Repository: grigorisg9gr/polynomial_nets Branch: master Commit: 210f0d34325d Files: 108 Total size: 17.3 MB Directory structure: gitextract_zpv_duhy/ ├── .gitignore ├── LICENSE.txt ├── classification-NO-activation-function/ │ ├── README.md │ ├── backbones/ │ │ ├── pinet_1x1_norelu.py │ │ ├── pinet_1x1_relu.py │ │ ├── pinet_norelu.py │ │ ├── pinet_relu.py │ │ └── resnet_norelu.py │ └── logs/ │ ├── pinet_1x1_norelu.log │ ├── pinet_1x1_relu.log │ ├── pinet_norelu.log │ ├── pinet_relu.log │ └── resnet_norelu.log ├── face_recognition/ │ ├── README.md │ ├── __init__.py │ ├── config.py │ ├── data/ │ │ ├── build_eval_pack.py │ │ ├── lfw/ │ │ │ └── pairs_label.txt │ │ └── rec2image.py │ ├── eval/ │ │ ├── lfw.py │ │ └── verification.py │ ├── flops_counter.py │ ├── image_iter.py │ ├── metric.py │ ├── models/ │ │ ├── MS1Mretina-polynet50-arcface/ │ │ │ ├── log_retina_r50newton_arcface.txt │ │ │ └── model-symbol.json │ │ └── MS1Mretina-resnet50-arcface/ │ │ ├── log_retina_r50_arcface.txt │ │ └── model-symbol.json │ ├── parall_module_local_v1.py │ ├── sample_config.py │ ├── symbol/ │ │ ├── fdensenet.py │ │ ├── fmnasnet.py │ │ ├── fmobilefacenet.py │ │ ├── fmobilenet.py │ │ ├── fresnet.py │ │ ├── memonger.py │ │ ├── symbol_utils.py │ │ └── vargfacenet.py │ ├── train.py │ ├── train_parall.py │ └── triplet_image_iter.py ├── image_generation_chainer/ │ ├── __init__.py │ ├── datasets/ │ │ └── mnist.py │ ├── dis_models/ │ │ ├── __init__.py │ │ ├── resblocks_dis.py │ │ └── snresnet_32.py │ ├── evaluations/ │ │ ├── __init__.py │ │ ├── extensions.py │ │ └── ndb.py │ ├── gen_models/ │ │ └── cnn_gen_custom_prodpoly.py │ ├── jobs/ │ │ └── pinet/ │ │ ├── fashionmnist_cnn_prodpoly_linear.yml │ │ └── train_mn_mnist.py │ ├── readme.rst │ ├── source/ │ │ ├── __init__.py │ │ ├── functions/ │ │ │ ├── __init__.py │ │ │ ├── cond_bn_wrapper.py │ │ │ ├── losses.py │ │ │ └── max_sv.py │ │ ├── inception/ │ │ │ ├── __init__.py │ │ │ ├── download.py │ │ │ ├── example.py │ │ │ ├── inception_score.py │ │ │ └── inception_score_tf.py │ │ ├── links/ │ │ │ ├── __init__.py │ │ │ ├── categorical_conditional_batch_normalization.py │ │ │ ├── conditional_batch_normalization.py │ │ │ ├── resnet_layer.py │ │ │ ├── sn_convolution_2d.py │ │ │ ├── sn_convolution_nd.py │ │ │ ├── sn_dilated_convolution_2d.py │ │ │ ├── sn_embed_id.py │ │ │ ├── sn_linear.py │ │ │ └── sn_symmetric_linear.py │ │ ├── misc_train_utils.py │ │ ├── miscs/ │ │ │ ├── __init__.py │ │ │ ├── model_moiving_average.py │ │ │ └── random_samples.py │ │ └── yaml_utils.py │ └── updaters/ │ └── gans/ │ └── updater.py ├── image_generation_pytorch/ │ ├── FID/ │ │ ├── LICENSE │ │ ├── README.md │ │ ├── __init__.py │ │ ├── fid_score.py │ │ └── inception.py │ ├── IS/ │ │ ├── LICENSE.md │ │ ├── README.md │ │ └── inception_score.py │ ├── README.rst │ ├── data_loader.py │ ├── logger.py │ ├── main.py │ ├── model.py │ └── solver.py ├── mesh_pi_nets/ │ ├── .gitignore │ ├── README.md │ ├── autoencoder_dataset.py │ ├── datasets/ │ │ ├── COMA/ │ │ │ └── templates/ │ │ │ ├── COMA_downsample/ │ │ │ │ └── downsampling_matrices.pkl │ │ │ └── template.obj │ │ └── DFAUST/ │ │ └── templates/ │ │ ├── COMA_downsample/ │ │ │ └── downsampling_matrices.pkl │ │ └── template.obj │ ├── mesh_sampling.py │ ├── models.py │ ├── shape_data.py │ ├── spiral_pi_nets.py │ ├── spiral_utils.py │ ├── test_funcs.py │ └── train_funcs.py └── readme.rst ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *~ *.pyc .idea/* *.egg-info/* *tmp*.py *tmp* *.*.backup **/__pycache__/ **/results/ **/results_*/ **/template_icl/ **/.ipynb_checkpoints/ **/demo/ .chainerui_commands *tmp_grigoris # personal testout files. personal.py ================================================ FILE: LICENSE.txt ================================================ Attribution-NonCommercial 4.0 International License Copyright (c) 2020, Grigorios G Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Jiankang Deng. 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Creative Commons may be contacted at creativecommons.org. ================================================ FILE: classification-NO-activation-function/README.md ================================================ # PiNet without Activation Function ## Abstract Typically, neural networks required activation functions to be able to approximate effectively complex distributions. ReLU-nets have been popular, e.g., ResNets. However, in the proposed polynomial nets, there is no strict requirement for activation functions as the Π-nets already include nonlinear interactions between the input elements. In fact, you could capture high-order correlations between the input elements without any activation functions, which is what we focus on in this experiment. In particular, we illustrate how Π-nets can learn classification even in the demanding ImageNet without activation functions. We hope that our code can inspire further experimentation with networks that do not require activation functions and can find alternative ways to express nonlinear relationships between the input elements.
## Implemenation Details Please follow [mmclassification](https://github.com/open-mmlab/mmclassification) to set up the training environment. Our models are trained by a single server with eight V100 GPUs. We slightly modifiy [ResNet](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/models/backbones/resnet.py) for different [experiments](https://github.com/grigorisg9gr/polynomial_nets/tree/master/no_relu/backbones). All other training details follow the standard [configuration](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py). ## Results ### ImageNet-1k | Model | ReLu | Conv-1x1 |Top-1 (%) | Top-5 (%) | Backbone | Logs | | :----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | | ResNet-18 | Yes | No | 69.90 | 89.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.log.json) | | ResNet-18 | No | No | 18.348 | 36.718 | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/resnet_norelu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/resnet_norelu.log) | | PiNet-18 | No | No | 63.666 | 84.340 | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/pinet_norelu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/pinet_norelu.log) | | PiNet-18 | No | Yes | 65.306 | 85.830 | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/pinet_1x1_norelu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/pinet_1x1_norelu.log) | | PiNet-18 | Yes | No | 70.350 | 89.434 | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/pinet_relu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/pinet_relu.log) | | PiNet-18 | Yes | Yes | 71.644 | 90.232 | [backbone](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/backbones/pinet_1x1_relu.py) | [log](https://github.com/grigorisg9gr/polynomial_nets/blob/master/no_relu/logs/pinet_1x1_relu.log) | ================================================ FILE: classification-NO-activation-function/backbones/pinet_1x1_norelu.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer, build_norm_layer, constant_init) from mmcv.cnn.bricks import DropPath from mmcv.runner import BaseModule from mmcv.utils.parrots_wrapper import _BatchNorm from ..builder import BACKBONES from .base_backbone import BaseBackbone eps = 1.0e-5 class BasicBlock(BaseModule): """BasicBlock for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the output channels of conv1. This is a reserved argument in BasicBlock and should always be 1. Default: 1. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): `pytorch` or `caffe`. It is unused and reserved for unified API with Bottleneck. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=1, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), drop_path_rate=0.0, act_cfg=dict(type='ReLU', inplace=True), init_cfg=None): super(BasicBlock, self).__init__(init_cfg=init_cfg) self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert self.expansion == 1 assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, out_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, 3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, 3, padding=1, bias=False) self.add_module(self.norm2_name, norm2) self.conv3 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) # self.relu = build_activation_layer(act_cfg) self.downsample = downsample # self.drop_path = DropPath(drop_prob=drop_path_rate # ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) # out = self.relu(out) out = self.conv2(out) out = self.norm2(out) if self.downsample is not None: identity = self.downsample(x) # out = self.drop_path(out) second = self.conv3(out*identity) out = out + identity + self.norm3(second) return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) # out = self.relu(out) return out class Bottleneck(BaseModule): """Bottleneck block for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the input/output channels of conv2. Default: 4. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: "pytorch". with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=4, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU', inplace=True), drop_path_rate=0.0, init_cfg=None): super(Bottleneck, self).__init__(init_cfg=init_cfg) assert style in ['pytorch', 'caffe'] self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg if self.style == 'pytorch': self.conv1_stride = 1 self.conv2_stride = stride else: self.conv1_stride = stride self.conv2_stride = 1 self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, self.mid_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, kernel_size=1, stride=self.conv1_stride, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, self.mid_channels, kernel_size=3, stride=self.conv2_stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm2_name, norm2) self.conv3 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) self.relu = build_activation_layer(act_cfg) self.downsample = downsample self.drop_path = DropPath(drop_prob=drop_path_rate ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) out = self.conv2(out) out = self.norm2(out) out = self.relu(out) out = self.conv3(out) out = self.norm3(out) if self.downsample is not None: identity = self.downsample(x) out = self.drop_path(out) out += identity return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out def get_expansion(block, expansion=None): """Get the expansion of a residual block. The block expansion will be obtained by the following order: 1. If ``expansion`` is given, just return it. 2. If ``block`` has the attribute ``expansion``, then return ``block.expansion``. 3. Return the default value according the the block type: 1 for ``BasicBlock`` and 4 for ``Bottleneck``. Args: block (class): The block class. expansion (int | None): The given expansion ratio. Returns: int: The expansion of the block. """ if isinstance(expansion, int): assert expansion > 0 elif expansion is None: if hasattr(block, 'expansion'): expansion = block.expansion elif issubclass(block, BasicBlock): expansion = 1 elif issubclass(block, Bottleneck): expansion = 4 else: raise TypeError(f'expansion is not specified for {block.__name__}') else: raise TypeError('expansion must be an integer or None') return expansion class ResLayer(nn.Sequential): """ResLayer to build ResNet style backbone. Args: block (nn.Module): Residual block used to build ResLayer. num_blocks (int): Number of blocks. in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int, optional): The expansion for BasicBlock/Bottleneck. If not specified, it will firstly be obtained via ``block.expansion``. If the block has no attribute "expansion", the following default values will be used: 1 for BasicBlock and 4 for Bottleneck. Default: None. stride (int): stride of the first block. Default: 1. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, block, num_blocks, in_channels, out_channels, expansion=None, stride=1, avg_down=False, conv_cfg=None, norm_cfg=dict(type='BN'), **kwargs): self.block = block self.expansion = get_expansion(block, expansion) downsample = None if stride != 1 or in_channels != out_channels: downsample = [] conv_stride = stride if avg_down and stride != 1: conv_stride = 1 downsample.append( nn.AvgPool2d( kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False)) downsample.extend([ build_conv_layer( conv_cfg, in_channels, out_channels, kernel_size=1, stride=conv_stride, bias=False), build_norm_layer(norm_cfg, out_channels)[1] ]) downsample = nn.Sequential(*downsample) layers = [] layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=stride, downsample=downsample, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) in_channels = out_channels for i in range(1, num_blocks): layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) super(ResLayer, self).__init__(*layers) @BACKBONES.register_module() class ResNet(BaseBackbone): """ResNet backbone. Please refer to the `paper `__ for details. Args: depth (int): Network depth, from {18, 34, 50, 101, 152}. in_channels (int): Number of input image channels. Default: 3. stem_channels (int): Output channels of the stem layer. Default: 64. base_channels (int): Middle channels of the first stage. Default: 64. num_stages (int): Stages of the network. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. Default: ``(1, 2, 2, 2)``. dilations (Sequence[int]): Dilation of each stage. Default: ``(1, 1, 1, 1)``. out_indices (Sequence[int]): Output from which stages. Default: ``(3, )``. style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv. Default: False. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1. conv_cfg (dict | None): The config dict for conv layers. Default: None. norm_cfg (dict): The config dict for norm layers. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True. Example: >>> from mmcls.models import ResNet >>> import torch >>> self = ResNet(depth=18) >>> self.eval() >>> inputs = torch.rand(1, 3, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 64, 8, 8) (1, 128, 4, 4) (1, 256, 2, 2) (1, 512, 1, 1) """ arch_settings = { 18: (BasicBlock, (2, 2, 2, 2)), 34: (BasicBlock, (3, 4, 6, 3)), 50: (Bottleneck, (3, 4, 6, 3)), 101: (Bottleneck, (3, 4, 23, 3)), 152: (Bottleneck, (3, 8, 36, 3)) } def __init__(self, depth, in_channels=3, stem_channels=64, base_channels=64, expansion=None, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3, ), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=False, with_cp=False, zero_init_residual=True, init_cfg=[ dict(type='Kaiming', layer=['Conv2d']), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ], drop_path_rate=0.0): super(ResNet, self).__init__(init_cfg) if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for resnet') self.depth = depth self.stem_channels = stem_channels self.base_channels = base_channels self.num_stages = num_stages assert num_stages >= 1 and num_stages <= 4 self.strides = strides self.dilations = dilations assert len(strides) == len(dilations) == num_stages self.out_indices = out_indices assert max(out_indices) < num_stages self.style = style self.deep_stem = deep_stem self.avg_down = avg_down self.frozen_stages = frozen_stages self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.with_cp = with_cp self.norm_eval = norm_eval self.zero_init_residual = zero_init_residual self.block, stage_blocks = self.arch_settings[depth] self.stage_blocks = stage_blocks[:num_stages] self.expansion = get_expansion(self.block, expansion) self._make_stem_layer(in_channels, stem_channels) self.res_layers = [] _in_channels = stem_channels _out_channels = base_channels * self.expansion for i, num_blocks in enumerate(self.stage_blocks): stride = strides[i] dilation = dilations[i] res_layer = self.make_res_layer( block=self.block, num_blocks=num_blocks, in_channels=_in_channels, out_channels=_out_channels, expansion=self.expansion, stride=stride, dilation=dilation, style=self.style, avg_down=self.avg_down, with_cp=with_cp, conv_cfg=conv_cfg, norm_cfg=norm_cfg, drop_path_rate=drop_path_rate) _in_channels = _out_channels _out_channels *= 2 layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) self.res_layers.append(layer_name) self._freeze_stages() self.feat_dim = res_layer[-1].out_channels def make_res_layer(self, **kwargs): return ResLayer(**kwargs) @property def norm1(self): return getattr(self, self.norm1_name) def _make_stem_layer(self, in_channels, stem_channels): if self.deep_stem: self.stem = nn.Sequential( ConvModule( in_channels, stem_channels // 2, kernel_size=3, stride=2, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels // 2, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True)) else: self.conv1 = build_conv_layer( self.conv_cfg, in_channels, stem_channels, kernel_size=7, stride=2, padding=3, bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, stem_channels, postfix=1) self.add_module(self.norm1_name, norm1) # self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def _freeze_stages(self): if self.frozen_stages >= 0: if self.deep_stem: self.stem.eval() for param in self.stem.parameters(): param.requires_grad = False else: self.norm1.eval() for m in [self.conv1, self.norm1]: for param in m.parameters(): param.requires_grad = False for i in range(1, self.frozen_stages + 1): m = getattr(self, f'layer{i}') m.eval() for param in m.parameters(): param.requires_grad = False def init_weights(self): super(ResNet, self).init_weights() if (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): # Suppress zero_init_residual if use pretrained model. return if self.zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): constant_init(m.norm3, 0) elif isinstance(m, BasicBlock): constant_init(m.norm2, 0) def forward(self, x): if self.deep_stem: x = self.stem(x) else: x = self.conv1(x) x = self.norm1(x) # x = self.relu(x) x = self.maxpool(x) outs = [] for i, layer_name in enumerate(self.res_layers): res_layer = getattr(self, layer_name) x = res_layer(x) if i in self.out_indices: outs.append(x) return tuple(outs) def train(self, mode=True): super(ResNet, self).train(mode) self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval() @BACKBONES.register_module() class ResNetV1c(ResNet): """ResNetV1c backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv in the input stem with three 3x3 convs. """ def __init__(self, **kwargs): super(ResNetV1c, self).__init__( deep_stem=True, avg_down=False, **kwargs) @BACKBONES.register_module() class ResNetV1d(ResNet): """ResNetV1d backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in the input stem with three 3x3 convs. And in the downsampling block, a 2x2 avg_pool with stride 2 is added before conv, whose stride is changed to 1. """ def __init__(self, **kwargs): super(ResNetV1d, self).__init__( deep_stem=True, avg_down=True, **kwargs) ================================================ FILE: classification-NO-activation-function/backbones/pinet_1x1_relu.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer, build_norm_layer, constant_init) from mmcv.cnn.bricks import DropPath from mmcv.runner import BaseModule from mmcv.utils.parrots_wrapper import _BatchNorm from ..builder import BACKBONES from .base_backbone import BaseBackbone eps = 1.0e-5 class BasicBlock(BaseModule): """BasicBlock for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the output channels of conv1. This is a reserved argument in BasicBlock and should always be 1. Default: 1. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): `pytorch` or `caffe`. It is unused and reserved for unified API with Bottleneck. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=1, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), drop_path_rate=0.0, act_cfg=dict(type='ReLU', inplace=True), init_cfg=None): super(BasicBlock, self).__init__(init_cfg=init_cfg) self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert self.expansion == 1 assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, out_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, 3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, 3, padding=1, bias=False) self.add_module(self.norm2_name, norm2) self.conv3 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) self.relu = build_activation_layer(act_cfg) self.downsample = downsample # self.drop_path = DropPath(drop_prob=drop_path_rate # ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) out = self.conv2(out) out = self.norm2(out) if self.downsample is not None: identity = self.downsample(x) # out = self.drop_path(out) second = self.conv3(out*identity) out = out + identity + self.norm3(second) return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out class Bottleneck(BaseModule): """Bottleneck block for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the input/output channels of conv2. Default: 4. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: "pytorch". with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=4, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU', inplace=True), drop_path_rate=0.0, init_cfg=None): super(Bottleneck, self).__init__(init_cfg=init_cfg) assert style in ['pytorch', 'caffe'] self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg if self.style == 'pytorch': self.conv1_stride = 1 self.conv2_stride = stride else: self.conv1_stride = stride self.conv2_stride = 1 self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, self.mid_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, kernel_size=1, stride=self.conv1_stride, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, self.mid_channels, kernel_size=3, stride=self.conv2_stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm2_name, norm2) self.conv3 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) self.relu = build_activation_layer(act_cfg) self.downsample = downsample self.drop_path = DropPath(drop_prob=drop_path_rate ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) out = self.conv2(out) out = self.norm2(out) out = self.relu(out) out = self.conv3(out) out = self.norm3(out) if self.downsample is not None: identity = self.downsample(x) out = self.drop_path(out) out += identity return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out def get_expansion(block, expansion=None): """Get the expansion of a residual block. The block expansion will be obtained by the following order: 1. If ``expansion`` is given, just return it. 2. If ``block`` has the attribute ``expansion``, then return ``block.expansion``. 3. Return the default value according the the block type: 1 for ``BasicBlock`` and 4 for ``Bottleneck``. Args: block (class): The block class. expansion (int | None): The given expansion ratio. Returns: int: The expansion of the block. """ if isinstance(expansion, int): assert expansion > 0 elif expansion is None: if hasattr(block, 'expansion'): expansion = block.expansion elif issubclass(block, BasicBlock): expansion = 1 elif issubclass(block, Bottleneck): expansion = 4 else: raise TypeError(f'expansion is not specified for {block.__name__}') else: raise TypeError('expansion must be an integer or None') return expansion class ResLayer(nn.Sequential): """ResLayer to build ResNet style backbone. Args: block (nn.Module): Residual block used to build ResLayer. num_blocks (int): Number of blocks. in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int, optional): The expansion for BasicBlock/Bottleneck. If not specified, it will firstly be obtained via ``block.expansion``. If the block has no attribute "expansion", the following default values will be used: 1 for BasicBlock and 4 for Bottleneck. Default: None. stride (int): stride of the first block. Default: 1. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, block, num_blocks, in_channels, out_channels, expansion=None, stride=1, avg_down=False, conv_cfg=None, norm_cfg=dict(type='BN'), **kwargs): self.block = block self.expansion = get_expansion(block, expansion) downsample = None if stride != 1 or in_channels != out_channels: downsample = [] conv_stride = stride if avg_down and stride != 1: conv_stride = 1 downsample.append( nn.AvgPool2d( kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False)) downsample.extend([ build_conv_layer( conv_cfg, in_channels, out_channels, kernel_size=1, stride=conv_stride, bias=False), build_norm_layer(norm_cfg, out_channels)[1] ]) downsample = nn.Sequential(*downsample) layers = [] layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=stride, downsample=downsample, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) in_channels = out_channels for i in range(1, num_blocks): layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) super(ResLayer, self).__init__(*layers) @BACKBONES.register_module() class ResNet(BaseBackbone): """ResNet backbone. Please refer to the `paper `__ for details. Args: depth (int): Network depth, from {18, 34, 50, 101, 152}. in_channels (int): Number of input image channels. Default: 3. stem_channels (int): Output channels of the stem layer. Default: 64. base_channels (int): Middle channels of the first stage. Default: 64. num_stages (int): Stages of the network. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. Default: ``(1, 2, 2, 2)``. dilations (Sequence[int]): Dilation of each stage. Default: ``(1, 1, 1, 1)``. out_indices (Sequence[int]): Output from which stages. Default: ``(3, )``. style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv. Default: False. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1. conv_cfg (dict | None): The config dict for conv layers. Default: None. norm_cfg (dict): The config dict for norm layers. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True. Example: >>> from mmcls.models import ResNet >>> import torch >>> self = ResNet(depth=18) >>> self.eval() >>> inputs = torch.rand(1, 3, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 64, 8, 8) (1, 128, 4, 4) (1, 256, 2, 2) (1, 512, 1, 1) """ arch_settings = { 18: (BasicBlock, (2, 2, 2, 2)), 34: (BasicBlock, (3, 4, 6, 3)), 50: (Bottleneck, (3, 4, 6, 3)), 101: (Bottleneck, (3, 4, 23, 3)), 152: (Bottleneck, (3, 8, 36, 3)) } def __init__(self, depth, in_channels=3, stem_channels=64, base_channels=64, expansion=None, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3, ), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=False, with_cp=False, zero_init_residual=True, init_cfg=[ dict(type='Kaiming', layer=['Conv2d']), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ], drop_path_rate=0.0): super(ResNet, self).__init__(init_cfg) if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for resnet') self.depth = depth self.stem_channels = stem_channels self.base_channels = base_channels self.num_stages = num_stages assert num_stages >= 1 and num_stages <= 4 self.strides = strides self.dilations = dilations assert len(strides) == len(dilations) == num_stages self.out_indices = out_indices assert max(out_indices) < num_stages self.style = style self.deep_stem = deep_stem self.avg_down = avg_down self.frozen_stages = frozen_stages self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.with_cp = with_cp self.norm_eval = norm_eval self.zero_init_residual = zero_init_residual self.block, stage_blocks = self.arch_settings[depth] self.stage_blocks = stage_blocks[:num_stages] self.expansion = get_expansion(self.block, expansion) self._make_stem_layer(in_channels, stem_channels) self.res_layers = [] _in_channels = stem_channels _out_channels = base_channels * self.expansion for i, num_blocks in enumerate(self.stage_blocks): stride = strides[i] dilation = dilations[i] res_layer = self.make_res_layer( block=self.block, num_blocks=num_blocks, in_channels=_in_channels, out_channels=_out_channels, expansion=self.expansion, stride=stride, dilation=dilation, style=self.style, avg_down=self.avg_down, with_cp=with_cp, conv_cfg=conv_cfg, norm_cfg=norm_cfg, drop_path_rate=drop_path_rate) _in_channels = _out_channels _out_channels *= 2 layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) self.res_layers.append(layer_name) self._freeze_stages() self.feat_dim = res_layer[-1].out_channels def make_res_layer(self, **kwargs): return ResLayer(**kwargs) @property def norm1(self): return getattr(self, self.norm1_name) def _make_stem_layer(self, in_channels, stem_channels): if self.deep_stem: self.stem = nn.Sequential( ConvModule( in_channels, stem_channels // 2, kernel_size=3, stride=2, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels // 2, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True)) else: self.conv1 = build_conv_layer( self.conv_cfg, in_channels, stem_channels, kernel_size=7, stride=2, padding=3, bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, stem_channels, postfix=1) self.add_module(self.norm1_name, norm1) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def _freeze_stages(self): if self.frozen_stages >= 0: if self.deep_stem: self.stem.eval() for param in self.stem.parameters(): param.requires_grad = False else: self.norm1.eval() for m in [self.conv1, self.norm1]: for param in m.parameters(): param.requires_grad = False for i in range(1, self.frozen_stages + 1): m = getattr(self, f'layer{i}') m.eval() for param in m.parameters(): param.requires_grad = False def init_weights(self): super(ResNet, self).init_weights() if (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): # Suppress zero_init_residual if use pretrained model. return if self.zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): constant_init(m.norm3, 0) elif isinstance(m, BasicBlock): constant_init(m.norm2, 0) def forward(self, x): if self.deep_stem: x = self.stem(x) else: x = self.conv1(x) x = self.norm1(x) x = self.relu(x) x = self.maxpool(x) outs = [] for i, layer_name in enumerate(self.res_layers): res_layer = getattr(self, layer_name) x = res_layer(x) if i in self.out_indices: outs.append(x) return tuple(outs) def train(self, mode=True): super(ResNet, self).train(mode) self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval() @BACKBONES.register_module() class ResNetV1c(ResNet): """ResNetV1c backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv in the input stem with three 3x3 convs. """ def __init__(self, **kwargs): super(ResNetV1c, self).__init__( deep_stem=True, avg_down=False, **kwargs) @BACKBONES.register_module() class ResNetV1d(ResNet): """ResNetV1d backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in the input stem with three 3x3 convs. And in the downsampling block, a 2x2 avg_pool with stride 2 is added before conv, whose stride is changed to 1. """ def __init__(self, **kwargs): super(ResNetV1d, self).__init__( deep_stem=True, avg_down=True, **kwargs) ================================================ FILE: classification-NO-activation-function/backbones/pinet_norelu.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer, build_norm_layer, constant_init) from mmcv.cnn.bricks import DropPath from mmcv.runner import BaseModule from mmcv.utils.parrots_wrapper import _BatchNorm from ..builder import BACKBONES from .base_backbone import BaseBackbone eps = 1.0e-5 class BasicBlock(BaseModule): """BasicBlock for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the output channels of conv1. This is a reserved argument in BasicBlock and should always be 1. Default: 1. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): `pytorch` or `caffe`. It is unused and reserved for unified API with Bottleneck. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=1, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), drop_path_rate=0.0, act_cfg=dict(type='ReLU', inplace=True), init_cfg=None): super(BasicBlock, self).__init__(init_cfg=init_cfg) self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert self.expansion == 1 assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, out_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, 3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, 3, padding=1, bias=False) self.add_module(self.norm2_name, norm2) # self.conv3 = build_conv_layer( # conv_cfg, # self.mid_channels, # out_channels, # kernel_size=1, # bias=False) self.add_module(self.norm3_name, norm3) # self.relu = build_activation_layer(act_cfg) self.downsample = downsample # self.drop_path = DropPath(drop_prob=drop_path_rate # ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) # out = self.relu(out) out = self.conv2(out) out = self.norm2(out) if self.downsample is not None: identity = self.downsample(x) # out = self.drop_path(out) # second = self.conv3(out*identity) second = out*identity out = out + identity + self.norm3(second) return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) # out = self.relu(out) return out class Bottleneck(BaseModule): """Bottleneck block for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the input/output channels of conv2. Default: 4. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: "pytorch". with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=4, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU', inplace=True), drop_path_rate=0.0, init_cfg=None): super(Bottleneck, self).__init__(init_cfg=init_cfg) assert style in ['pytorch', 'caffe'] self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg if self.style == 'pytorch': self.conv1_stride = 1 self.conv2_stride = stride else: self.conv1_stride = stride self.conv2_stride = 1 self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, self.mid_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, kernel_size=1, stride=self.conv1_stride, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, self.mid_channels, kernel_size=3, stride=self.conv2_stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm2_name, norm2) self.conv3 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) self.relu = build_activation_layer(act_cfg) self.downsample = downsample self.drop_path = DropPath(drop_prob=drop_path_rate ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) out = self.conv2(out) out = self.norm2(out) out = self.relu(out) out = self.conv3(out) out = self.norm3(out) if self.downsample is not None: identity = self.downsample(x) out = self.drop_path(out) out += identity return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out def get_expansion(block, expansion=None): """Get the expansion of a residual block. The block expansion will be obtained by the following order: 1. If ``expansion`` is given, just return it. 2. If ``block`` has the attribute ``expansion``, then return ``block.expansion``. 3. Return the default value according the the block type: 1 for ``BasicBlock`` and 4 for ``Bottleneck``. Args: block (class): The block class. expansion (int | None): The given expansion ratio. Returns: int: The expansion of the block. """ if isinstance(expansion, int): assert expansion > 0 elif expansion is None: if hasattr(block, 'expansion'): expansion = block.expansion elif issubclass(block, BasicBlock): expansion = 1 elif issubclass(block, Bottleneck): expansion = 4 else: raise TypeError(f'expansion is not specified for {block.__name__}') else: raise TypeError('expansion must be an integer or None') return expansion class ResLayer(nn.Sequential): """ResLayer to build ResNet style backbone. Args: block (nn.Module): Residual block used to build ResLayer. num_blocks (int): Number of blocks. in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int, optional): The expansion for BasicBlock/Bottleneck. If not specified, it will firstly be obtained via ``block.expansion``. If the block has no attribute "expansion", the following default values will be used: 1 for BasicBlock and 4 for Bottleneck. Default: None. stride (int): stride of the first block. Default: 1. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, block, num_blocks, in_channels, out_channels, expansion=None, stride=1, avg_down=False, conv_cfg=None, norm_cfg=dict(type='BN'), **kwargs): self.block = block self.expansion = get_expansion(block, expansion) downsample = None if stride != 1 or in_channels != out_channels: downsample = [] conv_stride = stride if avg_down and stride != 1: conv_stride = 1 downsample.append( nn.AvgPool2d( kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False)) downsample.extend([ build_conv_layer( conv_cfg, in_channels, out_channels, kernel_size=1, stride=conv_stride, bias=False), build_norm_layer(norm_cfg, out_channels)[1] ]) downsample = nn.Sequential(*downsample) layers = [] layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=stride, downsample=downsample, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) in_channels = out_channels for i in range(1, num_blocks): layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) super(ResLayer, self).__init__(*layers) @BACKBONES.register_module() class ResNet(BaseBackbone): """ResNet backbone. Please refer to the `paper `__ for details. Args: depth (int): Network depth, from {18, 34, 50, 101, 152}. in_channels (int): Number of input image channels. Default: 3. stem_channels (int): Output channels of the stem layer. Default: 64. base_channels (int): Middle channels of the first stage. Default: 64. num_stages (int): Stages of the network. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. Default: ``(1, 2, 2, 2)``. dilations (Sequence[int]): Dilation of each stage. Default: ``(1, 1, 1, 1)``. out_indices (Sequence[int]): Output from which stages. Default: ``(3, )``. style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv. Default: False. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1. conv_cfg (dict | None): The config dict for conv layers. Default: None. norm_cfg (dict): The config dict for norm layers. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True. Example: >>> from mmcls.models import ResNet >>> import torch >>> self = ResNet(depth=18) >>> self.eval() >>> inputs = torch.rand(1, 3, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 64, 8, 8) (1, 128, 4, 4) (1, 256, 2, 2) (1, 512, 1, 1) """ arch_settings = { 18: (BasicBlock, (2, 2, 2, 2)), 34: (BasicBlock, (3, 4, 6, 3)), 50: (Bottleneck, (3, 4, 6, 3)), 101: (Bottleneck, (3, 4, 23, 3)), 152: (Bottleneck, (3, 8, 36, 3)) } def __init__(self, depth, in_channels=3, stem_channels=64, base_channels=64, expansion=None, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3, ), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=False, with_cp=False, zero_init_residual=True, init_cfg=[ dict(type='Kaiming', layer=['Conv2d']), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ], drop_path_rate=0.0): super(ResNet, self).__init__(init_cfg) if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for resnet') self.depth = depth self.stem_channels = stem_channels self.base_channels = base_channels self.num_stages = num_stages assert num_stages >= 1 and num_stages <= 4 self.strides = strides self.dilations = dilations assert len(strides) == len(dilations) == num_stages self.out_indices = out_indices assert max(out_indices) < num_stages self.style = style self.deep_stem = deep_stem self.avg_down = avg_down self.frozen_stages = frozen_stages self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.with_cp = with_cp self.norm_eval = norm_eval self.zero_init_residual = zero_init_residual self.block, stage_blocks = self.arch_settings[depth] self.stage_blocks = stage_blocks[:num_stages] self.expansion = get_expansion(self.block, expansion) self._make_stem_layer(in_channels, stem_channels) self.res_layers = [] _in_channels = stem_channels _out_channels = base_channels * self.expansion for i, num_blocks in enumerate(self.stage_blocks): stride = strides[i] dilation = dilations[i] res_layer = self.make_res_layer( block=self.block, num_blocks=num_blocks, in_channels=_in_channels, out_channels=_out_channels, expansion=self.expansion, stride=stride, dilation=dilation, style=self.style, avg_down=self.avg_down, with_cp=with_cp, conv_cfg=conv_cfg, norm_cfg=norm_cfg, drop_path_rate=drop_path_rate) _in_channels = _out_channels _out_channels *= 2 layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) self.res_layers.append(layer_name) self._freeze_stages() self.feat_dim = res_layer[-1].out_channels def make_res_layer(self, **kwargs): return ResLayer(**kwargs) @property def norm1(self): return getattr(self, self.norm1_name) def _make_stem_layer(self, in_channels, stem_channels): if self.deep_stem: self.stem = nn.Sequential( ConvModule( in_channels, stem_channels // 2, kernel_size=3, stride=2, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels // 2, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True)) else: self.conv1 = build_conv_layer( self.conv_cfg, in_channels, stem_channels, kernel_size=7, stride=2, padding=3, bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, stem_channels, postfix=1) self.add_module(self.norm1_name, norm1) # self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def _freeze_stages(self): if self.frozen_stages >= 0: if self.deep_stem: self.stem.eval() for param in self.stem.parameters(): param.requires_grad = False else: self.norm1.eval() for m in [self.conv1, self.norm1]: for param in m.parameters(): param.requires_grad = False for i in range(1, self.frozen_stages + 1): m = getattr(self, f'layer{i}') m.eval() for param in m.parameters(): param.requires_grad = False def init_weights(self): super(ResNet, self).init_weights() if (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): # Suppress zero_init_residual if use pretrained model. return if self.zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): constant_init(m.norm3, 0) elif isinstance(m, BasicBlock): constant_init(m.norm2, 0) def forward(self, x): if self.deep_stem: x = self.stem(x) else: x = self.conv1(x) x = self.norm1(x) # x = self.relu(x) x = self.maxpool(x) outs = [] for i, layer_name in enumerate(self.res_layers): res_layer = getattr(self, layer_name) x = res_layer(x) if i in self.out_indices: outs.append(x) return tuple(outs) def train(self, mode=True): super(ResNet, self).train(mode) self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval() @BACKBONES.register_module() class ResNetV1c(ResNet): """ResNetV1c backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv in the input stem with three 3x3 convs. """ def __init__(self, **kwargs): super(ResNetV1c, self).__init__( deep_stem=True, avg_down=False, **kwargs) @BACKBONES.register_module() class ResNetV1d(ResNet): """ResNetV1d backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in the input stem with three 3x3 convs. And in the downsampling block, a 2x2 avg_pool with stride 2 is added before conv, whose stride is changed to 1. """ def __init__(self, **kwargs): super(ResNetV1d, self).__init__( deep_stem=True, avg_down=True, **kwargs) ================================================ FILE: classification-NO-activation-function/backbones/pinet_relu.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer, build_norm_layer, constant_init) from mmcv.cnn.bricks import DropPath from mmcv.runner import BaseModule from mmcv.utils.parrots_wrapper import _BatchNorm from ..builder import BACKBONES from .base_backbone import BaseBackbone eps = 1.0e-5 class BasicBlock(BaseModule): """BasicBlock for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the output channels of conv1. This is a reserved argument in BasicBlock and should always be 1. Default: 1. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): `pytorch` or `caffe`. It is unused and reserved for unified API with Bottleneck. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=1, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), drop_path_rate=0.0, act_cfg=dict(type='ReLU', inplace=True), init_cfg=None): super(BasicBlock, self).__init__(init_cfg=init_cfg) self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert self.expansion == 1 assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, out_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, 3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, 3, padding=1, bias=False) self.add_module(self.norm2_name, norm2) # self.conv3 = build_conv_layer( # conv_cfg, # self.mid_channels, # out_channels, # kernel_size=1, # bias=False) self.add_module(self.norm3_name, norm3) self.relu = build_activation_layer(act_cfg) self.downsample = downsample # self.drop_path = DropPath(drop_prob=drop_path_rate # ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) out = self.conv2(out) out = self.norm2(out) if self.downsample is not None: identity = self.downsample(x) # out = self.drop_path(out) # second = self.conv3(out*identity) second = out*identity out = out + identity + self.norm3(second) return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out class Bottleneck(BaseModule): """Bottleneck block for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the input/output channels of conv2. Default: 4. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: "pytorch". with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=4, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU', inplace=True), drop_path_rate=0.0, init_cfg=None): super(Bottleneck, self).__init__(init_cfg=init_cfg) assert style in ['pytorch', 'caffe'] self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg if self.style == 'pytorch': self.conv1_stride = 1 self.conv2_stride = stride else: self.conv1_stride = stride self.conv2_stride = 1 self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, self.mid_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, kernel_size=1, stride=self.conv1_stride, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, self.mid_channels, kernel_size=3, stride=self.conv2_stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm2_name, norm2) self.conv3 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) self.relu = build_activation_layer(act_cfg) self.downsample = downsample self.drop_path = DropPath(drop_prob=drop_path_rate ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) out = self.conv2(out) out = self.norm2(out) out = self.relu(out) out = self.conv3(out) out = self.norm3(out) if self.downsample is not None: identity = self.downsample(x) out = self.drop_path(out) out += identity return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out def get_expansion(block, expansion=None): """Get the expansion of a residual block. The block expansion will be obtained by the following order: 1. If ``expansion`` is given, just return it. 2. If ``block`` has the attribute ``expansion``, then return ``block.expansion``. 3. Return the default value according the the block type: 1 for ``BasicBlock`` and 4 for ``Bottleneck``. Args: block (class): The block class. expansion (int | None): The given expansion ratio. Returns: int: The expansion of the block. """ if isinstance(expansion, int): assert expansion > 0 elif expansion is None: if hasattr(block, 'expansion'): expansion = block.expansion elif issubclass(block, BasicBlock): expansion = 1 elif issubclass(block, Bottleneck): expansion = 4 else: raise TypeError(f'expansion is not specified for {block.__name__}') else: raise TypeError('expansion must be an integer or None') return expansion class ResLayer(nn.Sequential): """ResLayer to build ResNet style backbone. Args: block (nn.Module): Residual block used to build ResLayer. num_blocks (int): Number of blocks. in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int, optional): The expansion for BasicBlock/Bottleneck. If not specified, it will firstly be obtained via ``block.expansion``. If the block has no attribute "expansion", the following default values will be used: 1 for BasicBlock and 4 for Bottleneck. Default: None. stride (int): stride of the first block. Default: 1. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, block, num_blocks, in_channels, out_channels, expansion=None, stride=1, avg_down=False, conv_cfg=None, norm_cfg=dict(type='BN'), **kwargs): self.block = block self.expansion = get_expansion(block, expansion) downsample = None if stride != 1 or in_channels != out_channels: downsample = [] conv_stride = stride if avg_down and stride != 1: conv_stride = 1 downsample.append( nn.AvgPool2d( kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False)) downsample.extend([ build_conv_layer( conv_cfg, in_channels, out_channels, kernel_size=1, stride=conv_stride, bias=False), build_norm_layer(norm_cfg, out_channels)[1] ]) downsample = nn.Sequential(*downsample) layers = [] layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=stride, downsample=downsample, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) in_channels = out_channels for i in range(1, num_blocks): layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) super(ResLayer, self).__init__(*layers) @BACKBONES.register_module() class ResNet(BaseBackbone): """ResNet backbone. Please refer to the `paper `__ for details. Args: depth (int): Network depth, from {18, 34, 50, 101, 152}. in_channels (int): Number of input image channels. Default: 3. stem_channels (int): Output channels of the stem layer. Default: 64. base_channels (int): Middle channels of the first stage. Default: 64. num_stages (int): Stages of the network. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. Default: ``(1, 2, 2, 2)``. dilations (Sequence[int]): Dilation of each stage. Default: ``(1, 1, 1, 1)``. out_indices (Sequence[int]): Output from which stages. Default: ``(3, )``. style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv. Default: False. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1. conv_cfg (dict | None): The config dict for conv layers. Default: None. norm_cfg (dict): The config dict for norm layers. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True. Example: >>> from mmcls.models import ResNet >>> import torch >>> self = ResNet(depth=18) >>> self.eval() >>> inputs = torch.rand(1, 3, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 64, 8, 8) (1, 128, 4, 4) (1, 256, 2, 2) (1, 512, 1, 1) """ arch_settings = { 18: (BasicBlock, (2, 2, 2, 2)), 34: (BasicBlock, (3, 4, 6, 3)), 50: (Bottleneck, (3, 4, 6, 3)), 101: (Bottleneck, (3, 4, 23, 3)), 152: (Bottleneck, (3, 8, 36, 3)) } def __init__(self, depth, in_channels=3, stem_channels=64, base_channels=64, expansion=None, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3, ), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=False, with_cp=False, zero_init_residual=True, init_cfg=[ dict(type='Kaiming', layer=['Conv2d']), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ], drop_path_rate=0.0): super(ResNet, self).__init__(init_cfg) if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for resnet') self.depth = depth self.stem_channels = stem_channels self.base_channels = base_channels self.num_stages = num_stages assert num_stages >= 1 and num_stages <= 4 self.strides = strides self.dilations = dilations assert len(strides) == len(dilations) == num_stages self.out_indices = out_indices assert max(out_indices) < num_stages self.style = style self.deep_stem = deep_stem self.avg_down = avg_down self.frozen_stages = frozen_stages self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.with_cp = with_cp self.norm_eval = norm_eval self.zero_init_residual = zero_init_residual self.block, stage_blocks = self.arch_settings[depth] self.stage_blocks = stage_blocks[:num_stages] self.expansion = get_expansion(self.block, expansion) self._make_stem_layer(in_channels, stem_channels) self.res_layers = [] _in_channels = stem_channels _out_channels = base_channels * self.expansion for i, num_blocks in enumerate(self.stage_blocks): stride = strides[i] dilation = dilations[i] res_layer = self.make_res_layer( block=self.block, num_blocks=num_blocks, in_channels=_in_channels, out_channels=_out_channels, expansion=self.expansion, stride=stride, dilation=dilation, style=self.style, avg_down=self.avg_down, with_cp=with_cp, conv_cfg=conv_cfg, norm_cfg=norm_cfg, drop_path_rate=drop_path_rate) _in_channels = _out_channels _out_channels *= 2 layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) self.res_layers.append(layer_name) self._freeze_stages() self.feat_dim = res_layer[-1].out_channels def make_res_layer(self, **kwargs): return ResLayer(**kwargs) @property def norm1(self): return getattr(self, self.norm1_name) def _make_stem_layer(self, in_channels, stem_channels): if self.deep_stem: self.stem = nn.Sequential( ConvModule( in_channels, stem_channels // 2, kernel_size=3, stride=2, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels // 2, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True)) else: self.conv1 = build_conv_layer( self.conv_cfg, in_channels, stem_channels, kernel_size=7, stride=2, padding=3, bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, stem_channels, postfix=1) self.add_module(self.norm1_name, norm1) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def _freeze_stages(self): if self.frozen_stages >= 0: if self.deep_stem: self.stem.eval() for param in self.stem.parameters(): param.requires_grad = False else: self.norm1.eval() for m in [self.conv1, self.norm1]: for param in m.parameters(): param.requires_grad = False for i in range(1, self.frozen_stages + 1): m = getattr(self, f'layer{i}') m.eval() for param in m.parameters(): param.requires_grad = False def init_weights(self): super(ResNet, self).init_weights() if (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): # Suppress zero_init_residual if use pretrained model. return if self.zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): constant_init(m.norm3, 0) elif isinstance(m, BasicBlock): constant_init(m.norm2, 0) def forward(self, x): if self.deep_stem: x = self.stem(x) else: x = self.conv1(x) x = self.norm1(x) x = self.relu(x) x = self.maxpool(x) outs = [] for i, layer_name in enumerate(self.res_layers): res_layer = getattr(self, layer_name) x = res_layer(x) if i in self.out_indices: outs.append(x) return tuple(outs) def train(self, mode=True): super(ResNet, self).train(mode) self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval() @BACKBONES.register_module() class ResNetV1c(ResNet): """ResNetV1c backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv in the input stem with three 3x3 convs. """ def __init__(self, **kwargs): super(ResNetV1c, self).__init__( deep_stem=True, avg_down=False, **kwargs) @BACKBONES.register_module() class ResNetV1d(ResNet): """ResNetV1d backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in the input stem with three 3x3 convs. And in the downsampling block, a 2x2 avg_pool with stride 2 is added before conv, whose stride is changed to 1. """ def __init__(self, **kwargs): super(ResNetV1d, self).__init__( deep_stem=True, avg_down=True, **kwargs) ================================================ FILE: classification-NO-activation-function/backbones/resnet_norelu.py ================================================ # Copyright (c) OpenMMLab. All rights reserved. import torch.nn as nn import torch.utils.checkpoint as cp from mmcv.cnn import (ConvModule, build_activation_layer, build_conv_layer, build_norm_layer, constant_init) from mmcv.cnn.bricks import DropPath from mmcv.runner import BaseModule from mmcv.utils.parrots_wrapper import _BatchNorm from ..builder import BACKBONES from .base_backbone import BaseBackbone eps = 1.0e-5 class BasicBlock(BaseModule): """BasicBlock for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the output channels of conv1. This is a reserved argument in BasicBlock and should always be 1. Default: 1. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): `pytorch` or `caffe`. It is unused and reserved for unified API with Bottleneck. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=1, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), drop_path_rate=0.0, act_cfg=dict(type='ReLU', inplace=True), init_cfg=None): super(BasicBlock, self).__init__(init_cfg=init_cfg) self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert self.expansion == 1 assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, out_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, 3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, 3, padding=1, bias=False) self.add_module(self.norm2_name, norm2) # self.conv3 = build_conv_layer( # conv_cfg, # self.mid_channels, # out_channels, # kernel_size=1, # bias=False) # self.add_module(self.norm3_name, norm3) # self.relu = build_activation_layer(act_cfg) self.downsample = downsample # self.drop_path = DropPath(drop_prob=drop_path_rate # ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) # @property # def norm3(self): # return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) # out = self.relu(out) out = self.conv2(out) out = self.norm2(out) if self.downsample is not None: identity = self.downsample(x) # out = self.drop_path(out) # second = self.conv3(out*identity) # second = out*identity # out = out + identity + self.norm3(second) out = out + identity return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) # out = self.relu(out) return out class Bottleneck(BaseModule): """Bottleneck block for ResNet. Args: in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int): The ratio of ``out_channels/mid_channels`` where ``mid_channels`` is the input/output channels of conv2. Default: 4. stride (int): stride of the block. Default: 1 dilation (int): dilation of convolution. Default: 1 downsample (nn.Module, optional): downsample operation on identity branch. Default: None. style (str): ``"pytorch"`` or ``"caffe"``. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. Default: "pytorch". with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, in_channels, out_channels, expansion=4, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU', inplace=True), drop_path_rate=0.0, init_cfg=None): super(Bottleneck, self).__init__(init_cfg=init_cfg) assert style in ['pytorch', 'caffe'] self.in_channels = in_channels self.out_channels = out_channels self.expansion = expansion assert out_channels % expansion == 0 self.mid_channels = out_channels // expansion self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg if self.style == 'pytorch': self.conv1_stride = 1 self.conv2_stride = stride else: self.conv1_stride = stride self.conv2_stride = 1 self.norm1_name, norm1 = build_norm_layer( norm_cfg, self.mid_channels, postfix=1) self.norm2_name, norm2 = build_norm_layer( norm_cfg, self.mid_channels, postfix=2) self.norm3_name, norm3 = build_norm_layer( norm_cfg, out_channels, postfix=3) self.conv1 = build_conv_layer( conv_cfg, in_channels, self.mid_channels, kernel_size=1, stride=self.conv1_stride, bias=False) self.add_module(self.norm1_name, norm1) self.conv2 = build_conv_layer( conv_cfg, self.mid_channels, self.mid_channels, kernel_size=3, stride=self.conv2_stride, padding=dilation, dilation=dilation, bias=False) self.add_module(self.norm2_name, norm2) self.conv3 = build_conv_layer( conv_cfg, self.mid_channels, out_channels, kernel_size=1, bias=False) self.add_module(self.norm3_name, norm3) self.relu = build_activation_layer(act_cfg) self.downsample = downsample self.drop_path = DropPath(drop_prob=drop_path_rate ) if drop_path_rate > eps else nn.Identity() @property def norm1(self): return getattr(self, self.norm1_name) @property def norm2(self): return getattr(self, self.norm2_name) @property def norm3(self): return getattr(self, self.norm3_name) def forward(self, x): def _inner_forward(x): identity = x out = self.conv1(x) out = self.norm1(out) out = self.relu(out) out = self.conv2(out) out = self.norm2(out) out = self.relu(out) out = self.conv3(out) out = self.norm3(out) if self.downsample is not None: identity = self.downsample(x) out = self.drop_path(out) out += identity return out if self.with_cp and x.requires_grad: out = cp.checkpoint(_inner_forward, x) else: out = _inner_forward(x) out = self.relu(out) return out def get_expansion(block, expansion=None): """Get the expansion of a residual block. The block expansion will be obtained by the following order: 1. If ``expansion`` is given, just return it. 2. If ``block`` has the attribute ``expansion``, then return ``block.expansion``. 3. Return the default value according the the block type: 1 for ``BasicBlock`` and 4 for ``Bottleneck``. Args: block (class): The block class. expansion (int | None): The given expansion ratio. Returns: int: The expansion of the block. """ if isinstance(expansion, int): assert expansion > 0 elif expansion is None: if hasattr(block, 'expansion'): expansion = block.expansion elif issubclass(block, BasicBlock): expansion = 1 elif issubclass(block, Bottleneck): expansion = 4 else: raise TypeError(f'expansion is not specified for {block.__name__}') else: raise TypeError('expansion must be an integer or None') return expansion class ResLayer(nn.Sequential): """ResLayer to build ResNet style backbone. Args: block (nn.Module): Residual block used to build ResLayer. num_blocks (int): Number of blocks. in_channels (int): Input channels of this block. out_channels (int): Output channels of this block. expansion (int, optional): The expansion for BasicBlock/Bottleneck. If not specified, it will firstly be obtained via ``block.expansion``. If the block has no attribute "expansion", the following default values will be used: 1 for BasicBlock and 4 for Bottleneck. Default: None. stride (int): stride of the first block. Default: 1. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False conv_cfg (dict, optional): dictionary to construct and config conv layer. Default: None norm_cfg (dict): dictionary to construct and config norm layer. Default: dict(type='BN') """ def __init__(self, block, num_blocks, in_channels, out_channels, expansion=None, stride=1, avg_down=False, conv_cfg=None, norm_cfg=dict(type='BN'), **kwargs): self.block = block self.expansion = get_expansion(block, expansion) downsample = None if stride != 1 or in_channels != out_channels: downsample = [] conv_stride = stride if avg_down and stride != 1: conv_stride = 1 downsample.append( nn.AvgPool2d( kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False)) downsample.extend([ build_conv_layer( conv_cfg, in_channels, out_channels, kernel_size=1, stride=conv_stride, bias=False), build_norm_layer(norm_cfg, out_channels)[1] ]) downsample = nn.Sequential(*downsample) layers = [] layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=stride, downsample=downsample, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) in_channels = out_channels for i in range(1, num_blocks): layers.append( block( in_channels=in_channels, out_channels=out_channels, expansion=self.expansion, stride=1, conv_cfg=conv_cfg, norm_cfg=norm_cfg, **kwargs)) super(ResLayer, self).__init__(*layers) @BACKBONES.register_module() class ResNet(BaseBackbone): """ResNet backbone. Please refer to the `paper `__ for details. Args: depth (int): Network depth, from {18, 34, 50, 101, 152}. in_channels (int): Number of input image channels. Default: 3. stem_channels (int): Output channels of the stem layer. Default: 64. base_channels (int): Middle channels of the first stage. Default: 64. num_stages (int): Stages of the network. Default: 4. strides (Sequence[int]): Strides of the first block of each stage. Default: ``(1, 2, 2, 2)``. dilations (Sequence[int]): Dilation of each stage. Default: ``(1, 1, 1, 1)``. out_indices (Sequence[int]): Output from which stages. Default: ``(3, )``. style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two layer is the 3x3 conv layer, otherwise the stride-two layer is the first 1x1 conv layer. deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv. Default: False. avg_down (bool): Use AvgPool instead of stride conv when downsampling in the bottleneck. Default: False. frozen_stages (int): Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Default: -1. conv_cfg (dict | None): The config dict for conv layers. Default: None. norm_cfg (dict): The config dict for norm layers. norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. Default: False. with_cp (bool): Use checkpoint or not. Using checkpoint will save some memory while slowing down the training speed. Default: False. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. Default: True. Example: >>> from mmcls.models import ResNet >>> import torch >>> self = ResNet(depth=18) >>> self.eval() >>> inputs = torch.rand(1, 3, 32, 32) >>> level_outputs = self.forward(inputs) >>> for level_out in level_outputs: ... print(tuple(level_out.shape)) (1, 64, 8, 8) (1, 128, 4, 4) (1, 256, 2, 2) (1, 512, 1, 1) """ arch_settings = { 18: (BasicBlock, (2, 2, 2, 2)), 34: (BasicBlock, (3, 4, 6, 3)), 50: (Bottleneck, (3, 4, 6, 3)), 101: (Bottleneck, (3, 4, 23, 3)), 152: (Bottleneck, (3, 8, 36, 3)) } def __init__(self, depth, in_channels=3, stem_channels=64, base_channels=64, expansion=None, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3, ), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=-1, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=False, with_cp=False, zero_init_residual=True, init_cfg=[ dict(type='Kaiming', layer=['Conv2d']), dict( type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm']) ], drop_path_rate=0.0): super(ResNet, self).__init__(init_cfg) if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for resnet') self.depth = depth self.stem_channels = stem_channels self.base_channels = base_channels self.num_stages = num_stages assert num_stages >= 1 and num_stages <= 4 self.strides = strides self.dilations = dilations assert len(strides) == len(dilations) == num_stages self.out_indices = out_indices assert max(out_indices) < num_stages self.style = style self.deep_stem = deep_stem self.avg_down = avg_down self.frozen_stages = frozen_stages self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.with_cp = with_cp self.norm_eval = norm_eval self.zero_init_residual = zero_init_residual self.block, stage_blocks = self.arch_settings[depth] self.stage_blocks = stage_blocks[:num_stages] self.expansion = get_expansion(self.block, expansion) self._make_stem_layer(in_channels, stem_channels) self.res_layers = [] _in_channels = stem_channels _out_channels = base_channels * self.expansion for i, num_blocks in enumerate(self.stage_blocks): stride = strides[i] dilation = dilations[i] res_layer = self.make_res_layer( block=self.block, num_blocks=num_blocks, in_channels=_in_channels, out_channels=_out_channels, expansion=self.expansion, stride=stride, dilation=dilation, style=self.style, avg_down=self.avg_down, with_cp=with_cp, conv_cfg=conv_cfg, norm_cfg=norm_cfg, drop_path_rate=drop_path_rate) _in_channels = _out_channels _out_channels *= 2 layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) self.res_layers.append(layer_name) self._freeze_stages() self.feat_dim = res_layer[-1].out_channels def make_res_layer(self, **kwargs): return ResLayer(**kwargs) @property def norm1(self): return getattr(self, self.norm1_name) def _make_stem_layer(self, in_channels, stem_channels): if self.deep_stem: self.stem = nn.Sequential( ConvModule( in_channels, stem_channels // 2, kernel_size=3, stride=2, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels // 2, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True), ConvModule( stem_channels // 2, stem_channels, kernel_size=3, stride=1, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, inplace=True)) else: self.conv1 = build_conv_layer( self.conv_cfg, in_channels, stem_channels, kernel_size=7, stride=2, padding=3, bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, stem_channels, postfix=1) self.add_module(self.norm1_name, norm1) # self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def _freeze_stages(self): if self.frozen_stages >= 0: if self.deep_stem: self.stem.eval() for param in self.stem.parameters(): param.requires_grad = False else: self.norm1.eval() for m in [self.conv1, self.norm1]: for param in m.parameters(): param.requires_grad = False for i in range(1, self.frozen_stages + 1): m = getattr(self, f'layer{i}') m.eval() for param in m.parameters(): param.requires_grad = False def init_weights(self): super(ResNet, self).init_weights() if (isinstance(self.init_cfg, dict) and self.init_cfg['type'] == 'Pretrained'): # Suppress zero_init_residual if use pretrained model. return if self.zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): constant_init(m.norm3, 0) elif isinstance(m, BasicBlock): constant_init(m.norm2, 0) def forward(self, x): if self.deep_stem: x = self.stem(x) else: x = self.conv1(x) x = self.norm1(x) # x = self.relu(x) x = self.maxpool(x) outs = [] for i, layer_name in enumerate(self.res_layers): res_layer = getattr(self, layer_name) x = res_layer(x) if i in self.out_indices: outs.append(x) return tuple(outs) def train(self, mode=True): super(ResNet, self).train(mode) self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): m.eval() @BACKBONES.register_module() class ResNetV1c(ResNet): """ResNetV1c backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1c replaces the 7x7 conv in the input stem with three 3x3 convs. """ def __init__(self, **kwargs): super(ResNetV1c, self).__init__( deep_stem=True, avg_down=False, **kwargs) @BACKBONES.register_module() class ResNetV1d(ResNet): """ResNetV1d backbone. This variant is described in `Bag of Tricks. `_. Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in the input stem with three 3x3 convs. And in the downsampling block, a 2x2 avg_pool with stride 2 is added before conv, whose stride is changed to 1. """ def __init__(self, **kwargs): super(ResNetV1d, self).__init__( deep_stem=True, avg_down=True, **kwargs) ================================================ FILE: classification-NO-activation-function/logs/pinet_1x1_norelu.log ================================================ 2023-01-01 16:31:14,068 - mmcls - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.6.12 |Anaconda, Inc.| (default, Sep 8 2020, 23:10:56) [GCC 7.3.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 10.2, V10.2.8 GCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0 PyTorch: 1.9.0+cu102 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 10.2 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70 - CuDNN 7.6.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu102 OpenCV: 4.5.5 MMCV: 1.5.1 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler: 10.2 MMClassification: 0.24.0+91b85bb ------------------------------------------------------------ 2023-01-01 16:31:14,068 - mmcls - INFO - Distributed training: True 2023-01-01 16:31:14,230 - mmcls - INFO - Config: model = dict( type='ImageClassifier', backbone=dict( type='ResNet', depth=18, num_stages=4, out_indices=(3, ), style='pytorch'), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=1000, in_channels=512, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5))) dataset_type = 'ImageNet' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ] data = dict( samples_per_gpu=32, workers_per_gpu=2, train=dict( type='ImageNet', data_prefix='data/imagenet/train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ]), val=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]), test=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ])) evaluation = dict(interval=1, metric='accuracy') optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict(policy='step', step=[30, 60, 90]) runner = dict(type='EpochBasedRunner', max_epochs=100) checkpoint_config = dict(interval=50) log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] work_dir = './work_dirs/resnet18_8xb32_in1k_pi' gpu_ids = range(0, 8) 2023-01-01 16:31:19,484 - mmcls - INFO - Set random seed to 1232336006, deterministic: False 2023-01-01 16:31:19,669 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}] 2023-01-01 16:31:19,760 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01} Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv3.weight - torch.Size([64, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn3.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv3.weight - torch.Size([64, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn3.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv3.weight - torch.Size([128, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn3.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv3.weight - torch.Size([128, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn3.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv3.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv3.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv3.weight - torch.Size([512, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.downsample.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv3.weight - torch.Size([512, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier head.fc.weight - torch.Size([1000, 512]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([1000]): NormalInit: mean=0, std=0.01, bias=0 2023-01-01 16:31:48,444 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi 2023-01-01 16:31:48,444 - mmcls - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook -------------------- after_train_iter: (ABOVE_NORMAL) DistOptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (VERY_LOW ) TextLoggerHook -------------------- before_val_iter: (LOW ) IterTimerHook -------------------- after_val_iter: (LOW ) IterTimerHook -------------------- after_val_epoch: (VERY_LOW ) TextLoggerHook -------------------- after_run: (VERY_LOW ) TextLoggerHook -------------------- 2023-01-01 16:31:48,445 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs 2023-01-01 16:31:48,445 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend. 2023-01-01 16:32:01,103 - mmcls - INFO - Epoch [1][100/5005] lr: 1.000e-01, eta: 17:33:36, time: 0.126, data_time: 0.027, memory: 1224, loss: 6.7943 2023-01-01 16:32:09,823 - mmcls - INFO - Epoch [1][200/5005] lr: 1.000e-01, eta: 14:50:16, time: 0.087, data_time: 0.007, memory: 1224, loss: 6.5618 2023-01-01 16:32:18,667 - mmcls - INFO - Epoch [1][300/5005] lr: 1.000e-01, eta: 13:58:48, time: 0.088, data_time: 0.001, memory: 1224, loss: 6.3981 2023-01-01 16:32:28,033 - mmcls - INFO - Epoch [1][400/5005] lr: 1.000e-01, eta: 13:44:20, time: 0.094, data_time: 0.001, memory: 1224, loss: 6.2250 2023-01-01 16:32:36,751 - mmcls - INFO - Epoch [1][500/5005] lr: 1.000e-01, eta: 13:24:30, time: 0.087, data_time: 0.005, memory: 1224, loss: 6.0684 2023-01-01 16:32:45,889 - mmcls - INFO - Epoch [1][600/5005] lr: 1.000e-01, eta: 13:17:21, time: 0.092, data_time: 0.003, memory: 1224, loss: 5.9139 2023-01-01 16:32:54,902 - mmcls - INFO - Epoch [1][700/5005] lr: 1.000e-01, eta: 13:10:29, time: 0.090, data_time: 0.022, memory: 1224, loss: 5.7723 2023-01-01 16:33:04,093 - mmcls - INFO - Epoch [1][800/5005] lr: 1.000e-01, eta: 13:07:15, time: 0.092, data_time: 0.004, memory: 1224, loss: 5.6559 2023-01-01 16:33:13,416 - mmcls - INFO - Epoch [1][900/5005] lr: 1.000e-01, eta: 13:05:53, time: 0.093, data_time: 0.001, memory: 1224, loss: 5.5643 2023-01-01 16:33:22,401 - mmcls - INFO - Epoch [1][1000/5005] lr: 1.000e-01, eta: 13:01:53, time: 0.090, data_time: 0.002, memory: 1224, loss: 5.4418 2023-01-01 16:33:31,309 - mmcls - INFO - Epoch [1][1100/5005] lr: 1.000e-01, eta: 12:58:07, time: 0.089, data_time: 0.017, memory: 1224, loss: 5.3744 2023-01-01 16:33:40,254 - mmcls - INFO - Epoch [1][1200/5005] lr: 1.000e-01, eta: 12:55:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 5.2766 2023-01-01 16:33:49,209 - mmcls - INFO - Epoch [1][1300/5005] lr: 1.000e-01, eta: 12:52:39, time: 0.090, data_time: 0.003, memory: 1224, loss: 5.2192 2023-01-01 16:33:58,435 - mmcls - INFO - Epoch [1][1400/5005] lr: 1.000e-01, eta: 12:52:08, time: 0.092, data_time: 0.021, memory: 1224, loss: 5.1091 2023-01-01 16:34:07,654 - mmcls - INFO - Epoch [1][1500/5005] lr: 1.000e-01, eta: 12:51:40, time: 0.092, data_time: 0.002, memory: 1224, loss: 5.0672 2023-01-01 16:34:16,444 - mmcls - INFO - Epoch [1][1600/5005] lr: 1.000e-01, eta: 12:48:54, time: 0.088, data_time: 0.011, memory: 1224, loss: 4.9784 2023-01-01 16:34:25,494 - mmcls - INFO - Epoch [1][1700/5005] lr: 1.000e-01, eta: 12:47:49, time: 0.091, data_time: 0.014, memory: 1224, loss: 4.9488 2023-01-01 16:34:34,582 - mmcls - INFO - Epoch [1][1800/5005] lr: 1.000e-01, eta: 12:46:58, time: 0.091, data_time: 0.001, memory: 1224, loss: 4.8974 2023-01-01 16:34:43,683 - mmcls - INFO - Epoch [1][1900/5005] lr: 1.000e-01, eta: 12:46:14, time: 0.091, data_time: 0.011, memory: 1224, loss: 4.8291 2023-01-01 16:34:52,944 - mmcls - INFO - Epoch [1][2000/5005] lr: 1.000e-01, eta: 12:46:14, time: 0.093, data_time: 0.010, memory: 1224, loss: 4.7950 2023-01-01 16:35:01,799 - mmcls - INFO - Epoch [1][2100/5005] lr: 1.000e-01, eta: 12:44:38, time: 0.089, data_time: 0.002, memory: 1224, loss: 4.7290 2023-01-01 16:35:10,855 - mmcls - INFO - Epoch [1][2200/5005] lr: 1.000e-01, eta: 12:43:55, time: 0.091, data_time: 0.005, memory: 1224, loss: 4.6624 2023-01-01 16:35:19,750 - mmcls - INFO - Epoch [1][2300/5005] lr: 1.000e-01, eta: 12:42:40, time: 0.089, data_time: 0.009, memory: 1224, loss: 4.6366 2023-01-01 16:35:29,019 - mmcls - INFO - Epoch [1][2400/5005] lr: 1.000e-01, eta: 12:42:49, time: 0.093, data_time: 0.001, memory: 1224, loss: 4.5740 2023-01-01 16:35:37,864 - mmcls - INFO - Epoch [1][2500/5005] lr: 1.000e-01, eta: 12:41:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 4.5621 2023-01-01 16:35:46,875 - mmcls - INFO - Epoch [1][2600/5005] lr: 1.000e-01, eta: 12:40:50, time: 0.090, data_time: 0.004, memory: 1224, loss: 4.5201 2023-01-01 16:35:55,653 - mmcls - INFO - Epoch [1][2700/5005] lr: 1.000e-01, eta: 12:39:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 4.4704 2023-01-01 16:36:04,364 - mmcls - INFO - Epoch [1][2800/5005] lr: 1.000e-01, eta: 12:38:00, time: 0.087, data_time: 0.007, memory: 1224, loss: 4.4646 2023-01-01 16:36:13,390 - mmcls - INFO - Epoch [1][2900/5005] lr: 1.000e-01, eta: 12:37:32, time: 0.090, data_time: 0.027, memory: 1224, loss: 4.4182 2023-01-01 16:36:22,304 - mmcls - INFO - Epoch [1][3000/5005] lr: 1.000e-01, eta: 12:36:45, time: 0.089, data_time: 0.007, memory: 1224, loss: 4.3918 2023-01-01 16:36:31,063 - mmcls - INFO - Epoch [1][3100/5005] lr: 1.000e-01, eta: 12:35:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 4.3417 2023-01-01 16:36:40,190 - mmcls - INFO - Epoch [1][3200/5005] lr: 1.000e-01, eta: 12:35:30, time: 0.091, data_time: 0.001, memory: 1224, loss: 4.3231 2023-01-01 16:36:49,028 - mmcls - INFO - Epoch [1][3300/5005] lr: 1.000e-01, eta: 12:34:39, time: 0.088, data_time: 0.005, memory: 1224, loss: 4.3135 2023-01-01 16:36:58,094 - mmcls - INFO - Epoch [1][3400/5005] lr: 1.000e-01, eta: 12:34:23, time: 0.091, data_time: 0.004, memory: 1224, loss: 4.2897 2023-01-01 16:37:07,001 - mmcls - INFO - Epoch [1][3500/5005] lr: 1.000e-01, eta: 12:33:48, time: 0.089, data_time: 0.005, memory: 1224, loss: 4.2420 2023-01-01 16:37:15,801 - mmcls - INFO - Epoch [1][3600/5005] lr: 1.000e-01, eta: 12:32:56, time: 0.088, data_time: 0.007, memory: 1224, loss: 4.2271 2023-01-01 16:37:24,964 - mmcls - INFO - Epoch [1][3700/5005] lr: 1.000e-01, eta: 12:32:56, time: 0.092, data_time: 0.002, memory: 1224, loss: 4.2202 2023-01-01 16:37:34,440 - mmcls - INFO - Epoch [1][3800/5005] lr: 1.000e-01, eta: 12:33:37, time: 0.095, data_time: 0.011, memory: 1224, loss: 4.1578 2023-01-01 16:37:43,412 - mmcls - INFO - Epoch [1][3900/5005] lr: 1.000e-01, eta: 12:33:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 4.1460 2023-01-01 16:37:51,984 - mmcls - INFO - Epoch [1][4000/5005] lr: 1.000e-01, eta: 12:31:56, time: 0.086, data_time: 0.018, memory: 1224, loss: 4.1256 2023-01-01 16:38:01,139 - mmcls - INFO - Epoch [1][4100/5005] lr: 1.000e-01, eta: 12:31:55, time: 0.092, data_time: 0.002, memory: 1224, loss: 4.1535 2023-01-01 16:38:10,030 - mmcls - INFO - Epoch [1][4200/5005] lr: 1.000e-01, eta: 12:31:23, time: 0.089, data_time: 0.001, memory: 1224, loss: 4.1206 2023-01-01 16:38:19,057 - mmcls - INFO - Epoch [1][4300/5005] lr: 1.000e-01, eta: 12:31:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 4.0641 2023-01-01 16:38:28,015 - mmcls - INFO - Epoch [1][4400/5005] lr: 1.000e-01, eta: 12:30:43, time: 0.090, data_time: 0.018, memory: 1224, loss: 4.0750 2023-01-01 16:38:37,135 - mmcls - INFO - Epoch [1][4500/5005] lr: 1.000e-01, eta: 12:30:39, time: 0.091, data_time: 0.003, memory: 1224, loss: 4.0476 2023-01-01 16:38:46,191 - mmcls - INFO - Epoch [1][4600/5005] lr: 1.000e-01, eta: 12:30:28, time: 0.091, data_time: 0.006, memory: 1224, loss: 4.0405 2023-01-01 16:38:55,786 - mmcls - INFO - Epoch [1][4700/5005] lr: 1.000e-01, eta: 12:31:13, time: 0.096, data_time: 0.002, memory: 1224, loss: 4.0158 2023-01-01 16:39:04,790 - mmcls - INFO - Epoch [1][4800/5005] lr: 1.000e-01, eta: 12:30:55, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.9927 2023-01-01 16:39:13,801 - mmcls - INFO - Epoch [1][4900/5005] lr: 1.000e-01, eta: 12:30:37, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.9823 2023-01-01 16:39:22,580 - mmcls - INFO - Epoch [1][5000/5005] lr: 1.000e-01, eta: 12:29:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.9691 2023-01-01 16:39:46,049 - mmcls - INFO - Epoch(val) [1][196] accuracy_top-1: 27.0620, accuracy_top-5: 51.3400 2023-01-01 16:39:57,154 - mmcls - INFO - Epoch [2][100/5005] lr: 1.000e-01, eta: 12:32:19, time: 0.111, data_time: 0.050, memory: 1224, loss: 3.8937 2023-01-01 16:40:05,913 - mmcls - INFO - Epoch [2][200/5005] lr: 1.000e-01, eta: 12:31:35, time: 0.087, data_time: 0.008, memory: 1224, loss: 3.9343 2023-01-01 16:40:14,549 - mmcls - INFO - Epoch [2][300/5005] lr: 1.000e-01, eta: 12:30:42, time: 0.086, data_time: 0.016, memory: 1224, loss: 3.8934 2023-01-01 16:40:23,809 - mmcls - INFO - Epoch [2][400/5005] lr: 1.000e-01, eta: 12:30:48, time: 0.093, data_time: 0.011, memory: 1224, loss: 3.8838 2023-01-01 16:40:32,474 - mmcls - INFO - Epoch [2][500/5005] lr: 1.000e-01, eta: 12:30:00, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.8964 2023-01-01 16:40:41,451 - mmcls - INFO - Epoch [2][600/5005] lr: 1.000e-01, eta: 12:29:40, time: 0.090, data_time: 0.014, memory: 1224, loss: 3.8346 2023-01-01 16:40:50,179 - mmcls - INFO - Epoch [2][700/5005] lr: 1.000e-01, eta: 12:29:00, time: 0.087, data_time: 0.017, memory: 1224, loss: 3.8494 2023-01-01 16:40:59,384 - mmcls - INFO - Epoch [2][800/5005] lr: 1.000e-01, eta: 12:29:01, time: 0.092, data_time: 0.012, memory: 1224, loss: 3.8240 2023-01-01 16:41:08,143 - mmcls - INFO - Epoch [2][900/5005] lr: 1.000e-01, eta: 12:28:25, time: 0.088, data_time: 0.003, memory: 1224, loss: 3.8599 2023-01-01 16:41:17,200 - mmcls - INFO - Epoch [2][1000/5005] lr: 1.000e-01, eta: 12:28:14, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.8085 2023-01-01 16:41:26,152 - mmcls - INFO - Epoch [2][1100/5005] lr: 1.000e-01, eta: 12:27:54, time: 0.090, data_time: 0.016, memory: 1224, loss: 3.8072 2023-01-01 16:41:35,104 - mmcls - INFO - Epoch [2][1200/5005] lr: 1.000e-01, eta: 12:27:36, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.8290 2023-01-01 16:41:43,992 - mmcls - INFO - Epoch [2][1300/5005] lr: 1.000e-01, eta: 12:27:11, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.8225 2023-01-01 16:41:52,912 - mmcls - INFO - Epoch [2][1400/5005] lr: 1.000e-01, eta: 12:26:51, time: 0.089, data_time: 0.013, memory: 1224, loss: 3.7633 2023-01-01 16:42:01,992 - mmcls - INFO - Epoch [2][1500/5005] lr: 1.000e-01, eta: 12:26:42, time: 0.091, data_time: 0.012, memory: 1224, loss: 3.7492 2023-01-01 16:42:10,788 - mmcls - INFO - Epoch [2][1600/5005] lr: 1.000e-01, eta: 12:26:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.7819 2023-01-01 16:42:19,577 - mmcls - INFO - Epoch [2][1700/5005] lr: 1.000e-01, eta: 12:25:44, time: 0.088, data_time: 0.030, memory: 1224, loss: 3.7568 2023-01-01 16:42:28,423 - mmcls - INFO - Epoch [2][1800/5005] lr: 1.000e-01, eta: 12:25:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.7011 2023-01-01 16:42:37,539 - mmcls - INFO - Epoch [2][1900/5005] lr: 1.000e-01, eta: 12:25:14, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.6875 2023-01-01 16:42:46,593 - mmcls - INFO - Epoch [2][2000/5005] lr: 1.000e-01, eta: 12:25:04, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.7433 2023-01-01 16:42:55,891 - mmcls - INFO - Epoch [2][2100/5005] lr: 1.000e-01, eta: 12:25:11, time: 0.093, data_time: 0.013, memory: 1224, loss: 3.6791 2023-01-01 16:43:04,825 - mmcls - INFO - Epoch [2][2200/5005] lr: 1.000e-01, eta: 12:24:53, time: 0.089, data_time: 0.007, memory: 1224, loss: 3.7298 2023-01-01 16:43:13,790 - mmcls - INFO - Epoch [2][2300/5005] lr: 1.000e-01, eta: 12:24:39, time: 0.090, data_time: 0.011, memory: 1224, loss: 3.7094 2023-01-01 16:43:22,506 - mmcls - INFO - Epoch [2][2400/5005] lr: 1.000e-01, eta: 12:24:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.6950 2023-01-01 16:43:31,663 - mmcls - INFO - Epoch [2][2500/5005] lr: 1.000e-01, eta: 12:24:04, time: 0.092, data_time: 0.002, memory: 1224, loss: 3.6878 2023-01-01 16:43:40,408 - mmcls - INFO - Epoch [2][2600/5005] lr: 1.000e-01, eta: 12:23:35, time: 0.087, data_time: 0.009, memory: 1224, loss: 3.6642 2023-01-01 16:43:49,467 - mmcls - INFO - Epoch [2][2700/5005] lr: 1.000e-01, eta: 12:23:27, time: 0.091, data_time: 0.018, memory: 1224, loss: 3.6835 2023-01-01 16:43:58,312 - mmcls - INFO - Epoch [2][2800/5005] lr: 1.000e-01, eta: 12:23:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.6533 2023-01-01 16:44:06,958 - mmcls - INFO - Epoch [2][2900/5005] lr: 1.000e-01, eta: 12:22:30, time: 0.087, data_time: 0.031, memory: 1224, loss: 3.6510 2023-01-01 16:44:15,826 - mmcls - INFO - Epoch [2][3000/5005] lr: 1.000e-01, eta: 12:22:10, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.6303 2023-01-01 16:44:24,759 - mmcls - INFO - Epoch [2][3100/5005] lr: 1.000e-01, eta: 12:21:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.6254 2023-01-01 16:44:33,787 - mmcls - INFO - Epoch [2][3200/5005] lr: 1.000e-01, eta: 12:21:44, time: 0.090, data_time: 0.016, memory: 1224, loss: 3.6258 2023-01-01 16:44:42,652 - mmcls - INFO - Epoch [2][3300/5005] lr: 1.000e-01, eta: 12:21:25, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.6113 2023-01-01 16:44:51,325 - mmcls - INFO - Epoch [2][3400/5005] lr: 1.000e-01, eta: 12:20:54, time: 0.087, data_time: 0.006, memory: 1224, loss: 3.5648 2023-01-01 16:45:00,053 - mmcls - INFO - Epoch [2][3500/5005] lr: 1.000e-01, eta: 12:20:28, time: 0.087, data_time: 0.020, memory: 1224, loss: 3.5955 2023-01-01 16:45:08,746 - mmcls - INFO - Epoch [2][3600/5005] lr: 1.000e-01, eta: 12:19:59, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.5998 2023-01-01 16:45:17,468 - mmcls - INFO - Epoch [2][3700/5005] lr: 1.000e-01, eta: 12:19:33, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.5449 2023-01-01 16:45:26,400 - mmcls - INFO - Epoch [2][3800/5005] lr: 1.000e-01, eta: 12:19:19, time: 0.089, data_time: 0.020, memory: 1224, loss: 3.5836 2023-01-01 16:45:35,128 - mmcls - INFO - Epoch [2][3900/5005] lr: 1.000e-01, eta: 12:18:54, time: 0.087, data_time: 0.011, memory: 1224, loss: 3.5650 2023-01-01 16:45:44,410 - mmcls - INFO - Epoch [2][4000/5005] lr: 1.000e-01, eta: 12:18:59, time: 0.093, data_time: 0.008, memory: 1224, loss: 3.5710 2023-01-01 16:45:53,276 - mmcls - INFO - Epoch [2][4100/5005] lr: 1.000e-01, eta: 12:18:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.5962 2023-01-01 16:46:02,000 - mmcls - INFO - Epoch [2][4200/5005] lr: 1.000e-01, eta: 12:18:16, time: 0.087, data_time: 0.013, memory: 1224, loss: 3.5322 2023-01-01 16:46:11,414 - mmcls - INFO - Epoch [2][4300/5005] lr: 1.000e-01, eta: 12:18:28, time: 0.094, data_time: 0.005, memory: 1224, loss: 3.5285 2023-01-01 16:46:20,631 - mmcls - INFO - Epoch [2][4400/5005] lr: 1.000e-01, eta: 12:18:30, time: 0.092, data_time: 0.001, memory: 1224, loss: 3.5371 2023-01-01 16:46:29,368 - mmcls - INFO - Epoch [2][4500/5005] lr: 1.000e-01, eta: 12:18:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.5495 2023-01-01 16:46:38,562 - mmcls - INFO - Epoch [2][4600/5005] lr: 1.000e-01, eta: 12:18:06, time: 0.092, data_time: 0.004, memory: 1224, loss: 3.5300 2023-01-01 16:46:47,644 - mmcls - INFO - Epoch [2][4700/5005] lr: 1.000e-01, eta: 12:18:00, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.5532 2023-01-01 16:46:56,597 - mmcls - INFO - Epoch [2][4800/5005] lr: 1.000e-01, eta: 12:17:47, time: 0.090, data_time: 0.000, memory: 1224, loss: 3.5306 2023-01-01 16:47:05,637 - mmcls - INFO - Epoch [2][4900/5005] lr: 1.000e-01, eta: 12:17:39, time: 0.090, data_time: 0.009, memory: 1224, loss: 3.5050 2023-01-01 16:47:14,053 - mmcls - INFO - Epoch [2][5000/5005] lr: 1.000e-01, eta: 12:17:00, time: 0.084, data_time: 0.011, memory: 1224, loss: 3.5432 2023-01-01 16:47:37,395 - mmcls - INFO - Epoch(val) [2][196] accuracy_top-1: 31.7280, accuracy_top-5: 56.4860 2023-01-01 16:47:48,377 - mmcls - INFO - Epoch [3][100/5005] lr: 1.000e-01, eta: 12:18:04, time: 0.110, data_time: 0.034, memory: 1224, loss: 3.4193 2023-01-01 16:47:57,234 - mmcls - INFO - Epoch [3][200/5005] lr: 1.000e-01, eta: 12:17:46, time: 0.088, data_time: 0.006, memory: 1224, loss: 3.4269 2023-01-01 16:48:06,014 - mmcls - INFO - Epoch [3][300/5005] lr: 1.000e-01, eta: 12:17:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.4609 2023-01-01 16:48:14,886 - mmcls - INFO - Epoch [3][400/5005] lr: 1.000e-01, eta: 12:17:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.4043 2023-01-01 16:48:23,899 - mmcls - INFO - Epoch [3][500/5005] lr: 1.000e-01, eta: 12:16:59, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.4147 2023-01-01 16:48:32,630 - mmcls - INFO - Epoch [3][600/5005] lr: 1.000e-01, eta: 12:16:36, time: 0.087, data_time: 0.019, memory: 1224, loss: 3.4490 2023-01-01 16:48:41,614 - mmcls - INFO - Epoch [3][700/5005] lr: 1.000e-01, eta: 12:16:25, time: 0.090, data_time: 0.002, memory: 1224, loss: 3.4249 2023-01-01 16:48:50,202 - mmcls - INFO - Epoch [3][800/5005] lr: 1.000e-01, eta: 12:15:57, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.4669 2023-01-01 16:48:59,377 - mmcls - INFO - Epoch [3][900/5005] lr: 1.000e-01, eta: 12:15:55, time: 0.092, data_time: 0.004, memory: 1224, loss: 3.4597 2023-01-01 16:49:08,176 - mmcls - INFO - Epoch [3][1000/5005] lr: 1.000e-01, eta: 12:15:36, time: 0.088, data_time: 0.012, memory: 1224, loss: 3.4512 2023-01-01 16:49:17,045 - mmcls - INFO - Epoch [3][1100/5005] lr: 1.000e-01, eta: 12:15:21, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.4199 2023-01-01 16:49:26,353 - mmcls - INFO - Epoch [3][1200/5005] lr: 1.000e-01, eta: 12:15:24, time: 0.093, data_time: 0.002, memory: 1224, loss: 3.4413 2023-01-01 16:49:35,401 - mmcls - INFO - Epoch [3][1300/5005] lr: 1.000e-01, eta: 12:15:17, time: 0.090, data_time: 0.006, memory: 1224, loss: 3.4349 2023-01-01 16:49:44,438 - mmcls - INFO - Epoch [3][1400/5005] lr: 1.000e-01, eta: 12:15:08, time: 0.090, data_time: 0.005, memory: 1224, loss: 3.4003 2023-01-01 16:49:53,498 - mmcls - INFO - Epoch [3][1500/5005] lr: 1.000e-01, eta: 12:15:01, time: 0.091, data_time: 0.005, memory: 1224, loss: 3.3839 2023-01-01 16:50:02,469 - mmcls - INFO - Epoch [3][1600/5005] lr: 1.000e-01, eta: 12:14:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.3908 2023-01-01 16:50:11,371 - mmcls - INFO - Epoch [3][1700/5005] lr: 1.000e-01, eta: 12:14:36, time: 0.089, data_time: 0.010, memory: 1224, loss: 3.3845 2023-01-01 16:50:20,246 - mmcls - INFO - Epoch [3][1800/5005] lr: 1.000e-01, eta: 12:14:21, time: 0.089, data_time: 0.011, memory: 1224, loss: 3.3920 2023-01-01 16:50:29,299 - mmcls - INFO - Epoch [3][1900/5005] lr: 1.000e-01, eta: 12:14:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.4334 2023-01-01 16:50:38,340 - mmcls - INFO - Epoch [3][2000/5005] lr: 1.000e-01, eta: 12:14:05, time: 0.090, data_time: 0.016, memory: 1224, loss: 3.3754 2023-01-01 16:50:47,308 - mmcls - INFO - Epoch [3][2100/5005] lr: 1.000e-01, eta: 12:13:54, time: 0.090, data_time: 0.007, memory: 1224, loss: 3.4104 2023-01-01 16:50:56,408 - mmcls - INFO - Epoch [3][2200/5005] lr: 1.000e-01, eta: 12:13:49, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.3738 2023-01-01 16:51:05,321 - mmcls - INFO - Epoch [3][2300/5005] lr: 1.000e-01, eta: 12:13:35, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.3685 2023-01-01 16:51:14,103 - mmcls - INFO - Epoch [3][2400/5005] lr: 1.000e-01, eta: 12:13:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.4038 2023-01-01 16:51:23,118 - mmcls - INFO - Epoch [3][2500/5005] lr: 1.000e-01, eta: 12:13:08, time: 0.090, data_time: 0.015, memory: 1224, loss: 3.3754 2023-01-01 16:51:31,995 - mmcls - INFO - Epoch [3][2600/5005] lr: 1.000e-01, eta: 12:12:54, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.3689 2023-01-01 16:51:40,741 - mmcls - INFO - Epoch [3][2700/5005] lr: 1.000e-01, eta: 12:12:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.4128 2023-01-01 16:51:49,586 - mmcls - INFO - Epoch [3][2800/5005] lr: 1.000e-01, eta: 12:12:19, time: 0.088, data_time: 0.016, memory: 1224, loss: 3.3487 2023-01-01 16:51:58,464 - mmcls - INFO - Epoch [3][2900/5005] lr: 1.000e-01, eta: 12:12:05, time: 0.089, data_time: 0.007, memory: 1224, loss: 3.3723 2023-01-01 16:52:07,411 - mmcls - INFO - Epoch [3][3000/5005] lr: 1.000e-01, eta: 12:11:54, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.3516 2023-01-01 16:52:16,375 - mmcls - INFO - Epoch [3][3100/5005] lr: 1.000e-01, eta: 12:11:43, time: 0.090, data_time: 0.004, memory: 1224, loss: 3.3452 2023-01-01 16:52:25,536 - mmcls - INFO - Epoch [3][3200/5005] lr: 1.000e-01, eta: 12:11:40, time: 0.092, data_time: 0.001, memory: 1224, loss: 3.3139 2023-01-01 16:52:34,444 - mmcls - INFO - Epoch [3][3300/5005] lr: 1.000e-01, eta: 12:11:27, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.3368 2023-01-01 16:52:43,118 - mmcls - INFO - Epoch [3][3400/5005] lr: 1.000e-01, eta: 12:11:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.3583 2023-01-01 16:52:52,318 - mmcls - INFO - Epoch [3][3500/5005] lr: 1.000e-01, eta: 12:11:04, time: 0.092, data_time: 0.002, memory: 1224, loss: 3.3653 2023-01-01 16:53:01,027 - mmcls - INFO - Epoch [3][3600/5005] lr: 1.000e-01, eta: 12:10:44, time: 0.087, data_time: 0.028, memory: 1224, loss: 3.3778 2023-01-01 16:53:09,843 - mmcls - INFO - Epoch [3][3700/5005] lr: 1.000e-01, eta: 12:10:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.3397 2023-01-01 16:53:18,790 - mmcls - INFO - Epoch [3][3800/5005] lr: 1.000e-01, eta: 12:10:17, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.3653 2023-01-01 16:53:27,744 - mmcls - INFO - Epoch [3][3900/5005] lr: 1.000e-01, eta: 12:10:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.3169 2023-01-01 16:53:36,890 - mmcls - INFO - Epoch [3][4000/5005] lr: 1.000e-01, eta: 12:10:03, time: 0.092, data_time: 0.002, memory: 1224, loss: 3.3401 2023-01-01 16:53:45,638 - mmcls - INFO - Epoch [3][4100/5005] lr: 1.000e-01, eta: 12:09:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.3496 2023-01-01 16:53:54,609 - mmcls - INFO - Epoch [3][4200/5005] lr: 1.000e-01, eta: 12:09:34, time: 0.090, data_time: 0.020, memory: 1224, loss: 3.3620 2023-01-01 16:54:03,668 - mmcls - INFO - Epoch [3][4300/5005] lr: 1.000e-01, eta: 12:09:27, time: 0.091, data_time: 0.005, memory: 1224, loss: 3.3194 2023-01-01 16:54:12,414 - mmcls - INFO - Epoch [3][4400/5005] lr: 1.000e-01, eta: 12:09:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.3358 2023-01-01 16:54:21,176 - mmcls - INFO - Epoch [3][4500/5005] lr: 1.000e-01, eta: 12:08:53, time: 0.088, data_time: 0.003, memory: 1224, loss: 3.2975 2023-01-01 16:54:30,110 - mmcls - INFO - Epoch [3][4600/5005] lr: 1.000e-01, eta: 12:08:42, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.2991 2023-01-01 16:54:39,161 - mmcls - INFO - Epoch [3][4700/5005] lr: 1.000e-01, eta: 12:08:34, time: 0.091, data_time: 0.000, memory: 1224, loss: 3.2711 2023-01-01 16:54:48,080 - mmcls - INFO - Epoch [3][4800/5005] lr: 1.000e-01, eta: 12:08:23, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.3074 2023-01-01 16:54:57,040 - mmcls - INFO - Epoch [3][4900/5005] lr: 1.000e-01, eta: 12:08:12, time: 0.090, data_time: 0.004, memory: 1224, loss: 3.2890 2023-01-01 16:55:06,312 - mmcls - INFO - Epoch [3][5000/5005] lr: 1.000e-01, eta: 12:08:12, time: 0.093, data_time: 0.004, memory: 1224, loss: 3.2838 2023-01-01 16:55:29,302 - mmcls - INFO - Epoch(val) [3][196] accuracy_top-1: 38.3360, accuracy_top-5: 63.6980 2023-01-01 16:55:40,471 - mmcls - INFO - Epoch [4][100/5005] lr: 1.000e-01, eta: 12:08:57, time: 0.111, data_time: 0.037, memory: 1224, loss: 3.2448 2023-01-01 16:55:49,509 - mmcls - INFO - Epoch [4][200/5005] lr: 1.000e-01, eta: 12:08:49, time: 0.090, data_time: 0.014, memory: 1224, loss: 3.2449 2023-01-01 16:55:58,415 - mmcls - INFO - Epoch [4][300/5005] lr: 1.000e-01, eta: 12:08:36, time: 0.089, data_time: 0.010, memory: 1224, loss: 3.2429 2023-01-01 16:56:07,064 - mmcls - INFO - Epoch [4][400/5005] lr: 1.000e-01, eta: 12:08:16, time: 0.087, data_time: 0.005, memory: 1224, loss: 3.2606 2023-01-01 16:56:15,815 - mmcls - INFO - Epoch [4][500/5005] lr: 1.000e-01, eta: 12:07:59, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.2500 2023-01-01 16:56:24,856 - mmcls - INFO - Epoch [4][600/5005] lr: 1.000e-01, eta: 12:07:51, time: 0.090, data_time: 0.002, memory: 1224, loss: 3.2272 2023-01-01 16:56:33,422 - mmcls - INFO - Epoch [4][700/5005] lr: 1.000e-01, eta: 12:07:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.2703 2023-01-01 16:56:42,108 - mmcls - INFO - Epoch [4][800/5005] lr: 1.000e-01, eta: 12:07:10, time: 0.087, data_time: 0.024, memory: 1224, loss: 3.2516 2023-01-01 16:56:50,911 - mmcls - INFO - Epoch [4][900/5005] lr: 1.000e-01, eta: 12:06:54, time: 0.088, data_time: 0.015, memory: 1224, loss: 3.2497 2023-01-01 16:56:59,572 - mmcls - INFO - Epoch [4][1000/5005] lr: 1.000e-01, eta: 12:06:35, time: 0.087, data_time: 0.006, memory: 1224, loss: 3.2725 2023-01-01 16:57:08,858 - mmcls - INFO - Epoch [4][1100/5005] lr: 1.000e-01, eta: 12:06:35, time: 0.093, data_time: 0.009, memory: 1224, loss: 3.2645 2023-01-01 16:57:17,925 - mmcls - INFO - Epoch [4][1200/5005] lr: 1.000e-01, eta: 12:06:28, time: 0.091, data_time: 0.014, memory: 1224, loss: 3.2775 2023-01-01 16:57:26,580 - mmcls - INFO - Epoch [4][1300/5005] lr: 1.000e-01, eta: 12:06:08, time: 0.087, data_time: 0.008, memory: 1224, loss: 3.2889 2023-01-01 16:57:35,312 - mmcls - INFO - Epoch [4][1400/5005] lr: 1.000e-01, eta: 12:05:52, time: 0.087, data_time: 0.006, memory: 1224, loss: 3.2443 2023-01-01 16:57:43,975 - mmcls - INFO - Epoch [4][1500/5005] lr: 1.000e-01, eta: 12:05:33, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.2706 2023-01-01 16:57:52,704 - mmcls - INFO - Epoch [4][1600/5005] lr: 1.000e-01, eta: 12:05:16, time: 0.087, data_time: 0.005, memory: 1224, loss: 3.2458 2023-01-01 16:58:01,759 - mmcls - INFO - Epoch [4][1700/5005] lr: 1.000e-01, eta: 12:05:09, time: 0.091, data_time: 0.002, memory: 1224, loss: 3.2260 2023-01-01 16:58:10,565 - mmcls - INFO - Epoch [4][1800/5005] lr: 1.000e-01, eta: 12:04:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.2172 2023-01-01 16:58:19,623 - mmcls - INFO - Epoch [4][1900/5005] lr: 1.000e-01, eta: 12:04:47, time: 0.091, data_time: 0.005, memory: 1224, loss: 3.2001 2023-01-01 16:58:28,675 - mmcls - INFO - Epoch [4][2000/5005] lr: 1.000e-01, eta: 12:04:40, time: 0.090, data_time: 0.027, memory: 1224, loss: 3.2273 2023-01-01 16:58:37,648 - mmcls - INFO - Epoch [4][2100/5005] lr: 1.000e-01, eta: 12:04:30, time: 0.090, data_time: 0.019, memory: 1224, loss: 3.2260 2023-01-01 16:58:46,381 - mmcls - INFO - Epoch [4][2200/5005] lr: 1.000e-01, eta: 12:04:14, time: 0.087, data_time: 0.023, memory: 1224, loss: 3.2671 2023-01-01 16:58:55,019 - mmcls - INFO - Epoch [4][2300/5005] lr: 1.000e-01, eta: 12:03:55, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.2523 2023-01-01 16:59:03,956 - mmcls - INFO - Epoch [4][2400/5005] lr: 1.000e-01, eta: 12:03:45, time: 0.089, data_time: 0.008, memory: 1224, loss: 3.2252 2023-01-01 16:59:12,779 - mmcls - INFO - Epoch [4][2500/5005] lr: 1.000e-01, eta: 12:03:31, time: 0.088, data_time: 0.021, memory: 1224, loss: 3.2529 2023-01-01 16:59:21,630 - mmcls - INFO - Epoch [4][2600/5005] lr: 1.000e-01, eta: 12:03:18, time: 0.089, data_time: 0.013, memory: 1224, loss: 3.2290 2023-01-01 16:59:30,743 - mmcls - INFO - Epoch [4][2700/5005] lr: 1.000e-01, eta: 12:03:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.1988 2023-01-01 16:59:39,411 - mmcls - INFO - Epoch [4][2800/5005] lr: 1.000e-01, eta: 12:02:55, time: 0.087, data_time: 0.031, memory: 1224, loss: 3.2140 2023-01-01 16:59:48,480 - mmcls - INFO - Epoch [4][2900/5005] lr: 1.000e-01, eta: 12:02:49, time: 0.091, data_time: 0.002, memory: 1224, loss: 3.2289 2023-01-01 16:59:57,289 - mmcls - INFO - Epoch [4][3000/5005] lr: 1.000e-01, eta: 12:02:34, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.2148 2023-01-01 17:00:06,018 - mmcls - INFO - Epoch [4][3100/5005] lr: 1.000e-01, eta: 12:02:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.2195 2023-01-01 17:00:14,946 - mmcls - INFO - Epoch [4][3200/5005] lr: 1.000e-01, eta: 12:02:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.2138 2023-01-01 17:00:23,652 - mmcls - INFO - Epoch [4][3300/5005] lr: 1.000e-01, eta: 12:01:52, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.1915 2023-01-01 17:00:32,409 - mmcls - INFO - Epoch [4][3400/5005] lr: 1.000e-01, eta: 12:01:37, time: 0.088, data_time: 0.031, memory: 1224, loss: 3.2242 2023-01-01 17:00:41,202 - mmcls - INFO - Epoch [4][3500/5005] lr: 1.000e-01, eta: 12:01:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.2080 2023-01-01 17:00:50,144 - mmcls - INFO - Epoch [4][3600/5005] lr: 1.000e-01, eta: 12:01:13, time: 0.089, data_time: 0.016, memory: 1224, loss: 3.2262 2023-01-01 17:00:58,647 - mmcls - INFO - Epoch [4][3700/5005] lr: 1.000e-01, eta: 12:00:52, time: 0.085, data_time: 0.003, memory: 1224, loss: 3.2175 2023-01-01 17:01:07,467 - mmcls - INFO - Epoch [4][3800/5005] lr: 1.000e-01, eta: 12:00:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.2542 2023-01-01 17:01:17,001 - mmcls - INFO - Epoch [4][3900/5005] lr: 1.000e-01, eta: 12:00:44, time: 0.095, data_time: 0.004, memory: 1224, loss: 3.2338 2023-01-01 17:01:25,652 - mmcls - INFO - Epoch [4][4000/5005] lr: 1.000e-01, eta: 12:00:27, time: 0.087, data_time: 0.012, memory: 1224, loss: 3.2197 2023-01-01 17:01:34,642 - mmcls - INFO - Epoch [4][4100/5005] lr: 1.000e-01, eta: 12:00:18, time: 0.090, data_time: 0.013, memory: 1224, loss: 3.1930 2023-01-01 17:01:43,728 - mmcls - INFO - Epoch [4][4200/5005] lr: 1.000e-01, eta: 12:00:12, time: 0.091, data_time: 0.020, memory: 1224, loss: 3.1806 2023-01-01 17:01:52,480 - mmcls - INFO - Epoch [4][4300/5005] lr: 1.000e-01, eta: 11:59:57, time: 0.087, data_time: 0.018, memory: 1224, loss: 3.1539 2023-01-01 17:02:01,249 - mmcls - INFO - Epoch [4][4400/5005] lr: 1.000e-01, eta: 11:59:43, time: 0.088, data_time: 0.011, memory: 1224, loss: 3.2174 2023-01-01 17:02:10,267 - mmcls - INFO - Epoch [4][4500/5005] lr: 1.000e-01, eta: 11:59:35, time: 0.090, data_time: 0.019, memory: 1224, loss: 3.1706 2023-01-01 17:02:18,883 - mmcls - INFO - Epoch [4][4600/5005] lr: 1.000e-01, eta: 11:59:17, time: 0.086, data_time: 0.012, memory: 1224, loss: 3.1525 2023-01-01 17:02:27,768 - mmcls - INFO - Epoch [4][4700/5005] lr: 1.000e-01, eta: 11:59:06, time: 0.089, data_time: 0.016, memory: 1224, loss: 3.1732 2023-01-01 17:02:36,603 - mmcls - INFO - Epoch [4][4800/5005] lr: 1.000e-01, eta: 11:58:54, time: 0.088, data_time: 0.024, memory: 1224, loss: 3.2078 2023-01-01 17:02:45,610 - mmcls - INFO - Epoch [4][4900/5005] lr: 1.000e-01, eta: 11:58:45, time: 0.090, data_time: 0.041, memory: 1224, loss: 3.1928 2023-01-01 17:02:54,404 - mmcls - INFO - Epoch [4][5000/5005] lr: 1.000e-01, eta: 11:58:32, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.2033 2023-01-01 17:03:17,363 - mmcls - INFO - Epoch(val) [4][196] accuracy_top-1: 35.3300, accuracy_top-5: 60.1260 2023-01-01 17:03:28,690 - mmcls - INFO - Epoch [5][100/5005] lr: 1.000e-01, eta: 11:59:07, time: 0.113, data_time: 0.047, memory: 1224, loss: 3.1171 2023-01-01 17:03:37,473 - mmcls - INFO - Epoch [5][200/5005] lr: 1.000e-01, eta: 11:58:54, time: 0.088, data_time: 0.010, memory: 1224, loss: 3.1460 2023-01-01 17:03:46,147 - mmcls - INFO - Epoch [5][300/5005] lr: 1.000e-01, eta: 11:58:37, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.1248 2023-01-01 17:03:54,884 - mmcls - INFO - Epoch [5][400/5005] lr: 1.000e-01, eta: 11:58:23, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.1608 2023-01-01 17:04:03,514 - mmcls - INFO - Epoch [5][500/5005] lr: 1.000e-01, eta: 11:58:06, time: 0.086, data_time: 0.016, memory: 1224, loss: 3.1328 2023-01-01 17:04:12,491 - mmcls - INFO - Epoch [5][600/5005] lr: 1.000e-01, eta: 11:57:56, time: 0.090, data_time: 0.016, memory: 1224, loss: 3.1315 2023-01-01 17:04:21,387 - mmcls - INFO - Epoch [5][700/5005] lr: 1.000e-01, eta: 11:57:46, time: 0.089, data_time: 0.010, memory: 1224, loss: 3.1464 2023-01-01 17:04:30,263 - mmcls - INFO - Epoch [5][800/5005] lr: 1.000e-01, eta: 11:57:34, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.1444 2023-01-01 17:04:39,087 - mmcls - INFO - Epoch [5][900/5005] lr: 1.000e-01, eta: 11:57:22, time: 0.088, data_time: 0.029, memory: 1224, loss: 3.1346 2023-01-01 17:04:47,872 - mmcls - INFO - Epoch [5][1000/5005] lr: 1.000e-01, eta: 11:57:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.1337 2023-01-01 17:04:56,467 - mmcls - INFO - Epoch [5][1100/5005] lr: 1.000e-01, eta: 11:56:51, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.1561 2023-01-01 17:05:05,261 - mmcls - INFO - Epoch [5][1200/5005] lr: 1.000e-01, eta: 11:56:38, time: 0.088, data_time: 0.010, memory: 1224, loss: 3.1225 2023-01-01 17:05:14,148 - mmcls - INFO - Epoch [5][1300/5005] lr: 1.000e-01, eta: 11:56:27, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.1672 2023-01-01 17:05:23,196 - mmcls - INFO - Epoch [5][1400/5005] lr: 1.000e-01, eta: 11:56:20, time: 0.091, data_time: 0.010, memory: 1224, loss: 3.1250 2023-01-01 17:05:32,290 - mmcls - INFO - Epoch [5][1500/5005] lr: 1.000e-01, eta: 11:56:14, time: 0.091, data_time: 0.007, memory: 1224, loss: 3.1445 2023-01-01 17:05:41,368 - mmcls - INFO - Epoch [5][1600/5005] lr: 1.000e-01, eta: 11:56:07, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.1482 2023-01-01 17:05:50,117 - mmcls - INFO - Epoch [5][1700/5005] lr: 1.000e-01, eta: 11:55:53, time: 0.088, data_time: 0.006, memory: 1224, loss: 3.1415 2023-01-01 17:05:58,894 - mmcls - INFO - Epoch [5][1800/5005] lr: 1.000e-01, eta: 11:55:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.1379 2023-01-01 17:06:07,634 - mmcls - INFO - Epoch [5][1900/5005] lr: 1.000e-01, eta: 11:55:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 3.1370 2023-01-01 17:06:16,601 - mmcls - INFO - Epoch [5][2000/5005] lr: 1.000e-01, eta: 11:55:17, time: 0.090, data_time: 0.033, memory: 1224, loss: 3.1271 2023-01-01 17:06:25,524 - mmcls - INFO - Epoch [5][2100/5005] lr: 1.000e-01, eta: 11:55:07, time: 0.089, data_time: 0.026, memory: 1224, loss: 3.1369 2023-01-01 17:06:34,398 - mmcls - INFO - Epoch [5][2200/5005] lr: 1.000e-01, eta: 11:54:56, time: 0.089, data_time: 0.020, memory: 1224, loss: 3.1513 2023-01-01 17:06:43,241 - mmcls - INFO - Epoch [5][2300/5005] lr: 1.000e-01, eta: 11:54:44, time: 0.088, data_time: 0.020, memory: 1224, loss: 3.1288 2023-01-01 17:06:51,879 - mmcls - INFO - Epoch [5][2400/5005] lr: 1.000e-01, eta: 11:54:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.1363 2023-01-01 17:07:01,502 - mmcls - INFO - Epoch [5][2500/5005] lr: 1.000e-01, eta: 11:54:33, time: 0.096, data_time: 0.019, memory: 1224, loss: 3.1602 2023-01-01 17:07:10,308 - mmcls - INFO - Epoch [5][2600/5005] lr: 1.000e-01, eta: 11:54:21, time: 0.088, data_time: 0.011, memory: 1224, loss: 3.1627 2023-01-01 17:07:19,102 - mmcls - INFO - Epoch [5][2700/5005] lr: 1.000e-01, eta: 11:54:08, time: 0.088, data_time: 0.017, memory: 1224, loss: 3.1463 2023-01-01 17:07:27,648 - mmcls - INFO - Epoch [5][2800/5005] lr: 1.000e-01, eta: 11:53:50, time: 0.085, data_time: 0.004, memory: 1224, loss: 3.1158 2023-01-01 17:07:36,461 - mmcls - INFO - Epoch [5][2900/5005] lr: 1.000e-01, eta: 11:53:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.1317 2023-01-01 17:07:45,190 - mmcls - INFO - Epoch [5][3000/5005] lr: 1.000e-01, eta: 11:53:24, time: 0.087, data_time: 0.023, memory: 1224, loss: 3.1155 2023-01-01 17:07:54,062 - mmcls - INFO - Epoch [5][3100/5005] lr: 1.000e-01, eta: 11:53:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.1218 2023-01-01 17:08:03,170 - mmcls - INFO - Epoch [5][3200/5005] lr: 1.000e-01, eta: 11:53:07, time: 0.091, data_time: 0.013, memory: 1224, loss: 3.1428 2023-01-01 17:08:12,023 - mmcls - INFO - Epoch [5][3300/5005] lr: 1.000e-01, eta: 11:52:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.1194 2023-01-01 17:08:21,074 - mmcls - INFO - Epoch [5][3400/5005] lr: 1.000e-01, eta: 11:52:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.0907 2023-01-01 17:08:30,087 - mmcls - INFO - Epoch [5][3500/5005] lr: 1.000e-01, eta: 11:52:40, time: 0.090, data_time: 0.014, memory: 1224, loss: 3.0890 2023-01-01 17:08:39,004 - mmcls - INFO - Epoch [5][3600/5005] lr: 1.000e-01, eta: 11:52:31, time: 0.089, data_time: 0.022, memory: 1224, loss: 3.1272 2023-01-01 17:08:47,559 - mmcls - INFO - Epoch [5][3700/5005] lr: 1.000e-01, eta: 11:52:13, time: 0.086, data_time: 0.018, memory: 1224, loss: 3.1515 2023-01-01 17:08:56,407 - mmcls - INFO - Epoch [5][3800/5005] lr: 1.000e-01, eta: 11:52:02, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.1188 2023-01-01 17:09:05,343 - mmcls - INFO - Epoch [5][3900/5005] lr: 1.000e-01, eta: 11:51:53, time: 0.089, data_time: 0.008, memory: 1224, loss: 3.1060 2023-01-01 17:09:13,931 - mmcls - INFO - Epoch [5][4000/5005] lr: 1.000e-01, eta: 11:51:36, time: 0.086, data_time: 0.009, memory: 1224, loss: 3.1605 2023-01-01 17:09:22,848 - mmcls - INFO - Epoch [5][4100/5005] lr: 1.000e-01, eta: 11:51:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.1304 2023-01-01 17:09:31,658 - mmcls - INFO - Epoch [5][4200/5005] lr: 1.000e-01, eta: 11:51:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.0961 2023-01-01 17:09:40,307 - mmcls - INFO - Epoch [5][4300/5005] lr: 1.000e-01, eta: 11:50:59, time: 0.086, data_time: 0.005, memory: 1224, loss: 3.0905 2023-01-01 17:09:49,266 - mmcls - INFO - Epoch [5][4400/5005] lr: 1.000e-01, eta: 11:50:50, time: 0.090, data_time: 0.023, memory: 1224, loss: 3.0922 2023-01-01 17:09:58,136 - mmcls - INFO - Epoch [5][4500/5005] lr: 1.000e-01, eta: 11:50:40, time: 0.089, data_time: 0.008, memory: 1224, loss: 3.1105 2023-01-01 17:10:06,799 - mmcls - INFO - Epoch [5][4600/5005] lr: 1.000e-01, eta: 11:50:25, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.1382 2023-01-01 17:10:15,397 - mmcls - INFO - Epoch [5][4700/5005] lr: 1.000e-01, eta: 11:50:09, time: 0.086, data_time: 0.021, memory: 1224, loss: 3.1155 2023-01-01 17:10:24,271 - mmcls - INFO - Epoch [5][4800/5005] lr: 1.000e-01, eta: 11:49:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.1194 2023-01-01 17:10:32,812 - mmcls - INFO - Epoch [5][4900/5005] lr: 1.000e-01, eta: 11:49:42, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.0881 2023-01-01 17:10:41,946 - mmcls - INFO - Epoch [5][5000/5005] lr: 1.000e-01, eta: 11:49:36, time: 0.091, data_time: 0.010, memory: 1224, loss: 3.1169 2023-01-01 17:11:04,666 - mmcls - INFO - Epoch(val) [5][196] accuracy_top-1: 37.5040, accuracy_top-5: 62.3060 2023-01-01 17:11:15,661 - mmcls - INFO - Epoch [6][100/5005] lr: 1.000e-01, eta: 11:49:56, time: 0.110, data_time: 0.031, memory: 1224, loss: 3.0327 2023-01-01 17:11:24,223 - mmcls - INFO - Epoch [6][200/5005] lr: 1.000e-01, eta: 11:49:40, time: 0.086, data_time: 0.016, memory: 1224, loss: 3.0600 2023-01-01 17:11:33,009 - mmcls - INFO - Epoch [6][300/5005] lr: 1.000e-01, eta: 11:49:28, time: 0.088, data_time: 0.016, memory: 1224, loss: 3.0607 2023-01-01 17:11:42,070 - mmcls - INFO - Epoch [6][400/5005] lr: 1.000e-01, eta: 11:49:21, time: 0.091, data_time: 0.002, memory: 1224, loss: 3.0469 2023-01-01 17:11:50,736 - mmcls - INFO - Epoch [6][500/5005] lr: 1.000e-01, eta: 11:49:06, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.0827 2023-01-01 17:11:59,443 - mmcls - INFO - Epoch [6][600/5005] lr: 1.000e-01, eta: 11:48:53, time: 0.087, data_time: 0.008, memory: 1224, loss: 3.0820 2023-01-01 17:12:08,244 - mmcls - INFO - Epoch [6][700/5005] lr: 1.000e-01, eta: 11:48:41, time: 0.088, data_time: 0.010, memory: 1224, loss: 3.0681 2023-01-01 17:12:17,215 - mmcls - INFO - Epoch [6][800/5005] lr: 1.000e-01, eta: 11:48:32, time: 0.090, data_time: 0.000, memory: 1224, loss: 3.0837 2023-01-01 17:12:26,152 - mmcls - INFO - Epoch [6][900/5005] lr: 1.000e-01, eta: 11:48:23, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.0828 2023-01-01 17:12:34,912 - mmcls - INFO - Epoch [6][1000/5005] lr: 1.000e-01, eta: 11:48:10, time: 0.088, data_time: 0.037, memory: 1224, loss: 3.0302 2023-01-01 17:12:43,636 - mmcls - INFO - Epoch [6][1100/5005] lr: 1.000e-01, eta: 11:47:57, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.0992 2023-01-01 17:12:52,188 - mmcls - INFO - Epoch [6][1200/5005] lr: 1.000e-01, eta: 11:47:41, time: 0.086, data_time: 0.013, memory: 1224, loss: 3.0468 2023-01-01 17:13:00,929 - mmcls - INFO - Epoch [6][1300/5005] lr: 1.000e-01, eta: 11:47:28, time: 0.087, data_time: 0.003, memory: 1224, loss: 3.0498 2023-01-01 17:13:09,600 - mmcls - INFO - Epoch [6][1400/5005] lr: 1.000e-01, eta: 11:47:14, time: 0.087, data_time: 0.011, memory: 1224, loss: 3.0651 2023-01-01 17:13:18,185 - mmcls - INFO - Epoch [6][1500/5005] lr: 1.000e-01, eta: 11:46:58, time: 0.086, data_time: 0.007, memory: 1224, loss: 3.0792 2023-01-01 17:13:27,153 - mmcls - INFO - Epoch [6][1600/5005] lr: 1.000e-01, eta: 11:46:50, time: 0.090, data_time: 0.011, memory: 1224, loss: 3.0670 2023-01-01 17:13:36,057 - mmcls - INFO - Epoch [6][1700/5005] lr: 1.000e-01, eta: 11:46:40, time: 0.089, data_time: 0.007, memory: 1224, loss: 3.0871 2023-01-01 17:13:44,989 - mmcls - INFO - Epoch [6][1800/5005] lr: 1.000e-01, eta: 11:46:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.0289 2023-01-01 17:13:53,691 - mmcls - INFO - Epoch [6][1900/5005] lr: 1.000e-01, eta: 11:46:17, time: 0.087, data_time: 0.014, memory: 1224, loss: 3.0742 2023-01-01 17:14:02,433 - mmcls - INFO - Epoch [6][2000/5005] lr: 1.000e-01, eta: 11:46:05, time: 0.087, data_time: 0.016, memory: 1224, loss: 3.0958 2023-01-01 17:14:11,659 - mmcls - INFO - Epoch [6][2100/5005] lr: 1.000e-01, eta: 11:46:01, time: 0.092, data_time: 0.008, memory: 1224, loss: 3.0883 2023-01-01 17:14:20,125 - mmcls - INFO - Epoch [6][2200/5005] lr: 1.000e-01, eta: 11:45:43, time: 0.085, data_time: 0.017, memory: 1224, loss: 3.0921 2023-01-01 17:14:28,903 - mmcls - INFO - Epoch [6][2300/5005] lr: 1.000e-01, eta: 11:45:31, time: 0.088, data_time: 0.014, memory: 1224, loss: 3.0326 2023-01-01 17:14:37,721 - mmcls - INFO - Epoch [6][2400/5005] lr: 1.000e-01, eta: 11:45:20, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.0920 2023-01-01 17:14:46,362 - mmcls - INFO - Epoch [6][2500/5005] lr: 1.000e-01, eta: 11:45:06, time: 0.086, data_time: 0.005, memory: 1224, loss: 3.0888 2023-01-01 17:14:55,289 - mmcls - INFO - Epoch [6][2600/5005] lr: 1.000e-01, eta: 11:44:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.0545 2023-01-01 17:15:04,062 - mmcls - INFO - Epoch [6][2700/5005] lr: 1.000e-01, eta: 11:44:45, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.0943 2023-01-01 17:15:13,504 - mmcls - INFO - Epoch [6][2800/5005] lr: 1.000e-01, eta: 11:44:44, time: 0.094, data_time: 0.005, memory: 1224, loss: 3.0745 2023-01-01 17:15:22,241 - mmcls - INFO - Epoch [6][2900/5005] lr: 1.000e-01, eta: 11:44:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.0324 2023-01-01 17:15:31,027 - mmcls - INFO - Epoch [6][3000/5005] lr: 1.000e-01, eta: 11:44:20, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.0766 2023-01-01 17:15:39,913 - mmcls - INFO - Epoch [6][3100/5005] lr: 1.000e-01, eta: 11:44:10, time: 0.089, data_time: 0.013, memory: 1224, loss: 3.0506 2023-01-01 17:15:48,822 - mmcls - INFO - Epoch [6][3200/5005] lr: 1.000e-01, eta: 11:44:01, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.0673 2023-01-01 17:15:57,964 - mmcls - INFO - Epoch [6][3300/5005] lr: 1.000e-01, eta: 11:43:55, time: 0.091, data_time: 0.006, memory: 1224, loss: 3.0518 2023-01-01 17:16:06,751 - mmcls - INFO - Epoch [6][3400/5005] lr: 1.000e-01, eta: 11:43:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.0311 2023-01-01 17:16:15,667 - mmcls - INFO - Epoch [6][3500/5005] lr: 1.000e-01, eta: 11:43:34, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.0718 2023-01-01 17:16:24,656 - mmcls - INFO - Epoch [6][3600/5005] lr: 1.000e-01, eta: 11:43:26, time: 0.090, data_time: 0.020, memory: 1224, loss: 3.0704 2023-01-01 17:16:33,212 - mmcls - INFO - Epoch [6][3700/5005] lr: 1.000e-01, eta: 11:43:11, time: 0.085, data_time: 0.003, memory: 1224, loss: 3.0772 2023-01-01 17:16:42,377 - mmcls - INFO - Epoch [6][3800/5005] lr: 1.000e-01, eta: 11:43:05, time: 0.092, data_time: 0.003, memory: 1224, loss: 3.0421 2023-01-01 17:16:51,481 - mmcls - INFO - Epoch [6][3900/5005] lr: 1.000e-01, eta: 11:42:59, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.0320 2023-01-01 17:17:00,653 - mmcls - INFO - Epoch [6][4000/5005] lr: 1.000e-01, eta: 11:42:54, time: 0.092, data_time: 0.028, memory: 1224, loss: 3.0905 2023-01-01 17:17:09,221 - mmcls - INFO - Epoch [6][4100/5005] lr: 1.000e-01, eta: 11:42:39, time: 0.086, data_time: 0.020, memory: 1224, loss: 3.0758 2023-01-01 17:17:18,096 - mmcls - INFO - Epoch [6][4200/5005] lr: 1.000e-01, eta: 11:42:29, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.0431 2023-01-01 17:17:26,982 - mmcls - INFO - Epoch [6][4300/5005] lr: 1.000e-01, eta: 11:42:19, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.0363 2023-01-01 17:17:35,905 - mmcls - INFO - Epoch [6][4400/5005] lr: 1.000e-01, eta: 11:42:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.0449 2023-01-01 17:17:44,938 - mmcls - INFO - Epoch [6][4500/5005] lr: 1.000e-01, eta: 11:42:02, time: 0.090, data_time: 0.013, memory: 1224, loss: 3.0723 2023-01-01 17:17:54,182 - mmcls - INFO - Epoch [6][4600/5005] lr: 1.000e-01, eta: 11:41:58, time: 0.092, data_time: 0.017, memory: 1224, loss: 3.0462 2023-01-01 17:18:02,887 - mmcls - INFO - Epoch [6][4700/5005] lr: 1.000e-01, eta: 11:41:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.0306 2023-01-01 17:18:11,563 - mmcls - INFO - Epoch [6][4800/5005] lr: 1.000e-01, eta: 11:41:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.0683 2023-01-01 17:18:20,592 - mmcls - INFO - Epoch [6][4900/5005] lr: 1.000e-01, eta: 11:41:24, time: 0.090, data_time: 0.026, memory: 1224, loss: 3.0858 2023-01-01 17:18:29,191 - mmcls - INFO - Epoch [6][5000/5005] lr: 1.000e-01, eta: 11:41:10, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.0535 2023-01-01 17:18:52,021 - mmcls - INFO - Epoch(val) [6][196] accuracy_top-1: 40.1240, accuracy_top-5: 65.4240 2023-01-01 17:19:03,298 - mmcls - INFO - Epoch [7][100/5005] lr: 1.000e-01, eta: 11:41:29, time: 0.112, data_time: 0.033, memory: 1224, loss: 3.0050 2023-01-01 17:19:12,251 - mmcls - INFO - Epoch [7][200/5005] lr: 1.000e-01, eta: 11:41:21, time: 0.090, data_time: 0.004, memory: 1224, loss: 3.0111 2023-01-01 17:19:21,190 - mmcls - INFO - Epoch [7][300/5005] lr: 1.000e-01, eta: 11:41:11, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.9844 2023-01-01 17:19:29,934 - mmcls - INFO - Epoch [7][400/5005] lr: 1.000e-01, eta: 11:40:59, time: 0.087, data_time: 0.023, memory: 1224, loss: 3.0059 2023-01-01 17:19:38,818 - mmcls - INFO - Epoch [7][500/5005] lr: 1.000e-01, eta: 11:40:50, time: 0.089, data_time: 0.005, memory: 1224, loss: 3.0522 2023-01-01 17:19:47,427 - mmcls - INFO - Epoch [7][600/5005] lr: 1.000e-01, eta: 11:40:35, time: 0.086, data_time: 0.015, memory: 1224, loss: 3.0284 2023-01-01 17:19:56,404 - mmcls - INFO - Epoch [7][700/5005] lr: 1.000e-01, eta: 11:40:27, time: 0.090, data_time: 0.005, memory: 1224, loss: 3.0172 2023-01-01 17:20:04,890 - mmcls - INFO - Epoch [7][800/5005] lr: 1.000e-01, eta: 11:40:11, time: 0.085, data_time: 0.003, memory: 1224, loss: 3.0147 2023-01-01 17:20:13,758 - mmcls - INFO - Epoch [7][900/5005] lr: 1.000e-01, eta: 11:40:01, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.0045 2023-01-01 17:20:22,735 - mmcls - INFO - Epoch [7][1000/5005] lr: 1.000e-01, eta: 11:39:52, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.0035 2023-01-01 17:20:31,745 - mmcls - INFO - Epoch [7][1100/5005] lr: 1.000e-01, eta: 11:39:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 3.0162 2023-01-01 17:20:40,344 - mmcls - INFO - Epoch [7][1200/5005] lr: 1.000e-01, eta: 11:39:30, time: 0.086, data_time: 0.017, memory: 1224, loss: 3.0379 2023-01-01 17:20:49,081 - mmcls - INFO - Epoch [7][1300/5005] lr: 1.000e-01, eta: 11:39:18, time: 0.087, data_time: 0.025, memory: 1224, loss: 3.0156 2023-01-01 17:20:57,956 - mmcls - INFO - Epoch [7][1400/5005] lr: 1.000e-01, eta: 11:39:08, time: 0.089, data_time: 0.005, memory: 1224, loss: 3.0224 2023-01-01 17:21:06,858 - mmcls - INFO - Epoch [7][1500/5005] lr: 1.000e-01, eta: 11:38:58, time: 0.089, data_time: 0.026, memory: 1224, loss: 3.0242 2023-01-01 17:21:15,606 - mmcls - INFO - Epoch [7][1600/5005] lr: 1.000e-01, eta: 11:38:47, time: 0.087, data_time: 0.015, memory: 1224, loss: 3.0291 2023-01-01 17:21:24,518 - mmcls - INFO - Epoch [7][1700/5005] lr: 1.000e-01, eta: 11:38:37, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.0168 2023-01-01 17:21:33,624 - mmcls - INFO - Epoch [7][1800/5005] lr: 1.000e-01, eta: 11:38:31, time: 0.091, data_time: 0.015, memory: 1224, loss: 3.0193 2023-01-01 17:21:42,603 - mmcls - INFO - Epoch [7][1900/5005] lr: 1.000e-01, eta: 11:38:22, time: 0.090, data_time: 0.025, memory: 1224, loss: 3.0074 2023-01-01 17:21:51,272 - mmcls - INFO - Epoch [7][2000/5005] lr: 1.000e-01, eta: 11:38:09, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.9837 2023-01-01 17:22:00,348 - mmcls - INFO - Epoch [7][2100/5005] lr: 1.000e-01, eta: 11:38:02, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.0176 2023-01-01 17:22:08,888 - mmcls - INFO - Epoch [7][2200/5005] lr: 1.000e-01, eta: 11:37:48, time: 0.085, data_time: 0.021, memory: 1224, loss: 3.0029 2023-01-01 17:22:17,726 - mmcls - INFO - Epoch [7][2300/5005] lr: 1.000e-01, eta: 11:37:37, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.9938 2023-01-01 17:22:26,595 - mmcls - INFO - Epoch [7][2400/5005] lr: 1.000e-01, eta: 11:37:27, time: 0.089, data_time: 0.014, memory: 1224, loss: 3.0194 2023-01-01 17:22:35,831 - mmcls - INFO - Epoch [7][2500/5005] lr: 1.000e-01, eta: 11:37:22, time: 0.092, data_time: 0.043, memory: 1224, loss: 3.0205 2023-01-01 17:22:44,708 - mmcls - INFO - Epoch [7][2600/5005] lr: 1.000e-01, eta: 11:37:12, time: 0.089, data_time: 0.033, memory: 1224, loss: 3.0191 2023-01-01 17:22:53,574 - mmcls - INFO - Epoch [7][2700/5005] lr: 1.000e-01, eta: 11:37:03, time: 0.089, data_time: 0.007, memory: 1224, loss: 3.0532 2023-01-01 17:23:02,200 - mmcls - INFO - Epoch [7][2800/5005] lr: 1.000e-01, eta: 11:36:49, time: 0.086, data_time: 0.014, memory: 1224, loss: 3.0222 2023-01-01 17:23:11,079 - mmcls - INFO - Epoch [7][2900/5005] lr: 1.000e-01, eta: 11:36:39, time: 0.089, data_time: 0.024, memory: 1224, loss: 3.0170 2023-01-01 17:23:20,129 - mmcls - INFO - Epoch [7][3000/5005] lr: 1.000e-01, eta: 11:36:32, time: 0.091, data_time: 0.007, memory: 1224, loss: 3.0053 2023-01-01 17:23:28,992 - mmcls - INFO - Epoch [7][3100/5005] lr: 1.000e-01, eta: 11:36:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.0153 2023-01-01 17:23:37,812 - mmcls - INFO - Epoch [7][3200/5005] lr: 1.000e-01, eta: 11:36:11, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.0245 2023-01-01 17:23:46,623 - mmcls - INFO - Epoch [7][3300/5005] lr: 1.000e-01, eta: 11:36:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.0046 2023-01-01 17:23:55,369 - mmcls - INFO - Epoch [7][3400/5005] lr: 1.000e-01, eta: 11:35:49, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.9899 2023-01-01 17:24:03,821 - mmcls - INFO - Epoch [7][3500/5005] lr: 1.000e-01, eta: 11:35:33, time: 0.084, data_time: 0.024, memory: 1224, loss: 2.9821 2023-01-01 17:24:12,596 - mmcls - INFO - Epoch [7][3600/5005] lr: 1.000e-01, eta: 11:35:22, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.9858 2023-01-01 17:24:21,403 - mmcls - INFO - Epoch [7][3700/5005] lr: 1.000e-01, eta: 11:35:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.9862 2023-01-01 17:24:30,371 - mmcls - INFO - Epoch [7][3800/5005] lr: 1.000e-01, eta: 11:35:02, time: 0.090, data_time: 0.025, memory: 1224, loss: 3.0326 2023-01-01 17:24:39,293 - mmcls - INFO - Epoch [7][3900/5005] lr: 1.000e-01, eta: 11:34:53, time: 0.089, data_time: 0.009, memory: 1224, loss: 3.0219 2023-01-01 17:24:48,035 - mmcls - INFO - Epoch [7][4000/5005] lr: 1.000e-01, eta: 11:34:42, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9970 2023-01-01 17:24:56,688 - mmcls - INFO - Epoch [7][4100/5005] lr: 1.000e-01, eta: 11:34:29, time: 0.086, data_time: 0.014, memory: 1224, loss: 3.0158 2023-01-01 17:25:05,694 - mmcls - INFO - Epoch [7][4200/5005] lr: 1.000e-01, eta: 11:34:21, time: 0.090, data_time: 0.013, memory: 1224, loss: 3.0022 2023-01-01 17:25:14,435 - mmcls - INFO - Epoch [7][4300/5005] lr: 1.000e-01, eta: 11:34:09, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.9979 2023-01-01 17:25:23,143 - mmcls - INFO - Epoch [7][4400/5005] lr: 1.000e-01, eta: 11:33:57, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.9938 2023-01-01 17:25:31,762 - mmcls - INFO - Epoch [7][4500/5005] lr: 1.000e-01, eta: 11:33:44, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.9839 2023-01-01 17:25:40,634 - mmcls - INFO - Epoch [7][4600/5005] lr: 1.000e-01, eta: 11:33:34, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.9661 2023-01-01 17:25:49,523 - mmcls - INFO - Epoch [7][4700/5005] lr: 1.000e-01, eta: 11:33:25, time: 0.089, data_time: 0.019, memory: 1224, loss: 3.0334 2023-01-01 17:25:58,496 - mmcls - INFO - Epoch [7][4800/5005] lr: 1.000e-01, eta: 11:33:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.0384 2023-01-01 17:26:07,168 - mmcls - INFO - Epoch [7][4900/5005] lr: 1.000e-01, eta: 11:33:04, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.0438 2023-01-01 17:26:16,234 - mmcls - INFO - Epoch [7][5000/5005] lr: 1.000e-01, eta: 11:32:57, time: 0.090, data_time: 0.005, memory: 1224, loss: 3.0386 2023-01-01 17:26:39,414 - mmcls - INFO - Epoch(val) [7][196] accuracy_top-1: 43.1040, accuracy_top-5: 68.3360 2023-01-01 17:26:50,544 - mmcls - INFO - Epoch [8][100/5005] lr: 1.000e-01, eta: 11:33:10, time: 0.111, data_time: 0.034, memory: 1224, loss: 2.9871 2023-01-01 17:26:59,367 - mmcls - INFO - Epoch [8][200/5005] lr: 1.000e-01, eta: 11:33:00, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.9247 2023-01-01 17:27:08,051 - mmcls - INFO - Epoch [8][300/5005] lr: 1.000e-01, eta: 11:32:47, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.9394 2023-01-01 17:27:16,883 - mmcls - INFO - Epoch [8][400/5005] lr: 1.000e-01, eta: 11:32:37, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9488 2023-01-01 17:27:25,825 - mmcls - INFO - Epoch [8][500/5005] lr: 1.000e-01, eta: 11:32:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9593 2023-01-01 17:27:34,765 - mmcls - INFO - Epoch [8][600/5005] lr: 1.000e-01, eta: 11:32:19, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9896 2023-01-01 17:27:43,601 - mmcls - INFO - Epoch [8][700/5005] lr: 1.000e-01, eta: 11:32:09, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.9796 2023-01-01 17:27:52,384 - mmcls - INFO - Epoch [8][800/5005] lr: 1.000e-01, eta: 11:31:58, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.9740 2023-01-01 17:28:01,507 - mmcls - INFO - Epoch [8][900/5005] lr: 1.000e-01, eta: 11:31:52, time: 0.091, data_time: 0.017, memory: 1224, loss: 2.9819 2023-01-01 17:28:10,786 - mmcls - INFO - Epoch [8][1000/5005] lr: 1.000e-01, eta: 11:31:47, time: 0.093, data_time: 0.011, memory: 1224, loss: 2.9708 2023-01-01 17:28:19,483 - mmcls - INFO - Epoch [8][1100/5005] lr: 1.000e-01, eta: 11:31:35, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.9796 2023-01-01 17:28:28,388 - mmcls - INFO - Epoch [8][1200/5005] lr: 1.000e-01, eta: 11:31:26, time: 0.089, data_time: 0.009, memory: 1224, loss: 3.0125 2023-01-01 17:28:37,130 - mmcls - INFO - Epoch [8][1300/5005] lr: 1.000e-01, eta: 11:31:14, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.9668 2023-01-01 17:28:46,002 - mmcls - INFO - Epoch [8][1400/5005] lr: 1.000e-01, eta: 11:31:05, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.9714 2023-01-01 17:28:54,824 - mmcls - INFO - Epoch [8][1500/5005] lr: 1.000e-01, eta: 11:30:54, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.9816 2023-01-01 17:29:03,504 - mmcls - INFO - Epoch [8][1600/5005] lr: 1.000e-01, eta: 11:30:42, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.9691 2023-01-01 17:29:12,575 - mmcls - INFO - Epoch [8][1700/5005] lr: 1.000e-01, eta: 11:30:35, time: 0.091, data_time: 0.027, memory: 1224, loss: 2.9862 2023-01-01 17:29:21,447 - mmcls - INFO - Epoch [8][1800/5005] lr: 1.000e-01, eta: 11:30:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.9870 2023-01-01 17:29:30,430 - mmcls - INFO - Epoch [8][1900/5005] lr: 1.000e-01, eta: 11:30:17, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.9835 2023-01-01 17:29:39,032 - mmcls - INFO - Epoch [8][2000/5005] lr: 1.000e-01, eta: 11:30:04, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.9438 2023-01-01 17:29:47,999 - mmcls - INFO - Epoch [8][2100/5005] lr: 1.000e-01, eta: 11:29:55, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.9600 2023-01-01 17:29:56,793 - mmcls - INFO - Epoch [8][2200/5005] lr: 1.000e-01, eta: 11:29:44, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.9813 2023-01-01 17:30:06,032 - mmcls - INFO - Epoch [8][2300/5005] lr: 1.000e-01, eta: 11:29:39, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.9689 2023-01-01 17:30:15,107 - mmcls - INFO - Epoch [8][2400/5005] lr: 1.000e-01, eta: 11:29:32, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.0017 2023-01-01 17:30:24,103 - mmcls - INFO - Epoch [8][2500/5005] lr: 1.000e-01, eta: 11:29:24, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.9997 2023-01-01 17:30:32,911 - mmcls - INFO - Epoch [8][2600/5005] lr: 1.000e-01, eta: 11:29:13, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.9806 2023-01-01 17:30:41,555 - mmcls - INFO - Epoch [8][2700/5005] lr: 1.000e-01, eta: 11:29:01, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.9752 2023-01-01 17:30:50,399 - mmcls - INFO - Epoch [8][2800/5005] lr: 1.000e-01, eta: 11:28:51, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.9930 2023-01-01 17:30:59,372 - mmcls - INFO - Epoch [8][2900/5005] lr: 1.000e-01, eta: 11:28:42, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.9966 2023-01-01 17:31:08,811 - mmcls - INFO - Epoch [8][3000/5005] lr: 1.000e-01, eta: 11:28:40, time: 0.094, data_time: 0.006, memory: 1224, loss: 3.0226 2023-01-01 17:31:17,530 - mmcls - INFO - Epoch [8][3100/5005] lr: 1.000e-01, eta: 11:28:28, time: 0.087, data_time: 0.025, memory: 1224, loss: 3.0087 2023-01-01 17:31:26,139 - mmcls - INFO - Epoch [8][3200/5005] lr: 1.000e-01, eta: 11:28:15, time: 0.086, data_time: 0.007, memory: 1224, loss: 3.0116 2023-01-01 17:31:34,616 - mmcls - INFO - Epoch [8][3300/5005] lr: 1.000e-01, eta: 11:28:01, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.9914 2023-01-01 17:31:43,709 - mmcls - INFO - Epoch [8][3400/5005] lr: 1.000e-01, eta: 11:27:54, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.9976 2023-01-01 17:31:52,613 - mmcls - INFO - Epoch [8][3500/5005] lr: 1.000e-01, eta: 11:27:44, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.9766 2023-01-01 17:32:01,419 - mmcls - INFO - Epoch [8][3600/5005] lr: 1.000e-01, eta: 11:27:34, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.9641 2023-01-01 17:32:10,248 - mmcls - INFO - Epoch [8][3700/5005] lr: 1.000e-01, eta: 11:27:24, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.9954 2023-01-01 17:32:18,906 - mmcls - INFO - Epoch [8][3800/5005] lr: 1.000e-01, eta: 11:27:12, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.9927 2023-01-01 17:32:27,685 - mmcls - INFO - Epoch [8][3900/5005] lr: 1.000e-01, eta: 11:27:01, time: 0.088, data_time: 0.019, memory: 1224, loss: 3.0211 2023-01-01 17:32:36,416 - mmcls - INFO - Epoch [8][4000/5005] lr: 1.000e-01, eta: 11:26:50, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.9797 2023-01-01 17:32:45,283 - mmcls - INFO - Epoch [8][4100/5005] lr: 1.000e-01, eta: 11:26:40, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.9651 2023-01-01 17:32:54,009 - mmcls - INFO - Epoch [8][4200/5005] lr: 1.000e-01, eta: 11:26:29, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.9402 2023-01-01 17:33:02,657 - mmcls - INFO - Epoch [8][4300/5005] lr: 1.000e-01, eta: 11:26:16, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.9811 2023-01-01 17:33:11,421 - mmcls - INFO - Epoch [8][4400/5005] lr: 1.000e-01, eta: 11:26:05, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.0480 2023-01-01 17:33:20,182 - mmcls - INFO - Epoch [8][4500/5005] lr: 1.000e-01, eta: 11:25:54, time: 0.088, data_time: 0.034, memory: 1224, loss: 3.0165 2023-01-01 17:33:28,939 - mmcls - INFO - Epoch [8][4600/5005] lr: 1.000e-01, eta: 11:25:44, time: 0.088, data_time: 0.009, memory: 1224, loss: 3.0483 2023-01-01 17:33:37,500 - mmcls - INFO - Epoch [8][4700/5005] lr: 1.000e-01, eta: 11:25:30, time: 0.086, data_time: 0.012, memory: 1224, loss: 3.0128 2023-01-01 17:33:46,324 - mmcls - INFO - Epoch [8][4800/5005] lr: 1.000e-01, eta: 11:25:20, time: 0.088, data_time: 0.007, memory: 1224, loss: 3.0261 2023-01-01 17:33:55,115 - mmcls - INFO - Epoch [8][4900/5005] lr: 1.000e-01, eta: 11:25:10, time: 0.088, data_time: 0.003, memory: 1224, loss: 3.0043 2023-01-01 17:34:03,826 - mmcls - INFO - Epoch [8][5000/5005] lr: 1.000e-01, eta: 11:24:58, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.9815 2023-01-01 17:34:26,620 - mmcls - INFO - Epoch(val) [8][196] accuracy_top-1: 43.4240, accuracy_top-5: 68.6540 2023-01-01 17:34:37,723 - mmcls - INFO - Epoch [9][100/5005] lr: 1.000e-01, eta: 11:25:09, time: 0.111, data_time: 0.036, memory: 1224, loss: 2.9224 2023-01-01 17:34:46,519 - mmcls - INFO - Epoch [9][200/5005] lr: 1.000e-01, eta: 11:24:58, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.9173 2023-01-01 17:34:55,704 - mmcls - INFO - Epoch [9][300/5005] lr: 1.000e-01, eta: 11:24:52, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.9005 2023-01-01 17:35:04,732 - mmcls - INFO - Epoch [9][400/5005] lr: 1.000e-01, eta: 11:24:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.9122 2023-01-01 17:35:13,277 - mmcls - INFO - Epoch [9][500/5005] lr: 1.000e-01, eta: 11:24:31, time: 0.085, data_time: 0.000, memory: 1224, loss: 2.9260 2023-01-01 17:35:22,081 - mmcls - INFO - Epoch [9][600/5005] lr: 1.000e-01, eta: 11:24:21, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.9136 2023-01-01 17:35:30,909 - mmcls - INFO - Epoch [9][700/5005] lr: 1.000e-01, eta: 11:24:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9156 2023-01-01 17:35:39,738 - mmcls - INFO - Epoch [9][800/5005] lr: 1.000e-01, eta: 11:24:00, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.9782 2023-01-01 17:35:48,626 - mmcls - INFO - Epoch [9][900/5005] lr: 1.000e-01, eta: 11:23:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9208 2023-01-01 17:35:57,351 - mmcls - INFO - Epoch [9][1000/5005] lr: 1.000e-01, eta: 11:23:40, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.9361 2023-01-01 17:36:06,053 - mmcls - INFO - Epoch [9][1100/5005] lr: 1.000e-01, eta: 11:23:28, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.9480 2023-01-01 17:36:15,342 - mmcls - INFO - Epoch [9][1200/5005] lr: 1.000e-01, eta: 11:23:23, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.9287 2023-01-01 17:36:24,059 - mmcls - INFO - Epoch [9][1300/5005] lr: 1.000e-01, eta: 11:23:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9328 2023-01-01 17:36:33,185 - mmcls - INFO - Epoch [9][1400/5005] lr: 1.000e-01, eta: 11:23:05, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.9830 2023-01-01 17:36:41,989 - mmcls - INFO - Epoch [9][1500/5005] lr: 1.000e-01, eta: 11:22:55, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.9395 2023-01-01 17:36:50,837 - mmcls - INFO - Epoch [9][1600/5005] lr: 1.000e-01, eta: 11:22:45, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.9345 2023-01-01 17:36:59,337 - mmcls - INFO - Epoch [9][1700/5005] lr: 1.000e-01, eta: 11:22:32, time: 0.085, data_time: 0.021, memory: 1224, loss: 2.9607 2023-01-01 17:37:08,358 - mmcls - INFO - Epoch [9][1800/5005] lr: 1.000e-01, eta: 11:22:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.9294 2023-01-01 17:37:17,106 - mmcls - INFO - Epoch [9][1900/5005] lr: 1.000e-01, eta: 11:22:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.9440 2023-01-01 17:37:26,116 - mmcls - INFO - Epoch [9][2000/5005] lr: 1.000e-01, eta: 11:22:05, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.9361 2023-01-01 17:37:34,627 - mmcls - INFO - Epoch [9][2100/5005] lr: 1.000e-01, eta: 11:21:51, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.9483 2023-01-01 17:37:43,253 - mmcls - INFO - Epoch [9][2200/5005] lr: 1.000e-01, eta: 11:21:39, time: 0.086, data_time: 0.033, memory: 1224, loss: 2.9476 2023-01-01 17:37:51,992 - mmcls - INFO - Epoch [9][2300/5005] lr: 1.000e-01, eta: 11:21:28, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9299 2023-01-01 17:38:00,866 - mmcls - INFO - Epoch [9][2400/5005] lr: 1.000e-01, eta: 11:21:19, time: 0.089, data_time: 0.033, memory: 1224, loss: 2.9952 2023-01-01 17:38:09,623 - mmcls - INFO - Epoch [9][2500/5005] lr: 1.000e-01, eta: 11:21:08, time: 0.088, data_time: 0.031, memory: 1224, loss: 2.9627 2023-01-01 17:38:18,500 - mmcls - INFO - Epoch [9][2600/5005] lr: 1.000e-01, eta: 11:20:59, time: 0.089, data_time: 0.039, memory: 1224, loss: 2.9388 2023-01-01 17:38:27,149 - mmcls - INFO - Epoch [9][2700/5005] lr: 1.000e-01, eta: 11:20:47, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.9549 2023-01-01 17:38:35,981 - mmcls - INFO - Epoch [9][2800/5005] lr: 1.000e-01, eta: 11:20:37, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9707 2023-01-01 17:38:44,780 - mmcls - INFO - Epoch [9][2900/5005] lr: 1.000e-01, eta: 11:20:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9686 2023-01-01 17:38:53,564 - mmcls - INFO - Epoch [9][3000/5005] lr: 1.000e-01, eta: 11:20:16, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.9485 2023-01-01 17:39:02,284 - mmcls - INFO - Epoch [9][3100/5005] lr: 1.000e-01, eta: 11:20:05, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9829 2023-01-01 17:39:10,741 - mmcls - INFO - Epoch [9][3200/5005] lr: 1.000e-01, eta: 11:19:51, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.9409 2023-01-01 17:39:19,690 - mmcls - INFO - Epoch [9][3300/5005] lr: 1.000e-01, eta: 11:19:43, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.9579 2023-01-01 17:39:28,496 - mmcls - INFO - Epoch [9][3400/5005] lr: 1.000e-01, eta: 11:19:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9546 2023-01-01 17:39:37,394 - mmcls - INFO - Epoch [9][3500/5005] lr: 1.000e-01, eta: 11:19:23, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.9708 2023-01-01 17:39:46,357 - mmcls - INFO - Epoch [9][3600/5005] lr: 1.000e-01, eta: 11:19:15, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.9522 2023-01-01 17:39:55,207 - mmcls - INFO - Epoch [9][3700/5005] lr: 1.000e-01, eta: 11:19:05, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.9602 2023-01-01 17:40:04,106 - mmcls - INFO - Epoch [9][3800/5005] lr: 1.000e-01, eta: 11:18:56, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.9585 2023-01-01 17:40:12,923 - mmcls - INFO - Epoch [9][3900/5005] lr: 1.000e-01, eta: 11:18:46, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.9607 2023-01-01 17:40:21,890 - mmcls - INFO - Epoch [9][4000/5005] lr: 1.000e-01, eta: 11:18:37, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.9681 2023-01-01 17:40:30,706 - mmcls - INFO - Epoch [9][4100/5005] lr: 1.000e-01, eta: 11:18:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.9580 2023-01-01 17:40:39,428 - mmcls - INFO - Epoch [9][4200/5005] lr: 1.000e-01, eta: 11:18:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.9615 2023-01-01 17:40:48,444 - mmcls - INFO - Epoch [9][4300/5005] lr: 1.000e-01, eta: 11:18:09, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.9719 2023-01-01 17:40:57,457 - mmcls - INFO - Epoch [9][4400/5005] lr: 1.000e-01, eta: 11:18:00, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.9395 2023-01-01 17:41:06,582 - mmcls - INFO - Epoch [9][4500/5005] lr: 1.000e-01, eta: 11:17:54, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.9709 2023-01-01 17:41:15,458 - mmcls - INFO - Epoch [9][4600/5005] lr: 1.000e-01, eta: 11:17:44, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.9493 2023-01-01 17:41:24,139 - mmcls - INFO - Epoch [9][4700/5005] lr: 1.000e-01, eta: 11:17:33, time: 0.087, data_time: 0.032, memory: 1224, loss: 2.9401 2023-01-01 17:41:33,171 - mmcls - INFO - Epoch [9][4800/5005] lr: 1.000e-01, eta: 11:17:25, time: 0.090, data_time: 0.022, memory: 1224, loss: 2.9483 2023-01-01 17:41:42,037 - mmcls - INFO - Epoch [9][4900/5005] lr: 1.000e-01, eta: 11:17:16, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.9504 2023-01-01 17:41:50,838 - mmcls - INFO - Epoch [9][5000/5005] lr: 1.000e-01, eta: 11:17:06, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.9758 2023-01-01 17:42:13,632 - mmcls - INFO - Epoch(val) [9][196] accuracy_top-1: 42.0340, accuracy_top-5: 66.9720 2023-01-01 17:42:24,943 - mmcls - INFO - Epoch [10][100/5005] lr: 1.000e-01, eta: 11:17:16, time: 0.113, data_time: 0.045, memory: 1224, loss: 2.8815 2023-01-01 17:42:33,797 - mmcls - INFO - Epoch [10][200/5005] lr: 1.000e-01, eta: 11:17:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8680 2023-01-01 17:42:42,664 - mmcls - INFO - Epoch [10][300/5005] lr: 1.000e-01, eta: 11:16:57, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8874 2023-01-01 17:42:51,636 - mmcls - INFO - Epoch [10][400/5005] lr: 1.000e-01, eta: 11:16:48, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.9098 2023-01-01 17:43:00,551 - mmcls - INFO - Epoch [10][500/5005] lr: 1.000e-01, eta: 11:16:39, time: 0.089, data_time: 0.037, memory: 1224, loss: 2.9122 2023-01-01 17:43:09,621 - mmcls - INFO - Epoch [10][600/5005] lr: 1.000e-01, eta: 11:16:31, time: 0.091, data_time: 0.034, memory: 1224, loss: 2.9020 2023-01-01 17:43:18,517 - mmcls - INFO - Epoch [10][700/5005] lr: 1.000e-01, eta: 11:16:22, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.9223 2023-01-01 17:43:27,467 - mmcls - INFO - Epoch [10][800/5005] lr: 1.000e-01, eta: 11:16:14, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9317 2023-01-01 17:43:36,347 - mmcls - INFO - Epoch [10][900/5005] lr: 1.000e-01, eta: 11:16:04, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.9043 2023-01-01 17:43:45,827 - mmcls - INFO - Epoch [10][1000/5005] lr: 1.000e-01, eta: 11:16:01, time: 0.095, data_time: 0.048, memory: 1224, loss: 2.9532 2023-01-01 17:43:54,855 - mmcls - INFO - Epoch [10][1100/5005] lr: 1.000e-01, eta: 11:15:53, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.9246 2023-01-01 17:44:03,661 - mmcls - INFO - Epoch [10][1200/5005] lr: 1.000e-01, eta: 11:15:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9281 2023-01-01 17:44:12,160 - mmcls - INFO - Epoch [10][1300/5005] lr: 1.000e-01, eta: 11:15:30, time: 0.085, data_time: 0.016, memory: 1224, loss: 2.9264 2023-01-01 17:44:21,002 - mmcls - INFO - Epoch [10][1400/5005] lr: 1.000e-01, eta: 11:15:20, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.9372 2023-01-01 17:44:29,803 - mmcls - INFO - Epoch [10][1500/5005] lr: 1.000e-01, eta: 11:15:10, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.9113 2023-01-01 17:44:38,735 - mmcls - INFO - Epoch [10][1600/5005] lr: 1.000e-01, eta: 11:15:01, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.9197 2023-01-01 17:44:47,287 - mmcls - INFO - Epoch [10][1700/5005] lr: 1.000e-01, eta: 11:14:48, time: 0.085, data_time: 0.022, memory: 1224, loss: 2.9487 2023-01-01 17:44:56,362 - mmcls - INFO - Epoch [10][1800/5005] lr: 1.000e-01, eta: 11:14:41, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.9276 2023-01-01 17:45:05,157 - mmcls - INFO - Epoch [10][1900/5005] lr: 1.000e-01, eta: 11:14:31, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.9231 2023-01-01 17:45:13,782 - mmcls - INFO - Epoch [10][2000/5005] lr: 1.000e-01, eta: 11:14:19, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.9052 2023-01-01 17:45:22,804 - mmcls - INFO - Epoch [10][2100/5005] lr: 1.000e-01, eta: 11:14:11, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.9229 2023-01-01 17:45:31,583 - mmcls - INFO - Epoch [10][2200/5005] lr: 1.000e-01, eta: 11:14:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9083 2023-01-01 17:45:40,358 - mmcls - INFO - Epoch [10][2300/5005] lr: 1.000e-01, eta: 11:13:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9441 2023-01-01 17:45:49,105 - mmcls - INFO - Epoch [10][2400/5005] lr: 1.000e-01, eta: 11:13:40, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.9028 2023-01-01 17:45:57,996 - mmcls - INFO - Epoch [10][2500/5005] lr: 1.000e-01, eta: 11:13:31, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.9464 2023-01-01 17:46:07,032 - mmcls - INFO - Epoch [10][2600/5005] lr: 1.000e-01, eta: 11:13:23, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.9520 2023-01-01 17:46:15,740 - mmcls - INFO - Epoch [10][2700/5005] lr: 1.000e-01, eta: 11:13:12, time: 0.087, data_time: 0.023, memory: 1224, loss: 2.9161 2023-01-01 17:46:24,583 - mmcls - INFO - Epoch [10][2800/5005] lr: 1.000e-01, eta: 11:13:02, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.9055 2023-01-01 17:46:33,527 - mmcls - INFO - Epoch [10][2900/5005] lr: 1.000e-01, eta: 11:12:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9309 2023-01-01 17:46:42,056 - mmcls - INFO - Epoch [10][3000/5005] lr: 1.000e-01, eta: 11:12:41, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.9216 2023-01-01 17:46:51,200 - mmcls - INFO - Epoch [10][3100/5005] lr: 1.000e-01, eta: 11:12:34, time: 0.091, data_time: 0.024, memory: 1224, loss: 2.9311 2023-01-01 17:46:59,826 - mmcls - INFO - Epoch [10][3200/5005] lr: 1.000e-01, eta: 11:12:22, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.9680 2023-01-01 17:47:08,668 - mmcls - INFO - Epoch [10][3300/5005] lr: 1.000e-01, eta: 11:12:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9656 2023-01-01 17:47:17,352 - mmcls - INFO - Epoch [10][3400/5005] lr: 1.000e-01, eta: 11:12:01, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9638 2023-01-01 17:47:26,269 - mmcls - INFO - Epoch [10][3500/5005] lr: 1.000e-01, eta: 11:11:52, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.9439 2023-01-01 17:47:34,834 - mmcls - INFO - Epoch [10][3600/5005] lr: 1.000e-01, eta: 11:11:40, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.9444 2023-01-01 17:47:43,721 - mmcls - INFO - Epoch [10][3700/5005] lr: 1.000e-01, eta: 11:11:31, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.9297 2023-01-01 17:47:52,359 - mmcls - INFO - Epoch [10][3800/5005] lr: 1.000e-01, eta: 11:11:19, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.9401 2023-01-01 17:48:01,175 - mmcls - INFO - Epoch [10][3900/5005] lr: 1.000e-01, eta: 11:11:10, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.9423 2023-01-01 17:48:10,170 - mmcls - INFO - Epoch [10][4000/5005] lr: 1.000e-01, eta: 11:11:01, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.9434 2023-01-01 17:48:18,651 - mmcls - INFO - Epoch [10][4100/5005] lr: 1.000e-01, eta: 11:10:48, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.9034 2023-01-01 17:48:27,710 - mmcls - INFO - Epoch [10][4200/5005] lr: 1.000e-01, eta: 11:10:41, time: 0.091, data_time: 0.023, memory: 1224, loss: 2.9344 2023-01-01 17:48:36,367 - mmcls - INFO - Epoch [10][4300/5005] lr: 1.000e-01, eta: 11:10:30, time: 0.087, data_time: 0.027, memory: 1224, loss: 2.9181 2023-01-01 17:48:45,244 - mmcls - INFO - Epoch [10][4400/5005] lr: 1.000e-01, eta: 11:10:20, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.9413 2023-01-01 17:48:53,964 - mmcls - INFO - Epoch [10][4500/5005] lr: 1.000e-01, eta: 11:10:09, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.8755 2023-01-01 17:49:03,159 - mmcls - INFO - Epoch [10][4600/5005] lr: 1.000e-01, eta: 11:10:03, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.9379 2023-01-01 17:49:11,831 - mmcls - INFO - Epoch [10][4700/5005] lr: 1.000e-01, eta: 11:09:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9370 2023-01-01 17:49:20,920 - mmcls - INFO - Epoch [10][4800/5005] lr: 1.000e-01, eta: 11:09:45, time: 0.091, data_time: 0.034, memory: 1224, loss: 2.9478 2023-01-01 17:49:29,490 - mmcls - INFO - Epoch [10][4900/5005] lr: 1.000e-01, eta: 11:09:33, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.9568 2023-01-01 17:49:38,550 - mmcls - INFO - Epoch [10][5000/5005] lr: 1.000e-01, eta: 11:09:25, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.9213 2023-01-01 17:50:01,684 - mmcls - INFO - Epoch(val) [10][196] accuracy_top-1: 45.3460, accuracy_top-5: 70.5200 2023-01-01 17:50:12,569 - mmcls - INFO - Epoch [11][100/5005] lr: 1.000e-01, eta: 11:09:29, time: 0.109, data_time: 0.039, memory: 1224, loss: 2.8707 2023-01-01 17:50:21,246 - mmcls - INFO - Epoch [11][200/5005] lr: 1.000e-01, eta: 11:09:18, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.8847 2023-01-01 17:50:29,879 - mmcls - INFO - Epoch [11][300/5005] lr: 1.000e-01, eta: 11:09:06, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8744 2023-01-01 17:50:38,819 - mmcls - INFO - Epoch [11][400/5005] lr: 1.000e-01, eta: 11:08:58, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.8849 2023-01-01 17:50:47,471 - mmcls - INFO - Epoch [11][500/5005] lr: 1.000e-01, eta: 11:08:46, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.9209 2023-01-01 17:50:56,182 - mmcls - INFO - Epoch [11][600/5005] lr: 1.000e-01, eta: 11:08:36, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9191 2023-01-01 17:51:05,147 - mmcls - INFO - Epoch [11][700/5005] lr: 1.000e-01, eta: 11:08:27, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.8989 2023-01-01 17:51:13,914 - mmcls - INFO - Epoch [11][800/5005] lr: 1.000e-01, eta: 11:08:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9083 2023-01-01 17:51:22,587 - mmcls - INFO - Epoch [11][900/5005] lr: 1.000e-01, eta: 11:08:06, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.8683 2023-01-01 17:51:31,444 - mmcls - INFO - Epoch [11][1000/5005] lr: 1.000e-01, eta: 11:07:56, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9064 2023-01-01 17:51:40,046 - mmcls - INFO - Epoch [11][1100/5005] lr: 1.000e-01, eta: 11:07:45, time: 0.086, data_time: 0.037, memory: 1224, loss: 2.8866 2023-01-01 17:51:48,927 - mmcls - INFO - Epoch [11][1200/5005] lr: 1.000e-01, eta: 11:07:35, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.8735 2023-01-01 17:51:57,515 - mmcls - INFO - Epoch [11][1300/5005] lr: 1.000e-01, eta: 11:07:24, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.8838 2023-01-01 17:52:06,686 - mmcls - INFO - Epoch [11][1400/5005] lr: 1.000e-01, eta: 11:07:17, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8923 2023-01-01 17:52:15,466 - mmcls - INFO - Epoch [11][1500/5005] lr: 1.000e-01, eta: 11:07:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8600 2023-01-01 17:52:24,350 - mmcls - INFO - Epoch [11][1600/5005] lr: 1.000e-01, eta: 11:06:58, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.9340 2023-01-01 17:52:33,408 - mmcls - INFO - Epoch [11][1700/5005] lr: 1.000e-01, eta: 11:06:50, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.9463 2023-01-01 17:52:42,151 - mmcls - INFO - Epoch [11][1800/5005] lr: 1.000e-01, eta: 11:06:40, time: 0.087, data_time: 0.030, memory: 1224, loss: 2.9059 2023-01-01 17:52:50,974 - mmcls - INFO - Epoch [11][1900/5005] lr: 1.000e-01, eta: 11:06:30, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.8768 2023-01-01 17:52:59,921 - mmcls - INFO - Epoch [11][2000/5005] lr: 1.000e-01, eta: 11:06:21, time: 0.089, data_time: 0.025, memory: 1224, loss: 2.9123 2023-01-01 17:53:08,508 - mmcls - INFO - Epoch [11][2100/5005] lr: 1.000e-01, eta: 11:06:09, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.9216 2023-01-01 17:53:17,537 - mmcls - INFO - Epoch [11][2200/5005] lr: 1.000e-01, eta: 11:06:01, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.9117 2023-01-01 17:53:26,346 - mmcls - INFO - Epoch [11][2300/5005] lr: 1.000e-01, eta: 11:05:52, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.9033 2023-01-01 17:53:35,578 - mmcls - INFO - Epoch [11][2400/5005] lr: 1.000e-01, eta: 11:05:45, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.8952 2023-01-01 17:53:44,699 - mmcls - INFO - Epoch [11][2500/5005] lr: 1.000e-01, eta: 11:05:38, time: 0.091, data_time: 0.014, memory: 1224, loss: 2.8917 2023-01-01 17:53:53,755 - mmcls - INFO - Epoch [11][2600/5005] lr: 1.000e-01, eta: 11:05:30, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.8979 2023-01-01 17:54:02,375 - mmcls - INFO - Epoch [11][2700/5005] lr: 1.000e-01, eta: 11:05:19, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.9195 2023-01-01 17:54:11,401 - mmcls - INFO - Epoch [11][2800/5005] lr: 1.000e-01, eta: 11:05:11, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.9147 2023-01-01 17:54:20,070 - mmcls - INFO - Epoch [11][2900/5005] lr: 1.000e-01, eta: 11:05:00, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.9324 2023-01-01 17:54:28,990 - mmcls - INFO - Epoch [11][3000/5005] lr: 1.000e-01, eta: 11:04:51, time: 0.089, data_time: 0.034, memory: 1224, loss: 2.9132 2023-01-01 17:54:37,795 - mmcls - INFO - Epoch [11][3100/5005] lr: 1.000e-01, eta: 11:04:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9222 2023-01-01 17:54:46,343 - mmcls - INFO - Epoch [11][3200/5005] lr: 1.000e-01, eta: 11:04:29, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.8876 2023-01-01 17:54:55,159 - mmcls - INFO - Epoch [11][3300/5005] lr: 1.000e-01, eta: 11:04:20, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.9324 2023-01-01 17:55:04,097 - mmcls - INFO - Epoch [11][3400/5005] lr: 1.000e-01, eta: 11:04:11, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.8918 2023-01-01 17:55:13,127 - mmcls - INFO - Epoch [11][3500/5005] lr: 1.000e-01, eta: 11:04:03, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.8712 2023-01-01 17:55:21,890 - mmcls - INFO - Epoch [11][3600/5005] lr: 1.000e-01, eta: 11:03:53, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.9122 2023-01-01 17:55:30,681 - mmcls - INFO - Epoch [11][3700/5005] lr: 1.000e-01, eta: 11:03:43, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.9522 2023-01-01 17:55:39,423 - mmcls - INFO - Epoch [11][3800/5005] lr: 1.000e-01, eta: 11:03:32, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.8937 2023-01-01 17:55:48,574 - mmcls - INFO - Epoch [11][3900/5005] lr: 1.000e-01, eta: 11:03:26, time: 0.092, data_time: 0.031, memory: 1224, loss: 2.9054 2023-01-01 17:55:57,303 - mmcls - INFO - Epoch [11][4000/5005] lr: 1.000e-01, eta: 11:03:15, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.9155 2023-01-01 17:56:06,465 - mmcls - INFO - Epoch [11][4100/5005] lr: 1.000e-01, eta: 11:03:08, time: 0.092, data_time: 0.020, memory: 1224, loss: 2.9249 2023-01-01 17:56:15,337 - mmcls - INFO - Epoch [11][4200/5005] lr: 1.000e-01, eta: 11:02:59, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.9231 2023-01-01 17:56:23,777 - mmcls - INFO - Epoch [11][4300/5005] lr: 1.000e-01, eta: 11:02:46, time: 0.084, data_time: 0.005, memory: 1224, loss: 2.9136 2023-01-01 17:56:32,569 - mmcls - INFO - Epoch [11][4400/5005] lr: 1.000e-01, eta: 11:02:36, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.8811 2023-01-01 17:56:41,404 - mmcls - INFO - Epoch [11][4500/5005] lr: 1.000e-01, eta: 11:02:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8921 2023-01-01 17:56:50,284 - mmcls - INFO - Epoch [11][4600/5005] lr: 1.000e-01, eta: 11:02:17, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8787 2023-01-01 17:56:59,174 - mmcls - INFO - Epoch [11][4700/5005] lr: 1.000e-01, eta: 11:02:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9124 2023-01-01 17:57:08,458 - mmcls - INFO - Epoch [11][4800/5005] lr: 1.000e-01, eta: 11:02:02, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.8871 2023-01-01 17:57:17,246 - mmcls - INFO - Epoch [11][4900/5005] lr: 1.000e-01, eta: 11:01:52, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.9125 2023-01-01 17:57:26,064 - mmcls - INFO - Epoch [11][5000/5005] lr: 1.000e-01, eta: 11:01:43, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.9279 2023-01-01 17:57:48,939 - mmcls - INFO - Epoch(val) [11][196] accuracy_top-1: 42.1840, accuracy_top-5: 67.2420 2023-01-01 17:57:59,877 - mmcls - INFO - Epoch [12][100/5005] lr: 1.000e-01, eta: 11:01:46, time: 0.109, data_time: 0.051, memory: 1224, loss: 2.8673 2023-01-01 17:58:08,904 - mmcls - INFO - Epoch [12][200/5005] lr: 1.000e-01, eta: 11:01:38, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.8791 2023-01-01 17:58:17,944 - mmcls - INFO - Epoch [12][300/5005] lr: 1.000e-01, eta: 11:01:30, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.8446 2023-01-01 17:58:26,912 - mmcls - INFO - Epoch [12][400/5005] lr: 1.000e-01, eta: 11:01:21, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8587 2023-01-01 17:58:35,919 - mmcls - INFO - Epoch [12][500/5005] lr: 1.000e-01, eta: 11:01:13, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.8779 2023-01-01 17:58:44,590 - mmcls - INFO - Epoch [12][600/5005] lr: 1.000e-01, eta: 11:01:02, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.9075 2023-01-01 17:58:53,350 - mmcls - INFO - Epoch [12][700/5005] lr: 1.000e-01, eta: 11:00:52, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8611 2023-01-01 17:59:02,310 - mmcls - INFO - Epoch [12][800/5005] lr: 1.000e-01, eta: 11:00:44, time: 0.090, data_time: 0.034, memory: 1224, loss: 2.8811 2023-01-01 17:59:11,084 - mmcls - INFO - Epoch [12][900/5005] lr: 1.000e-01, eta: 11:00:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8743 2023-01-01 17:59:20,022 - mmcls - INFO - Epoch [12][1000/5005] lr: 1.000e-01, eta: 11:00:25, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8555 2023-01-01 17:59:29,077 - mmcls - INFO - Epoch [12][1100/5005] lr: 1.000e-01, eta: 11:00:17, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.8957 2023-01-01 17:59:37,920 - mmcls - INFO - Epoch [12][1200/5005] lr: 1.000e-01, eta: 11:00:08, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.9018 2023-01-01 17:59:46,730 - mmcls - INFO - Epoch [12][1300/5005] lr: 1.000e-01, eta: 10:59:58, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.8756 2023-01-01 17:59:55,834 - mmcls - INFO - Epoch [12][1400/5005] lr: 1.000e-01, eta: 10:59:50, time: 0.091, data_time: 0.016, memory: 1224, loss: 2.8914 2023-01-01 18:00:04,628 - mmcls - INFO - Epoch [12][1500/5005] lr: 1.000e-01, eta: 10:59:40, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8917 2023-01-01 18:00:13,860 - mmcls - INFO - Epoch [12][1600/5005] lr: 1.000e-01, eta: 10:59:34, time: 0.092, data_time: 0.009, memory: 1224, loss: 2.8893 2023-01-01 18:00:22,435 - mmcls - INFO - Epoch [12][1700/5005] lr: 1.000e-01, eta: 10:59:23, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.9054 2023-01-01 18:00:31,254 - mmcls - INFO - Epoch [12][1800/5005] lr: 1.000e-01, eta: 10:59:13, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.9176 2023-01-01 18:00:39,830 - mmcls - INFO - Epoch [12][1900/5005] lr: 1.000e-01, eta: 10:59:01, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.9184 2023-01-01 18:00:48,772 - mmcls - INFO - Epoch [12][2000/5005] lr: 1.000e-01, eta: 10:58:52, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.9177 2023-01-01 18:00:57,712 - mmcls - INFO - Epoch [12][2100/5005] lr: 1.000e-01, eta: 10:58:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9140 2023-01-01 18:01:06,628 - mmcls - INFO - Epoch [12][2200/5005] lr: 1.000e-01, eta: 10:58:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8947 2023-01-01 18:01:15,584 - mmcls - INFO - Epoch [12][2300/5005] lr: 1.000e-01, eta: 10:58:26, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.9106 2023-01-01 18:01:24,298 - mmcls - INFO - Epoch [12][2400/5005] lr: 1.000e-01, eta: 10:58:16, time: 0.087, data_time: 0.040, memory: 1224, loss: 2.9081 2023-01-01 18:01:33,144 - mmcls - INFO - Epoch [12][2500/5005] lr: 1.000e-01, eta: 10:58:06, time: 0.088, data_time: 0.039, memory: 1224, loss: 2.9039 2023-01-01 18:01:41,902 - mmcls - INFO - Epoch [12][2600/5005] lr: 1.000e-01, eta: 10:57:56, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.8512 2023-01-01 18:01:50,647 - mmcls - INFO - Epoch [12][2700/5005] lr: 1.000e-01, eta: 10:57:46, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.8872 2023-01-01 18:01:59,249 - mmcls - INFO - Epoch [12][2800/5005] lr: 1.000e-01, eta: 10:57:35, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.9379 2023-01-01 18:02:08,302 - mmcls - INFO - Epoch [12][2900/5005] lr: 1.000e-01, eta: 10:57:27, time: 0.091, data_time: 0.019, memory: 1224, loss: 2.9141 2023-01-01 18:02:17,116 - mmcls - INFO - Epoch [12][3000/5005] lr: 1.000e-01, eta: 10:57:17, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.9002 2023-01-01 18:02:25,954 - mmcls - INFO - Epoch [12][3100/5005] lr: 1.000e-01, eta: 10:57:08, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8741 2023-01-01 18:02:34,644 - mmcls - INFO - Epoch [12][3200/5005] lr: 1.000e-01, eta: 10:56:57, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8687 2023-01-01 18:02:43,541 - mmcls - INFO - Epoch [12][3300/5005] lr: 1.000e-01, eta: 10:56:48, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.8796 2023-01-01 18:02:52,356 - mmcls - INFO - Epoch [12][3400/5005] lr: 1.000e-01, eta: 10:56:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8753 2023-01-01 18:03:00,769 - mmcls - INFO - Epoch [12][3500/5005] lr: 1.000e-01, eta: 10:56:26, time: 0.084, data_time: 0.005, memory: 1224, loss: 2.9126 2023-01-01 18:03:10,077 - mmcls - INFO - Epoch [12][3600/5005] lr: 1.000e-01, eta: 10:56:20, time: 0.093, data_time: 0.006, memory: 1224, loss: 2.8754 2023-01-01 18:03:19,133 - mmcls - INFO - Epoch [12][3700/5005] lr: 1.000e-01, eta: 10:56:12, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.9340 2023-01-01 18:03:27,841 - mmcls - INFO - Epoch [12][3800/5005] lr: 1.000e-01, eta: 10:56:01, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.9144 2023-01-01 18:03:36,700 - mmcls - INFO - Epoch [12][3900/5005] lr: 1.000e-01, eta: 10:55:52, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.9014 2023-01-01 18:03:45,363 - mmcls - INFO - Epoch [12][4000/5005] lr: 1.000e-01, eta: 10:55:41, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.9268 2023-01-01 18:03:54,156 - mmcls - INFO - Epoch [12][4100/5005] lr: 1.000e-01, eta: 10:55:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8767 2023-01-01 18:04:03,168 - mmcls - INFO - Epoch [12][4200/5005] lr: 1.000e-01, eta: 10:55:23, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.8556 2023-01-01 18:04:11,978 - mmcls - INFO - Epoch [12][4300/5005] lr: 1.000e-01, eta: 10:55:14, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.9001 2023-01-01 18:04:20,510 - mmcls - INFO - Epoch [12][4400/5005] lr: 1.000e-01, eta: 10:55:02, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.8894 2023-01-01 18:04:29,409 - mmcls - INFO - Epoch [12][4500/5005] lr: 1.000e-01, eta: 10:54:53, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8725 2023-01-01 18:04:38,375 - mmcls - INFO - Epoch [12][4600/5005] lr: 1.000e-01, eta: 10:54:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8906 2023-01-01 18:04:47,189 - mmcls - INFO - Epoch [12][4700/5005] lr: 1.000e-01, eta: 10:54:35, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8901 2023-01-01 18:04:55,951 - mmcls - INFO - Epoch [12][4800/5005] lr: 1.000e-01, eta: 10:54:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8682 2023-01-01 18:05:04,692 - mmcls - INFO - Epoch [12][4900/5005] lr: 1.000e-01, eta: 10:54:15, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.8634 2023-01-01 18:05:13,941 - mmcls - INFO - Epoch [12][5000/5005] lr: 1.000e-01, eta: 10:54:08, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.8675 2023-01-01 18:05:36,797 - mmcls - INFO - Epoch(val) [12][196] accuracy_top-1: 42.3760, accuracy_top-5: 67.6460 2023-01-01 18:05:47,933 - mmcls - INFO - Epoch [13][100/5005] lr: 1.000e-01, eta: 10:54:12, time: 0.111, data_time: 0.033, memory: 1224, loss: 2.7944 2023-01-01 18:05:56,850 - mmcls - INFO - Epoch [13][200/5005] lr: 1.000e-01, eta: 10:54:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8828 2023-01-01 18:06:05,523 - mmcls - INFO - Epoch [13][300/5005] lr: 1.000e-01, eta: 10:53:52, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.8718 2023-01-01 18:06:14,172 - mmcls - INFO - Epoch [13][400/5005] lr: 1.000e-01, eta: 10:53:41, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.8808 2023-01-01 18:06:22,963 - mmcls - INFO - Epoch [13][500/5005] lr: 1.000e-01, eta: 10:53:31, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8511 2023-01-01 18:06:31,620 - mmcls - INFO - Epoch [13][600/5005] lr: 1.000e-01, eta: 10:53:21, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8709 2023-01-01 18:06:40,488 - mmcls - INFO - Epoch [13][700/5005] lr: 1.000e-01, eta: 10:53:11, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8854 2023-01-01 18:06:49,561 - mmcls - INFO - Epoch [13][800/5005] lr: 1.000e-01, eta: 10:53:04, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.8592 2023-01-01 18:06:58,220 - mmcls - INFO - Epoch [13][900/5005] lr: 1.000e-01, eta: 10:52:53, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8835 2023-01-01 18:07:07,670 - mmcls - INFO - Epoch [13][1000/5005] lr: 1.000e-01, eta: 10:52:48, time: 0.095, data_time: 0.003, memory: 1224, loss: 2.8671 2023-01-01 18:07:16,243 - mmcls - INFO - Epoch [13][1100/5005] lr: 1.000e-01, eta: 10:52:36, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.8600 2023-01-01 18:07:25,187 - mmcls - INFO - Epoch [13][1200/5005] lr: 1.000e-01, eta: 10:52:28, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.8603 2023-01-01 18:07:34,143 - mmcls - INFO - Epoch [13][1300/5005] lr: 1.000e-01, eta: 10:52:19, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.8564 2023-01-01 18:07:42,763 - mmcls - INFO - Epoch [13][1400/5005] lr: 1.000e-01, eta: 10:52:08, time: 0.086, data_time: 0.038, memory: 1224, loss: 2.9070 2023-01-01 18:07:51,535 - mmcls - INFO - Epoch [13][1500/5005] lr: 1.000e-01, eta: 10:51:58, time: 0.088, data_time: 0.040, memory: 1224, loss: 2.8831 2023-01-01 18:08:00,453 - mmcls - INFO - Epoch [13][1600/5005] lr: 1.000e-01, eta: 10:51:49, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.8517 2023-01-01 18:08:09,271 - mmcls - INFO - Epoch [13][1700/5005] lr: 1.000e-01, eta: 10:51:40, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8531 2023-01-01 18:08:18,535 - mmcls - INFO - Epoch [13][1800/5005] lr: 1.000e-01, eta: 10:51:33, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.8530 2023-01-01 18:08:27,396 - mmcls - INFO - Epoch [13][1900/5005] lr: 1.000e-01, eta: 10:51:24, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.9069 2023-01-01 18:08:36,331 - mmcls - INFO - Epoch [13][2000/5005] lr: 1.000e-01, eta: 10:51:15, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.8697 2023-01-01 18:08:45,154 - mmcls - INFO - Epoch [13][2100/5005] lr: 1.000e-01, eta: 10:51:06, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8786 2023-01-01 18:08:53,879 - mmcls - INFO - Epoch [13][2200/5005] lr: 1.000e-01, eta: 10:50:55, time: 0.087, data_time: 0.027, memory: 1224, loss: 2.8938 2023-01-01 18:09:02,754 - mmcls - INFO - Epoch [13][2300/5005] lr: 1.000e-01, eta: 10:50:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8565 2023-01-01 18:09:11,528 - mmcls - INFO - Epoch [13][2400/5005] lr: 1.000e-01, eta: 10:50:36, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8962 2023-01-01 18:09:20,164 - mmcls - INFO - Epoch [13][2500/5005] lr: 1.000e-01, eta: 10:50:26, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.8502 2023-01-01 18:09:29,173 - mmcls - INFO - Epoch [13][2600/5005] lr: 1.000e-01, eta: 10:50:17, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.8685 2023-01-01 18:09:38,010 - mmcls - INFO - Epoch [13][2700/5005] lr: 1.000e-01, eta: 10:50:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.9001 2023-01-01 18:09:47,068 - mmcls - INFO - Epoch [13][2800/5005] lr: 1.000e-01, eta: 10:50:00, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.8808 2023-01-01 18:09:55,855 - mmcls - INFO - Epoch [13][2900/5005] lr: 1.000e-01, eta: 10:49:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8508 2023-01-01 18:10:04,448 - mmcls - INFO - Epoch [13][3000/5005] lr: 1.000e-01, eta: 10:49:39, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.8626 2023-01-01 18:10:13,349 - mmcls - INFO - Epoch [13][3100/5005] lr: 1.000e-01, eta: 10:49:30, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.8511 2023-01-01 18:10:22,462 - mmcls - INFO - Epoch [13][3200/5005] lr: 1.000e-01, eta: 10:49:23, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.8690 2023-01-01 18:10:31,222 - mmcls - INFO - Epoch [13][3300/5005] lr: 1.000e-01, eta: 10:49:13, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8606 2023-01-01 18:10:39,964 - mmcls - INFO - Epoch [13][3400/5005] lr: 1.000e-01, eta: 10:49:03, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.8881 2023-01-01 18:10:48,979 - mmcls - INFO - Epoch [13][3500/5005] lr: 1.000e-01, eta: 10:48:54, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.8840 2023-01-01 18:10:57,570 - mmcls - INFO - Epoch [13][3600/5005] lr: 1.000e-01, eta: 10:48:43, time: 0.086, data_time: 0.028, memory: 1224, loss: 2.8745 2023-01-01 18:11:06,455 - mmcls - INFO - Epoch [13][3700/5005] lr: 1.000e-01, eta: 10:48:34, time: 0.089, data_time: 0.035, memory: 1224, loss: 2.8910 2023-01-01 18:11:15,481 - mmcls - INFO - Epoch [13][3800/5005] lr: 1.000e-01, eta: 10:48:26, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.8787 2023-01-01 18:11:24,208 - mmcls - INFO - Epoch [13][3900/5005] lr: 1.000e-01, eta: 10:48:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9135 2023-01-01 18:11:33,042 - mmcls - INFO - Epoch [13][4000/5005] lr: 1.000e-01, eta: 10:48:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8755 2023-01-01 18:11:41,579 - mmcls - INFO - Epoch [13][4100/5005] lr: 1.000e-01, eta: 10:47:55, time: 0.085, data_time: 0.009, memory: 1224, loss: 2.8592 2023-01-01 18:11:50,228 - mmcls - INFO - Epoch [13][4200/5005] lr: 1.000e-01, eta: 10:47:44, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.8966 2023-01-01 18:11:58,942 - mmcls - INFO - Epoch [13][4300/5005] lr: 1.000e-01, eta: 10:47:34, time: 0.087, data_time: 0.028, memory: 1224, loss: 2.9234 2023-01-01 18:12:07,770 - mmcls - INFO - Epoch [13][4400/5005] lr: 1.000e-01, eta: 10:47:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8636 2023-01-01 18:12:16,255 - mmcls - INFO - Epoch [13][4500/5005] lr: 1.000e-01, eta: 10:47:13, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.8677 2023-01-01 18:12:25,155 - mmcls - INFO - Epoch [13][4600/5005] lr: 1.000e-01, eta: 10:47:04, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.9348 2023-01-01 18:12:34,144 - mmcls - INFO - Epoch [13][4700/5005] lr: 1.000e-01, eta: 10:46:55, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.8846 2023-01-01 18:12:42,924 - mmcls - INFO - Epoch [13][4800/5005] lr: 1.000e-01, eta: 10:46:46, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.8541 2023-01-01 18:12:51,551 - mmcls - INFO - Epoch [13][4900/5005] lr: 1.000e-01, eta: 10:46:35, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.8595 2023-01-01 18:13:00,382 - mmcls - INFO - Epoch [13][5000/5005] lr: 1.000e-01, eta: 10:46:26, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8337 2023-01-01 18:13:23,299 - mmcls - INFO - Epoch(val) [13][196] accuracy_top-1: 44.2520, accuracy_top-5: 69.7160 2023-01-01 18:13:34,388 - mmcls - INFO - Epoch [14][100/5005] lr: 1.000e-01, eta: 10:46:28, time: 0.111, data_time: 0.057, memory: 1224, loss: 2.7935 2023-01-01 18:13:42,713 - mmcls - INFO - Epoch [14][200/5005] lr: 1.000e-01, eta: 10:46:15, time: 0.083, data_time: 0.011, memory: 1224, loss: 2.8015 2023-01-01 18:13:51,632 - mmcls - INFO - Epoch [14][300/5005] lr: 1.000e-01, eta: 10:46:06, time: 0.089, data_time: 0.000, memory: 1224, loss: 2.8187 2023-01-01 18:14:00,366 - mmcls - INFO - Epoch [14][400/5005] lr: 1.000e-01, eta: 10:45:56, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.8258 2023-01-01 18:14:09,510 - mmcls - INFO - Epoch [14][500/5005] lr: 1.000e-01, eta: 10:45:49, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.8141 2023-01-01 18:14:18,267 - mmcls - INFO - Epoch [14][600/5005] lr: 1.000e-01, eta: 10:45:39, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8197 2023-01-01 18:14:26,945 - mmcls - INFO - Epoch [14][700/5005] lr: 1.000e-01, eta: 10:45:28, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.8308 2023-01-01 18:14:35,877 - mmcls - INFO - Epoch [14][800/5005] lr: 1.000e-01, eta: 10:45:19, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8570 2023-01-01 18:14:44,651 - mmcls - INFO - Epoch [14][900/5005] lr: 1.000e-01, eta: 10:45:10, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.8157 2023-01-01 18:14:53,652 - mmcls - INFO - Epoch [14][1000/5005] lr: 1.000e-01, eta: 10:45:01, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8141 2023-01-01 18:15:02,556 - mmcls - INFO - Epoch [14][1100/5005] lr: 1.000e-01, eta: 10:44:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8398 2023-01-01 18:15:11,506 - mmcls - INFO - Epoch [14][1200/5005] lr: 1.000e-01, eta: 10:44:44, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.8283 2023-01-01 18:15:20,075 - mmcls - INFO - Epoch [14][1300/5005] lr: 1.000e-01, eta: 10:44:33, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8415 2023-01-01 18:15:28,992 - mmcls - INFO - Epoch [14][1400/5005] lr: 1.000e-01, eta: 10:44:24, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8332 2023-01-01 18:15:37,995 - mmcls - INFO - Epoch [14][1500/5005] lr: 1.000e-01, eta: 10:44:15, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.8736 2023-01-01 18:15:46,649 - mmcls - INFO - Epoch [14][1600/5005] lr: 1.000e-01, eta: 10:44:05, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9086 2023-01-01 18:15:55,293 - mmcls - INFO - Epoch [14][1700/5005] lr: 1.000e-01, eta: 10:43:54, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8565 2023-01-01 18:16:04,150 - mmcls - INFO - Epoch [14][1800/5005] lr: 1.000e-01, eta: 10:43:45, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.8812 2023-01-01 18:16:13,149 - mmcls - INFO - Epoch [14][1900/5005] lr: 1.000e-01, eta: 10:43:37, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8494 2023-01-01 18:16:21,869 - mmcls - INFO - Epoch [14][2000/5005] lr: 1.000e-01, eta: 10:43:27, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8601 2023-01-01 18:16:30,974 - mmcls - INFO - Epoch [14][2100/5005] lr: 1.000e-01, eta: 10:43:19, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.8450 2023-01-01 18:16:39,580 - mmcls - INFO - Epoch [14][2200/5005] lr: 1.000e-01, eta: 10:43:08, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8632 2023-01-01 18:16:48,450 - mmcls - INFO - Epoch [14][2300/5005] lr: 1.000e-01, eta: 10:42:59, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.8802 2023-01-01 18:16:57,077 - mmcls - INFO - Epoch [14][2400/5005] lr: 1.000e-01, eta: 10:42:48, time: 0.086, data_time: 0.041, memory: 1224, loss: 2.8535 2023-01-01 18:17:05,776 - mmcls - INFO - Epoch [14][2500/5005] lr: 1.000e-01, eta: 10:42:38, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.8662 2023-01-01 18:17:14,710 - mmcls - INFO - Epoch [14][2600/5005] lr: 1.000e-01, eta: 10:42:29, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8997 2023-01-01 18:17:23,267 - mmcls - INFO - Epoch [14][2700/5005] lr: 1.000e-01, eta: 10:42:18, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8571 2023-01-01 18:17:32,079 - mmcls - INFO - Epoch [14][2800/5005] lr: 1.000e-01, eta: 10:42:09, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8401 2023-01-01 18:17:41,105 - mmcls - INFO - Epoch [14][2900/5005] lr: 1.000e-01, eta: 10:42:00, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8582 2023-01-01 18:17:49,938 - mmcls - INFO - Epoch [14][3000/5005] lr: 1.000e-01, eta: 10:41:51, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.8416 2023-01-01 18:17:59,158 - mmcls - INFO - Epoch [14][3100/5005] lr: 1.000e-01, eta: 10:41:44, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8693 2023-01-01 18:18:07,769 - mmcls - INFO - Epoch [14][3200/5005] lr: 1.000e-01, eta: 10:41:33, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.8329 2023-01-01 18:18:16,808 - mmcls - INFO - Epoch [14][3300/5005] lr: 1.000e-01, eta: 10:41:25, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.8599 2023-01-01 18:18:26,008 - mmcls - INFO - Epoch [14][3400/5005] lr: 1.000e-01, eta: 10:41:18, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8525 2023-01-01 18:18:34,896 - mmcls - INFO - Epoch [14][3500/5005] lr: 1.000e-01, eta: 10:41:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8555 2023-01-01 18:18:43,933 - mmcls - INFO - Epoch [14][3600/5005] lr: 1.000e-01, eta: 10:41:01, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.8577 2023-01-01 18:18:53,024 - mmcls - INFO - Epoch [14][3700/5005] lr: 1.000e-01, eta: 10:40:53, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8617 2023-01-01 18:19:01,799 - mmcls - INFO - Epoch [14][3800/5005] lr: 1.000e-01, eta: 10:40:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8237 2023-01-01 18:19:10,610 - mmcls - INFO - Epoch [14][3900/5005] lr: 1.000e-01, eta: 10:40:34, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8735 2023-01-01 18:19:19,440 - mmcls - INFO - Epoch [14][4000/5005] lr: 1.000e-01, eta: 10:40:25, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8680 2023-01-01 18:19:28,010 - mmcls - INFO - Epoch [14][4100/5005] lr: 1.000e-01, eta: 10:40:14, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8647 2023-01-01 18:19:36,642 - mmcls - INFO - Epoch [14][4200/5005] lr: 1.000e-01, eta: 10:40:03, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.8996 2023-01-01 18:19:45,606 - mmcls - INFO - Epoch [14][4300/5005] lr: 1.000e-01, eta: 10:39:54, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.8546 2023-01-01 18:19:54,606 - mmcls - INFO - Epoch [14][4400/5005] lr: 1.000e-01, eta: 10:39:46, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.8890 2023-01-01 18:20:03,710 - mmcls - INFO - Epoch [14][4500/5005] lr: 1.000e-01, eta: 10:39:38, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.8840 2023-01-01 18:20:12,583 - mmcls - INFO - Epoch [14][4600/5005] lr: 1.000e-01, eta: 10:39:29, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8702 2023-01-01 18:20:21,737 - mmcls - INFO - Epoch [14][4700/5005] lr: 1.000e-01, eta: 10:39:22, time: 0.091, data_time: 0.034, memory: 1224, loss: 2.8727 2023-01-01 18:20:30,468 - mmcls - INFO - Epoch [14][4800/5005] lr: 1.000e-01, eta: 10:39:12, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.8806 2023-01-01 18:20:39,655 - mmcls - INFO - Epoch [14][4900/5005] lr: 1.000e-01, eta: 10:39:05, time: 0.092, data_time: 0.012, memory: 1224, loss: 2.8461 2023-01-01 18:20:48,303 - mmcls - INFO - Epoch [14][5000/5005] lr: 1.000e-01, eta: 10:38:54, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8838 2023-01-01 18:21:11,339 - mmcls - INFO - Epoch(val) [14][196] accuracy_top-1: 45.3740, accuracy_top-5: 70.6880 2023-01-01 18:21:22,555 - mmcls - INFO - Epoch [15][100/5005] lr: 1.000e-01, eta: 10:38:56, time: 0.112, data_time: 0.056, memory: 1224, loss: 2.7827 2023-01-01 18:21:31,378 - mmcls - INFO - Epoch [15][200/5005] lr: 1.000e-01, eta: 10:38:47, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7750 2023-01-01 18:21:40,179 - mmcls - INFO - Epoch [15][300/5005] lr: 1.000e-01, eta: 10:38:37, time: 0.088, data_time: 0.020, memory: 1224, loss: 2.7940 2023-01-01 18:21:49,112 - mmcls - INFO - Epoch [15][400/5005] lr: 1.000e-01, eta: 10:38:28, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.8238 2023-01-01 18:21:58,272 - mmcls - INFO - Epoch [15][500/5005] lr: 1.000e-01, eta: 10:38:21, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.8191 2023-01-01 18:22:07,238 - mmcls - INFO - Epoch [15][600/5005] lr: 1.000e-01, eta: 10:38:13, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8276 2023-01-01 18:22:15,995 - mmcls - INFO - Epoch [15][700/5005] lr: 1.000e-01, eta: 10:38:03, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.8021 2023-01-01 18:22:25,127 - mmcls - INFO - Epoch [15][800/5005] lr: 1.000e-01, eta: 10:37:55, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.8141 2023-01-01 18:22:34,012 - mmcls - INFO - Epoch [15][900/5005] lr: 1.000e-01, eta: 10:37:46, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.8106 2023-01-01 18:22:42,633 - mmcls - INFO - Epoch [15][1000/5005] lr: 1.000e-01, eta: 10:37:35, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.8216 2023-01-01 18:22:52,127 - mmcls - INFO - Epoch [15][1100/5005] lr: 1.000e-01, eta: 10:37:30, time: 0.095, data_time: 0.038, memory: 1224, loss: 2.8027 2023-01-01 18:23:00,988 - mmcls - INFO - Epoch [15][1200/5005] lr: 1.000e-01, eta: 10:37:21, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.8530 2023-01-01 18:23:09,868 - mmcls - INFO - Epoch [15][1300/5005] lr: 1.000e-01, eta: 10:37:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8490 2023-01-01 18:23:18,557 - mmcls - INFO - Epoch [15][1400/5005] lr: 1.000e-01, eta: 10:37:01, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.8358 2023-01-01 18:23:27,229 - mmcls - INFO - Epoch [15][1500/5005] lr: 1.000e-01, eta: 10:36:51, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8385 2023-01-01 18:23:35,992 - mmcls - INFO - Epoch [15][1600/5005] lr: 1.000e-01, eta: 10:36:41, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8280 2023-01-01 18:23:44,710 - mmcls - INFO - Epoch [15][1700/5005] lr: 1.000e-01, eta: 10:36:31, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8302 2023-01-01 18:23:53,416 - mmcls - INFO - Epoch [15][1800/5005] lr: 1.000e-01, eta: 10:36:21, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.8568 2023-01-01 18:24:02,300 - mmcls - INFO - Epoch [15][1900/5005] lr: 1.000e-01, eta: 10:36:12, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.8121 2023-01-01 18:24:11,093 - mmcls - INFO - Epoch [15][2000/5005] lr: 1.000e-01, eta: 10:36:03, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.8800 2023-01-01 18:24:20,081 - mmcls - INFO - Epoch [15][2100/5005] lr: 1.000e-01, eta: 10:35:54, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8038 2023-01-01 18:24:28,978 - mmcls - INFO - Epoch [15][2200/5005] lr: 1.000e-01, eta: 10:35:45, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.8681 2023-01-01 18:24:37,648 - mmcls - INFO - Epoch [15][2300/5005] lr: 1.000e-01, eta: 10:35:35, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.8630 2023-01-01 18:24:46,387 - mmcls - INFO - Epoch [15][2400/5005] lr: 1.000e-01, eta: 10:35:25, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.8245 2023-01-01 18:24:54,972 - mmcls - INFO - Epoch [15][2500/5005] lr: 1.000e-01, eta: 10:35:14, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.8395 2023-01-01 18:25:03,736 - mmcls - INFO - Epoch [15][2600/5005] lr: 1.000e-01, eta: 10:35:04, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.8361 2023-01-01 18:25:12,309 - mmcls - INFO - Epoch [15][2700/5005] lr: 1.000e-01, eta: 10:34:53, time: 0.086, data_time: 0.022, memory: 1224, loss: 2.8797 2023-01-01 18:25:20,935 - mmcls - INFO - Epoch [15][2800/5005] lr: 1.000e-01, eta: 10:34:43, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.8237 2023-01-01 18:25:29,622 - mmcls - INFO - Epoch [15][2900/5005] lr: 1.000e-01, eta: 10:34:33, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8554 2023-01-01 18:25:38,430 - mmcls - INFO - Epoch [15][3000/5005] lr: 1.000e-01, eta: 10:34:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8307 2023-01-01 18:25:47,256 - mmcls - INFO - Epoch [15][3100/5005] lr: 1.000e-01, eta: 10:34:14, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8273 2023-01-01 18:25:56,290 - mmcls - INFO - Epoch [15][3200/5005] lr: 1.000e-01, eta: 10:34:06, time: 0.090, data_time: 0.027, memory: 1224, loss: 2.8749 2023-01-01 18:26:05,095 - mmcls - INFO - Epoch [15][3300/5005] lr: 1.000e-01, eta: 10:33:56, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.8572 2023-01-01 18:26:13,844 - mmcls - INFO - Epoch [15][3400/5005] lr: 1.000e-01, eta: 10:33:46, time: 0.087, data_time: 0.040, memory: 1224, loss: 2.8320 2023-01-01 18:26:22,825 - mmcls - INFO - Epoch [15][3500/5005] lr: 1.000e-01, eta: 10:33:38, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.8556 2023-01-01 18:26:31,872 - mmcls - INFO - Epoch [15][3600/5005] lr: 1.000e-01, eta: 10:33:30, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8330 2023-01-01 18:26:40,707 - mmcls - INFO - Epoch [15][3700/5005] lr: 1.000e-01, eta: 10:33:20, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8733 2023-01-01 18:26:49,461 - mmcls - INFO - Epoch [15][3800/5005] lr: 1.000e-01, eta: 10:33:11, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.8646 2023-01-01 18:26:58,101 - mmcls - INFO - Epoch [15][3900/5005] lr: 1.000e-01, eta: 10:33:00, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.8307 2023-01-01 18:27:07,076 - mmcls - INFO - Epoch [15][4000/5005] lr: 1.000e-01, eta: 10:32:52, time: 0.090, data_time: 0.034, memory: 1224, loss: 2.8511 2023-01-01 18:27:15,736 - mmcls - INFO - Epoch [15][4100/5005] lr: 1.000e-01, eta: 10:32:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.8561 2023-01-01 18:27:24,586 - mmcls - INFO - Epoch [15][4200/5005] lr: 1.000e-01, eta: 10:32:32, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.8543 2023-01-01 18:27:33,353 - mmcls - INFO - Epoch [15][4300/5005] lr: 1.000e-01, eta: 10:32:23, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8844 2023-01-01 18:27:42,159 - mmcls - INFO - Epoch [15][4400/5005] lr: 1.000e-01, eta: 10:32:13, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8123 2023-01-01 18:27:51,126 - mmcls - INFO - Epoch [15][4500/5005] lr: 1.000e-01, eta: 10:32:05, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8486 2023-01-01 18:28:00,006 - mmcls - INFO - Epoch [15][4600/5005] lr: 1.000e-01, eta: 10:31:55, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.8573 2023-01-01 18:28:08,864 - mmcls - INFO - Epoch [15][4700/5005] lr: 1.000e-01, eta: 10:31:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8559 2023-01-01 18:28:17,676 - mmcls - INFO - Epoch [15][4800/5005] lr: 1.000e-01, eta: 10:31:37, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8512 2023-01-01 18:28:26,498 - mmcls - INFO - Epoch [15][4900/5005] lr: 1.000e-01, eta: 10:31:28, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8550 2023-01-01 18:28:35,832 - mmcls - INFO - Epoch [15][5000/5005] lr: 1.000e-01, eta: 10:31:21, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.8449 2023-01-01 18:28:58,864 - mmcls - INFO - Epoch(val) [15][196] accuracy_top-1: 44.9880, accuracy_top-5: 70.7200 2023-01-01 18:29:10,270 - mmcls - INFO - Epoch [16][100/5005] lr: 1.000e-01, eta: 10:31:23, time: 0.114, data_time: 0.030, memory: 1224, loss: 2.8095 2023-01-01 18:29:19,001 - mmcls - INFO - Epoch [16][200/5005] lr: 1.000e-01, eta: 10:31:13, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.7926 2023-01-01 18:29:27,687 - mmcls - INFO - Epoch [16][300/5005] lr: 1.000e-01, eta: 10:31:03, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.8496 2023-01-01 18:29:36,419 - mmcls - INFO - Epoch [16][400/5005] lr: 1.000e-01, eta: 10:30:53, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7977 2023-01-01 18:29:45,231 - mmcls - INFO - Epoch [16][500/5005] lr: 1.000e-01, eta: 10:30:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8101 2023-01-01 18:29:53,882 - mmcls - INFO - Epoch [16][600/5005] lr: 1.000e-01, eta: 10:30:33, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.8143 2023-01-01 18:30:02,783 - mmcls - INFO - Epoch [16][700/5005] lr: 1.000e-01, eta: 10:30:25, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.8410 2023-01-01 18:30:11,678 - mmcls - INFO - Epoch [16][800/5005] lr: 1.000e-01, eta: 10:30:16, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.8496 2023-01-01 18:30:20,643 - mmcls - INFO - Epoch [16][900/5005] lr: 1.000e-01, eta: 10:30:07, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.8208 2023-01-01 18:30:29,291 - mmcls - INFO - Epoch [16][1000/5005] lr: 1.000e-01, eta: 10:29:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8047 2023-01-01 18:30:38,051 - mmcls - INFO - Epoch [16][1100/5005] lr: 1.000e-01, eta: 10:29:47, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.8194 2023-01-01 18:30:47,150 - mmcls - INFO - Epoch [16][1200/5005] lr: 1.000e-01, eta: 10:29:39, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8257 2023-01-01 18:30:56,091 - mmcls - INFO - Epoch [16][1300/5005] lr: 1.000e-01, eta: 10:29:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8486 2023-01-01 18:31:04,676 - mmcls - INFO - Epoch [16][1400/5005] lr: 1.000e-01, eta: 10:29:20, time: 0.086, data_time: 0.023, memory: 1224, loss: 2.8375 2023-01-01 18:31:13,499 - mmcls - INFO - Epoch [16][1500/5005] lr: 1.000e-01, eta: 10:29:10, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.8230 2023-01-01 18:31:22,280 - mmcls - INFO - Epoch [16][1600/5005] lr: 1.000e-01, eta: 10:29:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8115 2023-01-01 18:31:30,996 - mmcls - INFO - Epoch [16][1700/5005] lr: 1.000e-01, eta: 10:28:51, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8087 2023-01-01 18:31:40,346 - mmcls - INFO - Epoch [16][1800/5005] lr: 1.000e-01, eta: 10:28:44, time: 0.093, data_time: 0.029, memory: 1224, loss: 2.8086 2023-01-01 18:31:49,320 - mmcls - INFO - Epoch [16][1900/5005] lr: 1.000e-01, eta: 10:28:36, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.7935 2023-01-01 18:31:58,031 - mmcls - INFO - Epoch [16][2000/5005] lr: 1.000e-01, eta: 10:28:26, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8225 2023-01-01 18:32:06,890 - mmcls - INFO - Epoch [16][2100/5005] lr: 1.000e-01, eta: 10:28:17, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8718 2023-01-01 18:32:15,762 - mmcls - INFO - Epoch [16][2200/5005] lr: 1.000e-01, eta: 10:28:08, time: 0.089, data_time: 0.000, memory: 1224, loss: 2.8225 2023-01-01 18:32:24,715 - mmcls - INFO - Epoch [16][2300/5005] lr: 1.000e-01, eta: 10:27:59, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8237 2023-01-01 18:32:33,347 - mmcls - INFO - Epoch [16][2400/5005] lr: 1.000e-01, eta: 10:27:49, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.8811 2023-01-01 18:32:42,021 - mmcls - INFO - Epoch [16][2500/5005] lr: 1.000e-01, eta: 10:27:38, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8028 2023-01-01 18:32:50,809 - mmcls - INFO - Epoch [16][2600/5005] lr: 1.000e-01, eta: 10:27:29, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8383 2023-01-01 18:32:59,567 - mmcls - INFO - Epoch [16][2700/5005] lr: 1.000e-01, eta: 10:27:19, time: 0.088, data_time: 0.029, memory: 1224, loss: 2.8618 2023-01-01 18:33:08,183 - mmcls - INFO - Epoch [16][2800/5005] lr: 1.000e-01, eta: 10:27:09, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.8458 2023-01-01 18:33:17,242 - mmcls - INFO - Epoch [16][2900/5005] lr: 1.000e-01, eta: 10:27:01, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.8384 2023-01-01 18:33:26,094 - mmcls - INFO - Epoch [16][3000/5005] lr: 1.000e-01, eta: 10:26:51, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.8183 2023-01-01 18:33:34,664 - mmcls - INFO - Epoch [16][3100/5005] lr: 1.000e-01, eta: 10:26:41, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.8433 2023-01-01 18:33:43,886 - mmcls - INFO - Epoch [16][3200/5005] lr: 1.000e-01, eta: 10:26:34, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.8285 2023-01-01 18:33:52,998 - mmcls - INFO - Epoch [16][3300/5005] lr: 1.000e-01, eta: 10:26:26, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8167 2023-01-01 18:34:01,682 - mmcls - INFO - Epoch [16][3400/5005] lr: 1.000e-01, eta: 10:26:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8381 2023-01-01 18:34:10,534 - mmcls - INFO - Epoch [16][3500/5005] lr: 1.000e-01, eta: 10:26:06, time: 0.088, data_time: 0.046, memory: 1224, loss: 2.8302 2023-01-01 18:34:19,423 - mmcls - INFO - Epoch [16][3600/5005] lr: 1.000e-01, eta: 10:25:57, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8409 2023-01-01 18:34:28,142 - mmcls - INFO - Epoch [16][3700/5005] lr: 1.000e-01, eta: 10:25:48, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.8514 2023-01-01 18:34:37,003 - mmcls - INFO - Epoch [16][3800/5005] lr: 1.000e-01, eta: 10:25:38, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.8885 2023-01-01 18:34:45,698 - mmcls - INFO - Epoch [16][3900/5005] lr: 1.000e-01, eta: 10:25:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8436 2023-01-01 18:34:54,538 - mmcls - INFO - Epoch [16][4000/5005] lr: 1.000e-01, eta: 10:25:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8959 2023-01-01 18:35:03,438 - mmcls - INFO - Epoch [16][4100/5005] lr: 1.000e-01, eta: 10:25:10, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.8188 2023-01-01 18:35:12,293 - mmcls - INFO - Epoch [16][4200/5005] lr: 1.000e-01, eta: 10:25:01, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.8424 2023-01-01 18:35:21,314 - mmcls - INFO - Epoch [16][4300/5005] lr: 1.000e-01, eta: 10:24:53, time: 0.090, data_time: 0.024, memory: 1224, loss: 2.8453 2023-01-01 18:35:30,097 - mmcls - INFO - Epoch [16][4400/5005] lr: 1.000e-01, eta: 10:24:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8293 2023-01-01 18:35:39,055 - mmcls - INFO - Epoch [16][4500/5005] lr: 1.000e-01, eta: 10:24:35, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8284 2023-01-01 18:35:47,733 - mmcls - INFO - Epoch [16][4600/5005] lr: 1.000e-01, eta: 10:24:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8238 2023-01-01 18:35:56,549 - mmcls - INFO - Epoch [16][4700/5005] lr: 1.000e-01, eta: 10:24:15, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8464 2023-01-01 18:36:05,587 - mmcls - INFO - Epoch [16][4800/5005] lr: 1.000e-01, eta: 10:24:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8328 2023-01-01 18:36:14,327 - mmcls - INFO - Epoch [16][4900/5005] lr: 1.000e-01, eta: 10:23:57, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8280 2023-01-01 18:36:23,038 - mmcls - INFO - Epoch [16][5000/5005] lr: 1.000e-01, eta: 10:23:47, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.8785 2023-01-01 18:36:46,223 - mmcls - INFO - Epoch(val) [16][196] accuracy_top-1: 44.7660, accuracy_top-5: 70.1300 2023-01-01 18:36:57,219 - mmcls - INFO - Epoch [17][100/5005] lr: 1.000e-01, eta: 10:23:46, time: 0.110, data_time: 0.053, memory: 1224, loss: 2.7977 2023-01-01 18:37:05,865 - mmcls - INFO - Epoch [17][200/5005] lr: 1.000e-01, eta: 10:23:36, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7951 2023-01-01 18:37:14,492 - mmcls - INFO - Epoch [17][300/5005] lr: 1.000e-01, eta: 10:23:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7738 2023-01-01 18:37:23,201 - mmcls - INFO - Epoch [17][400/5005] lr: 1.000e-01, eta: 10:23:16, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7861 2023-01-01 18:37:32,045 - mmcls - INFO - Epoch [17][500/5005] lr: 1.000e-01, eta: 10:23:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7930 2023-01-01 18:37:40,971 - mmcls - INFO - Epoch [17][600/5005] lr: 1.000e-01, eta: 10:22:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7740 2023-01-01 18:37:50,097 - mmcls - INFO - Epoch [17][700/5005] lr: 1.000e-01, eta: 10:22:50, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.7935 2023-01-01 18:37:58,872 - mmcls - INFO - Epoch [17][800/5005] lr: 1.000e-01, eta: 10:22:41, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8047 2023-01-01 18:38:07,508 - mmcls - INFO - Epoch [17][900/5005] lr: 1.000e-01, eta: 10:22:30, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.7949 2023-01-01 18:38:16,308 - mmcls - INFO - Epoch [17][1000/5005] lr: 1.000e-01, eta: 10:22:21, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.8195 2023-01-01 18:38:25,448 - mmcls - INFO - Epoch [17][1100/5005] lr: 1.000e-01, eta: 10:22:13, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.8391 2023-01-01 18:38:33,866 - mmcls - INFO - Epoch [17][1200/5005] lr: 1.000e-01, eta: 10:22:02, time: 0.084, data_time: 0.001, memory: 1224, loss: 2.7852 2023-01-01 18:38:43,149 - mmcls - INFO - Epoch [17][1300/5005] lr: 1.000e-01, eta: 10:21:55, time: 0.093, data_time: 0.003, memory: 1224, loss: 2.7957 2023-01-01 18:38:51,799 - mmcls - INFO - Epoch [17][1400/5005] lr: 1.000e-01, eta: 10:21:45, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8006 2023-01-01 18:39:00,697 - mmcls - INFO - Epoch [17][1500/5005] lr: 1.000e-01, eta: 10:21:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8105 2023-01-01 18:39:09,892 - mmcls - INFO - Epoch [17][1600/5005] lr: 1.000e-01, eta: 10:21:28, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.7843 2023-01-01 18:39:18,710 - mmcls - INFO - Epoch [17][1700/5005] lr: 1.000e-01, eta: 10:21:19, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8147 2023-01-01 18:39:27,421 - mmcls - INFO - Epoch [17][1800/5005] lr: 1.000e-01, eta: 10:21:09, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8156 2023-01-01 18:39:36,344 - mmcls - INFO - Epoch [17][1900/5005] lr: 1.000e-01, eta: 10:21:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8213 2023-01-01 18:39:44,973 - mmcls - INFO - Epoch [17][2000/5005] lr: 1.000e-01, eta: 10:20:50, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.8667 2023-01-01 18:39:53,846 - mmcls - INFO - Epoch [17][2100/5005] lr: 1.000e-01, eta: 10:20:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8406 2023-01-01 18:40:02,657 - mmcls - INFO - Epoch [17][2200/5005] lr: 1.000e-01, eta: 10:20:31, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8108 2023-01-01 18:40:11,283 - mmcls - INFO - Epoch [17][2300/5005] lr: 1.000e-01, eta: 10:20:21, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7983 2023-01-01 18:40:19,897 - mmcls - INFO - Epoch [17][2400/5005] lr: 1.000e-01, eta: 10:20:11, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.8337 2023-01-01 18:40:28,656 - mmcls - INFO - Epoch [17][2500/5005] lr: 1.000e-01, eta: 10:20:01, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.8307 2023-01-01 18:40:37,335 - mmcls - INFO - Epoch [17][2600/5005] lr: 1.000e-01, eta: 10:19:51, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8125 2023-01-01 18:40:46,215 - mmcls - INFO - Epoch [17][2700/5005] lr: 1.000e-01, eta: 10:19:42, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.8037 2023-01-01 18:40:54,979 - mmcls - INFO - Epoch [17][2800/5005] lr: 1.000e-01, eta: 10:19:32, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8419 2023-01-01 18:41:03,890 - mmcls - INFO - Epoch [17][2900/5005] lr: 1.000e-01, eta: 10:19:24, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.8006 2023-01-01 18:41:12,608 - mmcls - INFO - Epoch [17][3000/5005] lr: 1.000e-01, eta: 10:19:14, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.8589 2023-01-01 18:41:21,308 - mmcls - INFO - Epoch [17][3100/5005] lr: 1.000e-01, eta: 10:19:04, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.8310 2023-01-01 18:41:30,132 - mmcls - INFO - Epoch [17][3200/5005] lr: 1.000e-01, eta: 10:18:55, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8312 2023-01-01 18:41:38,928 - mmcls - INFO - Epoch [17][3300/5005] lr: 1.000e-01, eta: 10:18:45, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8254 2023-01-01 18:41:48,058 - mmcls - INFO - Epoch [17][3400/5005] lr: 1.000e-01, eta: 10:18:37, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.8047 2023-01-01 18:41:57,111 - mmcls - INFO - Epoch [17][3500/5005] lr: 1.000e-01, eta: 10:18:29, time: 0.091, data_time: 0.036, memory: 1224, loss: 2.8272 2023-01-01 18:42:06,276 - mmcls - INFO - Epoch [17][3600/5005] lr: 1.000e-01, eta: 10:18:22, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.8269 2023-01-01 18:42:15,053 - mmcls - INFO - Epoch [17][3700/5005] lr: 1.000e-01, eta: 10:18:12, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8375 2023-01-01 18:42:23,904 - mmcls - INFO - Epoch [17][3800/5005] lr: 1.000e-01, eta: 10:18:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8553 2023-01-01 18:42:32,797 - mmcls - INFO - Epoch [17][3900/5005] lr: 1.000e-01, eta: 10:17:54, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.8496 2023-01-01 18:42:41,770 - mmcls - INFO - Epoch [17][4000/5005] lr: 1.000e-01, eta: 10:17:45, time: 0.090, data_time: 0.038, memory: 1224, loss: 2.7984 2023-01-01 18:42:50,312 - mmcls - INFO - Epoch [17][4100/5005] lr: 1.000e-01, eta: 10:17:35, time: 0.085, data_time: 0.015, memory: 1224, loss: 2.8005 2023-01-01 18:42:59,047 - mmcls - INFO - Epoch [17][4200/5005] lr: 1.000e-01, eta: 10:17:25, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.8140 2023-01-01 18:43:07,743 - mmcls - INFO - Epoch [17][4300/5005] lr: 1.000e-01, eta: 10:17:15, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8645 2023-01-01 18:43:16,578 - mmcls - INFO - Epoch [17][4400/5005] lr: 1.000e-01, eta: 10:17:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8705 2023-01-01 18:43:25,228 - mmcls - INFO - Epoch [17][4500/5005] lr: 1.000e-01, eta: 10:16:56, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8311 2023-01-01 18:43:34,006 - mmcls - INFO - Epoch [17][4600/5005] lr: 1.000e-01, eta: 10:16:46, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.8238 2023-01-01 18:43:42,880 - mmcls - INFO - Epoch [17][4700/5005] lr: 1.000e-01, eta: 10:16:37, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.8192 2023-01-01 18:43:52,014 - mmcls - INFO - Epoch [17][4800/5005] lr: 1.000e-01, eta: 10:16:30, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.8418 2023-01-01 18:44:00,890 - mmcls - INFO - Epoch [17][4900/5005] lr: 1.000e-01, eta: 10:16:21, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.8147 2023-01-01 18:44:09,974 - mmcls - INFO - Epoch [17][5000/5005] lr: 1.000e-01, eta: 10:16:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8275 2023-01-01 18:44:32,758 - mmcls - INFO - Epoch(val) [17][196] accuracy_top-1: 45.6880, accuracy_top-5: 71.0200 2023-01-01 18:44:43,769 - mmcls - INFO - Epoch [18][100/5005] lr: 1.000e-01, eta: 10:16:11, time: 0.110, data_time: 0.044, memory: 1224, loss: 2.7641 2023-01-01 18:44:52,566 - mmcls - INFO - Epoch [18][200/5005] lr: 1.000e-01, eta: 10:16:02, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.7670 2023-01-01 18:45:01,538 - mmcls - INFO - Epoch [18][300/5005] lr: 1.000e-01, eta: 10:15:53, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.7539 2023-01-01 18:45:09,933 - mmcls - INFO - Epoch [18][400/5005] lr: 1.000e-01, eta: 10:15:42, time: 0.084, data_time: 0.006, memory: 1224, loss: 2.7645 2023-01-01 18:45:18,877 - mmcls - INFO - Epoch [18][500/5005] lr: 1.000e-01, eta: 10:15:33, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.7902 2023-01-01 18:45:27,808 - mmcls - INFO - Epoch [18][600/5005] lr: 1.000e-01, eta: 10:15:24, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.7845 2023-01-01 18:45:36,488 - mmcls - INFO - Epoch [18][700/5005] lr: 1.000e-01, eta: 10:15:14, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8050 2023-01-01 18:45:45,242 - mmcls - INFO - Epoch [18][800/5005] lr: 1.000e-01, eta: 10:15:05, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8243 2023-01-01 18:45:53,940 - mmcls - INFO - Epoch [18][900/5005] lr: 1.000e-01, eta: 10:14:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8323 2023-01-01 18:46:02,639 - mmcls - INFO - Epoch [18][1000/5005] lr: 1.000e-01, eta: 10:14:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8035 2023-01-01 18:46:11,615 - mmcls - INFO - Epoch [18][1100/5005] lr: 1.000e-01, eta: 10:14:36, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8227 2023-01-01 18:46:20,765 - mmcls - INFO - Epoch [18][1200/5005] lr: 1.000e-01, eta: 10:14:29, time: 0.092, data_time: 0.022, memory: 1224, loss: 2.8041 2023-01-01 18:46:29,498 - mmcls - INFO - Epoch [18][1300/5005] lr: 1.000e-01, eta: 10:14:19, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.8561 2023-01-01 18:46:38,437 - mmcls - INFO - Epoch [18][1400/5005] lr: 1.000e-01, eta: 10:14:10, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8598 2023-01-01 18:46:47,076 - mmcls - INFO - Epoch [18][1500/5005] lr: 1.000e-01, eta: 10:14:00, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.8182 2023-01-01 18:46:56,428 - mmcls - INFO - Epoch [18][1600/5005] lr: 1.000e-01, eta: 10:13:53, time: 0.093, data_time: 0.003, memory: 1224, loss: 2.8196 2023-01-01 18:47:05,376 - mmcls - INFO - Epoch [18][1700/5005] lr: 1.000e-01, eta: 10:13:45, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.8133 2023-01-01 18:47:14,091 - mmcls - INFO - Epoch [18][1800/5005] lr: 1.000e-01, eta: 10:13:35, time: 0.087, data_time: 0.025, memory: 1224, loss: 2.8085 2023-01-01 18:47:22,758 - mmcls - INFO - Epoch [18][1900/5005] lr: 1.000e-01, eta: 10:13:25, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8420 2023-01-01 18:47:31,511 - mmcls - INFO - Epoch [18][2000/5005] lr: 1.000e-01, eta: 10:13:15, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8074 2023-01-01 18:47:40,208 - mmcls - INFO - Epoch [18][2100/5005] lr: 1.000e-01, eta: 10:13:05, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.7817 2023-01-01 18:47:49,158 - mmcls - INFO - Epoch [18][2200/5005] lr: 1.000e-01, eta: 10:12:57, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.8264 2023-01-01 18:47:57,580 - mmcls - INFO - Epoch [18][2300/5005] lr: 1.000e-01, eta: 10:12:46, time: 0.084, data_time: 0.003, memory: 1224, loss: 2.8268 2023-01-01 18:48:06,359 - mmcls - INFO - Epoch [18][2400/5005] lr: 1.000e-01, eta: 10:12:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8124 2023-01-01 18:48:15,103 - mmcls - INFO - Epoch [18][2500/5005] lr: 1.000e-01, eta: 10:12:27, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7739 2023-01-01 18:48:23,914 - mmcls - INFO - Epoch [18][2600/5005] lr: 1.000e-01, eta: 10:12:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8010 2023-01-01 18:48:32,588 - mmcls - INFO - Epoch [18][2700/5005] lr: 1.000e-01, eta: 10:12:07, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.8083 2023-01-01 18:48:41,295 - mmcls - INFO - Epoch [18][2800/5005] lr: 1.000e-01, eta: 10:11:57, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8157 2023-01-01 18:48:50,621 - mmcls - INFO - Epoch [18][2900/5005] lr: 1.000e-01, eta: 10:11:51, time: 0.093, data_time: 0.018, memory: 1224, loss: 2.8425 2023-01-01 18:48:59,466 - mmcls - INFO - Epoch [18][3000/5005] lr: 1.000e-01, eta: 10:11:41, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8061 2023-01-01 18:49:08,640 - mmcls - INFO - Epoch [18][3100/5005] lr: 1.000e-01, eta: 10:11:34, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.8293 2023-01-01 18:49:17,763 - mmcls - INFO - Epoch [18][3200/5005] lr: 1.000e-01, eta: 10:11:26, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8266 2023-01-01 18:49:26,484 - mmcls - INFO - Epoch [18][3300/5005] lr: 1.000e-01, eta: 10:11:16, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.8096 2023-01-01 18:49:35,343 - mmcls - INFO - Epoch [18][3400/5005] lr: 1.000e-01, eta: 10:11:07, time: 0.089, data_time: 0.011, memory: 1224, loss: 3.0121 2023-01-01 18:49:44,134 - mmcls - INFO - Epoch [18][3500/5005] lr: 1.000e-01, eta: 10:10:58, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8624 2023-01-01 18:49:53,033 - mmcls - INFO - Epoch [18][3600/5005] lr: 1.000e-01, eta: 10:10:49, time: 0.089, data_time: 0.037, memory: 1224, loss: 2.8598 2023-01-01 18:50:01,728 - mmcls - INFO - Epoch [18][3700/5005] lr: 1.000e-01, eta: 10:10:39, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.8362 2023-01-01 18:50:10,607 - mmcls - INFO - Epoch [18][3800/5005] lr: 1.000e-01, eta: 10:10:30, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.8493 2023-01-01 18:50:19,260 - mmcls - INFO - Epoch [18][3900/5005] lr: 1.000e-01, eta: 10:10:20, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.8323 2023-01-01 18:50:28,246 - mmcls - INFO - Epoch [18][4000/5005] lr: 1.000e-01, eta: 10:10:11, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.8438 2023-01-01 18:50:36,992 - mmcls - INFO - Epoch [18][4100/5005] lr: 1.000e-01, eta: 10:10:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8233 2023-01-01 18:50:45,609 - mmcls - INFO - Epoch [18][4200/5005] lr: 1.000e-01, eta: 10:09:52, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8365 2023-01-01 18:50:54,474 - mmcls - INFO - Epoch [18][4300/5005] lr: 1.000e-01, eta: 10:09:42, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.7948 2023-01-01 18:51:03,288 - mmcls - INFO - Epoch [18][4400/5005] lr: 1.000e-01, eta: 10:09:33, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8139 2023-01-01 18:51:12,061 - mmcls - INFO - Epoch [18][4500/5005] lr: 1.000e-01, eta: 10:09:24, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.8378 2023-01-01 18:51:21,086 - mmcls - INFO - Epoch [18][4600/5005] lr: 1.000e-01, eta: 10:09:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8230 2023-01-01 18:51:29,938 - mmcls - INFO - Epoch [18][4700/5005] lr: 1.000e-01, eta: 10:09:06, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.8449 2023-01-01 18:51:39,217 - mmcls - INFO - Epoch [18][4800/5005] lr: 1.000e-01, eta: 10:08:59, time: 0.093, data_time: 0.004, memory: 1224, loss: 2.8339 2023-01-01 18:51:48,185 - mmcls - INFO - Epoch [18][4900/5005] lr: 1.000e-01, eta: 10:08:51, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.8216 2023-01-01 18:51:56,894 - mmcls - INFO - Epoch [18][5000/5005] lr: 1.000e-01, eta: 10:08:41, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8588 2023-01-01 18:52:19,940 - mmcls - INFO - Epoch(val) [18][196] accuracy_top-1: 45.6100, accuracy_top-5: 70.6080 2023-01-01 18:52:31,124 - mmcls - INFO - Epoch [19][100/5005] lr: 1.000e-01, eta: 10:08:40, time: 0.112, data_time: 0.050, memory: 1224, loss: 2.7749 2023-01-01 18:52:39,642 - mmcls - INFO - Epoch [19][200/5005] lr: 1.000e-01, eta: 10:08:29, time: 0.085, data_time: 0.028, memory: 1224, loss: 2.7703 2023-01-01 18:52:48,688 - mmcls - INFO - Epoch [19][300/5005] lr: 1.000e-01, eta: 10:08:21, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.7471 2023-01-01 18:52:57,359 - mmcls - INFO - Epoch [19][400/5005] lr: 1.000e-01, eta: 10:08:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7805 2023-01-01 18:53:06,218 - mmcls - INFO - Epoch [19][500/5005] lr: 1.000e-01, eta: 10:08:02, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7727 2023-01-01 18:53:14,762 - mmcls - INFO - Epoch [19][600/5005] lr: 1.000e-01, eta: 10:07:51, time: 0.085, data_time: 0.016, memory: 1224, loss: 2.7893 2023-01-01 18:53:23,395 - mmcls - INFO - Epoch [19][700/5005] lr: 1.000e-01, eta: 10:07:41, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.8259 2023-01-01 18:53:32,236 - mmcls - INFO - Epoch [19][800/5005] lr: 1.000e-01, eta: 10:07:32, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7705 2023-01-01 18:53:41,338 - mmcls - INFO - Epoch [19][900/5005] lr: 1.000e-01, eta: 10:07:24, time: 0.091, data_time: 0.021, memory: 1224, loss: 2.8097 2023-01-01 18:53:50,233 - mmcls - INFO - Epoch [19][1000/5005] lr: 1.000e-01, eta: 10:07:15, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.7955 2023-01-01 18:53:59,089 - mmcls - INFO - Epoch [19][1100/5005] lr: 1.000e-01, eta: 10:07:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8413 2023-01-01 18:54:07,937 - mmcls - INFO - Epoch [19][1200/5005] lr: 1.000e-01, eta: 10:06:57, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8032 2023-01-01 18:54:16,778 - mmcls - INFO - Epoch [19][1300/5005] lr: 1.000e-01, eta: 10:06:48, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7775 2023-01-01 18:54:25,377 - mmcls - INFO - Epoch [19][1400/5005] lr: 1.000e-01, eta: 10:06:37, time: 0.086, data_time: 0.025, memory: 1224, loss: 2.7895 2023-01-01 18:54:34,913 - mmcls - INFO - Epoch [19][1500/5005] lr: 1.000e-01, eta: 10:06:31, time: 0.095, data_time: 0.005, memory: 1224, loss: 2.7762 2023-01-01 18:54:43,975 - mmcls - INFO - Epoch [19][1600/5005] lr: 1.000e-01, eta: 10:06:23, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8146 2023-01-01 18:54:52,570 - mmcls - INFO - Epoch [19][1700/5005] lr: 1.000e-01, eta: 10:06:13, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8036 2023-01-01 18:55:01,494 - mmcls - INFO - Epoch [19][1800/5005] lr: 1.000e-01, eta: 10:06:04, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7734 2023-01-01 18:55:10,275 - mmcls - INFO - Epoch [19][1900/5005] lr: 1.000e-01, eta: 10:05:55, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8093 2023-01-01 18:55:19,194 - mmcls - INFO - Epoch [19][2000/5005] lr: 1.000e-01, eta: 10:05:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7880 2023-01-01 18:55:27,931 - mmcls - INFO - Epoch [19][2100/5005] lr: 1.000e-01, eta: 10:05:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8012 2023-01-01 18:55:36,600 - mmcls - INFO - Epoch [19][2200/5005] lr: 1.000e-01, eta: 10:05:26, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7891 2023-01-01 18:55:45,301 - mmcls - INFO - Epoch [19][2300/5005] lr: 1.000e-01, eta: 10:05:17, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.8284 2023-01-01 18:55:54,167 - mmcls - INFO - Epoch [19][2400/5005] lr: 1.000e-01, eta: 10:05:08, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.8413 2023-01-01 18:56:02,919 - mmcls - INFO - Epoch [19][2500/5005] lr: 1.000e-01, eta: 10:04:58, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.8005 2023-01-01 18:56:11,719 - mmcls - INFO - Epoch [19][2600/5005] lr: 1.000e-01, eta: 10:04:49, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.8138 2023-01-01 18:56:20,541 - mmcls - INFO - Epoch [19][2700/5005] lr: 1.000e-01, eta: 10:04:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7943 2023-01-01 18:56:29,244 - mmcls - INFO - Epoch [19][2800/5005] lr: 1.000e-01, eta: 10:04:30, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7979 2023-01-01 18:56:38,019 - mmcls - INFO - Epoch [19][2900/5005] lr: 1.000e-01, eta: 10:04:20, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.8026 2023-01-01 18:56:47,031 - mmcls - INFO - Epoch [19][3000/5005] lr: 1.000e-01, eta: 10:04:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8449 2023-01-01 18:56:55,812 - mmcls - INFO - Epoch [19][3100/5005] lr: 1.000e-01, eta: 10:04:02, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8306 2023-01-01 18:57:04,672 - mmcls - INFO - Epoch [19][3200/5005] lr: 1.000e-01, eta: 10:03:53, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8141 2023-01-01 18:57:13,602 - mmcls - INFO - Epoch [19][3300/5005] lr: 1.000e-01, eta: 10:03:45, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8094 2023-01-01 18:57:22,336 - mmcls - INFO - Epoch [19][3400/5005] lr: 1.000e-01, eta: 10:03:35, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.8063 2023-01-01 18:57:31,374 - mmcls - INFO - Epoch [19][3500/5005] lr: 1.000e-01, eta: 10:03:27, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.7800 2023-01-01 18:57:39,926 - mmcls - INFO - Epoch [19][3600/5005] lr: 1.000e-01, eta: 10:03:16, time: 0.085, data_time: 0.015, memory: 1224, loss: 2.8063 2023-01-01 18:57:48,838 - mmcls - INFO - Epoch [19][3700/5005] lr: 1.000e-01, eta: 10:03:07, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.7726 2023-01-01 18:57:57,957 - mmcls - INFO - Epoch [19][3800/5005] lr: 1.000e-01, eta: 10:02:59, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.8006 2023-01-01 18:58:06,724 - mmcls - INFO - Epoch [19][3900/5005] lr: 1.000e-01, eta: 10:02:50, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.7841 2023-01-01 18:58:15,574 - mmcls - INFO - Epoch [19][4000/5005] lr: 1.000e-01, eta: 10:02:41, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.7753 2023-01-01 18:58:24,481 - mmcls - INFO - Epoch [19][4100/5005] lr: 1.000e-01, eta: 10:02:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7995 2023-01-01 18:58:33,568 - mmcls - INFO - Epoch [19][4200/5005] lr: 1.000e-01, eta: 10:02:24, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8641 2023-01-01 18:58:42,465 - mmcls - INFO - Epoch [19][4300/5005] lr: 1.000e-01, eta: 10:02:15, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.8459 2023-01-01 18:58:51,214 - mmcls - INFO - Epoch [19][4400/5005] lr: 1.000e-01, eta: 10:02:05, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.8239 2023-01-01 18:58:59,995 - mmcls - INFO - Epoch [19][4500/5005] lr: 1.000e-01, eta: 10:01:56, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8008 2023-01-01 18:59:09,161 - mmcls - INFO - Epoch [19][4600/5005] lr: 1.000e-01, eta: 10:01:48, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8140 2023-01-01 18:59:17,727 - mmcls - INFO - Epoch [19][4700/5005] lr: 1.000e-01, eta: 10:01:38, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.8113 2023-01-01 18:59:26,499 - mmcls - INFO - Epoch [19][4800/5005] lr: 1.000e-01, eta: 10:01:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7750 2023-01-01 18:59:35,551 - mmcls - INFO - Epoch [19][4900/5005] lr: 1.000e-01, eta: 10:01:20, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.8337 2023-01-01 18:59:44,109 - mmcls - INFO - Epoch [19][5000/5005] lr: 1.000e-01, eta: 10:01:10, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.8160 2023-01-01 19:00:07,011 - mmcls - INFO - Epoch(val) [19][196] accuracy_top-1: 45.4540, accuracy_top-5: 70.8540 2023-01-01 19:00:18,086 - mmcls - INFO - Epoch [20][100/5005] lr: 1.000e-01, eta: 10:01:08, time: 0.111, data_time: 0.060, memory: 1224, loss: 2.7599 2023-01-01 19:00:26,845 - mmcls - INFO - Epoch [20][200/5005] lr: 1.000e-01, eta: 10:00:58, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7864 2023-01-01 19:00:35,373 - mmcls - INFO - Epoch [20][300/5005] lr: 1.000e-01, eta: 10:00:48, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.7772 2023-01-01 19:00:44,184 - mmcls - INFO - Epoch [20][400/5005] lr: 1.000e-01, eta: 10:00:39, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7764 2023-01-01 19:00:53,307 - mmcls - INFO - Epoch [20][500/5005] lr: 1.000e-01, eta: 10:00:31, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.7466 2023-01-01 19:01:02,342 - mmcls - INFO - Epoch [20][600/5005] lr: 1.000e-01, eta: 10:00:22, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.7797 2023-01-01 19:01:11,033 - mmcls - INFO - Epoch [20][700/5005] lr: 1.000e-01, eta: 10:00:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7599 2023-01-01 19:01:20,044 - mmcls - INFO - Epoch [20][800/5005] lr: 1.000e-01, eta: 10:00:04, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.7714 2023-01-01 19:01:28,735 - mmcls - INFO - Epoch [20][900/5005] lr: 1.000e-01, eta: 9:59:54, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8392 2023-01-01 19:01:37,389 - mmcls - INFO - Epoch [20][1000/5005] lr: 1.000e-01, eta: 9:59:44, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.7852 2023-01-01 19:01:46,018 - mmcls - INFO - Epoch [20][1100/5005] lr: 1.000e-01, eta: 9:59:34, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8574 2023-01-01 19:01:55,473 - mmcls - INFO - Epoch [20][1200/5005] lr: 1.000e-01, eta: 9:59:28, time: 0.094, data_time: 0.002, memory: 1224, loss: 2.7973 2023-01-01 19:02:03,920 - mmcls - INFO - Epoch [20][1300/5005] lr: 1.000e-01, eta: 9:59:17, time: 0.085, data_time: 0.013, memory: 1224, loss: 2.7977 2023-01-01 19:02:12,439 - mmcls - INFO - Epoch [20][1400/5005] lr: 1.000e-01, eta: 9:59:07, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.8222 2023-01-01 19:02:21,561 - mmcls - INFO - Epoch [20][1500/5005] lr: 1.000e-01, eta: 9:58:59, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8064 2023-01-01 19:02:30,543 - mmcls - INFO - Epoch [20][1600/5005] lr: 1.000e-01, eta: 9:58:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.8148 2023-01-01 19:02:39,574 - mmcls - INFO - Epoch [20][1700/5005] lr: 1.000e-01, eta: 9:58:42, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.8034 2023-01-01 19:02:48,347 - mmcls - INFO - Epoch [20][1800/5005] lr: 1.000e-01, eta: 9:58:32, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.8134 2023-01-01 19:02:57,028 - mmcls - INFO - Epoch [20][1900/5005] lr: 1.000e-01, eta: 9:58:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8395 2023-01-01 19:03:05,574 - mmcls - INFO - Epoch [20][2000/5005] lr: 1.000e-01, eta: 9:58:12, time: 0.085, data_time: 0.014, memory: 1224, loss: 2.8156 2023-01-01 19:03:15,375 - mmcls - INFO - Epoch [20][2100/5005] lr: 1.000e-01, eta: 9:58:07, time: 0.098, data_time: 0.046, memory: 1224, loss: 2.8219 2023-01-01 19:03:24,278 - mmcls - INFO - Epoch [20][2200/5005] lr: 1.000e-01, eta: 9:57:58, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.7853 2023-01-01 19:03:33,017 - mmcls - INFO - Epoch [20][2300/5005] lr: 1.000e-01, eta: 9:57:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8123 2023-01-01 19:03:41,729 - mmcls - INFO - Epoch [20][2400/5005] lr: 1.000e-01, eta: 9:57:39, time: 0.087, data_time: 0.025, memory: 1224, loss: 2.8268 2023-01-01 19:03:50,512 - mmcls - INFO - Epoch [20][2500/5005] lr: 1.000e-01, eta: 9:57:29, time: 0.088, data_time: 0.035, memory: 1224, loss: 2.8017 2023-01-01 19:03:59,116 - mmcls - INFO - Epoch [20][2600/5005] lr: 1.000e-01, eta: 9:57:19, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8310 2023-01-01 19:04:07,806 - mmcls - INFO - Epoch [20][2700/5005] lr: 1.000e-01, eta: 9:57:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.8070 2023-01-01 19:04:16,858 - mmcls - INFO - Epoch [20][2800/5005] lr: 1.000e-01, eta: 9:57:01, time: 0.091, data_time: 0.027, memory: 1224, loss: 2.8061 2023-01-01 19:04:25,769 - mmcls - INFO - Epoch [20][2900/5005] lr: 1.000e-01, eta: 9:56:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7847 2023-01-01 19:04:34,585 - mmcls - INFO - Epoch [20][3000/5005] lr: 1.000e-01, eta: 9:56:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8043 2023-01-01 19:04:43,210 - mmcls - INFO - Epoch [20][3100/5005] lr: 1.000e-01, eta: 9:56:33, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7938 2023-01-01 19:04:52,156 - mmcls - INFO - Epoch [20][3200/5005] lr: 1.000e-01, eta: 9:56:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.8047 2023-01-01 19:05:01,392 - mmcls - INFO - Epoch [20][3300/5005] lr: 1.000e-01, eta: 9:56:17, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.8220 2023-01-01 19:05:10,138 - mmcls - INFO - Epoch [20][3400/5005] lr: 1.000e-01, eta: 9:56:08, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7974 2023-01-01 19:05:18,742 - mmcls - INFO - Epoch [20][3500/5005] lr: 1.000e-01, eta: 9:55:57, time: 0.086, data_time: 0.026, memory: 1224, loss: 2.7968 2023-01-01 19:05:27,560 - mmcls - INFO - Epoch [20][3600/5005] lr: 1.000e-01, eta: 9:55:48, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.8283 2023-01-01 19:05:36,777 - mmcls - INFO - Epoch [20][3700/5005] lr: 1.000e-01, eta: 9:55:41, time: 0.092, data_time: 0.012, memory: 1224, loss: 2.8379 2023-01-01 19:05:45,520 - mmcls - INFO - Epoch [20][3800/5005] lr: 1.000e-01, eta: 9:55:31, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.7703 2023-01-01 19:05:55,399 - mmcls - INFO - Epoch [20][3900/5005] lr: 1.000e-01, eta: 9:55:26, time: 0.099, data_time: 0.003, memory: 1224, loss: 2.8143 2023-01-01 19:06:03,969 - mmcls - INFO - Epoch [20][4000/5005] lr: 1.000e-01, eta: 9:55:16, time: 0.086, data_time: 0.032, memory: 1224, loss: 2.8365 2023-01-01 19:06:12,782 - mmcls - INFO - Epoch [20][4100/5005] lr: 1.000e-01, eta: 9:55:07, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7920 2023-01-01 19:06:21,603 - mmcls - INFO - Epoch [20][4200/5005] lr: 1.000e-01, eta: 9:54:57, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.8165 2023-01-01 19:06:30,268 - mmcls - INFO - Epoch [20][4300/5005] lr: 1.000e-01, eta: 9:54:48, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.8343 2023-01-01 19:06:39,120 - mmcls - INFO - Epoch [20][4400/5005] lr: 1.000e-01, eta: 9:54:39, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8025 2023-01-01 19:06:47,756 - mmcls - INFO - Epoch [20][4500/5005] lr: 1.000e-01, eta: 9:54:29, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8174 2023-01-01 19:06:56,911 - mmcls - INFO - Epoch [20][4600/5005] lr: 1.000e-01, eta: 9:54:21, time: 0.092, data_time: 0.031, memory: 1224, loss: 2.7819 2023-01-01 19:07:05,631 - mmcls - INFO - Epoch [20][4700/5005] lr: 1.000e-01, eta: 9:54:11, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.7936 2023-01-01 19:07:15,036 - mmcls - INFO - Epoch [20][4800/5005] lr: 1.000e-01, eta: 9:54:04, time: 0.094, data_time: 0.007, memory: 1224, loss: 2.7941 2023-01-01 19:07:23,931 - mmcls - INFO - Epoch [20][4900/5005] lr: 1.000e-01, eta: 9:53:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7705 2023-01-01 19:07:32,603 - mmcls - INFO - Epoch [20][5000/5005] lr: 1.000e-01, eta: 9:53:46, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.7856 2023-01-01 19:07:55,584 - mmcls - INFO - Epoch(val) [20][196] accuracy_top-1: 45.4140, accuracy_top-5: 71.0980 2023-01-01 19:08:06,470 - mmcls - INFO - Epoch [21][100/5005] lr: 1.000e-01, eta: 9:53:42, time: 0.109, data_time: 0.048, memory: 1224, loss: 2.7447 2023-01-01 19:08:15,769 - mmcls - INFO - Epoch [21][200/5005] lr: 1.000e-01, eta: 9:53:35, time: 0.093, data_time: 0.020, memory: 1224, loss: 2.7467 2023-01-01 19:08:24,303 - mmcls - INFO - Epoch [21][300/5005] lr: 1.000e-01, eta: 9:53:25, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.7388 2023-01-01 19:08:33,220 - mmcls - INFO - Epoch [21][400/5005] lr: 1.000e-01, eta: 9:53:16, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.7951 2023-01-01 19:08:42,146 - mmcls - INFO - Epoch [21][500/5005] lr: 1.000e-01, eta: 9:53:07, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7818 2023-01-01 19:08:51,263 - mmcls - INFO - Epoch [21][600/5005] lr: 1.000e-01, eta: 9:52:59, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.7836 2023-01-01 19:09:00,092 - mmcls - INFO - Epoch [21][700/5005] lr: 1.000e-01, eta: 9:52:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7745 2023-01-01 19:09:08,785 - mmcls - INFO - Epoch [21][800/5005] lr: 1.000e-01, eta: 9:52:40, time: 0.087, data_time: 0.032, memory: 1224, loss: 2.7639 2023-01-01 19:09:17,655 - mmcls - INFO - Epoch [21][900/5005] lr: 1.000e-01, eta: 9:52:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7570 2023-01-01 19:09:26,419 - mmcls - INFO - Epoch [21][1000/5005] lr: 1.000e-01, eta: 9:52:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7890 2023-01-01 19:09:35,235 - mmcls - INFO - Epoch [21][1100/5005] lr: 1.000e-01, eta: 9:52:12, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7912 2023-01-01 19:09:43,981 - mmcls - INFO - Epoch [21][1200/5005] lr: 1.000e-01, eta: 9:52:03, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7578 2023-01-01 19:09:52,792 - mmcls - INFO - Epoch [21][1300/5005] lr: 1.000e-01, eta: 9:51:54, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7724 2023-01-01 19:10:01,597 - mmcls - INFO - Epoch [21][1400/5005] lr: 1.000e-01, eta: 9:51:44, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.7914 2023-01-01 19:10:10,417 - mmcls - INFO - Epoch [21][1500/5005] lr: 1.000e-01, eta: 9:51:35, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.7852 2023-01-01 19:10:19,413 - mmcls - INFO - Epoch [21][1600/5005] lr: 1.000e-01, eta: 9:51:27, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.7990 2023-01-01 19:10:27,999 - mmcls - INFO - Epoch [21][1700/5005] lr: 1.000e-01, eta: 9:51:16, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8021 2023-01-01 19:10:37,218 - mmcls - INFO - Epoch [21][1800/5005] lr: 1.000e-01, eta: 9:51:09, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.7961 2023-01-01 19:10:46,821 - mmcls - INFO - Epoch [21][1900/5005] lr: 1.000e-01, eta: 9:51:03, time: 0.096, data_time: 0.002, memory: 1224, loss: 2.8040 2023-01-01 19:10:55,504 - mmcls - INFO - Epoch [21][2000/5005] lr: 1.000e-01, eta: 9:50:53, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8158 2023-01-01 19:11:04,207 - mmcls - INFO - Epoch [21][2100/5005] lr: 1.000e-01, eta: 9:50:43, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.8143 2023-01-01 19:11:13,015 - mmcls - INFO - Epoch [21][2200/5005] lr: 1.000e-01, eta: 9:50:34, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8256 2023-01-01 19:11:22,024 - mmcls - INFO - Epoch [21][2300/5005] lr: 1.000e-01, eta: 9:50:26, time: 0.090, data_time: 0.042, memory: 1224, loss: 2.7909 2023-01-01 19:11:31,456 - mmcls - INFO - Epoch [21][2400/5005] lr: 1.000e-01, eta: 9:50:19, time: 0.094, data_time: 0.022, memory: 1224, loss: 2.7762 2023-01-01 19:11:40,086 - mmcls - INFO - Epoch [21][2500/5005] lr: 1.000e-01, eta: 9:50:09, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7941 2023-01-01 19:11:49,634 - mmcls - INFO - Epoch [21][2600/5005] lr: 1.000e-01, eta: 9:50:02, time: 0.096, data_time: 0.007, memory: 1224, loss: 2.8097 2023-01-01 19:11:58,347 - mmcls - INFO - Epoch [21][2700/5005] lr: 1.000e-01, eta: 9:49:53, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.8030 2023-01-01 19:12:07,261 - mmcls - INFO - Epoch [21][2800/5005] lr: 1.000e-01, eta: 9:49:44, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7940 2023-01-01 19:12:15,765 - mmcls - INFO - Epoch [21][2900/5005] lr: 1.000e-01, eta: 9:49:33, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.7860 2023-01-01 19:12:24,486 - mmcls - INFO - Epoch [21][3000/5005] lr: 1.000e-01, eta: 9:49:24, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8145 2023-01-01 19:12:33,028 - mmcls - INFO - Epoch [21][3100/5005] lr: 1.000e-01, eta: 9:49:14, time: 0.085, data_time: 0.013, memory: 1224, loss: 2.7976 2023-01-01 19:12:42,166 - mmcls - INFO - Epoch [21][3200/5005] lr: 1.000e-01, eta: 9:49:06, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.7971 2023-01-01 19:12:51,019 - mmcls - INFO - Epoch [21][3300/5005] lr: 1.000e-01, eta: 9:48:56, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7816 2023-01-01 19:12:59,767 - mmcls - INFO - Epoch [21][3400/5005] lr: 1.000e-01, eta: 9:48:47, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.7837 2023-01-01 19:13:08,665 - mmcls - INFO - Epoch [21][3500/5005] lr: 1.000e-01, eta: 9:48:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7720 2023-01-01 19:13:17,965 - mmcls - INFO - Epoch [21][3600/5005] lr: 1.000e-01, eta: 9:48:31, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.8038 2023-01-01 19:13:26,639 - mmcls - INFO - Epoch [21][3700/5005] lr: 1.000e-01, eta: 9:48:21, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.7998 2023-01-01 19:13:35,399 - mmcls - INFO - Epoch [21][3800/5005] lr: 1.000e-01, eta: 9:48:12, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.8485 2023-01-01 19:13:44,103 - mmcls - INFO - Epoch [21][3900/5005] lr: 1.000e-01, eta: 9:48:02, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.8130 2023-01-01 19:13:52,858 - mmcls - INFO - Epoch [21][4000/5005] lr: 1.000e-01, eta: 9:47:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7906 2023-01-01 19:14:01,832 - mmcls - INFO - Epoch [21][4100/5005] lr: 1.000e-01, eta: 9:47:44, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8171 2023-01-01 19:14:10,678 - mmcls - INFO - Epoch [21][4200/5005] lr: 1.000e-01, eta: 9:47:35, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.8001 2023-01-01 19:14:19,342 - mmcls - INFO - Epoch [21][4300/5005] lr: 1.000e-01, eta: 9:47:25, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.8178 2023-01-01 19:14:27,926 - mmcls - INFO - Epoch [21][4400/5005] lr: 1.000e-01, eta: 9:47:15, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.8051 2023-01-01 19:14:37,299 - mmcls - INFO - Epoch [21][4500/5005] lr: 1.000e-01, eta: 9:47:08, time: 0.094, data_time: 0.010, memory: 1224, loss: 2.7969 2023-01-01 19:14:46,266 - mmcls - INFO - Epoch [21][4600/5005] lr: 1.000e-01, eta: 9:46:59, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.7744 2023-01-01 19:14:55,134 - mmcls - INFO - Epoch [21][4700/5005] lr: 1.000e-01, eta: 9:46:50, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.7789 2023-01-01 19:15:03,673 - mmcls - INFO - Epoch [21][4800/5005] lr: 1.000e-01, eta: 9:46:40, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.8042 2023-01-01 19:15:12,253 - mmcls - INFO - Epoch [21][4900/5005] lr: 1.000e-01, eta: 9:46:30, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7768 2023-01-01 19:15:21,144 - mmcls - INFO - Epoch [21][5000/5005] lr: 1.000e-01, eta: 9:46:21, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.7915 2023-01-01 19:15:43,986 - mmcls - INFO - Epoch(val) [21][196] accuracy_top-1: 46.8700, accuracy_top-5: 72.3420 2023-01-01 19:15:55,256 - mmcls - INFO - Epoch [22][100/5005] lr: 1.000e-01, eta: 9:46:19, time: 0.112, data_time: 0.034, memory: 1224, loss: 2.7227 2023-01-01 19:16:04,162 - mmcls - INFO - Epoch [22][200/5005] lr: 1.000e-01, eta: 9:46:10, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7299 2023-01-01 19:16:13,024 - mmcls - INFO - Epoch [22][300/5005] lr: 1.000e-01, eta: 9:46:01, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7159 2023-01-01 19:16:21,695 - mmcls - INFO - Epoch [22][400/5005] lr: 1.000e-01, eta: 9:45:51, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7470 2023-01-01 19:16:30,567 - mmcls - INFO - Epoch [22][500/5005] lr: 1.000e-01, eta: 9:45:42, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7344 2023-01-01 19:16:39,223 - mmcls - INFO - Epoch [22][600/5005] lr: 1.000e-01, eta: 9:45:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7441 2023-01-01 19:16:48,083 - mmcls - INFO - Epoch [22][700/5005] lr: 1.000e-01, eta: 9:45:23, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7657 2023-01-01 19:16:56,824 - mmcls - INFO - Epoch [22][800/5005] lr: 1.000e-01, eta: 9:45:14, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7449 2023-01-01 19:17:05,984 - mmcls - INFO - Epoch [22][900/5005] lr: 1.000e-01, eta: 9:45:06, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.7592 2023-01-01 19:17:14,807 - mmcls - INFO - Epoch [22][1000/5005] lr: 1.000e-01, eta: 9:44:56, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7583 2023-01-01 19:17:23,528 - mmcls - INFO - Epoch [22][1100/5005] lr: 1.000e-01, eta: 9:44:47, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.7653 2023-01-01 19:17:32,403 - mmcls - INFO - Epoch [22][1200/5005] lr: 1.000e-01, eta: 9:44:38, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.7614 2023-01-01 19:17:40,890 - mmcls - INFO - Epoch [22][1300/5005] lr: 1.000e-01, eta: 9:44:27, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.8044 2023-01-01 19:17:49,729 - mmcls - INFO - Epoch [22][1400/5005] lr: 1.000e-01, eta: 9:44:18, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8192 2023-01-01 19:17:58,564 - mmcls - INFO - Epoch [22][1500/5005] lr: 1.000e-01, eta: 9:44:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7751 2023-01-01 19:18:07,499 - mmcls - INFO - Epoch [22][1600/5005] lr: 1.000e-01, eta: 9:44:00, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.7718 2023-01-01 19:18:16,448 - mmcls - INFO - Epoch [22][1700/5005] lr: 1.000e-01, eta: 9:43:52, time: 0.090, data_time: 0.022, memory: 1224, loss: 2.7638 2023-01-01 19:18:25,131 - mmcls - INFO - Epoch [22][1800/5005] lr: 1.000e-01, eta: 9:43:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7776 2023-01-01 19:18:33,936 - mmcls - INFO - Epoch [22][1900/5005] lr: 1.000e-01, eta: 9:43:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7820 2023-01-01 19:18:43,014 - mmcls - INFO - Epoch [22][2000/5005] lr: 1.000e-01, eta: 9:43:25, time: 0.091, data_time: 0.006, memory: 1224, loss: 2.7812 2023-01-01 19:18:51,638 - mmcls - INFO - Epoch [22][2100/5005] lr: 1.000e-01, eta: 9:43:15, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.7739 2023-01-01 19:19:00,674 - mmcls - INFO - Epoch [22][2200/5005] lr: 1.000e-01, eta: 9:43:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7759 2023-01-01 19:19:09,368 - mmcls - INFO - Epoch [22][2300/5005] lr: 1.000e-01, eta: 9:42:57, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7634 2023-01-01 19:19:18,146 - mmcls - INFO - Epoch [22][2400/5005] lr: 1.000e-01, eta: 9:42:47, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.8217 2023-01-01 19:19:27,130 - mmcls - INFO - Epoch [22][2500/5005] lr: 1.000e-01, eta: 9:42:39, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7715 2023-01-01 19:19:35,894 - mmcls - INFO - Epoch [22][2600/5005] lr: 1.000e-01, eta: 9:42:29, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7814 2023-01-01 19:19:44,696 - mmcls - INFO - Epoch [22][2700/5005] lr: 1.000e-01, eta: 9:42:20, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.8253 2023-01-01 19:19:53,672 - mmcls - INFO - Epoch [22][2800/5005] lr: 1.000e-01, eta: 9:42:11, time: 0.090, data_time: 0.020, memory: 1224, loss: 2.7687 2023-01-01 19:20:02,122 - mmcls - INFO - Epoch [22][2900/5005] lr: 1.000e-01, eta: 9:42:01, time: 0.085, data_time: 0.021, memory: 1224, loss: 2.7922 2023-01-01 19:20:10,858 - mmcls - INFO - Epoch [22][3000/5005] lr: 1.000e-01, eta: 9:41:51, time: 0.087, data_time: 0.040, memory: 1224, loss: 2.8140 2023-01-01 19:20:19,819 - mmcls - INFO - Epoch [22][3100/5005] lr: 1.000e-01, eta: 9:41:43, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.8460 2023-01-01 19:20:28,900 - mmcls - INFO - Epoch [22][3200/5005] lr: 1.000e-01, eta: 9:41:35, time: 0.091, data_time: 0.014, memory: 1224, loss: 2.8070 2023-01-01 19:20:37,477 - mmcls - INFO - Epoch [22][3300/5005] lr: 1.000e-01, eta: 9:41:24, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.7976 2023-01-01 19:20:46,207 - mmcls - INFO - Epoch [22][3400/5005] lr: 1.000e-01, eta: 9:41:15, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.7740 2023-01-01 19:20:55,053 - mmcls - INFO - Epoch [22][3500/5005] lr: 1.000e-01, eta: 9:41:06, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7780 2023-01-01 19:21:04,105 - mmcls - INFO - Epoch [22][3600/5005] lr: 1.000e-01, eta: 9:40:58, time: 0.091, data_time: 0.008, memory: 1224, loss: 2.7718 2023-01-01 19:21:12,752 - mmcls - INFO - Epoch [22][3700/5005] lr: 1.000e-01, eta: 9:40:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7656 2023-01-01 19:21:21,492 - mmcls - INFO - Epoch [22][3800/5005] lr: 1.000e-01, eta: 9:40:38, time: 0.087, data_time: 0.028, memory: 1224, loss: 2.7575 2023-01-01 19:21:30,543 - mmcls - INFO - Epoch [22][3900/5005] lr: 1.000e-01, eta: 9:40:30, time: 0.091, data_time: 0.031, memory: 1224, loss: 2.7498 2023-01-01 19:21:39,404 - mmcls - INFO - Epoch [22][4000/5005] lr: 1.000e-01, eta: 9:40:21, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.7613 2023-01-01 19:21:48,220 - mmcls - INFO - Epoch [22][4100/5005] lr: 1.000e-01, eta: 9:40:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7597 2023-01-01 19:21:57,237 - mmcls - INFO - Epoch [22][4200/5005] lr: 1.000e-01, eta: 9:40:03, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.7599 2023-01-01 19:22:06,161 - mmcls - INFO - Epoch [22][4300/5005] lr: 1.000e-01, eta: 9:39:54, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8074 2023-01-01 19:22:14,978 - mmcls - INFO - Epoch [22][4400/5005] lr: 1.000e-01, eta: 9:39:45, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7439 2023-01-01 19:22:23,794 - mmcls - INFO - Epoch [22][4500/5005] lr: 1.000e-01, eta: 9:39:36, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.8450 2023-01-01 19:22:32,406 - mmcls - INFO - Epoch [22][4600/5005] lr: 1.000e-01, eta: 9:39:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.8172 2023-01-01 19:22:41,218 - mmcls - INFO - Epoch [22][4700/5005] lr: 1.000e-01, eta: 9:39:17, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7959 2023-01-01 19:22:49,766 - mmcls - INFO - Epoch [22][4800/5005] lr: 1.000e-01, eta: 9:39:07, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.7802 2023-01-01 19:22:58,569 - mmcls - INFO - Epoch [22][4900/5005] lr: 1.000e-01, eta: 9:38:58, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8067 2023-01-01 19:23:07,683 - mmcls - INFO - Epoch [22][5000/5005] lr: 1.000e-01, eta: 9:38:49, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.7936 2023-01-01 19:23:30,419 - mmcls - INFO - Epoch(val) [22][196] accuracy_top-1: 44.6380, accuracy_top-5: 69.8540 2023-01-01 19:23:41,557 - mmcls - INFO - Epoch [23][100/5005] lr: 1.000e-01, eta: 9:38:46, time: 0.111, data_time: 0.048, memory: 1224, loss: 2.7187 2023-01-01 19:23:50,295 - mmcls - INFO - Epoch [23][200/5005] lr: 1.000e-01, eta: 9:38:37, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7041 2023-01-01 19:23:59,173 - mmcls - INFO - Epoch [23][300/5005] lr: 1.000e-01, eta: 9:38:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7659 2023-01-01 19:24:07,755 - mmcls - INFO - Epoch [23][400/5005] lr: 1.000e-01, eta: 9:38:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7833 2023-01-01 19:24:16,612 - mmcls - INFO - Epoch [23][500/5005] lr: 1.000e-01, eta: 9:38:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7519 2023-01-01 19:24:25,355 - mmcls - INFO - Epoch [23][600/5005] lr: 1.000e-01, eta: 9:37:59, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7706 2023-01-01 19:24:34,076 - mmcls - INFO - Epoch [23][700/5005] lr: 1.000e-01, eta: 9:37:50, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7611 2023-01-01 19:24:43,106 - mmcls - INFO - Epoch [23][800/5005] lr: 1.000e-01, eta: 9:37:42, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.7655 2023-01-01 19:24:51,828 - mmcls - INFO - Epoch [23][900/5005] lr: 1.000e-01, eta: 9:37:32, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.7517 2023-01-01 19:25:00,477 - mmcls - INFO - Epoch [23][1000/5005] lr: 1.000e-01, eta: 9:37:22, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7497 2023-01-01 19:25:09,452 - mmcls - INFO - Epoch [23][1100/5005] lr: 1.000e-01, eta: 9:37:14, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.7975 2023-01-01 19:25:18,252 - mmcls - INFO - Epoch [23][1200/5005] lr: 1.000e-01, eta: 9:37:04, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.7742 2023-01-01 19:25:27,179 - mmcls - INFO - Epoch [23][1300/5005] lr: 1.000e-01, eta: 9:36:56, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7442 2023-01-01 19:25:36,354 - mmcls - INFO - Epoch [23][1400/5005] lr: 1.000e-01, eta: 9:36:48, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.7587 2023-01-01 19:25:44,996 - mmcls - INFO - Epoch [23][1500/5005] lr: 1.000e-01, eta: 9:36:38, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7657 2023-01-01 19:25:53,964 - mmcls - INFO - Epoch [23][1600/5005] lr: 1.000e-01, eta: 9:36:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7924 2023-01-01 19:26:02,926 - mmcls - INFO - Epoch [23][1700/5005] lr: 1.000e-01, eta: 9:36:21, time: 0.090, data_time: 0.021, memory: 1224, loss: 2.8093 2023-01-01 19:26:11,611 - mmcls - INFO - Epoch [23][1800/5005] lr: 1.000e-01, eta: 9:36:11, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7722 2023-01-01 19:26:20,415 - mmcls - INFO - Epoch [23][1900/5005] lr: 1.000e-01, eta: 9:36:02, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7938 2023-01-01 19:26:29,329 - mmcls - INFO - Epoch [23][2000/5005] lr: 1.000e-01, eta: 9:35:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7847 2023-01-01 19:26:37,923 - mmcls - INFO - Epoch [23][2100/5005] lr: 1.000e-01, eta: 9:35:43, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7884 2023-01-01 19:26:46,572 - mmcls - INFO - Epoch [23][2200/5005] lr: 1.000e-01, eta: 9:35:33, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7556 2023-01-01 19:26:55,441 - mmcls - INFO - Epoch [23][2300/5005] lr: 1.000e-01, eta: 9:35:24, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.7973 2023-01-01 19:27:04,247 - mmcls - INFO - Epoch [23][2400/5005] lr: 1.000e-01, eta: 9:35:15, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8010 2023-01-01 19:27:13,015 - mmcls - INFO - Epoch [23][2500/5005] lr: 1.000e-01, eta: 9:35:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7827 2023-01-01 19:27:21,886 - mmcls - INFO - Epoch [23][2600/5005] lr: 1.000e-01, eta: 9:34:57, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8035 2023-01-01 19:27:30,632 - mmcls - INFO - Epoch [23][2700/5005] lr: 1.000e-01, eta: 9:34:47, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7645 2023-01-01 19:27:39,492 - mmcls - INFO - Epoch [23][2800/5005] lr: 1.000e-01, eta: 9:34:38, time: 0.089, data_time: 0.025, memory: 1224, loss: 2.7789 2023-01-01 19:27:48,382 - mmcls - INFO - Epoch [23][2900/5005] lr: 1.000e-01, eta: 9:34:29, time: 0.089, data_time: 0.033, memory: 1224, loss: 2.7809 2023-01-01 19:27:56,908 - mmcls - INFO - Epoch [23][3000/5005] lr: 1.000e-01, eta: 9:34:19, time: 0.085, data_time: 0.023, memory: 1224, loss: 2.7729 2023-01-01 19:28:05,749 - mmcls - INFO - Epoch [23][3100/5005] lr: 1.000e-01, eta: 9:34:10, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7980 2023-01-01 19:28:14,661 - mmcls - INFO - Epoch [23][3200/5005] lr: 1.000e-01, eta: 9:34:01, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.7804 2023-01-01 19:28:23,256 - mmcls - INFO - Epoch [23][3300/5005] lr: 1.000e-01, eta: 9:33:51, time: 0.086, data_time: 0.022, memory: 1224, loss: 2.7697 2023-01-01 19:28:31,840 - mmcls - INFO - Epoch [23][3400/5005] lr: 1.000e-01, eta: 9:33:41, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7951 2023-01-01 19:28:40,579 - mmcls - INFO - Epoch [23][3500/5005] lr: 1.000e-01, eta: 9:33:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8011 2023-01-01 19:28:49,220 - mmcls - INFO - Epoch [23][3600/5005] lr: 1.000e-01, eta: 9:33:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7970 2023-01-01 19:28:57,883 - mmcls - INFO - Epoch [23][3700/5005] lr: 1.000e-01, eta: 9:33:12, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.7685 2023-01-01 19:29:06,908 - mmcls - INFO - Epoch [23][3800/5005] lr: 1.000e-01, eta: 9:33:04, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.8043 2023-01-01 19:29:15,465 - mmcls - INFO - Epoch [23][3900/5005] lr: 1.000e-01, eta: 9:32:54, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7875 2023-01-01 19:29:24,606 - mmcls - INFO - Epoch [23][4000/5005] lr: 1.000e-01, eta: 9:32:46, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.7943 2023-01-01 19:29:33,570 - mmcls - INFO - Epoch [23][4100/5005] lr: 1.000e-01, eta: 9:32:37, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.7995 2023-01-01 19:29:42,362 - mmcls - INFO - Epoch [23][4200/5005] lr: 1.000e-01, eta: 9:32:28, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.7652 2023-01-01 19:29:51,167 - mmcls - INFO - Epoch [23][4300/5005] lr: 1.000e-01, eta: 9:32:19, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7956 2023-01-01 19:30:00,137 - mmcls - INFO - Epoch [23][4400/5005] lr: 1.000e-01, eta: 9:32:10, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8169 2023-01-01 19:30:09,359 - mmcls - INFO - Epoch [23][4500/5005] lr: 1.000e-01, eta: 9:32:02, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.7802 2023-01-01 19:30:18,432 - mmcls - INFO - Epoch [23][4600/5005] lr: 1.000e-01, eta: 9:31:54, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.7723 2023-01-01 19:30:27,008 - mmcls - INFO - Epoch [23][4700/5005] lr: 1.000e-01, eta: 9:31:44, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.8180 2023-01-01 19:30:35,596 - mmcls - INFO - Epoch [23][4800/5005] lr: 1.000e-01, eta: 9:31:34, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.8209 2023-01-01 19:30:44,382 - mmcls - INFO - Epoch [23][4900/5005] lr: 1.000e-01, eta: 9:31:25, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.7853 2023-01-01 19:30:53,134 - mmcls - INFO - Epoch [23][5000/5005] lr: 1.000e-01, eta: 9:31:16, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7570 2023-01-01 19:31:15,910 - mmcls - INFO - Epoch(val) [23][196] accuracy_top-1: 44.3780, accuracy_top-5: 69.8640 2023-01-01 19:31:27,019 - mmcls - INFO - Epoch [24][100/5005] lr: 1.000e-01, eta: 9:31:12, time: 0.111, data_time: 0.047, memory: 1224, loss: 2.7304 2023-01-01 19:31:35,725 - mmcls - INFO - Epoch [24][200/5005] lr: 1.000e-01, eta: 9:31:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7188 2023-01-01 19:31:44,347 - mmcls - INFO - Epoch [24][300/5005] lr: 1.000e-01, eta: 9:30:53, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7522 2023-01-01 19:31:53,106 - mmcls - INFO - Epoch [24][400/5005] lr: 1.000e-01, eta: 9:30:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7502 2023-01-01 19:32:01,928 - mmcls - INFO - Epoch [24][500/5005] lr: 1.000e-01, eta: 9:30:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7164 2023-01-01 19:32:10,778 - mmcls - INFO - Epoch [24][600/5005] lr: 1.000e-01, eta: 9:30:25, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.7338 2023-01-01 19:32:19,607 - mmcls - INFO - Epoch [24][700/5005] lr: 1.000e-01, eta: 9:30:16, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.7841 2023-01-01 19:32:28,093 - mmcls - INFO - Epoch [24][800/5005] lr: 1.000e-01, eta: 9:30:06, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.8070 2023-01-01 19:32:36,867 - mmcls - INFO - Epoch [24][900/5005] lr: 1.000e-01, eta: 9:29:56, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7978 2023-01-01 19:32:45,470 - mmcls - INFO - Epoch [24][1000/5005] lr: 1.000e-01, eta: 9:29:47, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.8224 2023-01-01 19:32:54,272 - mmcls - INFO - Epoch [24][1100/5005] lr: 1.000e-01, eta: 9:29:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7793 2023-01-01 19:33:02,931 - mmcls - INFO - Epoch [24][1200/5005] lr: 1.000e-01, eta: 9:29:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7881 2023-01-01 19:33:11,653 - mmcls - INFO - Epoch [24][1300/5005] lr: 1.000e-01, eta: 9:29:18, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7435 2023-01-01 19:33:20,637 - mmcls - INFO - Epoch [24][1400/5005] lr: 1.000e-01, eta: 9:29:10, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.7754 2023-01-01 19:33:29,507 - mmcls - INFO - Epoch [24][1500/5005] lr: 1.000e-01, eta: 9:29:01, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7443 2023-01-01 19:33:38,320 - mmcls - INFO - Epoch [24][1600/5005] lr: 1.000e-01, eta: 9:28:52, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7663 2023-01-01 19:33:47,055 - mmcls - INFO - Epoch [24][1700/5005] lr: 1.000e-01, eta: 9:28:42, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.7721 2023-01-01 19:33:55,657 - mmcls - INFO - Epoch [24][1800/5005] lr: 1.000e-01, eta: 9:28:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.7669 2023-01-01 19:34:04,519 - mmcls - INFO - Epoch [24][1900/5005] lr: 1.000e-01, eta: 9:28:23, time: 0.089, data_time: 0.028, memory: 1224, loss: 2.7907 2023-01-01 19:34:13,160 - mmcls - INFO - Epoch [24][2000/5005] lr: 1.000e-01, eta: 9:28:14, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.7793 2023-01-01 19:34:21,766 - mmcls - INFO - Epoch [24][2100/5005] lr: 1.000e-01, eta: 9:28:04, time: 0.086, data_time: 0.029, memory: 1224, loss: 2.7763 2023-01-01 19:34:30,751 - mmcls - INFO - Epoch [24][2200/5005] lr: 1.000e-01, eta: 9:27:55, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7788 2023-01-01 19:34:39,596 - mmcls - INFO - Epoch [24][2300/5005] lr: 1.000e-01, eta: 9:27:46, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7931 2023-01-01 19:34:48,416 - mmcls - INFO - Epoch [24][2400/5005] lr: 1.000e-01, eta: 9:27:37, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7902 2023-01-01 19:34:57,218 - mmcls - INFO - Epoch [24][2500/5005] lr: 1.000e-01, eta: 9:27:28, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7892 2023-01-01 19:35:06,135 - mmcls - INFO - Epoch [24][2600/5005] lr: 1.000e-01, eta: 9:27:19, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7552 2023-01-01 19:35:15,129 - mmcls - INFO - Epoch [24][2700/5005] lr: 1.000e-01, eta: 9:27:10, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.7964 2023-01-01 19:35:23,820 - mmcls - INFO - Epoch [24][2800/5005] lr: 1.000e-01, eta: 9:27:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7721 2023-01-01 19:35:32,506 - mmcls - INFO - Epoch [24][2900/5005] lr: 1.000e-01, eta: 9:26:51, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.8024 2023-01-01 19:35:41,204 - mmcls - INFO - Epoch [24][3000/5005] lr: 1.000e-01, eta: 9:26:42, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7913 2023-01-01 19:35:49,846 - mmcls - INFO - Epoch [24][3100/5005] lr: 1.000e-01, eta: 9:26:32, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7976 2023-01-01 19:35:58,814 - mmcls - INFO - Epoch [24][3200/5005] lr: 1.000e-01, eta: 9:26:23, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.7776 2023-01-01 19:36:07,771 - mmcls - INFO - Epoch [24][3300/5005] lr: 1.000e-01, eta: 9:26:15, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.7834 2023-01-01 19:36:17,000 - mmcls - INFO - Epoch [24][3400/5005] lr: 1.000e-01, eta: 9:26:07, time: 0.092, data_time: 0.025, memory: 1224, loss: 2.7832 2023-01-01 19:36:26,051 - mmcls - INFO - Epoch [24][3500/5005] lr: 1.000e-01, eta: 9:25:58, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.7628 2023-01-01 19:36:34,846 - mmcls - INFO - Epoch [24][3600/5005] lr: 1.000e-01, eta: 9:25:49, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7629 2023-01-01 19:36:43,775 - mmcls - INFO - Epoch [24][3700/5005] lr: 1.000e-01, eta: 9:25:40, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.7895 2023-01-01 19:36:53,037 - mmcls - INFO - Epoch [24][3800/5005] lr: 1.000e-01, eta: 9:25:33, time: 0.093, data_time: 0.027, memory: 1224, loss: 2.7788 2023-01-01 19:37:01,909 - mmcls - INFO - Epoch [24][3900/5005] lr: 1.000e-01, eta: 9:25:24, time: 0.089, data_time: 0.039, memory: 1224, loss: 2.7800 2023-01-01 19:37:10,627 - mmcls - INFO - Epoch [24][4000/5005] lr: 1.000e-01, eta: 9:25:14, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.7709 2023-01-01 19:37:19,437 - mmcls - INFO - Epoch [24][4100/5005] lr: 1.000e-01, eta: 9:25:05, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8120 2023-01-01 19:37:28,129 - mmcls - INFO - Epoch [24][4200/5005] lr: 1.000e-01, eta: 9:24:56, time: 0.087, data_time: 0.025, memory: 1224, loss: 2.7848 2023-01-01 19:37:37,120 - mmcls - INFO - Epoch [24][4300/5005] lr: 1.000e-01, eta: 9:24:47, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.7732 2023-01-01 19:37:45,913 - mmcls - INFO - Epoch [24][4400/5005] lr: 1.000e-01, eta: 9:24:38, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7751 2023-01-01 19:37:54,606 - mmcls - INFO - Epoch [24][4500/5005] lr: 1.000e-01, eta: 9:24:28, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7556 2023-01-01 19:38:03,423 - mmcls - INFO - Epoch [24][4600/5005] lr: 1.000e-01, eta: 9:24:19, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7778 2023-01-01 19:38:12,142 - mmcls - INFO - Epoch [24][4700/5005] lr: 1.000e-01, eta: 9:24:10, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7780 2023-01-01 19:38:20,667 - mmcls - INFO - Epoch [24][4800/5005] lr: 1.000e-01, eta: 9:24:00, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.7424 2023-01-01 19:38:29,721 - mmcls - INFO - Epoch [24][4900/5005] lr: 1.000e-01, eta: 9:23:51, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.7539 2023-01-01 19:38:38,451 - mmcls - INFO - Epoch [24][5000/5005] lr: 1.000e-01, eta: 9:23:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7717 2023-01-01 19:39:01,473 - mmcls - INFO - Epoch(val) [24][196] accuracy_top-1: 47.1760, accuracy_top-5: 72.0040 2023-01-01 19:39:12,312 - mmcls - INFO - Epoch [25][100/5005] lr: 1.000e-01, eta: 9:23:37, time: 0.108, data_time: 0.032, memory: 1224, loss: 2.7133 2023-01-01 19:39:21,333 - mmcls - INFO - Epoch [25][200/5005] lr: 1.000e-01, eta: 9:23:29, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7445 2023-01-01 19:39:30,170 - mmcls - INFO - Epoch [25][300/5005] lr: 1.000e-01, eta: 9:23:20, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7326 2023-01-01 19:39:38,893 - mmcls - INFO - Epoch [25][400/5005] lr: 1.000e-01, eta: 9:23:10, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.7569 2023-01-01 19:39:47,494 - mmcls - INFO - Epoch [25][500/5005] lr: 1.000e-01, eta: 9:23:00, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7322 2023-01-01 19:39:56,408 - mmcls - INFO - Epoch [25][600/5005] lr: 1.000e-01, eta: 9:22:52, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7255 2023-01-01 19:40:05,191 - mmcls - INFO - Epoch [25][700/5005] lr: 1.000e-01, eta: 9:22:42, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7286 2023-01-01 19:40:14,078 - mmcls - INFO - Epoch [25][800/5005] lr: 1.000e-01, eta: 9:22:33, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.7564 2023-01-01 19:40:22,861 - mmcls - INFO - Epoch [25][900/5005] lr: 1.000e-01, eta: 9:22:24, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7680 2023-01-01 19:40:31,803 - mmcls - INFO - Epoch [25][1000/5005] lr: 1.000e-01, eta: 9:22:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7381 2023-01-01 19:40:40,455 - mmcls - INFO - Epoch [25][1100/5005] lr: 1.000e-01, eta: 9:22:06, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.7329 2023-01-01 19:40:49,036 - mmcls - INFO - Epoch [25][1200/5005] lr: 1.000e-01, eta: 9:21:56, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.7580 2023-01-01 19:40:57,946 - mmcls - INFO - Epoch [25][1300/5005] lr: 1.000e-01, eta: 9:21:47, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.7665 2023-01-01 19:41:06,531 - mmcls - INFO - Epoch [25][1400/5005] lr: 1.000e-01, eta: 9:21:37, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.7535 2023-01-01 19:41:15,500 - mmcls - INFO - Epoch [25][1500/5005] lr: 1.000e-01, eta: 9:21:29, time: 0.090, data_time: 0.020, memory: 1224, loss: 2.7599 2023-01-01 19:41:24,189 - mmcls - INFO - Epoch [25][1600/5005] lr: 1.000e-01, eta: 9:21:19, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.7802 2023-01-01 19:41:32,930 - mmcls - INFO - Epoch [25][1700/5005] lr: 1.000e-01, eta: 9:21:10, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.7620 2023-01-01 19:41:41,562 - mmcls - INFO - Epoch [25][1800/5005] lr: 1.000e-01, eta: 9:21:00, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.7637 2023-01-01 19:41:50,538 - mmcls - INFO - Epoch [25][1900/5005] lr: 1.000e-01, eta: 9:20:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7903 2023-01-01 19:41:59,281 - mmcls - INFO - Epoch [25][2000/5005] lr: 1.000e-01, eta: 9:20:42, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.7938 2023-01-01 19:42:08,139 - mmcls - INFO - Epoch [25][2100/5005] lr: 1.000e-01, eta: 9:20:33, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.7797 2023-01-01 19:42:16,879 - mmcls - INFO - Epoch [25][2200/5005] lr: 1.000e-01, eta: 9:20:24, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7760 2023-01-01 19:42:25,693 - mmcls - INFO - Epoch [25][2300/5005] lr: 1.000e-01, eta: 9:20:15, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7595 2023-01-01 19:42:34,669 - mmcls - INFO - Epoch [25][2400/5005] lr: 1.000e-01, eta: 9:20:06, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.7672 2023-01-01 19:42:43,458 - mmcls - INFO - Epoch [25][2500/5005] lr: 1.000e-01, eta: 9:19:57, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.7564 2023-01-01 19:42:52,533 - mmcls - INFO - Epoch [25][2600/5005] lr: 1.000e-01, eta: 9:19:48, time: 0.091, data_time: 0.006, memory: 1224, loss: 2.7714 2023-01-01 19:43:01,039 - mmcls - INFO - Epoch [25][2700/5005] lr: 1.000e-01, eta: 9:19:38, time: 0.085, data_time: 0.012, memory: 1224, loss: 2.7853 2023-01-01 19:43:09,646 - mmcls - INFO - Epoch [25][2800/5005] lr: 1.000e-01, eta: 9:19:29, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7990 2023-01-01 19:43:18,460 - mmcls - INFO - Epoch [25][2900/5005] lr: 1.000e-01, eta: 9:19:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7810 2023-01-01 19:43:27,218 - mmcls - INFO - Epoch [25][3000/5005] lr: 1.000e-01, eta: 9:19:10, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.7777 2023-01-01 19:43:36,341 - mmcls - INFO - Epoch [25][3100/5005] lr: 1.000e-01, eta: 9:19:02, time: 0.091, data_time: 0.014, memory: 1224, loss: 2.7931 2023-01-01 19:43:44,761 - mmcls - INFO - Epoch [25][3200/5005] lr: 1.000e-01, eta: 9:18:52, time: 0.084, data_time: 0.015, memory: 1224, loss: 2.7844 2023-01-01 19:43:53,750 - mmcls - INFO - Epoch [25][3300/5005] lr: 1.000e-01, eta: 9:18:43, time: 0.090, data_time: 0.040, memory: 1224, loss: 2.7501 2023-01-01 19:44:02,613 - mmcls - INFO - Epoch [25][3400/5005] lr: 1.000e-01, eta: 9:18:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7945 2023-01-01 19:44:11,566 - mmcls - INFO - Epoch [25][3500/5005] lr: 1.000e-01, eta: 9:18:25, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.8023 2023-01-01 19:44:20,525 - mmcls - INFO - Epoch [25][3600/5005] lr: 1.000e-01, eta: 9:18:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.7731 2023-01-01 19:44:29,278 - mmcls - INFO - Epoch [25][3700/5005] lr: 1.000e-01, eta: 9:18:07, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7503 2023-01-01 19:44:38,066 - mmcls - INFO - Epoch [25][3800/5005] lr: 1.000e-01, eta: 9:17:58, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.8349 2023-01-01 19:44:46,698 - mmcls - INFO - Epoch [25][3900/5005] lr: 1.000e-01, eta: 9:17:48, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.7838 2023-01-01 19:44:55,525 - mmcls - INFO - Epoch [25][4000/5005] lr: 1.000e-01, eta: 9:17:39, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7829 2023-01-01 19:45:04,209 - mmcls - INFO - Epoch [25][4100/5005] lr: 1.000e-01, eta: 9:17:30, time: 0.087, data_time: 0.023, memory: 1224, loss: 2.7779 2023-01-01 19:45:13,084 - mmcls - INFO - Epoch [25][4200/5005] lr: 1.000e-01, eta: 9:17:21, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7788 2023-01-01 19:45:22,244 - mmcls - INFO - Epoch [25][4300/5005] lr: 1.000e-01, eta: 9:17:13, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.7808 2023-01-01 19:45:31,579 - mmcls - INFO - Epoch [25][4400/5005] lr: 1.000e-01, eta: 9:17:05, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.7370 2023-01-01 19:45:40,249 - mmcls - INFO - Epoch [25][4500/5005] lr: 1.000e-01, eta: 9:16:56, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.7778 2023-01-01 19:45:49,282 - mmcls - INFO - Epoch [25][4600/5005] lr: 1.000e-01, eta: 9:16:47, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7600 2023-01-01 19:45:57,823 - mmcls - INFO - Epoch [25][4700/5005] lr: 1.000e-01, eta: 9:16:37, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.7661 2023-01-01 19:46:06,771 - mmcls - INFO - Epoch [25][4800/5005] lr: 1.000e-01, eta: 9:16:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8104 2023-01-01 19:46:15,706 - mmcls - INFO - Epoch [25][4900/5005] lr: 1.000e-01, eta: 9:16:20, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.8708 2023-01-01 19:46:24,742 - mmcls - INFO - Epoch [25][5000/5005] lr: 1.000e-01, eta: 9:16:11, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.8146 2023-01-01 19:46:47,615 - mmcls - INFO - Epoch(val) [25][196] accuracy_top-1: 44.8280, accuracy_top-5: 69.8080 2023-01-01 19:46:58,445 - mmcls - INFO - Epoch [26][100/5005] lr: 1.000e-01, eta: 9:16:06, time: 0.108, data_time: 0.046, memory: 1224, loss: 2.7426 2023-01-01 19:47:07,302 - mmcls - INFO - Epoch [26][200/5005] lr: 1.000e-01, eta: 9:15:57, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7795 2023-01-01 19:47:15,917 - mmcls - INFO - Epoch [26][300/5005] lr: 1.000e-01, eta: 9:15:48, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.7257 2023-01-01 19:47:24,952 - mmcls - INFO - Epoch [26][400/5005] lr: 1.000e-01, eta: 9:15:39, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7517 2023-01-01 19:47:33,655 - mmcls - INFO - Epoch [26][500/5005] lr: 1.000e-01, eta: 9:15:30, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.7436 2023-01-01 19:47:42,858 - mmcls - INFO - Epoch [26][600/5005] lr: 1.000e-01, eta: 9:15:22, time: 0.092, data_time: 0.022, memory: 1224, loss: 2.7909 2023-01-01 19:47:51,524 - mmcls - INFO - Epoch [26][700/5005] lr: 1.000e-01, eta: 9:15:12, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.7748 2023-01-01 19:48:00,239 - mmcls - INFO - Epoch [26][800/5005] lr: 1.000e-01, eta: 9:15:03, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7508 2023-01-01 19:48:09,148 - mmcls - INFO - Epoch [26][900/5005] lr: 1.000e-01, eta: 9:14:54, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.7756 2023-01-01 19:48:17,714 - mmcls - INFO - Epoch [26][1000/5005] lr: 1.000e-01, eta: 9:14:44, time: 0.086, data_time: 0.028, memory: 1224, loss: 2.7737 2023-01-01 19:48:26,531 - mmcls - INFO - Epoch [26][1100/5005] lr: 1.000e-01, eta: 9:14:35, time: 0.088, data_time: 0.043, memory: 1224, loss: 2.7731 2023-01-01 19:48:35,410 - mmcls - INFO - Epoch [26][1200/5005] lr: 1.000e-01, eta: 9:14:26, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.7841 2023-01-01 19:48:44,053 - mmcls - INFO - Epoch [26][1300/5005] lr: 1.000e-01, eta: 9:14:17, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7683 2023-01-01 19:48:53,092 - mmcls - INFO - Epoch [26][1400/5005] lr: 1.000e-01, eta: 9:14:08, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.7608 2023-01-01 19:49:02,297 - mmcls - INFO - Epoch [26][1500/5005] lr: 1.000e-01, eta: 9:14:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.7686 2023-01-01 19:49:11,382 - mmcls - INFO - Epoch [26][1600/5005] lr: 1.000e-01, eta: 9:13:52, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.7538 2023-01-01 19:49:19,843 - mmcls - INFO - Epoch [26][1700/5005] lr: 1.000e-01, eta: 9:13:42, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.7633 2023-01-01 19:49:28,657 - mmcls - INFO - Epoch [26][1800/5005] lr: 1.000e-01, eta: 9:13:33, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.7768 2023-01-01 19:49:37,902 - mmcls - INFO - Epoch [26][1900/5005] lr: 1.000e-01, eta: 9:13:25, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.7470 2023-01-01 19:49:46,366 - mmcls - INFO - Epoch [26][2000/5005] lr: 1.000e-01, eta: 9:13:15, time: 0.085, data_time: 0.012, memory: 1224, loss: 2.7425 2023-01-01 19:49:54,907 - mmcls - INFO - Epoch [26][2100/5005] lr: 1.000e-01, eta: 9:13:05, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7686 2023-01-01 19:50:03,707 - mmcls - INFO - Epoch [26][2200/5005] lr: 1.000e-01, eta: 9:12:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7752 2023-01-01 19:50:12,541 - mmcls - INFO - Epoch [26][2300/5005] lr: 1.000e-01, eta: 9:12:46, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7799 2023-01-01 19:50:21,483 - mmcls - INFO - Epoch [26][2400/5005] lr: 1.000e-01, eta: 9:12:38, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7852 2023-01-01 19:50:30,109 - mmcls - INFO - Epoch [26][2500/5005] lr: 1.000e-01, eta: 9:12:28, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.7790 2023-01-01 19:50:38,838 - mmcls - INFO - Epoch [26][2600/5005] lr: 1.000e-01, eta: 9:12:19, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.7522 2023-01-01 19:50:47,919 - mmcls - INFO - Epoch [26][2700/5005] lr: 1.000e-01, eta: 9:12:10, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.7829 2023-01-01 19:50:56,825 - mmcls - INFO - Epoch [26][2800/5005] lr: 1.000e-01, eta: 9:12:01, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.8025 2023-01-01 19:51:05,522 - mmcls - INFO - Epoch [26][2900/5005] lr: 1.000e-01, eta: 9:11:52, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.8085 2023-01-01 19:51:14,360 - mmcls - INFO - Epoch [26][3000/5005] lr: 1.000e-01, eta: 9:11:43, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.7846 2023-01-01 19:51:23,238 - mmcls - INFO - Epoch [26][3100/5005] lr: 1.000e-01, eta: 9:11:34, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.7956 2023-01-01 19:51:32,179 - mmcls - INFO - Epoch [26][3200/5005] lr: 1.000e-01, eta: 9:11:25, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7814 2023-01-01 19:51:40,700 - mmcls - INFO - Epoch [26][3300/5005] lr: 1.000e-01, eta: 9:11:15, time: 0.085, data_time: 0.014, memory: 1224, loss: 2.7840 2023-01-01 19:51:49,491 - mmcls - INFO - Epoch [26][3400/5005] lr: 1.000e-01, eta: 9:11:06, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7754 2023-01-01 19:51:58,447 - mmcls - INFO - Epoch [26][3500/5005] lr: 1.000e-01, eta: 9:10:57, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7374 2023-01-01 19:52:07,168 - mmcls - INFO - Epoch [26][3600/5005] lr: 1.000e-01, eta: 9:10:48, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.7622 2023-01-01 19:52:15,726 - mmcls - INFO - Epoch [26][3700/5005] lr: 1.000e-01, eta: 9:10:38, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7688 2023-01-01 19:52:24,680 - mmcls - INFO - Epoch [26][3800/5005] lr: 1.000e-01, eta: 9:10:30, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7714 2023-01-01 19:52:33,529 - mmcls - INFO - Epoch [26][3900/5005] lr: 1.000e-01, eta: 9:10:21, time: 0.089, data_time: 0.000, memory: 1224, loss: 2.7733 2023-01-01 19:52:42,235 - mmcls - INFO - Epoch [26][4000/5005] lr: 1.000e-01, eta: 9:10:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7743 2023-01-01 19:52:51,044 - mmcls - INFO - Epoch [26][4100/5005] lr: 1.000e-01, eta: 9:10:02, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8079 2023-01-01 19:52:59,728 - mmcls - INFO - Epoch [26][4200/5005] lr: 1.000e-01, eta: 9:09:52, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7664 2023-01-01 19:53:08,602 - mmcls - INFO - Epoch [26][4300/5005] lr: 1.000e-01, eta: 9:09:44, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.7967 2023-01-01 19:53:17,435 - mmcls - INFO - Epoch [26][4400/5005] lr: 1.000e-01, eta: 9:09:35, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7679 2023-01-01 19:53:26,327 - mmcls - INFO - Epoch [26][4500/5005] lr: 1.000e-01, eta: 9:09:26, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7893 2023-01-01 19:53:35,167 - mmcls - INFO - Epoch [26][4600/5005] lr: 1.000e-01, eta: 9:09:17, time: 0.088, data_time: 0.030, memory: 1224, loss: 2.7883 2023-01-01 19:53:43,816 - mmcls - INFO - Epoch [26][4700/5005] lr: 1.000e-01, eta: 9:09:07, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.8162 2023-01-01 19:53:52,615 - mmcls - INFO - Epoch [26][4800/5005] lr: 1.000e-01, eta: 9:08:58, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.7702 2023-01-01 19:54:01,135 - mmcls - INFO - Epoch [26][4900/5005] lr: 1.000e-01, eta: 9:08:48, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.7672 2023-01-01 19:54:09,766 - mmcls - INFO - Epoch [26][5000/5005] lr: 1.000e-01, eta: 9:08:38, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.7781 2023-01-01 19:54:32,762 - mmcls - INFO - Epoch(val) [26][196] accuracy_top-1: 41.7900, accuracy_top-5: 65.7740 2023-01-01 19:54:43,944 - mmcls - INFO - Epoch [27][100/5005] lr: 1.000e-01, eta: 9:08:34, time: 0.112, data_time: 0.051, memory: 1224, loss: 2.7176 2023-01-01 19:54:52,773 - mmcls - INFO - Epoch [27][200/5005] lr: 1.000e-01, eta: 9:08:25, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.6871 2023-01-01 19:55:01,697 - mmcls - INFO - Epoch [27][300/5005] lr: 1.000e-01, eta: 9:08:16, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.7214 2023-01-01 19:55:10,914 - mmcls - INFO - Epoch [27][400/5005] lr: 1.000e-01, eta: 9:08:08, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.7323 2023-01-01 19:55:19,596 - mmcls - INFO - Epoch [27][500/5005] lr: 1.000e-01, eta: 9:07:59, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.7587 2023-01-01 19:55:28,221 - mmcls - INFO - Epoch [27][600/5005] lr: 1.000e-01, eta: 9:07:49, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.7364 2023-01-01 19:55:37,046 - mmcls - INFO - Epoch [27][700/5005] lr: 1.000e-01, eta: 9:07:40, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.7271 2023-01-01 19:55:45,913 - mmcls - INFO - Epoch [27][800/5005] lr: 1.000e-01, eta: 9:07:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7174 2023-01-01 19:55:54,914 - mmcls - INFO - Epoch [27][900/5005] lr: 1.000e-01, eta: 9:07:23, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.7439 2023-01-01 19:56:03,685 - mmcls - INFO - Epoch [27][1000/5005] lr: 1.000e-01, eta: 9:07:13, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7257 2023-01-01 19:56:12,727 - mmcls - INFO - Epoch [27][1100/5005] lr: 1.000e-01, eta: 9:07:05, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.7725 2023-01-01 19:56:22,231 - mmcls - INFO - Epoch [27][1200/5005] lr: 1.000e-01, eta: 9:06:58, time: 0.095, data_time: 0.008, memory: 1224, loss: 2.7633 2023-01-01 19:56:31,068 - mmcls - INFO - Epoch [27][1300/5005] lr: 1.000e-01, eta: 9:06:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7606 2023-01-01 19:56:39,575 - mmcls - INFO - Epoch [27][1400/5005] lr: 1.000e-01, eta: 9:06:39, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.7945 2023-01-01 19:56:48,249 - mmcls - INFO - Epoch [27][1500/5005] lr: 1.000e-01, eta: 9:06:29, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7665 2023-01-01 19:56:56,924 - mmcls - INFO - Epoch [27][1600/5005] lr: 1.000e-01, eta: 9:06:20, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7645 2023-01-01 19:57:05,652 - mmcls - INFO - Epoch [27][1700/5005] lr: 1.000e-01, eta: 9:06:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7569 2023-01-01 19:57:14,501 - mmcls - INFO - Epoch [27][1800/5005] lr: 1.000e-01, eta: 9:06:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7325 2023-01-01 19:57:23,249 - mmcls - INFO - Epoch [27][1900/5005] lr: 1.000e-01, eta: 9:05:52, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.7507 2023-01-01 19:57:32,102 - mmcls - INFO - Epoch [27][2000/5005] lr: 1.000e-01, eta: 9:05:43, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.7675 2023-01-01 19:57:40,620 - mmcls - INFO - Epoch [27][2100/5005] lr: 1.000e-01, eta: 9:05:33, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.7827 2023-01-01 19:57:49,372 - mmcls - INFO - Epoch [27][2200/5005] lr: 1.000e-01, eta: 9:05:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7723 2023-01-01 19:57:58,098 - mmcls - INFO - Epoch [27][2300/5005] lr: 1.000e-01, eta: 9:05:15, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7588 2023-01-01 19:58:07,127 - mmcls - INFO - Epoch [27][2400/5005] lr: 1.000e-01, eta: 9:05:06, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.7637 2023-01-01 19:58:15,815 - mmcls - INFO - Epoch [27][2500/5005] lr: 1.000e-01, eta: 9:04:57, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.7904 2023-01-01 19:58:24,660 - mmcls - INFO - Epoch [27][2600/5005] lr: 1.000e-01, eta: 9:04:48, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.7860 2023-01-01 19:58:33,140 - mmcls - INFO - Epoch [27][2700/5005] lr: 1.000e-01, eta: 9:04:38, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7680 2023-01-01 19:58:42,038 - mmcls - INFO - Epoch [27][2800/5005] lr: 1.000e-01, eta: 9:04:29, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.8031 2023-01-01 19:58:50,859 - mmcls - INFO - Epoch [27][2900/5005] lr: 1.000e-01, eta: 9:04:20, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.7935 2023-01-01 19:58:59,696 - mmcls - INFO - Epoch [27][3000/5005] lr: 1.000e-01, eta: 9:04:11, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.7772 2023-01-01 19:59:08,226 - mmcls - INFO - Epoch [27][3100/5005] lr: 1.000e-01, eta: 9:04:01, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.7737 2023-01-01 19:59:17,010 - mmcls - INFO - Epoch [27][3200/5005] lr: 1.000e-01, eta: 9:03:52, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7491 2023-01-01 19:59:25,599 - mmcls - INFO - Epoch [27][3300/5005] lr: 1.000e-01, eta: 9:03:42, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7319 2023-01-01 19:59:34,532 - mmcls - INFO - Epoch [27][3400/5005] lr: 1.000e-01, eta: 9:03:33, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.7490 2023-01-01 19:59:43,595 - mmcls - INFO - Epoch [27][3500/5005] lr: 1.000e-01, eta: 9:03:25, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.7564 2023-01-01 19:59:52,510 - mmcls - INFO - Epoch [27][3600/5005] lr: 1.000e-01, eta: 9:03:16, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7686 2023-01-01 20:00:01,236 - mmcls - INFO - Epoch [27][3700/5005] lr: 1.000e-01, eta: 9:03:07, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.8015 2023-01-01 20:00:09,947 - mmcls - INFO - Epoch [27][3800/5005] lr: 1.000e-01, eta: 9:02:57, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7945 2023-01-01 20:00:18,594 - mmcls - INFO - Epoch [27][3900/5005] lr: 1.000e-01, eta: 9:02:48, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.7905 2023-01-01 20:00:27,129 - mmcls - INFO - Epoch [27][4000/5005] lr: 1.000e-01, eta: 9:02:38, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7798 2023-01-01 20:00:36,001 - mmcls - INFO - Epoch [27][4100/5005] lr: 1.000e-01, eta: 9:02:29, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.7653 2023-01-01 20:00:45,293 - mmcls - INFO - Epoch [27][4200/5005] lr: 1.000e-01, eta: 9:02:21, time: 0.093, data_time: 0.019, memory: 1224, loss: 2.7978 2023-01-01 20:00:54,024 - mmcls - INFO - Epoch [27][4300/5005] lr: 1.000e-01, eta: 9:02:12, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7722 2023-01-01 20:01:02,820 - mmcls - INFO - Epoch [27][4400/5005] lr: 1.000e-01, eta: 9:02:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7811 2023-01-01 20:01:11,772 - mmcls - INFO - Epoch [27][4500/5005] lr: 1.000e-01, eta: 9:01:54, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7696 2023-01-01 20:01:20,764 - mmcls - INFO - Epoch [27][4600/5005] lr: 1.000e-01, eta: 9:01:45, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.7523 2023-01-01 20:01:29,683 - mmcls - INFO - Epoch [27][4700/5005] lr: 1.000e-01, eta: 9:01:37, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.7847 2023-01-01 20:01:38,332 - mmcls - INFO - Epoch [27][4800/5005] lr: 1.000e-01, eta: 9:01:27, time: 0.086, data_time: 0.024, memory: 1224, loss: 2.7706 2023-01-01 20:01:47,006 - mmcls - INFO - Epoch [27][4900/5005] lr: 1.000e-01, eta: 9:01:18, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7771 2023-01-01 20:01:55,835 - mmcls - INFO - Epoch [27][5000/5005] lr: 1.000e-01, eta: 9:01:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7912 2023-01-01 20:02:18,590 - mmcls - INFO - Epoch(val) [27][196] accuracy_top-1: 46.6480, accuracy_top-5: 72.1560 2023-01-01 20:02:29,471 - mmcls - INFO - Epoch [28][100/5005] lr: 1.000e-01, eta: 9:01:03, time: 0.109, data_time: 0.040, memory: 1224, loss: 2.7172 2023-01-01 20:02:38,397 - mmcls - INFO - Epoch [28][200/5005] lr: 1.000e-01, eta: 9:00:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7352 2023-01-01 20:02:47,104 - mmcls - INFO - Epoch [28][300/5005] lr: 1.000e-01, eta: 9:00:45, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7388 2023-01-01 20:02:55,641 - mmcls - INFO - Epoch [28][400/5005] lr: 1.000e-01, eta: 9:00:35, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.7009 2023-01-01 20:03:04,283 - mmcls - INFO - Epoch [28][500/5005] lr: 1.000e-01, eta: 9:00:26, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.7223 2023-01-01 20:03:13,120 - mmcls - INFO - Epoch [28][600/5005] lr: 1.000e-01, eta: 9:00:17, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7071 2023-01-01 20:03:21,788 - mmcls - INFO - Epoch [28][700/5005] lr: 1.000e-01, eta: 9:00:07, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7277 2023-01-01 20:03:30,669 - mmcls - INFO - Epoch [28][800/5005] lr: 1.000e-01, eta: 8:59:58, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.7619 2023-01-01 20:03:39,566 - mmcls - INFO - Epoch [28][900/5005] lr: 1.000e-01, eta: 8:59:49, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7439 2023-01-01 20:03:48,597 - mmcls - INFO - Epoch [28][1000/5005] lr: 1.000e-01, eta: 8:59:41, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7743 2023-01-01 20:03:57,306 - mmcls - INFO - Epoch [28][1100/5005] lr: 1.000e-01, eta: 8:59:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7485 2023-01-01 20:04:06,190 - mmcls - INFO - Epoch [28][1200/5005] lr: 1.000e-01, eta: 8:59:23, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7363 2023-01-01 20:04:14,998 - mmcls - INFO - Epoch [28][1300/5005] lr: 1.000e-01, eta: 8:59:14, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7374 2023-01-01 20:04:23,875 - mmcls - INFO - Epoch [28][1400/5005] lr: 1.000e-01, eta: 8:59:05, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.7404 2023-01-01 20:04:32,763 - mmcls - INFO - Epoch [28][1500/5005] lr: 1.000e-01, eta: 8:58:56, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.7693 2023-01-01 20:04:41,149 - mmcls - INFO - Epoch [28][1600/5005] lr: 1.000e-01, eta: 8:58:46, time: 0.084, data_time: 0.005, memory: 1224, loss: 2.7168 2023-01-01 20:04:50,141 - mmcls - INFO - Epoch [28][1700/5005] lr: 1.000e-01, eta: 8:58:37, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.7724 2023-01-01 20:04:59,054 - mmcls - INFO - Epoch [28][1800/5005] lr: 1.000e-01, eta: 8:58:28, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.7378 2023-01-01 20:05:07,600 - mmcls - INFO - Epoch [28][1900/5005] lr: 1.000e-01, eta: 8:58:18, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7347 2023-01-01 20:05:16,366 - mmcls - INFO - Epoch [28][2000/5005] lr: 1.000e-01, eta: 8:58:09, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.7503 2023-01-01 20:05:25,335 - mmcls - INFO - Epoch [28][2100/5005] lr: 1.000e-01, eta: 8:58:00, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.7751 2023-01-01 20:05:34,043 - mmcls - INFO - Epoch [28][2200/5005] lr: 1.000e-01, eta: 8:57:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7731 2023-01-01 20:05:42,878 - mmcls - INFO - Epoch [28][2300/5005] lr: 1.000e-01, eta: 8:57:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7701 2023-01-01 20:05:51,730 - mmcls - INFO - Epoch [28][2400/5005] lr: 1.000e-01, eta: 8:57:33, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7400 2023-01-01 20:06:00,396 - mmcls - INFO - Epoch [28][2500/5005] lr: 1.000e-01, eta: 8:57:24, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.7372 2023-01-01 20:06:09,190 - mmcls - INFO - Epoch [28][2600/5005] lr: 1.000e-01, eta: 8:57:15, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7703 2023-01-01 20:06:18,309 - mmcls - INFO - Epoch [28][2700/5005] lr: 1.000e-01, eta: 8:57:06, time: 0.091, data_time: 0.025, memory: 1224, loss: 2.7663 2023-01-01 20:06:27,208 - mmcls - INFO - Epoch [28][2800/5005] lr: 1.000e-01, eta: 8:56:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7655 2023-01-01 20:06:35,800 - mmcls - INFO - Epoch [28][2900/5005] lr: 1.000e-01, eta: 8:56:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7733 2023-01-01 20:06:45,012 - mmcls - INFO - Epoch [28][3000/5005] lr: 1.000e-01, eta: 8:56:40, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.7598 2023-01-01 20:06:53,743 - mmcls - INFO - Epoch [28][3100/5005] lr: 1.000e-01, eta: 8:56:30, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7615 2023-01-01 20:07:02,912 - mmcls - INFO - Epoch [28][3200/5005] lr: 1.000e-01, eta: 8:56:22, time: 0.092, data_time: 0.037, memory: 1224, loss: 2.8076 2023-01-01 20:07:11,461 - mmcls - INFO - Epoch [28][3300/5005] lr: 1.000e-01, eta: 8:56:12, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.7913 2023-01-01 20:07:20,306 - mmcls - INFO - Epoch [28][3400/5005] lr: 1.000e-01, eta: 8:56:04, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.7638 2023-01-01 20:07:28,722 - mmcls - INFO - Epoch [28][3500/5005] lr: 1.000e-01, eta: 8:55:53, time: 0.084, data_time: 0.029, memory: 1224, loss: 2.7822 2023-01-01 20:07:37,952 - mmcls - INFO - Epoch [28][3600/5005] lr: 1.000e-01, eta: 8:55:45, time: 0.092, data_time: 0.015, memory: 1224, loss: 2.7877 2023-01-01 20:07:47,475 - mmcls - INFO - Epoch [28][3700/5005] lr: 1.000e-01, eta: 8:55:38, time: 0.095, data_time: 0.014, memory: 1224, loss: 2.7793 2023-01-01 20:07:56,018 - mmcls - INFO - Epoch [28][3800/5005] lr: 1.000e-01, eta: 8:55:28, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.7544 2023-01-01 20:08:05,199 - mmcls - INFO - Epoch [28][3900/5005] lr: 1.000e-01, eta: 8:55:20, time: 0.092, data_time: 0.010, memory: 1224, loss: 2.7632 2023-01-01 20:08:14,179 - mmcls - INFO - Epoch [28][4000/5005] lr: 1.000e-01, eta: 8:55:12, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7644 2023-01-01 20:08:22,875 - mmcls - INFO - Epoch [28][4100/5005] lr: 1.000e-01, eta: 8:55:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7613 2023-01-01 20:08:31,619 - mmcls - INFO - Epoch [28][4200/5005] lr: 1.000e-01, eta: 8:54:53, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.7672 2023-01-01 20:08:40,189 - mmcls - INFO - Epoch [28][4300/5005] lr: 1.000e-01, eta: 8:54:43, time: 0.086, data_time: 0.025, memory: 1224, loss: 2.7507 2023-01-01 20:08:48,697 - mmcls - INFO - Epoch [28][4400/5005] lr: 1.000e-01, eta: 8:54:33, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.7728 2023-01-01 20:08:57,411 - mmcls - INFO - Epoch [28][4500/5005] lr: 1.000e-01, eta: 8:54:24, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7857 2023-01-01 20:09:06,132 - mmcls - INFO - Epoch [28][4600/5005] lr: 1.000e-01, eta: 8:54:15, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.7912 2023-01-01 20:09:15,199 - mmcls - INFO - Epoch [28][4700/5005] lr: 1.000e-01, eta: 8:54:06, time: 0.091, data_time: 0.024, memory: 1224, loss: 2.7575 2023-01-01 20:09:23,979 - mmcls - INFO - Epoch [28][4800/5005] lr: 1.000e-01, eta: 8:53:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7620 2023-01-01 20:09:33,009 - mmcls - INFO - Epoch [28][4900/5005] lr: 1.000e-01, eta: 8:53:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7796 2023-01-01 20:09:41,939 - mmcls - INFO - Epoch [28][5000/5005] lr: 1.000e-01, eta: 8:53:40, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.7677 2023-01-01 20:10:04,552 - mmcls - INFO - Epoch(val) [28][196] accuracy_top-1: 46.0460, accuracy_top-5: 71.5160 2023-01-01 20:10:15,453 - mmcls - INFO - Epoch [29][100/5005] lr: 1.000e-01, eta: 8:53:35, time: 0.109, data_time: 0.045, memory: 1224, loss: 2.7153 2023-01-01 20:10:24,538 - mmcls - INFO - Epoch [29][200/5005] lr: 1.000e-01, eta: 8:53:26, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.6954 2023-01-01 20:10:33,496 - mmcls - INFO - Epoch [29][300/5005] lr: 1.000e-01, eta: 8:53:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7148 2023-01-01 20:10:42,272 - mmcls - INFO - Epoch [29][400/5005] lr: 1.000e-01, eta: 8:53:08, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.7166 2023-01-01 20:10:51,015 - mmcls - INFO - Epoch [29][500/5005] lr: 1.000e-01, eta: 8:52:59, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7481 2023-01-01 20:11:00,028 - mmcls - INFO - Epoch [29][600/5005] lr: 1.000e-01, eta: 8:52:50, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.7401 2023-01-01 20:11:08,826 - mmcls - INFO - Epoch [29][700/5005] lr: 1.000e-01, eta: 8:52:41, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.7554 2023-01-01 20:11:17,388 - mmcls - INFO - Epoch [29][800/5005] lr: 1.000e-01, eta: 8:52:32, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.7556 2023-01-01 20:11:26,109 - mmcls - INFO - Epoch [29][900/5005] lr: 1.000e-01, eta: 8:52:22, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.7336 2023-01-01 20:11:34,943 - mmcls - INFO - Epoch [29][1000/5005] lr: 1.000e-01, eta: 8:52:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7336 2023-01-01 20:11:43,770 - mmcls - INFO - Epoch [29][1100/5005] lr: 1.000e-01, eta: 8:52:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7126 2023-01-01 20:11:52,561 - mmcls - INFO - Epoch [29][1200/5005] lr: 1.000e-01, eta: 8:51:55, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7351 2023-01-01 20:12:01,507 - mmcls - INFO - Epoch [29][1300/5005] lr: 1.000e-01, eta: 8:51:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7185 2023-01-01 20:12:10,597 - mmcls - INFO - Epoch [29][1400/5005] lr: 1.000e-01, eta: 8:51:38, time: 0.091, data_time: 0.018, memory: 1224, loss: 2.7401 2023-01-01 20:12:19,623 - mmcls - INFO - Epoch [29][1500/5005] lr: 1.000e-01, eta: 8:51:29, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7606 2023-01-01 20:12:28,260 - mmcls - INFO - Epoch [29][1600/5005] lr: 1.000e-01, eta: 8:51:20, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.7631 2023-01-01 20:12:36,988 - mmcls - INFO - Epoch [29][1700/5005] lr: 1.000e-01, eta: 8:51:11, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.7478 2023-01-01 20:12:45,832 - mmcls - INFO - Epoch [29][1800/5005] lr: 1.000e-01, eta: 8:51:02, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7355 2023-01-01 20:12:54,804 - mmcls - INFO - Epoch [29][1900/5005] lr: 1.000e-01, eta: 8:50:53, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7555 2023-01-01 20:13:03,878 - mmcls - INFO - Epoch [29][2000/5005] lr: 1.000e-01, eta: 8:50:45, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.7437 2023-01-01 20:13:12,672 - mmcls - INFO - Epoch [29][2100/5005] lr: 1.000e-01, eta: 8:50:35, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7221 2023-01-01 20:13:21,666 - mmcls - INFO - Epoch [29][2200/5005] lr: 1.000e-01, eta: 8:50:27, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.7684 2023-01-01 20:13:30,524 - mmcls - INFO - Epoch [29][2300/5005] lr: 1.000e-01, eta: 8:50:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7748 2023-01-01 20:13:39,169 - mmcls - INFO - Epoch [29][2400/5005] lr: 1.000e-01, eta: 8:50:08, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.7237 2023-01-01 20:13:48,152 - mmcls - INFO - Epoch [29][2500/5005] lr: 1.000e-01, eta: 8:50:00, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.7589 2023-01-01 20:13:56,809 - mmcls - INFO - Epoch [29][2600/5005] lr: 1.000e-01, eta: 8:49:50, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.7717 2023-01-01 20:14:06,180 - mmcls - INFO - Epoch [29][2700/5005] lr: 1.000e-01, eta: 8:49:43, time: 0.094, data_time: 0.004, memory: 1224, loss: 2.8122 2023-01-01 20:14:14,903 - mmcls - INFO - Epoch [29][2800/5005] lr: 1.000e-01, eta: 8:49:33, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.7647 2023-01-01 20:14:23,632 - mmcls - INFO - Epoch [29][2900/5005] lr: 1.000e-01, eta: 8:49:24, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.7618 2023-01-01 20:14:32,148 - mmcls - INFO - Epoch [29][3000/5005] lr: 1.000e-01, eta: 8:49:14, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.7527 2023-01-01 20:14:40,825 - mmcls - INFO - Epoch [29][3100/5005] lr: 1.000e-01, eta: 8:49:05, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.7685 2023-01-01 20:14:49,518 - mmcls - INFO - Epoch [29][3200/5005] lr: 1.000e-01, eta: 8:48:55, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7895 2023-01-01 20:14:58,385 - mmcls - INFO - Epoch [29][3300/5005] lr: 1.000e-01, eta: 8:48:47, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7705 2023-01-01 20:15:07,003 - mmcls - INFO - Epoch [29][3400/5005] lr: 1.000e-01, eta: 8:48:37, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.7782 2023-01-01 20:15:15,610 - mmcls - INFO - Epoch [29][3500/5005] lr: 1.000e-01, eta: 8:48:27, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.7496 2023-01-01 20:15:24,432 - mmcls - INFO - Epoch [29][3600/5005] lr: 1.000e-01, eta: 8:48:18, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7691 2023-01-01 20:15:33,359 - mmcls - INFO - Epoch [29][3700/5005] lr: 1.000e-01, eta: 8:48:10, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.7721 2023-01-01 20:15:41,920 - mmcls - INFO - Epoch [29][3800/5005] lr: 1.000e-01, eta: 8:48:00, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.7441 2023-01-01 20:15:50,633 - mmcls - INFO - Epoch [29][3900/5005] lr: 1.000e-01, eta: 8:47:51, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.7536 2023-01-01 20:15:59,384 - mmcls - INFO - Epoch [29][4000/5005] lr: 1.000e-01, eta: 8:47:41, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7585 2023-01-01 20:16:08,105 - mmcls - INFO - Epoch [29][4100/5005] lr: 1.000e-01, eta: 8:47:32, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7612 2023-01-01 20:16:16,901 - mmcls - INFO - Epoch [29][4200/5005] lr: 1.000e-01, eta: 8:47:23, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.7578 2023-01-01 20:16:25,730 - mmcls - INFO - Epoch [29][4300/5005] lr: 1.000e-01, eta: 8:47:14, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.7727 2023-01-01 20:16:34,473 - mmcls - INFO - Epoch [29][4400/5005] lr: 1.000e-01, eta: 8:47:05, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7902 2023-01-01 20:16:43,189 - mmcls - INFO - Epoch [29][4500/5005] lr: 1.000e-01, eta: 8:46:55, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.7702 2023-01-01 20:16:52,106 - mmcls - INFO - Epoch [29][4600/5005] lr: 1.000e-01, eta: 8:46:47, time: 0.089, data_time: 0.025, memory: 1224, loss: 2.7924 2023-01-01 20:17:01,253 - mmcls - INFO - Epoch [29][4700/5005] lr: 1.000e-01, eta: 8:46:38, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.7346 2023-01-01 20:17:10,187 - mmcls - INFO - Epoch [29][4800/5005] lr: 1.000e-01, eta: 8:46:30, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.7706 2023-01-01 20:17:19,209 - mmcls - INFO - Epoch [29][4900/5005] lr: 1.000e-01, eta: 8:46:21, time: 0.090, data_time: 0.020, memory: 1224, loss: 2.8165 2023-01-01 20:17:28,093 - mmcls - INFO - Epoch [29][5000/5005] lr: 1.000e-01, eta: 8:46:12, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.7818 2023-01-01 20:17:50,722 - mmcls - INFO - Epoch(val) [29][196] accuracy_top-1: 47.2720, accuracy_top-5: 72.7520 2023-01-01 20:18:01,820 - mmcls - INFO - Epoch [30][100/5005] lr: 1.000e-01, eta: 8:46:07, time: 0.111, data_time: 0.050, memory: 1224, loss: 2.7071 2023-01-01 20:18:10,605 - mmcls - INFO - Epoch [30][200/5005] lr: 1.000e-01, eta: 8:45:58, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.7325 2023-01-01 20:18:19,215 - mmcls - INFO - Epoch [30][300/5005] lr: 1.000e-01, eta: 8:45:48, time: 0.086, data_time: 0.029, memory: 1224, loss: 2.7330 2023-01-01 20:18:28,339 - mmcls - INFO - Epoch [30][400/5005] lr: 1.000e-01, eta: 8:45:40, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.7380 2023-01-01 20:18:37,208 - mmcls - INFO - Epoch [30][500/5005] lr: 1.000e-01, eta: 8:45:31, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.7402 2023-01-01 20:18:45,756 - mmcls - INFO - Epoch [30][600/5005] lr: 1.000e-01, eta: 8:45:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.7143 2023-01-01 20:18:54,490 - mmcls - INFO - Epoch [30][700/5005] lr: 1.000e-01, eta: 8:45:12, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.7246 2023-01-01 20:19:03,090 - mmcls - INFO - Epoch [30][800/5005] lr: 1.000e-01, eta: 8:45:03, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7671 2023-01-01 20:19:12,188 - mmcls - INFO - Epoch [30][900/5005] lr: 1.000e-01, eta: 8:44:54, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.7125 2023-01-01 20:19:20,864 - mmcls - INFO - Epoch [30][1000/5005] lr: 1.000e-01, eta: 8:44:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7587 2023-01-01 20:19:29,598 - mmcls - INFO - Epoch [30][1100/5005] lr: 1.000e-01, eta: 8:44:36, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7362 2023-01-01 20:19:38,217 - mmcls - INFO - Epoch [30][1200/5005] lr: 1.000e-01, eta: 8:44:26, time: 0.086, data_time: 0.016, memory: 1224, loss: 2.7383 2023-01-01 20:19:47,300 - mmcls - INFO - Epoch [30][1300/5005] lr: 1.000e-01, eta: 8:44:18, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.7227 2023-01-01 20:19:56,019 - mmcls - INFO - Epoch [30][1400/5005] lr: 1.000e-01, eta: 8:44:08, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7600 2023-01-01 20:20:04,756 - mmcls - INFO - Epoch [30][1500/5005] lr: 1.000e-01, eta: 8:43:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7282 2023-01-01 20:20:13,724 - mmcls - INFO - Epoch [30][1600/5005] lr: 1.000e-01, eta: 8:43:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7388 2023-01-01 20:20:22,700 - mmcls - INFO - Epoch [30][1700/5005] lr: 1.000e-01, eta: 8:43:42, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.7558 2023-01-01 20:20:31,726 - mmcls - INFO - Epoch [30][1800/5005] lr: 1.000e-01, eta: 8:43:33, time: 0.090, data_time: 0.021, memory: 1224, loss: 2.7489 2023-01-01 20:20:41,094 - mmcls - INFO - Epoch [30][1900/5005] lr: 1.000e-01, eta: 8:43:25, time: 0.094, data_time: 0.034, memory: 1224, loss: 2.7420 2023-01-01 20:20:50,176 - mmcls - INFO - Epoch [30][2000/5005] lr: 1.000e-01, eta: 8:43:17, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.7453 2023-01-01 20:20:59,021 - mmcls - INFO - Epoch [30][2100/5005] lr: 1.000e-01, eta: 8:43:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7445 2023-01-01 20:21:07,576 - mmcls - INFO - Epoch [30][2200/5005] lr: 1.000e-01, eta: 8:42:58, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7967 2023-01-01 20:21:16,688 - mmcls - INFO - Epoch [30][2300/5005] lr: 1.000e-01, eta: 8:42:50, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.7671 2023-01-01 20:21:25,292 - mmcls - INFO - Epoch [30][2400/5005] lr: 1.000e-01, eta: 8:42:40, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.7645 2023-01-01 20:21:34,010 - mmcls - INFO - Epoch [30][2500/5005] lr: 1.000e-01, eta: 8:42:31, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.7657 2023-01-01 20:21:42,718 - mmcls - INFO - Epoch [30][2600/5005] lr: 1.000e-01, eta: 8:42:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7422 2023-01-01 20:21:51,327 - mmcls - INFO - Epoch [30][2700/5005] lr: 1.000e-01, eta: 8:42:12, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.7602 2023-01-01 20:22:00,066 - mmcls - INFO - Epoch [30][2800/5005] lr: 1.000e-01, eta: 8:42:03, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.7501 2023-01-01 20:22:08,916 - mmcls - INFO - Epoch [30][2900/5005] lr: 1.000e-01, eta: 8:41:54, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.7615 2023-01-01 20:22:17,347 - mmcls - INFO - Epoch [30][3000/5005] lr: 1.000e-01, eta: 8:41:44, time: 0.084, data_time: 0.012, memory: 1224, loss: 2.7255 2023-01-01 20:22:26,330 - mmcls - INFO - Epoch [30][3100/5005] lr: 1.000e-01, eta: 8:41:35, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.7568 2023-01-01 20:22:35,577 - mmcls - INFO - Epoch [30][3200/5005] lr: 1.000e-01, eta: 8:41:27, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.7499 2023-01-01 20:22:44,594 - mmcls - INFO - Epoch [30][3300/5005] lr: 1.000e-01, eta: 8:41:19, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.7649 2023-01-01 20:22:53,453 - mmcls - INFO - Epoch [30][3400/5005] lr: 1.000e-01, eta: 8:41:10, time: 0.088, data_time: 0.038, memory: 1224, loss: 2.7258 2023-01-01 20:23:02,300 - mmcls - INFO - Epoch [30][3500/5005] lr: 1.000e-01, eta: 8:41:01, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7685 2023-01-01 20:23:10,837 - mmcls - INFO - Epoch [30][3600/5005] lr: 1.000e-01, eta: 8:40:51, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.8038 2023-01-01 20:23:19,708 - mmcls - INFO - Epoch [30][3700/5005] lr: 1.000e-01, eta: 8:40:42, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.7720 2023-01-01 20:23:28,298 - mmcls - INFO - Epoch [30][3800/5005] lr: 1.000e-01, eta: 8:40:33, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.7529 2023-01-01 20:23:37,471 - mmcls - INFO - Epoch [30][3900/5005] lr: 1.000e-01, eta: 8:40:25, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.7794 2023-01-01 20:23:46,132 - mmcls - INFO - Epoch [30][4000/5005] lr: 1.000e-01, eta: 8:40:15, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.7609 2023-01-01 20:23:55,140 - mmcls - INFO - Epoch [30][4100/5005] lr: 1.000e-01, eta: 8:40:07, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.7684 2023-01-01 20:24:03,797 - mmcls - INFO - Epoch [30][4200/5005] lr: 1.000e-01, eta: 8:39:57, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.7519 2023-01-01 20:24:12,673 - mmcls - INFO - Epoch [30][4300/5005] lr: 1.000e-01, eta: 8:39:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7702 2023-01-01 20:24:21,970 - mmcls - INFO - Epoch [30][4400/5005] lr: 1.000e-01, eta: 8:39:40, time: 0.093, data_time: 0.039, memory: 1224, loss: 2.7612 2023-01-01 20:24:30,724 - mmcls - INFO - Epoch [30][4500/5005] lr: 1.000e-01, eta: 8:39:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.7538 2023-01-01 20:24:39,602 - mmcls - INFO - Epoch [30][4600/5005] lr: 1.000e-01, eta: 8:39:22, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7690 2023-01-01 20:24:48,131 - mmcls - INFO - Epoch [30][4700/5005] lr: 1.000e-01, eta: 8:39:12, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.8017 2023-01-01 20:24:56,759 - mmcls - INFO - Epoch [30][4800/5005] lr: 1.000e-01, eta: 8:39:03, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.7774 2023-01-01 20:25:05,817 - mmcls - INFO - Epoch [30][4900/5005] lr: 1.000e-01, eta: 8:38:55, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.7521 2023-01-01 20:25:14,706 - mmcls - INFO - Epoch [30][5000/5005] lr: 1.000e-01, eta: 8:38:46, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.7527 2023-01-01 20:25:37,633 - mmcls - INFO - Epoch(val) [30][196] accuracy_top-1: 46.1860, accuracy_top-5: 71.7180 2023-01-01 20:25:48,535 - mmcls - INFO - Epoch [31][100/5005] lr: 1.000e-02, eta: 8:38:40, time: 0.109, data_time: 0.050, memory: 1224, loss: 2.5633 2023-01-01 20:25:57,317 - mmcls - INFO - Epoch [31][200/5005] lr: 1.000e-02, eta: 8:38:31, time: 0.088, data_time: 0.024, memory: 1224, loss: 2.4726 2023-01-01 20:26:06,199 - mmcls - INFO - Epoch [31][300/5005] lr: 1.000e-02, eta: 8:38:22, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4180 2023-01-01 20:26:15,137 - mmcls - INFO - Epoch [31][400/5005] lr: 1.000e-02, eta: 8:38:13, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.3921 2023-01-01 20:26:23,703 - mmcls - INFO - Epoch [31][500/5005] lr: 1.000e-02, eta: 8:38:04, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.3569 2023-01-01 20:26:32,974 - mmcls - INFO - Epoch [31][600/5005] lr: 1.000e-02, eta: 8:37:56, time: 0.093, data_time: 0.010, memory: 1224, loss: 2.3560 2023-01-01 20:26:41,739 - mmcls - INFO - Epoch [31][700/5005] lr: 1.000e-02, eta: 8:37:46, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3546 2023-01-01 20:26:50,449 - mmcls - INFO - Epoch [31][800/5005] lr: 1.000e-02, eta: 8:37:37, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.3425 2023-01-01 20:26:59,714 - mmcls - INFO - Epoch [31][900/5005] lr: 1.000e-02, eta: 8:37:29, time: 0.093, data_time: 0.006, memory: 1224, loss: 2.3273 2023-01-01 20:27:08,247 - mmcls - INFO - Epoch [31][1000/5005] lr: 1.000e-02, eta: 8:37:19, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.3128 2023-01-01 20:27:17,523 - mmcls - INFO - Epoch [31][1100/5005] lr: 1.000e-02, eta: 8:37:11, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.2898 2023-01-01 20:27:26,430 - mmcls - INFO - Epoch [31][1200/5005] lr: 1.000e-02, eta: 8:37:03, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.3238 2023-01-01 20:27:35,443 - mmcls - INFO - Epoch [31][1300/5005] lr: 1.000e-02, eta: 8:36:54, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.2919 2023-01-01 20:27:44,318 - mmcls - INFO - Epoch [31][1400/5005] lr: 1.000e-02, eta: 8:36:45, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.2855 2023-01-01 20:27:52,824 - mmcls - INFO - Epoch [31][1500/5005] lr: 1.000e-02, eta: 8:36:35, time: 0.085, data_time: 0.013, memory: 1224, loss: 2.2829 2023-01-01 20:28:01,342 - mmcls - INFO - Epoch [31][1600/5005] lr: 1.000e-02, eta: 8:36:26, time: 0.085, data_time: 0.019, memory: 1224, loss: 2.2696 2023-01-01 20:28:10,610 - mmcls - INFO - Epoch [31][1700/5005] lr: 1.000e-02, eta: 8:36:18, time: 0.093, data_time: 0.003, memory: 1224, loss: 2.2579 2023-01-01 20:28:19,418 - mmcls - INFO - Epoch [31][1800/5005] lr: 1.000e-02, eta: 8:36:08, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.2680 2023-01-01 20:28:28,116 - mmcls - INFO - Epoch [31][1900/5005] lr: 1.000e-02, eta: 8:35:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.2580 2023-01-01 20:28:37,218 - mmcls - INFO - Epoch [31][2000/5005] lr: 1.000e-02, eta: 8:35:51, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.2433 2023-01-01 20:28:46,121 - mmcls - INFO - Epoch [31][2100/5005] lr: 1.000e-02, eta: 8:35:42, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.2593 2023-01-01 20:28:55,100 - mmcls - INFO - Epoch [31][2200/5005] lr: 1.000e-02, eta: 8:35:33, time: 0.090, data_time: 0.000, memory: 1224, loss: 2.2512 2023-01-01 20:29:04,194 - mmcls - INFO - Epoch [31][2300/5005] lr: 1.000e-02, eta: 8:35:25, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.2417 2023-01-01 20:29:13,027 - mmcls - INFO - Epoch [31][2400/5005] lr: 1.000e-02, eta: 8:35:16, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.2109 2023-01-01 20:29:21,994 - mmcls - INFO - Epoch [31][2500/5005] lr: 1.000e-02, eta: 8:35:07, time: 0.090, data_time: 0.021, memory: 1224, loss: 2.2313 2023-01-01 20:29:30,906 - mmcls - INFO - Epoch [31][2600/5005] lr: 1.000e-02, eta: 8:34:58, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.2176 2023-01-01 20:29:39,623 - mmcls - INFO - Epoch [31][2700/5005] lr: 1.000e-02, eta: 8:34:49, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.2505 2023-01-01 20:29:48,562 - mmcls - INFO - Epoch [31][2800/5005] lr: 1.000e-02, eta: 8:34:40, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.2186 2023-01-01 20:29:57,364 - mmcls - INFO - Epoch [31][2900/5005] lr: 1.000e-02, eta: 8:34:31, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.2366 2023-01-01 20:30:06,104 - mmcls - INFO - Epoch [31][3000/5005] lr: 1.000e-02, eta: 8:34:22, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.2078 2023-01-01 20:30:14,540 - mmcls - INFO - Epoch [31][3100/5005] lr: 1.000e-02, eta: 8:34:12, time: 0.084, data_time: 0.010, memory: 1224, loss: 2.2215 2023-01-01 20:30:23,298 - mmcls - INFO - Epoch [31][3200/5005] lr: 1.000e-02, eta: 8:34:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.2162 2023-01-01 20:30:32,155 - mmcls - INFO - Epoch [31][3300/5005] lr: 1.000e-02, eta: 8:33:54, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.2413 2023-01-01 20:30:41,156 - mmcls - INFO - Epoch [31][3400/5005] lr: 1.000e-02, eta: 8:33:45, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.2208 2023-01-01 20:30:49,891 - mmcls - INFO - Epoch [31][3500/5005] lr: 1.000e-02, eta: 8:33:36, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.2211 2023-01-01 20:30:58,913 - mmcls - INFO - Epoch [31][3600/5005] lr: 1.000e-02, eta: 8:33:27, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.1944 2023-01-01 20:31:08,091 - mmcls - INFO - Epoch [31][3700/5005] lr: 1.000e-02, eta: 8:33:19, time: 0.092, data_time: 0.016, memory: 1224, loss: 2.2135 2023-01-01 20:31:17,057 - mmcls - INFO - Epoch [31][3800/5005] lr: 1.000e-02, eta: 8:33:11, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.2083 2023-01-01 20:31:25,896 - mmcls - INFO - Epoch [31][3900/5005] lr: 1.000e-02, eta: 8:33:02, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.2001 2023-01-01 20:31:34,838 - mmcls - INFO - Epoch [31][4000/5005] lr: 1.000e-02, eta: 8:32:53, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.2040 2023-01-01 20:31:43,475 - mmcls - INFO - Epoch [31][4100/5005] lr: 1.000e-02, eta: 8:32:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.2026 2023-01-01 20:31:52,257 - mmcls - INFO - Epoch [31][4200/5005] lr: 1.000e-02, eta: 8:32:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.2226 2023-01-01 20:32:01,111 - mmcls - INFO - Epoch [31][4300/5005] lr: 1.000e-02, eta: 8:32:25, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.1889 2023-01-01 20:32:09,916 - mmcls - INFO - Epoch [31][4400/5005] lr: 1.000e-02, eta: 8:32:16, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.1868 2023-01-01 20:32:18,690 - mmcls - INFO - Epoch [31][4500/5005] lr: 1.000e-02, eta: 8:32:07, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.1913 2023-01-01 20:32:27,465 - mmcls - INFO - Epoch [31][4600/5005] lr: 1.000e-02, eta: 8:31:58, time: 0.088, data_time: 0.041, memory: 1224, loss: 2.1809 2023-01-01 20:32:36,043 - mmcls - INFO - Epoch [31][4700/5005] lr: 1.000e-02, eta: 8:31:48, time: 0.086, data_time: 0.016, memory: 1224, loss: 2.1870 2023-01-01 20:32:44,667 - mmcls - INFO - Epoch [31][4800/5005] lr: 1.000e-02, eta: 8:31:39, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.1807 2023-01-01 20:32:53,387 - mmcls - INFO - Epoch [31][4900/5005] lr: 1.000e-02, eta: 8:31:30, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.1588 2023-01-01 20:33:02,211 - mmcls - INFO - Epoch [31][5000/5005] lr: 1.000e-02, eta: 8:31:21, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.1699 2023-01-01 20:33:25,066 - mmcls - INFO - Epoch(val) [31][196] accuracy_top-1: 58.0100, accuracy_top-5: 80.6720 2023-01-01 20:33:35,943 - mmcls - INFO - Epoch [32][100/5005] lr: 1.000e-02, eta: 8:31:15, time: 0.109, data_time: 0.068, memory: 1224, loss: 2.1557 2023-01-01 20:33:44,714 - mmcls - INFO - Epoch [32][200/5005] lr: 1.000e-02, eta: 8:31:06, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.1271 2023-01-01 20:33:53,622 - mmcls - INFO - Epoch [32][300/5005] lr: 1.000e-02, eta: 8:30:57, time: 0.089, data_time: 0.031, memory: 1224, loss: 2.1291 2023-01-01 20:34:02,367 - mmcls - INFO - Epoch [32][400/5005] lr: 1.000e-02, eta: 8:30:48, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.1274 2023-01-01 20:34:11,121 - mmcls - INFO - Epoch [32][500/5005] lr: 1.000e-02, eta: 8:30:38, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.1331 2023-01-01 20:34:19,843 - mmcls - INFO - Epoch [32][600/5005] lr: 1.000e-02, eta: 8:30:29, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.1304 2023-01-01 20:34:28,567 - mmcls - INFO - Epoch [32][700/5005] lr: 1.000e-02, eta: 8:30:20, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.1168 2023-01-01 20:34:37,530 - mmcls - INFO - Epoch [32][800/5005] lr: 1.000e-02, eta: 8:30:11, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.1541 2023-01-01 20:34:46,220 - mmcls - INFO - Epoch [32][900/5005] lr: 1.000e-02, eta: 8:30:02, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.1220 2023-01-01 20:34:55,184 - mmcls - INFO - Epoch [32][1000/5005] lr: 1.000e-02, eta: 8:29:53, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.1375 2023-01-01 20:35:04,107 - mmcls - INFO - Epoch [32][1100/5005] lr: 1.000e-02, eta: 8:29:44, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.1422 2023-01-01 20:35:12,981 - mmcls - INFO - Epoch [32][1200/5005] lr: 1.000e-02, eta: 8:29:35, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.1191 2023-01-01 20:35:21,399 - mmcls - INFO - Epoch [32][1300/5005] lr: 1.000e-02, eta: 8:29:26, time: 0.084, data_time: 0.018, memory: 1224, loss: 2.1336 2023-01-01 20:35:30,273 - mmcls - INFO - Epoch [32][1400/5005] lr: 1.000e-02, eta: 8:29:17, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.1359 2023-01-01 20:35:38,970 - mmcls - INFO - Epoch [32][1500/5005] lr: 1.000e-02, eta: 8:29:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.1335 2023-01-01 20:35:47,653 - mmcls - INFO - Epoch [32][1600/5005] lr: 1.000e-02, eta: 8:28:58, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.1389 2023-01-01 20:35:56,338 - mmcls - INFO - Epoch [32][1700/5005] lr: 1.000e-02, eta: 8:28:49, time: 0.087, data_time: 0.036, memory: 1224, loss: 2.1490 2023-01-01 20:36:05,107 - mmcls - INFO - Epoch [32][1800/5005] lr: 1.000e-02, eta: 8:28:40, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.1379 2023-01-01 20:36:14,519 - mmcls - INFO - Epoch [32][1900/5005] lr: 1.000e-02, eta: 8:28:32, time: 0.094, data_time: 0.020, memory: 1224, loss: 2.1357 2023-01-01 20:36:23,198 - mmcls - INFO - Epoch [32][2000/5005] lr: 1.000e-02, eta: 8:28:22, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.1300 2023-01-01 20:36:32,046 - mmcls - INFO - Epoch [32][2100/5005] lr: 1.000e-02, eta: 8:28:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.1237 2023-01-01 20:36:40,652 - mmcls - INFO - Epoch [32][2200/5005] lr: 1.000e-02, eta: 8:28:04, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.1317 2023-01-01 20:36:49,685 - mmcls - INFO - Epoch [32][2300/5005] lr: 1.000e-02, eta: 8:27:55, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.1148 2023-01-01 20:36:58,496 - mmcls - INFO - Epoch [32][2400/5005] lr: 1.000e-02, eta: 8:27:46, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.1260 2023-01-01 20:37:07,373 - mmcls - INFO - Epoch [32][2500/5005] lr: 1.000e-02, eta: 8:27:37, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.1136 2023-01-01 20:37:16,060 - mmcls - INFO - Epoch [32][2600/5005] lr: 1.000e-02, eta: 8:27:28, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.1033 2023-01-01 20:37:24,920 - mmcls - INFO - Epoch [32][2700/5005] lr: 1.000e-02, eta: 8:27:19, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.1109 2023-01-01 20:37:33,700 - mmcls - INFO - Epoch [32][2800/5005] lr: 1.000e-02, eta: 8:27:10, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.1300 2023-01-01 20:37:42,561 - mmcls - INFO - Epoch [32][2900/5005] lr: 1.000e-02, eta: 8:27:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.1245 2023-01-01 20:37:51,074 - mmcls - INFO - Epoch [32][3000/5005] lr: 1.000e-02, eta: 8:26:52, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.1290 2023-01-01 20:38:00,130 - mmcls - INFO - Epoch [32][3100/5005] lr: 1.000e-02, eta: 8:26:43, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.1403 2023-01-01 20:38:08,883 - mmcls - INFO - Epoch [32][3200/5005] lr: 1.000e-02, eta: 8:26:34, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.1370 2023-01-01 20:38:17,927 - mmcls - INFO - Epoch [32][3300/5005] lr: 1.000e-02, eta: 8:26:25, time: 0.090, data_time: 0.025, memory: 1224, loss: 2.1081 2023-01-01 20:38:26,720 - mmcls - INFO - Epoch [32][3400/5005] lr: 1.000e-02, eta: 8:26:16, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.1218 2023-01-01 20:38:35,570 - mmcls - INFO - Epoch [32][3500/5005] lr: 1.000e-02, eta: 8:26:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.1176 2023-01-01 20:38:44,136 - mmcls - INFO - Epoch [32][3600/5005] lr: 1.000e-02, eta: 8:25:58, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.1267 2023-01-01 20:38:52,799 - mmcls - INFO - Epoch [32][3700/5005] lr: 1.000e-02, eta: 8:25:48, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.1455 2023-01-01 20:39:01,553 - mmcls - INFO - Epoch [32][3800/5005] lr: 1.000e-02, eta: 8:25:39, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.1200 2023-01-01 20:39:10,243 - mmcls - INFO - Epoch [32][3900/5005] lr: 1.000e-02, eta: 8:25:30, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.1015 2023-01-01 20:39:19,482 - mmcls - INFO - Epoch [32][4000/5005] lr: 1.000e-02, eta: 8:25:22, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.1197 2023-01-01 20:39:28,261 - mmcls - INFO - Epoch [32][4100/5005] lr: 1.000e-02, eta: 8:25:13, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.0802 2023-01-01 20:39:36,769 - mmcls - INFO - Epoch [32][4200/5005] lr: 1.000e-02, eta: 8:25:03, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.1146 2023-01-01 20:39:45,691 - mmcls - INFO - Epoch [32][4300/5005] lr: 1.000e-02, eta: 8:24:54, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.1044 2023-01-01 20:39:54,604 - mmcls - INFO - Epoch [32][4400/5005] lr: 1.000e-02, eta: 8:24:45, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.1220 2023-01-01 20:40:03,493 - mmcls - INFO - Epoch [32][4500/5005] lr: 1.000e-02, eta: 8:24:36, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.1024 2023-01-01 20:40:12,249 - mmcls - INFO - Epoch [32][4600/5005] lr: 1.000e-02, eta: 8:24:27, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0998 2023-01-01 20:40:21,104 - mmcls - INFO - Epoch [32][4700/5005] lr: 1.000e-02, eta: 8:24:18, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.1278 2023-01-01 20:40:29,735 - mmcls - INFO - Epoch [32][4800/5005] lr: 1.000e-02, eta: 8:24:09, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.1059 2023-01-01 20:40:38,717 - mmcls - INFO - Epoch [32][4900/5005] lr: 1.000e-02, eta: 8:24:00, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.1058 2023-01-01 20:40:47,524 - mmcls - INFO - Epoch [32][5000/5005] lr: 1.000e-02, eta: 8:23:51, time: 0.088, data_time: 0.031, memory: 1224, loss: 2.1022 2023-01-01 20:41:10,338 - mmcls - INFO - Epoch(val) [32][196] accuracy_top-1: 58.3000, accuracy_top-5: 80.9400 2023-01-01 20:41:21,419 - mmcls - INFO - Epoch [33][100/5005] lr: 1.000e-02, eta: 8:23:46, time: 0.111, data_time: 0.032, memory: 1224, loss: 2.0597 2023-01-01 20:41:29,868 - mmcls - INFO - Epoch [33][200/5005] lr: 1.000e-02, eta: 8:23:36, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.0608 2023-01-01 20:41:38,456 - mmcls - INFO - Epoch [33][300/5005] lr: 1.000e-02, eta: 8:23:26, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.0562 2023-01-01 20:41:47,298 - mmcls - INFO - Epoch [33][400/5005] lr: 1.000e-02, eta: 8:23:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0791 2023-01-01 20:41:56,067 - mmcls - INFO - Epoch [33][500/5005] lr: 1.000e-02, eta: 8:23:08, time: 0.088, data_time: 0.020, memory: 1224, loss: 2.0586 2023-01-01 20:42:04,790 - mmcls - INFO - Epoch [33][600/5005] lr: 1.000e-02, eta: 8:22:59, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0842 2023-01-01 20:42:13,665 - mmcls - INFO - Epoch [33][700/5005] lr: 1.000e-02, eta: 8:22:50, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0767 2023-01-01 20:42:22,360 - mmcls - INFO - Epoch [33][800/5005] lr: 1.000e-02, eta: 8:22:41, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.1036 2023-01-01 20:42:31,059 - mmcls - INFO - Epoch [33][900/5005] lr: 1.000e-02, eta: 8:22:31, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.0852 2023-01-01 20:42:39,851 - mmcls - INFO - Epoch [33][1000/5005] lr: 1.000e-02, eta: 8:22:22, time: 0.088, data_time: 0.035, memory: 1224, loss: 2.0861 2023-01-01 20:42:48,519 - mmcls - INFO - Epoch [33][1100/5005] lr: 1.000e-02, eta: 8:22:13, time: 0.087, data_time: 0.039, memory: 1224, loss: 2.0638 2023-01-01 20:42:57,342 - mmcls - INFO - Epoch [33][1200/5005] lr: 1.000e-02, eta: 8:22:04, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0594 2023-01-01 20:43:05,967 - mmcls - INFO - Epoch [33][1300/5005] lr: 1.000e-02, eta: 8:21:55, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.1028 2023-01-01 20:43:14,518 - mmcls - INFO - Epoch [33][1400/5005] lr: 1.000e-02, eta: 8:21:45, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.0617 2023-01-01 20:43:23,413 - mmcls - INFO - Epoch [33][1500/5005] lr: 1.000e-02, eta: 8:21:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0880 2023-01-01 20:43:32,552 - mmcls - INFO - Epoch [33][1600/5005] lr: 1.000e-02, eta: 8:21:28, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.0487 2023-01-01 20:43:41,046 - mmcls - INFO - Epoch [33][1700/5005] lr: 1.000e-02, eta: 8:21:18, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.0882 2023-01-01 20:43:49,561 - mmcls - INFO - Epoch [33][1800/5005] lr: 1.000e-02, eta: 8:21:08, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.0911 2023-01-01 20:43:58,336 - mmcls - INFO - Epoch [33][1900/5005] lr: 1.000e-02, eta: 8:20:59, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0653 2023-01-01 20:44:07,383 - mmcls - INFO - Epoch [33][2000/5005] lr: 1.000e-02, eta: 8:20:51, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.0707 2023-01-01 20:44:16,154 - mmcls - INFO - Epoch [33][2100/5005] lr: 1.000e-02, eta: 8:20:42, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.0702 2023-01-01 20:44:25,002 - mmcls - INFO - Epoch [33][2200/5005] lr: 1.000e-02, eta: 8:20:33, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.0646 2023-01-01 20:44:33,667 - mmcls - INFO - Epoch [33][2300/5005] lr: 1.000e-02, eta: 8:20:23, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.0949 2023-01-01 20:44:42,403 - mmcls - INFO - Epoch [33][2400/5005] lr: 1.000e-02, eta: 8:20:14, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.0548 2023-01-01 20:44:51,354 - mmcls - INFO - Epoch [33][2500/5005] lr: 1.000e-02, eta: 8:20:06, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.0768 2023-01-01 20:45:00,200 - mmcls - INFO - Epoch [33][2600/5005] lr: 1.000e-02, eta: 8:19:57, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.0827 2023-01-01 20:45:09,328 - mmcls - INFO - Epoch [33][2700/5005] lr: 1.000e-02, eta: 8:19:48, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.0599 2023-01-01 20:45:18,212 - mmcls - INFO - Epoch [33][2800/5005] lr: 1.000e-02, eta: 8:19:39, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.0882 2023-01-01 20:45:27,301 - mmcls - INFO - Epoch [33][2900/5005] lr: 1.000e-02, eta: 8:19:31, time: 0.091, data_time: 0.024, memory: 1224, loss: 2.0539 2023-01-01 20:45:36,292 - mmcls - INFO - Epoch [33][3000/5005] lr: 1.000e-02, eta: 8:19:22, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.0917 2023-01-01 20:45:44,859 - mmcls - INFO - Epoch [33][3100/5005] lr: 1.000e-02, eta: 8:19:13, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.0650 2023-01-01 20:45:53,560 - mmcls - INFO - Epoch [33][3200/5005] lr: 1.000e-02, eta: 8:19:03, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0897 2023-01-01 20:46:02,470 - mmcls - INFO - Epoch [33][3300/5005] lr: 1.000e-02, eta: 8:18:55, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.0867 2023-01-01 20:46:11,226 - mmcls - INFO - Epoch [33][3400/5005] lr: 1.000e-02, eta: 8:18:45, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0792 2023-01-01 20:46:20,070 - mmcls - INFO - Epoch [33][3500/5005] lr: 1.000e-02, eta: 8:18:36, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.0691 2023-01-01 20:46:28,815 - mmcls - INFO - Epoch [33][3600/5005] lr: 1.000e-02, eta: 8:18:27, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0737 2023-01-01 20:46:37,662 - mmcls - INFO - Epoch [33][3700/5005] lr: 1.000e-02, eta: 8:18:18, time: 0.088, data_time: 0.040, memory: 1224, loss: 2.0461 2023-01-01 20:46:46,437 - mmcls - INFO - Epoch [33][3800/5005] lr: 1.000e-02, eta: 8:18:09, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.0868 2023-01-01 20:46:55,491 - mmcls - INFO - Epoch [33][3900/5005] lr: 1.000e-02, eta: 8:18:01, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.0776 2023-01-01 20:47:04,685 - mmcls - INFO - Epoch [33][4000/5005] lr: 1.000e-02, eta: 8:17:52, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.0717 2023-01-01 20:47:13,523 - mmcls - INFO - Epoch [33][4100/5005] lr: 1.000e-02, eta: 8:17:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0571 2023-01-01 20:47:22,268 - mmcls - INFO - Epoch [33][4200/5005] lr: 1.000e-02, eta: 8:17:34, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0711 2023-01-01 20:47:31,304 - mmcls - INFO - Epoch [33][4300/5005] lr: 1.000e-02, eta: 8:17:26, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.0816 2023-01-01 20:47:40,121 - mmcls - INFO - Epoch [33][4400/5005] lr: 1.000e-02, eta: 8:17:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0923 2023-01-01 20:47:48,863 - mmcls - INFO - Epoch [33][4500/5005] lr: 1.000e-02, eta: 8:17:08, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.0737 2023-01-01 20:47:57,699 - mmcls - INFO - Epoch [33][4600/5005] lr: 1.000e-02, eta: 8:16:59, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.0527 2023-01-01 20:48:06,721 - mmcls - INFO - Epoch [33][4700/5005] lr: 1.000e-02, eta: 8:16:50, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0830 2023-01-01 20:48:15,663 - mmcls - INFO - Epoch [33][4800/5005] lr: 1.000e-02, eta: 8:16:41, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0795 2023-01-01 20:48:24,834 - mmcls - INFO - Epoch [33][4900/5005] lr: 1.000e-02, eta: 8:16:33, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.0710 2023-01-01 20:48:33,352 - mmcls - INFO - Epoch [33][5000/5005] lr: 1.000e-02, eta: 8:16:23, time: 0.085, data_time: 0.029, memory: 1224, loss: 2.0783 2023-01-01 20:48:56,154 - mmcls - INFO - Epoch(val) [33][196] accuracy_top-1: 59.6980, accuracy_top-5: 81.7960 2023-01-01 20:49:06,856 - mmcls - INFO - Epoch [34][100/5005] lr: 1.000e-02, eta: 8:16:17, time: 0.107, data_time: 0.035, memory: 1224, loss: 2.0138 2023-01-01 20:49:15,712 - mmcls - INFO - Epoch [34][200/5005] lr: 1.000e-02, eta: 8:16:08, time: 0.089, data_time: 0.032, memory: 1224, loss: 2.0326 2023-01-01 20:49:24,944 - mmcls - INFO - Epoch [34][300/5005] lr: 1.000e-02, eta: 8:16:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.0201 2023-01-01 20:49:33,541 - mmcls - INFO - Epoch [34][400/5005] lr: 1.000e-02, eta: 8:15:50, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.0534 2023-01-01 20:49:42,132 - mmcls - INFO - Epoch [34][500/5005] lr: 1.000e-02, eta: 8:15:41, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0253 2023-01-01 20:49:50,964 - mmcls - INFO - Epoch [34][600/5005] lr: 1.000e-02, eta: 8:15:32, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0148 2023-01-01 20:49:59,695 - mmcls - INFO - Epoch [34][700/5005] lr: 1.000e-02, eta: 8:15:23, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.0518 2023-01-01 20:50:08,970 - mmcls - INFO - Epoch [34][800/5005] lr: 1.000e-02, eta: 8:15:14, time: 0.093, data_time: 0.003, memory: 1224, loss: 2.0145 2023-01-01 20:50:17,413 - mmcls - INFO - Epoch [34][900/5005] lr: 1.000e-02, eta: 8:15:05, time: 0.085, data_time: 0.018, memory: 1224, loss: 2.0398 2023-01-01 20:50:26,109 - mmcls - INFO - Epoch [34][1000/5005] lr: 1.000e-02, eta: 8:14:55, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0416 2023-01-01 20:50:34,852 - mmcls - INFO - Epoch [34][1100/5005] lr: 1.000e-02, eta: 8:14:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0263 2023-01-01 20:50:44,031 - mmcls - INFO - Epoch [34][1200/5005] lr: 1.000e-02, eta: 8:14:38, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.0521 2023-01-01 20:50:53,048 - mmcls - INFO - Epoch [34][1300/5005] lr: 1.000e-02, eta: 8:14:29, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0341 2023-01-01 20:51:01,681 - mmcls - INFO - Epoch [34][1400/5005] lr: 1.000e-02, eta: 8:14:20, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.0291 2023-01-01 20:51:10,693 - mmcls - INFO - Epoch [34][1500/5005] lr: 1.000e-02, eta: 8:14:11, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.0316 2023-01-01 20:51:19,413 - mmcls - INFO - Epoch [34][1600/5005] lr: 1.000e-02, eta: 8:14:02, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.0237 2023-01-01 20:51:27,865 - mmcls - INFO - Epoch [34][1700/5005] lr: 1.000e-02, eta: 8:13:52, time: 0.084, data_time: 0.019, memory: 1224, loss: 2.0263 2023-01-01 20:51:36,577 - mmcls - INFO - Epoch [34][1800/5005] lr: 1.000e-02, eta: 8:13:43, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0308 2023-01-01 20:51:45,341 - mmcls - INFO - Epoch [34][1900/5005] lr: 1.000e-02, eta: 8:13:34, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.0426 2023-01-01 20:51:54,084 - mmcls - INFO - Epoch [34][2000/5005] lr: 1.000e-02, eta: 8:13:25, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.0504 2023-01-01 20:52:02,823 - mmcls - INFO - Epoch [34][2100/5005] lr: 1.000e-02, eta: 8:13:16, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.0591 2023-01-01 20:52:11,811 - mmcls - INFO - Epoch [34][2200/5005] lr: 1.000e-02, eta: 8:13:07, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.0478 2023-01-01 20:52:20,416 - mmcls - INFO - Epoch [34][2300/5005] lr: 1.000e-02, eta: 8:12:58, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.0524 2023-01-01 20:52:29,160 - mmcls - INFO - Epoch [34][2400/5005] lr: 1.000e-02, eta: 8:12:48, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0216 2023-01-01 20:52:37,847 - mmcls - INFO - Epoch [34][2500/5005] lr: 1.000e-02, eta: 8:12:39, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0481 2023-01-01 20:52:46,704 - mmcls - INFO - Epoch [34][2600/5005] lr: 1.000e-02, eta: 8:12:30, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.0480 2023-01-01 20:52:55,696 - mmcls - INFO - Epoch [34][2700/5005] lr: 1.000e-02, eta: 8:12:22, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.0610 2023-01-01 20:53:04,660 - mmcls - INFO - Epoch [34][2800/5005] lr: 1.000e-02, eta: 8:12:13, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.0521 2023-01-01 20:53:13,397 - mmcls - INFO - Epoch [34][2900/5005] lr: 1.000e-02, eta: 8:12:04, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0638 2023-01-01 20:53:21,903 - mmcls - INFO - Epoch [34][3000/5005] lr: 1.000e-02, eta: 8:11:54, time: 0.085, data_time: 0.015, memory: 1224, loss: 2.0555 2023-01-01 20:53:30,789 - mmcls - INFO - Epoch [34][3100/5005] lr: 1.000e-02, eta: 8:11:45, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0382 2023-01-01 20:53:39,456 - mmcls - INFO - Epoch [34][3200/5005] lr: 1.000e-02, eta: 8:11:36, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.0516 2023-01-01 20:53:48,647 - mmcls - INFO - Epoch [34][3300/5005] lr: 1.000e-02, eta: 8:11:28, time: 0.092, data_time: 0.018, memory: 1224, loss: 2.0210 2023-01-01 20:53:57,678 - mmcls - INFO - Epoch [34][3400/5005] lr: 1.000e-02, eta: 8:11:19, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0499 2023-01-01 20:54:06,439 - mmcls - INFO - Epoch [34][3500/5005] lr: 1.000e-02, eta: 8:11:10, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0469 2023-01-01 20:54:15,122 - mmcls - INFO - Epoch [34][3600/5005] lr: 1.000e-02, eta: 8:11:01, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0547 2023-01-01 20:54:23,988 - mmcls - INFO - Epoch [34][3700/5005] lr: 1.000e-02, eta: 8:10:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0412 2023-01-01 20:54:32,708 - mmcls - INFO - Epoch [34][3800/5005] lr: 1.000e-02, eta: 8:10:43, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0477 2023-01-01 20:54:41,705 - mmcls - INFO - Epoch [34][3900/5005] lr: 1.000e-02, eta: 8:10:34, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0402 2023-01-01 20:54:50,750 - mmcls - INFO - Epoch [34][4000/5005] lr: 1.000e-02, eta: 8:10:25, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0336 2023-01-01 20:54:59,643 - mmcls - INFO - Epoch [34][4100/5005] lr: 1.000e-02, eta: 8:10:16, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.0651 2023-01-01 20:55:08,636 - mmcls - INFO - Epoch [34][4200/5005] lr: 1.000e-02, eta: 8:10:08, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0448 2023-01-01 20:55:17,301 - mmcls - INFO - Epoch [34][4300/5005] lr: 1.000e-02, eta: 8:09:58, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0612 2023-01-01 20:55:26,087 - mmcls - INFO - Epoch [34][4400/5005] lr: 1.000e-02, eta: 8:09:49, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0272 2023-01-01 20:55:34,897 - mmcls - INFO - Epoch [34][4500/5005] lr: 1.000e-02, eta: 8:09:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0322 2023-01-01 20:55:43,354 - mmcls - INFO - Epoch [34][4600/5005] lr: 1.000e-02, eta: 8:09:31, time: 0.085, data_time: 0.027, memory: 1224, loss: 2.0507 2023-01-01 20:55:52,271 - mmcls - INFO - Epoch [34][4700/5005] lr: 1.000e-02, eta: 8:09:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0432 2023-01-01 20:56:01,119 - mmcls - INFO - Epoch [34][4800/5005] lr: 1.000e-02, eta: 8:09:13, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0647 2023-01-01 20:56:10,109 - mmcls - INFO - Epoch [34][4900/5005] lr: 1.000e-02, eta: 8:09:04, time: 0.090, data_time: 0.033, memory: 1224, loss: 2.0481 2023-01-01 20:56:18,944 - mmcls - INFO - Epoch [34][5000/5005] lr: 1.000e-02, eta: 8:08:55, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0590 2023-01-01 20:56:41,792 - mmcls - INFO - Epoch(val) [34][196] accuracy_top-1: 59.9340, accuracy_top-5: 81.9720 2023-01-01 20:56:53,119 - mmcls - INFO - Epoch [35][100/5005] lr: 1.000e-02, eta: 8:08:50, time: 0.113, data_time: 0.037, memory: 1224, loss: 1.9842 2023-01-01 20:57:01,808 - mmcls - INFO - Epoch [35][200/5005] lr: 1.000e-02, eta: 8:08:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.0082 2023-01-01 20:57:10,538 - mmcls - INFO - Epoch [35][300/5005] lr: 1.000e-02, eta: 8:08:31, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0205 2023-01-01 20:57:19,451 - mmcls - INFO - Epoch [35][400/5005] lr: 1.000e-02, eta: 8:08:23, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.0001 2023-01-01 20:57:28,046 - mmcls - INFO - Epoch [35][500/5005] lr: 1.000e-02, eta: 8:08:13, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.9796 2023-01-01 20:57:36,761 - mmcls - INFO - Epoch [35][600/5005] lr: 1.000e-02, eta: 8:08:04, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0037 2023-01-01 20:57:45,609 - mmcls - INFO - Epoch [35][700/5005] lr: 1.000e-02, eta: 8:07:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0019 2023-01-01 20:57:54,375 - mmcls - INFO - Epoch [35][800/5005] lr: 1.000e-02, eta: 8:07:46, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0166 2023-01-01 20:58:03,067 - mmcls - INFO - Epoch [35][900/5005] lr: 1.000e-02, eta: 8:07:37, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0181 2023-01-01 20:58:12,234 - mmcls - INFO - Epoch [35][1000/5005] lr: 1.000e-02, eta: 8:07:28, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.9936 2023-01-01 20:58:20,895 - mmcls - INFO - Epoch [35][1100/5005] lr: 1.000e-02, eta: 8:07:19, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.0135 2023-01-01 20:58:29,891 - mmcls - INFO - Epoch [35][1200/5005] lr: 1.000e-02, eta: 8:07:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0151 2023-01-01 20:58:38,548 - mmcls - INFO - Epoch [35][1300/5005] lr: 1.000e-02, eta: 8:07:01, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.0202 2023-01-01 20:58:47,242 - mmcls - INFO - Epoch [35][1400/5005] lr: 1.000e-02, eta: 8:06:52, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0158 2023-01-01 20:58:56,042 - mmcls - INFO - Epoch [35][1500/5005] lr: 1.000e-02, eta: 8:06:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0557 2023-01-01 20:59:05,022 - mmcls - INFO - Epoch [35][1600/5005] lr: 1.000e-02, eta: 8:06:34, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.0388 2023-01-01 20:59:13,748 - mmcls - INFO - Epoch [35][1700/5005] lr: 1.000e-02, eta: 8:06:25, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0465 2023-01-01 20:59:22,338 - mmcls - INFO - Epoch [35][1800/5005] lr: 1.000e-02, eta: 8:06:15, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.0442 2023-01-01 20:59:31,472 - mmcls - INFO - Epoch [35][1900/5005] lr: 1.000e-02, eta: 8:06:07, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9881 2023-01-01 20:59:40,202 - mmcls - INFO - Epoch [35][2000/5005] lr: 1.000e-02, eta: 8:05:58, time: 0.087, data_time: 0.028, memory: 1224, loss: 2.0312 2023-01-01 20:59:48,971 - mmcls - INFO - Epoch [35][2100/5005] lr: 1.000e-02, eta: 8:05:49, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.0247 2023-01-01 20:59:57,908 - mmcls - INFO - Epoch [35][2200/5005] lr: 1.000e-02, eta: 8:05:40, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.0195 2023-01-01 21:00:06,661 - mmcls - INFO - Epoch [35][2300/5005] lr: 1.000e-02, eta: 8:05:31, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0497 2023-01-01 21:00:15,551 - mmcls - INFO - Epoch [35][2400/5005] lr: 1.000e-02, eta: 8:05:22, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0302 2023-01-01 21:00:24,044 - mmcls - INFO - Epoch [35][2500/5005] lr: 1.000e-02, eta: 8:05:12, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.0316 2023-01-01 21:00:32,710 - mmcls - INFO - Epoch [35][2600/5005] lr: 1.000e-02, eta: 8:05:03, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.0612 2023-01-01 21:00:41,321 - mmcls - INFO - Epoch [35][2700/5005] lr: 1.000e-02, eta: 8:04:54, time: 0.086, data_time: 0.024, memory: 1224, loss: 2.0289 2023-01-01 21:00:50,484 - mmcls - INFO - Epoch [35][2800/5005] lr: 1.000e-02, eta: 8:04:45, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.0192 2023-01-01 21:00:59,121 - mmcls - INFO - Epoch [35][2900/5005] lr: 1.000e-02, eta: 8:04:36, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.0315 2023-01-01 21:01:07,776 - mmcls - INFO - Epoch [35][3000/5005] lr: 1.000e-02, eta: 8:04:27, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.0071 2023-01-01 21:01:16,666 - mmcls - INFO - Epoch [35][3100/5005] lr: 1.000e-02, eta: 8:04:18, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0364 2023-01-01 21:01:25,529 - mmcls - INFO - Epoch [35][3200/5005] lr: 1.000e-02, eta: 8:04:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.0163 2023-01-01 21:01:34,426 - mmcls - INFO - Epoch [35][3300/5005] lr: 1.000e-02, eta: 8:04:00, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.0440 2023-01-01 21:01:43,201 - mmcls - INFO - Epoch [35][3400/5005] lr: 1.000e-02, eta: 8:03:51, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0327 2023-01-01 21:01:51,986 - mmcls - INFO - Epoch [35][3500/5005] lr: 1.000e-02, eta: 8:03:42, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0724 2023-01-01 21:02:00,702 - mmcls - INFO - Epoch [35][3600/5005] lr: 1.000e-02, eta: 8:03:33, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.0401 2023-01-01 21:02:09,350 - mmcls - INFO - Epoch [35][3700/5005] lr: 1.000e-02, eta: 8:03:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0098 2023-01-01 21:02:18,368 - mmcls - INFO - Epoch [35][3800/5005] lr: 1.000e-02, eta: 8:03:15, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.0357 2023-01-01 21:02:27,262 - mmcls - INFO - Epoch [35][3900/5005] lr: 1.000e-02, eta: 8:03:06, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.0170 2023-01-01 21:02:36,153 - mmcls - INFO - Epoch [35][4000/5005] lr: 1.000e-02, eta: 8:02:57, time: 0.089, data_time: 0.039, memory: 1224, loss: 2.0174 2023-01-01 21:02:45,180 - mmcls - INFO - Epoch [35][4100/5005] lr: 1.000e-02, eta: 8:02:48, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0220 2023-01-01 21:02:54,183 - mmcls - INFO - Epoch [35][4200/5005] lr: 1.000e-02, eta: 8:02:40, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0402 2023-01-01 21:03:03,110 - mmcls - INFO - Epoch [35][4300/5005] lr: 1.000e-02, eta: 8:02:31, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.0275 2023-01-01 21:03:12,119 - mmcls - INFO - Epoch [35][4400/5005] lr: 1.000e-02, eta: 8:02:22, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.0406 2023-01-01 21:03:20,950 - mmcls - INFO - Epoch [35][4500/5005] lr: 1.000e-02, eta: 8:02:13, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.0444 2023-01-01 21:03:29,699 - mmcls - INFO - Epoch [35][4600/5005] lr: 1.000e-02, eta: 8:02:04, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.0392 2023-01-01 21:03:38,541 - mmcls - INFO - Epoch [35][4700/5005] lr: 1.000e-02, eta: 8:01:55, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0516 2023-01-01 21:03:47,175 - mmcls - INFO - Epoch [35][4800/5005] lr: 1.000e-02, eta: 8:01:46, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0668 2023-01-01 21:03:55,980 - mmcls - INFO - Epoch [35][4900/5005] lr: 1.000e-02, eta: 8:01:37, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0396 2023-01-01 21:04:05,115 - mmcls - INFO - Epoch [35][5000/5005] lr: 1.000e-02, eta: 8:01:29, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.0373 2023-01-01 21:04:27,674 - mmcls - INFO - Epoch(val) [35][196] accuracy_top-1: 59.6540, accuracy_top-5: 82.0160 2023-01-01 21:04:38,726 - mmcls - INFO - Epoch [36][100/5005] lr: 1.000e-02, eta: 8:01:22, time: 0.110, data_time: 0.035, memory: 1224, loss: 2.0153 2023-01-01 21:04:47,272 - mmcls - INFO - Epoch [36][200/5005] lr: 1.000e-02, eta: 8:01:13, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9783 2023-01-01 21:04:56,199 - mmcls - INFO - Epoch [36][300/5005] lr: 1.000e-02, eta: 8:01:04, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9755 2023-01-01 21:05:05,114 - mmcls - INFO - Epoch [36][400/5005] lr: 1.000e-02, eta: 8:00:55, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.9788 2023-01-01 21:05:14,217 - mmcls - INFO - Epoch [36][500/5005] lr: 1.000e-02, eta: 8:00:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9882 2023-01-01 21:05:23,150 - mmcls - INFO - Epoch [36][600/5005] lr: 1.000e-02, eta: 8:00:38, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.0071 2023-01-01 21:05:32,166 - mmcls - INFO - Epoch [36][700/5005] lr: 1.000e-02, eta: 8:00:29, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0043 2023-01-01 21:05:40,693 - mmcls - INFO - Epoch [36][800/5005] lr: 1.000e-02, eta: 8:00:20, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.9942 2023-01-01 21:05:49,555 - mmcls - INFO - Epoch [36][900/5005] lr: 1.000e-02, eta: 8:00:11, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9971 2023-01-01 21:05:58,001 - mmcls - INFO - Epoch [36][1000/5005] lr: 1.000e-02, eta: 8:00:01, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.0144 2023-01-01 21:06:06,915 - mmcls - INFO - Epoch [36][1100/5005] lr: 1.000e-02, eta: 7:59:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0003 2023-01-01 21:06:15,336 - mmcls - INFO - Epoch [36][1200/5005] lr: 1.000e-02, eta: 7:59:43, time: 0.084, data_time: 0.008, memory: 1224, loss: 1.9985 2023-01-01 21:06:24,261 - mmcls - INFO - Epoch [36][1300/5005] lr: 1.000e-02, eta: 7:59:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9945 2023-01-01 21:06:33,378 - mmcls - INFO - Epoch [36][1400/5005] lr: 1.000e-02, eta: 7:59:25, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.0160 2023-01-01 21:06:42,187 - mmcls - INFO - Epoch [36][1500/5005] lr: 1.000e-02, eta: 7:59:16, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0066 2023-01-01 21:06:50,946 - mmcls - INFO - Epoch [36][1600/5005] lr: 1.000e-02, eta: 7:59:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0199 2023-01-01 21:06:59,854 - mmcls - INFO - Epoch [36][1700/5005] lr: 1.000e-02, eta: 7:58:59, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.0262 2023-01-01 21:07:08,677 - mmcls - INFO - Epoch [36][1800/5005] lr: 1.000e-02, eta: 7:58:50, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0105 2023-01-01 21:07:17,435 - mmcls - INFO - Epoch [36][1900/5005] lr: 1.000e-02, eta: 7:58:40, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.0168 2023-01-01 21:07:26,118 - mmcls - INFO - Epoch [36][2000/5005] lr: 1.000e-02, eta: 7:58:31, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0247 2023-01-01 21:07:34,809 - mmcls - INFO - Epoch [36][2100/5005] lr: 1.000e-02, eta: 7:58:22, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0099 2023-01-01 21:07:43,685 - mmcls - INFO - Epoch [36][2200/5005] lr: 1.000e-02, eta: 7:58:13, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9837 2023-01-01 21:07:52,431 - mmcls - INFO - Epoch [36][2300/5005] lr: 1.000e-02, eta: 7:58:04, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.0155 2023-01-01 21:08:01,099 - mmcls - INFO - Epoch [36][2400/5005] lr: 1.000e-02, eta: 7:57:55, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.0159 2023-01-01 21:08:10,325 - mmcls - INFO - Epoch [36][2500/5005] lr: 1.000e-02, eta: 7:57:46, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9955 2023-01-01 21:08:19,041 - mmcls - INFO - Epoch [36][2600/5005] lr: 1.000e-02, eta: 7:57:37, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0166 2023-01-01 21:08:28,007 - mmcls - INFO - Epoch [36][2700/5005] lr: 1.000e-02, eta: 7:57:29, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0133 2023-01-01 21:08:36,616 - mmcls - INFO - Epoch [36][2800/5005] lr: 1.000e-02, eta: 7:57:19, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0216 2023-01-01 21:08:45,305 - mmcls - INFO - Epoch [36][2900/5005] lr: 1.000e-02, eta: 7:57:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9979 2023-01-01 21:08:54,004 - mmcls - INFO - Epoch [36][3000/5005] lr: 1.000e-02, eta: 7:57:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0172 2023-01-01 21:09:02,591 - mmcls - INFO - Epoch [36][3100/5005] lr: 1.000e-02, eta: 7:56:51, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.0167 2023-01-01 21:09:11,236 - mmcls - INFO - Epoch [36][3200/5005] lr: 1.000e-02, eta: 7:56:42, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9899 2023-01-01 21:09:19,984 - mmcls - INFO - Epoch [36][3300/5005] lr: 1.000e-02, eta: 7:56:33, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0128 2023-01-01 21:09:29,024 - mmcls - INFO - Epoch [36][3400/5005] lr: 1.000e-02, eta: 7:56:24, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.0196 2023-01-01 21:09:38,146 - mmcls - INFO - Epoch [36][3500/5005] lr: 1.000e-02, eta: 7:56:16, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9803 2023-01-01 21:09:47,014 - mmcls - INFO - Epoch [36][3600/5005] lr: 1.000e-02, eta: 7:56:07, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.0396 2023-01-01 21:09:55,851 - mmcls - INFO - Epoch [36][3700/5005] lr: 1.000e-02, eta: 7:55:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0044 2023-01-01 21:10:04,608 - mmcls - INFO - Epoch [36][3800/5005] lr: 1.000e-02, eta: 7:55:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0199 2023-01-01 21:10:13,247 - mmcls - INFO - Epoch [36][3900/5005] lr: 1.000e-02, eta: 7:55:40, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.0069 2023-01-01 21:10:21,908 - mmcls - INFO - Epoch [36][4000/5005] lr: 1.000e-02, eta: 7:55:30, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.0287 2023-01-01 21:10:30,988 - mmcls - INFO - Epoch [36][4100/5005] lr: 1.000e-02, eta: 7:55:22, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.0189 2023-01-01 21:10:39,779 - mmcls - INFO - Epoch [36][4200/5005] lr: 1.000e-02, eta: 7:55:13, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.0374 2023-01-01 21:10:48,432 - mmcls - INFO - Epoch [36][4300/5005] lr: 1.000e-02, eta: 7:55:03, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.9757 2023-01-01 21:10:57,103 - mmcls - INFO - Epoch [36][4400/5005] lr: 1.000e-02, eta: 7:54:54, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.0044 2023-01-01 21:11:05,907 - mmcls - INFO - Epoch [36][4500/5005] lr: 1.000e-02, eta: 7:54:45, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0206 2023-01-01 21:11:14,851 - mmcls - INFO - Epoch [36][4600/5005] lr: 1.000e-02, eta: 7:54:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0302 2023-01-01 21:11:23,769 - mmcls - INFO - Epoch [36][4700/5005] lr: 1.000e-02, eta: 7:54:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9940 2023-01-01 21:11:32,726 - mmcls - INFO - Epoch [36][4800/5005] lr: 1.000e-02, eta: 7:54:19, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0242 2023-01-01 21:11:41,478 - mmcls - INFO - Epoch [36][4900/5005] lr: 1.000e-02, eta: 7:54:10, time: 0.087, data_time: 0.037, memory: 1224, loss: 2.0044 2023-01-01 21:11:50,395 - mmcls - INFO - Epoch [36][5000/5005] lr: 1.000e-02, eta: 7:54:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0051 2023-01-01 21:12:13,154 - mmcls - INFO - Epoch(val) [36][196] accuracy_top-1: 59.8420, accuracy_top-5: 82.1840 2023-01-01 21:12:24,198 - mmcls - INFO - Epoch [37][100/5005] lr: 1.000e-02, eta: 7:53:55, time: 0.110, data_time: 0.046, memory: 1224, loss: 1.9891 2023-01-01 21:12:33,053 - mmcls - INFO - Epoch [37][200/5005] lr: 1.000e-02, eta: 7:53:46, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.9620 2023-01-01 21:12:42,346 - mmcls - INFO - Epoch [37][300/5005] lr: 1.000e-02, eta: 7:53:38, time: 0.093, data_time: 0.011, memory: 1224, loss: 1.9992 2023-01-01 21:12:50,953 - mmcls - INFO - Epoch [37][400/5005] lr: 1.000e-02, eta: 7:53:28, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9836 2023-01-01 21:12:59,928 - mmcls - INFO - Epoch [37][500/5005] lr: 1.000e-02, eta: 7:53:19, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.9830 2023-01-01 21:13:08,556 - mmcls - INFO - Epoch [37][600/5005] lr: 1.000e-02, eta: 7:53:10, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9715 2023-01-01 21:13:17,224 - mmcls - INFO - Epoch [37][700/5005] lr: 1.000e-02, eta: 7:53:01, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9938 2023-01-01 21:13:26,148 - mmcls - INFO - Epoch [37][800/5005] lr: 1.000e-02, eta: 7:52:52, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9829 2023-01-01 21:13:34,579 - mmcls - INFO - Epoch [37][900/5005] lr: 1.000e-02, eta: 7:52:42, time: 0.084, data_time: 0.003, memory: 1224, loss: 1.9892 2023-01-01 21:13:43,467 - mmcls - INFO - Epoch [37][1000/5005] lr: 1.000e-02, eta: 7:52:34, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0020 2023-01-01 21:13:52,562 - mmcls - INFO - Epoch [37][1100/5005] lr: 1.000e-02, eta: 7:52:25, time: 0.091, data_time: 0.028, memory: 1224, loss: 1.9837 2023-01-01 21:14:01,789 - mmcls - INFO - Epoch [37][1200/5005] lr: 1.000e-02, eta: 7:52:17, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.0050 2023-01-01 21:14:10,659 - mmcls - INFO - Epoch [37][1300/5005] lr: 1.000e-02, eta: 7:52:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0061 2023-01-01 21:14:19,494 - mmcls - INFO - Epoch [37][1400/5005] lr: 1.000e-02, eta: 7:51:59, time: 0.088, data_time: 0.027, memory: 1224, loss: 2.0022 2023-01-01 21:14:28,240 - mmcls - INFO - Epoch [37][1500/5005] lr: 1.000e-02, eta: 7:51:50, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9798 2023-01-01 21:14:37,229 - mmcls - INFO - Epoch [37][1600/5005] lr: 1.000e-02, eta: 7:51:41, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9909 2023-01-01 21:14:46,089 - mmcls - INFO - Epoch [37][1700/5005] lr: 1.000e-02, eta: 7:51:32, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9925 2023-01-01 21:14:54,898 - mmcls - INFO - Epoch [37][1800/5005] lr: 1.000e-02, eta: 7:51:23, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0007 2023-01-01 21:15:03,642 - mmcls - INFO - Epoch [37][1900/5005] lr: 1.000e-02, eta: 7:51:14, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9825 2023-01-01 21:15:12,448 - mmcls - INFO - Epoch [37][2000/5005] lr: 1.000e-02, eta: 7:51:05, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9852 2023-01-01 21:15:21,221 - mmcls - INFO - Epoch [37][2100/5005] lr: 1.000e-02, eta: 7:50:56, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0168 2023-01-01 21:15:30,072 - mmcls - INFO - Epoch [37][2200/5005] lr: 1.000e-02, eta: 7:50:47, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0190 2023-01-01 21:15:38,571 - mmcls - INFO - Epoch [37][2300/5005] lr: 1.000e-02, eta: 7:50:38, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.9916 2023-01-01 21:15:47,333 - mmcls - INFO - Epoch [37][2400/5005] lr: 1.000e-02, eta: 7:50:28, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0078 2023-01-01 21:15:56,318 - mmcls - INFO - Epoch [37][2500/5005] lr: 1.000e-02, eta: 7:50:20, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9654 2023-01-01 21:16:04,639 - mmcls - INFO - Epoch [37][2600/5005] lr: 1.000e-02, eta: 7:50:10, time: 0.083, data_time: 0.003, memory: 1224, loss: 1.9990 2023-01-01 21:16:13,427 - mmcls - INFO - Epoch [37][2700/5005] lr: 1.000e-02, eta: 7:50:01, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.0155 2023-01-01 21:16:22,342 - mmcls - INFO - Epoch [37][2800/5005] lr: 1.000e-02, eta: 7:49:52, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.0243 2023-01-01 21:16:31,597 - mmcls - INFO - Epoch [37][2900/5005] lr: 1.000e-02, eta: 7:49:44, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.0250 2023-01-01 21:16:40,568 - mmcls - INFO - Epoch [37][3000/5005] lr: 1.000e-02, eta: 7:49:35, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0248 2023-01-01 21:16:49,549 - mmcls - INFO - Epoch [37][3100/5005] lr: 1.000e-02, eta: 7:49:26, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.0066 2023-01-01 21:16:57,996 - mmcls - INFO - Epoch [37][3200/5005] lr: 1.000e-02, eta: 7:49:17, time: 0.085, data_time: 0.031, memory: 1224, loss: 2.0176 2023-01-01 21:17:06,871 - mmcls - INFO - Epoch [37][3300/5005] lr: 1.000e-02, eta: 7:49:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9998 2023-01-01 21:17:15,980 - mmcls - INFO - Epoch [37][3400/5005] lr: 1.000e-02, eta: 7:48:59, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.0160 2023-01-01 21:17:24,720 - mmcls - INFO - Epoch [37][3500/5005] lr: 1.000e-02, eta: 7:48:50, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.0241 2023-01-01 21:17:33,572 - mmcls - INFO - Epoch [37][3600/5005] lr: 1.000e-02, eta: 7:48:41, time: 0.088, data_time: 0.046, memory: 1224, loss: 2.0108 2023-01-01 21:17:42,886 - mmcls - INFO - Epoch [37][3700/5005] lr: 1.000e-02, eta: 7:48:33, time: 0.093, data_time: 0.006, memory: 1224, loss: 2.0424 2023-01-01 21:17:51,411 - mmcls - INFO - Epoch [37][3800/5005] lr: 1.000e-02, eta: 7:48:24, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.9907 2023-01-01 21:18:00,522 - mmcls - INFO - Epoch [37][3900/5005] lr: 1.000e-02, eta: 7:48:15, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9912 2023-01-01 21:18:09,337 - mmcls - INFO - Epoch [37][4000/5005] lr: 1.000e-02, eta: 7:48:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9959 2023-01-01 21:18:18,300 - mmcls - INFO - Epoch [37][4100/5005] lr: 1.000e-02, eta: 7:47:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0197 2023-01-01 21:18:26,807 - mmcls - INFO - Epoch [37][4200/5005] lr: 1.000e-02, eta: 7:47:48, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.0110 2023-01-01 21:18:35,639 - mmcls - INFO - Epoch [37][4300/5005] lr: 1.000e-02, eta: 7:47:39, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.0000 2023-01-01 21:18:44,353 - mmcls - INFO - Epoch [37][4400/5005] lr: 1.000e-02, eta: 7:47:30, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9875 2023-01-01 21:18:53,186 - mmcls - INFO - Epoch [37][4500/5005] lr: 1.000e-02, eta: 7:47:21, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.0020 2023-01-01 21:19:01,861 - mmcls - INFO - Epoch [37][4600/5005] lr: 1.000e-02, eta: 7:47:12, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9862 2023-01-01 21:19:10,672 - mmcls - INFO - Epoch [37][4700/5005] lr: 1.000e-02, eta: 7:47:03, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.0173 2023-01-01 21:19:19,535 - mmcls - INFO - Epoch [37][4800/5005] lr: 1.000e-02, eta: 7:46:54, time: 0.089, data_time: 0.033, memory: 1224, loss: 2.0081 2023-01-01 21:19:28,227 - mmcls - INFO - Epoch [37][4900/5005] lr: 1.000e-02, eta: 7:46:45, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9991 2023-01-01 21:19:37,007 - mmcls - INFO - Epoch [37][5000/5005] lr: 1.000e-02, eta: 7:46:36, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0292 2023-01-01 21:19:59,637 - mmcls - INFO - Epoch(val) [37][196] accuracy_top-1: 59.9580, accuracy_top-5: 82.0760 2023-01-01 21:20:10,932 - mmcls - INFO - Epoch [38][100/5005] lr: 1.000e-02, eta: 7:46:30, time: 0.113, data_time: 0.056, memory: 1224, loss: 1.9783 2023-01-01 21:20:19,534 - mmcls - INFO - Epoch [38][200/5005] lr: 1.000e-02, eta: 7:46:20, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9710 2023-01-01 21:20:28,187 - mmcls - INFO - Epoch [38][300/5005] lr: 1.000e-02, eta: 7:46:11, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9801 2023-01-01 21:20:36,878 - mmcls - INFO - Epoch [38][400/5005] lr: 1.000e-02, eta: 7:46:02, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9699 2023-01-01 21:20:45,597 - mmcls - INFO - Epoch [38][500/5005] lr: 1.000e-02, eta: 7:45:53, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9811 2023-01-01 21:20:54,238 - mmcls - INFO - Epoch [38][600/5005] lr: 1.000e-02, eta: 7:45:43, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.9629 2023-01-01 21:21:03,328 - mmcls - INFO - Epoch [38][700/5005] lr: 1.000e-02, eta: 7:45:35, time: 0.091, data_time: 0.040, memory: 1224, loss: 1.9715 2023-01-01 21:21:12,195 - mmcls - INFO - Epoch [38][800/5005] lr: 1.000e-02, eta: 7:45:26, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9521 2023-01-01 21:21:20,853 - mmcls - INFO - Epoch [38][900/5005] lr: 1.000e-02, eta: 7:45:17, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0034 2023-01-01 21:21:29,635 - mmcls - INFO - Epoch [38][1000/5005] lr: 1.000e-02, eta: 7:45:08, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9988 2023-01-01 21:21:38,334 - mmcls - INFO - Epoch [38][1100/5005] lr: 1.000e-02, eta: 7:44:58, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9791 2023-01-01 21:21:47,381 - mmcls - INFO - Epoch [38][1200/5005] lr: 1.000e-02, eta: 7:44:50, time: 0.090, data_time: 0.050, memory: 1224, loss: 1.9687 2023-01-01 21:21:56,469 - mmcls - INFO - Epoch [38][1300/5005] lr: 1.000e-02, eta: 7:44:41, time: 0.091, data_time: 0.030, memory: 1224, loss: 1.9900 2023-01-01 21:22:05,060 - mmcls - INFO - Epoch [38][1400/5005] lr: 1.000e-02, eta: 7:44:32, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.9753 2023-01-01 21:22:14,090 - mmcls - INFO - Epoch [38][1500/5005] lr: 1.000e-02, eta: 7:44:23, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.0061 2023-01-01 21:22:22,830 - mmcls - INFO - Epoch [38][1600/5005] lr: 1.000e-02, eta: 7:44:14, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9766 2023-01-01 21:22:31,551 - mmcls - INFO - Epoch [38][1700/5005] lr: 1.000e-02, eta: 7:44:05, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9793 2023-01-01 21:22:40,341 - mmcls - INFO - Epoch [38][1800/5005] lr: 1.000e-02, eta: 7:43:56, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9629 2023-01-01 21:22:49,001 - mmcls - INFO - Epoch [38][1900/5005] lr: 1.000e-02, eta: 7:43:47, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0169 2023-01-01 21:22:57,763 - mmcls - INFO - Epoch [38][2000/5005] lr: 1.000e-02, eta: 7:43:38, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0075 2023-01-01 21:23:06,191 - mmcls - INFO - Epoch [38][2100/5005] lr: 1.000e-02, eta: 7:43:28, time: 0.084, data_time: 0.020, memory: 1224, loss: 1.9871 2023-01-01 21:23:14,878 - mmcls - INFO - Epoch [38][2200/5005] lr: 1.000e-02, eta: 7:43:19, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9904 2023-01-01 21:23:23,505 - mmcls - INFO - Epoch [38][2300/5005] lr: 1.000e-02, eta: 7:43:10, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9887 2023-01-01 21:23:32,449 - mmcls - INFO - Epoch [38][2400/5005] lr: 1.000e-02, eta: 7:43:01, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9911 2023-01-01 21:23:41,356 - mmcls - INFO - Epoch [38][2500/5005] lr: 1.000e-02, eta: 7:42:52, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.0088 2023-01-01 21:23:50,279 - mmcls - INFO - Epoch [38][2600/5005] lr: 1.000e-02, eta: 7:42:43, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.0140 2023-01-01 21:23:58,852 - mmcls - INFO - Epoch [38][2700/5005] lr: 1.000e-02, eta: 7:42:34, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9738 2023-01-01 21:24:07,834 - mmcls - INFO - Epoch [38][2800/5005] lr: 1.000e-02, eta: 7:42:25, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.0138 2023-01-01 21:24:16,732 - mmcls - INFO - Epoch [38][2900/5005] lr: 1.000e-02, eta: 7:42:16, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.0144 2023-01-01 21:24:25,730 - mmcls - INFO - Epoch [38][3000/5005] lr: 1.000e-02, eta: 7:42:07, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.0150 2023-01-01 21:24:34,447 - mmcls - INFO - Epoch [38][3100/5005] lr: 1.000e-02, eta: 7:41:58, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9928 2023-01-01 21:24:43,384 - mmcls - INFO - Epoch [38][3200/5005] lr: 1.000e-02, eta: 7:41:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0181 2023-01-01 21:24:52,162 - mmcls - INFO - Epoch [38][3300/5005] lr: 1.000e-02, eta: 7:41:40, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0021 2023-01-01 21:25:00,847 - mmcls - INFO - Epoch [38][3400/5005] lr: 1.000e-02, eta: 7:41:31, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.9890 2023-01-01 21:25:09,862 - mmcls - INFO - Epoch [38][3500/5005] lr: 1.000e-02, eta: 7:41:23, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9884 2023-01-01 21:25:18,667 - mmcls - INFO - Epoch [38][3600/5005] lr: 1.000e-02, eta: 7:41:14, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9896 2023-01-01 21:25:27,730 - mmcls - INFO - Epoch [38][3700/5005] lr: 1.000e-02, eta: 7:41:05, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.0287 2023-01-01 21:25:36,522 - mmcls - INFO - Epoch [38][3800/5005] lr: 1.000e-02, eta: 7:40:56, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.0057 2023-01-01 21:25:45,056 - mmcls - INFO - Epoch [38][3900/5005] lr: 1.000e-02, eta: 7:40:47, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.0240 2023-01-01 21:25:53,892 - mmcls - INFO - Epoch [38][4000/5005] lr: 1.000e-02, eta: 7:40:38, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9931 2023-01-01 21:26:02,455 - mmcls - INFO - Epoch [38][4100/5005] lr: 1.000e-02, eta: 7:40:28, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.9881 2023-01-01 21:26:11,485 - mmcls - INFO - Epoch [38][4200/5005] lr: 1.000e-02, eta: 7:40:20, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0198 2023-01-01 21:26:20,754 - mmcls - INFO - Epoch [38][4300/5005] lr: 1.000e-02, eta: 7:40:11, time: 0.093, data_time: 0.019, memory: 1224, loss: 2.0022 2023-01-01 21:26:29,670 - mmcls - INFO - Epoch [38][4400/5005] lr: 1.000e-02, eta: 7:40:03, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.0011 2023-01-01 21:26:38,150 - mmcls - INFO - Epoch [38][4500/5005] lr: 1.000e-02, eta: 7:39:53, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.9922 2023-01-01 21:26:46,979 - mmcls - INFO - Epoch [38][4600/5005] lr: 1.000e-02, eta: 7:39:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9908 2023-01-01 21:26:55,951 - mmcls - INFO - Epoch [38][4700/5005] lr: 1.000e-02, eta: 7:39:35, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0053 2023-01-01 21:27:04,804 - mmcls - INFO - Epoch [38][4800/5005] lr: 1.000e-02, eta: 7:39:26, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9979 2023-01-01 21:27:13,558 - mmcls - INFO - Epoch [38][4900/5005] lr: 1.000e-02, eta: 7:39:17, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.0238 2023-01-01 21:27:22,159 - mmcls - INFO - Epoch [38][5000/5005] lr: 1.000e-02, eta: 7:39:08, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.0032 2023-01-01 21:27:44,974 - mmcls - INFO - Epoch(val) [38][196] accuracy_top-1: 60.1100, accuracy_top-5: 82.4720 2023-01-01 21:27:56,253 - mmcls - INFO - Epoch [39][100/5005] lr: 1.000e-02, eta: 7:39:02, time: 0.112, data_time: 0.056, memory: 1224, loss: 1.9716 2023-01-01 21:28:04,993 - mmcls - INFO - Epoch [39][200/5005] lr: 1.000e-02, eta: 7:38:53, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9561 2023-01-01 21:28:13,591 - mmcls - INFO - Epoch [39][300/5005] lr: 1.000e-02, eta: 7:38:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9695 2023-01-01 21:28:22,412 - mmcls - INFO - Epoch [39][400/5005] lr: 1.000e-02, eta: 7:38:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9679 2023-01-01 21:28:31,015 - mmcls - INFO - Epoch [39][500/5005] lr: 1.000e-02, eta: 7:38:25, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9903 2023-01-01 21:28:39,731 - mmcls - INFO - Epoch [39][600/5005] lr: 1.000e-02, eta: 7:38:16, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.9873 2023-01-01 21:28:48,484 - mmcls - INFO - Epoch [39][700/5005] lr: 1.000e-02, eta: 7:38:07, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9448 2023-01-01 21:28:57,191 - mmcls - INFO - Epoch [39][800/5005] lr: 1.000e-02, eta: 7:37:58, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9809 2023-01-01 21:29:05,968 - mmcls - INFO - Epoch [39][900/5005] lr: 1.000e-02, eta: 7:37:49, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9661 2023-01-01 21:29:14,824 - mmcls - INFO - Epoch [39][1000/5005] lr: 1.000e-02, eta: 7:37:40, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.9584 2023-01-01 21:29:23,282 - mmcls - INFO - Epoch [39][1100/5005] lr: 1.000e-02, eta: 7:37:30, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.9662 2023-01-01 21:29:32,350 - mmcls - INFO - Epoch [39][1200/5005] lr: 1.000e-02, eta: 7:37:22, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.9704 2023-01-01 21:29:41,035 - mmcls - INFO - Epoch [39][1300/5005] lr: 1.000e-02, eta: 7:37:12, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9984 2023-01-01 21:29:49,789 - mmcls - INFO - Epoch [39][1400/5005] lr: 1.000e-02, eta: 7:37:03, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.0038 2023-01-01 21:29:58,532 - mmcls - INFO - Epoch [39][1500/5005] lr: 1.000e-02, eta: 7:36:54, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9973 2023-01-01 21:30:07,150 - mmcls - INFO - Epoch [39][1600/5005] lr: 1.000e-02, eta: 7:36:45, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9774 2023-01-01 21:30:16,102 - mmcls - INFO - Epoch [39][1700/5005] lr: 1.000e-02, eta: 7:36:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9993 2023-01-01 21:30:25,065 - mmcls - INFO - Epoch [39][1800/5005] lr: 1.000e-02, eta: 7:36:27, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0034 2023-01-01 21:30:34,067 - mmcls - INFO - Epoch [39][1900/5005] lr: 1.000e-02, eta: 7:36:19, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.0081 2023-01-01 21:30:42,927 - mmcls - INFO - Epoch [39][2000/5005] lr: 1.000e-02, eta: 7:36:10, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.9662 2023-01-01 21:30:51,734 - mmcls - INFO - Epoch [39][2100/5005] lr: 1.000e-02, eta: 7:36:01, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9818 2023-01-01 21:31:00,206 - mmcls - INFO - Epoch [39][2200/5005] lr: 1.000e-02, eta: 7:35:51, time: 0.085, data_time: 0.024, memory: 1224, loss: 1.9855 2023-01-01 21:31:09,051 - mmcls - INFO - Epoch [39][2300/5005] lr: 1.000e-02, eta: 7:35:42, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9811 2023-01-01 21:31:17,764 - mmcls - INFO - Epoch [39][2400/5005] lr: 1.000e-02, eta: 7:35:33, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9913 2023-01-01 21:31:26,545 - mmcls - INFO - Epoch [39][2500/5005] lr: 1.000e-02, eta: 7:35:24, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9788 2023-01-01 21:31:35,754 - mmcls - INFO - Epoch [39][2600/5005] lr: 1.000e-02, eta: 7:35:16, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.0000 2023-01-01 21:31:44,640 - mmcls - INFO - Epoch [39][2700/5005] lr: 1.000e-02, eta: 7:35:07, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.9918 2023-01-01 21:31:53,744 - mmcls - INFO - Epoch [39][2800/5005] lr: 1.000e-02, eta: 7:34:59, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.9661 2023-01-01 21:32:02,281 - mmcls - INFO - Epoch [39][2900/5005] lr: 1.000e-02, eta: 7:34:49, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.9849 2023-01-01 21:32:10,860 - mmcls - INFO - Epoch [39][3000/5005] lr: 1.000e-02, eta: 7:34:40, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.0024 2023-01-01 21:32:19,854 - mmcls - INFO - Epoch [39][3100/5005] lr: 1.000e-02, eta: 7:34:31, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0073 2023-01-01 21:32:28,463 - mmcls - INFO - Epoch [39][3200/5005] lr: 1.000e-02, eta: 7:34:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9900 2023-01-01 21:32:36,899 - mmcls - INFO - Epoch [39][3300/5005] lr: 1.000e-02, eta: 7:34:12, time: 0.084, data_time: 0.011, memory: 1224, loss: 1.9958 2023-01-01 21:32:45,970 - mmcls - INFO - Epoch [39][3400/5005] lr: 1.000e-02, eta: 7:34:04, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9831 2023-01-01 21:32:54,803 - mmcls - INFO - Epoch [39][3500/5005] lr: 1.000e-02, eta: 7:33:55, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.0107 2023-01-01 21:33:03,507 - mmcls - INFO - Epoch [39][3600/5005] lr: 1.000e-02, eta: 7:33:46, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.0079 2023-01-01 21:33:12,212 - mmcls - INFO - Epoch [39][3700/5005] lr: 1.000e-02, eta: 7:33:36, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0094 2023-01-01 21:33:21,041 - mmcls - INFO - Epoch [39][3800/5005] lr: 1.000e-02, eta: 7:33:27, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9772 2023-01-01 21:33:29,806 - mmcls - INFO - Epoch [39][3900/5005] lr: 1.000e-02, eta: 7:33:18, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.0024 2023-01-01 21:33:38,815 - mmcls - INFO - Epoch [39][4000/5005] lr: 1.000e-02, eta: 7:33:10, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.0008 2023-01-01 21:33:47,621 - mmcls - INFO - Epoch [39][4100/5005] lr: 1.000e-02, eta: 7:33:01, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0101 2023-01-01 21:33:56,280 - mmcls - INFO - Epoch [39][4200/5005] lr: 1.000e-02, eta: 7:32:51, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.9968 2023-01-01 21:34:04,956 - mmcls - INFO - Epoch [39][4300/5005] lr: 1.000e-02, eta: 7:32:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9680 2023-01-01 21:34:13,806 - mmcls - INFO - Epoch [39][4400/5005] lr: 1.000e-02, eta: 7:32:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9960 2023-01-01 21:34:22,492 - mmcls - INFO - Epoch [39][4500/5005] lr: 1.000e-02, eta: 7:32:24, time: 0.087, data_time: 0.031, memory: 1224, loss: 2.0099 2023-01-01 21:34:31,460 - mmcls - INFO - Epoch [39][4600/5005] lr: 1.000e-02, eta: 7:32:15, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.0028 2023-01-01 21:34:40,479 - mmcls - INFO - Epoch [39][4700/5005] lr: 1.000e-02, eta: 7:32:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0218 2023-01-01 21:34:49,075 - mmcls - INFO - Epoch [39][4800/5005] lr: 1.000e-02, eta: 7:31:57, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.0157 2023-01-01 21:34:58,064 - mmcls - INFO - Epoch [39][4900/5005] lr: 1.000e-02, eta: 7:31:49, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9985 2023-01-01 21:35:06,931 - mmcls - INFO - Epoch [39][5000/5005] lr: 1.000e-02, eta: 7:31:40, time: 0.089, data_time: 0.029, memory: 1224, loss: 2.0098 2023-01-01 21:35:29,959 - mmcls - INFO - Epoch(val) [39][196] accuracy_top-1: 60.1380, accuracy_top-5: 82.2380 2023-01-01 21:35:41,327 - mmcls - INFO - Epoch [40][100/5005] lr: 1.000e-02, eta: 7:31:34, time: 0.113, data_time: 0.041, memory: 1224, loss: 1.9633 2023-01-01 21:35:49,822 - mmcls - INFO - Epoch [40][200/5005] lr: 1.000e-02, eta: 7:31:24, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.9596 2023-01-01 21:35:58,464 - mmcls - INFO - Epoch [40][300/5005] lr: 1.000e-02, eta: 7:31:15, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9569 2023-01-01 21:36:07,177 - mmcls - INFO - Epoch [40][400/5005] lr: 1.000e-02, eta: 7:31:06, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.9534 2023-01-01 21:36:15,925 - mmcls - INFO - Epoch [40][500/5005] lr: 1.000e-02, eta: 7:30:57, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9483 2023-01-01 21:36:24,880 - mmcls - INFO - Epoch [40][600/5005] lr: 1.000e-02, eta: 7:30:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9422 2023-01-01 21:36:33,594 - mmcls - INFO - Epoch [40][700/5005] lr: 1.000e-02, eta: 7:30:39, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9570 2023-01-01 21:36:42,781 - mmcls - INFO - Epoch [40][800/5005] lr: 1.000e-02, eta: 7:30:31, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.9759 2023-01-01 21:36:51,920 - mmcls - INFO - Epoch [40][900/5005] lr: 1.000e-02, eta: 7:30:22, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9794 2023-01-01 21:37:01,018 - mmcls - INFO - Epoch [40][1000/5005] lr: 1.000e-02, eta: 7:30:14, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.9866 2023-01-01 21:37:09,690 - mmcls - INFO - Epoch [40][1100/5005] lr: 1.000e-02, eta: 7:30:04, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9477 2023-01-01 21:37:18,237 - mmcls - INFO - Epoch [40][1200/5005] lr: 1.000e-02, eta: 7:29:55, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.9452 2023-01-01 21:37:27,049 - mmcls - INFO - Epoch [40][1300/5005] lr: 1.000e-02, eta: 7:29:46, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9701 2023-01-01 21:37:35,846 - mmcls - INFO - Epoch [40][1400/5005] lr: 1.000e-02, eta: 7:29:37, time: 0.088, data_time: 0.032, memory: 1224, loss: 1.9594 2023-01-01 21:37:44,725 - mmcls - INFO - Epoch [40][1500/5005] lr: 1.000e-02, eta: 7:29:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9813 2023-01-01 21:37:53,706 - mmcls - INFO - Epoch [40][1600/5005] lr: 1.000e-02, eta: 7:29:19, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9888 2023-01-01 21:38:02,285 - mmcls - INFO - Epoch [40][1700/5005] lr: 1.000e-02, eta: 7:29:10, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9900 2023-01-01 21:38:11,002 - mmcls - INFO - Epoch [40][1800/5005] lr: 1.000e-02, eta: 7:29:01, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9814 2023-01-01 21:38:19,827 - mmcls - INFO - Epoch [40][1900/5005] lr: 1.000e-02, eta: 7:28:52, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9811 2023-01-01 21:38:28,655 - mmcls - INFO - Epoch [40][2000/5005] lr: 1.000e-02, eta: 7:28:43, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9609 2023-01-01 21:38:37,239 - mmcls - INFO - Epoch [40][2100/5005] lr: 1.000e-02, eta: 7:28:34, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9737 2023-01-01 21:38:46,198 - mmcls - INFO - Epoch [40][2200/5005] lr: 1.000e-02, eta: 7:28:25, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9839 2023-01-01 21:38:55,066 - mmcls - INFO - Epoch [40][2300/5005] lr: 1.000e-02, eta: 7:28:16, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9734 2023-01-01 21:39:03,770 - mmcls - INFO - Epoch [40][2400/5005] lr: 1.000e-02, eta: 7:28:07, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9775 2023-01-01 21:39:12,485 - mmcls - INFO - Epoch [40][2500/5005] lr: 1.000e-02, eta: 7:27:58, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9600 2023-01-01 21:39:21,299 - mmcls - INFO - Epoch [40][2600/5005] lr: 1.000e-02, eta: 7:27:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0121 2023-01-01 21:39:29,854 - mmcls - INFO - Epoch [40][2700/5005] lr: 1.000e-02, eta: 7:27:39, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9987 2023-01-01 21:39:38,564 - mmcls - INFO - Epoch [40][2800/5005] lr: 1.000e-02, eta: 7:27:30, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9744 2023-01-01 21:39:47,674 - mmcls - INFO - Epoch [40][2900/5005] lr: 1.000e-02, eta: 7:27:22, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.9926 2023-01-01 21:39:56,519 - mmcls - INFO - Epoch [40][3000/5005] lr: 1.000e-02, eta: 7:27:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0007 2023-01-01 21:40:05,452 - mmcls - INFO - Epoch [40][3100/5005] lr: 1.000e-02, eta: 7:27:04, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.0027 2023-01-01 21:40:14,517 - mmcls - INFO - Epoch [40][3200/5005] lr: 1.000e-02, eta: 7:26:55, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9963 2023-01-01 21:40:23,173 - mmcls - INFO - Epoch [40][3300/5005] lr: 1.000e-02, eta: 7:26:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0160 2023-01-01 21:40:31,790 - mmcls - INFO - Epoch [40][3400/5005] lr: 1.000e-02, eta: 7:26:37, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.9950 2023-01-01 21:40:40,298 - mmcls - INFO - Epoch [40][3500/5005] lr: 1.000e-02, eta: 7:26:28, time: 0.085, data_time: 0.024, memory: 1224, loss: 1.9940 2023-01-01 21:40:49,437 - mmcls - INFO - Epoch [40][3600/5005] lr: 1.000e-02, eta: 7:26:19, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.9804 2023-01-01 21:40:58,494 - mmcls - INFO - Epoch [40][3700/5005] lr: 1.000e-02, eta: 7:26:10, time: 0.091, data_time: 0.015, memory: 1224, loss: 2.0457 2023-01-01 21:41:07,236 - mmcls - INFO - Epoch [40][3800/5005] lr: 1.000e-02, eta: 7:26:01, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9911 2023-01-01 21:41:15,796 - mmcls - INFO - Epoch [40][3900/5005] lr: 1.000e-02, eta: 7:25:52, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9765 2023-01-01 21:41:24,581 - mmcls - INFO - Epoch [40][4000/5005] lr: 1.000e-02, eta: 7:25:43, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0215 2023-01-01 21:41:33,225 - mmcls - INFO - Epoch [40][4100/5005] lr: 1.000e-02, eta: 7:25:34, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9983 2023-01-01 21:41:42,458 - mmcls - INFO - Epoch [40][4200/5005] lr: 1.000e-02, eta: 7:25:25, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.0088 2023-01-01 21:41:50,987 - mmcls - INFO - Epoch [40][4300/5005] lr: 1.000e-02, eta: 7:25:16, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.0228 2023-01-01 21:42:00,021 - mmcls - INFO - Epoch [40][4400/5005] lr: 1.000e-02, eta: 7:25:07, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.0208 2023-01-01 21:42:09,018 - mmcls - INFO - Epoch [40][4500/5005] lr: 1.000e-02, eta: 7:24:59, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.0376 2023-01-01 21:42:18,047 - mmcls - INFO - Epoch [40][4600/5005] lr: 1.000e-02, eta: 7:24:50, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0042 2023-01-01 21:42:26,963 - mmcls - INFO - Epoch [40][4700/5005] lr: 1.000e-02, eta: 7:24:41, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.0077 2023-01-01 21:42:35,801 - mmcls - INFO - Epoch [40][4800/5005] lr: 1.000e-02, eta: 7:24:32, time: 0.088, data_time: 0.036, memory: 1224, loss: 1.9915 2023-01-01 21:42:44,446 - mmcls - INFO - Epoch [40][4900/5005] lr: 1.000e-02, eta: 7:24:23, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.0001 2023-01-01 21:42:53,256 - mmcls - INFO - Epoch [40][5000/5005] lr: 1.000e-02, eta: 7:24:14, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9710 2023-01-01 21:43:16,056 - mmcls - INFO - Epoch(val) [40][196] accuracy_top-1: 59.7400, accuracy_top-5: 82.0020 2023-01-01 21:43:26,838 - mmcls - INFO - Epoch [41][100/5005] lr: 1.000e-02, eta: 7:24:07, time: 0.108, data_time: 0.042, memory: 1224, loss: 1.9626 2023-01-01 21:43:35,684 - mmcls - INFO - Epoch [41][200/5005] lr: 1.000e-02, eta: 7:23:58, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.9440 2023-01-01 21:43:44,539 - mmcls - INFO - Epoch [41][300/5005] lr: 1.000e-02, eta: 7:23:49, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.9651 2023-01-01 21:43:53,323 - mmcls - INFO - Epoch [41][400/5005] lr: 1.000e-02, eta: 7:23:40, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.9888 2023-01-01 21:44:01,952 - mmcls - INFO - Epoch [41][500/5005] lr: 1.000e-02, eta: 7:23:31, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9643 2023-01-01 21:44:10,608 - mmcls - INFO - Epoch [41][600/5005] lr: 1.000e-02, eta: 7:23:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9396 2023-01-01 21:44:19,684 - mmcls - INFO - Epoch [41][700/5005] lr: 1.000e-02, eta: 7:23:13, time: 0.091, data_time: 0.023, memory: 1224, loss: 1.9401 2023-01-01 21:44:28,967 - mmcls - INFO - Epoch [41][800/5005] lr: 1.000e-02, eta: 7:23:05, time: 0.093, data_time: 0.000, memory: 1224, loss: 1.9789 2023-01-01 21:44:37,621 - mmcls - INFO - Epoch [41][900/5005] lr: 1.000e-02, eta: 7:22:56, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9753 2023-01-01 21:44:46,588 - mmcls - INFO - Epoch [41][1000/5005] lr: 1.000e-02, eta: 7:22:47, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9788 2023-01-01 21:44:55,298 - mmcls - INFO - Epoch [41][1100/5005] lr: 1.000e-02, eta: 7:22:38, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9762 2023-01-01 21:45:04,154 - mmcls - INFO - Epoch [41][1200/5005] lr: 1.000e-02, eta: 7:22:29, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9901 2023-01-01 21:45:13,115 - mmcls - INFO - Epoch [41][1300/5005] lr: 1.000e-02, eta: 7:22:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9788 2023-01-01 21:45:21,863 - mmcls - INFO - Epoch [41][1400/5005] lr: 1.000e-02, eta: 7:22:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9971 2023-01-01 21:45:30,560 - mmcls - INFO - Epoch [41][1500/5005] lr: 1.000e-02, eta: 7:22:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9915 2023-01-01 21:45:39,577 - mmcls - INFO - Epoch [41][1600/5005] lr: 1.000e-02, eta: 7:21:53, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9784 2023-01-01 21:45:48,258 - mmcls - INFO - Epoch [41][1700/5005] lr: 1.000e-02, eta: 7:21:44, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9770 2023-01-01 21:45:57,030 - mmcls - INFO - Epoch [41][1800/5005] lr: 1.000e-02, eta: 7:21:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9475 2023-01-01 21:46:05,830 - mmcls - INFO - Epoch [41][1900/5005] lr: 1.000e-02, eta: 7:21:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9576 2023-01-01 21:46:14,566 - mmcls - INFO - Epoch [41][2000/5005] lr: 1.000e-02, eta: 7:21:17, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9857 2023-01-01 21:46:23,338 - mmcls - INFO - Epoch [41][2100/5005] lr: 1.000e-02, eta: 7:21:08, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.9810 2023-01-01 21:46:32,250 - mmcls - INFO - Epoch [41][2200/5005] lr: 1.000e-02, eta: 7:20:59, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9614 2023-01-01 21:46:41,108 - mmcls - INFO - Epoch [41][2300/5005] lr: 1.000e-02, eta: 7:20:50, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9861 2023-01-01 21:46:49,829 - mmcls - INFO - Epoch [41][2400/5005] lr: 1.000e-02, eta: 7:20:41, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9821 2023-01-01 21:46:58,534 - mmcls - INFO - Epoch [41][2500/5005] lr: 1.000e-02, eta: 7:20:32, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9829 2023-01-01 21:47:07,576 - mmcls - INFO - Epoch [41][2600/5005] lr: 1.000e-02, eta: 7:20:23, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.0155 2023-01-01 21:47:16,477 - mmcls - INFO - Epoch [41][2700/5005] lr: 1.000e-02, eta: 7:20:14, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0011 2023-01-01 21:47:25,234 - mmcls - INFO - Epoch [41][2800/5005] lr: 1.000e-02, eta: 7:20:05, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9756 2023-01-01 21:47:34,062 - mmcls - INFO - Epoch [41][2900/5005] lr: 1.000e-02, eta: 7:19:56, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.9744 2023-01-01 21:47:42,694 - mmcls - INFO - Epoch [41][3000/5005] lr: 1.000e-02, eta: 7:19:47, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9974 2023-01-01 21:47:51,619 - mmcls - INFO - Epoch [41][3100/5005] lr: 1.000e-02, eta: 7:19:38, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.9466 2023-01-01 21:48:00,495 - mmcls - INFO - Epoch [41][3200/5005] lr: 1.000e-02, eta: 7:19:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9884 2023-01-01 21:48:09,808 - mmcls - INFO - Epoch [41][3300/5005] lr: 1.000e-02, eta: 7:19:21, time: 0.093, data_time: 0.007, memory: 1224, loss: 2.0207 2023-01-01 21:48:18,540 - mmcls - INFO - Epoch [41][3400/5005] lr: 1.000e-02, eta: 7:19:12, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9994 2023-01-01 21:48:27,502 - mmcls - INFO - Epoch [41][3500/5005] lr: 1.000e-02, eta: 7:19:03, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.9754 2023-01-01 21:48:36,178 - mmcls - INFO - Epoch [41][3600/5005] lr: 1.000e-02, eta: 7:18:54, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.9866 2023-01-01 21:48:44,683 - mmcls - INFO - Epoch [41][3700/5005] lr: 1.000e-02, eta: 7:18:45, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9967 2023-01-01 21:48:53,569 - mmcls - INFO - Epoch [41][3800/5005] lr: 1.000e-02, eta: 7:18:36, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.9914 2023-01-01 21:49:02,229 - mmcls - INFO - Epoch [41][3900/5005] lr: 1.000e-02, eta: 7:18:27, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.0341 2023-01-01 21:49:11,050 - mmcls - INFO - Epoch [41][4000/5005] lr: 1.000e-02, eta: 7:18:18, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.0013 2023-01-01 21:49:20,384 - mmcls - INFO - Epoch [41][4100/5005] lr: 1.000e-02, eta: 7:18:10, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.9797 2023-01-01 21:49:29,305 - mmcls - INFO - Epoch [41][4200/5005] lr: 1.000e-02, eta: 7:18:01, time: 0.089, data_time: 0.027, memory: 1224, loss: 2.0109 2023-01-01 21:49:38,061 - mmcls - INFO - Epoch [41][4300/5005] lr: 1.000e-02, eta: 7:17:52, time: 0.088, data_time: 0.032, memory: 1224, loss: 2.0087 2023-01-01 21:49:46,619 - mmcls - INFO - Epoch [41][4400/5005] lr: 1.000e-02, eta: 7:17:42, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.0201 2023-01-01 21:49:55,248 - mmcls - INFO - Epoch [41][4500/5005] lr: 1.000e-02, eta: 7:17:33, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.9949 2023-01-01 21:50:04,425 - mmcls - INFO - Epoch [41][4600/5005] lr: 1.000e-02, eta: 7:17:25, time: 0.092, data_time: 0.019, memory: 1224, loss: 2.0125 2023-01-01 21:50:13,381 - mmcls - INFO - Epoch [41][4700/5005] lr: 1.000e-02, eta: 7:17:16, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.0039 2023-01-01 21:50:22,422 - mmcls - INFO - Epoch [41][4800/5005] lr: 1.000e-02, eta: 7:17:07, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.0415 2023-01-01 21:50:31,229 - mmcls - INFO - Epoch [41][4900/5005] lr: 1.000e-02, eta: 7:16:58, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0149 2023-01-01 21:50:40,092 - mmcls - INFO - Epoch [41][5000/5005] lr: 1.000e-02, eta: 7:16:49, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.0131 2023-01-01 21:51:02,682 - mmcls - INFO - Epoch(val) [41][196] accuracy_top-1: 60.1460, accuracy_top-5: 82.4200 2023-01-01 21:51:13,537 - mmcls - INFO - Epoch [42][100/5005] lr: 1.000e-02, eta: 7:16:42, time: 0.108, data_time: 0.041, memory: 1224, loss: 1.9721 2023-01-01 21:51:22,418 - mmcls - INFO - Epoch [42][200/5005] lr: 1.000e-02, eta: 7:16:33, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9434 2023-01-01 21:51:31,191 - mmcls - INFO - Epoch [42][300/5005] lr: 1.000e-02, eta: 7:16:24, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9429 2023-01-01 21:51:40,038 - mmcls - INFO - Epoch [42][400/5005] lr: 1.000e-02, eta: 7:16:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9518 2023-01-01 21:51:48,913 - mmcls - INFO - Epoch [42][500/5005] lr: 1.000e-02, eta: 7:16:07, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9602 2023-01-01 21:51:57,525 - mmcls - INFO - Epoch [42][600/5005] lr: 1.000e-02, eta: 7:15:57, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9585 2023-01-01 21:52:06,257 - mmcls - INFO - Epoch [42][700/5005] lr: 1.000e-02, eta: 7:15:48, time: 0.087, data_time: 0.034, memory: 1224, loss: 1.9848 2023-01-01 21:52:15,105 - mmcls - INFO - Epoch [42][800/5005] lr: 1.000e-02, eta: 7:15:39, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9562 2023-01-01 21:52:23,930 - mmcls - INFO - Epoch [42][900/5005] lr: 1.000e-02, eta: 7:15:30, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9558 2023-01-01 21:52:32,981 - mmcls - INFO - Epoch [42][1000/5005] lr: 1.000e-02, eta: 7:15:22, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9609 2023-01-01 21:52:41,688 - mmcls - INFO - Epoch [42][1100/5005] lr: 1.000e-02, eta: 7:15:13, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.9659 2023-01-01 21:52:50,378 - mmcls - INFO - Epoch [42][1200/5005] lr: 1.000e-02, eta: 7:15:04, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9903 2023-01-01 21:52:59,189 - mmcls - INFO - Epoch [42][1300/5005] lr: 1.000e-02, eta: 7:14:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9701 2023-01-01 21:53:08,172 - mmcls - INFO - Epoch [42][1400/5005] lr: 1.000e-02, eta: 7:14:46, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0111 2023-01-01 21:53:16,946 - mmcls - INFO - Epoch [42][1500/5005] lr: 1.000e-02, eta: 7:14:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9948 2023-01-01 21:53:26,049 - mmcls - INFO - Epoch [42][1600/5005] lr: 1.000e-02, eta: 7:14:28, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9565 2023-01-01 21:53:35,031 - mmcls - INFO - Epoch [42][1700/5005] lr: 1.000e-02, eta: 7:14:20, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9859 2023-01-01 21:53:44,213 - mmcls - INFO - Epoch [42][1800/5005] lr: 1.000e-02, eta: 7:14:11, time: 0.092, data_time: 0.044, memory: 1224, loss: 1.9721 2023-01-01 21:53:53,465 - mmcls - INFO - Epoch [42][1900/5005] lr: 1.000e-02, eta: 7:14:03, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.9934 2023-01-01 21:54:02,165 - mmcls - INFO - Epoch [42][2000/5005] lr: 1.000e-02, eta: 7:13:54, time: 0.087, data_time: 0.038, memory: 1224, loss: 2.0165 2023-01-01 21:54:10,901 - mmcls - INFO - Epoch [42][2100/5005] lr: 1.000e-02, eta: 7:13:45, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.9883 2023-01-01 21:54:19,671 - mmcls - INFO - Epoch [42][2200/5005] lr: 1.000e-02, eta: 7:13:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9753 2023-01-01 21:54:28,246 - mmcls - INFO - Epoch [42][2300/5005] lr: 1.000e-02, eta: 7:13:26, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9873 2023-01-01 21:54:37,073 - mmcls - INFO - Epoch [42][2400/5005] lr: 1.000e-02, eta: 7:13:17, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9565 2023-01-01 21:54:45,785 - mmcls - INFO - Epoch [42][2500/5005] lr: 1.000e-02, eta: 7:13:08, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9878 2023-01-01 21:54:54,396 - mmcls - INFO - Epoch [42][2600/5005] lr: 1.000e-02, eta: 7:12:59, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9880 2023-01-01 21:55:03,404 - mmcls - INFO - Epoch [42][2700/5005] lr: 1.000e-02, eta: 7:12:50, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.9737 2023-01-01 21:55:12,158 - mmcls - INFO - Epoch [42][2800/5005] lr: 1.000e-02, eta: 7:12:41, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9949 2023-01-01 21:55:21,238 - mmcls - INFO - Epoch [42][2900/5005] lr: 1.000e-02, eta: 7:12:33, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.9937 2023-01-01 21:55:29,929 - mmcls - INFO - Epoch [42][3000/5005] lr: 1.000e-02, eta: 7:12:23, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9938 2023-01-01 21:55:38,691 - mmcls - INFO - Epoch [42][3100/5005] lr: 1.000e-02, eta: 7:12:14, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.9962 2023-01-01 21:55:47,529 - mmcls - INFO - Epoch [42][3200/5005] lr: 1.000e-02, eta: 7:12:05, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9725 2023-01-01 21:55:56,280 - mmcls - INFO - Epoch [42][3300/5005] lr: 1.000e-02, eta: 7:11:56, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.9933 2023-01-01 21:56:04,922 - mmcls - INFO - Epoch [42][3400/5005] lr: 1.000e-02, eta: 7:11:47, time: 0.087, data_time: 0.027, memory: 1224, loss: 2.0084 2023-01-01 21:56:14,161 - mmcls - INFO - Epoch [42][3500/5005] lr: 1.000e-02, eta: 7:11:39, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.9746 2023-01-01 21:56:23,014 - mmcls - INFO - Epoch [42][3600/5005] lr: 1.000e-02, eta: 7:11:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0211 2023-01-01 21:56:31,621 - mmcls - INFO - Epoch [42][3700/5005] lr: 1.000e-02, eta: 7:11:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9802 2023-01-01 21:56:40,447 - mmcls - INFO - Epoch [42][3800/5005] lr: 1.000e-02, eta: 7:11:12, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.0079 2023-01-01 21:56:49,089 - mmcls - INFO - Epoch [42][3900/5005] lr: 1.000e-02, eta: 7:11:03, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.0166 2023-01-01 21:56:57,885 - mmcls - INFO - Epoch [42][4000/5005] lr: 1.000e-02, eta: 7:10:54, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0262 2023-01-01 21:57:06,621 - mmcls - INFO - Epoch [42][4100/5005] lr: 1.000e-02, eta: 7:10:44, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.0088 2023-01-01 21:57:15,455 - mmcls - INFO - Epoch [42][4200/5005] lr: 1.000e-02, eta: 7:10:36, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9827 2023-01-01 21:57:24,215 - mmcls - INFO - Epoch [42][4300/5005] lr: 1.000e-02, eta: 7:10:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0102 2023-01-01 21:57:32,863 - mmcls - INFO - Epoch [42][4400/5005] lr: 1.000e-02, eta: 7:10:17, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9975 2023-01-01 21:57:41,689 - mmcls - INFO - Epoch [42][4500/5005] lr: 1.000e-02, eta: 7:10:08, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0102 2023-01-01 21:57:50,365 - mmcls - INFO - Epoch [42][4600/5005] lr: 1.000e-02, eta: 7:09:59, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.0131 2023-01-01 21:57:59,328 - mmcls - INFO - Epoch [42][4700/5005] lr: 1.000e-02, eta: 7:09:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0032 2023-01-01 21:58:08,289 - mmcls - INFO - Epoch [42][4800/5005] lr: 1.000e-02, eta: 7:09:42, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.0020 2023-01-01 21:58:17,194 - mmcls - INFO - Epoch [42][4900/5005] lr: 1.000e-02, eta: 7:09:33, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0042 2023-01-01 21:58:26,532 - mmcls - INFO - Epoch [42][5000/5005] lr: 1.000e-02, eta: 7:09:25, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.0050 2023-01-01 21:58:49,292 - mmcls - INFO - Epoch(val) [42][196] accuracy_top-1: 60.2500, accuracy_top-5: 82.4340 2023-01-01 21:59:00,376 - mmcls - INFO - Epoch [43][100/5005] lr: 1.000e-02, eta: 7:09:18, time: 0.111, data_time: 0.040, memory: 1224, loss: 1.9543 2023-01-01 21:59:09,445 - mmcls - INFO - Epoch [43][200/5005] lr: 1.000e-02, eta: 7:09:09, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.9598 2023-01-01 21:59:18,105 - mmcls - INFO - Epoch [43][300/5005] lr: 1.000e-02, eta: 7:09:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9631 2023-01-01 21:59:26,908 - mmcls - INFO - Epoch [43][400/5005] lr: 1.000e-02, eta: 7:08:51, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.9834 2023-01-01 21:59:35,655 - mmcls - INFO - Epoch [43][500/5005] lr: 1.000e-02, eta: 7:08:42, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9775 2023-01-01 21:59:44,358 - mmcls - INFO - Epoch [43][600/5005] lr: 1.000e-02, eta: 7:08:33, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9622 2023-01-01 21:59:53,459 - mmcls - INFO - Epoch [43][700/5005] lr: 1.000e-02, eta: 7:08:24, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9347 2023-01-01 22:00:01,988 - mmcls - INFO - Epoch [43][800/5005] lr: 1.000e-02, eta: 7:08:15, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9557 2023-01-01 22:00:10,941 - mmcls - INFO - Epoch [43][900/5005] lr: 1.000e-02, eta: 7:08:06, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.9650 2023-01-01 22:00:19,828 - mmcls - INFO - Epoch [43][1000/5005] lr: 1.000e-02, eta: 7:07:57, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9689 2023-01-01 22:00:28,480 - mmcls - INFO - Epoch [43][1100/5005] lr: 1.000e-02, eta: 7:07:48, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9662 2023-01-01 22:00:37,270 - mmcls - INFO - Epoch [43][1200/5005] lr: 1.000e-02, eta: 7:07:39, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0032 2023-01-01 22:00:46,204 - mmcls - INFO - Epoch [43][1300/5005] lr: 1.000e-02, eta: 7:07:30, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.9961 2023-01-01 22:00:54,988 - mmcls - INFO - Epoch [43][1400/5005] lr: 1.000e-02, eta: 7:07:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9822 2023-01-01 22:01:04,049 - mmcls - INFO - Epoch [43][1500/5005] lr: 1.000e-02, eta: 7:07:13, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.9730 2023-01-01 22:01:12,968 - mmcls - INFO - Epoch [43][1600/5005] lr: 1.000e-02, eta: 7:07:04, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.0200 2023-01-01 22:01:22,048 - mmcls - INFO - Epoch [43][1700/5005] lr: 1.000e-02, eta: 7:06:55, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9647 2023-01-01 22:01:30,597 - mmcls - INFO - Epoch [43][1800/5005] lr: 1.000e-02, eta: 7:06:46, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.0039 2023-01-01 22:01:39,519 - mmcls - INFO - Epoch [43][1900/5005] lr: 1.000e-02, eta: 7:06:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9793 2023-01-01 22:01:48,262 - mmcls - INFO - Epoch [43][2000/5005] lr: 1.000e-02, eta: 7:06:28, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9827 2023-01-01 22:01:57,062 - mmcls - INFO - Epoch [43][2100/5005] lr: 1.000e-02, eta: 7:06:19, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9911 2023-01-01 22:02:05,982 - mmcls - INFO - Epoch [43][2200/5005] lr: 1.000e-02, eta: 7:06:10, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9853 2023-01-01 22:02:14,674 - mmcls - INFO - Epoch [43][2300/5005] lr: 1.000e-02, eta: 7:06:01, time: 0.087, data_time: 0.036, memory: 1224, loss: 1.9887 2023-01-01 22:02:23,296 - mmcls - INFO - Epoch [43][2400/5005] lr: 1.000e-02, eta: 7:05:52, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9852 2023-01-01 22:02:32,069 - mmcls - INFO - Epoch [43][2500/5005] lr: 1.000e-02, eta: 7:05:43, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0018 2023-01-01 22:02:40,856 - mmcls - INFO - Epoch [43][2600/5005] lr: 1.000e-02, eta: 7:05:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9805 2023-01-01 22:02:49,684 - mmcls - INFO - Epoch [43][2700/5005] lr: 1.000e-02, eta: 7:05:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0048 2023-01-01 22:02:58,740 - mmcls - INFO - Epoch [43][2800/5005] lr: 1.000e-02, eta: 7:05:16, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9770 2023-01-01 22:03:07,285 - mmcls - INFO - Epoch [43][2900/5005] lr: 1.000e-02, eta: 7:05:07, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.9701 2023-01-01 22:03:15,906 - mmcls - INFO - Epoch [43][3000/5005] lr: 1.000e-02, eta: 7:04:58, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9857 2023-01-01 22:03:24,415 - mmcls - INFO - Epoch [43][3100/5005] lr: 1.000e-02, eta: 7:04:48, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9919 2023-01-01 22:03:33,185 - mmcls - INFO - Epoch [43][3200/5005] lr: 1.000e-02, eta: 7:04:39, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9995 2023-01-01 22:03:42,020 - mmcls - INFO - Epoch [43][3300/5005] lr: 1.000e-02, eta: 7:04:30, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0059 2023-01-01 22:03:50,891 - mmcls - INFO - Epoch [43][3400/5005] lr: 1.000e-02, eta: 7:04:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.0069 2023-01-01 22:03:59,637 - mmcls - INFO - Epoch [43][3500/5005] lr: 1.000e-02, eta: 7:04:12, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9900 2023-01-01 22:04:08,224 - mmcls - INFO - Epoch [43][3600/5005] lr: 1.000e-02, eta: 7:04:03, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9981 2023-01-01 22:04:17,113 - mmcls - INFO - Epoch [43][3700/5005] lr: 1.000e-02, eta: 7:03:54, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.0301 2023-01-01 22:04:25,863 - mmcls - INFO - Epoch [43][3800/5005] lr: 1.000e-02, eta: 7:03:45, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9814 2023-01-01 22:04:34,610 - mmcls - INFO - Epoch [43][3900/5005] lr: 1.000e-02, eta: 7:03:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9925 2023-01-01 22:04:43,201 - mmcls - INFO - Epoch [43][4000/5005] lr: 1.000e-02, eta: 7:03:27, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.9893 2023-01-01 22:04:51,951 - mmcls - INFO - Epoch [43][4100/5005] lr: 1.000e-02, eta: 7:03:18, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.9826 2023-01-01 22:05:00,941 - mmcls - INFO - Epoch [43][4200/5005] lr: 1.000e-02, eta: 7:03:09, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.9609 2023-01-01 22:05:09,498 - mmcls - INFO - Epoch [43][4300/5005] lr: 1.000e-02, eta: 7:03:00, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9838 2023-01-01 22:05:18,596 - mmcls - INFO - Epoch [43][4400/5005] lr: 1.000e-02, eta: 7:02:51, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.0039 2023-01-01 22:05:27,411 - mmcls - INFO - Epoch [43][4500/5005] lr: 1.000e-02, eta: 7:02:42, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.9942 2023-01-01 22:05:36,519 - mmcls - INFO - Epoch [43][4600/5005] lr: 1.000e-02, eta: 7:02:34, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9952 2023-01-01 22:05:45,154 - mmcls - INFO - Epoch [43][4700/5005] lr: 1.000e-02, eta: 7:02:25, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9911 2023-01-01 22:05:54,394 - mmcls - INFO - Epoch [43][4800/5005] lr: 1.000e-02, eta: 7:02:16, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.0122 2023-01-01 22:06:03,317 - mmcls - INFO - Epoch [43][4900/5005] lr: 1.000e-02, eta: 7:02:07, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9743 2023-01-01 22:06:12,271 - mmcls - INFO - Epoch [43][5000/5005] lr: 1.000e-02, eta: 7:01:59, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9938 2023-01-01 22:06:35,255 - mmcls - INFO - Epoch(val) [43][196] accuracy_top-1: 58.9880, accuracy_top-5: 81.5180 2023-01-01 22:06:46,397 - mmcls - INFO - Epoch [44][100/5005] lr: 1.000e-02, eta: 7:01:52, time: 0.111, data_time: 0.042, memory: 1224, loss: 1.9538 2023-01-01 22:06:55,369 - mmcls - INFO - Epoch [44][200/5005] lr: 1.000e-02, eta: 7:01:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9397 2023-01-01 22:07:04,413 - mmcls - INFO - Epoch [44][300/5005] lr: 1.000e-02, eta: 7:01:34, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9440 2023-01-01 22:07:12,896 - mmcls - INFO - Epoch [44][400/5005] lr: 1.000e-02, eta: 7:01:25, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9648 2023-01-01 22:07:21,913 - mmcls - INFO - Epoch [44][500/5005] lr: 1.000e-02, eta: 7:01:16, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9362 2023-01-01 22:07:30,738 - mmcls - INFO - Epoch [44][600/5005] lr: 1.000e-02, eta: 7:01:07, time: 0.088, data_time: 0.036, memory: 1224, loss: 1.9585 2023-01-01 22:07:39,842 - mmcls - INFO - Epoch [44][700/5005] lr: 1.000e-02, eta: 7:00:59, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.9414 2023-01-01 22:07:48,411 - mmcls - INFO - Epoch [44][800/5005] lr: 1.000e-02, eta: 7:00:49, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9659 2023-01-01 22:07:57,271 - mmcls - INFO - Epoch [44][900/5005] lr: 1.000e-02, eta: 7:00:41, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.9740 2023-01-01 22:08:05,824 - mmcls - INFO - Epoch [44][1000/5005] lr: 1.000e-02, eta: 7:00:31, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9624 2023-01-01 22:08:14,364 - mmcls - INFO - Epoch [44][1100/5005] lr: 1.000e-02, eta: 7:00:22, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9713 2023-01-01 22:08:23,332 - mmcls - INFO - Epoch [44][1200/5005] lr: 1.000e-02, eta: 7:00:13, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.9555 2023-01-01 22:08:32,144 - mmcls - INFO - Epoch [44][1300/5005] lr: 1.000e-02, eta: 7:00:04, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.9780 2023-01-01 22:08:40,928 - mmcls - INFO - Epoch [44][1400/5005] lr: 1.000e-02, eta: 6:59:55, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9576 2023-01-01 22:08:49,669 - mmcls - INFO - Epoch [44][1500/5005] lr: 1.000e-02, eta: 6:59:46, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9873 2023-01-01 22:08:58,459 - mmcls - INFO - Epoch [44][1600/5005] lr: 1.000e-02, eta: 6:59:37, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.9639 2023-01-01 22:09:07,208 - mmcls - INFO - Epoch [44][1700/5005] lr: 1.000e-02, eta: 6:59:28, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9800 2023-01-01 22:09:15,927 - mmcls - INFO - Epoch [44][1800/5005] lr: 1.000e-02, eta: 6:59:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0027 2023-01-01 22:09:24,716 - mmcls - INFO - Epoch [44][1900/5005] lr: 1.000e-02, eta: 6:59:10, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9543 2023-01-01 22:09:33,627 - mmcls - INFO - Epoch [44][2000/5005] lr: 1.000e-02, eta: 6:59:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9872 2023-01-01 22:09:42,239 - mmcls - INFO - Epoch [44][2100/5005] lr: 1.000e-02, eta: 6:58:52, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9748 2023-01-01 22:09:50,948 - mmcls - INFO - Epoch [44][2200/5005] lr: 1.000e-02, eta: 6:58:43, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9699 2023-01-01 22:09:59,780 - mmcls - INFO - Epoch [44][2300/5005] lr: 1.000e-02, eta: 6:58:34, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9956 2023-01-01 22:10:08,407 - mmcls - INFO - Epoch [44][2400/5005] lr: 1.000e-02, eta: 6:58:25, time: 0.086, data_time: 0.016, memory: 1224, loss: 2.0078 2023-01-01 22:10:17,315 - mmcls - INFO - Epoch [44][2500/5005] lr: 1.000e-02, eta: 6:58:16, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9806 2023-01-01 22:10:26,330 - mmcls - INFO - Epoch [44][2600/5005] lr: 1.000e-02, eta: 6:58:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9993 2023-01-01 22:10:34,871 - mmcls - INFO - Epoch [44][2700/5005] lr: 1.000e-02, eta: 6:57:58, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.0132 2023-01-01 22:10:43,778 - mmcls - INFO - Epoch [44][2800/5005] lr: 1.000e-02, eta: 6:57:49, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9746 2023-01-01 22:10:52,659 - mmcls - INFO - Epoch [44][2900/5005] lr: 1.000e-02, eta: 6:57:40, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9905 2023-01-01 22:11:01,396 - mmcls - INFO - Epoch [44][3000/5005] lr: 1.000e-02, eta: 6:57:31, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9877 2023-01-01 22:11:10,147 - mmcls - INFO - Epoch [44][3100/5005] lr: 1.000e-02, eta: 6:57:22, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.9986 2023-01-01 22:11:18,819 - mmcls - INFO - Epoch [44][3200/5005] lr: 1.000e-02, eta: 6:57:13, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.0021 2023-01-01 22:11:27,435 - mmcls - INFO - Epoch [44][3300/5005] lr: 1.000e-02, eta: 6:57:04, time: 0.086, data_time: 0.026, memory: 1224, loss: 2.0049 2023-01-01 22:11:36,112 - mmcls - INFO - Epoch [44][3400/5005] lr: 1.000e-02, eta: 6:56:55, time: 0.087, data_time: 0.039, memory: 1224, loss: 2.0108 2023-01-01 22:11:44,925 - mmcls - INFO - Epoch [44][3500/5005] lr: 1.000e-02, eta: 6:56:46, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.0099 2023-01-01 22:11:53,644 - mmcls - INFO - Epoch [44][3600/5005] lr: 1.000e-02, eta: 6:56:37, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9849 2023-01-01 22:12:02,388 - mmcls - INFO - Epoch [44][3700/5005] lr: 1.000e-02, eta: 6:56:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9945 2023-01-01 22:12:11,259 - mmcls - INFO - Epoch [44][3800/5005] lr: 1.000e-02, eta: 6:56:19, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.0150 2023-01-01 22:12:20,381 - mmcls - INFO - Epoch [44][3900/5005] lr: 1.000e-02, eta: 6:56:10, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9821 2023-01-01 22:12:29,052 - mmcls - INFO - Epoch [44][4000/5005] lr: 1.000e-02, eta: 6:56:01, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.0252 2023-01-01 22:12:37,973 - mmcls - INFO - Epoch [44][4100/5005] lr: 1.000e-02, eta: 6:55:52, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.0007 2023-01-01 22:12:46,734 - mmcls - INFO - Epoch [44][4200/5005] lr: 1.000e-02, eta: 6:55:43, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9891 2023-01-01 22:12:55,593 - mmcls - INFO - Epoch [44][4300/5005] lr: 1.000e-02, eta: 6:55:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9783 2023-01-01 22:13:04,220 - mmcls - INFO - Epoch [44][4400/5005] lr: 1.000e-02, eta: 6:55:25, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9780 2023-01-01 22:13:13,241 - mmcls - INFO - Epoch [44][4500/5005] lr: 1.000e-02, eta: 6:55:16, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9969 2023-01-01 22:13:21,906 - mmcls - INFO - Epoch [44][4600/5005] lr: 1.000e-02, eta: 6:55:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0039 2023-01-01 22:13:30,481 - mmcls - INFO - Epoch [44][4700/5005] lr: 1.000e-02, eta: 6:54:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0019 2023-01-01 22:13:39,228 - mmcls - INFO - Epoch [44][4800/5005] lr: 1.000e-02, eta: 6:54:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0268 2023-01-01 22:13:48,357 - mmcls - INFO - Epoch [44][4900/5005] lr: 1.000e-02, eta: 6:54:40, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.0143 2023-01-01 22:13:57,396 - mmcls - INFO - Epoch [44][5000/5005] lr: 1.000e-02, eta: 6:54:32, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.0219 2023-01-01 22:14:19,926 - mmcls - INFO - Epoch(val) [44][196] accuracy_top-1: 60.0740, accuracy_top-5: 82.3200 2023-01-01 22:14:30,936 - mmcls - INFO - Epoch [45][100/5005] lr: 1.000e-02, eta: 6:54:25, time: 0.110, data_time: 0.048, memory: 1224, loss: 1.9773 2023-01-01 22:14:39,689 - mmcls - INFO - Epoch [45][200/5005] lr: 1.000e-02, eta: 6:54:16, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.9544 2023-01-01 22:14:48,759 - mmcls - INFO - Epoch [45][300/5005] lr: 1.000e-02, eta: 6:54:07, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9753 2023-01-01 22:14:57,601 - mmcls - INFO - Epoch [45][400/5005] lr: 1.000e-02, eta: 6:53:58, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9565 2023-01-01 22:15:06,152 - mmcls - INFO - Epoch [45][500/5005] lr: 1.000e-02, eta: 6:53:49, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.9583 2023-01-01 22:15:15,083 - mmcls - INFO - Epoch [45][600/5005] lr: 1.000e-02, eta: 6:53:40, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.9509 2023-01-01 22:15:23,607 - mmcls - INFO - Epoch [45][700/5005] lr: 1.000e-02, eta: 6:53:31, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.9662 2023-01-01 22:15:32,611 - mmcls - INFO - Epoch [45][800/5005] lr: 1.000e-02, eta: 6:53:22, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.9630 2023-01-01 22:15:41,735 - mmcls - INFO - Epoch [45][900/5005] lr: 1.000e-02, eta: 6:53:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9597 2023-01-01 22:15:50,259 - mmcls - INFO - Epoch [45][1000/5005] lr: 1.000e-02, eta: 6:53:04, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.9673 2023-01-01 22:15:59,112 - mmcls - INFO - Epoch [45][1100/5005] lr: 1.000e-02, eta: 6:52:55, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9633 2023-01-01 22:16:07,848 - mmcls - INFO - Epoch [45][1200/5005] lr: 1.000e-02, eta: 6:52:46, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9475 2023-01-01 22:16:16,485 - mmcls - INFO - Epoch [45][1300/5005] lr: 1.000e-02, eta: 6:52:37, time: 0.086, data_time: 0.030, memory: 1224, loss: 1.9815 2023-01-01 22:16:25,093 - mmcls - INFO - Epoch [45][1400/5005] lr: 1.000e-02, eta: 6:52:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9580 2023-01-01 22:16:33,631 - mmcls - INFO - Epoch [45][1500/5005] lr: 1.000e-02, eta: 6:52:18, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.9495 2023-01-01 22:16:42,416 - mmcls - INFO - Epoch [45][1600/5005] lr: 1.000e-02, eta: 6:52:09, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.9599 2023-01-01 22:16:51,142 - mmcls - INFO - Epoch [45][1700/5005] lr: 1.000e-02, eta: 6:52:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.0034 2023-01-01 22:17:00,359 - mmcls - INFO - Epoch [45][1800/5005] lr: 1.000e-02, eta: 6:51:52, time: 0.092, data_time: 0.000, memory: 1224, loss: 1.9618 2023-01-01 22:17:09,382 - mmcls - INFO - Epoch [45][1900/5005] lr: 1.000e-02, eta: 6:51:43, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.9545 2023-01-01 22:17:18,262 - mmcls - INFO - Epoch [45][2000/5005] lr: 1.000e-02, eta: 6:51:34, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9778 2023-01-01 22:17:27,186 - mmcls - INFO - Epoch [45][2100/5005] lr: 1.000e-02, eta: 6:51:26, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.9717 2023-01-01 22:17:35,783 - mmcls - INFO - Epoch [45][2200/5005] lr: 1.000e-02, eta: 6:51:16, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.0058 2023-01-01 22:17:44,470 - mmcls - INFO - Epoch [45][2300/5005] lr: 1.000e-02, eta: 6:51:07, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.9616 2023-01-01 22:17:53,113 - mmcls - INFO - Epoch [45][2400/5005] lr: 1.000e-02, eta: 6:50:58, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9691 2023-01-01 22:18:02,102 - mmcls - INFO - Epoch [45][2500/5005] lr: 1.000e-02, eta: 6:50:49, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9982 2023-01-01 22:18:10,947 - mmcls - INFO - Epoch [45][2600/5005] lr: 1.000e-02, eta: 6:50:40, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.0059 2023-01-01 22:18:19,628 - mmcls - INFO - Epoch [45][2700/5005] lr: 1.000e-02, eta: 6:50:31, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.9944 2023-01-01 22:18:28,346 - mmcls - INFO - Epoch [45][2800/5005] lr: 1.000e-02, eta: 6:50:22, time: 0.087, data_time: 0.042, memory: 1224, loss: 1.9821 2023-01-01 22:18:37,190 - mmcls - INFO - Epoch [45][2900/5005] lr: 1.000e-02, eta: 6:50:13, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9866 2023-01-01 22:18:45,830 - mmcls - INFO - Epoch [45][3000/5005] lr: 1.000e-02, eta: 6:50:04, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9930 2023-01-01 22:18:54,768 - mmcls - INFO - Epoch [45][3100/5005] lr: 1.000e-02, eta: 6:49:55, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9896 2023-01-01 22:19:03,460 - mmcls - INFO - Epoch [45][3200/5005] lr: 1.000e-02, eta: 6:49:46, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.9784 2023-01-01 22:19:12,093 - mmcls - INFO - Epoch [45][3300/5005] lr: 1.000e-02, eta: 6:49:37, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9775 2023-01-01 22:19:20,898 - mmcls - INFO - Epoch [45][3400/5005] lr: 1.000e-02, eta: 6:49:28, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.0002 2023-01-01 22:19:29,827 - mmcls - INFO - Epoch [45][3500/5005] lr: 1.000e-02, eta: 6:49:19, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9742 2023-01-01 22:19:38,228 - mmcls - INFO - Epoch [45][3600/5005] lr: 1.000e-02, eta: 6:49:10, time: 0.084, data_time: 0.011, memory: 1224, loss: 1.9808 2023-01-01 22:19:46,942 - mmcls - INFO - Epoch [45][3700/5005] lr: 1.000e-02, eta: 6:49:01, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.9719 2023-01-01 22:19:55,806 - mmcls - INFO - Epoch [45][3800/5005] lr: 1.000e-02, eta: 6:48:52, time: 0.089, data_time: 0.037, memory: 1224, loss: 1.9952 2023-01-01 22:20:04,734 - mmcls - INFO - Epoch [45][3900/5005] lr: 1.000e-02, eta: 6:48:43, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9781 2023-01-01 22:20:13,721 - mmcls - INFO - Epoch [45][4000/5005] lr: 1.000e-02, eta: 6:48:34, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.9893 2023-01-01 22:20:22,868 - mmcls - INFO - Epoch [45][4100/5005] lr: 1.000e-02, eta: 6:48:26, time: 0.092, data_time: 0.013, memory: 1224, loss: 1.9751 2023-01-01 22:20:31,743 - mmcls - INFO - Epoch [45][4200/5005] lr: 1.000e-02, eta: 6:48:17, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9991 2023-01-01 22:20:40,303 - mmcls - INFO - Epoch [45][4300/5005] lr: 1.000e-02, eta: 6:48:08, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.9969 2023-01-01 22:20:49,010 - mmcls - INFO - Epoch [45][4400/5005] lr: 1.000e-02, eta: 6:47:59, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9999 2023-01-01 22:20:57,800 - mmcls - INFO - Epoch [45][4500/5005] lr: 1.000e-02, eta: 6:47:50, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9923 2023-01-01 22:21:06,485 - mmcls - INFO - Epoch [45][4600/5005] lr: 1.000e-02, eta: 6:47:40, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9889 2023-01-01 22:21:15,445 - mmcls - INFO - Epoch [45][4700/5005] lr: 1.000e-02, eta: 6:47:32, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9776 2023-01-01 22:21:24,328 - mmcls - INFO - Epoch [45][4800/5005] lr: 1.000e-02, eta: 6:47:23, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.9914 2023-01-01 22:21:33,035 - mmcls - INFO - Epoch [45][4900/5005] lr: 1.000e-02, eta: 6:47:14, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.0196 2023-01-01 22:21:41,984 - mmcls - INFO - Epoch [45][5000/5005] lr: 1.000e-02, eta: 6:47:05, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.0049 2023-01-01 22:22:04,915 - mmcls - INFO - Epoch(val) [45][196] accuracy_top-1: 59.9800, accuracy_top-5: 82.2840 2023-01-01 22:22:15,716 - mmcls - INFO - Epoch [46][100/5005] lr: 1.000e-02, eta: 6:46:57, time: 0.108, data_time: 0.040, memory: 1224, loss: 1.9558 2023-01-01 22:22:24,968 - mmcls - INFO - Epoch [46][200/5005] lr: 1.000e-02, eta: 6:46:49, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.9559 2023-01-01 22:22:33,660 - mmcls - INFO - Epoch [46][300/5005] lr: 1.000e-02, eta: 6:46:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9331 2023-01-01 22:22:42,483 - mmcls - INFO - Epoch [46][400/5005] lr: 1.000e-02, eta: 6:46:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9462 2023-01-01 22:22:51,296 - mmcls - INFO - Epoch [46][500/5005] lr: 1.000e-02, eta: 6:46:22, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9699 2023-01-01 22:23:00,372 - mmcls - INFO - Epoch [46][600/5005] lr: 1.000e-02, eta: 6:46:13, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.9612 2023-01-01 22:23:09,365 - mmcls - INFO - Epoch [46][700/5005] lr: 1.000e-02, eta: 6:46:05, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.9596 2023-01-01 22:23:18,459 - mmcls - INFO - Epoch [46][800/5005] lr: 1.000e-02, eta: 6:45:56, time: 0.091, data_time: 0.027, memory: 1224, loss: 1.9675 2023-01-01 22:23:27,584 - mmcls - INFO - Epoch [46][900/5005] lr: 1.000e-02, eta: 6:45:48, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.9823 2023-01-01 22:23:36,089 - mmcls - INFO - Epoch [46][1000/5005] lr: 1.000e-02, eta: 6:45:38, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.9681 2023-01-01 22:23:45,000 - mmcls - INFO - Epoch [46][1100/5005] lr: 1.000e-02, eta: 6:45:29, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9721 2023-01-01 22:23:53,716 - mmcls - INFO - Epoch [46][1200/5005] lr: 1.000e-02, eta: 6:45:20, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9756 2023-01-01 22:24:02,503 - mmcls - INFO - Epoch [46][1300/5005] lr: 1.000e-02, eta: 6:45:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9684 2023-01-01 22:24:11,310 - mmcls - INFO - Epoch [46][1400/5005] lr: 1.000e-02, eta: 6:45:02, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.9644 2023-01-01 22:24:20,158 - mmcls - INFO - Epoch [46][1500/5005] lr: 1.000e-02, eta: 6:44:53, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9677 2023-01-01 22:24:28,871 - mmcls - INFO - Epoch [46][1600/5005] lr: 1.000e-02, eta: 6:44:44, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9747 2023-01-01 22:24:37,638 - mmcls - INFO - Epoch [46][1700/5005] lr: 1.000e-02, eta: 6:44:35, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9806 2023-01-01 22:24:46,269 - mmcls - INFO - Epoch [46][1800/5005] lr: 1.000e-02, eta: 6:44:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9736 2023-01-01 22:24:54,912 - mmcls - INFO - Epoch [46][1900/5005] lr: 1.000e-02, eta: 6:44:17, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0075 2023-01-01 22:25:03,823 - mmcls - INFO - Epoch [46][2000/5005] lr: 1.000e-02, eta: 6:44:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9765 2023-01-01 22:25:12,449 - mmcls - INFO - Epoch [46][2100/5005] lr: 1.000e-02, eta: 6:43:59, time: 0.086, data_time: 0.031, memory: 1224, loss: 1.9795 2023-01-01 22:25:21,103 - mmcls - INFO - Epoch [46][2200/5005] lr: 1.000e-02, eta: 6:43:50, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.9791 2023-01-01 22:25:30,285 - mmcls - INFO - Epoch [46][2300/5005] lr: 1.000e-02, eta: 6:43:41, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9955 2023-01-01 22:25:39,391 - mmcls - INFO - Epoch [46][2400/5005] lr: 1.000e-02, eta: 6:43:33, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9640 2023-01-01 22:25:48,355 - mmcls - INFO - Epoch [46][2500/5005] lr: 1.000e-02, eta: 6:43:24, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9758 2023-01-01 22:25:57,376 - mmcls - INFO - Epoch [46][2600/5005] lr: 1.000e-02, eta: 6:43:15, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9810 2023-01-01 22:26:06,268 - mmcls - INFO - Epoch [46][2700/5005] lr: 1.000e-02, eta: 6:43:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9806 2023-01-01 22:26:14,886 - mmcls - INFO - Epoch [46][2800/5005] lr: 1.000e-02, eta: 6:42:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.0001 2023-01-01 22:26:23,916 - mmcls - INFO - Epoch [46][2900/5005] lr: 1.000e-02, eta: 6:42:49, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.0063 2023-01-01 22:26:32,695 - mmcls - INFO - Epoch [46][3000/5005] lr: 1.000e-02, eta: 6:42:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0072 2023-01-01 22:26:41,444 - mmcls - INFO - Epoch [46][3100/5005] lr: 1.000e-02, eta: 6:42:31, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9813 2023-01-01 22:26:50,172 - mmcls - INFO - Epoch [46][3200/5005] lr: 1.000e-02, eta: 6:42:22, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9799 2023-01-01 22:26:58,955 - mmcls - INFO - Epoch [46][3300/5005] lr: 1.000e-02, eta: 6:42:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9887 2023-01-01 22:27:07,566 - mmcls - INFO - Epoch [46][3400/5005] lr: 1.000e-02, eta: 6:42:03, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.0079 2023-01-01 22:27:16,229 - mmcls - INFO - Epoch [46][3500/5005] lr: 1.000e-02, eta: 6:41:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9707 2023-01-01 22:27:25,204 - mmcls - INFO - Epoch [46][3600/5005] lr: 1.000e-02, eta: 6:41:46, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9809 2023-01-01 22:27:34,018 - mmcls - INFO - Epoch [46][3700/5005] lr: 1.000e-02, eta: 6:41:37, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.0062 2023-01-01 22:27:42,673 - mmcls - INFO - Epoch [46][3800/5005] lr: 1.000e-02, eta: 6:41:27, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0000 2023-01-01 22:27:51,321 - mmcls - INFO - Epoch [46][3900/5005] lr: 1.000e-02, eta: 6:41:18, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9771 2023-01-01 22:28:00,025 - mmcls - INFO - Epoch [46][4000/5005] lr: 1.000e-02, eta: 6:41:09, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9837 2023-01-01 22:28:08,890 - mmcls - INFO - Epoch [46][4100/5005] lr: 1.000e-02, eta: 6:41:00, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9821 2023-01-01 22:28:17,588 - mmcls - INFO - Epoch [46][4200/5005] lr: 1.000e-02, eta: 6:40:51, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.0003 2023-01-01 22:28:26,586 - mmcls - INFO - Epoch [46][4300/5005] lr: 1.000e-02, eta: 6:40:43, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.9898 2023-01-01 22:28:35,586 - mmcls - INFO - Epoch [46][4400/5005] lr: 1.000e-02, eta: 6:40:34, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9894 2023-01-01 22:28:44,321 - mmcls - INFO - Epoch [46][4500/5005] lr: 1.000e-02, eta: 6:40:25, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.0103 2023-01-01 22:28:52,888 - mmcls - INFO - Epoch [46][4600/5005] lr: 1.000e-02, eta: 6:40:16, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.0016 2023-01-01 22:29:01,533 - mmcls - INFO - Epoch [46][4700/5005] lr: 1.000e-02, eta: 6:40:06, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9945 2023-01-01 22:29:10,657 - mmcls - INFO - Epoch [46][4800/5005] lr: 1.000e-02, eta: 6:39:58, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.0209 2023-01-01 22:29:19,866 - mmcls - INFO - Epoch [46][4900/5005] lr: 1.000e-02, eta: 6:39:49, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.9855 2023-01-01 22:29:28,820 - mmcls - INFO - Epoch [46][5000/5005] lr: 1.000e-02, eta: 6:39:41, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.0082 2023-01-01 22:29:51,562 - mmcls - INFO - Epoch(val) [46][196] accuracy_top-1: 60.1540, accuracy_top-5: 82.4240 2023-01-01 22:30:02,745 - mmcls - INFO - Epoch [47][100/5005] lr: 1.000e-02, eta: 6:39:33, time: 0.112, data_time: 0.064, memory: 1224, loss: 1.9347 2023-01-01 22:30:11,476 - mmcls - INFO - Epoch [47][200/5005] lr: 1.000e-02, eta: 6:39:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9294 2023-01-01 22:30:20,231 - mmcls - INFO - Epoch [47][300/5005] lr: 1.000e-02, eta: 6:39:15, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.9479 2023-01-01 22:30:28,865 - mmcls - INFO - Epoch [47][400/5005] lr: 1.000e-02, eta: 6:39:06, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.9499 2023-01-01 22:30:37,719 - mmcls - INFO - Epoch [47][500/5005] lr: 1.000e-02, eta: 6:38:57, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.9551 2023-01-01 22:30:46,574 - mmcls - INFO - Epoch [47][600/5005] lr: 1.000e-02, eta: 6:38:48, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.9452 2023-01-01 22:30:55,146 - mmcls - INFO - Epoch [47][700/5005] lr: 1.000e-02, eta: 6:38:39, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9622 2023-01-01 22:31:04,239 - mmcls - INFO - Epoch [47][800/5005] lr: 1.000e-02, eta: 6:38:31, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.9897 2023-01-01 22:31:12,745 - mmcls - INFO - Epoch [47][900/5005] lr: 1.000e-02, eta: 6:38:21, time: 0.085, data_time: 0.036, memory: 1224, loss: 1.9671 2023-01-01 22:31:21,475 - mmcls - INFO - Epoch [47][1000/5005] lr: 1.000e-02, eta: 6:38:12, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9741 2023-01-01 22:31:30,275 - mmcls - INFO - Epoch [47][1100/5005] lr: 1.000e-02, eta: 6:38:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9603 2023-01-01 22:31:38,900 - mmcls - INFO - Epoch [47][1200/5005] lr: 1.000e-02, eta: 6:37:54, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9545 2023-01-01 22:31:47,666 - mmcls - INFO - Epoch [47][1300/5005] lr: 1.000e-02, eta: 6:37:45, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9501 2023-01-01 22:31:56,364 - mmcls - INFO - Epoch [47][1400/5005] lr: 1.000e-02, eta: 6:37:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9916 2023-01-01 22:32:05,722 - mmcls - INFO - Epoch [47][1500/5005] lr: 1.000e-02, eta: 6:37:28, time: 0.094, data_time: 0.018, memory: 1224, loss: 1.9951 2023-01-01 22:32:14,476 - mmcls - INFO - Epoch [47][1600/5005] lr: 1.000e-02, eta: 6:37:19, time: 0.088, data_time: 0.035, memory: 1224, loss: 1.9696 2023-01-01 22:32:23,441 - mmcls - INFO - Epoch [47][1700/5005] lr: 1.000e-02, eta: 6:37:10, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9574 2023-01-01 22:32:32,662 - mmcls - INFO - Epoch [47][1800/5005] lr: 1.000e-02, eta: 6:37:02, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.9826 2023-01-01 22:32:41,670 - mmcls - INFO - Epoch [47][1900/5005] lr: 1.000e-02, eta: 6:36:53, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9744 2023-01-01 22:32:50,751 - mmcls - INFO - Epoch [47][2000/5005] lr: 1.000e-02, eta: 6:36:44, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.9650 2023-01-01 22:32:59,479 - mmcls - INFO - Epoch [47][2100/5005] lr: 1.000e-02, eta: 6:36:35, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.9618 2023-01-01 22:33:08,155 - mmcls - INFO - Epoch [47][2200/5005] lr: 1.000e-02, eta: 6:36:26, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9835 2023-01-01 22:33:17,196 - mmcls - INFO - Epoch [47][2300/5005] lr: 1.000e-02, eta: 6:36:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9663 2023-01-01 22:33:26,076 - mmcls - INFO - Epoch [47][2400/5005] lr: 1.000e-02, eta: 6:36:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9600 2023-01-01 22:33:35,027 - mmcls - INFO - Epoch [47][2500/5005] lr: 1.000e-02, eta: 6:36:00, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.0168 2023-01-01 22:33:43,667 - mmcls - INFO - Epoch [47][2600/5005] lr: 1.000e-02, eta: 6:35:51, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9639 2023-01-01 22:33:53,218 - mmcls - INFO - Epoch [47][2700/5005] lr: 1.000e-02, eta: 6:35:42, time: 0.095, data_time: 0.018, memory: 1224, loss: 1.9691 2023-01-01 22:34:02,022 - mmcls - INFO - Epoch [47][2800/5005] lr: 1.000e-02, eta: 6:35:33, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9954 2023-01-01 22:34:11,015 - mmcls - INFO - Epoch [47][2900/5005] lr: 1.000e-02, eta: 6:35:25, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.9947 2023-01-01 22:34:19,499 - mmcls - INFO - Epoch [47][3000/5005] lr: 1.000e-02, eta: 6:35:15, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9637 2023-01-01 22:34:28,190 - mmcls - INFO - Epoch [47][3100/5005] lr: 1.000e-02, eta: 6:35:06, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.0162 2023-01-01 22:34:36,894 - mmcls - INFO - Epoch [47][3200/5005] lr: 1.000e-02, eta: 6:34:57, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.0215 2023-01-01 22:34:45,847 - mmcls - INFO - Epoch [47][3300/5005] lr: 1.000e-02, eta: 6:34:48, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.9878 2023-01-01 22:34:54,312 - mmcls - INFO - Epoch [47][3400/5005] lr: 1.000e-02, eta: 6:34:39, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.9978 2023-01-01 22:35:02,908 - mmcls - INFO - Epoch [47][3500/5005] lr: 1.000e-02, eta: 6:34:30, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.0133 2023-01-01 22:35:11,645 - mmcls - INFO - Epoch [47][3600/5005] lr: 1.000e-02, eta: 6:34:21, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9963 2023-01-01 22:35:20,436 - mmcls - INFO - Epoch [47][3700/5005] lr: 1.000e-02, eta: 6:34:12, time: 0.088, data_time: 0.024, memory: 1224, loss: 2.0108 2023-01-01 22:35:29,215 - mmcls - INFO - Epoch [47][3800/5005] lr: 1.000e-02, eta: 6:34:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9803 2023-01-01 22:35:37,763 - mmcls - INFO - Epoch [47][3900/5005] lr: 1.000e-02, eta: 6:33:54, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.9702 2023-01-01 22:35:46,855 - mmcls - INFO - Epoch [47][4000/5005] lr: 1.000e-02, eta: 6:33:45, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.9678 2023-01-01 22:35:55,717 - mmcls - INFO - Epoch [47][4100/5005] lr: 1.000e-02, eta: 6:33:36, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.0200 2023-01-01 22:36:04,413 - mmcls - INFO - Epoch [47][4200/5005] lr: 1.000e-02, eta: 6:33:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.0120 2023-01-01 22:36:13,165 - mmcls - INFO - Epoch [47][4300/5005] lr: 1.000e-02, eta: 6:33:18, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9894 2023-01-01 22:36:21,919 - mmcls - INFO - Epoch [47][4400/5005] lr: 1.000e-02, eta: 6:33:09, time: 0.088, data_time: 0.037, memory: 1224, loss: 1.9911 2023-01-01 22:36:30,913 - mmcls - INFO - Epoch [47][4500/5005] lr: 1.000e-02, eta: 6:33:00, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.9695 2023-01-01 22:36:40,011 - mmcls - INFO - Epoch [47][4600/5005] lr: 1.000e-02, eta: 6:32:52, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.0177 2023-01-01 22:36:49,161 - mmcls - INFO - Epoch [47][4700/5005] lr: 1.000e-02, eta: 6:32:43, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.9954 2023-01-01 22:36:57,799 - mmcls - INFO - Epoch [47][4800/5005] lr: 1.000e-02, eta: 6:32:34, time: 0.086, data_time: 0.025, memory: 1224, loss: 2.0167 2023-01-01 22:37:06,483 - mmcls - INFO - Epoch [47][4900/5005] lr: 1.000e-02, eta: 6:32:25, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.0093 2023-01-01 22:37:15,515 - mmcls - INFO - Epoch [47][5000/5005] lr: 1.000e-02, eta: 6:32:16, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.0198 2023-01-01 22:37:38,230 - mmcls - INFO - Epoch(val) [47][196] accuracy_top-1: 60.1000, accuracy_top-5: 82.1960 2023-01-01 22:37:49,028 - mmcls - INFO - Epoch [48][100/5005] lr: 1.000e-02, eta: 6:32:09, time: 0.108, data_time: 0.037, memory: 1224, loss: 1.9578 2023-01-01 22:37:57,843 - mmcls - INFO - Epoch [48][200/5005] lr: 1.000e-02, eta: 6:32:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9525 2023-01-01 22:38:06,609 - mmcls - INFO - Epoch [48][300/5005] lr: 1.000e-02, eta: 6:31:51, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9597 2023-01-01 22:38:15,370 - mmcls - INFO - Epoch [48][400/5005] lr: 1.000e-02, eta: 6:31:42, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9493 2023-01-01 22:38:24,140 - mmcls - INFO - Epoch [48][500/5005] lr: 1.000e-02, eta: 6:31:33, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9762 2023-01-01 22:38:32,941 - mmcls - INFO - Epoch [48][600/5005] lr: 1.000e-02, eta: 6:31:24, time: 0.088, data_time: 0.030, memory: 1224, loss: 1.9700 2023-01-01 22:38:41,851 - mmcls - INFO - Epoch [48][700/5005] lr: 1.000e-02, eta: 6:31:15, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9494 2023-01-01 22:38:50,921 - mmcls - INFO - Epoch [48][800/5005] lr: 1.000e-02, eta: 6:31:06, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9534 2023-01-01 22:38:59,734 - mmcls - INFO - Epoch [48][900/5005] lr: 1.000e-02, eta: 6:30:57, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9586 2023-01-01 22:39:08,724 - mmcls - INFO - Epoch [48][1000/5005] lr: 1.000e-02, eta: 6:30:49, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9642 2023-01-01 22:39:17,327 - mmcls - INFO - Epoch [48][1100/5005] lr: 1.000e-02, eta: 6:30:39, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9529 2023-01-01 22:39:26,349 - mmcls - INFO - Epoch [48][1200/5005] lr: 1.000e-02, eta: 6:30:31, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9697 2023-01-01 22:39:34,950 - mmcls - INFO - Epoch [48][1300/5005] lr: 1.000e-02, eta: 6:30:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9840 2023-01-01 22:39:43,660 - mmcls - INFO - Epoch [48][1400/5005] lr: 1.000e-02, eta: 6:30:12, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9798 2023-01-01 22:39:52,324 - mmcls - INFO - Epoch [48][1500/5005] lr: 1.000e-02, eta: 6:30:03, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9449 2023-01-01 22:40:01,155 - mmcls - INFO - Epoch [48][1600/5005] lr: 1.000e-02, eta: 6:29:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9586 2023-01-01 22:40:09,788 - mmcls - INFO - Epoch [48][1700/5005] lr: 1.000e-02, eta: 6:29:45, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.9709 2023-01-01 22:40:18,731 - mmcls - INFO - Epoch [48][1800/5005] lr: 1.000e-02, eta: 6:29:36, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.9833 2023-01-01 22:40:27,409 - mmcls - INFO - Epoch [48][1900/5005] lr: 1.000e-02, eta: 6:29:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9762 2023-01-01 22:40:36,369 - mmcls - INFO - Epoch [48][2000/5005] lr: 1.000e-02, eta: 6:29:19, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9493 2023-01-01 22:40:44,735 - mmcls - INFO - Epoch [48][2100/5005] lr: 1.000e-02, eta: 6:29:09, time: 0.084, data_time: 0.023, memory: 1224, loss: 2.0041 2023-01-01 22:40:53,742 - mmcls - INFO - Epoch [48][2200/5005] lr: 1.000e-02, eta: 6:29:00, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9569 2023-01-01 22:41:02,433 - mmcls - INFO - Epoch [48][2300/5005] lr: 1.000e-02, eta: 6:28:51, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9905 2023-01-01 22:41:11,322 - mmcls - INFO - Epoch [48][2400/5005] lr: 1.000e-02, eta: 6:28:43, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.9834 2023-01-01 22:41:19,975 - mmcls - INFO - Epoch [48][2500/5005] lr: 1.000e-02, eta: 6:28:33, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9676 2023-01-01 22:41:29,025 - mmcls - INFO - Epoch [48][2600/5005] lr: 1.000e-02, eta: 6:28:25, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9898 2023-01-01 22:41:37,900 - mmcls - INFO - Epoch [48][2700/5005] lr: 1.000e-02, eta: 6:28:16, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9709 2023-01-01 22:41:46,534 - mmcls - INFO - Epoch [48][2800/5005] lr: 1.000e-02, eta: 6:28:07, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.9800 2023-01-01 22:41:55,047 - mmcls - INFO - Epoch [48][2900/5005] lr: 1.000e-02, eta: 6:27:57, time: 0.085, data_time: 0.021, memory: 1224, loss: 1.9893 2023-01-01 22:42:04,130 - mmcls - INFO - Epoch [48][3000/5005] lr: 1.000e-02, eta: 6:27:49, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.9856 2023-01-01 22:42:12,770 - mmcls - INFO - Epoch [48][3100/5005] lr: 1.000e-02, eta: 6:27:40, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.9869 2023-01-01 22:42:21,538 - mmcls - INFO - Epoch [48][3200/5005] lr: 1.000e-02, eta: 6:27:31, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9758 2023-01-01 22:42:30,424 - mmcls - INFO - Epoch [48][3300/5005] lr: 1.000e-02, eta: 6:27:22, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9768 2023-01-01 22:42:39,180 - mmcls - INFO - Epoch [48][3400/5005] lr: 1.000e-02, eta: 6:27:13, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.0173 2023-01-01 22:42:47,811 - mmcls - INFO - Epoch [48][3500/5005] lr: 1.000e-02, eta: 6:27:04, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.0173 2023-01-01 22:42:56,494 - mmcls - INFO - Epoch [48][3600/5005] lr: 1.000e-02, eta: 6:26:55, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.0000 2023-01-01 22:43:05,254 - mmcls - INFO - Epoch [48][3700/5005] lr: 1.000e-02, eta: 6:26:46, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9823 2023-01-01 22:43:13,911 - mmcls - INFO - Epoch [48][3800/5005] lr: 1.000e-02, eta: 6:26:37, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9777 2023-01-01 22:43:22,616 - mmcls - INFO - Epoch [48][3900/5005] lr: 1.000e-02, eta: 6:26:27, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9976 2023-01-01 22:43:31,425 - mmcls - INFO - Epoch [48][4000/5005] lr: 1.000e-02, eta: 6:26:19, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9884 2023-01-01 22:43:40,198 - mmcls - INFO - Epoch [48][4100/5005] lr: 1.000e-02, eta: 6:26:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9745 2023-01-01 22:43:49,340 - mmcls - INFO - Epoch [48][4200/5005] lr: 1.000e-02, eta: 6:26:01, time: 0.091, data_time: 0.034, memory: 1224, loss: 1.9883 2023-01-01 22:43:58,121 - mmcls - INFO - Epoch [48][4300/5005] lr: 1.000e-02, eta: 6:25:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.0062 2023-01-01 22:44:07,078 - mmcls - INFO - Epoch [48][4400/5005] lr: 1.000e-02, eta: 6:25:43, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9907 2023-01-01 22:44:15,980 - mmcls - INFO - Epoch [48][4500/5005] lr: 1.000e-02, eta: 6:25:34, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.0024 2023-01-01 22:44:24,706 - mmcls - INFO - Epoch [48][4600/5005] lr: 1.000e-02, eta: 6:25:25, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9819 2023-01-01 22:44:33,727 - mmcls - INFO - Epoch [48][4700/5005] lr: 1.000e-02, eta: 6:25:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9606 2023-01-01 22:44:42,441 - mmcls - INFO - Epoch [48][4800/5005] lr: 1.000e-02, eta: 6:25:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9792 2023-01-01 22:44:51,469 - mmcls - INFO - Epoch [48][4900/5005] lr: 1.000e-02, eta: 6:24:59, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9479 2023-01-01 22:45:00,303 - mmcls - INFO - Epoch [48][5000/5005] lr: 1.000e-02, eta: 6:24:50, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0090 2023-01-01 22:45:22,927 - mmcls - INFO - Epoch(val) [48][196] accuracy_top-1: 60.3420, accuracy_top-5: 82.5240 2023-01-01 22:45:34,233 - mmcls - INFO - Epoch [49][100/5005] lr: 1.000e-02, eta: 6:24:43, time: 0.113, data_time: 0.037, memory: 1224, loss: 1.9590 2023-01-01 22:45:43,009 - mmcls - INFO - Epoch [49][200/5005] lr: 1.000e-02, eta: 6:24:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9365 2023-01-01 22:45:51,777 - mmcls - INFO - Epoch [49][300/5005] lr: 1.000e-02, eta: 6:24:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9842 2023-01-01 22:46:00,795 - mmcls - INFO - Epoch [49][400/5005] lr: 1.000e-02, eta: 6:24:16, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9669 2023-01-01 22:46:09,775 - mmcls - INFO - Epoch [49][500/5005] lr: 1.000e-02, eta: 6:24:07, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.9644 2023-01-01 22:46:18,750 - mmcls - INFO - Epoch [49][600/5005] lr: 1.000e-02, eta: 6:23:59, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9721 2023-01-01 22:46:27,265 - mmcls - INFO - Epoch [49][700/5005] lr: 1.000e-02, eta: 6:23:49, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.9596 2023-01-01 22:46:36,172 - mmcls - INFO - Epoch [49][800/5005] lr: 1.000e-02, eta: 6:23:40, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9360 2023-01-01 22:46:44,998 - mmcls - INFO - Epoch [49][900/5005] lr: 1.000e-02, eta: 6:23:32, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9588 2023-01-01 22:46:53,959 - mmcls - INFO - Epoch [49][1000/5005] lr: 1.000e-02, eta: 6:23:23, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.9641 2023-01-01 22:47:02,735 - mmcls - INFO - Epoch [49][1100/5005] lr: 1.000e-02, eta: 6:23:14, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9313 2023-01-01 22:47:11,702 - mmcls - INFO - Epoch [49][1200/5005] lr: 1.000e-02, eta: 6:23:05, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.9711 2023-01-01 22:47:20,887 - mmcls - INFO - Epoch [49][1300/5005] lr: 1.000e-02, eta: 6:22:56, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.9933 2023-01-01 22:47:29,394 - mmcls - INFO - Epoch [49][1400/5005] lr: 1.000e-02, eta: 6:22:47, time: 0.085, data_time: 0.021, memory: 1224, loss: 1.9661 2023-01-01 22:47:38,184 - mmcls - INFO - Epoch [49][1500/5005] lr: 1.000e-02, eta: 6:22:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9583 2023-01-01 22:47:46,879 - mmcls - INFO - Epoch [49][1600/5005] lr: 1.000e-02, eta: 6:22:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9995 2023-01-01 22:47:55,477 - mmcls - INFO - Epoch [49][1700/5005] lr: 1.000e-02, eta: 6:22:20, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.9741 2023-01-01 22:48:04,409 - mmcls - INFO - Epoch [49][1800/5005] lr: 1.000e-02, eta: 6:22:11, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9743 2023-01-01 22:48:12,960 - mmcls - INFO - Epoch [49][1900/5005] lr: 1.000e-02, eta: 6:22:02, time: 0.085, data_time: 0.000, memory: 1224, loss: 1.9863 2023-01-01 22:48:21,960 - mmcls - INFO - Epoch [49][2000/5005] lr: 1.000e-02, eta: 6:21:53, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9886 2023-01-01 22:48:30,848 - mmcls - INFO - Epoch [49][2100/5005] lr: 1.000e-02, eta: 6:21:44, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9472 2023-01-01 22:48:39,764 - mmcls - INFO - Epoch [49][2200/5005] lr: 1.000e-02, eta: 6:21:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9635 2023-01-01 22:48:48,486 - mmcls - INFO - Epoch [49][2300/5005] lr: 1.000e-02, eta: 6:21:27, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9913 2023-01-01 22:48:57,090 - mmcls - INFO - Epoch [49][2400/5005] lr: 1.000e-02, eta: 6:21:17, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9958 2023-01-01 22:49:06,123 - mmcls - INFO - Epoch [49][2500/5005] lr: 1.000e-02, eta: 6:21:09, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9927 2023-01-01 22:49:14,552 - mmcls - INFO - Epoch [49][2600/5005] lr: 1.000e-02, eta: 6:20:59, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.9975 2023-01-01 22:49:23,286 - mmcls - INFO - Epoch [49][2700/5005] lr: 1.000e-02, eta: 6:20:50, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9829 2023-01-01 22:49:32,120 - mmcls - INFO - Epoch [49][2800/5005] lr: 1.000e-02, eta: 6:20:41, time: 0.088, data_time: 0.024, memory: 1224, loss: 2.0035 2023-01-01 22:49:40,694 - mmcls - INFO - Epoch [49][2900/5005] lr: 1.000e-02, eta: 6:20:32, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.0016 2023-01-01 22:49:49,378 - mmcls - INFO - Epoch [49][3000/5005] lr: 1.000e-02, eta: 6:20:23, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.0061 2023-01-01 22:49:57,875 - mmcls - INFO - Epoch [49][3100/5005] lr: 1.000e-02, eta: 6:20:14, time: 0.085, data_time: 0.018, memory: 1224, loss: 2.0095 2023-01-01 22:50:06,999 - mmcls - INFO - Epoch [49][3200/5005] lr: 1.000e-02, eta: 6:20:05, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.9940 2023-01-01 22:50:15,738 - mmcls - INFO - Epoch [49][3300/5005] lr: 1.000e-02, eta: 6:19:56, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9881 2023-01-01 22:50:24,655 - mmcls - INFO - Epoch [49][3400/5005] lr: 1.000e-02, eta: 6:19:47, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.0057 2023-01-01 22:50:33,297 - mmcls - INFO - Epoch [49][3500/5005] lr: 1.000e-02, eta: 6:19:38, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9741 2023-01-01 22:50:42,534 - mmcls - INFO - Epoch [49][3600/5005] lr: 1.000e-02, eta: 6:19:30, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.0054 2023-01-01 22:50:51,366 - mmcls - INFO - Epoch [49][3700/5005] lr: 1.000e-02, eta: 6:19:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9649 2023-01-01 22:51:00,176 - mmcls - INFO - Epoch [49][3800/5005] lr: 1.000e-02, eta: 6:19:12, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.0115 2023-01-01 22:51:08,801 - mmcls - INFO - Epoch [49][3900/5005] lr: 1.000e-02, eta: 6:19:03, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9832 2023-01-01 22:51:17,793 - mmcls - INFO - Epoch [49][4000/5005] lr: 1.000e-02, eta: 6:18:54, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.9944 2023-01-01 22:51:26,685 - mmcls - INFO - Epoch [49][4100/5005] lr: 1.000e-02, eta: 6:18:45, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9732 2023-01-01 22:51:35,656 - mmcls - INFO - Epoch [49][4200/5005] lr: 1.000e-02, eta: 6:18:36, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9783 2023-01-01 22:51:44,301 - mmcls - INFO - Epoch [49][4300/5005] lr: 1.000e-02, eta: 6:18:27, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9744 2023-01-01 22:51:52,975 - mmcls - INFO - Epoch [49][4400/5005] lr: 1.000e-02, eta: 6:18:18, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.9992 2023-01-01 22:52:01,717 - mmcls - INFO - Epoch [49][4500/5005] lr: 1.000e-02, eta: 6:18:09, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9815 2023-01-01 22:52:10,751 - mmcls - INFO - Epoch [49][4600/5005] lr: 1.000e-02, eta: 6:18:01, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.0049 2023-01-01 22:52:19,712 - mmcls - INFO - Epoch [49][4700/5005] lr: 1.000e-02, eta: 6:17:52, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9965 2023-01-01 22:52:28,503 - mmcls - INFO - Epoch [49][4800/5005] lr: 1.000e-02, eta: 6:17:43, time: 0.088, data_time: 0.034, memory: 1224, loss: 1.9909 2023-01-01 22:52:37,348 - mmcls - INFO - Epoch [49][4900/5005] lr: 1.000e-02, eta: 6:17:34, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.9894 2023-01-01 22:52:46,416 - mmcls - INFO - Epoch [49][5000/5005] lr: 1.000e-02, eta: 6:17:25, time: 0.091, data_time: 0.024, memory: 1224, loss: 1.9886 2023-01-01 22:53:09,217 - mmcls - INFO - Epoch(val) [49][196] accuracy_top-1: 60.4660, accuracy_top-5: 82.5460 2023-01-01 22:53:20,115 - mmcls - INFO - Epoch [50][100/5005] lr: 1.000e-02, eta: 6:17:18, time: 0.109, data_time: 0.029, memory: 1224, loss: 1.9053 2023-01-01 22:53:28,984 - mmcls - INFO - Epoch [50][200/5005] lr: 1.000e-02, eta: 6:17:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9643 2023-01-01 22:53:37,736 - mmcls - INFO - Epoch [50][300/5005] lr: 1.000e-02, eta: 6:17:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9354 2023-01-01 22:53:46,298 - mmcls - INFO - Epoch [50][400/5005] lr: 1.000e-02, eta: 6:16:51, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9214 2023-01-01 22:53:55,159 - mmcls - INFO - Epoch [50][500/5005] lr: 1.000e-02, eta: 6:16:42, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9215 2023-01-01 22:54:04,086 - mmcls - INFO - Epoch [50][600/5005] lr: 1.000e-02, eta: 6:16:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9283 2023-01-01 22:54:12,977 - mmcls - INFO - Epoch [50][700/5005] lr: 1.000e-02, eta: 6:16:24, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9781 2023-01-01 22:54:21,840 - mmcls - INFO - Epoch [50][800/5005] lr: 1.000e-02, eta: 6:16:15, time: 0.089, data_time: 0.036, memory: 1224, loss: 1.9918 2023-01-01 22:54:30,482 - mmcls - INFO - Epoch [50][900/5005] lr: 1.000e-02, eta: 6:16:06, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9887 2023-01-01 22:54:39,604 - mmcls - INFO - Epoch [50][1000/5005] lr: 1.000e-02, eta: 6:15:57, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.9611 2023-01-01 22:54:48,545 - mmcls - INFO - Epoch [50][1100/5005] lr: 1.000e-02, eta: 6:15:49, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.9711 2023-01-01 22:54:57,208 - mmcls - INFO - Epoch [50][1200/5005] lr: 1.000e-02, eta: 6:15:39, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9708 2023-01-01 22:55:06,139 - mmcls - INFO - Epoch [50][1300/5005] lr: 1.000e-02, eta: 6:15:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9643 2023-01-01 22:55:15,003 - mmcls - INFO - Epoch [50][1400/5005] lr: 1.000e-02, eta: 6:15:22, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9589 2023-01-01 22:55:23,952 - mmcls - INFO - Epoch [50][1500/5005] lr: 1.000e-02, eta: 6:15:13, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9685 2023-01-01 22:55:32,700 - mmcls - INFO - Epoch [50][1600/5005] lr: 1.000e-02, eta: 6:15:04, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9579 2023-01-01 22:55:41,595 - mmcls - INFO - Epoch [50][1700/5005] lr: 1.000e-02, eta: 6:14:55, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9651 2023-01-01 22:55:50,134 - mmcls - INFO - Epoch [50][1800/5005] lr: 1.000e-02, eta: 6:14:46, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.9523 2023-01-01 22:55:59,034 - mmcls - INFO - Epoch [50][1900/5005] lr: 1.000e-02, eta: 6:14:37, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.9808 2023-01-01 22:56:08,004 - mmcls - INFO - Epoch [50][2000/5005] lr: 1.000e-02, eta: 6:14:28, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.9632 2023-01-01 22:56:16,794 - mmcls - INFO - Epoch [50][2100/5005] lr: 1.000e-02, eta: 6:14:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9929 2023-01-01 22:56:25,752 - mmcls - INFO - Epoch [50][2200/5005] lr: 1.000e-02, eta: 6:14:11, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9801 2023-01-01 22:56:34,557 - mmcls - INFO - Epoch [50][2300/5005] lr: 1.000e-02, eta: 6:14:02, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9645 2023-01-01 22:56:43,693 - mmcls - INFO - Epoch [50][2400/5005] lr: 1.000e-02, eta: 6:13:53, time: 0.091, data_time: 0.033, memory: 1224, loss: 1.9535 2023-01-01 22:56:52,252 - mmcls - INFO - Epoch [50][2500/5005] lr: 1.000e-02, eta: 6:13:44, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9919 2023-01-01 22:57:01,345 - mmcls - INFO - Epoch [50][2600/5005] lr: 1.000e-02, eta: 6:13:35, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.9938 2023-01-01 22:57:10,106 - mmcls - INFO - Epoch [50][2700/5005] lr: 1.000e-02, eta: 6:13:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0055 2023-01-01 22:57:18,656 - mmcls - INFO - Epoch [50][2800/5005] lr: 1.000e-02, eta: 6:13:17, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9890 2023-01-01 22:57:27,964 - mmcls - INFO - Epoch [50][2900/5005] lr: 1.000e-02, eta: 6:13:09, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.9726 2023-01-01 22:57:37,049 - mmcls - INFO - Epoch [50][3000/5005] lr: 1.000e-02, eta: 6:13:00, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9728 2023-01-01 22:57:46,085 - mmcls - INFO - Epoch [50][3100/5005] lr: 1.000e-02, eta: 6:12:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9635 2023-01-01 22:57:54,935 - mmcls - INFO - Epoch [50][3200/5005] lr: 1.000e-02, eta: 6:12:42, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9718 2023-01-01 22:58:03,626 - mmcls - INFO - Epoch [50][3300/5005] lr: 1.000e-02, eta: 6:12:33, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.0128 2023-01-01 22:58:12,180 - mmcls - INFO - Epoch [50][3400/5005] lr: 1.000e-02, eta: 6:12:24, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.0037 2023-01-01 22:58:20,966 - mmcls - INFO - Epoch [50][3500/5005] lr: 1.000e-02, eta: 6:12:15, time: 0.088, data_time: 0.031, memory: 1224, loss: 1.9911 2023-01-01 22:58:29,845 - mmcls - INFO - Epoch [50][3600/5005] lr: 1.000e-02, eta: 6:12:06, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9765 2023-01-01 22:58:38,962 - mmcls - INFO - Epoch [50][3700/5005] lr: 1.000e-02, eta: 6:11:58, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.9714 2023-01-01 22:58:47,909 - mmcls - INFO - Epoch [50][3800/5005] lr: 1.000e-02, eta: 6:11:49, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9541 2023-01-01 22:58:56,956 - mmcls - INFO - Epoch [50][3900/5005] lr: 1.000e-02, eta: 6:11:40, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.9923 2023-01-01 22:59:05,810 - mmcls - INFO - Epoch [50][4000/5005] lr: 1.000e-02, eta: 6:11:31, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0144 2023-01-01 22:59:14,638 - mmcls - INFO - Epoch [50][4100/5005] lr: 1.000e-02, eta: 6:11:22, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.9716 2023-01-01 22:59:23,424 - mmcls - INFO - Epoch [50][4200/5005] lr: 1.000e-02, eta: 6:11:13, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9847 2023-01-01 22:59:32,090 - mmcls - INFO - Epoch [50][4300/5005] lr: 1.000e-02, eta: 6:11:04, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9828 2023-01-01 22:59:40,795 - mmcls - INFO - Epoch [50][4400/5005] lr: 1.000e-02, eta: 6:10:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9959 2023-01-01 22:59:49,756 - mmcls - INFO - Epoch [50][4500/5005] lr: 1.000e-02, eta: 6:10:46, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9908 2023-01-01 22:59:58,285 - mmcls - INFO - Epoch [50][4600/5005] lr: 1.000e-02, eta: 6:10:37, time: 0.085, data_time: 0.000, memory: 1224, loss: 1.9705 2023-01-01 23:00:07,045 - mmcls - INFO - Epoch [50][4700/5005] lr: 1.000e-02, eta: 6:10:28, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.0253 2023-01-01 23:00:15,828 - mmcls - INFO - Epoch [50][4800/5005] lr: 1.000e-02, eta: 6:10:19, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0062 2023-01-01 23:00:24,713 - mmcls - INFO - Epoch [50][4900/5005] lr: 1.000e-02, eta: 6:10:10, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9883 2023-01-01 23:00:33,528 - mmcls - INFO - Epoch [50][5000/5005] lr: 1.000e-02, eta: 6:10:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9957 2023-01-01 23:00:33,918 - mmcls - INFO - Saving checkpoint at 50 epochs 2023-01-01 23:00:55,815 - mmcls - INFO - Epoch(val) [50][196] accuracy_top-1: 60.4500, accuracy_top-5: 82.4180 2023-01-01 23:01:06,759 - mmcls - INFO - Epoch [51][100/5005] lr: 1.000e-02, eta: 6:09:54, time: 0.109, data_time: 0.045, memory: 1224, loss: 1.9902 2023-01-01 23:01:15,721 - mmcls - INFO - Epoch [51][200/5005] lr: 1.000e-02, eta: 6:09:45, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.9386 2023-01-01 23:01:24,587 - mmcls - INFO - Epoch [51][300/5005] lr: 1.000e-02, eta: 6:09:36, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.9356 2023-01-01 23:01:33,226 - mmcls - INFO - Epoch [51][400/5005] lr: 1.000e-02, eta: 6:09:27, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.9235 2023-01-01 23:01:42,174 - mmcls - INFO - Epoch [51][500/5005] lr: 1.000e-02, eta: 6:09:18, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9672 2023-01-01 23:01:50,962 - mmcls - INFO - Epoch [51][600/5005] lr: 1.000e-02, eta: 6:09:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9666 2023-01-01 23:01:59,628 - mmcls - INFO - Epoch [51][700/5005] lr: 1.000e-02, eta: 6:09:00, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9581 2023-01-01 23:02:08,664 - mmcls - INFO - Epoch [51][800/5005] lr: 1.000e-02, eta: 6:08:51, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.9895 2023-01-01 23:02:17,253 - mmcls - INFO - Epoch [51][900/5005] lr: 1.000e-02, eta: 6:08:42, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.9840 2023-01-01 23:02:26,043 - mmcls - INFO - Epoch [51][1000/5005] lr: 1.000e-02, eta: 6:08:33, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.9704 2023-01-01 23:02:34,929 - mmcls - INFO - Epoch [51][1100/5005] lr: 1.000e-02, eta: 6:08:24, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.9425 2023-01-01 23:02:43,486 - mmcls - INFO - Epoch [51][1200/5005] lr: 1.000e-02, eta: 6:08:15, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.9574 2023-01-01 23:02:52,444 - mmcls - INFO - Epoch [51][1300/5005] lr: 1.000e-02, eta: 6:08:07, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9460 2023-01-01 23:03:01,010 - mmcls - INFO - Epoch [51][1400/5005] lr: 1.000e-02, eta: 6:07:57, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.9506 2023-01-01 23:03:09,935 - mmcls - INFO - Epoch [51][1500/5005] lr: 1.000e-02, eta: 6:07:49, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.9457 2023-01-01 23:03:18,474 - mmcls - INFO - Epoch [51][1600/5005] lr: 1.000e-02, eta: 6:07:39, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9663 2023-01-01 23:03:27,404 - mmcls - INFO - Epoch [51][1700/5005] lr: 1.000e-02, eta: 6:07:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9675 2023-01-01 23:03:36,201 - mmcls - INFO - Epoch [51][1800/5005] lr: 1.000e-02, eta: 6:07:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9832 2023-01-01 23:03:45,069 - mmcls - INFO - Epoch [51][1900/5005] lr: 1.000e-02, eta: 6:07:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9577 2023-01-01 23:03:54,077 - mmcls - INFO - Epoch [51][2000/5005] lr: 1.000e-02, eta: 6:07:04, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9649 2023-01-01 23:04:02,668 - mmcls - INFO - Epoch [51][2100/5005] lr: 1.000e-02, eta: 6:06:55, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9540 2023-01-01 23:04:11,109 - mmcls - INFO - Epoch [51][2200/5005] lr: 1.000e-02, eta: 6:06:45, time: 0.084, data_time: 0.012, memory: 1224, loss: 1.9609 2023-01-01 23:04:19,840 - mmcls - INFO - Epoch [51][2300/5005] lr: 1.000e-02, eta: 6:06:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9726 2023-01-01 23:04:28,693 - mmcls - INFO - Epoch [51][2400/5005] lr: 1.000e-02, eta: 6:06:28, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9788 2023-01-01 23:04:37,358 - mmcls - INFO - Epoch [51][2500/5005] lr: 1.000e-02, eta: 6:06:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9953 2023-01-01 23:04:46,149 - mmcls - INFO - Epoch [51][2600/5005] lr: 1.000e-02, eta: 6:06:10, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.9816 2023-01-01 23:04:54,955 - mmcls - INFO - Epoch [51][2700/5005] lr: 1.000e-02, eta: 6:06:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9756 2023-01-01 23:05:03,585 - mmcls - INFO - Epoch [51][2800/5005] lr: 1.000e-02, eta: 6:05:52, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9689 2023-01-01 23:05:12,285 - mmcls - INFO - Epoch [51][2900/5005] lr: 1.000e-02, eta: 6:05:42, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.9691 2023-01-01 23:05:20,919 - mmcls - INFO - Epoch [51][3000/5005] lr: 1.000e-02, eta: 6:05:33, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9731 2023-01-01 23:05:29,915 - mmcls - INFO - Epoch [51][3100/5005] lr: 1.000e-02, eta: 6:05:25, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.9700 2023-01-01 23:05:38,581 - mmcls - INFO - Epoch [51][3200/5005] lr: 1.000e-02, eta: 6:05:16, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9703 2023-01-01 23:05:47,404 - mmcls - INFO - Epoch [51][3300/5005] lr: 1.000e-02, eta: 6:05:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0010 2023-01-01 23:05:56,064 - mmcls - INFO - Epoch [51][3400/5005] lr: 1.000e-02, eta: 6:04:58, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9789 2023-01-01 23:06:04,704 - mmcls - INFO - Epoch [51][3500/5005] lr: 1.000e-02, eta: 6:04:48, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.9957 2023-01-01 23:06:13,257 - mmcls - INFO - Epoch [51][3600/5005] lr: 1.000e-02, eta: 6:04:39, time: 0.086, data_time: 0.029, memory: 1224, loss: 2.0024 2023-01-01 23:06:22,066 - mmcls - INFO - Epoch [51][3700/5005] lr: 1.000e-02, eta: 6:04:30, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.0008 2023-01-01 23:06:30,965 - mmcls - INFO - Epoch [51][3800/5005] lr: 1.000e-02, eta: 6:04:21, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9906 2023-01-01 23:06:39,888 - mmcls - INFO - Epoch [51][3900/5005] lr: 1.000e-02, eta: 6:04:13, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9992 2023-01-01 23:06:48,927 - mmcls - INFO - Epoch [51][4000/5005] lr: 1.000e-02, eta: 6:04:04, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.9849 2023-01-01 23:06:57,523 - mmcls - INFO - Epoch [51][4100/5005] lr: 1.000e-02, eta: 6:03:55, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.9672 2023-01-01 23:07:06,085 - mmcls - INFO - Epoch [51][4200/5005] lr: 1.000e-02, eta: 6:03:46, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9998 2023-01-01 23:07:14,660 - mmcls - INFO - Epoch [51][4300/5005] lr: 1.000e-02, eta: 6:03:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9718 2023-01-01 23:07:23,702 - mmcls - INFO - Epoch [51][4400/5005] lr: 1.000e-02, eta: 6:03:28, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9671 2023-01-01 23:07:32,488 - mmcls - INFO - Epoch [51][4500/5005] lr: 1.000e-02, eta: 6:03:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9783 2023-01-01 23:07:41,458 - mmcls - INFO - Epoch [51][4600/5005] lr: 1.000e-02, eta: 6:03:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9917 2023-01-01 23:07:50,230 - mmcls - INFO - Epoch [51][4700/5005] lr: 1.000e-02, eta: 6:03:01, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.0263 2023-01-01 23:07:58,986 - mmcls - INFO - Epoch [51][4800/5005] lr: 1.000e-02, eta: 6:02:52, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.0047 2023-01-01 23:08:07,821 - mmcls - INFO - Epoch [51][4900/5005] lr: 1.000e-02, eta: 6:02:43, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.0004 2023-01-01 23:08:16,665 - mmcls - INFO - Epoch [51][5000/5005] lr: 1.000e-02, eta: 6:02:34, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.0038 2023-01-01 23:08:39,283 - mmcls - INFO - Epoch(val) [51][196] accuracy_top-1: 60.3500, accuracy_top-5: 82.4160 2023-01-01 23:08:50,230 - mmcls - INFO - Epoch [52][100/5005] lr: 1.000e-02, eta: 6:02:27, time: 0.109, data_time: 0.036, memory: 1224, loss: 1.9442 2023-01-01 23:08:59,044 - mmcls - INFO - Epoch [52][200/5005] lr: 1.000e-02, eta: 6:02:18, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9450 2023-01-01 23:09:07,449 - mmcls - INFO - Epoch [52][300/5005] lr: 1.000e-02, eta: 6:02:08, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.9275 2023-01-01 23:09:16,379 - mmcls - INFO - Epoch [52][400/5005] lr: 1.000e-02, eta: 6:01:59, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.9607 2023-01-01 23:09:25,386 - mmcls - INFO - Epoch [52][500/5005] lr: 1.000e-02, eta: 6:01:51, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9659 2023-01-01 23:09:34,201 - mmcls - INFO - Epoch [52][600/5005] lr: 1.000e-02, eta: 6:01:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9346 2023-01-01 23:09:43,242 - mmcls - INFO - Epoch [52][700/5005] lr: 1.000e-02, eta: 6:01:33, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9507 2023-01-01 23:09:52,161 - mmcls - INFO - Epoch [52][800/5005] lr: 1.000e-02, eta: 6:01:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9510 2023-01-01 23:10:01,032 - mmcls - INFO - Epoch [52][900/5005] lr: 1.000e-02, eta: 6:01:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9474 2023-01-01 23:10:09,702 - mmcls - INFO - Epoch [52][1000/5005] lr: 1.000e-02, eta: 6:01:06, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.9502 2023-01-01 23:10:18,325 - mmcls - INFO - Epoch [52][1100/5005] lr: 1.000e-02, eta: 6:00:57, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.9494 2023-01-01 23:10:27,159 - mmcls - INFO - Epoch [52][1200/5005] lr: 1.000e-02, eta: 6:00:48, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9298 2023-01-01 23:10:35,663 - mmcls - INFO - Epoch [52][1300/5005] lr: 1.000e-02, eta: 6:00:39, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.9670 2023-01-01 23:10:44,434 - mmcls - INFO - Epoch [52][1400/5005] lr: 1.000e-02, eta: 6:00:30, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9452 2023-01-01 23:10:53,368 - mmcls - INFO - Epoch [52][1500/5005] lr: 1.000e-02, eta: 6:00:21, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9566 2023-01-01 23:11:02,203 - mmcls - INFO - Epoch [52][1600/5005] lr: 1.000e-02, eta: 6:00:12, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9508 2023-01-01 23:11:10,788 - mmcls - INFO - Epoch [52][1700/5005] lr: 1.000e-02, eta: 6:00:03, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9594 2023-01-01 23:11:19,579 - mmcls - INFO - Epoch [52][1800/5005] lr: 1.000e-02, eta: 5:59:54, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9651 2023-01-01 23:11:28,160 - mmcls - INFO - Epoch [52][1900/5005] lr: 1.000e-02, eta: 5:59:45, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.9805 2023-01-01 23:11:36,852 - mmcls - INFO - Epoch [52][2000/5005] lr: 1.000e-02, eta: 5:59:36, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9467 2023-01-01 23:11:45,507 - mmcls - INFO - Epoch [52][2100/5005] lr: 1.000e-02, eta: 5:59:27, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.9596 2023-01-01 23:11:54,319 - mmcls - INFO - Epoch [52][2200/5005] lr: 1.000e-02, eta: 5:59:18, time: 0.088, data_time: 0.034, memory: 1224, loss: 1.9626 2023-01-01 23:12:02,939 - mmcls - INFO - Epoch [52][2300/5005] lr: 1.000e-02, eta: 5:59:09, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9408 2023-01-01 23:12:12,036 - mmcls - INFO - Epoch [52][2400/5005] lr: 1.000e-02, eta: 5:59:00, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.9816 2023-01-01 23:12:20,801 - mmcls - INFO - Epoch [52][2500/5005] lr: 1.000e-02, eta: 5:58:51, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.9964 2023-01-01 23:12:29,812 - mmcls - INFO - Epoch [52][2600/5005] lr: 1.000e-02, eta: 5:58:43, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.0016 2023-01-01 23:12:38,748 - mmcls - INFO - Epoch [52][2700/5005] lr: 1.000e-02, eta: 5:58:34, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.9971 2023-01-01 23:12:47,554 - mmcls - INFO - Epoch [52][2800/5005] lr: 1.000e-02, eta: 5:58:25, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9741 2023-01-01 23:12:56,736 - mmcls - INFO - Epoch [52][2900/5005] lr: 1.000e-02, eta: 5:58:16, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.9694 2023-01-01 23:13:05,679 - mmcls - INFO - Epoch [52][3000/5005] lr: 1.000e-02, eta: 5:58:08, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.9855 2023-01-01 23:13:14,502 - mmcls - INFO - Epoch [52][3100/5005] lr: 1.000e-02, eta: 5:57:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9754 2023-01-01 23:13:23,105 - mmcls - INFO - Epoch [52][3200/5005] lr: 1.000e-02, eta: 5:57:50, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9758 2023-01-01 23:13:31,874 - mmcls - INFO - Epoch [52][3300/5005] lr: 1.000e-02, eta: 5:57:41, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.0068 2023-01-01 23:13:40,765 - mmcls - INFO - Epoch [52][3400/5005] lr: 1.000e-02, eta: 5:57:32, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9642 2023-01-01 23:13:49,766 - mmcls - INFO - Epoch [52][3500/5005] lr: 1.000e-02, eta: 5:57:23, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.9672 2023-01-01 23:13:58,416 - mmcls - INFO - Epoch [52][3600/5005] lr: 1.000e-02, eta: 5:57:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9794 2023-01-01 23:14:07,003 - mmcls - INFO - Epoch [52][3700/5005] lr: 1.000e-02, eta: 5:57:05, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9601 2023-01-01 23:14:15,841 - mmcls - INFO - Epoch [52][3800/5005] lr: 1.000e-02, eta: 5:56:56, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9840 2023-01-01 23:14:24,741 - mmcls - INFO - Epoch [52][3900/5005] lr: 1.000e-02, eta: 5:56:47, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.9789 2023-01-01 23:14:33,547 - mmcls - INFO - Epoch [52][4000/5005] lr: 1.000e-02, eta: 5:56:38, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9911 2023-01-01 23:14:42,566 - mmcls - INFO - Epoch [52][4100/5005] lr: 1.000e-02, eta: 5:56:29, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9941 2023-01-01 23:14:51,427 - mmcls - INFO - Epoch [52][4200/5005] lr: 1.000e-02, eta: 5:56:20, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.9979 2023-01-01 23:15:00,019 - mmcls - INFO - Epoch [52][4300/5005] lr: 1.000e-02, eta: 5:56:11, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.9922 2023-01-01 23:15:08,586 - mmcls - INFO - Epoch [52][4400/5005] lr: 1.000e-02, eta: 5:56:02, time: 0.085, data_time: 0.023, memory: 1224, loss: 1.9759 2023-01-01 23:15:17,244 - mmcls - INFO - Epoch [52][4500/5005] lr: 1.000e-02, eta: 5:55:53, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.0211 2023-01-01 23:15:25,828 - mmcls - INFO - Epoch [52][4600/5005] lr: 1.000e-02, eta: 5:55:44, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.0107 2023-01-01 23:15:34,725 - mmcls - INFO - Epoch [52][4700/5005] lr: 1.000e-02, eta: 5:55:35, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0002 2023-01-01 23:15:43,771 - mmcls - INFO - Epoch [52][4800/5005] lr: 1.000e-02, eta: 5:55:26, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.0086 2023-01-01 23:15:52,653 - mmcls - INFO - Epoch [52][4900/5005] lr: 1.000e-02, eta: 5:55:18, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9902 2023-01-01 23:16:01,884 - mmcls - INFO - Epoch [52][5000/5005] lr: 1.000e-02, eta: 5:55:09, time: 0.092, data_time: 0.015, memory: 1224, loss: 2.0084 2023-01-01 23:16:24,583 - mmcls - INFO - Epoch(val) [52][196] accuracy_top-1: 60.3560, accuracy_top-5: 82.5160 2023-01-01 23:16:35,960 - mmcls - INFO - Epoch [53][100/5005] lr: 1.000e-02, eta: 5:55:02, time: 0.113, data_time: 0.044, memory: 1224, loss: 1.9439 2023-01-01 23:16:45,274 - mmcls - INFO - Epoch [53][200/5005] lr: 1.000e-02, eta: 5:54:53, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.9322 2023-01-01 23:16:54,452 - mmcls - INFO - Epoch [53][300/5005] lr: 1.000e-02, eta: 5:54:45, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.9497 2023-01-01 23:17:03,263 - mmcls - INFO - Epoch [53][400/5005] lr: 1.000e-02, eta: 5:54:36, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9246 2023-01-01 23:17:12,070 - mmcls - INFO - Epoch [53][500/5005] lr: 1.000e-02, eta: 5:54:27, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.9265 2023-01-01 23:17:20,847 - mmcls - INFO - Epoch [53][600/5005] lr: 1.000e-02, eta: 5:54:18, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9401 2023-01-01 23:17:29,418 - mmcls - INFO - Epoch [53][700/5005] lr: 1.000e-02, eta: 5:54:09, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9408 2023-01-01 23:17:38,253 - mmcls - INFO - Epoch [53][800/5005] lr: 1.000e-02, eta: 5:54:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9448 2023-01-01 23:17:47,000 - mmcls - INFO - Epoch [53][900/5005] lr: 1.000e-02, eta: 5:53:51, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9678 2023-01-01 23:17:55,626 - mmcls - INFO - Epoch [53][1000/5005] lr: 1.000e-02, eta: 5:53:42, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.9499 2023-01-01 23:18:04,277 - mmcls - INFO - Epoch [53][1100/5005] lr: 1.000e-02, eta: 5:53:33, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9389 2023-01-01 23:18:13,092 - mmcls - INFO - Epoch [53][1200/5005] lr: 1.000e-02, eta: 5:53:24, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9629 2023-01-01 23:18:22,466 - mmcls - INFO - Epoch [53][1300/5005] lr: 1.000e-02, eta: 5:53:15, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.9607 2023-01-01 23:18:31,189 - mmcls - INFO - Epoch [53][1400/5005] lr: 1.000e-02, eta: 5:53:06, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.9813 2023-01-01 23:18:39,981 - mmcls - INFO - Epoch [53][1500/5005] lr: 1.000e-02, eta: 5:52:57, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9438 2023-01-01 23:18:48,545 - mmcls - INFO - Epoch [53][1600/5005] lr: 1.000e-02, eta: 5:52:48, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9869 2023-01-01 23:18:57,923 - mmcls - INFO - Epoch [53][1700/5005] lr: 1.000e-02, eta: 5:52:40, time: 0.094, data_time: 0.009, memory: 1224, loss: 1.9917 2023-01-01 23:19:06,869 - mmcls - INFO - Epoch [53][1800/5005] lr: 1.000e-02, eta: 5:52:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9626 2023-01-01 23:19:15,678 - mmcls - INFO - Epoch [53][1900/5005] lr: 1.000e-02, eta: 5:52:22, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.9658 2023-01-01 23:19:24,420 - mmcls - INFO - Epoch [53][2000/5005] lr: 1.000e-02, eta: 5:52:13, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9756 2023-01-01 23:19:33,395 - mmcls - INFO - Epoch [53][2100/5005] lr: 1.000e-02, eta: 5:52:04, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9810 2023-01-01 23:19:42,031 - mmcls - INFO - Epoch [53][2200/5005] lr: 1.000e-02, eta: 5:51:55, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9498 2023-01-01 23:19:50,782 - mmcls - INFO - Epoch [53][2300/5005] lr: 1.000e-02, eta: 5:51:46, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9575 2023-01-01 23:19:59,538 - mmcls - INFO - Epoch [53][2400/5005] lr: 1.000e-02, eta: 5:51:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9760 2023-01-01 23:20:08,626 - mmcls - INFO - Epoch [53][2500/5005] lr: 1.000e-02, eta: 5:51:28, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.9933 2023-01-01 23:20:17,610 - mmcls - INFO - Epoch [53][2600/5005] lr: 1.000e-02, eta: 5:51:20, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.0084 2023-01-01 23:20:26,272 - mmcls - INFO - Epoch [53][2700/5005] lr: 1.000e-02, eta: 5:51:11, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9976 2023-01-01 23:20:35,009 - mmcls - INFO - Epoch [53][2800/5005] lr: 1.000e-02, eta: 5:51:02, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.9739 2023-01-01 23:20:43,787 - mmcls - INFO - Epoch [53][2900/5005] lr: 1.000e-02, eta: 5:50:53, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.9752 2023-01-01 23:20:52,841 - mmcls - INFO - Epoch [53][3000/5005] lr: 1.000e-02, eta: 5:50:44, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.9975 2023-01-01 23:21:01,688 - mmcls - INFO - Epoch [53][3100/5005] lr: 1.000e-02, eta: 5:50:35, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9853 2023-01-01 23:21:10,361 - mmcls - INFO - Epoch [53][3200/5005] lr: 1.000e-02, eta: 5:50:26, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9867 2023-01-01 23:21:19,098 - mmcls - INFO - Epoch [53][3300/5005] lr: 1.000e-02, eta: 5:50:17, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9835 2023-01-01 23:21:27,859 - mmcls - INFO - Epoch [53][3400/5005] lr: 1.000e-02, eta: 5:50:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9696 2023-01-01 23:21:36,675 - mmcls - INFO - Epoch [53][3500/5005] lr: 1.000e-02, eta: 5:49:59, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9884 2023-01-01 23:21:45,723 - mmcls - INFO - Epoch [53][3600/5005] lr: 1.000e-02, eta: 5:49:50, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9787 2023-01-01 23:21:54,432 - mmcls - INFO - Epoch [53][3700/5005] lr: 1.000e-02, eta: 5:49:41, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.0000 2023-01-01 23:22:03,365 - mmcls - INFO - Epoch [53][3800/5005] lr: 1.000e-02, eta: 5:49:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9714 2023-01-01 23:22:11,993 - mmcls - INFO - Epoch [53][3900/5005] lr: 1.000e-02, eta: 5:49:24, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.0106 2023-01-01 23:22:20,741 - mmcls - INFO - Epoch [53][4000/5005] lr: 1.000e-02, eta: 5:49:15, time: 0.087, data_time: 0.037, memory: 1224, loss: 1.9760 2023-01-01 23:22:29,430 - mmcls - INFO - Epoch [53][4100/5005] lr: 1.000e-02, eta: 5:49:06, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.0002 2023-01-01 23:22:38,223 - mmcls - INFO - Epoch [53][4200/5005] lr: 1.000e-02, eta: 5:48:57, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.0037 2023-01-01 23:22:47,155 - mmcls - INFO - Epoch [53][4300/5005] lr: 1.000e-02, eta: 5:48:48, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0106 2023-01-01 23:22:55,782 - mmcls - INFO - Epoch [53][4400/5005] lr: 1.000e-02, eta: 5:48:39, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9896 2023-01-01 23:23:04,446 - mmcls - INFO - Epoch [53][4500/5005] lr: 1.000e-02, eta: 5:48:30, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9885 2023-01-01 23:23:13,620 - mmcls - INFO - Epoch [53][4600/5005] lr: 1.000e-02, eta: 5:48:21, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.9877 2023-01-01 23:23:22,211 - mmcls - INFO - Epoch [53][4700/5005] lr: 1.000e-02, eta: 5:48:12, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9855 2023-01-01 23:23:30,691 - mmcls - INFO - Epoch [53][4800/5005] lr: 1.000e-02, eta: 5:48:03, time: 0.085, data_time: 0.014, memory: 1224, loss: 2.0039 2023-01-01 23:23:39,523 - mmcls - INFO - Epoch [53][4900/5005] lr: 1.000e-02, eta: 5:47:54, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9756 2023-01-01 23:23:47,996 - mmcls - INFO - Epoch [53][5000/5005] lr: 1.000e-02, eta: 5:47:45, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.9730 2023-01-01 23:24:10,754 - mmcls - INFO - Epoch(val) [53][196] accuracy_top-1: 60.4640, accuracy_top-5: 82.5920 2023-01-01 23:24:21,526 - mmcls - INFO - Epoch [54][100/5005] lr: 1.000e-02, eta: 5:47:37, time: 0.108, data_time: 0.063, memory: 1224, loss: 1.9007 2023-01-01 23:24:30,422 - mmcls - INFO - Epoch [54][200/5005] lr: 1.000e-02, eta: 5:47:28, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.9210 2023-01-01 23:24:39,636 - mmcls - INFO - Epoch [54][300/5005] lr: 1.000e-02, eta: 5:47:19, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.9149 2023-01-01 23:24:48,573 - mmcls - INFO - Epoch [54][400/5005] lr: 1.000e-02, eta: 5:47:10, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9380 2023-01-01 23:24:57,444 - mmcls - INFO - Epoch [54][500/5005] lr: 1.000e-02, eta: 5:47:01, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.9498 2023-01-01 23:25:06,183 - mmcls - INFO - Epoch [54][600/5005] lr: 1.000e-02, eta: 5:46:52, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9195 2023-01-01 23:25:14,835 - mmcls - INFO - Epoch [54][700/5005] lr: 1.000e-02, eta: 5:46:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9607 2023-01-01 23:25:23,909 - mmcls - INFO - Epoch [54][800/5005] lr: 1.000e-02, eta: 5:46:35, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.9674 2023-01-01 23:25:32,410 - mmcls - INFO - Epoch [54][900/5005] lr: 1.000e-02, eta: 5:46:25, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.9722 2023-01-01 23:25:41,218 - mmcls - INFO - Epoch [54][1000/5005] lr: 1.000e-02, eta: 5:46:17, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9337 2023-01-01 23:25:50,264 - mmcls - INFO - Epoch [54][1100/5005] lr: 1.000e-02, eta: 5:46:08, time: 0.090, data_time: 0.033, memory: 1224, loss: 1.9355 2023-01-01 23:25:59,291 - mmcls - INFO - Epoch [54][1200/5005] lr: 1.000e-02, eta: 5:45:59, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.9701 2023-01-01 23:26:08,483 - mmcls - INFO - Epoch [54][1300/5005] lr: 1.000e-02, eta: 5:45:51, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.9463 2023-01-01 23:26:17,211 - mmcls - INFO - Epoch [54][1400/5005] lr: 1.000e-02, eta: 5:45:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9365 2023-01-01 23:26:26,104 - mmcls - INFO - Epoch [54][1500/5005] lr: 1.000e-02, eta: 5:45:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9635 2023-01-01 23:26:34,849 - mmcls - INFO - Epoch [54][1600/5005] lr: 1.000e-02, eta: 5:45:24, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9673 2023-01-01 23:26:43,791 - mmcls - INFO - Epoch [54][1700/5005] lr: 1.000e-02, eta: 5:45:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9819 2023-01-01 23:26:52,537 - mmcls - INFO - Epoch [54][1800/5005] lr: 1.000e-02, eta: 5:45:06, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.9681 2023-01-01 23:27:01,097 - mmcls - INFO - Epoch [54][1900/5005] lr: 1.000e-02, eta: 5:44:57, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.9457 2023-01-01 23:27:10,074 - mmcls - INFO - Epoch [54][2000/5005] lr: 1.000e-02, eta: 5:44:48, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9531 2023-01-01 23:27:19,027 - mmcls - INFO - Epoch [54][2100/5005] lr: 1.000e-02, eta: 5:44:39, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9516 2023-01-01 23:27:28,017 - mmcls - INFO - Epoch [54][2200/5005] lr: 1.000e-02, eta: 5:44:30, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9666 2023-01-01 23:27:36,544 - mmcls - INFO - Epoch [54][2300/5005] lr: 1.000e-02, eta: 5:44:21, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.9493 2023-01-01 23:27:45,302 - mmcls - INFO - Epoch [54][2400/5005] lr: 1.000e-02, eta: 5:44:12, time: 0.088, data_time: 0.040, memory: 1224, loss: 1.9935 2023-01-01 23:27:54,005 - mmcls - INFO - Epoch [54][2500/5005] lr: 1.000e-02, eta: 5:44:03, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9701 2023-01-01 23:28:02,447 - mmcls - INFO - Epoch [54][2600/5005] lr: 1.000e-02, eta: 5:43:54, time: 0.084, data_time: 0.026, memory: 1224, loss: 1.9684 2023-01-01 23:28:11,305 - mmcls - INFO - Epoch [54][2700/5005] lr: 1.000e-02, eta: 5:43:45, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.9862 2023-01-01 23:28:20,332 - mmcls - INFO - Epoch [54][2800/5005] lr: 1.000e-02, eta: 5:43:36, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9579 2023-01-01 23:28:29,223 - mmcls - INFO - Epoch [54][2900/5005] lr: 1.000e-02, eta: 5:43:28, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9606 2023-01-01 23:28:38,202 - mmcls - INFO - Epoch [54][3000/5005] lr: 1.000e-02, eta: 5:43:19, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9595 2023-01-01 23:28:46,925 - mmcls - INFO - Epoch [54][3100/5005] lr: 1.000e-02, eta: 5:43:10, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9758 2023-01-01 23:28:55,529 - mmcls - INFO - Epoch [54][3200/5005] lr: 1.000e-02, eta: 5:43:01, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9922 2023-01-01 23:29:04,298 - mmcls - INFO - Epoch [54][3300/5005] lr: 1.000e-02, eta: 5:42:52, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9732 2023-01-01 23:29:13,090 - mmcls - INFO - Epoch [54][3400/5005] lr: 1.000e-02, eta: 5:42:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9945 2023-01-01 23:29:22,114 - mmcls - INFO - Epoch [54][3500/5005] lr: 1.000e-02, eta: 5:42:34, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9569 2023-01-01 23:29:31,293 - mmcls - INFO - Epoch [54][3600/5005] lr: 1.000e-02, eta: 5:42:25, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.9692 2023-01-01 23:29:39,900 - mmcls - INFO - Epoch [54][3700/5005] lr: 1.000e-02, eta: 5:42:16, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.9779 2023-01-01 23:29:48,592 - mmcls - INFO - Epoch [54][3800/5005] lr: 1.000e-02, eta: 5:42:07, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9864 2023-01-01 23:29:57,405 - mmcls - INFO - Epoch [54][3900/5005] lr: 1.000e-02, eta: 5:41:58, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9920 2023-01-01 23:30:06,231 - mmcls - INFO - Epoch [54][4000/5005] lr: 1.000e-02, eta: 5:41:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.0015 2023-01-01 23:30:14,930 - mmcls - INFO - Epoch [54][4100/5005] lr: 1.000e-02, eta: 5:41:40, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9724 2023-01-01 23:30:23,534 - mmcls - INFO - Epoch [54][4200/5005] lr: 1.000e-02, eta: 5:41:31, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9972 2023-01-01 23:30:32,296 - mmcls - INFO - Epoch [54][4300/5005] lr: 1.000e-02, eta: 5:41:22, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9598 2023-01-01 23:30:41,099 - mmcls - INFO - Epoch [54][4400/5005] lr: 1.000e-02, eta: 5:41:13, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9832 2023-01-01 23:30:50,145 - mmcls - INFO - Epoch [54][4500/5005] lr: 1.000e-02, eta: 5:41:05, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9775 2023-01-01 23:30:58,913 - mmcls - INFO - Epoch [54][4600/5005] lr: 1.000e-02, eta: 5:40:56, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.9709 2023-01-01 23:31:07,940 - mmcls - INFO - Epoch [54][4700/5005] lr: 1.000e-02, eta: 5:40:47, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9934 2023-01-01 23:31:16,460 - mmcls - INFO - Epoch [54][4800/5005] lr: 1.000e-02, eta: 5:40:38, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.9702 2023-01-01 23:31:25,163 - mmcls - INFO - Epoch [54][4900/5005] lr: 1.000e-02, eta: 5:40:29, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.9986 2023-01-01 23:31:33,835 - mmcls - INFO - Epoch [54][5000/5005] lr: 1.000e-02, eta: 5:40:20, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9940 2023-01-01 23:31:56,392 - mmcls - INFO - Epoch(val) [54][196] accuracy_top-1: 59.8220, accuracy_top-5: 82.2980 2023-01-01 23:32:07,444 - mmcls - INFO - Epoch [55][100/5005] lr: 1.000e-02, eta: 5:40:12, time: 0.110, data_time: 0.037, memory: 1224, loss: 1.9251 2023-01-01 23:32:16,173 - mmcls - INFO - Epoch [55][200/5005] lr: 1.000e-02, eta: 5:40:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9289 2023-01-01 23:32:25,087 - mmcls - INFO - Epoch [55][300/5005] lr: 1.000e-02, eta: 5:39:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9177 2023-01-01 23:32:33,576 - mmcls - INFO - Epoch [55][400/5005] lr: 1.000e-02, eta: 5:39:45, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.9380 2023-01-01 23:32:42,157 - mmcls - INFO - Epoch [55][500/5005] lr: 1.000e-02, eta: 5:39:36, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9301 2023-01-01 23:32:50,909 - mmcls - INFO - Epoch [55][600/5005] lr: 1.000e-02, eta: 5:39:27, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9528 2023-01-01 23:32:59,684 - mmcls - INFO - Epoch [55][700/5005] lr: 1.000e-02, eta: 5:39:18, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9635 2023-01-01 23:33:08,627 - mmcls - INFO - Epoch [55][800/5005] lr: 1.000e-02, eta: 5:39:09, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.9654 2023-01-01 23:33:17,538 - mmcls - INFO - Epoch [55][900/5005] lr: 1.000e-02, eta: 5:39:00, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9696 2023-01-01 23:33:26,223 - mmcls - INFO - Epoch [55][1000/5005] lr: 1.000e-02, eta: 5:38:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9458 2023-01-01 23:33:35,045 - mmcls - INFO - Epoch [55][1100/5005] lr: 1.000e-02, eta: 5:38:42, time: 0.088, data_time: 0.030, memory: 1224, loss: 1.9634 2023-01-01 23:33:44,092 - mmcls - INFO - Epoch [55][1200/5005] lr: 1.000e-02, eta: 5:38:34, time: 0.091, data_time: 0.023, memory: 1224, loss: 1.9432 2023-01-01 23:33:52,838 - mmcls - INFO - Epoch [55][1300/5005] lr: 1.000e-02, eta: 5:38:25, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.9665 2023-01-01 23:34:01,363 - mmcls - INFO - Epoch [55][1400/5005] lr: 1.000e-02, eta: 5:38:16, time: 0.085, data_time: 0.035, memory: 1224, loss: 1.9750 2023-01-01 23:34:10,096 - mmcls - INFO - Epoch [55][1500/5005] lr: 1.000e-02, eta: 5:38:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9723 2023-01-01 23:34:18,748 - mmcls - INFO - Epoch [55][1600/5005] lr: 1.000e-02, eta: 5:37:57, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.9510 2023-01-01 23:34:27,374 - mmcls - INFO - Epoch [55][1700/5005] lr: 1.000e-02, eta: 5:37:48, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9776 2023-01-01 23:34:36,374 - mmcls - INFO - Epoch [55][1800/5005] lr: 1.000e-02, eta: 5:37:40, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.9527 2023-01-01 23:34:45,207 - mmcls - INFO - Epoch [55][1900/5005] lr: 1.000e-02, eta: 5:37:31, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9494 2023-01-01 23:34:54,024 - mmcls - INFO - Epoch [55][2000/5005] lr: 1.000e-02, eta: 5:37:22, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9609 2023-01-01 23:35:02,937 - mmcls - INFO - Epoch [55][2100/5005] lr: 1.000e-02, eta: 5:37:13, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9703 2023-01-01 23:35:11,963 - mmcls - INFO - Epoch [55][2200/5005] lr: 1.000e-02, eta: 5:37:04, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.9657 2023-01-01 23:35:20,798 - mmcls - INFO - Epoch [55][2300/5005] lr: 1.000e-02, eta: 5:36:55, time: 0.088, data_time: 0.030, memory: 1224, loss: 1.9634 2023-01-01 23:35:30,130 - mmcls - INFO - Epoch [55][2400/5005] lr: 1.000e-02, eta: 5:36:47, time: 0.093, data_time: 0.014, memory: 1224, loss: 1.9779 2023-01-01 23:35:38,872 - mmcls - INFO - Epoch [55][2500/5005] lr: 1.000e-02, eta: 5:36:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9875 2023-01-01 23:35:47,629 - mmcls - INFO - Epoch [55][2600/5005] lr: 1.000e-02, eta: 5:36:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9420 2023-01-01 23:35:56,444 - mmcls - INFO - Epoch [55][2700/5005] lr: 1.000e-02, eta: 5:36:20, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9780 2023-01-01 23:36:05,345 - mmcls - INFO - Epoch [55][2800/5005] lr: 1.000e-02, eta: 5:36:11, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9553 2023-01-01 23:36:14,190 - mmcls - INFO - Epoch [55][2900/5005] lr: 1.000e-02, eta: 5:36:02, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9740 2023-01-01 23:36:22,886 - mmcls - INFO - Epoch [55][3000/5005] lr: 1.000e-02, eta: 5:35:53, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9821 2023-01-01 23:36:31,760 - mmcls - INFO - Epoch [55][3100/5005] lr: 1.000e-02, eta: 5:35:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9630 2023-01-01 23:36:40,310 - mmcls - INFO - Epoch [55][3200/5005] lr: 1.000e-02, eta: 5:35:35, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.9881 2023-01-01 23:36:49,258 - mmcls - INFO - Epoch [55][3300/5005] lr: 1.000e-02, eta: 5:35:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9664 2023-01-01 23:36:57,981 - mmcls - INFO - Epoch [55][3400/5005] lr: 1.000e-02, eta: 5:35:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9676 2023-01-01 23:37:06,876 - mmcls - INFO - Epoch [55][3500/5005] lr: 1.000e-02, eta: 5:35:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9821 2023-01-01 23:37:15,851 - mmcls - INFO - Epoch [55][3600/5005] lr: 1.000e-02, eta: 5:35:00, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9795 2023-01-01 23:37:24,831 - mmcls - INFO - Epoch [55][3700/5005] lr: 1.000e-02, eta: 5:34:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9922 2023-01-01 23:37:33,680 - mmcls - INFO - Epoch [55][3800/5005] lr: 1.000e-02, eta: 5:34:42, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9836 2023-01-01 23:37:42,260 - mmcls - INFO - Epoch [55][3900/5005] lr: 1.000e-02, eta: 5:34:33, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9958 2023-01-01 23:37:51,191 - mmcls - INFO - Epoch [55][4000/5005] lr: 1.000e-02, eta: 5:34:24, time: 0.089, data_time: 0.034, memory: 1224, loss: 1.9751 2023-01-01 23:37:59,980 - mmcls - INFO - Epoch [55][4100/5005] lr: 1.000e-02, eta: 5:34:15, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9581 2023-01-01 23:38:08,790 - mmcls - INFO - Epoch [55][4200/5005] lr: 1.000e-02, eta: 5:34:06, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9701 2023-01-01 23:38:17,588 - mmcls - INFO - Epoch [55][4300/5005] lr: 1.000e-02, eta: 5:33:57, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9718 2023-01-01 23:38:26,175 - mmcls - INFO - Epoch [55][4400/5005] lr: 1.000e-02, eta: 5:33:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9879 2023-01-01 23:38:35,460 - mmcls - INFO - Epoch [55][4500/5005] lr: 1.000e-02, eta: 5:33:40, time: 0.093, data_time: 0.018, memory: 1224, loss: 1.9645 2023-01-01 23:38:44,179 - mmcls - INFO - Epoch [55][4600/5005] lr: 1.000e-02, eta: 5:33:31, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.9695 2023-01-01 23:38:53,143 - mmcls - INFO - Epoch [55][4700/5005] lr: 1.000e-02, eta: 5:33:22, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9872 2023-01-01 23:39:01,786 - mmcls - INFO - Epoch [55][4800/5005] lr: 1.000e-02, eta: 5:33:13, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.9957 2023-01-01 23:39:10,552 - mmcls - INFO - Epoch [55][4900/5005] lr: 1.000e-02, eta: 5:33:04, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.0027 2023-01-01 23:39:19,250 - mmcls - INFO - Epoch [55][5000/5005] lr: 1.000e-02, eta: 5:32:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9940 2023-01-01 23:39:41,916 - mmcls - INFO - Epoch(val) [55][196] accuracy_top-1: 59.9000, accuracy_top-5: 82.1740 2023-01-01 23:39:53,060 - mmcls - INFO - Epoch [56][100/5005] lr: 1.000e-02, eta: 5:32:47, time: 0.111, data_time: 0.064, memory: 1224, loss: 1.9604 2023-01-01 23:40:01,862 - mmcls - INFO - Epoch [56][200/5005] lr: 1.000e-02, eta: 5:32:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9316 2023-01-01 23:40:10,596 - mmcls - INFO - Epoch [56][300/5005] lr: 1.000e-02, eta: 5:32:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9207 2023-01-01 23:40:19,619 - mmcls - INFO - Epoch [56][400/5005] lr: 1.000e-02, eta: 5:32:21, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9428 2023-01-01 23:40:28,412 - mmcls - INFO - Epoch [56][500/5005] lr: 1.000e-02, eta: 5:32:12, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9413 2023-01-01 23:40:37,165 - mmcls - INFO - Epoch [56][600/5005] lr: 1.000e-02, eta: 5:32:03, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.9519 2023-01-01 23:40:46,047 - mmcls - INFO - Epoch [56][700/5005] lr: 1.000e-02, eta: 5:31:54, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9277 2023-01-01 23:40:54,468 - mmcls - INFO - Epoch [56][800/5005] lr: 1.000e-02, eta: 5:31:45, time: 0.084, data_time: 0.013, memory: 1224, loss: 1.9339 2023-01-01 23:41:03,253 - mmcls - INFO - Epoch [56][900/5005] lr: 1.000e-02, eta: 5:31:36, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.9453 2023-01-01 23:41:12,086 - mmcls - INFO - Epoch [56][1000/5005] lr: 1.000e-02, eta: 5:31:27, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.9467 2023-01-01 23:41:20,876 - mmcls - INFO - Epoch [56][1100/5005] lr: 1.000e-02, eta: 5:31:18, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9340 2023-01-01 23:41:29,675 - mmcls - INFO - Epoch [56][1200/5005] lr: 1.000e-02, eta: 5:31:09, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9470 2023-01-01 23:41:38,392 - mmcls - INFO - Epoch [56][1300/5005] lr: 1.000e-02, eta: 5:31:00, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9588 2023-01-01 23:41:47,417 - mmcls - INFO - Epoch [56][1400/5005] lr: 1.000e-02, eta: 5:30:51, time: 0.090, data_time: 0.033, memory: 1224, loss: 1.9516 2023-01-01 23:41:56,119 - mmcls - INFO - Epoch [56][1500/5005] lr: 1.000e-02, eta: 5:30:42, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9847 2023-01-01 23:42:05,070 - mmcls - INFO - Epoch [56][1600/5005] lr: 1.000e-02, eta: 5:30:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9604 2023-01-01 23:42:13,875 - mmcls - INFO - Epoch [56][1700/5005] lr: 1.000e-02, eta: 5:30:24, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.9832 2023-01-01 23:42:22,631 - mmcls - INFO - Epoch [56][1800/5005] lr: 1.000e-02, eta: 5:30:15, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9917 2023-01-01 23:42:31,754 - mmcls - INFO - Epoch [56][1900/5005] lr: 1.000e-02, eta: 5:30:07, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.9624 2023-01-01 23:42:40,284 - mmcls - INFO - Epoch [56][2000/5005] lr: 1.000e-02, eta: 5:29:58, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.0073 2023-01-01 23:42:49,088 - mmcls - INFO - Epoch [56][2100/5005] lr: 1.000e-02, eta: 5:29:49, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9772 2023-01-01 23:42:57,994 - mmcls - INFO - Epoch [56][2200/5005] lr: 1.000e-02, eta: 5:29:40, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9847 2023-01-01 23:43:06,857 - mmcls - INFO - Epoch [56][2300/5005] lr: 1.000e-02, eta: 5:29:31, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.9547 2023-01-01 23:43:15,602 - mmcls - INFO - Epoch [56][2400/5005] lr: 1.000e-02, eta: 5:29:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9606 2023-01-01 23:43:24,413 - mmcls - INFO - Epoch [56][2500/5005] lr: 1.000e-02, eta: 5:29:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9787 2023-01-01 23:43:33,280 - mmcls - INFO - Epoch [56][2600/5005] lr: 1.000e-02, eta: 5:29:04, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9614 2023-01-01 23:43:42,090 - mmcls - INFO - Epoch [56][2700/5005] lr: 1.000e-02, eta: 5:28:55, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9709 2023-01-01 23:43:50,734 - mmcls - INFO - Epoch [56][2800/5005] lr: 1.000e-02, eta: 5:28:46, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9784 2023-01-01 23:43:59,676 - mmcls - INFO - Epoch [56][2900/5005] lr: 1.000e-02, eta: 5:28:37, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9675 2023-01-01 23:44:08,673 - mmcls - INFO - Epoch [56][3000/5005] lr: 1.000e-02, eta: 5:28:29, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.9499 2023-01-01 23:44:17,271 - mmcls - INFO - Epoch [56][3100/5005] lr: 1.000e-02, eta: 5:28:20, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.9749 2023-01-01 23:44:26,213 - mmcls - INFO - Epoch [56][3200/5005] lr: 1.000e-02, eta: 5:28:11, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.9659 2023-01-01 23:44:34,920 - mmcls - INFO - Epoch [56][3300/5005] lr: 1.000e-02, eta: 5:28:02, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9621 2023-01-01 23:44:43,600 - mmcls - INFO - Epoch [56][3400/5005] lr: 1.000e-02, eta: 5:27:53, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.9684 2023-01-01 23:44:52,401 - mmcls - INFO - Epoch [56][3500/5005] lr: 1.000e-02, eta: 5:27:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9570 2023-01-01 23:45:00,982 - mmcls - INFO - Epoch [56][3600/5005] lr: 1.000e-02, eta: 5:27:35, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.9890 2023-01-01 23:45:09,678 - mmcls - INFO - Epoch [56][3700/5005] lr: 1.000e-02, eta: 5:27:26, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.9764 2023-01-01 23:45:18,707 - mmcls - INFO - Epoch [56][3800/5005] lr: 1.000e-02, eta: 5:27:17, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.0242 2023-01-01 23:45:27,492 - mmcls - INFO - Epoch [56][3900/5005] lr: 1.000e-02, eta: 5:27:08, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9535 2023-01-01 23:45:36,210 - mmcls - INFO - Epoch [56][4000/5005] lr: 1.000e-02, eta: 5:26:59, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.9592 2023-01-01 23:45:44,915 - mmcls - INFO - Epoch [56][4100/5005] lr: 1.000e-02, eta: 5:26:50, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.9798 2023-01-01 23:45:53,995 - mmcls - INFO - Epoch [56][4200/5005] lr: 1.000e-02, eta: 5:26:41, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.9516 2023-01-01 23:46:02,615 - mmcls - INFO - Epoch [56][4300/5005] lr: 1.000e-02, eta: 5:26:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.0050 2023-01-01 23:46:11,332 - mmcls - INFO - Epoch [56][4400/5005] lr: 1.000e-02, eta: 5:26:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9950 2023-01-01 23:46:19,891 - mmcls - INFO - Epoch [56][4500/5005] lr: 1.000e-02, eta: 5:26:14, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.9840 2023-01-01 23:46:29,040 - mmcls - INFO - Epoch [56][4600/5005] lr: 1.000e-02, eta: 5:26:06, time: 0.091, data_time: 0.022, memory: 1224, loss: 1.9503 2023-01-01 23:46:38,040 - mmcls - INFO - Epoch [56][4700/5005] lr: 1.000e-02, eta: 5:25:57, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.9671 2023-01-01 23:46:46,684 - mmcls - INFO - Epoch [56][4800/5005] lr: 1.000e-02, eta: 5:25:48, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.9789 2023-01-01 23:46:55,266 - mmcls - INFO - Epoch [56][4900/5005] lr: 1.000e-02, eta: 5:25:39, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.0100 2023-01-01 23:47:03,995 - mmcls - INFO - Epoch [56][5000/5005] lr: 1.000e-02, eta: 5:25:30, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9936 2023-01-01 23:47:26,752 - mmcls - INFO - Epoch(val) [56][196] accuracy_top-1: 60.0100, accuracy_top-5: 82.2900 2023-01-01 23:47:37,578 - mmcls - INFO - Epoch [57][100/5005] lr: 1.000e-02, eta: 5:25:22, time: 0.108, data_time: 0.049, memory: 1224, loss: 1.9029 2023-01-01 23:47:46,464 - mmcls - INFO - Epoch [57][200/5005] lr: 1.000e-02, eta: 5:25:13, time: 0.089, data_time: 0.041, memory: 1224, loss: 1.9220 2023-01-01 23:47:55,291 - mmcls - INFO - Epoch [57][300/5005] lr: 1.000e-02, eta: 5:25:04, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9311 2023-01-01 23:48:03,941 - mmcls - INFO - Epoch [57][400/5005] lr: 1.000e-02, eta: 5:24:55, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.9484 2023-01-01 23:48:12,532 - mmcls - INFO - Epoch [57][500/5005] lr: 1.000e-02, eta: 5:24:46, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.9811 2023-01-01 23:48:21,212 - mmcls - INFO - Epoch [57][600/5005] lr: 1.000e-02, eta: 5:24:37, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.9505 2023-01-01 23:48:29,989 - mmcls - INFO - Epoch [57][700/5005] lr: 1.000e-02, eta: 5:24:28, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9381 2023-01-01 23:48:38,616 - mmcls - INFO - Epoch [57][800/5005] lr: 1.000e-02, eta: 5:24:19, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9467 2023-01-01 23:48:47,329 - mmcls - INFO - Epoch [57][900/5005] lr: 1.000e-02, eta: 5:24:10, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9402 2023-01-01 23:48:56,208 - mmcls - INFO - Epoch [57][1000/5005] lr: 1.000e-02, eta: 5:24:01, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.9511 2023-01-01 23:49:05,049 - mmcls - INFO - Epoch [57][1100/5005] lr: 1.000e-02, eta: 5:23:52, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9444 2023-01-01 23:49:14,222 - mmcls - INFO - Epoch [57][1200/5005] lr: 1.000e-02, eta: 5:23:43, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9449 2023-01-01 23:49:22,904 - mmcls - INFO - Epoch [57][1300/5005] lr: 1.000e-02, eta: 5:23:34, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.9206 2023-01-01 23:49:31,785 - mmcls - INFO - Epoch [57][1400/5005] lr: 1.000e-02, eta: 5:23:25, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.9394 2023-01-01 23:49:40,533 - mmcls - INFO - Epoch [57][1500/5005] lr: 1.000e-02, eta: 5:23:16, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.9435 2023-01-01 23:49:49,399 - mmcls - INFO - Epoch [57][1600/5005] lr: 1.000e-02, eta: 5:23:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.9652 2023-01-01 23:49:58,273 - mmcls - INFO - Epoch [57][1700/5005] lr: 1.000e-02, eta: 5:22:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9777 2023-01-01 23:50:07,118 - mmcls - INFO - Epoch [57][1800/5005] lr: 1.000e-02, eta: 5:22:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9491 2023-01-01 23:50:15,744 - mmcls - INFO - Epoch [57][1900/5005] lr: 1.000e-02, eta: 5:22:41, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.9614 2023-01-01 23:50:24,706 - mmcls - INFO - Epoch [57][2000/5005] lr: 1.000e-02, eta: 5:22:32, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9497 2023-01-01 23:50:33,547 - mmcls - INFO - Epoch [57][2100/5005] lr: 1.000e-02, eta: 5:22:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9541 2023-01-01 23:50:42,408 - mmcls - INFO - Epoch [57][2200/5005] lr: 1.000e-02, eta: 5:22:14, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9720 2023-01-01 23:50:51,300 - mmcls - INFO - Epoch [57][2300/5005] lr: 1.000e-02, eta: 5:22:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9684 2023-01-01 23:51:00,125 - mmcls - INFO - Epoch [57][2400/5005] lr: 1.000e-02, eta: 5:21:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9524 2023-01-01 23:51:08,745 - mmcls - INFO - Epoch [57][2500/5005] lr: 1.000e-02, eta: 5:21:47, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9289 2023-01-01 23:51:17,503 - mmcls - INFO - Epoch [57][2600/5005] lr: 1.000e-02, eta: 5:21:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9657 2023-01-01 23:51:26,183 - mmcls - INFO - Epoch [57][2700/5005] lr: 1.000e-02, eta: 5:21:29, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9670 2023-01-01 23:51:35,001 - mmcls - INFO - Epoch [57][2800/5005] lr: 1.000e-02, eta: 5:21:20, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9581 2023-01-01 23:51:43,933 - mmcls - INFO - Epoch [57][2900/5005] lr: 1.000e-02, eta: 5:21:12, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.9701 2023-01-01 23:51:52,691 - mmcls - INFO - Epoch [57][3000/5005] lr: 1.000e-02, eta: 5:21:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9180 2023-01-01 23:52:01,937 - mmcls - INFO - Epoch [57][3100/5005] lr: 1.000e-02, eta: 5:20:54, time: 0.092, data_time: 0.013, memory: 1224, loss: 1.9738 2023-01-01 23:52:10,978 - mmcls - INFO - Epoch [57][3200/5005] lr: 1.000e-02, eta: 5:20:45, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.0045 2023-01-01 23:52:19,744 - mmcls - INFO - Epoch [57][3300/5005] lr: 1.000e-02, eta: 5:20:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9814 2023-01-01 23:52:28,808 - mmcls - INFO - Epoch [57][3400/5005] lr: 1.000e-02, eta: 5:20:28, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9877 2023-01-01 23:52:37,571 - mmcls - INFO - Epoch [57][3500/5005] lr: 1.000e-02, eta: 5:20:19, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.9752 2023-01-01 23:52:46,369 - mmcls - INFO - Epoch [57][3600/5005] lr: 1.000e-02, eta: 5:20:10, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9691 2023-01-01 23:52:55,258 - mmcls - INFO - Epoch [57][3700/5005] lr: 1.000e-02, eta: 5:20:01, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9870 2023-01-01 23:53:04,006 - mmcls - INFO - Epoch [57][3800/5005] lr: 1.000e-02, eta: 5:19:52, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9613 2023-01-01 23:53:12,725 - mmcls - INFO - Epoch [57][3900/5005] lr: 1.000e-02, eta: 5:19:43, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9951 2023-01-01 23:53:21,277 - mmcls - INFO - Epoch [57][4000/5005] lr: 1.000e-02, eta: 5:19:34, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9677 2023-01-01 23:53:30,168 - mmcls - INFO - Epoch [57][4100/5005] lr: 1.000e-02, eta: 5:19:25, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9630 2023-01-01 23:53:38,805 - mmcls - INFO - Epoch [57][4200/5005] lr: 1.000e-02, eta: 5:19:16, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9701 2023-01-01 23:53:47,381 - mmcls - INFO - Epoch [57][4300/5005] lr: 1.000e-02, eta: 5:19:07, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.9697 2023-01-01 23:53:56,142 - mmcls - INFO - Epoch [57][4400/5005] lr: 1.000e-02, eta: 5:18:58, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9967 2023-01-01 23:54:04,874 - mmcls - INFO - Epoch [57][4500/5005] lr: 1.000e-02, eta: 5:18:49, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9818 2023-01-01 23:54:14,146 - mmcls - INFO - Epoch [57][4600/5005] lr: 1.000e-02, eta: 5:18:40, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.9735 2023-01-01 23:54:22,985 - mmcls - INFO - Epoch [57][4700/5005] lr: 1.000e-02, eta: 5:18:32, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9558 2023-01-01 23:54:31,902 - mmcls - INFO - Epoch [57][4800/5005] lr: 1.000e-02, eta: 5:18:23, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9609 2023-01-01 23:54:40,714 - mmcls - INFO - Epoch [57][4900/5005] lr: 1.000e-02, eta: 5:18:14, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9717 2023-01-01 23:54:49,707 - mmcls - INFO - Epoch [57][5000/5005] lr: 1.000e-02, eta: 5:18:05, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9461 2023-01-01 23:55:12,430 - mmcls - INFO - Epoch(val) [57][196] accuracy_top-1: 60.8100, accuracy_top-5: 82.8580 2023-01-01 23:55:23,683 - mmcls - INFO - Epoch [58][100/5005] lr: 1.000e-02, eta: 5:17:57, time: 0.112, data_time: 0.040, memory: 1224, loss: 1.9078 2023-01-01 23:55:32,616 - mmcls - INFO - Epoch [58][200/5005] lr: 1.000e-02, eta: 5:17:48, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9148 2023-01-01 23:55:41,188 - mmcls - INFO - Epoch [58][300/5005] lr: 1.000e-02, eta: 5:17:39, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9173 2023-01-01 23:55:49,911 - mmcls - INFO - Epoch [58][400/5005] lr: 1.000e-02, eta: 5:17:30, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9447 2023-01-01 23:55:58,724 - mmcls - INFO - Epoch [58][500/5005] lr: 1.000e-02, eta: 5:17:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9073 2023-01-01 23:56:07,580 - mmcls - INFO - Epoch [58][600/5005] lr: 1.000e-02, eta: 5:17:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9209 2023-01-01 23:56:16,648 - mmcls - INFO - Epoch [58][700/5005] lr: 1.000e-02, eta: 5:17:04, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.9310 2023-01-01 23:56:25,406 - mmcls - INFO - Epoch [58][800/5005] lr: 1.000e-02, eta: 5:16:55, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9270 2023-01-01 23:56:34,061 - mmcls - INFO - Epoch [58][900/5005] lr: 1.000e-02, eta: 5:16:46, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.9307 2023-01-01 23:56:42,999 - mmcls - INFO - Epoch [58][1000/5005] lr: 1.000e-02, eta: 5:16:37, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.9624 2023-01-01 23:56:51,849 - mmcls - INFO - Epoch [58][1100/5005] lr: 1.000e-02, eta: 5:16:28, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9164 2023-01-01 23:57:00,539 - mmcls - INFO - Epoch [58][1200/5005] lr: 1.000e-02, eta: 5:16:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9248 2023-01-01 23:57:09,534 - mmcls - INFO - Epoch [58][1300/5005] lr: 1.000e-02, eta: 5:16:10, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9220 2023-01-01 23:57:18,620 - mmcls - INFO - Epoch [58][1400/5005] lr: 1.000e-02, eta: 5:16:02, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.9386 2023-01-01 23:57:27,449 - mmcls - INFO - Epoch [58][1500/5005] lr: 1.000e-02, eta: 5:15:53, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9557 2023-01-01 23:57:36,212 - mmcls - INFO - Epoch [58][1600/5005] lr: 1.000e-02, eta: 5:15:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9446 2023-01-01 23:57:45,332 - mmcls - INFO - Epoch [58][1700/5005] lr: 1.000e-02, eta: 5:15:35, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9507 2023-01-01 23:57:54,298 - mmcls - INFO - Epoch [58][1800/5005] lr: 1.000e-02, eta: 5:15:26, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9426 2023-01-01 23:58:02,886 - mmcls - INFO - Epoch [58][1900/5005] lr: 1.000e-02, eta: 5:15:17, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9548 2023-01-01 23:58:11,978 - mmcls - INFO - Epoch [58][2000/5005] lr: 1.000e-02, eta: 5:15:09, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.9404 2023-01-01 23:58:20,730 - mmcls - INFO - Epoch [58][2100/5005] lr: 1.000e-02, eta: 5:15:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9487 2023-01-01 23:58:29,512 - mmcls - INFO - Epoch [58][2200/5005] lr: 1.000e-02, eta: 5:14:51, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9441 2023-01-01 23:58:38,247 - mmcls - INFO - Epoch [58][2300/5005] lr: 1.000e-02, eta: 5:14:42, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.9436 2023-01-01 23:58:46,915 - mmcls - INFO - Epoch [58][2400/5005] lr: 1.000e-02, eta: 5:14:33, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.9804 2023-01-01 23:58:55,617 - mmcls - INFO - Epoch [58][2500/5005] lr: 1.000e-02, eta: 5:14:24, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9624 2023-01-01 23:59:04,543 - mmcls - INFO - Epoch [58][2600/5005] lr: 1.000e-02, eta: 5:14:15, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.9527 2023-01-01 23:59:13,355 - mmcls - INFO - Epoch [58][2700/5005] lr: 1.000e-02, eta: 5:14:06, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9588 2023-01-01 23:59:21,914 - mmcls - INFO - Epoch [58][2800/5005] lr: 1.000e-02, eta: 5:13:57, time: 0.086, data_time: 0.034, memory: 1224, loss: 1.9967 2023-01-01 23:59:30,799 - mmcls - INFO - Epoch [58][2900/5005] lr: 1.000e-02, eta: 5:13:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9711 2023-01-01 23:59:39,484 - mmcls - INFO - Epoch [58][3000/5005] lr: 1.000e-02, eta: 5:13:39, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9582 2023-01-01 23:59:48,300 - mmcls - INFO - Epoch [58][3100/5005] lr: 1.000e-02, eta: 5:13:30, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.0012 2023-01-01 23:59:57,412 - mmcls - INFO - Epoch [58][3200/5005] lr: 1.000e-02, eta: 5:13:21, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.9729 2023-01-02 00:00:06,071 - mmcls - INFO - Epoch [58][3300/5005] lr: 1.000e-02, eta: 5:13:12, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9747 2023-01-02 00:00:14,747 - mmcls - INFO - Epoch [58][3400/5005] lr: 1.000e-02, eta: 5:13:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9839 2023-01-02 00:00:23,505 - mmcls - INFO - Epoch [58][3500/5005] lr: 1.000e-02, eta: 5:12:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9786 2023-01-02 00:00:32,269 - mmcls - INFO - Epoch [58][3600/5005] lr: 1.000e-02, eta: 5:12:45, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9650 2023-01-02 00:00:41,037 - mmcls - INFO - Epoch [58][3700/5005] lr: 1.000e-02, eta: 5:12:37, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.9763 2023-01-02 00:00:49,872 - mmcls - INFO - Epoch [58][3800/5005] lr: 1.000e-02, eta: 5:12:28, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9741 2023-01-02 00:00:58,701 - mmcls - INFO - Epoch [58][3900/5005] lr: 1.000e-02, eta: 5:12:19, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9837 2023-01-02 00:01:07,855 - mmcls - INFO - Epoch [58][4000/5005] lr: 1.000e-02, eta: 5:12:10, time: 0.092, data_time: 0.033, memory: 1224, loss: 1.9610 2023-01-02 00:01:16,450 - mmcls - INFO - Epoch [58][4100/5005] lr: 1.000e-02, eta: 5:12:01, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9943 2023-01-02 00:01:25,206 - mmcls - INFO - Epoch [58][4200/5005] lr: 1.000e-02, eta: 5:11:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9469 2023-01-02 00:01:33,997 - mmcls - INFO - Epoch [58][4300/5005] lr: 1.000e-02, eta: 5:11:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9775 2023-01-02 00:01:43,181 - mmcls - INFO - Epoch [58][4400/5005] lr: 1.000e-02, eta: 5:11:34, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9804 2023-01-02 00:01:52,123 - mmcls - INFO - Epoch [58][4500/5005] lr: 1.000e-02, eta: 5:11:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9873 2023-01-02 00:02:00,882 - mmcls - INFO - Epoch [58][4600/5005] lr: 1.000e-02, eta: 5:11:17, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9756 2023-01-02 00:02:09,486 - mmcls - INFO - Epoch [58][4700/5005] lr: 1.000e-02, eta: 5:11:08, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.9695 2023-01-02 00:02:18,299 - mmcls - INFO - Epoch [58][4800/5005] lr: 1.000e-02, eta: 5:10:59, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.9530 2023-01-02 00:02:27,125 - mmcls - INFO - Epoch [58][4900/5005] lr: 1.000e-02, eta: 5:10:50, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9811 2023-01-02 00:02:35,758 - mmcls - INFO - Epoch [58][5000/5005] lr: 1.000e-02, eta: 5:10:41, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.0103 2023-01-02 00:02:58,623 - mmcls - INFO - Epoch(val) [58][196] accuracy_top-1: 56.9520, accuracy_top-5: 79.4000 2023-01-02 00:03:09,615 - mmcls - INFO - Epoch [59][100/5005] lr: 1.000e-02, eta: 5:10:33, time: 0.110, data_time: 0.064, memory: 1224, loss: 1.9117 2023-01-02 00:03:18,425 - mmcls - INFO - Epoch [59][200/5005] lr: 1.000e-02, eta: 5:10:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.8996 2023-01-02 00:03:27,171 - mmcls - INFO - Epoch [59][300/5005] lr: 1.000e-02, eta: 5:10:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9168 2023-01-02 00:03:35,883 - mmcls - INFO - Epoch [59][400/5005] lr: 1.000e-02, eta: 5:10:06, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.9250 2023-01-02 00:03:44,862 - mmcls - INFO - Epoch [59][500/5005] lr: 1.000e-02, eta: 5:09:57, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.9460 2023-01-02 00:03:53,861 - mmcls - INFO - Epoch [59][600/5005] lr: 1.000e-02, eta: 5:09:48, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.9422 2023-01-02 00:04:02,555 - mmcls - INFO - Epoch [59][700/5005] lr: 1.000e-02, eta: 5:09:39, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9282 2023-01-02 00:04:11,128 - mmcls - INFO - Epoch [59][800/5005] lr: 1.000e-02, eta: 5:09:30, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.9409 2023-01-02 00:04:20,441 - mmcls - INFO - Epoch [59][900/5005] lr: 1.000e-02, eta: 5:09:22, time: 0.093, data_time: 0.015, memory: 1224, loss: 1.9482 2023-01-02 00:04:29,471 - mmcls - INFO - Epoch [59][1000/5005] lr: 1.000e-02, eta: 5:09:13, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9492 2023-01-02 00:04:38,611 - mmcls - INFO - Epoch [59][1100/5005] lr: 1.000e-02, eta: 5:09:04, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.9584 2023-01-02 00:04:47,434 - mmcls - INFO - Epoch [59][1200/5005] lr: 1.000e-02, eta: 5:08:55, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.9287 2023-01-02 00:04:55,934 - mmcls - INFO - Epoch [59][1300/5005] lr: 1.000e-02, eta: 5:08:46, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.9319 2023-01-02 00:05:04,804 - mmcls - INFO - Epoch [59][1400/5005] lr: 1.000e-02, eta: 5:08:37, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9566 2023-01-02 00:05:13,270 - mmcls - INFO - Epoch [59][1500/5005] lr: 1.000e-02, eta: 5:08:28, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.9677 2023-01-02 00:05:22,122 - mmcls - INFO - Epoch [59][1600/5005] lr: 1.000e-02, eta: 5:08:19, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9819 2023-01-02 00:05:31,095 - mmcls - INFO - Epoch [59][1700/5005] lr: 1.000e-02, eta: 5:08:10, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9480 2023-01-02 00:05:39,664 - mmcls - INFO - Epoch [59][1800/5005] lr: 1.000e-02, eta: 5:08:01, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9359 2023-01-02 00:05:48,428 - mmcls - INFO - Epoch [59][1900/5005] lr: 1.000e-02, eta: 5:07:52, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9358 2023-01-02 00:05:57,188 - mmcls - INFO - Epoch [59][2000/5005] lr: 1.000e-02, eta: 5:07:44, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.9420 2023-01-02 00:06:06,115 - mmcls - INFO - Epoch [59][2100/5005] lr: 1.000e-02, eta: 5:07:35, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9679 2023-01-02 00:06:15,108 - mmcls - INFO - Epoch [59][2200/5005] lr: 1.000e-02, eta: 5:07:26, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.9563 2023-01-02 00:06:24,046 - mmcls - INFO - Epoch [59][2300/5005] lr: 1.000e-02, eta: 5:07:17, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9378 2023-01-02 00:06:32,541 - mmcls - INFO - Epoch [59][2400/5005] lr: 1.000e-02, eta: 5:07:08, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.0141 2023-01-02 00:06:41,178 - mmcls - INFO - Epoch [59][2500/5005] lr: 1.000e-02, eta: 5:06:59, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9545 2023-01-02 00:06:50,047 - mmcls - INFO - Epoch [59][2600/5005] lr: 1.000e-02, eta: 5:06:50, time: 0.089, data_time: 0.039, memory: 1224, loss: 1.9822 2023-01-02 00:06:58,817 - mmcls - INFO - Epoch [59][2700/5005] lr: 1.000e-02, eta: 5:06:41, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9363 2023-01-02 00:07:07,781 - mmcls - INFO - Epoch [59][2800/5005] lr: 1.000e-02, eta: 5:06:32, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.9343 2023-01-02 00:07:16,459 - mmcls - INFO - Epoch [59][2900/5005] lr: 1.000e-02, eta: 5:06:23, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9619 2023-01-02 00:07:25,473 - mmcls - INFO - Epoch [59][3000/5005] lr: 1.000e-02, eta: 5:06:15, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.9568 2023-01-02 00:07:34,290 - mmcls - INFO - Epoch [59][3100/5005] lr: 1.000e-02, eta: 5:06:06, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.9952 2023-01-02 00:07:43,150 - mmcls - INFO - Epoch [59][3200/5005] lr: 1.000e-02, eta: 5:05:57, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.9841 2023-01-02 00:07:52,134 - mmcls - INFO - Epoch [59][3300/5005] lr: 1.000e-02, eta: 5:05:48, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.9759 2023-01-02 00:08:00,973 - mmcls - INFO - Epoch [59][3400/5005] lr: 1.000e-02, eta: 5:05:39, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.9833 2023-01-02 00:08:09,769 - mmcls - INFO - Epoch [59][3500/5005] lr: 1.000e-02, eta: 5:05:30, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.9412 2023-01-02 00:08:18,586 - mmcls - INFO - Epoch [59][3600/5005] lr: 1.000e-02, eta: 5:05:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9659 2023-01-02 00:08:27,360 - mmcls - INFO - Epoch [59][3700/5005] lr: 1.000e-02, eta: 5:05:12, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.9628 2023-01-02 00:08:36,293 - mmcls - INFO - Epoch [59][3800/5005] lr: 1.000e-02, eta: 5:05:04, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.9757 2023-01-02 00:08:45,247 - mmcls - INFO - Epoch [59][3900/5005] lr: 1.000e-02, eta: 5:04:55, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.9837 2023-01-02 00:08:53,979 - mmcls - INFO - Epoch [59][4000/5005] lr: 1.000e-02, eta: 5:04:46, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.0063 2023-01-02 00:09:03,042 - mmcls - INFO - Epoch [59][4100/5005] lr: 1.000e-02, eta: 5:04:37, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.9886 2023-01-02 00:09:11,794 - mmcls - INFO - Epoch [59][4200/5005] lr: 1.000e-02, eta: 5:04:28, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.9907 2023-01-02 00:09:20,583 - mmcls - INFO - Epoch [59][4300/5005] lr: 1.000e-02, eta: 5:04:19, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.9440 2023-01-02 00:09:29,264 - mmcls - INFO - Epoch [59][4400/5005] lr: 1.000e-02, eta: 5:04:10, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9738 2023-01-02 00:09:38,066 - mmcls - INFO - Epoch [59][4500/5005] lr: 1.000e-02, eta: 5:04:01, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.9361 2023-01-02 00:09:47,024 - mmcls - INFO - Epoch [59][4600/5005] lr: 1.000e-02, eta: 5:03:52, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.9244 2023-01-02 00:09:55,699 - mmcls - INFO - Epoch [59][4700/5005] lr: 1.000e-02, eta: 5:03:43, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.9512 2023-01-02 00:10:04,559 - mmcls - INFO - Epoch [59][4800/5005] lr: 1.000e-02, eta: 5:03:35, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.9739 2023-01-02 00:10:13,653 - mmcls - INFO - Epoch [59][4900/5005] lr: 1.000e-02, eta: 5:03:26, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.9673 2023-01-02 00:10:22,375 - mmcls - INFO - Epoch [59][5000/5005] lr: 1.000e-02, eta: 5:03:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9811 2023-01-02 00:10:45,349 - mmcls - INFO - Epoch(val) [59][196] accuracy_top-1: 60.4760, accuracy_top-5: 82.8280 2023-01-02 00:10:56,698 - mmcls - INFO - Epoch [60][100/5005] lr: 1.000e-02, eta: 5:03:09, time: 0.113, data_time: 0.031, memory: 1224, loss: 1.9168 2023-01-02 00:11:05,510 - mmcls - INFO - Epoch [60][200/5005] lr: 1.000e-02, eta: 5:03:00, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9079 2023-01-02 00:11:14,343 - mmcls - INFO - Epoch [60][300/5005] lr: 1.000e-02, eta: 5:02:51, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.9226 2023-01-02 00:11:23,059 - mmcls - INFO - Epoch [60][400/5005] lr: 1.000e-02, eta: 5:02:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9461 2023-01-02 00:11:31,824 - mmcls - INFO - Epoch [60][500/5005] lr: 1.000e-02, eta: 5:02:33, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.8977 2023-01-02 00:11:40,625 - mmcls - INFO - Epoch [60][600/5005] lr: 1.000e-02, eta: 5:02:24, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9101 2023-01-02 00:11:49,454 - mmcls - INFO - Epoch [60][700/5005] lr: 1.000e-02, eta: 5:02:15, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.9490 2023-01-02 00:11:58,144 - mmcls - INFO - Epoch [60][800/5005] lr: 1.000e-02, eta: 5:02:06, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.9041 2023-01-02 00:12:06,753 - mmcls - INFO - Epoch [60][900/5005] lr: 1.000e-02, eta: 5:01:57, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.9200 2023-01-02 00:12:15,584 - mmcls - INFO - Epoch [60][1000/5005] lr: 1.000e-02, eta: 5:01:49, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.9350 2023-01-02 00:12:24,363 - mmcls - INFO - Epoch [60][1100/5005] lr: 1.000e-02, eta: 5:01:40, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.9376 2023-01-02 00:12:33,124 - mmcls - INFO - Epoch [60][1200/5005] lr: 1.000e-02, eta: 5:01:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9267 2023-01-02 00:12:41,713 - mmcls - INFO - Epoch [60][1300/5005] lr: 1.000e-02, eta: 5:01:22, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9515 2023-01-02 00:12:50,488 - mmcls - INFO - Epoch [60][1400/5005] lr: 1.000e-02, eta: 5:01:13, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9413 2023-01-02 00:12:59,013 - mmcls - INFO - Epoch [60][1500/5005] lr: 1.000e-02, eta: 5:01:04, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.9373 2023-01-02 00:13:07,561 - mmcls - INFO - Epoch [60][1600/5005] lr: 1.000e-02, eta: 5:00:54, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.9342 2023-01-02 00:13:16,531 - mmcls - INFO - Epoch [60][1700/5005] lr: 1.000e-02, eta: 5:00:46, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9626 2023-01-02 00:13:25,113 - mmcls - INFO - Epoch [60][1800/5005] lr: 1.000e-02, eta: 5:00:37, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.9630 2023-01-02 00:13:33,895 - mmcls - INFO - Epoch [60][1900/5005] lr: 1.000e-02, eta: 5:00:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.9427 2023-01-02 00:13:43,165 - mmcls - INFO - Epoch [60][2000/5005] lr: 1.000e-02, eta: 5:00:19, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.9498 2023-01-02 00:13:52,200 - mmcls - INFO - Epoch [60][2100/5005] lr: 1.000e-02, eta: 5:00:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.9610 2023-01-02 00:14:01,189 - mmcls - INFO - Epoch [60][2200/5005] lr: 1.000e-02, eta: 5:00:02, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.9394 2023-01-02 00:14:09,763 - mmcls - INFO - Epoch [60][2300/5005] lr: 1.000e-02, eta: 4:59:52, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.9616 2023-01-02 00:14:18,626 - mmcls - INFO - Epoch [60][2400/5005] lr: 1.000e-02, eta: 4:59:44, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.9696 2023-01-02 00:14:27,547 - mmcls - INFO - Epoch [60][2500/5005] lr: 1.000e-02, eta: 4:59:35, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.9580 2023-01-02 00:14:36,444 - mmcls - INFO - Epoch [60][2600/5005] lr: 1.000e-02, eta: 4:59:26, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.9644 2023-01-02 00:14:45,182 - mmcls - INFO - Epoch [60][2700/5005] lr: 1.000e-02, eta: 4:59:17, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.9729 2023-01-02 00:14:54,129 - mmcls - INFO - Epoch [60][2800/5005] lr: 1.000e-02, eta: 4:59:08, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.9495 2023-01-02 00:15:02,897 - mmcls - INFO - Epoch [60][2900/5005] lr: 1.000e-02, eta: 4:58:59, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.9447 2023-01-02 00:15:11,619 - mmcls - INFO - Epoch [60][3000/5005] lr: 1.000e-02, eta: 4:58:50, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.9519 2023-01-02 00:15:20,520 - mmcls - INFO - Epoch [60][3100/5005] lr: 1.000e-02, eta: 4:58:41, time: 0.089, data_time: 0.036, memory: 1224, loss: 1.9379 2023-01-02 00:15:29,374 - mmcls - INFO - Epoch [60][3200/5005] lr: 1.000e-02, eta: 4:58:33, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.9709 2023-01-02 00:15:38,460 - mmcls - INFO - Epoch [60][3300/5005] lr: 1.000e-02, eta: 4:58:24, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.9814 2023-01-02 00:15:47,105 - mmcls - INFO - Epoch [60][3400/5005] lr: 1.000e-02, eta: 4:58:15, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.9842 2023-01-02 00:15:56,054 - mmcls - INFO - Epoch [60][3500/5005] lr: 1.000e-02, eta: 4:58:06, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.9720 2023-01-02 00:16:04,746 - mmcls - INFO - Epoch [60][3600/5005] lr: 1.000e-02, eta: 4:57:57, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9735 2023-01-02 00:16:13,589 - mmcls - INFO - Epoch [60][3700/5005] lr: 1.000e-02, eta: 4:57:48, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.9772 2023-01-02 00:16:22,212 - mmcls - INFO - Epoch [60][3800/5005] lr: 1.000e-02, eta: 4:57:39, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.9566 2023-01-02 00:16:31,134 - mmcls - INFO - Epoch [60][3900/5005] lr: 1.000e-02, eta: 4:57:30, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.9619 2023-01-02 00:16:40,115 - mmcls - INFO - Epoch [60][4000/5005] lr: 1.000e-02, eta: 4:57:21, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.9997 2023-01-02 00:16:48,867 - mmcls - INFO - Epoch [60][4100/5005] lr: 1.000e-02, eta: 4:57:12, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.9996 2023-01-02 00:16:57,757 - mmcls - INFO - Epoch [60][4200/5005] lr: 1.000e-02, eta: 4:57:04, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.9872 2023-01-02 00:17:06,552 - mmcls - INFO - Epoch [60][4300/5005] lr: 1.000e-02, eta: 4:56:55, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.9667 2023-01-02 00:17:15,240 - mmcls - INFO - Epoch [60][4400/5005] lr: 1.000e-02, eta: 4:56:46, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.9829 2023-01-02 00:17:24,090 - mmcls - INFO - Epoch [60][4500/5005] lr: 1.000e-02, eta: 4:56:37, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.9650 2023-01-02 00:17:32,691 - mmcls - INFO - Epoch [60][4600/5005] lr: 1.000e-02, eta: 4:56:28, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.9467 2023-01-02 00:17:41,642 - mmcls - INFO - Epoch [60][4700/5005] lr: 1.000e-02, eta: 4:56:19, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.9647 2023-01-02 00:17:50,716 - mmcls - INFO - Epoch [60][4800/5005] lr: 1.000e-02, eta: 4:56:10, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.9752 2023-01-02 00:17:59,725 - mmcls - INFO - Epoch [60][4900/5005] lr: 1.000e-02, eta: 4:56:01, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.9637 2023-01-02 00:18:08,310 - mmcls - INFO - Epoch [60][5000/5005] lr: 1.000e-02, eta: 4:55:52, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.0175 2023-01-02 00:18:31,043 - mmcls - INFO - Epoch(val) [60][196] accuracy_top-1: 60.1400, accuracy_top-5: 82.4700 2023-01-02 00:18:41,934 - mmcls - INFO - Epoch [61][100/5005] lr: 1.000e-03, eta: 4:55:44, time: 0.109, data_time: 0.037, memory: 1224, loss: 1.8820 2023-01-02 00:18:50,510 - mmcls - INFO - Epoch [61][200/5005] lr: 1.000e-03, eta: 4:55:35, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.8527 2023-01-02 00:18:59,355 - mmcls - INFO - Epoch [61][300/5005] lr: 1.000e-03, eta: 4:55:26, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.8555 2023-01-02 00:19:07,959 - mmcls - INFO - Epoch [61][400/5005] lr: 1.000e-03, eta: 4:55:17, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.8368 2023-01-02 00:19:16,642 - mmcls - INFO - Epoch [61][500/5005] lr: 1.000e-03, eta: 4:55:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.8076 2023-01-02 00:19:25,408 - mmcls - INFO - Epoch [61][600/5005] lr: 1.000e-03, eta: 4:54:59, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.8417 2023-01-02 00:19:34,035 - mmcls - INFO - Epoch [61][700/5005] lr: 1.000e-03, eta: 4:54:50, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.8405 2023-01-02 00:19:43,068 - mmcls - INFO - Epoch [61][800/5005] lr: 1.000e-03, eta: 4:54:41, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.8338 2023-01-02 00:19:51,811 - mmcls - INFO - Epoch [61][900/5005] lr: 1.000e-03, eta: 4:54:32, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.7922 2023-01-02 00:20:00,545 - mmcls - INFO - Epoch [61][1000/5005] lr: 1.000e-03, eta: 4:54:24, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.8000 2023-01-02 00:20:09,501 - mmcls - INFO - Epoch [61][1100/5005] lr: 1.000e-03, eta: 4:54:15, time: 0.090, data_time: 0.033, memory: 1224, loss: 1.8239 2023-01-02 00:20:18,313 - mmcls - INFO - Epoch [61][1200/5005] lr: 1.000e-03, eta: 4:54:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7871 2023-01-02 00:20:27,140 - mmcls - INFO - Epoch [61][1300/5005] lr: 1.000e-03, eta: 4:53:57, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.8189 2023-01-02 00:20:35,801 - mmcls - INFO - Epoch [61][1400/5005] lr: 1.000e-03, eta: 4:53:48, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.7826 2023-01-02 00:20:44,506 - mmcls - INFO - Epoch [61][1500/5005] lr: 1.000e-03, eta: 4:53:39, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7758 2023-01-02 00:20:53,411 - mmcls - INFO - Epoch [61][1600/5005] lr: 1.000e-03, eta: 4:53:30, time: 0.089, data_time: 0.038, memory: 1224, loss: 1.7833 2023-01-02 00:21:01,953 - mmcls - INFO - Epoch [61][1700/5005] lr: 1.000e-03, eta: 4:53:21, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.7730 2023-01-02 00:21:10,652 - mmcls - INFO - Epoch [61][1800/5005] lr: 1.000e-03, eta: 4:53:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7827 2023-01-02 00:21:19,427 - mmcls - INFO - Epoch [61][1900/5005] lr: 1.000e-03, eta: 4:53:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7971 2023-01-02 00:21:28,259 - mmcls - INFO - Epoch [61][2000/5005] lr: 1.000e-03, eta: 4:52:54, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7989 2023-01-02 00:21:37,001 - mmcls - INFO - Epoch [61][2100/5005] lr: 1.000e-03, eta: 4:52:45, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.7766 2023-01-02 00:21:45,593 - mmcls - INFO - Epoch [61][2200/5005] lr: 1.000e-03, eta: 4:52:36, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7625 2023-01-02 00:21:54,317 - mmcls - INFO - Epoch [61][2300/5005] lr: 1.000e-03, eta: 4:52:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7761 2023-01-02 00:22:02,946 - mmcls - INFO - Epoch [61][2400/5005] lr: 1.000e-03, eta: 4:52:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7801 2023-01-02 00:22:12,026 - mmcls - INFO - Epoch [61][2500/5005] lr: 1.000e-03, eta: 4:52:10, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.7562 2023-01-02 00:22:21,313 - mmcls - INFO - Epoch [61][2600/5005] lr: 1.000e-03, eta: 4:52:01, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.7806 2023-01-02 00:22:29,864 - mmcls - INFO - Epoch [61][2700/5005] lr: 1.000e-03, eta: 4:51:52, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.7608 2023-01-02 00:22:38,638 - mmcls - INFO - Epoch [61][2800/5005] lr: 1.000e-03, eta: 4:51:43, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7528 2023-01-02 00:22:47,355 - mmcls - INFO - Epoch [61][2900/5005] lr: 1.000e-03, eta: 4:51:34, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.7833 2023-01-02 00:22:55,944 - mmcls - INFO - Epoch [61][3000/5005] lr: 1.000e-03, eta: 4:51:25, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.7765 2023-01-02 00:23:04,689 - mmcls - INFO - Epoch [61][3100/5005] lr: 1.000e-03, eta: 4:51:16, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.7819 2023-01-02 00:23:13,358 - mmcls - INFO - Epoch [61][3200/5005] lr: 1.000e-03, eta: 4:51:07, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7715 2023-01-02 00:23:22,286 - mmcls - INFO - Epoch [61][3300/5005] lr: 1.000e-03, eta: 4:50:58, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.7649 2023-01-02 00:23:30,853 - mmcls - INFO - Epoch [61][3400/5005] lr: 1.000e-03, eta: 4:50:49, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.7554 2023-01-02 00:23:39,594 - mmcls - INFO - Epoch [61][3500/5005] lr: 1.000e-03, eta: 4:50:40, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7664 2023-01-02 00:23:48,459 - mmcls - INFO - Epoch [61][3600/5005] lr: 1.000e-03, eta: 4:50:31, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7816 2023-01-02 00:23:57,804 - mmcls - INFO - Epoch [61][3700/5005] lr: 1.000e-03, eta: 4:50:23, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.7710 2023-01-02 00:24:06,560 - mmcls - INFO - Epoch [61][3800/5005] lr: 1.000e-03, eta: 4:50:14, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7570 2023-01-02 00:24:15,796 - mmcls - INFO - Epoch [61][3900/5005] lr: 1.000e-03, eta: 4:50:05, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.7719 2023-01-02 00:24:24,427 - mmcls - INFO - Epoch [61][4000/5005] lr: 1.000e-03, eta: 4:49:56, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.7801 2023-01-02 00:24:33,282 - mmcls - INFO - Epoch [61][4100/5005] lr: 1.000e-03, eta: 4:49:47, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7731 2023-01-02 00:24:42,301 - mmcls - INFO - Epoch [61][4200/5005] lr: 1.000e-03, eta: 4:49:38, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7713 2023-01-02 00:24:51,105 - mmcls - INFO - Epoch [61][4300/5005] lr: 1.000e-03, eta: 4:49:30, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.7974 2023-01-02 00:24:59,838 - mmcls - INFO - Epoch [61][4400/5005] lr: 1.000e-03, eta: 4:49:21, time: 0.087, data_time: 0.038, memory: 1224, loss: 1.7858 2023-01-02 00:25:08,471 - mmcls - INFO - Epoch [61][4500/5005] lr: 1.000e-03, eta: 4:49:12, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.7479 2023-01-02 00:25:17,242 - mmcls - INFO - Epoch [61][4600/5005] lr: 1.000e-03, eta: 4:49:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7596 2023-01-02 00:25:26,272 - mmcls - INFO - Epoch [61][4700/5005] lr: 1.000e-03, eta: 4:48:54, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7585 2023-01-02 00:25:35,163 - mmcls - INFO - Epoch [61][4800/5005] lr: 1.000e-03, eta: 4:48:45, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.7735 2023-01-02 00:25:43,953 - mmcls - INFO - Epoch [61][4900/5005] lr: 1.000e-03, eta: 4:48:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7423 2023-01-02 00:25:52,622 - mmcls - INFO - Epoch [61][5000/5005] lr: 1.000e-03, eta: 4:48:27, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7735 2023-01-02 00:26:15,217 - mmcls - INFO - Epoch(val) [61][196] accuracy_top-1: 63.4000, accuracy_top-5: 84.7080 2023-01-02 00:26:26,570 - mmcls - INFO - Epoch [62][100/5005] lr: 1.000e-03, eta: 4:48:19, time: 0.113, data_time: 0.050, memory: 1224, loss: 1.7562 2023-01-02 00:26:35,545 - mmcls - INFO - Epoch [62][200/5005] lr: 1.000e-03, eta: 4:48:10, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.7317 2023-01-02 00:26:44,257 - mmcls - INFO - Epoch [62][300/5005] lr: 1.000e-03, eta: 4:48:01, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.7547 2023-01-02 00:26:52,952 - mmcls - INFO - Epoch [62][400/5005] lr: 1.000e-03, eta: 4:47:52, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.7504 2023-01-02 00:27:01,671 - mmcls - INFO - Epoch [62][500/5005] lr: 1.000e-03, eta: 4:47:43, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.7419 2023-01-02 00:27:10,679 - mmcls - INFO - Epoch [62][600/5005] lr: 1.000e-03, eta: 4:47:35, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.7638 2023-01-02 00:27:19,824 - mmcls - INFO - Epoch [62][700/5005] lr: 1.000e-03, eta: 4:47:26, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.7359 2023-01-02 00:27:28,545 - mmcls - INFO - Epoch [62][800/5005] lr: 1.000e-03, eta: 4:47:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7453 2023-01-02 00:27:37,475 - mmcls - INFO - Epoch [62][900/5005] lr: 1.000e-03, eta: 4:47:08, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.7696 2023-01-02 00:27:46,458 - mmcls - INFO - Epoch [62][1000/5005] lr: 1.000e-03, eta: 4:46:59, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7331 2023-01-02 00:27:55,215 - mmcls - INFO - Epoch [62][1100/5005] lr: 1.000e-03, eta: 4:46:50, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.7344 2023-01-02 00:28:04,467 - mmcls - INFO - Epoch [62][1200/5005] lr: 1.000e-03, eta: 4:46:42, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.7395 2023-01-02 00:28:13,362 - mmcls - INFO - Epoch [62][1300/5005] lr: 1.000e-03, eta: 4:46:33, time: 0.089, data_time: 0.035, memory: 1224, loss: 1.7224 2023-01-02 00:28:22,141 - mmcls - INFO - Epoch [62][1400/5005] lr: 1.000e-03, eta: 4:46:24, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7502 2023-01-02 00:28:31,030 - mmcls - INFO - Epoch [62][1500/5005] lr: 1.000e-03, eta: 4:46:15, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.7508 2023-01-02 00:28:39,814 - mmcls - INFO - Epoch [62][1600/5005] lr: 1.000e-03, eta: 4:46:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.7293 2023-01-02 00:28:48,573 - mmcls - INFO - Epoch [62][1700/5005] lr: 1.000e-03, eta: 4:45:57, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.7367 2023-01-02 00:28:57,618 - mmcls - INFO - Epoch [62][1800/5005] lr: 1.000e-03, eta: 4:45:49, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.7374 2023-01-02 00:29:06,211 - mmcls - INFO - Epoch [62][1900/5005] lr: 1.000e-03, eta: 4:45:40, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.7651 2023-01-02 00:29:15,054 - mmcls - INFO - Epoch [62][2000/5005] lr: 1.000e-03, eta: 4:45:31, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7330 2023-01-02 00:29:23,897 - mmcls - INFO - Epoch [62][2100/5005] lr: 1.000e-03, eta: 4:45:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7282 2023-01-02 00:29:32,911 - mmcls - INFO - Epoch [62][2200/5005] lr: 1.000e-03, eta: 4:45:13, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.7437 2023-01-02 00:29:41,837 - mmcls - INFO - Epoch [62][2300/5005] lr: 1.000e-03, eta: 4:45:04, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.7374 2023-01-02 00:29:50,498 - mmcls - INFO - Epoch [62][2400/5005] lr: 1.000e-03, eta: 4:44:55, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7464 2023-01-02 00:29:59,163 - mmcls - INFO - Epoch [62][2500/5005] lr: 1.000e-03, eta: 4:44:46, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7222 2023-01-02 00:30:07,947 - mmcls - INFO - Epoch [62][2600/5005] lr: 1.000e-03, eta: 4:44:37, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7390 2023-01-02 00:30:16,723 - mmcls - INFO - Epoch [62][2700/5005] lr: 1.000e-03, eta: 4:44:28, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.7374 2023-01-02 00:30:25,600 - mmcls - INFO - Epoch [62][2800/5005] lr: 1.000e-03, eta: 4:44:19, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7184 2023-01-02 00:30:34,369 - mmcls - INFO - Epoch [62][2900/5005] lr: 1.000e-03, eta: 4:44:11, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.7384 2023-01-02 00:30:43,090 - mmcls - INFO - Epoch [62][3000/5005] lr: 1.000e-03, eta: 4:44:02, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.7386 2023-01-02 00:30:51,800 - mmcls - INFO - Epoch [62][3100/5005] lr: 1.000e-03, eta: 4:43:53, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7306 2023-01-02 00:31:00,783 - mmcls - INFO - Epoch [62][3200/5005] lr: 1.000e-03, eta: 4:43:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.7306 2023-01-02 00:31:09,599 - mmcls - INFO - Epoch [62][3300/5005] lr: 1.000e-03, eta: 4:43:35, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.7566 2023-01-02 00:31:18,228 - mmcls - INFO - Epoch [62][3400/5005] lr: 1.000e-03, eta: 4:43:26, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.7256 2023-01-02 00:31:26,893 - mmcls - INFO - Epoch [62][3500/5005] lr: 1.000e-03, eta: 4:43:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7558 2023-01-02 00:31:35,935 - mmcls - INFO - Epoch [62][3600/5005] lr: 1.000e-03, eta: 4:43:08, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.7439 2023-01-02 00:31:44,713 - mmcls - INFO - Epoch [62][3700/5005] lr: 1.000e-03, eta: 4:42:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7288 2023-01-02 00:31:53,625 - mmcls - INFO - Epoch [62][3800/5005] lr: 1.000e-03, eta: 4:42:50, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.7522 2023-01-02 00:32:02,503 - mmcls - INFO - Epoch [62][3900/5005] lr: 1.000e-03, eta: 4:42:41, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.7438 2023-01-02 00:32:11,556 - mmcls - INFO - Epoch [62][4000/5005] lr: 1.000e-03, eta: 4:42:33, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.7464 2023-01-02 00:32:20,699 - mmcls - INFO - Epoch [62][4100/5005] lr: 1.000e-03, eta: 4:42:24, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.7346 2023-01-02 00:32:29,269 - mmcls - INFO - Epoch [62][4200/5005] lr: 1.000e-03, eta: 4:42:15, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7246 2023-01-02 00:32:38,550 - mmcls - INFO - Epoch [62][4300/5005] lr: 1.000e-03, eta: 4:42:06, time: 0.093, data_time: 0.021, memory: 1224, loss: 1.7417 2023-01-02 00:32:47,355 - mmcls - INFO - Epoch [62][4400/5005] lr: 1.000e-03, eta: 4:41:57, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.7725 2023-01-02 00:32:56,153 - mmcls - INFO - Epoch [62][4500/5005] lr: 1.000e-03, eta: 4:41:49, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.7568 2023-01-02 00:33:04,740 - mmcls - INFO - Epoch [62][4600/5005] lr: 1.000e-03, eta: 4:41:40, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.7234 2023-01-02 00:33:13,589 - mmcls - INFO - Epoch [62][4700/5005] lr: 1.000e-03, eta: 4:41:31, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.7368 2023-01-02 00:33:22,239 - mmcls - INFO - Epoch [62][4800/5005] lr: 1.000e-03, eta: 4:41:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7351 2023-01-02 00:33:30,814 - mmcls - INFO - Epoch [62][4900/5005] lr: 1.000e-03, eta: 4:41:13, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.7086 2023-01-02 00:33:39,890 - mmcls - INFO - Epoch [62][5000/5005] lr: 1.000e-03, eta: 4:41:04, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.7516 2023-01-02 00:34:02,672 - mmcls - INFO - Epoch(val) [62][196] accuracy_top-1: 63.7500, accuracy_top-5: 84.8740 2023-01-02 00:34:14,064 - mmcls - INFO - Epoch [63][100/5005] lr: 1.000e-03, eta: 4:40:56, time: 0.114, data_time: 0.060, memory: 1224, loss: 1.7324 2023-01-02 00:34:22,623 - mmcls - INFO - Epoch [63][200/5005] lr: 1.000e-03, eta: 4:40:47, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.7129 2023-01-02 00:34:31,240 - mmcls - INFO - Epoch [63][300/5005] lr: 1.000e-03, eta: 4:40:38, time: 0.086, data_time: 0.029, memory: 1224, loss: 1.7143 2023-01-02 00:34:39,818 - mmcls - INFO - Epoch [63][400/5005] lr: 1.000e-03, eta: 4:40:29, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.7287 2023-01-02 00:34:49,028 - mmcls - INFO - Epoch [63][500/5005] lr: 1.000e-03, eta: 4:40:20, time: 0.092, data_time: 0.013, memory: 1224, loss: 1.6999 2023-01-02 00:34:57,823 - mmcls - INFO - Epoch [63][600/5005] lr: 1.000e-03, eta: 4:40:11, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.7223 2023-01-02 00:35:06,546 - mmcls - INFO - Epoch [63][700/5005] lr: 1.000e-03, eta: 4:40:02, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.7066 2023-01-02 00:35:15,184 - mmcls - INFO - Epoch [63][800/5005] lr: 1.000e-03, eta: 4:39:53, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.7540 2023-01-02 00:35:24,208 - mmcls - INFO - Epoch [63][900/5005] lr: 1.000e-03, eta: 4:39:44, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7181 2023-01-02 00:35:33,187 - mmcls - INFO - Epoch [63][1000/5005] lr: 1.000e-03, eta: 4:39:36, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6915 2023-01-02 00:35:41,865 - mmcls - INFO - Epoch [63][1100/5005] lr: 1.000e-03, eta: 4:39:27, time: 0.087, data_time: 0.041, memory: 1224, loss: 1.6967 2023-01-02 00:35:50,641 - mmcls - INFO - Epoch [63][1200/5005] lr: 1.000e-03, eta: 4:39:18, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.7325 2023-01-02 00:35:59,930 - mmcls - INFO - Epoch [63][1300/5005] lr: 1.000e-03, eta: 4:39:09, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.7361 2023-01-02 00:36:08,777 - mmcls - INFO - Epoch [63][1400/5005] lr: 1.000e-03, eta: 4:39:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7241 2023-01-02 00:36:17,636 - mmcls - INFO - Epoch [63][1500/5005] lr: 1.000e-03, eta: 4:38:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7078 2023-01-02 00:36:26,373 - mmcls - INFO - Epoch [63][1600/5005] lr: 1.000e-03, eta: 4:38:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7141 2023-01-02 00:36:35,656 - mmcls - INFO - Epoch [63][1700/5005] lr: 1.000e-03, eta: 4:38:34, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.7339 2023-01-02 00:36:44,850 - mmcls - INFO - Epoch [63][1800/5005] lr: 1.000e-03, eta: 4:38:25, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.7298 2023-01-02 00:36:53,710 - mmcls - INFO - Epoch [63][1900/5005] lr: 1.000e-03, eta: 4:38:16, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7242 2023-01-02 00:37:03,094 - mmcls - INFO - Epoch [63][2000/5005] lr: 1.000e-03, eta: 4:38:08, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.7478 2023-01-02 00:37:11,771 - mmcls - INFO - Epoch [63][2100/5005] lr: 1.000e-03, eta: 4:37:59, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.7286 2023-01-02 00:37:20,457 - mmcls - INFO - Epoch [63][2200/5005] lr: 1.000e-03, eta: 4:37:50, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7278 2023-01-02 00:37:29,302 - mmcls - INFO - Epoch [63][2300/5005] lr: 1.000e-03, eta: 4:37:41, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.7049 2023-01-02 00:37:38,008 - mmcls - INFO - Epoch [63][2400/5005] lr: 1.000e-03, eta: 4:37:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7216 2023-01-02 00:37:46,860 - mmcls - INFO - Epoch [63][2500/5005] lr: 1.000e-03, eta: 4:37:23, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.7545 2023-01-02 00:37:55,646 - mmcls - INFO - Epoch [63][2600/5005] lr: 1.000e-03, eta: 4:37:14, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.7257 2023-01-02 00:38:04,645 - mmcls - INFO - Epoch [63][2700/5005] lr: 1.000e-03, eta: 4:37:05, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.7139 2023-01-02 00:38:13,527 - mmcls - INFO - Epoch [63][2800/5005] lr: 1.000e-03, eta: 4:36:56, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.7207 2023-01-02 00:38:22,291 - mmcls - INFO - Epoch [63][2900/5005] lr: 1.000e-03, eta: 4:36:47, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7143 2023-01-02 00:38:30,760 - mmcls - INFO - Epoch [63][3000/5005] lr: 1.000e-03, eta: 4:36:38, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.7008 2023-01-02 00:38:39,626 - mmcls - INFO - Epoch [63][3100/5005] lr: 1.000e-03, eta: 4:36:29, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.7040 2023-01-02 00:38:48,335 - mmcls - INFO - Epoch [63][3200/5005] lr: 1.000e-03, eta: 4:36:21, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.7217 2023-01-02 00:38:57,044 - mmcls - INFO - Epoch [63][3300/5005] lr: 1.000e-03, eta: 4:36:12, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.7239 2023-01-02 00:39:05,692 - mmcls - INFO - Epoch [63][3400/5005] lr: 1.000e-03, eta: 4:36:03, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.7269 2023-01-02 00:39:14,899 - mmcls - INFO - Epoch [63][3500/5005] lr: 1.000e-03, eta: 4:35:54, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.7219 2023-01-02 00:39:23,281 - mmcls - INFO - Epoch [63][3600/5005] lr: 1.000e-03, eta: 4:35:45, time: 0.084, data_time: 0.006, memory: 1224, loss: 1.7269 2023-01-02 00:39:32,382 - mmcls - INFO - Epoch [63][3700/5005] lr: 1.000e-03, eta: 4:35:36, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.7252 2023-01-02 00:39:41,190 - mmcls - INFO - Epoch [63][3800/5005] lr: 1.000e-03, eta: 4:35:27, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.7348 2023-01-02 00:39:49,732 - mmcls - INFO - Epoch [63][3900/5005] lr: 1.000e-03, eta: 4:35:18, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.7398 2023-01-02 00:39:58,312 - mmcls - INFO - Epoch [63][4000/5005] lr: 1.000e-03, eta: 4:35:09, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.7196 2023-01-02 00:40:06,975 - mmcls - INFO - Epoch [63][4100/5005] lr: 1.000e-03, eta: 4:35:00, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7086 2023-01-02 00:40:16,039 - mmcls - INFO - Epoch [63][4200/5005] lr: 1.000e-03, eta: 4:34:51, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.7408 2023-01-02 00:40:24,827 - mmcls - INFO - Epoch [63][4300/5005] lr: 1.000e-03, eta: 4:34:42, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6949 2023-01-02 00:40:33,822 - mmcls - INFO - Epoch [63][4400/5005] lr: 1.000e-03, eta: 4:34:34, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6958 2023-01-02 00:40:42,790 - mmcls - INFO - Epoch [63][4500/5005] lr: 1.000e-03, eta: 4:34:25, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.7311 2023-01-02 00:40:51,479 - mmcls - INFO - Epoch [63][4600/5005] lr: 1.000e-03, eta: 4:34:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7082 2023-01-02 00:41:00,603 - mmcls - INFO - Epoch [63][4700/5005] lr: 1.000e-03, eta: 4:34:07, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.7212 2023-01-02 00:41:09,443 - mmcls - INFO - Epoch [63][4800/5005] lr: 1.000e-03, eta: 4:33:58, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.7030 2023-01-02 00:41:18,413 - mmcls - INFO - Epoch [63][4900/5005] lr: 1.000e-03, eta: 4:33:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.7478 2023-01-02 00:41:27,362 - mmcls - INFO - Epoch [63][5000/5005] lr: 1.000e-03, eta: 4:33:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7395 2023-01-02 00:41:50,084 - mmcls - INFO - Epoch(val) [63][196] accuracy_top-1: 63.9220, accuracy_top-5: 84.8700 2023-01-02 00:42:01,208 - mmcls - INFO - Epoch [64][100/5005] lr: 1.000e-03, eta: 4:33:32, time: 0.111, data_time: 0.046, memory: 1224, loss: 1.7199 2023-01-02 00:42:10,654 - mmcls - INFO - Epoch [64][200/5005] lr: 1.000e-03, eta: 4:33:24, time: 0.094, data_time: 0.016, memory: 1224, loss: 1.6922 2023-01-02 00:42:19,447 - mmcls - INFO - Epoch [64][300/5005] lr: 1.000e-03, eta: 4:33:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7291 2023-01-02 00:42:28,274 - mmcls - INFO - Epoch [64][400/5005] lr: 1.000e-03, eta: 4:33:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7209 2023-01-02 00:42:36,801 - mmcls - INFO - Epoch [64][500/5005] lr: 1.000e-03, eta: 4:32:57, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.7217 2023-01-02 00:42:45,622 - mmcls - INFO - Epoch [64][600/5005] lr: 1.000e-03, eta: 4:32:48, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.7147 2023-01-02 00:42:54,575 - mmcls - INFO - Epoch [64][700/5005] lr: 1.000e-03, eta: 4:32:39, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6951 2023-01-02 00:43:03,574 - mmcls - INFO - Epoch [64][800/5005] lr: 1.000e-03, eta: 4:32:30, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.7191 2023-01-02 00:43:12,444 - mmcls - INFO - Epoch [64][900/5005] lr: 1.000e-03, eta: 4:32:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7158 2023-01-02 00:43:21,315 - mmcls - INFO - Epoch [64][1000/5005] lr: 1.000e-03, eta: 4:32:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7232 2023-01-02 00:43:30,299 - mmcls - INFO - Epoch [64][1100/5005] lr: 1.000e-03, eta: 4:32:04, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.7378 2023-01-02 00:43:39,239 - mmcls - INFO - Epoch [64][1200/5005] lr: 1.000e-03, eta: 4:31:55, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6919 2023-01-02 00:43:47,975 - mmcls - INFO - Epoch [64][1300/5005] lr: 1.000e-03, eta: 4:31:46, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7110 2023-01-02 00:43:56,976 - mmcls - INFO - Epoch [64][1400/5005] lr: 1.000e-03, eta: 4:31:37, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.7197 2023-01-02 00:44:05,620 - mmcls - INFO - Epoch [64][1500/5005] lr: 1.000e-03, eta: 4:31:28, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.7023 2023-01-02 00:44:14,241 - mmcls - INFO - Epoch [64][1600/5005] lr: 1.000e-03, eta: 4:31:19, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6917 2023-01-02 00:44:22,789 - mmcls - INFO - Epoch [64][1700/5005] lr: 1.000e-03, eta: 4:31:10, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.7164 2023-01-02 00:44:31,404 - mmcls - INFO - Epoch [64][1800/5005] lr: 1.000e-03, eta: 4:31:01, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7182 2023-01-02 00:44:40,266 - mmcls - INFO - Epoch [64][1900/5005] lr: 1.000e-03, eta: 4:30:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7189 2023-01-02 00:44:48,956 - mmcls - INFO - Epoch [64][2000/5005] lr: 1.000e-03, eta: 4:30:43, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7191 2023-01-02 00:44:57,662 - mmcls - INFO - Epoch [64][2100/5005] lr: 1.000e-03, eta: 4:30:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6991 2023-01-02 00:45:06,578 - mmcls - INFO - Epoch [64][2200/5005] lr: 1.000e-03, eta: 4:30:26, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6976 2023-01-02 00:45:15,386 - mmcls - INFO - Epoch [64][2300/5005] lr: 1.000e-03, eta: 4:30:17, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.7166 2023-01-02 00:45:24,029 - mmcls - INFO - Epoch [64][2400/5005] lr: 1.000e-03, eta: 4:30:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7090 2023-01-02 00:45:33,018 - mmcls - INFO - Epoch [64][2500/5005] lr: 1.000e-03, eta: 4:29:59, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7140 2023-01-02 00:45:41,827 - mmcls - INFO - Epoch [64][2600/5005] lr: 1.000e-03, eta: 4:29:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7193 2023-01-02 00:45:50,496 - mmcls - INFO - Epoch [64][2700/5005] lr: 1.000e-03, eta: 4:29:41, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7171 2023-01-02 00:45:59,805 - mmcls - INFO - Epoch [64][2800/5005] lr: 1.000e-03, eta: 4:29:32, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.7181 2023-01-02 00:46:08,633 - mmcls - INFO - Epoch [64][2900/5005] lr: 1.000e-03, eta: 4:29:23, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6892 2023-01-02 00:46:17,752 - mmcls - INFO - Epoch [64][3000/5005] lr: 1.000e-03, eta: 4:29:15, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.7280 2023-01-02 00:46:26,407 - mmcls - INFO - Epoch [64][3100/5005] lr: 1.000e-03, eta: 4:29:06, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6794 2023-01-02 00:46:35,358 - mmcls - INFO - Epoch [64][3200/5005] lr: 1.000e-03, eta: 4:28:57, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.7045 2023-01-02 00:46:44,196 - mmcls - INFO - Epoch [64][3300/5005] lr: 1.000e-03, eta: 4:28:48, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6967 2023-01-02 00:46:52,990 - mmcls - INFO - Epoch [64][3400/5005] lr: 1.000e-03, eta: 4:28:39, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.7226 2023-01-02 00:47:01,867 - mmcls - INFO - Epoch [64][3500/5005] lr: 1.000e-03, eta: 4:28:30, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.7216 2023-01-02 00:47:10,483 - mmcls - INFO - Epoch [64][3600/5005] lr: 1.000e-03, eta: 4:28:21, time: 0.086, data_time: 0.037, memory: 1224, loss: 1.6961 2023-01-02 00:47:19,352 - mmcls - INFO - Epoch [64][3700/5005] lr: 1.000e-03, eta: 4:28:12, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.7148 2023-01-02 00:47:28,313 - mmcls - INFO - Epoch [64][3800/5005] lr: 1.000e-03, eta: 4:28:04, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.7057 2023-01-02 00:47:36,852 - mmcls - INFO - Epoch [64][3900/5005] lr: 1.000e-03, eta: 4:27:55, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.7260 2023-01-02 00:47:45,695 - mmcls - INFO - Epoch [64][4000/5005] lr: 1.000e-03, eta: 4:27:46, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.7151 2023-01-02 00:47:55,060 - mmcls - INFO - Epoch [64][4100/5005] lr: 1.000e-03, eta: 4:27:37, time: 0.094, data_time: 0.006, memory: 1224, loss: 1.7103 2023-01-02 00:48:04,068 - mmcls - INFO - Epoch [64][4200/5005] lr: 1.000e-03, eta: 4:27:28, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6918 2023-01-02 00:48:13,061 - mmcls - INFO - Epoch [64][4300/5005] lr: 1.000e-03, eta: 4:27:19, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.7182 2023-01-02 00:48:21,811 - mmcls - INFO - Epoch [64][4400/5005] lr: 1.000e-03, eta: 4:27:11, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7046 2023-01-02 00:48:30,470 - mmcls - INFO - Epoch [64][4500/5005] lr: 1.000e-03, eta: 4:27:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6854 2023-01-02 00:48:39,149 - mmcls - INFO - Epoch [64][4600/5005] lr: 1.000e-03, eta: 4:26:53, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7232 2023-01-02 00:48:47,864 - mmcls - INFO - Epoch [64][4700/5005] lr: 1.000e-03, eta: 4:26:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6985 2023-01-02 00:48:56,728 - mmcls - INFO - Epoch [64][4800/5005] lr: 1.000e-03, eta: 4:26:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6980 2023-01-02 00:49:05,355 - mmcls - INFO - Epoch [64][4900/5005] lr: 1.000e-03, eta: 4:26:26, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6964 2023-01-02 00:49:14,126 - mmcls - INFO - Epoch [64][5000/5005] lr: 1.000e-03, eta: 4:26:17, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6856 2023-01-02 00:49:36,924 - mmcls - INFO - Epoch(val) [64][196] accuracy_top-1: 64.1920, accuracy_top-5: 85.0520 2023-01-02 00:49:47,959 - mmcls - INFO - Epoch [65][100/5005] lr: 1.000e-03, eta: 4:26:08, time: 0.110, data_time: 0.042, memory: 1224, loss: 1.6809 2023-01-02 00:49:56,659 - mmcls - INFO - Epoch [65][200/5005] lr: 1.000e-03, eta: 4:26:00, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.7185 2023-01-02 00:50:05,316 - mmcls - INFO - Epoch [65][300/5005] lr: 1.000e-03, eta: 4:25:51, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.7126 2023-01-02 00:50:14,198 - mmcls - INFO - Epoch [65][400/5005] lr: 1.000e-03, eta: 4:25:42, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6935 2023-01-02 00:50:23,165 - mmcls - INFO - Epoch [65][500/5005] lr: 1.000e-03, eta: 4:25:33, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6753 2023-01-02 00:50:32,034 - mmcls - INFO - Epoch [65][600/5005] lr: 1.000e-03, eta: 4:25:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6816 2023-01-02 00:50:40,866 - mmcls - INFO - Epoch [65][700/5005] lr: 1.000e-03, eta: 4:25:15, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.6834 2023-01-02 00:50:49,668 - mmcls - INFO - Epoch [65][800/5005] lr: 1.000e-03, eta: 4:25:06, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7122 2023-01-02 00:50:58,440 - mmcls - INFO - Epoch [65][900/5005] lr: 1.000e-03, eta: 4:24:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6984 2023-01-02 00:51:07,198 - mmcls - INFO - Epoch [65][1000/5005] lr: 1.000e-03, eta: 4:24:48, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.7013 2023-01-02 00:51:16,102 - mmcls - INFO - Epoch [65][1100/5005] lr: 1.000e-03, eta: 4:24:40, time: 0.089, data_time: 0.040, memory: 1224, loss: 1.6874 2023-01-02 00:51:25,144 - mmcls - INFO - Epoch [65][1200/5005] lr: 1.000e-03, eta: 4:24:31, time: 0.090, data_time: 0.029, memory: 1224, loss: 1.6911 2023-01-02 00:51:34,081 - mmcls - INFO - Epoch [65][1300/5005] lr: 1.000e-03, eta: 4:24:22, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.7010 2023-01-02 00:51:42,804 - mmcls - INFO - Epoch [65][1400/5005] lr: 1.000e-03, eta: 4:24:13, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6869 2023-01-02 00:51:51,353 - mmcls - INFO - Epoch [65][1500/5005] lr: 1.000e-03, eta: 4:24:04, time: 0.085, data_time: 0.016, memory: 1224, loss: 1.7006 2023-01-02 00:52:00,043 - mmcls - INFO - Epoch [65][1600/5005] lr: 1.000e-03, eta: 4:23:55, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7286 2023-01-02 00:52:09,033 - mmcls - INFO - Epoch [65][1700/5005] lr: 1.000e-03, eta: 4:23:46, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6817 2023-01-02 00:52:17,669 - mmcls - INFO - Epoch [65][1800/5005] lr: 1.000e-03, eta: 4:23:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6969 2023-01-02 00:52:26,515 - mmcls - INFO - Epoch [65][1900/5005] lr: 1.000e-03, eta: 4:23:28, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7046 2023-01-02 00:52:35,211 - mmcls - INFO - Epoch [65][2000/5005] lr: 1.000e-03, eta: 4:23:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6816 2023-01-02 00:52:44,121 - mmcls - INFO - Epoch [65][2100/5005] lr: 1.000e-03, eta: 4:23:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7016 2023-01-02 00:52:52,829 - mmcls - INFO - Epoch [65][2200/5005] lr: 1.000e-03, eta: 4:23:02, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6911 2023-01-02 00:53:01,560 - mmcls - INFO - Epoch [65][2300/5005] lr: 1.000e-03, eta: 4:22:53, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6998 2023-01-02 00:53:10,501 - mmcls - INFO - Epoch [65][2400/5005] lr: 1.000e-03, eta: 4:22:44, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6974 2023-01-02 00:53:19,409 - mmcls - INFO - Epoch [65][2500/5005] lr: 1.000e-03, eta: 4:22:35, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6855 2023-01-02 00:53:28,093 - mmcls - INFO - Epoch [65][2600/5005] lr: 1.000e-03, eta: 4:22:26, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6924 2023-01-02 00:53:37,185 - mmcls - INFO - Epoch [65][2700/5005] lr: 1.000e-03, eta: 4:22:17, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.7119 2023-01-02 00:53:46,068 - mmcls - INFO - Epoch [65][2800/5005] lr: 1.000e-03, eta: 4:22:08, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6988 2023-01-02 00:53:54,613 - mmcls - INFO - Epoch [65][2900/5005] lr: 1.000e-03, eta: 4:21:59, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6922 2023-01-02 00:54:03,414 - mmcls - INFO - Epoch [65][3000/5005] lr: 1.000e-03, eta: 4:21:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6957 2023-01-02 00:54:12,233 - mmcls - INFO - Epoch [65][3100/5005] lr: 1.000e-03, eta: 4:21:41, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7057 2023-01-02 00:54:20,854 - mmcls - INFO - Epoch [65][3200/5005] lr: 1.000e-03, eta: 4:21:32, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6762 2023-01-02 00:54:29,570 - mmcls - INFO - Epoch [65][3300/5005] lr: 1.000e-03, eta: 4:21:24, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6960 2023-01-02 00:54:38,474 - mmcls - INFO - Epoch [65][3400/5005] lr: 1.000e-03, eta: 4:21:15, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6967 2023-01-02 00:54:47,216 - mmcls - INFO - Epoch [65][3500/5005] lr: 1.000e-03, eta: 4:21:06, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6950 2023-01-02 00:54:56,095 - mmcls - INFO - Epoch [65][3600/5005] lr: 1.000e-03, eta: 4:20:57, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6994 2023-01-02 00:55:05,182 - mmcls - INFO - Epoch [65][3700/5005] lr: 1.000e-03, eta: 4:20:48, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.7220 2023-01-02 00:55:14,002 - mmcls - INFO - Epoch [65][3800/5005] lr: 1.000e-03, eta: 4:20:39, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.7116 2023-01-02 00:55:22,841 - mmcls - INFO - Epoch [65][3900/5005] lr: 1.000e-03, eta: 4:20:30, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6953 2023-01-02 00:55:31,547 - mmcls - INFO - Epoch [65][4000/5005] lr: 1.000e-03, eta: 4:20:21, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7120 2023-01-02 00:55:40,345 - mmcls - INFO - Epoch [65][4100/5005] lr: 1.000e-03, eta: 4:20:13, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.7140 2023-01-02 00:55:49,204 - mmcls - INFO - Epoch [65][4200/5005] lr: 1.000e-03, eta: 4:20:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7037 2023-01-02 00:55:58,000 - mmcls - INFO - Epoch [65][4300/5005] lr: 1.000e-03, eta: 4:19:55, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.7186 2023-01-02 00:56:06,869 - mmcls - INFO - Epoch [65][4400/5005] lr: 1.000e-03, eta: 4:19:46, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6833 2023-01-02 00:56:15,773 - mmcls - INFO - Epoch [65][4500/5005] lr: 1.000e-03, eta: 4:19:37, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6920 2023-01-02 00:56:24,524 - mmcls - INFO - Epoch [65][4600/5005] lr: 1.000e-03, eta: 4:19:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7033 2023-01-02 00:56:33,163 - mmcls - INFO - Epoch [65][4700/5005] lr: 1.000e-03, eta: 4:19:19, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.7033 2023-01-02 00:56:42,018 - mmcls - INFO - Epoch [65][4800/5005] lr: 1.000e-03, eta: 4:19:10, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.7082 2023-01-02 00:56:50,849 - mmcls - INFO - Epoch [65][4900/5005] lr: 1.000e-03, eta: 4:19:01, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.7053 2023-01-02 00:56:59,705 - mmcls - INFO - Epoch [65][5000/5005] lr: 1.000e-03, eta: 4:18:52, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7081 2023-01-02 00:57:22,730 - mmcls - INFO - Epoch(val) [65][196] accuracy_top-1: 64.3040, accuracy_top-5: 85.0380 2023-01-02 00:57:33,826 - mmcls - INFO - Epoch [66][100/5005] lr: 1.000e-03, eta: 4:18:44, time: 0.111, data_time: 0.047, memory: 1224, loss: 1.6958 2023-01-02 00:57:42,492 - mmcls - INFO - Epoch [66][200/5005] lr: 1.000e-03, eta: 4:18:35, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6867 2023-01-02 00:57:51,317 - mmcls - INFO - Epoch [66][300/5005] lr: 1.000e-03, eta: 4:18:26, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6807 2023-01-02 00:58:00,198 - mmcls - INFO - Epoch [66][400/5005] lr: 1.000e-03, eta: 4:18:17, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6799 2023-01-02 00:58:09,087 - mmcls - INFO - Epoch [66][500/5005] lr: 1.000e-03, eta: 4:18:09, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6830 2023-01-02 00:58:17,658 - mmcls - INFO - Epoch [66][600/5005] lr: 1.000e-03, eta: 4:17:59, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6903 2023-01-02 00:58:26,279 - mmcls - INFO - Epoch [66][700/5005] lr: 1.000e-03, eta: 4:17:50, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.6476 2023-01-02 00:58:35,387 - mmcls - INFO - Epoch [66][800/5005] lr: 1.000e-03, eta: 4:17:42, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6739 2023-01-02 00:58:44,467 - mmcls - INFO - Epoch [66][900/5005] lr: 1.000e-03, eta: 4:17:33, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.7020 2023-01-02 00:58:53,301 - mmcls - INFO - Epoch [66][1000/5005] lr: 1.000e-03, eta: 4:17:24, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6838 2023-01-02 00:59:01,932 - mmcls - INFO - Epoch [66][1100/5005] lr: 1.000e-03, eta: 4:17:15, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6850 2023-01-02 00:59:10,798 - mmcls - INFO - Epoch [66][1200/5005] lr: 1.000e-03, eta: 4:17:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6927 2023-01-02 00:59:19,745 - mmcls - INFO - Epoch [66][1300/5005] lr: 1.000e-03, eta: 4:16:57, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7167 2023-01-02 00:59:28,302 - mmcls - INFO - Epoch [66][1400/5005] lr: 1.000e-03, eta: 4:16:48, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6824 2023-01-02 00:59:37,237 - mmcls - INFO - Epoch [66][1500/5005] lr: 1.000e-03, eta: 4:16:40, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6992 2023-01-02 00:59:46,532 - mmcls - INFO - Epoch [66][1600/5005] lr: 1.000e-03, eta: 4:16:31, time: 0.093, data_time: 0.022, memory: 1224, loss: 1.6906 2023-01-02 00:59:55,162 - mmcls - INFO - Epoch [66][1700/5005] lr: 1.000e-03, eta: 4:16:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6911 2023-01-02 01:00:03,998 - mmcls - INFO - Epoch [66][1800/5005] lr: 1.000e-03, eta: 4:16:13, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6864 2023-01-02 01:00:12,630 - mmcls - INFO - Epoch [66][1900/5005] lr: 1.000e-03, eta: 4:16:04, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6926 2023-01-02 01:00:21,847 - mmcls - INFO - Epoch [66][2000/5005] lr: 1.000e-03, eta: 4:15:55, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.6866 2023-01-02 01:00:30,932 - mmcls - INFO - Epoch [66][2100/5005] lr: 1.000e-03, eta: 4:15:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.7071 2023-01-02 01:00:39,672 - mmcls - INFO - Epoch [66][2200/5005] lr: 1.000e-03, eta: 4:15:38, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6880 2023-01-02 01:00:48,386 - mmcls - INFO - Epoch [66][2300/5005] lr: 1.000e-03, eta: 4:15:29, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6951 2023-01-02 01:00:57,136 - mmcls - INFO - Epoch [66][2400/5005] lr: 1.000e-03, eta: 4:15:20, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7029 2023-01-02 01:01:06,129 - mmcls - INFO - Epoch [66][2500/5005] lr: 1.000e-03, eta: 4:15:11, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6883 2023-01-02 01:01:14,773 - mmcls - INFO - Epoch [66][2600/5005] lr: 1.000e-03, eta: 4:15:02, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7018 2023-01-02 01:01:23,458 - mmcls - INFO - Epoch [66][2700/5005] lr: 1.000e-03, eta: 4:14:53, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.7005 2023-01-02 01:01:32,318 - mmcls - INFO - Epoch [66][2800/5005] lr: 1.000e-03, eta: 4:14:44, time: 0.089, data_time: 0.046, memory: 1224, loss: 1.6815 2023-01-02 01:01:40,801 - mmcls - INFO - Epoch [66][2900/5005] lr: 1.000e-03, eta: 4:14:35, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6831 2023-01-02 01:01:49,968 - mmcls - INFO - Epoch [66][3000/5005] lr: 1.000e-03, eta: 4:14:26, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6863 2023-01-02 01:01:58,575 - mmcls - INFO - Epoch [66][3100/5005] lr: 1.000e-03, eta: 4:14:17, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6879 2023-01-02 01:02:07,428 - mmcls - INFO - Epoch [66][3200/5005] lr: 1.000e-03, eta: 4:14:09, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.7086 2023-01-02 01:02:16,206 - mmcls - INFO - Epoch [66][3300/5005] lr: 1.000e-03, eta: 4:14:00, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6964 2023-01-02 01:02:25,005 - mmcls - INFO - Epoch [66][3400/5005] lr: 1.000e-03, eta: 4:13:51, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6805 2023-01-02 01:02:34,038 - mmcls - INFO - Epoch [66][3500/5005] lr: 1.000e-03, eta: 4:13:42, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6867 2023-01-02 01:02:42,485 - mmcls - INFO - Epoch [66][3600/5005] lr: 1.000e-03, eta: 4:13:33, time: 0.084, data_time: 0.005, memory: 1224, loss: 1.7007 2023-01-02 01:02:51,144 - mmcls - INFO - Epoch [66][3700/5005] lr: 1.000e-03, eta: 4:13:24, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7042 2023-01-02 01:03:00,168 - mmcls - INFO - Epoch [66][3800/5005] lr: 1.000e-03, eta: 4:13:15, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.7126 2023-01-02 01:03:08,617 - mmcls - INFO - Epoch [66][3900/5005] lr: 1.000e-03, eta: 4:13:06, time: 0.084, data_time: 0.021, memory: 1224, loss: 1.6920 2023-01-02 01:03:17,473 - mmcls - INFO - Epoch [66][4000/5005] lr: 1.000e-03, eta: 4:12:57, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6750 2023-01-02 01:03:26,041 - mmcls - INFO - Epoch [66][4100/5005] lr: 1.000e-03, eta: 4:12:48, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6644 2023-01-02 01:03:34,903 - mmcls - INFO - Epoch [66][4200/5005] lr: 1.000e-03, eta: 4:12:39, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6792 2023-01-02 01:03:43,816 - mmcls - INFO - Epoch [66][4300/5005] lr: 1.000e-03, eta: 4:12:30, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6979 2023-01-02 01:03:53,092 - mmcls - INFO - Epoch [66][4400/5005] lr: 1.000e-03, eta: 4:12:22, time: 0.093, data_time: 0.009, memory: 1224, loss: 1.7070 2023-01-02 01:04:02,049 - mmcls - INFO - Epoch [66][4500/5005] lr: 1.000e-03, eta: 4:12:13, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6889 2023-01-02 01:04:10,687 - mmcls - INFO - Epoch [66][4600/5005] lr: 1.000e-03, eta: 4:12:04, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.7059 2023-01-02 01:04:19,492 - mmcls - INFO - Epoch [66][4700/5005] lr: 1.000e-03, eta: 4:11:55, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6909 2023-01-02 01:04:28,330 - mmcls - INFO - Epoch [66][4800/5005] lr: 1.000e-03, eta: 4:11:46, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6824 2023-01-02 01:04:37,122 - mmcls - INFO - Epoch [66][4900/5005] lr: 1.000e-03, eta: 4:11:37, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6736 2023-01-02 01:04:45,977 - mmcls - INFO - Epoch [66][5000/5005] lr: 1.000e-03, eta: 4:11:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7062 2023-01-02 01:05:08,807 - mmcls - INFO - Epoch(val) [66][196] accuracy_top-1: 64.4140, accuracy_top-5: 85.1420 2023-01-02 01:05:19,511 - mmcls - INFO - Epoch [67][100/5005] lr: 1.000e-03, eta: 4:11:20, time: 0.107, data_time: 0.053, memory: 1224, loss: 1.6863 2023-01-02 01:05:28,450 - mmcls - INFO - Epoch [67][200/5005] lr: 1.000e-03, eta: 4:11:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6887 2023-01-02 01:05:37,128 - mmcls - INFO - Epoch [67][300/5005] lr: 1.000e-03, eta: 4:11:02, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6875 2023-01-02 01:05:45,902 - mmcls - INFO - Epoch [67][400/5005] lr: 1.000e-03, eta: 4:10:53, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6689 2023-01-02 01:05:54,945 - mmcls - INFO - Epoch [67][500/5005] lr: 1.000e-03, eta: 4:10:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6756 2023-01-02 01:06:03,736 - mmcls - INFO - Epoch [67][600/5005] lr: 1.000e-03, eta: 4:10:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6866 2023-01-02 01:06:12,174 - mmcls - INFO - Epoch [67][700/5005] lr: 1.000e-03, eta: 4:10:26, time: 0.084, data_time: 0.026, memory: 1224, loss: 1.6816 2023-01-02 01:06:21,050 - mmcls - INFO - Epoch [67][800/5005] lr: 1.000e-03, eta: 4:10:17, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6757 2023-01-02 01:06:29,924 - mmcls - INFO - Epoch [67][900/5005] lr: 1.000e-03, eta: 4:10:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6872 2023-01-02 01:06:38,574 - mmcls - INFO - Epoch [67][1000/5005] lr: 1.000e-03, eta: 4:10:00, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6825 2023-01-02 01:06:47,321 - mmcls - INFO - Epoch [67][1100/5005] lr: 1.000e-03, eta: 4:09:51, time: 0.088, data_time: 0.030, memory: 1224, loss: 1.6786 2023-01-02 01:06:56,396 - mmcls - INFO - Epoch [67][1200/5005] lr: 1.000e-03, eta: 4:09:42, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6924 2023-01-02 01:07:05,590 - mmcls - INFO - Epoch [67][1300/5005] lr: 1.000e-03, eta: 4:09:33, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6858 2023-01-02 01:07:14,431 - mmcls - INFO - Epoch [67][1400/5005] lr: 1.000e-03, eta: 4:09:24, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6790 2023-01-02 01:07:23,456 - mmcls - INFO - Epoch [67][1500/5005] lr: 1.000e-03, eta: 4:09:16, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6688 2023-01-02 01:07:32,170 - mmcls - INFO - Epoch [67][1600/5005] lr: 1.000e-03, eta: 4:09:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6819 2023-01-02 01:07:40,817 - mmcls - INFO - Epoch [67][1700/5005] lr: 1.000e-03, eta: 4:08:58, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6902 2023-01-02 01:07:49,832 - mmcls - INFO - Epoch [67][1800/5005] lr: 1.000e-03, eta: 4:08:49, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6909 2023-01-02 01:07:58,655 - mmcls - INFO - Epoch [67][1900/5005] lr: 1.000e-03, eta: 4:08:40, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6995 2023-01-02 01:08:07,187 - mmcls - INFO - Epoch [67][2000/5005] lr: 1.000e-03, eta: 4:08:31, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.7030 2023-01-02 01:08:15,781 - mmcls - INFO - Epoch [67][2100/5005] lr: 1.000e-03, eta: 4:08:22, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6760 2023-01-02 01:08:24,530 - mmcls - INFO - Epoch [67][2200/5005] lr: 1.000e-03, eta: 4:08:13, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6890 2023-01-02 01:08:33,312 - mmcls - INFO - Epoch [67][2300/5005] lr: 1.000e-03, eta: 4:08:04, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.7103 2023-01-02 01:08:42,175 - mmcls - INFO - Epoch [67][2400/5005] lr: 1.000e-03, eta: 4:07:55, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6902 2023-01-02 01:08:50,826 - mmcls - INFO - Epoch [67][2500/5005] lr: 1.000e-03, eta: 4:07:46, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.6792 2023-01-02 01:08:59,672 - mmcls - INFO - Epoch [67][2600/5005] lr: 1.000e-03, eta: 4:07:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6927 2023-01-02 01:09:08,569 - mmcls - INFO - Epoch [67][2700/5005] lr: 1.000e-03, eta: 4:07:28, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6664 2023-01-02 01:09:17,453 - mmcls - INFO - Epoch [67][2800/5005] lr: 1.000e-03, eta: 4:07:20, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6738 2023-01-02 01:09:25,986 - mmcls - INFO - Epoch [67][2900/5005] lr: 1.000e-03, eta: 4:07:11, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.6923 2023-01-02 01:09:34,596 - mmcls - INFO - Epoch [67][3000/5005] lr: 1.000e-03, eta: 4:07:02, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6875 2023-01-02 01:09:43,269 - mmcls - INFO - Epoch [67][3100/5005] lr: 1.000e-03, eta: 4:06:53, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6847 2023-01-02 01:09:52,083 - mmcls - INFO - Epoch [67][3200/5005] lr: 1.000e-03, eta: 4:06:44, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6707 2023-01-02 01:10:00,769 - mmcls - INFO - Epoch [67][3300/5005] lr: 1.000e-03, eta: 4:06:35, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.6749 2023-01-02 01:10:09,647 - mmcls - INFO - Epoch [67][3400/5005] lr: 1.000e-03, eta: 4:06:26, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.6992 2023-01-02 01:10:18,405 - mmcls - INFO - Epoch [67][3500/5005] lr: 1.000e-03, eta: 4:06:17, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6992 2023-01-02 01:10:27,283 - mmcls - INFO - Epoch [67][3600/5005] lr: 1.000e-03, eta: 4:06:08, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6905 2023-01-02 01:10:36,476 - mmcls - INFO - Epoch [67][3700/5005] lr: 1.000e-03, eta: 4:05:59, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.7022 2023-01-02 01:10:45,281 - mmcls - INFO - Epoch [67][3800/5005] lr: 1.000e-03, eta: 4:05:51, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6847 2023-01-02 01:10:54,248 - mmcls - INFO - Epoch [67][3900/5005] lr: 1.000e-03, eta: 4:05:42, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6821 2023-01-02 01:11:03,140 - mmcls - INFO - Epoch [67][4000/5005] lr: 1.000e-03, eta: 4:05:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6847 2023-01-02 01:11:11,936 - mmcls - INFO - Epoch [67][4100/5005] lr: 1.000e-03, eta: 4:05:24, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6834 2023-01-02 01:11:20,771 - mmcls - INFO - Epoch [67][4200/5005] lr: 1.000e-03, eta: 4:05:15, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6999 2023-01-02 01:11:29,558 - mmcls - INFO - Epoch [67][4300/5005] lr: 1.000e-03, eta: 4:05:06, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.7061 2023-01-02 01:11:38,523 - mmcls - INFO - Epoch [67][4400/5005] lr: 1.000e-03, eta: 4:04:57, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.7076 2023-01-02 01:11:47,031 - mmcls - INFO - Epoch [67][4500/5005] lr: 1.000e-03, eta: 4:04:48, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.6935 2023-01-02 01:11:55,759 - mmcls - INFO - Epoch [67][4600/5005] lr: 1.000e-03, eta: 4:04:39, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6782 2023-01-02 01:12:04,645 - mmcls - INFO - Epoch [67][4700/5005] lr: 1.000e-03, eta: 4:04:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6836 2023-01-02 01:12:13,365 - mmcls - INFO - Epoch [67][4800/5005] lr: 1.000e-03, eta: 4:04:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6669 2023-01-02 01:12:22,225 - mmcls - INFO - Epoch [67][4900/5005] lr: 1.000e-03, eta: 4:04:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7084 2023-01-02 01:12:31,111 - mmcls - INFO - Epoch [67][5000/5005] lr: 1.000e-03, eta: 4:04:04, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6826 2023-01-02 01:12:53,802 - mmcls - INFO - Epoch(val) [67][196] accuracy_top-1: 64.5880, accuracy_top-5: 85.2920 2023-01-02 01:13:05,335 - mmcls - INFO - Epoch [68][100/5005] lr: 1.000e-03, eta: 4:03:56, time: 0.115, data_time: 0.039, memory: 1224, loss: 1.6635 2023-01-02 01:13:13,859 - mmcls - INFO - Epoch [68][200/5005] lr: 1.000e-03, eta: 4:03:47, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6835 2023-01-02 01:13:22,653 - mmcls - INFO - Epoch [68][300/5005] lr: 1.000e-03, eta: 4:03:38, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6799 2023-01-02 01:13:31,368 - mmcls - INFO - Epoch [68][400/5005] lr: 1.000e-03, eta: 4:03:29, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6648 2023-01-02 01:13:40,402 - mmcls - INFO - Epoch [68][500/5005] lr: 1.000e-03, eta: 4:03:20, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6831 2023-01-02 01:13:49,291 - mmcls - INFO - Epoch [68][600/5005] lr: 1.000e-03, eta: 4:03:11, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6559 2023-01-02 01:13:57,836 - mmcls - INFO - Epoch [68][700/5005] lr: 1.000e-03, eta: 4:03:02, time: 0.085, data_time: 0.028, memory: 1224, loss: 1.6819 2023-01-02 01:14:06,463 - mmcls - INFO - Epoch [68][800/5005] lr: 1.000e-03, eta: 4:02:53, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6876 2023-01-02 01:14:15,276 - mmcls - INFO - Epoch [68][900/5005] lr: 1.000e-03, eta: 4:02:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6838 2023-01-02 01:14:24,199 - mmcls - INFO - Epoch [68][1000/5005] lr: 1.000e-03, eta: 4:02:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6662 2023-01-02 01:14:32,931 - mmcls - INFO - Epoch [68][1100/5005] lr: 1.000e-03, eta: 4:02:26, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6699 2023-01-02 01:14:41,572 - mmcls - INFO - Epoch [68][1200/5005] lr: 1.000e-03, eta: 4:02:17, time: 0.086, data_time: 0.033, memory: 1224, loss: 1.6819 2023-01-02 01:14:50,453 - mmcls - INFO - Epoch [68][1300/5005] lr: 1.000e-03, eta: 4:02:09, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6742 2023-01-02 01:14:59,828 - mmcls - INFO - Epoch [68][1400/5005] lr: 1.000e-03, eta: 4:02:00, time: 0.094, data_time: 0.017, memory: 1224, loss: 1.6806 2023-01-02 01:15:08,654 - mmcls - INFO - Epoch [68][1500/5005] lr: 1.000e-03, eta: 4:01:51, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6630 2023-01-02 01:15:17,610 - mmcls - INFO - Epoch [68][1600/5005] lr: 1.000e-03, eta: 4:01:42, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6583 2023-01-02 01:15:26,393 - mmcls - INFO - Epoch [68][1700/5005] lr: 1.000e-03, eta: 4:01:33, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6845 2023-01-02 01:15:35,150 - mmcls - INFO - Epoch [68][1800/5005] lr: 1.000e-03, eta: 4:01:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6755 2023-01-02 01:15:43,750 - mmcls - INFO - Epoch [68][1900/5005] lr: 1.000e-03, eta: 4:01:15, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6648 2023-01-02 01:15:52,375 - mmcls - INFO - Epoch [68][2000/5005] lr: 1.000e-03, eta: 4:01:06, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6742 2023-01-02 01:16:01,385 - mmcls - INFO - Epoch [68][2100/5005] lr: 1.000e-03, eta: 4:00:58, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6701 2023-01-02 01:16:10,317 - mmcls - INFO - Epoch [68][2200/5005] lr: 1.000e-03, eta: 4:00:49, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6594 2023-01-02 01:16:18,812 - mmcls - INFO - Epoch [68][2300/5005] lr: 1.000e-03, eta: 4:00:40, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.6517 2023-01-02 01:16:27,804 - mmcls - INFO - Epoch [68][2400/5005] lr: 1.000e-03, eta: 4:00:31, time: 0.090, data_time: 0.027, memory: 1224, loss: 1.6736 2023-01-02 01:16:36,789 - mmcls - INFO - Epoch [68][2500/5005] lr: 1.000e-03, eta: 4:00:22, time: 0.090, data_time: 0.029, memory: 1224, loss: 1.6684 2023-01-02 01:16:45,399 - mmcls - INFO - Epoch [68][2600/5005] lr: 1.000e-03, eta: 4:00:13, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6716 2023-01-02 01:16:54,332 - mmcls - INFO - Epoch [68][2700/5005] lr: 1.000e-03, eta: 4:00:04, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6749 2023-01-02 01:17:03,004 - mmcls - INFO - Epoch [68][2800/5005] lr: 1.000e-03, eta: 3:59:55, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6995 2023-01-02 01:17:11,979 - mmcls - INFO - Epoch [68][2900/5005] lr: 1.000e-03, eta: 3:59:47, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6824 2023-01-02 01:17:20,572 - mmcls - INFO - Epoch [68][3000/5005] lr: 1.000e-03, eta: 3:59:38, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6690 2023-01-02 01:17:29,374 - mmcls - INFO - Epoch [68][3100/5005] lr: 1.000e-03, eta: 3:59:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6809 2023-01-02 01:17:38,094 - mmcls - INFO - Epoch [68][3200/5005] lr: 1.000e-03, eta: 3:59:20, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6746 2023-01-02 01:17:46,975 - mmcls - INFO - Epoch [68][3300/5005] lr: 1.000e-03, eta: 3:59:11, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6742 2023-01-02 01:17:55,496 - mmcls - INFO - Epoch [68][3400/5005] lr: 1.000e-03, eta: 3:59:02, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6904 2023-01-02 01:18:04,928 - mmcls - INFO - Epoch [68][3500/5005] lr: 1.000e-03, eta: 3:58:53, time: 0.094, data_time: 0.002, memory: 1224, loss: 1.6839 2023-01-02 01:18:13,988 - mmcls - INFO - Epoch [68][3600/5005] lr: 1.000e-03, eta: 3:58:44, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.7108 2023-01-02 01:18:22,897 - mmcls - INFO - Epoch [68][3700/5005] lr: 1.000e-03, eta: 3:58:36, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.7027 2023-01-02 01:18:31,580 - mmcls - INFO - Epoch [68][3800/5005] lr: 1.000e-03, eta: 3:58:27, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6787 2023-01-02 01:18:40,490 - mmcls - INFO - Epoch [68][3900/5005] lr: 1.000e-03, eta: 3:58:18, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6815 2023-01-02 01:18:49,083 - mmcls - INFO - Epoch [68][4000/5005] lr: 1.000e-03, eta: 3:58:09, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6746 2023-01-02 01:18:57,931 - mmcls - INFO - Epoch [68][4100/5005] lr: 1.000e-03, eta: 3:58:00, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6864 2023-01-02 01:19:07,335 - mmcls - INFO - Epoch [68][4200/5005] lr: 1.000e-03, eta: 3:57:51, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.6643 2023-01-02 01:19:15,840 - mmcls - INFO - Epoch [68][4300/5005] lr: 1.000e-03, eta: 3:57:42, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6880 2023-01-02 01:19:24,782 - mmcls - INFO - Epoch [68][4400/5005] lr: 1.000e-03, eta: 3:57:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6906 2023-01-02 01:19:33,525 - mmcls - INFO - Epoch [68][4500/5005] lr: 1.000e-03, eta: 3:57:25, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6853 2023-01-02 01:19:42,514 - mmcls - INFO - Epoch [68][4600/5005] lr: 1.000e-03, eta: 3:57:16, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6891 2023-01-02 01:19:51,404 - mmcls - INFO - Epoch [68][4700/5005] lr: 1.000e-03, eta: 3:57:07, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6801 2023-01-02 01:20:00,244 - mmcls - INFO - Epoch [68][4800/5005] lr: 1.000e-03, eta: 3:56:58, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6816 2023-01-02 01:20:09,305 - mmcls - INFO - Epoch [68][4900/5005] lr: 1.000e-03, eta: 3:56:49, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.7069 2023-01-02 01:20:18,703 - mmcls - INFO - Epoch [68][5000/5005] lr: 1.000e-03, eta: 3:56:41, time: 0.094, data_time: 0.003, memory: 1224, loss: 1.6925 2023-01-02 01:20:41,232 - mmcls - INFO - Epoch(val) [68][196] accuracy_top-1: 64.4880, accuracy_top-5: 85.2320 2023-01-02 01:20:52,100 - mmcls - INFO - Epoch [69][100/5005] lr: 1.000e-03, eta: 3:56:32, time: 0.108, data_time: 0.060, memory: 1224, loss: 1.6487 2023-01-02 01:21:01,010 - mmcls - INFO - Epoch [69][200/5005] lr: 1.000e-03, eta: 3:56:23, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6684 2023-01-02 01:21:09,756 - mmcls - INFO - Epoch [69][300/5005] lr: 1.000e-03, eta: 3:56:14, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6616 2023-01-02 01:21:18,714 - mmcls - INFO - Epoch [69][400/5005] lr: 1.000e-03, eta: 3:56:05, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.6857 2023-01-02 01:21:27,502 - mmcls - INFO - Epoch [69][500/5005] lr: 1.000e-03, eta: 3:55:56, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6711 2023-01-02 01:21:36,457 - mmcls - INFO - Epoch [69][600/5005] lr: 1.000e-03, eta: 3:55:48, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6804 2023-01-02 01:21:45,463 - mmcls - INFO - Epoch [69][700/5005] lr: 1.000e-03, eta: 3:55:39, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6636 2023-01-02 01:21:53,959 - mmcls - INFO - Epoch [69][800/5005] lr: 1.000e-03, eta: 3:55:30, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6976 2023-01-02 01:22:02,815 - mmcls - INFO - Epoch [69][900/5005] lr: 1.000e-03, eta: 3:55:21, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6465 2023-01-02 01:22:11,682 - mmcls - INFO - Epoch [69][1000/5005] lr: 1.000e-03, eta: 3:55:12, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6644 2023-01-02 01:22:20,631 - mmcls - INFO - Epoch [69][1100/5005] lr: 1.000e-03, eta: 3:55:03, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6934 2023-01-02 01:22:29,406 - mmcls - INFO - Epoch [69][1200/5005] lr: 1.000e-03, eta: 3:54:54, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6755 2023-01-02 01:22:38,071 - mmcls - INFO - Epoch [69][1300/5005] lr: 1.000e-03, eta: 3:54:45, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6610 2023-01-02 01:22:47,008 - mmcls - INFO - Epoch [69][1400/5005] lr: 1.000e-03, eta: 3:54:37, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6674 2023-01-02 01:22:55,804 - mmcls - INFO - Epoch [69][1500/5005] lr: 1.000e-03, eta: 3:54:28, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6784 2023-01-02 01:23:04,860 - mmcls - INFO - Epoch [69][1600/5005] lr: 1.000e-03, eta: 3:54:19, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6534 2023-01-02 01:23:13,443 - mmcls - INFO - Epoch [69][1700/5005] lr: 1.000e-03, eta: 3:54:10, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6687 2023-01-02 01:23:22,225 - mmcls - INFO - Epoch [69][1800/5005] lr: 1.000e-03, eta: 3:54:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6923 2023-01-02 01:23:31,064 - mmcls - INFO - Epoch [69][1900/5005] lr: 1.000e-03, eta: 3:53:52, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6382 2023-01-02 01:23:39,929 - mmcls - INFO - Epoch [69][2000/5005] lr: 1.000e-03, eta: 3:53:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6740 2023-01-02 01:23:48,663 - mmcls - INFO - Epoch [69][2100/5005] lr: 1.000e-03, eta: 3:53:34, time: 0.087, data_time: 0.040, memory: 1224, loss: 1.6674 2023-01-02 01:23:57,370 - mmcls - INFO - Epoch [69][2200/5005] lr: 1.000e-03, eta: 3:53:25, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6528 2023-01-02 01:24:05,914 - mmcls - INFO - Epoch [69][2300/5005] lr: 1.000e-03, eta: 3:53:16, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6647 2023-01-02 01:24:14,615 - mmcls - INFO - Epoch [69][2400/5005] lr: 1.000e-03, eta: 3:53:07, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6856 2023-01-02 01:24:23,281 - mmcls - INFO - Epoch [69][2500/5005] lr: 1.000e-03, eta: 3:52:58, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6625 2023-01-02 01:24:32,251 - mmcls - INFO - Epoch [69][2600/5005] lr: 1.000e-03, eta: 3:52:50, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6619 2023-01-02 01:24:41,290 - mmcls - INFO - Epoch [69][2700/5005] lr: 1.000e-03, eta: 3:52:41, time: 0.090, data_time: 0.028, memory: 1224, loss: 1.6768 2023-01-02 01:24:50,162 - mmcls - INFO - Epoch [69][2800/5005] lr: 1.000e-03, eta: 3:52:32, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6634 2023-01-02 01:24:59,027 - mmcls - INFO - Epoch [69][2900/5005] lr: 1.000e-03, eta: 3:52:23, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6883 2023-01-02 01:25:07,537 - mmcls - INFO - Epoch [69][3000/5005] lr: 1.000e-03, eta: 3:52:14, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6758 2023-01-02 01:25:16,383 - mmcls - INFO - Epoch [69][3100/5005] lr: 1.000e-03, eta: 3:52:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6606 2023-01-02 01:25:25,138 - mmcls - INFO - Epoch [69][3200/5005] lr: 1.000e-03, eta: 3:51:56, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.6844 2023-01-02 01:25:34,001 - mmcls - INFO - Epoch [69][3300/5005] lr: 1.000e-03, eta: 3:51:47, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6663 2023-01-02 01:25:42,730 - mmcls - INFO - Epoch [69][3400/5005] lr: 1.000e-03, eta: 3:51:38, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6638 2023-01-02 01:25:51,582 - mmcls - INFO - Epoch [69][3500/5005] lr: 1.000e-03, eta: 3:51:30, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6831 2023-01-02 01:26:00,399 - mmcls - INFO - Epoch [69][3600/5005] lr: 1.000e-03, eta: 3:51:21, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6898 2023-01-02 01:26:09,079 - mmcls - INFO - Epoch [69][3700/5005] lr: 1.000e-03, eta: 3:51:12, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6674 2023-01-02 01:26:18,170 - mmcls - INFO - Epoch [69][3800/5005] lr: 1.000e-03, eta: 3:51:03, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6751 2023-01-02 01:26:26,685 - mmcls - INFO - Epoch [69][3900/5005] lr: 1.000e-03, eta: 3:50:54, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6717 2023-01-02 01:26:35,545 - mmcls - INFO - Epoch [69][4000/5005] lr: 1.000e-03, eta: 3:50:45, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6580 2023-01-02 01:26:44,341 - mmcls - INFO - Epoch [69][4100/5005] lr: 1.000e-03, eta: 3:50:36, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6577 2023-01-02 01:26:53,376 - mmcls - INFO - Epoch [69][4200/5005] lr: 1.000e-03, eta: 3:50:27, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6903 2023-01-02 01:27:02,000 - mmcls - INFO - Epoch [69][4300/5005] lr: 1.000e-03, eta: 3:50:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6955 2023-01-02 01:27:10,785 - mmcls - INFO - Epoch [69][4400/5005] lr: 1.000e-03, eta: 3:50:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6778 2023-01-02 01:27:19,379 - mmcls - INFO - Epoch [69][4500/5005] lr: 1.000e-03, eta: 3:50:01, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7026 2023-01-02 01:27:28,076 - mmcls - INFO - Epoch [69][4600/5005] lr: 1.000e-03, eta: 3:49:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6926 2023-01-02 01:27:37,128 - mmcls - INFO - Epoch [69][4700/5005] lr: 1.000e-03, eta: 3:49:43, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6871 2023-01-02 01:27:46,227 - mmcls - INFO - Epoch [69][4800/5005] lr: 1.000e-03, eta: 3:49:34, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6773 2023-01-02 01:27:54,839 - mmcls - INFO - Epoch [69][4900/5005] lr: 1.000e-03, eta: 3:49:25, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6454 2023-01-02 01:28:03,946 - mmcls - INFO - Epoch [69][5000/5005] lr: 1.000e-03, eta: 3:49:16, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.6695 2023-01-02 01:28:26,915 - mmcls - INFO - Epoch(val) [69][196] accuracy_top-1: 64.4920, accuracy_top-5: 85.3100 2023-01-02 01:28:38,235 - mmcls - INFO - Epoch [70][100/5005] lr: 1.000e-03, eta: 3:49:08, time: 0.113, data_time: 0.034, memory: 1224, loss: 1.6800 2023-01-02 01:28:47,169 - mmcls - INFO - Epoch [70][200/5005] lr: 1.000e-03, eta: 3:48:59, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6601 2023-01-02 01:28:55,879 - mmcls - INFO - Epoch [70][300/5005] lr: 1.000e-03, eta: 3:48:50, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6576 2023-01-02 01:29:04,550 - mmcls - INFO - Epoch [70][400/5005] lr: 1.000e-03, eta: 3:48:41, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6754 2023-01-02 01:29:13,517 - mmcls - INFO - Epoch [70][500/5005] lr: 1.000e-03, eta: 3:48:32, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6503 2023-01-02 01:29:22,342 - mmcls - INFO - Epoch [70][600/5005] lr: 1.000e-03, eta: 3:48:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6655 2023-01-02 01:29:31,310 - mmcls - INFO - Epoch [70][700/5005] lr: 1.000e-03, eta: 3:48:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6776 2023-01-02 01:29:40,091 - mmcls - INFO - Epoch [70][800/5005] lr: 1.000e-03, eta: 3:48:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6534 2023-01-02 01:29:48,817 - mmcls - INFO - Epoch [70][900/5005] lr: 1.000e-03, eta: 3:47:57, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6852 2023-01-02 01:29:57,705 - mmcls - INFO - Epoch [70][1000/5005] lr: 1.000e-03, eta: 3:47:48, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6570 2023-01-02 01:30:06,534 - mmcls - INFO - Epoch [70][1100/5005] lr: 1.000e-03, eta: 3:47:39, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6439 2023-01-02 01:30:15,477 - mmcls - INFO - Epoch [70][1200/5005] lr: 1.000e-03, eta: 3:47:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6567 2023-01-02 01:30:24,667 - mmcls - INFO - Epoch [70][1300/5005] lr: 1.000e-03, eta: 3:47:21, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6457 2023-01-02 01:30:33,760 - mmcls - INFO - Epoch [70][1400/5005] lr: 1.000e-03, eta: 3:47:13, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6743 2023-01-02 01:30:42,527 - mmcls - INFO - Epoch [70][1500/5005] lr: 1.000e-03, eta: 3:47:04, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6579 2023-01-02 01:30:51,509 - mmcls - INFO - Epoch [70][1600/5005] lr: 1.000e-03, eta: 3:46:55, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6561 2023-01-02 01:31:00,457 - mmcls - INFO - Epoch [70][1700/5005] lr: 1.000e-03, eta: 3:46:46, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6707 2023-01-02 01:31:09,228 - mmcls - INFO - Epoch [70][1800/5005] lr: 1.000e-03, eta: 3:46:37, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.6721 2023-01-02 01:31:18,098 - mmcls - INFO - Epoch [70][1900/5005] lr: 1.000e-03, eta: 3:46:28, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6501 2023-01-02 01:31:27,316 - mmcls - INFO - Epoch [70][2000/5005] lr: 1.000e-03, eta: 3:46:20, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.6706 2023-01-02 01:31:35,916 - mmcls - INFO - Epoch [70][2100/5005] lr: 1.000e-03, eta: 3:46:11, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6736 2023-01-02 01:31:44,453 - mmcls - INFO - Epoch [70][2200/5005] lr: 1.000e-03, eta: 3:46:02, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6761 2023-01-02 01:31:53,199 - mmcls - INFO - Epoch [70][2300/5005] lr: 1.000e-03, eta: 3:45:53, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6487 2023-01-02 01:32:02,135 - mmcls - INFO - Epoch [70][2400/5005] lr: 1.000e-03, eta: 3:45:44, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6540 2023-01-02 01:32:10,861 - mmcls - INFO - Epoch [70][2500/5005] lr: 1.000e-03, eta: 3:45:35, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6615 2023-01-02 01:32:19,515 - mmcls - INFO - Epoch [70][2600/5005] lr: 1.000e-03, eta: 3:45:26, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6754 2023-01-02 01:32:28,055 - mmcls - INFO - Epoch [70][2700/5005] lr: 1.000e-03, eta: 3:45:17, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6790 2023-01-02 01:32:36,927 - mmcls - INFO - Epoch [70][2800/5005] lr: 1.000e-03, eta: 3:45:08, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6742 2023-01-02 01:32:45,793 - mmcls - INFO - Epoch [70][2900/5005] lr: 1.000e-03, eta: 3:44:59, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.6670 2023-01-02 01:32:54,369 - mmcls - INFO - Epoch [70][3000/5005] lr: 1.000e-03, eta: 3:44:50, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6738 2023-01-02 01:33:03,224 - mmcls - INFO - Epoch [70][3100/5005] lr: 1.000e-03, eta: 3:44:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6801 2023-01-02 01:33:12,054 - mmcls - INFO - Epoch [70][3200/5005] lr: 1.000e-03, eta: 3:44:33, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6255 2023-01-02 01:33:20,923 - mmcls - INFO - Epoch [70][3300/5005] lr: 1.000e-03, eta: 3:44:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6929 2023-01-02 01:33:29,648 - mmcls - INFO - Epoch [70][3400/5005] lr: 1.000e-03, eta: 3:44:15, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6856 2023-01-02 01:33:38,463 - mmcls - INFO - Epoch [70][3500/5005] lr: 1.000e-03, eta: 3:44:06, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6802 2023-01-02 01:33:46,856 - mmcls - INFO - Epoch [70][3600/5005] lr: 1.000e-03, eta: 3:43:57, time: 0.084, data_time: 0.012, memory: 1224, loss: 1.6571 2023-01-02 01:33:55,591 - mmcls - INFO - Epoch [70][3700/5005] lr: 1.000e-03, eta: 3:43:48, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6963 2023-01-02 01:34:04,271 - mmcls - INFO - Epoch [70][3800/5005] lr: 1.000e-03, eta: 3:43:39, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.7006 2023-01-02 01:34:12,760 - mmcls - INFO - Epoch [70][3900/5005] lr: 1.000e-03, eta: 3:43:30, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.6734 2023-01-02 01:34:21,413 - mmcls - INFO - Epoch [70][4000/5005] lr: 1.000e-03, eta: 3:43:21, time: 0.086, data_time: 0.037, memory: 1224, loss: 1.6754 2023-01-02 01:34:30,513 - mmcls - INFO - Epoch [70][4100/5005] lr: 1.000e-03, eta: 3:43:12, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6884 2023-01-02 01:34:39,285 - mmcls - INFO - Epoch [70][4200/5005] lr: 1.000e-03, eta: 3:43:03, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.7046 2023-01-02 01:34:47,862 - mmcls - INFO - Epoch [70][4300/5005] lr: 1.000e-03, eta: 3:42:54, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6692 2023-01-02 01:34:57,229 - mmcls - INFO - Epoch [70][4400/5005] lr: 1.000e-03, eta: 3:42:46, time: 0.094, data_time: 0.014, memory: 1224, loss: 1.6704 2023-01-02 01:35:05,809 - mmcls - INFO - Epoch [70][4500/5005] lr: 1.000e-03, eta: 3:42:37, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6599 2023-01-02 01:35:14,685 - mmcls - INFO - Epoch [70][4600/5005] lr: 1.000e-03, eta: 3:42:28, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6787 2023-01-02 01:35:23,603 - mmcls - INFO - Epoch [70][4700/5005] lr: 1.000e-03, eta: 3:42:19, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.6588 2023-01-02 01:35:32,241 - mmcls - INFO - Epoch [70][4800/5005] lr: 1.000e-03, eta: 3:42:10, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6694 2023-01-02 01:35:41,252 - mmcls - INFO - Epoch [70][4900/5005] lr: 1.000e-03, eta: 3:42:01, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6818 2023-01-02 01:35:49,975 - mmcls - INFO - Epoch [70][5000/5005] lr: 1.000e-03, eta: 3:41:52, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6671 2023-01-02 01:36:12,806 - mmcls - INFO - Epoch(val) [70][196] accuracy_top-1: 64.3920, accuracy_top-5: 85.1680 2023-01-02 01:36:23,835 - mmcls - INFO - Epoch [71][100/5005] lr: 1.000e-03, eta: 3:41:44, time: 0.110, data_time: 0.054, memory: 1224, loss: 1.6545 2023-01-02 01:36:32,528 - mmcls - INFO - Epoch [71][200/5005] lr: 1.000e-03, eta: 3:41:35, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6564 2023-01-02 01:36:41,188 - mmcls - INFO - Epoch [71][300/5005] lr: 1.000e-03, eta: 3:41:26, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6765 2023-01-02 01:36:50,275 - mmcls - INFO - Epoch [71][400/5005] lr: 1.000e-03, eta: 3:41:17, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.6735 2023-01-02 01:36:58,802 - mmcls - INFO - Epoch [71][500/5005] lr: 1.000e-03, eta: 3:41:08, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6747 2023-01-02 01:37:07,413 - mmcls - INFO - Epoch [71][600/5005] lr: 1.000e-03, eta: 3:40:59, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6539 2023-01-02 01:37:16,152 - mmcls - INFO - Epoch [71][700/5005] lr: 1.000e-03, eta: 3:40:50, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.7041 2023-01-02 01:37:24,998 - mmcls - INFO - Epoch [71][800/5005] lr: 1.000e-03, eta: 3:40:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6488 2023-01-02 01:37:33,917 - mmcls - INFO - Epoch [71][900/5005] lr: 1.000e-03, eta: 3:40:32, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6669 2023-01-02 01:37:42,903 - mmcls - INFO - Epoch [71][1000/5005] lr: 1.000e-03, eta: 3:40:24, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6692 2023-01-02 01:37:51,623 - mmcls - INFO - Epoch [71][1100/5005] lr: 1.000e-03, eta: 3:40:15, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6602 2023-01-02 01:38:00,506 - mmcls - INFO - Epoch [71][1200/5005] lr: 1.000e-03, eta: 3:40:06, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6696 2023-01-02 01:38:09,200 - mmcls - INFO - Epoch [71][1300/5005] lr: 1.000e-03, eta: 3:39:57, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6636 2023-01-02 01:38:17,977 - mmcls - INFO - Epoch [71][1400/5005] lr: 1.000e-03, eta: 3:39:48, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6644 2023-01-02 01:38:26,829 - mmcls - INFO - Epoch [71][1500/5005] lr: 1.000e-03, eta: 3:39:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6543 2023-01-02 01:38:35,631 - mmcls - INFO - Epoch [71][1600/5005] lr: 1.000e-03, eta: 3:39:30, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6776 2023-01-02 01:38:44,313 - mmcls - INFO - Epoch [71][1700/5005] lr: 1.000e-03, eta: 3:39:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6518 2023-01-02 01:38:52,774 - mmcls - INFO - Epoch [71][1800/5005] lr: 1.000e-03, eta: 3:39:12, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6491 2023-01-02 01:39:01,376 - mmcls - INFO - Epoch [71][1900/5005] lr: 1.000e-03, eta: 3:39:03, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6675 2023-01-02 01:39:10,129 - mmcls - INFO - Epoch [71][2000/5005] lr: 1.000e-03, eta: 3:38:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6631 2023-01-02 01:39:18,923 - mmcls - INFO - Epoch [71][2100/5005] lr: 1.000e-03, eta: 3:38:45, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6699 2023-01-02 01:39:27,928 - mmcls - INFO - Epoch [71][2200/5005] lr: 1.000e-03, eta: 3:38:37, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6699 2023-01-02 01:39:36,521 - mmcls - INFO - Epoch [71][2300/5005] lr: 1.000e-03, eta: 3:38:28, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6562 2023-01-02 01:39:45,327 - mmcls - INFO - Epoch [71][2400/5005] lr: 1.000e-03, eta: 3:38:19, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6726 2023-01-02 01:39:53,997 - mmcls - INFO - Epoch [71][2500/5005] lr: 1.000e-03, eta: 3:38:10, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6769 2023-01-02 01:40:02,660 - mmcls - INFO - Epoch [71][2600/5005] lr: 1.000e-03, eta: 3:38:01, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6810 2023-01-02 01:40:11,270 - mmcls - INFO - Epoch [71][2700/5005] lr: 1.000e-03, eta: 3:37:52, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6612 2023-01-02 01:40:20,299 - mmcls - INFO - Epoch [71][2800/5005] lr: 1.000e-03, eta: 3:37:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6437 2023-01-02 01:40:29,004 - mmcls - INFO - Epoch [71][2900/5005] lr: 1.000e-03, eta: 3:37:34, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6828 2023-01-02 01:40:37,806 - mmcls - INFO - Epoch [71][3000/5005] lr: 1.000e-03, eta: 3:37:25, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6392 2023-01-02 01:40:46,862 - mmcls - INFO - Epoch [71][3100/5005] lr: 1.000e-03, eta: 3:37:16, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.6615 2023-01-02 01:40:55,733 - mmcls - INFO - Epoch [71][3200/5005] lr: 1.000e-03, eta: 3:37:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6847 2023-01-02 01:41:04,600 - mmcls - INFO - Epoch [71][3300/5005] lr: 1.000e-03, eta: 3:36:59, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6754 2023-01-02 01:41:13,515 - mmcls - INFO - Epoch [71][3400/5005] lr: 1.000e-03, eta: 3:36:50, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6631 2023-01-02 01:41:22,306 - mmcls - INFO - Epoch [71][3500/5005] lr: 1.000e-03, eta: 3:36:41, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6982 2023-01-02 01:41:30,940 - mmcls - INFO - Epoch [71][3600/5005] lr: 1.000e-03, eta: 3:36:32, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6564 2023-01-02 01:41:39,840 - mmcls - INFO - Epoch [71][3700/5005] lr: 1.000e-03, eta: 3:36:23, time: 0.089, data_time: 0.031, memory: 1224, loss: 1.6909 2023-01-02 01:41:48,520 - mmcls - INFO - Epoch [71][3800/5005] lr: 1.000e-03, eta: 3:36:14, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6866 2023-01-02 01:41:57,558 - mmcls - INFO - Epoch [71][3900/5005] lr: 1.000e-03, eta: 3:36:05, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6668 2023-01-02 01:42:06,246 - mmcls - INFO - Epoch [71][4000/5005] lr: 1.000e-03, eta: 3:35:56, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6633 2023-01-02 01:42:14,965 - mmcls - INFO - Epoch [71][4100/5005] lr: 1.000e-03, eta: 3:35:48, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6888 2023-01-02 01:42:23,588 - mmcls - INFO - Epoch [71][4200/5005] lr: 1.000e-03, eta: 3:35:39, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.6479 2023-01-02 01:42:32,408 - mmcls - INFO - Epoch [71][4300/5005] lr: 1.000e-03, eta: 3:35:30, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6642 2023-01-02 01:42:41,109 - mmcls - INFO - Epoch [71][4400/5005] lr: 1.000e-03, eta: 3:35:21, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6527 2023-01-02 01:42:50,115 - mmcls - INFO - Epoch [71][4500/5005] lr: 1.000e-03, eta: 3:35:12, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6695 2023-01-02 01:42:59,172 - mmcls - INFO - Epoch [71][4600/5005] lr: 1.000e-03, eta: 3:35:03, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.7000 2023-01-02 01:43:08,543 - mmcls - INFO - Epoch [71][4700/5005] lr: 1.000e-03, eta: 3:34:54, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.6625 2023-01-02 01:43:17,199 - mmcls - INFO - Epoch [71][4800/5005] lr: 1.000e-03, eta: 3:34:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6756 2023-01-02 01:43:26,403 - mmcls - INFO - Epoch [71][4900/5005] lr: 1.000e-03, eta: 3:34:37, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.6341 2023-01-02 01:43:35,185 - mmcls - INFO - Epoch [71][5000/5005] lr: 1.000e-03, eta: 3:34:28, time: 0.088, data_time: 0.039, memory: 1224, loss: 1.6799 2023-01-02 01:43:58,018 - mmcls - INFO - Epoch(val) [71][196] accuracy_top-1: 64.4880, accuracy_top-5: 85.2800 2023-01-02 01:44:08,935 - mmcls - INFO - Epoch [72][100/5005] lr: 1.000e-03, eta: 3:34:19, time: 0.109, data_time: 0.033, memory: 1224, loss: 1.6588 2023-01-02 01:44:17,679 - mmcls - INFO - Epoch [72][200/5005] lr: 1.000e-03, eta: 3:34:10, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6466 2023-01-02 01:44:26,924 - mmcls - INFO - Epoch [72][300/5005] lr: 1.000e-03, eta: 3:34:02, time: 0.092, data_time: 0.030, memory: 1224, loss: 1.6582 2023-01-02 01:44:35,556 - mmcls - INFO - Epoch [72][400/5005] lr: 1.000e-03, eta: 3:33:53, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6470 2023-01-02 01:44:44,339 - mmcls - INFO - Epoch [72][500/5005] lr: 1.000e-03, eta: 3:33:44, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6440 2023-01-02 01:44:53,235 - mmcls - INFO - Epoch [72][600/5005] lr: 1.000e-03, eta: 3:33:35, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6604 2023-01-02 01:45:02,008 - mmcls - INFO - Epoch [72][700/5005] lr: 1.000e-03, eta: 3:33:26, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6362 2023-01-02 01:45:11,056 - mmcls - INFO - Epoch [72][800/5005] lr: 1.000e-03, eta: 3:33:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6405 2023-01-02 01:45:20,380 - mmcls - INFO - Epoch [72][900/5005] lr: 1.000e-03, eta: 3:33:09, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.6459 2023-01-02 01:45:29,267 - mmcls - INFO - Epoch [72][1000/5005] lr: 1.000e-03, eta: 3:33:00, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6395 2023-01-02 01:45:38,395 - mmcls - INFO - Epoch [72][1100/5005] lr: 1.000e-03, eta: 3:32:51, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6626 2023-01-02 01:45:47,382 - mmcls - INFO - Epoch [72][1200/5005] lr: 1.000e-03, eta: 3:32:42, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.6599 2023-01-02 01:45:56,424 - mmcls - INFO - Epoch [72][1300/5005] lr: 1.000e-03, eta: 3:32:33, time: 0.091, data_time: 0.035, memory: 1224, loss: 1.6603 2023-01-02 01:46:05,296 - mmcls - INFO - Epoch [72][1400/5005] lr: 1.000e-03, eta: 3:32:24, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6722 2023-01-02 01:46:13,830 - mmcls - INFO - Epoch [72][1500/5005] lr: 1.000e-03, eta: 3:32:15, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6734 2023-01-02 01:46:22,710 - mmcls - INFO - Epoch [72][1600/5005] lr: 1.000e-03, eta: 3:32:07, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6552 2023-01-02 01:46:31,501 - mmcls - INFO - Epoch [72][1700/5005] lr: 1.000e-03, eta: 3:31:58, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6686 2023-01-02 01:46:40,472 - mmcls - INFO - Epoch [72][1800/5005] lr: 1.000e-03, eta: 3:31:49, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6474 2023-01-02 01:46:49,526 - mmcls - INFO - Epoch [72][1900/5005] lr: 1.000e-03, eta: 3:31:40, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6467 2023-01-02 01:46:58,162 - mmcls - INFO - Epoch [72][2000/5005] lr: 1.000e-03, eta: 3:31:31, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6518 2023-01-02 01:47:06,900 - mmcls - INFO - Epoch [72][2100/5005] lr: 1.000e-03, eta: 3:31:22, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6597 2023-01-02 01:47:15,981 - mmcls - INFO - Epoch [72][2200/5005] lr: 1.000e-03, eta: 3:31:13, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6499 2023-01-02 01:47:24,987 - mmcls - INFO - Epoch [72][2300/5005] lr: 1.000e-03, eta: 3:31:05, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6636 2023-01-02 01:47:33,661 - mmcls - INFO - Epoch [72][2400/5005] lr: 1.000e-03, eta: 3:30:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6523 2023-01-02 01:47:42,486 - mmcls - INFO - Epoch [72][2500/5005] lr: 1.000e-03, eta: 3:30:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6475 2023-01-02 01:47:51,314 - mmcls - INFO - Epoch [72][2600/5005] lr: 1.000e-03, eta: 3:30:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6503 2023-01-02 01:48:00,012 - mmcls - INFO - Epoch [72][2700/5005] lr: 1.000e-03, eta: 3:30:29, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6764 2023-01-02 01:48:08,719 - mmcls - INFO - Epoch [72][2800/5005] lr: 1.000e-03, eta: 3:30:20, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6594 2023-01-02 01:48:17,482 - mmcls - INFO - Epoch [72][2900/5005] lr: 1.000e-03, eta: 3:30:11, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6766 2023-01-02 01:48:26,464 - mmcls - INFO - Epoch [72][3000/5005] lr: 1.000e-03, eta: 3:30:02, time: 0.090, data_time: 0.035, memory: 1224, loss: 1.6521 2023-01-02 01:48:35,321 - mmcls - INFO - Epoch [72][3100/5005] lr: 1.000e-03, eta: 3:29:53, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6695 2023-01-02 01:48:43,993 - mmcls - INFO - Epoch [72][3200/5005] lr: 1.000e-03, eta: 3:29:44, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6745 2023-01-02 01:48:53,002 - mmcls - INFO - Epoch [72][3300/5005] lr: 1.000e-03, eta: 3:29:36, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6536 2023-01-02 01:49:01,700 - mmcls - INFO - Epoch [72][3400/5005] lr: 1.000e-03, eta: 3:29:27, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6704 2023-01-02 01:49:10,417 - mmcls - INFO - Epoch [72][3500/5005] lr: 1.000e-03, eta: 3:29:18, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6579 2023-01-02 01:49:19,139 - mmcls - INFO - Epoch [72][3600/5005] lr: 1.000e-03, eta: 3:29:09, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6661 2023-01-02 01:49:27,729 - mmcls - INFO - Epoch [72][3700/5005] lr: 1.000e-03, eta: 3:29:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6740 2023-01-02 01:49:36,334 - mmcls - INFO - Epoch [72][3800/5005] lr: 1.000e-03, eta: 3:28:51, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6817 2023-01-02 01:49:45,818 - mmcls - INFO - Epoch [72][3900/5005] lr: 1.000e-03, eta: 3:28:42, time: 0.095, data_time: 0.003, memory: 1224, loss: 1.6595 2023-01-02 01:49:54,339 - mmcls - INFO - Epoch [72][4000/5005] lr: 1.000e-03, eta: 3:28:33, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.6894 2023-01-02 01:50:03,131 - mmcls - INFO - Epoch [72][4100/5005] lr: 1.000e-03, eta: 3:28:24, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6483 2023-01-02 01:50:11,991 - mmcls - INFO - Epoch [72][4200/5005] lr: 1.000e-03, eta: 3:28:16, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6694 2023-01-02 01:50:20,731 - mmcls - INFO - Epoch [72][4300/5005] lr: 1.000e-03, eta: 3:28:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6819 2023-01-02 01:50:29,506 - mmcls - INFO - Epoch [72][4400/5005] lr: 1.000e-03, eta: 3:27:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6645 2023-01-02 01:50:38,529 - mmcls - INFO - Epoch [72][4500/5005] lr: 1.000e-03, eta: 3:27:49, time: 0.090, data_time: 0.036, memory: 1224, loss: 1.6746 2023-01-02 01:50:47,442 - mmcls - INFO - Epoch [72][4600/5005] lr: 1.000e-03, eta: 3:27:40, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6723 2023-01-02 01:50:56,362 - mmcls - INFO - Epoch [72][4700/5005] lr: 1.000e-03, eta: 3:27:31, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.6717 2023-01-02 01:51:05,157 - mmcls - INFO - Epoch [72][4800/5005] lr: 1.000e-03, eta: 3:27:22, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6654 2023-01-02 01:51:14,052 - mmcls - INFO - Epoch [72][4900/5005] lr: 1.000e-03, eta: 3:27:13, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6709 2023-01-02 01:51:22,847 - mmcls - INFO - Epoch [72][5000/5005] lr: 1.000e-03, eta: 3:27:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6777 2023-01-02 01:51:45,508 - mmcls - INFO - Epoch(val) [72][196] accuracy_top-1: 64.5620, accuracy_top-5: 85.2540 2023-01-02 01:51:56,457 - mmcls - INFO - Epoch [73][100/5005] lr: 1.000e-03, eta: 3:26:56, time: 0.109, data_time: 0.040, memory: 1224, loss: 1.6394 2023-01-02 01:52:05,219 - mmcls - INFO - Epoch [73][200/5005] lr: 1.000e-03, eta: 3:26:47, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6593 2023-01-02 01:52:14,320 - mmcls - INFO - Epoch [73][300/5005] lr: 1.000e-03, eta: 3:26:38, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6557 2023-01-02 01:52:23,041 - mmcls - INFO - Epoch [73][400/5005] lr: 1.000e-03, eta: 3:26:29, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6482 2023-01-02 01:52:32,018 - mmcls - INFO - Epoch [73][500/5005] lr: 1.000e-03, eta: 3:26:20, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6511 2023-01-02 01:52:41,122 - mmcls - INFO - Epoch [73][600/5005] lr: 1.000e-03, eta: 3:26:12, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6414 2023-01-02 01:52:49,985 - mmcls - INFO - Epoch [73][700/5005] lr: 1.000e-03, eta: 3:26:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6353 2023-01-02 01:52:58,496 - mmcls - INFO - Epoch [73][800/5005] lr: 1.000e-03, eta: 3:25:54, time: 0.085, data_time: 0.019, memory: 1224, loss: 1.6358 2023-01-02 01:53:07,178 - mmcls - INFO - Epoch [73][900/5005] lr: 1.000e-03, eta: 3:25:45, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6560 2023-01-02 01:53:16,344 - mmcls - INFO - Epoch [73][1000/5005] lr: 1.000e-03, eta: 3:25:36, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.6619 2023-01-02 01:53:25,102 - mmcls - INFO - Epoch [73][1100/5005] lr: 1.000e-03, eta: 3:25:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6360 2023-01-02 01:53:34,084 - mmcls - INFO - Epoch [73][1200/5005] lr: 1.000e-03, eta: 3:25:18, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6421 2023-01-02 01:53:43,102 - mmcls - INFO - Epoch [73][1300/5005] lr: 1.000e-03, eta: 3:25:10, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.6628 2023-01-02 01:53:51,946 - mmcls - INFO - Epoch [73][1400/5005] lr: 1.000e-03, eta: 3:25:01, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6603 2023-01-02 01:54:01,146 - mmcls - INFO - Epoch [73][1500/5005] lr: 1.000e-03, eta: 3:24:52, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.6448 2023-01-02 01:54:09,984 - mmcls - INFO - Epoch [73][1600/5005] lr: 1.000e-03, eta: 3:24:43, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6542 2023-01-02 01:54:18,778 - mmcls - INFO - Epoch [73][1700/5005] lr: 1.000e-03, eta: 3:24:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6626 2023-01-02 01:54:27,379 - mmcls - INFO - Epoch [73][1800/5005] lr: 1.000e-03, eta: 3:24:25, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6520 2023-01-02 01:54:36,162 - mmcls - INFO - Epoch [73][1900/5005] lr: 1.000e-03, eta: 3:24:16, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6539 2023-01-02 01:54:44,833 - mmcls - INFO - Epoch [73][2000/5005] lr: 1.000e-03, eta: 3:24:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6347 2023-01-02 01:54:53,552 - mmcls - INFO - Epoch [73][2100/5005] lr: 1.000e-03, eta: 3:23:58, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.6582 2023-01-02 01:55:02,331 - mmcls - INFO - Epoch [73][2200/5005] lr: 1.000e-03, eta: 3:23:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6488 2023-01-02 01:55:11,092 - mmcls - INFO - Epoch [73][2300/5005] lr: 1.000e-03, eta: 3:23:41, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6731 2023-01-02 01:55:19,743 - mmcls - INFO - Epoch [73][2400/5005] lr: 1.000e-03, eta: 3:23:32, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6582 2023-01-02 01:55:28,371 - mmcls - INFO - Epoch [73][2500/5005] lr: 1.000e-03, eta: 3:23:23, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6524 2023-01-02 01:55:36,914 - mmcls - INFO - Epoch [73][2600/5005] lr: 1.000e-03, eta: 3:23:14, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.6496 2023-01-02 01:55:45,691 - mmcls - INFO - Epoch [73][2700/5005] lr: 1.000e-03, eta: 3:23:05, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6708 2023-01-02 01:55:54,466 - mmcls - INFO - Epoch [73][2800/5005] lr: 1.000e-03, eta: 3:22:56, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6409 2023-01-02 01:56:03,450 - mmcls - INFO - Epoch [73][2900/5005] lr: 1.000e-03, eta: 3:22:47, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6881 2023-01-02 01:56:12,286 - mmcls - INFO - Epoch [73][3000/5005] lr: 1.000e-03, eta: 3:22:38, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6582 2023-01-02 01:56:20,972 - mmcls - INFO - Epoch [73][3100/5005] lr: 1.000e-03, eta: 3:22:29, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6645 2023-01-02 01:56:30,122 - mmcls - INFO - Epoch [73][3200/5005] lr: 1.000e-03, eta: 3:22:21, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6620 2023-01-02 01:56:39,032 - mmcls - INFO - Epoch [73][3300/5005] lr: 1.000e-03, eta: 3:22:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6495 2023-01-02 01:56:48,004 - mmcls - INFO - Epoch [73][3400/5005] lr: 1.000e-03, eta: 3:22:03, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.6473 2023-01-02 01:56:56,941 - mmcls - INFO - Epoch [73][3500/5005] lr: 1.000e-03, eta: 3:21:54, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6762 2023-01-02 01:57:05,549 - mmcls - INFO - Epoch [73][3600/5005] lr: 1.000e-03, eta: 3:21:45, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6502 2023-01-02 01:57:14,475 - mmcls - INFO - Epoch [73][3700/5005] lr: 1.000e-03, eta: 3:21:36, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6602 2023-01-02 01:57:23,297 - mmcls - INFO - Epoch [73][3800/5005] lr: 1.000e-03, eta: 3:21:27, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6797 2023-01-02 01:57:32,040 - mmcls - INFO - Epoch [73][3900/5005] lr: 1.000e-03, eta: 3:21:18, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6614 2023-01-02 01:57:41,001 - mmcls - INFO - Epoch [73][4000/5005] lr: 1.000e-03, eta: 3:21:10, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6614 2023-01-02 01:57:49,789 - mmcls - INFO - Epoch [73][4100/5005] lr: 1.000e-03, eta: 3:21:01, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6638 2023-01-02 01:57:58,849 - mmcls - INFO - Epoch [73][4200/5005] lr: 1.000e-03, eta: 3:20:52, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.6873 2023-01-02 01:58:07,788 - mmcls - INFO - Epoch [73][4300/5005] lr: 1.000e-03, eta: 3:20:43, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6423 2023-01-02 01:58:16,679 - mmcls - INFO - Epoch [73][4400/5005] lr: 1.000e-03, eta: 3:20:34, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6651 2023-01-02 01:58:25,509 - mmcls - INFO - Epoch [73][4500/5005] lr: 1.000e-03, eta: 3:20:25, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6417 2023-01-02 01:58:34,380 - mmcls - INFO - Epoch [73][4600/5005] lr: 1.000e-03, eta: 3:20:17, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6825 2023-01-02 01:58:43,245 - mmcls - INFO - Epoch [73][4700/5005] lr: 1.000e-03, eta: 3:20:08, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6676 2023-01-02 01:58:52,398 - mmcls - INFO - Epoch [73][4800/5005] lr: 1.000e-03, eta: 3:19:59, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.6828 2023-01-02 01:59:01,030 - mmcls - INFO - Epoch [73][4900/5005] lr: 1.000e-03, eta: 3:19:50, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6738 2023-01-02 01:59:09,666 - mmcls - INFO - Epoch [73][5000/5005] lr: 1.000e-03, eta: 3:19:41, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6659 2023-01-02 01:59:32,338 - mmcls - INFO - Epoch(val) [73][196] accuracy_top-1: 64.4780, accuracy_top-5: 85.3260 2023-01-02 01:59:43,311 - mmcls - INFO - Epoch [74][100/5005] lr: 1.000e-03, eta: 3:19:32, time: 0.109, data_time: 0.042, memory: 1224, loss: 1.6189 2023-01-02 01:59:52,109 - mmcls - INFO - Epoch [74][200/5005] lr: 1.000e-03, eta: 3:19:23, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6083 2023-01-02 02:00:01,011 - mmcls - INFO - Epoch [74][300/5005] lr: 1.000e-03, eta: 3:19:15, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6489 2023-01-02 02:00:10,017 - mmcls - INFO - Epoch [74][400/5005] lr: 1.000e-03, eta: 3:19:06, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6528 2023-01-02 02:00:18,906 - mmcls - INFO - Epoch [74][500/5005] lr: 1.000e-03, eta: 3:18:57, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6340 2023-01-02 02:00:27,904 - mmcls - INFO - Epoch [74][600/5005] lr: 1.000e-03, eta: 3:18:48, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.6665 2023-01-02 02:00:36,550 - mmcls - INFO - Epoch [74][700/5005] lr: 1.000e-03, eta: 3:18:39, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.6248 2023-01-02 02:00:45,206 - mmcls - INFO - Epoch [74][800/5005] lr: 1.000e-03, eta: 3:18:30, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.6679 2023-01-02 02:00:53,968 - mmcls - INFO - Epoch [74][900/5005] lr: 1.000e-03, eta: 3:18:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6257 2023-01-02 02:01:03,145 - mmcls - INFO - Epoch [74][1000/5005] lr: 1.000e-03, eta: 3:18:12, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6523 2023-01-02 02:01:11,786 - mmcls - INFO - Epoch [74][1100/5005] lr: 1.000e-03, eta: 3:18:04, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6585 2023-01-02 02:01:20,682 - mmcls - INFO - Epoch [74][1200/5005] lr: 1.000e-03, eta: 3:17:55, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6472 2023-01-02 02:01:29,353 - mmcls - INFO - Epoch [74][1300/5005] lr: 1.000e-03, eta: 3:17:46, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6540 2023-01-02 02:01:38,312 - mmcls - INFO - Epoch [74][1400/5005] lr: 1.000e-03, eta: 3:17:37, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.6513 2023-01-02 02:01:47,089 - mmcls - INFO - Epoch [74][1500/5005] lr: 1.000e-03, eta: 3:17:28, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6479 2023-01-02 02:01:55,918 - mmcls - INFO - Epoch [74][1600/5005] lr: 1.000e-03, eta: 3:17:19, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6451 2023-01-02 02:02:05,013 - mmcls - INFO - Epoch [74][1700/5005] lr: 1.000e-03, eta: 3:17:10, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6666 2023-01-02 02:02:13,768 - mmcls - INFO - Epoch [74][1800/5005] lr: 1.000e-03, eta: 3:17:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6817 2023-01-02 02:02:22,264 - mmcls - INFO - Epoch [74][1900/5005] lr: 1.000e-03, eta: 3:16:52, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6360 2023-01-02 02:02:31,008 - mmcls - INFO - Epoch [74][2000/5005] lr: 1.000e-03, eta: 3:16:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6621 2023-01-02 02:02:39,726 - mmcls - INFO - Epoch [74][2100/5005] lr: 1.000e-03, eta: 3:16:35, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6434 2023-01-02 02:02:48,479 - mmcls - INFO - Epoch [74][2200/5005] lr: 1.000e-03, eta: 3:16:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6680 2023-01-02 02:02:56,972 - mmcls - INFO - Epoch [74][2300/5005] lr: 1.000e-03, eta: 3:16:17, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6434 2023-01-02 02:03:05,538 - mmcls - INFO - Epoch [74][2400/5005] lr: 1.000e-03, eta: 3:16:08, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6570 2023-01-02 02:03:14,121 - mmcls - INFO - Epoch [74][2500/5005] lr: 1.000e-03, eta: 3:15:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6698 2023-01-02 02:03:22,927 - mmcls - INFO - Epoch [74][2600/5005] lr: 1.000e-03, eta: 3:15:50, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6586 2023-01-02 02:03:31,940 - mmcls - INFO - Epoch [74][2700/5005] lr: 1.000e-03, eta: 3:15:41, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6596 2023-01-02 02:03:40,580 - mmcls - INFO - Epoch [74][2800/5005] lr: 1.000e-03, eta: 3:15:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6665 2023-01-02 02:03:49,571 - mmcls - INFO - Epoch [74][2900/5005] lr: 1.000e-03, eta: 3:15:23, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6440 2023-01-02 02:03:58,289 - mmcls - INFO - Epoch [74][3000/5005] lr: 1.000e-03, eta: 3:15:14, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6591 2023-01-02 02:04:07,118 - mmcls - INFO - Epoch [74][3100/5005] lr: 1.000e-03, eta: 3:15:05, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6433 2023-01-02 02:04:15,720 - mmcls - INFO - Epoch [74][3200/5005] lr: 1.000e-03, eta: 3:14:57, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6416 2023-01-02 02:04:24,495 - mmcls - INFO - Epoch [74][3300/5005] lr: 1.000e-03, eta: 3:14:48, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6576 2023-01-02 02:04:33,277 - mmcls - INFO - Epoch [74][3400/5005] lr: 1.000e-03, eta: 3:14:39, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6720 2023-01-02 02:04:42,039 - mmcls - INFO - Epoch [74][3500/5005] lr: 1.000e-03, eta: 3:14:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6230 2023-01-02 02:04:51,207 - mmcls - INFO - Epoch [74][3600/5005] lr: 1.000e-03, eta: 3:14:21, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6528 2023-01-02 02:05:00,534 - mmcls - INFO - Epoch [74][3700/5005] lr: 1.000e-03, eta: 3:14:12, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.6531 2023-01-02 02:05:09,321 - mmcls - INFO - Epoch [74][3800/5005] lr: 1.000e-03, eta: 3:14:03, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6491 2023-01-02 02:05:18,333 - mmcls - INFO - Epoch [74][3900/5005] lr: 1.000e-03, eta: 3:13:55, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6832 2023-01-02 02:05:27,010 - mmcls - INFO - Epoch [74][4000/5005] lr: 1.000e-03, eta: 3:13:46, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6640 2023-01-02 02:05:35,756 - mmcls - INFO - Epoch [74][4100/5005] lr: 1.000e-03, eta: 3:13:37, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6359 2023-01-02 02:05:44,452 - mmcls - INFO - Epoch [74][4200/5005] lr: 1.000e-03, eta: 3:13:28, time: 0.087, data_time: 0.033, memory: 1224, loss: 1.6542 2023-01-02 02:05:53,164 - mmcls - INFO - Epoch [74][4300/5005] lr: 1.000e-03, eta: 3:13:19, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6446 2023-01-02 02:06:02,188 - mmcls - INFO - Epoch [74][4400/5005] lr: 1.000e-03, eta: 3:13:10, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6475 2023-01-02 02:06:11,090 - mmcls - INFO - Epoch [74][4500/5005] lr: 1.000e-03, eta: 3:13:01, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6565 2023-01-02 02:06:19,863 - mmcls - INFO - Epoch [74][4600/5005] lr: 1.000e-03, eta: 3:12:52, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6714 2023-01-02 02:06:28,775 - mmcls - INFO - Epoch [74][4700/5005] lr: 1.000e-03, eta: 3:12:44, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6768 2023-01-02 02:06:37,579 - mmcls - INFO - Epoch [74][4800/5005] lr: 1.000e-03, eta: 3:12:35, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6465 2023-01-02 02:06:46,407 - mmcls - INFO - Epoch [74][4900/5005] lr: 1.000e-03, eta: 3:12:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6763 2023-01-02 02:06:55,098 - mmcls - INFO - Epoch [74][5000/5005] lr: 1.000e-03, eta: 3:12:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6879 2023-01-02 02:07:17,716 - mmcls - INFO - Epoch(val) [74][196] accuracy_top-1: 64.5000, accuracy_top-5: 85.2900 2023-01-02 02:07:28,733 - mmcls - INFO - Epoch [75][100/5005] lr: 1.000e-03, eta: 3:12:08, time: 0.110, data_time: 0.035, memory: 1224, loss: 1.6405 2023-01-02 02:07:38,101 - mmcls - INFO - Epoch [75][200/5005] lr: 1.000e-03, eta: 3:11:59, time: 0.094, data_time: 0.010, memory: 1224, loss: 1.6473 2023-01-02 02:07:47,005 - mmcls - INFO - Epoch [75][300/5005] lr: 1.000e-03, eta: 3:11:51, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6463 2023-01-02 02:07:55,811 - mmcls - INFO - Epoch [75][400/5005] lr: 1.000e-03, eta: 3:11:42, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6376 2023-01-02 02:08:04,767 - mmcls - INFO - Epoch [75][500/5005] lr: 1.000e-03, eta: 3:11:33, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6369 2023-01-02 02:08:13,748 - mmcls - INFO - Epoch [75][600/5005] lr: 1.000e-03, eta: 3:11:24, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.6329 2023-01-02 02:08:22,582 - mmcls - INFO - Epoch [75][700/5005] lr: 1.000e-03, eta: 3:11:15, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6493 2023-01-02 02:08:31,224 - mmcls - INFO - Epoch [75][800/5005] lr: 1.000e-03, eta: 3:11:06, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.6557 2023-01-02 02:08:40,211 - mmcls - INFO - Epoch [75][900/5005] lr: 1.000e-03, eta: 3:10:57, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6370 2023-01-02 02:08:49,215 - mmcls - INFO - Epoch [75][1000/5005] lr: 1.000e-03, eta: 3:10:49, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6433 2023-01-02 02:08:57,795 - mmcls - INFO - Epoch [75][1100/5005] lr: 1.000e-03, eta: 3:10:40, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6311 2023-01-02 02:09:06,953 - mmcls - INFO - Epoch [75][1200/5005] lr: 1.000e-03, eta: 3:10:31, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6464 2023-01-02 02:09:15,855 - mmcls - INFO - Epoch [75][1300/5005] lr: 1.000e-03, eta: 3:10:22, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6633 2023-01-02 02:09:24,593 - mmcls - INFO - Epoch [75][1400/5005] lr: 1.000e-03, eta: 3:10:13, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6635 2023-01-02 02:09:33,377 - mmcls - INFO - Epoch [75][1500/5005] lr: 1.000e-03, eta: 3:10:04, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6496 2023-01-02 02:09:42,297 - mmcls - INFO - Epoch [75][1600/5005] lr: 1.000e-03, eta: 3:09:55, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6532 2023-01-02 02:09:50,688 - mmcls - INFO - Epoch [75][1700/5005] lr: 1.000e-03, eta: 3:09:46, time: 0.084, data_time: 0.006, memory: 1224, loss: 1.6716 2023-01-02 02:09:59,481 - mmcls - INFO - Epoch [75][1800/5005] lr: 1.000e-03, eta: 3:09:37, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6585 2023-01-02 02:10:08,769 - mmcls - INFO - Epoch [75][1900/5005] lr: 1.000e-03, eta: 3:09:29, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.6439 2023-01-02 02:10:17,473 - mmcls - INFO - Epoch [75][2000/5005] lr: 1.000e-03, eta: 3:09:20, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6634 2023-01-02 02:10:26,026 - mmcls - INFO - Epoch [75][2100/5005] lr: 1.000e-03, eta: 3:09:11, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6598 2023-01-02 02:10:34,799 - mmcls - INFO - Epoch [75][2200/5005] lr: 1.000e-03, eta: 3:09:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6690 2023-01-02 02:10:43,599 - mmcls - INFO - Epoch [75][2300/5005] lr: 1.000e-03, eta: 3:08:53, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6524 2023-01-02 02:10:52,208 - mmcls - INFO - Epoch [75][2400/5005] lr: 1.000e-03, eta: 3:08:44, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6402 2023-01-02 02:11:01,002 - mmcls - INFO - Epoch [75][2500/5005] lr: 1.000e-03, eta: 3:08:35, time: 0.088, data_time: 0.032, memory: 1224, loss: 1.6424 2023-01-02 02:11:10,158 - mmcls - INFO - Epoch [75][2600/5005] lr: 1.000e-03, eta: 3:08:26, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6600 2023-01-02 02:11:18,707 - mmcls - INFO - Epoch [75][2700/5005] lr: 1.000e-03, eta: 3:08:17, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6560 2023-01-02 02:11:27,523 - mmcls - INFO - Epoch [75][2800/5005] lr: 1.000e-03, eta: 3:08:09, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6430 2023-01-02 02:11:36,915 - mmcls - INFO - Epoch [75][2900/5005] lr: 1.000e-03, eta: 3:08:00, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.6505 2023-01-02 02:11:45,736 - mmcls - INFO - Epoch [75][3000/5005] lr: 1.000e-03, eta: 3:07:51, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6621 2023-01-02 02:11:54,206 - mmcls - INFO - Epoch [75][3100/5005] lr: 1.000e-03, eta: 3:07:42, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.6498 2023-01-02 02:12:03,046 - mmcls - INFO - Epoch [75][3200/5005] lr: 1.000e-03, eta: 3:07:33, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6357 2023-01-02 02:12:11,758 - mmcls - INFO - Epoch [75][3300/5005] lr: 1.000e-03, eta: 3:07:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6458 2023-01-02 02:12:20,215 - mmcls - INFO - Epoch [75][3400/5005] lr: 1.000e-03, eta: 3:07:15, time: 0.084, data_time: 0.008, memory: 1224, loss: 1.6638 2023-01-02 02:12:29,008 - mmcls - INFO - Epoch [75][3500/5005] lr: 1.000e-03, eta: 3:07:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6484 2023-01-02 02:12:37,644 - mmcls - INFO - Epoch [75][3600/5005] lr: 1.000e-03, eta: 3:06:57, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6506 2023-01-02 02:12:46,517 - mmcls - INFO - Epoch [75][3700/5005] lr: 1.000e-03, eta: 3:06:48, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6588 2023-01-02 02:12:55,295 - mmcls - INFO - Epoch [75][3800/5005] lr: 1.000e-03, eta: 3:06:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6354 2023-01-02 02:13:04,143 - mmcls - INFO - Epoch [75][3900/5005] lr: 1.000e-03, eta: 3:06:31, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6408 2023-01-02 02:13:12,971 - mmcls - INFO - Epoch [75][4000/5005] lr: 1.000e-03, eta: 3:06:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6589 2023-01-02 02:13:21,695 - mmcls - INFO - Epoch [75][4100/5005] lr: 1.000e-03, eta: 3:06:13, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6581 2023-01-02 02:13:30,699 - mmcls - INFO - Epoch [75][4200/5005] lr: 1.000e-03, eta: 3:06:04, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6431 2023-01-02 02:13:39,255 - mmcls - INFO - Epoch [75][4300/5005] lr: 1.000e-03, eta: 3:05:55, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.6700 2023-01-02 02:13:48,044 - mmcls - INFO - Epoch [75][4400/5005] lr: 1.000e-03, eta: 3:05:46, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.6501 2023-01-02 02:13:56,970 - mmcls - INFO - Epoch [75][4500/5005] lr: 1.000e-03, eta: 3:05:37, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6618 2023-01-02 02:14:05,910 - mmcls - INFO - Epoch [75][4600/5005] lr: 1.000e-03, eta: 3:05:29, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6434 2023-01-02 02:14:14,657 - mmcls - INFO - Epoch [75][4700/5005] lr: 1.000e-03, eta: 3:05:20, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6764 2023-01-02 02:14:23,271 - mmcls - INFO - Epoch [75][4800/5005] lr: 1.000e-03, eta: 3:05:11, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6609 2023-01-02 02:14:32,042 - mmcls - INFO - Epoch [75][4900/5005] lr: 1.000e-03, eta: 3:05:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6544 2023-01-02 02:14:41,078 - mmcls - INFO - Epoch [75][5000/5005] lr: 1.000e-03, eta: 3:04:53, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6636 2023-01-02 02:15:03,748 - mmcls - INFO - Epoch(val) [75][196] accuracy_top-1: 64.6880, accuracy_top-5: 85.3680 2023-01-02 02:15:14,598 - mmcls - INFO - Epoch [76][100/5005] lr: 1.000e-03, eta: 3:04:44, time: 0.108, data_time: 0.071, memory: 1224, loss: 1.6514 2023-01-02 02:15:23,457 - mmcls - INFO - Epoch [76][200/5005] lr: 1.000e-03, eta: 3:04:35, time: 0.089, data_time: 0.029, memory: 1224, loss: 1.6374 2023-01-02 02:15:32,086 - mmcls - INFO - Epoch [76][300/5005] lr: 1.000e-03, eta: 3:04:26, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6517 2023-01-02 02:15:40,851 - mmcls - INFO - Epoch [76][400/5005] lr: 1.000e-03, eta: 3:04:17, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6260 2023-01-02 02:15:49,842 - mmcls - INFO - Epoch [76][500/5005] lr: 1.000e-03, eta: 3:04:09, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6501 2023-01-02 02:15:58,631 - mmcls - INFO - Epoch [76][600/5005] lr: 1.000e-03, eta: 3:04:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6224 2023-01-02 02:16:07,971 - mmcls - INFO - Epoch [76][700/5005] lr: 1.000e-03, eta: 3:03:51, time: 0.093, data_time: 0.010, memory: 1224, loss: 1.6331 2023-01-02 02:16:16,577 - mmcls - INFO - Epoch [76][800/5005] lr: 1.000e-03, eta: 3:03:42, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6422 2023-01-02 02:16:25,495 - mmcls - INFO - Epoch [76][900/5005] lr: 1.000e-03, eta: 3:03:33, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6299 2023-01-02 02:16:34,121 - mmcls - INFO - Epoch [76][1000/5005] lr: 1.000e-03, eta: 3:03:24, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6363 2023-01-02 02:16:42,867 - mmcls - INFO - Epoch [76][1100/5005] lr: 1.000e-03, eta: 3:03:15, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6486 2023-01-02 02:16:51,693 - mmcls - INFO - Epoch [76][1200/5005] lr: 1.000e-03, eta: 3:03:07, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6539 2023-01-02 02:17:00,280 - mmcls - INFO - Epoch [76][1300/5005] lr: 1.000e-03, eta: 3:02:58, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6531 2023-01-02 02:17:08,861 - mmcls - INFO - Epoch [76][1400/5005] lr: 1.000e-03, eta: 3:02:49, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6712 2023-01-02 02:17:17,876 - mmcls - INFO - Epoch [76][1500/5005] lr: 1.000e-03, eta: 3:02:40, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6538 2023-01-02 02:17:26,727 - mmcls - INFO - Epoch [76][1600/5005] lr: 1.000e-03, eta: 3:02:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6369 2023-01-02 02:17:35,538 - mmcls - INFO - Epoch [76][1700/5005] lr: 1.000e-03, eta: 3:02:22, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6471 2023-01-02 02:17:44,442 - mmcls - INFO - Epoch [76][1800/5005] lr: 1.000e-03, eta: 3:02:13, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6373 2023-01-02 02:17:53,098 - mmcls - INFO - Epoch [76][1900/5005] lr: 1.000e-03, eta: 3:02:04, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6583 2023-01-02 02:18:02,055 - mmcls - INFO - Epoch [76][2000/5005] lr: 1.000e-03, eta: 3:01:55, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6576 2023-01-02 02:18:11,216 - mmcls - INFO - Epoch [76][2100/5005] lr: 1.000e-03, eta: 3:01:47, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6393 2023-01-02 02:18:19,895 - mmcls - INFO - Epoch [76][2200/5005] lr: 1.000e-03, eta: 3:01:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6475 2023-01-02 02:18:28,778 - mmcls - INFO - Epoch [76][2300/5005] lr: 1.000e-03, eta: 3:01:29, time: 0.089, data_time: 0.038, memory: 1224, loss: 1.6565 2023-01-02 02:18:37,704 - mmcls - INFO - Epoch [76][2400/5005] lr: 1.000e-03, eta: 3:01:20, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6395 2023-01-02 02:18:46,471 - mmcls - INFO - Epoch [76][2500/5005] lr: 1.000e-03, eta: 3:01:11, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6432 2023-01-02 02:18:55,495 - mmcls - INFO - Epoch [76][2600/5005] lr: 1.000e-03, eta: 3:01:02, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6732 2023-01-02 02:19:04,454 - mmcls - INFO - Epoch [76][2700/5005] lr: 1.000e-03, eta: 3:00:53, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6519 2023-01-02 02:19:13,244 - mmcls - INFO - Epoch [76][2800/5005] lr: 1.000e-03, eta: 3:00:45, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6434 2023-01-02 02:19:22,186 - mmcls - INFO - Epoch [76][2900/5005] lr: 1.000e-03, eta: 3:00:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6466 2023-01-02 02:19:31,172 - mmcls - INFO - Epoch [76][3000/5005] lr: 1.000e-03, eta: 3:00:27, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6414 2023-01-02 02:19:39,689 - mmcls - INFO - Epoch [76][3100/5005] lr: 1.000e-03, eta: 3:00:18, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6676 2023-01-02 02:19:48,873 - mmcls - INFO - Epoch [76][3200/5005] lr: 1.000e-03, eta: 3:00:09, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6302 2023-01-02 02:19:57,654 - mmcls - INFO - Epoch [76][3300/5005] lr: 1.000e-03, eta: 3:00:00, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6568 2023-01-02 02:20:06,259 - mmcls - INFO - Epoch [76][3400/5005] lr: 1.000e-03, eta: 2:59:51, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.6747 2023-01-02 02:20:15,398 - mmcls - INFO - Epoch [76][3500/5005] lr: 1.000e-03, eta: 2:59:43, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6639 2023-01-02 02:20:24,239 - mmcls - INFO - Epoch [76][3600/5005] lr: 1.000e-03, eta: 2:59:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6526 2023-01-02 02:20:32,861 - mmcls - INFO - Epoch [76][3700/5005] lr: 1.000e-03, eta: 2:59:25, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6565 2023-01-02 02:20:41,575 - mmcls - INFO - Epoch [76][3800/5005] lr: 1.000e-03, eta: 2:59:16, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6565 2023-01-02 02:20:50,474 - mmcls - INFO - Epoch [76][3900/5005] lr: 1.000e-03, eta: 2:59:07, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6613 2023-01-02 02:20:59,167 - mmcls - INFO - Epoch [76][4000/5005] lr: 1.000e-03, eta: 2:58:58, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6607 2023-01-02 02:21:08,126 - mmcls - INFO - Epoch [76][4100/5005] lr: 1.000e-03, eta: 2:58:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6326 2023-01-02 02:21:16,990 - mmcls - INFO - Epoch [76][4200/5005] lr: 1.000e-03, eta: 2:58:40, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6547 2023-01-02 02:21:25,468 - mmcls - INFO - Epoch [76][4300/5005] lr: 1.000e-03, eta: 2:58:31, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.6326 2023-01-02 02:21:34,566 - mmcls - INFO - Epoch [76][4400/5005] lr: 1.000e-03, eta: 2:58:23, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.6556 2023-01-02 02:21:43,568 - mmcls - INFO - Epoch [76][4500/5005] lr: 1.000e-03, eta: 2:58:14, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6529 2023-01-02 02:21:52,341 - mmcls - INFO - Epoch [76][4600/5005] lr: 1.000e-03, eta: 2:58:05, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6711 2023-01-02 02:22:01,333 - mmcls - INFO - Epoch [76][4700/5005] lr: 1.000e-03, eta: 2:57:56, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6281 2023-01-02 02:22:10,181 - mmcls - INFO - Epoch [76][4800/5005] lr: 1.000e-03, eta: 2:57:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6621 2023-01-02 02:22:18,707 - mmcls - INFO - Epoch [76][4900/5005] lr: 1.000e-03, eta: 2:57:38, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6577 2023-01-02 02:22:27,340 - mmcls - INFO - Epoch [76][5000/5005] lr: 1.000e-03, eta: 2:57:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6469 2023-01-02 02:22:50,257 - mmcls - INFO - Epoch(val) [76][196] accuracy_top-1: 64.5620, accuracy_top-5: 85.4020 2023-01-02 02:23:01,602 - mmcls - INFO - Epoch [77][100/5005] lr: 1.000e-03, eta: 2:57:21, time: 0.113, data_time: 0.041, memory: 1224, loss: 1.6337 2023-01-02 02:23:10,323 - mmcls - INFO - Epoch [77][200/5005] lr: 1.000e-03, eta: 2:57:12, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6391 2023-01-02 02:23:19,162 - mmcls - INFO - Epoch [77][300/5005] lr: 1.000e-03, eta: 2:57:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6470 2023-01-02 02:23:27,939 - mmcls - INFO - Epoch [77][400/5005] lr: 1.000e-03, eta: 2:56:54, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6414 2023-01-02 02:23:36,742 - mmcls - INFO - Epoch [77][500/5005] lr: 1.000e-03, eta: 2:56:45, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.6534 2023-01-02 02:23:45,576 - mmcls - INFO - Epoch [77][600/5005] lr: 1.000e-03, eta: 2:56:36, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6267 2023-01-02 02:23:54,358 - mmcls - INFO - Epoch [77][700/5005] lr: 1.000e-03, eta: 2:56:27, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6326 2023-01-02 02:24:02,958 - mmcls - INFO - Epoch [77][800/5005] lr: 1.000e-03, eta: 2:56:18, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6443 2023-01-02 02:24:11,783 - mmcls - INFO - Epoch [77][900/5005] lr: 1.000e-03, eta: 2:56:09, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6682 2023-01-02 02:24:20,626 - mmcls - INFO - Epoch [77][1000/5005] lr: 1.000e-03, eta: 2:56:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6443 2023-01-02 02:24:29,415 - mmcls - INFO - Epoch [77][1100/5005] lr: 1.000e-03, eta: 2:55:52, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6372 2023-01-02 02:24:38,174 - mmcls - INFO - Epoch [77][1200/5005] lr: 1.000e-03, eta: 2:55:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6335 2023-01-02 02:24:46,632 - mmcls - INFO - Epoch [77][1300/5005] lr: 1.000e-03, eta: 2:55:34, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.6226 2023-01-02 02:24:55,755 - mmcls - INFO - Epoch [77][1400/5005] lr: 1.000e-03, eta: 2:55:25, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6364 2023-01-02 02:25:04,488 - mmcls - INFO - Epoch [77][1500/5005] lr: 1.000e-03, eta: 2:55:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6514 2023-01-02 02:25:13,328 - mmcls - INFO - Epoch [77][1600/5005] lr: 1.000e-03, eta: 2:55:07, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6670 2023-01-02 02:25:22,289 - mmcls - INFO - Epoch [77][1700/5005] lr: 1.000e-03, eta: 2:54:58, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.6403 2023-01-02 02:25:30,927 - mmcls - INFO - Epoch [77][1800/5005] lr: 1.000e-03, eta: 2:54:49, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6348 2023-01-02 02:25:39,958 - mmcls - INFO - Epoch [77][1900/5005] lr: 1.000e-03, eta: 2:54:41, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6340 2023-01-02 02:25:49,089 - mmcls - INFO - Epoch [77][2000/5005] lr: 1.000e-03, eta: 2:54:32, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.6297 2023-01-02 02:25:57,876 - mmcls - INFO - Epoch [77][2100/5005] lr: 1.000e-03, eta: 2:54:23, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6470 2023-01-02 02:26:06,749 - mmcls - INFO - Epoch [77][2200/5005] lr: 1.000e-03, eta: 2:54:14, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6332 2023-01-02 02:26:15,599 - mmcls - INFO - Epoch [77][2300/5005] lr: 1.000e-03, eta: 2:54:05, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6414 2023-01-02 02:26:24,158 - mmcls - INFO - Epoch [77][2400/5005] lr: 1.000e-03, eta: 2:53:56, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6495 2023-01-02 02:26:33,046 - mmcls - INFO - Epoch [77][2500/5005] lr: 1.000e-03, eta: 2:53:47, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6550 2023-01-02 02:26:42,194 - mmcls - INFO - Epoch [77][2600/5005] lr: 1.000e-03, eta: 2:53:39, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6516 2023-01-02 02:26:50,992 - mmcls - INFO - Epoch [77][2700/5005] lr: 1.000e-03, eta: 2:53:30, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6304 2023-01-02 02:26:59,613 - mmcls - INFO - Epoch [77][2800/5005] lr: 1.000e-03, eta: 2:53:21, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6446 2023-01-02 02:27:08,402 - mmcls - INFO - Epoch [77][2900/5005] lr: 1.000e-03, eta: 2:53:12, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.6553 2023-01-02 02:27:17,347 - mmcls - INFO - Epoch [77][3000/5005] lr: 1.000e-03, eta: 2:53:03, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6612 2023-01-02 02:27:26,105 - mmcls - INFO - Epoch [77][3100/5005] lr: 1.000e-03, eta: 2:52:54, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6626 2023-01-02 02:27:35,103 - mmcls - INFO - Epoch [77][3200/5005] lr: 1.000e-03, eta: 2:52:45, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6463 2023-01-02 02:27:43,860 - mmcls - INFO - Epoch [77][3300/5005] lr: 1.000e-03, eta: 2:52:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6446 2023-01-02 02:27:52,649 - mmcls - INFO - Epoch [77][3400/5005] lr: 1.000e-03, eta: 2:52:27, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6370 2023-01-02 02:28:01,160 - mmcls - INFO - Epoch [77][3500/5005] lr: 1.000e-03, eta: 2:52:18, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6583 2023-01-02 02:28:10,454 - mmcls - INFO - Epoch [77][3600/5005] lr: 1.000e-03, eta: 2:52:10, time: 0.093, data_time: 0.007, memory: 1224, loss: 1.6465 2023-01-02 02:28:19,120 - mmcls - INFO - Epoch [77][3700/5005] lr: 1.000e-03, eta: 2:52:01, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6361 2023-01-02 02:28:27,891 - mmcls - INFO - Epoch [77][3800/5005] lr: 1.000e-03, eta: 2:51:52, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6469 2023-01-02 02:28:36,548 - mmcls - INFO - Epoch [77][3900/5005] lr: 1.000e-03, eta: 2:51:43, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.6383 2023-01-02 02:28:45,239 - mmcls - INFO - Epoch [77][4000/5005] lr: 1.000e-03, eta: 2:51:34, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6557 2023-01-02 02:28:54,035 - mmcls - INFO - Epoch [77][4100/5005] lr: 1.000e-03, eta: 2:51:25, time: 0.088, data_time: 0.038, memory: 1224, loss: 1.6504 2023-01-02 02:29:02,884 - mmcls - INFO - Epoch [77][4200/5005] lr: 1.000e-03, eta: 2:51:16, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6502 2023-01-02 02:29:11,593 - mmcls - INFO - Epoch [77][4300/5005] lr: 1.000e-03, eta: 2:51:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6432 2023-01-02 02:29:20,271 - mmcls - INFO - Epoch [77][4400/5005] lr: 1.000e-03, eta: 2:50:59, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6534 2023-01-02 02:29:29,426 - mmcls - INFO - Epoch [77][4500/5005] lr: 1.000e-03, eta: 2:50:50, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.6534 2023-01-02 02:29:38,449 - mmcls - INFO - Epoch [77][4600/5005] lr: 1.000e-03, eta: 2:50:41, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6632 2023-01-02 02:29:47,338 - mmcls - INFO - Epoch [77][4700/5005] lr: 1.000e-03, eta: 2:50:32, time: 0.089, data_time: 0.035, memory: 1224, loss: 1.6348 2023-01-02 02:29:56,126 - mmcls - INFO - Epoch [77][4800/5005] lr: 1.000e-03, eta: 2:50:23, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6625 2023-01-02 02:30:04,828 - mmcls - INFO - Epoch [77][4900/5005] lr: 1.000e-03, eta: 2:50:14, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6594 2023-01-02 02:30:13,383 - mmcls - INFO - Epoch [77][5000/5005] lr: 1.000e-03, eta: 2:50:05, time: 0.085, data_time: 0.020, memory: 1224, loss: 1.6586 2023-01-02 02:30:36,061 - mmcls - INFO - Epoch(val) [77][196] accuracy_top-1: 64.4820, accuracy_top-5: 85.3660 2023-01-02 02:30:47,125 - mmcls - INFO - Epoch [78][100/5005] lr: 1.000e-03, eta: 2:49:56, time: 0.110, data_time: 0.057, memory: 1224, loss: 1.6397 2023-01-02 02:30:55,905 - mmcls - INFO - Epoch [78][200/5005] lr: 1.000e-03, eta: 2:49:48, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6362 2023-01-02 02:31:04,989 - mmcls - INFO - Epoch [78][300/5005] lr: 1.000e-03, eta: 2:49:39, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6528 2023-01-02 02:31:13,735 - mmcls - INFO - Epoch [78][400/5005] lr: 1.000e-03, eta: 2:49:30, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6210 2023-01-02 02:31:22,676 - mmcls - INFO - Epoch [78][500/5005] lr: 1.000e-03, eta: 2:49:21, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6315 2023-01-02 02:31:31,381 - mmcls - INFO - Epoch [78][600/5005] lr: 1.000e-03, eta: 2:49:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6387 2023-01-02 02:31:40,286 - mmcls - INFO - Epoch [78][700/5005] lr: 1.000e-03, eta: 2:49:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6425 2023-01-02 02:31:49,080 - mmcls - INFO - Epoch [78][800/5005] lr: 1.000e-03, eta: 2:48:54, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6421 2023-01-02 02:31:57,834 - mmcls - INFO - Epoch [78][900/5005] lr: 1.000e-03, eta: 2:48:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6205 2023-01-02 02:32:06,510 - mmcls - INFO - Epoch [78][1000/5005] lr: 1.000e-03, eta: 2:48:37, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6588 2023-01-02 02:32:15,460 - mmcls - INFO - Epoch [78][1100/5005] lr: 1.000e-03, eta: 2:48:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6440 2023-01-02 02:32:24,130 - mmcls - INFO - Epoch [78][1200/5005] lr: 1.000e-03, eta: 2:48:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6213 2023-01-02 02:32:32,939 - mmcls - INFO - Epoch [78][1300/5005] lr: 1.000e-03, eta: 2:48:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6334 2023-01-02 02:32:41,590 - mmcls - INFO - Epoch [78][1400/5005] lr: 1.000e-03, eta: 2:48:01, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6337 2023-01-02 02:32:50,241 - mmcls - INFO - Epoch [78][1500/5005] lr: 1.000e-03, eta: 2:47:52, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6349 2023-01-02 02:32:59,147 - mmcls - INFO - Epoch [78][1600/5005] lr: 1.000e-03, eta: 2:47:43, time: 0.089, data_time: 0.042, memory: 1224, loss: 1.6534 2023-01-02 02:33:08,272 - mmcls - INFO - Epoch [78][1700/5005] lr: 1.000e-03, eta: 2:47:34, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.6444 2023-01-02 02:33:17,004 - mmcls - INFO - Epoch [78][1800/5005] lr: 1.000e-03, eta: 2:47:26, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6550 2023-01-02 02:33:25,608 - mmcls - INFO - Epoch [78][1900/5005] lr: 1.000e-03, eta: 2:47:17, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6465 2023-01-02 02:33:34,531 - mmcls - INFO - Epoch [78][2000/5005] lr: 1.000e-03, eta: 2:47:08, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6382 2023-01-02 02:33:43,695 - mmcls - INFO - Epoch [78][2100/5005] lr: 1.000e-03, eta: 2:46:59, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6534 2023-01-02 02:33:52,358 - mmcls - INFO - Epoch [78][2200/5005] lr: 1.000e-03, eta: 2:46:50, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6524 2023-01-02 02:34:01,289 - mmcls - INFO - Epoch [78][2300/5005] lr: 1.000e-03, eta: 2:46:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6532 2023-01-02 02:34:10,130 - mmcls - INFO - Epoch [78][2400/5005] lr: 1.000e-03, eta: 2:46:32, time: 0.088, data_time: 0.038, memory: 1224, loss: 1.6472 2023-01-02 02:34:18,970 - mmcls - INFO - Epoch [78][2500/5005] lr: 1.000e-03, eta: 2:46:23, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.6267 2023-01-02 02:34:28,067 - mmcls - INFO - Epoch [78][2600/5005] lr: 1.000e-03, eta: 2:46:15, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6578 2023-01-02 02:34:36,754 - mmcls - INFO - Epoch [78][2700/5005] lr: 1.000e-03, eta: 2:46:06, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.6583 2023-01-02 02:34:45,644 - mmcls - INFO - Epoch [78][2800/5005] lr: 1.000e-03, eta: 2:45:57, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6322 2023-01-02 02:34:54,313 - mmcls - INFO - Epoch [78][2900/5005] lr: 1.000e-03, eta: 2:45:48, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6630 2023-01-02 02:35:03,459 - mmcls - INFO - Epoch [78][3000/5005] lr: 1.000e-03, eta: 2:45:39, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6678 2023-01-02 02:35:12,254 - mmcls - INFO - Epoch [78][3100/5005] lr: 1.000e-03, eta: 2:45:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6627 2023-01-02 02:35:21,050 - mmcls - INFO - Epoch [78][3200/5005] lr: 1.000e-03, eta: 2:45:21, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6499 2023-01-02 02:35:30,096 - mmcls - INFO - Epoch [78][3300/5005] lr: 1.000e-03, eta: 2:45:13, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6427 2023-01-02 02:35:39,257 - mmcls - INFO - Epoch [78][3400/5005] lr: 1.000e-03, eta: 2:45:04, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.6359 2023-01-02 02:35:48,012 - mmcls - INFO - Epoch [78][3500/5005] lr: 1.000e-03, eta: 2:44:55, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.6383 2023-01-02 02:35:56,990 - mmcls - INFO - Epoch [78][3600/5005] lr: 1.000e-03, eta: 2:44:46, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6310 2023-01-02 02:36:05,936 - mmcls - INFO - Epoch [78][3700/5005] lr: 1.000e-03, eta: 2:44:37, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6517 2023-01-02 02:36:14,745 - mmcls - INFO - Epoch [78][3800/5005] lr: 1.000e-03, eta: 2:44:28, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6672 2023-01-02 02:36:23,674 - mmcls - INFO - Epoch [78][3900/5005] lr: 1.000e-03, eta: 2:44:19, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6526 2023-01-02 02:36:32,597 - mmcls - INFO - Epoch [78][4000/5005] lr: 1.000e-03, eta: 2:44:11, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6275 2023-01-02 02:36:42,109 - mmcls - INFO - Epoch [78][4100/5005] lr: 1.000e-03, eta: 2:44:02, time: 0.095, data_time: 0.036, memory: 1224, loss: 1.6562 2023-01-02 02:36:50,990 - mmcls - INFO - Epoch [78][4200/5005] lr: 1.000e-03, eta: 2:43:53, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6776 2023-01-02 02:36:59,755 - mmcls - INFO - Epoch [78][4300/5005] lr: 1.000e-03, eta: 2:43:44, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6406 2023-01-02 02:37:08,668 - mmcls - INFO - Epoch [78][4400/5005] lr: 1.000e-03, eta: 2:43:35, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6405 2023-01-02 02:37:17,290 - mmcls - INFO - Epoch [78][4500/5005] lr: 1.000e-03, eta: 2:43:26, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6527 2023-01-02 02:37:26,118 - mmcls - INFO - Epoch [78][4600/5005] lr: 1.000e-03, eta: 2:43:18, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6456 2023-01-02 02:37:34,849 - mmcls - INFO - Epoch [78][4700/5005] lr: 1.000e-03, eta: 2:43:09, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6610 2023-01-02 02:37:43,607 - mmcls - INFO - Epoch [78][4800/5005] lr: 1.000e-03, eta: 2:43:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6619 2023-01-02 02:37:52,389 - mmcls - INFO - Epoch [78][4900/5005] lr: 1.000e-03, eta: 2:42:51, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6634 2023-01-02 02:38:01,347 - mmcls - INFO - Epoch [78][5000/5005] lr: 1.000e-03, eta: 2:42:42, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6602 2023-01-02 02:38:24,368 - mmcls - INFO - Epoch(val) [78][196] accuracy_top-1: 64.9080, accuracy_top-5: 85.4420 2023-01-02 02:38:35,407 - mmcls - INFO - Epoch [79][100/5005] lr: 1.000e-03, eta: 2:42:33, time: 0.110, data_time: 0.057, memory: 1224, loss: 1.6334 2023-01-02 02:38:44,061 - mmcls - INFO - Epoch [79][200/5005] lr: 1.000e-03, eta: 2:42:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6303 2023-01-02 02:38:52,831 - mmcls - INFO - Epoch [79][300/5005] lr: 1.000e-03, eta: 2:42:15, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6311 2023-01-02 02:39:01,604 - mmcls - INFO - Epoch [79][400/5005] lr: 1.000e-03, eta: 2:42:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6188 2023-01-02 02:39:10,296 - mmcls - INFO - Epoch [79][500/5005] lr: 1.000e-03, eta: 2:41:58, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6389 2023-01-02 02:39:19,030 - mmcls - INFO - Epoch [79][600/5005] lr: 1.000e-03, eta: 2:41:49, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6555 2023-01-02 02:39:27,778 - mmcls - INFO - Epoch [79][700/5005] lr: 1.000e-03, eta: 2:41:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6223 2023-01-02 02:39:36,540 - mmcls - INFO - Epoch [79][800/5005] lr: 1.000e-03, eta: 2:41:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6330 2023-01-02 02:39:45,129 - mmcls - INFO - Epoch [79][900/5005] lr: 1.000e-03, eta: 2:41:22, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6589 2023-01-02 02:39:53,931 - mmcls - INFO - Epoch [79][1000/5005] lr: 1.000e-03, eta: 2:41:13, time: 0.088, data_time: 0.036, memory: 1224, loss: 1.6567 2023-01-02 02:40:02,907 - mmcls - INFO - Epoch [79][1100/5005] lr: 1.000e-03, eta: 2:41:04, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.6412 2023-01-02 02:40:11,712 - mmcls - INFO - Epoch [79][1200/5005] lr: 1.000e-03, eta: 2:40:55, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6393 2023-01-02 02:40:20,660 - mmcls - INFO - Epoch [79][1300/5005] lr: 1.000e-03, eta: 2:40:46, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6442 2023-01-02 02:40:29,354 - mmcls - INFO - Epoch [79][1400/5005] lr: 1.000e-03, eta: 2:40:38, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6294 2023-01-02 02:40:38,069 - mmcls - INFO - Epoch [79][1500/5005] lr: 1.000e-03, eta: 2:40:29, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6506 2023-01-02 02:40:46,630 - mmcls - INFO - Epoch [79][1600/5005] lr: 1.000e-03, eta: 2:40:20, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6607 2023-01-02 02:40:55,449 - mmcls - INFO - Epoch [79][1700/5005] lr: 1.000e-03, eta: 2:40:11, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6465 2023-01-02 02:41:04,284 - mmcls - INFO - Epoch [79][1800/5005] lr: 1.000e-03, eta: 2:40:02, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6384 2023-01-02 02:41:13,194 - mmcls - INFO - Epoch [79][1900/5005] lr: 1.000e-03, eta: 2:39:53, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6691 2023-01-02 02:41:21,977 - mmcls - INFO - Epoch [79][2000/5005] lr: 1.000e-03, eta: 2:39:44, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6531 2023-01-02 02:41:30,781 - mmcls - INFO - Epoch [79][2100/5005] lr: 1.000e-03, eta: 2:39:35, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6431 2023-01-02 02:41:39,419 - mmcls - INFO - Epoch [79][2200/5005] lr: 1.000e-03, eta: 2:39:26, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6493 2023-01-02 02:41:48,516 - mmcls - INFO - Epoch [79][2300/5005] lr: 1.000e-03, eta: 2:39:18, time: 0.091, data_time: 0.023, memory: 1224, loss: 1.6454 2023-01-02 02:41:57,171 - mmcls - INFO - Epoch [79][2400/5005] lr: 1.000e-03, eta: 2:39:09, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6183 2023-01-02 02:42:06,334 - mmcls - INFO - Epoch [79][2500/5005] lr: 1.000e-03, eta: 2:39:00, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6473 2023-01-02 02:42:15,116 - mmcls - INFO - Epoch [79][2600/5005] lr: 1.000e-03, eta: 2:38:51, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6279 2023-01-02 02:42:24,392 - mmcls - INFO - Epoch [79][2700/5005] lr: 1.000e-03, eta: 2:38:42, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.6471 2023-01-02 02:42:33,361 - mmcls - INFO - Epoch [79][2800/5005] lr: 1.000e-03, eta: 2:38:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6453 2023-01-02 02:42:41,966 - mmcls - INFO - Epoch [79][2900/5005] lr: 1.000e-03, eta: 2:38:24, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6473 2023-01-02 02:42:50,740 - mmcls - INFO - Epoch [79][3000/5005] lr: 1.000e-03, eta: 2:38:16, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6614 2023-01-02 02:42:59,528 - mmcls - INFO - Epoch [79][3100/5005] lr: 1.000e-03, eta: 2:38:07, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.6064 2023-01-02 02:43:08,401 - mmcls - INFO - Epoch [79][3200/5005] lr: 1.000e-03, eta: 2:37:58, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6371 2023-01-02 02:43:17,239 - mmcls - INFO - Epoch [79][3300/5005] lr: 1.000e-03, eta: 2:37:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6198 2023-01-02 02:43:26,564 - mmcls - INFO - Epoch [79][3400/5005] lr: 1.000e-03, eta: 2:37:40, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.6446 2023-01-02 02:43:35,327 - mmcls - INFO - Epoch [79][3500/5005] lr: 1.000e-03, eta: 2:37:31, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6433 2023-01-02 02:43:44,444 - mmcls - INFO - Epoch [79][3600/5005] lr: 1.000e-03, eta: 2:37:23, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6436 2023-01-02 02:43:53,178 - mmcls - INFO - Epoch [79][3700/5005] lr: 1.000e-03, eta: 2:37:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6646 2023-01-02 02:44:02,011 - mmcls - INFO - Epoch [79][3800/5005] lr: 1.000e-03, eta: 2:37:05, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6518 2023-01-02 02:44:10,780 - mmcls - INFO - Epoch [79][3900/5005] lr: 1.000e-03, eta: 2:36:56, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6390 2023-01-02 02:44:19,609 - mmcls - INFO - Epoch [79][4000/5005] lr: 1.000e-03, eta: 2:36:47, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6332 2023-01-02 02:44:28,196 - mmcls - INFO - Epoch [79][4100/5005] lr: 1.000e-03, eta: 2:36:38, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6265 2023-01-02 02:44:37,138 - mmcls - INFO - Epoch [79][4200/5005] lr: 1.000e-03, eta: 2:36:29, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6560 2023-01-02 02:44:45,569 - mmcls - INFO - Epoch [79][4300/5005] lr: 1.000e-03, eta: 2:36:20, time: 0.084, data_time: 0.008, memory: 1224, loss: 1.6292 2023-01-02 02:44:54,536 - mmcls - INFO - Epoch [79][4400/5005] lr: 1.000e-03, eta: 2:36:11, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6675 2023-01-02 02:45:03,194 - mmcls - INFO - Epoch [79][4500/5005] lr: 1.000e-03, eta: 2:36:02, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6532 2023-01-02 02:45:11,794 - mmcls - INFO - Epoch [79][4600/5005] lr: 1.000e-03, eta: 2:35:53, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6593 2023-01-02 02:45:20,436 - mmcls - INFO - Epoch [79][4700/5005] lr: 1.000e-03, eta: 2:35:45, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6395 2023-01-02 02:45:29,208 - mmcls - INFO - Epoch [79][4800/5005] lr: 1.000e-03, eta: 2:35:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6708 2023-01-02 02:45:38,121 - mmcls - INFO - Epoch [79][4900/5005] lr: 1.000e-03, eta: 2:35:27, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6629 2023-01-02 02:45:47,081 - mmcls - INFO - Epoch [79][5000/5005] lr: 1.000e-03, eta: 2:35:18, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6542 2023-01-02 02:46:09,852 - mmcls - INFO - Epoch(val) [79][196] accuracy_top-1: 64.7960, accuracy_top-5: 85.3960 2023-01-02 02:46:20,796 - mmcls - INFO - Epoch [80][100/5005] lr: 1.000e-03, eta: 2:35:09, time: 0.109, data_time: 0.058, memory: 1224, loss: 1.6095 2023-01-02 02:46:29,936 - mmcls - INFO - Epoch [80][200/5005] lr: 1.000e-03, eta: 2:35:00, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6244 2023-01-02 02:46:38,697 - mmcls - INFO - Epoch [80][300/5005] lr: 1.000e-03, eta: 2:34:51, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6340 2023-01-02 02:46:47,497 - mmcls - INFO - Epoch [80][400/5005] lr: 1.000e-03, eta: 2:34:43, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6367 2023-01-02 02:46:56,194 - mmcls - INFO - Epoch [80][500/5005] lr: 1.000e-03, eta: 2:34:34, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6170 2023-01-02 02:47:05,041 - mmcls - INFO - Epoch [80][600/5005] lr: 1.000e-03, eta: 2:34:25, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6076 2023-01-02 02:47:13,870 - mmcls - INFO - Epoch [80][700/5005] lr: 1.000e-03, eta: 2:34:16, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6186 2023-01-02 02:47:22,851 - mmcls - INFO - Epoch [80][800/5005] lr: 1.000e-03, eta: 2:34:07, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6352 2023-01-02 02:47:31,632 - mmcls - INFO - Epoch [80][900/5005] lr: 1.000e-03, eta: 2:33:58, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6583 2023-01-02 02:47:40,243 - mmcls - INFO - Epoch [80][1000/5005] lr: 1.000e-03, eta: 2:33:49, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6287 2023-01-02 02:47:48,948 - mmcls - INFO - Epoch [80][1100/5005] lr: 1.000e-03, eta: 2:33:40, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6334 2023-01-02 02:47:57,586 - mmcls - INFO - Epoch [80][1200/5005] lr: 1.000e-03, eta: 2:33:31, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.6460 2023-01-02 02:48:06,082 - mmcls - INFO - Epoch [80][1300/5005] lr: 1.000e-03, eta: 2:33:22, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.6395 2023-01-02 02:48:14,683 - mmcls - INFO - Epoch [80][1400/5005] lr: 1.000e-03, eta: 2:33:13, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6480 2023-01-02 02:48:23,555 - mmcls - INFO - Epoch [80][1500/5005] lr: 1.000e-03, eta: 2:33:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6480 2023-01-02 02:48:32,425 - mmcls - INFO - Epoch [80][1600/5005] lr: 1.000e-03, eta: 2:32:56, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6292 2023-01-02 02:48:41,113 - mmcls - INFO - Epoch [80][1700/5005] lr: 1.000e-03, eta: 2:32:47, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6351 2023-01-02 02:48:49,695 - mmcls - INFO - Epoch [80][1800/5005] lr: 1.000e-03, eta: 2:32:38, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6414 2023-01-02 02:48:58,498 - mmcls - INFO - Epoch [80][1900/5005] lr: 1.000e-03, eta: 2:32:29, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6448 2023-01-02 02:49:07,334 - mmcls - INFO - Epoch [80][2000/5005] lr: 1.000e-03, eta: 2:32:20, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6487 2023-01-02 02:49:16,644 - mmcls - INFO - Epoch [80][2100/5005] lr: 1.000e-03, eta: 2:32:11, time: 0.093, data_time: 0.036, memory: 1224, loss: 1.6332 2023-01-02 02:49:25,492 - mmcls - INFO - Epoch [80][2200/5005] lr: 1.000e-03, eta: 2:32:03, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.6415 2023-01-02 02:49:34,242 - mmcls - INFO - Epoch [80][2300/5005] lr: 1.000e-03, eta: 2:31:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6443 2023-01-02 02:49:43,092 - mmcls - INFO - Epoch [80][2400/5005] lr: 1.000e-03, eta: 2:31:45, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6342 2023-01-02 02:49:51,827 - mmcls - INFO - Epoch [80][2500/5005] lr: 1.000e-03, eta: 2:31:36, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6440 2023-01-02 02:50:00,591 - mmcls - INFO - Epoch [80][2600/5005] lr: 1.000e-03, eta: 2:31:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6401 2023-01-02 02:50:09,433 - mmcls - INFO - Epoch [80][2700/5005] lr: 1.000e-03, eta: 2:31:18, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6377 2023-01-02 02:50:18,147 - mmcls - INFO - Epoch [80][2800/5005] lr: 1.000e-03, eta: 2:31:09, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6345 2023-01-02 02:50:26,901 - mmcls - INFO - Epoch [80][2900/5005] lr: 1.000e-03, eta: 2:31:00, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6287 2023-01-02 02:50:35,733 - mmcls - INFO - Epoch [80][3000/5005] lr: 1.000e-03, eta: 2:30:51, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6362 2023-01-02 02:50:44,537 - mmcls - INFO - Epoch [80][3100/5005] lr: 1.000e-03, eta: 2:30:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6277 2023-01-02 02:50:53,147 - mmcls - INFO - Epoch [80][3200/5005] lr: 1.000e-03, eta: 2:30:34, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6614 2023-01-02 02:51:01,856 - mmcls - INFO - Epoch [80][3300/5005] lr: 1.000e-03, eta: 2:30:25, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6436 2023-01-02 02:51:10,812 - mmcls - INFO - Epoch [80][3400/5005] lr: 1.000e-03, eta: 2:30:16, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6193 2023-01-02 02:51:19,458 - mmcls - INFO - Epoch [80][3500/5005] lr: 1.000e-03, eta: 2:30:07, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6391 2023-01-02 02:51:28,657 - mmcls - INFO - Epoch [80][3600/5005] lr: 1.000e-03, eta: 2:29:58, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.6531 2023-01-02 02:51:37,505 - mmcls - INFO - Epoch [80][3700/5005] lr: 1.000e-03, eta: 2:29:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6316 2023-01-02 02:51:46,237 - mmcls - INFO - Epoch [80][3800/5005] lr: 1.000e-03, eta: 2:29:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6341 2023-01-02 02:51:54,934 - mmcls - INFO - Epoch [80][3900/5005] lr: 1.000e-03, eta: 2:29:31, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6356 2023-01-02 02:52:03,444 - mmcls - INFO - Epoch [80][4000/5005] lr: 1.000e-03, eta: 2:29:23, time: 0.085, data_time: 0.016, memory: 1224, loss: 1.6417 2023-01-02 02:52:12,094 - mmcls - INFO - Epoch [80][4100/5005] lr: 1.000e-03, eta: 2:29:14, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6648 2023-01-02 02:52:20,945 - mmcls - INFO - Epoch [80][4200/5005] lr: 1.000e-03, eta: 2:29:05, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6548 2023-01-02 02:52:29,877 - mmcls - INFO - Epoch [80][4300/5005] lr: 1.000e-03, eta: 2:28:56, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6191 2023-01-02 02:52:38,675 - mmcls - INFO - Epoch [80][4400/5005] lr: 1.000e-03, eta: 2:28:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6240 2023-01-02 02:52:47,542 - mmcls - INFO - Epoch [80][4500/5005] lr: 1.000e-03, eta: 2:28:38, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6477 2023-01-02 02:52:56,368 - mmcls - INFO - Epoch [80][4600/5005] lr: 1.000e-03, eta: 2:28:29, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6724 2023-01-02 02:53:05,818 - mmcls - INFO - Epoch [80][4700/5005] lr: 1.000e-03, eta: 2:28:21, time: 0.095, data_time: 0.024, memory: 1224, loss: 1.6841 2023-01-02 02:53:14,708 - mmcls - INFO - Epoch [80][4800/5005] lr: 1.000e-03, eta: 2:28:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6294 2023-01-02 02:53:23,450 - mmcls - INFO - Epoch [80][4900/5005] lr: 1.000e-03, eta: 2:28:03, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6432 2023-01-02 02:53:32,162 - mmcls - INFO - Epoch [80][5000/5005] lr: 1.000e-03, eta: 2:27:54, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6389 2023-01-02 02:53:54,854 - mmcls - INFO - Epoch(val) [80][196] accuracy_top-1: 64.7060, accuracy_top-5: 85.4100 2023-01-02 02:54:05,802 - mmcls - INFO - Epoch [81][100/5005] lr: 1.000e-03, eta: 2:27:45, time: 0.109, data_time: 0.044, memory: 1224, loss: 1.6285 2023-01-02 02:54:14,529 - mmcls - INFO - Epoch [81][200/5005] lr: 1.000e-03, eta: 2:27:36, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6256 2023-01-02 02:54:23,133 - mmcls - INFO - Epoch [81][300/5005] lr: 1.000e-03, eta: 2:27:27, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6204 2023-01-02 02:54:32,114 - mmcls - INFO - Epoch [81][400/5005] lr: 1.000e-03, eta: 2:27:18, time: 0.090, data_time: 0.029, memory: 1224, loss: 1.6244 2023-01-02 02:54:41,094 - mmcls - INFO - Epoch [81][500/5005] lr: 1.000e-03, eta: 2:27:09, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6042 2023-01-02 02:54:50,063 - mmcls - INFO - Epoch [81][600/5005] lr: 1.000e-03, eta: 2:27:01, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6270 2023-01-02 02:54:58,937 - mmcls - INFO - Epoch [81][700/5005] lr: 1.000e-03, eta: 2:26:52, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6326 2023-01-02 02:55:07,712 - mmcls - INFO - Epoch [81][800/5005] lr: 1.000e-03, eta: 2:26:43, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6456 2023-01-02 02:55:16,850 - mmcls - INFO - Epoch [81][900/5005] lr: 1.000e-03, eta: 2:26:34, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6217 2023-01-02 02:55:25,463 - mmcls - INFO - Epoch [81][1000/5005] lr: 1.000e-03, eta: 2:26:25, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6359 2023-01-02 02:55:34,055 - mmcls - INFO - Epoch [81][1100/5005] lr: 1.000e-03, eta: 2:26:16, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6336 2023-01-02 02:55:42,892 - mmcls - INFO - Epoch [81][1200/5005] lr: 1.000e-03, eta: 2:26:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6184 2023-01-02 02:55:51,756 - mmcls - INFO - Epoch [81][1300/5005] lr: 1.000e-03, eta: 2:25:58, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6289 2023-01-02 02:56:00,622 - mmcls - INFO - Epoch [81][1400/5005] lr: 1.000e-03, eta: 2:25:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6403 2023-01-02 02:56:09,694 - mmcls - INFO - Epoch [81][1500/5005] lr: 1.000e-03, eta: 2:25:41, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6451 2023-01-02 02:56:18,361 - mmcls - INFO - Epoch [81][1600/5005] lr: 1.000e-03, eta: 2:25:32, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6444 2023-01-02 02:56:27,087 - mmcls - INFO - Epoch [81][1700/5005] lr: 1.000e-03, eta: 2:25:23, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6491 2023-01-02 02:56:35,802 - mmcls - INFO - Epoch [81][1800/5005] lr: 1.000e-03, eta: 2:25:14, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6005 2023-01-02 02:56:44,900 - mmcls - INFO - Epoch [81][1900/5005] lr: 1.000e-03, eta: 2:25:05, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6395 2023-01-02 02:56:53,645 - mmcls - INFO - Epoch [81][2000/5005] lr: 1.000e-03, eta: 2:24:56, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6403 2023-01-02 02:57:02,496 - mmcls - INFO - Epoch [81][2100/5005] lr: 1.000e-03, eta: 2:24:48, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6303 2023-01-02 02:57:11,015 - mmcls - INFO - Epoch [81][2200/5005] lr: 1.000e-03, eta: 2:24:39, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6268 2023-01-02 02:57:20,194 - mmcls - INFO - Epoch [81][2300/5005] lr: 1.000e-03, eta: 2:24:30, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6412 2023-01-02 02:57:29,023 - mmcls - INFO - Epoch [81][2400/5005] lr: 1.000e-03, eta: 2:24:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6555 2023-01-02 02:57:37,882 - mmcls - INFO - Epoch [81][2500/5005] lr: 1.000e-03, eta: 2:24:12, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6249 2023-01-02 02:57:46,994 - mmcls - INFO - Epoch [81][2600/5005] lr: 1.000e-03, eta: 2:24:03, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6266 2023-01-02 02:57:55,900 - mmcls - INFO - Epoch [81][2700/5005] lr: 1.000e-03, eta: 2:23:54, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6337 2023-01-02 02:58:04,760 - mmcls - INFO - Epoch [81][2800/5005] lr: 1.000e-03, eta: 2:23:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6085 2023-01-02 02:58:13,592 - mmcls - INFO - Epoch [81][2900/5005] lr: 1.000e-03, eta: 2:23:37, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6353 2023-01-02 02:58:22,538 - mmcls - INFO - Epoch [81][3000/5005] lr: 1.000e-03, eta: 2:23:28, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6525 2023-01-02 02:58:31,362 - mmcls - INFO - Epoch [81][3100/5005] lr: 1.000e-03, eta: 2:23:19, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6143 2023-01-02 02:58:39,982 - mmcls - INFO - Epoch [81][3200/5005] lr: 1.000e-03, eta: 2:23:10, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6477 2023-01-02 02:58:48,674 - mmcls - INFO - Epoch [81][3300/5005] lr: 1.000e-03, eta: 2:23:01, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6525 2023-01-02 02:58:57,323 - mmcls - INFO - Epoch [81][3400/5005] lr: 1.000e-03, eta: 2:22:52, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6313 2023-01-02 02:59:06,238 - mmcls - INFO - Epoch [81][3500/5005] lr: 1.000e-03, eta: 2:22:43, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6293 2023-01-02 02:59:15,177 - mmcls - INFO - Epoch [81][3600/5005] lr: 1.000e-03, eta: 2:22:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6677 2023-01-02 02:59:23,970 - mmcls - INFO - Epoch [81][3700/5005] lr: 1.000e-03, eta: 2:22:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6272 2023-01-02 02:59:32,743 - mmcls - INFO - Epoch [81][3800/5005] lr: 1.000e-03, eta: 2:22:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6554 2023-01-02 02:59:41,392 - mmcls - INFO - Epoch [81][3900/5005] lr: 1.000e-03, eta: 2:22:08, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6137 2023-01-02 02:59:49,973 - mmcls - INFO - Epoch [81][4000/5005] lr: 1.000e-03, eta: 2:21:59, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6514 2023-01-02 02:59:58,762 - mmcls - INFO - Epoch [81][4100/5005] lr: 1.000e-03, eta: 2:21:50, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6536 2023-01-02 03:00:07,817 - mmcls - INFO - Epoch [81][4200/5005] lr: 1.000e-03, eta: 2:21:41, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.6399 2023-01-02 03:00:16,575 - mmcls - INFO - Epoch [81][4300/5005] lr: 1.000e-03, eta: 2:21:32, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6394 2023-01-02 03:00:25,261 - mmcls - INFO - Epoch [81][4400/5005] lr: 1.000e-03, eta: 2:21:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6468 2023-01-02 03:00:34,094 - mmcls - INFO - Epoch [81][4500/5005] lr: 1.000e-03, eta: 2:21:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6427 2023-01-02 03:00:43,174 - mmcls - INFO - Epoch [81][4600/5005] lr: 1.000e-03, eta: 2:21:06, time: 0.091, data_time: 0.024, memory: 1224, loss: 1.6276 2023-01-02 03:00:51,746 - mmcls - INFO - Epoch [81][4700/5005] lr: 1.000e-03, eta: 2:20:57, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6528 2023-01-02 03:01:00,370 - mmcls - INFO - Epoch [81][4800/5005] lr: 1.000e-03, eta: 2:20:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6386 2023-01-02 03:01:09,094 - mmcls - INFO - Epoch [81][4900/5005] lr: 1.000e-03, eta: 2:20:39, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6545 2023-01-02 03:01:17,845 - mmcls - INFO - Epoch [81][5000/5005] lr: 1.000e-03, eta: 2:20:30, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6526 2023-01-02 03:01:40,521 - mmcls - INFO - Epoch(val) [81][196] accuracy_top-1: 64.7660, accuracy_top-5: 85.3940 2023-01-02 03:01:51,569 - mmcls - INFO - Epoch [82][100/5005] lr: 1.000e-03, eta: 2:20:21, time: 0.110, data_time: 0.049, memory: 1224, loss: 1.6287 2023-01-02 03:02:01,080 - mmcls - INFO - Epoch [82][200/5005] lr: 1.000e-03, eta: 2:20:12, time: 0.095, data_time: 0.010, memory: 1224, loss: 1.6244 2023-01-02 03:02:09,981 - mmcls - INFO - Epoch [82][300/5005] lr: 1.000e-03, eta: 2:20:04, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6210 2023-01-02 03:02:18,790 - mmcls - INFO - Epoch [82][400/5005] lr: 1.000e-03, eta: 2:19:55, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6265 2023-01-02 03:02:27,695 - mmcls - INFO - Epoch [82][500/5005] lr: 1.000e-03, eta: 2:19:46, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6180 2023-01-02 03:02:36,212 - mmcls - INFO - Epoch [82][600/5005] lr: 1.000e-03, eta: 2:19:37, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6230 2023-01-02 03:02:44,940 - mmcls - INFO - Epoch [82][700/5005] lr: 1.000e-03, eta: 2:19:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6253 2023-01-02 03:02:53,829 - mmcls - INFO - Epoch [82][800/5005] lr: 1.000e-03, eta: 2:19:19, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6295 2023-01-02 03:03:02,423 - mmcls - INFO - Epoch [82][900/5005] lr: 1.000e-03, eta: 2:19:10, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6210 2023-01-02 03:03:10,947 - mmcls - INFO - Epoch [82][1000/5005] lr: 1.000e-03, eta: 2:19:01, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6474 2023-01-02 03:03:19,825 - mmcls - INFO - Epoch [82][1100/5005] lr: 1.000e-03, eta: 2:18:52, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6163 2023-01-02 03:03:28,798 - mmcls - INFO - Epoch [82][1200/5005] lr: 1.000e-03, eta: 2:18:44, time: 0.090, data_time: 0.031, memory: 1224, loss: 1.6447 2023-01-02 03:03:37,485 - mmcls - INFO - Epoch [82][1300/5005] lr: 1.000e-03, eta: 2:18:35, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6268 2023-01-02 03:03:46,295 - mmcls - INFO - Epoch [82][1400/5005] lr: 1.000e-03, eta: 2:18:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6671 2023-01-02 03:03:55,016 - mmcls - INFO - Epoch [82][1500/5005] lr: 1.000e-03, eta: 2:18:17, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6387 2023-01-02 03:04:03,904 - mmcls - INFO - Epoch [82][1600/5005] lr: 1.000e-03, eta: 2:18:08, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6320 2023-01-02 03:04:12,835 - mmcls - INFO - Epoch [82][1700/5005] lr: 1.000e-03, eta: 2:17:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6382 2023-01-02 03:04:21,672 - mmcls - INFO - Epoch [82][1800/5005] lr: 1.000e-03, eta: 2:17:50, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6317 2023-01-02 03:04:30,430 - mmcls - INFO - Epoch [82][1900/5005] lr: 1.000e-03, eta: 2:17:41, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6297 2023-01-02 03:04:39,288 - mmcls - INFO - Epoch [82][2000/5005] lr: 1.000e-03, eta: 2:17:33, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6219 2023-01-02 03:04:47,931 - mmcls - INFO - Epoch [82][2100/5005] lr: 1.000e-03, eta: 2:17:24, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6114 2023-01-02 03:04:56,648 - mmcls - INFO - Epoch [82][2200/5005] lr: 1.000e-03, eta: 2:17:15, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6294 2023-01-02 03:05:05,421 - mmcls - INFO - Epoch [82][2300/5005] lr: 1.000e-03, eta: 2:17:06, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6349 2023-01-02 03:05:14,173 - mmcls - INFO - Epoch [82][2400/5005] lr: 1.000e-03, eta: 2:16:57, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6478 2023-01-02 03:05:23,290 - mmcls - INFO - Epoch [82][2500/5005] lr: 1.000e-03, eta: 2:16:48, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6432 2023-01-02 03:05:31,966 - mmcls - INFO - Epoch [82][2600/5005] lr: 1.000e-03, eta: 2:16:39, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6333 2023-01-02 03:05:40,878 - mmcls - INFO - Epoch [82][2700/5005] lr: 1.000e-03, eta: 2:16:30, time: 0.089, data_time: 0.035, memory: 1224, loss: 1.6463 2023-01-02 03:05:49,629 - mmcls - INFO - Epoch [82][2800/5005] lr: 1.000e-03, eta: 2:16:21, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6385 2023-01-02 03:05:58,352 - mmcls - INFO - Epoch [82][2900/5005] lr: 1.000e-03, eta: 2:16:13, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6387 2023-01-02 03:06:07,166 - mmcls - INFO - Epoch [82][3000/5005] lr: 1.000e-03, eta: 2:16:04, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6479 2023-01-02 03:06:16,497 - mmcls - INFO - Epoch [82][3100/5005] lr: 1.000e-03, eta: 2:15:55, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.6351 2023-01-02 03:06:25,018 - mmcls - INFO - Epoch [82][3200/5005] lr: 1.000e-03, eta: 2:15:46, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6401 2023-01-02 03:06:34,160 - mmcls - INFO - Epoch [82][3300/5005] lr: 1.000e-03, eta: 2:15:37, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.6333 2023-01-02 03:06:42,932 - mmcls - INFO - Epoch [82][3400/5005] lr: 1.000e-03, eta: 2:15:28, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6251 2023-01-02 03:06:51,621 - mmcls - INFO - Epoch [82][3500/5005] lr: 1.000e-03, eta: 2:15:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6405 2023-01-02 03:07:00,405 - mmcls - INFO - Epoch [82][3600/5005] lr: 1.000e-03, eta: 2:15:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6261 2023-01-02 03:07:09,231 - mmcls - INFO - Epoch [82][3700/5005] lr: 1.000e-03, eta: 2:15:02, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6268 2023-01-02 03:07:18,012 - mmcls - INFO - Epoch [82][3800/5005] lr: 1.000e-03, eta: 2:14:53, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6182 2023-01-02 03:07:26,857 - mmcls - INFO - Epoch [82][3900/5005] lr: 1.000e-03, eta: 2:14:44, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6366 2023-01-02 03:07:35,506 - mmcls - INFO - Epoch [82][4000/5005] lr: 1.000e-03, eta: 2:14:35, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6623 2023-01-02 03:07:44,244 - mmcls - INFO - Epoch [82][4100/5005] lr: 1.000e-03, eta: 2:14:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6393 2023-01-02 03:07:52,944 - mmcls - INFO - Epoch [82][4200/5005] lr: 1.000e-03, eta: 2:14:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6479 2023-01-02 03:08:01,897 - mmcls - INFO - Epoch [82][4300/5005] lr: 1.000e-03, eta: 2:14:08, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6416 2023-01-02 03:08:10,522 - mmcls - INFO - Epoch [82][4400/5005] lr: 1.000e-03, eta: 2:13:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6580 2023-01-02 03:08:19,233 - mmcls - INFO - Epoch [82][4500/5005] lr: 1.000e-03, eta: 2:13:50, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6385 2023-01-02 03:08:28,342 - mmcls - INFO - Epoch [82][4600/5005] lr: 1.000e-03, eta: 2:13:42, time: 0.091, data_time: 0.049, memory: 1224, loss: 1.6286 2023-01-02 03:08:37,191 - mmcls - INFO - Epoch [82][4700/5005] lr: 1.000e-03, eta: 2:13:33, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6670 2023-01-02 03:08:45,918 - mmcls - INFO - Epoch [82][4800/5005] lr: 1.000e-03, eta: 2:13:24, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6585 2023-01-02 03:08:54,763 - mmcls - INFO - Epoch [82][4900/5005] lr: 1.000e-03, eta: 2:13:15, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6501 2023-01-02 03:09:03,758 - mmcls - INFO - Epoch [82][5000/5005] lr: 1.000e-03, eta: 2:13:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6276 2023-01-02 03:09:26,393 - mmcls - INFO - Epoch(val) [82][196] accuracy_top-1: 64.7200, accuracy_top-5: 85.4040 2023-01-02 03:09:37,539 - mmcls - INFO - Epoch [83][100/5005] lr: 1.000e-03, eta: 2:12:57, time: 0.111, data_time: 0.036, memory: 1224, loss: 1.6035 2023-01-02 03:09:46,280 - mmcls - INFO - Epoch [83][200/5005] lr: 1.000e-03, eta: 2:12:48, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6344 2023-01-02 03:09:55,355 - mmcls - INFO - Epoch [83][300/5005] lr: 1.000e-03, eta: 2:12:40, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6015 2023-01-02 03:10:04,171 - mmcls - INFO - Epoch [83][400/5005] lr: 1.000e-03, eta: 2:12:31, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.6067 2023-01-02 03:10:12,813 - mmcls - INFO - Epoch [83][500/5005] lr: 1.000e-03, eta: 2:12:22, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.6088 2023-01-02 03:10:21,774 - mmcls - INFO - Epoch [83][600/5005] lr: 1.000e-03, eta: 2:12:13, time: 0.090, data_time: 0.034, memory: 1224, loss: 1.6605 2023-01-02 03:10:30,427 - mmcls - INFO - Epoch [83][700/5005] lr: 1.000e-03, eta: 2:12:04, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6228 2023-01-02 03:10:39,119 - mmcls - INFO - Epoch [83][800/5005] lr: 1.000e-03, eta: 2:11:55, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6322 2023-01-02 03:10:47,985 - mmcls - INFO - Epoch [83][900/5005] lr: 1.000e-03, eta: 2:11:46, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6273 2023-01-02 03:10:56,536 - mmcls - INFO - Epoch [83][1000/5005] lr: 1.000e-03, eta: 2:11:37, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.6261 2023-01-02 03:11:05,575 - mmcls - INFO - Epoch [83][1100/5005] lr: 1.000e-03, eta: 2:11:29, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.6184 2023-01-02 03:11:14,424 - mmcls - INFO - Epoch [83][1200/5005] lr: 1.000e-03, eta: 2:11:20, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6226 2023-01-02 03:11:23,424 - mmcls - INFO - Epoch [83][1300/5005] lr: 1.000e-03, eta: 2:11:11, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6296 2023-01-02 03:11:32,220 - mmcls - INFO - Epoch [83][1400/5005] lr: 1.000e-03, eta: 2:11:02, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6185 2023-01-02 03:11:41,155 - mmcls - INFO - Epoch [83][1500/5005] lr: 1.000e-03, eta: 2:10:53, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6359 2023-01-02 03:11:49,933 - mmcls - INFO - Epoch [83][1600/5005] lr: 1.000e-03, eta: 2:10:44, time: 0.088, data_time: 0.038, memory: 1224, loss: 1.6410 2023-01-02 03:11:58,955 - mmcls - INFO - Epoch [83][1700/5005] lr: 1.000e-03, eta: 2:10:35, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6258 2023-01-02 03:12:07,780 - mmcls - INFO - Epoch [83][1800/5005] lr: 1.000e-03, eta: 2:10:26, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6239 2023-01-02 03:12:16,830 - mmcls - INFO - Epoch [83][1900/5005] lr: 1.000e-03, eta: 2:10:18, time: 0.090, data_time: 0.038, memory: 1224, loss: 1.6425 2023-01-02 03:12:25,587 - mmcls - INFO - Epoch [83][2000/5005] lr: 1.000e-03, eta: 2:10:09, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6225 2023-01-02 03:12:34,385 - mmcls - INFO - Epoch [83][2100/5005] lr: 1.000e-03, eta: 2:10:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6415 2023-01-02 03:12:43,028 - mmcls - INFO - Epoch [83][2200/5005] lr: 1.000e-03, eta: 2:09:51, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6649 2023-01-02 03:12:51,713 - mmcls - INFO - Epoch [83][2300/5005] lr: 1.000e-03, eta: 2:09:42, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6377 2023-01-02 03:13:00,386 - mmcls - INFO - Epoch [83][2400/5005] lr: 1.000e-03, eta: 2:09:33, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6514 2023-01-02 03:13:09,432 - mmcls - INFO - Epoch [83][2500/5005] lr: 1.000e-03, eta: 2:09:24, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6385 2023-01-02 03:13:18,454 - mmcls - INFO - Epoch [83][2600/5005] lr: 1.000e-03, eta: 2:09:16, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6263 2023-01-02 03:13:27,243 - mmcls - INFO - Epoch [83][2700/5005] lr: 1.000e-03, eta: 2:09:07, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6349 2023-01-02 03:13:35,932 - mmcls - INFO - Epoch [83][2800/5005] lr: 1.000e-03, eta: 2:08:58, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6305 2023-01-02 03:13:45,139 - mmcls - INFO - Epoch [83][2900/5005] lr: 1.000e-03, eta: 2:08:49, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6445 2023-01-02 03:13:53,729 - mmcls - INFO - Epoch [83][3000/5005] lr: 1.000e-03, eta: 2:08:40, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6402 2023-01-02 03:14:02,961 - mmcls - INFO - Epoch [83][3100/5005] lr: 1.000e-03, eta: 2:08:31, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6125 2023-01-02 03:14:11,652 - mmcls - INFO - Epoch [83][3200/5005] lr: 1.000e-03, eta: 2:08:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6359 2023-01-02 03:14:20,216 - mmcls - INFO - Epoch [83][3300/5005] lr: 1.000e-03, eta: 2:08:13, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6348 2023-01-02 03:14:28,935 - mmcls - INFO - Epoch [83][3400/5005] lr: 1.000e-03, eta: 2:08:05, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6638 2023-01-02 03:14:37,735 - mmcls - INFO - Epoch [83][3500/5005] lr: 1.000e-03, eta: 2:07:56, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6436 2023-01-02 03:14:46,952 - mmcls - INFO - Epoch [83][3600/5005] lr: 1.000e-03, eta: 2:07:47, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6437 2023-01-02 03:14:55,993 - mmcls - INFO - Epoch [83][3700/5005] lr: 1.000e-03, eta: 2:07:38, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6509 2023-01-02 03:15:04,540 - mmcls - INFO - Epoch [83][3800/5005] lr: 1.000e-03, eta: 2:07:29, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6014 2023-01-02 03:15:13,273 - mmcls - INFO - Epoch [83][3900/5005] lr: 1.000e-03, eta: 2:07:20, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6522 2023-01-02 03:15:22,040 - mmcls - INFO - Epoch [83][4000/5005] lr: 1.000e-03, eta: 2:07:11, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6459 2023-01-02 03:15:30,782 - mmcls - INFO - Epoch [83][4100/5005] lr: 1.000e-03, eta: 2:07:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6224 2023-01-02 03:15:39,387 - mmcls - INFO - Epoch [83][4200/5005] lr: 1.000e-03, eta: 2:06:53, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6678 2023-01-02 03:15:48,521 - mmcls - INFO - Epoch [83][4300/5005] lr: 1.000e-03, eta: 2:06:45, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6276 2023-01-02 03:15:57,607 - mmcls - INFO - Epoch [83][4400/5005] lr: 1.000e-03, eta: 2:06:36, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6343 2023-01-02 03:16:06,468 - mmcls - INFO - Epoch [83][4500/5005] lr: 1.000e-03, eta: 2:06:27, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6312 2023-01-02 03:16:15,147 - mmcls - INFO - Epoch [83][4600/5005] lr: 1.000e-03, eta: 2:06:18, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6414 2023-01-02 03:16:24,105 - mmcls - INFO - Epoch [83][4700/5005] lr: 1.000e-03, eta: 2:06:09, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.6499 2023-01-02 03:16:32,656 - mmcls - INFO - Epoch [83][4800/5005] lr: 1.000e-03, eta: 2:06:00, time: 0.086, data_time: 0.028, memory: 1224, loss: 1.6213 2023-01-02 03:16:41,717 - mmcls - INFO - Epoch [83][4900/5005] lr: 1.000e-03, eta: 2:05:51, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6405 2023-01-02 03:16:50,655 - mmcls - INFO - Epoch [83][5000/5005] lr: 1.000e-03, eta: 2:05:43, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6274 2023-01-02 03:17:13,408 - mmcls - INFO - Epoch(val) [83][196] accuracy_top-1: 64.7820, accuracy_top-5: 85.3960 2023-01-02 03:17:24,365 - mmcls - INFO - Epoch [84][100/5005] lr: 1.000e-03, eta: 2:05:34, time: 0.109, data_time: 0.057, memory: 1224, loss: 1.6256 2023-01-02 03:17:33,379 - mmcls - INFO - Epoch [84][200/5005] lr: 1.000e-03, eta: 2:05:25, time: 0.090, data_time: 0.045, memory: 1224, loss: 1.6095 2023-01-02 03:17:42,072 - mmcls - INFO - Epoch [84][300/5005] lr: 1.000e-03, eta: 2:05:16, time: 0.087, data_time: 0.039, memory: 1224, loss: 1.6195 2023-01-02 03:17:51,207 - mmcls - INFO - Epoch [84][400/5005] lr: 1.000e-03, eta: 2:05:07, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6344 2023-01-02 03:17:59,979 - mmcls - INFO - Epoch [84][500/5005] lr: 1.000e-03, eta: 2:04:58, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6251 2023-01-02 03:18:08,947 - mmcls - INFO - Epoch [84][600/5005] lr: 1.000e-03, eta: 2:04:49, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6377 2023-01-02 03:18:17,823 - mmcls - INFO - Epoch [84][700/5005] lr: 1.000e-03, eta: 2:04:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6492 2023-01-02 03:18:26,563 - mmcls - INFO - Epoch [84][800/5005] lr: 1.000e-03, eta: 2:04:32, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6326 2023-01-02 03:18:35,542 - mmcls - INFO - Epoch [84][900/5005] lr: 1.000e-03, eta: 2:04:23, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6143 2023-01-02 03:18:44,457 - mmcls - INFO - Epoch [84][1000/5005] lr: 1.000e-03, eta: 2:04:14, time: 0.089, data_time: 0.038, memory: 1224, loss: 1.6289 2023-01-02 03:18:53,079 - mmcls - INFO - Epoch [84][1100/5005] lr: 1.000e-03, eta: 2:04:05, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6356 2023-01-02 03:19:02,098 - mmcls - INFO - Epoch [84][1200/5005] lr: 1.000e-03, eta: 2:03:56, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6286 2023-01-02 03:19:10,812 - mmcls - INFO - Epoch [84][1300/5005] lr: 1.000e-03, eta: 2:03:47, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6279 2023-01-02 03:19:19,762 - mmcls - INFO - Epoch [84][1400/5005] lr: 1.000e-03, eta: 2:03:38, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6307 2023-01-02 03:19:28,447 - mmcls - INFO - Epoch [84][1500/5005] lr: 1.000e-03, eta: 2:03:30, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5950 2023-01-02 03:19:37,255 - mmcls - INFO - Epoch [84][1600/5005] lr: 1.000e-03, eta: 2:03:21, time: 0.088, data_time: 0.040, memory: 1224, loss: 1.6349 2023-01-02 03:19:46,311 - mmcls - INFO - Epoch [84][1700/5005] lr: 1.000e-03, eta: 2:03:12, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6271 2023-01-02 03:19:54,934 - mmcls - INFO - Epoch [84][1800/5005] lr: 1.000e-03, eta: 2:03:03, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6100 2023-01-02 03:20:03,686 - mmcls - INFO - Epoch [84][1900/5005] lr: 1.000e-03, eta: 2:02:54, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6276 2023-01-02 03:20:12,958 - mmcls - INFO - Epoch [84][2000/5005] lr: 1.000e-03, eta: 2:02:45, time: 0.093, data_time: 0.010, memory: 1224, loss: 1.6232 2023-01-02 03:20:21,710 - mmcls - INFO - Epoch [84][2100/5005] lr: 1.000e-03, eta: 2:02:36, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6354 2023-01-02 03:20:30,260 - mmcls - INFO - Epoch [84][2200/5005] lr: 1.000e-03, eta: 2:02:27, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6251 2023-01-02 03:20:39,318 - mmcls - INFO - Epoch [84][2300/5005] lr: 1.000e-03, eta: 2:02:19, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6279 2023-01-02 03:20:48,256 - mmcls - INFO - Epoch [84][2400/5005] lr: 1.000e-03, eta: 2:02:10, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6301 2023-01-02 03:20:56,828 - mmcls - INFO - Epoch [84][2500/5005] lr: 1.000e-03, eta: 2:02:01, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6175 2023-01-02 03:21:05,657 - mmcls - INFO - Epoch [84][2600/5005] lr: 1.000e-03, eta: 2:01:52, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6281 2023-01-02 03:21:14,574 - mmcls - INFO - Epoch [84][2700/5005] lr: 1.000e-03, eta: 2:01:43, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6403 2023-01-02 03:21:23,417 - mmcls - INFO - Epoch [84][2800/5005] lr: 1.000e-03, eta: 2:01:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6109 2023-01-02 03:21:32,352 - mmcls - INFO - Epoch [84][2900/5005] lr: 1.000e-03, eta: 2:01:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6357 2023-01-02 03:21:41,144 - mmcls - INFO - Epoch [84][3000/5005] lr: 1.000e-03, eta: 2:01:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6242 2023-01-02 03:21:50,067 - mmcls - INFO - Epoch [84][3100/5005] lr: 1.000e-03, eta: 2:01:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6193 2023-01-02 03:21:58,810 - mmcls - INFO - Epoch [84][3200/5005] lr: 1.000e-03, eta: 2:00:59, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6314 2023-01-02 03:22:07,598 - mmcls - INFO - Epoch [84][3300/5005] lr: 1.000e-03, eta: 2:00:50, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6499 2023-01-02 03:22:16,495 - mmcls - INFO - Epoch [84][3400/5005] lr: 1.000e-03, eta: 2:00:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6393 2023-01-02 03:22:25,221 - mmcls - INFO - Epoch [84][3500/5005] lr: 1.000e-03, eta: 2:00:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6380 2023-01-02 03:22:34,007 - mmcls - INFO - Epoch [84][3600/5005] lr: 1.000e-03, eta: 2:00:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6168 2023-01-02 03:22:42,814 - mmcls - INFO - Epoch [84][3700/5005] lr: 1.000e-03, eta: 2:00:14, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6440 2023-01-02 03:22:51,690 - mmcls - INFO - Epoch [84][3800/5005] lr: 1.000e-03, eta: 2:00:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6370 2023-01-02 03:23:00,389 - mmcls - INFO - Epoch [84][3900/5005] lr: 1.000e-03, eta: 1:59:57, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6380 2023-01-02 03:23:09,107 - mmcls - INFO - Epoch [84][4000/5005] lr: 1.000e-03, eta: 1:59:48, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6297 2023-01-02 03:23:17,985 - mmcls - INFO - Epoch [84][4100/5005] lr: 1.000e-03, eta: 1:59:39, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6399 2023-01-02 03:23:26,701 - mmcls - INFO - Epoch [84][4200/5005] lr: 1.000e-03, eta: 1:59:30, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.6387 2023-01-02 03:23:35,435 - mmcls - INFO - Epoch [84][4300/5005] lr: 1.000e-03, eta: 1:59:21, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6486 2023-01-02 03:23:43,887 - mmcls - INFO - Epoch [84][4400/5005] lr: 1.000e-03, eta: 1:59:12, time: 0.084, data_time: 0.021, memory: 1224, loss: 1.6440 2023-01-02 03:23:52,636 - mmcls - INFO - Epoch [84][4500/5005] lr: 1.000e-03, eta: 1:59:03, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6533 2023-01-02 03:24:01,546 - mmcls - INFO - Epoch [84][4600/5005] lr: 1.000e-03, eta: 1:58:54, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6531 2023-01-02 03:24:10,475 - mmcls - INFO - Epoch [84][4700/5005] lr: 1.000e-03, eta: 1:58:46, time: 0.089, data_time: 0.037, memory: 1224, loss: 1.6394 2023-01-02 03:24:19,137 - mmcls - INFO - Epoch [84][4800/5005] lr: 1.000e-03, eta: 1:58:37, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.6741 2023-01-02 03:24:28,074 - mmcls - INFO - Epoch [84][4900/5005] lr: 1.000e-03, eta: 1:58:28, time: 0.089, data_time: 0.033, memory: 1224, loss: 1.6522 2023-01-02 03:24:36,808 - mmcls - INFO - Epoch [84][5000/5005] lr: 1.000e-03, eta: 1:58:19, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.6601 2023-01-02 03:24:59,423 - mmcls - INFO - Epoch(val) [84][196] accuracy_top-1: 64.8040, accuracy_top-5: 85.4840 2023-01-02 03:25:10,826 - mmcls - INFO - Epoch [85][100/5005] lr: 1.000e-03, eta: 1:58:10, time: 0.114, data_time: 0.046, memory: 1224, loss: 1.6264 2023-01-02 03:25:19,726 - mmcls - INFO - Epoch [85][200/5005] lr: 1.000e-03, eta: 1:58:01, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6372 2023-01-02 03:25:28,404 - mmcls - INFO - Epoch [85][300/5005] lr: 1.000e-03, eta: 1:57:52, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.6223 2023-01-02 03:25:37,117 - mmcls - INFO - Epoch [85][400/5005] lr: 1.000e-03, eta: 1:57:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6292 2023-01-02 03:25:46,015 - mmcls - INFO - Epoch [85][500/5005] lr: 1.000e-03, eta: 1:57:34, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6118 2023-01-02 03:25:55,015 - mmcls - INFO - Epoch [85][600/5005] lr: 1.000e-03, eta: 1:57:26, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6333 2023-01-02 03:26:03,626 - mmcls - INFO - Epoch [85][700/5005] lr: 1.000e-03, eta: 1:57:17, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6364 2023-01-02 03:26:12,026 - mmcls - INFO - Epoch [85][800/5005] lr: 1.000e-03, eta: 1:57:08, time: 0.084, data_time: 0.028, memory: 1224, loss: 1.6245 2023-01-02 03:26:20,774 - mmcls - INFO - Epoch [85][900/5005] lr: 1.000e-03, eta: 1:56:59, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6473 2023-01-02 03:26:29,751 - mmcls - INFO - Epoch [85][1000/5005] lr: 1.000e-03, eta: 1:56:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6301 2023-01-02 03:26:38,350 - mmcls - INFO - Epoch [85][1100/5005] lr: 1.000e-03, eta: 1:56:41, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6122 2023-01-02 03:26:47,202 - mmcls - INFO - Epoch [85][1200/5005] lr: 1.000e-03, eta: 1:56:32, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6311 2023-01-02 03:26:55,867 - mmcls - INFO - Epoch [85][1300/5005] lr: 1.000e-03, eta: 1:56:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6205 2023-01-02 03:27:05,042 - mmcls - INFO - Epoch [85][1400/5005] lr: 1.000e-03, eta: 1:56:15, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6253 2023-01-02 03:27:13,692 - mmcls - INFO - Epoch [85][1500/5005] lr: 1.000e-03, eta: 1:56:06, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.6197 2023-01-02 03:27:22,637 - mmcls - INFO - Epoch [85][1600/5005] lr: 1.000e-03, eta: 1:55:57, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.6158 2023-01-02 03:27:31,326 - mmcls - INFO - Epoch [85][1700/5005] lr: 1.000e-03, eta: 1:55:48, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6096 2023-01-02 03:27:40,136 - mmcls - INFO - Epoch [85][1800/5005] lr: 1.000e-03, eta: 1:55:39, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6421 2023-01-02 03:27:49,105 - mmcls - INFO - Epoch [85][1900/5005] lr: 1.000e-03, eta: 1:55:30, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6188 2023-01-02 03:27:58,313 - mmcls - INFO - Epoch [85][2000/5005] lr: 1.000e-03, eta: 1:55:21, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.6057 2023-01-02 03:28:06,993 - mmcls - INFO - Epoch [85][2100/5005] lr: 1.000e-03, eta: 1:55:12, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6161 2023-01-02 03:28:15,786 - mmcls - INFO - Epoch [85][2200/5005] lr: 1.000e-03, eta: 1:55:04, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6206 2023-01-02 03:28:24,673 - mmcls - INFO - Epoch [85][2300/5005] lr: 1.000e-03, eta: 1:54:55, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6135 2023-01-02 03:28:33,584 - mmcls - INFO - Epoch [85][2400/5005] lr: 1.000e-03, eta: 1:54:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6386 2023-01-02 03:28:42,486 - mmcls - INFO - Epoch [85][2500/5005] lr: 1.000e-03, eta: 1:54:37, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6435 2023-01-02 03:28:51,610 - mmcls - INFO - Epoch [85][2600/5005] lr: 1.000e-03, eta: 1:54:28, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6115 2023-01-02 03:29:00,343 - mmcls - INFO - Epoch [85][2700/5005] lr: 1.000e-03, eta: 1:54:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6262 2023-01-02 03:29:08,936 - mmcls - INFO - Epoch [85][2800/5005] lr: 1.000e-03, eta: 1:54:10, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6350 2023-01-02 03:29:17,725 - mmcls - INFO - Epoch [85][2900/5005] lr: 1.000e-03, eta: 1:54:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6267 2023-01-02 03:29:26,515 - mmcls - INFO - Epoch [85][3000/5005] lr: 1.000e-03, eta: 1:53:53, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6248 2023-01-02 03:29:35,766 - mmcls - INFO - Epoch [85][3100/5005] lr: 1.000e-03, eta: 1:53:44, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6391 2023-01-02 03:29:44,462 - mmcls - INFO - Epoch [85][3200/5005] lr: 1.000e-03, eta: 1:53:35, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6309 2023-01-02 03:29:53,301 - mmcls - INFO - Epoch [85][3300/5005] lr: 1.000e-03, eta: 1:53:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6522 2023-01-02 03:30:02,049 - mmcls - INFO - Epoch [85][3400/5005] lr: 1.000e-03, eta: 1:53:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6286 2023-01-02 03:30:10,826 - mmcls - INFO - Epoch [85][3500/5005] lr: 1.000e-03, eta: 1:53:08, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6298 2023-01-02 03:30:19,587 - mmcls - INFO - Epoch [85][3600/5005] lr: 1.000e-03, eta: 1:52:59, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6658 2023-01-02 03:30:28,283 - mmcls - INFO - Epoch [85][3700/5005] lr: 1.000e-03, eta: 1:52:51, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.5881 2023-01-02 03:30:37,069 - mmcls - INFO - Epoch [85][3800/5005] lr: 1.000e-03, eta: 1:52:42, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6531 2023-01-02 03:30:46,020 - mmcls - INFO - Epoch [85][3900/5005] lr: 1.000e-03, eta: 1:52:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6282 2023-01-02 03:30:54,946 - mmcls - INFO - Epoch [85][4000/5005] lr: 1.000e-03, eta: 1:52:24, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6228 2023-01-02 03:31:03,926 - mmcls - INFO - Epoch [85][4100/5005] lr: 1.000e-03, eta: 1:52:15, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6527 2023-01-02 03:31:12,734 - mmcls - INFO - Epoch [85][4200/5005] lr: 1.000e-03, eta: 1:52:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6218 2023-01-02 03:31:21,449 - mmcls - INFO - Epoch [85][4300/5005] lr: 1.000e-03, eta: 1:51:57, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6377 2023-01-02 03:31:30,066 - mmcls - INFO - Epoch [85][4400/5005] lr: 1.000e-03, eta: 1:51:48, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6373 2023-01-02 03:31:38,799 - mmcls - INFO - Epoch [85][4500/5005] lr: 1.000e-03, eta: 1:51:40, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6357 2023-01-02 03:31:47,573 - mmcls - INFO - Epoch [85][4600/5005] lr: 1.000e-03, eta: 1:51:31, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6368 2023-01-02 03:31:56,596 - mmcls - INFO - Epoch [85][4700/5005] lr: 1.000e-03, eta: 1:51:22, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6516 2023-01-02 03:32:05,458 - mmcls - INFO - Epoch [85][4800/5005] lr: 1.000e-03, eta: 1:51:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6316 2023-01-02 03:32:14,235 - mmcls - INFO - Epoch [85][4900/5005] lr: 1.000e-03, eta: 1:51:04, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6407 2023-01-02 03:32:22,770 - mmcls - INFO - Epoch [85][5000/5005] lr: 1.000e-03, eta: 1:50:55, time: 0.085, data_time: 0.023, memory: 1224, loss: 1.6545 2023-01-02 03:32:45,345 - mmcls - INFO - Epoch(val) [85][196] accuracy_top-1: 64.6760, accuracy_top-5: 85.3460 2023-01-02 03:32:56,112 - mmcls - INFO - Epoch [86][100/5005] lr: 1.000e-03, eta: 1:50:46, time: 0.107, data_time: 0.044, memory: 1224, loss: 1.6182 2023-01-02 03:33:05,423 - mmcls - INFO - Epoch [86][200/5005] lr: 1.000e-03, eta: 1:50:37, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.6036 2023-01-02 03:33:14,182 - mmcls - INFO - Epoch [86][300/5005] lr: 1.000e-03, eta: 1:50:28, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5935 2023-01-02 03:33:22,985 - mmcls - INFO - Epoch [86][400/5005] lr: 1.000e-03, eta: 1:50:20, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6237 2023-01-02 03:33:31,783 - mmcls - INFO - Epoch [86][500/5005] lr: 1.000e-03, eta: 1:50:11, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6152 2023-01-02 03:33:40,531 - mmcls - INFO - Epoch [86][600/5005] lr: 1.000e-03, eta: 1:50:02, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.6259 2023-01-02 03:33:49,141 - mmcls - INFO - Epoch [86][700/5005] lr: 1.000e-03, eta: 1:49:53, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6190 2023-01-02 03:33:57,935 - mmcls - INFO - Epoch [86][800/5005] lr: 1.000e-03, eta: 1:49:44, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6406 2023-01-02 03:34:06,762 - mmcls - INFO - Epoch [86][900/5005] lr: 1.000e-03, eta: 1:49:35, time: 0.088, data_time: 0.035, memory: 1224, loss: 1.6405 2023-01-02 03:34:15,756 - mmcls - INFO - Epoch [86][1000/5005] lr: 1.000e-03, eta: 1:49:26, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.6300 2023-01-02 03:34:24,690 - mmcls - INFO - Epoch [86][1100/5005] lr: 1.000e-03, eta: 1:49:17, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6489 2023-01-02 03:34:33,422 - mmcls - INFO - Epoch [86][1200/5005] lr: 1.000e-03, eta: 1:49:09, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6330 2023-01-02 03:34:42,512 - mmcls - INFO - Epoch [86][1300/5005] lr: 1.000e-03, eta: 1:49:00, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6103 2023-01-02 03:34:51,169 - mmcls - INFO - Epoch [86][1400/5005] lr: 1.000e-03, eta: 1:48:51, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6165 2023-01-02 03:35:00,144 - mmcls - INFO - Epoch [86][1500/5005] lr: 1.000e-03, eta: 1:48:42, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6112 2023-01-02 03:35:09,048 - mmcls - INFO - Epoch [86][1600/5005] lr: 1.000e-03, eta: 1:48:33, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6368 2023-01-02 03:35:18,012 - mmcls - INFO - Epoch [86][1700/5005] lr: 1.000e-03, eta: 1:48:24, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6291 2023-01-02 03:35:26,609 - mmcls - INFO - Epoch [86][1800/5005] lr: 1.000e-03, eta: 1:48:15, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6429 2023-01-02 03:35:35,431 - mmcls - INFO - Epoch [86][1900/5005] lr: 1.000e-03, eta: 1:48:06, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6050 2023-01-02 03:35:43,839 - mmcls - INFO - Epoch [86][2000/5005] lr: 1.000e-03, eta: 1:47:58, time: 0.084, data_time: 0.004, memory: 1224, loss: 1.6483 2023-01-02 03:35:52,472 - mmcls - INFO - Epoch [86][2100/5005] lr: 1.000e-03, eta: 1:47:49, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6312 2023-01-02 03:36:01,387 - mmcls - INFO - Epoch [86][2200/5005] lr: 1.000e-03, eta: 1:47:40, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6331 2023-01-02 03:36:09,887 - mmcls - INFO - Epoch [86][2300/5005] lr: 1.000e-03, eta: 1:47:31, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.6254 2023-01-02 03:36:18,854 - mmcls - INFO - Epoch [86][2400/5005] lr: 1.000e-03, eta: 1:47:22, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6195 2023-01-02 03:36:27,501 - mmcls - INFO - Epoch [86][2500/5005] lr: 1.000e-03, eta: 1:47:13, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6041 2023-01-02 03:36:36,182 - mmcls - INFO - Epoch [86][2600/5005] lr: 1.000e-03, eta: 1:47:04, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6239 2023-01-02 03:36:45,245 - mmcls - INFO - Epoch [86][2700/5005] lr: 1.000e-03, eta: 1:46:55, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.6075 2023-01-02 03:36:54,106 - mmcls - INFO - Epoch [86][2800/5005] lr: 1.000e-03, eta: 1:46:46, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6036 2023-01-02 03:37:02,781 - mmcls - INFO - Epoch [86][2900/5005] lr: 1.000e-03, eta: 1:46:38, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6160 2023-01-02 03:37:11,491 - mmcls - INFO - Epoch [86][3000/5005] lr: 1.000e-03, eta: 1:46:29, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6185 2023-01-02 03:37:20,573 - mmcls - INFO - Epoch [86][3100/5005] lr: 1.000e-03, eta: 1:46:20, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.6314 2023-01-02 03:37:29,706 - mmcls - INFO - Epoch [86][3200/5005] lr: 1.000e-03, eta: 1:46:11, time: 0.091, data_time: 0.043, memory: 1224, loss: 1.6319 2023-01-02 03:37:38,676 - mmcls - INFO - Epoch [86][3300/5005] lr: 1.000e-03, eta: 1:46:02, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6534 2023-01-02 03:37:47,795 - mmcls - INFO - Epoch [86][3400/5005] lr: 1.000e-03, eta: 1:45:53, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.6218 2023-01-02 03:37:56,893 - mmcls - INFO - Epoch [86][3500/5005] lr: 1.000e-03, eta: 1:45:45, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.6167 2023-01-02 03:38:05,689 - mmcls - INFO - Epoch [86][3600/5005] lr: 1.000e-03, eta: 1:45:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6315 2023-01-02 03:38:14,503 - mmcls - INFO - Epoch [86][3700/5005] lr: 1.000e-03, eta: 1:45:27, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6218 2023-01-02 03:38:23,263 - mmcls - INFO - Epoch [86][3800/5005] lr: 1.000e-03, eta: 1:45:18, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6315 2023-01-02 03:38:32,241 - mmcls - INFO - Epoch [86][3900/5005] lr: 1.000e-03, eta: 1:45:09, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.6203 2023-01-02 03:38:40,864 - mmcls - INFO - Epoch [86][4000/5005] lr: 1.000e-03, eta: 1:45:00, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6344 2023-01-02 03:38:49,818 - mmcls - INFO - Epoch [86][4100/5005] lr: 1.000e-03, eta: 1:44:51, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6265 2023-01-02 03:38:58,635 - mmcls - INFO - Epoch [86][4200/5005] lr: 1.000e-03, eta: 1:44:42, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6435 2023-01-02 03:39:07,567 - mmcls - INFO - Epoch [86][4300/5005] lr: 1.000e-03, eta: 1:44:34, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6176 2023-01-02 03:39:16,377 - mmcls - INFO - Epoch [86][4400/5005] lr: 1.000e-03, eta: 1:44:25, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6227 2023-01-02 03:39:25,230 - mmcls - INFO - Epoch [86][4500/5005] lr: 1.000e-03, eta: 1:44:16, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6387 2023-01-02 03:39:34,019 - mmcls - INFO - Epoch [86][4600/5005] lr: 1.000e-03, eta: 1:44:07, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6336 2023-01-02 03:39:43,073 - mmcls - INFO - Epoch [86][4700/5005] lr: 1.000e-03, eta: 1:43:58, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6261 2023-01-02 03:39:51,851 - mmcls - INFO - Epoch [86][4800/5005] lr: 1.000e-03, eta: 1:43:49, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6544 2023-01-02 03:40:00,490 - mmcls - INFO - Epoch [86][4900/5005] lr: 1.000e-03, eta: 1:43:40, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6334 2023-01-02 03:40:09,315 - mmcls - INFO - Epoch [86][5000/5005] lr: 1.000e-03, eta: 1:43:31, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6547 2023-01-02 03:40:32,107 - mmcls - INFO - Epoch(val) [86][196] accuracy_top-1: 64.8300, accuracy_top-5: 85.5140 2023-01-02 03:40:43,568 - mmcls - INFO - Epoch [87][100/5005] lr: 1.000e-03, eta: 1:43:23, time: 0.114, data_time: 0.066, memory: 1224, loss: 1.6390 2023-01-02 03:40:52,588 - mmcls - INFO - Epoch [87][200/5005] lr: 1.000e-03, eta: 1:43:14, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6155 2023-01-02 03:41:01,186 - mmcls - INFO - Epoch [87][300/5005] lr: 1.000e-03, eta: 1:43:05, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6164 2023-01-02 03:41:10,422 - mmcls - INFO - Epoch [87][400/5005] lr: 1.000e-03, eta: 1:42:56, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.5939 2023-01-02 03:41:19,715 - mmcls - INFO - Epoch [87][500/5005] lr: 1.000e-03, eta: 1:42:47, time: 0.093, data_time: 0.013, memory: 1224, loss: 1.5971 2023-01-02 03:41:28,574 - mmcls - INFO - Epoch [87][600/5005] lr: 1.000e-03, eta: 1:42:38, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6329 2023-01-02 03:41:37,517 - mmcls - INFO - Epoch [87][700/5005] lr: 1.000e-03, eta: 1:42:29, time: 0.089, data_time: 0.033, memory: 1224, loss: 1.6233 2023-01-02 03:41:46,722 - mmcls - INFO - Epoch [87][800/5005] lr: 1.000e-03, eta: 1:42:21, time: 0.092, data_time: 0.027, memory: 1224, loss: 1.6266 2023-01-02 03:41:55,213 - mmcls - INFO - Epoch [87][900/5005] lr: 1.000e-03, eta: 1:42:12, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.5927 2023-01-02 03:42:04,232 - mmcls - INFO - Epoch [87][1000/5005] lr: 1.000e-03, eta: 1:42:03, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6217 2023-01-02 03:42:12,802 - mmcls - INFO - Epoch [87][1100/5005] lr: 1.000e-03, eta: 1:41:54, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6086 2023-01-02 03:42:21,385 - mmcls - INFO - Epoch [87][1200/5005] lr: 1.000e-03, eta: 1:41:45, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6155 2023-01-02 03:42:30,105 - mmcls - INFO - Epoch [87][1300/5005] lr: 1.000e-03, eta: 1:41:36, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6112 2023-01-02 03:42:38,966 - mmcls - INFO - Epoch [87][1400/5005] lr: 1.000e-03, eta: 1:41:27, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6367 2023-01-02 03:42:48,026 - mmcls - INFO - Epoch [87][1500/5005] lr: 1.000e-03, eta: 1:41:18, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.6289 2023-01-02 03:42:56,676 - mmcls - INFO - Epoch [87][1600/5005] lr: 1.000e-03, eta: 1:41:10, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.6031 2023-01-02 03:43:05,436 - mmcls - INFO - Epoch [87][1700/5005] lr: 1.000e-03, eta: 1:41:01, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6283 2023-01-02 03:43:14,241 - mmcls - INFO - Epoch [87][1800/5005] lr: 1.000e-03, eta: 1:40:52, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6005 2023-01-02 03:43:23,050 - mmcls - INFO - Epoch [87][1900/5005] lr: 1.000e-03, eta: 1:40:43, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6392 2023-01-02 03:43:31,731 - mmcls - INFO - Epoch [87][2000/5005] lr: 1.000e-03, eta: 1:40:34, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6363 2023-01-02 03:43:40,727 - mmcls - INFO - Epoch [87][2100/5005] lr: 1.000e-03, eta: 1:40:25, time: 0.090, data_time: 0.028, memory: 1224, loss: 1.6290 2023-01-02 03:43:49,651 - mmcls - INFO - Epoch [87][2200/5005] lr: 1.000e-03, eta: 1:40:16, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6458 2023-01-02 03:43:58,168 - mmcls - INFO - Epoch [87][2300/5005] lr: 1.000e-03, eta: 1:40:07, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6235 2023-01-02 03:44:06,852 - mmcls - INFO - Epoch [87][2400/5005] lr: 1.000e-03, eta: 1:39:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6187 2023-01-02 03:44:15,325 - mmcls - INFO - Epoch [87][2500/5005] lr: 1.000e-03, eta: 1:39:50, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6374 2023-01-02 03:44:24,130 - mmcls - INFO - Epoch [87][2600/5005] lr: 1.000e-03, eta: 1:39:41, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6459 2023-01-02 03:44:32,962 - mmcls - INFO - Epoch [87][2700/5005] lr: 1.000e-03, eta: 1:39:32, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6277 2023-01-02 03:44:41,705 - mmcls - INFO - Epoch [87][2800/5005] lr: 1.000e-03, eta: 1:39:23, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6293 2023-01-02 03:44:50,492 - mmcls - INFO - Epoch [87][2900/5005] lr: 1.000e-03, eta: 1:39:14, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6218 2023-01-02 03:44:58,931 - mmcls - INFO - Epoch [87][3000/5005] lr: 1.000e-03, eta: 1:39:05, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.6287 2023-01-02 03:45:07,549 - mmcls - INFO - Epoch [87][3100/5005] lr: 1.000e-03, eta: 1:38:56, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6323 2023-01-02 03:45:16,663 - mmcls - INFO - Epoch [87][3200/5005] lr: 1.000e-03, eta: 1:38:47, time: 0.091, data_time: 0.000, memory: 1224, loss: 1.6347 2023-01-02 03:45:25,615 - mmcls - INFO - Epoch [87][3300/5005] lr: 1.000e-03, eta: 1:38:39, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6454 2023-01-02 03:45:34,322 - mmcls - INFO - Epoch [87][3400/5005] lr: 1.000e-03, eta: 1:38:30, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6359 2023-01-02 03:45:43,060 - mmcls - INFO - Epoch [87][3500/5005] lr: 1.000e-03, eta: 1:38:21, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6326 2023-01-02 03:45:51,773 - mmcls - INFO - Epoch [87][3600/5005] lr: 1.000e-03, eta: 1:38:12, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6297 2023-01-02 03:46:00,522 - mmcls - INFO - Epoch [87][3700/5005] lr: 1.000e-03, eta: 1:38:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6535 2023-01-02 03:46:09,390 - mmcls - INFO - Epoch [87][3800/5005] lr: 1.000e-03, eta: 1:37:54, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6396 2023-01-02 03:46:18,331 - mmcls - INFO - Epoch [87][3900/5005] lr: 1.000e-03, eta: 1:37:45, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6049 2023-01-02 03:46:27,051 - mmcls - INFO - Epoch [87][4000/5005] lr: 1.000e-03, eta: 1:37:36, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6137 2023-01-02 03:46:36,081 - mmcls - INFO - Epoch [87][4100/5005] lr: 1.000e-03, eta: 1:37:28, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6409 2023-01-02 03:46:44,777 - mmcls - INFO - Epoch [87][4200/5005] lr: 1.000e-03, eta: 1:37:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6333 2023-01-02 03:46:53,432 - mmcls - INFO - Epoch [87][4300/5005] lr: 1.000e-03, eta: 1:37:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6294 2023-01-02 03:47:02,554 - mmcls - INFO - Epoch [87][4400/5005] lr: 1.000e-03, eta: 1:37:01, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6417 2023-01-02 03:47:11,596 - mmcls - INFO - Epoch [87][4500/5005] lr: 1.000e-03, eta: 1:36:52, time: 0.090, data_time: 0.026, memory: 1224, loss: 1.6374 2023-01-02 03:47:20,410 - mmcls - INFO - Epoch [87][4600/5005] lr: 1.000e-03, eta: 1:36:43, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6533 2023-01-02 03:47:29,231 - mmcls - INFO - Epoch [87][4700/5005] lr: 1.000e-03, eta: 1:36:34, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6336 2023-01-02 03:47:38,049 - mmcls - INFO - Epoch [87][4800/5005] lr: 1.000e-03, eta: 1:36:26, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6341 2023-01-02 03:47:46,887 - mmcls - INFO - Epoch [87][4900/5005] lr: 1.000e-03, eta: 1:36:17, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6224 2023-01-02 03:47:55,640 - mmcls - INFO - Epoch [87][5000/5005] lr: 1.000e-03, eta: 1:36:08, time: 0.087, data_time: 0.035, memory: 1224, loss: 1.6125 2023-01-02 03:48:18,495 - mmcls - INFO - Epoch(val) [87][196] accuracy_top-1: 64.8440, accuracy_top-5: 85.4340 2023-01-02 03:48:29,441 - mmcls - INFO - Epoch [88][100/5005] lr: 1.000e-03, eta: 1:35:59, time: 0.109, data_time: 0.040, memory: 1224, loss: 1.6147 2023-01-02 03:48:38,359 - mmcls - INFO - Epoch [88][200/5005] lr: 1.000e-03, eta: 1:35:50, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6271 2023-01-02 03:48:47,233 - mmcls - INFO - Epoch [88][300/5005] lr: 1.000e-03, eta: 1:35:41, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6137 2023-01-02 03:48:56,156 - mmcls - INFO - Epoch [88][400/5005] lr: 1.000e-03, eta: 1:35:32, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6088 2023-01-02 03:49:05,139 - mmcls - INFO - Epoch [88][500/5005] lr: 1.000e-03, eta: 1:35:23, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6013 2023-01-02 03:49:14,286 - mmcls - INFO - Epoch [88][600/5005] lr: 1.000e-03, eta: 1:35:14, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6222 2023-01-02 03:49:23,109 - mmcls - INFO - Epoch [88][700/5005] lr: 1.000e-03, eta: 1:35:06, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6275 2023-01-02 03:49:31,915 - mmcls - INFO - Epoch [88][800/5005] lr: 1.000e-03, eta: 1:34:57, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6033 2023-01-02 03:49:40,775 - mmcls - INFO - Epoch [88][900/5005] lr: 1.000e-03, eta: 1:34:48, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6276 2023-01-02 03:49:49,223 - mmcls - INFO - Epoch [88][1000/5005] lr: 1.000e-03, eta: 1:34:39, time: 0.084, data_time: 0.029, memory: 1224, loss: 1.5990 2023-01-02 03:49:58,115 - mmcls - INFO - Epoch [88][1100/5005] lr: 1.000e-03, eta: 1:34:30, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6177 2023-01-02 03:50:07,181 - mmcls - INFO - Epoch [88][1200/5005] lr: 1.000e-03, eta: 1:34:21, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.6035 2023-01-02 03:50:15,647 - mmcls - INFO - Epoch [88][1300/5005] lr: 1.000e-03, eta: 1:34:12, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6467 2023-01-02 03:50:24,253 - mmcls - INFO - Epoch [88][1400/5005] lr: 1.000e-03, eta: 1:34:03, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.6272 2023-01-02 03:50:33,657 - mmcls - INFO - Epoch [88][1500/5005] lr: 1.000e-03, eta: 1:33:55, time: 0.094, data_time: 0.002, memory: 1224, loss: 1.6210 2023-01-02 03:50:42,918 - mmcls - INFO - Epoch [88][1600/5005] lr: 1.000e-03, eta: 1:33:46, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.6090 2023-01-02 03:50:51,925 - mmcls - INFO - Epoch [88][1700/5005] lr: 1.000e-03, eta: 1:33:37, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6366 2023-01-02 03:51:00,533 - mmcls - INFO - Epoch [88][1800/5005] lr: 1.000e-03, eta: 1:33:28, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6163 2023-01-02 03:51:09,289 - mmcls - INFO - Epoch [88][1900/5005] lr: 1.000e-03, eta: 1:33:19, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6291 2023-01-02 03:51:17,873 - mmcls - INFO - Epoch [88][2000/5005] lr: 1.000e-03, eta: 1:33:10, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6118 2023-01-02 03:51:26,609 - mmcls - INFO - Epoch [88][2100/5005] lr: 1.000e-03, eta: 1:33:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6161 2023-01-02 03:51:35,486 - mmcls - INFO - Epoch [88][2200/5005] lr: 1.000e-03, eta: 1:32:53, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6355 2023-01-02 03:51:44,295 - mmcls - INFO - Epoch [88][2300/5005] lr: 1.000e-03, eta: 1:32:44, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6298 2023-01-02 03:51:53,039 - mmcls - INFO - Epoch [88][2400/5005] lr: 1.000e-03, eta: 1:32:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6318 2023-01-02 03:52:01,940 - mmcls - INFO - Epoch [88][2500/5005] lr: 1.000e-03, eta: 1:32:26, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6274 2023-01-02 03:52:10,649 - mmcls - INFO - Epoch [88][2600/5005] lr: 1.000e-03, eta: 1:32:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6173 2023-01-02 03:52:19,421 - mmcls - INFO - Epoch [88][2700/5005] lr: 1.000e-03, eta: 1:32:08, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6363 2023-01-02 03:52:28,078 - mmcls - INFO - Epoch [88][2800/5005] lr: 1.000e-03, eta: 1:31:59, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6391 2023-01-02 03:52:36,847 - mmcls - INFO - Epoch [88][2900/5005] lr: 1.000e-03, eta: 1:31:50, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6440 2023-01-02 03:52:45,652 - mmcls - INFO - Epoch [88][3000/5005] lr: 1.000e-03, eta: 1:31:41, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6450 2023-01-02 03:52:54,538 - mmcls - INFO - Epoch [88][3100/5005] lr: 1.000e-03, eta: 1:31:33, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6170 2023-01-02 03:53:03,071 - mmcls - INFO - Epoch [88][3200/5005] lr: 1.000e-03, eta: 1:31:24, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6171 2023-01-02 03:53:11,831 - mmcls - INFO - Epoch [88][3300/5005] lr: 1.000e-03, eta: 1:31:15, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6371 2023-01-02 03:53:20,647 - mmcls - INFO - Epoch [88][3400/5005] lr: 1.000e-03, eta: 1:31:06, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6130 2023-01-02 03:53:29,366 - mmcls - INFO - Epoch [88][3500/5005] lr: 1.000e-03, eta: 1:30:57, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6215 2023-01-02 03:53:38,092 - mmcls - INFO - Epoch [88][3600/5005] lr: 1.000e-03, eta: 1:30:48, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6302 2023-01-02 03:53:46,917 - mmcls - INFO - Epoch [88][3700/5005] lr: 1.000e-03, eta: 1:30:39, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6153 2023-01-02 03:53:55,880 - mmcls - INFO - Epoch [88][3800/5005] lr: 1.000e-03, eta: 1:30:30, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6319 2023-01-02 03:54:04,747 - mmcls - INFO - Epoch [88][3900/5005] lr: 1.000e-03, eta: 1:30:22, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6244 2023-01-02 03:54:13,681 - mmcls - INFO - Epoch [88][4000/5005] lr: 1.000e-03, eta: 1:30:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6320 2023-01-02 03:54:22,450 - mmcls - INFO - Epoch [88][4100/5005] lr: 1.000e-03, eta: 1:30:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6179 2023-01-02 03:54:31,117 - mmcls - INFO - Epoch [88][4200/5005] lr: 1.000e-03, eta: 1:29:55, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6181 2023-01-02 03:54:40,146 - mmcls - INFO - Epoch [88][4300/5005] lr: 1.000e-03, eta: 1:29:46, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6282 2023-01-02 03:54:48,798 - mmcls - INFO - Epoch [88][4400/5005] lr: 1.000e-03, eta: 1:29:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6219 2023-01-02 03:54:57,697 - mmcls - INFO - Epoch [88][4500/5005] lr: 1.000e-03, eta: 1:29:28, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.6024 2023-01-02 03:55:06,328 - mmcls - INFO - Epoch [88][4600/5005] lr: 1.000e-03, eta: 1:29:19, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6448 2023-01-02 03:55:15,017 - mmcls - INFO - Epoch [88][4700/5005] lr: 1.000e-03, eta: 1:29:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6119 2023-01-02 03:55:23,793 - mmcls - INFO - Epoch [88][4800/5005] lr: 1.000e-03, eta: 1:29:02, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6168 2023-01-02 03:55:32,634 - mmcls - INFO - Epoch [88][4900/5005] lr: 1.000e-03, eta: 1:28:53, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6306 2023-01-02 03:55:41,462 - mmcls - INFO - Epoch [88][5000/5005] lr: 1.000e-03, eta: 1:28:44, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6359 2023-01-02 03:56:04,023 - mmcls - INFO - Epoch(val) [88][196] accuracy_top-1: 64.8560, accuracy_top-5: 85.4560 2023-01-02 03:56:15,221 - mmcls - INFO - Epoch [89][100/5005] lr: 1.000e-03, eta: 1:28:35, time: 0.112, data_time: 0.054, memory: 1224, loss: 1.6215 2023-01-02 03:56:23,895 - mmcls - INFO - Epoch [89][200/5005] lr: 1.000e-03, eta: 1:28:26, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6146 2023-01-02 03:56:32,856 - mmcls - INFO - Epoch [89][300/5005] lr: 1.000e-03, eta: 1:28:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5773 2023-01-02 03:56:41,922 - mmcls - INFO - Epoch [89][400/5005] lr: 1.000e-03, eta: 1:28:08, time: 0.091, data_time: 0.024, memory: 1224, loss: 1.6194 2023-01-02 03:56:51,050 - mmcls - INFO - Epoch [89][500/5005] lr: 1.000e-03, eta: 1:28:00, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.6205 2023-01-02 03:57:00,014 - mmcls - INFO - Epoch [89][600/5005] lr: 1.000e-03, eta: 1:27:51, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6079 2023-01-02 03:57:08,719 - mmcls - INFO - Epoch [89][700/5005] lr: 1.000e-03, eta: 1:27:42, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6030 2023-01-02 03:57:17,623 - mmcls - INFO - Epoch [89][800/5005] lr: 1.000e-03, eta: 1:27:33, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6213 2023-01-02 03:57:26,307 - mmcls - INFO - Epoch [89][900/5005] lr: 1.000e-03, eta: 1:27:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6282 2023-01-02 03:57:35,297 - mmcls - INFO - Epoch [89][1000/5005] lr: 1.000e-03, eta: 1:27:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6086 2023-01-02 03:57:44,239 - mmcls - INFO - Epoch [89][1100/5005] lr: 1.000e-03, eta: 1:27:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6112 2023-01-02 03:57:53,403 - mmcls - INFO - Epoch [89][1200/5005] lr: 1.000e-03, eta: 1:26:58, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6256 2023-01-02 03:58:02,151 - mmcls - INFO - Epoch [89][1300/5005] lr: 1.000e-03, eta: 1:26:49, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.5982 2023-01-02 03:58:11,413 - mmcls - INFO - Epoch [89][1400/5005] lr: 1.000e-03, eta: 1:26:40, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.6181 2023-01-02 03:58:20,270 - mmcls - INFO - Epoch [89][1500/5005] lr: 1.000e-03, eta: 1:26:31, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6503 2023-01-02 03:58:28,933 - mmcls - INFO - Epoch [89][1600/5005] lr: 1.000e-03, eta: 1:26:22, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6307 2023-01-02 03:58:37,744 - mmcls - INFO - Epoch [89][1700/5005] lr: 1.000e-03, eta: 1:26:13, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6325 2023-01-02 03:58:46,507 - mmcls - INFO - Epoch [89][1800/5005] lr: 1.000e-03, eta: 1:26:04, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6128 2023-01-02 03:58:55,232 - mmcls - INFO - Epoch [89][1900/5005] lr: 1.000e-03, eta: 1:25:55, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6164 2023-01-02 03:59:03,897 - mmcls - INFO - Epoch [89][2000/5005] lr: 1.000e-03, eta: 1:25:47, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6215 2023-01-02 03:59:12,868 - mmcls - INFO - Epoch [89][2100/5005] lr: 1.000e-03, eta: 1:25:38, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6160 2023-01-02 03:59:21,330 - mmcls - INFO - Epoch [89][2200/5005] lr: 1.000e-03, eta: 1:25:29, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.6232 2023-01-02 03:59:30,265 - mmcls - INFO - Epoch [89][2300/5005] lr: 1.000e-03, eta: 1:25:20, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6421 2023-01-02 03:59:38,988 - mmcls - INFO - Epoch [89][2400/5005] lr: 1.000e-03, eta: 1:25:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5982 2023-01-02 03:59:47,742 - mmcls - INFO - Epoch [89][2500/5005] lr: 1.000e-03, eta: 1:25:02, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6337 2023-01-02 03:59:56,451 - mmcls - INFO - Epoch [89][2600/5005] lr: 1.000e-03, eta: 1:24:53, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6182 2023-01-02 04:00:05,352 - mmcls - INFO - Epoch [89][2700/5005] lr: 1.000e-03, eta: 1:24:44, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6491 2023-01-02 04:00:14,448 - mmcls - INFO - Epoch [89][2800/5005] lr: 1.000e-03, eta: 1:24:36, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6143 2023-01-02 04:00:23,188 - mmcls - INFO - Epoch [89][2900/5005] lr: 1.000e-03, eta: 1:24:27, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6374 2023-01-02 04:00:32,154 - mmcls - INFO - Epoch [89][3000/5005] lr: 1.000e-03, eta: 1:24:18, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6470 2023-01-02 04:00:40,950 - mmcls - INFO - Epoch [89][3100/5005] lr: 1.000e-03, eta: 1:24:09, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6456 2023-01-02 04:00:49,472 - mmcls - INFO - Epoch [89][3200/5005] lr: 1.000e-03, eta: 1:24:00, time: 0.085, data_time: 0.030, memory: 1224, loss: 1.6066 2023-01-02 04:00:58,418 - mmcls - INFO - Epoch [89][3300/5005] lr: 1.000e-03, eta: 1:23:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6121 2023-01-02 04:01:07,059 - mmcls - INFO - Epoch [89][3400/5005] lr: 1.000e-03, eta: 1:23:42, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6303 2023-01-02 04:01:16,036 - mmcls - INFO - Epoch [89][3500/5005] lr: 1.000e-03, eta: 1:23:33, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6189 2023-01-02 04:01:24,732 - mmcls - INFO - Epoch [89][3600/5005] lr: 1.000e-03, eta: 1:23:25, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6163 2023-01-02 04:01:33,445 - mmcls - INFO - Epoch [89][3700/5005] lr: 1.000e-03, eta: 1:23:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6161 2023-01-02 04:01:42,096 - mmcls - INFO - Epoch [89][3800/5005] lr: 1.000e-03, eta: 1:23:07, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6234 2023-01-02 04:01:50,921 - mmcls - INFO - Epoch [89][3900/5005] lr: 1.000e-03, eta: 1:22:58, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6289 2023-01-02 04:02:00,387 - mmcls - INFO - Epoch [89][4000/5005] lr: 1.000e-03, eta: 1:22:49, time: 0.095, data_time: 0.002, memory: 1224, loss: 1.6307 2023-01-02 04:02:09,156 - mmcls - INFO - Epoch [89][4100/5005] lr: 1.000e-03, eta: 1:22:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6293 2023-01-02 04:02:17,730 - mmcls - INFO - Epoch [89][4200/5005] lr: 1.000e-03, eta: 1:22:31, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6420 2023-01-02 04:02:26,635 - mmcls - INFO - Epoch [89][4300/5005] lr: 1.000e-03, eta: 1:22:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6380 2023-01-02 04:02:35,366 - mmcls - INFO - Epoch [89][4400/5005] lr: 1.000e-03, eta: 1:22:14, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6479 2023-01-02 04:02:44,306 - mmcls - INFO - Epoch [89][4500/5005] lr: 1.000e-03, eta: 1:22:05, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6467 2023-01-02 04:02:52,924 - mmcls - INFO - Epoch [89][4600/5005] lr: 1.000e-03, eta: 1:21:56, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6544 2023-01-02 04:03:02,015 - mmcls - INFO - Epoch [89][4700/5005] lr: 1.000e-03, eta: 1:21:47, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6275 2023-01-02 04:03:10,793 - mmcls - INFO - Epoch [89][4800/5005] lr: 1.000e-03, eta: 1:21:38, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6371 2023-01-02 04:03:19,233 - mmcls - INFO - Epoch [89][4900/5005] lr: 1.000e-03, eta: 1:21:29, time: 0.084, data_time: 0.007, memory: 1224, loss: 1.6205 2023-01-02 04:03:28,040 - mmcls - INFO - Epoch [89][5000/5005] lr: 1.000e-03, eta: 1:21:20, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6316 2023-01-02 04:03:50,748 - mmcls - INFO - Epoch(val) [89][196] accuracy_top-1: 64.7040, accuracy_top-5: 85.5680 2023-01-02 04:04:01,760 - mmcls - INFO - Epoch [90][100/5005] lr: 1.000e-03, eta: 1:21:11, time: 0.110, data_time: 0.059, memory: 1224, loss: 1.6009 2023-01-02 04:04:10,833 - mmcls - INFO - Epoch [90][200/5005] lr: 1.000e-03, eta: 1:21:02, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6269 2023-01-02 04:04:19,662 - mmcls - INFO - Epoch [90][300/5005] lr: 1.000e-03, eta: 1:20:54, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6164 2023-01-02 04:04:28,340 - mmcls - INFO - Epoch [90][400/5005] lr: 1.000e-03, eta: 1:20:45, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6004 2023-01-02 04:04:37,327 - mmcls - INFO - Epoch [90][500/5005] lr: 1.000e-03, eta: 1:20:36, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.6168 2023-01-02 04:04:46,946 - mmcls - INFO - Epoch [90][600/5005] lr: 1.000e-03, eta: 1:20:27, time: 0.096, data_time: 0.003, memory: 1224, loss: 1.6270 2023-01-02 04:04:56,168 - mmcls - INFO - Epoch [90][700/5005] lr: 1.000e-03, eta: 1:20:18, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6005 2023-01-02 04:05:04,959 - mmcls - INFO - Epoch [90][800/5005] lr: 1.000e-03, eta: 1:20:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6297 2023-01-02 04:05:13,963 - mmcls - INFO - Epoch [90][900/5005] lr: 1.000e-03, eta: 1:20:00, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6098 2023-01-02 04:05:22,752 - mmcls - INFO - Epoch [90][1000/5005] lr: 1.000e-03, eta: 1:19:52, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6125 2023-01-02 04:05:31,410 - mmcls - INFO - Epoch [90][1100/5005] lr: 1.000e-03, eta: 1:19:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6152 2023-01-02 04:05:40,036 - mmcls - INFO - Epoch [90][1200/5005] lr: 1.000e-03, eta: 1:19:34, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6190 2023-01-02 04:05:49,054 - mmcls - INFO - Epoch [90][1300/5005] lr: 1.000e-03, eta: 1:19:25, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6190 2023-01-02 04:05:57,690 - mmcls - INFO - Epoch [90][1400/5005] lr: 1.000e-03, eta: 1:19:16, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6333 2023-01-02 04:06:06,417 - mmcls - INFO - Epoch [90][1500/5005] lr: 1.000e-03, eta: 1:19:07, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6514 2023-01-02 04:06:15,348 - mmcls - INFO - Epoch [90][1600/5005] lr: 1.000e-03, eta: 1:18:58, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6293 2023-01-02 04:06:24,729 - mmcls - INFO - Epoch [90][1700/5005] lr: 1.000e-03, eta: 1:18:50, time: 0.094, data_time: 0.008, memory: 1224, loss: 1.6333 2023-01-02 04:06:33,505 - mmcls - INFO - Epoch [90][1800/5005] lr: 1.000e-03, eta: 1:18:41, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6184 2023-01-02 04:06:42,497 - mmcls - INFO - Epoch [90][1900/5005] lr: 1.000e-03, eta: 1:18:32, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6176 2023-01-02 04:06:51,318 - mmcls - INFO - Epoch [90][2000/5005] lr: 1.000e-03, eta: 1:18:23, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.5938 2023-01-02 04:06:59,815 - mmcls - INFO - Epoch [90][2100/5005] lr: 1.000e-03, eta: 1:18:14, time: 0.085, data_time: 0.029, memory: 1224, loss: 1.6198 2023-01-02 04:07:08,525 - mmcls - INFO - Epoch [90][2200/5005] lr: 1.000e-03, eta: 1:18:05, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6320 2023-01-02 04:07:17,480 - mmcls - INFO - Epoch [90][2300/5005] lr: 1.000e-03, eta: 1:17:56, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5892 2023-01-02 04:07:26,276 - mmcls - INFO - Epoch [90][2400/5005] lr: 1.000e-03, eta: 1:17:47, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6075 2023-01-02 04:07:35,183 - mmcls - INFO - Epoch [90][2500/5005] lr: 1.000e-03, eta: 1:17:39, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6547 2023-01-02 04:07:44,325 - mmcls - INFO - Epoch [90][2600/5005] lr: 1.000e-03, eta: 1:17:30, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.5934 2023-01-02 04:07:53,029 - mmcls - INFO - Epoch [90][2700/5005] lr: 1.000e-03, eta: 1:17:21, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6224 2023-01-02 04:08:01,864 - mmcls - INFO - Epoch [90][2800/5005] lr: 1.000e-03, eta: 1:17:12, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.5985 2023-01-02 04:08:10,448 - mmcls - INFO - Epoch [90][2900/5005] lr: 1.000e-03, eta: 1:17:03, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6205 2023-01-02 04:08:19,158 - mmcls - INFO - Epoch [90][3000/5005] lr: 1.000e-03, eta: 1:16:54, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6098 2023-01-02 04:08:28,057 - mmcls - INFO - Epoch [90][3100/5005] lr: 1.000e-03, eta: 1:16:45, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6221 2023-01-02 04:08:36,724 - mmcls - INFO - Epoch [90][3200/5005] lr: 1.000e-03, eta: 1:16:36, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6514 2023-01-02 04:08:45,453 - mmcls - INFO - Epoch [90][3300/5005] lr: 1.000e-03, eta: 1:16:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6459 2023-01-02 04:08:54,047 - mmcls - INFO - Epoch [90][3400/5005] lr: 1.000e-03, eta: 1:16:19, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6386 2023-01-02 04:09:02,847 - mmcls - INFO - Epoch [90][3500/5005] lr: 1.000e-03, eta: 1:16:10, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6051 2023-01-02 04:09:11,614 - mmcls - INFO - Epoch [90][3600/5005] lr: 1.000e-03, eta: 1:16:01, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6448 2023-01-02 04:09:20,355 - mmcls - INFO - Epoch [90][3700/5005] lr: 1.000e-03, eta: 1:15:52, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6139 2023-01-02 04:09:29,301 - mmcls - INFO - Epoch [90][3800/5005] lr: 1.000e-03, eta: 1:15:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6283 2023-01-02 04:09:38,004 - mmcls - INFO - Epoch [90][3900/5005] lr: 1.000e-03, eta: 1:15:34, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6429 2023-01-02 04:09:46,652 - mmcls - INFO - Epoch [90][4000/5005] lr: 1.000e-03, eta: 1:15:25, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6207 2023-01-02 04:09:55,539 - mmcls - INFO - Epoch [90][4100/5005] lr: 1.000e-03, eta: 1:15:17, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6471 2023-01-02 04:10:04,329 - mmcls - INFO - Epoch [90][4200/5005] lr: 1.000e-03, eta: 1:15:08, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.6315 2023-01-02 04:10:12,911 - mmcls - INFO - Epoch [90][4300/5005] lr: 1.000e-03, eta: 1:14:59, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6541 2023-01-02 04:10:21,857 - mmcls - INFO - Epoch [90][4400/5005] lr: 1.000e-03, eta: 1:14:50, time: 0.089, data_time: 0.031, memory: 1224, loss: 1.6321 2023-01-02 04:10:30,540 - mmcls - INFO - Epoch [90][4500/5005] lr: 1.000e-03, eta: 1:14:41, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6301 2023-01-02 04:10:39,430 - mmcls - INFO - Epoch [90][4600/5005] lr: 1.000e-03, eta: 1:14:32, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6198 2023-01-02 04:10:48,135 - mmcls - INFO - Epoch [90][4700/5005] lr: 1.000e-03, eta: 1:14:23, time: 0.087, data_time: 0.035, memory: 1224, loss: 1.6209 2023-01-02 04:10:57,362 - mmcls - INFO - Epoch [90][4800/5005] lr: 1.000e-03, eta: 1:14:14, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.6212 2023-01-02 04:11:06,266 - mmcls - INFO - Epoch [90][4900/5005] lr: 1.000e-03, eta: 1:14:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6150 2023-01-02 04:11:15,746 - mmcls - INFO - Epoch [90][5000/5005] lr: 1.000e-03, eta: 1:13:57, time: 0.095, data_time: 0.004, memory: 1224, loss: 1.6168 2023-01-02 04:11:38,285 - mmcls - INFO - Epoch(val) [90][196] accuracy_top-1: 64.8200, accuracy_top-5: 85.5240 2023-01-02 04:11:49,036 - mmcls - INFO - Epoch [91][100/5005] lr: 1.000e-04, eta: 1:13:48, time: 0.107, data_time: 0.033, memory: 1224, loss: 1.6003 2023-01-02 04:11:58,015 - mmcls - INFO - Epoch [91][200/5005] lr: 1.000e-04, eta: 1:13:39, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.5916 2023-01-02 04:12:06,545 - mmcls - INFO - Epoch [91][300/5005] lr: 1.000e-04, eta: 1:13:30, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.5975 2023-01-02 04:12:15,304 - mmcls - INFO - Epoch [91][400/5005] lr: 1.000e-04, eta: 1:13:21, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6271 2023-01-02 04:12:24,134 - mmcls - INFO - Epoch [91][500/5005] lr: 1.000e-04, eta: 1:13:12, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6159 2023-01-02 04:12:32,968 - mmcls - INFO - Epoch [91][600/5005] lr: 1.000e-04, eta: 1:13:03, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6006 2023-01-02 04:12:41,619 - mmcls - INFO - Epoch [91][700/5005] lr: 1.000e-04, eta: 1:12:54, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6018 2023-01-02 04:12:50,321 - mmcls - INFO - Epoch [91][800/5005] lr: 1.000e-04, eta: 1:12:46, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.5878 2023-01-02 04:12:59,516 - mmcls - INFO - Epoch [91][900/5005] lr: 1.000e-04, eta: 1:12:37, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.5952 2023-01-02 04:13:08,597 - mmcls - INFO - Epoch [91][1000/5005] lr: 1.000e-04, eta: 1:12:28, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.5767 2023-01-02 04:13:17,346 - mmcls - INFO - Epoch [91][1100/5005] lr: 1.000e-04, eta: 1:12:19, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.5858 2023-01-02 04:13:26,200 - mmcls - INFO - Epoch [91][1200/5005] lr: 1.000e-04, eta: 1:12:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5897 2023-01-02 04:13:34,953 - mmcls - INFO - Epoch [91][1300/5005] lr: 1.000e-04, eta: 1:12:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5803 2023-01-02 04:13:43,956 - mmcls - INFO - Epoch [91][1400/5005] lr: 1.000e-04, eta: 1:11:52, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5765 2023-01-02 04:13:52,773 - mmcls - INFO - Epoch [91][1500/5005] lr: 1.000e-04, eta: 1:11:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5779 2023-01-02 04:14:01,566 - mmcls - INFO - Epoch [91][1600/5005] lr: 1.000e-04, eta: 1:11:35, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.5794 2023-01-02 04:14:10,284 - mmcls - INFO - Epoch [91][1700/5005] lr: 1.000e-04, eta: 1:11:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.5815 2023-01-02 04:14:19,115 - mmcls - INFO - Epoch [91][1800/5005] lr: 1.000e-04, eta: 1:11:17, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6153 2023-01-02 04:14:27,965 - mmcls - INFO - Epoch [91][1900/5005] lr: 1.000e-04, eta: 1:11:08, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.6108 2023-01-02 04:14:36,742 - mmcls - INFO - Epoch [91][2000/5005] lr: 1.000e-04, eta: 1:10:59, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6157 2023-01-02 04:14:45,770 - mmcls - INFO - Epoch [91][2100/5005] lr: 1.000e-04, eta: 1:10:50, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6066 2023-01-02 04:14:54,380 - mmcls - INFO - Epoch [91][2200/5005] lr: 1.000e-04, eta: 1:10:41, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.5990 2023-01-02 04:15:03,141 - mmcls - INFO - Epoch [91][2300/5005] lr: 1.000e-04, eta: 1:10:33, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.5947 2023-01-02 04:15:12,162 - mmcls - INFO - Epoch [91][2400/5005] lr: 1.000e-04, eta: 1:10:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5756 2023-01-02 04:15:20,994 - mmcls - INFO - Epoch [91][2500/5005] lr: 1.000e-04, eta: 1:10:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5945 2023-01-02 04:15:29,683 - mmcls - INFO - Epoch [91][2600/5005] lr: 1.000e-04, eta: 1:10:06, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5855 2023-01-02 04:15:38,473 - mmcls - INFO - Epoch [91][2700/5005] lr: 1.000e-04, eta: 1:09:57, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6078 2023-01-02 04:15:47,009 - mmcls - INFO - Epoch [91][2800/5005] lr: 1.000e-04, eta: 1:09:48, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.6010 2023-01-02 04:15:55,771 - mmcls - INFO - Epoch [91][2900/5005] lr: 1.000e-04, eta: 1:09:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5921 2023-01-02 04:16:04,647 - mmcls - INFO - Epoch [91][3000/5005] lr: 1.000e-04, eta: 1:09:30, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6065 2023-01-02 04:16:13,189 - mmcls - INFO - Epoch [91][3100/5005] lr: 1.000e-04, eta: 1:09:22, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.5978 2023-01-02 04:16:22,134 - mmcls - INFO - Epoch [91][3200/5005] lr: 1.000e-04, eta: 1:09:13, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.5893 2023-01-02 04:16:30,894 - mmcls - INFO - Epoch [91][3300/5005] lr: 1.000e-04, eta: 1:09:04, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5849 2023-01-02 04:16:39,398 - mmcls - INFO - Epoch [91][3400/5005] lr: 1.000e-04, eta: 1:08:55, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.5782 2023-01-02 04:16:48,195 - mmcls - INFO - Epoch [91][3500/5005] lr: 1.000e-04, eta: 1:08:46, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5843 2023-01-02 04:16:57,056 - mmcls - INFO - Epoch [91][3600/5005] lr: 1.000e-04, eta: 1:08:37, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.5896 2023-01-02 04:17:05,841 - mmcls - INFO - Epoch [91][3700/5005] lr: 1.000e-04, eta: 1:08:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6016 2023-01-02 04:17:14,853 - mmcls - INFO - Epoch [91][3800/5005] lr: 1.000e-04, eta: 1:08:19, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.5912 2023-01-02 04:17:23,849 - mmcls - INFO - Epoch [91][3900/5005] lr: 1.000e-04, eta: 1:08:11, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5921 2023-01-02 04:17:32,708 - mmcls - INFO - Epoch [91][4000/5005] lr: 1.000e-04, eta: 1:08:02, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.5821 2023-01-02 04:17:41,757 - mmcls - INFO - Epoch [91][4100/5005] lr: 1.000e-04, eta: 1:07:53, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5976 2023-01-02 04:17:50,565 - mmcls - INFO - Epoch [91][4200/5005] lr: 1.000e-04, eta: 1:07:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5821 2023-01-02 04:17:59,419 - mmcls - INFO - Epoch [91][4300/5005] lr: 1.000e-04, eta: 1:07:35, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.5931 2023-01-02 04:18:08,373 - mmcls - INFO - Epoch [91][4400/5005] lr: 1.000e-04, eta: 1:07:26, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.5833 2023-01-02 04:18:17,392 - mmcls - INFO - Epoch [91][4500/5005] lr: 1.000e-04, eta: 1:07:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5982 2023-01-02 04:18:26,026 - mmcls - INFO - Epoch [91][4600/5005] lr: 1.000e-04, eta: 1:07:09, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5872 2023-01-02 04:18:34,964 - mmcls - INFO - Epoch [91][4700/5005] lr: 1.000e-04, eta: 1:07:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5848 2023-01-02 04:18:43,511 - mmcls - INFO - Epoch [91][4800/5005] lr: 1.000e-04, eta: 1:06:51, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.5911 2023-01-02 04:18:52,171 - mmcls - INFO - Epoch [91][4900/5005] lr: 1.000e-04, eta: 1:06:42, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.5775 2023-01-02 04:19:01,041 - mmcls - INFO - Epoch [91][5000/5005] lr: 1.000e-04, eta: 1:06:33, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6061 2023-01-02 04:19:23,827 - mmcls - INFO - Epoch(val) [91][196] accuracy_top-1: 65.2800, accuracy_top-5: 85.6880 2023-01-02 04:19:34,739 - mmcls - INFO - Epoch [92][100/5005] lr: 1.000e-04, eta: 1:06:24, time: 0.109, data_time: 0.031, memory: 1224, loss: 1.5935 2023-01-02 04:19:43,735 - mmcls - INFO - Epoch [92][200/5005] lr: 1.000e-04, eta: 1:06:15, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.5738 2023-01-02 04:19:52,225 - mmcls - INFO - Epoch [92][300/5005] lr: 1.000e-04, eta: 1:06:06, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.5890 2023-01-02 04:20:01,154 - mmcls - INFO - Epoch [92][400/5005] lr: 1.000e-04, eta: 1:05:57, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5823 2023-01-02 04:20:09,867 - mmcls - INFO - Epoch [92][500/5005] lr: 1.000e-04, eta: 1:05:48, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5852 2023-01-02 04:20:18,824 - mmcls - INFO - Epoch [92][600/5005] lr: 1.000e-04, eta: 1:05:40, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5940 2023-01-02 04:20:27,772 - mmcls - INFO - Epoch [92][700/5005] lr: 1.000e-04, eta: 1:05:31, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6046 2023-01-02 04:20:36,417 - mmcls - INFO - Epoch [92][800/5005] lr: 1.000e-04, eta: 1:05:22, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.5787 2023-01-02 04:20:45,201 - mmcls - INFO - Epoch [92][900/5005] lr: 1.000e-04, eta: 1:05:13, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.5699 2023-01-02 04:20:54,390 - mmcls - INFO - Epoch [92][1000/5005] lr: 1.000e-04, eta: 1:05:04, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.5703 2023-01-02 04:21:03,112 - mmcls - INFO - Epoch [92][1100/5005] lr: 1.000e-04, eta: 1:04:55, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.5859 2023-01-02 04:21:11,782 - mmcls - INFO - Epoch [92][1200/5005] lr: 1.000e-04, eta: 1:04:46, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.5946 2023-01-02 04:21:20,622 - mmcls - INFO - Epoch [92][1300/5005] lr: 1.000e-04, eta: 1:04:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5979 2023-01-02 04:21:29,787 - mmcls - INFO - Epoch [92][1400/5005] lr: 1.000e-04, eta: 1:04:29, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.5935 2023-01-02 04:21:38,386 - mmcls - INFO - Epoch [92][1500/5005] lr: 1.000e-04, eta: 1:04:20, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.5707 2023-01-02 04:21:47,645 - mmcls - INFO - Epoch [92][1600/5005] lr: 1.000e-04, eta: 1:04:11, time: 0.093, data_time: 0.037, memory: 1224, loss: 1.6059 2023-01-02 04:21:56,831 - mmcls - INFO - Epoch [92][1700/5005] lr: 1.000e-04, eta: 1:04:02, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.5819 2023-01-02 04:22:05,422 - mmcls - INFO - Epoch [92][1800/5005] lr: 1.000e-04, eta: 1:03:53, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.5893 2023-01-02 04:22:14,327 - mmcls - INFO - Epoch [92][1900/5005] lr: 1.000e-04, eta: 1:03:44, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.5783 2023-01-02 04:22:23,153 - mmcls - INFO - Epoch [92][2000/5005] lr: 1.000e-04, eta: 1:03:35, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5875 2023-01-02 04:22:31,848 - mmcls - INFO - Epoch [92][2100/5005] lr: 1.000e-04, eta: 1:03:27, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.5725 2023-01-02 04:22:40,528 - mmcls - INFO - Epoch [92][2200/5005] lr: 1.000e-04, eta: 1:03:18, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5976 2023-01-02 04:22:49,313 - mmcls - INFO - Epoch [92][2300/5005] lr: 1.000e-04, eta: 1:03:09, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6083 2023-01-02 04:22:57,916 - mmcls - INFO - Epoch [92][2400/5005] lr: 1.000e-04, eta: 1:03:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5774 2023-01-02 04:23:06,841 - mmcls - INFO - Epoch [92][2500/5005] lr: 1.000e-04, eta: 1:02:51, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.5920 2023-01-02 04:23:15,761 - mmcls - INFO - Epoch [92][2600/5005] lr: 1.000e-04, eta: 1:02:42, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.5907 2023-01-02 04:23:24,890 - mmcls - INFO - Epoch [92][2700/5005] lr: 1.000e-04, eta: 1:02:33, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5794 2023-01-02 04:23:33,815 - mmcls - INFO - Epoch [92][2800/5005] lr: 1.000e-04, eta: 1:02:24, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.5769 2023-01-02 04:23:42,415 - mmcls - INFO - Epoch [92][2900/5005] lr: 1.000e-04, eta: 1:02:16, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.5604 2023-01-02 04:23:51,207 - mmcls - INFO - Epoch [92][3000/5005] lr: 1.000e-04, eta: 1:02:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6091 2023-01-02 04:23:59,949 - mmcls - INFO - Epoch [92][3100/5005] lr: 1.000e-04, eta: 1:01:58, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.5842 2023-01-02 04:24:08,742 - mmcls - INFO - Epoch [92][3200/5005] lr: 1.000e-04, eta: 1:01:49, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.5825 2023-01-02 04:24:17,522 - mmcls - INFO - Epoch [92][3300/5005] lr: 1.000e-04, eta: 1:01:40, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6103 2023-01-02 04:24:25,993 - mmcls - INFO - Epoch [92][3400/5005] lr: 1.000e-04, eta: 1:01:31, time: 0.085, data_time: 0.016, memory: 1224, loss: 1.5906 2023-01-02 04:24:35,116 - mmcls - INFO - Epoch [92][3500/5005] lr: 1.000e-04, eta: 1:01:22, time: 0.091, data_time: 0.024, memory: 1224, loss: 1.5936 2023-01-02 04:24:44,070 - mmcls - INFO - Epoch [92][3600/5005] lr: 1.000e-04, eta: 1:01:14, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6065 2023-01-02 04:24:52,929 - mmcls - INFO - Epoch [92][3700/5005] lr: 1.000e-04, eta: 1:01:05, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.5935 2023-01-02 04:25:01,915 - mmcls - INFO - Epoch [92][3800/5005] lr: 1.000e-04, eta: 1:00:56, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.5824 2023-01-02 04:25:10,761 - mmcls - INFO - Epoch [92][3900/5005] lr: 1.000e-04, eta: 1:00:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5802 2023-01-02 04:25:19,530 - mmcls - INFO - Epoch [92][4000/5005] lr: 1.000e-04, eta: 1:00:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5792 2023-01-02 04:25:28,379 - mmcls - INFO - Epoch [92][4100/5005] lr: 1.000e-04, eta: 1:00:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5786 2023-01-02 04:25:37,338 - mmcls - INFO - Epoch [92][4200/5005] lr: 1.000e-04, eta: 1:00:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5899 2023-01-02 04:25:46,018 - mmcls - INFO - Epoch [92][4300/5005] lr: 1.000e-04, eta: 1:00:11, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.5971 2023-01-02 04:25:54,825 - mmcls - INFO - Epoch [92][4400/5005] lr: 1.000e-04, eta: 1:00:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6025 2023-01-02 04:26:03,608 - mmcls - INFO - Epoch [92][4500/5005] lr: 1.000e-04, eta: 0:59:54, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.5887 2023-01-02 04:26:12,240 - mmcls - INFO - Epoch [92][4600/5005] lr: 1.000e-04, eta: 0:59:45, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.5719 2023-01-02 04:26:20,793 - mmcls - INFO - Epoch [92][4700/5005] lr: 1.000e-04, eta: 0:59:36, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.5831 2023-01-02 04:26:29,480 - mmcls - INFO - Epoch [92][4800/5005] lr: 1.000e-04, eta: 0:59:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5925 2023-01-02 04:26:38,371 - mmcls - INFO - Epoch [92][4900/5005] lr: 1.000e-04, eta: 0:59:18, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5827 2023-01-02 04:26:47,327 - mmcls - INFO - Epoch [92][5000/5005] lr: 1.000e-04, eta: 0:59:09, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.5784 2023-01-02 04:27:10,071 - mmcls - INFO - Epoch(val) [92][196] accuracy_top-1: 65.2480, accuracy_top-5: 85.6640 2023-01-02 04:27:21,351 - mmcls - INFO - Epoch [93][100/5005] lr: 1.000e-04, eta: 0:59:00, time: 0.113, data_time: 0.064, memory: 1224, loss: 1.5837 2023-01-02 04:27:30,175 - mmcls - INFO - Epoch [93][200/5005] lr: 1.000e-04, eta: 0:58:51, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.5890 2023-01-02 04:27:39,083 - mmcls - INFO - Epoch [93][300/5005] lr: 1.000e-04, eta: 0:58:42, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5653 2023-01-02 04:27:47,834 - mmcls - INFO - Epoch [93][400/5005] lr: 1.000e-04, eta: 0:58:34, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.5835 2023-01-02 04:27:56,337 - mmcls - INFO - Epoch [93][500/5005] lr: 1.000e-04, eta: 0:58:25, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.6042 2023-01-02 04:28:05,141 - mmcls - INFO - Epoch [93][600/5005] lr: 1.000e-04, eta: 0:58:16, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6196 2023-01-02 04:28:13,947 - mmcls - INFO - Epoch [93][700/5005] lr: 1.000e-04, eta: 0:58:07, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.5924 2023-01-02 04:28:22,782 - mmcls - INFO - Epoch [93][800/5005] lr: 1.000e-04, eta: 0:57:58, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.5679 2023-01-02 04:28:31,712 - mmcls - INFO - Epoch [93][900/5005] lr: 1.000e-04, eta: 0:57:49, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6009 2023-01-02 04:28:40,463 - mmcls - INFO - Epoch [93][1000/5005] lr: 1.000e-04, eta: 0:57:40, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.5829 2023-01-02 04:28:49,386 - mmcls - INFO - Epoch [93][1100/5005] lr: 1.000e-04, eta: 0:57:31, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.5886 2023-01-02 04:28:58,223 - mmcls - INFO - Epoch [93][1200/5005] lr: 1.000e-04, eta: 0:57:23, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5727 2023-01-02 04:29:06,883 - mmcls - INFO - Epoch [93][1300/5005] lr: 1.000e-04, eta: 0:57:14, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5750 2023-01-02 04:29:15,895 - mmcls - INFO - Epoch [93][1400/5005] lr: 1.000e-04, eta: 0:57:05, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.5660 2023-01-02 04:29:24,872 - mmcls - INFO - Epoch [93][1500/5005] lr: 1.000e-04, eta: 0:56:56, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.5803 2023-01-02 04:29:33,752 - mmcls - INFO - Epoch [93][1600/5005] lr: 1.000e-04, eta: 0:56:47, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.5935 2023-01-02 04:29:42,332 - mmcls - INFO - Epoch [93][1700/5005] lr: 1.000e-04, eta: 0:56:38, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.5759 2023-01-02 04:29:51,286 - mmcls - INFO - Epoch [93][1800/5005] lr: 1.000e-04, eta: 0:56:29, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.5750 2023-01-02 04:30:00,010 - mmcls - INFO - Epoch [93][1900/5005] lr: 1.000e-04, eta: 0:56:21, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5682 2023-01-02 04:30:08,831 - mmcls - INFO - Epoch [93][2000/5005] lr: 1.000e-04, eta: 0:56:12, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.5921 2023-01-02 04:30:17,842 - mmcls - INFO - Epoch [93][2100/5005] lr: 1.000e-04, eta: 0:56:03, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5806 2023-01-02 04:30:26,219 - mmcls - INFO - Epoch [93][2200/5005] lr: 1.000e-04, eta: 0:55:54, time: 0.084, data_time: 0.014, memory: 1224, loss: 1.5755 2023-01-02 04:30:34,889 - mmcls - INFO - Epoch [93][2300/5005] lr: 1.000e-04, eta: 0:55:45, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.5887 2023-01-02 04:30:43,571 - mmcls - INFO - Epoch [93][2400/5005] lr: 1.000e-04, eta: 0:55:36, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5927 2023-01-02 04:30:52,425 - mmcls - INFO - Epoch [93][2500/5005] lr: 1.000e-04, eta: 0:55:27, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5886 2023-01-02 04:31:01,117 - mmcls - INFO - Epoch [93][2600/5005] lr: 1.000e-04, eta: 0:55:18, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5782 2023-01-02 04:31:09,982 - mmcls - INFO - Epoch [93][2700/5005] lr: 1.000e-04, eta: 0:55:10, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6072 2023-01-02 04:31:18,742 - mmcls - INFO - Epoch [93][2800/5005] lr: 1.000e-04, eta: 0:55:01, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.5624 2023-01-02 04:31:27,383 - mmcls - INFO - Epoch [93][2900/5005] lr: 1.000e-04, eta: 0:54:52, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.5895 2023-01-02 04:31:36,071 - mmcls - INFO - Epoch [93][3000/5005] lr: 1.000e-04, eta: 0:54:43, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.5859 2023-01-02 04:31:44,799 - mmcls - INFO - Epoch [93][3100/5005] lr: 1.000e-04, eta: 0:54:34, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5829 2023-01-02 04:31:53,777 - mmcls - INFO - Epoch [93][3200/5005] lr: 1.000e-04, eta: 0:54:25, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.5764 2023-01-02 04:32:02,437 - mmcls - INFO - Epoch [93][3300/5005] lr: 1.000e-04, eta: 0:54:16, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5989 2023-01-02 04:32:11,324 - mmcls - INFO - Epoch [93][3400/5005] lr: 1.000e-04, eta: 0:54:07, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6012 2023-01-02 04:32:19,957 - mmcls - INFO - Epoch [93][3500/5005] lr: 1.000e-04, eta: 0:53:59, time: 0.086, data_time: 0.034, memory: 1224, loss: 1.5876 2023-01-02 04:32:28,918 - mmcls - INFO - Epoch [93][3600/5005] lr: 1.000e-04, eta: 0:53:50, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.5864 2023-01-02 04:32:37,790 - mmcls - INFO - Epoch [93][3700/5005] lr: 1.000e-04, eta: 0:53:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5774 2023-01-02 04:32:46,479 - mmcls - INFO - Epoch [93][3800/5005] lr: 1.000e-04, eta: 0:53:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5766 2023-01-02 04:32:55,796 - mmcls - INFO - Epoch [93][3900/5005] lr: 1.000e-04, eta: 0:53:23, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.5645 2023-01-02 04:33:04,800 - mmcls - INFO - Epoch [93][4000/5005] lr: 1.000e-04, eta: 0:53:14, time: 0.090, data_time: 0.037, memory: 1224, loss: 1.5929 2023-01-02 04:33:13,850 - mmcls - INFO - Epoch [93][4100/5005] lr: 1.000e-04, eta: 0:53:05, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.5589 2023-01-02 04:33:22,731 - mmcls - INFO - Epoch [93][4200/5005] lr: 1.000e-04, eta: 0:52:57, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.5754 2023-01-02 04:33:31,368 - mmcls - INFO - Epoch [93][4300/5005] lr: 1.000e-04, eta: 0:52:48, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.5949 2023-01-02 04:33:40,103 - mmcls - INFO - Epoch [93][4400/5005] lr: 1.000e-04, eta: 0:52:39, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6140 2023-01-02 04:33:48,835 - mmcls - INFO - Epoch [93][4500/5005] lr: 1.000e-04, eta: 0:52:30, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5927 2023-01-02 04:33:58,068 - mmcls - INFO - Epoch [93][4600/5005] lr: 1.000e-04, eta: 0:52:21, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.5967 2023-01-02 04:34:07,091 - mmcls - INFO - Epoch [93][4700/5005] lr: 1.000e-04, eta: 0:52:12, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.5925 2023-01-02 04:34:15,665 - mmcls - INFO - Epoch [93][4800/5005] lr: 1.000e-04, eta: 0:52:03, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5844 2023-01-02 04:34:24,564 - mmcls - INFO - Epoch [93][4900/5005] lr: 1.000e-04, eta: 0:51:54, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5934 2023-01-02 04:34:33,390 - mmcls - INFO - Epoch [93][5000/5005] lr: 1.000e-04, eta: 0:51:46, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5982 2023-01-02 04:34:56,325 - mmcls - INFO - Epoch(val) [93][196] accuracy_top-1: 65.2780, accuracy_top-5: 85.6780 2023-01-02 04:35:07,165 - mmcls - INFO - Epoch [94][100/5005] lr: 1.000e-04, eta: 0:51:36, time: 0.108, data_time: 0.054, memory: 1224, loss: 1.6063 2023-01-02 04:35:15,837 - mmcls - INFO - Epoch [94][200/5005] lr: 1.000e-04, eta: 0:51:28, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5751 2023-01-02 04:35:24,565 - mmcls - INFO - Epoch [94][300/5005] lr: 1.000e-04, eta: 0:51:19, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5738 2023-01-02 04:35:33,254 - mmcls - INFO - Epoch [94][400/5005] lr: 1.000e-04, eta: 0:51:10, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5644 2023-01-02 04:35:42,438 - mmcls - INFO - Epoch [94][500/5005] lr: 1.000e-04, eta: 0:51:01, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.5660 2023-01-02 04:35:51,154 - mmcls - INFO - Epoch [94][600/5005] lr: 1.000e-04, eta: 0:50:52, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.5648 2023-01-02 04:36:00,096 - mmcls - INFO - Epoch [94][700/5005] lr: 1.000e-04, eta: 0:50:43, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.5752 2023-01-02 04:36:08,835 - mmcls - INFO - Epoch [94][800/5005] lr: 1.000e-04, eta: 0:50:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5792 2023-01-02 04:36:17,868 - mmcls - INFO - Epoch [94][900/5005] lr: 1.000e-04, eta: 0:50:25, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.5738 2023-01-02 04:36:26,632 - mmcls - INFO - Epoch [94][1000/5005] lr: 1.000e-04, eta: 0:50:17, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.5689 2023-01-02 04:36:35,601 - mmcls - INFO - Epoch [94][1100/5005] lr: 1.000e-04, eta: 0:50:08, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5923 2023-01-02 04:36:44,760 - mmcls - INFO - Epoch [94][1200/5005] lr: 1.000e-04, eta: 0:49:59, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.5914 2023-01-02 04:36:53,407 - mmcls - INFO - Epoch [94][1300/5005] lr: 1.000e-04, eta: 0:49:50, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.5871 2023-01-02 04:37:02,338 - mmcls - INFO - Epoch [94][1400/5005] lr: 1.000e-04, eta: 0:49:41, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5817 2023-01-02 04:37:11,215 - mmcls - INFO - Epoch [94][1500/5005] lr: 1.000e-04, eta: 0:49:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5675 2023-01-02 04:37:20,059 - mmcls - INFO - Epoch [94][1600/5005] lr: 1.000e-04, eta: 0:49:23, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5887 2023-01-02 04:37:28,932 - mmcls - INFO - Epoch [94][1700/5005] lr: 1.000e-04, eta: 0:49:15, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6058 2023-01-02 04:37:37,685 - mmcls - INFO - Epoch [94][1800/5005] lr: 1.000e-04, eta: 0:49:06, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6017 2023-01-02 04:37:46,569 - mmcls - INFO - Epoch [94][1900/5005] lr: 1.000e-04, eta: 0:48:57, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.5719 2023-01-02 04:37:55,333 - mmcls - INFO - Epoch [94][2000/5005] lr: 1.000e-04, eta: 0:48:48, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.5952 2023-01-02 04:38:04,065 - mmcls - INFO - Epoch [94][2100/5005] lr: 1.000e-04, eta: 0:48:39, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5829 2023-01-02 04:38:13,042 - mmcls - INFO - Epoch [94][2200/5005] lr: 1.000e-04, eta: 0:48:30, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.5722 2023-01-02 04:38:21,747 - mmcls - INFO - Epoch [94][2300/5005] lr: 1.000e-04, eta: 0:48:21, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.5950 2023-01-02 04:38:30,593 - mmcls - INFO - Epoch [94][2400/5005] lr: 1.000e-04, eta: 0:48:13, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5826 2023-01-02 04:38:39,301 - mmcls - INFO - Epoch [94][2500/5005] lr: 1.000e-04, eta: 0:48:04, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5730 2023-01-02 04:38:48,117 - mmcls - INFO - Epoch [94][2600/5005] lr: 1.000e-04, eta: 0:47:55, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5834 2023-01-02 04:38:57,185 - mmcls - INFO - Epoch [94][2700/5005] lr: 1.000e-04, eta: 0:47:46, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.5700 2023-01-02 04:39:05,943 - mmcls - INFO - Epoch [94][2800/5005] lr: 1.000e-04, eta: 0:47:37, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5845 2023-01-02 04:39:14,559 - mmcls - INFO - Epoch [94][2900/5005] lr: 1.000e-04, eta: 0:47:28, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.5917 2023-01-02 04:39:23,153 - mmcls - INFO - Epoch [94][3000/5005] lr: 1.000e-04, eta: 0:47:19, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6038 2023-01-02 04:39:31,982 - mmcls - INFO - Epoch [94][3100/5005] lr: 1.000e-04, eta: 0:47:10, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5760 2023-01-02 04:39:40,624 - mmcls - INFO - Epoch [94][3200/5005] lr: 1.000e-04, eta: 0:47:02, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.5763 2023-01-02 04:39:49,500 - mmcls - INFO - Epoch [94][3300/5005] lr: 1.000e-04, eta: 0:46:53, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6078 2023-01-02 04:39:58,319 - mmcls - INFO - Epoch [94][3400/5005] lr: 1.000e-04, eta: 0:46:44, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5581 2023-01-02 04:40:07,383 - mmcls - INFO - Epoch [94][3500/5005] lr: 1.000e-04, eta: 0:46:35, time: 0.091, data_time: 0.026, memory: 1224, loss: 1.5817 2023-01-02 04:40:16,683 - mmcls - INFO - Epoch [94][3600/5005] lr: 1.000e-04, eta: 0:46:26, time: 0.093, data_time: 0.017, memory: 1224, loss: 1.5856 2023-01-02 04:40:25,304 - mmcls - INFO - Epoch [94][3700/5005] lr: 1.000e-04, eta: 0:46:17, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.5846 2023-01-02 04:40:33,970 - mmcls - INFO - Epoch [94][3800/5005] lr: 1.000e-04, eta: 0:46:08, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.5624 2023-01-02 04:40:43,139 - mmcls - INFO - Epoch [94][3900/5005] lr: 1.000e-04, eta: 0:46:00, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.5912 2023-01-02 04:40:52,119 - mmcls - INFO - Epoch [94][4000/5005] lr: 1.000e-04, eta: 0:45:51, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.5975 2023-01-02 04:41:00,882 - mmcls - INFO - Epoch [94][4100/5005] lr: 1.000e-04, eta: 0:45:42, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6021 2023-01-02 04:41:09,855 - mmcls - INFO - Epoch [94][4200/5005] lr: 1.000e-04, eta: 0:45:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5860 2023-01-02 04:41:18,577 - mmcls - INFO - Epoch [94][4300/5005] lr: 1.000e-04, eta: 0:45:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5926 2023-01-02 04:41:27,300 - mmcls - INFO - Epoch [94][4400/5005] lr: 1.000e-04, eta: 0:45:15, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.5916 2023-01-02 04:41:36,615 - mmcls - INFO - Epoch [94][4500/5005] lr: 1.000e-04, eta: 0:45:06, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.5859 2023-01-02 04:41:45,401 - mmcls - INFO - Epoch [94][4600/5005] lr: 1.000e-04, eta: 0:44:57, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5569 2023-01-02 04:41:54,037 - mmcls - INFO - Epoch [94][4700/5005] lr: 1.000e-04, eta: 0:44:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5745 2023-01-02 04:42:03,207 - mmcls - INFO - Epoch [94][4800/5005] lr: 1.000e-04, eta: 0:44:40, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.5786 2023-01-02 04:42:11,975 - mmcls - INFO - Epoch [94][4900/5005] lr: 1.000e-04, eta: 0:44:31, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.5861 2023-01-02 04:42:20,967 - mmcls - INFO - Epoch [94][5000/5005] lr: 1.000e-04, eta: 0:44:22, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.5854 2023-01-02 04:42:43,696 - mmcls - INFO - Epoch(val) [94][196] accuracy_top-1: 65.3460, accuracy_top-5: 85.7620 2023-01-02 04:42:55,064 - mmcls - INFO - Epoch [95][100/5005] lr: 1.000e-04, eta: 0:44:13, time: 0.113, data_time: 0.056, memory: 1224, loss: 1.5675 2023-01-02 04:43:03,695 - mmcls - INFO - Epoch [95][200/5005] lr: 1.000e-04, eta: 0:44:04, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5733 2023-01-02 04:43:12,425 - mmcls - INFO - Epoch [95][300/5005] lr: 1.000e-04, eta: 0:43:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5899 2023-01-02 04:43:21,397 - mmcls - INFO - Epoch [95][400/5005] lr: 1.000e-04, eta: 0:43:46, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.5941 2023-01-02 04:43:30,186 - mmcls - INFO - Epoch [95][500/5005] lr: 1.000e-04, eta: 0:43:37, time: 0.088, data_time: 0.037, memory: 1224, loss: 1.5965 2023-01-02 04:43:38,895 - mmcls - INFO - Epoch [95][600/5005] lr: 1.000e-04, eta: 0:43:28, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.5927 2023-01-02 04:43:47,798 - mmcls - INFO - Epoch [95][700/5005] lr: 1.000e-04, eta: 0:43:20, time: 0.089, data_time: 0.044, memory: 1224, loss: 1.5806 2023-01-02 04:43:56,666 - mmcls - INFO - Epoch [95][800/5005] lr: 1.000e-04, eta: 0:43:11, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.5814 2023-01-02 04:44:05,306 - mmcls - INFO - Epoch [95][900/5005] lr: 1.000e-04, eta: 0:43:02, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.5772 2023-01-02 04:44:14,220 - mmcls - INFO - Epoch [95][1000/5005] lr: 1.000e-04, eta: 0:42:53, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.5922 2023-01-02 04:44:23,224 - mmcls - INFO - Epoch [95][1100/5005] lr: 1.000e-04, eta: 0:42:44, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.5767 2023-01-02 04:44:31,768 - mmcls - INFO - Epoch [95][1200/5005] lr: 1.000e-04, eta: 0:42:35, time: 0.085, data_time: 0.030, memory: 1224, loss: 1.5576 2023-01-02 04:44:40,440 - mmcls - INFO - Epoch [95][1300/5005] lr: 1.000e-04, eta: 0:42:26, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.5602 2023-01-02 04:44:49,255 - mmcls - INFO - Epoch [95][1400/5005] lr: 1.000e-04, eta: 0:42:18, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.5986 2023-01-02 04:44:58,030 - mmcls - INFO - Epoch [95][1500/5005] lr: 1.000e-04, eta: 0:42:09, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.5686 2023-01-02 04:45:06,973 - mmcls - INFO - Epoch [95][1600/5005] lr: 1.000e-04, eta: 0:42:00, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.5829 2023-01-02 04:45:15,933 - mmcls - INFO - Epoch [95][1700/5005] lr: 1.000e-04, eta: 0:41:51, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.5762 2023-01-02 04:45:24,830 - mmcls - INFO - Epoch [95][1800/5005] lr: 1.000e-04, eta: 0:41:42, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5664 2023-01-02 04:45:33,474 - mmcls - INFO - Epoch [95][1900/5005] lr: 1.000e-04, eta: 0:41:33, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.5717 2023-01-02 04:45:42,248 - mmcls - INFO - Epoch [95][2000/5005] lr: 1.000e-04, eta: 0:41:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5699 2023-01-02 04:45:50,934 - mmcls - INFO - Epoch [95][2100/5005] lr: 1.000e-04, eta: 0:41:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5873 2023-01-02 04:45:59,523 - mmcls - INFO - Epoch [95][2200/5005] lr: 1.000e-04, eta: 0:41:07, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.5870 2023-01-02 04:46:08,066 - mmcls - INFO - Epoch [95][2300/5005] lr: 1.000e-04, eta: 0:40:58, time: 0.085, data_time: 0.020, memory: 1224, loss: 1.5792 2023-01-02 04:46:16,825 - mmcls - INFO - Epoch [95][2400/5005] lr: 1.000e-04, eta: 0:40:49, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5804 2023-01-02 04:46:25,552 - mmcls - INFO - Epoch [95][2500/5005] lr: 1.000e-04, eta: 0:40:40, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.5681 2023-01-02 04:46:34,445 - mmcls - INFO - Epoch [95][2600/5005] lr: 1.000e-04, eta: 0:40:31, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5868 2023-01-02 04:46:43,308 - mmcls - INFO - Epoch [95][2700/5005] lr: 1.000e-04, eta: 0:40:22, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.5860 2023-01-02 04:46:52,096 - mmcls - INFO - Epoch [95][2800/5005] lr: 1.000e-04, eta: 0:40:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5782 2023-01-02 04:47:01,096 - mmcls - INFO - Epoch [95][2900/5005] lr: 1.000e-04, eta: 0:40:04, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.5784 2023-01-02 04:47:09,908 - mmcls - INFO - Epoch [95][3000/5005] lr: 1.000e-04, eta: 0:39:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5770 2023-01-02 04:47:19,011 - mmcls - INFO - Epoch [95][3100/5005] lr: 1.000e-04, eta: 0:39:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.5802 2023-01-02 04:47:28,066 - mmcls - INFO - Epoch [95][3200/5005] lr: 1.000e-04, eta: 0:39:38, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.5827 2023-01-02 04:47:36,760 - mmcls - INFO - Epoch [95][3300/5005] lr: 1.000e-04, eta: 0:39:29, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.5739 2023-01-02 04:47:45,536 - mmcls - INFO - Epoch [95][3400/5005] lr: 1.000e-04, eta: 0:39:20, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.5796 2023-01-02 04:47:54,413 - mmcls - INFO - Epoch [95][3500/5005] lr: 1.000e-04, eta: 0:39:11, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.5825 2023-01-02 04:48:03,406 - mmcls - INFO - Epoch [95][3600/5005] lr: 1.000e-04, eta: 0:39:02, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.5778 2023-01-02 04:48:12,120 - mmcls - INFO - Epoch [95][3700/5005] lr: 1.000e-04, eta: 0:38:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5867 2023-01-02 04:48:20,940 - mmcls - INFO - Epoch [95][3800/5005] lr: 1.000e-04, eta: 0:38:45, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6139 2023-01-02 04:48:30,004 - mmcls - INFO - Epoch [95][3900/5005] lr: 1.000e-04, eta: 0:38:36, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5872 2023-01-02 04:48:39,222 - mmcls - INFO - Epoch [95][4000/5005] lr: 1.000e-04, eta: 0:38:27, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.5785 2023-01-02 04:48:48,281 - mmcls - INFO - Epoch [95][4100/5005] lr: 1.000e-04, eta: 0:38:18, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5895 2023-01-02 04:48:56,904 - mmcls - INFO - Epoch [95][4200/5005] lr: 1.000e-04, eta: 0:38:09, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6094 2023-01-02 04:49:05,903 - mmcls - INFO - Epoch [95][4300/5005] lr: 1.000e-04, eta: 0:38:00, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.5677 2023-01-02 04:49:14,829 - mmcls - INFO - Epoch [95][4400/5005] lr: 1.000e-04, eta: 0:37:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6011 2023-01-02 04:49:23,368 - mmcls - INFO - Epoch [95][4500/5005] lr: 1.000e-04, eta: 0:37:43, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.5661 2023-01-02 04:49:32,144 - mmcls - INFO - Epoch [95][4600/5005] lr: 1.000e-04, eta: 0:37:34, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.5934 2023-01-02 04:49:41,197 - mmcls - INFO - Epoch [95][4700/5005] lr: 1.000e-04, eta: 0:37:25, time: 0.091, data_time: 0.031, memory: 1224, loss: 1.5740 2023-01-02 04:49:50,063 - mmcls - INFO - Epoch [95][4800/5005] lr: 1.000e-04, eta: 0:37:16, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5845 2023-01-02 04:49:58,841 - mmcls - INFO - Epoch [95][4900/5005] lr: 1.000e-04, eta: 0:37:07, time: 0.088, data_time: 0.044, memory: 1224, loss: 1.5835 2023-01-02 04:50:07,672 - mmcls - INFO - Epoch [95][5000/5005] lr: 1.000e-04, eta: 0:36:58, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.5867 2023-01-02 04:50:30,329 - mmcls - INFO - Epoch(val) [95][196] accuracy_top-1: 65.3760, accuracy_top-5: 85.7900 2023-01-02 04:50:41,245 - mmcls - INFO - Epoch [96][100/5005] lr: 1.000e-04, eta: 0:36:49, time: 0.109, data_time: 0.051, memory: 1224, loss: 1.5557 2023-01-02 04:50:49,791 - mmcls - INFO - Epoch [96][200/5005] lr: 1.000e-04, eta: 0:36:40, time: 0.085, data_time: 0.016, memory: 1224, loss: 1.5764 2023-01-02 04:50:58,392 - mmcls - INFO - Epoch [96][300/5005] lr: 1.000e-04, eta: 0:36:31, time: 0.086, data_time: 0.026, memory: 1224, loss: 1.5813 2023-01-02 04:51:07,274 - mmcls - INFO - Epoch [96][400/5005] lr: 1.000e-04, eta: 0:36:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5697 2023-01-02 04:51:16,255 - mmcls - INFO - Epoch [96][500/5005] lr: 1.000e-04, eta: 0:36:14, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5877 2023-01-02 04:51:25,138 - mmcls - INFO - Epoch [96][600/5005] lr: 1.000e-04, eta: 0:36:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5761 2023-01-02 04:51:34,011 - mmcls - INFO - Epoch [96][700/5005] lr: 1.000e-04, eta: 0:35:56, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.5665 2023-01-02 04:51:43,150 - mmcls - INFO - Epoch [96][800/5005] lr: 1.000e-04, eta: 0:35:47, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.5855 2023-01-02 04:51:51,784 - mmcls - INFO - Epoch [96][900/5005] lr: 1.000e-04, eta: 0:35:38, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5790 2023-01-02 04:52:00,861 - mmcls - INFO - Epoch [96][1000/5005] lr: 1.000e-04, eta: 0:35:29, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6128 2023-01-02 04:52:09,578 - mmcls - INFO - Epoch [96][1100/5005] lr: 1.000e-04, eta: 0:35:20, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5575 2023-01-02 04:52:18,268 - mmcls - INFO - Epoch [96][1200/5005] lr: 1.000e-04, eta: 0:35:12, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5917 2023-01-02 04:52:27,367 - mmcls - INFO - Epoch [96][1300/5005] lr: 1.000e-04, eta: 0:35:03, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.5866 2023-01-02 04:52:35,892 - mmcls - INFO - Epoch [96][1400/5005] lr: 1.000e-04, eta: 0:34:54, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.5878 2023-01-02 04:52:44,735 - mmcls - INFO - Epoch [96][1500/5005] lr: 1.000e-04, eta: 0:34:45, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.5796 2023-01-02 04:52:53,350 - mmcls - INFO - Epoch [96][1600/5005] lr: 1.000e-04, eta: 0:34:36, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5801 2023-01-02 04:53:01,991 - mmcls - INFO - Epoch [96][1700/5005] lr: 1.000e-04, eta: 0:34:27, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5766 2023-01-02 04:53:10,799 - mmcls - INFO - Epoch [96][1800/5005] lr: 1.000e-04, eta: 0:34:18, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.5864 2023-01-02 04:53:19,681 - mmcls - INFO - Epoch [96][1900/5005] lr: 1.000e-04, eta: 0:34:09, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.5858 2023-01-02 04:53:28,739 - mmcls - INFO - Epoch [96][2000/5005] lr: 1.000e-04, eta: 0:34:01, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5910 2023-01-02 04:53:37,375 - mmcls - INFO - Epoch [96][2100/5005] lr: 1.000e-04, eta: 0:33:52, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5842 2023-01-02 04:53:46,346 - mmcls - INFO - Epoch [96][2200/5005] lr: 1.000e-04, eta: 0:33:43, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.5741 2023-01-02 04:53:55,210 - mmcls - INFO - Epoch [96][2300/5005] lr: 1.000e-04, eta: 0:33:34, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5737 2023-01-02 04:54:03,992 - mmcls - INFO - Epoch [96][2400/5005] lr: 1.000e-04, eta: 0:33:25, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.5792 2023-01-02 04:54:12,700 - mmcls - INFO - Epoch [96][2500/5005] lr: 1.000e-04, eta: 0:33:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5855 2023-01-02 04:54:21,737 - mmcls - INFO - Epoch [96][2600/5005] lr: 1.000e-04, eta: 0:33:07, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.5881 2023-01-02 04:54:30,659 - mmcls - INFO - Epoch [96][2700/5005] lr: 1.000e-04, eta: 0:32:59, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.5764 2023-01-02 04:54:39,296 - mmcls - INFO - Epoch [96][2800/5005] lr: 1.000e-04, eta: 0:32:50, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.5720 2023-01-02 04:54:48,170 - mmcls - INFO - Epoch [96][2900/5005] lr: 1.000e-04, eta: 0:32:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5871 2023-01-02 04:54:57,067 - mmcls - INFO - Epoch [96][3000/5005] lr: 1.000e-04, eta: 0:32:32, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.5751 2023-01-02 04:55:05,783 - mmcls - INFO - Epoch [96][3100/5005] lr: 1.000e-04, eta: 0:32:23, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5813 2023-01-02 04:55:14,803 - mmcls - INFO - Epoch [96][3200/5005] lr: 1.000e-04, eta: 0:32:14, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.5897 2023-01-02 04:55:23,929 - mmcls - INFO - Epoch [96][3300/5005] lr: 1.000e-04, eta: 0:32:05, time: 0.091, data_time: 0.028, memory: 1224, loss: 1.5860 2023-01-02 04:55:32,787 - mmcls - INFO - Epoch [96][3400/5005] lr: 1.000e-04, eta: 0:31:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5886 2023-01-02 04:55:41,398 - mmcls - INFO - Epoch [96][3500/5005] lr: 1.000e-04, eta: 0:31:48, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.5938 2023-01-02 04:55:50,377 - mmcls - INFO - Epoch [96][3600/5005] lr: 1.000e-04, eta: 0:31:39, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.5729 2023-01-02 04:55:58,978 - mmcls - INFO - Epoch [96][3700/5005] lr: 1.000e-04, eta: 0:31:30, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.5848 2023-01-02 04:56:07,830 - mmcls - INFO - Epoch [96][3800/5005] lr: 1.000e-04, eta: 0:31:21, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.5828 2023-01-02 04:56:16,273 - mmcls - INFO - Epoch [96][3900/5005] lr: 1.000e-04, eta: 0:31:12, time: 0.084, data_time: 0.024, memory: 1224, loss: 1.5719 2023-01-02 04:56:25,169 - mmcls - INFO - Epoch [96][4000/5005] lr: 1.000e-04, eta: 0:31:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5766 2023-01-02 04:56:34,216 - mmcls - INFO - Epoch [96][4100/5005] lr: 1.000e-04, eta: 0:30:54, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.5759 2023-01-02 04:56:43,151 - mmcls - INFO - Epoch [96][4200/5005] lr: 1.000e-04, eta: 0:30:46, time: 0.089, data_time: 0.037, memory: 1224, loss: 1.5854 2023-01-02 04:56:52,225 - mmcls - INFO - Epoch [96][4300/5005] lr: 1.000e-04, eta: 0:30:37, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.5789 2023-01-02 04:57:00,792 - mmcls - INFO - Epoch [96][4400/5005] lr: 1.000e-04, eta: 0:30:28, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.5734 2023-01-02 04:57:09,698 - mmcls - INFO - Epoch [96][4500/5005] lr: 1.000e-04, eta: 0:30:19, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5678 2023-01-02 04:57:18,666 - mmcls - INFO - Epoch [96][4600/5005] lr: 1.000e-04, eta: 0:30:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5761 2023-01-02 04:57:27,577 - mmcls - INFO - Epoch [96][4700/5005] lr: 1.000e-04, eta: 0:30:01, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5873 2023-01-02 04:57:36,328 - mmcls - INFO - Epoch [96][4800/5005] lr: 1.000e-04, eta: 0:29:52, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.5818 2023-01-02 04:57:45,061 - mmcls - INFO - Epoch [96][4900/5005] lr: 1.000e-04, eta: 0:29:44, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.5916 2023-01-02 04:57:53,744 - mmcls - INFO - Epoch [96][5000/5005] lr: 1.000e-04, eta: 0:29:35, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.5729 2023-01-02 04:58:16,383 - mmcls - INFO - Epoch(val) [96][196] accuracy_top-1: 65.4020, accuracy_top-5: 85.7920 2023-01-02 04:58:27,557 - mmcls - INFO - Epoch [97][100/5005] lr: 1.000e-04, eta: 0:29:25, time: 0.111, data_time: 0.039, memory: 1224, loss: 1.5681 2023-01-02 04:58:36,458 - mmcls - INFO - Epoch [97][200/5005] lr: 1.000e-04, eta: 0:29:17, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.5833 2023-01-02 04:58:45,350 - mmcls - INFO - Epoch [97][300/5005] lr: 1.000e-04, eta: 0:29:08, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.5962 2023-01-02 04:58:54,166 - mmcls - INFO - Epoch [97][400/5005] lr: 1.000e-04, eta: 0:28:59, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6109 2023-01-02 04:59:03,038 - mmcls - INFO - Epoch [97][500/5005] lr: 1.000e-04, eta: 0:28:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5786 2023-01-02 04:59:11,532 - mmcls - INFO - Epoch [97][600/5005] lr: 1.000e-04, eta: 0:28:41, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.5891 2023-01-02 04:59:20,504 - mmcls - INFO - Epoch [97][700/5005] lr: 1.000e-04, eta: 0:28:32, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5744 2023-01-02 04:59:29,669 - mmcls - INFO - Epoch [97][800/5005] lr: 1.000e-04, eta: 0:28:23, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.5586 2023-01-02 04:59:38,558 - mmcls - INFO - Epoch [97][900/5005] lr: 1.000e-04, eta: 0:28:15, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.5872 2023-01-02 04:59:47,174 - mmcls - INFO - Epoch [97][1000/5005] lr: 1.000e-04, eta: 0:28:06, time: 0.086, data_time: 0.029, memory: 1224, loss: 1.5386 2023-01-02 04:59:55,762 - mmcls - INFO - Epoch [97][1100/5005] lr: 1.000e-04, eta: 0:27:57, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.5718 2023-01-02 05:00:04,482 - mmcls - INFO - Epoch [97][1200/5005] lr: 1.000e-04, eta: 0:27:48, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.5551 2023-01-02 05:00:13,083 - mmcls - INFO - Epoch [97][1300/5005] lr: 1.000e-04, eta: 0:27:39, time: 0.086, data_time: 0.039, memory: 1224, loss: 1.5914 2023-01-02 05:00:21,789 - mmcls - INFO - Epoch [97][1400/5005] lr: 1.000e-04, eta: 0:27:30, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.5902 2023-01-02 05:00:30,546 - mmcls - INFO - Epoch [97][1500/5005] lr: 1.000e-04, eta: 0:27:21, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.5716 2023-01-02 05:00:39,716 - mmcls - INFO - Epoch [97][1600/5005] lr: 1.000e-04, eta: 0:27:12, time: 0.092, data_time: 0.027, memory: 1224, loss: 1.5884 2023-01-02 05:00:48,866 - mmcls - INFO - Epoch [97][1700/5005] lr: 1.000e-04, eta: 0:27:04, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.5615 2023-01-02 05:00:57,439 - mmcls - INFO - Epoch [97][1800/5005] lr: 1.000e-04, eta: 0:26:55, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.5816 2023-01-02 05:01:06,116 - mmcls - INFO - Epoch [97][1900/5005] lr: 1.000e-04, eta: 0:26:46, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5735 2023-01-02 05:01:14,958 - mmcls - INFO - Epoch [97][2000/5005] lr: 1.000e-04, eta: 0:26:37, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5757 2023-01-02 05:01:23,612 - mmcls - INFO - Epoch [97][2100/5005] lr: 1.000e-04, eta: 0:26:28, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.5884 2023-01-02 05:01:32,534 - mmcls - INFO - Epoch [97][2200/5005] lr: 1.000e-04, eta: 0:26:19, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.5661 2023-01-02 05:01:41,446 - mmcls - INFO - Epoch [97][2300/5005] lr: 1.000e-04, eta: 0:26:10, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.5846 2023-01-02 05:01:50,141 - mmcls - INFO - Epoch [97][2400/5005] lr: 1.000e-04, eta: 0:26:01, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5757 2023-01-02 05:01:59,007 - mmcls - INFO - Epoch [97][2500/5005] lr: 1.000e-04, eta: 0:25:53, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5769 2023-01-02 05:02:07,792 - mmcls - INFO - Epoch [97][2600/5005] lr: 1.000e-04, eta: 0:25:44, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.5935 2023-01-02 05:02:16,589 - mmcls - INFO - Epoch [97][2700/5005] lr: 1.000e-04, eta: 0:25:35, time: 0.088, data_time: 0.041, memory: 1224, loss: 1.5904 2023-01-02 05:02:25,829 - mmcls - INFO - Epoch [97][2800/5005] lr: 1.000e-04, eta: 0:25:26, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.5790 2023-01-02 05:02:34,687 - mmcls - INFO - Epoch [97][2900/5005] lr: 1.000e-04, eta: 0:25:17, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5663 2023-01-02 05:02:43,349 - mmcls - INFO - Epoch [97][3000/5005] lr: 1.000e-04, eta: 0:25:08, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.5921 2023-01-02 05:02:52,269 - mmcls - INFO - Epoch [97][3100/5005] lr: 1.000e-04, eta: 0:24:59, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5785 2023-01-02 05:03:00,906 - mmcls - INFO - Epoch [97][3200/5005] lr: 1.000e-04, eta: 0:24:51, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.5922 2023-01-02 05:03:09,658 - mmcls - INFO - Epoch [97][3300/5005] lr: 1.000e-04, eta: 0:24:42, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.5664 2023-01-02 05:03:18,667 - mmcls - INFO - Epoch [97][3400/5005] lr: 1.000e-04, eta: 0:24:33, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.5941 2023-01-02 05:03:27,392 - mmcls - INFO - Epoch [97][3500/5005] lr: 1.000e-04, eta: 0:24:24, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.5735 2023-01-02 05:03:36,424 - mmcls - INFO - Epoch [97][3600/5005] lr: 1.000e-04, eta: 0:24:15, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.6001 2023-01-02 05:03:45,023 - mmcls - INFO - Epoch [97][3700/5005] lr: 1.000e-04, eta: 0:24:06, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6003 2023-01-02 05:03:53,911 - mmcls - INFO - Epoch [97][3800/5005] lr: 1.000e-04, eta: 0:23:57, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.5592 2023-01-02 05:04:02,644 - mmcls - INFO - Epoch [97][3900/5005] lr: 1.000e-04, eta: 0:23:48, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.5670 2023-01-02 05:04:11,577 - mmcls - INFO - Epoch [97][4000/5005] lr: 1.000e-04, eta: 0:23:40, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5689 2023-01-02 05:04:20,572 - mmcls - INFO - Epoch [97][4100/5005] lr: 1.000e-04, eta: 0:23:31, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.5810 2023-01-02 05:04:29,429 - mmcls - INFO - Epoch [97][4200/5005] lr: 1.000e-04, eta: 0:23:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5756 2023-01-02 05:04:38,027 - mmcls - INFO - Epoch [97][4300/5005] lr: 1.000e-04, eta: 0:23:13, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.5470 2023-01-02 05:04:46,970 - mmcls - INFO - Epoch [97][4400/5005] lr: 1.000e-04, eta: 0:23:04, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5823 2023-01-02 05:04:55,945 - mmcls - INFO - Epoch [97][4500/5005] lr: 1.000e-04, eta: 0:22:55, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.5798 2023-01-02 05:05:04,834 - mmcls - INFO - Epoch [97][4600/5005] lr: 1.000e-04, eta: 0:22:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6110 2023-01-02 05:05:13,542 - mmcls - INFO - Epoch [97][4700/5005] lr: 1.000e-04, eta: 0:22:38, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.5613 2023-01-02 05:05:22,378 - mmcls - INFO - Epoch [97][4800/5005] lr: 1.000e-04, eta: 0:22:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5628 2023-01-02 05:05:31,154 - mmcls - INFO - Epoch [97][4900/5005] lr: 1.000e-04, eta: 0:22:20, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5461 2023-01-02 05:05:40,095 - mmcls - INFO - Epoch [97][5000/5005] lr: 1.000e-04, eta: 0:22:11, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5682 2023-01-02 05:06:02,820 - mmcls - INFO - Epoch(val) [97][196] accuracy_top-1: 65.3600, accuracy_top-5: 85.7300 2023-01-02 05:06:14,233 - mmcls - INFO - Epoch [98][100/5005] lr: 1.000e-04, eta: 0:22:02, time: 0.114, data_time: 0.053, memory: 1224, loss: 1.5806 2023-01-02 05:06:22,902 - mmcls - INFO - Epoch [98][200/5005] lr: 1.000e-04, eta: 0:21:53, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5645 2023-01-02 05:06:31,714 - mmcls - INFO - Epoch [98][300/5005] lr: 1.000e-04, eta: 0:21:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5616 2023-01-02 05:06:40,404 - mmcls - INFO - Epoch [98][400/5005] lr: 1.000e-04, eta: 0:21:35, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.5705 2023-01-02 05:06:48,979 - mmcls - INFO - Epoch [98][500/5005] lr: 1.000e-04, eta: 0:21:26, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.5688 2023-01-02 05:06:57,872 - mmcls - INFO - Epoch [98][600/5005] lr: 1.000e-04, eta: 0:21:17, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5775 2023-01-02 05:07:06,525 - mmcls - INFO - Epoch [98][700/5005] lr: 1.000e-04, eta: 0:21:09, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5709 2023-01-02 05:07:15,538 - mmcls - INFO - Epoch [98][800/5005] lr: 1.000e-04, eta: 0:21:00, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.5743 2023-01-02 05:07:24,307 - mmcls - INFO - Epoch [98][900/5005] lr: 1.000e-04, eta: 0:20:51, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.5915 2023-01-02 05:07:32,891 - mmcls - INFO - Epoch [98][1000/5005] lr: 1.000e-04, eta: 0:20:42, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.5666 2023-01-02 05:07:41,478 - mmcls - INFO - Epoch [98][1100/5005] lr: 1.000e-04, eta: 0:20:33, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5965 2023-01-02 05:07:50,466 - mmcls - INFO - Epoch [98][1200/5005] lr: 1.000e-04, eta: 0:20:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5781 2023-01-02 05:07:59,517 - mmcls - INFO - Epoch [98][1300/5005] lr: 1.000e-04, eta: 0:20:15, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.5721 2023-01-02 05:08:08,671 - mmcls - INFO - Epoch [98][1400/5005] lr: 1.000e-04, eta: 0:20:06, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.5852 2023-01-02 05:08:17,371 - mmcls - INFO - Epoch [98][1500/5005] lr: 1.000e-04, eta: 0:19:58, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5790 2023-01-02 05:08:26,104 - mmcls - INFO - Epoch [98][1600/5005] lr: 1.000e-04, eta: 0:19:49, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.5722 2023-01-02 05:08:34,721 - mmcls - INFO - Epoch [98][1700/5005] lr: 1.000e-04, eta: 0:19:40, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6041 2023-01-02 05:08:43,525 - mmcls - INFO - Epoch [98][1800/5005] lr: 1.000e-04, eta: 0:19:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5810 2023-01-02 05:08:52,287 - mmcls - INFO - Epoch [98][1900/5005] lr: 1.000e-04, eta: 0:19:22, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5709 2023-01-02 05:09:01,057 - mmcls - INFO - Epoch [98][2000/5005] lr: 1.000e-04, eta: 0:19:13, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5842 2023-01-02 05:09:09,786 - mmcls - INFO - Epoch [98][2100/5005] lr: 1.000e-04, eta: 0:19:04, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.5804 2023-01-02 05:09:18,978 - mmcls - INFO - Epoch [98][2200/5005] lr: 1.000e-04, eta: 0:18:56, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.5472 2023-01-02 05:09:27,918 - mmcls - INFO - Epoch [98][2300/5005] lr: 1.000e-04, eta: 0:18:47, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5772 2023-01-02 05:09:36,662 - mmcls - INFO - Epoch [98][2400/5005] lr: 1.000e-04, eta: 0:18:38, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.5903 2023-01-02 05:09:45,268 - mmcls - INFO - Epoch [98][2500/5005] lr: 1.000e-04, eta: 0:18:29, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6041 2023-01-02 05:09:54,361 - mmcls - INFO - Epoch [98][2600/5005] lr: 1.000e-04, eta: 0:18:20, time: 0.091, data_time: 0.037, memory: 1224, loss: 1.5762 2023-01-02 05:10:03,308 - mmcls - INFO - Epoch [98][2700/5005] lr: 1.000e-04, eta: 0:18:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5596 2023-01-02 05:10:12,103 - mmcls - INFO - Epoch [98][2800/5005] lr: 1.000e-04, eta: 0:18:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5693 2023-01-02 05:10:20,800 - mmcls - INFO - Epoch [98][2900/5005] lr: 1.000e-04, eta: 0:17:53, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5662 2023-01-02 05:10:29,501 - mmcls - INFO - Epoch [98][3000/5005] lr: 1.000e-04, eta: 0:17:45, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.5730 2023-01-02 05:10:38,404 - mmcls - INFO - Epoch [98][3100/5005] lr: 1.000e-04, eta: 0:17:36, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.5897 2023-01-02 05:10:47,157 - mmcls - INFO - Epoch [98][3200/5005] lr: 1.000e-04, eta: 0:17:27, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.5691 2023-01-02 05:10:55,705 - mmcls - INFO - Epoch [98][3300/5005] lr: 1.000e-04, eta: 0:17:18, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.5719 2023-01-02 05:11:04,550 - mmcls - INFO - Epoch [98][3400/5005] lr: 1.000e-04, eta: 0:17:09, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5854 2023-01-02 05:11:13,337 - mmcls - INFO - Epoch [98][3500/5005] lr: 1.000e-04, eta: 0:17:00, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5969 2023-01-02 05:11:22,338 - mmcls - INFO - Epoch [98][3600/5005] lr: 1.000e-04, eta: 0:16:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5729 2023-01-02 05:11:30,960 - mmcls - INFO - Epoch [98][3700/5005] lr: 1.000e-04, eta: 0:16:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5956 2023-01-02 05:11:39,470 - mmcls - INFO - Epoch [98][3800/5005] lr: 1.000e-04, eta: 0:16:34, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.5842 2023-01-02 05:11:48,180 - mmcls - INFO - Epoch [98][3900/5005] lr: 1.000e-04, eta: 0:16:25, time: 0.087, data_time: 0.033, memory: 1224, loss: 1.5665 2023-01-02 05:11:57,008 - mmcls - INFO - Epoch [98][4000/5005] lr: 1.000e-04, eta: 0:16:16, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5642 2023-01-02 05:12:05,770 - mmcls - INFO - Epoch [98][4100/5005] lr: 1.000e-04, eta: 0:16:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6066 2023-01-02 05:12:14,451 - mmcls - INFO - Epoch [98][4200/5005] lr: 1.000e-04, eta: 0:15:58, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5847 2023-01-02 05:12:23,621 - mmcls - INFO - Epoch [98][4300/5005] lr: 1.000e-04, eta: 0:15:49, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.5872 2023-01-02 05:12:32,429 - mmcls - INFO - Epoch [98][4400/5005] lr: 1.000e-04, eta: 0:15:40, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.5678 2023-01-02 05:12:41,454 - mmcls - INFO - Epoch [98][4500/5005] lr: 1.000e-04, eta: 0:15:32, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.5910 2023-01-02 05:12:50,219 - mmcls - INFO - Epoch [98][4600/5005] lr: 1.000e-04, eta: 0:15:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5602 2023-01-02 05:12:58,993 - mmcls - INFO - Epoch [98][4700/5005] lr: 1.000e-04, eta: 0:15:14, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5951 2023-01-02 05:13:07,831 - mmcls - INFO - Epoch [98][4800/5005] lr: 1.000e-04, eta: 0:15:05, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5786 2023-01-02 05:13:16,675 - mmcls - INFO - Epoch [98][4900/5005] lr: 1.000e-04, eta: 0:14:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5910 2023-01-02 05:13:25,592 - mmcls - INFO - Epoch [98][5000/5005] lr: 1.000e-04, eta: 0:14:47, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.5727 2023-01-02 05:13:48,367 - mmcls - INFO - Epoch(val) [98][196] accuracy_top-1: 65.3100, accuracy_top-5: 85.7620 2023-01-02 05:13:59,478 - mmcls - INFO - Epoch [99][100/5005] lr: 1.000e-04, eta: 0:14:38, time: 0.111, data_time: 0.029, memory: 1224, loss: 1.5952 2023-01-02 05:14:08,192 - mmcls - INFO - Epoch [99][200/5005] lr: 1.000e-04, eta: 0:14:29, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5776 2023-01-02 05:14:17,008 - mmcls - INFO - Epoch [99][300/5005] lr: 1.000e-04, eta: 0:14:20, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5773 2023-01-02 05:14:25,792 - mmcls - INFO - Epoch [99][400/5005] lr: 1.000e-04, eta: 0:14:11, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.5783 2023-01-02 05:14:34,594 - mmcls - INFO - Epoch [99][500/5005] lr: 1.000e-04, eta: 0:14:03, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.5777 2023-01-02 05:14:43,660 - mmcls - INFO - Epoch [99][600/5005] lr: 1.000e-04, eta: 0:13:54, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.5802 2023-01-02 05:14:52,399 - mmcls - INFO - Epoch [99][700/5005] lr: 1.000e-04, eta: 0:13:45, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5844 2023-01-02 05:15:01,367 - mmcls - INFO - Epoch [99][800/5005] lr: 1.000e-04, eta: 0:13:36, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.5671 2023-01-02 05:15:10,046 - mmcls - INFO - Epoch [99][900/5005] lr: 1.000e-04, eta: 0:13:27, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.5708 2023-01-02 05:15:18,777 - mmcls - INFO - Epoch [99][1000/5005] lr: 1.000e-04, eta: 0:13:18, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.5947 2023-01-02 05:15:27,427 - mmcls - INFO - Epoch [99][1100/5005] lr: 1.000e-04, eta: 0:13:09, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5742 2023-01-02 05:15:36,323 - mmcls - INFO - Epoch [99][1200/5005] lr: 1.000e-04, eta: 0:13:00, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5808 2023-01-02 05:15:45,127 - mmcls - INFO - Epoch [99][1300/5005] lr: 1.000e-04, eta: 0:12:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5850 2023-01-02 05:15:53,837 - mmcls - INFO - Epoch [99][1400/5005] lr: 1.000e-04, eta: 0:12:43, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5833 2023-01-02 05:16:02,534 - mmcls - INFO - Epoch [99][1500/5005] lr: 1.000e-04, eta: 0:12:34, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.5725 2023-01-02 05:16:11,495 - mmcls - INFO - Epoch [99][1600/5005] lr: 1.000e-04, eta: 0:12:25, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.5656 2023-01-02 05:16:20,448 - mmcls - INFO - Epoch [99][1700/5005] lr: 1.000e-04, eta: 0:12:16, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.5889 2023-01-02 05:16:29,219 - mmcls - INFO - Epoch [99][1800/5005] lr: 1.000e-04, eta: 0:12:07, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.5870 2023-01-02 05:16:38,108 - mmcls - INFO - Epoch [99][1900/5005] lr: 1.000e-04, eta: 0:11:58, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5605 2023-01-02 05:16:47,035 - mmcls - INFO - Epoch [99][2000/5005] lr: 1.000e-04, eta: 0:11:50, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5767 2023-01-02 05:16:55,814 - mmcls - INFO - Epoch [99][2100/5005] lr: 1.000e-04, eta: 0:11:41, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5535 2023-01-02 05:17:04,380 - mmcls - INFO - Epoch [99][2200/5005] lr: 1.000e-04, eta: 0:11:32, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.5794 2023-01-02 05:17:13,424 - mmcls - INFO - Epoch [99][2300/5005] lr: 1.000e-04, eta: 0:11:23, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.5709 2023-01-02 05:17:22,292 - mmcls - INFO - Epoch [99][2400/5005] lr: 1.000e-04, eta: 0:11:14, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.5686 2023-01-02 05:17:31,463 - mmcls - INFO - Epoch [99][2500/5005] lr: 1.000e-04, eta: 0:11:05, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.5923 2023-01-02 05:17:40,264 - mmcls - INFO - Epoch [99][2600/5005] lr: 1.000e-04, eta: 0:10:56, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.5554 2023-01-02 05:17:48,864 - mmcls - INFO - Epoch [99][2700/5005] lr: 1.000e-04, eta: 0:10:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5728 2023-01-02 05:17:57,578 - mmcls - INFO - Epoch [99][2800/5005] lr: 1.000e-04, eta: 0:10:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5723 2023-01-02 05:18:06,488 - mmcls - INFO - Epoch [99][2900/5005] lr: 1.000e-04, eta: 0:10:30, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.5436 2023-01-02 05:18:15,319 - mmcls - INFO - Epoch [99][3000/5005] lr: 1.000e-04, eta: 0:10:21, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.5868 2023-01-02 05:18:24,163 - mmcls - INFO - Epoch [99][3100/5005] lr: 1.000e-04, eta: 0:10:12, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5692 2023-01-02 05:18:33,037 - mmcls - INFO - Epoch [99][3200/5005] lr: 1.000e-04, eta: 0:10:03, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.5730 2023-01-02 05:18:41,871 - mmcls - INFO - Epoch [99][3300/5005] lr: 1.000e-04, eta: 0:09:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5882 2023-01-02 05:18:50,476 - mmcls - INFO - Epoch [99][3400/5005] lr: 1.000e-04, eta: 0:09:45, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.5885 2023-01-02 05:18:59,279 - mmcls - INFO - Epoch [99][3500/5005] lr: 1.000e-04, eta: 0:09:37, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.5993 2023-01-02 05:19:08,216 - mmcls - INFO - Epoch [99][3600/5005] lr: 1.000e-04, eta: 0:09:28, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.5715 2023-01-02 05:19:16,852 - mmcls - INFO - Epoch [99][3700/5005] lr: 1.000e-04, eta: 0:09:19, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.5743 2023-01-02 05:19:26,033 - mmcls - INFO - Epoch [99][3800/5005] lr: 1.000e-04, eta: 0:09:10, time: 0.092, data_time: 0.044, memory: 1224, loss: 1.5668 2023-01-02 05:19:34,808 - mmcls - INFO - Epoch [99][3900/5005] lr: 1.000e-04, eta: 0:09:01, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.5769 2023-01-02 05:19:43,752 - mmcls - INFO - Epoch [99][4000/5005] lr: 1.000e-04, eta: 0:08:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5396 2023-01-02 05:19:52,361 - mmcls - INFO - Epoch [99][4100/5005] lr: 1.000e-04, eta: 0:08:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.5724 2023-01-02 05:20:01,172 - mmcls - INFO - Epoch [99][4200/5005] lr: 1.000e-04, eta: 0:08:35, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.5776 2023-01-02 05:20:10,107 - mmcls - INFO - Epoch [99][4300/5005] lr: 1.000e-04, eta: 0:08:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5851 2023-01-02 05:20:19,129 - mmcls - INFO - Epoch [99][4400/5005] lr: 1.000e-04, eta: 0:08:17, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.5845 2023-01-02 05:20:27,902 - mmcls - INFO - Epoch [99][4500/5005] lr: 1.000e-04, eta: 0:08:08, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.5589 2023-01-02 05:20:36,774 - mmcls - INFO - Epoch [99][4600/5005] lr: 1.000e-04, eta: 0:07:59, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.5616 2023-01-02 05:20:45,642 - mmcls - INFO - Epoch [99][4700/5005] lr: 1.000e-04, eta: 0:07:50, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5876 2023-01-02 05:20:54,242 - mmcls - INFO - Epoch [99][4800/5005] lr: 1.000e-04, eta: 0:07:41, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6037 2023-01-02 05:21:03,181 - mmcls - INFO - Epoch [99][4900/5005] lr: 1.000e-04, eta: 0:07:32, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.5876 2023-01-02 05:21:11,924 - mmcls - INFO - Epoch [99][5000/5005] lr: 1.000e-04, eta: 0:07:24, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.5844 2023-01-02 05:21:34,694 - mmcls - INFO - Epoch(val) [99][196] accuracy_top-1: 65.4180, accuracy_top-5: 85.7900 2023-01-02 05:21:45,488 - mmcls - INFO - Epoch [100][100/5005] lr: 1.000e-04, eta: 0:07:14, time: 0.108, data_time: 0.039, memory: 1224, loss: 1.5828 2023-01-02 05:21:54,328 - mmcls - INFO - Epoch [100][200/5005] lr: 1.000e-04, eta: 0:07:05, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.5668 2023-01-02 05:22:02,958 - mmcls - INFO - Epoch [100][300/5005] lr: 1.000e-04, eta: 0:06:57, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.5605 2023-01-02 05:22:12,085 - mmcls - INFO - Epoch [100][400/5005] lr: 1.000e-04, eta: 0:06:48, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.5737 2023-01-02 05:22:20,695 - mmcls - INFO - Epoch [100][500/5005] lr: 1.000e-04, eta: 0:06:39, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5879 2023-01-02 05:22:29,342 - mmcls - INFO - Epoch [100][600/5005] lr: 1.000e-04, eta: 0:06:30, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.5756 2023-01-02 05:22:38,262 - mmcls - INFO - Epoch [100][700/5005] lr: 1.000e-04, eta: 0:06:21, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.5823 2023-01-02 05:22:47,470 - mmcls - INFO - Epoch [100][800/5005] lr: 1.000e-04, eta: 0:06:12, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.5769 2023-01-02 05:22:56,258 - mmcls - INFO - Epoch [100][900/5005] lr: 1.000e-04, eta: 0:06:03, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.5704 2023-01-02 05:23:05,225 - mmcls - INFO - Epoch [100][1000/5005] lr: 1.000e-04, eta: 0:05:55, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.5853 2023-01-02 05:23:13,941 - mmcls - INFO - Epoch [100][1100/5005] lr: 1.000e-04, eta: 0:05:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5654 2023-01-02 05:23:22,624 - mmcls - INFO - Epoch [100][1200/5005] lr: 1.000e-04, eta: 0:05:37, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.5782 2023-01-02 05:23:31,406 - mmcls - INFO - Epoch [100][1300/5005] lr: 1.000e-04, eta: 0:05:28, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.5709 2023-01-02 05:23:40,115 - mmcls - INFO - Epoch [100][1400/5005] lr: 1.000e-04, eta: 0:05:19, time: 0.087, data_time: 0.035, memory: 1224, loss: 1.5866 2023-01-02 05:23:48,751 - mmcls - INFO - Epoch [100][1500/5005] lr: 1.000e-04, eta: 0:05:10, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.5704 2023-01-02 05:23:57,689 - mmcls - INFO - Epoch [100][1600/5005] lr: 1.000e-04, eta: 0:05:01, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.5837 2023-01-02 05:24:06,370 - mmcls - INFO - Epoch [100][1700/5005] lr: 1.000e-04, eta: 0:04:52, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.5736 2023-01-02 05:24:15,264 - mmcls - INFO - Epoch [100][1800/5005] lr: 1.000e-04, eta: 0:04:44, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.5853 2023-01-02 05:24:23,923 - mmcls - INFO - Epoch [100][1900/5005] lr: 1.000e-04, eta: 0:04:35, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.5694 2023-01-02 05:24:32,855 - mmcls - INFO - Epoch [100][2000/5005] lr: 1.000e-04, eta: 0:04:26, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.5935 2023-01-02 05:24:41,667 - mmcls - INFO - Epoch [100][2100/5005] lr: 1.000e-04, eta: 0:04:17, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.5708 2023-01-02 05:24:50,456 - mmcls - INFO - Epoch [100][2200/5005] lr: 1.000e-04, eta: 0:04:08, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.5762 2023-01-02 05:24:59,258 - mmcls - INFO - Epoch [100][2300/5005] lr: 1.000e-04, eta: 0:03:59, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.5510 2023-01-02 05:25:08,024 - mmcls - INFO - Epoch [100][2400/5005] lr: 1.000e-04, eta: 0:03:50, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.5720 2023-01-02 05:25:17,077 - mmcls - INFO - Epoch [100][2500/5005] lr: 1.000e-04, eta: 0:03:42, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.5912 2023-01-02 05:25:25,866 - mmcls - INFO - Epoch [100][2600/5005] lr: 1.000e-04, eta: 0:03:33, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.5695 2023-01-02 05:25:34,639 - mmcls - INFO - Epoch [100][2700/5005] lr: 1.000e-04, eta: 0:03:24, time: 0.088, data_time: 0.039, memory: 1224, loss: 1.5668 2023-01-02 05:25:43,456 - mmcls - INFO - Epoch [100][2800/5005] lr: 1.000e-04, eta: 0:03:15, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.5741 2023-01-02 05:25:52,142 - mmcls - INFO - Epoch [100][2900/5005] lr: 1.000e-04, eta: 0:03:06, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.5732 2023-01-02 05:26:00,763 - mmcls - INFO - Epoch [100][3000/5005] lr: 1.000e-04, eta: 0:02:57, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.5811 2023-01-02 05:26:09,707 - mmcls - INFO - Epoch [100][3100/5005] lr: 1.000e-04, eta: 0:02:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5964 2023-01-02 05:26:18,639 - mmcls - INFO - Epoch [100][3200/5005] lr: 1.000e-04, eta: 0:02:40, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.5928 2023-01-02 05:26:27,503 - mmcls - INFO - Epoch [100][3300/5005] lr: 1.000e-04, eta: 0:02:31, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5621 2023-01-02 05:26:35,935 - mmcls - INFO - Epoch [100][3400/5005] lr: 1.000e-04, eta: 0:02:22, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.5775 2023-01-02 05:26:44,864 - mmcls - INFO - Epoch [100][3500/5005] lr: 1.000e-04, eta: 0:02:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5721 2023-01-02 05:26:53,689 - mmcls - INFO - Epoch [100][3600/5005] lr: 1.000e-04, eta: 0:02:04, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.5808 2023-01-02 05:27:02,382 - mmcls - INFO - Epoch [100][3700/5005] lr: 1.000e-04, eta: 0:01:55, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.5721 2023-01-02 05:27:11,198 - mmcls - INFO - Epoch [100][3800/5005] lr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6036 2023-01-02 05:27:19,968 - mmcls - INFO - Epoch [100][3900/5005] lr: 1.000e-04, eta: 0:01:37, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.5860 2023-01-02 05:27:28,549 - mmcls - INFO - Epoch [100][4000/5005] lr: 1.000e-04, eta: 0:01:29, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.5708 2023-01-02 05:27:37,287 - mmcls - INFO - Epoch [100][4100/5005] lr: 1.000e-04, eta: 0:01:20, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.5870 2023-01-02 05:27:46,162 - mmcls - INFO - Epoch [100][4200/5005] lr: 1.000e-04, eta: 0:01:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.5577 2023-01-02 05:27:54,969 - mmcls - INFO - Epoch [100][4300/5005] lr: 1.000e-04, eta: 0:01:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.5683 2023-01-02 05:28:03,770 - mmcls - INFO - Epoch [100][4400/5005] lr: 1.000e-04, eta: 0:00:53, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.5880 2023-01-02 05:28:13,064 - mmcls - INFO - Epoch [100][4500/5005] lr: 1.000e-04, eta: 0:00:44, time: 0.093, data_time: 0.030, memory: 1224, loss: 1.5715 2023-01-02 05:28:21,743 - mmcls - INFO - Epoch [100][4600/5005] lr: 1.000e-04, eta: 0:00:35, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.5760 2023-01-02 05:28:31,001 - mmcls - INFO - Epoch [100][4700/5005] lr: 1.000e-04, eta: 0:00:27, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.5630 2023-01-02 05:28:39,656 - mmcls - INFO - Epoch [100][4800/5005] lr: 1.000e-04, eta: 0:00:18, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5797 2023-01-02 05:28:48,438 - mmcls - INFO - Epoch [100][4900/5005] lr: 1.000e-04, eta: 0:00:09, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.5830 2023-01-02 05:28:57,236 - mmcls - INFO - Epoch [100][5000/5005] lr: 1.000e-04, eta: 0:00:00, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6048 2023-01-02 05:28:57,585 - mmcls - INFO - Saving checkpoint at 100 epochs 2023-01-02 05:29:19,997 - mmcls - INFO - Epoch(val) [100][196] accuracy_top-1: 65.3060, accuracy_top-5: 85.8300 ================================================ FILE: classification-NO-activation-function/logs/pinet_1x1_relu.log ================================================ 2022-12-31 14:18:36,463 - mmcls - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.6.12 |Anaconda, Inc.| (default, Sep 8 2020, 23:10:56) [GCC 7.3.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 10.2, V10.2.8 GCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0 PyTorch: 1.9.0+cu102 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 10.2 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70 - CuDNN 7.6.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu102 OpenCV: 4.5.5 MMCV: 1.5.1 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler: 10.2 MMClassification: 0.24.0+91b85bb ------------------------------------------------------------ 2022-12-31 14:18:36,464 - mmcls - INFO - Distributed training: True 2022-12-31 14:18:36,596 - mmcls - INFO - Config: model = dict( type='ImageClassifier', backbone=dict( type='ResNet', depth=18, num_stages=4, out_indices=(3, ), style='pytorch'), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=1000, in_channels=512, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5))) dataset_type = 'ImageNet' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ] data = dict( samples_per_gpu=32, workers_per_gpu=2, train=dict( type='ImageNet', data_prefix='data/imagenet/train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ]), val=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]), test=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ])) evaluation = dict(interval=1, metric='accuracy') optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict(policy='step', step=[30, 60, 90]) runner = dict(type='EpochBasedRunner', max_epochs=100) checkpoint_config = dict(interval=50) log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] work_dir = './work_dirs/resnet18_8xb32_in1k_pi' gpu_ids = range(0, 8) 2022-12-31 14:18:42,027 - mmcls - INFO - Set random seed to 1565778062, deterministic: False 2022-12-31 14:18:42,212 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}] 2022-12-31 14:18:42,302 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01} Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv3.weight - torch.Size([64, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn3.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv3.weight - torch.Size([64, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn3.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv3.weight - torch.Size([128, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn3.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv3.weight - torch.Size([128, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn3.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv3.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv3.weight - torch.Size([256, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv3.weight - torch.Size([512, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.downsample.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv3.weight - torch.Size([512, 512, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier head.fc.weight - torch.Size([1000, 512]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([1000]): NormalInit: mean=0, std=0.01, bias=0 2022-12-31 14:19:10,732 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi 2022-12-31 14:19:10,733 - mmcls - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook -------------------- after_train_iter: (ABOVE_NORMAL) DistOptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (VERY_LOW ) TextLoggerHook -------------------- before_val_iter: (LOW ) IterTimerHook -------------------- after_val_iter: (LOW ) IterTimerHook -------------------- after_val_epoch: (VERY_LOW ) TextLoggerHook -------------------- after_run: (VERY_LOW ) TextLoggerHook -------------------- 2022-12-31 14:19:10,733 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs 2022-12-31 14:19:10,733 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend. 2022-12-31 14:19:22,898 - mmcls - INFO - Epoch [1][100/5005] lr: 1.000e-01, eta: 16:52:13, time: 0.121, data_time: 0.031, memory: 1224, loss: 6.9145 2022-12-31 14:19:31,850 - mmcls - INFO - Epoch [1][200/5005] lr: 1.000e-01, eta: 14:39:06, time: 0.089, data_time: 0.004, memory: 1224, loss: 6.8518 2022-12-31 14:19:41,054 - mmcls - INFO - Epoch [1][300/5005] lr: 1.000e-01, eta: 14:01:42, time: 0.092, data_time: 0.001, memory: 1224, loss: 6.7409 2022-12-31 14:19:49,951 - mmcls - INFO - Epoch [1][400/5005] lr: 1.000e-01, eta: 13:36:37, time: 0.089, data_time: 0.003, memory: 1224, loss: 6.6521 2022-12-31 14:19:58,856 - mmcls - INFO - Epoch [1][500/5005] lr: 1.000e-01, eta: 13:21:33, time: 0.089, data_time: 0.007, memory: 1224, loss: 6.5536 2022-12-31 14:20:07,854 - mmcls - INFO - Epoch [1][600/5005] lr: 1.000e-01, eta: 13:12:45, time: 0.090, data_time: 0.016, memory: 1224, loss: 6.4413 2022-12-31 14:20:16,790 - mmcls - INFO - Epoch [1][700/5005] lr: 1.000e-01, eta: 13:05:42, time: 0.089, data_time: 0.011, memory: 1224, loss: 6.3203 2022-12-31 14:20:25,666 - mmcls - INFO - Epoch [1][800/5005] lr: 1.000e-01, eta: 12:59:45, time: 0.089, data_time: 0.020, memory: 1224, loss: 6.2093 2022-12-31 14:20:35,032 - mmcls - INFO - Epoch [1][900/5005] lr: 1.000e-01, eta: 12:59:36, time: 0.094, data_time: 0.013, memory: 1224, loss: 6.1252 2022-12-31 14:20:43,830 - mmcls - INFO - Epoch [1][1000/5005] lr: 1.000e-01, eta: 12:54:43, time: 0.088, data_time: 0.003, memory: 1224, loss: 6.0635 2022-12-31 14:20:52,938 - mmcls - INFO - Epoch [1][1100/5005] lr: 1.000e-01, eta: 12:53:05, time: 0.091, data_time: 0.001, memory: 1224, loss: 5.9582 2022-12-31 14:21:02,051 - mmcls - INFO - Epoch [1][1200/5005] lr: 1.000e-01, eta: 12:51:42, time: 0.091, data_time: 0.004, memory: 1224, loss: 5.9076 2022-12-31 14:21:11,011 - mmcls - INFO - Epoch [1][1300/5005] lr: 1.000e-01, eta: 12:49:34, time: 0.090, data_time: 0.003, memory: 1224, loss: 5.8187 2022-12-31 14:21:19,851 - mmcls - INFO - Epoch [1][1400/5005] lr: 1.000e-01, eta: 12:46:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 5.7441 2022-12-31 14:21:28,545 - mmcls - INFO - Epoch [1][1500/5005] lr: 1.000e-01, eta: 12:43:51, time: 0.087, data_time: 0.002, memory: 1224, loss: 5.6767 2022-12-31 14:21:37,376 - mmcls - INFO - Epoch [1][1600/5005] lr: 1.000e-01, eta: 12:41:51, time: 0.088, data_time: 0.001, memory: 1224, loss: 5.5923 2022-12-31 14:21:46,032 - mmcls - INFO - Epoch [1][1700/5005] lr: 1.000e-01, eta: 12:39:12, time: 0.087, data_time: 0.002, memory: 1224, loss: 5.5427 2022-12-31 14:21:54,978 - mmcls - INFO - Epoch [1][1800/5005] lr: 1.000e-01, eta: 12:38:12, time: 0.089, data_time: 0.008, memory: 1224, loss: 5.4727 2022-12-31 14:22:03,883 - mmcls - INFO - Epoch [1][1900/5005] lr: 1.000e-01, eta: 12:37:06, time: 0.089, data_time: 0.003, memory: 1224, loss: 5.4028 2022-12-31 14:22:13,080 - mmcls - INFO - Epoch [1][2000/5005] lr: 1.000e-01, eta: 12:37:19, time: 0.092, data_time: 0.004, memory: 1224, loss: 5.3603 2022-12-31 14:22:22,170 - mmcls - INFO - Epoch [1][2100/5005] lr: 1.000e-01, eta: 12:37:03, time: 0.091, data_time: 0.001, memory: 1224, loss: 5.2915 2022-12-31 14:22:31,340 - mmcls - INFO - Epoch [1][2200/5005] lr: 1.000e-01, eta: 12:37:05, time: 0.092, data_time: 0.006, memory: 1224, loss: 5.2445 2022-12-31 14:22:40,014 - mmcls - INFO - Epoch [1][2300/5005] lr: 1.000e-01, eta: 12:35:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 5.1960 2022-12-31 14:22:49,307 - mmcls - INFO - Epoch [1][2400/5005] lr: 1.000e-01, eta: 12:35:53, time: 0.093, data_time: 0.018, memory: 1224, loss: 5.1426 2022-12-31 14:22:58,326 - mmcls - INFO - Epoch [1][2500/5005] lr: 1.000e-01, eta: 12:35:26, time: 0.090, data_time: 0.007, memory: 1224, loss: 5.1003 2022-12-31 14:23:07,195 - mmcls - INFO - Epoch [1][2600/5005] lr: 1.000e-01, eta: 12:34:34, time: 0.089, data_time: 0.022, memory: 1224, loss: 5.0493 2022-12-31 14:23:15,877 - mmcls - INFO - Epoch [1][2700/5005] lr: 1.000e-01, eta: 12:33:07, time: 0.087, data_time: 0.023, memory: 1224, loss: 4.9949 2022-12-31 14:23:24,754 - mmcls - INFO - Epoch [1][2800/5005] lr: 1.000e-01, eta: 12:32:23, time: 0.089, data_time: 0.044, memory: 1224, loss: 4.9564 2022-12-31 14:23:33,581 - mmcls - INFO - Epoch [1][2900/5005] lr: 1.000e-01, eta: 12:31:32, time: 0.088, data_time: 0.002, memory: 1224, loss: 4.9208 2022-12-31 14:23:42,518 - mmcls - INFO - Epoch [1][3000/5005] lr: 1.000e-01, eta: 12:31:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 4.8527 2022-12-31 14:23:51,224 - mmcls - INFO - Epoch [1][3100/5005] lr: 1.000e-01, eta: 12:29:57, time: 0.087, data_time: 0.004, memory: 1224, loss: 4.8234 2022-12-31 14:23:59,964 - mmcls - INFO - Epoch [1][3200/5005] lr: 1.000e-01, eta: 12:29:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 4.7827 2022-12-31 14:24:08,866 - mmcls - INFO - Epoch [1][3300/5005] lr: 1.000e-01, eta: 12:28:30, time: 0.089, data_time: 0.008, memory: 1224, loss: 4.7637 2022-12-31 14:24:17,668 - mmcls - INFO - Epoch [1][3400/5005] lr: 1.000e-01, eta: 12:27:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 4.6780 2022-12-31 14:24:26,718 - mmcls - INFO - Epoch [1][3500/5005] lr: 1.000e-01, eta: 12:27:41, time: 0.090, data_time: 0.017, memory: 1224, loss: 4.6568 2022-12-31 14:24:35,579 - mmcls - INFO - Epoch [1][3600/5005] lr: 1.000e-01, eta: 12:27:10, time: 0.089, data_time: 0.010, memory: 1224, loss: 4.6368 2022-12-31 14:24:44,300 - mmcls - INFO - Epoch [1][3700/5005] lr: 1.000e-01, eta: 12:26:19, time: 0.087, data_time: 0.004, memory: 1224, loss: 4.5910 2022-12-31 14:24:53,198 - mmcls - INFO - Epoch [1][3800/5005] lr: 1.000e-01, eta: 12:25:55, time: 0.089, data_time: 0.015, memory: 1224, loss: 4.5157 2022-12-31 14:25:02,149 - mmcls - INFO - Epoch [1][3900/5005] lr: 1.000e-01, eta: 12:25:38, time: 0.089, data_time: 0.004, memory: 1224, loss: 4.5032 2022-12-31 14:25:11,094 - mmcls - INFO - Epoch [1][4000/5005] lr: 1.000e-01, eta: 12:25:22, time: 0.090, data_time: 0.002, memory: 1224, loss: 4.4981 2022-12-31 14:25:20,282 - mmcls - INFO - Epoch [1][4100/5005] lr: 1.000e-01, eta: 12:25:35, time: 0.092, data_time: 0.006, memory: 1224, loss: 4.4685 2022-12-31 14:25:29,050 - mmcls - INFO - Epoch [1][4200/5005] lr: 1.000e-01, eta: 12:24:56, time: 0.088, data_time: 0.030, memory: 1224, loss: 4.4190 2022-12-31 14:25:38,232 - mmcls - INFO - Epoch [1][4300/5005] lr: 1.000e-01, eta: 12:25:08, time: 0.092, data_time: 0.001, memory: 1224, loss: 4.3722 2022-12-31 14:25:46,963 - mmcls - INFO - Epoch [1][4400/5005] lr: 1.000e-01, eta: 12:24:28, time: 0.087, data_time: 0.004, memory: 1224, loss: 4.3420 2022-12-31 14:25:55,527 - mmcls - INFO - Epoch [1][4500/5005] lr: 1.000e-01, eta: 12:23:29, time: 0.086, data_time: 0.013, memory: 1224, loss: 4.3088 2022-12-31 14:26:04,728 - mmcls - INFO - Epoch [1][4600/5005] lr: 1.000e-01, eta: 12:23:43, time: 0.092, data_time: 0.004, memory: 1224, loss: 4.2734 2022-12-31 14:26:13,559 - mmcls - INFO - Epoch [1][4700/5005] lr: 1.000e-01, eta: 12:23:16, time: 0.088, data_time: 0.004, memory: 1224, loss: 4.2628 2022-12-31 14:26:22,360 - mmcls - INFO - Epoch [1][4800/5005] lr: 1.000e-01, eta: 12:22:47, time: 0.088, data_time: 0.014, memory: 1224, loss: 4.2119 2022-12-31 14:26:31,331 - mmcls - INFO - Epoch [1][4900/5005] lr: 1.000e-01, eta: 12:22:36, time: 0.090, data_time: 0.001, memory: 1224, loss: 4.2229 2022-12-31 14:26:40,473 - mmcls - INFO - Epoch [1][5000/5005] lr: 1.000e-01, eta: 12:22:42, time: 0.091, data_time: 0.001, memory: 1224, loss: 4.1944 2022-12-31 14:27:03,974 - mmcls - INFO - Epoch(val) [1][196] accuracy_top-1: 20.7600, accuracy_top-5: 43.2760 2022-12-31 14:27:15,074 - mmcls - INFO - Epoch [2][100/5005] lr: 1.000e-01, eta: 12:25:12, time: 0.111, data_time: 0.039, memory: 1224, loss: 4.1041 2022-12-31 14:27:24,030 - mmcls - INFO - Epoch [2][200/5005] lr: 1.000e-01, eta: 12:24:55, time: 0.089, data_time: 0.002, memory: 1224, loss: 4.0855 2022-12-31 14:27:32,472 - mmcls - INFO - Epoch [2][300/5005] lr: 1.000e-01, eta: 12:23:53, time: 0.085, data_time: 0.001, memory: 1224, loss: 4.0948 2022-12-31 14:27:41,188 - mmcls - INFO - Epoch [2][400/5005] lr: 1.000e-01, eta: 12:23:15, time: 0.087, data_time: 0.029, memory: 1224, loss: 4.0651 2022-12-31 14:27:49,794 - mmcls - INFO - Epoch [2][500/5005] lr: 1.000e-01, eta: 12:22:30, time: 0.086, data_time: 0.008, memory: 1224, loss: 4.0343 2022-12-31 14:27:58,571 - mmcls - INFO - Epoch [2][600/5005] lr: 1.000e-01, eta: 12:22:01, time: 0.088, data_time: 0.014, memory: 1224, loss: 4.0408 2022-12-31 14:28:07,228 - mmcls - INFO - Epoch [2][700/5005] lr: 1.000e-01, eta: 12:21:23, time: 0.087, data_time: 0.013, memory: 1224, loss: 3.9827 2022-12-31 14:28:16,123 - mmcls - INFO - Epoch [2][800/5005] lr: 1.000e-01, eta: 12:21:06, time: 0.089, data_time: 0.011, memory: 1224, loss: 3.9743 2022-12-31 14:28:24,918 - mmcls - INFO - Epoch [2][900/5005] lr: 1.000e-01, eta: 12:20:41, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.9803 2022-12-31 14:28:33,967 - mmcls - INFO - Epoch [2][1000/5005] lr: 1.000e-01, eta: 12:20:37, time: 0.090, data_time: 0.018, memory: 1224, loss: 3.9164 2022-12-31 14:28:42,561 - mmcls - INFO - Epoch [2][1100/5005] lr: 1.000e-01, eta: 12:19:56, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.9209 2022-12-31 14:28:51,197 - mmcls - INFO - Epoch [2][1200/5005] lr: 1.000e-01, eta: 12:19:20, time: 0.086, data_time: 0.021, memory: 1224, loss: 3.8949 2022-12-31 14:28:59,852 - mmcls - INFO - Epoch [2][1300/5005] lr: 1.000e-01, eta: 12:18:46, time: 0.087, data_time: 0.023, memory: 1224, loss: 3.8661 2022-12-31 14:29:08,620 - mmcls - INFO - Epoch [2][1400/5005] lr: 1.000e-01, eta: 12:18:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.8579 2022-12-31 14:29:17,517 - mmcls - INFO - Epoch [2][1500/5005] lr: 1.000e-01, eta: 12:18:06, time: 0.089, data_time: 0.015, memory: 1224, loss: 3.8111 2022-12-31 14:29:26,524 - mmcls - INFO - Epoch [2][1600/5005] lr: 1.000e-01, eta: 12:18:00, time: 0.090, data_time: 0.007, memory: 1224, loss: 3.8050 2022-12-31 14:29:35,640 - mmcls - INFO - Epoch [2][1700/5005] lr: 1.000e-01, eta: 12:18:03, time: 0.091, data_time: 0.001, memory: 1224, loss: 3.8072 2022-12-31 14:29:44,325 - mmcls - INFO - Epoch [2][1800/5005] lr: 1.000e-01, eta: 12:17:33, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.7828 2022-12-31 14:29:53,069 - mmcls - INFO - Epoch [2][1900/5005] lr: 1.000e-01, eta: 12:17:08, time: 0.087, data_time: 0.020, memory: 1224, loss: 3.7542 2022-12-31 14:30:01,949 - mmcls - INFO - Epoch [2][2000/5005] lr: 1.000e-01, eta: 12:16:53, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.7437 2022-12-31 14:30:10,410 - mmcls - INFO - Epoch [2][2100/5005] lr: 1.000e-01, eta: 12:16:10, time: 0.085, data_time: 0.008, memory: 1224, loss: 3.7587 2022-12-31 14:30:19,379 - mmcls - INFO - Epoch [2][2200/5005] lr: 1.000e-01, eta: 12:16:02, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.7093 2022-12-31 14:30:27,992 - mmcls - INFO - Epoch [2][2300/5005] lr: 1.000e-01, eta: 12:15:30, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.6989 2022-12-31 14:30:36,778 - mmcls - INFO - Epoch [2][2400/5005] lr: 1.000e-01, eta: 12:15:11, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.6647 2022-12-31 14:30:45,653 - mmcls - INFO - Epoch [2][2500/5005] lr: 1.000e-01, eta: 12:14:57, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.6560 2022-12-31 14:30:54,536 - mmcls - INFO - Epoch [2][2600/5005] lr: 1.000e-01, eta: 12:14:44, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.6465 2022-12-31 14:31:03,396 - mmcls - INFO - Epoch [2][2700/5005] lr: 1.000e-01, eta: 12:14:29, time: 0.089, data_time: 0.011, memory: 1224, loss: 3.6454 2022-12-31 14:31:12,052 - mmcls - INFO - Epoch [2][2800/5005] lr: 1.000e-01, eta: 12:14:02, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.6156 2022-12-31 14:31:20,950 - mmcls - INFO - Epoch [2][2900/5005] lr: 1.000e-01, eta: 12:13:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.6129 2022-12-31 14:31:29,712 - mmcls - INFO - Epoch [2][3000/5005] lr: 1.000e-01, eta: 12:13:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.5735 2022-12-31 14:31:38,610 - mmcls - INFO - Epoch [2][3100/5005] lr: 1.000e-01, eta: 12:13:19, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.5776 2022-12-31 14:31:47,386 - mmcls - INFO - Epoch [2][3200/5005] lr: 1.000e-01, eta: 12:13:01, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.5465 2022-12-31 14:31:56,301 - mmcls - INFO - Epoch [2][3300/5005] lr: 1.000e-01, eta: 12:12:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.5932 2022-12-31 14:32:05,080 - mmcls - INFO - Epoch [2][3400/5005] lr: 1.000e-01, eta: 12:12:33, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.5164 2022-12-31 14:32:13,837 - mmcls - INFO - Epoch [2][3500/5005] lr: 1.000e-01, eta: 12:12:14, time: 0.088, data_time: 0.005, memory: 1224, loss: 3.5148 2022-12-31 14:32:22,707 - mmcls - INFO - Epoch [2][3600/5005] lr: 1.000e-01, eta: 12:12:01, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.5002 2022-12-31 14:32:31,334 - mmcls - INFO - Epoch [2][3700/5005] lr: 1.000e-01, eta: 12:11:35, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.4858 2022-12-31 14:32:39,921 - mmcls - INFO - Epoch [2][3800/5005] lr: 1.000e-01, eta: 12:11:07, time: 0.086, data_time: 0.009, memory: 1224, loss: 3.4673 2022-12-31 14:32:48,669 - mmcls - INFO - Epoch [2][3900/5005] lr: 1.000e-01, eta: 12:10:49, time: 0.087, data_time: 0.004, memory: 1224, loss: 3.4662 2022-12-31 14:32:57,538 - mmcls - INFO - Epoch [2][4000/5005] lr: 1.000e-01, eta: 12:10:38, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.4286 2022-12-31 14:33:06,331 - mmcls - INFO - Epoch [2][4100/5005] lr: 1.000e-01, eta: 12:10:21, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.4441 2022-12-31 14:33:14,985 - mmcls - INFO - Epoch [2][4200/5005] lr: 1.000e-01, eta: 12:09:58, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.4411 2022-12-31 14:33:23,761 - mmcls - INFO - Epoch [2][4300/5005] lr: 1.000e-01, eta: 12:09:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.4258 2022-12-31 14:33:32,343 - mmcls - INFO - Epoch [2][4400/5005] lr: 1.000e-01, eta: 12:09:16, time: 0.086, data_time: 0.018, memory: 1224, loss: 3.4391 2022-12-31 14:33:41,417 - mmcls - INFO - Epoch [2][4500/5005] lr: 1.000e-01, eta: 12:09:15, time: 0.091, data_time: 0.003, memory: 1224, loss: 3.4259 2022-12-31 14:33:50,320 - mmcls - INFO - Epoch [2][4600/5005] lr: 1.000e-01, eta: 12:09:06, time: 0.089, data_time: 0.006, memory: 1224, loss: 3.3824 2022-12-31 14:33:59,398 - mmcls - INFO - Epoch [2][4700/5005] lr: 1.000e-01, eta: 12:09:06, time: 0.091, data_time: 0.002, memory: 1224, loss: 3.4116 2022-12-31 14:34:07,923 - mmcls - INFO - Epoch [2][4800/5005] lr: 1.000e-01, eta: 12:08:37, time: 0.085, data_time: 0.001, memory: 1224, loss: 3.3384 2022-12-31 14:34:16,735 - mmcls - INFO - Epoch [2][4900/5005] lr: 1.000e-01, eta: 12:08:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.3673 2022-12-31 14:34:25,571 - mmcls - INFO - Epoch [2][5000/5005] lr: 1.000e-01, eta: 12:08:11, time: 0.088, data_time: 0.011, memory: 1224, loss: 3.3482 2022-12-31 14:34:48,688 - mmcls - INFO - Epoch(val) [2][196] accuracy_top-1: 34.1100, accuracy_top-5: 60.4580 2022-12-31 14:34:59,706 - mmcls - INFO - Epoch [3][100/5005] lr: 1.000e-01, eta: 12:09:21, time: 0.110, data_time: 0.048, memory: 1224, loss: 3.3193 2022-12-31 14:35:08,315 - mmcls - INFO - Epoch [3][200/5005] lr: 1.000e-01, eta: 12:08:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.2824 2022-12-31 14:35:17,258 - mmcls - INFO - Epoch [3][300/5005] lr: 1.000e-01, eta: 12:08:49, time: 0.089, data_time: 0.013, memory: 1224, loss: 3.2799 2022-12-31 14:35:26,022 - mmcls - INFO - Epoch [3][400/5005] lr: 1.000e-01, eta: 12:08:32, time: 0.087, data_time: 0.031, memory: 1224, loss: 3.3000 2022-12-31 14:35:34,512 - mmcls - INFO - Epoch [3][500/5005] lr: 1.000e-01, eta: 12:08:04, time: 0.085, data_time: 0.012, memory: 1224, loss: 3.2747 2022-12-31 14:35:43,298 - mmcls - INFO - Epoch [3][600/5005] lr: 1.000e-01, eta: 12:07:49, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.2599 2022-12-31 14:35:52,057 - mmcls - INFO - Epoch [3][700/5005] lr: 1.000e-01, eta: 12:07:33, time: 0.088, data_time: 0.007, memory: 1224, loss: 3.2774 2022-12-31 14:36:00,936 - mmcls - INFO - Epoch [3][800/5005] lr: 1.000e-01, eta: 12:07:23, time: 0.089, data_time: 0.015, memory: 1224, loss: 3.2444 2022-12-31 14:36:09,834 - mmcls - INFO - Epoch [3][900/5005] lr: 1.000e-01, eta: 12:07:12, time: 0.089, data_time: 0.012, memory: 1224, loss: 3.2548 2022-12-31 14:36:18,689 - mmcls - INFO - Epoch [3][1000/5005] lr: 1.000e-01, eta: 12:07:01, time: 0.089, data_time: 0.014, memory: 1224, loss: 3.2401 2022-12-31 14:36:27,459 - mmcls - INFO - Epoch [3][1100/5005] lr: 1.000e-01, eta: 12:06:45, time: 0.088, data_time: 0.002, memory: 1224, loss: 3.2274 2022-12-31 14:36:36,026 - mmcls - INFO - Epoch [3][1200/5005] lr: 1.000e-01, eta: 12:06:22, time: 0.086, data_time: 0.002, memory: 1224, loss: 3.2263 2022-12-31 14:36:44,692 - mmcls - INFO - Epoch [3][1300/5005] lr: 1.000e-01, eta: 12:06:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 3.2057 2022-12-31 14:36:53,390 - mmcls - INFO - Epoch [3][1400/5005] lr: 1.000e-01, eta: 12:05:45, time: 0.087, data_time: 0.011, memory: 1224, loss: 3.2175 2022-12-31 14:37:02,083 - mmcls - INFO - Epoch [3][1500/5005] lr: 1.000e-01, eta: 12:05:27, time: 0.087, data_time: 0.002, memory: 1224, loss: 3.2217 2022-12-31 14:37:10,809 - mmcls - INFO - Epoch [3][1600/5005] lr: 1.000e-01, eta: 12:05:11, time: 0.087, data_time: 0.020, memory: 1224, loss: 3.2073 2022-12-31 14:37:19,821 - mmcls - INFO - Epoch [3][1700/5005] lr: 1.000e-01, eta: 12:05:06, time: 0.090, data_time: 0.013, memory: 1224, loss: 3.1729 2022-12-31 14:37:28,486 - mmcls - INFO - Epoch [3][1800/5005] lr: 1.000e-01, eta: 12:04:47, time: 0.087, data_time: 0.009, memory: 1224, loss: 3.1837 2022-12-31 14:37:37,577 - mmcls - INFO - Epoch [3][1900/5005] lr: 1.000e-01, eta: 12:04:47, time: 0.091, data_time: 0.007, memory: 1224, loss: 3.1651 2022-12-31 14:37:46,480 - mmcls - INFO - Epoch [3][2000/5005] lr: 1.000e-01, eta: 12:04:38, time: 0.089, data_time: 0.002, memory: 1224, loss: 3.2049 2022-12-31 14:37:55,285 - mmcls - INFO - Epoch [3][2100/5005] lr: 1.000e-01, eta: 12:04:25, time: 0.088, data_time: 0.000, memory: 1224, loss: 3.1770 2022-12-31 14:38:04,212 - mmcls - INFO - Epoch [3][2200/5005] lr: 1.000e-01, eta: 12:04:17, time: 0.089, data_time: 0.001, memory: 1224, loss: 3.1993 2022-12-31 14:38:13,021 - mmcls - INFO - Epoch [3][2300/5005] lr: 1.000e-01, eta: 12:04:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.1848 2022-12-31 14:38:21,785 - mmcls - INFO - Epoch [3][2400/5005] lr: 1.000e-01, eta: 12:03:50, time: 0.088, data_time: 0.013, memory: 1224, loss: 3.1480 2022-12-31 14:38:30,312 - mmcls - INFO - Epoch [3][2500/5005] lr: 1.000e-01, eta: 12:03:27, time: 0.085, data_time: 0.001, memory: 1224, loss: 3.1263 2022-12-31 14:38:39,520 - mmcls - INFO - Epoch [3][2600/5005] lr: 1.000e-01, eta: 12:03:30, time: 0.092, data_time: 0.006, memory: 1224, loss: 3.1352 2022-12-31 14:38:48,295 - mmcls - INFO - Epoch [3][2700/5005] lr: 1.000e-01, eta: 12:03:16, time: 0.088, data_time: 0.004, memory: 1224, loss: 3.1117 2022-12-31 14:38:56,926 - mmcls - INFO - Epoch [3][2800/5005] lr: 1.000e-01, eta: 12:02:57, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.1322 2022-12-31 14:39:05,884 - mmcls - INFO - Epoch [3][2900/5005] lr: 1.000e-01, eta: 12:02:51, time: 0.090, data_time: 0.006, memory: 1224, loss: 3.0916 2022-12-31 14:39:14,710 - mmcls - INFO - Epoch [3][3000/5005] lr: 1.000e-01, eta: 12:02:39, time: 0.088, data_time: 0.013, memory: 1224, loss: 3.1319 2022-12-31 14:39:23,457 - mmcls - INFO - Epoch [3][3100/5005] lr: 1.000e-01, eta: 12:02:25, time: 0.087, data_time: 0.008, memory: 1224, loss: 3.1089 2022-12-31 14:39:32,203 - mmcls - INFO - Epoch [3][3200/5005] lr: 1.000e-01, eta: 12:02:11, time: 0.088, data_time: 0.016, memory: 1224, loss: 3.0940 2022-12-31 14:39:40,901 - mmcls - INFO - Epoch [3][3300/5005] lr: 1.000e-01, eta: 12:01:55, time: 0.087, data_time: 0.009, memory: 1224, loss: 3.0975 2022-12-31 14:39:49,652 - mmcls - INFO - Epoch [3][3400/5005] lr: 1.000e-01, eta: 12:01:41, time: 0.087, data_time: 0.003, memory: 1224, loss: 3.1034 2022-12-31 14:39:58,465 - mmcls - INFO - Epoch [3][3500/5005] lr: 1.000e-01, eta: 12:01:29, time: 0.088, data_time: 0.009, memory: 1224, loss: 3.0861 2022-12-31 14:40:07,112 - mmcls - INFO - Epoch [3][3600/5005] lr: 1.000e-01, eta: 12:01:11, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.1162 2022-12-31 14:40:15,997 - mmcls - INFO - Epoch [3][3700/5005] lr: 1.000e-01, eta: 12:01:02, time: 0.089, data_time: 0.003, memory: 1224, loss: 3.0855 2022-12-31 14:40:24,984 - mmcls - INFO - Epoch [3][3800/5005] lr: 1.000e-01, eta: 12:00:57, time: 0.090, data_time: 0.010, memory: 1224, loss: 3.0695 2022-12-31 14:40:33,785 - mmcls - INFO - Epoch [3][3900/5005] lr: 1.000e-01, eta: 12:00:45, time: 0.088, data_time: 0.016, memory: 1224, loss: 3.0792 2022-12-31 14:40:42,800 - mmcls - INFO - Epoch [3][4000/5005] lr: 1.000e-01, eta: 12:00:40, time: 0.090, data_time: 0.022, memory: 1224, loss: 3.0494 2022-12-31 14:40:51,394 - mmcls - INFO - Epoch [3][4100/5005] lr: 1.000e-01, eta: 12:00:21, time: 0.086, data_time: 0.014, memory: 1224, loss: 3.0773 2022-12-31 14:41:00,124 - mmcls - INFO - Epoch [3][4200/5005] lr: 1.000e-01, eta: 12:00:07, time: 0.087, data_time: 0.007, memory: 1224, loss: 3.0690 2022-12-31 14:41:08,985 - mmcls - INFO - Epoch [3][4300/5005] lr: 1.000e-01, eta: 11:59:57, time: 0.089, data_time: 0.004, memory: 1224, loss: 3.0531 2022-12-31 14:41:17,959 - mmcls - INFO - Epoch [3][4400/5005] lr: 1.000e-01, eta: 11:59:51, time: 0.090, data_time: 0.001, memory: 1224, loss: 3.0540 2022-12-31 14:41:26,784 - mmcls - INFO - Epoch [3][4500/5005] lr: 1.000e-01, eta: 11:59:40, time: 0.088, data_time: 0.009, memory: 1224, loss: 3.0270 2022-12-31 14:41:35,514 - mmcls - INFO - Epoch [3][4600/5005] lr: 1.000e-01, eta: 11:59:26, time: 0.087, data_time: 0.010, memory: 1224, loss: 3.0466 2022-12-31 14:41:44,141 - mmcls - INFO - Epoch [3][4700/5005] lr: 1.000e-01, eta: 11:59:09, time: 0.086, data_time: 0.001, memory: 1224, loss: 3.0467 2022-12-31 14:41:53,496 - mmcls - INFO - Epoch [3][4800/5005] lr: 1.000e-01, eta: 11:59:15, time: 0.093, data_time: 0.001, memory: 1224, loss: 3.0222 2022-12-31 14:42:02,267 - mmcls - INFO - Epoch [3][4900/5005] lr: 1.000e-01, eta: 11:59:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 3.0122 2022-12-31 14:42:11,093 - mmcls - INFO - Epoch [3][5000/5005] lr: 1.000e-01, eta: 11:58:52, time: 0.088, data_time: 0.003, memory: 1224, loss: 3.0146 2022-12-31 14:42:33,775 - mmcls - INFO - Epoch(val) [3][196] accuracy_top-1: 40.5460, accuracy_top-5: 67.2300 2022-12-31 14:42:44,699 - mmcls - INFO - Epoch [4][100/5005] lr: 1.000e-01, eta: 11:59:33, time: 0.109, data_time: 0.037, memory: 1224, loss: 2.9622 2022-12-31 14:42:53,712 - mmcls - INFO - Epoch [4][200/5005] lr: 1.000e-01, eta: 11:59:28, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.9904 2022-12-31 14:43:02,304 - mmcls - INFO - Epoch [4][300/5005] lr: 1.000e-01, eta: 11:59:09, time: 0.086, data_time: 0.036, memory: 1224, loss: 2.9785 2022-12-31 14:43:11,166 - mmcls - INFO - Epoch [4][400/5005] lr: 1.000e-01, eta: 11:58:59, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.9614 2022-12-31 14:43:19,750 - mmcls - INFO - Epoch [4][500/5005] lr: 1.000e-01, eta: 11:58:41, time: 0.086, data_time: 0.024, memory: 1224, loss: 2.9235 2022-12-31 14:43:28,751 - mmcls - INFO - Epoch [4][600/5005] lr: 1.000e-01, eta: 11:58:36, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.9435 2022-12-31 14:43:37,699 - mmcls - INFO - Epoch [4][700/5005] lr: 1.000e-01, eta: 11:58:28, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.9521 2022-12-31 14:43:46,633 - mmcls - INFO - Epoch [4][800/5005] lr: 1.000e-01, eta: 11:58:21, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.9636 2022-12-31 14:43:55,344 - mmcls - INFO - Epoch [4][900/5005] lr: 1.000e-01, eta: 11:58:06, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9487 2022-12-31 14:44:03,986 - mmcls - INFO - Epoch [4][1000/5005] lr: 1.000e-01, eta: 11:57:50, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.9723 2022-12-31 14:44:13,065 - mmcls - INFO - Epoch [4][1100/5005] lr: 1.000e-01, eta: 11:57:47, time: 0.091, data_time: 0.006, memory: 1224, loss: 2.9366 2022-12-31 14:44:22,207 - mmcls - INFO - Epoch [4][1200/5005] lr: 1.000e-01, eta: 11:57:45, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.9551 2022-12-31 14:44:31,170 - mmcls - INFO - Epoch [4][1300/5005] lr: 1.000e-01, eta: 11:57:38, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.9588 2022-12-31 14:44:40,013 - mmcls - INFO - Epoch [4][1400/5005] lr: 1.000e-01, eta: 11:57:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.9379 2022-12-31 14:44:48,722 - mmcls - INFO - Epoch [4][1500/5005] lr: 1.000e-01, eta: 11:57:14, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.9477 2022-12-31 14:44:57,518 - mmcls - INFO - Epoch [4][1600/5005] lr: 1.000e-01, eta: 11:57:02, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.9251 2022-12-31 14:45:06,534 - mmcls - INFO - Epoch [4][1700/5005] lr: 1.000e-01, eta: 11:56:57, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.9208 2022-12-31 14:45:15,869 - mmcls - INFO - Epoch [4][1800/5005] lr: 1.000e-01, eta: 11:57:00, time: 0.093, data_time: 0.022, memory: 1224, loss: 2.9051 2022-12-31 14:45:24,612 - mmcls - INFO - Epoch [4][1900/5005] lr: 1.000e-01, eta: 11:56:47, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.9243 2022-12-31 14:45:33,615 - mmcls - INFO - Epoch [4][2000/5005] lr: 1.000e-01, eta: 11:56:41, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.9177 2022-12-31 14:45:42,033 - mmcls - INFO - Epoch [4][2100/5005] lr: 1.000e-01, eta: 11:56:19, time: 0.084, data_time: 0.006, memory: 1224, loss: 2.9285 2022-12-31 14:45:50,687 - mmcls - INFO - Epoch [4][2200/5005] lr: 1.000e-01, eta: 11:56:04, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.9136 2022-12-31 14:45:59,385 - mmcls - INFO - Epoch [4][2300/5005] lr: 1.000e-01, eta: 11:55:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.9264 2022-12-31 14:46:07,965 - mmcls - INFO - Epoch [4][2400/5005] lr: 1.000e-01, eta: 11:55:32, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.9109 2022-12-31 14:46:16,653 - mmcls - INFO - Epoch [4][2500/5005] lr: 1.000e-01, eta: 11:55:17, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.8960 2022-12-31 14:46:25,443 - mmcls - INFO - Epoch [4][2600/5005] lr: 1.000e-01, eta: 11:55:06, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.8966 2022-12-31 14:46:34,328 - mmcls - INFO - Epoch [4][2700/5005] lr: 1.000e-01, eta: 11:54:57, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.9077 2022-12-31 14:46:43,320 - mmcls - INFO - Epoch [4][2800/5005] lr: 1.000e-01, eta: 11:54:51, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.9034 2022-12-31 14:46:51,938 - mmcls - INFO - Epoch [4][2900/5005] lr: 1.000e-01, eta: 11:54:35, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.8933 2022-12-31 14:47:01,156 - mmcls - INFO - Epoch [4][3000/5005] lr: 1.000e-01, eta: 11:54:35, time: 0.092, data_time: 0.009, memory: 1224, loss: 2.9160 2022-12-31 14:47:10,069 - mmcls - INFO - Epoch [4][3100/5005] lr: 1.000e-01, eta: 11:54:26, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8961 2022-12-31 14:47:19,292 - mmcls - INFO - Epoch [4][3200/5005] lr: 1.000e-01, eta: 11:54:27, time: 0.092, data_time: 0.016, memory: 1224, loss: 2.9042 2022-12-31 14:47:28,153 - mmcls - INFO - Epoch [4][3300/5005] lr: 1.000e-01, eta: 11:54:17, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.8993 2022-12-31 14:47:36,882 - mmcls - INFO - Epoch [4][3400/5005] lr: 1.000e-01, eta: 11:54:04, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.8810 2022-12-31 14:47:45,988 - mmcls - INFO - Epoch [4][3500/5005] lr: 1.000e-01, eta: 11:54:01, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8713 2022-12-31 14:47:54,759 - mmcls - INFO - Epoch [4][3600/5005] lr: 1.000e-01, eta: 11:53:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.8889 2022-12-31 14:48:03,586 - mmcls - INFO - Epoch [4][3700/5005] lr: 1.000e-01, eta: 11:53:38, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8625 2022-12-31 14:48:12,573 - mmcls - INFO - Epoch [4][3800/5005] lr: 1.000e-01, eta: 11:53:32, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.9068 2022-12-31 14:48:21,363 - mmcls - INFO - Epoch [4][3900/5005] lr: 1.000e-01, eta: 11:53:20, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.8967 2022-12-31 14:48:30,150 - mmcls - INFO - Epoch [4][4000/5005] lr: 1.000e-01, eta: 11:53:09, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.8667 2022-12-31 14:48:38,921 - mmcls - INFO - Epoch [4][4100/5005] lr: 1.000e-01, eta: 11:52:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.8725 2022-12-31 14:48:48,044 - mmcls - INFO - Epoch [4][4200/5005] lr: 1.000e-01, eta: 11:52:54, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.8794 2022-12-31 14:48:57,116 - mmcls - INFO - Epoch [4][4300/5005] lr: 1.000e-01, eta: 11:52:50, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.8764 2022-12-31 14:49:06,187 - mmcls - INFO - Epoch [4][4400/5005] lr: 1.000e-01, eta: 11:52:45, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.8564 2022-12-31 14:49:14,621 - mmcls - INFO - Epoch [4][4500/5005] lr: 1.000e-01, eta: 11:52:25, time: 0.084, data_time: 0.002, memory: 1224, loss: 2.8397 2022-12-31 14:49:23,222 - mmcls - INFO - Epoch [4][4600/5005] lr: 1.000e-01, eta: 11:52:09, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.8573 2022-12-31 14:49:32,068 - mmcls - INFO - Epoch [4][4700/5005] lr: 1.000e-01, eta: 11:51:59, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8272 2022-12-31 14:49:40,763 - mmcls - INFO - Epoch [4][4800/5005] lr: 1.000e-01, eta: 11:51:46, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.8633 2022-12-31 14:49:49,579 - mmcls - INFO - Epoch [4][4900/5005] lr: 1.000e-01, eta: 11:51:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.8223 2022-12-31 14:49:58,350 - mmcls - INFO - Epoch [4][5000/5005] lr: 1.000e-01, eta: 11:51:24, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.8250 2022-12-31 14:50:21,161 - mmcls - INFO - Epoch(val) [4][196] accuracy_top-1: 41.2460, accuracy_top-5: 67.6780 2022-12-31 14:50:31,966 - mmcls - INFO - Epoch [5][100/5005] lr: 1.000e-01, eta: 11:51:49, time: 0.108, data_time: 0.033, memory: 1224, loss: 2.7883 2022-12-31 14:50:40,973 - mmcls - INFO - Epoch [5][200/5005] lr: 1.000e-01, eta: 11:51:43, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.8051 2022-12-31 14:50:49,854 - mmcls - INFO - Epoch [5][300/5005] lr: 1.000e-01, eta: 11:51:34, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7634 2022-12-31 14:50:58,444 - mmcls - INFO - Epoch [5][400/5005] lr: 1.000e-01, eta: 11:51:18, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.8121 2022-12-31 14:51:07,186 - mmcls - INFO - Epoch [5][500/5005] lr: 1.000e-01, eta: 11:51:05, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.7923 2022-12-31 14:51:15,860 - mmcls - INFO - Epoch [5][600/5005] lr: 1.000e-01, eta: 11:50:51, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.7920 2022-12-31 14:51:24,831 - mmcls - INFO - Epoch [5][700/5005] lr: 1.000e-01, eta: 11:50:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.8065 2022-12-31 14:51:33,743 - mmcls - INFO - Epoch [5][800/5005] lr: 1.000e-01, eta: 11:50:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7872 2022-12-31 14:51:42,547 - mmcls - INFO - Epoch [5][900/5005] lr: 1.000e-01, eta: 11:50:25, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.8043 2022-12-31 14:51:51,374 - mmcls - INFO - Epoch [5][1000/5005] lr: 1.000e-01, eta: 11:50:15, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.7884 2022-12-31 14:52:00,312 - mmcls - INFO - Epoch [5][1100/5005] lr: 1.000e-01, eta: 11:50:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.8122 2022-12-31 14:52:09,317 - mmcls - INFO - Epoch [5][1200/5005] lr: 1.000e-01, eta: 11:50:01, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7952 2022-12-31 14:52:18,275 - mmcls - INFO - Epoch [5][1300/5005] lr: 1.000e-01, eta: 11:49:53, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.7806 2022-12-31 14:52:26,986 - mmcls - INFO - Epoch [5][1400/5005] lr: 1.000e-01, eta: 11:49:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7990 2022-12-31 14:52:35,689 - mmcls - INFO - Epoch [5][1500/5005] lr: 1.000e-01, eta: 11:49:28, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.8061 2022-12-31 14:52:44,465 - mmcls - INFO - Epoch [5][1600/5005] lr: 1.000e-01, eta: 11:49:16, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7924 2022-12-31 14:52:53,376 - mmcls - INFO - Epoch [5][1700/5005] lr: 1.000e-01, eta: 11:49:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.8078 2022-12-31 14:53:02,157 - mmcls - INFO - Epoch [5][1800/5005] lr: 1.000e-01, eta: 11:48:57, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.7638 2022-12-31 14:53:11,319 - mmcls - INFO - Epoch [5][1900/5005] lr: 1.000e-01, eta: 11:48:54, time: 0.092, data_time: 0.011, memory: 1224, loss: 2.7742 2022-12-31 14:53:20,154 - mmcls - INFO - Epoch [5][2000/5005] lr: 1.000e-01, eta: 11:48:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.7921 2022-12-31 14:53:28,881 - mmcls - INFO - Epoch [5][2100/5005] lr: 1.000e-01, eta: 11:48:31, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.7961 2022-12-31 14:53:37,583 - mmcls - INFO - Epoch [5][2200/5005] lr: 1.000e-01, eta: 11:48:19, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.7684 2022-12-31 14:53:46,082 - mmcls - INFO - Epoch [5][2300/5005] lr: 1.000e-01, eta: 11:48:01, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.7883 2022-12-31 14:53:54,858 - mmcls - INFO - Epoch [5][2400/5005] lr: 1.000e-01, eta: 11:47:50, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7898 2022-12-31 14:54:04,019 - mmcls - INFO - Epoch [5][2500/5005] lr: 1.000e-01, eta: 11:47:47, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.8040 2022-12-31 14:54:13,033 - mmcls - INFO - Epoch [5][2600/5005] lr: 1.000e-01, eta: 11:47:41, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7966 2022-12-31 14:54:21,878 - mmcls - INFO - Epoch [5][2700/5005] lr: 1.000e-01, eta: 11:47:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7814 2022-12-31 14:54:30,733 - mmcls - INFO - Epoch [5][2800/5005] lr: 1.000e-01, eta: 11:47:22, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.7720 2022-12-31 14:54:39,710 - mmcls - INFO - Epoch [5][2900/5005] lr: 1.000e-01, eta: 11:47:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.7566 2022-12-31 14:54:48,358 - mmcls - INFO - Epoch [5][3000/5005] lr: 1.000e-01, eta: 11:47:01, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7665 2022-12-31 14:54:57,349 - mmcls - INFO - Epoch [5][3100/5005] lr: 1.000e-01, eta: 11:46:54, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.8099 2022-12-31 14:55:05,981 - mmcls - INFO - Epoch [5][3200/5005] lr: 1.000e-01, eta: 11:46:40, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.7850 2022-12-31 14:55:14,880 - mmcls - INFO - Epoch [5][3300/5005] lr: 1.000e-01, eta: 11:46:31, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.7789 2022-12-31 14:55:23,765 - mmcls - INFO - Epoch [5][3400/5005] lr: 1.000e-01, eta: 11:46:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7536 2022-12-31 14:55:32,453 - mmcls - INFO - Epoch [5][3500/5005] lr: 1.000e-01, eta: 11:46:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.7590 2022-12-31 14:55:41,428 - mmcls - INFO - Epoch [5][3600/5005] lr: 1.000e-01, eta: 11:46:02, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.7605 2022-12-31 14:55:50,242 - mmcls - INFO - Epoch [5][3700/5005] lr: 1.000e-01, eta: 11:45:52, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7922 2022-12-31 14:55:59,221 - mmcls - INFO - Epoch [5][3800/5005] lr: 1.000e-01, eta: 11:45:45, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.7962 2022-12-31 14:56:08,001 - mmcls - INFO - Epoch [5][3900/5005] lr: 1.000e-01, eta: 11:45:34, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.7540 2022-12-31 14:56:16,467 - mmcls - INFO - Epoch [5][4000/5005] lr: 1.000e-01, eta: 11:45:17, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.7862 2022-12-31 14:56:25,141 - mmcls - INFO - Epoch [5][4100/5005] lr: 1.000e-01, eta: 11:45:04, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.7692 2022-12-31 14:56:33,732 - mmcls - INFO - Epoch [5][4200/5005] lr: 1.000e-01, eta: 11:44:50, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7535 2022-12-31 14:56:42,554 - mmcls - INFO - Epoch [5][4300/5005] lr: 1.000e-01, eta: 11:44:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7215 2022-12-31 14:56:51,176 - mmcls - INFO - Epoch [5][4400/5005] lr: 1.000e-01, eta: 11:44:26, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.7596 2022-12-31 14:56:59,998 - mmcls - INFO - Epoch [5][4500/5005] lr: 1.000e-01, eta: 11:44:15, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7443 2022-12-31 14:57:09,124 - mmcls - INFO - Epoch [5][4600/5005] lr: 1.000e-01, eta: 11:44:11, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.7595 2022-12-31 14:57:17,754 - mmcls - INFO - Epoch [5][4700/5005] lr: 1.000e-01, eta: 11:43:58, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.7294 2022-12-31 14:57:26,792 - mmcls - INFO - Epoch [5][4800/5005] lr: 1.000e-01, eta: 11:43:52, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.7469 2022-12-31 14:57:35,358 - mmcls - INFO - Epoch [5][4900/5005] lr: 1.000e-01, eta: 11:43:37, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.7681 2022-12-31 14:57:44,150 - mmcls - INFO - Epoch [5][5000/5005] lr: 1.000e-01, eta: 11:43:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.7587 2022-12-31 14:58:06,839 - mmcls - INFO - Epoch(val) [5][196] accuracy_top-1: 43.8300, accuracy_top-5: 70.7380 2022-12-31 14:58:17,741 - mmcls - INFO - Epoch [6][100/5005] lr: 1.000e-01, eta: 11:43:47, time: 0.109, data_time: 0.049, memory: 1224, loss: 2.6893 2022-12-31 14:58:27,059 - mmcls - INFO - Epoch [6][200/5005] lr: 1.000e-01, eta: 11:43:46, time: 0.093, data_time: 0.013, memory: 1224, loss: 2.7007 2022-12-31 14:58:36,238 - mmcls - INFO - Epoch [6][300/5005] lr: 1.000e-01, eta: 11:43:43, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.6929 2022-12-31 14:58:44,827 - mmcls - INFO - Epoch [6][400/5005] lr: 1.000e-01, eta: 11:43:28, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.6818 2022-12-31 14:58:53,437 - mmcls - INFO - Epoch [6][500/5005] lr: 1.000e-01, eta: 11:43:14, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.6739 2022-12-31 14:59:02,054 - mmcls - INFO - Epoch [6][600/5005] lr: 1.000e-01, eta: 11:43:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.6953 2022-12-31 14:59:10,913 - mmcls - INFO - Epoch [6][700/5005] lr: 1.000e-01, eta: 11:42:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7038 2022-12-31 14:59:19,337 - mmcls - INFO - Epoch [6][800/5005] lr: 1.000e-01, eta: 11:42:34, time: 0.084, data_time: 0.003, memory: 1224, loss: 2.7204 2022-12-31 14:59:28,286 - mmcls - INFO - Epoch [6][900/5005] lr: 1.000e-01, eta: 11:42:26, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.6825 2022-12-31 14:59:37,057 - mmcls - INFO - Epoch [6][1000/5005] lr: 1.000e-01, eta: 11:42:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6981 2022-12-31 14:59:46,071 - mmcls - INFO - Epoch [6][1100/5005] lr: 1.000e-01, eta: 11:42:09, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.7227 2022-12-31 14:59:54,952 - mmcls - INFO - Epoch [6][1200/5005] lr: 1.000e-01, eta: 11:42:00, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.7004 2022-12-31 15:00:03,477 - mmcls - INFO - Epoch [6][1300/5005] lr: 1.000e-01, eta: 11:41:44, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.7428 2022-12-31 15:00:12,375 - mmcls - INFO - Epoch [6][1400/5005] lr: 1.000e-01, eta: 11:41:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.6873 2022-12-31 15:00:21,187 - mmcls - INFO - Epoch [6][1500/5005] lr: 1.000e-01, eta: 11:41:26, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.7018 2022-12-31 15:00:29,874 - mmcls - INFO - Epoch [6][1600/5005] lr: 1.000e-01, eta: 11:41:13, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.7077 2022-12-31 15:00:38,932 - mmcls - INFO - Epoch [6][1700/5005] lr: 1.000e-01, eta: 11:41:08, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.6731 2022-12-31 15:00:47,751 - mmcls - INFO - Epoch [6][1800/5005] lr: 1.000e-01, eta: 11:40:57, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.7253 2022-12-31 15:00:56,605 - mmcls - INFO - Epoch [6][1900/5005] lr: 1.000e-01, eta: 11:40:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.6952 2022-12-31 15:01:05,967 - mmcls - INFO - Epoch [6][2000/5005] lr: 1.000e-01, eta: 11:40:48, time: 0.094, data_time: 0.001, memory: 1224, loss: 2.6848 2022-12-31 15:01:14,806 - mmcls - INFO - Epoch [6][2100/5005] lr: 1.000e-01, eta: 11:40:38, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.6948 2022-12-31 15:01:23,604 - mmcls - INFO - Epoch [6][2200/5005] lr: 1.000e-01, eta: 11:40:28, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.7245 2022-12-31 15:01:32,163 - mmcls - INFO - Epoch [6][2300/5005] lr: 1.000e-01, eta: 11:40:13, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.6792 2022-12-31 15:01:40,865 - mmcls - INFO - Epoch [6][2400/5005] lr: 1.000e-01, eta: 11:40:02, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.7051 2022-12-31 15:01:49,355 - mmcls - INFO - Epoch [6][2500/5005] lr: 1.000e-01, eta: 11:39:46, time: 0.085, data_time: 0.016, memory: 1224, loss: 2.6977 2022-12-31 15:01:58,195 - mmcls - INFO - Epoch [6][2600/5005] lr: 1.000e-01, eta: 11:39:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.6944 2022-12-31 15:02:07,045 - mmcls - INFO - Epoch [6][2700/5005] lr: 1.000e-01, eta: 11:39:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.7474 2022-12-31 15:02:15,818 - mmcls - INFO - Epoch [6][2800/5005] lr: 1.000e-01, eta: 11:39:16, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6692 2022-12-31 15:02:24,673 - mmcls - INFO - Epoch [6][2900/5005] lr: 1.000e-01, eta: 11:39:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.6828 2022-12-31 15:02:33,455 - mmcls - INFO - Epoch [6][3000/5005] lr: 1.000e-01, eta: 11:38:57, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.7192 2022-12-31 15:02:42,292 - mmcls - INFO - Epoch [6][3100/5005] lr: 1.000e-01, eta: 11:38:47, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.7162 2022-12-31 15:02:50,966 - mmcls - INFO - Epoch [6][3200/5005] lr: 1.000e-01, eta: 11:38:35, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.6855 2022-12-31 15:02:59,857 - mmcls - INFO - Epoch [6][3300/5005] lr: 1.000e-01, eta: 11:38:26, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.7006 2022-12-31 15:03:08,603 - mmcls - INFO - Epoch [6][3400/5005] lr: 1.000e-01, eta: 11:38:15, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.6787 2022-12-31 15:03:17,278 - mmcls - INFO - Epoch [6][3500/5005] lr: 1.000e-01, eta: 11:38:03, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.6887 2022-12-31 15:03:26,136 - mmcls - INFO - Epoch [6][3600/5005] lr: 1.000e-01, eta: 11:37:54, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.6949 2022-12-31 15:03:34,877 - mmcls - INFO - Epoch [6][3700/5005] lr: 1.000e-01, eta: 11:37:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.6962 2022-12-31 15:03:43,675 - mmcls - INFO - Epoch [6][3800/5005] lr: 1.000e-01, eta: 11:37:33, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.6899 2022-12-31 15:03:52,594 - mmcls - INFO - Epoch [6][3900/5005] lr: 1.000e-01, eta: 11:37:25, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.6670 2022-12-31 15:04:01,373 - mmcls - INFO - Epoch [6][4000/5005] lr: 1.000e-01, eta: 11:37:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6850 2022-12-31 15:04:10,062 - mmcls - INFO - Epoch [6][4100/5005] lr: 1.000e-01, eta: 11:37:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6992 2022-12-31 15:04:18,661 - mmcls - INFO - Epoch [6][4200/5005] lr: 1.000e-01, eta: 11:36:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.6771 2022-12-31 15:04:27,584 - mmcls - INFO - Epoch [6][4300/5005] lr: 1.000e-01, eta: 11:36:41, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.6620 2022-12-31 15:04:36,582 - mmcls - INFO - Epoch [6][4400/5005] lr: 1.000e-01, eta: 11:36:34, time: 0.090, data_time: 0.032, memory: 1224, loss: 2.6976 2022-12-31 15:04:45,206 - mmcls - INFO - Epoch [6][4500/5005] lr: 1.000e-01, eta: 11:36:21, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.6796 2022-12-31 15:04:54,207 - mmcls - INFO - Epoch [6][4600/5005] lr: 1.000e-01, eta: 11:36:14, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.6723 2022-12-31 15:05:02,929 - mmcls - INFO - Epoch [6][4700/5005] lr: 1.000e-01, eta: 11:36:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6702 2022-12-31 15:05:11,662 - mmcls - INFO - Epoch [6][4800/5005] lr: 1.000e-01, eta: 11:35:52, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.7053 2022-12-31 15:05:20,317 - mmcls - INFO - Epoch [6][4900/5005] lr: 1.000e-01, eta: 11:35:40, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.6731 2022-12-31 15:05:29,118 - mmcls - INFO - Epoch [6][5000/5005] lr: 1.000e-01, eta: 11:35:30, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.6835 2022-12-31 15:05:52,166 - mmcls - INFO - Epoch(val) [6][196] accuracy_top-1: 47.2820, accuracy_top-5: 73.3220 2022-12-31 15:06:03,536 - mmcls - INFO - Epoch [7][100/5005] lr: 1.000e-01, eta: 11:35:52, time: 0.113, data_time: 0.040, memory: 1224, loss: 2.6088 2022-12-31 15:06:12,436 - mmcls - INFO - Epoch [7][200/5005] lr: 1.000e-01, eta: 11:35:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.6177 2022-12-31 15:06:21,117 - mmcls - INFO - Epoch [7][300/5005] lr: 1.000e-01, eta: 11:35:32, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.6288 2022-12-31 15:06:29,694 - mmcls - INFO - Epoch [7][400/5005] lr: 1.000e-01, eta: 11:35:18, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.6409 2022-12-31 15:06:38,435 - mmcls - INFO - Epoch [7][500/5005] lr: 1.000e-01, eta: 11:35:07, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.6469 2022-12-31 15:06:46,994 - mmcls - INFO - Epoch [7][600/5005] lr: 1.000e-01, eta: 11:34:54, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.6223 2022-12-31 15:06:55,795 - mmcls - INFO - Epoch [7][700/5005] lr: 1.000e-01, eta: 11:34:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6451 2022-12-31 15:07:04,489 - mmcls - INFO - Epoch [7][800/5005] lr: 1.000e-01, eta: 11:34:32, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.6186 2022-12-31 15:07:13,359 - mmcls - INFO - Epoch [7][900/5005] lr: 1.000e-01, eta: 11:34:23, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.6461 2022-12-31 15:07:22,223 - mmcls - INFO - Epoch [7][1000/5005] lr: 1.000e-01, eta: 11:34:14, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.6442 2022-12-31 15:07:31,095 - mmcls - INFO - Epoch [7][1100/5005] lr: 1.000e-01, eta: 11:34:05, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.6460 2022-12-31 15:07:39,884 - mmcls - INFO - Epoch [7][1200/5005] lr: 1.000e-01, eta: 11:33:55, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.6180 2022-12-31 15:07:48,622 - mmcls - INFO - Epoch [7][1300/5005] lr: 1.000e-01, eta: 11:33:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6479 2022-12-31 15:07:57,505 - mmcls - INFO - Epoch [7][1400/5005] lr: 1.000e-01, eta: 11:33:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.6648 2022-12-31 15:08:06,316 - mmcls - INFO - Epoch [7][1500/5005] lr: 1.000e-01, eta: 11:33:25, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.6383 2022-12-31 15:08:15,064 - mmcls - INFO - Epoch [7][1600/5005] lr: 1.000e-01, eta: 11:33:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6210 2022-12-31 15:08:23,752 - mmcls - INFO - Epoch [7][1700/5005] lr: 1.000e-01, eta: 11:33:03, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.6263 2022-12-31 15:08:32,420 - mmcls - INFO - Epoch [7][1800/5005] lr: 1.000e-01, eta: 11:32:51, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.6527 2022-12-31 15:08:41,319 - mmcls - INFO - Epoch [7][1900/5005] lr: 1.000e-01, eta: 11:32:43, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.6263 2022-12-31 15:08:50,053 - mmcls - INFO - Epoch [7][2000/5005] lr: 1.000e-01, eta: 11:32:32, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.6357 2022-12-31 15:08:58,744 - mmcls - INFO - Epoch [7][2100/5005] lr: 1.000e-01, eta: 11:32:20, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.6575 2022-12-31 15:09:07,201 - mmcls - INFO - Epoch [7][2200/5005] lr: 1.000e-01, eta: 11:32:05, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.6480 2022-12-31 15:09:15,981 - mmcls - INFO - Epoch [7][2300/5005] lr: 1.000e-01, eta: 11:31:55, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.6372 2022-12-31 15:09:25,050 - mmcls - INFO - Epoch [7][2400/5005] lr: 1.000e-01, eta: 11:31:49, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.6518 2022-12-31 15:09:33,603 - mmcls - INFO - Epoch [7][2500/5005] lr: 1.000e-01, eta: 11:31:36, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.6611 2022-12-31 15:09:42,451 - mmcls - INFO - Epoch [7][2600/5005] lr: 1.000e-01, eta: 11:31:27, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.6516 2022-12-31 15:09:51,369 - mmcls - INFO - Epoch [7][2700/5005] lr: 1.000e-01, eta: 11:31:19, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.6536 2022-12-31 15:10:00,007 - mmcls - INFO - Epoch [7][2800/5005] lr: 1.000e-01, eta: 11:31:06, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.6542 2022-12-31 15:10:08,936 - mmcls - INFO - Epoch [7][2900/5005] lr: 1.000e-01, eta: 11:30:58, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.6261 2022-12-31 15:10:17,586 - mmcls - INFO - Epoch [7][3000/5005] lr: 1.000e-01, eta: 11:30:47, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.6405 2022-12-31 15:10:26,191 - mmcls - INFO - Epoch [7][3100/5005] lr: 1.000e-01, eta: 11:30:34, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.6436 2022-12-31 15:10:35,221 - mmcls - INFO - Epoch [7][3200/5005] lr: 1.000e-01, eta: 11:30:27, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.6491 2022-12-31 15:10:43,968 - mmcls - INFO - Epoch [7][3300/5005] lr: 1.000e-01, eta: 11:30:17, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.6373 2022-12-31 15:10:52,707 - mmcls - INFO - Epoch [7][3400/5005] lr: 1.000e-01, eta: 11:30:06, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.6456 2022-12-31 15:11:01,714 - mmcls - INFO - Epoch [7][3500/5005] lr: 1.000e-01, eta: 11:29:59, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.6242 2022-12-31 15:11:10,532 - mmcls - INFO - Epoch [7][3600/5005] lr: 1.000e-01, eta: 11:29:50, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.6193 2022-12-31 15:11:19,335 - mmcls - INFO - Epoch [7][3700/5005] lr: 1.000e-01, eta: 11:29:40, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.6401 2022-12-31 15:11:28,194 - mmcls - INFO - Epoch [7][3800/5005] lr: 1.000e-01, eta: 11:29:31, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.6497 2022-12-31 15:11:37,225 - mmcls - INFO - Epoch [7][3900/5005] lr: 1.000e-01, eta: 11:29:25, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.6341 2022-12-31 15:11:46,056 - mmcls - INFO - Epoch [7][4000/5005] lr: 1.000e-01, eta: 11:29:15, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.6525 2022-12-31 15:11:54,579 - mmcls - INFO - Epoch [7][4100/5005] lr: 1.000e-01, eta: 11:29:01, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.6422 2022-12-31 15:12:03,264 - mmcls - INFO - Epoch [7][4200/5005] lr: 1.000e-01, eta: 11:28:50, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.6371 2022-12-31 15:12:12,138 - mmcls - INFO - Epoch [7][4300/5005] lr: 1.000e-01, eta: 11:28:42, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.6161 2022-12-31 15:12:20,750 - mmcls - INFO - Epoch [7][4400/5005] lr: 1.000e-01, eta: 11:28:29, time: 0.086, data_time: 0.023, memory: 1224, loss: 2.6268 2022-12-31 15:12:29,645 - mmcls - INFO - Epoch [7][4500/5005] lr: 1.000e-01, eta: 11:28:21, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.6604 2022-12-31 15:12:38,824 - mmcls - INFO - Epoch [7][4600/5005] lr: 1.000e-01, eta: 11:28:16, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.5926 2022-12-31 15:12:47,392 - mmcls - INFO - Epoch [7][4700/5005] lr: 1.000e-01, eta: 11:28:03, time: 0.086, data_time: 0.026, memory: 1224, loss: 2.6255 2022-12-31 15:12:56,519 - mmcls - INFO - Epoch [7][4800/5005] lr: 1.000e-01, eta: 11:27:58, time: 0.091, data_time: 0.031, memory: 1224, loss: 2.6187 2022-12-31 15:13:05,079 - mmcls - INFO - Epoch [7][4900/5005] lr: 1.000e-01, eta: 11:27:45, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.6444 2022-12-31 15:13:13,626 - mmcls - INFO - Epoch [7][5000/5005] lr: 1.000e-01, eta: 11:27:32, time: 0.085, data_time: 0.015, memory: 1224, loss: 2.6461 2022-12-31 15:13:36,207 - mmcls - INFO - Epoch(val) [7][196] accuracy_top-1: 48.1260, accuracy_top-5: 74.4340 2022-12-31 15:13:46,896 - mmcls - INFO - Epoch [8][100/5005] lr: 1.000e-01, eta: 11:27:41, time: 0.107, data_time: 0.055, memory: 1224, loss: 2.5821 2022-12-31 15:13:55,808 - mmcls - INFO - Epoch [8][200/5005] lr: 1.000e-01, eta: 11:27:32, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.5662 2022-12-31 15:14:05,003 - mmcls - INFO - Epoch [8][300/5005] lr: 1.000e-01, eta: 11:27:28, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.5782 2022-12-31 15:14:14,089 - mmcls - INFO - Epoch [8][400/5005] lr: 1.000e-01, eta: 11:27:22, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.5682 2022-12-31 15:14:22,969 - mmcls - INFO - Epoch [8][500/5005] lr: 1.000e-01, eta: 11:27:13, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5902 2022-12-31 15:14:31,934 - mmcls - INFO - Epoch [8][600/5005] lr: 1.000e-01, eta: 11:27:05, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.5969 2022-12-31 15:14:41,007 - mmcls - INFO - Epoch [8][700/5005] lr: 1.000e-01, eta: 11:26:59, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.5988 2022-12-31 15:14:49,747 - mmcls - INFO - Epoch [8][800/5005] lr: 1.000e-01, eta: 11:26:49, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.5951 2022-12-31 15:14:58,760 - mmcls - INFO - Epoch [8][900/5005] lr: 1.000e-01, eta: 11:26:42, time: 0.090, data_time: 0.035, memory: 1224, loss: 2.5924 2022-12-31 15:15:07,801 - mmcls - INFO - Epoch [8][1000/5005] lr: 1.000e-01, eta: 11:26:35, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.5824 2022-12-31 15:15:16,549 - mmcls - INFO - Epoch [8][1100/5005] lr: 1.000e-01, eta: 11:26:25, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.5937 2022-12-31 15:15:25,130 - mmcls - INFO - Epoch [8][1200/5005] lr: 1.000e-01, eta: 11:26:12, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.6002 2022-12-31 15:15:33,976 - mmcls - INFO - Epoch [8][1300/5005] lr: 1.000e-01, eta: 11:26:03, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5670 2022-12-31 15:15:42,749 - mmcls - INFO - Epoch [8][1400/5005] lr: 1.000e-01, eta: 11:25:53, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5978 2022-12-31 15:15:51,703 - mmcls - INFO - Epoch [8][1500/5005] lr: 1.000e-01, eta: 11:25:45, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.6279 2022-12-31 15:16:00,268 - mmcls - INFO - Epoch [8][1600/5005] lr: 1.000e-01, eta: 11:25:32, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.6054 2022-12-31 15:16:09,050 - mmcls - INFO - Epoch [8][1700/5005] lr: 1.000e-01, eta: 11:25:22, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.6068 2022-12-31 15:16:17,808 - mmcls - INFO - Epoch [8][1800/5005] lr: 1.000e-01, eta: 11:25:12, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.6008 2022-12-31 15:16:26,545 - mmcls - INFO - Epoch [8][1900/5005] lr: 1.000e-01, eta: 11:25:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6054 2022-12-31 15:16:35,240 - mmcls - INFO - Epoch [8][2000/5005] lr: 1.000e-01, eta: 11:24:50, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.6303 2022-12-31 15:16:44,033 - mmcls - INFO - Epoch [8][2100/5005] lr: 1.000e-01, eta: 11:24:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5889 2022-12-31 15:16:52,729 - mmcls - INFO - Epoch [8][2200/5005] lr: 1.000e-01, eta: 11:24:30, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.5894 2022-12-31 15:17:01,658 - mmcls - INFO - Epoch [8][2300/5005] lr: 1.000e-01, eta: 11:24:22, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5924 2022-12-31 15:17:10,256 - mmcls - INFO - Epoch [8][2400/5005] lr: 1.000e-01, eta: 11:24:09, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.5895 2022-12-31 15:17:19,099 - mmcls - INFO - Epoch [8][2500/5005] lr: 1.000e-01, eta: 11:24:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.6159 2022-12-31 15:17:27,649 - mmcls - INFO - Epoch [8][2600/5005] lr: 1.000e-01, eta: 11:23:48, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5980 2022-12-31 15:17:36,415 - mmcls - INFO - Epoch [8][2700/5005] lr: 1.000e-01, eta: 11:23:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5956 2022-12-31 15:17:45,711 - mmcls - INFO - Epoch [8][2800/5005] lr: 1.000e-01, eta: 11:23:34, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.5685 2022-12-31 15:17:54,315 - mmcls - INFO - Epoch [8][2900/5005] lr: 1.000e-01, eta: 11:23:22, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5870 2022-12-31 15:18:02,880 - mmcls - INFO - Epoch [8][3000/5005] lr: 1.000e-01, eta: 11:23:09, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.5852 2022-12-31 15:18:11,716 - mmcls - INFO - Epoch [8][3100/5005] lr: 1.000e-01, eta: 11:23:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.6227 2022-12-31 15:18:20,183 - mmcls - INFO - Epoch [8][3200/5005] lr: 1.000e-01, eta: 11:22:46, time: 0.085, data_time: 0.018, memory: 1224, loss: 2.6005 2022-12-31 15:18:28,992 - mmcls - INFO - Epoch [8][3300/5005] lr: 1.000e-01, eta: 11:22:37, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.6146 2022-12-31 15:18:38,003 - mmcls - INFO - Epoch [8][3400/5005] lr: 1.000e-01, eta: 11:22:30, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5997 2022-12-31 15:18:47,050 - mmcls - INFO - Epoch [8][3500/5005] lr: 1.000e-01, eta: 11:22:23, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.5990 2022-12-31 15:18:55,798 - mmcls - INFO - Epoch [8][3600/5005] lr: 1.000e-01, eta: 11:22:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5693 2022-12-31 15:19:04,646 - mmcls - INFO - Epoch [8][3700/5005] lr: 1.000e-01, eta: 11:22:04, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.5968 2022-12-31 15:19:13,483 - mmcls - INFO - Epoch [8][3800/5005] lr: 1.000e-01, eta: 11:21:55, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5945 2022-12-31 15:19:21,952 - mmcls - INFO - Epoch [8][3900/5005] lr: 1.000e-01, eta: 11:21:41, time: 0.085, data_time: 0.027, memory: 1224, loss: 2.6096 2022-12-31 15:19:30,564 - mmcls - INFO - Epoch [8][4000/5005] lr: 1.000e-01, eta: 11:21:29, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.5969 2022-12-31 15:19:39,441 - mmcls - INFO - Epoch [8][4100/5005] lr: 1.000e-01, eta: 11:21:20, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5996 2022-12-31 15:19:48,421 - mmcls - INFO - Epoch [8][4200/5005] lr: 1.000e-01, eta: 11:21:13, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.6211 2022-12-31 15:19:57,227 - mmcls - INFO - Epoch [8][4300/5005] lr: 1.000e-01, eta: 11:21:04, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.5814 2022-12-31 15:20:06,146 - mmcls - INFO - Epoch [8][4400/5005] lr: 1.000e-01, eta: 11:20:55, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.5784 2022-12-31 15:20:15,131 - mmcls - INFO - Epoch [8][4500/5005] lr: 1.000e-01, eta: 11:20:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.5960 2022-12-31 15:20:23,748 - mmcls - INFO - Epoch [8][4600/5005] lr: 1.000e-01, eta: 11:20:36, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.6127 2022-12-31 15:20:32,807 - mmcls - INFO - Epoch [8][4700/5005] lr: 1.000e-01, eta: 11:20:30, time: 0.091, data_time: 0.026, memory: 1224, loss: 2.6233 2022-12-31 15:20:41,335 - mmcls - INFO - Epoch [8][4800/5005] lr: 1.000e-01, eta: 11:20:17, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.5825 2022-12-31 15:20:50,055 - mmcls - INFO - Epoch [8][4900/5005] lr: 1.000e-01, eta: 11:20:07, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.5849 2022-12-31 15:20:58,832 - mmcls - INFO - Epoch [8][5000/5005] lr: 1.000e-01, eta: 11:19:57, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.5951 2022-12-31 15:21:21,664 - mmcls - INFO - Epoch(val) [8][196] accuracy_top-1: 47.0460, accuracy_top-5: 73.6240 2022-12-31 15:21:32,664 - mmcls - INFO - Epoch [9][100/5005] lr: 1.000e-01, eta: 11:20:07, time: 0.110, data_time: 0.069, memory: 1224, loss: 2.5470 2022-12-31 15:21:41,336 - mmcls - INFO - Epoch [9][200/5005] lr: 1.000e-01, eta: 11:19:55, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.5385 2022-12-31 15:21:50,290 - mmcls - INFO - Epoch [9][300/5005] lr: 1.000e-01, eta: 11:19:48, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5646 2022-12-31 15:21:59,242 - mmcls - INFO - Epoch [9][400/5005] lr: 1.000e-01, eta: 11:19:40, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.5224 2022-12-31 15:22:07,846 - mmcls - INFO - Epoch [9][500/5005] lr: 1.000e-01, eta: 11:19:28, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5394 2022-12-31 15:22:16,576 - mmcls - INFO - Epoch [9][600/5005] lr: 1.000e-01, eta: 11:19:18, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.5607 2022-12-31 15:22:25,330 - mmcls - INFO - Epoch [9][700/5005] lr: 1.000e-01, eta: 11:19:07, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.5482 2022-12-31 15:22:34,246 - mmcls - INFO - Epoch [9][800/5005] lr: 1.000e-01, eta: 11:18:59, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.5662 2022-12-31 15:22:43,081 - mmcls - INFO - Epoch [9][900/5005] lr: 1.000e-01, eta: 11:18:50, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.5844 2022-12-31 15:22:51,893 - mmcls - INFO - Epoch [9][1000/5005] lr: 1.000e-01, eta: 11:18:40, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.5427 2022-12-31 15:23:00,583 - mmcls - INFO - Epoch [9][1100/5005] lr: 1.000e-01, eta: 11:18:30, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.5482 2022-12-31 15:23:09,238 - mmcls - INFO - Epoch [9][1200/5005] lr: 1.000e-01, eta: 11:18:19, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.5804 2022-12-31 15:23:17,901 - mmcls - INFO - Epoch [9][1300/5005] lr: 1.000e-01, eta: 11:18:08, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.5432 2022-12-31 15:23:26,748 - mmcls - INFO - Epoch [9][1400/5005] lr: 1.000e-01, eta: 11:17:58, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5675 2022-12-31 15:23:35,676 - mmcls - INFO - Epoch [9][1500/5005] lr: 1.000e-01, eta: 11:17:50, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.5535 2022-12-31 15:23:44,702 - mmcls - INFO - Epoch [9][1600/5005] lr: 1.000e-01, eta: 11:17:43, time: 0.090, data_time: 0.016, memory: 1224, loss: 2.5630 2022-12-31 15:23:53,905 - mmcls - INFO - Epoch [9][1700/5005] lr: 1.000e-01, eta: 11:17:38, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.5722 2022-12-31 15:24:02,539 - mmcls - INFO - Epoch [9][1800/5005] lr: 1.000e-01, eta: 11:17:27, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.5382 2022-12-31 15:24:11,466 - mmcls - INFO - Epoch [9][1900/5005] lr: 1.000e-01, eta: 11:17:19, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5764 2022-12-31 15:24:20,359 - mmcls - INFO - Epoch [9][2000/5005] lr: 1.000e-01, eta: 11:17:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5691 2022-12-31 15:24:29,692 - mmcls - INFO - Epoch [9][2100/5005] lr: 1.000e-01, eta: 11:17:06, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.5539 2022-12-31 15:24:38,446 - mmcls - INFO - Epoch [9][2200/5005] lr: 1.000e-01, eta: 11:16:56, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5631 2022-12-31 15:24:47,273 - mmcls - INFO - Epoch [9][2300/5005] lr: 1.000e-01, eta: 11:16:47, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5956 2022-12-31 15:24:56,202 - mmcls - INFO - Epoch [9][2400/5005] lr: 1.000e-01, eta: 11:16:39, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.5459 2022-12-31 15:25:04,881 - mmcls - INFO - Epoch [9][2500/5005] lr: 1.000e-01, eta: 11:16:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5778 2022-12-31 15:25:13,756 - mmcls - INFO - Epoch [9][2600/5005] lr: 1.000e-01, eta: 11:16:19, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.5624 2022-12-31 15:25:22,634 - mmcls - INFO - Epoch [9][2700/5005] lr: 1.000e-01, eta: 11:16:10, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.5659 2022-12-31 15:25:31,172 - mmcls - INFO - Epoch [9][2800/5005] lr: 1.000e-01, eta: 11:15:58, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.5674 2022-12-31 15:25:39,702 - mmcls - INFO - Epoch [9][2900/5005] lr: 1.000e-01, eta: 11:15:46, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.5895 2022-12-31 15:25:48,304 - mmcls - INFO - Epoch [9][3000/5005] lr: 1.000e-01, eta: 11:15:34, time: 0.086, data_time: 0.022, memory: 1224, loss: 2.5504 2022-12-31 15:25:57,198 - mmcls - INFO - Epoch [9][3100/5005] lr: 1.000e-01, eta: 11:15:26, time: 0.089, data_time: 0.026, memory: 1224, loss: 2.5824 2022-12-31 15:26:06,288 - mmcls - INFO - Epoch [9][3200/5005] lr: 1.000e-01, eta: 11:15:19, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.5560 2022-12-31 15:26:15,103 - mmcls - INFO - Epoch [9][3300/5005] lr: 1.000e-01, eta: 11:15:10, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.5586 2022-12-31 15:26:23,676 - mmcls - INFO - Epoch [9][3400/5005] lr: 1.000e-01, eta: 11:14:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5429 2022-12-31 15:26:32,276 - mmcls - INFO - Epoch [9][3500/5005] lr: 1.000e-01, eta: 11:14:46, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5699 2022-12-31 15:26:40,847 - mmcls - INFO - Epoch [9][3600/5005] lr: 1.000e-01, eta: 11:14:34, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.5654 2022-12-31 15:26:49,897 - mmcls - INFO - Epoch [9][3700/5005] lr: 1.000e-01, eta: 11:14:27, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.5844 2022-12-31 15:26:58,832 - mmcls - INFO - Epoch [9][3800/5005] lr: 1.000e-01, eta: 11:14:19, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5626 2022-12-31 15:27:07,721 - mmcls - INFO - Epoch [9][3900/5005] lr: 1.000e-01, eta: 11:14:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5798 2022-12-31 15:27:16,339 - mmcls - INFO - Epoch [9][4000/5005] lr: 1.000e-01, eta: 11:13:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5674 2022-12-31 15:27:25,036 - mmcls - INFO - Epoch [9][4100/5005] lr: 1.000e-01, eta: 11:13:49, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.5567 2022-12-31 15:27:33,674 - mmcls - INFO - Epoch [9][4200/5005] lr: 1.000e-01, eta: 11:13:38, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5567 2022-12-31 15:27:42,516 - mmcls - INFO - Epoch [9][4300/5005] lr: 1.000e-01, eta: 11:13:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5704 2022-12-31 15:27:51,695 - mmcls - INFO - Epoch [9][4400/5005] lr: 1.000e-01, eta: 11:13:23, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.5449 2022-12-31 15:28:00,734 - mmcls - INFO - Epoch [9][4500/5005] lr: 1.000e-01, eta: 11:13:16, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5831 2022-12-31 15:28:09,252 - mmcls - INFO - Epoch [9][4600/5005] lr: 1.000e-01, eta: 11:13:04, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.5550 2022-12-31 15:28:18,098 - mmcls - INFO - Epoch [9][4700/5005] lr: 1.000e-01, eta: 11:12:55, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.5601 2022-12-31 15:28:26,761 - mmcls - INFO - Epoch [9][4800/5005] lr: 1.000e-01, eta: 11:12:44, time: 0.087, data_time: 0.030, memory: 1224, loss: 2.5357 2022-12-31 15:28:35,642 - mmcls - INFO - Epoch [9][4900/5005] lr: 1.000e-01, eta: 11:12:35, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.5600 2022-12-31 15:28:44,277 - mmcls - INFO - Epoch [9][5000/5005] lr: 1.000e-01, eta: 11:12:24, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5707 2022-12-31 15:29:06,928 - mmcls - INFO - Epoch(val) [9][196] accuracy_top-1: 47.8140, accuracy_top-5: 73.7100 2022-12-31 15:29:17,829 - mmcls - INFO - Epoch [10][100/5005] lr: 1.000e-01, eta: 11:12:31, time: 0.109, data_time: 0.047, memory: 1224, loss: 2.5026 2022-12-31 15:29:26,541 - mmcls - INFO - Epoch [10][200/5005] lr: 1.000e-01, eta: 11:12:20, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4951 2022-12-31 15:29:35,280 - mmcls - INFO - Epoch [10][300/5005] lr: 1.000e-01, eta: 11:12:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.5498 2022-12-31 15:29:43,904 - mmcls - INFO - Epoch [10][400/5005] lr: 1.000e-01, eta: 11:11:59, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5160 2022-12-31 15:29:52,702 - mmcls - INFO - Epoch [10][500/5005] lr: 1.000e-01, eta: 11:11:50, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.4854 2022-12-31 15:30:01,435 - mmcls - INFO - Epoch [10][600/5005] lr: 1.000e-01, eta: 11:11:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5491 2022-12-31 15:30:10,134 - mmcls - INFO - Epoch [10][700/5005] lr: 1.000e-01, eta: 11:11:29, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.5246 2022-12-31 15:30:18,781 - mmcls - INFO - Epoch [10][800/5005] lr: 1.000e-01, eta: 11:11:18, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.5131 2022-12-31 15:30:27,621 - mmcls - INFO - Epoch [10][900/5005] lr: 1.000e-01, eta: 11:11:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5135 2022-12-31 15:30:37,177 - mmcls - INFO - Epoch [10][1000/5005] lr: 1.000e-01, eta: 11:11:07, time: 0.096, data_time: 0.000, memory: 1224, loss: 2.5489 2022-12-31 15:30:46,178 - mmcls - INFO - Epoch [10][1100/5005] lr: 1.000e-01, eta: 11:10:59, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.5020 2022-12-31 15:30:54,790 - mmcls - INFO - Epoch [10][1200/5005] lr: 1.000e-01, eta: 11:10:48, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.5232 2022-12-31 15:31:03,487 - mmcls - INFO - Epoch [10][1300/5005] lr: 1.000e-01, eta: 11:10:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5584 2022-12-31 15:31:12,196 - mmcls - INFO - Epoch [10][1400/5005] lr: 1.000e-01, eta: 11:10:27, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.5249 2022-12-31 15:31:20,860 - mmcls - INFO - Epoch [10][1500/5005] lr: 1.000e-01, eta: 11:10:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.5573 2022-12-31 15:31:29,509 - mmcls - INFO - Epoch [10][1600/5005] lr: 1.000e-01, eta: 11:10:06, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.5528 2022-12-31 15:31:38,222 - mmcls - INFO - Epoch [10][1700/5005] lr: 1.000e-01, eta: 11:09:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.5601 2022-12-31 15:31:46,704 - mmcls - INFO - Epoch [10][1800/5005] lr: 1.000e-01, eta: 11:09:43, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.5543 2022-12-31 15:31:55,521 - mmcls - INFO - Epoch [10][1900/5005] lr: 1.000e-01, eta: 11:09:33, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.5153 2022-12-31 15:32:04,081 - mmcls - INFO - Epoch [10][2000/5005] lr: 1.000e-01, eta: 11:09:22, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.5488 2022-12-31 15:32:12,892 - mmcls - INFO - Epoch [10][2100/5005] lr: 1.000e-01, eta: 11:09:12, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.5359 2022-12-31 15:32:21,828 - mmcls - INFO - Epoch [10][2200/5005] lr: 1.000e-01, eta: 11:09:04, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.5381 2022-12-31 15:32:30,786 - mmcls - INFO - Epoch [10][2300/5005] lr: 1.000e-01, eta: 11:08:56, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5366 2022-12-31 15:32:39,538 - mmcls - INFO - Epoch [10][2400/5005] lr: 1.000e-01, eta: 11:08:47, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5205 2022-12-31 15:32:48,171 - mmcls - INFO - Epoch [10][2500/5005] lr: 1.000e-01, eta: 11:08:35, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5544 2022-12-31 15:32:56,844 - mmcls - INFO - Epoch [10][2600/5005] lr: 1.000e-01, eta: 11:08:25, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.5453 2022-12-31 15:33:05,989 - mmcls - INFO - Epoch [10][2700/5005] lr: 1.000e-01, eta: 11:08:19, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.5416 2022-12-31 15:33:14,811 - mmcls - INFO - Epoch [10][2800/5005] lr: 1.000e-01, eta: 11:08:10, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5323 2022-12-31 15:33:23,394 - mmcls - INFO - Epoch [10][2900/5005] lr: 1.000e-01, eta: 11:07:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5571 2022-12-31 15:33:32,036 - mmcls - INFO - Epoch [10][3000/5005] lr: 1.000e-01, eta: 11:07:47, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.5412 2022-12-31 15:33:41,145 - mmcls - INFO - Epoch [10][3100/5005] lr: 1.000e-01, eta: 11:07:41, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.5362 2022-12-31 15:33:49,941 - mmcls - INFO - Epoch [10][3200/5005] lr: 1.000e-01, eta: 11:07:31, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.5302 2022-12-31 15:33:58,775 - mmcls - INFO - Epoch [10][3300/5005] lr: 1.000e-01, eta: 11:07:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5480 2022-12-31 15:34:07,628 - mmcls - INFO - Epoch [10][3400/5005] lr: 1.000e-01, eta: 11:07:13, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.5142 2022-12-31 15:34:16,485 - mmcls - INFO - Epoch [10][3500/5005] lr: 1.000e-01, eta: 11:07:05, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.5379 2022-12-31 15:34:25,012 - mmcls - INFO - Epoch [10][3600/5005] lr: 1.000e-01, eta: 11:06:53, time: 0.085, data_time: 0.018, memory: 1224, loss: 2.5709 2022-12-31 15:34:34,200 - mmcls - INFO - Epoch [10][3700/5005] lr: 1.000e-01, eta: 11:06:47, time: 0.092, data_time: 0.020, memory: 1224, loss: 2.5636 2022-12-31 15:34:43,217 - mmcls - INFO - Epoch [10][3800/5005] lr: 1.000e-01, eta: 11:06:39, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5276 2022-12-31 15:34:52,019 - mmcls - INFO - Epoch [10][3900/5005] lr: 1.000e-01, eta: 11:06:30, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.5123 2022-12-31 15:35:01,073 - mmcls - INFO - Epoch [10][4000/5005] lr: 1.000e-01, eta: 11:06:23, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.5466 2022-12-31 15:35:10,126 - mmcls - INFO - Epoch [10][4100/5005] lr: 1.000e-01, eta: 11:06:16, time: 0.090, data_time: 0.000, memory: 1224, loss: 2.5657 2022-12-31 15:35:19,016 - mmcls - INFO - Epoch [10][4200/5005] lr: 1.000e-01, eta: 11:06:07, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.5342 2022-12-31 15:35:27,751 - mmcls - INFO - Epoch [10][4300/5005] lr: 1.000e-01, eta: 11:05:57, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.5336 2022-12-31 15:35:36,447 - mmcls - INFO - Epoch [10][4400/5005] lr: 1.000e-01, eta: 11:05:47, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.5541 2022-12-31 15:35:45,489 - mmcls - INFO - Epoch [10][4500/5005] lr: 1.000e-01, eta: 11:05:40, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.5669 2022-12-31 15:35:54,141 - mmcls - INFO - Epoch [10][4600/5005] lr: 1.000e-01, eta: 11:05:29, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.5311 2022-12-31 15:36:03,078 - mmcls - INFO - Epoch [10][4700/5005] lr: 1.000e-01, eta: 11:05:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5630 2022-12-31 15:36:12,265 - mmcls - INFO - Epoch [10][4800/5005] lr: 1.000e-01, eta: 11:05:15, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.5508 2022-12-31 15:36:21,055 - mmcls - INFO - Epoch [10][4900/5005] lr: 1.000e-01, eta: 11:05:06, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.5491 2022-12-31 15:36:29,952 - mmcls - INFO - Epoch [10][5000/5005] lr: 1.000e-01, eta: 11:04:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5393 2022-12-31 15:36:52,425 - mmcls - INFO - Epoch(val) [10][196] accuracy_top-1: 48.2000, accuracy_top-5: 74.2600 2022-12-31 15:37:03,626 - mmcls - INFO - Epoch [11][100/5005] lr: 1.000e-01, eta: 11:05:05, time: 0.112, data_time: 0.044, memory: 1224, loss: 2.4948 2022-12-31 15:37:12,223 - mmcls - INFO - Epoch [11][200/5005] lr: 1.000e-01, eta: 11:04:54, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4970 2022-12-31 15:37:21,106 - mmcls - INFO - Epoch [11][300/5005] lr: 1.000e-01, eta: 11:04:45, time: 0.089, data_time: 0.021, memory: 1224, loss: 2.4925 2022-12-31 15:37:29,721 - mmcls - INFO - Epoch [11][400/5005] lr: 1.000e-01, eta: 11:04:34, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.5309 2022-12-31 15:37:38,339 - mmcls - INFO - Epoch [11][500/5005] lr: 1.000e-01, eta: 11:04:23, time: 0.086, data_time: 0.030, memory: 1224, loss: 2.5005 2022-12-31 15:37:47,040 - mmcls - INFO - Epoch [11][600/5005] lr: 1.000e-01, eta: 11:04:12, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.5051 2022-12-31 15:37:56,210 - mmcls - INFO - Epoch [11][700/5005] lr: 1.000e-01, eta: 11:04:06, time: 0.092, data_time: 0.008, memory: 1224, loss: 2.4759 2022-12-31 15:38:05,170 - mmcls - INFO - Epoch [11][800/5005] lr: 1.000e-01, eta: 11:03:58, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.5091 2022-12-31 15:38:14,128 - mmcls - INFO - Epoch [11][900/5005] lr: 1.000e-01, eta: 11:03:50, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.5044 2022-12-31 15:38:23,120 - mmcls - INFO - Epoch [11][1000/5005] lr: 1.000e-01, eta: 11:03:43, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5219 2022-12-31 15:38:32,017 - mmcls - INFO - Epoch [11][1100/5005] lr: 1.000e-01, eta: 11:03:34, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.5270 2022-12-31 15:38:40,517 - mmcls - INFO - Epoch [11][1200/5005] lr: 1.000e-01, eta: 11:03:22, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.5282 2022-12-31 15:38:49,287 - mmcls - INFO - Epoch [11][1300/5005] lr: 1.000e-01, eta: 11:03:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5136 2022-12-31 15:38:58,197 - mmcls - INFO - Epoch [11][1400/5005] lr: 1.000e-01, eta: 11:03:04, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.5117 2022-12-31 15:39:07,076 - mmcls - INFO - Epoch [11][1500/5005] lr: 1.000e-01, eta: 11:02:55, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4965 2022-12-31 15:39:15,694 - mmcls - INFO - Epoch [11][1600/5005] lr: 1.000e-01, eta: 11:02:44, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.5614 2022-12-31 15:39:24,629 - mmcls - INFO - Epoch [11][1700/5005] lr: 1.000e-01, eta: 11:02:36, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.5015 2022-12-31 15:39:33,671 - mmcls - INFO - Epoch [11][1800/5005] lr: 1.000e-01, eta: 11:02:29, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.4924 2022-12-31 15:39:42,444 - mmcls - INFO - Epoch [11][1900/5005] lr: 1.000e-01, eta: 11:02:19, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.5291 2022-12-31 15:39:51,079 - mmcls - INFO - Epoch [11][2000/5005] lr: 1.000e-01, eta: 11:02:08, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.5520 2022-12-31 15:39:59,929 - mmcls - INFO - Epoch [11][2100/5005] lr: 1.000e-01, eta: 11:01:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5200 2022-12-31 15:40:09,046 - mmcls - INFO - Epoch [11][2200/5005] lr: 1.000e-01, eta: 11:01:53, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.5247 2022-12-31 15:40:17,987 - mmcls - INFO - Epoch [11][2300/5005] lr: 1.000e-01, eta: 11:01:45, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.5151 2022-12-31 15:40:26,669 - mmcls - INFO - Epoch [11][2400/5005] lr: 1.000e-01, eta: 11:01:34, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.5131 2022-12-31 15:40:35,484 - mmcls - INFO - Epoch [11][2500/5005] lr: 1.000e-01, eta: 11:01:25, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5215 2022-12-31 15:40:44,466 - mmcls - INFO - Epoch [11][2600/5005] lr: 1.000e-01, eta: 11:01:17, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.5283 2022-12-31 15:40:53,050 - mmcls - INFO - Epoch [11][2700/5005] lr: 1.000e-01, eta: 11:01:06, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.5431 2022-12-31 15:41:02,239 - mmcls - INFO - Epoch [11][2800/5005] lr: 1.000e-01, eta: 11:01:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.5155 2022-12-31 15:41:10,868 - mmcls - INFO - Epoch [11][2900/5005] lr: 1.000e-01, eta: 11:00:49, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5317 2022-12-31 15:41:19,459 - mmcls - INFO - Epoch [11][3000/5005] lr: 1.000e-01, eta: 11:00:38, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.5417 2022-12-31 15:41:28,265 - mmcls - INFO - Epoch [11][3100/5005] lr: 1.000e-01, eta: 11:00:29, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5413 2022-12-31 15:41:37,017 - mmcls - INFO - Epoch [11][3200/5005] lr: 1.000e-01, eta: 11:00:19, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.5251 2022-12-31 15:41:45,823 - mmcls - INFO - Epoch [11][3300/5005] lr: 1.000e-01, eta: 11:00:10, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.5115 2022-12-31 15:41:54,717 - mmcls - INFO - Epoch [11][3400/5005] lr: 1.000e-01, eta: 11:00:01, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.5483 2022-12-31 15:42:03,809 - mmcls - INFO - Epoch [11][3500/5005] lr: 1.000e-01, eta: 10:59:54, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4943 2022-12-31 15:42:12,504 - mmcls - INFO - Epoch [11][3600/5005] lr: 1.000e-01, eta: 10:59:44, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.5004 2022-12-31 15:42:21,115 - mmcls - INFO - Epoch [11][3700/5005] lr: 1.000e-01, eta: 10:59:33, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.5196 2022-12-31 15:42:29,930 - mmcls - INFO - Epoch [11][3800/5005] lr: 1.000e-01, eta: 10:59:24, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.5044 2022-12-31 15:42:39,188 - mmcls - INFO - Epoch [11][3900/5005] lr: 1.000e-01, eta: 10:59:18, time: 0.093, data_time: 0.024, memory: 1224, loss: 2.5173 2022-12-31 15:42:48,217 - mmcls - INFO - Epoch [11][4000/5005] lr: 1.000e-01, eta: 10:59:11, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.5321 2022-12-31 15:42:57,074 - mmcls - INFO - Epoch [11][4100/5005] lr: 1.000e-01, eta: 10:59:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.5051 2022-12-31 15:43:05,725 - mmcls - INFO - Epoch [11][4200/5005] lr: 1.000e-01, eta: 10:58:51, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.5089 2022-12-31 15:43:14,285 - mmcls - INFO - Epoch [11][4300/5005] lr: 1.000e-01, eta: 10:58:40, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.5199 2022-12-31 15:43:23,154 - mmcls - INFO - Epoch [11][4400/5005] lr: 1.000e-01, eta: 10:58:31, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.5150 2022-12-31 15:43:31,984 - mmcls - INFO - Epoch [11][4500/5005] lr: 1.000e-01, eta: 10:58:22, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.5278 2022-12-31 15:43:40,846 - mmcls - INFO - Epoch [11][4600/5005] lr: 1.000e-01, eta: 10:58:13, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.5317 2022-12-31 15:43:49,739 - mmcls - INFO - Epoch [11][4700/5005] lr: 1.000e-01, eta: 10:58:05, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.5059 2022-12-31 15:43:58,323 - mmcls - INFO - Epoch [11][4800/5005] lr: 1.000e-01, eta: 10:57:53, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.5094 2022-12-31 15:44:07,065 - mmcls - INFO - Epoch [11][4900/5005] lr: 1.000e-01, eta: 10:57:44, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.5236 2022-12-31 15:44:15,691 - mmcls - INFO - Epoch [11][5000/5005] lr: 1.000e-01, eta: 10:57:33, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.5408 2022-12-31 15:44:38,248 - mmcls - INFO - Epoch(val) [11][196] accuracy_top-1: 50.1380, accuracy_top-5: 75.9240 2022-12-31 15:44:49,440 - mmcls - INFO - Epoch [12][100/5005] lr: 1.000e-01, eta: 10:57:39, time: 0.112, data_time: 0.051, memory: 1224, loss: 2.4657 2022-12-31 15:44:57,953 - mmcls - INFO - Epoch [12][200/5005] lr: 1.000e-01, eta: 10:57:27, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.4795 2022-12-31 15:45:06,679 - mmcls - INFO - Epoch [12][300/5005] lr: 1.000e-01, eta: 10:57:17, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4873 2022-12-31 15:45:15,725 - mmcls - INFO - Epoch [12][400/5005] lr: 1.000e-01, eta: 10:57:10, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.5003 2022-12-31 15:45:24,555 - mmcls - INFO - Epoch [12][500/5005] lr: 1.000e-01, eta: 10:57:01, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.4837 2022-12-31 15:45:33,417 - mmcls - INFO - Epoch [12][600/5005] lr: 1.000e-01, eta: 10:56:52, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.4970 2022-12-31 15:45:42,369 - mmcls - INFO - Epoch [12][700/5005] lr: 1.000e-01, eta: 10:56:44, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4768 2022-12-31 15:45:51,082 - mmcls - INFO - Epoch [12][800/5005] lr: 1.000e-01, eta: 10:56:34, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4884 2022-12-31 15:45:59,850 - mmcls - INFO - Epoch [12][900/5005] lr: 1.000e-01, eta: 10:56:24, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4964 2022-12-31 15:46:08,794 - mmcls - INFO - Epoch [12][1000/5005] lr: 1.000e-01, eta: 10:56:16, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4934 2022-12-31 15:46:17,643 - mmcls - INFO - Epoch [12][1100/5005] lr: 1.000e-01, eta: 10:56:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5017 2022-12-31 15:46:26,810 - mmcls - INFO - Epoch [12][1200/5005] lr: 1.000e-01, eta: 10:56:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4697 2022-12-31 15:46:35,763 - mmcls - INFO - Epoch [12][1300/5005] lr: 1.000e-01, eta: 10:55:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5131 2022-12-31 15:46:44,465 - mmcls - INFO - Epoch [12][1400/5005] lr: 1.000e-01, eta: 10:55:42, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.4963 2022-12-31 15:46:53,592 - mmcls - INFO - Epoch [12][1500/5005] lr: 1.000e-01, eta: 10:55:35, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4795 2022-12-31 15:47:02,411 - mmcls - INFO - Epoch [12][1600/5005] lr: 1.000e-01, eta: 10:55:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.5083 2022-12-31 15:47:11,150 - mmcls - INFO - Epoch [12][1700/5005] lr: 1.000e-01, eta: 10:55:16, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.5149 2022-12-31 15:47:19,861 - mmcls - INFO - Epoch [12][1800/5005] lr: 1.000e-01, eta: 10:55:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4863 2022-12-31 15:47:28,674 - mmcls - INFO - Epoch [12][1900/5005] lr: 1.000e-01, eta: 10:54:57, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5122 2022-12-31 15:47:37,522 - mmcls - INFO - Epoch [12][2000/5005] lr: 1.000e-01, eta: 10:54:48, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5021 2022-12-31 15:47:46,143 - mmcls - INFO - Epoch [12][2100/5005] lr: 1.000e-01, eta: 10:54:37, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4939 2022-12-31 15:47:54,621 - mmcls - INFO - Epoch [12][2200/5005] lr: 1.000e-01, eta: 10:54:26, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.5144 2022-12-31 15:48:03,155 - mmcls - INFO - Epoch [12][2300/5005] lr: 1.000e-01, eta: 10:54:14, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.5395 2022-12-31 15:48:11,749 - mmcls - INFO - Epoch [12][2400/5005] lr: 1.000e-01, eta: 10:54:03, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.5182 2022-12-31 15:48:20,552 - mmcls - INFO - Epoch [12][2500/5005] lr: 1.000e-01, eta: 10:53:54, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.5240 2022-12-31 15:48:29,389 - mmcls - INFO - Epoch [12][2600/5005] lr: 1.000e-01, eta: 10:53:45, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.5163 2022-12-31 15:48:38,213 - mmcls - INFO - Epoch [12][2700/5005] lr: 1.000e-01, eta: 10:53:36, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.5058 2022-12-31 15:48:46,825 - mmcls - INFO - Epoch [12][2800/5005] lr: 1.000e-01, eta: 10:53:25, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5150 2022-12-31 15:48:55,660 - mmcls - INFO - Epoch [12][2900/5005] lr: 1.000e-01, eta: 10:53:16, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.5183 2022-12-31 15:49:04,590 - mmcls - INFO - Epoch [12][3000/5005] lr: 1.000e-01, eta: 10:53:08, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4974 2022-12-31 15:49:13,474 - mmcls - INFO - Epoch [12][3100/5005] lr: 1.000e-01, eta: 10:52:59, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.4805 2022-12-31 15:49:22,368 - mmcls - INFO - Epoch [12][3200/5005] lr: 1.000e-01, eta: 10:52:51, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.4870 2022-12-31 15:49:31,013 - mmcls - INFO - Epoch [12][3300/5005] lr: 1.000e-01, eta: 10:52:40, time: 0.086, data_time: 0.028, memory: 1224, loss: 2.4991 2022-12-31 15:49:40,000 - mmcls - INFO - Epoch [12][3400/5005] lr: 1.000e-01, eta: 10:52:32, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.4933 2022-12-31 15:49:48,976 - mmcls - INFO - Epoch [12][3500/5005] lr: 1.000e-01, eta: 10:52:24, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.5144 2022-12-31 15:49:57,831 - mmcls - INFO - Epoch [12][3600/5005] lr: 1.000e-01, eta: 10:52:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5007 2022-12-31 15:50:06,417 - mmcls - INFO - Epoch [12][3700/5005] lr: 1.000e-01, eta: 10:52:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4788 2022-12-31 15:50:15,135 - mmcls - INFO - Epoch [12][3800/5005] lr: 1.000e-01, eta: 10:51:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4907 2022-12-31 15:50:23,809 - mmcls - INFO - Epoch [12][3900/5005] lr: 1.000e-01, eta: 10:51:44, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4866 2022-12-31 15:50:32,405 - mmcls - INFO - Epoch [12][4000/5005] lr: 1.000e-01, eta: 10:51:34, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4777 2022-12-31 15:50:41,046 - mmcls - INFO - Epoch [12][4100/5005] lr: 1.000e-01, eta: 10:51:23, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.5136 2022-12-31 15:50:49,945 - mmcls - INFO - Epoch [12][4200/5005] lr: 1.000e-01, eta: 10:51:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5082 2022-12-31 15:50:58,640 - mmcls - INFO - Epoch [12][4300/5005] lr: 1.000e-01, eta: 10:51:05, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.5092 2022-12-31 15:51:07,359 - mmcls - INFO - Epoch [12][4400/5005] lr: 1.000e-01, eta: 10:50:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.5194 2022-12-31 15:51:16,234 - mmcls - INFO - Epoch [12][4500/5005] lr: 1.000e-01, eta: 10:50:46, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4935 2022-12-31 15:51:24,716 - mmcls - INFO - Epoch [12][4600/5005] lr: 1.000e-01, eta: 10:50:34, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.5198 2022-12-31 15:51:33,290 - mmcls - INFO - Epoch [12][4700/5005] lr: 1.000e-01, eta: 10:50:23, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.5282 2022-12-31 15:51:42,331 - mmcls - INFO - Epoch [12][4800/5005] lr: 1.000e-01, eta: 10:50:16, time: 0.090, data_time: 0.024, memory: 1224, loss: 2.5059 2022-12-31 15:51:51,471 - mmcls - INFO - Epoch [12][4900/5005] lr: 1.000e-01, eta: 10:50:09, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.5229 2022-12-31 15:52:00,101 - mmcls - INFO - Epoch [12][5000/5005] lr: 1.000e-01, eta: 10:49:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.5254 2022-12-31 15:52:22,758 - mmcls - INFO - Epoch(val) [12][196] accuracy_top-1: 50.3200, accuracy_top-5: 75.9940 2022-12-31 15:52:34,201 - mmcls - INFO - Epoch [13][100/5005] lr: 1.000e-01, eta: 10:50:05, time: 0.114, data_time: 0.047, memory: 1224, loss: 2.4527 2022-12-31 15:52:42,848 - mmcls - INFO - Epoch [13][200/5005] lr: 1.000e-01, eta: 10:49:54, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4649 2022-12-31 15:52:51,538 - mmcls - INFO - Epoch [13][300/5005] lr: 1.000e-01, eta: 10:49:44, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.4735 2022-12-31 15:53:01,265 - mmcls - INFO - Epoch [13][400/5005] lr: 1.000e-01, eta: 10:49:42, time: 0.097, data_time: 0.001, memory: 1224, loss: 2.4369 2022-12-31 15:53:10,070 - mmcls - INFO - Epoch [13][500/5005] lr: 1.000e-01, eta: 10:49:33, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4861 2022-12-31 15:53:18,741 - mmcls - INFO - Epoch [13][600/5005] lr: 1.000e-01, eta: 10:49:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4640 2022-12-31 15:53:27,585 - mmcls - INFO - Epoch [13][700/5005] lr: 1.000e-01, eta: 10:49:13, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4801 2022-12-31 15:53:36,581 - mmcls - INFO - Epoch [13][800/5005] lr: 1.000e-01, eta: 10:49:05, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.4527 2022-12-31 15:53:45,295 - mmcls - INFO - Epoch [13][900/5005] lr: 1.000e-01, eta: 10:48:56, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4586 2022-12-31 15:53:53,815 - mmcls - INFO - Epoch [13][1000/5005] lr: 1.000e-01, eta: 10:48:44, time: 0.085, data_time: 0.017, memory: 1224, loss: 2.4892 2022-12-31 15:54:02,420 - mmcls - INFO - Epoch [13][1100/5005] lr: 1.000e-01, eta: 10:48:34, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.4938 2022-12-31 15:54:11,308 - mmcls - INFO - Epoch [13][1200/5005] lr: 1.000e-01, eta: 10:48:25, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.5020 2022-12-31 15:54:20,215 - mmcls - INFO - Epoch [13][1300/5005] lr: 1.000e-01, eta: 10:48:16, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4744 2022-12-31 15:54:28,995 - mmcls - INFO - Epoch [13][1400/5005] lr: 1.000e-01, eta: 10:48:07, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4757 2022-12-31 15:54:37,861 - mmcls - INFO - Epoch [13][1500/5005] lr: 1.000e-01, eta: 10:47:58, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4755 2022-12-31 15:54:46,578 - mmcls - INFO - Epoch [13][1600/5005] lr: 1.000e-01, eta: 10:47:48, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4910 2022-12-31 15:54:55,498 - mmcls - INFO - Epoch [13][1700/5005] lr: 1.000e-01, eta: 10:47:40, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4832 2022-12-31 15:55:04,451 - mmcls - INFO - Epoch [13][1800/5005] lr: 1.000e-01, eta: 10:47:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.5114 2022-12-31 15:55:13,271 - mmcls - INFO - Epoch [13][1900/5005] lr: 1.000e-01, eta: 10:47:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4888 2022-12-31 15:55:22,028 - mmcls - INFO - Epoch [13][2000/5005] lr: 1.000e-01, eta: 10:47:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4926 2022-12-31 15:55:30,704 - mmcls - INFO - Epoch [13][2100/5005] lr: 1.000e-01, eta: 10:47:03, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.5128 2022-12-31 15:55:39,596 - mmcls - INFO - Epoch [13][2200/5005] lr: 1.000e-01, eta: 10:46:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4945 2022-12-31 15:55:48,124 - mmcls - INFO - Epoch [13][2300/5005] lr: 1.000e-01, eta: 10:46:43, time: 0.085, data_time: 0.025, memory: 1224, loss: 2.5005 2022-12-31 15:55:56,745 - mmcls - INFO - Epoch [13][2400/5005] lr: 1.000e-01, eta: 10:46:33, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4693 2022-12-31 15:56:05,821 - mmcls - INFO - Epoch [13][2500/5005] lr: 1.000e-01, eta: 10:46:25, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.4917 2022-12-31 15:56:14,652 - mmcls - INFO - Epoch [13][2600/5005] lr: 1.000e-01, eta: 10:46:16, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4883 2022-12-31 15:56:23,653 - mmcls - INFO - Epoch [13][2700/5005] lr: 1.000e-01, eta: 10:46:08, time: 0.090, data_time: 0.012, memory: 1224, loss: 2.4773 2022-12-31 15:56:32,483 - mmcls - INFO - Epoch [13][2800/5005] lr: 1.000e-01, eta: 10:45:59, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.4738 2022-12-31 15:56:41,226 - mmcls - INFO - Epoch [13][2900/5005] lr: 1.000e-01, eta: 10:45:50, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.5010 2022-12-31 15:56:50,174 - mmcls - INFO - Epoch [13][3000/5005] lr: 1.000e-01, eta: 10:45:41, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.5079 2022-12-31 15:56:58,687 - mmcls - INFO - Epoch [13][3100/5005] lr: 1.000e-01, eta: 10:45:30, time: 0.085, data_time: 0.013, memory: 1224, loss: 2.5016 2022-12-31 15:57:07,273 - mmcls - INFO - Epoch [13][3200/5005] lr: 1.000e-01, eta: 10:45:19, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.4800 2022-12-31 15:57:16,334 - mmcls - INFO - Epoch [13][3300/5005] lr: 1.000e-01, eta: 10:45:12, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.4774 2022-12-31 15:57:25,032 - mmcls - INFO - Epoch [13][3400/5005] lr: 1.000e-01, eta: 10:45:02, time: 0.087, data_time: 0.023, memory: 1224, loss: 2.4703 2022-12-31 15:57:33,773 - mmcls - INFO - Epoch [13][3500/5005] lr: 1.000e-01, eta: 10:44:53, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4943 2022-12-31 15:57:42,713 - mmcls - INFO - Epoch [13][3600/5005] lr: 1.000e-01, eta: 10:44:44, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.5187 2022-12-31 15:57:51,335 - mmcls - INFO - Epoch [13][3700/5005] lr: 1.000e-01, eta: 10:44:34, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.4818 2022-12-31 15:58:00,239 - mmcls - INFO - Epoch [13][3800/5005] lr: 1.000e-01, eta: 10:44:25, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.4726 2022-12-31 15:58:09,220 - mmcls - INFO - Epoch [13][3900/5005] lr: 1.000e-01, eta: 10:44:17, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.5007 2022-12-31 15:58:18,025 - mmcls - INFO - Epoch [13][4000/5005] lr: 1.000e-01, eta: 10:44:08, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.5121 2022-12-31 15:58:26,876 - mmcls - INFO - Epoch [13][4100/5005] lr: 1.000e-01, eta: 10:43:59, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.4899 2022-12-31 15:58:35,821 - mmcls - INFO - Epoch [13][4200/5005] lr: 1.000e-01, eta: 10:43:51, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.4990 2022-12-31 15:58:44,405 - mmcls - INFO - Epoch [13][4300/5005] lr: 1.000e-01, eta: 10:43:40, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4998 2022-12-31 15:58:53,099 - mmcls - INFO - Epoch [13][4400/5005] lr: 1.000e-01, eta: 10:43:30, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4953 2022-12-31 15:59:02,065 - mmcls - INFO - Epoch [13][4500/5005] lr: 1.000e-01, eta: 10:43:22, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.4943 2022-12-31 15:59:11,238 - mmcls - INFO - Epoch [13][4600/5005] lr: 1.000e-01, eta: 10:43:15, time: 0.092, data_time: 0.030, memory: 1224, loss: 2.5301 2022-12-31 15:59:20,089 - mmcls - INFO - Epoch [13][4700/5005] lr: 1.000e-01, eta: 10:43:06, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.5164 2022-12-31 15:59:29,122 - mmcls - INFO - Epoch [13][4800/5005] lr: 1.000e-01, eta: 10:42:59, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.4985 2022-12-31 15:59:37,899 - mmcls - INFO - Epoch [13][4900/5005] lr: 1.000e-01, eta: 10:42:49, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4638 2022-12-31 15:59:46,743 - mmcls - INFO - Epoch [13][5000/5005] lr: 1.000e-01, eta: 10:42:40, time: 0.088, data_time: 0.020, memory: 1224, loss: 2.5045 2022-12-31 16:00:09,620 - mmcls - INFO - Epoch(val) [13][196] accuracy_top-1: 51.3240, accuracy_top-5: 77.0860 2022-12-31 16:00:20,547 - mmcls - INFO - Epoch [14][100/5005] lr: 1.000e-01, eta: 10:42:42, time: 0.109, data_time: 0.033, memory: 1224, loss: 2.4495 2022-12-31 16:00:29,188 - mmcls - INFO - Epoch [14][200/5005] lr: 1.000e-01, eta: 10:42:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4230 2022-12-31 16:00:37,766 - mmcls - INFO - Epoch [14][300/5005] lr: 1.000e-01, eta: 10:42:21, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4356 2022-12-31 16:00:46,336 - mmcls - INFO - Epoch [14][400/5005] lr: 1.000e-01, eta: 10:42:10, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4451 2022-12-31 16:00:55,070 - mmcls - INFO - Epoch [14][500/5005] lr: 1.000e-01, eta: 10:42:00, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4302 2022-12-31 16:01:03,678 - mmcls - INFO - Epoch [14][600/5005] lr: 1.000e-01, eta: 10:41:50, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4537 2022-12-31 16:01:12,149 - mmcls - INFO - Epoch [14][700/5005] lr: 1.000e-01, eta: 10:41:38, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.4494 2022-12-31 16:01:20,910 - mmcls - INFO - Epoch [14][800/5005] lr: 1.000e-01, eta: 10:41:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4769 2022-12-31 16:01:29,660 - mmcls - INFO - Epoch [14][900/5005] lr: 1.000e-01, eta: 10:41:19, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4753 2022-12-31 16:01:38,261 - mmcls - INFO - Epoch [14][1000/5005] lr: 1.000e-01, eta: 10:41:09, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4830 2022-12-31 16:01:47,125 - mmcls - INFO - Epoch [14][1100/5005] lr: 1.000e-01, eta: 10:41:00, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4490 2022-12-31 16:01:56,108 - mmcls - INFO - Epoch [14][1200/5005] lr: 1.000e-01, eta: 10:40:52, time: 0.090, data_time: 0.035, memory: 1224, loss: 2.4775 2022-12-31 16:02:04,778 - mmcls - INFO - Epoch [14][1300/5005] lr: 1.000e-01, eta: 10:40:42, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4724 2022-12-31 16:02:13,343 - mmcls - INFO - Epoch [14][1400/5005] lr: 1.000e-01, eta: 10:40:31, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4791 2022-12-31 16:02:22,288 - mmcls - INFO - Epoch [14][1500/5005] lr: 1.000e-01, eta: 10:40:23, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4848 2022-12-31 16:02:30,877 - mmcls - INFO - Epoch [14][1600/5005] lr: 1.000e-01, eta: 10:40:12, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4629 2022-12-31 16:02:39,554 - mmcls - INFO - Epoch [14][1700/5005] lr: 1.000e-01, eta: 10:40:02, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4571 2022-12-31 16:02:48,168 - mmcls - INFO - Epoch [14][1800/5005] lr: 1.000e-01, eta: 10:39:52, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4794 2022-12-31 16:02:56,777 - mmcls - INFO - Epoch [14][1900/5005] lr: 1.000e-01, eta: 10:39:42, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4783 2022-12-31 16:03:05,563 - mmcls - INFO - Epoch [14][2000/5005] lr: 1.000e-01, eta: 10:39:32, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.4768 2022-12-31 16:03:14,397 - mmcls - INFO - Epoch [14][2100/5005] lr: 1.000e-01, eta: 10:39:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4768 2022-12-31 16:03:23,324 - mmcls - INFO - Epoch [14][2200/5005] lr: 1.000e-01, eta: 10:39:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4733 2022-12-31 16:03:31,945 - mmcls - INFO - Epoch [14][2300/5005] lr: 1.000e-01, eta: 10:39:04, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4873 2022-12-31 16:03:41,115 - mmcls - INFO - Epoch [14][2400/5005] lr: 1.000e-01, eta: 10:38:58, time: 0.092, data_time: 0.017, memory: 1224, loss: 2.4756 2022-12-31 16:03:49,873 - mmcls - INFO - Epoch [14][2500/5005] lr: 1.000e-01, eta: 10:38:48, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.5116 2022-12-31 16:03:58,602 - mmcls - INFO - Epoch [14][2600/5005] lr: 1.000e-01, eta: 10:38:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4737 2022-12-31 16:04:07,438 - mmcls - INFO - Epoch [14][2700/5005] lr: 1.000e-01, eta: 10:38:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4620 2022-12-31 16:04:16,376 - mmcls - INFO - Epoch [14][2800/5005] lr: 1.000e-01, eta: 10:38:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4822 2022-12-31 16:04:25,220 - mmcls - INFO - Epoch [14][2900/5005] lr: 1.000e-01, eta: 10:38:12, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4850 2022-12-31 16:04:33,906 - mmcls - INFO - Epoch [14][3000/5005] lr: 1.000e-01, eta: 10:38:02, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4599 2022-12-31 16:04:42,345 - mmcls - INFO - Epoch [14][3100/5005] lr: 1.000e-01, eta: 10:37:51, time: 0.084, data_time: 0.003, memory: 1224, loss: 2.4816 2022-12-31 16:04:51,193 - mmcls - INFO - Epoch [14][3200/5005] lr: 1.000e-01, eta: 10:37:42, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.4484 2022-12-31 16:05:00,116 - mmcls - INFO - Epoch [14][3300/5005] lr: 1.000e-01, eta: 10:37:34, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4535 2022-12-31 16:05:08,739 - mmcls - INFO - Epoch [14][3400/5005] lr: 1.000e-01, eta: 10:37:23, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4599 2022-12-31 16:05:17,646 - mmcls - INFO - Epoch [14][3500/5005] lr: 1.000e-01, eta: 10:37:15, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4854 2022-12-31 16:05:26,766 - mmcls - INFO - Epoch [14][3600/5005] lr: 1.000e-01, eta: 10:37:08, time: 0.091, data_time: 0.021, memory: 1224, loss: 2.4883 2022-12-31 16:05:35,610 - mmcls - INFO - Epoch [14][3700/5005] lr: 1.000e-01, eta: 10:36:59, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4957 2022-12-31 16:05:44,370 - mmcls - INFO - Epoch [14][3800/5005] lr: 1.000e-01, eta: 10:36:49, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.4737 2022-12-31 16:05:53,143 - mmcls - INFO - Epoch [14][3900/5005] lr: 1.000e-01, eta: 10:36:40, time: 0.088, data_time: 0.023, memory: 1224, loss: 2.4808 2022-12-31 16:06:01,976 - mmcls - INFO - Epoch [14][4000/5005] lr: 1.000e-01, eta: 10:36:31, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4637 2022-12-31 16:06:10,631 - mmcls - INFO - Epoch [14][4100/5005] lr: 1.000e-01, eta: 10:36:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4899 2022-12-31 16:06:19,185 - mmcls - INFO - Epoch [14][4200/5005] lr: 1.000e-01, eta: 10:36:10, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.4630 2022-12-31 16:06:28,351 - mmcls - INFO - Epoch [14][4300/5005] lr: 1.000e-01, eta: 10:36:03, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.5068 2022-12-31 16:06:37,189 - mmcls - INFO - Epoch [14][4400/5005] lr: 1.000e-01, eta: 10:35:54, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4542 2022-12-31 16:06:46,059 - mmcls - INFO - Epoch [14][4500/5005] lr: 1.000e-01, eta: 10:35:46, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4627 2022-12-31 16:06:54,811 - mmcls - INFO - Epoch [14][4600/5005] lr: 1.000e-01, eta: 10:35:36, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.5087 2022-12-31 16:07:03,455 - mmcls - INFO - Epoch [14][4700/5005] lr: 1.000e-01, eta: 10:35:26, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.4682 2022-12-31 16:07:12,175 - mmcls - INFO - Epoch [14][4800/5005] lr: 1.000e-01, eta: 10:35:16, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4871 2022-12-31 16:07:20,658 - mmcls - INFO - Epoch [14][4900/5005] lr: 1.000e-01, eta: 10:35:05, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.4842 2022-12-31 16:07:29,570 - mmcls - INFO - Epoch [14][5000/5005] lr: 1.000e-01, eta: 10:34:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4791 2022-12-31 16:07:52,379 - mmcls - INFO - Epoch(val) [14][196] accuracy_top-1: 49.6700, accuracy_top-5: 75.7400 2022-12-31 16:08:03,845 - mmcls - INFO - Epoch [15][100/5005] lr: 1.000e-01, eta: 10:35:01, time: 0.114, data_time: 0.055, memory: 1224, loss: 2.4360 2022-12-31 16:08:12,697 - mmcls - INFO - Epoch [15][200/5005] lr: 1.000e-01, eta: 10:34:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4160 2022-12-31 16:08:21,286 - mmcls - INFO - Epoch [15][300/5005] lr: 1.000e-01, eta: 10:34:41, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4307 2022-12-31 16:08:30,087 - mmcls - INFO - Epoch [15][400/5005] lr: 1.000e-01, eta: 10:34:32, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4339 2022-12-31 16:08:38,729 - mmcls - INFO - Epoch [15][500/5005] lr: 1.000e-01, eta: 10:34:22, time: 0.086, data_time: 0.025, memory: 1224, loss: 2.4197 2022-12-31 16:08:47,915 - mmcls - INFO - Epoch [15][600/5005] lr: 1.000e-01, eta: 10:34:15, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.4441 2022-12-31 16:08:56,667 - mmcls - INFO - Epoch [15][700/5005] lr: 1.000e-01, eta: 10:34:06, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4648 2022-12-31 16:09:05,296 - mmcls - INFO - Epoch [15][800/5005] lr: 1.000e-01, eta: 10:33:55, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4347 2022-12-31 16:09:13,930 - mmcls - INFO - Epoch [15][900/5005] lr: 1.000e-01, eta: 10:33:45, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4634 2022-12-31 16:09:22,833 - mmcls - INFO - Epoch [15][1000/5005] lr: 1.000e-01, eta: 10:33:37, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4477 2022-12-31 16:09:31,711 - mmcls - INFO - Epoch [15][1100/5005] lr: 1.000e-01, eta: 10:33:28, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4767 2022-12-31 16:09:40,635 - mmcls - INFO - Epoch [15][1200/5005] lr: 1.000e-01, eta: 10:33:19, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4778 2022-12-31 16:09:49,522 - mmcls - INFO - Epoch [15][1300/5005] lr: 1.000e-01, eta: 10:33:11, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4415 2022-12-31 16:09:58,181 - mmcls - INFO - Epoch [15][1400/5005] lr: 1.000e-01, eta: 10:33:01, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4740 2022-12-31 16:10:07,261 - mmcls - INFO - Epoch [15][1500/5005] lr: 1.000e-01, eta: 10:32:53, time: 0.091, data_time: 0.008, memory: 1224, loss: 2.4589 2022-12-31 16:10:16,116 - mmcls - INFO - Epoch [15][1600/5005] lr: 1.000e-01, eta: 10:32:44, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4624 2022-12-31 16:10:24,794 - mmcls - INFO - Epoch [15][1700/5005] lr: 1.000e-01, eta: 10:32:35, time: 0.087, data_time: 0.032, memory: 1224, loss: 2.4836 2022-12-31 16:10:33,594 - mmcls - INFO - Epoch [15][1800/5005] lr: 1.000e-01, eta: 10:32:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4619 2022-12-31 16:10:42,620 - mmcls - INFO - Epoch [15][1900/5005] lr: 1.000e-01, eta: 10:32:18, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.4565 2022-12-31 16:10:51,620 - mmcls - INFO - Epoch [15][2000/5005] lr: 1.000e-01, eta: 10:32:10, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.4647 2022-12-31 16:11:00,145 - mmcls - INFO - Epoch [15][2100/5005] lr: 1.000e-01, eta: 10:31:59, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4491 2022-12-31 16:11:08,946 - mmcls - INFO - Epoch [15][2200/5005] lr: 1.000e-01, eta: 10:31:50, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4859 2022-12-31 16:11:17,866 - mmcls - INFO - Epoch [15][2300/5005] lr: 1.000e-01, eta: 10:31:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4829 2022-12-31 16:11:26,672 - mmcls - INFO - Epoch [15][2400/5005] lr: 1.000e-01, eta: 10:31:32, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4378 2022-12-31 16:11:35,602 - mmcls - INFO - Epoch [15][2500/5005] lr: 1.000e-01, eta: 10:31:24, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4747 2022-12-31 16:11:44,399 - mmcls - INFO - Epoch [15][2600/5005] lr: 1.000e-01, eta: 10:31:14, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4880 2022-12-31 16:11:53,166 - mmcls - INFO - Epoch [15][2700/5005] lr: 1.000e-01, eta: 10:31:05, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4461 2022-12-31 16:12:02,046 - mmcls - INFO - Epoch [15][2800/5005] lr: 1.000e-01, eta: 10:30:56, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4728 2022-12-31 16:12:11,608 - mmcls - INFO - Epoch [15][2900/5005] lr: 1.000e-01, eta: 10:30:52, time: 0.096, data_time: 0.014, memory: 1224, loss: 2.4897 2022-12-31 16:12:20,364 - mmcls - INFO - Epoch [15][3000/5005] lr: 1.000e-01, eta: 10:30:42, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4795 2022-12-31 16:12:29,026 - mmcls - INFO - Epoch [15][3100/5005] lr: 1.000e-01, eta: 10:30:32, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4616 2022-12-31 16:12:37,784 - mmcls - INFO - Epoch [15][3200/5005] lr: 1.000e-01, eta: 10:30:23, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4612 2022-12-31 16:12:46,519 - mmcls - INFO - Epoch [15][3300/5005] lr: 1.000e-01, eta: 10:30:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4841 2022-12-31 16:12:55,397 - mmcls - INFO - Epoch [15][3400/5005] lr: 1.000e-01, eta: 10:30:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4920 2022-12-31 16:13:03,842 - mmcls - INFO - Epoch [15][3500/5005] lr: 1.000e-01, eta: 10:29:53, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.4691 2022-12-31 16:13:12,755 - mmcls - INFO - Epoch [15][3600/5005] lr: 1.000e-01, eta: 10:29:45, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4988 2022-12-31 16:13:21,918 - mmcls - INFO - Epoch [15][3700/5005] lr: 1.000e-01, eta: 10:29:38, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.4449 2022-12-31 16:13:30,936 - mmcls - INFO - Epoch [15][3800/5005] lr: 1.000e-01, eta: 10:29:30, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4774 2022-12-31 16:13:39,590 - mmcls - INFO - Epoch [15][3900/5005] lr: 1.000e-01, eta: 10:29:20, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4776 2022-12-31 16:13:48,405 - mmcls - INFO - Epoch [15][4000/5005] lr: 1.000e-01, eta: 10:29:11, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4812 2022-12-31 16:13:56,932 - mmcls - INFO - Epoch [15][4100/5005] lr: 1.000e-01, eta: 10:29:00, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4739 2022-12-31 16:14:06,000 - mmcls - INFO - Epoch [15][4200/5005] lr: 1.000e-01, eta: 10:28:52, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4637 2022-12-31 16:14:14,791 - mmcls - INFO - Epoch [15][4300/5005] lr: 1.000e-01, eta: 10:28:43, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4776 2022-12-31 16:14:23,616 - mmcls - INFO - Epoch [15][4400/5005] lr: 1.000e-01, eta: 10:28:34, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4607 2022-12-31 16:14:32,371 - mmcls - INFO - Epoch [15][4500/5005] lr: 1.000e-01, eta: 10:28:25, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4888 2022-12-31 16:14:41,505 - mmcls - INFO - Epoch [15][4600/5005] lr: 1.000e-01, eta: 10:28:18, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.4546 2022-12-31 16:14:50,224 - mmcls - INFO - Epoch [15][4700/5005] lr: 1.000e-01, eta: 10:28:08, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4723 2022-12-31 16:14:58,897 - mmcls - INFO - Epoch [15][4800/5005] lr: 1.000e-01, eta: 10:27:58, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4497 2022-12-31 16:15:07,497 - mmcls - INFO - Epoch [15][4900/5005] lr: 1.000e-01, eta: 10:27:48, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.4644 2022-12-31 16:15:16,135 - mmcls - INFO - Epoch [15][5000/5005] lr: 1.000e-01, eta: 10:27:38, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4775 2022-12-31 16:15:38,877 - mmcls - INFO - Epoch(val) [15][196] accuracy_top-1: 50.8780, accuracy_top-5: 76.1600 2022-12-31 16:15:49,872 - mmcls - INFO - Epoch [16][100/5005] lr: 1.000e-01, eta: 10:27:38, time: 0.110, data_time: 0.054, memory: 1224, loss: 2.4295 2022-12-31 16:15:58,678 - mmcls - INFO - Epoch [16][200/5005] lr: 1.000e-01, eta: 10:27:29, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4282 2022-12-31 16:16:07,419 - mmcls - INFO - Epoch [16][300/5005] lr: 1.000e-01, eta: 10:27:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4579 2022-12-31 16:16:16,233 - mmcls - INFO - Epoch [16][400/5005] lr: 1.000e-01, eta: 10:27:10, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4480 2022-12-31 16:16:24,786 - mmcls - INFO - Epoch [16][500/5005] lr: 1.000e-01, eta: 10:27:00, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4379 2022-12-31 16:16:34,244 - mmcls - INFO - Epoch [16][600/5005] lr: 1.000e-01, eta: 10:26:54, time: 0.095, data_time: 0.001, memory: 1224, loss: 2.4416 2022-12-31 16:16:43,724 - mmcls - INFO - Epoch [16][700/5005] lr: 1.000e-01, eta: 10:26:49, time: 0.095, data_time: 0.000, memory: 1224, loss: 2.4240 2022-12-31 16:16:52,454 - mmcls - INFO - Epoch [16][800/5005] lr: 1.000e-01, eta: 10:26:39, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.4181 2022-12-31 16:17:01,252 - mmcls - INFO - Epoch [16][900/5005] lr: 1.000e-01, eta: 10:26:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4401 2022-12-31 16:17:10,037 - mmcls - INFO - Epoch [16][1000/5005] lr: 1.000e-01, eta: 10:26:21, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4630 2022-12-31 16:17:18,937 - mmcls - INFO - Epoch [16][1100/5005] lr: 1.000e-01, eta: 10:26:12, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4704 2022-12-31 16:17:27,590 - mmcls - INFO - Epoch [16][1200/5005] lr: 1.000e-01, eta: 10:26:02, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.4446 2022-12-31 16:17:36,814 - mmcls - INFO - Epoch [16][1300/5005] lr: 1.000e-01, eta: 10:25:55, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.4621 2022-12-31 16:17:45,686 - mmcls - INFO - Epoch [16][1400/5005] lr: 1.000e-01, eta: 10:25:47, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4444 2022-12-31 16:17:54,426 - mmcls - INFO - Epoch [16][1500/5005] lr: 1.000e-01, eta: 10:25:37, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4568 2022-12-31 16:18:03,050 - mmcls - INFO - Epoch [16][1600/5005] lr: 1.000e-01, eta: 10:25:27, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4364 2022-12-31 16:18:11,803 - mmcls - INFO - Epoch [16][1700/5005] lr: 1.000e-01, eta: 10:25:18, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4721 2022-12-31 16:18:20,937 - mmcls - INFO - Epoch [16][1800/5005] lr: 1.000e-01, eta: 10:25:10, time: 0.091, data_time: 0.007, memory: 1224, loss: 2.4421 2022-12-31 16:18:30,047 - mmcls - INFO - Epoch [16][1900/5005] lr: 1.000e-01, eta: 10:25:03, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.4276 2022-12-31 16:18:38,768 - mmcls - INFO - Epoch [16][2000/5005] lr: 1.000e-01, eta: 10:24:53, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.4552 2022-12-31 16:18:47,224 - mmcls - INFO - Epoch [16][2100/5005] lr: 1.000e-01, eta: 10:24:42, time: 0.085, data_time: 0.009, memory: 1224, loss: 2.4383 2022-12-31 16:18:55,896 - mmcls - INFO - Epoch [16][2200/5005] lr: 1.000e-01, eta: 10:24:32, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4626 2022-12-31 16:19:04,524 - mmcls - INFO - Epoch [16][2300/5005] lr: 1.000e-01, eta: 10:24:22, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4739 2022-12-31 16:19:13,410 - mmcls - INFO - Epoch [16][2400/5005] lr: 1.000e-01, eta: 10:24:13, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4689 2022-12-31 16:19:21,985 - mmcls - INFO - Epoch [16][2500/5005] lr: 1.000e-01, eta: 10:24:03, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4581 2022-12-31 16:19:31,016 - mmcls - INFO - Epoch [16][2600/5005] lr: 1.000e-01, eta: 10:23:55, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.4522 2022-12-31 16:19:39,691 - mmcls - INFO - Epoch [16][2700/5005] lr: 1.000e-01, eta: 10:23:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4334 2022-12-31 16:19:48,505 - mmcls - INFO - Epoch [16][2800/5005] lr: 1.000e-01, eta: 10:23:36, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4147 2022-12-31 16:19:57,165 - mmcls - INFO - Epoch [16][2900/5005] lr: 1.000e-01, eta: 10:23:26, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4612 2022-12-31 16:20:06,048 - mmcls - INFO - Epoch [16][3000/5005] lr: 1.000e-01, eta: 10:23:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4440 2022-12-31 16:20:15,533 - mmcls - INFO - Epoch [16][3100/5005] lr: 1.000e-01, eta: 10:23:12, time: 0.095, data_time: 0.001, memory: 1224, loss: 2.4526 2022-12-31 16:20:24,202 - mmcls - INFO - Epoch [16][3200/5005] lr: 1.000e-01, eta: 10:23:02, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4696 2022-12-31 16:20:33,205 - mmcls - INFO - Epoch [16][3300/5005] lr: 1.000e-01, eta: 10:22:54, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4730 2022-12-31 16:20:42,009 - mmcls - INFO - Epoch [16][3400/5005] lr: 1.000e-01, eta: 10:22:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4197 2022-12-31 16:20:51,012 - mmcls - INFO - Epoch [16][3500/5005] lr: 1.000e-01, eta: 10:22:37, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4504 2022-12-31 16:21:00,200 - mmcls - INFO - Epoch [16][3600/5005] lr: 1.000e-01, eta: 10:22:30, time: 0.092, data_time: 0.031, memory: 1224, loss: 2.4720 2022-12-31 16:21:08,889 - mmcls - INFO - Epoch [16][3700/5005] lr: 1.000e-01, eta: 10:22:20, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4570 2022-12-31 16:21:17,621 - mmcls - INFO - Epoch [16][3800/5005] lr: 1.000e-01, eta: 10:22:11, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4897 2022-12-31 16:21:26,107 - mmcls - INFO - Epoch [16][3900/5005] lr: 1.000e-01, eta: 10:22:00, time: 0.085, data_time: 0.009, memory: 1224, loss: 2.4628 2022-12-31 16:21:35,286 - mmcls - INFO - Epoch [16][4000/5005] lr: 1.000e-01, eta: 10:21:53, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4913 2022-12-31 16:21:44,278 - mmcls - INFO - Epoch [16][4100/5005] lr: 1.000e-01, eta: 10:21:45, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4466 2022-12-31 16:21:52,942 - mmcls - INFO - Epoch [16][4200/5005] lr: 1.000e-01, eta: 10:21:35, time: 0.087, data_time: 0.025, memory: 1224, loss: 2.4503 2022-12-31 16:22:01,777 - mmcls - INFO - Epoch [16][4300/5005] lr: 1.000e-01, eta: 10:21:26, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4595 2022-12-31 16:22:10,573 - mmcls - INFO - Epoch [16][4400/5005] lr: 1.000e-01, eta: 10:21:17, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4270 2022-12-31 16:22:19,580 - mmcls - INFO - Epoch [16][4500/5005] lr: 1.000e-01, eta: 10:21:09, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.4610 2022-12-31 16:22:28,539 - mmcls - INFO - Epoch [16][4600/5005] lr: 1.000e-01, eta: 10:21:00, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4607 2022-12-31 16:22:37,044 - mmcls - INFO - Epoch [16][4700/5005] lr: 1.000e-01, eta: 10:20:50, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.4621 2022-12-31 16:22:45,684 - mmcls - INFO - Epoch [16][4800/5005] lr: 1.000e-01, eta: 10:20:40, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4532 2022-12-31 16:22:54,523 - mmcls - INFO - Epoch [16][4900/5005] lr: 1.000e-01, eta: 10:20:31, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4443 2022-12-31 16:23:03,150 - mmcls - INFO - Epoch [16][5000/5005] lr: 1.000e-01, eta: 10:20:21, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4538 2022-12-31 16:23:25,962 - mmcls - INFO - Epoch(val) [16][196] accuracy_top-1: 51.3140, accuracy_top-5: 77.0000 2022-12-31 16:23:37,427 - mmcls - INFO - Epoch [17][100/5005] lr: 1.000e-01, eta: 10:20:23, time: 0.114, data_time: 0.042, memory: 1224, loss: 2.4110 2022-12-31 16:23:46,386 - mmcls - INFO - Epoch [17][200/5005] lr: 1.000e-01, eta: 10:20:14, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4273 2022-12-31 16:23:54,877 - mmcls - INFO - Epoch [17][300/5005] lr: 1.000e-01, eta: 10:20:04, time: 0.085, data_time: 0.012, memory: 1224, loss: 2.4095 2022-12-31 16:24:03,565 - mmcls - INFO - Epoch [17][400/5005] lr: 1.000e-01, eta: 10:19:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4213 2022-12-31 16:24:12,192 - mmcls - INFO - Epoch [17][500/5005] lr: 1.000e-01, eta: 10:19:44, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.4095 2022-12-31 16:24:20,882 - mmcls - INFO - Epoch [17][600/5005] lr: 1.000e-01, eta: 10:19:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4084 2022-12-31 16:24:29,552 - mmcls - INFO - Epoch [17][700/5005] lr: 1.000e-01, eta: 10:19:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4505 2022-12-31 16:24:38,055 - mmcls - INFO - Epoch [17][800/5005] lr: 1.000e-01, eta: 10:19:14, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.4578 2022-12-31 16:24:46,938 - mmcls - INFO - Epoch [17][900/5005] lr: 1.000e-01, eta: 10:19:05, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4112 2022-12-31 16:24:55,747 - mmcls - INFO - Epoch [17][1000/5005] lr: 1.000e-01, eta: 10:18:56, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.4348 2022-12-31 16:25:04,285 - mmcls - INFO - Epoch [17][1100/5005] lr: 1.000e-01, eta: 10:18:45, time: 0.085, data_time: 0.014, memory: 1224, loss: 2.4318 2022-12-31 16:25:13,224 - mmcls - INFO - Epoch [17][1200/5005] lr: 1.000e-01, eta: 10:18:37, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4055 2022-12-31 16:25:22,162 - mmcls - INFO - Epoch [17][1300/5005] lr: 1.000e-01, eta: 10:18:28, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.4424 2022-12-31 16:25:30,824 - mmcls - INFO - Epoch [17][1400/5005] lr: 1.000e-01, eta: 10:18:19, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4300 2022-12-31 16:25:39,315 - mmcls - INFO - Epoch [17][1500/5005] lr: 1.000e-01, eta: 10:18:08, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.4311 2022-12-31 16:25:47,803 - mmcls - INFO - Epoch [17][1600/5005] lr: 1.000e-01, eta: 10:17:57, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4422 2022-12-31 16:25:56,501 - mmcls - INFO - Epoch [17][1700/5005] lr: 1.000e-01, eta: 10:17:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4727 2022-12-31 16:26:05,254 - mmcls - INFO - Epoch [17][1800/5005] lr: 1.000e-01, eta: 10:17:38, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4615 2022-12-31 16:26:14,146 - mmcls - INFO - Epoch [17][1900/5005] lr: 1.000e-01, eta: 10:17:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4689 2022-12-31 16:26:23,279 - mmcls - INFO - Epoch [17][2000/5005] lr: 1.000e-01, eta: 10:17:22, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.4502 2022-12-31 16:26:32,558 - mmcls - INFO - Epoch [17][2100/5005] lr: 1.000e-01, eta: 10:17:15, time: 0.093, data_time: 0.004, memory: 1224, loss: 2.4577 2022-12-31 16:26:41,426 - mmcls - INFO - Epoch [17][2200/5005] lr: 1.000e-01, eta: 10:17:07, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.4593 2022-12-31 16:26:50,071 - mmcls - INFO - Epoch [17][2300/5005] lr: 1.000e-01, eta: 10:16:57, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4441 2022-12-31 16:26:58,708 - mmcls - INFO - Epoch [17][2400/5005] lr: 1.000e-01, eta: 10:16:47, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.4483 2022-12-31 16:27:07,360 - mmcls - INFO - Epoch [17][2500/5005] lr: 1.000e-01, eta: 10:16:37, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4328 2022-12-31 16:27:16,212 - mmcls - INFO - Epoch [17][2600/5005] lr: 1.000e-01, eta: 10:16:28, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4519 2022-12-31 16:27:24,741 - mmcls - INFO - Epoch [17][2700/5005] lr: 1.000e-01, eta: 10:16:17, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.4450 2022-12-31 16:27:33,601 - mmcls - INFO - Epoch [17][2800/5005] lr: 1.000e-01, eta: 10:16:09, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.4393 2022-12-31 16:27:42,457 - mmcls - INFO - Epoch [17][2900/5005] lr: 1.000e-01, eta: 10:16:00, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.4688 2022-12-31 16:27:51,196 - mmcls - INFO - Epoch [17][3000/5005] lr: 1.000e-01, eta: 10:15:50, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.4609 2022-12-31 16:27:59,839 - mmcls - INFO - Epoch [17][3100/5005] lr: 1.000e-01, eta: 10:15:40, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4464 2022-12-31 16:28:08,919 - mmcls - INFO - Epoch [17][3200/5005] lr: 1.000e-01, eta: 10:15:33, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.4566 2022-12-31 16:28:17,757 - mmcls - INFO - Epoch [17][3300/5005] lr: 1.000e-01, eta: 10:15:24, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4451 2022-12-31 16:28:26,832 - mmcls - INFO - Epoch [17][3400/5005] lr: 1.000e-01, eta: 10:15:16, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4320 2022-12-31 16:28:35,692 - mmcls - INFO - Epoch [17][3500/5005] lr: 1.000e-01, eta: 10:15:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4298 2022-12-31 16:28:44,534 - mmcls - INFO - Epoch [17][3600/5005] lr: 1.000e-01, eta: 10:14:58, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4560 2022-12-31 16:28:53,506 - mmcls - INFO - Epoch [17][3700/5005] lr: 1.000e-01, eta: 10:14:50, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4484 2022-12-31 16:29:02,024 - mmcls - INFO - Epoch [17][3800/5005] lr: 1.000e-01, eta: 10:14:40, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.4678 2022-12-31 16:29:11,014 - mmcls - INFO - Epoch [17][3900/5005] lr: 1.000e-01, eta: 10:14:31, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.4560 2022-12-31 16:29:19,758 - mmcls - INFO - Epoch [17][4000/5005] lr: 1.000e-01, eta: 10:14:22, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4495 2022-12-31 16:29:28,425 - mmcls - INFO - Epoch [17][4100/5005] lr: 1.000e-01, eta: 10:14:12, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4426 2022-12-31 16:29:37,396 - mmcls - INFO - Epoch [17][4200/5005] lr: 1.000e-01, eta: 10:14:04, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.4673 2022-12-31 16:29:46,082 - mmcls - INFO - Epoch [17][4300/5005] lr: 1.000e-01, eta: 10:13:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4620 2022-12-31 16:29:54,889 - mmcls - INFO - Epoch [17][4400/5005] lr: 1.000e-01, eta: 10:13:45, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4572 2022-12-31 16:30:03,605 - mmcls - INFO - Epoch [17][4500/5005] lr: 1.000e-01, eta: 10:13:36, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4669 2022-12-31 16:30:12,356 - mmcls - INFO - Epoch [17][4600/5005] lr: 1.000e-01, eta: 10:13:26, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4609 2022-12-31 16:30:21,079 - mmcls - INFO - Epoch [17][4700/5005] lr: 1.000e-01, eta: 10:13:17, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4526 2022-12-31 16:30:29,708 - mmcls - INFO - Epoch [17][4800/5005] lr: 1.000e-01, eta: 10:13:07, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4543 2022-12-31 16:30:38,151 - mmcls - INFO - Epoch [17][4900/5005] lr: 1.000e-01, eta: 10:12:56, time: 0.084, data_time: 0.010, memory: 1224, loss: 2.4532 2022-12-31 16:30:47,178 - mmcls - INFO - Epoch [17][5000/5005] lr: 1.000e-01, eta: 10:12:48, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4363 2022-12-31 16:31:09,804 - mmcls - INFO - Epoch(val) [17][196] accuracy_top-1: 51.3020, accuracy_top-5: 77.0040 2022-12-31 16:31:20,963 - mmcls - INFO - Epoch [18][100/5005] lr: 1.000e-01, eta: 10:12:48, time: 0.111, data_time: 0.055, memory: 1224, loss: 2.3922 2022-12-31 16:31:29,814 - mmcls - INFO - Epoch [18][200/5005] lr: 1.000e-01, eta: 10:12:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4067 2022-12-31 16:31:38,745 - mmcls - INFO - Epoch [18][300/5005] lr: 1.000e-01, eta: 10:12:30, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.3841 2022-12-31 16:31:47,510 - mmcls - INFO - Epoch [18][400/5005] lr: 1.000e-01, eta: 10:12:21, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4095 2022-12-31 16:31:56,653 - mmcls - INFO - Epoch [18][500/5005] lr: 1.000e-01, eta: 10:12:14, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.4357 2022-12-31 16:32:05,120 - mmcls - INFO - Epoch [18][600/5005] lr: 1.000e-01, eta: 10:12:03, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.4257 2022-12-31 16:32:14,134 - mmcls - INFO - Epoch [18][700/5005] lr: 1.000e-01, eta: 10:11:55, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.4274 2022-12-31 16:32:22,915 - mmcls - INFO - Epoch [18][800/5005] lr: 1.000e-01, eta: 10:11:46, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4438 2022-12-31 16:32:31,582 - mmcls - INFO - Epoch [18][900/5005] lr: 1.000e-01, eta: 10:11:36, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.4166 2022-12-31 16:32:40,450 - mmcls - INFO - Epoch [18][1000/5005] lr: 1.000e-01, eta: 10:11:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4416 2022-12-31 16:32:49,285 - mmcls - INFO - Epoch [18][1100/5005] lr: 1.000e-01, eta: 10:11:18, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4408 2022-12-31 16:32:57,901 - mmcls - INFO - Epoch [18][1200/5005] lr: 1.000e-01, eta: 10:11:08, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4415 2022-12-31 16:33:06,391 - mmcls - INFO - Epoch [18][1300/5005] lr: 1.000e-01, eta: 10:10:58, time: 0.085, data_time: 0.010, memory: 1224, loss: 2.4083 2022-12-31 16:33:15,240 - mmcls - INFO - Epoch [18][1400/5005] lr: 1.000e-01, eta: 10:10:49, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4316 2022-12-31 16:33:24,107 - mmcls - INFO - Epoch [18][1500/5005] lr: 1.000e-01, eta: 10:10:40, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4415 2022-12-31 16:33:33,094 - mmcls - INFO - Epoch [18][1600/5005] lr: 1.000e-01, eta: 10:10:32, time: 0.090, data_time: 0.023, memory: 1224, loss: 2.4307 2022-12-31 16:33:42,255 - mmcls - INFO - Epoch [18][1700/5005] lr: 1.000e-01, eta: 10:10:24, time: 0.092, data_time: 0.018, memory: 1224, loss: 2.4474 2022-12-31 16:33:50,846 - mmcls - INFO - Epoch [18][1800/5005] lr: 1.000e-01, eta: 10:10:14, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.4493 2022-12-31 16:33:59,634 - mmcls - INFO - Epoch [18][1900/5005] lr: 1.000e-01, eta: 10:10:05, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.4586 2022-12-31 16:34:08,266 - mmcls - INFO - Epoch [18][2000/5005] lr: 1.000e-01, eta: 10:09:55, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4344 2022-12-31 16:34:17,080 - mmcls - INFO - Epoch [18][2100/5005] lr: 1.000e-01, eta: 10:09:46, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4443 2022-12-31 16:34:25,913 - mmcls - INFO - Epoch [18][2200/5005] lr: 1.000e-01, eta: 10:09:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4438 2022-12-31 16:34:34,873 - mmcls - INFO - Epoch [18][2300/5005] lr: 1.000e-01, eta: 10:09:29, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.4581 2022-12-31 16:34:43,841 - mmcls - INFO - Epoch [18][2400/5005] lr: 1.000e-01, eta: 10:09:21, time: 0.090, data_time: 0.025, memory: 1224, loss: 2.4124 2022-12-31 16:34:52,722 - mmcls - INFO - Epoch [18][2500/5005] lr: 1.000e-01, eta: 10:09:12, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4511 2022-12-31 16:35:01,245 - mmcls - INFO - Epoch [18][2600/5005] lr: 1.000e-01, eta: 10:09:01, time: 0.085, data_time: 0.025, memory: 1224, loss: 2.4413 2022-12-31 16:35:10,025 - mmcls - INFO - Epoch [18][2700/5005] lr: 1.000e-01, eta: 10:08:52, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.4294 2022-12-31 16:35:19,163 - mmcls - INFO - Epoch [18][2800/5005] lr: 1.000e-01, eta: 10:08:45, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.4242 2022-12-31 16:35:28,057 - mmcls - INFO - Epoch [18][2900/5005] lr: 1.000e-01, eta: 10:08:36, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.4258 2022-12-31 16:35:36,722 - mmcls - INFO - Epoch [18][3000/5005] lr: 1.000e-01, eta: 10:08:26, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4296 2022-12-31 16:35:45,346 - mmcls - INFO - Epoch [18][3100/5005] lr: 1.000e-01, eta: 10:08:16, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4288 2022-12-31 16:35:54,055 - mmcls - INFO - Epoch [18][3200/5005] lr: 1.000e-01, eta: 10:08:07, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4463 2022-12-31 16:36:02,739 - mmcls - INFO - Epoch [18][3300/5005] lr: 1.000e-01, eta: 10:07:57, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4324 2022-12-31 16:36:11,451 - mmcls - INFO - Epoch [18][3400/5005] lr: 1.000e-01, eta: 10:07:48, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4588 2022-12-31 16:36:20,382 - mmcls - INFO - Epoch [18][3500/5005] lr: 1.000e-01, eta: 10:07:39, time: 0.089, data_time: 0.023, memory: 1224, loss: 2.4368 2022-12-31 16:36:29,057 - mmcls - INFO - Epoch [18][3600/5005] lr: 1.000e-01, eta: 10:07:30, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4371 2022-12-31 16:36:37,771 - mmcls - INFO - Epoch [18][3700/5005] lr: 1.000e-01, eta: 10:07:20, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4392 2022-12-31 16:36:46,474 - mmcls - INFO - Epoch [18][3800/5005] lr: 1.000e-01, eta: 10:07:10, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.4464 2022-12-31 16:36:55,220 - mmcls - INFO - Epoch [18][3900/5005] lr: 1.000e-01, eta: 10:07:01, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4410 2022-12-31 16:37:04,004 - mmcls - INFO - Epoch [18][4000/5005] lr: 1.000e-01, eta: 10:06:52, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4610 2022-12-31 16:37:12,565 - mmcls - INFO - Epoch [18][4100/5005] lr: 1.000e-01, eta: 10:06:42, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.4517 2022-12-31 16:37:21,755 - mmcls - INFO - Epoch [18][4200/5005] lr: 1.000e-01, eta: 10:06:35, time: 0.092, data_time: 0.003, memory: 1224, loss: 2.4888 2022-12-31 16:37:30,547 - mmcls - INFO - Epoch [18][4300/5005] lr: 1.000e-01, eta: 10:06:25, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4175 2022-12-31 16:37:39,203 - mmcls - INFO - Epoch [18][4400/5005] lr: 1.000e-01, eta: 10:06:16, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.4467 2022-12-31 16:37:47,800 - mmcls - INFO - Epoch [18][4500/5005] lr: 1.000e-01, eta: 10:06:06, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4367 2022-12-31 16:37:57,266 - mmcls - INFO - Epoch [18][4600/5005] lr: 1.000e-01, eta: 10:06:00, time: 0.095, data_time: 0.013, memory: 1224, loss: 2.4568 2022-12-31 16:38:05,964 - mmcls - INFO - Epoch [18][4700/5005] lr: 1.000e-01, eta: 10:05:50, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4438 2022-12-31 16:38:14,690 - mmcls - INFO - Epoch [18][4800/5005] lr: 1.000e-01, eta: 10:05:41, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4583 2022-12-31 16:38:23,567 - mmcls - INFO - Epoch [18][4900/5005] lr: 1.000e-01, eta: 10:05:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4216 2022-12-31 16:38:32,325 - mmcls - INFO - Epoch [18][5000/5005] lr: 1.000e-01, eta: 10:05:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4393 2022-12-31 16:38:54,926 - mmcls - INFO - Epoch(val) [18][196] accuracy_top-1: 50.4360, accuracy_top-5: 76.0140 2022-12-31 16:39:06,142 - mmcls - INFO - Epoch [19][100/5005] lr: 1.000e-01, eta: 10:05:22, time: 0.112, data_time: 0.030, memory: 1224, loss: 2.3814 2022-12-31 16:39:14,585 - mmcls - INFO - Epoch [19][200/5005] lr: 1.000e-01, eta: 10:05:11, time: 0.084, data_time: 0.002, memory: 1224, loss: 2.3917 2022-12-31 16:39:23,386 - mmcls - INFO - Epoch [19][300/5005] lr: 1.000e-01, eta: 10:05:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3942 2022-12-31 16:39:32,243 - mmcls - INFO - Epoch [19][400/5005] lr: 1.000e-01, eta: 10:04:53, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4078 2022-12-31 16:39:41,058 - mmcls - INFO - Epoch [19][500/5005] lr: 1.000e-01, eta: 10:04:44, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4249 2022-12-31 16:39:49,623 - mmcls - INFO - Epoch [19][600/5005] lr: 1.000e-01, eta: 10:04:34, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.4166 2022-12-31 16:39:58,394 - mmcls - INFO - Epoch [19][700/5005] lr: 1.000e-01, eta: 10:04:25, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.4209 2022-12-31 16:40:07,164 - mmcls - INFO - Epoch [19][800/5005] lr: 1.000e-01, eta: 10:04:16, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4274 2022-12-31 16:40:16,009 - mmcls - INFO - Epoch [19][900/5005] lr: 1.000e-01, eta: 10:04:07, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4058 2022-12-31 16:40:24,814 - mmcls - INFO - Epoch [19][1000/5005] lr: 1.000e-01, eta: 10:03:58, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.4295 2022-12-31 16:40:33,508 - mmcls - INFO - Epoch [19][1100/5005] lr: 1.000e-01, eta: 10:03:48, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4149 2022-12-31 16:40:42,207 - mmcls - INFO - Epoch [19][1200/5005] lr: 1.000e-01, eta: 10:03:39, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.4176 2022-12-31 16:40:50,781 - mmcls - INFO - Epoch [19][1300/5005] lr: 1.000e-01, eta: 10:03:29, time: 0.086, data_time: 0.023, memory: 1224, loss: 2.4361 2022-12-31 16:40:59,749 - mmcls - INFO - Epoch [19][1400/5005] lr: 1.000e-01, eta: 10:03:20, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.4291 2022-12-31 16:41:08,661 - mmcls - INFO - Epoch [19][1500/5005] lr: 1.000e-01, eta: 10:03:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4213 2022-12-31 16:41:17,528 - mmcls - INFO - Epoch [19][1600/5005] lr: 1.000e-01, eta: 10:03:03, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4203 2022-12-31 16:41:26,515 - mmcls - INFO - Epoch [19][1700/5005] lr: 1.000e-01, eta: 10:02:55, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4231 2022-12-31 16:41:35,352 - mmcls - INFO - Epoch [19][1800/5005] lr: 1.000e-01, eta: 10:02:46, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4303 2022-12-31 16:41:44,050 - mmcls - INFO - Epoch [19][1900/5005] lr: 1.000e-01, eta: 10:02:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4192 2022-12-31 16:41:52,634 - mmcls - INFO - Epoch [19][2000/5005] lr: 1.000e-01, eta: 10:02:26, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4425 2022-12-31 16:42:01,370 - mmcls - INFO - Epoch [19][2100/5005] lr: 1.000e-01, eta: 10:02:17, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4497 2022-12-31 16:42:10,284 - mmcls - INFO - Epoch [19][2200/5005] lr: 1.000e-01, eta: 10:02:08, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4436 2022-12-31 16:42:18,964 - mmcls - INFO - Epoch [19][2300/5005] lr: 1.000e-01, eta: 10:01:59, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.4300 2022-12-31 16:42:27,629 - mmcls - INFO - Epoch [19][2400/5005] lr: 1.000e-01, eta: 10:01:49, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.4539 2022-12-31 16:42:36,658 - mmcls - INFO - Epoch [19][2500/5005] lr: 1.000e-01, eta: 10:01:41, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.4141 2022-12-31 16:42:45,306 - mmcls - INFO - Epoch [19][2600/5005] lr: 1.000e-01, eta: 10:01:31, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4288 2022-12-31 16:42:53,956 - mmcls - INFO - Epoch [19][2700/5005] lr: 1.000e-01, eta: 10:01:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4325 2022-12-31 16:43:02,909 - mmcls - INFO - Epoch [19][2800/5005] lr: 1.000e-01, eta: 10:01:13, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4352 2022-12-31 16:43:11,824 - mmcls - INFO - Epoch [19][2900/5005] lr: 1.000e-01, eta: 10:01:04, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4319 2022-12-31 16:43:20,668 - mmcls - INFO - Epoch [19][3000/5005] lr: 1.000e-01, eta: 10:00:56, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.4357 2022-12-31 16:43:29,423 - mmcls - INFO - Epoch [19][3100/5005] lr: 1.000e-01, eta: 10:00:46, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4106 2022-12-31 16:43:38,156 - mmcls - INFO - Epoch [19][3200/5005] lr: 1.000e-01, eta: 10:00:37, time: 0.087, data_time: 0.019, memory: 1224, loss: 2.4256 2022-12-31 16:43:46,970 - mmcls - INFO - Epoch [19][3300/5005] lr: 1.000e-01, eta: 10:00:28, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4144 2022-12-31 16:43:55,434 - mmcls - INFO - Epoch [19][3400/5005] lr: 1.000e-01, eta: 10:00:17, time: 0.085, data_time: 0.005, memory: 1224, loss: 2.4823 2022-12-31 16:44:04,590 - mmcls - INFO - Epoch [19][3500/5005] lr: 1.000e-01, eta: 10:00:10, time: 0.092, data_time: 0.010, memory: 1224, loss: 2.4379 2022-12-31 16:44:13,305 - mmcls - INFO - Epoch [19][3600/5005] lr: 1.000e-01, eta: 10:00:00, time: 0.087, data_time: 0.032, memory: 1224, loss: 2.4609 2022-12-31 16:44:22,114 - mmcls - INFO - Epoch [19][3700/5005] lr: 1.000e-01, eta: 9:59:51, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4340 2022-12-31 16:44:30,793 - mmcls - INFO - Epoch [19][3800/5005] lr: 1.000e-01, eta: 9:59:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.4279 2022-12-31 16:44:39,516 - mmcls - INFO - Epoch [19][3900/5005] lr: 1.000e-01, eta: 9:59:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4380 2022-12-31 16:44:48,159 - mmcls - INFO - Epoch [19][4000/5005] lr: 1.000e-01, eta: 9:59:23, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4343 2022-12-31 16:44:56,772 - mmcls - INFO - Epoch [19][4100/5005] lr: 1.000e-01, eta: 9:59:13, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4296 2022-12-31 16:45:05,593 - mmcls - INFO - Epoch [19][4200/5005] lr: 1.000e-01, eta: 9:59:04, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.4242 2022-12-31 16:45:14,336 - mmcls - INFO - Epoch [19][4300/5005] lr: 1.000e-01, eta: 9:58:54, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4300 2022-12-31 16:45:23,330 - mmcls - INFO - Epoch [19][4400/5005] lr: 1.000e-01, eta: 9:58:46, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4497 2022-12-31 16:45:31,950 - mmcls - INFO - Epoch [19][4500/5005] lr: 1.000e-01, eta: 9:58:36, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4410 2022-12-31 16:45:40,617 - mmcls - INFO - Epoch [19][4600/5005] lr: 1.000e-01, eta: 9:58:27, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4347 2022-12-31 16:45:49,773 - mmcls - INFO - Epoch [19][4700/5005] lr: 1.000e-01, eta: 9:58:19, time: 0.092, data_time: 0.040, memory: 1224, loss: 2.4380 2022-12-31 16:45:58,605 - mmcls - INFO - Epoch [19][4800/5005] lr: 1.000e-01, eta: 9:58:10, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4254 2022-12-31 16:46:07,364 - mmcls - INFO - Epoch [19][4900/5005] lr: 1.000e-01, eta: 9:58:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4393 2022-12-31 16:46:15,767 - mmcls - INFO - Epoch [19][5000/5005] lr: 1.000e-01, eta: 9:57:50, time: 0.084, data_time: 0.001, memory: 1224, loss: 2.4458 2022-12-31 16:46:38,464 - mmcls - INFO - Epoch(val) [19][196] accuracy_top-1: 49.8560, accuracy_top-5: 76.0680 2022-12-31 16:46:49,880 - mmcls - INFO - Epoch [20][100/5005] lr: 1.000e-01, eta: 9:57:50, time: 0.114, data_time: 0.039, memory: 1224, loss: 2.4087 2022-12-31 16:46:58,628 - mmcls - INFO - Epoch [20][200/5005] lr: 1.000e-01, eta: 9:57:41, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3807 2022-12-31 16:47:07,538 - mmcls - INFO - Epoch [20][300/5005] lr: 1.000e-01, eta: 9:57:32, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.4114 2022-12-31 16:47:16,493 - mmcls - INFO - Epoch [20][400/5005] lr: 1.000e-01, eta: 9:57:24, time: 0.090, data_time: 0.011, memory: 1224, loss: 2.3709 2022-12-31 16:47:25,419 - mmcls - INFO - Epoch [20][500/5005] lr: 1.000e-01, eta: 9:57:15, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.3984 2022-12-31 16:47:34,058 - mmcls - INFO - Epoch [20][600/5005] lr: 1.000e-01, eta: 9:57:05, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4043 2022-12-31 16:47:43,636 - mmcls - INFO - Epoch [20][700/5005] lr: 1.000e-01, eta: 9:57:00, time: 0.096, data_time: 0.001, memory: 1224, loss: 2.4115 2022-12-31 16:47:52,518 - mmcls - INFO - Epoch [20][800/5005] lr: 1.000e-01, eta: 9:56:51, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.3993 2022-12-31 16:48:01,628 - mmcls - INFO - Epoch [20][900/5005] lr: 1.000e-01, eta: 9:56:43, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.3910 2022-12-31 16:48:10,158 - mmcls - INFO - Epoch [20][1000/5005] lr: 1.000e-01, eta: 9:56:33, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.3857 2022-12-31 16:48:18,744 - mmcls - INFO - Epoch [20][1100/5005] lr: 1.000e-01, eta: 9:56:23, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4160 2022-12-31 16:48:27,330 - mmcls - INFO - Epoch [20][1200/5005] lr: 1.000e-01, eta: 9:56:13, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4014 2022-12-31 16:48:36,096 - mmcls - INFO - Epoch [20][1300/5005] lr: 1.000e-01, eta: 9:56:04, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.4218 2022-12-31 16:48:44,734 - mmcls - INFO - Epoch [20][1400/5005] lr: 1.000e-01, eta: 9:55:54, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.4293 2022-12-31 16:48:53,402 - mmcls - INFO - Epoch [20][1500/5005] lr: 1.000e-01, eta: 9:55:44, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.3855 2022-12-31 16:49:02,481 - mmcls - INFO - Epoch [20][1600/5005] lr: 1.000e-01, eta: 9:55:37, time: 0.091, data_time: 0.004, memory: 1224, loss: 2.4292 2022-12-31 16:49:11,427 - mmcls - INFO - Epoch [20][1700/5005] lr: 1.000e-01, eta: 9:55:28, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4261 2022-12-31 16:49:20,291 - mmcls - INFO - Epoch [20][1800/5005] lr: 1.000e-01, eta: 9:55:19, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4440 2022-12-31 16:49:29,029 - mmcls - INFO - Epoch [20][1900/5005] lr: 1.000e-01, eta: 9:55:10, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.4324 2022-12-31 16:49:37,820 - mmcls - INFO - Epoch [20][2000/5005] lr: 1.000e-01, eta: 9:55:01, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4173 2022-12-31 16:49:46,673 - mmcls - INFO - Epoch [20][2100/5005] lr: 1.000e-01, eta: 9:54:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4309 2022-12-31 16:49:55,633 - mmcls - INFO - Epoch [20][2200/5005] lr: 1.000e-01, eta: 9:54:44, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.4093 2022-12-31 16:50:05,257 - mmcls - INFO - Epoch [20][2300/5005] lr: 1.000e-01, eta: 9:54:38, time: 0.096, data_time: 0.022, memory: 1224, loss: 2.4318 2022-12-31 16:50:13,914 - mmcls - INFO - Epoch [20][2400/5005] lr: 1.000e-01, eta: 9:54:28, time: 0.087, data_time: 0.033, memory: 1224, loss: 2.4461 2022-12-31 16:50:22,771 - mmcls - INFO - Epoch [20][2500/5005] lr: 1.000e-01, eta: 9:54:20, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4588 2022-12-31 16:50:31,380 - mmcls - INFO - Epoch [20][2600/5005] lr: 1.000e-01, eta: 9:54:10, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4349 2022-12-31 16:50:39,988 - mmcls - INFO - Epoch [20][2700/5005] lr: 1.000e-01, eta: 9:54:00, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.4425 2022-12-31 16:50:48,686 - mmcls - INFO - Epoch [20][2800/5005] lr: 1.000e-01, eta: 9:53:50, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4474 2022-12-31 16:50:57,478 - mmcls - INFO - Epoch [20][2900/5005] lr: 1.000e-01, eta: 9:53:41, time: 0.088, data_time: 0.031, memory: 1224, loss: 2.4355 2022-12-31 16:51:06,064 - mmcls - INFO - Epoch [20][3000/5005] lr: 1.000e-01, eta: 9:53:31, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.4260 2022-12-31 16:51:14,820 - mmcls - INFO - Epoch [20][3100/5005] lr: 1.000e-01, eta: 9:53:22, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4023 2022-12-31 16:51:23,671 - mmcls - INFO - Epoch [20][3200/5005] lr: 1.000e-01, eta: 9:53:13, time: 0.089, data_time: 0.019, memory: 1224, loss: 2.4383 2022-12-31 16:51:32,523 - mmcls - INFO - Epoch [20][3300/5005] lr: 1.000e-01, eta: 9:53:04, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4408 2022-12-31 16:51:41,574 - mmcls - INFO - Epoch [20][3400/5005] lr: 1.000e-01, eta: 9:52:56, time: 0.091, data_time: 0.018, memory: 1224, loss: 2.4335 2022-12-31 16:51:50,364 - mmcls - INFO - Epoch [20][3500/5005] lr: 1.000e-01, eta: 9:52:47, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.4623 2022-12-31 16:51:58,968 - mmcls - INFO - Epoch [20][3600/5005] lr: 1.000e-01, eta: 9:52:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4328 2022-12-31 16:52:07,994 - mmcls - INFO - Epoch [20][3700/5005] lr: 1.000e-01, eta: 9:52:29, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4229 2022-12-31 16:52:16,643 - mmcls - INFO - Epoch [20][3800/5005] lr: 1.000e-01, eta: 9:52:20, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.4239 2022-12-31 16:52:25,356 - mmcls - INFO - Epoch [20][3900/5005] lr: 1.000e-01, eta: 9:52:10, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4314 2022-12-31 16:52:34,234 - mmcls - INFO - Epoch [20][4000/5005] lr: 1.000e-01, eta: 9:52:01, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4412 2022-12-31 16:52:42,948 - mmcls - INFO - Epoch [20][4100/5005] lr: 1.000e-01, eta: 9:51:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4496 2022-12-31 16:52:51,731 - mmcls - INFO - Epoch [20][4200/5005] lr: 1.000e-01, eta: 9:51:43, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.4234 2022-12-31 16:53:00,471 - mmcls - INFO - Epoch [20][4300/5005] lr: 1.000e-01, eta: 9:51:34, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4630 2022-12-31 16:53:09,160 - mmcls - INFO - Epoch [20][4400/5005] lr: 1.000e-01, eta: 9:51:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4374 2022-12-31 16:53:18,310 - mmcls - INFO - Epoch [20][4500/5005] lr: 1.000e-01, eta: 9:51:17, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4297 2022-12-31 16:53:27,246 - mmcls - INFO - Epoch [20][4600/5005] lr: 1.000e-01, eta: 9:51:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4261 2022-12-31 16:53:35,927 - mmcls - INFO - Epoch [20][4700/5005] lr: 1.000e-01, eta: 9:50:58, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.4373 2022-12-31 16:53:44,535 - mmcls - INFO - Epoch [20][4800/5005] lr: 1.000e-01, eta: 9:50:49, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4425 2022-12-31 16:53:52,991 - mmcls - INFO - Epoch [20][4900/5005] lr: 1.000e-01, eta: 9:50:38, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.3891 2022-12-31 16:54:01,815 - mmcls - INFO - Epoch [20][5000/5005] lr: 1.000e-01, eta: 9:50:29, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4467 2022-12-31 16:54:25,159 - mmcls - INFO - Epoch(val) [20][196] accuracy_top-1: 51.2320, accuracy_top-5: 77.1460 2022-12-31 16:54:35,955 - mmcls - INFO - Epoch [21][100/5005] lr: 1.000e-01, eta: 9:50:26, time: 0.108, data_time: 0.039, memory: 1224, loss: 2.4004 2022-12-31 16:54:44,562 - mmcls - INFO - Epoch [21][200/5005] lr: 1.000e-01, eta: 9:50:16, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.3831 2022-12-31 16:54:53,303 - mmcls - INFO - Epoch [21][300/5005] lr: 1.000e-01, eta: 9:50:07, time: 0.087, data_time: 0.017, memory: 1224, loss: 2.4050 2022-12-31 16:55:02,742 - mmcls - INFO - Epoch [21][400/5005] lr: 1.000e-01, eta: 9:50:00, time: 0.094, data_time: 0.031, memory: 1224, loss: 2.3921 2022-12-31 16:55:11,285 - mmcls - INFO - Epoch [21][500/5005] lr: 1.000e-01, eta: 9:49:50, time: 0.085, data_time: 0.011, memory: 1224, loss: 2.3578 2022-12-31 16:55:20,197 - mmcls - INFO - Epoch [21][600/5005] lr: 1.000e-01, eta: 9:49:42, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.3926 2022-12-31 16:55:28,931 - mmcls - INFO - Epoch [21][700/5005] lr: 1.000e-01, eta: 9:49:32, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.3955 2022-12-31 16:55:38,204 - mmcls - INFO - Epoch [21][800/5005] lr: 1.000e-01, eta: 9:49:25, time: 0.093, data_time: 0.010, memory: 1224, loss: 2.3993 2022-12-31 16:55:46,959 - mmcls - INFO - Epoch [21][900/5005] lr: 1.000e-01, eta: 9:49:16, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.3855 2022-12-31 16:55:55,901 - mmcls - INFO - Epoch [21][1000/5005] lr: 1.000e-01, eta: 9:49:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4207 2022-12-31 16:56:04,737 - mmcls - INFO - Epoch [21][1100/5005] lr: 1.000e-01, eta: 9:48:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4363 2022-12-31 16:56:13,713 - mmcls - INFO - Epoch [21][1200/5005] lr: 1.000e-01, eta: 9:48:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4099 2022-12-31 16:56:22,541 - mmcls - INFO - Epoch [21][1300/5005] lr: 1.000e-01, eta: 9:48:41, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.3977 2022-12-31 16:56:31,507 - mmcls - INFO - Epoch [21][1400/5005] lr: 1.000e-01, eta: 9:48:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4128 2022-12-31 16:56:40,472 - mmcls - INFO - Epoch [21][1500/5005] lr: 1.000e-01, eta: 9:48:24, time: 0.089, data_time: 0.013, memory: 1224, loss: 2.4033 2022-12-31 16:56:49,220 - mmcls - INFO - Epoch [21][1600/5005] lr: 1.000e-01, eta: 9:48:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4188 2022-12-31 16:56:57,888 - mmcls - INFO - Epoch [21][1700/5005] lr: 1.000e-01, eta: 9:48:06, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4181 2022-12-31 16:57:07,016 - mmcls - INFO - Epoch [21][1800/5005] lr: 1.000e-01, eta: 9:47:58, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.4178 2022-12-31 16:57:15,705 - mmcls - INFO - Epoch [21][1900/5005] lr: 1.000e-01, eta: 9:47:48, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3970 2022-12-31 16:57:24,784 - mmcls - INFO - Epoch [21][2000/5005] lr: 1.000e-01, eta: 9:47:40, time: 0.091, data_time: 0.013, memory: 1224, loss: 2.4009 2022-12-31 16:57:33,388 - mmcls - INFO - Epoch [21][2100/5005] lr: 1.000e-01, eta: 9:47:30, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.4382 2022-12-31 16:57:42,399 - mmcls - INFO - Epoch [21][2200/5005] lr: 1.000e-01, eta: 9:47:22, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.3973 2022-12-31 16:57:51,230 - mmcls - INFO - Epoch [21][2300/5005] lr: 1.000e-01, eta: 9:47:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4084 2022-12-31 16:58:00,561 - mmcls - INFO - Epoch [21][2400/5005] lr: 1.000e-01, eta: 9:47:06, time: 0.093, data_time: 0.008, memory: 1224, loss: 2.4492 2022-12-31 16:58:09,241 - mmcls - INFO - Epoch [21][2500/5005] lr: 1.000e-01, eta: 9:46:57, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4013 2022-12-31 16:58:17,898 - mmcls - INFO - Epoch [21][2600/5005] lr: 1.000e-01, eta: 9:46:47, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4387 2022-12-31 16:58:27,294 - mmcls - INFO - Epoch [21][2700/5005] lr: 1.000e-01, eta: 9:46:40, time: 0.094, data_time: 0.012, memory: 1224, loss: 2.4215 2022-12-31 16:58:35,970 - mmcls - INFO - Epoch [21][2800/5005] lr: 1.000e-01, eta: 9:46:31, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4617 2022-12-31 16:58:44,721 - mmcls - INFO - Epoch [21][2900/5005] lr: 1.000e-01, eta: 9:46:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4199 2022-12-31 16:58:53,461 - mmcls - INFO - Epoch [21][3000/5005] lr: 1.000e-01, eta: 9:46:12, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4567 2022-12-31 16:59:02,257 - mmcls - INFO - Epoch [21][3100/5005] lr: 1.000e-01, eta: 9:46:03, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4128 2022-12-31 16:59:11,119 - mmcls - INFO - Epoch [21][3200/5005] lr: 1.000e-01, eta: 9:45:55, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.4251 2022-12-31 16:59:19,969 - mmcls - INFO - Epoch [21][3300/5005] lr: 1.000e-01, eta: 9:45:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4239 2022-12-31 16:59:29,165 - mmcls - INFO - Epoch [21][3400/5005] lr: 1.000e-01, eta: 9:45:38, time: 0.092, data_time: 0.020, memory: 1224, loss: 2.4322 2022-12-31 16:59:37,752 - mmcls - INFO - Epoch [21][3500/5005] lr: 1.000e-01, eta: 9:45:28, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4340 2022-12-31 16:59:46,737 - mmcls - INFO - Epoch [21][3600/5005] lr: 1.000e-01, eta: 9:45:20, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4367 2022-12-31 16:59:55,378 - mmcls - INFO - Epoch [21][3700/5005] lr: 1.000e-01, eta: 9:45:10, time: 0.086, data_time: 0.020, memory: 1224, loss: 2.4392 2022-12-31 17:00:04,087 - mmcls - INFO - Epoch [21][3800/5005] lr: 1.000e-01, eta: 9:45:01, time: 0.087, data_time: 0.029, memory: 1224, loss: 2.4152 2022-12-31 17:00:12,701 - mmcls - INFO - Epoch [21][3900/5005] lr: 1.000e-01, eta: 9:44:51, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.4263 2022-12-31 17:00:21,428 - mmcls - INFO - Epoch [21][4000/5005] lr: 1.000e-01, eta: 9:44:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.4380 2022-12-31 17:00:30,092 - mmcls - INFO - Epoch [21][4100/5005] lr: 1.000e-01, eta: 9:44:32, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4201 2022-12-31 17:00:38,948 - mmcls - INFO - Epoch [21][4200/5005] lr: 1.000e-01, eta: 9:44:23, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.4348 2022-12-31 17:00:47,698 - mmcls - INFO - Epoch [21][4300/5005] lr: 1.000e-01, eta: 9:44:14, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4059 2022-12-31 17:00:56,543 - mmcls - INFO - Epoch [21][4400/5005] lr: 1.000e-01, eta: 9:44:05, time: 0.088, data_time: 0.014, memory: 1224, loss: 2.4103 2022-12-31 17:01:05,272 - mmcls - INFO - Epoch [21][4500/5005] lr: 1.000e-01, eta: 9:43:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4195 2022-12-31 17:01:13,923 - mmcls - INFO - Epoch [21][4600/5005] lr: 1.000e-01, eta: 9:43:46, time: 0.086, data_time: 0.016, memory: 1224, loss: 2.4477 2022-12-31 17:01:22,861 - mmcls - INFO - Epoch [21][4700/5005] lr: 1.000e-01, eta: 9:43:38, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.4172 2022-12-31 17:01:31,625 - mmcls - INFO - Epoch [21][4800/5005] lr: 1.000e-01, eta: 9:43:29, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4544 2022-12-31 17:01:40,213 - mmcls - INFO - Epoch [21][4900/5005] lr: 1.000e-01, eta: 9:43:19, time: 0.086, data_time: 0.034, memory: 1224, loss: 2.4045 2022-12-31 17:01:49,789 - mmcls - INFO - Epoch [21][5000/5005] lr: 1.000e-01, eta: 9:43:13, time: 0.096, data_time: 0.012, memory: 1224, loss: 2.4499 2022-12-31 17:02:12,553 - mmcls - INFO - Epoch(val) [21][196] accuracy_top-1: 50.6840, accuracy_top-5: 76.6440 2022-12-31 17:02:23,555 - mmcls - INFO - Epoch [22][100/5005] lr: 1.000e-01, eta: 9:43:10, time: 0.110, data_time: 0.036, memory: 1224, loss: 2.3784 2022-12-31 17:02:32,349 - mmcls - INFO - Epoch [22][200/5005] lr: 1.000e-01, eta: 9:43:01, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.3758 2022-12-31 17:02:41,673 - mmcls - INFO - Epoch [22][300/5005] lr: 1.000e-01, eta: 9:42:54, time: 0.093, data_time: 0.010, memory: 1224, loss: 2.3608 2022-12-31 17:02:50,176 - mmcls - INFO - Epoch [22][400/5005] lr: 1.000e-01, eta: 9:42:43, time: 0.085, data_time: 0.025, memory: 1224, loss: 2.3893 2022-12-31 17:02:58,633 - mmcls - INFO - Epoch [22][500/5005] lr: 1.000e-01, eta: 9:42:33, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.3683 2022-12-31 17:03:07,436 - mmcls - INFO - Epoch [22][600/5005] lr: 1.000e-01, eta: 9:42:24, time: 0.088, data_time: 0.032, memory: 1224, loss: 2.3808 2022-12-31 17:03:16,137 - mmcls - INFO - Epoch [22][700/5005] lr: 1.000e-01, eta: 9:42:15, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.3798 2022-12-31 17:03:24,896 - mmcls - INFO - Epoch [22][800/5005] lr: 1.000e-01, eta: 9:42:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4183 2022-12-31 17:03:33,790 - mmcls - INFO - Epoch [22][900/5005] lr: 1.000e-01, eta: 9:41:57, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4126 2022-12-31 17:03:42,578 - mmcls - INFO - Epoch [22][1000/5005] lr: 1.000e-01, eta: 9:41:48, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4168 2022-12-31 17:03:51,276 - mmcls - INFO - Epoch [22][1100/5005] lr: 1.000e-01, eta: 9:41:38, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4084 2022-12-31 17:03:59,886 - mmcls - INFO - Epoch [22][1200/5005] lr: 1.000e-01, eta: 9:41:29, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.3794 2022-12-31 17:04:09,599 - mmcls - INFO - Epoch [22][1300/5005] lr: 1.000e-01, eta: 9:41:23, time: 0.097, data_time: 0.016, memory: 1224, loss: 2.3887 2022-12-31 17:04:18,366 - mmcls - INFO - Epoch [22][1400/5005] lr: 1.000e-01, eta: 9:41:14, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4217 2022-12-31 17:04:26,988 - mmcls - INFO - Epoch [22][1500/5005] lr: 1.000e-01, eta: 9:41:04, time: 0.086, data_time: 0.019, memory: 1224, loss: 2.3852 2022-12-31 17:04:36,025 - mmcls - INFO - Epoch [22][1600/5005] lr: 1.000e-01, eta: 9:40:56, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.3993 2022-12-31 17:04:44,810 - mmcls - INFO - Epoch [22][1700/5005] lr: 1.000e-01, eta: 9:40:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4189 2022-12-31 17:04:53,659 - mmcls - INFO - Epoch [22][1800/5005] lr: 1.000e-01, eta: 9:40:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4067 2022-12-31 17:05:02,531 - mmcls - INFO - Epoch [22][1900/5005] lr: 1.000e-01, eta: 9:40:29, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.3934 2022-12-31 17:05:11,075 - mmcls - INFO - Epoch [22][2000/5005] lr: 1.000e-01, eta: 9:40:19, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.4108 2022-12-31 17:05:19,742 - mmcls - INFO - Epoch [22][2100/5005] lr: 1.000e-01, eta: 9:40:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4290 2022-12-31 17:05:28,392 - mmcls - INFO - Epoch [22][2200/5005] lr: 1.000e-01, eta: 9:40:00, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4246 2022-12-31 17:05:37,154 - mmcls - INFO - Epoch [22][2300/5005] lr: 1.000e-01, eta: 9:39:51, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4298 2022-12-31 17:05:46,037 - mmcls - INFO - Epoch [22][2400/5005] lr: 1.000e-01, eta: 9:39:42, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4329 2022-12-31 17:05:55,077 - mmcls - INFO - Epoch [22][2500/5005] lr: 1.000e-01, eta: 9:39:34, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.4167 2022-12-31 17:06:03,862 - mmcls - INFO - Epoch [22][2600/5005] lr: 1.000e-01, eta: 9:39:25, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.4319 2022-12-31 17:06:12,587 - mmcls - INFO - Epoch [22][2700/5005] lr: 1.000e-01, eta: 9:39:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4187 2022-12-31 17:06:21,272 - mmcls - INFO - Epoch [22][2800/5005] lr: 1.000e-01, eta: 9:39:06, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4091 2022-12-31 17:06:29,841 - mmcls - INFO - Epoch [22][2900/5005] lr: 1.000e-01, eta: 9:38:56, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.4370 2022-12-31 17:06:38,517 - mmcls - INFO - Epoch [22][3000/5005] lr: 1.000e-01, eta: 9:38:47, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.4214 2022-12-31 17:06:47,561 - mmcls - INFO - Epoch [22][3100/5005] lr: 1.000e-01, eta: 9:38:39, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.4112 2022-12-31 17:06:56,522 - mmcls - INFO - Epoch [22][3200/5005] lr: 1.000e-01, eta: 9:38:30, time: 0.090, data_time: 0.018, memory: 1224, loss: 2.4457 2022-12-31 17:07:05,681 - mmcls - INFO - Epoch [22][3300/5005] lr: 1.000e-01, eta: 9:38:22, time: 0.092, data_time: 0.000, memory: 1224, loss: 2.4133 2022-12-31 17:07:14,532 - mmcls - INFO - Epoch [22][3400/5005] lr: 1.000e-01, eta: 9:38:13, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4310 2022-12-31 17:07:23,174 - mmcls - INFO - Epoch [22][3500/5005] lr: 1.000e-01, eta: 9:38:04, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4269 2022-12-31 17:07:31,919 - mmcls - INFO - Epoch [22][3600/5005] lr: 1.000e-01, eta: 9:37:55, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4319 2022-12-31 17:07:40,654 - mmcls - INFO - Epoch [22][3700/5005] lr: 1.000e-01, eta: 9:37:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3976 2022-12-31 17:07:49,308 - mmcls - INFO - Epoch [22][3800/5005] lr: 1.000e-01, eta: 9:37:36, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4342 2022-12-31 17:07:57,810 - mmcls - INFO - Epoch [22][3900/5005] lr: 1.000e-01, eta: 9:37:26, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4306 2022-12-31 17:08:06,644 - mmcls - INFO - Epoch [22][4000/5005] lr: 1.000e-01, eta: 9:37:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4288 2022-12-31 17:08:15,357 - mmcls - INFO - Epoch [22][4100/5005] lr: 1.000e-01, eta: 9:37:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4065 2022-12-31 17:08:24,554 - mmcls - INFO - Epoch [22][4200/5005] lr: 1.000e-01, eta: 9:37:00, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4043 2022-12-31 17:08:33,288 - mmcls - INFO - Epoch [22][4300/5005] lr: 1.000e-01, eta: 9:36:51, time: 0.087, data_time: 0.023, memory: 1224, loss: 2.4152 2022-12-31 17:08:42,189 - mmcls - INFO - Epoch [22][4400/5005] lr: 1.000e-01, eta: 9:36:42, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.4191 2022-12-31 17:08:50,759 - mmcls - INFO - Epoch [22][4500/5005] lr: 1.000e-01, eta: 9:36:32, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4410 2022-12-31 17:08:59,549 - mmcls - INFO - Epoch [22][4600/5005] lr: 1.000e-01, eta: 9:36:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4127 2022-12-31 17:09:08,289 - mmcls - INFO - Epoch [22][4700/5005] lr: 1.000e-01, eta: 9:36:14, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.4035 2022-12-31 17:09:17,153 - mmcls - INFO - Epoch [22][4800/5005] lr: 1.000e-01, eta: 9:36:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3968 2022-12-31 17:09:26,342 - mmcls - INFO - Epoch [22][4900/5005] lr: 1.000e-01, eta: 9:35:57, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.4106 2022-12-31 17:09:35,096 - mmcls - INFO - Epoch [22][5000/5005] lr: 1.000e-01, eta: 9:35:48, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.4401 2022-12-31 17:09:57,766 - mmcls - INFO - Epoch(val) [22][196] accuracy_top-1: 51.9980, accuracy_top-5: 77.2480 2022-12-31 17:10:08,652 - mmcls - INFO - Epoch [23][100/5005] lr: 1.000e-01, eta: 9:35:44, time: 0.109, data_time: 0.043, memory: 1224, loss: 2.3654 2022-12-31 17:10:17,818 - mmcls - INFO - Epoch [23][200/5005] lr: 1.000e-01, eta: 9:35:37, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.3778 2022-12-31 17:10:26,508 - mmcls - INFO - Epoch [23][300/5005] lr: 1.000e-01, eta: 9:35:27, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.3672 2022-12-31 17:10:35,264 - mmcls - INFO - Epoch [23][400/5005] lr: 1.000e-01, eta: 9:35:18, time: 0.088, data_time: 0.028, memory: 1224, loss: 2.3438 2022-12-31 17:10:44,049 - mmcls - INFO - Epoch [23][500/5005] lr: 1.000e-01, eta: 9:35:09, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4034 2022-12-31 17:10:52,802 - mmcls - INFO - Epoch [23][600/5005] lr: 1.000e-01, eta: 9:35:00, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3626 2022-12-31 17:11:01,802 - mmcls - INFO - Epoch [23][700/5005] lr: 1.000e-01, eta: 9:34:51, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.4028 2022-12-31 17:11:10,487 - mmcls - INFO - Epoch [23][800/5005] lr: 1.000e-01, eta: 9:34:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3758 2022-12-31 17:11:19,399 - mmcls - INFO - Epoch [23][900/5005] lr: 1.000e-01, eta: 9:34:33, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.3987 2022-12-31 17:11:28,146 - mmcls - INFO - Epoch [23][1000/5005] lr: 1.000e-01, eta: 9:34:24, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4180 2022-12-31 17:11:37,422 - mmcls - INFO - Epoch [23][1100/5005] lr: 1.000e-01, eta: 9:34:17, time: 0.093, data_time: 0.009, memory: 1224, loss: 2.3990 2022-12-31 17:11:46,275 - mmcls - INFO - Epoch [23][1200/5005] lr: 1.000e-01, eta: 9:34:08, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.4134 2022-12-31 17:11:54,870 - mmcls - INFO - Epoch [23][1300/5005] lr: 1.000e-01, eta: 9:33:58, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.3948 2022-12-31 17:12:03,582 - mmcls - INFO - Epoch [23][1400/5005] lr: 1.000e-01, eta: 9:33:49, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.3761 2022-12-31 17:12:12,342 - mmcls - INFO - Epoch [23][1500/5005] lr: 1.000e-01, eta: 9:33:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3880 2022-12-31 17:12:21,239 - mmcls - INFO - Epoch [23][1600/5005] lr: 1.000e-01, eta: 9:33:31, time: 0.089, data_time: 0.006, memory: 1224, loss: 2.4204 2022-12-31 17:12:29,885 - mmcls - INFO - Epoch [23][1700/5005] lr: 1.000e-01, eta: 9:33:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4298 2022-12-31 17:12:38,588 - mmcls - INFO - Epoch [23][1800/5005] lr: 1.000e-01, eta: 9:33:12, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4132 2022-12-31 17:12:47,295 - mmcls - INFO - Epoch [23][1900/5005] lr: 1.000e-01, eta: 9:33:03, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.4156 2022-12-31 17:12:55,981 - mmcls - INFO - Epoch [23][2000/5005] lr: 1.000e-01, eta: 9:32:53, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.4238 2022-12-31 17:13:04,645 - mmcls - INFO - Epoch [23][2100/5005] lr: 1.000e-01, eta: 9:32:44, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4358 2022-12-31 17:13:13,252 - mmcls - INFO - Epoch [23][2200/5005] lr: 1.000e-01, eta: 9:32:34, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.3996 2022-12-31 17:13:22,178 - mmcls - INFO - Epoch [23][2300/5005] lr: 1.000e-01, eta: 9:32:25, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4049 2022-12-31 17:13:30,663 - mmcls - INFO - Epoch [23][2400/5005] lr: 1.000e-01, eta: 9:32:15, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.4332 2022-12-31 17:13:39,438 - mmcls - INFO - Epoch [23][2500/5005] lr: 1.000e-01, eta: 9:32:06, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.4047 2022-12-31 17:13:47,938 - mmcls - INFO - Epoch [23][2600/5005] lr: 1.000e-01, eta: 9:31:56, time: 0.085, data_time: 0.030, memory: 1224, loss: 2.4228 2022-12-31 17:13:56,844 - mmcls - INFO - Epoch [23][2700/5005] lr: 1.000e-01, eta: 9:31:47, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.4107 2022-12-31 17:14:05,724 - mmcls - INFO - Epoch [23][2800/5005] lr: 1.000e-01, eta: 9:31:39, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.4036 2022-12-31 17:14:14,503 - mmcls - INFO - Epoch [23][2900/5005] lr: 1.000e-01, eta: 9:31:30, time: 0.088, data_time: 0.017, memory: 1224, loss: 2.4210 2022-12-31 17:14:23,717 - mmcls - INFO - Epoch [23][3000/5005] lr: 1.000e-01, eta: 9:31:22, time: 0.092, data_time: 0.005, memory: 1224, loss: 2.4177 2022-12-31 17:14:32,408 - mmcls - INFO - Epoch [23][3100/5005] lr: 1.000e-01, eta: 9:31:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4212 2022-12-31 17:14:40,990 - mmcls - INFO - Epoch [23][3200/5005] lr: 1.000e-01, eta: 9:31:03, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.4056 2022-12-31 17:14:49,952 - mmcls - INFO - Epoch [23][3300/5005] lr: 1.000e-01, eta: 9:30:54, time: 0.090, data_time: 0.019, memory: 1224, loss: 2.4216 2022-12-31 17:14:58,566 - mmcls - INFO - Epoch [23][3400/5005] lr: 1.000e-01, eta: 9:30:45, time: 0.086, data_time: 0.027, memory: 1224, loss: 2.4073 2022-12-31 17:15:07,798 - mmcls - INFO - Epoch [23][3500/5005] lr: 1.000e-01, eta: 9:30:37, time: 0.092, data_time: 0.011, memory: 1224, loss: 2.4229 2022-12-31 17:15:17,183 - mmcls - INFO - Epoch [23][3600/5005] lr: 1.000e-01, eta: 9:30:30, time: 0.094, data_time: 0.011, memory: 1224, loss: 2.4299 2022-12-31 17:15:26,017 - mmcls - INFO - Epoch [23][3700/5005] lr: 1.000e-01, eta: 9:30:21, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.4279 2022-12-31 17:15:34,997 - mmcls - INFO - Epoch [23][3800/5005] lr: 1.000e-01, eta: 9:30:13, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4438 2022-12-31 17:15:43,791 - mmcls - INFO - Epoch [23][3900/5005] lr: 1.000e-01, eta: 9:30:04, time: 0.088, data_time: 0.009, memory: 1224, loss: 2.4088 2022-12-31 17:15:52,926 - mmcls - INFO - Epoch [23][4000/5005] lr: 1.000e-01, eta: 9:29:56, time: 0.091, data_time: 0.021, memory: 1224, loss: 2.3956 2022-12-31 17:16:01,593 - mmcls - INFO - Epoch [23][4100/5005] lr: 1.000e-01, eta: 9:29:46, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.4365 2022-12-31 17:16:10,316 - mmcls - INFO - Epoch [23][4200/5005] lr: 1.000e-01, eta: 9:29:37, time: 0.087, data_time: 0.020, memory: 1224, loss: 2.4172 2022-12-31 17:16:19,026 - mmcls - INFO - Epoch [23][4300/5005] lr: 1.000e-01, eta: 9:29:28, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.4074 2022-12-31 17:16:27,979 - mmcls - INFO - Epoch [23][4400/5005] lr: 1.000e-01, eta: 9:29:19, time: 0.090, data_time: 0.007, memory: 1224, loss: 2.4107 2022-12-31 17:16:37,004 - mmcls - INFO - Epoch [23][4500/5005] lr: 1.000e-01, eta: 9:29:11, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.4120 2022-12-31 17:16:45,804 - mmcls - INFO - Epoch [23][4600/5005] lr: 1.000e-01, eta: 9:29:02, time: 0.088, data_time: 0.025, memory: 1224, loss: 2.4281 2022-12-31 17:16:54,293 - mmcls - INFO - Epoch [23][4700/5005] lr: 1.000e-01, eta: 9:28:52, time: 0.085, data_time: 0.006, memory: 1224, loss: 2.4144 2022-12-31 17:17:03,507 - mmcls - INFO - Epoch [23][4800/5005] lr: 1.000e-01, eta: 9:28:44, time: 0.092, data_time: 0.006, memory: 1224, loss: 2.4171 2022-12-31 17:17:12,345 - mmcls - INFO - Epoch [23][4900/5005] lr: 1.000e-01, eta: 9:28:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4026 2022-12-31 17:17:20,980 - mmcls - INFO - Epoch [23][5000/5005] lr: 1.000e-01, eta: 9:28:26, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4022 2022-12-31 17:17:43,795 - mmcls - INFO - Epoch(val) [23][196] accuracy_top-1: 50.9380, accuracy_top-5: 76.6960 2022-12-31 17:17:55,020 - mmcls - INFO - Epoch [24][100/5005] lr: 1.000e-01, eta: 9:28:23, time: 0.112, data_time: 0.060, memory: 1224, loss: 2.3756 2022-12-31 17:18:04,230 - mmcls - INFO - Epoch [24][200/5005] lr: 1.000e-01, eta: 9:28:15, time: 0.092, data_time: 0.010, memory: 1224, loss: 2.3690 2022-12-31 17:18:13,016 - mmcls - INFO - Epoch [24][300/5005] lr: 1.000e-01, eta: 9:28:06, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.3634 2022-12-31 17:18:21,951 - mmcls - INFO - Epoch [24][400/5005] lr: 1.000e-01, eta: 9:27:58, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.3946 2022-12-31 17:18:30,661 - mmcls - INFO - Epoch [24][500/5005] lr: 1.000e-01, eta: 9:27:48, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.3878 2022-12-31 17:18:39,622 - mmcls - INFO - Epoch [24][600/5005] lr: 1.000e-01, eta: 9:27:40, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.3817 2022-12-31 17:18:48,428 - mmcls - INFO - Epoch [24][700/5005] lr: 1.000e-01, eta: 9:27:31, time: 0.088, data_time: 0.031, memory: 1224, loss: 2.3823 2022-12-31 17:18:57,170 - mmcls - INFO - Epoch [24][800/5005] lr: 1.000e-01, eta: 9:27:22, time: 0.087, data_time: 0.022, memory: 1224, loss: 2.4212 2022-12-31 17:19:05,862 - mmcls - INFO - Epoch [24][900/5005] lr: 1.000e-01, eta: 9:27:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4116 2022-12-31 17:19:14,442 - mmcls - INFO - Epoch [24][1000/5005] lr: 1.000e-01, eta: 9:27:02, time: 0.086, data_time: 0.017, memory: 1224, loss: 2.3980 2022-12-31 17:19:23,068 - mmcls - INFO - Epoch [24][1100/5005] lr: 1.000e-01, eta: 9:26:53, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.3945 2022-12-31 17:19:31,822 - mmcls - INFO - Epoch [24][1200/5005] lr: 1.000e-01, eta: 9:26:44, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3924 2022-12-31 17:19:40,405 - mmcls - INFO - Epoch [24][1300/5005] lr: 1.000e-01, eta: 9:26:34, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.3972 2022-12-31 17:19:49,608 - mmcls - INFO - Epoch [24][1400/5005] lr: 1.000e-01, eta: 9:26:26, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.4048 2022-12-31 17:19:58,451 - mmcls - INFO - Epoch [24][1500/5005] lr: 1.000e-01, eta: 9:26:17, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.3952 2022-12-31 17:20:07,481 - mmcls - INFO - Epoch [24][1600/5005] lr: 1.000e-01, eta: 9:26:09, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.3906 2022-12-31 17:20:16,289 - mmcls - INFO - Epoch [24][1700/5005] lr: 1.000e-01, eta: 9:26:00, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4045 2022-12-31 17:20:24,878 - mmcls - INFO - Epoch [24][1800/5005] lr: 1.000e-01, eta: 9:25:50, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.3838 2022-12-31 17:20:33,623 - mmcls - INFO - Epoch [24][1900/5005] lr: 1.000e-01, eta: 9:25:41, time: 0.087, data_time: 0.035, memory: 1224, loss: 2.4226 2022-12-31 17:20:42,365 - mmcls - INFO - Epoch [24][2000/5005] lr: 1.000e-01, eta: 9:25:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3981 2022-12-31 17:20:51,123 - mmcls - INFO - Epoch [24][2100/5005] lr: 1.000e-01, eta: 9:25:23, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4340 2022-12-31 17:20:59,803 - mmcls - INFO - Epoch [24][2200/5005] lr: 1.000e-01, eta: 9:25:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4215 2022-12-31 17:21:08,882 - mmcls - INFO - Epoch [24][2300/5005] lr: 1.000e-01, eta: 9:25:05, time: 0.091, data_time: 0.006, memory: 1224, loss: 2.3891 2022-12-31 17:21:17,722 - mmcls - INFO - Epoch [24][2400/5005] lr: 1.000e-01, eta: 9:24:56, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4015 2022-12-31 17:21:26,465 - mmcls - INFO - Epoch [24][2500/5005] lr: 1.000e-01, eta: 9:24:47, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.4135 2022-12-31 17:21:35,052 - mmcls - INFO - Epoch [24][2600/5005] lr: 1.000e-01, eta: 9:24:37, time: 0.086, data_time: 0.006, memory: 1224, loss: 2.3711 2022-12-31 17:21:43,710 - mmcls - INFO - Epoch [24][2700/5005] lr: 1.000e-01, eta: 9:24:28, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3950 2022-12-31 17:21:52,338 - mmcls - INFO - Epoch [24][2800/5005] lr: 1.000e-01, eta: 9:24:18, time: 0.086, data_time: 0.022, memory: 1224, loss: 2.4010 2022-12-31 17:22:01,191 - mmcls - INFO - Epoch [24][2900/5005] lr: 1.000e-01, eta: 9:24:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4215 2022-12-31 17:22:10,292 - mmcls - INFO - Epoch [24][3000/5005] lr: 1.000e-01, eta: 9:24:02, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.4043 2022-12-31 17:22:19,044 - mmcls - INFO - Epoch [24][3100/5005] lr: 1.000e-01, eta: 9:23:52, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4267 2022-12-31 17:22:27,758 - mmcls - INFO - Epoch [24][3200/5005] lr: 1.000e-01, eta: 9:23:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.4006 2022-12-31 17:22:36,566 - mmcls - INFO - Epoch [24][3300/5005] lr: 1.000e-01, eta: 9:23:34, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.3927 2022-12-31 17:22:45,356 - mmcls - INFO - Epoch [24][3400/5005] lr: 1.000e-01, eta: 9:23:25, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4224 2022-12-31 17:22:54,529 - mmcls - INFO - Epoch [24][3500/5005] lr: 1.000e-01, eta: 9:23:17, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.4081 2022-12-31 17:23:03,150 - mmcls - INFO - Epoch [24][3600/5005] lr: 1.000e-01, eta: 9:23:08, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.3924 2022-12-31 17:23:11,892 - mmcls - INFO - Epoch [24][3700/5005] lr: 1.000e-01, eta: 9:22:58, time: 0.087, data_time: 0.015, memory: 1224, loss: 2.4302 2022-12-31 17:23:21,689 - mmcls - INFO - Epoch [24][3800/5005] lr: 1.000e-01, eta: 9:22:53, time: 0.098, data_time: 0.001, memory: 1224, loss: 2.4000 2022-12-31 17:23:30,328 - mmcls - INFO - Epoch [24][3900/5005] lr: 1.000e-01, eta: 9:22:43, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4140 2022-12-31 17:23:38,925 - mmcls - INFO - Epoch [24][4000/5005] lr: 1.000e-01, eta: 9:22:33, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4255 2022-12-31 17:23:47,576 - mmcls - INFO - Epoch [24][4100/5005] lr: 1.000e-01, eta: 9:22:24, time: 0.086, data_time: 0.009, memory: 1224, loss: 2.3964 2022-12-31 17:23:56,400 - mmcls - INFO - Epoch [24][4200/5005] lr: 1.000e-01, eta: 9:22:15, time: 0.088, data_time: 0.008, memory: 1224, loss: 2.4100 2022-12-31 17:24:05,611 - mmcls - INFO - Epoch [24][4300/5005] lr: 1.000e-01, eta: 9:22:07, time: 0.092, data_time: 0.002, memory: 1224, loss: 2.4041 2022-12-31 17:24:14,260 - mmcls - INFO - Epoch [24][4400/5005] lr: 1.000e-01, eta: 9:21:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4333 2022-12-31 17:24:23,064 - mmcls - INFO - Epoch [24][4500/5005] lr: 1.000e-01, eta: 9:21:49, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.3924 2022-12-31 17:24:31,853 - mmcls - INFO - Epoch [24][4600/5005] lr: 1.000e-01, eta: 9:21:40, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.3991 2022-12-31 17:24:40,599 - mmcls - INFO - Epoch [24][4700/5005] lr: 1.000e-01, eta: 9:21:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3777 2022-12-31 17:24:49,235 - mmcls - INFO - Epoch [24][4800/5005] lr: 1.000e-01, eta: 9:21:21, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.4342 2022-12-31 17:24:57,756 - mmcls - INFO - Epoch [24][4900/5005] lr: 1.000e-01, eta: 9:21:11, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.4217 2022-12-31 17:25:06,520 - mmcls - INFO - Epoch [24][5000/5005] lr: 1.000e-01, eta: 9:21:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3913 2022-12-31 17:25:29,430 - mmcls - INFO - Epoch(val) [24][196] accuracy_top-1: 52.0580, accuracy_top-5: 77.5200 2022-12-31 17:25:40,723 - mmcls - INFO - Epoch [25][100/5005] lr: 1.000e-01, eta: 9:20:59, time: 0.113, data_time: 0.031, memory: 1224, loss: 2.3601 2022-12-31 17:25:49,649 - mmcls - INFO - Epoch [25][200/5005] lr: 1.000e-01, eta: 9:20:50, time: 0.089, data_time: 0.018, memory: 1224, loss: 2.3385 2022-12-31 17:25:58,603 - mmcls - INFO - Epoch [25][300/5005] lr: 1.000e-01, eta: 9:20:42, time: 0.089, data_time: 0.027, memory: 1224, loss: 2.3693 2022-12-31 17:26:07,560 - mmcls - INFO - Epoch [25][400/5005] lr: 1.000e-01, eta: 9:20:33, time: 0.090, data_time: 0.017, memory: 1224, loss: 2.4133 2022-12-31 17:26:16,259 - mmcls - INFO - Epoch [25][500/5005] lr: 1.000e-01, eta: 9:20:24, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.3695 2022-12-31 17:26:24,892 - mmcls - INFO - Epoch [25][600/5005] lr: 1.000e-01, eta: 9:20:14, time: 0.086, data_time: 0.011, memory: 1224, loss: 2.3636 2022-12-31 17:26:33,653 - mmcls - INFO - Epoch [25][700/5005] lr: 1.000e-01, eta: 9:20:05, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3817 2022-12-31 17:26:42,283 - mmcls - INFO - Epoch [25][800/5005] lr: 1.000e-01, eta: 9:19:56, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.3833 2022-12-31 17:26:51,474 - mmcls - INFO - Epoch [25][900/5005] lr: 1.000e-01, eta: 9:19:48, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.3911 2022-12-31 17:27:00,105 - mmcls - INFO - Epoch [25][1000/5005] lr: 1.000e-01, eta: 9:19:38, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.3970 2022-12-31 17:27:08,877 - mmcls - INFO - Epoch [25][1100/5005] lr: 1.000e-01, eta: 9:19:29, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.3563 2022-12-31 17:27:17,721 - mmcls - INFO - Epoch [25][1200/5005] lr: 1.000e-01, eta: 9:19:20, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3900 2022-12-31 17:27:26,549 - mmcls - INFO - Epoch [25][1300/5005] lr: 1.000e-01, eta: 9:19:12, time: 0.088, data_time: 0.020, memory: 1224, loss: 2.3868 2022-12-31 17:27:35,663 - mmcls - INFO - Epoch [25][1400/5005] lr: 1.000e-01, eta: 9:19:04, time: 0.091, data_time: 0.010, memory: 1224, loss: 2.3813 2022-12-31 17:27:44,226 - mmcls - INFO - Epoch [25][1500/5005] lr: 1.000e-01, eta: 9:18:54, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4028 2022-12-31 17:27:53,241 - mmcls - INFO - Epoch [25][1600/5005] lr: 1.000e-01, eta: 9:18:45, time: 0.090, data_time: 0.006, memory: 1224, loss: 2.3832 2022-12-31 17:28:01,906 - mmcls - INFO - Epoch [25][1700/5005] lr: 1.000e-01, eta: 9:18:36, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4222 2022-12-31 17:28:10,786 - mmcls - INFO - Epoch [25][1800/5005] lr: 1.000e-01, eta: 9:18:27, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.4115 2022-12-31 17:28:19,404 - mmcls - INFO - Epoch [25][1900/5005] lr: 1.000e-01, eta: 9:18:18, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.4090 2022-12-31 17:28:28,223 - mmcls - INFO - Epoch [25][2000/5005] lr: 1.000e-01, eta: 9:18:09, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3943 2022-12-31 17:28:37,137 - mmcls - INFO - Epoch [25][2100/5005] lr: 1.000e-01, eta: 9:18:00, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4103 2022-12-31 17:28:45,845 - mmcls - INFO - Epoch [25][2200/5005] lr: 1.000e-01, eta: 9:17:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3884 2022-12-31 17:28:54,506 - mmcls - INFO - Epoch [25][2300/5005] lr: 1.000e-01, eta: 9:17:41, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.4046 2022-12-31 17:29:03,252 - mmcls - INFO - Epoch [25][2400/5005] lr: 1.000e-01, eta: 9:17:32, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.3909 2022-12-31 17:29:12,070 - mmcls - INFO - Epoch [25][2500/5005] lr: 1.000e-01, eta: 9:17:23, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4184 2022-12-31 17:29:20,775 - mmcls - INFO - Epoch [25][2600/5005] lr: 1.000e-01, eta: 9:17:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.3990 2022-12-31 17:29:29,804 - mmcls - INFO - Epoch [25][2700/5005] lr: 1.000e-01, eta: 9:17:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4070 2022-12-31 17:29:38,506 - mmcls - INFO - Epoch [25][2800/5005] lr: 1.000e-01, eta: 9:16:56, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.4165 2022-12-31 17:29:47,305 - mmcls - INFO - Epoch [25][2900/5005] lr: 1.000e-01, eta: 9:16:47, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.3872 2022-12-31 17:29:56,070 - mmcls - INFO - Epoch [25][3000/5005] lr: 1.000e-01, eta: 9:16:38, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3808 2022-12-31 17:30:04,930 - mmcls - INFO - Epoch [25][3100/5005] lr: 1.000e-01, eta: 9:16:29, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4145 2022-12-31 17:30:13,628 - mmcls - INFO - Epoch [25][3200/5005] lr: 1.000e-01, eta: 9:16:20, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3965 2022-12-31 17:30:22,418 - mmcls - INFO - Epoch [25][3300/5005] lr: 1.000e-01, eta: 9:16:11, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3930 2022-12-31 17:30:31,186 - mmcls - INFO - Epoch [25][3400/5005] lr: 1.000e-01, eta: 9:16:02, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4074 2022-12-31 17:30:39,980 - mmcls - INFO - Epoch [25][3500/5005] lr: 1.000e-01, eta: 9:15:53, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4185 2022-12-31 17:30:48,627 - mmcls - INFO - Epoch [25][3600/5005] lr: 1.000e-01, eta: 9:15:43, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4075 2022-12-31 17:30:57,321 - mmcls - INFO - Epoch [25][3700/5005] lr: 1.000e-01, eta: 9:15:34, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.4037 2022-12-31 17:31:06,027 - mmcls - INFO - Epoch [25][3800/5005] lr: 1.000e-01, eta: 9:15:25, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.3940 2022-12-31 17:31:15,063 - mmcls - INFO - Epoch [25][3900/5005] lr: 1.000e-01, eta: 9:15:16, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4204 2022-12-31 17:31:23,966 - mmcls - INFO - Epoch [25][4000/5005] lr: 1.000e-01, eta: 9:15:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4034 2022-12-31 17:31:32,585 - mmcls - INFO - Epoch [25][4100/5005] lr: 1.000e-01, eta: 9:14:58, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.4028 2022-12-31 17:31:41,336 - mmcls - INFO - Epoch [25][4200/5005] lr: 1.000e-01, eta: 9:14:49, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4247 2022-12-31 17:31:50,245 - mmcls - INFO - Epoch [25][4300/5005] lr: 1.000e-01, eta: 9:14:40, time: 0.089, data_time: 0.022, memory: 1224, loss: 2.4011 2022-12-31 17:31:58,862 - mmcls - INFO - Epoch [25][4400/5005] lr: 1.000e-01, eta: 9:14:31, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.3771 2022-12-31 17:32:07,577 - mmcls - INFO - Epoch [25][4500/5005] lr: 1.000e-01, eta: 9:14:22, time: 0.087, data_time: 0.018, memory: 1224, loss: 2.3915 2022-12-31 17:32:16,209 - mmcls - INFO - Epoch [25][4600/5005] lr: 1.000e-01, eta: 9:14:12, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.4332 2022-12-31 17:32:25,215 - mmcls - INFO - Epoch [25][4700/5005] lr: 1.000e-01, eta: 9:14:04, time: 0.090, data_time: 0.015, memory: 1224, loss: 2.4064 2022-12-31 17:32:34,048 - mmcls - INFO - Epoch [25][4800/5005] lr: 1.000e-01, eta: 9:13:55, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4110 2022-12-31 17:32:43,265 - mmcls - INFO - Epoch [25][4900/5005] lr: 1.000e-01, eta: 9:13:47, time: 0.092, data_time: 0.007, memory: 1224, loss: 2.4094 2022-12-31 17:32:52,044 - mmcls - INFO - Epoch [25][5000/5005] lr: 1.000e-01, eta: 9:13:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 2.4124 2022-12-31 17:33:15,028 - mmcls - INFO - Epoch(val) [25][196] accuracy_top-1: 52.3140, accuracy_top-5: 77.8040 2022-12-31 17:33:25,865 - mmcls - INFO - Epoch [26][100/5005] lr: 1.000e-01, eta: 9:13:33, time: 0.108, data_time: 0.032, memory: 1224, loss: 2.3777 2022-12-31 17:33:34,735 - mmcls - INFO - Epoch [26][200/5005] lr: 1.000e-01, eta: 9:13:24, time: 0.089, data_time: 0.034, memory: 1224, loss: 2.3646 2022-12-31 17:33:43,572 - mmcls - INFO - Epoch [26][300/5005] lr: 1.000e-01, eta: 9:13:16, time: 0.088, data_time: 0.012, memory: 1224, loss: 2.3617 2022-12-31 17:33:52,770 - mmcls - INFO - Epoch [26][400/5005] lr: 1.000e-01, eta: 9:13:08, time: 0.092, data_time: 0.014, memory: 1224, loss: 2.3656 2022-12-31 17:34:01,749 - mmcls - INFO - Epoch [26][500/5005] lr: 1.000e-01, eta: 9:12:59, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.3654 2022-12-31 17:34:10,649 - mmcls - INFO - Epoch [26][600/5005] lr: 1.000e-01, eta: 9:12:51, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.3606 2022-12-31 17:34:19,308 - mmcls - INFO - Epoch [26][700/5005] lr: 1.000e-01, eta: 9:12:41, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3760 2022-12-31 17:34:27,954 - mmcls - INFO - Epoch [26][800/5005] lr: 1.000e-01, eta: 9:12:32, time: 0.086, data_time: 0.013, memory: 1224, loss: 2.3738 2022-12-31 17:34:37,107 - mmcls - INFO - Epoch [26][900/5005] lr: 1.000e-01, eta: 9:12:24, time: 0.092, data_time: 0.013, memory: 1224, loss: 2.3962 2022-12-31 17:34:45,917 - mmcls - INFO - Epoch [26][1000/5005] lr: 1.000e-01, eta: 9:12:15, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.3793 2022-12-31 17:34:54,744 - mmcls - INFO - Epoch [26][1100/5005] lr: 1.000e-01, eta: 9:12:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3894 2022-12-31 17:35:03,697 - mmcls - INFO - Epoch [26][1200/5005] lr: 1.000e-01, eta: 9:11:57, time: 0.089, data_time: 0.027, memory: 1224, loss: 2.3823 2022-12-31 17:35:12,383 - mmcls - INFO - Epoch [26][1300/5005] lr: 1.000e-01, eta: 9:11:48, time: 0.087, data_time: 0.016, memory: 1224, loss: 2.3936 2022-12-31 17:35:21,137 - mmcls - INFO - Epoch [26][1400/5005] lr: 1.000e-01, eta: 9:11:39, time: 0.087, data_time: 0.010, memory: 1224, loss: 2.3947 2022-12-31 17:35:30,045 - mmcls - INFO - Epoch [26][1500/5005] lr: 1.000e-01, eta: 9:11:30, time: 0.089, data_time: 0.008, memory: 1224, loss: 2.3835 2022-12-31 17:35:38,705 - mmcls - INFO - Epoch [26][1600/5005] lr: 1.000e-01, eta: 9:11:21, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4182 2022-12-31 17:35:47,910 - mmcls - INFO - Epoch [26][1700/5005] lr: 1.000e-01, eta: 9:11:13, time: 0.092, data_time: 0.006, memory: 1224, loss: 2.3727 2022-12-31 17:35:56,736 - mmcls - INFO - Epoch [26][1800/5005] lr: 1.000e-01, eta: 9:11:04, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4019 2022-12-31 17:36:05,728 - mmcls - INFO - Epoch [26][1900/5005] lr: 1.000e-01, eta: 9:10:56, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3850 2022-12-31 17:36:14,655 - mmcls - INFO - Epoch [26][2000/5005] lr: 1.000e-01, eta: 9:10:47, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.4105 2022-12-31 17:36:23,317 - mmcls - INFO - Epoch [26][2100/5005] lr: 1.000e-01, eta: 9:10:37, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.4255 2022-12-31 17:36:32,235 - mmcls - INFO - Epoch [26][2200/5005] lr: 1.000e-01, eta: 9:10:29, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.3763 2022-12-31 17:36:41,283 - mmcls - INFO - Epoch [26][2300/5005] lr: 1.000e-01, eta: 9:10:21, time: 0.091, data_time: 0.003, memory: 1224, loss: 2.4173 2022-12-31 17:36:49,898 - mmcls - INFO - Epoch [26][2400/5005] lr: 1.000e-01, eta: 9:10:11, time: 0.086, data_time: 0.010, memory: 1224, loss: 2.3802 2022-12-31 17:36:58,648 - mmcls - INFO - Epoch [26][2500/5005] lr: 1.000e-01, eta: 9:10:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4000 2022-12-31 17:37:07,264 - mmcls - INFO - Epoch [26][2600/5005] lr: 1.000e-01, eta: 9:09:52, time: 0.086, data_time: 0.018, memory: 1224, loss: 2.3873 2022-12-31 17:37:16,031 - mmcls - INFO - Epoch [26][2700/5005] lr: 1.000e-01, eta: 9:09:43, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.4098 2022-12-31 17:37:24,794 - mmcls - INFO - Epoch [26][2800/5005] lr: 1.000e-01, eta: 9:09:34, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.4126 2022-12-31 17:37:33,658 - mmcls - INFO - Epoch [26][2900/5005] lr: 1.000e-01, eta: 9:09:25, time: 0.089, data_time: 0.032, memory: 1224, loss: 2.3944 2022-12-31 17:37:42,560 - mmcls - INFO - Epoch [26][3000/5005] lr: 1.000e-01, eta: 9:09:17, time: 0.089, data_time: 0.011, memory: 1224, loss: 2.4071 2022-12-31 17:37:50,961 - mmcls - INFO - Epoch [26][3100/5005] lr: 1.000e-01, eta: 9:09:06, time: 0.084, data_time: 0.003, memory: 1224, loss: 2.4019 2022-12-31 17:37:59,832 - mmcls - INFO - Epoch [26][3200/5005] lr: 1.000e-01, eta: 9:08:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4167 2022-12-31 17:38:08,345 - mmcls - INFO - Epoch [26][3300/5005] lr: 1.000e-01, eta: 9:08:48, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.4104 2022-12-31 17:38:16,995 - mmcls - INFO - Epoch [26][3400/5005] lr: 1.000e-01, eta: 9:08:38, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4053 2022-12-31 17:38:25,781 - mmcls - INFO - Epoch [26][3500/5005] lr: 1.000e-01, eta: 9:08:29, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4133 2022-12-31 17:38:34,422 - mmcls - INFO - Epoch [26][3600/5005] lr: 1.000e-01, eta: 9:08:20, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.3939 2022-12-31 17:38:43,793 - mmcls - INFO - Epoch [26][3700/5005] lr: 1.000e-01, eta: 9:08:13, time: 0.094, data_time: 0.001, memory: 1224, loss: 2.4013 2022-12-31 17:38:52,549 - mmcls - INFO - Epoch [26][3800/5005] lr: 1.000e-01, eta: 9:08:03, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4234 2022-12-31 17:39:01,441 - mmcls - INFO - Epoch [26][3900/5005] lr: 1.000e-01, eta: 9:07:55, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.4197 2022-12-31 17:39:10,317 - mmcls - INFO - Epoch [26][4000/5005] lr: 1.000e-01, eta: 9:07:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4166 2022-12-31 17:39:19,202 - mmcls - INFO - Epoch [26][4100/5005] lr: 1.000e-01, eta: 9:07:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3902 2022-12-31 17:39:27,648 - mmcls - INFO - Epoch [26][4200/5005] lr: 1.000e-01, eta: 9:07:27, time: 0.084, data_time: 0.012, memory: 1224, loss: 2.4299 2022-12-31 17:39:36,558 - mmcls - INFO - Epoch [26][4300/5005] lr: 1.000e-01, eta: 9:07:18, time: 0.089, data_time: 0.015, memory: 1224, loss: 2.4112 2022-12-31 17:39:45,333 - mmcls - INFO - Epoch [26][4400/5005] lr: 1.000e-01, eta: 9:07:09, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.4024 2022-12-31 17:39:54,128 - mmcls - INFO - Epoch [26][4500/5005] lr: 1.000e-01, eta: 9:07:00, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.4097 2022-12-31 17:40:02,789 - mmcls - INFO - Epoch [26][4600/5005] lr: 1.000e-01, eta: 9:06:51, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4085 2022-12-31 17:40:11,560 - mmcls - INFO - Epoch [26][4700/5005] lr: 1.000e-01, eta: 9:06:42, time: 0.088, data_time: 0.019, memory: 1224, loss: 2.4104 2022-12-31 17:40:20,514 - mmcls - INFO - Epoch [26][4800/5005] lr: 1.000e-01, eta: 9:06:33, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.4117 2022-12-31 17:40:29,316 - mmcls - INFO - Epoch [26][4900/5005] lr: 1.000e-01, eta: 9:06:24, time: 0.088, data_time: 0.026, memory: 1224, loss: 2.4205 2022-12-31 17:40:38,078 - mmcls - INFO - Epoch [26][5000/5005] lr: 1.000e-01, eta: 9:06:15, time: 0.088, data_time: 0.011, memory: 1224, loss: 2.4024 2022-12-31 17:41:00,723 - mmcls - INFO - Epoch(val) [26][196] accuracy_top-1: 51.3820, accuracy_top-5: 77.2600 2022-12-31 17:41:11,756 - mmcls - INFO - Epoch [27][100/5005] lr: 1.000e-01, eta: 9:06:11, time: 0.110, data_time: 0.063, memory: 1224, loss: 2.3570 2022-12-31 17:41:20,661 - mmcls - INFO - Epoch [27][200/5005] lr: 1.000e-01, eta: 9:06:02, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.3511 2022-12-31 17:41:29,446 - mmcls - INFO - Epoch [27][300/5005] lr: 1.000e-01, eta: 9:05:53, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3544 2022-12-31 17:41:38,198 - mmcls - INFO - Epoch [27][400/5005] lr: 1.000e-01, eta: 9:05:44, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.3602 2022-12-31 17:41:46,861 - mmcls - INFO - Epoch [27][500/5005] lr: 1.000e-01, eta: 9:05:35, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.4103 2022-12-31 17:41:56,069 - mmcls - INFO - Epoch [27][600/5005] lr: 1.000e-01, eta: 9:05:27, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.3531 2022-12-31 17:42:04,731 - mmcls - INFO - Epoch [27][700/5005] lr: 1.000e-01, eta: 9:05:17, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.4062 2022-12-31 17:42:13,735 - mmcls - INFO - Epoch [27][800/5005] lr: 1.000e-01, eta: 9:05:09, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3800 2022-12-31 17:42:22,503 - mmcls - INFO - Epoch [27][900/5005] lr: 1.000e-01, eta: 9:05:00, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.3705 2022-12-31 17:42:31,381 - mmcls - INFO - Epoch [27][1000/5005] lr: 1.000e-01, eta: 9:04:51, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.3835 2022-12-31 17:42:40,238 - mmcls - INFO - Epoch [27][1100/5005] lr: 1.000e-01, eta: 9:04:42, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.3686 2022-12-31 17:42:49,000 - mmcls - INFO - Epoch [27][1200/5005] lr: 1.000e-01, eta: 9:04:33, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3797 2022-12-31 17:42:57,633 - mmcls - INFO - Epoch [27][1300/5005] lr: 1.000e-01, eta: 9:04:24, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.3889 2022-12-31 17:43:06,309 - mmcls - INFO - Epoch [27][1400/5005] lr: 1.000e-01, eta: 9:04:14, time: 0.087, data_time: 0.003, memory: 1224, loss: 2.3886 2022-12-31 17:43:15,363 - mmcls - INFO - Epoch [27][1500/5005] lr: 1.000e-01, eta: 9:04:06, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.3660 2022-12-31 17:43:24,055 - mmcls - INFO - Epoch [27][1600/5005] lr: 1.000e-01, eta: 9:03:57, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3836 2022-12-31 17:43:32,962 - mmcls - INFO - Epoch [27][1700/5005] lr: 1.000e-01, eta: 9:03:48, time: 0.089, data_time: 0.024, memory: 1224, loss: 2.3693 2022-12-31 17:43:41,779 - mmcls - INFO - Epoch [27][1800/5005] lr: 1.000e-01, eta: 9:03:39, time: 0.088, data_time: 0.021, memory: 1224, loss: 2.3854 2022-12-31 17:43:50,627 - mmcls - INFO - Epoch [27][1900/5005] lr: 1.000e-01, eta: 9:03:30, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3850 2022-12-31 17:43:59,189 - mmcls - INFO - Epoch [27][2000/5005] lr: 1.000e-01, eta: 9:03:21, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.3552 2022-12-31 17:44:08,088 - mmcls - INFO - Epoch [27][2100/5005] lr: 1.000e-01, eta: 9:03:12, time: 0.089, data_time: 0.031, memory: 1224, loss: 2.4041 2022-12-31 17:44:17,081 - mmcls - INFO - Epoch [27][2200/5005] lr: 1.000e-01, eta: 9:03:03, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.4191 2022-12-31 17:44:25,912 - mmcls - INFO - Epoch [27][2300/5005] lr: 1.000e-01, eta: 9:02:55, time: 0.088, data_time: 0.029, memory: 1224, loss: 2.3758 2022-12-31 17:44:35,097 - mmcls - INFO - Epoch [27][2400/5005] lr: 1.000e-01, eta: 9:02:47, time: 0.092, data_time: 0.004, memory: 1224, loss: 2.4102 2022-12-31 17:44:44,027 - mmcls - INFO - Epoch [27][2500/5005] lr: 1.000e-01, eta: 9:02:38, time: 0.089, data_time: 0.038, memory: 1224, loss: 2.4004 2022-12-31 17:44:52,712 - mmcls - INFO - Epoch [27][2600/5005] lr: 1.000e-01, eta: 9:02:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 2.3980 2022-12-31 17:45:01,465 - mmcls - INFO - Epoch [27][2700/5005] lr: 1.000e-01, eta: 9:02:20, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.4008 2022-12-31 17:45:09,996 - mmcls - INFO - Epoch [27][2800/5005] lr: 1.000e-01, eta: 9:02:10, time: 0.085, data_time: 0.028, memory: 1224, loss: 2.3729 2022-12-31 17:45:18,742 - mmcls - INFO - Epoch [27][2900/5005] lr: 1.000e-01, eta: 9:02:01, time: 0.087, data_time: 0.040, memory: 1224, loss: 2.4121 2022-12-31 17:45:27,416 - mmcls - INFO - Epoch [27][3000/5005] lr: 1.000e-01, eta: 9:01:51, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.4029 2022-12-31 17:45:36,148 - mmcls - INFO - Epoch [27][3100/5005] lr: 1.000e-01, eta: 9:01:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4114 2022-12-31 17:45:45,116 - mmcls - INFO - Epoch [27][3200/5005] lr: 1.000e-01, eta: 9:01:34, time: 0.090, data_time: 0.029, memory: 1224, loss: 2.4123 2022-12-31 17:45:53,772 - mmcls - INFO - Epoch [27][3300/5005] lr: 1.000e-01, eta: 9:01:24, time: 0.087, data_time: 0.007, memory: 1224, loss: 2.4039 2022-12-31 17:46:02,892 - mmcls - INFO - Epoch [27][3400/5005] lr: 1.000e-01, eta: 9:01:16, time: 0.091, data_time: 0.009, memory: 1224, loss: 2.3832 2022-12-31 17:46:11,642 - mmcls - INFO - Epoch [27][3500/5005] lr: 1.000e-01, eta: 9:01:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3976 2022-12-31 17:46:20,624 - mmcls - INFO - Epoch [27][3600/5005] lr: 1.000e-01, eta: 9:00:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4344 2022-12-31 17:46:29,927 - mmcls - INFO - Epoch [27][3700/5005] lr: 1.000e-01, eta: 9:00:51, time: 0.093, data_time: 0.001, memory: 1224, loss: 2.3941 2022-12-31 17:46:39,012 - mmcls - INFO - Epoch [27][3800/5005] lr: 1.000e-01, eta: 9:00:43, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.4113 2022-12-31 17:46:47,887 - mmcls - INFO - Epoch [27][3900/5005] lr: 1.000e-01, eta: 9:00:34, time: 0.089, data_time: 0.017, memory: 1224, loss: 2.4207 2022-12-31 17:46:56,921 - mmcls - INFO - Epoch [27][4000/5005] lr: 1.000e-01, eta: 9:00:26, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.4077 2022-12-31 17:47:05,538 - mmcls - INFO - Epoch [27][4100/5005] lr: 1.000e-01, eta: 9:00:16, time: 0.086, data_time: 0.012, memory: 1224, loss: 2.4030 2022-12-31 17:47:14,194 - mmcls - INFO - Epoch [27][4200/5005] lr: 1.000e-01, eta: 9:00:07, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.3738 2022-12-31 17:47:23,229 - mmcls - INFO - Epoch [27][4300/5005] lr: 1.000e-01, eta: 8:59:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3890 2022-12-31 17:47:32,467 - mmcls - INFO - Epoch [27][4400/5005] lr: 1.000e-01, eta: 8:59:51, time: 0.092, data_time: 0.001, memory: 1224, loss: 2.3795 2022-12-31 17:47:41,097 - mmcls - INFO - Epoch [27][4500/5005] lr: 1.000e-01, eta: 8:59:41, time: 0.086, data_time: 0.015, memory: 1224, loss: 2.3956 2022-12-31 17:47:49,815 - mmcls - INFO - Epoch [27][4600/5005] lr: 1.000e-01, eta: 8:59:32, time: 0.087, data_time: 0.014, memory: 1224, loss: 2.4216 2022-12-31 17:47:59,059 - mmcls - INFO - Epoch [27][4700/5005] lr: 1.000e-01, eta: 8:59:24, time: 0.093, data_time: 0.023, memory: 1224, loss: 2.3826 2022-12-31 17:48:07,732 - mmcls - INFO - Epoch [27][4800/5005] lr: 1.000e-01, eta: 8:59:15, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3978 2022-12-31 17:48:16,384 - mmcls - INFO - Epoch [27][4900/5005] lr: 1.000e-01, eta: 8:59:05, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.3674 2022-12-31 17:48:25,341 - mmcls - INFO - Epoch [27][5000/5005] lr: 1.000e-01, eta: 8:58:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3719 2022-12-31 17:48:47,905 - mmcls - INFO - Epoch(val) [27][196] accuracy_top-1: 52.4000, accuracy_top-5: 77.7660 2022-12-31 17:48:59,038 - mmcls - INFO - Epoch [28][100/5005] lr: 1.000e-01, eta: 8:58:52, time: 0.111, data_time: 0.048, memory: 1224, loss: 2.3523 2022-12-31 17:49:07,628 - mmcls - INFO - Epoch [28][200/5005] lr: 1.000e-01, eta: 8:58:43, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.3574 2022-12-31 17:49:16,389 - mmcls - INFO - Epoch [28][300/5005] lr: 1.000e-01, eta: 8:58:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3914 2022-12-31 17:49:25,331 - mmcls - INFO - Epoch [28][400/5005] lr: 1.000e-01, eta: 8:58:25, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3576 2022-12-31 17:49:33,821 - mmcls - INFO - Epoch [28][500/5005] lr: 1.000e-01, eta: 8:58:15, time: 0.085, data_time: 0.020, memory: 1224, loss: 2.3615 2022-12-31 17:49:42,706 - mmcls - INFO - Epoch [28][600/5005] lr: 1.000e-01, eta: 8:58:07, time: 0.089, data_time: 0.016, memory: 1224, loss: 2.3703 2022-12-31 17:49:51,927 - mmcls - INFO - Epoch [28][700/5005] lr: 1.000e-01, eta: 8:57:59, time: 0.092, data_time: 0.013, memory: 1224, loss: 2.3967 2022-12-31 17:50:00,605 - mmcls - INFO - Epoch [28][800/5005] lr: 1.000e-01, eta: 8:57:49, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.3832 2022-12-31 17:50:09,486 - mmcls - INFO - Epoch [28][900/5005] lr: 1.000e-01, eta: 8:57:41, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.3628 2022-12-31 17:50:18,256 - mmcls - INFO - Epoch [28][1000/5005] lr: 1.000e-01, eta: 8:57:31, time: 0.088, data_time: 0.018, memory: 1224, loss: 2.3536 2022-12-31 17:50:27,275 - mmcls - INFO - Epoch [28][1100/5005] lr: 1.000e-01, eta: 8:57:23, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3529 2022-12-31 17:50:36,095 - mmcls - INFO - Epoch [28][1200/5005] lr: 1.000e-01, eta: 8:57:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3994 2022-12-31 17:50:45,094 - mmcls - INFO - Epoch [28][1300/5005] lr: 1.000e-01, eta: 8:57:06, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.3775 2022-12-31 17:50:53,979 - mmcls - INFO - Epoch [28][1400/5005] lr: 1.000e-01, eta: 8:56:57, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3704 2022-12-31 17:51:02,769 - mmcls - INFO - Epoch [28][1500/5005] lr: 1.000e-01, eta: 8:56:48, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3952 2022-12-31 17:51:11,365 - mmcls - INFO - Epoch [28][1600/5005] lr: 1.000e-01, eta: 8:56:38, time: 0.086, data_time: 0.008, memory: 1224, loss: 2.3901 2022-12-31 17:51:20,185 - mmcls - INFO - Epoch [28][1700/5005] lr: 1.000e-01, eta: 8:56:29, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.3916 2022-12-31 17:51:29,079 - mmcls - INFO - Epoch [28][1800/5005] lr: 1.000e-01, eta: 8:56:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.4001 2022-12-31 17:51:38,047 - mmcls - INFO - Epoch [28][1900/5005] lr: 1.000e-01, eta: 8:56:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.3902 2022-12-31 17:51:46,907 - mmcls - INFO - Epoch [28][2000/5005] lr: 1.000e-01, eta: 8:56:03, time: 0.089, data_time: 0.001, memory: 1224, loss: 2.3602 2022-12-31 17:51:55,676 - mmcls - INFO - Epoch [28][2100/5005] lr: 1.000e-01, eta: 8:55:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3828 2022-12-31 17:52:04,348 - mmcls - INFO - Epoch [28][2200/5005] lr: 1.000e-01, eta: 8:55:45, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.3843 2022-12-31 17:52:13,616 - mmcls - INFO - Epoch [28][2300/5005] lr: 1.000e-01, eta: 8:55:37, time: 0.093, data_time: 0.004, memory: 1224, loss: 2.3889 2022-12-31 17:52:22,414 - mmcls - INFO - Epoch [28][2400/5005] lr: 1.000e-01, eta: 8:55:28, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.4056 2022-12-31 17:52:31,243 - mmcls - INFO - Epoch [28][2500/5005] lr: 1.000e-01, eta: 8:55:19, time: 0.088, data_time: 0.007, memory: 1224, loss: 2.3829 2022-12-31 17:52:39,740 - mmcls - INFO - Epoch [28][2600/5005] lr: 1.000e-01, eta: 8:55:09, time: 0.085, data_time: 0.007, memory: 1224, loss: 2.3894 2022-12-31 17:52:48,447 - mmcls - INFO - Epoch [28][2700/5005] lr: 1.000e-01, eta: 8:55:00, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4108 2022-12-31 17:52:57,411 - mmcls - INFO - Epoch [28][2800/5005] lr: 1.000e-01, eta: 8:54:52, time: 0.090, data_time: 0.004, memory: 1224, loss: 2.3899 2022-12-31 17:53:06,253 - mmcls - INFO - Epoch [28][2900/5005] lr: 1.000e-01, eta: 8:54:43, time: 0.088, data_time: 0.015, memory: 1224, loss: 2.3791 2022-12-31 17:53:14,866 - mmcls - INFO - Epoch [28][3000/5005] lr: 1.000e-01, eta: 8:54:33, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.3775 2022-12-31 17:53:23,517 - mmcls - INFO - Epoch [28][3100/5005] lr: 1.000e-01, eta: 8:54:24, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.4068 2022-12-31 17:53:32,628 - mmcls - INFO - Epoch [28][3200/5005] lr: 1.000e-01, eta: 8:54:16, time: 0.091, data_time: 0.011, memory: 1224, loss: 2.3921 2022-12-31 17:53:41,542 - mmcls - INFO - Epoch [28][3300/5005] lr: 1.000e-01, eta: 8:54:07, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4241 2022-12-31 17:53:50,472 - mmcls - INFO - Epoch [28][3400/5005] lr: 1.000e-01, eta: 8:53:58, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.3925 2022-12-31 17:53:59,573 - mmcls - INFO - Epoch [28][3500/5005] lr: 1.000e-01, eta: 8:53:50, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.4007 2022-12-31 17:54:08,187 - mmcls - INFO - Epoch [28][3600/5005] lr: 1.000e-01, eta: 8:53:41, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.3955 2022-12-31 17:54:16,738 - mmcls - INFO - Epoch [28][3700/5005] lr: 1.000e-01, eta: 8:53:31, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.4046 2022-12-31 17:54:25,411 - mmcls - INFO - Epoch [28][3800/5005] lr: 1.000e-01, eta: 8:53:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 2.4053 2022-12-31 17:54:34,202 - mmcls - INFO - Epoch [28][3900/5005] lr: 1.000e-01, eta: 8:53:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3989 2022-12-31 17:54:43,079 - mmcls - INFO - Epoch [28][4000/5005] lr: 1.000e-01, eta: 8:53:04, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.3956 2022-12-31 17:54:52,085 - mmcls - INFO - Epoch [28][4100/5005] lr: 1.000e-01, eta: 8:52:56, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.4032 2022-12-31 17:55:00,565 - mmcls - INFO - Epoch [28][4200/5005] lr: 1.000e-01, eta: 8:52:46, time: 0.085, data_time: 0.004, memory: 1224, loss: 2.3926 2022-12-31 17:55:09,359 - mmcls - INFO - Epoch [28][4300/5005] lr: 1.000e-01, eta: 8:52:37, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4026 2022-12-31 17:55:17,846 - mmcls - INFO - Epoch [28][4400/5005] lr: 1.000e-01, eta: 8:52:27, time: 0.085, data_time: 0.002, memory: 1224, loss: 2.4062 2022-12-31 17:55:26,463 - mmcls - INFO - Epoch [28][4500/5005] lr: 1.000e-01, eta: 8:52:17, time: 0.086, data_time: 0.024, memory: 1224, loss: 2.3906 2022-12-31 17:55:35,358 - mmcls - INFO - Epoch [28][4600/5005] lr: 1.000e-01, eta: 8:52:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.4192 2022-12-31 17:55:44,397 - mmcls - INFO - Epoch [28][4700/5005] lr: 1.000e-01, eta: 8:52:00, time: 0.090, data_time: 0.021, memory: 1224, loss: 2.3995 2022-12-31 17:55:53,296 - mmcls - INFO - Epoch [28][4800/5005] lr: 1.000e-01, eta: 8:51:52, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.3949 2022-12-31 17:56:01,959 - mmcls - INFO - Epoch [28][4900/5005] lr: 1.000e-01, eta: 8:51:42, time: 0.087, data_time: 0.013, memory: 1224, loss: 2.3981 2022-12-31 17:56:10,963 - mmcls - INFO - Epoch [28][5000/5005] lr: 1.000e-01, eta: 8:51:34, time: 0.090, data_time: 0.031, memory: 1224, loss: 2.4134 2022-12-31 17:56:33,493 - mmcls - INFO - Epoch(val) [28][196] accuracy_top-1: 50.3460, accuracy_top-5: 76.1560 2022-12-31 17:56:44,648 - mmcls - INFO - Epoch [29][100/5005] lr: 1.000e-01, eta: 8:51:29, time: 0.111, data_time: 0.027, memory: 1224, loss: 2.3304 2022-12-31 17:56:53,596 - mmcls - INFO - Epoch [29][200/5005] lr: 1.000e-01, eta: 8:51:21, time: 0.089, data_time: 0.039, memory: 1224, loss: 2.3693 2022-12-31 17:57:02,450 - mmcls - INFO - Epoch [29][300/5005] lr: 1.000e-01, eta: 8:51:12, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.3476 2022-12-31 17:57:11,071 - mmcls - INFO - Epoch [29][400/5005] lr: 1.000e-01, eta: 8:51:02, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.3509 2022-12-31 17:57:20,465 - mmcls - INFO - Epoch [29][500/5005] lr: 1.000e-01, eta: 8:50:55, time: 0.094, data_time: 0.006, memory: 1224, loss: 2.3778 2022-12-31 17:57:29,539 - mmcls - INFO - Epoch [29][600/5005] lr: 1.000e-01, eta: 8:50:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 2.3671 2022-12-31 17:57:38,521 - mmcls - INFO - Epoch [29][700/5005] lr: 1.000e-01, eta: 8:50:38, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3619 2022-12-31 17:57:47,284 - mmcls - INFO - Epoch [29][800/5005] lr: 1.000e-01, eta: 8:50:29, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.3616 2022-12-31 17:57:56,272 - mmcls - INFO - Epoch [29][900/5005] lr: 1.000e-01, eta: 8:50:20, time: 0.090, data_time: 0.009, memory: 1224, loss: 2.3534 2022-12-31 17:58:05,240 - mmcls - INFO - Epoch [29][1000/5005] lr: 1.000e-01, eta: 8:50:12, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.3539 2022-12-31 17:58:14,665 - mmcls - INFO - Epoch [29][1100/5005] lr: 1.000e-01, eta: 8:50:04, time: 0.094, data_time: 0.001, memory: 1224, loss: 2.3756 2022-12-31 17:58:23,649 - mmcls - INFO - Epoch [29][1200/5005] lr: 1.000e-01, eta: 8:49:56, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.3431 2022-12-31 17:58:32,280 - mmcls - INFO - Epoch [29][1300/5005] lr: 1.000e-01, eta: 8:49:47, time: 0.086, data_time: 0.021, memory: 1224, loss: 2.3613 2022-12-31 17:58:40,986 - mmcls - INFO - Epoch [29][1400/5005] lr: 1.000e-01, eta: 8:49:37, time: 0.087, data_time: 0.005, memory: 1224, loss: 2.3927 2022-12-31 17:58:49,596 - mmcls - INFO - Epoch [29][1500/5005] lr: 1.000e-01, eta: 8:49:28, time: 0.086, data_time: 0.005, memory: 1224, loss: 2.3641 2022-12-31 17:58:58,341 - mmcls - INFO - Epoch [29][1600/5005] lr: 1.000e-01, eta: 8:49:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3821 2022-12-31 17:59:07,211 - mmcls - INFO - Epoch [29][1700/5005] lr: 1.000e-01, eta: 8:49:10, time: 0.089, data_time: 0.003, memory: 1224, loss: 2.3641 2022-12-31 17:59:15,978 - mmcls - INFO - Epoch [29][1800/5005] lr: 1.000e-01, eta: 8:49:01, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4052 2022-12-31 17:59:24,647 - mmcls - INFO - Epoch [29][1900/5005] lr: 1.000e-01, eta: 8:48:52, time: 0.087, data_time: 0.028, memory: 1224, loss: 2.3832 2022-12-31 17:59:33,406 - mmcls - INFO - Epoch [29][2000/5005] lr: 1.000e-01, eta: 8:48:42, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3641 2022-12-31 17:59:42,529 - mmcls - INFO - Epoch [29][2100/5005] lr: 1.000e-01, eta: 8:48:34, time: 0.091, data_time: 0.000, memory: 1224, loss: 2.3607 2022-12-31 17:59:51,248 - mmcls - INFO - Epoch [29][2200/5005] lr: 1.000e-01, eta: 8:48:25, time: 0.087, data_time: 0.004, memory: 1224, loss: 2.3833 2022-12-31 18:00:00,176 - mmcls - INFO - Epoch [29][2300/5005] lr: 1.000e-01, eta: 8:48:16, time: 0.089, data_time: 0.012, memory: 1224, loss: 2.3675 2022-12-31 18:00:09,094 - mmcls - INFO - Epoch [29][2400/5005] lr: 1.000e-01, eta: 8:48:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.4205 2022-12-31 18:00:17,817 - mmcls - INFO - Epoch [29][2500/5005] lr: 1.000e-01, eta: 8:47:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3878 2022-12-31 18:00:26,909 - mmcls - INFO - Epoch [29][2600/5005] lr: 1.000e-01, eta: 8:47:50, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.3894 2022-12-31 18:00:35,696 - mmcls - INFO - Epoch [29][2700/5005] lr: 1.000e-01, eta: 8:47:41, time: 0.088, data_time: 0.003, memory: 1224, loss: 2.4277 2022-12-31 18:00:44,380 - mmcls - INFO - Epoch [29][2800/5005] lr: 1.000e-01, eta: 8:47:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3920 2022-12-31 18:00:52,980 - mmcls - INFO - Epoch [29][2900/5005] lr: 1.000e-01, eta: 8:47:23, time: 0.086, data_time: 0.002, memory: 1224, loss: 2.3986 2022-12-31 18:01:02,037 - mmcls - INFO - Epoch [29][3000/5005] lr: 1.000e-01, eta: 8:47:14, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.4229 2022-12-31 18:01:10,575 - mmcls - INFO - Epoch [29][3100/5005] lr: 1.000e-01, eta: 8:47:04, time: 0.085, data_time: 0.029, memory: 1224, loss: 2.4171 2022-12-31 18:01:19,329 - mmcls - INFO - Epoch [29][3200/5005] lr: 1.000e-01, eta: 8:46:55, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.4014 2022-12-31 18:01:28,301 - mmcls - INFO - Epoch [29][3300/5005] lr: 1.000e-01, eta: 8:46:47, time: 0.090, data_time: 0.003, memory: 1224, loss: 2.3822 2022-12-31 18:01:37,260 - mmcls - INFO - Epoch [29][3400/5005] lr: 1.000e-01, eta: 8:46:38, time: 0.090, data_time: 0.002, memory: 1224, loss: 2.3772 2022-12-31 18:01:46,034 - mmcls - INFO - Epoch [29][3500/5005] lr: 1.000e-01, eta: 8:46:29, time: 0.088, data_time: 0.004, memory: 1224, loss: 2.3673 2022-12-31 18:01:54,903 - mmcls - INFO - Epoch [29][3600/5005] lr: 1.000e-01, eta: 8:46:20, time: 0.089, data_time: 0.028, memory: 1224, loss: 2.3963 2022-12-31 18:02:03,572 - mmcls - INFO - Epoch [29][3700/5005] lr: 1.000e-01, eta: 8:46:11, time: 0.087, data_time: 0.021, memory: 1224, loss: 2.3870 2022-12-31 18:02:12,486 - mmcls - INFO - Epoch [29][3800/5005] lr: 1.000e-01, eta: 8:46:02, time: 0.089, data_time: 0.004, memory: 1224, loss: 2.3716 2022-12-31 18:02:20,952 - mmcls - INFO - Epoch [29][3900/5005] lr: 1.000e-01, eta: 8:45:53, time: 0.085, data_time: 0.003, memory: 1224, loss: 2.3957 2022-12-31 18:02:29,552 - mmcls - INFO - Epoch [29][4000/5005] lr: 1.000e-01, eta: 8:45:43, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.4138 2022-12-31 18:02:38,364 - mmcls - INFO - Epoch [29][4100/5005] lr: 1.000e-01, eta: 8:45:34, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.4098 2022-12-31 18:02:47,162 - mmcls - INFO - Epoch [29][4200/5005] lr: 1.000e-01, eta: 8:45:25, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3983 2022-12-31 18:02:55,803 - mmcls - INFO - Epoch [29][4300/5005] lr: 1.000e-01, eta: 8:45:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4071 2022-12-31 18:03:04,497 - mmcls - INFO - Epoch [29][4400/5005] lr: 1.000e-01, eta: 8:45:07, time: 0.087, data_time: 0.031, memory: 1224, loss: 2.4130 2022-12-31 18:03:13,332 - mmcls - INFO - Epoch [29][4500/5005] lr: 1.000e-01, eta: 8:44:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4052 2022-12-31 18:03:22,301 - mmcls - INFO - Epoch [29][4600/5005] lr: 1.000e-01, eta: 8:44:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.4086 2022-12-31 18:03:31,008 - mmcls - INFO - Epoch [29][4700/5005] lr: 1.000e-01, eta: 8:44:40, time: 0.087, data_time: 0.002, memory: 1224, loss: 2.3969 2022-12-31 18:03:39,771 - mmcls - INFO - Epoch [29][4800/5005] lr: 1.000e-01, eta: 8:44:31, time: 0.088, data_time: 0.016, memory: 1224, loss: 2.3941 2022-12-31 18:03:48,702 - mmcls - INFO - Epoch [29][4900/5005] lr: 1.000e-01, eta: 8:44:22, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.3801 2022-12-31 18:03:57,347 - mmcls - INFO - Epoch [29][5000/5005] lr: 1.000e-01, eta: 8:44:13, time: 0.086, data_time: 0.001, memory: 1224, loss: 2.4149 2022-12-31 18:04:20,026 - mmcls - INFO - Epoch(val) [29][196] accuracy_top-1: 51.2260, accuracy_top-5: 76.8380 2022-12-31 18:04:30,929 - mmcls - INFO - Epoch [30][100/5005] lr: 1.000e-01, eta: 8:44:07, time: 0.109, data_time: 0.029, memory: 1224, loss: 2.3656 2022-12-31 18:04:39,624 - mmcls - INFO - Epoch [30][200/5005] lr: 1.000e-01, eta: 8:43:58, time: 0.087, data_time: 0.008, memory: 1224, loss: 2.3131 2022-12-31 18:04:48,279 - mmcls - INFO - Epoch [30][300/5005] lr: 1.000e-01, eta: 8:43:49, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.3301 2022-12-31 18:04:57,177 - mmcls - INFO - Epoch [30][400/5005] lr: 1.000e-01, eta: 8:43:40, time: 0.089, data_time: 0.007, memory: 1224, loss: 2.3250 2022-12-31 18:05:06,373 - mmcls - INFO - Epoch [30][500/5005] lr: 1.000e-01, eta: 8:43:32, time: 0.092, data_time: 0.009, memory: 1224, loss: 2.3337 2022-12-31 18:05:15,159 - mmcls - INFO - Epoch [30][600/5005] lr: 1.000e-01, eta: 8:43:23, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.3683 2022-12-31 18:05:23,973 - mmcls - INFO - Epoch [30][700/5005] lr: 1.000e-01, eta: 8:43:14, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.3651 2022-12-31 18:05:32,830 - mmcls - INFO - Epoch [30][800/5005] lr: 1.000e-01, eta: 8:43:05, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.3384 2022-12-31 18:05:41,573 - mmcls - INFO - Epoch [30][900/5005] lr: 1.000e-01, eta: 8:42:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3636 2022-12-31 18:05:50,281 - mmcls - INFO - Epoch [30][1000/5005] lr: 1.000e-01, eta: 8:42:47, time: 0.087, data_time: 0.009, memory: 1224, loss: 2.3892 2022-12-31 18:05:58,782 - mmcls - INFO - Epoch [30][1100/5005] lr: 1.000e-01, eta: 8:42:37, time: 0.085, data_time: 0.008, memory: 1224, loss: 2.3608 2022-12-31 18:06:07,784 - mmcls - INFO - Epoch [30][1200/5005] lr: 1.000e-01, eta: 8:42:29, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.3732 2022-12-31 18:06:16,435 - mmcls - INFO - Epoch [30][1300/5005] lr: 1.000e-01, eta: 8:42:19, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.3888 2022-12-31 18:06:25,164 - mmcls - INFO - Epoch [30][1400/5005] lr: 1.000e-01, eta: 8:42:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3808 2022-12-31 18:06:33,820 - mmcls - INFO - Epoch [30][1500/5005] lr: 1.000e-01, eta: 8:42:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.3909 2022-12-31 18:06:42,368 - mmcls - INFO - Epoch [30][1600/5005] lr: 1.000e-01, eta: 8:41:51, time: 0.085, data_time: 0.001, memory: 1224, loss: 2.3648 2022-12-31 18:06:51,416 - mmcls - INFO - Epoch [30][1700/5005] lr: 1.000e-01, eta: 8:41:43, time: 0.091, data_time: 0.002, memory: 1224, loss: 2.3581 2022-12-31 18:07:00,403 - mmcls - INFO - Epoch [30][1800/5005] lr: 1.000e-01, eta: 8:41:34, time: 0.090, data_time: 0.014, memory: 1224, loss: 2.3694 2022-12-31 18:07:09,318 - mmcls - INFO - Epoch [30][1900/5005] lr: 1.000e-01, eta: 8:41:26, time: 0.089, data_time: 0.020, memory: 1224, loss: 2.3672 2022-12-31 18:07:18,164 - mmcls - INFO - Epoch [30][2000/5005] lr: 1.000e-01, eta: 8:41:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 2.3870 2022-12-31 18:07:26,801 - mmcls - INFO - Epoch [30][2100/5005] lr: 1.000e-01, eta: 8:41:07, time: 0.086, data_time: 0.007, memory: 1224, loss: 2.3942 2022-12-31 18:07:35,570 - mmcls - INFO - Epoch [30][2200/5005] lr: 1.000e-01, eta: 8:40:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.4325 2022-12-31 18:07:44,248 - mmcls - INFO - Epoch [30][2300/5005] lr: 1.000e-01, eta: 8:40:49, time: 0.087, data_time: 0.011, memory: 1224, loss: 2.3797 2022-12-31 18:07:53,193 - mmcls - INFO - Epoch [30][2400/5005] lr: 1.000e-01, eta: 8:40:41, time: 0.089, data_time: 0.005, memory: 1224, loss: 2.3728 2022-12-31 18:08:02,071 - mmcls - INFO - Epoch [30][2500/5005] lr: 1.000e-01, eta: 8:40:32, time: 0.089, data_time: 0.009, memory: 1224, loss: 2.3818 2022-12-31 18:08:10,876 - mmcls - INFO - Epoch [30][2600/5005] lr: 1.000e-01, eta: 8:40:23, time: 0.088, data_time: 0.022, memory: 1224, loss: 2.4185 2022-12-31 18:08:19,968 - mmcls - INFO - Epoch [30][2700/5005] lr: 1.000e-01, eta: 8:40:14, time: 0.091, data_time: 0.027, memory: 1224, loss: 2.3837 2022-12-31 18:08:28,928 - mmcls - INFO - Epoch [30][2800/5005] lr: 1.000e-01, eta: 8:40:06, time: 0.090, data_time: 0.005, memory: 1224, loss: 2.3684 2022-12-31 18:08:37,711 - mmcls - INFO - Epoch [30][2900/5005] lr: 1.000e-01, eta: 8:39:57, time: 0.088, data_time: 0.006, memory: 1224, loss: 2.3910 2022-12-31 18:08:46,777 - mmcls - INFO - Epoch [30][3000/5005] lr: 1.000e-01, eta: 8:39:49, time: 0.091, data_time: 0.012, memory: 1224, loss: 2.3943 2022-12-31 18:08:55,481 - mmcls - INFO - Epoch [30][3100/5005] lr: 1.000e-01, eta: 8:39:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4051 2022-12-31 18:09:04,742 - mmcls - INFO - Epoch [30][3200/5005] lr: 1.000e-01, eta: 8:39:31, time: 0.093, data_time: 0.002, memory: 1224, loss: 2.3875 2022-12-31 18:09:13,779 - mmcls - INFO - Epoch [30][3300/5005] lr: 1.000e-01, eta: 8:39:23, time: 0.090, data_time: 0.031, memory: 1224, loss: 2.4099 2022-12-31 18:09:22,618 - mmcls - INFO - Epoch [30][3400/5005] lr: 1.000e-01, eta: 8:39:14, time: 0.088, data_time: 0.013, memory: 1224, loss: 2.3638 2022-12-31 18:09:31,417 - mmcls - INFO - Epoch [30][3500/5005] lr: 1.000e-01, eta: 8:39:05, time: 0.088, data_time: 0.010, memory: 1224, loss: 2.3898 2022-12-31 18:09:40,404 - mmcls - INFO - Epoch [30][3600/5005] lr: 1.000e-01, eta: 8:38:57, time: 0.090, data_time: 0.013, memory: 1224, loss: 2.3902 2022-12-31 18:09:49,065 - mmcls - INFO - Epoch [30][3700/5005] lr: 1.000e-01, eta: 8:38:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 2.4024 2022-12-31 18:09:57,788 - mmcls - INFO - Epoch [30][3800/5005] lr: 1.000e-01, eta: 8:38:38, time: 0.087, data_time: 0.024, memory: 1224, loss: 2.3889 2022-12-31 18:10:06,292 - mmcls - INFO - Epoch [30][3900/5005] lr: 1.000e-01, eta: 8:38:29, time: 0.085, data_time: 0.024, memory: 1224, loss: 2.3903 2022-12-31 18:10:15,201 - mmcls - INFO - Epoch [30][4000/5005] lr: 1.000e-01, eta: 8:38:20, time: 0.089, data_time: 0.014, memory: 1224, loss: 2.4152 2022-12-31 18:10:23,596 - mmcls - INFO - Epoch [30][4100/5005] lr: 1.000e-01, eta: 8:38:10, time: 0.084, data_time: 0.017, memory: 1224, loss: 2.3904 2022-12-31 18:10:32,131 - mmcls - INFO - Epoch [30][4200/5005] lr: 1.000e-01, eta: 8:38:00, time: 0.085, data_time: 0.029, memory: 1224, loss: 2.3939 2022-12-31 18:10:40,685 - mmcls - INFO - Epoch [30][4300/5005] lr: 1.000e-01, eta: 8:37:51, time: 0.086, data_time: 0.004, memory: 1224, loss: 2.3993 2022-12-31 18:10:49,539 - mmcls - INFO - Epoch [30][4400/5005] lr: 1.000e-01, eta: 8:37:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 2.3824 2022-12-31 18:10:58,392 - mmcls - INFO - Epoch [30][4500/5005] lr: 1.000e-01, eta: 8:37:33, time: 0.089, data_time: 0.010, memory: 1224, loss: 2.3962 2022-12-31 18:11:07,506 - mmcls - INFO - Epoch [30][4600/5005] lr: 1.000e-01, eta: 8:37:25, time: 0.091, data_time: 0.005, memory: 1224, loss: 2.3824 2022-12-31 18:11:16,477 - mmcls - INFO - Epoch [30][4700/5005] lr: 1.000e-01, eta: 8:37:16, time: 0.090, data_time: 0.010, memory: 1224, loss: 2.3793 2022-12-31 18:11:25,106 - mmcls - INFO - Epoch [30][4800/5005] lr: 1.000e-01, eta: 8:37:07, time: 0.086, data_time: 0.014, memory: 1224, loss: 2.3938 2022-12-31 18:11:34,098 - mmcls - INFO - Epoch [30][4900/5005] lr: 1.000e-01, eta: 8:36:58, time: 0.090, data_time: 0.008, memory: 1224, loss: 2.4065 2022-12-31 18:11:42,688 - mmcls - INFO - Epoch [30][5000/5005] lr: 1.000e-01, eta: 8:36:49, time: 0.086, data_time: 0.003, memory: 1224, loss: 2.4086 2022-12-31 18:12:05,584 - mmcls - INFO - Epoch(val) [30][196] accuracy_top-1: 52.4360, accuracy_top-5: 77.7300 2022-12-31 18:12:16,418 - mmcls - INFO - Epoch [31][100/5005] lr: 1.000e-02, eta: 8:36:43, time: 0.108, data_time: 0.044, memory: 1224, loss: 2.2000 2022-12-31 18:12:25,443 - mmcls - INFO - Epoch [31][200/5005] lr: 1.000e-02, eta: 8:36:35, time: 0.090, data_time: 0.001, memory: 1224, loss: 2.0742 2022-12-31 18:12:34,302 - mmcls - INFO - Epoch [31][300/5005] lr: 1.000e-02, eta: 8:36:26, time: 0.089, data_time: 0.002, memory: 1224, loss: 2.0043 2022-12-31 18:12:42,957 - mmcls - INFO - Epoch [31][400/5005] lr: 1.000e-02, eta: 8:36:16, time: 0.087, data_time: 0.026, memory: 1224, loss: 2.0216 2022-12-31 18:12:51,758 - mmcls - INFO - Epoch [31][500/5005] lr: 1.000e-02, eta: 8:36:08, time: 0.088, data_time: 0.024, memory: 1224, loss: 2.0123 2022-12-31 18:13:00,203 - mmcls - INFO - Epoch [31][600/5005] lr: 1.000e-02, eta: 8:35:58, time: 0.084, data_time: 0.017, memory: 1224, loss: 2.0126 2022-12-31 18:13:09,016 - mmcls - INFO - Epoch [31][700/5005] lr: 1.000e-02, eta: 8:35:49, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.9754 2022-12-31 18:13:17,746 - mmcls - INFO - Epoch [31][800/5005] lr: 1.000e-02, eta: 8:35:40, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.9608 2022-12-31 18:13:26,641 - mmcls - INFO - Epoch [31][900/5005] lr: 1.000e-02, eta: 8:35:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.9392 2022-12-31 18:13:35,849 - mmcls - INFO - Epoch [31][1000/5005] lr: 1.000e-02, eta: 8:35:23, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.9639 2022-12-31 18:13:44,634 - mmcls - INFO - Epoch [31][1100/5005] lr: 1.000e-02, eta: 8:35:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.9418 2022-12-31 18:13:53,230 - mmcls - INFO - Epoch [31][1200/5005] lr: 1.000e-02, eta: 8:35:04, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9103 2022-12-31 18:14:02,352 - mmcls - INFO - Epoch [31][1300/5005] lr: 1.000e-02, eta: 8:34:56, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.9392 2022-12-31 18:14:10,926 - mmcls - INFO - Epoch [31][1400/5005] lr: 1.000e-02, eta: 8:34:47, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.9278 2022-12-31 18:14:19,934 - mmcls - INFO - Epoch [31][1500/5005] lr: 1.000e-02, eta: 8:34:38, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.9195 2022-12-31 18:14:28,613 - mmcls - INFO - Epoch [31][1600/5005] lr: 1.000e-02, eta: 8:34:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.9164 2022-12-31 18:14:37,339 - mmcls - INFO - Epoch [31][1700/5005] lr: 1.000e-02, eta: 8:34:20, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.8955 2022-12-31 18:14:46,029 - mmcls - INFO - Epoch [31][1800/5005] lr: 1.000e-02, eta: 8:34:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.9211 2022-12-31 18:14:54,818 - mmcls - INFO - Epoch [31][1900/5005] lr: 1.000e-02, eta: 8:34:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.8903 2022-12-31 18:15:03,973 - mmcls - INFO - Epoch [31][2000/5005] lr: 1.000e-02, eta: 8:33:53, time: 0.092, data_time: 0.013, memory: 1224, loss: 1.8817 2022-12-31 18:15:12,656 - mmcls - INFO - Epoch [31][2100/5005] lr: 1.000e-02, eta: 8:33:44, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.9021 2022-12-31 18:15:21,508 - mmcls - INFO - Epoch [31][2200/5005] lr: 1.000e-02, eta: 8:33:35, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.8682 2022-12-31 18:15:30,219 - mmcls - INFO - Epoch [31][2300/5005] lr: 1.000e-02, eta: 8:33:26, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.8828 2022-12-31 18:15:38,911 - mmcls - INFO - Epoch [31][2400/5005] lr: 1.000e-02, eta: 8:33:17, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.8530 2022-12-31 18:15:47,539 - mmcls - INFO - Epoch [31][2500/5005] lr: 1.000e-02, eta: 8:33:08, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.8704 2022-12-31 18:15:56,408 - mmcls - INFO - Epoch [31][2600/5005] lr: 1.000e-02, eta: 8:32:59, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.8586 2022-12-31 18:16:05,290 - mmcls - INFO - Epoch [31][2700/5005] lr: 1.000e-02, eta: 8:32:50, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.8601 2022-12-31 18:16:14,164 - mmcls - INFO - Epoch [31][2800/5005] lr: 1.000e-02, eta: 8:32:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.8742 2022-12-31 18:16:22,746 - mmcls - INFO - Epoch [31][2900/5005] lr: 1.000e-02, eta: 8:32:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.8539 2022-12-31 18:16:31,515 - mmcls - INFO - Epoch [31][3000/5005] lr: 1.000e-02, eta: 8:32:23, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.8504 2022-12-31 18:16:40,451 - mmcls - INFO - Epoch [31][3100/5005] lr: 1.000e-02, eta: 8:32:14, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.8531 2022-12-31 18:16:49,148 - mmcls - INFO - Epoch [31][3200/5005] lr: 1.000e-02, eta: 8:32:05, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.8508 2022-12-31 18:16:58,133 - mmcls - INFO - Epoch [31][3300/5005] lr: 1.000e-02, eta: 8:31:56, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.8590 2022-12-31 18:17:06,946 - mmcls - INFO - Epoch [31][3400/5005] lr: 1.000e-02, eta: 8:31:47, time: 0.088, data_time: 0.036, memory: 1224, loss: 1.8539 2022-12-31 18:17:15,935 - mmcls - INFO - Epoch [31][3500/5005] lr: 1.000e-02, eta: 8:31:39, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.8660 2022-12-31 18:17:24,616 - mmcls - INFO - Epoch [31][3600/5005] lr: 1.000e-02, eta: 8:31:30, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.8398 2022-12-31 18:17:33,193 - mmcls - INFO - Epoch [31][3700/5005] lr: 1.000e-02, eta: 8:31:20, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.8505 2022-12-31 18:17:42,139 - mmcls - INFO - Epoch [31][3800/5005] lr: 1.000e-02, eta: 8:31:11, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.8491 2022-12-31 18:17:51,098 - mmcls - INFO - Epoch [31][3900/5005] lr: 1.000e-02, eta: 8:31:03, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.8441 2022-12-31 18:17:59,987 - mmcls - INFO - Epoch [31][4000/5005] lr: 1.000e-02, eta: 8:30:54, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.8312 2022-12-31 18:18:08,617 - mmcls - INFO - Epoch [31][4100/5005] lr: 1.000e-02, eta: 8:30:45, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.8304 2022-12-31 18:18:17,209 - mmcls - INFO - Epoch [31][4200/5005] lr: 1.000e-02, eta: 8:30:35, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.8551 2022-12-31 18:18:25,865 - mmcls - INFO - Epoch [31][4300/5005] lr: 1.000e-02, eta: 8:30:26, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.8363 2022-12-31 18:18:35,159 - mmcls - INFO - Epoch [31][4400/5005] lr: 1.000e-02, eta: 8:30:18, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.8273 2022-12-31 18:18:44,010 - mmcls - INFO - Epoch [31][4500/5005] lr: 1.000e-02, eta: 8:30:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.8143 2022-12-31 18:18:52,872 - mmcls - INFO - Epoch [31][4600/5005] lr: 1.000e-02, eta: 8:30:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.8376 2022-12-31 18:19:01,958 - mmcls - INFO - Epoch [31][4700/5005] lr: 1.000e-02, eta: 8:29:52, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.8384 2022-12-31 18:19:10,672 - mmcls - INFO - Epoch [31][4800/5005] lr: 1.000e-02, eta: 8:29:43, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.8179 2022-12-31 18:19:19,601 - mmcls - INFO - Epoch [31][4900/5005] lr: 1.000e-02, eta: 8:29:34, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.8179 2022-12-31 18:19:28,425 - mmcls - INFO - Epoch [31][5000/5005] lr: 1.000e-02, eta: 8:29:25, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.8281 2022-12-31 18:19:51,202 - mmcls - INFO - Epoch(val) [31][196] accuracy_top-1: 64.1660, accuracy_top-5: 85.8000 2022-12-31 18:20:02,369 - mmcls - INFO - Epoch [32][100/5005] lr: 1.000e-02, eta: 8:29:20, time: 0.111, data_time: 0.029, memory: 1224, loss: 1.7959 2022-12-31 18:20:11,166 - mmcls - INFO - Epoch [32][200/5005] lr: 1.000e-02, eta: 8:29:11, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7959 2022-12-31 18:20:19,941 - mmcls - INFO - Epoch [32][300/5005] lr: 1.000e-02, eta: 8:29:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.7833 2022-12-31 18:20:28,869 - mmcls - INFO - Epoch [32][400/5005] lr: 1.000e-02, eta: 8:28:54, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.7887 2022-12-31 18:20:37,805 - mmcls - INFO - Epoch [32][500/5005] lr: 1.000e-02, eta: 8:28:45, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.8076 2022-12-31 18:20:46,590 - mmcls - INFO - Epoch [32][600/5005] lr: 1.000e-02, eta: 8:28:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7730 2022-12-31 18:20:55,474 - mmcls - INFO - Epoch [32][700/5005] lr: 1.000e-02, eta: 8:28:27, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.7822 2022-12-31 18:21:04,373 - mmcls - INFO - Epoch [32][800/5005] lr: 1.000e-02, eta: 8:28:18, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7964 2022-12-31 18:21:12,933 - mmcls - INFO - Epoch [32][900/5005] lr: 1.000e-02, eta: 8:28:09, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.7899 2022-12-31 18:21:21,826 - mmcls - INFO - Epoch [32][1000/5005] lr: 1.000e-02, eta: 8:28:00, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.8128 2022-12-31 18:21:30,638 - mmcls - INFO - Epoch [32][1100/5005] lr: 1.000e-02, eta: 8:27:51, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.7668 2022-12-31 18:21:39,335 - mmcls - INFO - Epoch [32][1200/5005] lr: 1.000e-02, eta: 8:27:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7968 2022-12-31 18:21:47,855 - mmcls - INFO - Epoch [32][1300/5005] lr: 1.000e-02, eta: 8:27:32, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.7845 2022-12-31 18:21:56,765 - mmcls - INFO - Epoch [32][1400/5005] lr: 1.000e-02, eta: 8:27:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7592 2022-12-31 18:22:05,798 - mmcls - INFO - Epoch [32][1500/5005] lr: 1.000e-02, eta: 8:27:15, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.7805 2022-12-31 18:22:14,230 - mmcls - INFO - Epoch [32][1600/5005] lr: 1.000e-02, eta: 8:27:05, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.7744 2022-12-31 18:22:22,990 - mmcls - INFO - Epoch [32][1700/5005] lr: 1.000e-02, eta: 8:26:56, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.7779 2022-12-31 18:22:31,798 - mmcls - INFO - Epoch [32][1800/5005] lr: 1.000e-02, eta: 8:26:47, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.7829 2022-12-31 18:22:40,677 - mmcls - INFO - Epoch [32][1900/5005] lr: 1.000e-02, eta: 8:26:39, time: 0.089, data_time: 0.036, memory: 1224, loss: 1.7918 2022-12-31 18:22:49,320 - mmcls - INFO - Epoch [32][2000/5005] lr: 1.000e-02, eta: 8:26:29, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7878 2022-12-31 18:22:58,289 - mmcls - INFO - Epoch [32][2100/5005] lr: 1.000e-02, eta: 8:26:21, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.7773 2022-12-31 18:23:06,930 - mmcls - INFO - Epoch [32][2200/5005] lr: 1.000e-02, eta: 8:26:11, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7646 2022-12-31 18:23:16,177 - mmcls - INFO - Epoch [32][2300/5005] lr: 1.000e-02, eta: 8:26:03, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.7944 2022-12-31 18:23:25,162 - mmcls - INFO - Epoch [32][2400/5005] lr: 1.000e-02, eta: 8:25:55, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.7846 2022-12-31 18:23:33,846 - mmcls - INFO - Epoch [32][2500/5005] lr: 1.000e-02, eta: 8:25:46, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.7716 2022-12-31 18:23:42,693 - mmcls - INFO - Epoch [32][2600/5005] lr: 1.000e-02, eta: 8:25:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7998 2022-12-31 18:23:51,441 - mmcls - INFO - Epoch [32][2700/5005] lr: 1.000e-02, eta: 8:25:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.8080 2022-12-31 18:24:00,649 - mmcls - INFO - Epoch [32][2800/5005] lr: 1.000e-02, eta: 8:25:20, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.7491 2022-12-31 18:24:09,642 - mmcls - INFO - Epoch [32][2900/5005] lr: 1.000e-02, eta: 8:25:11, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.7656 2022-12-31 18:24:18,371 - mmcls - INFO - Epoch [32][3000/5005] lr: 1.000e-02, eta: 8:25:02, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.7801 2022-12-31 18:24:27,074 - mmcls - INFO - Epoch [32][3100/5005] lr: 1.000e-02, eta: 8:24:53, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.7934 2022-12-31 18:24:35,760 - mmcls - INFO - Epoch [32][3200/5005] lr: 1.000e-02, eta: 8:24:44, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.7832 2022-12-31 18:24:44,344 - mmcls - INFO - Epoch [32][3300/5005] lr: 1.000e-02, eta: 8:24:34, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.7744 2022-12-31 18:24:53,344 - mmcls - INFO - Epoch [32][3400/5005] lr: 1.000e-02, eta: 8:24:26, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.7735 2022-12-31 18:25:01,972 - mmcls - INFO - Epoch [32][3500/5005] lr: 1.000e-02, eta: 8:24:16, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7713 2022-12-31 18:25:10,778 - mmcls - INFO - Epoch [32][3600/5005] lr: 1.000e-02, eta: 8:24:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7737 2022-12-31 18:25:19,415 - mmcls - INFO - Epoch [32][3700/5005] lr: 1.000e-02, eta: 8:23:58, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.7742 2022-12-31 18:25:28,274 - mmcls - INFO - Epoch [32][3800/5005] lr: 1.000e-02, eta: 8:23:49, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.7522 2022-12-31 18:25:37,082 - mmcls - INFO - Epoch [32][3900/5005] lr: 1.000e-02, eta: 8:23:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7763 2022-12-31 18:25:45,966 - mmcls - INFO - Epoch [32][4000/5005] lr: 1.000e-02, eta: 8:23:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7584 2022-12-31 18:25:54,667 - mmcls - INFO - Epoch [32][4100/5005] lr: 1.000e-02, eta: 8:23:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7511 2022-12-31 18:26:03,340 - mmcls - INFO - Epoch [32][4200/5005] lr: 1.000e-02, eta: 8:23:13, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7572 2022-12-31 18:26:12,019 - mmcls - INFO - Epoch [32][4300/5005] lr: 1.000e-02, eta: 8:23:04, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7512 2022-12-31 18:26:20,597 - mmcls - INFO - Epoch [32][4400/5005] lr: 1.000e-02, eta: 8:22:54, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.7564 2022-12-31 18:26:29,503 - mmcls - INFO - Epoch [32][4500/5005] lr: 1.000e-02, eta: 8:22:46, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.7462 2022-12-31 18:26:38,564 - mmcls - INFO - Epoch [32][4600/5005] lr: 1.000e-02, eta: 8:22:37, time: 0.091, data_time: 0.026, memory: 1224, loss: 1.7699 2022-12-31 18:26:47,609 - mmcls - INFO - Epoch [32][4700/5005] lr: 1.000e-02, eta: 8:22:29, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.7689 2022-12-31 18:26:56,283 - mmcls - INFO - Epoch [32][4800/5005] lr: 1.000e-02, eta: 8:22:20, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.7575 2022-12-31 18:27:05,158 - mmcls - INFO - Epoch [32][4900/5005] lr: 1.000e-02, eta: 8:22:11, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.7552 2022-12-31 18:27:14,358 - mmcls - INFO - Epoch [32][5000/5005] lr: 1.000e-02, eta: 8:22:03, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.7786 2022-12-31 18:27:37,053 - mmcls - INFO - Epoch(val) [32][196] accuracy_top-1: 65.1880, accuracy_top-5: 86.3360 2022-12-31 18:27:48,063 - mmcls - INFO - Epoch [33][100/5005] lr: 1.000e-02, eta: 8:21:57, time: 0.110, data_time: 0.057, memory: 1224, loss: 1.7436 2022-12-31 18:27:56,866 - mmcls - INFO - Epoch [33][200/5005] lr: 1.000e-02, eta: 8:21:48, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7166 2022-12-31 18:28:05,469 - mmcls - INFO - Epoch [33][300/5005] lr: 1.000e-02, eta: 8:21:39, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7425 2022-12-31 18:28:14,740 - mmcls - INFO - Epoch [33][400/5005] lr: 1.000e-02, eta: 8:21:31, time: 0.093, data_time: 0.019, memory: 1224, loss: 1.7259 2022-12-31 18:28:23,591 - mmcls - INFO - Epoch [33][500/5005] lr: 1.000e-02, eta: 8:21:22, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7348 2022-12-31 18:28:32,694 - mmcls - INFO - Epoch [33][600/5005] lr: 1.000e-02, eta: 8:21:13, time: 0.091, data_time: 0.037, memory: 1224, loss: 1.7341 2022-12-31 18:28:41,534 - mmcls - INFO - Epoch [33][700/5005] lr: 1.000e-02, eta: 8:21:05, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.7379 2022-12-31 18:28:50,238 - mmcls - INFO - Epoch [33][800/5005] lr: 1.000e-02, eta: 8:20:55, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7513 2022-12-31 18:28:58,747 - mmcls - INFO - Epoch [33][900/5005] lr: 1.000e-02, eta: 8:20:46, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.7262 2022-12-31 18:29:07,919 - mmcls - INFO - Epoch [33][1000/5005] lr: 1.000e-02, eta: 8:20:38, time: 0.092, data_time: 0.019, memory: 1224, loss: 1.6994 2022-12-31 18:29:16,579 - mmcls - INFO - Epoch [33][1100/5005] lr: 1.000e-02, eta: 8:20:28, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7017 2022-12-31 18:29:25,318 - mmcls - INFO - Epoch [33][1200/5005] lr: 1.000e-02, eta: 8:20:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7344 2022-12-31 18:29:34,259 - mmcls - INFO - Epoch [33][1300/5005] lr: 1.000e-02, eta: 8:20:11, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.7551 2022-12-31 18:29:43,160 - mmcls - INFO - Epoch [33][1400/5005] lr: 1.000e-02, eta: 8:20:02, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.7143 2022-12-31 18:29:51,848 - mmcls - INFO - Epoch [33][1500/5005] lr: 1.000e-02, eta: 8:19:53, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.7361 2022-12-31 18:30:01,130 - mmcls - INFO - Epoch [33][1600/5005] lr: 1.000e-02, eta: 8:19:45, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.7610 2022-12-31 18:30:10,105 - mmcls - INFO - Epoch [33][1700/5005] lr: 1.000e-02, eta: 8:19:36, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.7321 2022-12-31 18:30:19,221 - mmcls - INFO - Epoch [33][1800/5005] lr: 1.000e-02, eta: 8:19:28, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.7265 2022-12-31 18:30:27,909 - mmcls - INFO - Epoch [33][1900/5005] lr: 1.000e-02, eta: 8:19:19, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.7236 2022-12-31 18:30:36,618 - mmcls - INFO - Epoch [33][2000/5005] lr: 1.000e-02, eta: 8:19:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7438 2022-12-31 18:30:45,355 - mmcls - INFO - Epoch [33][2100/5005] lr: 1.000e-02, eta: 8:19:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7444 2022-12-31 18:30:54,013 - mmcls - INFO - Epoch [33][2200/5005] lr: 1.000e-02, eta: 8:18:51, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.7250 2022-12-31 18:31:02,859 - mmcls - INFO - Epoch [33][2300/5005] lr: 1.000e-02, eta: 8:18:42, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.7142 2022-12-31 18:31:12,011 - mmcls - INFO - Epoch [33][2400/5005] lr: 1.000e-02, eta: 8:18:34, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.7258 2022-12-31 18:31:20,896 - mmcls - INFO - Epoch [33][2500/5005] lr: 1.000e-02, eta: 8:18:25, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7291 2022-12-31 18:31:30,291 - mmcls - INFO - Epoch [33][2600/5005] lr: 1.000e-02, eta: 8:18:17, time: 0.094, data_time: 0.011, memory: 1224, loss: 1.7225 2022-12-31 18:31:38,890 - mmcls - INFO - Epoch [33][2700/5005] lr: 1.000e-02, eta: 8:18:08, time: 0.086, data_time: 0.035, memory: 1224, loss: 1.7383 2022-12-31 18:31:47,929 - mmcls - INFO - Epoch [33][2800/5005] lr: 1.000e-02, eta: 8:18:00, time: 0.090, data_time: 0.028, memory: 1224, loss: 1.7441 2022-12-31 18:31:56,546 - mmcls - INFO - Epoch [33][2900/5005] lr: 1.000e-02, eta: 8:17:50, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.7209 2022-12-31 18:32:05,231 - mmcls - INFO - Epoch [33][3000/5005] lr: 1.000e-02, eta: 8:17:41, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7351 2022-12-31 18:32:13,978 - mmcls - INFO - Epoch [33][3100/5005] lr: 1.000e-02, eta: 8:17:32, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.7307 2022-12-31 18:32:22,725 - mmcls - INFO - Epoch [33][3200/5005] lr: 1.000e-02, eta: 8:17:23, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7226 2022-12-31 18:32:31,532 - mmcls - INFO - Epoch [33][3300/5005] lr: 1.000e-02, eta: 8:17:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7324 2022-12-31 18:32:40,662 - mmcls - INFO - Epoch [33][3400/5005] lr: 1.000e-02, eta: 8:17:06, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.7260 2022-12-31 18:32:49,256 - mmcls - INFO - Epoch [33][3500/5005] lr: 1.000e-02, eta: 8:16:56, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7236 2022-12-31 18:32:57,927 - mmcls - INFO - Epoch [33][3600/5005] lr: 1.000e-02, eta: 8:16:47, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.7300 2022-12-31 18:33:06,700 - mmcls - INFO - Epoch [33][3700/5005] lr: 1.000e-02, eta: 8:16:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7268 2022-12-31 18:33:15,304 - mmcls - INFO - Epoch [33][3800/5005] lr: 1.000e-02, eta: 8:16:29, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.7314 2022-12-31 18:33:23,924 - mmcls - INFO - Epoch [33][3900/5005] lr: 1.000e-02, eta: 8:16:19, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.7252 2022-12-31 18:33:32,648 - mmcls - INFO - Epoch [33][4000/5005] lr: 1.000e-02, eta: 8:16:10, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.7140 2022-12-31 18:33:41,583 - mmcls - INFO - Epoch [33][4100/5005] lr: 1.000e-02, eta: 8:16:02, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6926 2022-12-31 18:33:50,456 - mmcls - INFO - Epoch [33][4200/5005] lr: 1.000e-02, eta: 8:15:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7276 2022-12-31 18:33:59,350 - mmcls - INFO - Epoch [33][4300/5005] lr: 1.000e-02, eta: 8:15:44, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.7138 2022-12-31 18:34:08,163 - mmcls - INFO - Epoch [33][4400/5005] lr: 1.000e-02, eta: 8:15:35, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.7199 2022-12-31 18:34:16,939 - mmcls - INFO - Epoch [33][4500/5005] lr: 1.000e-02, eta: 8:15:26, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.7408 2022-12-31 18:34:25,696 - mmcls - INFO - Epoch [33][4600/5005] lr: 1.000e-02, eta: 8:15:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7341 2022-12-31 18:34:34,349 - mmcls - INFO - Epoch [33][4700/5005] lr: 1.000e-02, eta: 8:15:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7724 2022-12-31 18:34:43,285 - mmcls - INFO - Epoch [33][4800/5005] lr: 1.000e-02, eta: 8:14:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7411 2022-12-31 18:34:51,872 - mmcls - INFO - Epoch [33][4900/5005] lr: 1.000e-02, eta: 8:14:50, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.7271 2022-12-31 18:35:00,645 - mmcls - INFO - Epoch [33][5000/5005] lr: 1.000e-02, eta: 8:14:41, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.7071 2022-12-31 18:35:23,407 - mmcls - INFO - Epoch(val) [33][196] accuracy_top-1: 65.8040, accuracy_top-5: 86.8480 2022-12-31 18:35:34,526 - mmcls - INFO - Epoch [34][100/5005] lr: 1.000e-02, eta: 8:14:35, time: 0.111, data_time: 0.040, memory: 1224, loss: 1.6903 2022-12-31 18:35:43,417 - mmcls - INFO - Epoch [34][200/5005] lr: 1.000e-02, eta: 8:14:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6712 2022-12-31 18:35:52,629 - mmcls - INFO - Epoch [34][300/5005] lr: 1.000e-02, eta: 8:14:18, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.6885 2022-12-31 18:36:01,514 - mmcls - INFO - Epoch [34][400/5005] lr: 1.000e-02, eta: 8:14:09, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.7061 2022-12-31 18:36:10,208 - mmcls - INFO - Epoch [34][500/5005] lr: 1.000e-02, eta: 8:14:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.7066 2022-12-31 18:36:18,978 - mmcls - INFO - Epoch [34][600/5005] lr: 1.000e-02, eta: 8:13:51, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6967 2022-12-31 18:36:27,715 - mmcls - INFO - Epoch [34][700/5005] lr: 1.000e-02, eta: 8:13:42, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6960 2022-12-31 18:36:36,462 - mmcls - INFO - Epoch [34][800/5005] lr: 1.000e-02, eta: 8:13:33, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.7075 2022-12-31 18:36:45,057 - mmcls - INFO - Epoch [34][900/5005] lr: 1.000e-02, eta: 8:13:24, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6996 2022-12-31 18:36:53,653 - mmcls - INFO - Epoch [34][1000/5005] lr: 1.000e-02, eta: 8:13:14, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7062 2022-12-31 18:37:02,325 - mmcls - INFO - Epoch [34][1100/5005] lr: 1.000e-02, eta: 8:13:05, time: 0.087, data_time: 0.031, memory: 1224, loss: 1.7103 2022-12-31 18:37:11,122 - mmcls - INFO - Epoch [34][1200/5005] lr: 1.000e-02, eta: 8:12:56, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.7086 2022-12-31 18:37:19,920 - mmcls - INFO - Epoch [34][1300/5005] lr: 1.000e-02, eta: 8:12:47, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6714 2022-12-31 18:37:28,807 - mmcls - INFO - Epoch [34][1400/5005] lr: 1.000e-02, eta: 8:12:38, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6932 2022-12-31 18:37:37,573 - mmcls - INFO - Epoch [34][1500/5005] lr: 1.000e-02, eta: 8:12:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6976 2022-12-31 18:37:46,198 - mmcls - INFO - Epoch [34][1600/5005] lr: 1.000e-02, eta: 8:12:20, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.7050 2022-12-31 18:37:55,106 - mmcls - INFO - Epoch [34][1700/5005] lr: 1.000e-02, eta: 8:12:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7054 2022-12-31 18:38:03,665 - mmcls - INFO - Epoch [34][1800/5005] lr: 1.000e-02, eta: 8:12:02, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6942 2022-12-31 18:38:12,789 - mmcls - INFO - Epoch [34][1900/5005] lr: 1.000e-02, eta: 8:11:54, time: 0.091, data_time: 0.040, memory: 1224, loss: 1.7151 2022-12-31 18:38:21,440 - mmcls - INFO - Epoch [34][2000/5005] lr: 1.000e-02, eta: 8:11:44, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.7182 2022-12-31 18:38:30,001 - mmcls - INFO - Epoch [34][2100/5005] lr: 1.000e-02, eta: 8:11:35, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.7174 2022-12-31 18:38:38,768 - mmcls - INFO - Epoch [34][2200/5005] lr: 1.000e-02, eta: 8:11:26, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.7127 2022-12-31 18:38:47,738 - mmcls - INFO - Epoch [34][2300/5005] lr: 1.000e-02, eta: 8:11:17, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6925 2022-12-31 18:38:56,481 - mmcls - INFO - Epoch [34][2400/5005] lr: 1.000e-02, eta: 8:11:08, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.7044 2022-12-31 18:39:05,244 - mmcls - INFO - Epoch [34][2500/5005] lr: 1.000e-02, eta: 8:10:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.7056 2022-12-31 18:39:13,926 - mmcls - INFO - Epoch [34][2600/5005] lr: 1.000e-02, eta: 8:10:50, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.7054 2022-12-31 18:39:22,874 - mmcls - INFO - Epoch [34][2700/5005] lr: 1.000e-02, eta: 8:10:41, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.7284 2022-12-31 18:39:31,670 - mmcls - INFO - Epoch [34][2800/5005] lr: 1.000e-02, eta: 8:10:32, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6928 2022-12-31 18:39:40,871 - mmcls - INFO - Epoch [34][2900/5005] lr: 1.000e-02, eta: 8:10:24, time: 0.092, data_time: 0.019, memory: 1224, loss: 1.6969 2022-12-31 18:39:49,645 - mmcls - INFO - Epoch [34][3000/5005] lr: 1.000e-02, eta: 8:10:15, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6811 2022-12-31 18:39:58,467 - mmcls - INFO - Epoch [34][3100/5005] lr: 1.000e-02, eta: 8:10:06, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6879 2022-12-31 18:40:06,998 - mmcls - INFO - Epoch [34][3200/5005] lr: 1.000e-02, eta: 8:09:57, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6905 2022-12-31 18:40:16,349 - mmcls - INFO - Epoch [34][3300/5005] lr: 1.000e-02, eta: 8:09:49, time: 0.094, data_time: 0.028, memory: 1224, loss: 1.6957 2022-12-31 18:40:24,978 - mmcls - INFO - Epoch [34][3400/5005] lr: 1.000e-02, eta: 8:09:40, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7017 2022-12-31 18:40:33,976 - mmcls - INFO - Epoch [34][3500/5005] lr: 1.000e-02, eta: 8:09:31, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.7219 2022-12-31 18:40:42,897 - mmcls - INFO - Epoch [34][3600/5005] lr: 1.000e-02, eta: 8:09:22, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.7015 2022-12-31 18:40:51,798 - mmcls - INFO - Epoch [34][3700/5005] lr: 1.000e-02, eta: 8:09:14, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.7170 2022-12-31 18:41:00,676 - mmcls - INFO - Epoch [34][3800/5005] lr: 1.000e-02, eta: 8:09:05, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.7046 2022-12-31 18:41:09,378 - mmcls - INFO - Epoch [34][3900/5005] lr: 1.000e-02, eta: 8:08:56, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.7169 2022-12-31 18:41:18,122 - mmcls - INFO - Epoch [34][4000/5005] lr: 1.000e-02, eta: 8:08:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6930 2022-12-31 18:41:26,762 - mmcls - INFO - Epoch [34][4100/5005] lr: 1.000e-02, eta: 8:08:37, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.7137 2022-12-31 18:41:35,542 - mmcls - INFO - Epoch [34][4200/5005] lr: 1.000e-02, eta: 8:08:28, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7154 2022-12-31 18:41:44,485 - mmcls - INFO - Epoch [34][4300/5005] lr: 1.000e-02, eta: 8:08:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7175 2022-12-31 18:41:53,067 - mmcls - INFO - Epoch [34][4400/5005] lr: 1.000e-02, eta: 8:08:10, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6991 2022-12-31 18:42:01,668 - mmcls - INFO - Epoch [34][4500/5005] lr: 1.000e-02, eta: 8:08:01, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6939 2022-12-31 18:42:10,585 - mmcls - INFO - Epoch [34][4600/5005] lr: 1.000e-02, eta: 8:07:52, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6943 2022-12-31 18:42:19,505 - mmcls - INFO - Epoch [34][4700/5005] lr: 1.000e-02, eta: 8:07:43, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.7177 2022-12-31 18:42:28,444 - mmcls - INFO - Epoch [34][4800/5005] lr: 1.000e-02, eta: 8:07:35, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6961 2022-12-31 18:42:37,198 - mmcls - INFO - Epoch [34][4900/5005] lr: 1.000e-02, eta: 8:07:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.7003 2022-12-31 18:42:46,558 - mmcls - INFO - Epoch [34][5000/5005] lr: 1.000e-02, eta: 8:07:18, time: 0.094, data_time: 0.014, memory: 1224, loss: 1.7108 2022-12-31 18:43:09,599 - mmcls - INFO - Epoch(val) [34][196] accuracy_top-1: 66.2280, accuracy_top-5: 87.1200 2022-12-31 18:43:20,329 - mmcls - INFO - Epoch [35][100/5005] lr: 1.000e-02, eta: 8:07:11, time: 0.107, data_time: 0.035, memory: 1224, loss: 1.6794 2022-12-31 18:43:29,216 - mmcls - INFO - Epoch [35][200/5005] lr: 1.000e-02, eta: 8:07:03, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6676 2022-12-31 18:43:38,234 - mmcls - INFO - Epoch [35][300/5005] lr: 1.000e-02, eta: 8:06:54, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6774 2022-12-31 18:43:46,880 - mmcls - INFO - Epoch [35][400/5005] lr: 1.000e-02, eta: 8:06:45, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6544 2022-12-31 18:43:55,598 - mmcls - INFO - Epoch [35][500/5005] lr: 1.000e-02, eta: 8:06:36, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6928 2022-12-31 18:44:04,240 - mmcls - INFO - Epoch [35][600/5005] lr: 1.000e-02, eta: 8:06:26, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6566 2022-12-31 18:44:12,947 - mmcls - INFO - Epoch [35][700/5005] lr: 1.000e-02, eta: 8:06:17, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6814 2022-12-31 18:44:21,814 - mmcls - INFO - Epoch [35][800/5005] lr: 1.000e-02, eta: 8:06:08, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6834 2022-12-31 18:44:30,581 - mmcls - INFO - Epoch [35][900/5005] lr: 1.000e-02, eta: 8:05:59, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6997 2022-12-31 18:44:39,514 - mmcls - INFO - Epoch [35][1000/5005] lr: 1.000e-02, eta: 8:05:51, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6826 2022-12-31 18:44:48,257 - mmcls - INFO - Epoch [35][1100/5005] lr: 1.000e-02, eta: 8:05:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6714 2022-12-31 18:44:56,879 - mmcls - INFO - Epoch [35][1200/5005] lr: 1.000e-02, eta: 8:05:32, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6812 2022-12-31 18:45:05,569 - mmcls - INFO - Epoch [35][1300/5005] lr: 1.000e-02, eta: 8:05:23, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6701 2022-12-31 18:45:14,393 - mmcls - INFO - Epoch [35][1400/5005] lr: 1.000e-02, eta: 8:05:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6597 2022-12-31 18:45:23,077 - mmcls - INFO - Epoch [35][1500/5005] lr: 1.000e-02, eta: 8:05:05, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.7104 2022-12-31 18:45:32,081 - mmcls - INFO - Epoch [35][1600/5005] lr: 1.000e-02, eta: 8:04:57, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6885 2022-12-31 18:45:40,951 - mmcls - INFO - Epoch [35][1700/5005] lr: 1.000e-02, eta: 8:04:48, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6873 2022-12-31 18:45:50,252 - mmcls - INFO - Epoch [35][1800/5005] lr: 1.000e-02, eta: 8:04:40, time: 0.093, data_time: 0.017, memory: 1224, loss: 1.6750 2022-12-31 18:45:58,773 - mmcls - INFO - Epoch [35][1900/5005] lr: 1.000e-02, eta: 8:04:30, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6811 2022-12-31 18:46:07,266 - mmcls - INFO - Epoch [35][2000/5005] lr: 1.000e-02, eta: 8:04:21, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.7000 2022-12-31 18:46:16,042 - mmcls - INFO - Epoch [35][2100/5005] lr: 1.000e-02, eta: 8:04:12, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6987 2022-12-31 18:46:25,025 - mmcls - INFO - Epoch [35][2200/5005] lr: 1.000e-02, eta: 8:04:03, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6755 2022-12-31 18:46:33,521 - mmcls - INFO - Epoch [35][2300/5005] lr: 1.000e-02, eta: 8:03:54, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6579 2022-12-31 18:46:42,278 - mmcls - INFO - Epoch [35][2400/5005] lr: 1.000e-02, eta: 8:03:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6759 2022-12-31 18:46:50,990 - mmcls - INFO - Epoch [35][2500/5005] lr: 1.000e-02, eta: 8:03:35, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6895 2022-12-31 18:47:00,513 - mmcls - INFO - Epoch [35][2600/5005] lr: 1.000e-02, eta: 8:03:28, time: 0.095, data_time: 0.006, memory: 1224, loss: 1.6803 2022-12-31 18:47:09,241 - mmcls - INFO - Epoch [35][2700/5005] lr: 1.000e-02, eta: 8:03:19, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6716 2022-12-31 18:47:18,067 - mmcls - INFO - Epoch [35][2800/5005] lr: 1.000e-02, eta: 8:03:10, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6676 2022-12-31 18:47:26,738 - mmcls - INFO - Epoch [35][2900/5005] lr: 1.000e-02, eta: 8:03:01, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6966 2022-12-31 18:47:35,557 - mmcls - INFO - Epoch [35][3000/5005] lr: 1.000e-02, eta: 8:02:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6930 2022-12-31 18:47:44,301 - mmcls - INFO - Epoch [35][3100/5005] lr: 1.000e-02, eta: 8:02:43, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6710 2022-12-31 18:47:53,159 - mmcls - INFO - Epoch [35][3200/5005] lr: 1.000e-02, eta: 8:02:34, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6926 2022-12-31 18:48:01,676 - mmcls - INFO - Epoch [35][3300/5005] lr: 1.000e-02, eta: 8:02:24, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.6967 2022-12-31 18:48:10,258 - mmcls - INFO - Epoch [35][3400/5005] lr: 1.000e-02, eta: 8:02:15, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.7065 2022-12-31 18:48:19,041 - mmcls - INFO - Epoch [35][3500/5005] lr: 1.000e-02, eta: 8:02:06, time: 0.088, data_time: 0.019, memory: 1224, loss: 1.6878 2022-12-31 18:48:27,865 - mmcls - INFO - Epoch [35][3600/5005] lr: 1.000e-02, eta: 8:01:57, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6562 2022-12-31 18:48:36,897 - mmcls - INFO - Epoch [35][3700/5005] lr: 1.000e-02, eta: 8:01:49, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6913 2022-12-31 18:48:45,486 - mmcls - INFO - Epoch [35][3800/5005] lr: 1.000e-02, eta: 8:01:39, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.7078 2022-12-31 18:48:54,291 - mmcls - INFO - Epoch [35][3900/5005] lr: 1.000e-02, eta: 8:01:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6856 2022-12-31 18:49:02,868 - mmcls - INFO - Epoch [35][4000/5005] lr: 1.000e-02, eta: 8:01:21, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6531 2022-12-31 18:49:11,546 - mmcls - INFO - Epoch [35][4100/5005] lr: 1.000e-02, eta: 8:01:12, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6979 2022-12-31 18:49:20,144 - mmcls - INFO - Epoch [35][4200/5005] lr: 1.000e-02, eta: 8:01:03, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6889 2022-12-31 18:49:28,831 - mmcls - INFO - Epoch [35][4300/5005] lr: 1.000e-02, eta: 8:00:53, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.7054 2022-12-31 18:49:37,589 - mmcls - INFO - Epoch [35][4400/5005] lr: 1.000e-02, eta: 8:00:44, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6733 2022-12-31 18:49:46,356 - mmcls - INFO - Epoch [35][4500/5005] lr: 1.000e-02, eta: 8:00:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6964 2022-12-31 18:49:55,116 - mmcls - INFO - Epoch [35][4600/5005] lr: 1.000e-02, eta: 8:00:26, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.7150 2022-12-31 18:50:04,094 - mmcls - INFO - Epoch [35][4700/5005] lr: 1.000e-02, eta: 8:00:18, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.6716 2022-12-31 18:50:12,836 - mmcls - INFO - Epoch [35][4800/5005] lr: 1.000e-02, eta: 8:00:09, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6774 2022-12-31 18:50:21,692 - mmcls - INFO - Epoch [35][4900/5005] lr: 1.000e-02, eta: 8:00:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6771 2022-12-31 18:50:30,631 - mmcls - INFO - Epoch [35][5000/5005] lr: 1.000e-02, eta: 7:59:51, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.7025 2022-12-31 18:50:53,358 - mmcls - INFO - Epoch(val) [35][196] accuracy_top-1: 66.1840, accuracy_top-5: 87.1980 2022-12-31 18:51:04,200 - mmcls - INFO - Epoch [36][100/5005] lr: 1.000e-02, eta: 7:59:45, time: 0.108, data_time: 0.045, memory: 1224, loss: 1.6436 2022-12-31 18:51:13,035 - mmcls - INFO - Epoch [36][200/5005] lr: 1.000e-02, eta: 7:59:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6382 2022-12-31 18:51:21,809 - mmcls - INFO - Epoch [36][300/5005] lr: 1.000e-02, eta: 7:59:27, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6703 2022-12-31 18:51:30,311 - mmcls - INFO - Epoch [36][400/5005] lr: 1.000e-02, eta: 7:59:17, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6526 2022-12-31 18:51:38,998 - mmcls - INFO - Epoch [36][500/5005] lr: 1.000e-02, eta: 7:59:08, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6545 2022-12-31 18:51:47,785 - mmcls - INFO - Epoch [36][600/5005] lr: 1.000e-02, eta: 7:58:59, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6518 2022-12-31 18:51:56,713 - mmcls - INFO - Epoch [36][700/5005] lr: 1.000e-02, eta: 7:58:50, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6533 2022-12-31 18:52:05,258 - mmcls - INFO - Epoch [36][800/5005] lr: 1.000e-02, eta: 7:58:41, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6668 2022-12-31 18:52:14,112 - mmcls - INFO - Epoch [36][900/5005] lr: 1.000e-02, eta: 7:58:32, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6528 2022-12-31 18:52:22,865 - mmcls - INFO - Epoch [36][1000/5005] lr: 1.000e-02, eta: 7:58:23, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6611 2022-12-31 18:52:31,716 - mmcls - INFO - Epoch [36][1100/5005] lr: 1.000e-02, eta: 7:58:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.7029 2022-12-31 18:52:40,738 - mmcls - INFO - Epoch [36][1200/5005] lr: 1.000e-02, eta: 7:58:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6548 2022-12-31 18:52:49,441 - mmcls - INFO - Epoch [36][1300/5005] lr: 1.000e-02, eta: 7:57:57, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6428 2022-12-31 18:52:58,231 - mmcls - INFO - Epoch [36][1400/5005] lr: 1.000e-02, eta: 7:57:48, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6609 2022-12-31 18:53:07,168 - mmcls - INFO - Epoch [36][1500/5005] lr: 1.000e-02, eta: 7:57:39, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6422 2022-12-31 18:53:16,194 - mmcls - INFO - Epoch [36][1600/5005] lr: 1.000e-02, eta: 7:57:30, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6754 2022-12-31 18:53:24,957 - mmcls - INFO - Epoch [36][1700/5005] lr: 1.000e-02, eta: 7:57:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6768 2022-12-31 18:53:33,759 - mmcls - INFO - Epoch [36][1800/5005] lr: 1.000e-02, eta: 7:57:12, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6592 2022-12-31 18:53:42,288 - mmcls - INFO - Epoch [36][1900/5005] lr: 1.000e-02, eta: 7:57:03, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6679 2022-12-31 18:53:50,952 - mmcls - INFO - Epoch [36][2000/5005] lr: 1.000e-02, eta: 7:56:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6503 2022-12-31 18:53:59,560 - mmcls - INFO - Epoch [36][2100/5005] lr: 1.000e-02, eta: 7:56:45, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6729 2022-12-31 18:54:08,381 - mmcls - INFO - Epoch [36][2200/5005] lr: 1.000e-02, eta: 7:56:36, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6677 2022-12-31 18:54:17,254 - mmcls - INFO - Epoch [36][2300/5005] lr: 1.000e-02, eta: 7:56:27, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6797 2022-12-31 18:54:26,412 - mmcls - INFO - Epoch [36][2400/5005] lr: 1.000e-02, eta: 7:56:19, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6851 2022-12-31 18:54:35,184 - mmcls - INFO - Epoch [36][2500/5005] lr: 1.000e-02, eta: 7:56:10, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6593 2022-12-31 18:54:44,060 - mmcls - INFO - Epoch [36][2600/5005] lr: 1.000e-02, eta: 7:56:01, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6638 2022-12-31 18:54:52,983 - mmcls - INFO - Epoch [36][2700/5005] lr: 1.000e-02, eta: 7:55:52, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6676 2022-12-31 18:55:01,784 - mmcls - INFO - Epoch [36][2800/5005] lr: 1.000e-02, eta: 7:55:43, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6709 2022-12-31 18:55:11,040 - mmcls - INFO - Epoch [36][2900/5005] lr: 1.000e-02, eta: 7:55:35, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.6822 2022-12-31 18:55:19,641 - mmcls - INFO - Epoch [36][3000/5005] lr: 1.000e-02, eta: 7:55:26, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6838 2022-12-31 18:55:28,170 - mmcls - INFO - Epoch [36][3100/5005] lr: 1.000e-02, eta: 7:55:16, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6967 2022-12-31 18:55:37,229 - mmcls - INFO - Epoch [36][3200/5005] lr: 1.000e-02, eta: 7:55:08, time: 0.091, data_time: 0.022, memory: 1224, loss: 1.6674 2022-12-31 18:55:46,032 - mmcls - INFO - Epoch [36][3300/5005] lr: 1.000e-02, eta: 7:54:59, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6660 2022-12-31 18:55:54,700 - mmcls - INFO - Epoch [36][3400/5005] lr: 1.000e-02, eta: 7:54:50, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6699 2022-12-31 18:56:03,933 - mmcls - INFO - Epoch [36][3500/5005] lr: 1.000e-02, eta: 7:54:42, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6722 2022-12-31 18:56:12,948 - mmcls - INFO - Epoch [36][3600/5005] lr: 1.000e-02, eta: 7:54:33, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.6896 2022-12-31 18:56:21,652 - mmcls - INFO - Epoch [36][3700/5005] lr: 1.000e-02, eta: 7:54:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6672 2022-12-31 18:56:30,354 - mmcls - INFO - Epoch [36][3800/5005] lr: 1.000e-02, eta: 7:54:15, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6725 2022-12-31 18:56:39,278 - mmcls - INFO - Epoch [36][3900/5005] lr: 1.000e-02, eta: 7:54:06, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6844 2022-12-31 18:56:47,947 - mmcls - INFO - Epoch [36][4000/5005] lr: 1.000e-02, eta: 7:53:57, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6694 2022-12-31 18:56:56,777 - mmcls - INFO - Epoch [36][4100/5005] lr: 1.000e-02, eta: 7:53:48, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6936 2022-12-31 18:57:05,290 - mmcls - INFO - Epoch [36][4200/5005] lr: 1.000e-02, eta: 7:53:38, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.6607 2022-12-31 18:57:14,372 - mmcls - INFO - Epoch [36][4300/5005] lr: 1.000e-02, eta: 7:53:30, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6691 2022-12-31 18:57:23,031 - mmcls - INFO - Epoch [36][4400/5005] lr: 1.000e-02, eta: 7:53:21, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6710 2022-12-31 18:57:31,808 - mmcls - INFO - Epoch [36][4500/5005] lr: 1.000e-02, eta: 7:53:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6745 2022-12-31 18:57:40,684 - mmcls - INFO - Epoch [36][4600/5005] lr: 1.000e-02, eta: 7:53:03, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6760 2022-12-31 18:57:49,435 - mmcls - INFO - Epoch [36][4700/5005] lr: 1.000e-02, eta: 7:52:54, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6668 2022-12-31 18:57:58,363 - mmcls - INFO - Epoch [36][4800/5005] lr: 1.000e-02, eta: 7:52:45, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6575 2022-12-31 18:58:07,532 - mmcls - INFO - Epoch [36][4900/5005] lr: 1.000e-02, eta: 7:52:37, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.6996 2022-12-31 18:58:16,134 - mmcls - INFO - Epoch [36][5000/5005] lr: 1.000e-02, eta: 7:52:28, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6675 2022-12-31 18:58:38,767 - mmcls - INFO - Epoch(val) [36][196] accuracy_top-1: 66.1900, accuracy_top-5: 87.1380 2022-12-31 18:58:49,829 - mmcls - INFO - Epoch [37][100/5005] lr: 1.000e-02, eta: 7:52:22, time: 0.110, data_time: 0.036, memory: 1224, loss: 1.6474 2022-12-31 18:58:58,261 - mmcls - INFO - Epoch [37][200/5005] lr: 1.000e-02, eta: 7:52:12, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.6419 2022-12-31 18:59:07,032 - mmcls - INFO - Epoch [37][300/5005] lr: 1.000e-02, eta: 7:52:03, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6305 2022-12-31 18:59:15,966 - mmcls - INFO - Epoch [37][400/5005] lr: 1.000e-02, eta: 7:51:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6270 2022-12-31 18:59:24,651 - mmcls - INFO - Epoch [37][500/5005] lr: 1.000e-02, eta: 7:51:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6483 2022-12-31 18:59:33,534 - mmcls - INFO - Epoch [37][600/5005] lr: 1.000e-02, eta: 7:51:36, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6265 2022-12-31 18:59:42,051 - mmcls - INFO - Epoch [37][700/5005] lr: 1.000e-02, eta: 7:51:27, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.6360 2022-12-31 18:59:50,761 - mmcls - INFO - Epoch [37][800/5005] lr: 1.000e-02, eta: 7:51:18, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6553 2022-12-31 18:59:59,828 - mmcls - INFO - Epoch [37][900/5005] lr: 1.000e-02, eta: 7:51:09, time: 0.091, data_time: 0.031, memory: 1224, loss: 1.6580 2022-12-31 19:00:09,362 - mmcls - INFO - Epoch [37][1000/5005] lr: 1.000e-02, eta: 7:51:02, time: 0.095, data_time: 0.010, memory: 1224, loss: 1.6614 2022-12-31 19:00:18,307 - mmcls - INFO - Epoch [37][1100/5005] lr: 1.000e-02, eta: 7:50:53, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6472 2022-12-31 19:00:26,821 - mmcls - INFO - Epoch [37][1200/5005] lr: 1.000e-02, eta: 7:50:44, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6546 2022-12-31 19:00:35,500 - mmcls - INFO - Epoch [37][1300/5005] lr: 1.000e-02, eta: 7:50:34, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6521 2022-12-31 19:00:44,661 - mmcls - INFO - Epoch [37][1400/5005] lr: 1.000e-02, eta: 7:50:26, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.6692 2022-12-31 19:00:53,781 - mmcls - INFO - Epoch [37][1500/5005] lr: 1.000e-02, eta: 7:50:18, time: 0.091, data_time: 0.018, memory: 1224, loss: 1.6361 2022-12-31 19:01:02,429 - mmcls - INFO - Epoch [37][1600/5005] lr: 1.000e-02, eta: 7:50:08, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6651 2022-12-31 19:01:11,060 - mmcls - INFO - Epoch [37][1700/5005] lr: 1.000e-02, eta: 7:49:59, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6722 2022-12-31 19:01:20,012 - mmcls - INFO - Epoch [37][1800/5005] lr: 1.000e-02, eta: 7:49:51, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6467 2022-12-31 19:01:29,070 - mmcls - INFO - Epoch [37][1900/5005] lr: 1.000e-02, eta: 7:49:42, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6516 2022-12-31 19:01:37,687 - mmcls - INFO - Epoch [37][2000/5005] lr: 1.000e-02, eta: 7:49:33, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6550 2022-12-31 19:01:46,342 - mmcls - INFO - Epoch [37][2100/5005] lr: 1.000e-02, eta: 7:49:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6526 2022-12-31 19:01:55,276 - mmcls - INFO - Epoch [37][2200/5005] lr: 1.000e-02, eta: 7:49:15, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.6659 2022-12-31 19:02:04,148 - mmcls - INFO - Epoch [37][2300/5005] lr: 1.000e-02, eta: 7:49:06, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6720 2022-12-31 19:02:12,878 - mmcls - INFO - Epoch [37][2400/5005] lr: 1.000e-02, eta: 7:48:57, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6620 2022-12-31 19:02:21,555 - mmcls - INFO - Epoch [37][2500/5005] lr: 1.000e-02, eta: 7:48:48, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6837 2022-12-31 19:02:29,866 - mmcls - INFO - Epoch [37][2600/5005] lr: 1.000e-02, eta: 7:48:38, time: 0.083, data_time: 0.003, memory: 1224, loss: 1.6721 2022-12-31 19:02:38,988 - mmcls - INFO - Epoch [37][2700/5005] lr: 1.000e-02, eta: 7:48:30, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6829 2022-12-31 19:02:47,611 - mmcls - INFO - Epoch [37][2800/5005] lr: 1.000e-02, eta: 7:48:20, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6620 2022-12-31 19:02:56,458 - mmcls - INFO - Epoch [37][2900/5005] lr: 1.000e-02, eta: 7:48:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6380 2022-12-31 19:03:05,198 - mmcls - INFO - Epoch [37][3000/5005] lr: 1.000e-02, eta: 7:48:03, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6625 2022-12-31 19:03:14,107 - mmcls - INFO - Epoch [37][3100/5005] lr: 1.000e-02, eta: 7:47:54, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6732 2022-12-31 19:03:22,755 - mmcls - INFO - Epoch [37][3200/5005] lr: 1.000e-02, eta: 7:47:45, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6712 2022-12-31 19:03:31,970 - mmcls - INFO - Epoch [37][3300/5005] lr: 1.000e-02, eta: 7:47:36, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.6801 2022-12-31 19:03:40,586 - mmcls - INFO - Epoch [37][3400/5005] lr: 1.000e-02, eta: 7:47:27, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6757 2022-12-31 19:03:49,162 - mmcls - INFO - Epoch [37][3500/5005] lr: 1.000e-02, eta: 7:47:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6712 2022-12-31 19:03:57,862 - mmcls - INFO - Epoch [37][3600/5005] lr: 1.000e-02, eta: 7:47:09, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6979 2022-12-31 19:04:06,700 - mmcls - INFO - Epoch [37][3700/5005] lr: 1.000e-02, eta: 7:47:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6779 2022-12-31 19:04:15,543 - mmcls - INFO - Epoch [37][3800/5005] lr: 1.000e-02, eta: 7:46:51, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6781 2022-12-31 19:04:24,554 - mmcls - INFO - Epoch [37][3900/5005] lr: 1.000e-02, eta: 7:46:42, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6691 2022-12-31 19:04:33,402 - mmcls - INFO - Epoch [37][4000/5005] lr: 1.000e-02, eta: 7:46:34, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.7028 2022-12-31 19:04:42,344 - mmcls - INFO - Epoch [37][4100/5005] lr: 1.000e-02, eta: 7:46:25, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6731 2022-12-31 19:04:51,026 - mmcls - INFO - Epoch [37][4200/5005] lr: 1.000e-02, eta: 7:46:16, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6714 2022-12-31 19:04:59,855 - mmcls - INFO - Epoch [37][4300/5005] lr: 1.000e-02, eta: 7:46:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6833 2022-12-31 19:05:08,798 - mmcls - INFO - Epoch [37][4400/5005] lr: 1.000e-02, eta: 7:45:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6478 2022-12-31 19:05:17,669 - mmcls - INFO - Epoch [37][4500/5005] lr: 1.000e-02, eta: 7:45:49, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6481 2022-12-31 19:05:26,147 - mmcls - INFO - Epoch [37][4600/5005] lr: 1.000e-02, eta: 7:45:40, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6736 2022-12-31 19:05:35,100 - mmcls - INFO - Epoch [37][4700/5005] lr: 1.000e-02, eta: 7:45:31, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.6868 2022-12-31 19:05:44,177 - mmcls - INFO - Epoch [37][4800/5005] lr: 1.000e-02, eta: 7:45:23, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.6768 2022-12-31 19:05:52,777 - mmcls - INFO - Epoch [37][4900/5005] lr: 1.000e-02, eta: 7:45:13, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6746 2022-12-31 19:06:02,142 - mmcls - INFO - Epoch [37][5000/5005] lr: 1.000e-02, eta: 7:45:05, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.6750 2022-12-31 19:06:24,843 - mmcls - INFO - Epoch(val) [37][196] accuracy_top-1: 66.4720, accuracy_top-5: 87.2420 2022-12-31 19:06:35,795 - mmcls - INFO - Epoch [38][100/5005] lr: 1.000e-02, eta: 7:44:59, time: 0.109, data_time: 0.034, memory: 1224, loss: 1.6378 2022-12-31 19:06:44,570 - mmcls - INFO - Epoch [38][200/5005] lr: 1.000e-02, eta: 7:44:50, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6480 2022-12-31 19:06:53,706 - mmcls - INFO - Epoch [38][300/5005] lr: 1.000e-02, eta: 7:44:42, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6365 2022-12-31 19:07:02,348 - mmcls - INFO - Epoch [38][400/5005] lr: 1.000e-02, eta: 7:44:32, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6323 2022-12-31 19:07:11,184 - mmcls - INFO - Epoch [38][500/5005] lr: 1.000e-02, eta: 7:44:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6411 2022-12-31 19:07:20,045 - mmcls - INFO - Epoch [38][600/5005] lr: 1.000e-02, eta: 7:44:15, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6271 2022-12-31 19:07:28,980 - mmcls - INFO - Epoch [38][700/5005] lr: 1.000e-02, eta: 7:44:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6173 2022-12-31 19:07:38,134 - mmcls - INFO - Epoch [38][800/5005] lr: 1.000e-02, eta: 7:43:58, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.6442 2022-12-31 19:07:47,138 - mmcls - INFO - Epoch [38][900/5005] lr: 1.000e-02, eta: 7:43:49, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6391 2022-12-31 19:07:55,783 - mmcls - INFO - Epoch [38][1000/5005] lr: 1.000e-02, eta: 7:43:40, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6486 2022-12-31 19:08:04,598 - mmcls - INFO - Epoch [38][1100/5005] lr: 1.000e-02, eta: 7:43:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6641 2022-12-31 19:08:13,313 - mmcls - INFO - Epoch [38][1200/5005] lr: 1.000e-02, eta: 7:43:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6721 2022-12-31 19:08:22,189 - mmcls - INFO - Epoch [38][1300/5005] lr: 1.000e-02, eta: 7:43:13, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6491 2022-12-31 19:08:30,795 - mmcls - INFO - Epoch [38][1400/5005] lr: 1.000e-02, eta: 7:43:04, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6634 2022-12-31 19:08:39,695 - mmcls - INFO - Epoch [38][1500/5005] lr: 1.000e-02, eta: 7:42:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6505 2022-12-31 19:08:48,607 - mmcls - INFO - Epoch [38][1600/5005] lr: 1.000e-02, eta: 7:42:46, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6601 2022-12-31 19:08:57,247 - mmcls - INFO - Epoch [38][1700/5005] lr: 1.000e-02, eta: 7:42:37, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6657 2022-12-31 19:09:05,965 - mmcls - INFO - Epoch [38][1800/5005] lr: 1.000e-02, eta: 7:42:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6535 2022-12-31 19:09:14,405 - mmcls - INFO - Epoch [38][1900/5005] lr: 1.000e-02, eta: 7:42:18, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.6389 2022-12-31 19:09:23,532 - mmcls - INFO - Epoch [38][2000/5005] lr: 1.000e-02, eta: 7:42:10, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.6552 2022-12-31 19:09:32,238 - mmcls - INFO - Epoch [38][2100/5005] lr: 1.000e-02, eta: 7:42:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6353 2022-12-31 19:09:41,064 - mmcls - INFO - Epoch [38][2200/5005] lr: 1.000e-02, eta: 7:41:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6342 2022-12-31 19:09:49,974 - mmcls - INFO - Epoch [38][2300/5005] lr: 1.000e-02, eta: 7:41:43, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6869 2022-12-31 19:09:58,869 - mmcls - INFO - Epoch [38][2400/5005] lr: 1.000e-02, eta: 7:41:34, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6545 2022-12-31 19:10:07,800 - mmcls - INFO - Epoch [38][2500/5005] lr: 1.000e-02, eta: 7:41:26, time: 0.089, data_time: 0.022, memory: 1224, loss: 1.6434 2022-12-31 19:10:16,590 - mmcls - INFO - Epoch [38][2600/5005] lr: 1.000e-02, eta: 7:41:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6472 2022-12-31 19:10:25,265 - mmcls - INFO - Epoch [38][2700/5005] lr: 1.000e-02, eta: 7:41:08, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6653 2022-12-31 19:10:34,080 - mmcls - INFO - Epoch [38][2800/5005] lr: 1.000e-02, eta: 7:40:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6661 2022-12-31 19:10:43,077 - mmcls - INFO - Epoch [38][2900/5005] lr: 1.000e-02, eta: 7:40:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6554 2022-12-31 19:10:51,946 - mmcls - INFO - Epoch [38][3000/5005] lr: 1.000e-02, eta: 7:40:41, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6698 2022-12-31 19:11:00,773 - mmcls - INFO - Epoch [38][3100/5005] lr: 1.000e-02, eta: 7:40:32, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6432 2022-12-31 19:11:09,557 - mmcls - INFO - Epoch [38][3200/5005] lr: 1.000e-02, eta: 7:40:23, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6582 2022-12-31 19:11:18,375 - mmcls - INFO - Epoch [38][3300/5005] lr: 1.000e-02, eta: 7:40:15, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6671 2022-12-31 19:11:27,666 - mmcls - INFO - Epoch [38][3400/5005] lr: 1.000e-02, eta: 7:40:06, time: 0.093, data_time: 0.009, memory: 1224, loss: 1.6490 2022-12-31 19:11:36,673 - mmcls - INFO - Epoch [38][3500/5005] lr: 1.000e-02, eta: 7:39:58, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.6921 2022-12-31 19:11:45,279 - mmcls - INFO - Epoch [38][3600/5005] lr: 1.000e-02, eta: 7:39:49, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.6442 2022-12-31 19:11:54,027 - mmcls - INFO - Epoch [38][3700/5005] lr: 1.000e-02, eta: 7:39:40, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6795 2022-12-31 19:12:02,740 - mmcls - INFO - Epoch [38][3800/5005] lr: 1.000e-02, eta: 7:39:30, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6707 2022-12-31 19:12:12,216 - mmcls - INFO - Epoch [38][3900/5005] lr: 1.000e-02, eta: 7:39:23, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.6404 2022-12-31 19:12:20,616 - mmcls - INFO - Epoch [38][4000/5005] lr: 1.000e-02, eta: 7:39:13, time: 0.084, data_time: 0.004, memory: 1224, loss: 1.6817 2022-12-31 19:12:29,310 - mmcls - INFO - Epoch [38][4100/5005] lr: 1.000e-02, eta: 7:39:04, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6766 2022-12-31 19:12:38,203 - mmcls - INFO - Epoch [38][4200/5005] lr: 1.000e-02, eta: 7:38:55, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6699 2022-12-31 19:12:46,918 - mmcls - INFO - Epoch [38][4300/5005] lr: 1.000e-02, eta: 7:38:46, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6752 2022-12-31 19:12:55,794 - mmcls - INFO - Epoch [38][4400/5005] lr: 1.000e-02, eta: 7:38:37, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6876 2022-12-31 19:13:04,541 - mmcls - INFO - Epoch [38][4500/5005] lr: 1.000e-02, eta: 7:38:28, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6543 2022-12-31 19:13:13,256 - mmcls - INFO - Epoch [38][4600/5005] lr: 1.000e-02, eta: 7:38:19, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6868 2022-12-31 19:13:22,154 - mmcls - INFO - Epoch [38][4700/5005] lr: 1.000e-02, eta: 7:38:10, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6760 2022-12-31 19:13:30,875 - mmcls - INFO - Epoch [38][4800/5005] lr: 1.000e-02, eta: 7:38:01, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6721 2022-12-31 19:13:39,569 - mmcls - INFO - Epoch [38][4900/5005] lr: 1.000e-02, eta: 7:37:52, time: 0.087, data_time: 0.044, memory: 1224, loss: 1.6694 2022-12-31 19:13:48,565 - mmcls - INFO - Epoch [38][5000/5005] lr: 1.000e-02, eta: 7:37:44, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6707 2022-12-31 19:14:11,175 - mmcls - INFO - Epoch(val) [38][196] accuracy_top-1: 66.4360, accuracy_top-5: 87.3880 2022-12-31 19:14:21,990 - mmcls - INFO - Epoch [39][100/5005] lr: 1.000e-02, eta: 7:37:37, time: 0.108, data_time: 0.049, memory: 1224, loss: 1.6103 2022-12-31 19:14:30,728 - mmcls - INFO - Epoch [39][200/5005] lr: 1.000e-02, eta: 7:37:28, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6458 2022-12-31 19:14:39,562 - mmcls - INFO - Epoch [39][300/5005] lr: 1.000e-02, eta: 7:37:19, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6186 2022-12-31 19:14:48,472 - mmcls - INFO - Epoch [39][400/5005] lr: 1.000e-02, eta: 7:37:10, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6271 2022-12-31 19:14:57,212 - mmcls - INFO - Epoch [39][500/5005] lr: 1.000e-02, eta: 7:37:01, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6261 2022-12-31 19:15:06,032 - mmcls - INFO - Epoch [39][600/5005] lr: 1.000e-02, eta: 7:36:52, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6619 2022-12-31 19:15:14,924 - mmcls - INFO - Epoch [39][700/5005] lr: 1.000e-02, eta: 7:36:43, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6243 2022-12-31 19:15:23,860 - mmcls - INFO - Epoch [39][800/5005] lr: 1.000e-02, eta: 7:36:35, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6471 2022-12-31 19:15:32,521 - mmcls - INFO - Epoch [39][900/5005] lr: 1.000e-02, eta: 7:36:25, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6588 2022-12-31 19:15:41,106 - mmcls - INFO - Epoch [39][1000/5005] lr: 1.000e-02, eta: 7:36:16, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6537 2022-12-31 19:15:50,229 - mmcls - INFO - Epoch [39][1100/5005] lr: 1.000e-02, eta: 7:36:08, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6288 2022-12-31 19:15:58,963 - mmcls - INFO - Epoch [39][1200/5005] lr: 1.000e-02, eta: 7:35:59, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6429 2022-12-31 19:16:07,962 - mmcls - INFO - Epoch [39][1300/5005] lr: 1.000e-02, eta: 7:35:50, time: 0.090, data_time: 0.024, memory: 1224, loss: 1.6434 2022-12-31 19:16:16,670 - mmcls - INFO - Epoch [39][1400/5005] lr: 1.000e-02, eta: 7:35:41, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6355 2022-12-31 19:16:25,471 - mmcls - INFO - Epoch [39][1500/5005] lr: 1.000e-02, eta: 7:35:32, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6450 2022-12-31 19:16:34,406 - mmcls - INFO - Epoch [39][1600/5005] lr: 1.000e-02, eta: 7:35:23, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6743 2022-12-31 19:16:43,134 - mmcls - INFO - Epoch [39][1700/5005] lr: 1.000e-02, eta: 7:35:14, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6672 2022-12-31 19:16:52,038 - mmcls - INFO - Epoch [39][1800/5005] lr: 1.000e-02, eta: 7:35:06, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.6629 2022-12-31 19:17:00,429 - mmcls - INFO - Epoch [39][1900/5005] lr: 1.000e-02, eta: 7:34:56, time: 0.084, data_time: 0.000, memory: 1224, loss: 1.6632 2022-12-31 19:17:09,337 - mmcls - INFO - Epoch [39][2000/5005] lr: 1.000e-02, eta: 7:34:47, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6564 2022-12-31 19:17:18,067 - mmcls - INFO - Epoch [39][2100/5005] lr: 1.000e-02, eta: 7:34:38, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6454 2022-12-31 19:17:26,824 - mmcls - INFO - Epoch [39][2200/5005] lr: 1.000e-02, eta: 7:34:29, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6725 2022-12-31 19:17:35,566 - mmcls - INFO - Epoch [39][2300/5005] lr: 1.000e-02, eta: 7:34:20, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6407 2022-12-31 19:17:44,480 - mmcls - INFO - Epoch [39][2400/5005] lr: 1.000e-02, eta: 7:34:11, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6456 2022-12-31 19:17:53,475 - mmcls - INFO - Epoch [39][2500/5005] lr: 1.000e-02, eta: 7:34:03, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6590 2022-12-31 19:18:02,455 - mmcls - INFO - Epoch [39][2600/5005] lr: 1.000e-02, eta: 7:33:54, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.6588 2022-12-31 19:18:11,374 - mmcls - INFO - Epoch [39][2700/5005] lr: 1.000e-02, eta: 7:33:45, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6715 2022-12-31 19:18:20,171 - mmcls - INFO - Epoch [39][2800/5005] lr: 1.000e-02, eta: 7:33:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6435 2022-12-31 19:18:29,171 - mmcls - INFO - Epoch [39][2900/5005] lr: 1.000e-02, eta: 7:33:28, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6549 2022-12-31 19:18:37,906 - mmcls - INFO - Epoch [39][3000/5005] lr: 1.000e-02, eta: 7:33:19, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6575 2022-12-31 19:18:46,722 - mmcls - INFO - Epoch [39][3100/5005] lr: 1.000e-02, eta: 7:33:10, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6757 2022-12-31 19:18:55,630 - mmcls - INFO - Epoch [39][3200/5005] lr: 1.000e-02, eta: 7:33:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6648 2022-12-31 19:19:04,716 - mmcls - INFO - Epoch [39][3300/5005] lr: 1.000e-02, eta: 7:32:53, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6477 2022-12-31 19:19:13,560 - mmcls - INFO - Epoch [39][3400/5005] lr: 1.000e-02, eta: 7:32:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6665 2022-12-31 19:19:22,121 - mmcls - INFO - Epoch [39][3500/5005] lr: 1.000e-02, eta: 7:32:35, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6407 2022-12-31 19:19:30,881 - mmcls - INFO - Epoch [39][3600/5005] lr: 1.000e-02, eta: 7:32:25, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6434 2022-12-31 19:19:39,552 - mmcls - INFO - Epoch [39][3700/5005] lr: 1.000e-02, eta: 7:32:16, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6708 2022-12-31 19:19:48,248 - mmcls - INFO - Epoch [39][3800/5005] lr: 1.000e-02, eta: 7:32:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6577 2022-12-31 19:19:57,186 - mmcls - INFO - Epoch [39][3900/5005] lr: 1.000e-02, eta: 7:31:59, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6597 2022-12-31 19:20:06,359 - mmcls - INFO - Epoch [39][4000/5005] lr: 1.000e-02, eta: 7:31:50, time: 0.092, data_time: 0.014, memory: 1224, loss: 1.6428 2022-12-31 19:20:15,112 - mmcls - INFO - Epoch [39][4100/5005] lr: 1.000e-02, eta: 7:31:41, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6740 2022-12-31 19:20:23,808 - mmcls - INFO - Epoch [39][4200/5005] lr: 1.000e-02, eta: 7:31:32, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6562 2022-12-31 19:20:32,514 - mmcls - INFO - Epoch [39][4300/5005] lr: 1.000e-02, eta: 7:31:23, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6778 2022-12-31 19:20:41,504 - mmcls - INFO - Epoch [39][4400/5005] lr: 1.000e-02, eta: 7:31:14, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6728 2022-12-31 19:20:50,299 - mmcls - INFO - Epoch [39][4500/5005] lr: 1.000e-02, eta: 7:31:05, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6895 2022-12-31 19:20:59,274 - mmcls - INFO - Epoch [39][4600/5005] lr: 1.000e-02, eta: 7:30:57, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6777 2022-12-31 19:21:07,874 - mmcls - INFO - Epoch [39][4700/5005] lr: 1.000e-02, eta: 7:30:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6866 2022-12-31 19:21:16,547 - mmcls - INFO - Epoch [39][4800/5005] lr: 1.000e-02, eta: 7:30:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6675 2022-12-31 19:21:25,382 - mmcls - INFO - Epoch [39][4900/5005] lr: 1.000e-02, eta: 7:30:30, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6562 2022-12-31 19:21:34,271 - mmcls - INFO - Epoch [39][5000/5005] lr: 1.000e-02, eta: 7:30:21, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6756 2022-12-31 19:21:56,905 - mmcls - INFO - Epoch(val) [39][196] accuracy_top-1: 66.0640, accuracy_top-5: 87.0240 2022-12-31 19:22:07,883 - mmcls - INFO - Epoch [40][100/5005] lr: 1.000e-02, eta: 7:30:14, time: 0.109, data_time: 0.038, memory: 1224, loss: 1.6272 2022-12-31 19:22:16,786 - mmcls - INFO - Epoch [40][200/5005] lr: 1.000e-02, eta: 7:30:05, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6072 2022-12-31 19:22:25,609 - mmcls - INFO - Epoch [40][300/5005] lr: 1.000e-02, eta: 7:29:56, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6365 2022-12-31 19:22:34,234 - mmcls - INFO - Epoch [40][400/5005] lr: 1.000e-02, eta: 7:29:47, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6354 2022-12-31 19:22:42,964 - mmcls - INFO - Epoch [40][500/5005] lr: 1.000e-02, eta: 7:29:38, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6307 2022-12-31 19:22:51,882 - mmcls - INFO - Epoch [40][600/5005] lr: 1.000e-02, eta: 7:29:29, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6458 2022-12-31 19:23:00,368 - mmcls - INFO - Epoch [40][700/5005] lr: 1.000e-02, eta: 7:29:20, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6405 2022-12-31 19:23:09,104 - mmcls - INFO - Epoch [40][800/5005] lr: 1.000e-02, eta: 7:29:11, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6068 2022-12-31 19:23:18,022 - mmcls - INFO - Epoch [40][900/5005] lr: 1.000e-02, eta: 7:29:02, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6336 2022-12-31 19:23:26,747 - mmcls - INFO - Epoch [40][1000/5005] lr: 1.000e-02, eta: 7:28:53, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6389 2022-12-31 19:23:35,517 - mmcls - INFO - Epoch [40][1100/5005] lr: 1.000e-02, eta: 7:28:44, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6142 2022-12-31 19:23:44,272 - mmcls - INFO - Epoch [40][1200/5005] lr: 1.000e-02, eta: 7:28:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6524 2022-12-31 19:23:52,709 - mmcls - INFO - Epoch [40][1300/5005] lr: 1.000e-02, eta: 7:28:26, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.6586 2022-12-31 19:24:01,434 - mmcls - INFO - Epoch [40][1400/5005] lr: 1.000e-02, eta: 7:28:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6485 2022-12-31 19:24:10,290 - mmcls - INFO - Epoch [40][1500/5005] lr: 1.000e-02, eta: 7:28:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6453 2022-12-31 19:24:18,857 - mmcls - INFO - Epoch [40][1600/5005] lr: 1.000e-02, eta: 7:27:58, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6485 2022-12-31 19:24:27,547 - mmcls - INFO - Epoch [40][1700/5005] lr: 1.000e-02, eta: 7:27:49, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6363 2022-12-31 19:24:36,311 - mmcls - INFO - Epoch [40][1800/5005] lr: 1.000e-02, eta: 7:27:40, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6586 2022-12-31 19:24:44,941 - mmcls - INFO - Epoch [40][1900/5005] lr: 1.000e-02, eta: 7:27:31, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6588 2022-12-31 19:24:53,833 - mmcls - INFO - Epoch [40][2000/5005] lr: 1.000e-02, eta: 7:27:22, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6755 2022-12-31 19:25:02,584 - mmcls - INFO - Epoch [40][2100/5005] lr: 1.000e-02, eta: 7:27:13, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6312 2022-12-31 19:25:11,712 - mmcls - INFO - Epoch [40][2200/5005] lr: 1.000e-02, eta: 7:27:05, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6400 2022-12-31 19:25:20,669 - mmcls - INFO - Epoch [40][2300/5005] lr: 1.000e-02, eta: 7:26:56, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6490 2022-12-31 19:25:29,671 - mmcls - INFO - Epoch [40][2400/5005] lr: 1.000e-02, eta: 7:26:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6386 2022-12-31 19:25:38,351 - mmcls - INFO - Epoch [40][2500/5005] lr: 1.000e-02, eta: 7:26:39, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.6337 2022-12-31 19:25:47,068 - mmcls - INFO - Epoch [40][2600/5005] lr: 1.000e-02, eta: 7:26:30, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6373 2022-12-31 19:25:55,677 - mmcls - INFO - Epoch [40][2700/5005] lr: 1.000e-02, eta: 7:26:20, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.6527 2022-12-31 19:26:04,429 - mmcls - INFO - Epoch [40][2800/5005] lr: 1.000e-02, eta: 7:26:11, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6727 2022-12-31 19:26:13,137 - mmcls - INFO - Epoch [40][2900/5005] lr: 1.000e-02, eta: 7:26:02, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6434 2022-12-31 19:26:21,811 - mmcls - INFO - Epoch [40][3000/5005] lr: 1.000e-02, eta: 7:25:53, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6648 2022-12-31 19:26:31,222 - mmcls - INFO - Epoch [40][3100/5005] lr: 1.000e-02, eta: 7:25:45, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.6575 2022-12-31 19:26:40,145 - mmcls - INFO - Epoch [40][3200/5005] lr: 1.000e-02, eta: 7:25:36, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6575 2022-12-31 19:26:48,993 - mmcls - INFO - Epoch [40][3300/5005] lr: 1.000e-02, eta: 7:25:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6518 2022-12-31 19:26:57,580 - mmcls - INFO - Epoch [40][3400/5005] lr: 1.000e-02, eta: 7:25:18, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6767 2022-12-31 19:27:06,302 - mmcls - INFO - Epoch [40][3500/5005] lr: 1.000e-02, eta: 7:25:09, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6490 2022-12-31 19:27:15,028 - mmcls - INFO - Epoch [40][3600/5005] lr: 1.000e-02, eta: 7:25:00, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6650 2022-12-31 19:27:24,093 - mmcls - INFO - Epoch [40][3700/5005] lr: 1.000e-02, eta: 7:24:52, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6537 2022-12-31 19:27:33,293 - mmcls - INFO - Epoch [40][3800/5005] lr: 1.000e-02, eta: 7:24:43, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6666 2022-12-31 19:27:42,259 - mmcls - INFO - Epoch [40][3900/5005] lr: 1.000e-02, eta: 7:24:35, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6448 2022-12-31 19:27:50,907 - mmcls - INFO - Epoch [40][4000/5005] lr: 1.000e-02, eta: 7:24:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6526 2022-12-31 19:27:59,904 - mmcls - INFO - Epoch [40][4100/5005] lr: 1.000e-02, eta: 7:24:17, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6623 2022-12-31 19:28:08,538 - mmcls - INFO - Epoch [40][4200/5005] lr: 1.000e-02, eta: 7:24:08, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6802 2022-12-31 19:28:17,479 - mmcls - INFO - Epoch [40][4300/5005] lr: 1.000e-02, eta: 7:23:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6664 2022-12-31 19:28:26,248 - mmcls - INFO - Epoch [40][4400/5005] lr: 1.000e-02, eta: 7:23:50, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6594 2022-12-31 19:28:35,160 - mmcls - INFO - Epoch [40][4500/5005] lr: 1.000e-02, eta: 7:23:41, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6584 2022-12-31 19:28:43,906 - mmcls - INFO - Epoch [40][4600/5005] lr: 1.000e-02, eta: 7:23:32, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6710 2022-12-31 19:28:52,600 - mmcls - INFO - Epoch [40][4700/5005] lr: 1.000e-02, eta: 7:23:23, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6682 2022-12-31 19:29:01,317 - mmcls - INFO - Epoch [40][4800/5005] lr: 1.000e-02, eta: 7:23:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6676 2022-12-31 19:29:10,033 - mmcls - INFO - Epoch [40][4900/5005] lr: 1.000e-02, eta: 7:23:05, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6622 2022-12-31 19:29:18,614 - mmcls - INFO - Epoch [40][5000/5005] lr: 1.000e-02, eta: 7:22:56, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6935 2022-12-31 19:29:41,402 - mmcls - INFO - Epoch(val) [40][196] accuracy_top-1: 66.4980, accuracy_top-5: 87.2980 2022-12-31 19:29:52,542 - mmcls - INFO - Epoch [41][100/5005] lr: 1.000e-02, eta: 7:22:49, time: 0.111, data_time: 0.067, memory: 1224, loss: 1.6306 2022-12-31 19:30:01,180 - mmcls - INFO - Epoch [41][200/5005] lr: 1.000e-02, eta: 7:22:40, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.5957 2022-12-31 19:30:09,966 - mmcls - INFO - Epoch [41][300/5005] lr: 1.000e-02, eta: 7:22:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6138 2022-12-31 19:30:18,886 - mmcls - INFO - Epoch [41][400/5005] lr: 1.000e-02, eta: 7:22:22, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6436 2022-12-31 19:30:27,541 - mmcls - INFO - Epoch [41][500/5005] lr: 1.000e-02, eta: 7:22:13, time: 0.087, data_time: 0.035, memory: 1224, loss: 1.6460 2022-12-31 19:30:36,344 - mmcls - INFO - Epoch [41][600/5005] lr: 1.000e-02, eta: 7:22:04, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6159 2022-12-31 19:30:44,963 - mmcls - INFO - Epoch [41][700/5005] lr: 1.000e-02, eta: 7:21:55, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6440 2022-12-31 19:30:53,873 - mmcls - INFO - Epoch [41][800/5005] lr: 1.000e-02, eta: 7:21:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6508 2022-12-31 19:31:02,730 - mmcls - INFO - Epoch [41][900/5005] lr: 1.000e-02, eta: 7:21:38, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6393 2022-12-31 19:31:11,634 - mmcls - INFO - Epoch [41][1000/5005] lr: 1.000e-02, eta: 7:21:29, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6251 2022-12-31 19:31:20,476 - mmcls - INFO - Epoch [41][1100/5005] lr: 1.000e-02, eta: 7:21:20, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6347 2022-12-31 19:31:29,316 - mmcls - INFO - Epoch [41][1200/5005] lr: 1.000e-02, eta: 7:21:11, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6258 2022-12-31 19:31:38,039 - mmcls - INFO - Epoch [41][1300/5005] lr: 1.000e-02, eta: 7:21:02, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6498 2022-12-31 19:31:46,587 - mmcls - INFO - Epoch [41][1400/5005] lr: 1.000e-02, eta: 7:20:53, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6474 2022-12-31 19:31:55,327 - mmcls - INFO - Epoch [41][1500/5005] lr: 1.000e-02, eta: 7:20:44, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6568 2022-12-31 19:32:04,068 - mmcls - INFO - Epoch [41][1600/5005] lr: 1.000e-02, eta: 7:20:35, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6525 2022-12-31 19:32:12,867 - mmcls - INFO - Epoch [41][1700/5005] lr: 1.000e-02, eta: 7:20:26, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6472 2022-12-31 19:32:21,661 - mmcls - INFO - Epoch [41][1800/5005] lr: 1.000e-02, eta: 7:20:17, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6440 2022-12-31 19:32:30,508 - mmcls - INFO - Epoch [41][1900/5005] lr: 1.000e-02, eta: 7:20:08, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.6744 2022-12-31 19:32:39,774 - mmcls - INFO - Epoch [41][2000/5005] lr: 1.000e-02, eta: 7:20:00, time: 0.093, data_time: 0.013, memory: 1224, loss: 1.6583 2022-12-31 19:32:48,771 - mmcls - INFO - Epoch [41][2100/5005] lr: 1.000e-02, eta: 7:19:51, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6526 2022-12-31 19:32:57,380 - mmcls - INFO - Epoch [41][2200/5005] lr: 1.000e-02, eta: 7:19:42, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6581 2022-12-31 19:33:06,170 - mmcls - INFO - Epoch [41][2300/5005] lr: 1.000e-02, eta: 7:19:33, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6529 2022-12-31 19:33:14,822 - mmcls - INFO - Epoch [41][2400/5005] lr: 1.000e-02, eta: 7:19:24, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6677 2022-12-31 19:33:24,074 - mmcls - INFO - Epoch [41][2500/5005] lr: 1.000e-02, eta: 7:19:15, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.6658 2022-12-31 19:33:33,048 - mmcls - INFO - Epoch [41][2600/5005] lr: 1.000e-02, eta: 7:19:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6723 2022-12-31 19:33:41,821 - mmcls - INFO - Epoch [41][2700/5005] lr: 1.000e-02, eta: 7:18:58, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6736 2022-12-31 19:33:50,796 - mmcls - INFO - Epoch [41][2800/5005] lr: 1.000e-02, eta: 7:18:49, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6599 2022-12-31 19:33:59,980 - mmcls - INFO - Epoch [41][2900/5005] lr: 1.000e-02, eta: 7:18:41, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.6468 2022-12-31 19:34:09,013 - mmcls - INFO - Epoch [41][3000/5005] lr: 1.000e-02, eta: 7:18:32, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6607 2022-12-31 19:34:18,012 - mmcls - INFO - Epoch [41][3100/5005] lr: 1.000e-02, eta: 7:18:24, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.6630 2022-12-31 19:34:26,703 - mmcls - INFO - Epoch [41][3200/5005] lr: 1.000e-02, eta: 7:18:15, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.6575 2022-12-31 19:34:35,485 - mmcls - INFO - Epoch [41][3300/5005] lr: 1.000e-02, eta: 7:18:06, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6677 2022-12-31 19:34:44,198 - mmcls - INFO - Epoch [41][3400/5005] lr: 1.000e-02, eta: 7:17:56, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6578 2022-12-31 19:34:52,955 - mmcls - INFO - Epoch [41][3500/5005] lr: 1.000e-02, eta: 7:17:48, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6619 2022-12-31 19:35:01,617 - mmcls - INFO - Epoch [41][3600/5005] lr: 1.000e-02, eta: 7:17:38, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6718 2022-12-31 19:35:10,557 - mmcls - INFO - Epoch [41][3700/5005] lr: 1.000e-02, eta: 7:17:30, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6686 2022-12-31 19:35:19,233 - mmcls - INFO - Epoch [41][3800/5005] lr: 1.000e-02, eta: 7:17:21, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6497 2022-12-31 19:35:28,109 - mmcls - INFO - Epoch [41][3900/5005] lr: 1.000e-02, eta: 7:17:12, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6673 2022-12-31 19:35:36,711 - mmcls - INFO - Epoch [41][4000/5005] lr: 1.000e-02, eta: 7:17:03, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6705 2022-12-31 19:35:45,632 - mmcls - INFO - Epoch [41][4100/5005] lr: 1.000e-02, eta: 7:16:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6786 2022-12-31 19:35:54,240 - mmcls - INFO - Epoch [41][4200/5005] lr: 1.000e-02, eta: 7:16:45, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6651 2022-12-31 19:36:02,916 - mmcls - INFO - Epoch [41][4300/5005] lr: 1.000e-02, eta: 7:16:35, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6764 2022-12-31 19:36:12,029 - mmcls - INFO - Epoch [41][4400/5005] lr: 1.000e-02, eta: 7:16:27, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.6587 2022-12-31 19:36:20,598 - mmcls - INFO - Epoch [41][4500/5005] lr: 1.000e-02, eta: 7:16:18, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6703 2022-12-31 19:36:29,393 - mmcls - INFO - Epoch [41][4600/5005] lr: 1.000e-02, eta: 7:16:09, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6763 2022-12-31 19:36:38,336 - mmcls - INFO - Epoch [41][4700/5005] lr: 1.000e-02, eta: 7:16:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6586 2022-12-31 19:36:47,450 - mmcls - INFO - Epoch [41][4800/5005] lr: 1.000e-02, eta: 7:15:52, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6639 2022-12-31 19:36:56,126 - mmcls - INFO - Epoch [41][4900/5005] lr: 1.000e-02, eta: 7:15:43, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6755 2022-12-31 19:37:04,929 - mmcls - INFO - Epoch [41][5000/5005] lr: 1.000e-02, eta: 7:15:34, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6795 2022-12-31 19:37:27,892 - mmcls - INFO - Epoch(val) [41][196] accuracy_top-1: 65.6960, accuracy_top-5: 86.8600 2022-12-31 19:37:38,852 - mmcls - INFO - Epoch [42][100/5005] lr: 1.000e-02, eta: 7:15:27, time: 0.109, data_time: 0.043, memory: 1224, loss: 1.6067 2022-12-31 19:37:47,600 - mmcls - INFO - Epoch [42][200/5005] lr: 1.000e-02, eta: 7:15:18, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6167 2022-12-31 19:37:56,579 - mmcls - INFO - Epoch [42][300/5005] lr: 1.000e-02, eta: 7:15:09, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6260 2022-12-31 19:38:05,477 - mmcls - INFO - Epoch [42][400/5005] lr: 1.000e-02, eta: 7:15:00, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6379 2022-12-31 19:38:14,543 - mmcls - INFO - Epoch [42][500/5005] lr: 1.000e-02, eta: 7:14:52, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6194 2022-12-31 19:38:23,093 - mmcls - INFO - Epoch [42][600/5005] lr: 1.000e-02, eta: 7:14:42, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.5997 2022-12-31 19:38:31,882 - mmcls - INFO - Epoch [42][700/5005] lr: 1.000e-02, eta: 7:14:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6119 2022-12-31 19:38:40,626 - mmcls - INFO - Epoch [42][800/5005] lr: 1.000e-02, eta: 7:14:24, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6341 2022-12-31 19:38:49,440 - mmcls - INFO - Epoch [42][900/5005] lr: 1.000e-02, eta: 7:14:16, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6255 2022-12-31 19:38:58,431 - mmcls - INFO - Epoch [42][1000/5005] lr: 1.000e-02, eta: 7:14:07, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6159 2022-12-31 19:39:07,009 - mmcls - INFO - Epoch [42][1100/5005] lr: 1.000e-02, eta: 7:13:58, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6303 2022-12-31 19:39:15,766 - mmcls - INFO - Epoch [42][1200/5005] lr: 1.000e-02, eta: 7:13:49, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6461 2022-12-31 19:39:24,648 - mmcls - INFO - Epoch [42][1300/5005] lr: 1.000e-02, eta: 7:13:40, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6418 2022-12-31 19:39:33,207 - mmcls - INFO - Epoch [42][1400/5005] lr: 1.000e-02, eta: 7:13:31, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6652 2022-12-31 19:39:42,432 - mmcls - INFO - Epoch [42][1500/5005] lr: 1.000e-02, eta: 7:13:22, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.6372 2022-12-31 19:39:51,249 - mmcls - INFO - Epoch [42][1600/5005] lr: 1.000e-02, eta: 7:13:13, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6562 2022-12-31 19:39:59,885 - mmcls - INFO - Epoch [42][1700/5005] lr: 1.000e-02, eta: 7:13:04, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6526 2022-12-31 19:40:08,962 - mmcls - INFO - Epoch [42][1800/5005] lr: 1.000e-02, eta: 7:12:56, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6324 2022-12-31 19:40:17,845 - mmcls - INFO - Epoch [42][1900/5005] lr: 1.000e-02, eta: 7:12:47, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6459 2022-12-31 19:40:26,618 - mmcls - INFO - Epoch [42][2000/5005] lr: 1.000e-02, eta: 7:12:38, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6461 2022-12-31 19:40:35,412 - mmcls - INFO - Epoch [42][2100/5005] lr: 1.000e-02, eta: 7:12:29, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6596 2022-12-31 19:40:44,189 - mmcls - INFO - Epoch [42][2200/5005] lr: 1.000e-02, eta: 7:12:20, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6706 2022-12-31 19:40:53,216 - mmcls - INFO - Epoch [42][2300/5005] lr: 1.000e-02, eta: 7:12:11, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6547 2022-12-31 19:41:01,857 - mmcls - INFO - Epoch [42][2400/5005] lr: 1.000e-02, eta: 7:12:02, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6503 2022-12-31 19:41:10,328 - mmcls - INFO - Epoch [42][2500/5005] lr: 1.000e-02, eta: 7:11:53, time: 0.085, data_time: 0.028, memory: 1224, loss: 1.6632 2022-12-31 19:41:19,430 - mmcls - INFO - Epoch [42][2600/5005] lr: 1.000e-02, eta: 7:11:44, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.6697 2022-12-31 19:41:28,272 - mmcls - INFO - Epoch [42][2700/5005] lr: 1.000e-02, eta: 7:11:36, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6449 2022-12-31 19:41:37,200 - mmcls - INFO - Epoch [42][2800/5005] lr: 1.000e-02, eta: 7:11:27, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6292 2022-12-31 19:41:45,684 - mmcls - INFO - Epoch [42][2900/5005] lr: 1.000e-02, eta: 7:11:17, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6894 2022-12-31 19:41:54,338 - mmcls - INFO - Epoch [42][3000/5005] lr: 1.000e-02, eta: 7:11:08, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6778 2022-12-31 19:42:03,135 - mmcls - INFO - Epoch [42][3100/5005] lr: 1.000e-02, eta: 7:10:59, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6781 2022-12-31 19:42:11,998 - mmcls - INFO - Epoch [42][3200/5005] lr: 1.000e-02, eta: 7:10:51, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6458 2022-12-31 19:42:20,932 - mmcls - INFO - Epoch [42][3300/5005] lr: 1.000e-02, eta: 7:10:42, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.6500 2022-12-31 19:42:29,555 - mmcls - INFO - Epoch [42][3400/5005] lr: 1.000e-02, eta: 7:10:33, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6516 2022-12-31 19:42:38,083 - mmcls - INFO - Epoch [42][3500/5005] lr: 1.000e-02, eta: 7:10:23, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6697 2022-12-31 19:42:46,774 - mmcls - INFO - Epoch [42][3600/5005] lr: 1.000e-02, eta: 7:10:14, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6563 2022-12-31 19:42:55,327 - mmcls - INFO - Epoch [42][3700/5005] lr: 1.000e-02, eta: 7:10:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6642 2022-12-31 19:43:04,066 - mmcls - INFO - Epoch [42][3800/5005] lr: 1.000e-02, eta: 7:09:56, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6714 2022-12-31 19:43:12,883 - mmcls - INFO - Epoch [42][3900/5005] lr: 1.000e-02, eta: 7:09:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6669 2022-12-31 19:43:21,595 - mmcls - INFO - Epoch [42][4000/5005] lr: 1.000e-02, eta: 7:09:38, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6328 2022-12-31 19:43:30,361 - mmcls - INFO - Epoch [42][4100/5005] lr: 1.000e-02, eta: 7:09:29, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6801 2022-12-31 19:43:39,519 - mmcls - INFO - Epoch [42][4200/5005] lr: 1.000e-02, eta: 7:09:21, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.6545 2022-12-31 19:43:48,428 - mmcls - INFO - Epoch [42][4300/5005] lr: 1.000e-02, eta: 7:09:12, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6535 2022-12-31 19:43:57,519 - mmcls - INFO - Epoch [42][4400/5005] lr: 1.000e-02, eta: 7:09:03, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6709 2022-12-31 19:44:06,075 - mmcls - INFO - Epoch [42][4500/5005] lr: 1.000e-02, eta: 7:08:54, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6624 2022-12-31 19:44:15,039 - mmcls - INFO - Epoch [42][4600/5005] lr: 1.000e-02, eta: 7:08:45, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.6792 2022-12-31 19:44:24,006 - mmcls - INFO - Epoch [42][4700/5005] lr: 1.000e-02, eta: 7:08:37, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6720 2022-12-31 19:44:32,818 - mmcls - INFO - Epoch [42][4800/5005] lr: 1.000e-02, eta: 7:08:28, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6815 2022-12-31 19:44:41,500 - mmcls - INFO - Epoch [42][4900/5005] lr: 1.000e-02, eta: 7:08:19, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6603 2022-12-31 19:44:50,214 - mmcls - INFO - Epoch [42][5000/5005] lr: 1.000e-02, eta: 7:08:10, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6679 2022-12-31 19:45:12,924 - mmcls - INFO - Epoch(val) [42][196] accuracy_top-1: 65.5240, accuracy_top-5: 86.8380 2022-12-31 19:45:24,126 - mmcls - INFO - Epoch [43][100/5005] lr: 1.000e-02, eta: 7:08:03, time: 0.112, data_time: 0.057, memory: 1224, loss: 1.6401 2022-12-31 19:45:32,657 - mmcls - INFO - Epoch [43][200/5005] lr: 1.000e-02, eta: 7:07:54, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6057 2022-12-31 19:45:41,475 - mmcls - INFO - Epoch [43][300/5005] lr: 1.000e-02, eta: 7:07:45, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6359 2022-12-31 19:45:50,189 - mmcls - INFO - Epoch [43][400/5005] lr: 1.000e-02, eta: 7:07:36, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6369 2022-12-31 19:45:59,007 - mmcls - INFO - Epoch [43][500/5005] lr: 1.000e-02, eta: 7:07:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6381 2022-12-31 19:46:07,898 - mmcls - INFO - Epoch [43][600/5005] lr: 1.000e-02, eta: 7:07:18, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6334 2022-12-31 19:46:17,267 - mmcls - INFO - Epoch [43][700/5005] lr: 1.000e-02, eta: 7:07:10, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.6310 2022-12-31 19:46:25,976 - mmcls - INFO - Epoch [43][800/5005] lr: 1.000e-02, eta: 7:07:01, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6383 2022-12-31 19:46:34,797 - mmcls - INFO - Epoch [43][900/5005] lr: 1.000e-02, eta: 7:06:52, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6190 2022-12-31 19:46:43,684 - mmcls - INFO - Epoch [43][1000/5005] lr: 1.000e-02, eta: 7:06:43, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6603 2022-12-31 19:46:52,408 - mmcls - INFO - Epoch [43][1100/5005] lr: 1.000e-02, eta: 7:06:34, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.6253 2022-12-31 19:47:01,302 - mmcls - INFO - Epoch [43][1200/5005] lr: 1.000e-02, eta: 7:06:25, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6624 2022-12-31 19:47:10,021 - mmcls - INFO - Epoch [43][1300/5005] lr: 1.000e-02, eta: 7:06:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6600 2022-12-31 19:47:18,634 - mmcls - INFO - Epoch [43][1400/5005] lr: 1.000e-02, eta: 7:06:07, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6606 2022-12-31 19:47:27,626 - mmcls - INFO - Epoch [43][1500/5005] lr: 1.000e-02, eta: 7:05:59, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6428 2022-12-31 19:47:36,558 - mmcls - INFO - Epoch [43][1600/5005] lr: 1.000e-02, eta: 7:05:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6561 2022-12-31 19:47:45,306 - mmcls - INFO - Epoch [43][1700/5005] lr: 1.000e-02, eta: 7:05:41, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6417 2022-12-31 19:47:54,061 - mmcls - INFO - Epoch [43][1800/5005] lr: 1.000e-02, eta: 7:05:32, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6425 2022-12-31 19:48:02,861 - mmcls - INFO - Epoch [43][1900/5005] lr: 1.000e-02, eta: 7:05:23, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6574 2022-12-31 19:48:11,639 - mmcls - INFO - Epoch [43][2000/5005] lr: 1.000e-02, eta: 7:05:14, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6442 2022-12-31 19:48:20,271 - mmcls - INFO - Epoch [43][2100/5005] lr: 1.000e-02, eta: 7:05:05, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6425 2022-12-31 19:48:28,973 - mmcls - INFO - Epoch [43][2200/5005] lr: 1.000e-02, eta: 7:04:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6627 2022-12-31 19:48:37,919 - mmcls - INFO - Epoch [43][2300/5005] lr: 1.000e-02, eta: 7:04:47, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6454 2022-12-31 19:48:46,625 - mmcls - INFO - Epoch [43][2400/5005] lr: 1.000e-02, eta: 7:04:38, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6503 2022-12-31 19:48:55,326 - mmcls - INFO - Epoch [43][2500/5005] lr: 1.000e-02, eta: 7:04:29, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6477 2022-12-31 19:49:03,958 - mmcls - INFO - Epoch [43][2600/5005] lr: 1.000e-02, eta: 7:04:20, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6671 2022-12-31 19:49:12,830 - mmcls - INFO - Epoch [43][2700/5005] lr: 1.000e-02, eta: 7:04:11, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6303 2022-12-31 19:49:21,675 - mmcls - INFO - Epoch [43][2800/5005] lr: 1.000e-02, eta: 7:04:02, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6666 2022-12-31 19:49:30,612 - mmcls - INFO - Epoch [43][2900/5005] lr: 1.000e-02, eta: 7:03:53, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6781 2022-12-31 19:49:39,417 - mmcls - INFO - Epoch [43][3000/5005] lr: 1.000e-02, eta: 7:03:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6774 2022-12-31 19:49:47,925 - mmcls - INFO - Epoch [43][3100/5005] lr: 1.000e-02, eta: 7:03:35, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6741 2022-12-31 19:49:56,669 - mmcls - INFO - Epoch [43][3200/5005] lr: 1.000e-02, eta: 7:03:26, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6605 2022-12-31 19:50:05,394 - mmcls - INFO - Epoch [43][3300/5005] lr: 1.000e-02, eta: 7:03:17, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6599 2022-12-31 19:50:14,671 - mmcls - INFO - Epoch [43][3400/5005] lr: 1.000e-02, eta: 7:03:09, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.6612 2022-12-31 19:50:23,186 - mmcls - INFO - Epoch [43][3500/5005] lr: 1.000e-02, eta: 7:03:00, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.6628 2022-12-31 19:50:31,831 - mmcls - INFO - Epoch [43][3600/5005] lr: 1.000e-02, eta: 7:02:50, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6617 2022-12-31 19:50:40,472 - mmcls - INFO - Epoch [43][3700/5005] lr: 1.000e-02, eta: 7:02:41, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6580 2022-12-31 19:50:49,374 - mmcls - INFO - Epoch [43][3800/5005] lr: 1.000e-02, eta: 7:02:33, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6481 2022-12-31 19:50:58,304 - mmcls - INFO - Epoch [43][3900/5005] lr: 1.000e-02, eta: 7:02:24, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6644 2022-12-31 19:51:06,926 - mmcls - INFO - Epoch [43][4000/5005] lr: 1.000e-02, eta: 7:02:15, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.6582 2022-12-31 19:51:15,692 - mmcls - INFO - Epoch [43][4100/5005] lr: 1.000e-02, eta: 7:02:06, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6516 2022-12-31 19:51:24,617 - mmcls - INFO - Epoch [43][4200/5005] lr: 1.000e-02, eta: 7:01:57, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6771 2022-12-31 19:51:33,439 - mmcls - INFO - Epoch [43][4300/5005] lr: 1.000e-02, eta: 7:01:48, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6629 2022-12-31 19:51:42,079 - mmcls - INFO - Epoch [43][4400/5005] lr: 1.000e-02, eta: 7:01:39, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6435 2022-12-31 19:51:50,855 - mmcls - INFO - Epoch [43][4500/5005] lr: 1.000e-02, eta: 7:01:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6584 2022-12-31 19:51:59,744 - mmcls - INFO - Epoch [43][4600/5005] lr: 1.000e-02, eta: 7:01:21, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6864 2022-12-31 19:52:08,421 - mmcls - INFO - Epoch [43][4700/5005] lr: 1.000e-02, eta: 7:01:12, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6585 2022-12-31 19:52:16,992 - mmcls - INFO - Epoch [43][4800/5005] lr: 1.000e-02, eta: 7:01:03, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6583 2022-12-31 19:52:26,124 - mmcls - INFO - Epoch [43][4900/5005] lr: 1.000e-02, eta: 7:00:54, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.6634 2022-12-31 19:52:34,813 - mmcls - INFO - Epoch [43][5000/5005] lr: 1.000e-02, eta: 7:00:45, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6581 2022-12-31 19:52:57,625 - mmcls - INFO - Epoch(val) [43][196] accuracy_top-1: 66.1840, accuracy_top-5: 87.1460 2022-12-31 19:53:08,794 - mmcls - INFO - Epoch [44][100/5005] lr: 1.000e-02, eta: 7:00:38, time: 0.111, data_time: 0.042, memory: 1224, loss: 1.6342 2022-12-31 19:53:17,396 - mmcls - INFO - Epoch [44][200/5005] lr: 1.000e-02, eta: 7:00:29, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6201 2022-12-31 19:53:26,741 - mmcls - INFO - Epoch [44][300/5005] lr: 1.000e-02, eta: 7:00:21, time: 0.094, data_time: 0.001, memory: 1224, loss: 1.6425 2022-12-31 19:53:35,766 - mmcls - INFO - Epoch [44][400/5005] lr: 1.000e-02, eta: 7:00:12, time: 0.090, data_time: 0.031, memory: 1224, loss: 1.6217 2022-12-31 19:53:44,941 - mmcls - INFO - Epoch [44][500/5005] lr: 1.000e-02, eta: 7:00:04, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.6309 2022-12-31 19:53:53,740 - mmcls - INFO - Epoch [44][600/5005] lr: 1.000e-02, eta: 6:59:55, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6160 2022-12-31 19:54:02,485 - mmcls - INFO - Epoch [44][700/5005] lr: 1.000e-02, eta: 6:59:46, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.6138 2022-12-31 19:54:10,994 - mmcls - INFO - Epoch [44][800/5005] lr: 1.000e-02, eta: 6:59:37, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.6341 2022-12-31 19:54:19,856 - mmcls - INFO - Epoch [44][900/5005] lr: 1.000e-02, eta: 6:59:28, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6568 2022-12-31 19:54:28,797 - mmcls - INFO - Epoch [44][1000/5005] lr: 1.000e-02, eta: 6:59:19, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6387 2022-12-31 19:54:37,579 - mmcls - INFO - Epoch [44][1100/5005] lr: 1.000e-02, eta: 6:59:10, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6432 2022-12-31 19:54:46,331 - mmcls - INFO - Epoch [44][1200/5005] lr: 1.000e-02, eta: 6:59:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6539 2022-12-31 19:54:55,110 - mmcls - INFO - Epoch [44][1300/5005] lr: 1.000e-02, eta: 6:58:52, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6618 2022-12-31 19:55:03,885 - mmcls - INFO - Epoch [44][1400/5005] lr: 1.000e-02, eta: 6:58:43, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6369 2022-12-31 19:55:13,039 - mmcls - INFO - Epoch [44][1500/5005] lr: 1.000e-02, eta: 6:58:35, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6325 2022-12-31 19:55:21,727 - mmcls - INFO - Epoch [44][1600/5005] lr: 1.000e-02, eta: 6:58:26, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6434 2022-12-31 19:55:30,632 - mmcls - INFO - Epoch [44][1700/5005] lr: 1.000e-02, eta: 6:58:17, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6444 2022-12-31 19:55:39,481 - mmcls - INFO - Epoch [44][1800/5005] lr: 1.000e-02, eta: 6:58:08, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6583 2022-12-31 19:55:48,203 - mmcls - INFO - Epoch [44][1900/5005] lr: 1.000e-02, eta: 6:57:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6387 2022-12-31 19:55:57,392 - mmcls - INFO - Epoch [44][2000/5005] lr: 1.000e-02, eta: 6:57:51, time: 0.092, data_time: 0.010, memory: 1224, loss: 1.6551 2022-12-31 19:56:06,255 - mmcls - INFO - Epoch [44][2100/5005] lr: 1.000e-02, eta: 6:57:42, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6447 2022-12-31 19:56:15,072 - mmcls - INFO - Epoch [44][2200/5005] lr: 1.000e-02, eta: 6:57:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6794 2022-12-31 19:56:23,869 - mmcls - INFO - Epoch [44][2300/5005] lr: 1.000e-02, eta: 6:57:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6652 2022-12-31 19:56:33,063 - mmcls - INFO - Epoch [44][2400/5005] lr: 1.000e-02, eta: 6:57:16, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.6680 2022-12-31 19:56:41,897 - mmcls - INFO - Epoch [44][2500/5005] lr: 1.000e-02, eta: 6:57:07, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6529 2022-12-31 19:56:50,649 - mmcls - INFO - Epoch [44][2600/5005] lr: 1.000e-02, eta: 6:56:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6596 2022-12-31 19:56:59,599 - mmcls - INFO - Epoch [44][2700/5005] lr: 1.000e-02, eta: 6:56:49, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6574 2022-12-31 19:57:08,634 - mmcls - INFO - Epoch [44][2800/5005] lr: 1.000e-02, eta: 6:56:41, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6582 2022-12-31 19:57:17,134 - mmcls - INFO - Epoch [44][2900/5005] lr: 1.000e-02, eta: 6:56:31, time: 0.085, data_time: 0.028, memory: 1224, loss: 1.6388 2022-12-31 19:57:25,814 - mmcls - INFO - Epoch [44][3000/5005] lr: 1.000e-02, eta: 6:56:22, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6587 2022-12-31 19:57:34,645 - mmcls - INFO - Epoch [44][3100/5005] lr: 1.000e-02, eta: 6:56:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6462 2022-12-31 19:57:43,497 - mmcls - INFO - Epoch [44][3200/5005] lr: 1.000e-02, eta: 6:56:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6283 2022-12-31 19:57:52,214 - mmcls - INFO - Epoch [44][3300/5005] lr: 1.000e-02, eta: 6:55:56, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6516 2022-12-31 19:58:00,909 - mmcls - INFO - Epoch [44][3400/5005] lr: 1.000e-02, eta: 6:55:46, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6509 2022-12-31 19:58:09,584 - mmcls - INFO - Epoch [44][3500/5005] lr: 1.000e-02, eta: 6:55:37, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6598 2022-12-31 19:58:18,373 - mmcls - INFO - Epoch [44][3600/5005] lr: 1.000e-02, eta: 6:55:28, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6707 2022-12-31 19:58:27,574 - mmcls - INFO - Epoch [44][3700/5005] lr: 1.000e-02, eta: 6:55:20, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6572 2022-12-31 19:58:36,326 - mmcls - INFO - Epoch [44][3800/5005] lr: 1.000e-02, eta: 6:55:11, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6506 2022-12-31 19:58:44,999 - mmcls - INFO - Epoch [44][3900/5005] lr: 1.000e-02, eta: 6:55:02, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6615 2022-12-31 19:58:53,775 - mmcls - INFO - Epoch [44][4000/5005] lr: 1.000e-02, eta: 6:54:53, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6694 2022-12-31 19:59:02,741 - mmcls - INFO - Epoch [44][4100/5005] lr: 1.000e-02, eta: 6:54:44, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.6580 2022-12-31 19:59:11,325 - mmcls - INFO - Epoch [44][4200/5005] lr: 1.000e-02, eta: 6:54:35, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6669 2022-12-31 19:59:20,042 - mmcls - INFO - Epoch [44][4300/5005] lr: 1.000e-02, eta: 6:54:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6841 2022-12-31 19:59:29,013 - mmcls - INFO - Epoch [44][4400/5005] lr: 1.000e-02, eta: 6:54:17, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6647 2022-12-31 19:59:37,762 - mmcls - INFO - Epoch [44][4500/5005] lr: 1.000e-02, eta: 6:54:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6635 2022-12-31 19:59:46,392 - mmcls - INFO - Epoch [44][4600/5005] lr: 1.000e-02, eta: 6:53:59, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6379 2022-12-31 19:59:55,206 - mmcls - INFO - Epoch [44][4700/5005] lr: 1.000e-02, eta: 6:53:50, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6721 2022-12-31 20:00:04,232 - mmcls - INFO - Epoch [44][4800/5005] lr: 1.000e-02, eta: 6:53:42, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6827 2022-12-31 20:00:13,239 - mmcls - INFO - Epoch [44][4900/5005] lr: 1.000e-02, eta: 6:53:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6713 2022-12-31 20:00:22,031 - mmcls - INFO - Epoch [44][5000/5005] lr: 1.000e-02, eta: 6:53:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6843 2022-12-31 20:00:44,783 - mmcls - INFO - Epoch(val) [44][196] accuracy_top-1: 65.8820, accuracy_top-5: 87.0060 2022-12-31 20:00:55,801 - mmcls - INFO - Epoch [45][100/5005] lr: 1.000e-02, eta: 6:53:17, time: 0.110, data_time: 0.050, memory: 1224, loss: 1.6251 2022-12-31 20:01:04,382 - mmcls - INFO - Epoch [45][200/5005] lr: 1.000e-02, eta: 6:53:08, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6050 2022-12-31 20:01:13,129 - mmcls - INFO - Epoch [45][300/5005] lr: 1.000e-02, eta: 6:52:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6304 2022-12-31 20:01:21,743 - mmcls - INFO - Epoch [45][400/5005] lr: 1.000e-02, eta: 6:52:50, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6159 2022-12-31 20:01:30,566 - mmcls - INFO - Epoch [45][500/5005] lr: 1.000e-02, eta: 6:52:41, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.6383 2022-12-31 20:01:39,493 - mmcls - INFO - Epoch [45][600/5005] lr: 1.000e-02, eta: 6:52:32, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6335 2022-12-31 20:01:48,537 - mmcls - INFO - Epoch [45][700/5005] lr: 1.000e-02, eta: 6:52:24, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6303 2022-12-31 20:01:57,602 - mmcls - INFO - Epoch [45][800/5005] lr: 1.000e-02, eta: 6:52:15, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6350 2022-12-31 20:02:06,470 - mmcls - INFO - Epoch [45][900/5005] lr: 1.000e-02, eta: 6:52:06, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6391 2022-12-31 20:02:15,262 - mmcls - INFO - Epoch [45][1000/5005] lr: 1.000e-02, eta: 6:51:57, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6398 2022-12-31 20:02:24,141 - mmcls - INFO - Epoch [45][1100/5005] lr: 1.000e-02, eta: 6:51:48, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6507 2022-12-31 20:02:32,850 - mmcls - INFO - Epoch [45][1200/5005] lr: 1.000e-02, eta: 6:51:39, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6309 2022-12-31 20:02:41,513 - mmcls - INFO - Epoch [45][1300/5005] lr: 1.000e-02, eta: 6:51:30, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6340 2022-12-31 20:02:50,482 - mmcls - INFO - Epoch [45][1400/5005] lr: 1.000e-02, eta: 6:51:22, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.6377 2022-12-31 20:02:59,139 - mmcls - INFO - Epoch [45][1500/5005] lr: 1.000e-02, eta: 6:51:12, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6289 2022-12-31 20:03:08,062 - mmcls - INFO - Epoch [45][1600/5005] lr: 1.000e-02, eta: 6:51:04, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6720 2022-12-31 20:03:16,810 - mmcls - INFO - Epoch [45][1700/5005] lr: 1.000e-02, eta: 6:50:55, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6330 2022-12-31 20:03:25,602 - mmcls - INFO - Epoch [45][1800/5005] lr: 1.000e-02, eta: 6:50:46, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6475 2022-12-31 20:03:34,640 - mmcls - INFO - Epoch [45][1900/5005] lr: 1.000e-02, eta: 6:50:37, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6656 2022-12-31 20:03:43,689 - mmcls - INFO - Epoch [45][2000/5005] lr: 1.000e-02, eta: 6:50:29, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.6512 2022-12-31 20:03:52,677 - mmcls - INFO - Epoch [45][2100/5005] lr: 1.000e-02, eta: 6:50:20, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6633 2022-12-31 20:04:01,313 - mmcls - INFO - Epoch [45][2200/5005] lr: 1.000e-02, eta: 6:50:11, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6553 2022-12-31 20:04:10,137 - mmcls - INFO - Epoch [45][2300/5005] lr: 1.000e-02, eta: 6:50:02, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6456 2022-12-31 20:04:19,002 - mmcls - INFO - Epoch [45][2400/5005] lr: 1.000e-02, eta: 6:49:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6670 2022-12-31 20:04:27,574 - mmcls - INFO - Epoch [45][2500/5005] lr: 1.000e-02, eta: 6:49:44, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6478 2022-12-31 20:04:36,636 - mmcls - INFO - Epoch [45][2600/5005] lr: 1.000e-02, eta: 6:49:35, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6578 2022-12-31 20:04:45,218 - mmcls - INFO - Epoch [45][2700/5005] lr: 1.000e-02, eta: 6:49:26, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6598 2022-12-31 20:04:53,949 - mmcls - INFO - Epoch [45][2800/5005] lr: 1.000e-02, eta: 6:49:17, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6612 2022-12-31 20:05:02,605 - mmcls - INFO - Epoch [45][2900/5005] lr: 1.000e-02, eta: 6:49:08, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6722 2022-12-31 20:05:11,679 - mmcls - INFO - Epoch [45][3000/5005] lr: 1.000e-02, eta: 6:48:59, time: 0.091, data_time: 0.028, memory: 1224, loss: 1.6603 2022-12-31 20:05:20,438 - mmcls - INFO - Epoch [45][3100/5005] lr: 1.000e-02, eta: 6:48:50, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6654 2022-12-31 20:05:29,197 - mmcls - INFO - Epoch [45][3200/5005] lr: 1.000e-02, eta: 6:48:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6528 2022-12-31 20:05:37,913 - mmcls - INFO - Epoch [45][3300/5005] lr: 1.000e-02, eta: 6:48:32, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6419 2022-12-31 20:05:46,849 - mmcls - INFO - Epoch [45][3400/5005] lr: 1.000e-02, eta: 6:48:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6624 2022-12-31 20:05:55,651 - mmcls - INFO - Epoch [45][3500/5005] lr: 1.000e-02, eta: 6:48:15, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6559 2022-12-31 20:06:04,652 - mmcls - INFO - Epoch [45][3600/5005] lr: 1.000e-02, eta: 6:48:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6601 2022-12-31 20:06:13,387 - mmcls - INFO - Epoch [45][3700/5005] lr: 1.000e-02, eta: 6:47:57, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6733 2022-12-31 20:06:22,226 - mmcls - INFO - Epoch [45][3800/5005] lr: 1.000e-02, eta: 6:47:48, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6628 2022-12-31 20:06:30,654 - mmcls - INFO - Epoch [45][3900/5005] lr: 1.000e-02, eta: 6:47:39, time: 0.084, data_time: 0.009, memory: 1224, loss: 1.6608 2022-12-31 20:06:39,537 - mmcls - INFO - Epoch [45][4000/5005] lr: 1.000e-02, eta: 6:47:30, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6653 2022-12-31 20:06:48,666 - mmcls - INFO - Epoch [45][4100/5005] lr: 1.000e-02, eta: 6:47:22, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6427 2022-12-31 20:06:57,322 - mmcls - INFO - Epoch [45][4200/5005] lr: 1.000e-02, eta: 6:47:13, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6390 2022-12-31 20:07:06,234 - mmcls - INFO - Epoch [45][4300/5005] lr: 1.000e-02, eta: 6:47:04, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6718 2022-12-31 20:07:15,433 - mmcls - INFO - Epoch [45][4400/5005] lr: 1.000e-02, eta: 6:46:55, time: 0.092, data_time: 0.015, memory: 1224, loss: 1.6808 2022-12-31 20:07:24,007 - mmcls - INFO - Epoch [45][4500/5005] lr: 1.000e-02, eta: 6:46:46, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.6730 2022-12-31 20:07:32,841 - mmcls - INFO - Epoch [45][4600/5005] lr: 1.000e-02, eta: 6:46:37, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6558 2022-12-31 20:07:41,508 - mmcls - INFO - Epoch [45][4700/5005] lr: 1.000e-02, eta: 6:46:28, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6623 2022-12-31 20:07:50,716 - mmcls - INFO - Epoch [45][4800/5005] lr: 1.000e-02, eta: 6:46:20, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6641 2022-12-31 20:07:59,425 - mmcls - INFO - Epoch [45][4900/5005] lr: 1.000e-02, eta: 6:46:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6706 2022-12-31 20:08:08,273 - mmcls - INFO - Epoch [45][5000/5005] lr: 1.000e-02, eta: 6:46:02, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6731 2022-12-31 20:08:30,993 - mmcls - INFO - Epoch(val) [45][196] accuracy_top-1: 65.6440, accuracy_top-5: 86.9980 2022-12-31 20:08:42,005 - mmcls - INFO - Epoch [46][100/5005] lr: 1.000e-02, eta: 6:45:55, time: 0.110, data_time: 0.041, memory: 1224, loss: 1.6267 2022-12-31 20:08:50,792 - mmcls - INFO - Epoch [46][200/5005] lr: 1.000e-02, eta: 6:45:46, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6179 2022-12-31 20:08:59,339 - mmcls - INFO - Epoch [46][300/5005] lr: 1.000e-02, eta: 6:45:37, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6384 2022-12-31 20:09:08,177 - mmcls - INFO - Epoch [46][400/5005] lr: 1.000e-02, eta: 6:45:28, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6170 2022-12-31 20:09:16,896 - mmcls - INFO - Epoch [46][500/5005] lr: 1.000e-02, eta: 6:45:19, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6287 2022-12-31 20:09:25,671 - mmcls - INFO - Epoch [46][600/5005] lr: 1.000e-02, eta: 6:45:10, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6402 2022-12-31 20:09:34,461 - mmcls - INFO - Epoch [46][700/5005] lr: 1.000e-02, eta: 6:45:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6271 2022-12-31 20:09:43,327 - mmcls - INFO - Epoch [46][800/5005] lr: 1.000e-02, eta: 6:44:52, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6559 2022-12-31 20:09:51,991 - mmcls - INFO - Epoch [46][900/5005] lr: 1.000e-02, eta: 6:44:43, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6444 2022-12-31 20:10:00,975 - mmcls - INFO - Epoch [46][1000/5005] lr: 1.000e-02, eta: 6:44:34, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6304 2022-12-31 20:10:09,982 - mmcls - INFO - Epoch [46][1100/5005] lr: 1.000e-02, eta: 6:44:26, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6532 2022-12-31 20:10:18,871 - mmcls - INFO - Epoch [46][1200/5005] lr: 1.000e-02, eta: 6:44:17, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6506 2022-12-31 20:10:27,799 - mmcls - INFO - Epoch [46][1300/5005] lr: 1.000e-02, eta: 6:44:08, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.6311 2022-12-31 20:10:36,614 - mmcls - INFO - Epoch [46][1400/5005] lr: 1.000e-02, eta: 6:43:59, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6463 2022-12-31 20:10:45,546 - mmcls - INFO - Epoch [46][1500/5005] lr: 1.000e-02, eta: 6:43:50, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6315 2022-12-31 20:10:54,291 - mmcls - INFO - Epoch [46][1600/5005] lr: 1.000e-02, eta: 6:43:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6733 2022-12-31 20:11:02,921 - mmcls - INFO - Epoch [46][1700/5005] lr: 1.000e-02, eta: 6:43:32, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6654 2022-12-31 20:11:12,398 - mmcls - INFO - Epoch [46][1800/5005] lr: 1.000e-02, eta: 6:43:24, time: 0.095, data_time: 0.011, memory: 1224, loss: 1.6373 2022-12-31 20:11:21,266 - mmcls - INFO - Epoch [46][1900/5005] lr: 1.000e-02, eta: 6:43:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6635 2022-12-31 20:11:30,153 - mmcls - INFO - Epoch [46][2000/5005] lr: 1.000e-02, eta: 6:43:06, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6527 2022-12-31 20:11:39,270 - mmcls - INFO - Epoch [46][2100/5005] lr: 1.000e-02, eta: 6:42:58, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6438 2022-12-31 20:11:47,959 - mmcls - INFO - Epoch [46][2200/5005] lr: 1.000e-02, eta: 6:42:49, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6542 2022-12-31 20:11:56,974 - mmcls - INFO - Epoch [46][2300/5005] lr: 1.000e-02, eta: 6:42:40, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6433 2022-12-31 20:12:05,746 - mmcls - INFO - Epoch [46][2400/5005] lr: 1.000e-02, eta: 6:42:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6494 2022-12-31 20:12:14,492 - mmcls - INFO - Epoch [46][2500/5005] lr: 1.000e-02, eta: 6:42:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6467 2022-12-31 20:12:23,293 - mmcls - INFO - Epoch [46][2600/5005] lr: 1.000e-02, eta: 6:42:13, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.6925 2022-12-31 20:12:32,232 - mmcls - INFO - Epoch [46][2700/5005] lr: 1.000e-02, eta: 6:42:05, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6420 2022-12-31 20:12:41,285 - mmcls - INFO - Epoch [46][2800/5005] lr: 1.000e-02, eta: 6:41:56, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.6397 2022-12-31 20:12:50,223 - mmcls - INFO - Epoch [46][2900/5005] lr: 1.000e-02, eta: 6:41:47, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6533 2022-12-31 20:12:59,105 - mmcls - INFO - Epoch [46][3000/5005] lr: 1.000e-02, eta: 6:41:38, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6509 2022-12-31 20:13:07,988 - mmcls - INFO - Epoch [46][3100/5005] lr: 1.000e-02, eta: 6:41:30, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6493 2022-12-31 20:13:17,375 - mmcls - INFO - Epoch [46][3200/5005] lr: 1.000e-02, eta: 6:41:21, time: 0.094, data_time: 0.012, memory: 1224, loss: 1.6720 2022-12-31 20:13:26,620 - mmcls - INFO - Epoch [46][3300/5005] lr: 1.000e-02, eta: 6:41:13, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.6703 2022-12-31 20:13:35,472 - mmcls - INFO - Epoch [46][3400/5005] lr: 1.000e-02, eta: 6:41:04, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6301 2022-12-31 20:13:43,935 - mmcls - INFO - Epoch [46][3500/5005] lr: 1.000e-02, eta: 6:40:55, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6671 2022-12-31 20:13:52,533 - mmcls - INFO - Epoch [46][3600/5005] lr: 1.000e-02, eta: 6:40:46, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6413 2022-12-31 20:14:01,343 - mmcls - INFO - Epoch [46][3700/5005] lr: 1.000e-02, eta: 6:40:37, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6731 2022-12-31 20:14:10,071 - mmcls - INFO - Epoch [46][3800/5005] lr: 1.000e-02, eta: 6:40:28, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6688 2022-12-31 20:14:19,025 - mmcls - INFO - Epoch [46][3900/5005] lr: 1.000e-02, eta: 6:40:19, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6585 2022-12-31 20:14:28,117 - mmcls - INFO - Epoch [46][4000/5005] lr: 1.000e-02, eta: 6:40:11, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.6441 2022-12-31 20:14:36,667 - mmcls - INFO - Epoch [46][4100/5005] lr: 1.000e-02, eta: 6:40:01, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.6834 2022-12-31 20:14:45,523 - mmcls - INFO - Epoch [46][4200/5005] lr: 1.000e-02, eta: 6:39:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6664 2022-12-31 20:14:54,279 - mmcls - INFO - Epoch [46][4300/5005] lr: 1.000e-02, eta: 6:39:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6649 2022-12-31 20:15:03,100 - mmcls - INFO - Epoch [46][4400/5005] lr: 1.000e-02, eta: 6:39:35, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6885 2022-12-31 20:15:11,986 - mmcls - INFO - Epoch [46][4500/5005] lr: 1.000e-02, eta: 6:39:26, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6785 2022-12-31 20:15:20,700 - mmcls - INFO - Epoch [46][4600/5005] lr: 1.000e-02, eta: 6:39:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6804 2022-12-31 20:15:29,387 - mmcls - INFO - Epoch [46][4700/5005] lr: 1.000e-02, eta: 6:39:08, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6722 2022-12-31 20:15:38,146 - mmcls - INFO - Epoch [46][4800/5005] lr: 1.000e-02, eta: 6:38:59, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6813 2022-12-31 20:15:46,790 - mmcls - INFO - Epoch [46][4900/5005] lr: 1.000e-02, eta: 6:38:50, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6786 2022-12-31 20:15:55,730 - mmcls - INFO - Epoch [46][5000/5005] lr: 1.000e-02, eta: 6:38:41, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6889 2022-12-31 20:16:18,373 - mmcls - INFO - Epoch(val) [46][196] accuracy_top-1: 65.6980, accuracy_top-5: 86.9320 2022-12-31 20:16:29,572 - mmcls - INFO - Epoch [47][100/5005] lr: 1.000e-02, eta: 6:38:34, time: 0.112, data_time: 0.042, memory: 1224, loss: 1.6542 2022-12-31 20:16:38,254 - mmcls - INFO - Epoch [47][200/5005] lr: 1.000e-02, eta: 6:38:25, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.5992 2022-12-31 20:16:47,192 - mmcls - INFO - Epoch [47][300/5005] lr: 1.000e-02, eta: 6:38:16, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6223 2022-12-31 20:16:55,952 - mmcls - INFO - Epoch [47][400/5005] lr: 1.000e-02, eta: 6:38:07, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6113 2022-12-31 20:17:04,860 - mmcls - INFO - Epoch [47][500/5005] lr: 1.000e-02, eta: 6:37:58, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6440 2022-12-31 20:17:13,457 - mmcls - INFO - Epoch [47][600/5005] lr: 1.000e-02, eta: 6:37:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6187 2022-12-31 20:17:22,400 - mmcls - INFO - Epoch [47][700/5005] lr: 1.000e-02, eta: 6:37:40, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6267 2022-12-31 20:17:31,231 - mmcls - INFO - Epoch [47][800/5005] lr: 1.000e-02, eta: 6:37:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6213 2022-12-31 20:17:39,894 - mmcls - INFO - Epoch [47][900/5005] lr: 1.000e-02, eta: 6:37:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6214 2022-12-31 20:17:48,529 - mmcls - INFO - Epoch [47][1000/5005] lr: 1.000e-02, eta: 6:37:13, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6380 2022-12-31 20:17:57,242 - mmcls - INFO - Epoch [47][1100/5005] lr: 1.000e-02, eta: 6:37:04, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6359 2022-12-31 20:18:06,050 - mmcls - INFO - Epoch [47][1200/5005] lr: 1.000e-02, eta: 6:36:55, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6684 2022-12-31 20:18:14,727 - mmcls - INFO - Epoch [47][1300/5005] lr: 1.000e-02, eta: 6:36:46, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6626 2022-12-31 20:18:23,620 - mmcls - INFO - Epoch [47][1400/5005] lr: 1.000e-02, eta: 6:36:38, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.6550 2022-12-31 20:18:32,241 - mmcls - INFO - Epoch [47][1500/5005] lr: 1.000e-02, eta: 6:36:28, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.6360 2022-12-31 20:18:40,935 - mmcls - INFO - Epoch [47][1600/5005] lr: 1.000e-02, eta: 6:36:19, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.6515 2022-12-31 20:18:49,665 - mmcls - INFO - Epoch [47][1700/5005] lr: 1.000e-02, eta: 6:36:10, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6341 2022-12-31 20:18:58,452 - mmcls - INFO - Epoch [47][1800/5005] lr: 1.000e-02, eta: 6:36:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6478 2022-12-31 20:19:07,241 - mmcls - INFO - Epoch [47][1900/5005] lr: 1.000e-02, eta: 6:35:53, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.6842 2022-12-31 20:19:16,033 - mmcls - INFO - Epoch [47][2000/5005] lr: 1.000e-02, eta: 6:35:44, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6339 2022-12-31 20:19:24,863 - mmcls - INFO - Epoch [47][2100/5005] lr: 1.000e-02, eta: 6:35:35, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6400 2022-12-31 20:19:33,668 - mmcls - INFO - Epoch [47][2200/5005] lr: 1.000e-02, eta: 6:35:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6718 2022-12-31 20:19:42,621 - mmcls - INFO - Epoch [47][2300/5005] lr: 1.000e-02, eta: 6:35:17, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6711 2022-12-31 20:19:51,860 - mmcls - INFO - Epoch [47][2400/5005] lr: 1.000e-02, eta: 6:35:09, time: 0.092, data_time: 0.021, memory: 1224, loss: 1.6484 2022-12-31 20:20:00,625 - mmcls - INFO - Epoch [47][2500/5005] lr: 1.000e-02, eta: 6:35:00, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.6726 2022-12-31 20:20:09,197 - mmcls - INFO - Epoch [47][2600/5005] lr: 1.000e-02, eta: 6:34:51, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6651 2022-12-31 20:20:17,893 - mmcls - INFO - Epoch [47][2700/5005] lr: 1.000e-02, eta: 6:34:42, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6534 2022-12-31 20:20:27,090 - mmcls - INFO - Epoch [47][2800/5005] lr: 1.000e-02, eta: 6:34:33, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6679 2022-12-31 20:20:35,804 - mmcls - INFO - Epoch [47][2900/5005] lr: 1.000e-02, eta: 6:34:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6684 2022-12-31 20:20:44,554 - mmcls - INFO - Epoch [47][3000/5005] lr: 1.000e-02, eta: 6:34:15, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.6544 2022-12-31 20:20:53,483 - mmcls - INFO - Epoch [47][3100/5005] lr: 1.000e-02, eta: 6:34:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6396 2022-12-31 20:21:02,158 - mmcls - INFO - Epoch [47][3200/5005] lr: 1.000e-02, eta: 6:33:57, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6511 2022-12-31 20:21:11,227 - mmcls - INFO - Epoch [47][3300/5005] lr: 1.000e-02, eta: 6:33:49, time: 0.091, data_time: 0.026, memory: 1224, loss: 1.6566 2022-12-31 20:21:19,877 - mmcls - INFO - Epoch [47][3400/5005] lr: 1.000e-02, eta: 6:33:40, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6749 2022-12-31 20:21:28,826 - mmcls - INFO - Epoch [47][3500/5005] lr: 1.000e-02, eta: 6:33:31, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6677 2022-12-31 20:21:37,866 - mmcls - INFO - Epoch [47][3600/5005] lr: 1.000e-02, eta: 6:33:22, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.6708 2022-12-31 20:21:46,365 - mmcls - INFO - Epoch [47][3700/5005] lr: 1.000e-02, eta: 6:33:13, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.6369 2022-12-31 20:21:54,979 - mmcls - INFO - Epoch [47][3800/5005] lr: 1.000e-02, eta: 6:33:04, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6544 2022-12-31 20:22:03,662 - mmcls - INFO - Epoch [47][3900/5005] lr: 1.000e-02, eta: 6:32:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6709 2022-12-31 20:22:12,362 - mmcls - INFO - Epoch [47][4000/5005] lr: 1.000e-02, eta: 6:32:46, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6569 2022-12-31 20:22:21,202 - mmcls - INFO - Epoch [47][4100/5005] lr: 1.000e-02, eta: 6:32:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6956 2022-12-31 20:22:29,910 - mmcls - INFO - Epoch [47][4200/5005] lr: 1.000e-02, eta: 6:32:28, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6724 2022-12-31 20:22:38,896 - mmcls - INFO - Epoch [47][4300/5005] lr: 1.000e-02, eta: 6:32:19, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6744 2022-12-31 20:22:48,133 - mmcls - INFO - Epoch [47][4400/5005] lr: 1.000e-02, eta: 6:32:11, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6715 2022-12-31 20:22:56,980 - mmcls - INFO - Epoch [47][4500/5005] lr: 1.000e-02, eta: 6:32:02, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6410 2022-12-31 20:23:05,681 - mmcls - INFO - Epoch [47][4600/5005] lr: 1.000e-02, eta: 6:31:53, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6930 2022-12-31 20:23:14,524 - mmcls - INFO - Epoch [47][4700/5005] lr: 1.000e-02, eta: 6:31:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6721 2022-12-31 20:23:23,581 - mmcls - INFO - Epoch [47][4800/5005] lr: 1.000e-02, eta: 6:31:36, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.6630 2022-12-31 20:23:32,235 - mmcls - INFO - Epoch [47][4900/5005] lr: 1.000e-02, eta: 6:31:26, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6838 2022-12-31 20:23:40,977 - mmcls - INFO - Epoch [47][5000/5005] lr: 1.000e-02, eta: 6:31:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6439 2022-12-31 20:24:03,582 - mmcls - INFO - Epoch(val) [47][196] accuracy_top-1: 65.7820, accuracy_top-5: 86.8500 2022-12-31 20:24:14,677 - mmcls - INFO - Epoch [48][100/5005] lr: 1.000e-02, eta: 6:31:10, time: 0.111, data_time: 0.042, memory: 1224, loss: 1.6260 2022-12-31 20:24:23,504 - mmcls - INFO - Epoch [48][200/5005] lr: 1.000e-02, eta: 6:31:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6213 2022-12-31 20:24:32,468 - mmcls - INFO - Epoch [48][300/5005] lr: 1.000e-02, eta: 6:30:53, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6220 2022-12-31 20:24:41,674 - mmcls - INFO - Epoch [48][400/5005] lr: 1.000e-02, eta: 6:30:44, time: 0.092, data_time: 0.020, memory: 1224, loss: 1.6218 2022-12-31 20:24:50,403 - mmcls - INFO - Epoch [48][500/5005] lr: 1.000e-02, eta: 6:30:35, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6276 2022-12-31 20:24:59,175 - mmcls - INFO - Epoch [48][600/5005] lr: 1.000e-02, eta: 6:30:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6224 2022-12-31 20:25:07,903 - mmcls - INFO - Epoch [48][700/5005] lr: 1.000e-02, eta: 6:30:17, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6211 2022-12-31 20:25:16,661 - mmcls - INFO - Epoch [48][800/5005] lr: 1.000e-02, eta: 6:30:08, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6481 2022-12-31 20:25:25,310 - mmcls - INFO - Epoch [48][900/5005] lr: 1.000e-02, eta: 6:29:59, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6203 2022-12-31 20:25:34,075 - mmcls - INFO - Epoch [48][1000/5005] lr: 1.000e-02, eta: 6:29:50, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6417 2022-12-31 20:25:42,742 - mmcls - INFO - Epoch [48][1100/5005] lr: 1.000e-02, eta: 6:29:41, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6236 2022-12-31 20:25:51,529 - mmcls - INFO - Epoch [48][1200/5005] lr: 1.000e-02, eta: 6:29:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6280 2022-12-31 20:26:00,168 - mmcls - INFO - Epoch [48][1300/5005] lr: 1.000e-02, eta: 6:29:23, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6508 2022-12-31 20:26:08,831 - mmcls - INFO - Epoch [48][1400/5005] lr: 1.000e-02, eta: 6:29:14, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6234 2022-12-31 20:26:17,522 - mmcls - INFO - Epoch [48][1500/5005] lr: 1.000e-02, eta: 6:29:05, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6461 2022-12-31 20:26:26,457 - mmcls - INFO - Epoch [48][1600/5005] lr: 1.000e-02, eta: 6:28:56, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6476 2022-12-31 20:26:35,095 - mmcls - INFO - Epoch [48][1700/5005] lr: 1.000e-02, eta: 6:28:47, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.6254 2022-12-31 20:26:43,673 - mmcls - INFO - Epoch [48][1800/5005] lr: 1.000e-02, eta: 6:28:38, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6626 2022-12-31 20:26:52,715 - mmcls - INFO - Epoch [48][1900/5005] lr: 1.000e-02, eta: 6:28:29, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6519 2022-12-31 20:27:01,488 - mmcls - INFO - Epoch [48][2000/5005] lr: 1.000e-02, eta: 6:28:20, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6461 2022-12-31 20:27:10,375 - mmcls - INFO - Epoch [48][2100/5005] lr: 1.000e-02, eta: 6:28:12, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6652 2022-12-31 20:27:19,241 - mmcls - INFO - Epoch [48][2200/5005] lr: 1.000e-02, eta: 6:28:03, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6490 2022-12-31 20:27:28,065 - mmcls - INFO - Epoch [48][2300/5005] lr: 1.000e-02, eta: 6:27:54, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6721 2022-12-31 20:27:37,142 - mmcls - INFO - Epoch [48][2400/5005] lr: 1.000e-02, eta: 6:27:45, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.6625 2022-12-31 20:27:45,884 - mmcls - INFO - Epoch [48][2500/5005] lr: 1.000e-02, eta: 6:27:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6668 2022-12-31 20:27:54,456 - mmcls - INFO - Epoch [48][2600/5005] lr: 1.000e-02, eta: 6:27:27, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6588 2022-12-31 20:28:03,236 - mmcls - INFO - Epoch [48][2700/5005] lr: 1.000e-02, eta: 6:27:18, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.6488 2022-12-31 20:28:12,122 - mmcls - INFO - Epoch [48][2800/5005] lr: 1.000e-02, eta: 6:27:09, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6581 2022-12-31 20:28:20,658 - mmcls - INFO - Epoch [48][2900/5005] lr: 1.000e-02, eta: 6:27:00, time: 0.085, data_time: 0.019, memory: 1224, loss: 1.6795 2022-12-31 20:28:29,559 - mmcls - INFO - Epoch [48][3000/5005] lr: 1.000e-02, eta: 6:26:51, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6712 2022-12-31 20:28:38,640 - mmcls - INFO - Epoch [48][3100/5005] lr: 1.000e-02, eta: 6:26:43, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6464 2022-12-31 20:28:47,378 - mmcls - INFO - Epoch [48][3200/5005] lr: 1.000e-02, eta: 6:26:34, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6504 2022-12-31 20:28:56,505 - mmcls - INFO - Epoch [48][3300/5005] lr: 1.000e-02, eta: 6:26:25, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.6394 2022-12-31 20:29:05,572 - mmcls - INFO - Epoch [48][3400/5005] lr: 1.000e-02, eta: 6:26:17, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6769 2022-12-31 20:29:14,334 - mmcls - INFO - Epoch [48][3500/5005] lr: 1.000e-02, eta: 6:26:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6711 2022-12-31 20:29:23,074 - mmcls - INFO - Epoch [48][3600/5005] lr: 1.000e-02, eta: 6:25:59, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6781 2022-12-31 20:29:31,718 - mmcls - INFO - Epoch [48][3700/5005] lr: 1.000e-02, eta: 6:25:50, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6396 2022-12-31 20:29:40,698 - mmcls - INFO - Epoch [48][3800/5005] lr: 1.000e-02, eta: 6:25:41, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6705 2022-12-31 20:29:49,973 - mmcls - INFO - Epoch [48][3900/5005] lr: 1.000e-02, eta: 6:25:33, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.6763 2022-12-31 20:29:58,737 - mmcls - INFO - Epoch [48][4000/5005] lr: 1.000e-02, eta: 6:25:24, time: 0.088, data_time: 0.031, memory: 1224, loss: 1.6756 2022-12-31 20:30:07,436 - mmcls - INFO - Epoch [48][4100/5005] lr: 1.000e-02, eta: 6:25:15, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6697 2022-12-31 20:30:16,301 - mmcls - INFO - Epoch [48][4200/5005] lr: 1.000e-02, eta: 6:25:06, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6524 2022-12-31 20:30:24,943 - mmcls - INFO - Epoch [48][4300/5005] lr: 1.000e-02, eta: 6:24:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6756 2022-12-31 20:30:33,554 - mmcls - INFO - Epoch [48][4400/5005] lr: 1.000e-02, eta: 6:24:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6895 2022-12-31 20:30:42,272 - mmcls - INFO - Epoch [48][4500/5005] lr: 1.000e-02, eta: 6:24:39, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6557 2022-12-31 20:30:51,031 - mmcls - INFO - Epoch [48][4600/5005] lr: 1.000e-02, eta: 6:24:30, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.6774 2022-12-31 20:30:59,909 - mmcls - INFO - Epoch [48][4700/5005] lr: 1.000e-02, eta: 6:24:21, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6604 2022-12-31 20:31:08,525 - mmcls - INFO - Epoch [48][4800/5005] lr: 1.000e-02, eta: 6:24:12, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6584 2022-12-31 20:31:17,511 - mmcls - INFO - Epoch [48][4900/5005] lr: 1.000e-02, eta: 6:24:03, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.6578 2022-12-31 20:31:26,585 - mmcls - INFO - Epoch [48][5000/5005] lr: 1.000e-02, eta: 6:23:54, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6717 2022-12-31 20:31:49,223 - mmcls - INFO - Epoch(val) [48][196] accuracy_top-1: 65.7800, accuracy_top-5: 87.0580 2022-12-31 20:32:00,324 - mmcls - INFO - Epoch [49][100/5005] lr: 1.000e-02, eta: 6:23:47, time: 0.111, data_time: 0.046, memory: 1224, loss: 1.6101 2022-12-31 20:32:09,213 - mmcls - INFO - Epoch [49][200/5005] lr: 1.000e-02, eta: 6:23:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6182 2022-12-31 20:32:18,073 - mmcls - INFO - Epoch [49][300/5005] lr: 1.000e-02, eta: 6:23:29, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6276 2022-12-31 20:32:27,039 - mmcls - INFO - Epoch [49][400/5005] lr: 1.000e-02, eta: 6:23:21, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6283 2022-12-31 20:32:36,007 - mmcls - INFO - Epoch [49][500/5005] lr: 1.000e-02, eta: 6:23:12, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.6456 2022-12-31 20:32:45,203 - mmcls - INFO - Epoch [49][600/5005] lr: 1.000e-02, eta: 6:23:03, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.6370 2022-12-31 20:32:54,194 - mmcls - INFO - Epoch [49][700/5005] lr: 1.000e-02, eta: 6:22:55, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6425 2022-12-31 20:33:03,101 - mmcls - INFO - Epoch [49][800/5005] lr: 1.000e-02, eta: 6:22:46, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.5934 2022-12-31 20:33:11,889 - mmcls - INFO - Epoch [49][900/5005] lr: 1.000e-02, eta: 6:22:37, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6171 2022-12-31 20:33:20,568 - mmcls - INFO - Epoch [49][1000/5005] lr: 1.000e-02, eta: 6:22:28, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6592 2022-12-31 20:33:29,316 - mmcls - INFO - Epoch [49][1100/5005] lr: 1.000e-02, eta: 6:22:19, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6162 2022-12-31 20:33:37,862 - mmcls - INFO - Epoch [49][1200/5005] lr: 1.000e-02, eta: 6:22:10, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.6602 2022-12-31 20:33:46,560 - mmcls - INFO - Epoch [49][1300/5005] lr: 1.000e-02, eta: 6:22:01, time: 0.087, data_time: 0.036, memory: 1224, loss: 1.6392 2022-12-31 20:33:55,339 - mmcls - INFO - Epoch [49][1400/5005] lr: 1.000e-02, eta: 6:21:52, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6641 2022-12-31 20:34:04,320 - mmcls - INFO - Epoch [49][1500/5005] lr: 1.000e-02, eta: 6:21:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6377 2022-12-31 20:34:13,041 - mmcls - INFO - Epoch [49][1600/5005] lr: 1.000e-02, eta: 6:21:34, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6408 2022-12-31 20:34:21,754 - mmcls - INFO - Epoch [49][1700/5005] lr: 1.000e-02, eta: 6:21:25, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6552 2022-12-31 20:34:30,770 - mmcls - INFO - Epoch [49][1800/5005] lr: 1.000e-02, eta: 6:21:17, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6612 2022-12-31 20:34:39,549 - mmcls - INFO - Epoch [49][1900/5005] lr: 1.000e-02, eta: 6:21:08, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6530 2022-12-31 20:34:48,353 - mmcls - INFO - Epoch [49][2000/5005] lr: 1.000e-02, eta: 6:20:59, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6468 2022-12-31 20:34:57,063 - mmcls - INFO - Epoch [49][2100/5005] lr: 1.000e-02, eta: 6:20:50, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6507 2022-12-31 20:35:05,795 - mmcls - INFO - Epoch [49][2200/5005] lr: 1.000e-02, eta: 6:20:41, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.6425 2022-12-31 20:35:14,637 - mmcls - INFO - Epoch [49][2300/5005] lr: 1.000e-02, eta: 6:20:32, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6577 2022-12-31 20:35:23,367 - mmcls - INFO - Epoch [49][2400/5005] lr: 1.000e-02, eta: 6:20:23, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6473 2022-12-31 20:35:32,028 - mmcls - INFO - Epoch [49][2500/5005] lr: 1.000e-02, eta: 6:20:14, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6535 2022-12-31 20:35:41,261 - mmcls - INFO - Epoch [49][2600/5005] lr: 1.000e-02, eta: 6:20:05, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6588 2022-12-31 20:35:49,887 - mmcls - INFO - Epoch [49][2700/5005] lr: 1.000e-02, eta: 6:19:56, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6608 2022-12-31 20:35:58,856 - mmcls - INFO - Epoch [49][2800/5005] lr: 1.000e-02, eta: 6:19:48, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6577 2022-12-31 20:36:07,768 - mmcls - INFO - Epoch [49][2900/5005] lr: 1.000e-02, eta: 6:19:39, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6671 2022-12-31 20:36:16,641 - mmcls - INFO - Epoch [49][3000/5005] lr: 1.000e-02, eta: 6:19:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6641 2022-12-31 20:36:25,360 - mmcls - INFO - Epoch [49][3100/5005] lr: 1.000e-02, eta: 6:19:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6657 2022-12-31 20:36:34,462 - mmcls - INFO - Epoch [49][3200/5005] lr: 1.000e-02, eta: 6:19:12, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6483 2022-12-31 20:36:43,191 - mmcls - INFO - Epoch [49][3300/5005] lr: 1.000e-02, eta: 6:19:03, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6448 2022-12-31 20:36:52,002 - mmcls - INFO - Epoch [49][3400/5005] lr: 1.000e-02, eta: 6:18:55, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6672 2022-12-31 20:37:00,758 - mmcls - INFO - Epoch [49][3500/5005] lr: 1.000e-02, eta: 6:18:46, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6684 2022-12-31 20:37:09,635 - mmcls - INFO - Epoch [49][3600/5005] lr: 1.000e-02, eta: 6:18:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6414 2022-12-31 20:37:18,394 - mmcls - INFO - Epoch [49][3700/5005] lr: 1.000e-02, eta: 6:18:28, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6666 2022-12-31 20:37:27,111 - mmcls - INFO - Epoch [49][3800/5005] lr: 1.000e-02, eta: 6:18:19, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6507 2022-12-31 20:37:35,666 - mmcls - INFO - Epoch [49][3900/5005] lr: 1.000e-02, eta: 6:18:10, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6581 2022-12-31 20:37:44,602 - mmcls - INFO - Epoch [49][4000/5005] lr: 1.000e-02, eta: 6:18:01, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6830 2022-12-31 20:37:53,496 - mmcls - INFO - Epoch [49][4100/5005] lr: 1.000e-02, eta: 6:17:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6652 2022-12-31 20:38:02,238 - mmcls - INFO - Epoch [49][4200/5005] lr: 1.000e-02, eta: 6:17:43, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6790 2022-12-31 20:38:11,343 - mmcls - INFO - Epoch [49][4300/5005] lr: 1.000e-02, eta: 6:17:35, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6841 2022-12-31 20:38:19,829 - mmcls - INFO - Epoch [49][4400/5005] lr: 1.000e-02, eta: 6:17:25, time: 0.085, data_time: 0.027, memory: 1224, loss: 1.6531 2022-12-31 20:38:28,522 - mmcls - INFO - Epoch [49][4500/5005] lr: 1.000e-02, eta: 6:17:16, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6880 2022-12-31 20:38:37,331 - mmcls - INFO - Epoch [49][4600/5005] lr: 1.000e-02, eta: 6:17:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6636 2022-12-31 20:38:46,237 - mmcls - INFO - Epoch [49][4700/5005] lr: 1.000e-02, eta: 6:16:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.7037 2022-12-31 20:38:55,311 - mmcls - INFO - Epoch [49][4800/5005] lr: 1.000e-02, eta: 6:16:50, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6724 2022-12-31 20:39:03,840 - mmcls - INFO - Epoch [49][4900/5005] lr: 1.000e-02, eta: 6:16:41, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.6749 2022-12-31 20:39:12,588 - mmcls - INFO - Epoch [49][5000/5005] lr: 1.000e-02, eta: 6:16:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6754 2022-12-31 20:39:35,222 - mmcls - INFO - Epoch(val) [49][196] accuracy_top-1: 66.1600, accuracy_top-5: 86.9960 2022-12-31 20:39:46,302 - mmcls - INFO - Epoch [50][100/5005] lr: 1.000e-02, eta: 6:16:24, time: 0.111, data_time: 0.051, memory: 1224, loss: 1.6133 2022-12-31 20:39:55,211 - mmcls - INFO - Epoch [50][200/5005] lr: 1.000e-02, eta: 6:16:16, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6046 2022-12-31 20:40:04,089 - mmcls - INFO - Epoch [50][300/5005] lr: 1.000e-02, eta: 6:16:07, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6392 2022-12-31 20:40:12,771 - mmcls - INFO - Epoch [50][400/5005] lr: 1.000e-02, eta: 6:15:58, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6348 2022-12-31 20:40:21,423 - mmcls - INFO - Epoch [50][500/5005] lr: 1.000e-02, eta: 6:15:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6296 2022-12-31 20:40:30,177 - mmcls - INFO - Epoch [50][600/5005] lr: 1.000e-02, eta: 6:15:40, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6370 2022-12-31 20:40:39,086 - mmcls - INFO - Epoch [50][700/5005] lr: 1.000e-02, eta: 6:15:31, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6393 2022-12-31 20:40:48,301 - mmcls - INFO - Epoch [50][800/5005] lr: 1.000e-02, eta: 6:15:22, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.6361 2022-12-31 20:40:57,149 - mmcls - INFO - Epoch [50][900/5005] lr: 1.000e-02, eta: 6:15:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6345 2022-12-31 20:41:06,092 - mmcls - INFO - Epoch [50][1000/5005] lr: 1.000e-02, eta: 6:15:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6629 2022-12-31 20:41:14,953 - mmcls - INFO - Epoch [50][1100/5005] lr: 1.000e-02, eta: 6:14:56, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6340 2022-12-31 20:41:23,708 - mmcls - INFO - Epoch [50][1200/5005] lr: 1.000e-02, eta: 6:14:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6278 2022-12-31 20:41:32,368 - mmcls - INFO - Epoch [50][1300/5005] lr: 1.000e-02, eta: 6:14:38, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6498 2022-12-31 20:41:41,268 - mmcls - INFO - Epoch [50][1400/5005] lr: 1.000e-02, eta: 6:14:29, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6544 2022-12-31 20:41:49,807 - mmcls - INFO - Epoch [50][1500/5005] lr: 1.000e-02, eta: 6:14:20, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6523 2022-12-31 20:41:58,596 - mmcls - INFO - Epoch [50][1600/5005] lr: 1.000e-02, eta: 6:14:11, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6508 2022-12-31 20:42:07,061 - mmcls - INFO - Epoch [50][1700/5005] lr: 1.000e-02, eta: 6:14:02, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6420 2022-12-31 20:42:15,605 - mmcls - INFO - Epoch [50][1800/5005] lr: 1.000e-02, eta: 6:13:53, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6254 2022-12-31 20:42:24,352 - mmcls - INFO - Epoch [50][1900/5005] lr: 1.000e-02, eta: 6:13:44, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6457 2022-12-31 20:42:33,298 - mmcls - INFO - Epoch [50][2000/5005] lr: 1.000e-02, eta: 6:13:35, time: 0.089, data_time: 0.044, memory: 1224, loss: 1.6248 2022-12-31 20:42:42,079 - mmcls - INFO - Epoch [50][2100/5005] lr: 1.000e-02, eta: 6:13:26, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6264 2022-12-31 20:42:50,809 - mmcls - INFO - Epoch [50][2200/5005] lr: 1.000e-02, eta: 6:13:17, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6649 2022-12-31 20:42:59,744 - mmcls - INFO - Epoch [50][2300/5005] lr: 1.000e-02, eta: 6:13:08, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6503 2022-12-31 20:43:08,496 - mmcls - INFO - Epoch [50][2400/5005] lr: 1.000e-02, eta: 6:12:59, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6613 2022-12-31 20:43:17,224 - mmcls - INFO - Epoch [50][2500/5005] lr: 1.000e-02, eta: 6:12:50, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6721 2022-12-31 20:43:26,201 - mmcls - INFO - Epoch [50][2600/5005] lr: 1.000e-02, eta: 6:12:42, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6639 2022-12-31 20:43:34,880 - mmcls - INFO - Epoch [50][2700/5005] lr: 1.000e-02, eta: 6:12:33, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6454 2022-12-31 20:43:44,003 - mmcls - INFO - Epoch [50][2800/5005] lr: 1.000e-02, eta: 6:12:24, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6428 2022-12-31 20:43:52,730 - mmcls - INFO - Epoch [50][2900/5005] lr: 1.000e-02, eta: 6:12:15, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6221 2022-12-31 20:44:01,360 - mmcls - INFO - Epoch [50][3000/5005] lr: 1.000e-02, eta: 6:12:06, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6534 2022-12-31 20:44:10,243 - mmcls - INFO - Epoch [50][3100/5005] lr: 1.000e-02, eta: 6:11:57, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6653 2022-12-31 20:44:19,231 - mmcls - INFO - Epoch [50][3200/5005] lr: 1.000e-02, eta: 6:11:48, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.6524 2022-12-31 20:44:28,129 - mmcls - INFO - Epoch [50][3300/5005] lr: 1.000e-02, eta: 6:11:40, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6355 2022-12-31 20:44:37,080 - mmcls - INFO - Epoch [50][3400/5005] lr: 1.000e-02, eta: 6:11:31, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6666 2022-12-31 20:44:45,918 - mmcls - INFO - Epoch [50][3500/5005] lr: 1.000e-02, eta: 6:11:22, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6643 2022-12-31 20:44:55,003 - mmcls - INFO - Epoch [50][3600/5005] lr: 1.000e-02, eta: 6:11:13, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6675 2022-12-31 20:45:04,147 - mmcls - INFO - Epoch [50][3700/5005] lr: 1.000e-02, eta: 6:11:05, time: 0.091, data_time: 0.029, memory: 1224, loss: 1.6814 2022-12-31 20:45:12,823 - mmcls - INFO - Epoch [50][3800/5005] lr: 1.000e-02, eta: 6:10:56, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.6527 2022-12-31 20:45:21,800 - mmcls - INFO - Epoch [50][3900/5005] lr: 1.000e-02, eta: 6:10:47, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6751 2022-12-31 20:45:30,441 - mmcls - INFO - Epoch [50][4000/5005] lr: 1.000e-02, eta: 6:10:38, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6494 2022-12-31 20:45:39,164 - mmcls - INFO - Epoch [50][4100/5005] lr: 1.000e-02, eta: 6:10:29, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6416 2022-12-31 20:45:47,738 - mmcls - INFO - Epoch [50][4200/5005] lr: 1.000e-02, eta: 6:10:20, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6505 2022-12-31 20:45:56,521 - mmcls - INFO - Epoch [50][4300/5005] lr: 1.000e-02, eta: 6:10:11, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6645 2022-12-31 20:46:05,044 - mmcls - INFO - Epoch [50][4400/5005] lr: 1.000e-02, eta: 6:10:02, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6621 2022-12-31 20:46:13,924 - mmcls - INFO - Epoch [50][4500/5005] lr: 1.000e-02, eta: 6:09:53, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6895 2022-12-31 20:46:22,902 - mmcls - INFO - Epoch [50][4600/5005] lr: 1.000e-02, eta: 6:09:44, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6627 2022-12-31 20:46:32,047 - mmcls - INFO - Epoch [50][4700/5005] lr: 1.000e-02, eta: 6:09:36, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.6633 2022-12-31 20:46:40,618 - mmcls - INFO - Epoch [50][4800/5005] lr: 1.000e-02, eta: 6:09:27, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6567 2022-12-31 20:46:49,326 - mmcls - INFO - Epoch [50][4900/5005] lr: 1.000e-02, eta: 6:09:18, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6868 2022-12-31 20:46:58,132 - mmcls - INFO - Epoch [50][5000/5005] lr: 1.000e-02, eta: 6:09:09, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6668 2022-12-31 20:46:58,579 - mmcls - INFO - Saving checkpoint at 50 epochs 2022-12-31 20:47:20,888 - mmcls - INFO - Epoch(val) [50][196] accuracy_top-1: 65.7480, accuracy_top-5: 87.1020 2022-12-31 20:47:32,005 - mmcls - INFO - Epoch [51][100/5005] lr: 1.000e-02, eta: 6:09:01, time: 0.111, data_time: 0.040, memory: 1224, loss: 1.6347 2022-12-31 20:47:40,878 - mmcls - INFO - Epoch [51][200/5005] lr: 1.000e-02, eta: 6:08:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.5984 2022-12-31 20:47:49,685 - mmcls - INFO - Epoch [51][300/5005] lr: 1.000e-02, eta: 6:08:43, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6390 2022-12-31 20:47:58,720 - mmcls - INFO - Epoch [51][400/5005] lr: 1.000e-02, eta: 6:08:35, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6097 2022-12-31 20:48:07,626 - mmcls - INFO - Epoch [51][500/5005] lr: 1.000e-02, eta: 6:08:26, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.5980 2022-12-31 20:48:16,754 - mmcls - INFO - Epoch [51][600/5005] lr: 1.000e-02, eta: 6:08:17, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6589 2022-12-31 20:48:25,621 - mmcls - INFO - Epoch [51][700/5005] lr: 1.000e-02, eta: 6:08:09, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6254 2022-12-31 20:48:34,297 - mmcls - INFO - Epoch [51][800/5005] lr: 1.000e-02, eta: 6:08:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6494 2022-12-31 20:48:43,206 - mmcls - INFO - Epoch [51][900/5005] lr: 1.000e-02, eta: 6:07:51, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6303 2022-12-31 20:48:52,141 - mmcls - INFO - Epoch [51][1000/5005] lr: 1.000e-02, eta: 6:07:42, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6269 2022-12-31 20:49:00,930 - mmcls - INFO - Epoch [51][1100/5005] lr: 1.000e-02, eta: 6:07:33, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6414 2022-12-31 20:49:09,596 - mmcls - INFO - Epoch [51][1200/5005] lr: 1.000e-02, eta: 6:07:24, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6313 2022-12-31 20:49:18,406 - mmcls - INFO - Epoch [51][1300/5005] lr: 1.000e-02, eta: 6:07:15, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6442 2022-12-31 20:49:27,464 - mmcls - INFO - Epoch [51][1400/5005] lr: 1.000e-02, eta: 6:07:06, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6476 2022-12-31 20:49:36,303 - mmcls - INFO - Epoch [51][1500/5005] lr: 1.000e-02, eta: 6:06:58, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.6471 2022-12-31 20:49:45,232 - mmcls - INFO - Epoch [51][1600/5005] lr: 1.000e-02, eta: 6:06:49, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6518 2022-12-31 20:49:54,396 - mmcls - INFO - Epoch [51][1700/5005] lr: 1.000e-02, eta: 6:06:40, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6565 2022-12-31 20:50:02,948 - mmcls - INFO - Epoch [51][1800/5005] lr: 1.000e-02, eta: 6:06:31, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6411 2022-12-31 20:50:12,003 - mmcls - INFO - Epoch [51][1900/5005] lr: 1.000e-02, eta: 6:06:23, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6267 2022-12-31 20:50:20,814 - mmcls - INFO - Epoch [51][2000/5005] lr: 1.000e-02, eta: 6:06:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6791 2022-12-31 20:50:29,659 - mmcls - INFO - Epoch [51][2100/5005] lr: 1.000e-02, eta: 6:06:05, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6358 2022-12-31 20:50:38,508 - mmcls - INFO - Epoch [51][2200/5005] lr: 1.000e-02, eta: 6:05:56, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6353 2022-12-31 20:50:47,183 - mmcls - INFO - Epoch [51][2300/5005] lr: 1.000e-02, eta: 6:05:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6607 2022-12-31 20:50:55,929 - mmcls - INFO - Epoch [51][2400/5005] lr: 1.000e-02, eta: 6:05:38, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6686 2022-12-31 20:51:04,680 - mmcls - INFO - Epoch [51][2500/5005] lr: 1.000e-02, eta: 6:05:29, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6550 2022-12-31 20:51:13,275 - mmcls - INFO - Epoch [51][2600/5005] lr: 1.000e-02, eta: 6:05:20, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.6521 2022-12-31 20:51:22,133 - mmcls - INFO - Epoch [51][2700/5005] lr: 1.000e-02, eta: 6:05:11, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.6486 2022-12-31 20:51:30,937 - mmcls - INFO - Epoch [51][2800/5005] lr: 1.000e-02, eta: 6:05:02, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6664 2022-12-31 20:51:39,731 - mmcls - INFO - Epoch [51][2900/5005] lr: 1.000e-02, eta: 6:04:53, time: 0.088, data_time: 0.039, memory: 1224, loss: 1.6375 2022-12-31 20:51:48,744 - mmcls - INFO - Epoch [51][3000/5005] lr: 1.000e-02, eta: 6:04:45, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6530 2022-12-31 20:51:57,725 - mmcls - INFO - Epoch [51][3100/5005] lr: 1.000e-02, eta: 6:04:36, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6426 2022-12-31 20:52:06,492 - mmcls - INFO - Epoch [51][3200/5005] lr: 1.000e-02, eta: 6:04:27, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6667 2022-12-31 20:52:15,284 - mmcls - INFO - Epoch [51][3300/5005] lr: 1.000e-02, eta: 6:04:18, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6435 2022-12-31 20:52:24,281 - mmcls - INFO - Epoch [51][3400/5005] lr: 1.000e-02, eta: 6:04:09, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6701 2022-12-31 20:52:33,313 - mmcls - INFO - Epoch [51][3500/5005] lr: 1.000e-02, eta: 6:04:01, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6752 2022-12-31 20:52:42,180 - mmcls - INFO - Epoch [51][3600/5005] lr: 1.000e-02, eta: 6:03:52, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6818 2022-12-31 20:52:50,932 - mmcls - INFO - Epoch [51][3700/5005] lr: 1.000e-02, eta: 6:03:43, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6583 2022-12-31 20:52:59,625 - mmcls - INFO - Epoch [51][3800/5005] lr: 1.000e-02, eta: 6:03:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6504 2022-12-31 20:53:08,681 - mmcls - INFO - Epoch [51][3900/5005] lr: 1.000e-02, eta: 6:03:25, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6613 2022-12-31 20:53:17,687 - mmcls - INFO - Epoch [51][4000/5005] lr: 1.000e-02, eta: 6:03:16, time: 0.090, data_time: 0.026, memory: 1224, loss: 1.6628 2022-12-31 20:53:26,437 - mmcls - INFO - Epoch [51][4100/5005] lr: 1.000e-02, eta: 6:03:07, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6324 2022-12-31 20:53:35,293 - mmcls - INFO - Epoch [51][4200/5005] lr: 1.000e-02, eta: 6:02:59, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6482 2022-12-31 20:53:44,290 - mmcls - INFO - Epoch [51][4300/5005] lr: 1.000e-02, eta: 6:02:50, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6737 2022-12-31 20:53:53,262 - mmcls - INFO - Epoch [51][4400/5005] lr: 1.000e-02, eta: 6:02:41, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.6695 2022-12-31 20:54:01,805 - mmcls - INFO - Epoch [51][4500/5005] lr: 1.000e-02, eta: 6:02:32, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6592 2022-12-31 20:54:10,725 - mmcls - INFO - Epoch [51][4600/5005] lr: 1.000e-02, eta: 6:02:23, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6669 2022-12-31 20:54:19,852 - mmcls - INFO - Epoch [51][4700/5005] lr: 1.000e-02, eta: 6:02:15, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6666 2022-12-31 20:54:28,528 - mmcls - INFO - Epoch [51][4800/5005] lr: 1.000e-02, eta: 6:02:06, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6617 2022-12-31 20:54:37,069 - mmcls - INFO - Epoch [51][4900/5005] lr: 1.000e-02, eta: 6:01:56, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6656 2022-12-31 20:54:46,189 - mmcls - INFO - Epoch [51][5000/5005] lr: 1.000e-02, eta: 6:01:48, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.6727 2022-12-31 20:55:08,896 - mmcls - INFO - Epoch(val) [51][196] accuracy_top-1: 65.9360, accuracy_top-5: 87.0100 2022-12-31 20:55:19,712 - mmcls - INFO - Epoch [52][100/5005] lr: 1.000e-02, eta: 6:01:40, time: 0.108, data_time: 0.049, memory: 1224, loss: 1.6296 2022-12-31 20:55:28,455 - mmcls - INFO - Epoch [52][200/5005] lr: 1.000e-02, eta: 6:01:31, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6344 2022-12-31 20:55:37,137 - mmcls - INFO - Epoch [52][300/5005] lr: 1.000e-02, eta: 6:01:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6250 2022-12-31 20:55:46,274 - mmcls - INFO - Epoch [52][400/5005] lr: 1.000e-02, eta: 6:01:13, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6180 2022-12-31 20:55:54,874 - mmcls - INFO - Epoch [52][500/5005] lr: 1.000e-02, eta: 6:01:04, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.6222 2022-12-31 20:56:03,809 - mmcls - INFO - Epoch [52][600/5005] lr: 1.000e-02, eta: 6:00:56, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6310 2022-12-31 20:56:12,546 - mmcls - INFO - Epoch [52][700/5005] lr: 1.000e-02, eta: 6:00:47, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.6218 2022-12-31 20:56:21,580 - mmcls - INFO - Epoch [52][800/5005] lr: 1.000e-02, eta: 6:00:38, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.6414 2022-12-31 20:56:30,238 - mmcls - INFO - Epoch [52][900/5005] lr: 1.000e-02, eta: 6:00:29, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6165 2022-12-31 20:56:39,378 - mmcls - INFO - Epoch [52][1000/5005] lr: 1.000e-02, eta: 6:00:20, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.6224 2022-12-31 20:56:48,350 - mmcls - INFO - Epoch [52][1100/5005] lr: 1.000e-02, eta: 6:00:12, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6379 2022-12-31 20:56:57,371 - mmcls - INFO - Epoch [52][1200/5005] lr: 1.000e-02, eta: 6:00:03, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6393 2022-12-31 20:57:06,271 - mmcls - INFO - Epoch [52][1300/5005] lr: 1.000e-02, eta: 5:59:54, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6408 2022-12-31 20:57:15,150 - mmcls - INFO - Epoch [52][1400/5005] lr: 1.000e-02, eta: 5:59:45, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6303 2022-12-31 20:57:23,943 - mmcls - INFO - Epoch [52][1500/5005] lr: 1.000e-02, eta: 5:59:36, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6400 2022-12-31 20:57:32,813 - mmcls - INFO - Epoch [52][1600/5005] lr: 1.000e-02, eta: 5:59:28, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6191 2022-12-31 20:57:41,806 - mmcls - INFO - Epoch [52][1700/5005] lr: 1.000e-02, eta: 5:59:19, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6147 2022-12-31 20:57:50,935 - mmcls - INFO - Epoch [52][1800/5005] lr: 1.000e-02, eta: 5:59:10, time: 0.091, data_time: 0.022, memory: 1224, loss: 1.6333 2022-12-31 20:58:00,029 - mmcls - INFO - Epoch [52][1900/5005] lr: 1.000e-02, eta: 5:59:02, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6720 2022-12-31 20:58:08,594 - mmcls - INFO - Epoch [52][2000/5005] lr: 1.000e-02, eta: 5:58:52, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6618 2022-12-31 20:58:17,352 - mmcls - INFO - Epoch [52][2100/5005] lr: 1.000e-02, eta: 5:58:44, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6493 2022-12-31 20:58:26,244 - mmcls - INFO - Epoch [52][2200/5005] lr: 1.000e-02, eta: 5:58:35, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6615 2022-12-31 20:58:35,447 - mmcls - INFO - Epoch [52][2300/5005] lr: 1.000e-02, eta: 5:58:26, time: 0.092, data_time: 0.021, memory: 1224, loss: 1.6742 2022-12-31 20:58:44,048 - mmcls - INFO - Epoch [52][2400/5005] lr: 1.000e-02, eta: 5:58:17, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6362 2022-12-31 20:58:52,845 - mmcls - INFO - Epoch [52][2500/5005] lr: 1.000e-02, eta: 5:58:08, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6753 2022-12-31 20:59:01,590 - mmcls - INFO - Epoch [52][2600/5005] lr: 1.000e-02, eta: 5:57:59, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6594 2022-12-31 20:59:10,328 - mmcls - INFO - Epoch [52][2700/5005] lr: 1.000e-02, eta: 5:57:50, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6418 2022-12-31 20:59:19,398 - mmcls - INFO - Epoch [52][2800/5005] lr: 1.000e-02, eta: 5:57:42, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6551 2022-12-31 20:59:28,271 - mmcls - INFO - Epoch [52][2900/5005] lr: 1.000e-02, eta: 5:57:33, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6692 2022-12-31 20:59:37,036 - mmcls - INFO - Epoch [52][3000/5005] lr: 1.000e-02, eta: 5:57:24, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6498 2022-12-31 20:59:45,693 - mmcls - INFO - Epoch [52][3100/5005] lr: 1.000e-02, eta: 5:57:15, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6686 2022-12-31 20:59:54,468 - mmcls - INFO - Epoch [52][3200/5005] lr: 1.000e-02, eta: 5:57:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6457 2022-12-31 21:00:03,347 - mmcls - INFO - Epoch [52][3300/5005] lr: 1.000e-02, eta: 5:56:57, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6683 2022-12-31 21:00:12,108 - mmcls - INFO - Epoch [52][3400/5005] lr: 1.000e-02, eta: 5:56:48, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6437 2022-12-31 21:00:20,822 - mmcls - INFO - Epoch [52][3500/5005] lr: 1.000e-02, eta: 5:56:39, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6661 2022-12-31 21:00:29,558 - mmcls - INFO - Epoch [52][3600/5005] lr: 1.000e-02, eta: 5:56:30, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6436 2022-12-31 21:00:38,407 - mmcls - INFO - Epoch [52][3700/5005] lr: 1.000e-02, eta: 5:56:21, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6548 2022-12-31 21:00:47,226 - mmcls - INFO - Epoch [52][3800/5005] lr: 1.000e-02, eta: 5:56:12, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6528 2022-12-31 21:00:55,793 - mmcls - INFO - Epoch [52][3900/5005] lr: 1.000e-02, eta: 5:56:03, time: 0.086, data_time: 0.026, memory: 1224, loss: 1.6577 2022-12-31 21:01:04,315 - mmcls - INFO - Epoch [52][4000/5005] lr: 1.000e-02, eta: 5:55:54, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6388 2022-12-31 21:01:13,036 - mmcls - INFO - Epoch [52][4100/5005] lr: 1.000e-02, eta: 5:55:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6411 2022-12-31 21:01:21,923 - mmcls - INFO - Epoch [52][4200/5005] lr: 1.000e-02, eta: 5:55:36, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6552 2022-12-31 21:01:30,981 - mmcls - INFO - Epoch [52][4300/5005] lr: 1.000e-02, eta: 5:55:28, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6921 2022-12-31 21:01:39,653 - mmcls - INFO - Epoch [52][4400/5005] lr: 1.000e-02, eta: 5:55:19, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6573 2022-12-31 21:01:48,416 - mmcls - INFO - Epoch [52][4500/5005] lr: 1.000e-02, eta: 5:55:10, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6809 2022-12-31 21:01:56,979 - mmcls - INFO - Epoch [52][4600/5005] lr: 1.000e-02, eta: 5:55:01, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6650 2022-12-31 21:02:06,011 - mmcls - INFO - Epoch [52][4700/5005] lr: 1.000e-02, eta: 5:54:52, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6474 2022-12-31 21:02:15,151 - mmcls - INFO - Epoch [52][4800/5005] lr: 1.000e-02, eta: 5:54:43, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6634 2022-12-31 21:02:24,196 - mmcls - INFO - Epoch [52][4900/5005] lr: 1.000e-02, eta: 5:54:35, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6376 2022-12-31 21:02:32,912 - mmcls - INFO - Epoch [52][5000/5005] lr: 1.000e-02, eta: 5:54:26, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6521 2022-12-31 21:02:55,530 - mmcls - INFO - Epoch(val) [52][196] accuracy_top-1: 66.1820, accuracy_top-5: 87.1020 2022-12-31 21:03:06,530 - mmcls - INFO - Epoch [53][100/5005] lr: 1.000e-02, eta: 5:54:18, time: 0.110, data_time: 0.054, memory: 1224, loss: 1.6410 2022-12-31 21:03:15,220 - mmcls - INFO - Epoch [53][200/5005] lr: 1.000e-02, eta: 5:54:09, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.5974 2022-12-31 21:03:24,090 - mmcls - INFO - Epoch [53][300/5005] lr: 1.000e-02, eta: 5:54:00, time: 0.089, data_time: 0.037, memory: 1224, loss: 1.6205 2022-12-31 21:03:33,080 - mmcls - INFO - Epoch [53][400/5005] lr: 1.000e-02, eta: 5:53:51, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6156 2022-12-31 21:03:42,096 - mmcls - INFO - Epoch [53][500/5005] lr: 1.000e-02, eta: 5:53:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6288 2022-12-31 21:03:51,098 - mmcls - INFO - Epoch [53][600/5005] lr: 1.000e-02, eta: 5:53:34, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.6283 2022-12-31 21:03:59,963 - mmcls - INFO - Epoch [53][700/5005] lr: 1.000e-02, eta: 5:53:25, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6435 2022-12-31 21:04:09,053 - mmcls - INFO - Epoch [53][800/5005] lr: 1.000e-02, eta: 5:53:16, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.6461 2022-12-31 21:04:17,818 - mmcls - INFO - Epoch [53][900/5005] lr: 1.000e-02, eta: 5:53:08, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6292 2022-12-31 21:04:26,676 - mmcls - INFO - Epoch [53][1000/5005] lr: 1.000e-02, eta: 5:52:59, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6579 2022-12-31 21:04:35,438 - mmcls - INFO - Epoch [53][1100/5005] lr: 1.000e-02, eta: 5:52:50, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.6224 2022-12-31 21:04:44,212 - mmcls - INFO - Epoch [53][1200/5005] lr: 1.000e-02, eta: 5:52:41, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6316 2022-12-31 21:04:53,349 - mmcls - INFO - Epoch [53][1300/5005] lr: 1.000e-02, eta: 5:52:32, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6368 2022-12-31 21:05:01,900 - mmcls - INFO - Epoch [53][1400/5005] lr: 1.000e-02, eta: 5:52:23, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6461 2022-12-31 21:05:10,660 - mmcls - INFO - Epoch [53][1500/5005] lr: 1.000e-02, eta: 5:52:14, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6216 2022-12-31 21:05:19,431 - mmcls - INFO - Epoch [53][1600/5005] lr: 1.000e-02, eta: 5:52:05, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6283 2022-12-31 21:05:28,229 - mmcls - INFO - Epoch [53][1700/5005] lr: 1.000e-02, eta: 5:51:56, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6622 2022-12-31 21:05:36,909 - mmcls - INFO - Epoch [53][1800/5005] lr: 1.000e-02, eta: 5:51:47, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.6398 2022-12-31 21:05:45,948 - mmcls - INFO - Epoch [53][1900/5005] lr: 1.000e-02, eta: 5:51:39, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6506 2022-12-31 21:05:54,931 - mmcls - INFO - Epoch [53][2000/5005] lr: 1.000e-02, eta: 5:51:30, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6509 2022-12-31 21:06:03,809 - mmcls - INFO - Epoch [53][2100/5005] lr: 1.000e-02, eta: 5:51:21, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6548 2022-12-31 21:06:12,725 - mmcls - INFO - Epoch [53][2200/5005] lr: 1.000e-02, eta: 5:51:12, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6228 2022-12-31 21:06:21,355 - mmcls - INFO - Epoch [53][2300/5005] lr: 1.000e-02, eta: 5:51:03, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6329 2022-12-31 21:06:29,902 - mmcls - INFO - Epoch [53][2400/5005] lr: 1.000e-02, eta: 5:50:54, time: 0.085, data_time: 0.024, memory: 1224, loss: 1.6612 2022-12-31 21:06:38,597 - mmcls - INFO - Epoch [53][2500/5005] lr: 1.000e-02, eta: 5:50:45, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6565 2022-12-31 21:06:47,320 - mmcls - INFO - Epoch [53][2600/5005] lr: 1.000e-02, eta: 5:50:36, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6480 2022-12-31 21:06:56,089 - mmcls - INFO - Epoch [53][2700/5005] lr: 1.000e-02, eta: 5:50:27, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6490 2022-12-31 21:07:04,936 - mmcls - INFO - Epoch [53][2800/5005] lr: 1.000e-02, eta: 5:50:18, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6748 2022-12-31 21:07:13,441 - mmcls - INFO - Epoch [53][2900/5005] lr: 1.000e-02, eta: 5:50:09, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6553 2022-12-31 21:07:22,236 - mmcls - INFO - Epoch [53][3000/5005] lr: 1.000e-02, eta: 5:50:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6531 2022-12-31 21:07:31,012 - mmcls - INFO - Epoch [53][3100/5005] lr: 1.000e-02, eta: 5:49:51, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6587 2022-12-31 21:07:39,726 - mmcls - INFO - Epoch [53][3200/5005] lr: 1.000e-02, eta: 5:49:42, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6525 2022-12-31 21:07:48,979 - mmcls - INFO - Epoch [53][3300/5005] lr: 1.000e-02, eta: 5:49:34, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.6518 2022-12-31 21:07:57,860 - mmcls - INFO - Epoch [53][3400/5005] lr: 1.000e-02, eta: 5:49:25, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6448 2022-12-31 21:08:06,746 - mmcls - INFO - Epoch [53][3500/5005] lr: 1.000e-02, eta: 5:49:16, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.6719 2022-12-31 21:08:15,529 - mmcls - INFO - Epoch [53][3600/5005] lr: 1.000e-02, eta: 5:49:07, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6549 2022-12-31 21:08:24,135 - mmcls - INFO - Epoch [53][3700/5005] lr: 1.000e-02, eta: 5:48:58, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.6499 2022-12-31 21:08:33,166 - mmcls - INFO - Epoch [53][3800/5005] lr: 1.000e-02, eta: 5:48:50, time: 0.090, data_time: 0.029, memory: 1224, loss: 1.6611 2022-12-31 21:08:41,628 - mmcls - INFO - Epoch [53][3900/5005] lr: 1.000e-02, eta: 5:48:40, time: 0.085, data_time: 0.020, memory: 1224, loss: 1.6484 2022-12-31 21:08:50,493 - mmcls - INFO - Epoch [53][4000/5005] lr: 1.000e-02, eta: 5:48:32, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.6616 2022-12-31 21:08:59,645 - mmcls - INFO - Epoch [53][4100/5005] lr: 1.000e-02, eta: 5:48:23, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6635 2022-12-31 21:09:08,735 - mmcls - INFO - Epoch [53][4200/5005] lr: 1.000e-02, eta: 5:48:14, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.6687 2022-12-31 21:09:17,481 - mmcls - INFO - Epoch [53][4300/5005] lr: 1.000e-02, eta: 5:48:05, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6686 2022-12-31 21:09:26,100 - mmcls - INFO - Epoch [53][4400/5005] lr: 1.000e-02, eta: 5:47:56, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6537 2022-12-31 21:09:35,441 - mmcls - INFO - Epoch [53][4500/5005] lr: 1.000e-02, eta: 5:47:48, time: 0.093, data_time: 0.016, memory: 1224, loss: 1.6651 2022-12-31 21:09:44,229 - mmcls - INFO - Epoch [53][4600/5005] lr: 1.000e-02, eta: 5:47:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6552 2022-12-31 21:09:53,004 - mmcls - INFO - Epoch [53][4700/5005] lr: 1.000e-02, eta: 5:47:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6599 2022-12-31 21:10:01,679 - mmcls - INFO - Epoch [53][4800/5005] lr: 1.000e-02, eta: 5:47:21, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6745 2022-12-31 21:10:10,295 - mmcls - INFO - Epoch [53][4900/5005] lr: 1.000e-02, eta: 5:47:12, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6586 2022-12-31 21:10:19,209 - mmcls - INFO - Epoch [53][5000/5005] lr: 1.000e-02, eta: 5:47:03, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6427 2022-12-31 21:10:41,884 - mmcls - INFO - Epoch(val) [53][196] accuracy_top-1: 66.2100, accuracy_top-5: 87.2500 2022-12-31 21:10:53,238 - mmcls - INFO - Epoch [54][100/5005] lr: 1.000e-02, eta: 5:46:56, time: 0.113, data_time: 0.041, memory: 1224, loss: 1.6296 2022-12-31 21:11:01,857 - mmcls - INFO - Epoch [54][200/5005] lr: 1.000e-02, eta: 5:46:47, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6162 2022-12-31 21:11:10,893 - mmcls - INFO - Epoch [54][300/5005] lr: 1.000e-02, eta: 5:46:38, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6517 2022-12-31 21:11:19,494 - mmcls - INFO - Epoch [54][400/5005] lr: 1.000e-02, eta: 5:46:29, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.5965 2022-12-31 21:11:28,384 - mmcls - INFO - Epoch [54][500/5005] lr: 1.000e-02, eta: 5:46:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6024 2022-12-31 21:11:37,088 - mmcls - INFO - Epoch [54][600/5005] lr: 1.000e-02, eta: 5:46:11, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6145 2022-12-31 21:11:46,021 - mmcls - INFO - Epoch [54][700/5005] lr: 1.000e-02, eta: 5:46:02, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6344 2022-12-31 21:11:54,603 - mmcls - INFO - Epoch [54][800/5005] lr: 1.000e-02, eta: 5:45:53, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.6271 2022-12-31 21:12:03,288 - mmcls - INFO - Epoch [54][900/5005] lr: 1.000e-02, eta: 5:45:44, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6376 2022-12-31 21:12:12,371 - mmcls - INFO - Epoch [54][1000/5005] lr: 1.000e-02, eta: 5:45:36, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6050 2022-12-31 21:12:21,275 - mmcls - INFO - Epoch [54][1100/5005] lr: 1.000e-02, eta: 5:45:27, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.6422 2022-12-31 21:12:30,222 - mmcls - INFO - Epoch [54][1200/5005] lr: 1.000e-02, eta: 5:45:18, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6529 2022-12-31 21:12:38,962 - mmcls - INFO - Epoch [54][1300/5005] lr: 1.000e-02, eta: 5:45:09, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6135 2022-12-31 21:12:47,736 - mmcls - INFO - Epoch [54][1400/5005] lr: 1.000e-02, eta: 5:45:00, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6317 2022-12-31 21:12:56,422 - mmcls - INFO - Epoch [54][1500/5005] lr: 1.000e-02, eta: 5:44:51, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6332 2022-12-31 21:13:04,925 - mmcls - INFO - Epoch [54][1600/5005] lr: 1.000e-02, eta: 5:44:42, time: 0.085, data_time: 0.022, memory: 1224, loss: 1.6428 2022-12-31 21:13:13,824 - mmcls - INFO - Epoch [54][1700/5005] lr: 1.000e-02, eta: 5:44:33, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.6438 2022-12-31 21:13:22,750 - mmcls - INFO - Epoch [54][1800/5005] lr: 1.000e-02, eta: 5:44:24, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6276 2022-12-31 21:13:31,705 - mmcls - INFO - Epoch [54][1900/5005] lr: 1.000e-02, eta: 5:44:16, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6634 2022-12-31 21:13:40,457 - mmcls - INFO - Epoch [54][2000/5005] lr: 1.000e-02, eta: 5:44:07, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6598 2022-12-31 21:13:49,121 - mmcls - INFO - Epoch [54][2100/5005] lr: 1.000e-02, eta: 5:43:58, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6450 2022-12-31 21:13:57,789 - mmcls - INFO - Epoch [54][2200/5005] lr: 1.000e-02, eta: 5:43:49, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6455 2022-12-31 21:14:07,183 - mmcls - INFO - Epoch [54][2300/5005] lr: 1.000e-02, eta: 5:43:40, time: 0.094, data_time: 0.002, memory: 1224, loss: 1.6338 2022-12-31 21:14:15,953 - mmcls - INFO - Epoch [54][2400/5005] lr: 1.000e-02, eta: 5:43:31, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6598 2022-12-31 21:14:24,721 - mmcls - INFO - Epoch [54][2500/5005] lr: 1.000e-02, eta: 5:43:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6432 2022-12-31 21:14:33,567 - mmcls - INFO - Epoch [54][2600/5005] lr: 1.000e-02, eta: 5:43:13, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6582 2022-12-31 21:14:42,381 - mmcls - INFO - Epoch [54][2700/5005] lr: 1.000e-02, eta: 5:43:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6476 2022-12-31 21:14:51,229 - mmcls - INFO - Epoch [54][2800/5005] lr: 1.000e-02, eta: 5:42:56, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6392 2022-12-31 21:15:00,033 - mmcls - INFO - Epoch [54][2900/5005] lr: 1.000e-02, eta: 5:42:47, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6627 2022-12-31 21:15:08,726 - mmcls - INFO - Epoch [54][3000/5005] lr: 1.000e-02, eta: 5:42:38, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6743 2022-12-31 21:15:17,489 - mmcls - INFO - Epoch [54][3100/5005] lr: 1.000e-02, eta: 5:42:29, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6488 2022-12-31 21:15:25,995 - mmcls - INFO - Epoch [54][3200/5005] lr: 1.000e-02, eta: 5:42:20, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6344 2022-12-31 21:15:34,743 - mmcls - INFO - Epoch [54][3300/5005] lr: 1.000e-02, eta: 5:42:11, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6433 2022-12-31 21:15:43,509 - mmcls - INFO - Epoch [54][3400/5005] lr: 1.000e-02, eta: 5:42:02, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6597 2022-12-31 21:15:52,207 - mmcls - INFO - Epoch [54][3500/5005] lr: 1.000e-02, eta: 5:41:53, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.6717 2022-12-31 21:16:01,210 - mmcls - INFO - Epoch [54][3600/5005] lr: 1.000e-02, eta: 5:41:44, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.6518 2022-12-31 21:16:10,151 - mmcls - INFO - Epoch [54][3700/5005] lr: 1.000e-02, eta: 5:41:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6682 2022-12-31 21:16:19,055 - mmcls - INFO - Epoch [54][3800/5005] lr: 1.000e-02, eta: 5:41:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6540 2022-12-31 21:16:27,852 - mmcls - INFO - Epoch [54][3900/5005] lr: 1.000e-02, eta: 5:41:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6616 2022-12-31 21:16:36,878 - mmcls - INFO - Epoch [54][4000/5005] lr: 1.000e-02, eta: 5:41:09, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6532 2022-12-31 21:16:45,335 - mmcls - INFO - Epoch [54][4100/5005] lr: 1.000e-02, eta: 5:41:00, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.6472 2022-12-31 21:16:54,246 - mmcls - INFO - Epoch [54][4200/5005] lr: 1.000e-02, eta: 5:40:51, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6717 2022-12-31 21:17:03,170 - mmcls - INFO - Epoch [54][4300/5005] lr: 1.000e-02, eta: 5:40:42, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6486 2022-12-31 21:17:11,875 - mmcls - INFO - Epoch [54][4400/5005] lr: 1.000e-02, eta: 5:40:33, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6571 2022-12-31 21:17:20,934 - mmcls - INFO - Epoch [54][4500/5005] lr: 1.000e-02, eta: 5:40:25, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.6567 2022-12-31 21:17:29,846 - mmcls - INFO - Epoch [54][4600/5005] lr: 1.000e-02, eta: 5:40:16, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6857 2022-12-31 21:17:38,462 - mmcls - INFO - Epoch [54][4700/5005] lr: 1.000e-02, eta: 5:40:07, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6729 2022-12-31 21:17:47,412 - mmcls - INFO - Epoch [54][4800/5005] lr: 1.000e-02, eta: 5:39:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6660 2022-12-31 21:17:56,047 - mmcls - INFO - Epoch [54][4900/5005] lr: 1.000e-02, eta: 5:39:49, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6742 2022-12-31 21:18:04,902 - mmcls - INFO - Epoch [54][5000/5005] lr: 1.000e-02, eta: 5:39:40, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6535 2022-12-31 21:18:27,561 - mmcls - INFO - Epoch(val) [54][196] accuracy_top-1: 65.9220, accuracy_top-5: 87.0200 2022-12-31 21:18:38,371 - mmcls - INFO - Epoch [55][100/5005] lr: 1.000e-02, eta: 5:39:32, time: 0.108, data_time: 0.065, memory: 1224, loss: 1.6249 2022-12-31 21:18:47,137 - mmcls - INFO - Epoch [55][200/5005] lr: 1.000e-02, eta: 5:39:23, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.6204 2022-12-31 21:18:56,384 - mmcls - INFO - Epoch [55][300/5005] lr: 1.000e-02, eta: 5:39:15, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6329 2022-12-31 21:19:05,555 - mmcls - INFO - Epoch [55][400/5005] lr: 1.000e-02, eta: 5:39:06, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.5984 2022-12-31 21:19:14,329 - mmcls - INFO - Epoch [55][500/5005] lr: 1.000e-02, eta: 5:38:57, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.6172 2022-12-31 21:19:23,128 - mmcls - INFO - Epoch [55][600/5005] lr: 1.000e-02, eta: 5:38:48, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.6400 2022-12-31 21:19:32,001 - mmcls - INFO - Epoch [55][700/5005] lr: 1.000e-02, eta: 5:38:39, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6223 2022-12-31 21:19:40,993 - mmcls - INFO - Epoch [55][800/5005] lr: 1.000e-02, eta: 5:38:31, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6233 2022-12-31 21:19:49,868 - mmcls - INFO - Epoch [55][900/5005] lr: 1.000e-02, eta: 5:38:22, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6161 2022-12-31 21:19:58,633 - mmcls - INFO - Epoch [55][1000/5005] lr: 1.000e-02, eta: 5:38:13, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6472 2022-12-31 21:20:07,432 - mmcls - INFO - Epoch [55][1100/5005] lr: 1.000e-02, eta: 5:38:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6428 2022-12-31 21:20:16,303 - mmcls - INFO - Epoch [55][1200/5005] lr: 1.000e-02, eta: 5:37:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6599 2022-12-31 21:20:25,368 - mmcls - INFO - Epoch [55][1300/5005] lr: 1.000e-02, eta: 5:37:46, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6234 2022-12-31 21:20:34,010 - mmcls - INFO - Epoch [55][1400/5005] lr: 1.000e-02, eta: 5:37:37, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.6107 2022-12-31 21:20:42,975 - mmcls - INFO - Epoch [55][1500/5005] lr: 1.000e-02, eta: 5:37:29, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6360 2022-12-31 21:20:51,924 - mmcls - INFO - Epoch [55][1600/5005] lr: 1.000e-02, eta: 5:37:20, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.6204 2022-12-31 21:21:00,827 - mmcls - INFO - Epoch [55][1700/5005] lr: 1.000e-02, eta: 5:37:11, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.6407 2022-12-31 21:21:09,683 - mmcls - INFO - Epoch [55][1800/5005] lr: 1.000e-02, eta: 5:37:02, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6227 2022-12-31 21:21:18,589 - mmcls - INFO - Epoch [55][1900/5005] lr: 1.000e-02, eta: 5:36:53, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.6139 2022-12-31 21:21:27,197 - mmcls - INFO - Epoch [55][2000/5005] lr: 1.000e-02, eta: 5:36:44, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6294 2022-12-31 21:21:36,066 - mmcls - INFO - Epoch [55][2100/5005] lr: 1.000e-02, eta: 5:36:35, time: 0.089, data_time: 0.032, memory: 1224, loss: 1.6502 2022-12-31 21:21:44,866 - mmcls - INFO - Epoch [55][2200/5005] lr: 1.000e-02, eta: 5:36:27, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6328 2022-12-31 21:21:53,389 - mmcls - INFO - Epoch [55][2300/5005] lr: 1.000e-02, eta: 5:36:17, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6276 2022-12-31 21:22:02,446 - mmcls - INFO - Epoch [55][2400/5005] lr: 1.000e-02, eta: 5:36:09, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6656 2022-12-31 21:22:11,224 - mmcls - INFO - Epoch [55][2500/5005] lr: 1.000e-02, eta: 5:36:00, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6399 2022-12-31 21:22:19,934 - mmcls - INFO - Epoch [55][2600/5005] lr: 1.000e-02, eta: 5:35:51, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6448 2022-12-31 21:22:28,766 - mmcls - INFO - Epoch [55][2700/5005] lr: 1.000e-02, eta: 5:35:42, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6302 2022-12-31 21:22:37,475 - mmcls - INFO - Epoch [55][2800/5005] lr: 1.000e-02, eta: 5:35:33, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.6494 2022-12-31 21:22:46,370 - mmcls - INFO - Epoch [55][2900/5005] lr: 1.000e-02, eta: 5:35:24, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.6626 2022-12-31 21:22:54,990 - mmcls - INFO - Epoch [55][3000/5005] lr: 1.000e-02, eta: 5:35:15, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6754 2022-12-31 21:23:03,737 - mmcls - INFO - Epoch [55][3100/5005] lr: 1.000e-02, eta: 5:35:06, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6593 2022-12-31 21:23:12,765 - mmcls - INFO - Epoch [55][3200/5005] lr: 1.000e-02, eta: 5:34:58, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6461 2022-12-31 21:23:21,395 - mmcls - INFO - Epoch [55][3300/5005] lr: 1.000e-02, eta: 5:34:48, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6218 2022-12-31 21:23:30,152 - mmcls - INFO - Epoch [55][3400/5005] lr: 1.000e-02, eta: 5:34:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6622 2022-12-31 21:23:39,130 - mmcls - INFO - Epoch [55][3500/5005] lr: 1.000e-02, eta: 5:34:31, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.6861 2022-12-31 21:23:47,941 - mmcls - INFO - Epoch [55][3600/5005] lr: 1.000e-02, eta: 5:34:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6489 2022-12-31 21:23:56,732 - mmcls - INFO - Epoch [55][3700/5005] lr: 1.000e-02, eta: 5:34:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6657 2022-12-31 21:24:05,737 - mmcls - INFO - Epoch [55][3800/5005] lr: 1.000e-02, eta: 5:34:04, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6568 2022-12-31 21:24:14,988 - mmcls - INFO - Epoch [55][3900/5005] lr: 1.000e-02, eta: 5:33:56, time: 0.092, data_time: 0.010, memory: 1224, loss: 1.6524 2022-12-31 21:24:23,690 - mmcls - INFO - Epoch [55][4000/5005] lr: 1.000e-02, eta: 5:33:47, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6477 2022-12-31 21:24:32,827 - mmcls - INFO - Epoch [55][4100/5005] lr: 1.000e-02, eta: 5:33:38, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6548 2022-12-31 21:24:41,796 - mmcls - INFO - Epoch [55][4200/5005] lr: 1.000e-02, eta: 5:33:29, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.6486 2022-12-31 21:24:50,734 - mmcls - INFO - Epoch [55][4300/5005] lr: 1.000e-02, eta: 5:33:21, time: 0.089, data_time: 0.034, memory: 1224, loss: 1.6815 2022-12-31 21:24:59,318 - mmcls - INFO - Epoch [55][4400/5005] lr: 1.000e-02, eta: 5:33:12, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6845 2022-12-31 21:25:08,012 - mmcls - INFO - Epoch [55][4500/5005] lr: 1.000e-02, eta: 5:33:03, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6559 2022-12-31 21:25:17,138 - mmcls - INFO - Epoch [55][4600/5005] lr: 1.000e-02, eta: 5:32:54, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6458 2022-12-31 21:25:25,803 - mmcls - INFO - Epoch [55][4700/5005] lr: 1.000e-02, eta: 5:32:45, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6490 2022-12-31 21:25:34,419 - mmcls - INFO - Epoch [55][4800/5005] lr: 1.000e-02, eta: 5:32:36, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6726 2022-12-31 21:25:43,466 - mmcls - INFO - Epoch [55][4900/5005] lr: 1.000e-02, eta: 5:32:27, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6596 2022-12-31 21:25:52,315 - mmcls - INFO - Epoch [55][5000/5005] lr: 1.000e-02, eta: 5:32:18, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.6483 2022-12-31 21:26:14,985 - mmcls - INFO - Epoch(val) [55][196] accuracy_top-1: 65.4740, accuracy_top-5: 86.8940 2022-12-31 21:26:25,781 - mmcls - INFO - Epoch [56][100/5005] lr: 1.000e-02, eta: 5:32:10, time: 0.108, data_time: 0.047, memory: 1224, loss: 1.5985 2022-12-31 21:26:34,431 - mmcls - INFO - Epoch [56][200/5005] lr: 1.000e-02, eta: 5:32:01, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6143 2022-12-31 21:26:43,120 - mmcls - INFO - Epoch [56][300/5005] lr: 1.000e-02, eta: 5:31:52, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6119 2022-12-31 21:26:51,863 - mmcls - INFO - Epoch [56][400/5005] lr: 1.000e-02, eta: 5:31:43, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6210 2022-12-31 21:27:00,427 - mmcls - INFO - Epoch [56][500/5005] lr: 1.000e-02, eta: 5:31:34, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6137 2022-12-31 21:27:09,085 - mmcls - INFO - Epoch [56][600/5005] lr: 1.000e-02, eta: 5:31:25, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6314 2022-12-31 21:27:17,951 - mmcls - INFO - Epoch [56][700/5005] lr: 1.000e-02, eta: 5:31:16, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6334 2022-12-31 21:27:26,886 - mmcls - INFO - Epoch [56][800/5005] lr: 1.000e-02, eta: 5:31:08, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6300 2022-12-31 21:27:35,701 - mmcls - INFO - Epoch [56][900/5005] lr: 1.000e-02, eta: 5:30:59, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.6461 2022-12-31 21:27:44,816 - mmcls - INFO - Epoch [56][1000/5005] lr: 1.000e-02, eta: 5:30:50, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.6426 2022-12-31 21:27:53,475 - mmcls - INFO - Epoch [56][1100/5005] lr: 1.000e-02, eta: 5:30:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.6586 2022-12-31 21:28:02,281 - mmcls - INFO - Epoch [56][1200/5005] lr: 1.000e-02, eta: 5:30:32, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6559 2022-12-31 21:28:11,070 - mmcls - INFO - Epoch [56][1300/5005] lr: 1.000e-02, eta: 5:30:23, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6340 2022-12-31 21:28:19,777 - mmcls - INFO - Epoch [56][1400/5005] lr: 1.000e-02, eta: 5:30:14, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6398 2022-12-31 21:28:28,523 - mmcls - INFO - Epoch [56][1500/5005] lr: 1.000e-02, eta: 5:30:05, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6298 2022-12-31 21:28:37,232 - mmcls - INFO - Epoch [56][1600/5005] lr: 1.000e-02, eta: 5:29:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6317 2022-12-31 21:28:46,006 - mmcls - INFO - Epoch [56][1700/5005] lr: 1.000e-02, eta: 5:29:47, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6287 2022-12-31 21:28:54,670 - mmcls - INFO - Epoch [56][1800/5005] lr: 1.000e-02, eta: 5:29:38, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6280 2022-12-31 21:29:03,469 - mmcls - INFO - Epoch [56][1900/5005] lr: 1.000e-02, eta: 5:29:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6437 2022-12-31 21:29:12,207 - mmcls - INFO - Epoch [56][2000/5005] lr: 1.000e-02, eta: 5:29:21, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6393 2022-12-31 21:29:21,301 - mmcls - INFO - Epoch [56][2100/5005] lr: 1.000e-02, eta: 5:29:12, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6441 2022-12-31 21:29:30,456 - mmcls - INFO - Epoch [56][2200/5005] lr: 1.000e-02, eta: 5:29:03, time: 0.092, data_time: 0.032, memory: 1224, loss: 1.6406 2022-12-31 21:29:39,101 - mmcls - INFO - Epoch [56][2300/5005] lr: 1.000e-02, eta: 5:28:54, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6592 2022-12-31 21:29:48,164 - mmcls - INFO - Epoch [56][2400/5005] lr: 1.000e-02, eta: 5:28:46, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6386 2022-12-31 21:29:56,967 - mmcls - INFO - Epoch [56][2500/5005] lr: 1.000e-02, eta: 5:28:37, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6471 2022-12-31 21:30:05,628 - mmcls - INFO - Epoch [56][2600/5005] lr: 1.000e-02, eta: 5:28:28, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6507 2022-12-31 21:30:14,213 - mmcls - INFO - Epoch [56][2700/5005] lr: 1.000e-02, eta: 5:28:19, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6170 2022-12-31 21:30:23,149 - mmcls - INFO - Epoch [56][2800/5005] lr: 1.000e-02, eta: 5:28:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6539 2022-12-31 21:30:32,045 - mmcls - INFO - Epoch [56][2900/5005] lr: 1.000e-02, eta: 5:28:01, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6592 2022-12-31 21:30:40,607 - mmcls - INFO - Epoch [56][3000/5005] lr: 1.000e-02, eta: 5:27:52, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6295 2022-12-31 21:30:49,749 - mmcls - INFO - Epoch [56][3100/5005] lr: 1.000e-02, eta: 5:27:43, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.6342 2022-12-31 21:30:58,698 - mmcls - INFO - Epoch [56][3200/5005] lr: 1.000e-02, eta: 5:27:35, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.6501 2022-12-31 21:31:07,754 - mmcls - INFO - Epoch [56][3300/5005] lr: 1.000e-02, eta: 5:27:26, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.6640 2022-12-31 21:31:16,530 - mmcls - INFO - Epoch [56][3400/5005] lr: 1.000e-02, eta: 5:27:17, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6626 2022-12-31 21:31:25,365 - mmcls - INFO - Epoch [56][3500/5005] lr: 1.000e-02, eta: 5:27:08, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6140 2022-12-31 21:31:34,156 - mmcls - INFO - Epoch [56][3600/5005] lr: 1.000e-02, eta: 5:26:59, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6460 2022-12-31 21:31:43,059 - mmcls - INFO - Epoch [56][3700/5005] lr: 1.000e-02, eta: 5:26:50, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6495 2022-12-31 21:31:52,121 - mmcls - INFO - Epoch [56][3800/5005] lr: 1.000e-02, eta: 5:26:42, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6643 2022-12-31 21:32:00,616 - mmcls - INFO - Epoch [56][3900/5005] lr: 1.000e-02, eta: 5:26:33, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.6526 2022-12-31 21:32:09,383 - mmcls - INFO - Epoch [56][4000/5005] lr: 1.000e-02, eta: 5:26:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6656 2022-12-31 21:32:18,443 - mmcls - INFO - Epoch [56][4100/5005] lr: 1.000e-02, eta: 5:26:15, time: 0.091, data_time: 0.016, memory: 1224, loss: 1.6542 2022-12-31 21:32:27,223 - mmcls - INFO - Epoch [56][4200/5005] lr: 1.000e-02, eta: 5:26:06, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6443 2022-12-31 21:32:36,464 - mmcls - INFO - Epoch [56][4300/5005] lr: 1.000e-02, eta: 5:25:57, time: 0.092, data_time: 0.010, memory: 1224, loss: 1.6417 2022-12-31 21:32:45,522 - mmcls - INFO - Epoch [56][4400/5005] lr: 1.000e-02, eta: 5:25:49, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.6566 2022-12-31 21:32:54,289 - mmcls - INFO - Epoch [56][4500/5005] lr: 1.000e-02, eta: 5:25:40, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.6651 2022-12-31 21:33:02,915 - mmcls - INFO - Epoch [56][4600/5005] lr: 1.000e-02, eta: 5:25:31, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6483 2022-12-31 21:33:11,827 - mmcls - INFO - Epoch [56][4700/5005] lr: 1.000e-02, eta: 5:25:22, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6507 2022-12-31 21:33:20,825 - mmcls - INFO - Epoch [56][4800/5005] lr: 1.000e-02, eta: 5:25:13, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6547 2022-12-31 21:33:29,705 - mmcls - INFO - Epoch [56][4900/5005] lr: 1.000e-02, eta: 5:25:04, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6647 2022-12-31 21:33:38,487 - mmcls - INFO - Epoch [56][5000/5005] lr: 1.000e-02, eta: 5:24:56, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6701 2022-12-31 21:34:01,223 - mmcls - INFO - Epoch(val) [56][196] accuracy_top-1: 66.2260, accuracy_top-5: 87.0920 2022-12-31 21:34:12,415 - mmcls - INFO - Epoch [57][100/5005] lr: 1.000e-02, eta: 5:24:48, time: 0.112, data_time: 0.041, memory: 1224, loss: 1.6251 2022-12-31 21:34:21,391 - mmcls - INFO - Epoch [57][200/5005] lr: 1.000e-02, eta: 5:24:39, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.5955 2022-12-31 21:34:30,314 - mmcls - INFO - Epoch [57][300/5005] lr: 1.000e-02, eta: 5:24:30, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6260 2022-12-31 21:34:38,914 - mmcls - INFO - Epoch [57][400/5005] lr: 1.000e-02, eta: 5:24:21, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.6028 2022-12-31 21:34:47,832 - mmcls - INFO - Epoch [57][500/5005] lr: 1.000e-02, eta: 5:24:12, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.5966 2022-12-31 21:34:56,789 - mmcls - INFO - Epoch [57][600/5005] lr: 1.000e-02, eta: 5:24:04, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6043 2022-12-31 21:35:05,869 - mmcls - INFO - Epoch [57][700/5005] lr: 1.000e-02, eta: 5:23:55, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6232 2022-12-31 21:35:14,625 - mmcls - INFO - Epoch [57][800/5005] lr: 1.000e-02, eta: 5:23:46, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6223 2022-12-31 21:35:23,312 - mmcls - INFO - Epoch [57][900/5005] lr: 1.000e-02, eta: 5:23:37, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6124 2022-12-31 21:35:32,180 - mmcls - INFO - Epoch [57][1000/5005] lr: 1.000e-02, eta: 5:23:28, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.6457 2022-12-31 21:35:41,040 - mmcls - INFO - Epoch [57][1100/5005] lr: 1.000e-02, eta: 5:23:19, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.6156 2022-12-31 21:35:49,674 - mmcls - INFO - Epoch [57][1200/5005] lr: 1.000e-02, eta: 5:23:10, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.6325 2022-12-31 21:35:58,463 - mmcls - INFO - Epoch [57][1300/5005] lr: 1.000e-02, eta: 5:23:01, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6241 2022-12-31 21:36:07,261 - mmcls - INFO - Epoch [57][1400/5005] lr: 1.000e-02, eta: 5:22:52, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6405 2022-12-31 21:36:15,910 - mmcls - INFO - Epoch [57][1500/5005] lr: 1.000e-02, eta: 5:22:43, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.6364 2022-12-31 21:36:24,960 - mmcls - INFO - Epoch [57][1600/5005] lr: 1.000e-02, eta: 5:22:35, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6057 2022-12-31 21:36:33,599 - mmcls - INFO - Epoch [57][1700/5005] lr: 1.000e-02, eta: 5:22:26, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.6594 2022-12-31 21:36:42,213 - mmcls - INFO - Epoch [57][1800/5005] lr: 1.000e-02, eta: 5:22:17, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.6285 2022-12-31 21:36:50,818 - mmcls - INFO - Epoch [57][1900/5005] lr: 1.000e-02, eta: 5:22:08, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6521 2022-12-31 21:37:00,009 - mmcls - INFO - Epoch [57][2000/5005] lr: 1.000e-02, eta: 5:21:59, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6440 2022-12-31 21:37:08,979 - mmcls - INFO - Epoch [57][2100/5005] lr: 1.000e-02, eta: 5:21:50, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.6267 2022-12-31 21:37:17,963 - mmcls - INFO - Epoch [57][2200/5005] lr: 1.000e-02, eta: 5:21:41, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6465 2022-12-31 21:37:26,983 - mmcls - INFO - Epoch [57][2300/5005] lr: 1.000e-02, eta: 5:21:33, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6185 2022-12-31 21:37:35,493 - mmcls - INFO - Epoch [57][2400/5005] lr: 1.000e-02, eta: 5:21:24, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.6328 2022-12-31 21:37:44,281 - mmcls - INFO - Epoch [57][2500/5005] lr: 1.000e-02, eta: 5:21:15, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.6261 2022-12-31 21:37:52,791 - mmcls - INFO - Epoch [57][2600/5005] lr: 1.000e-02, eta: 5:21:06, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6591 2022-12-31 21:38:01,696 - mmcls - INFO - Epoch [57][2700/5005] lr: 1.000e-02, eta: 5:20:57, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6335 2022-12-31 21:38:10,481 - mmcls - INFO - Epoch [57][2800/5005] lr: 1.000e-02, eta: 5:20:48, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6370 2022-12-31 21:38:19,250 - mmcls - INFO - Epoch [57][2900/5005] lr: 1.000e-02, eta: 5:20:39, time: 0.088, data_time: 0.037, memory: 1224, loss: 1.6226 2022-12-31 21:38:28,005 - mmcls - INFO - Epoch [57][3000/5005] lr: 1.000e-02, eta: 5:20:30, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6495 2022-12-31 21:38:37,109 - mmcls - INFO - Epoch [57][3100/5005] lr: 1.000e-02, eta: 5:20:21, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6610 2022-12-31 21:38:46,170 - mmcls - INFO - Epoch [57][3200/5005] lr: 1.000e-02, eta: 5:20:13, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.6643 2022-12-31 21:38:55,012 - mmcls - INFO - Epoch [57][3300/5005] lr: 1.000e-02, eta: 5:20:04, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6521 2022-12-31 21:39:04,256 - mmcls - INFO - Epoch [57][3400/5005] lr: 1.000e-02, eta: 5:19:55, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.6561 2022-12-31 21:39:12,793 - mmcls - INFO - Epoch [57][3500/5005] lr: 1.000e-02, eta: 5:19:46, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6647 2022-12-31 21:39:21,477 - mmcls - INFO - Epoch [57][3600/5005] lr: 1.000e-02, eta: 5:19:37, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6261 2022-12-31 21:39:30,203 - mmcls - INFO - Epoch [57][3700/5005] lr: 1.000e-02, eta: 5:19:28, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.6873 2022-12-31 21:39:38,947 - mmcls - INFO - Epoch [57][3800/5005] lr: 1.000e-02, eta: 5:19:19, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.6465 2022-12-31 21:39:47,590 - mmcls - INFO - Epoch [57][3900/5005] lr: 1.000e-02, eta: 5:19:10, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6590 2022-12-31 21:39:56,349 - mmcls - INFO - Epoch [57][4000/5005] lr: 1.000e-02, eta: 5:19:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6668 2022-12-31 21:40:04,977 - mmcls - INFO - Epoch [57][4100/5005] lr: 1.000e-02, eta: 5:18:52, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.6600 2022-12-31 21:40:13,896 - mmcls - INFO - Epoch [57][4200/5005] lr: 1.000e-02, eta: 5:18:44, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.6772 2022-12-31 21:40:22,505 - mmcls - INFO - Epoch [57][4300/5005] lr: 1.000e-02, eta: 5:18:35, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.6449 2022-12-31 21:40:31,545 - mmcls - INFO - Epoch [57][4400/5005] lr: 1.000e-02, eta: 5:18:26, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.6458 2022-12-31 21:40:40,104 - mmcls - INFO - Epoch [57][4500/5005] lr: 1.000e-02, eta: 5:18:17, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.6531 2022-12-31 21:40:48,907 - mmcls - INFO - Epoch [57][4600/5005] lr: 1.000e-02, eta: 5:18:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6728 2022-12-31 21:40:57,462 - mmcls - INFO - Epoch [57][4700/5005] lr: 1.000e-02, eta: 5:17:59, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6641 2022-12-31 21:41:06,652 - mmcls - INFO - Epoch [57][4800/5005] lr: 1.000e-02, eta: 5:17:50, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6583 2022-12-31 21:41:15,433 - mmcls - INFO - Epoch [57][4900/5005] lr: 1.000e-02, eta: 5:17:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6420 2022-12-31 21:41:24,117 - mmcls - INFO - Epoch [57][5000/5005] lr: 1.000e-02, eta: 5:17:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6486 2022-12-31 21:41:46,707 - mmcls - INFO - Epoch(val) [57][196] accuracy_top-1: 66.0460, accuracy_top-5: 87.2300 2022-12-31 21:41:57,710 - mmcls - INFO - Epoch [58][100/5005] lr: 1.000e-02, eta: 5:17:24, time: 0.110, data_time: 0.035, memory: 1224, loss: 1.5943 2022-12-31 21:42:06,412 - mmcls - INFO - Epoch [58][200/5005] lr: 1.000e-02, eta: 5:17:15, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.5771 2022-12-31 21:42:15,253 - mmcls - INFO - Epoch [58][300/5005] lr: 1.000e-02, eta: 5:17:06, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.6126 2022-12-31 21:42:23,837 - mmcls - INFO - Epoch [58][400/5005] lr: 1.000e-02, eta: 5:16:57, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.6286 2022-12-31 21:42:33,187 - mmcls - INFO - Epoch [58][500/5005] lr: 1.000e-02, eta: 5:16:49, time: 0.093, data_time: 0.013, memory: 1224, loss: 1.6011 2022-12-31 21:42:41,693 - mmcls - INFO - Epoch [58][600/5005] lr: 1.000e-02, eta: 5:16:40, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.6316 2022-12-31 21:42:50,348 - mmcls - INFO - Epoch [58][700/5005] lr: 1.000e-02, eta: 5:16:31, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.5970 2022-12-31 21:42:59,081 - mmcls - INFO - Epoch [58][800/5005] lr: 1.000e-02, eta: 5:16:22, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.5954 2022-12-31 21:43:07,828 - mmcls - INFO - Epoch [58][900/5005] lr: 1.000e-02, eta: 5:16:13, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.6232 2022-12-31 21:43:16,719 - mmcls - INFO - Epoch [58][1000/5005] lr: 1.000e-02, eta: 5:16:04, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.6332 2022-12-31 21:43:25,806 - mmcls - INFO - Epoch [58][1100/5005] lr: 1.000e-02, eta: 5:15:55, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.6302 2022-12-31 21:43:34,792 - mmcls - INFO - Epoch [58][1200/5005] lr: 1.000e-02, eta: 5:15:47, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.6221 2022-12-31 21:43:43,563 - mmcls - INFO - Epoch [58][1300/5005] lr: 1.000e-02, eta: 5:15:38, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.6309 2022-12-31 21:43:52,604 - mmcls - INFO - Epoch [58][1400/5005] lr: 1.000e-02, eta: 5:15:29, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6468 2022-12-31 21:44:01,607 - mmcls - INFO - Epoch [58][1500/5005] lr: 1.000e-02, eta: 5:15:20, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.6294 2022-12-31 21:44:10,202 - mmcls - INFO - Epoch [58][1600/5005] lr: 1.000e-02, eta: 5:15:11, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6528 2022-12-31 21:44:19,343 - mmcls - INFO - Epoch [58][1700/5005] lr: 1.000e-02, eta: 5:15:03, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6278 2022-12-31 21:44:28,151 - mmcls - INFO - Epoch [58][1800/5005] lr: 1.000e-02, eta: 5:14:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6426 2022-12-31 21:44:37,155 - mmcls - INFO - Epoch [58][1900/5005] lr: 1.000e-02, eta: 5:14:45, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6221 2022-12-31 21:44:45,912 - mmcls - INFO - Epoch [58][2000/5005] lr: 1.000e-02, eta: 5:14:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6459 2022-12-31 21:44:54,668 - mmcls - INFO - Epoch [58][2100/5005] lr: 1.000e-02, eta: 5:14:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6409 2022-12-31 21:45:03,304 - mmcls - INFO - Epoch [58][2200/5005] lr: 1.000e-02, eta: 5:14:18, time: 0.086, data_time: 0.034, memory: 1224, loss: 1.6423 2022-12-31 21:45:12,072 - mmcls - INFO - Epoch [58][2300/5005] lr: 1.000e-02, eta: 5:14:09, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6165 2022-12-31 21:45:21,111 - mmcls - INFO - Epoch [58][2400/5005] lr: 1.000e-02, eta: 5:14:00, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6362 2022-12-31 21:45:29,912 - mmcls - INFO - Epoch [58][2500/5005] lr: 1.000e-02, eta: 5:13:52, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.6465 2022-12-31 21:45:39,353 - mmcls - INFO - Epoch [58][2600/5005] lr: 1.000e-02, eta: 5:13:43, time: 0.094, data_time: 0.002, memory: 1224, loss: 1.6380 2022-12-31 21:45:48,449 - mmcls - INFO - Epoch [58][2700/5005] lr: 1.000e-02, eta: 5:13:34, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6301 2022-12-31 21:45:57,222 - mmcls - INFO - Epoch [58][2800/5005] lr: 1.000e-02, eta: 5:13:26, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6690 2022-12-31 21:46:06,117 - mmcls - INFO - Epoch [58][2900/5005] lr: 1.000e-02, eta: 5:13:17, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6152 2022-12-31 21:46:14,771 - mmcls - INFO - Epoch [58][3000/5005] lr: 1.000e-02, eta: 5:13:08, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.6495 2022-12-31 21:46:23,902 - mmcls - INFO - Epoch [58][3100/5005] lr: 1.000e-02, eta: 5:12:59, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.6446 2022-12-31 21:46:32,582 - mmcls - INFO - Epoch [58][3200/5005] lr: 1.000e-02, eta: 5:12:50, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6417 2022-12-31 21:46:41,408 - mmcls - INFO - Epoch [58][3300/5005] lr: 1.000e-02, eta: 5:12:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6825 2022-12-31 21:46:49,924 - mmcls - INFO - Epoch [58][3400/5005] lr: 1.000e-02, eta: 5:12:32, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6628 2022-12-31 21:46:58,650 - mmcls - INFO - Epoch [58][3500/5005] lr: 1.000e-02, eta: 5:12:23, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6352 2022-12-31 21:47:07,390 - mmcls - INFO - Epoch [58][3600/5005] lr: 1.000e-02, eta: 5:12:14, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6419 2022-12-31 21:47:16,186 - mmcls - INFO - Epoch [58][3700/5005] lr: 1.000e-02, eta: 5:12:05, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6818 2022-12-31 21:47:25,168 - mmcls - INFO - Epoch [58][3800/5005] lr: 1.000e-02, eta: 5:11:57, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6450 2022-12-31 21:47:33,726 - mmcls - INFO - Epoch [58][3900/5005] lr: 1.000e-02, eta: 5:11:47, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6269 2022-12-31 21:47:42,600 - mmcls - INFO - Epoch [58][4000/5005] lr: 1.000e-02, eta: 5:11:39, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.6592 2022-12-31 21:47:51,251 - mmcls - INFO - Epoch [58][4100/5005] lr: 1.000e-02, eta: 5:11:30, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6671 2022-12-31 21:48:00,216 - mmcls - INFO - Epoch [58][4200/5005] lr: 1.000e-02, eta: 5:11:21, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6521 2022-12-31 21:48:08,723 - mmcls - INFO - Epoch [58][4300/5005] lr: 1.000e-02, eta: 5:11:12, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.6728 2022-12-31 21:48:17,373 - mmcls - INFO - Epoch [58][4400/5005] lr: 1.000e-02, eta: 5:11:03, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6359 2022-12-31 21:48:26,474 - mmcls - INFO - Epoch [58][4500/5005] lr: 1.000e-02, eta: 5:10:54, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.6672 2022-12-31 21:48:35,095 - mmcls - INFO - Epoch [58][4600/5005] lr: 1.000e-02, eta: 5:10:45, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6542 2022-12-31 21:48:44,108 - mmcls - INFO - Epoch [58][4700/5005] lr: 1.000e-02, eta: 5:10:36, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.6439 2022-12-31 21:48:53,146 - mmcls - INFO - Epoch [58][4800/5005] lr: 1.000e-02, eta: 5:10:28, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6400 2022-12-31 21:49:01,813 - mmcls - INFO - Epoch [58][4900/5005] lr: 1.000e-02, eta: 5:10:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6617 2022-12-31 21:49:10,693 - mmcls - INFO - Epoch [58][5000/5005] lr: 1.000e-02, eta: 5:10:10, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.6672 2022-12-31 21:49:33,400 - mmcls - INFO - Epoch(val) [58][196] accuracy_top-1: 65.6820, accuracy_top-5: 86.8980 2022-12-31 21:49:44,270 - mmcls - INFO - Epoch [59][100/5005] lr: 1.000e-02, eta: 5:10:02, time: 0.108, data_time: 0.036, memory: 1224, loss: 1.6147 2022-12-31 21:49:53,155 - mmcls - INFO - Epoch [59][200/5005] lr: 1.000e-02, eta: 5:09:53, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6205 2022-12-31 21:50:01,991 - mmcls - INFO - Epoch [59][300/5005] lr: 1.000e-02, eta: 5:09:44, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6244 2022-12-31 21:50:10,702 - mmcls - INFO - Epoch [59][400/5005] lr: 1.000e-02, eta: 5:09:35, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6370 2022-12-31 21:50:19,220 - mmcls - INFO - Epoch [59][500/5005] lr: 1.000e-02, eta: 5:09:26, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6313 2022-12-31 21:50:27,746 - mmcls - INFO - Epoch [59][600/5005] lr: 1.000e-02, eta: 5:09:17, time: 0.085, data_time: 0.025, memory: 1224, loss: 1.6071 2022-12-31 21:50:36,513 - mmcls - INFO - Epoch [59][700/5005] lr: 1.000e-02, eta: 5:09:08, time: 0.088, data_time: 0.038, memory: 1224, loss: 1.6308 2022-12-31 21:50:45,204 - mmcls - INFO - Epoch [59][800/5005] lr: 1.000e-02, eta: 5:08:59, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.6055 2022-12-31 21:50:53,977 - mmcls - INFO - Epoch [59][900/5005] lr: 1.000e-02, eta: 5:08:50, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.6263 2022-12-31 21:51:02,585 - mmcls - INFO - Epoch [59][1000/5005] lr: 1.000e-02, eta: 5:08:41, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.6193 2022-12-31 21:51:11,665 - mmcls - INFO - Epoch [59][1100/5005] lr: 1.000e-02, eta: 5:08:32, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.6131 2022-12-31 21:51:20,454 - mmcls - INFO - Epoch [59][1200/5005] lr: 1.000e-02, eta: 5:08:23, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6224 2022-12-31 21:51:29,226 - mmcls - INFO - Epoch [59][1300/5005] lr: 1.000e-02, eta: 5:08:14, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6306 2022-12-31 21:51:37,839 - mmcls - INFO - Epoch [59][1400/5005] lr: 1.000e-02, eta: 5:08:05, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.6330 2022-12-31 21:51:46,578 - mmcls - INFO - Epoch [59][1500/5005] lr: 1.000e-02, eta: 5:07:56, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.6378 2022-12-31 21:51:55,258 - mmcls - INFO - Epoch [59][1600/5005] lr: 1.000e-02, eta: 5:07:48, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6257 2022-12-31 21:52:03,921 - mmcls - INFO - Epoch [59][1700/5005] lr: 1.000e-02, eta: 5:07:39, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6286 2022-12-31 21:52:12,773 - mmcls - INFO - Epoch [59][1800/5005] lr: 1.000e-02, eta: 5:07:30, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.6401 2022-12-31 21:52:21,896 - mmcls - INFO - Epoch [59][1900/5005] lr: 1.000e-02, eta: 5:07:21, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.6580 2022-12-31 21:52:30,647 - mmcls - INFO - Epoch [59][2000/5005] lr: 1.000e-02, eta: 5:07:12, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.6215 2022-12-31 21:52:39,652 - mmcls - INFO - Epoch [59][2100/5005] lr: 1.000e-02, eta: 5:07:03, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.6401 2022-12-31 21:52:48,326 - mmcls - INFO - Epoch [59][2200/5005] lr: 1.000e-02, eta: 5:06:54, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6328 2022-12-31 21:52:57,101 - mmcls - INFO - Epoch [59][2300/5005] lr: 1.000e-02, eta: 5:06:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6313 2022-12-31 21:53:06,282 - mmcls - INFO - Epoch [59][2400/5005] lr: 1.000e-02, eta: 5:06:37, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.6375 2022-12-31 21:53:15,199 - mmcls - INFO - Epoch [59][2500/5005] lr: 1.000e-02, eta: 5:06:28, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6485 2022-12-31 21:53:23,847 - mmcls - INFO - Epoch [59][2600/5005] lr: 1.000e-02, eta: 5:06:19, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.6456 2022-12-31 21:53:33,018 - mmcls - INFO - Epoch [59][2700/5005] lr: 1.000e-02, eta: 5:06:10, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.6445 2022-12-31 21:53:42,008 - mmcls - INFO - Epoch [59][2800/5005] lr: 1.000e-02, eta: 5:06:02, time: 0.090, data_time: 0.039, memory: 1224, loss: 1.6413 2022-12-31 21:53:50,725 - mmcls - INFO - Epoch [59][2900/5005] lr: 1.000e-02, eta: 5:05:53, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6583 2022-12-31 21:53:59,454 - mmcls - INFO - Epoch [59][3000/5005] lr: 1.000e-02, eta: 5:05:44, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.6224 2022-12-31 21:54:08,322 - mmcls - INFO - Epoch [59][3100/5005] lr: 1.000e-02, eta: 5:05:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.6532 2022-12-31 21:54:17,414 - mmcls - INFO - Epoch [59][3200/5005] lr: 1.000e-02, eta: 5:05:26, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.6494 2022-12-31 21:54:26,374 - mmcls - INFO - Epoch [59][3300/5005] lr: 1.000e-02, eta: 5:05:17, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.6151 2022-12-31 21:54:35,474 - mmcls - INFO - Epoch [59][3400/5005] lr: 1.000e-02, eta: 5:05:09, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6295 2022-12-31 21:54:44,345 - mmcls - INFO - Epoch [59][3500/5005] lr: 1.000e-02, eta: 5:05:00, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6542 2022-12-31 21:54:53,473 - mmcls - INFO - Epoch [59][3600/5005] lr: 1.000e-02, eta: 5:04:51, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6380 2022-12-31 21:55:02,468 - mmcls - INFO - Epoch [59][3700/5005] lr: 1.000e-02, eta: 5:04:42, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.6410 2022-12-31 21:55:11,241 - mmcls - INFO - Epoch [59][3800/5005] lr: 1.000e-02, eta: 5:04:34, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6407 2022-12-31 21:55:20,307 - mmcls - INFO - Epoch [59][3900/5005] lr: 1.000e-02, eta: 5:04:25, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.6453 2022-12-31 21:55:29,447 - mmcls - INFO - Epoch [59][4000/5005] lr: 1.000e-02, eta: 5:04:16, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.6476 2022-12-31 21:55:37,971 - mmcls - INFO - Epoch [59][4100/5005] lr: 1.000e-02, eta: 5:04:07, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.6212 2022-12-31 21:55:46,863 - mmcls - INFO - Epoch [59][4200/5005] lr: 1.000e-02, eta: 5:03:58, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.6502 2022-12-31 21:55:55,660 - mmcls - INFO - Epoch [59][4300/5005] lr: 1.000e-02, eta: 5:03:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6472 2022-12-31 21:56:04,526 - mmcls - INFO - Epoch [59][4400/5005] lr: 1.000e-02, eta: 5:03:41, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.6772 2022-12-31 21:56:13,217 - mmcls - INFO - Epoch [59][4500/5005] lr: 1.000e-02, eta: 5:03:32, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6549 2022-12-31 21:56:21,962 - mmcls - INFO - Epoch [59][4600/5005] lr: 1.000e-02, eta: 5:03:23, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6528 2022-12-31 21:56:30,849 - mmcls - INFO - Epoch [59][4700/5005] lr: 1.000e-02, eta: 5:03:14, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6257 2022-12-31 21:56:39,506 - mmcls - INFO - Epoch [59][4800/5005] lr: 1.000e-02, eta: 5:03:05, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.6602 2022-12-31 21:56:48,320 - mmcls - INFO - Epoch [59][4900/5005] lr: 1.000e-02, eta: 5:02:56, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.6635 2022-12-31 21:56:57,573 - mmcls - INFO - Epoch [59][5000/5005] lr: 1.000e-02, eta: 5:02:47, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.6590 2022-12-31 21:57:20,128 - mmcls - INFO - Epoch(val) [59][196] accuracy_top-1: 65.7640, accuracy_top-5: 86.6940 2022-12-31 21:57:31,398 - mmcls - INFO - Epoch [60][100/5005] lr: 1.000e-02, eta: 5:02:39, time: 0.112, data_time: 0.054, memory: 1224, loss: 1.5921 2022-12-31 21:57:40,127 - mmcls - INFO - Epoch [60][200/5005] lr: 1.000e-02, eta: 5:02:31, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.6077 2022-12-31 21:57:48,942 - mmcls - INFO - Epoch [60][300/5005] lr: 1.000e-02, eta: 5:02:22, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6283 2022-12-31 21:57:57,689 - mmcls - INFO - Epoch [60][400/5005] lr: 1.000e-02, eta: 5:02:13, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6163 2022-12-31 21:58:06,392 - mmcls - INFO - Epoch [60][500/5005] lr: 1.000e-02, eta: 5:02:04, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.6153 2022-12-31 21:58:15,068 - mmcls - INFO - Epoch [60][600/5005] lr: 1.000e-02, eta: 5:01:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6229 2022-12-31 21:58:24,314 - mmcls - INFO - Epoch [60][700/5005] lr: 1.000e-02, eta: 5:01:46, time: 0.092, data_time: 0.008, memory: 1224, loss: 1.6153 2022-12-31 21:58:33,195 - mmcls - INFO - Epoch [60][800/5005] lr: 1.000e-02, eta: 5:01:37, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.6564 2022-12-31 21:58:42,026 - mmcls - INFO - Epoch [60][900/5005] lr: 1.000e-02, eta: 5:01:28, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.6045 2022-12-31 21:58:51,002 - mmcls - INFO - Epoch [60][1000/5005] lr: 1.000e-02, eta: 5:01:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6174 2022-12-31 21:58:59,648 - mmcls - INFO - Epoch [60][1100/5005] lr: 1.000e-02, eta: 5:01:11, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6129 2022-12-31 21:59:08,433 - mmcls - INFO - Epoch [60][1200/5005] lr: 1.000e-02, eta: 5:01:02, time: 0.088, data_time: 0.029, memory: 1224, loss: 1.6126 2022-12-31 21:59:17,457 - mmcls - INFO - Epoch [60][1300/5005] lr: 1.000e-02, eta: 5:00:53, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.6327 2022-12-31 21:59:26,262 - mmcls - INFO - Epoch [60][1400/5005] lr: 1.000e-02, eta: 5:00:44, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6309 2022-12-31 21:59:34,908 - mmcls - INFO - Epoch [60][1500/5005] lr: 1.000e-02, eta: 5:00:35, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6186 2022-12-31 21:59:43,604 - mmcls - INFO - Epoch [60][1600/5005] lr: 1.000e-02, eta: 5:00:26, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.6359 2022-12-31 21:59:52,416 - mmcls - INFO - Epoch [60][1700/5005] lr: 1.000e-02, eta: 5:00:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6290 2022-12-31 22:00:01,223 - mmcls - INFO - Epoch [60][1800/5005] lr: 1.000e-02, eta: 5:00:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.6314 2022-12-31 22:00:09,946 - mmcls - INFO - Epoch [60][1900/5005] lr: 1.000e-02, eta: 5:00:00, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6264 2022-12-31 22:00:18,642 - mmcls - INFO - Epoch [60][2000/5005] lr: 1.000e-02, eta: 4:59:51, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.6383 2022-12-31 22:00:27,310 - mmcls - INFO - Epoch [60][2100/5005] lr: 1.000e-02, eta: 4:59:42, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.6539 2022-12-31 22:00:35,882 - mmcls - INFO - Epoch [60][2200/5005] lr: 1.000e-02, eta: 4:59:33, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.6320 2022-12-31 22:00:44,681 - mmcls - INFO - Epoch [60][2300/5005] lr: 1.000e-02, eta: 4:59:24, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.6451 2022-12-31 22:00:53,341 - mmcls - INFO - Epoch [60][2400/5005] lr: 1.000e-02, eta: 4:59:15, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.6493 2022-12-31 22:01:02,360 - mmcls - INFO - Epoch [60][2500/5005] lr: 1.000e-02, eta: 4:59:06, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.6081 2022-12-31 22:01:11,064 - mmcls - INFO - Epoch [60][2600/5005] lr: 1.000e-02, eta: 4:58:57, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.6499 2022-12-31 22:01:20,007 - mmcls - INFO - Epoch [60][2700/5005] lr: 1.000e-02, eta: 4:58:48, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6383 2022-12-31 22:01:28,658 - mmcls - INFO - Epoch [60][2800/5005] lr: 1.000e-02, eta: 4:58:39, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.6486 2022-12-31 22:01:37,561 - mmcls - INFO - Epoch [60][2900/5005] lr: 1.000e-02, eta: 4:58:30, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.6194 2022-12-31 22:01:46,640 - mmcls - INFO - Epoch [60][3000/5005] lr: 1.000e-02, eta: 4:58:22, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6407 2022-12-31 22:01:55,317 - mmcls - INFO - Epoch [60][3100/5005] lr: 1.000e-02, eta: 4:58:13, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.6352 2022-12-31 22:02:04,220 - mmcls - INFO - Epoch [60][3200/5005] lr: 1.000e-02, eta: 4:58:04, time: 0.089, data_time: 0.034, memory: 1224, loss: 1.6260 2022-12-31 22:02:12,928 - mmcls - INFO - Epoch [60][3300/5005] lr: 1.000e-02, eta: 4:57:55, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.6186 2022-12-31 22:02:22,002 - mmcls - INFO - Epoch [60][3400/5005] lr: 1.000e-02, eta: 4:57:46, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.6495 2022-12-31 22:02:30,633 - mmcls - INFO - Epoch [60][3500/5005] lr: 1.000e-02, eta: 4:57:37, time: 0.086, data_time: 0.025, memory: 1224, loss: 1.6499 2022-12-31 22:02:39,472 - mmcls - INFO - Epoch [60][3600/5005] lr: 1.000e-02, eta: 4:57:28, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.6572 2022-12-31 22:02:48,154 - mmcls - INFO - Epoch [60][3700/5005] lr: 1.000e-02, eta: 4:57:19, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.6485 2022-12-31 22:02:57,057 - mmcls - INFO - Epoch [60][3800/5005] lr: 1.000e-02, eta: 4:57:11, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.6396 2022-12-31 22:03:05,809 - mmcls - INFO - Epoch [60][3900/5005] lr: 1.000e-02, eta: 4:57:02, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.6452 2022-12-31 22:03:14,642 - mmcls - INFO - Epoch [60][4000/5005] lr: 1.000e-02, eta: 4:56:53, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.6371 2022-12-31 22:03:23,156 - mmcls - INFO - Epoch [60][4100/5005] lr: 1.000e-02, eta: 4:56:44, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.6505 2022-12-31 22:03:31,873 - mmcls - INFO - Epoch [60][4200/5005] lr: 1.000e-02, eta: 4:56:35, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6665 2022-12-31 22:03:40,724 - mmcls - INFO - Epoch [60][4300/5005] lr: 1.000e-02, eta: 4:56:26, time: 0.089, data_time: 0.029, memory: 1224, loss: 1.6340 2022-12-31 22:03:49,197 - mmcls - INFO - Epoch [60][4400/5005] lr: 1.000e-02, eta: 4:56:17, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.6585 2022-12-31 22:03:57,900 - mmcls - INFO - Epoch [60][4500/5005] lr: 1.000e-02, eta: 4:56:08, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.6544 2022-12-31 22:04:06,777 - mmcls - INFO - Epoch [60][4600/5005] lr: 1.000e-02, eta: 4:55:59, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.6616 2022-12-31 22:04:15,430 - mmcls - INFO - Epoch [60][4700/5005] lr: 1.000e-02, eta: 4:55:50, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.6332 2022-12-31 22:04:24,252 - mmcls - INFO - Epoch [60][4800/5005] lr: 1.000e-02, eta: 4:55:41, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.6525 2022-12-31 22:04:32,908 - mmcls - INFO - Epoch [60][4900/5005] lr: 1.000e-02, eta: 4:55:32, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.6772 2022-12-31 22:04:41,519 - mmcls - INFO - Epoch [60][5000/5005] lr: 1.000e-02, eta: 4:55:23, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.6569 2022-12-31 22:05:04,258 - mmcls - INFO - Epoch(val) [60][196] accuracy_top-1: 65.8700, accuracy_top-5: 87.0500 2022-12-31 22:05:15,086 - mmcls - INFO - Epoch [61][100/5005] lr: 1.000e-03, eta: 4:55:15, time: 0.108, data_time: 0.041, memory: 1224, loss: 1.5603 2022-12-31 22:05:24,158 - mmcls - INFO - Epoch [61][200/5005] lr: 1.000e-03, eta: 4:55:06, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.5258 2022-12-31 22:05:33,221 - mmcls - INFO - Epoch [61][300/5005] lr: 1.000e-03, eta: 4:54:57, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.5039 2022-12-31 22:05:41,769 - mmcls - INFO - Epoch [61][400/5005] lr: 1.000e-03, eta: 4:54:48, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.4802 2022-12-31 22:05:50,972 - mmcls - INFO - Epoch [61][500/5005] lr: 1.000e-03, eta: 4:54:40, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.4945 2022-12-31 22:05:59,878 - mmcls - INFO - Epoch [61][600/5005] lr: 1.000e-03, eta: 4:54:31, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.4956 2022-12-31 22:06:08,779 - mmcls - INFO - Epoch [61][700/5005] lr: 1.000e-03, eta: 4:54:22, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.4753 2022-12-31 22:06:17,911 - mmcls - INFO - Epoch [61][800/5005] lr: 1.000e-03, eta: 4:54:13, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.4793 2022-12-31 22:06:26,682 - mmcls - INFO - Epoch [61][900/5005] lr: 1.000e-03, eta: 4:54:04, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.4518 2022-12-31 22:06:35,504 - mmcls - INFO - Epoch [61][1000/5005] lr: 1.000e-03, eta: 4:53:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.4720 2022-12-31 22:06:44,340 - mmcls - INFO - Epoch [61][1100/5005] lr: 1.000e-03, eta: 4:53:47, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.4676 2022-12-31 22:06:53,182 - mmcls - INFO - Epoch [61][1200/5005] lr: 1.000e-03, eta: 4:53:38, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.4642 2022-12-31 22:07:01,815 - mmcls - INFO - Epoch [61][1300/5005] lr: 1.000e-03, eta: 4:53:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.4537 2022-12-31 22:07:10,707 - mmcls - INFO - Epoch [61][1400/5005] lr: 1.000e-03, eta: 4:53:20, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.4442 2022-12-31 22:07:19,703 - mmcls - INFO - Epoch [61][1500/5005] lr: 1.000e-03, eta: 4:53:11, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.4599 2022-12-31 22:07:28,506 - mmcls - INFO - Epoch [61][1600/5005] lr: 1.000e-03, eta: 4:53:02, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.4585 2022-12-31 22:07:37,198 - mmcls - INFO - Epoch [61][1700/5005] lr: 1.000e-03, eta: 4:52:53, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.4555 2022-12-31 22:07:46,041 - mmcls - INFO - Epoch [61][1800/5005] lr: 1.000e-03, eta: 4:52:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.4671 2022-12-31 22:07:54,787 - mmcls - INFO - Epoch [61][1900/5005] lr: 1.000e-03, eta: 4:52:36, time: 0.087, data_time: 0.027, memory: 1224, loss: 1.4313 2022-12-31 22:08:03,674 - mmcls - INFO - Epoch [61][2000/5005] lr: 1.000e-03, eta: 4:52:27, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.4336 2022-12-31 22:08:12,883 - mmcls - INFO - Epoch [61][2100/5005] lr: 1.000e-03, eta: 4:52:18, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.4574 2022-12-31 22:08:21,889 - mmcls - INFO - Epoch [61][2200/5005] lr: 1.000e-03, eta: 4:52:09, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.4446 2022-12-31 22:08:30,256 - mmcls - INFO - Epoch [61][2300/5005] lr: 1.000e-03, eta: 4:52:00, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.4476 2022-12-31 22:08:38,971 - mmcls - INFO - Epoch [61][2400/5005] lr: 1.000e-03, eta: 4:51:51, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.4453 2022-12-31 22:08:47,914 - mmcls - INFO - Epoch [61][2500/5005] lr: 1.000e-03, eta: 4:51:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4252 2022-12-31 22:08:56,759 - mmcls - INFO - Epoch [61][2600/5005] lr: 1.000e-03, eta: 4:51:34, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.4542 2022-12-31 22:09:05,638 - mmcls - INFO - Epoch [61][2700/5005] lr: 1.000e-03, eta: 4:51:25, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.4286 2022-12-31 22:09:14,222 - mmcls - INFO - Epoch [61][2800/5005] lr: 1.000e-03, eta: 4:51:16, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.4510 2022-12-31 22:09:22,993 - mmcls - INFO - Epoch [61][2900/5005] lr: 1.000e-03, eta: 4:51:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.4430 2022-12-31 22:09:31,803 - mmcls - INFO - Epoch [61][3000/5005] lr: 1.000e-03, eta: 4:50:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.4392 2022-12-31 22:09:40,783 - mmcls - INFO - Epoch [61][3100/5005] lr: 1.000e-03, eta: 4:50:49, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.4463 2022-12-31 22:09:49,509 - mmcls - INFO - Epoch [61][3200/5005] lr: 1.000e-03, eta: 4:50:40, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.4447 2022-12-31 22:09:58,273 - mmcls - INFO - Epoch [61][3300/5005] lr: 1.000e-03, eta: 4:50:31, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.4540 2022-12-31 22:10:07,044 - mmcls - INFO - Epoch [61][3400/5005] lr: 1.000e-03, eta: 4:50:22, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.4278 2022-12-31 22:10:15,786 - mmcls - INFO - Epoch [61][3500/5005] lr: 1.000e-03, eta: 4:50:14, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.4510 2022-12-31 22:10:24,669 - mmcls - INFO - Epoch [61][3600/5005] lr: 1.000e-03, eta: 4:50:05, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.4473 2022-12-31 22:10:33,515 - mmcls - INFO - Epoch [61][3700/5005] lr: 1.000e-03, eta: 4:49:56, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.4514 2022-12-31 22:10:42,287 - mmcls - INFO - Epoch [61][3800/5005] lr: 1.000e-03, eta: 4:49:47, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.4287 2022-12-31 22:10:50,867 - mmcls - INFO - Epoch [61][3900/5005] lr: 1.000e-03, eta: 4:49:38, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.4227 2022-12-31 22:10:59,556 - mmcls - INFO - Epoch [61][4000/5005] lr: 1.000e-03, eta: 4:49:29, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.4243 2022-12-31 22:11:08,499 - mmcls - INFO - Epoch [61][4100/5005] lr: 1.000e-03, eta: 4:49:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4082 2022-12-31 22:11:17,674 - mmcls - INFO - Epoch [61][4200/5005] lr: 1.000e-03, eta: 4:49:12, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.4580 2022-12-31 22:11:26,160 - mmcls - INFO - Epoch [61][4300/5005] lr: 1.000e-03, eta: 4:49:02, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.4499 2022-12-31 22:11:35,186 - mmcls - INFO - Epoch [61][4400/5005] lr: 1.000e-03, eta: 4:48:54, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.4472 2022-12-31 22:11:43,987 - mmcls - INFO - Epoch [61][4500/5005] lr: 1.000e-03, eta: 4:48:45, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.4386 2022-12-31 22:11:52,891 - mmcls - INFO - Epoch [61][4600/5005] lr: 1.000e-03, eta: 4:48:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4268 2022-12-31 22:12:01,652 - mmcls - INFO - Epoch [61][4700/5005] lr: 1.000e-03, eta: 4:48:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.4223 2022-12-31 22:12:10,697 - mmcls - INFO - Epoch [61][4800/5005] lr: 1.000e-03, eta: 4:48:18, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.4146 2022-12-31 22:12:19,585 - mmcls - INFO - Epoch [61][4900/5005] lr: 1.000e-03, eta: 4:48:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4094 2022-12-31 22:12:28,542 - mmcls - INFO - Epoch [61][5000/5005] lr: 1.000e-03, eta: 4:48:01, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4094 2022-12-31 22:12:51,237 - mmcls - INFO - Epoch(val) [61][196] accuracy_top-1: 69.7560, accuracy_top-5: 89.1280 2022-12-31 22:13:02,437 - mmcls - INFO - Epoch [62][100/5005] lr: 1.000e-03, eta: 4:47:53, time: 0.112, data_time: 0.055, memory: 1224, loss: 1.4027 2022-12-31 22:13:11,102 - mmcls - INFO - Epoch [62][200/5005] lr: 1.000e-03, eta: 4:47:44, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.4195 2022-12-31 22:13:19,716 - mmcls - INFO - Epoch [62][300/5005] lr: 1.000e-03, eta: 4:47:35, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.4232 2022-12-31 22:13:28,674 - mmcls - INFO - Epoch [62][400/5005] lr: 1.000e-03, eta: 4:47:26, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.3997 2022-12-31 22:13:37,421 - mmcls - INFO - Epoch [62][500/5005] lr: 1.000e-03, eta: 4:47:17, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.4216 2022-12-31 22:13:46,340 - mmcls - INFO - Epoch [62][600/5005] lr: 1.000e-03, eta: 4:47:08, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.4227 2022-12-31 22:13:55,044 - mmcls - INFO - Epoch [62][700/5005] lr: 1.000e-03, eta: 4:46:59, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.4171 2022-12-31 22:14:03,668 - mmcls - INFO - Epoch [62][800/5005] lr: 1.000e-03, eta: 4:46:50, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.4308 2022-12-31 22:14:13,002 - mmcls - INFO - Epoch [62][900/5005] lr: 1.000e-03, eta: 4:46:42, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.4013 2022-12-31 22:14:21,630 - mmcls - INFO - Epoch [62][1000/5005] lr: 1.000e-03, eta: 4:46:33, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.4108 2022-12-31 22:14:30,542 - mmcls - INFO - Epoch [62][1100/5005] lr: 1.000e-03, eta: 4:46:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4206 2022-12-31 22:14:39,196 - mmcls - INFO - Epoch [62][1200/5005] lr: 1.000e-03, eta: 4:46:15, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.4198 2022-12-31 22:14:47,872 - mmcls - INFO - Epoch [62][1300/5005] lr: 1.000e-03, eta: 4:46:06, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3885 2022-12-31 22:14:57,003 - mmcls - INFO - Epoch [62][1400/5005] lr: 1.000e-03, eta: 4:45:57, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.4075 2022-12-31 22:15:05,478 - mmcls - INFO - Epoch [62][1500/5005] lr: 1.000e-03, eta: 4:45:48, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.4241 2022-12-31 22:15:14,232 - mmcls - INFO - Epoch [62][1600/5005] lr: 1.000e-03, eta: 4:45:39, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.4104 2022-12-31 22:15:22,593 - mmcls - INFO - Epoch [62][1700/5005] lr: 1.000e-03, eta: 4:45:30, time: 0.084, data_time: 0.014, memory: 1224, loss: 1.3927 2022-12-31 22:15:31,479 - mmcls - INFO - Epoch [62][1800/5005] lr: 1.000e-03, eta: 4:45:21, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.4145 2022-12-31 22:15:40,375 - mmcls - INFO - Epoch [62][1900/5005] lr: 1.000e-03, eta: 4:45:12, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4115 2022-12-31 22:15:49,303 - mmcls - INFO - Epoch [62][2000/5005] lr: 1.000e-03, eta: 4:45:04, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.3972 2022-12-31 22:15:58,412 - mmcls - INFO - Epoch [62][2100/5005] lr: 1.000e-03, eta: 4:44:55, time: 0.091, data_time: 0.029, memory: 1224, loss: 1.4187 2022-12-31 22:16:07,350 - mmcls - INFO - Epoch [62][2200/5005] lr: 1.000e-03, eta: 4:44:46, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.4186 2022-12-31 22:16:16,213 - mmcls - INFO - Epoch [62][2300/5005] lr: 1.000e-03, eta: 4:44:37, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.4136 2022-12-31 22:16:24,808 - mmcls - INFO - Epoch [62][2400/5005] lr: 1.000e-03, eta: 4:44:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.4125 2022-12-31 22:16:33,641 - mmcls - INFO - Epoch [62][2500/5005] lr: 1.000e-03, eta: 4:44:19, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.4256 2022-12-31 22:16:42,323 - mmcls - INFO - Epoch [62][2600/5005] lr: 1.000e-03, eta: 4:44:10, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.4050 2022-12-31 22:16:50,934 - mmcls - INFO - Epoch [62][2700/5005] lr: 1.000e-03, eta: 4:44:01, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.4036 2022-12-31 22:16:59,449 - mmcls - INFO - Epoch [62][2800/5005] lr: 1.000e-03, eta: 4:43:52, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.3877 2022-12-31 22:17:08,589 - mmcls - INFO - Epoch [62][2900/5005] lr: 1.000e-03, eta: 4:43:44, time: 0.091, data_time: 0.030, memory: 1224, loss: 1.4080 2022-12-31 22:17:17,493 - mmcls - INFO - Epoch [62][3000/5005] lr: 1.000e-03, eta: 4:43:35, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.4149 2022-12-31 22:17:26,215 - mmcls - INFO - Epoch [62][3100/5005] lr: 1.000e-03, eta: 4:43:26, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3932 2022-12-31 22:17:35,208 - mmcls - INFO - Epoch [62][3200/5005] lr: 1.000e-03, eta: 4:43:17, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.4107 2022-12-31 22:17:43,860 - mmcls - INFO - Epoch [62][3300/5005] lr: 1.000e-03, eta: 4:43:08, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.4033 2022-12-31 22:17:52,529 - mmcls - INFO - Epoch [62][3400/5005] lr: 1.000e-03, eta: 4:42:59, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.4236 2022-12-31 22:18:01,438 - mmcls - INFO - Epoch [62][3500/5005] lr: 1.000e-03, eta: 4:42:50, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.4130 2022-12-31 22:18:10,259 - mmcls - INFO - Epoch [62][3600/5005] lr: 1.000e-03, eta: 4:42:41, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.4063 2022-12-31 22:18:19,139 - mmcls - INFO - Epoch [62][3700/5005] lr: 1.000e-03, eta: 4:42:33, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.4095 2022-12-31 22:18:27,803 - mmcls - INFO - Epoch [62][3800/5005] lr: 1.000e-03, eta: 4:42:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.4192 2022-12-31 22:18:36,769 - mmcls - INFO - Epoch [62][3900/5005] lr: 1.000e-03, eta: 4:42:15, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.4010 2022-12-31 22:18:45,376 - mmcls - INFO - Epoch [62][4000/5005] lr: 1.000e-03, eta: 4:42:06, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3891 2022-12-31 22:18:54,413 - mmcls - INFO - Epoch [62][4100/5005] lr: 1.000e-03, eta: 4:41:57, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.4343 2022-12-31 22:19:03,401 - mmcls - INFO - Epoch [62][4200/5005] lr: 1.000e-03, eta: 4:41:48, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.4075 2022-12-31 22:19:12,170 - mmcls - INFO - Epoch [62][4300/5005] lr: 1.000e-03, eta: 4:41:39, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.3889 2022-12-31 22:19:20,702 - mmcls - INFO - Epoch [62][4400/5005] lr: 1.000e-03, eta: 4:41:30, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3858 2022-12-31 22:19:29,491 - mmcls - INFO - Epoch [62][4500/5005] lr: 1.000e-03, eta: 4:41:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.4077 2022-12-31 22:19:38,459 - mmcls - INFO - Epoch [62][4600/5005] lr: 1.000e-03, eta: 4:41:13, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.3908 2022-12-31 22:19:47,147 - mmcls - INFO - Epoch [62][4700/5005] lr: 1.000e-03, eta: 4:41:04, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3722 2022-12-31 22:19:55,835 - mmcls - INFO - Epoch [62][4800/5005] lr: 1.000e-03, eta: 4:40:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.4011 2022-12-31 22:20:04,687 - mmcls - INFO - Epoch [62][4900/5005] lr: 1.000e-03, eta: 4:40:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.4089 2022-12-31 22:20:13,926 - mmcls - INFO - Epoch [62][5000/5005] lr: 1.000e-03, eta: 4:40:37, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.4081 2022-12-31 22:20:36,702 - mmcls - INFO - Epoch(val) [62][196] accuracy_top-1: 69.9340, accuracy_top-5: 89.3200 2022-12-31 22:20:47,685 - mmcls - INFO - Epoch [63][100/5005] lr: 1.000e-03, eta: 4:40:29, time: 0.110, data_time: 0.034, memory: 1224, loss: 1.3962 2022-12-31 22:20:56,257 - mmcls - INFO - Epoch [63][200/5005] lr: 1.000e-03, eta: 4:40:20, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3745 2022-12-31 22:21:04,925 - mmcls - INFO - Epoch [63][300/5005] lr: 1.000e-03, eta: 4:40:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3873 2022-12-31 22:21:13,918 - mmcls - INFO - Epoch [63][400/5005] lr: 1.000e-03, eta: 4:40:02, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3939 2022-12-31 22:21:22,855 - mmcls - INFO - Epoch [63][500/5005] lr: 1.000e-03, eta: 4:39:54, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.3831 2022-12-31 22:21:31,782 - mmcls - INFO - Epoch [63][600/5005] lr: 1.000e-03, eta: 4:39:45, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3730 2022-12-31 22:21:40,575 - mmcls - INFO - Epoch [63][700/5005] lr: 1.000e-03, eta: 4:39:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3748 2022-12-31 22:21:49,650 - mmcls - INFO - Epoch [63][800/5005] lr: 1.000e-03, eta: 4:39:27, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.3974 2022-12-31 22:21:58,504 - mmcls - INFO - Epoch [63][900/5005] lr: 1.000e-03, eta: 4:39:18, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3876 2022-12-31 22:22:07,171 - mmcls - INFO - Epoch [63][1000/5005] lr: 1.000e-03, eta: 4:39:09, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.3790 2022-12-31 22:22:16,100 - mmcls - INFO - Epoch [63][1100/5005] lr: 1.000e-03, eta: 4:39:00, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.4058 2022-12-31 22:22:24,742 - mmcls - INFO - Epoch [63][1200/5005] lr: 1.000e-03, eta: 4:38:51, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3838 2022-12-31 22:22:33,923 - mmcls - INFO - Epoch [63][1300/5005] lr: 1.000e-03, eta: 4:38:43, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.3697 2022-12-31 22:22:42,764 - mmcls - INFO - Epoch [63][1400/5005] lr: 1.000e-03, eta: 4:38:34, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3817 2022-12-31 22:22:51,665 - mmcls - INFO - Epoch [63][1500/5005] lr: 1.000e-03, eta: 4:38:25, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.3908 2022-12-31 22:23:00,161 - mmcls - INFO - Epoch [63][1600/5005] lr: 1.000e-03, eta: 4:38:16, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.3915 2022-12-31 22:23:08,940 - mmcls - INFO - Epoch [63][1700/5005] lr: 1.000e-03, eta: 4:38:07, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3854 2022-12-31 22:23:17,573 - mmcls - INFO - Epoch [63][1800/5005] lr: 1.000e-03, eta: 4:37:58, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3999 2022-12-31 22:23:26,564 - mmcls - INFO - Epoch [63][1900/5005] lr: 1.000e-03, eta: 4:37:49, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.3759 2022-12-31 22:23:35,731 - mmcls - INFO - Epoch [63][2000/5005] lr: 1.000e-03, eta: 4:37:41, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.4128 2022-12-31 22:23:44,439 - mmcls - INFO - Epoch [63][2100/5005] lr: 1.000e-03, eta: 4:37:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3813 2022-12-31 22:23:53,236 - mmcls - INFO - Epoch [63][2200/5005] lr: 1.000e-03, eta: 4:37:23, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3803 2022-12-31 22:24:02,063 - mmcls - INFO - Epoch [63][2300/5005] lr: 1.000e-03, eta: 4:37:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3971 2022-12-31 22:24:10,707 - mmcls - INFO - Epoch [63][2400/5005] lr: 1.000e-03, eta: 4:37:05, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.3854 2022-12-31 22:24:19,436 - mmcls - INFO - Epoch [63][2500/5005] lr: 1.000e-03, eta: 4:36:56, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3913 2022-12-31 22:24:28,471 - mmcls - INFO - Epoch [63][2600/5005] lr: 1.000e-03, eta: 4:36:47, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.4173 2022-12-31 22:24:37,362 - mmcls - INFO - Epoch [63][2700/5005] lr: 1.000e-03, eta: 4:36:39, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3670 2022-12-31 22:24:46,236 - mmcls - INFO - Epoch [63][2800/5005] lr: 1.000e-03, eta: 4:36:30, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.4081 2022-12-31 22:24:55,515 - mmcls - INFO - Epoch [63][2900/5005] lr: 1.000e-03, eta: 4:36:21, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.4055 2022-12-31 22:25:04,042 - mmcls - INFO - Epoch [63][3000/5005] lr: 1.000e-03, eta: 4:36:12, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3868 2022-12-31 22:25:13,083 - mmcls - INFO - Epoch [63][3100/5005] lr: 1.000e-03, eta: 4:36:03, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3896 2022-12-31 22:25:21,985 - mmcls - INFO - Epoch [63][3200/5005] lr: 1.000e-03, eta: 4:35:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3752 2022-12-31 22:25:30,746 - mmcls - INFO - Epoch [63][3300/5005] lr: 1.000e-03, eta: 4:35:46, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3853 2022-12-31 22:25:39,425 - mmcls - INFO - Epoch [63][3400/5005] lr: 1.000e-03, eta: 4:35:37, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.4137 2022-12-31 22:25:48,240 - mmcls - INFO - Epoch [63][3500/5005] lr: 1.000e-03, eta: 4:35:28, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3950 2022-12-31 22:25:57,005 - mmcls - INFO - Epoch [63][3600/5005] lr: 1.000e-03, eta: 4:35:19, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3917 2022-12-31 22:26:05,699 - mmcls - INFO - Epoch [63][3700/5005] lr: 1.000e-03, eta: 4:35:10, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3788 2022-12-31 22:26:14,666 - mmcls - INFO - Epoch [63][3800/5005] lr: 1.000e-03, eta: 4:35:01, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.3996 2022-12-31 22:26:23,249 - mmcls - INFO - Epoch [63][3900/5005] lr: 1.000e-03, eta: 4:34:52, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.3834 2022-12-31 22:26:32,017 - mmcls - INFO - Epoch [63][4000/5005] lr: 1.000e-03, eta: 4:34:43, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.3935 2022-12-31 22:26:40,978 - mmcls - INFO - Epoch [63][4100/5005] lr: 1.000e-03, eta: 4:34:34, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.4070 2022-12-31 22:26:49,715 - mmcls - INFO - Epoch [63][4200/5005] lr: 1.000e-03, eta: 4:34:25, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3850 2022-12-31 22:26:58,932 - mmcls - INFO - Epoch [63][4300/5005] lr: 1.000e-03, eta: 4:34:17, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.3834 2022-12-31 22:27:07,711 - mmcls - INFO - Epoch [63][4400/5005] lr: 1.000e-03, eta: 4:34:08, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3845 2022-12-31 22:27:16,873 - mmcls - INFO - Epoch [63][4500/5005] lr: 1.000e-03, eta: 4:33:59, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.3705 2022-12-31 22:27:25,676 - mmcls - INFO - Epoch [63][4600/5005] lr: 1.000e-03, eta: 4:33:50, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3854 2022-12-31 22:27:34,508 - mmcls - INFO - Epoch [63][4700/5005] lr: 1.000e-03, eta: 4:33:42, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.4019 2022-12-31 22:27:43,525 - mmcls - INFO - Epoch [63][4800/5005] lr: 1.000e-03, eta: 4:33:33, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.4001 2022-12-31 22:27:52,138 - mmcls - INFO - Epoch [63][4900/5005] lr: 1.000e-03, eta: 4:33:24, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.3843 2022-12-31 22:28:00,904 - mmcls - INFO - Epoch [63][5000/5005] lr: 1.000e-03, eta: 4:33:15, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3693 2022-12-31 22:28:23,380 - mmcls - INFO - Epoch(val) [63][196] accuracy_top-1: 70.2380, accuracy_top-5: 89.3960 2022-12-31 22:28:34,260 - mmcls - INFO - Epoch [64][100/5005] lr: 1.000e-03, eta: 4:33:06, time: 0.109, data_time: 0.046, memory: 1224, loss: 1.4006 2022-12-31 22:28:43,193 - mmcls - INFO - Epoch [64][200/5005] lr: 1.000e-03, eta: 4:32:58, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3802 2022-12-31 22:28:52,129 - mmcls - INFO - Epoch [64][300/5005] lr: 1.000e-03, eta: 4:32:49, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.3693 2022-12-31 22:29:01,051 - mmcls - INFO - Epoch [64][400/5005] lr: 1.000e-03, eta: 4:32:40, time: 0.089, data_time: 0.018, memory: 1224, loss: 1.3744 2022-12-31 22:29:09,970 - mmcls - INFO - Epoch [64][500/5005] lr: 1.000e-03, eta: 4:32:31, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3582 2022-12-31 22:29:18,820 - mmcls - INFO - Epoch [64][600/5005] lr: 1.000e-03, eta: 4:32:22, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.3706 2022-12-31 22:29:27,634 - mmcls - INFO - Epoch [64][700/5005] lr: 1.000e-03, eta: 4:32:14, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3790 2022-12-31 22:29:36,354 - mmcls - INFO - Epoch [64][800/5005] lr: 1.000e-03, eta: 4:32:05, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3876 2022-12-31 22:29:45,151 - mmcls - INFO - Epoch [64][900/5005] lr: 1.000e-03, eta: 4:31:56, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3794 2022-12-31 22:29:53,648 - mmcls - INFO - Epoch [64][1000/5005] lr: 1.000e-03, eta: 4:31:47, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3697 2022-12-31 22:30:02,719 - mmcls - INFO - Epoch [64][1100/5005] lr: 1.000e-03, eta: 4:31:38, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3939 2022-12-31 22:30:11,850 - mmcls - INFO - Epoch [64][1200/5005] lr: 1.000e-03, eta: 4:31:29, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3657 2022-12-31 22:30:20,744 - mmcls - INFO - Epoch [64][1300/5005] lr: 1.000e-03, eta: 4:31:20, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3828 2022-12-31 22:30:29,479 - mmcls - INFO - Epoch [64][1400/5005] lr: 1.000e-03, eta: 4:31:11, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3976 2022-12-31 22:30:38,333 - mmcls - INFO - Epoch [64][1500/5005] lr: 1.000e-03, eta: 4:31:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3741 2022-12-31 22:30:46,819 - mmcls - INFO - Epoch [64][1600/5005] lr: 1.000e-03, eta: 4:30:54, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3735 2022-12-31 22:30:55,825 - mmcls - INFO - Epoch [64][1700/5005] lr: 1.000e-03, eta: 4:30:45, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3740 2022-12-31 22:31:04,757 - mmcls - INFO - Epoch [64][1800/5005] lr: 1.000e-03, eta: 4:30:36, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.3851 2022-12-31 22:31:13,772 - mmcls - INFO - Epoch [64][1900/5005] lr: 1.000e-03, eta: 4:30:27, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.3696 2022-12-31 22:31:22,353 - mmcls - INFO - Epoch [64][2000/5005] lr: 1.000e-03, eta: 4:30:18, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3810 2022-12-31 22:31:31,138 - mmcls - INFO - Epoch [64][2100/5005] lr: 1.000e-03, eta: 4:30:09, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3665 2022-12-31 22:31:39,946 - mmcls - INFO - Epoch [64][2200/5005] lr: 1.000e-03, eta: 4:30:00, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3783 2022-12-31 22:31:49,220 - mmcls - INFO - Epoch [64][2300/5005] lr: 1.000e-03, eta: 4:29:52, time: 0.093, data_time: 0.009, memory: 1224, loss: 1.3732 2022-12-31 22:31:57,906 - mmcls - INFO - Epoch [64][2400/5005] lr: 1.000e-03, eta: 4:29:43, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.3813 2022-12-31 22:32:06,709 - mmcls - INFO - Epoch [64][2500/5005] lr: 1.000e-03, eta: 4:29:34, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.3756 2022-12-31 22:32:15,558 - mmcls - INFO - Epoch [64][2600/5005] lr: 1.000e-03, eta: 4:29:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3842 2022-12-31 22:32:24,201 - mmcls - INFO - Epoch [64][2700/5005] lr: 1.000e-03, eta: 4:29:16, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3744 2022-12-31 22:32:33,056 - mmcls - INFO - Epoch [64][2800/5005] lr: 1.000e-03, eta: 4:29:07, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3821 2022-12-31 22:32:41,770 - mmcls - INFO - Epoch [64][2900/5005] lr: 1.000e-03, eta: 4:28:58, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.3490 2022-12-31 22:32:50,951 - mmcls - INFO - Epoch [64][3000/5005] lr: 1.000e-03, eta: 4:28:50, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.3647 2022-12-31 22:33:00,221 - mmcls - INFO - Epoch [64][3100/5005] lr: 1.000e-03, eta: 4:28:41, time: 0.093, data_time: 0.006, memory: 1224, loss: 1.3734 2022-12-31 22:33:09,342 - mmcls - INFO - Epoch [64][3200/5005] lr: 1.000e-03, eta: 4:28:32, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.3734 2022-12-31 22:33:18,166 - mmcls - INFO - Epoch [64][3300/5005] lr: 1.000e-03, eta: 4:28:23, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3506 2022-12-31 22:33:26,945 - mmcls - INFO - Epoch [64][3400/5005] lr: 1.000e-03, eta: 4:28:15, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3725 2022-12-31 22:33:35,540 - mmcls - INFO - Epoch [64][3500/5005] lr: 1.000e-03, eta: 4:28:06, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3677 2022-12-31 22:33:44,584 - mmcls - INFO - Epoch [64][3600/5005] lr: 1.000e-03, eta: 4:27:57, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3694 2022-12-31 22:33:53,796 - mmcls - INFO - Epoch [64][3700/5005] lr: 1.000e-03, eta: 4:27:48, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.3683 2022-12-31 22:34:02,752 - mmcls - INFO - Epoch [64][3800/5005] lr: 1.000e-03, eta: 4:27:39, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.3608 2022-12-31 22:34:11,357 - mmcls - INFO - Epoch [64][3900/5005] lr: 1.000e-03, eta: 4:27:30, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3768 2022-12-31 22:34:20,118 - mmcls - INFO - Epoch [64][4000/5005] lr: 1.000e-03, eta: 4:27:21, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3687 2022-12-31 22:34:28,739 - mmcls - INFO - Epoch [64][4100/5005] lr: 1.000e-03, eta: 4:27:13, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.3743 2022-12-31 22:34:37,417 - mmcls - INFO - Epoch [64][4200/5005] lr: 1.000e-03, eta: 4:27:04, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.3759 2022-12-31 22:34:46,075 - mmcls - INFO - Epoch [64][4300/5005] lr: 1.000e-03, eta: 4:26:55, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3823 2022-12-31 22:34:54,858 - mmcls - INFO - Epoch [64][4400/5005] lr: 1.000e-03, eta: 4:26:46, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3738 2022-12-31 22:35:03,571 - mmcls - INFO - Epoch [64][4500/5005] lr: 1.000e-03, eta: 4:26:37, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3571 2022-12-31 22:35:12,426 - mmcls - INFO - Epoch [64][4600/5005] lr: 1.000e-03, eta: 4:26:28, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3480 2022-12-31 22:35:21,826 - mmcls - INFO - Epoch [64][4700/5005] lr: 1.000e-03, eta: 4:26:19, time: 0.094, data_time: 0.006, memory: 1224, loss: 1.3635 2022-12-31 22:35:30,618 - mmcls - INFO - Epoch [64][4800/5005] lr: 1.000e-03, eta: 4:26:10, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.3879 2022-12-31 22:35:39,254 - mmcls - INFO - Epoch [64][4900/5005] lr: 1.000e-03, eta: 4:26:01, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3897 2022-12-31 22:35:47,814 - mmcls - INFO - Epoch [64][5000/5005] lr: 1.000e-03, eta: 4:25:52, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.3858 2022-12-31 22:36:10,624 - mmcls - INFO - Epoch(val) [64][196] accuracy_top-1: 70.4280, accuracy_top-5: 89.5100 2022-12-31 22:36:21,486 - mmcls - INFO - Epoch [65][100/5005] lr: 1.000e-03, eta: 4:25:44, time: 0.108, data_time: 0.037, memory: 1224, loss: 1.3422 2022-12-31 22:36:30,392 - mmcls - INFO - Epoch [65][200/5005] lr: 1.000e-03, eta: 4:25:35, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.3661 2022-12-31 22:36:39,208 - mmcls - INFO - Epoch [65][300/5005] lr: 1.000e-03, eta: 4:25:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3690 2022-12-31 22:36:48,133 - mmcls - INFO - Epoch [65][400/5005] lr: 1.000e-03, eta: 4:25:18, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.3624 2022-12-31 22:36:57,116 - mmcls - INFO - Epoch [65][500/5005] lr: 1.000e-03, eta: 4:25:09, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.3762 2022-12-31 22:37:05,766 - mmcls - INFO - Epoch [65][600/5005] lr: 1.000e-03, eta: 4:25:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3752 2022-12-31 22:37:14,423 - mmcls - INFO - Epoch [65][700/5005] lr: 1.000e-03, eta: 4:24:51, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3746 2022-12-31 22:37:23,421 - mmcls - INFO - Epoch [65][800/5005] lr: 1.000e-03, eta: 4:24:42, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.3833 2022-12-31 22:37:32,368 - mmcls - INFO - Epoch [65][900/5005] lr: 1.000e-03, eta: 4:24:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3649 2022-12-31 22:37:40,825 - mmcls - INFO - Epoch [65][1000/5005] lr: 1.000e-03, eta: 4:24:24, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.3805 2022-12-31 22:37:49,437 - mmcls - INFO - Epoch [65][1100/5005] lr: 1.000e-03, eta: 4:24:15, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3555 2022-12-31 22:37:58,125 - mmcls - INFO - Epoch [65][1200/5005] lr: 1.000e-03, eta: 4:24:06, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3573 2022-12-31 22:38:06,793 - mmcls - INFO - Epoch [65][1300/5005] lr: 1.000e-03, eta: 4:23:57, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.3718 2022-12-31 22:38:15,488 - mmcls - INFO - Epoch [65][1400/5005] lr: 1.000e-03, eta: 4:23:48, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.3680 2022-12-31 22:38:24,253 - mmcls - INFO - Epoch [65][1500/5005] lr: 1.000e-03, eta: 4:23:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3645 2022-12-31 22:38:32,824 - mmcls - INFO - Epoch [65][1600/5005] lr: 1.000e-03, eta: 4:23:30, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3335 2022-12-31 22:38:41,766 - mmcls - INFO - Epoch [65][1700/5005] lr: 1.000e-03, eta: 4:23:22, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3579 2022-12-31 22:38:50,530 - mmcls - INFO - Epoch [65][1800/5005] lr: 1.000e-03, eta: 4:23:13, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3798 2022-12-31 22:38:59,175 - mmcls - INFO - Epoch [65][1900/5005] lr: 1.000e-03, eta: 4:23:04, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3561 2022-12-31 22:39:08,129 - mmcls - INFO - Epoch [65][2000/5005] lr: 1.000e-03, eta: 4:22:55, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.3658 2022-12-31 22:39:17,024 - mmcls - INFO - Epoch [65][2100/5005] lr: 1.000e-03, eta: 4:22:46, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.3635 2022-12-31 22:39:25,577 - mmcls - INFO - Epoch [65][2200/5005] lr: 1.000e-03, eta: 4:22:37, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.3619 2022-12-31 22:39:34,217 - mmcls - INFO - Epoch [65][2300/5005] lr: 1.000e-03, eta: 4:22:28, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3793 2022-12-31 22:39:42,901 - mmcls - INFO - Epoch [65][2400/5005] lr: 1.000e-03, eta: 4:22:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3620 2022-12-31 22:39:52,100 - mmcls - INFO - Epoch [65][2500/5005] lr: 1.000e-03, eta: 4:22:10, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.3945 2022-12-31 22:40:00,858 - mmcls - INFO - Epoch [65][2600/5005] lr: 1.000e-03, eta: 4:22:02, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3774 2022-12-31 22:40:09,705 - mmcls - INFO - Epoch [65][2700/5005] lr: 1.000e-03, eta: 4:21:53, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3759 2022-12-31 22:40:18,789 - mmcls - INFO - Epoch [65][2800/5005] lr: 1.000e-03, eta: 4:21:44, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.3708 2022-12-31 22:40:27,435 - mmcls - INFO - Epoch [65][2900/5005] lr: 1.000e-03, eta: 4:21:35, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3823 2022-12-31 22:40:36,009 - mmcls - INFO - Epoch [65][3000/5005] lr: 1.000e-03, eta: 4:21:26, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3549 2022-12-31 22:40:44,820 - mmcls - INFO - Epoch [65][3100/5005] lr: 1.000e-03, eta: 4:21:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3654 2022-12-31 22:40:53,601 - mmcls - INFO - Epoch [65][3200/5005] lr: 1.000e-03, eta: 4:21:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3615 2022-12-31 22:41:02,727 - mmcls - INFO - Epoch [65][3300/5005] lr: 1.000e-03, eta: 4:21:00, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.3571 2022-12-31 22:41:11,532 - mmcls - INFO - Epoch [65][3400/5005] lr: 1.000e-03, eta: 4:20:51, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3578 2022-12-31 22:41:20,129 - mmcls - INFO - Epoch [65][3500/5005] lr: 1.000e-03, eta: 4:20:42, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3662 2022-12-31 22:41:28,811 - mmcls - INFO - Epoch [65][3600/5005] lr: 1.000e-03, eta: 4:20:33, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3630 2022-12-31 22:41:37,886 - mmcls - INFO - Epoch [65][3700/5005] lr: 1.000e-03, eta: 4:20:24, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3748 2022-12-31 22:41:46,793 - mmcls - INFO - Epoch [65][3800/5005] lr: 1.000e-03, eta: 4:20:15, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.3553 2022-12-31 22:41:55,873 - mmcls - INFO - Epoch [65][3900/5005] lr: 1.000e-03, eta: 4:20:06, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3680 2022-12-31 22:42:04,501 - mmcls - INFO - Epoch [65][4000/5005] lr: 1.000e-03, eta: 4:19:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3665 2022-12-31 22:42:13,408 - mmcls - INFO - Epoch [65][4100/5005] lr: 1.000e-03, eta: 4:19:49, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.3532 2022-12-31 22:42:22,397 - mmcls - INFO - Epoch [65][4200/5005] lr: 1.000e-03, eta: 4:19:40, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.3474 2022-12-31 22:42:31,323 - mmcls - INFO - Epoch [65][4300/5005] lr: 1.000e-03, eta: 4:19:31, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3547 2022-12-31 22:42:40,190 - mmcls - INFO - Epoch [65][4400/5005] lr: 1.000e-03, eta: 4:19:22, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3454 2022-12-31 22:42:48,797 - mmcls - INFO - Epoch [65][4500/5005] lr: 1.000e-03, eta: 4:19:13, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3740 2022-12-31 22:42:57,521 - mmcls - INFO - Epoch [65][4600/5005] lr: 1.000e-03, eta: 4:19:04, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.3529 2022-12-31 22:43:06,374 - mmcls - INFO - Epoch [65][4700/5005] lr: 1.000e-03, eta: 4:18:55, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.3889 2022-12-31 22:43:15,151 - mmcls - INFO - Epoch [65][4800/5005] lr: 1.000e-03, eta: 4:18:47, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3860 2022-12-31 22:43:24,051 - mmcls - INFO - Epoch [65][4900/5005] lr: 1.000e-03, eta: 4:18:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3639 2022-12-31 22:43:33,026 - mmcls - INFO - Epoch [65][5000/5005] lr: 1.000e-03, eta: 4:18:29, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3649 2022-12-31 22:43:55,695 - mmcls - INFO - Epoch(val) [65][196] accuracy_top-1: 70.3720, accuracy_top-5: 89.4000 2022-12-31 22:44:06,879 - mmcls - INFO - Epoch [66][100/5005] lr: 1.000e-03, eta: 4:18:21, time: 0.112, data_time: 0.035, memory: 1224, loss: 1.3452 2022-12-31 22:44:15,787 - mmcls - INFO - Epoch [66][200/5005] lr: 1.000e-03, eta: 4:18:12, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3467 2022-12-31 22:44:24,291 - mmcls - INFO - Epoch [66][300/5005] lr: 1.000e-03, eta: 4:18:03, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.3626 2022-12-31 22:44:33,087 - mmcls - INFO - Epoch [66][400/5005] lr: 1.000e-03, eta: 4:17:54, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3334 2022-12-31 22:44:42,180 - mmcls - INFO - Epoch [66][500/5005] lr: 1.000e-03, eta: 4:17:45, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3702 2022-12-31 22:44:50,719 - mmcls - INFO - Epoch [66][600/5005] lr: 1.000e-03, eta: 4:17:36, time: 0.085, data_time: 0.004, memory: 1224, loss: 1.3776 2022-12-31 22:44:59,360 - mmcls - INFO - Epoch [66][700/5005] lr: 1.000e-03, eta: 4:17:27, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3502 2022-12-31 22:45:08,066 - mmcls - INFO - Epoch [66][800/5005] lr: 1.000e-03, eta: 4:17:18, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3525 2022-12-31 22:45:17,014 - mmcls - INFO - Epoch [66][900/5005] lr: 1.000e-03, eta: 4:17:09, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3598 2022-12-31 22:45:26,039 - mmcls - INFO - Epoch [66][1000/5005] lr: 1.000e-03, eta: 4:17:01, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3777 2022-12-31 22:45:34,754 - mmcls - INFO - Epoch [66][1100/5005] lr: 1.000e-03, eta: 4:16:52, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3467 2022-12-31 22:45:43,633 - mmcls - INFO - Epoch [66][1200/5005] lr: 1.000e-03, eta: 4:16:43, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3449 2022-12-31 22:45:52,542 - mmcls - INFO - Epoch [66][1300/5005] lr: 1.000e-03, eta: 4:16:34, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3720 2022-12-31 22:46:01,317 - mmcls - INFO - Epoch [66][1400/5005] lr: 1.000e-03, eta: 4:16:25, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3627 2022-12-31 22:46:10,302 - mmcls - INFO - Epoch [66][1500/5005] lr: 1.000e-03, eta: 4:16:16, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3662 2022-12-31 22:46:19,151 - mmcls - INFO - Epoch [66][1600/5005] lr: 1.000e-03, eta: 4:16:08, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.3620 2022-12-31 22:46:27,686 - mmcls - INFO - Epoch [66][1700/5005] lr: 1.000e-03, eta: 4:15:58, time: 0.085, data_time: 0.029, memory: 1224, loss: 1.3340 2022-12-31 22:46:36,554 - mmcls - INFO - Epoch [66][1800/5005] lr: 1.000e-03, eta: 4:15:50, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3451 2022-12-31 22:46:45,561 - mmcls - INFO - Epoch [66][1900/5005] lr: 1.000e-03, eta: 4:15:41, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.3500 2022-12-31 22:46:54,420 - mmcls - INFO - Epoch [66][2000/5005] lr: 1.000e-03, eta: 4:15:32, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.3520 2022-12-31 22:47:03,482 - mmcls - INFO - Epoch [66][2100/5005] lr: 1.000e-03, eta: 4:15:23, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3692 2022-12-31 22:47:12,009 - mmcls - INFO - Epoch [66][2200/5005] lr: 1.000e-03, eta: 4:15:14, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.3513 2022-12-31 22:47:20,631 - mmcls - INFO - Epoch [66][2300/5005] lr: 1.000e-03, eta: 4:15:05, time: 0.086, data_time: 0.037, memory: 1224, loss: 1.3799 2022-12-31 22:47:29,286 - mmcls - INFO - Epoch [66][2400/5005] lr: 1.000e-03, eta: 4:14:56, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3671 2022-12-31 22:47:38,134 - mmcls - INFO - Epoch [66][2500/5005] lr: 1.000e-03, eta: 4:14:47, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.3681 2022-12-31 22:47:46,706 - mmcls - INFO - Epoch [66][2600/5005] lr: 1.000e-03, eta: 4:14:38, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3416 2022-12-31 22:47:55,541 - mmcls - INFO - Epoch [66][2700/5005] lr: 1.000e-03, eta: 4:14:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3651 2022-12-31 22:48:04,273 - mmcls - INFO - Epoch [66][2800/5005] lr: 1.000e-03, eta: 4:14:21, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.3497 2022-12-31 22:48:12,853 - mmcls - INFO - Epoch [66][2900/5005] lr: 1.000e-03, eta: 4:14:12, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.3651 2022-12-31 22:48:21,724 - mmcls - INFO - Epoch [66][3000/5005] lr: 1.000e-03, eta: 4:14:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3668 2022-12-31 22:48:30,522 - mmcls - INFO - Epoch [66][3100/5005] lr: 1.000e-03, eta: 4:13:54, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.3594 2022-12-31 22:48:39,080 - mmcls - INFO - Epoch [66][3200/5005] lr: 1.000e-03, eta: 4:13:45, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3413 2022-12-31 22:48:47,718 - mmcls - INFO - Epoch [66][3300/5005] lr: 1.000e-03, eta: 4:13:36, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.3492 2022-12-31 22:48:56,395 - mmcls - INFO - Epoch [66][3400/5005] lr: 1.000e-03, eta: 4:13:27, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3494 2022-12-31 22:49:05,336 - mmcls - INFO - Epoch [66][3500/5005] lr: 1.000e-03, eta: 4:13:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3459 2022-12-31 22:49:14,037 - mmcls - INFO - Epoch [66][3600/5005] lr: 1.000e-03, eta: 4:13:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3781 2022-12-31 22:49:22,719 - mmcls - INFO - Epoch [66][3700/5005] lr: 1.000e-03, eta: 4:13:00, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3427 2022-12-31 22:49:31,822 - mmcls - INFO - Epoch [66][3800/5005] lr: 1.000e-03, eta: 4:12:52, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.3495 2022-12-31 22:49:40,510 - mmcls - INFO - Epoch [66][3900/5005] lr: 1.000e-03, eta: 4:12:43, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3696 2022-12-31 22:49:49,442 - mmcls - INFO - Epoch [66][4000/5005] lr: 1.000e-03, eta: 4:12:34, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3626 2022-12-31 22:49:57,948 - mmcls - INFO - Epoch [66][4100/5005] lr: 1.000e-03, eta: 4:12:25, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.3630 2022-12-31 22:50:06,747 - mmcls - INFO - Epoch [66][4200/5005] lr: 1.000e-03, eta: 4:12:16, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3759 2022-12-31 22:50:15,518 - mmcls - INFO - Epoch [66][4300/5005] lr: 1.000e-03, eta: 4:12:07, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3511 2022-12-31 22:50:24,460 - mmcls - INFO - Epoch [66][4400/5005] lr: 1.000e-03, eta: 4:11:58, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.3482 2022-12-31 22:50:33,189 - mmcls - INFO - Epoch [66][4500/5005] lr: 1.000e-03, eta: 4:11:49, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.3592 2022-12-31 22:50:41,856 - mmcls - INFO - Epoch [66][4600/5005] lr: 1.000e-03, eta: 4:11:40, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3274 2022-12-31 22:50:50,645 - mmcls - INFO - Epoch [66][4700/5005] lr: 1.000e-03, eta: 4:11:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3437 2022-12-31 22:50:59,316 - mmcls - INFO - Epoch [66][4800/5005] lr: 1.000e-03, eta: 4:11:23, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3518 2022-12-31 22:51:08,381 - mmcls - INFO - Epoch [66][4900/5005] lr: 1.000e-03, eta: 4:11:14, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3515 2022-12-31 22:51:17,174 - mmcls - INFO - Epoch [66][5000/5005] lr: 1.000e-03, eta: 4:11:05, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.3675 2022-12-31 22:51:39,875 - mmcls - INFO - Epoch(val) [66][196] accuracy_top-1: 70.4720, accuracy_top-5: 89.5600 2022-12-31 22:51:51,093 - mmcls - INFO - Epoch [67][100/5005] lr: 1.000e-03, eta: 4:10:57, time: 0.112, data_time: 0.051, memory: 1224, loss: 1.3531 2022-12-31 22:51:59,759 - mmcls - INFO - Epoch [67][200/5005] lr: 1.000e-03, eta: 4:10:48, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3537 2022-12-31 22:52:08,488 - mmcls - INFO - Epoch [67][300/5005] lr: 1.000e-03, eta: 4:10:39, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3440 2022-12-31 22:52:17,154 - mmcls - INFO - Epoch [67][400/5005] lr: 1.000e-03, eta: 4:10:30, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3492 2022-12-31 22:52:25,813 - mmcls - INFO - Epoch [67][500/5005] lr: 1.000e-03, eta: 4:10:21, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3390 2022-12-31 22:52:34,756 - mmcls - INFO - Epoch [67][600/5005] lr: 1.000e-03, eta: 4:10:12, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.3504 2022-12-31 22:52:43,443 - mmcls - INFO - Epoch [67][700/5005] lr: 1.000e-03, eta: 4:10:03, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3433 2022-12-31 22:52:52,305 - mmcls - INFO - Epoch [67][800/5005] lr: 1.000e-03, eta: 4:09:54, time: 0.088, data_time: 0.046, memory: 1224, loss: 1.3470 2022-12-31 22:53:00,819 - mmcls - INFO - Epoch [67][900/5005] lr: 1.000e-03, eta: 4:09:45, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.3564 2022-12-31 22:53:09,579 - mmcls - INFO - Epoch [67][1000/5005] lr: 1.000e-03, eta: 4:09:36, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3429 2022-12-31 22:53:18,273 - mmcls - INFO - Epoch [67][1100/5005] lr: 1.000e-03, eta: 4:09:27, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3503 2022-12-31 22:53:27,046 - mmcls - INFO - Epoch [67][1200/5005] lr: 1.000e-03, eta: 4:09:18, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3500 2022-12-31 22:53:35,954 - mmcls - INFO - Epoch [67][1300/5005] lr: 1.000e-03, eta: 4:09:10, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3624 2022-12-31 22:53:44,759 - mmcls - INFO - Epoch [67][1400/5005] lr: 1.000e-03, eta: 4:09:01, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3468 2022-12-31 22:53:54,074 - mmcls - INFO - Epoch [67][1500/5005] lr: 1.000e-03, eta: 4:08:52, time: 0.093, data_time: 0.004, memory: 1224, loss: 1.3320 2022-12-31 22:54:03,051 - mmcls - INFO - Epoch [67][1600/5005] lr: 1.000e-03, eta: 4:08:43, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.3607 2022-12-31 22:54:11,783 - mmcls - INFO - Epoch [67][1700/5005] lr: 1.000e-03, eta: 4:08:34, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3575 2022-12-31 22:54:20,799 - mmcls - INFO - Epoch [67][1800/5005] lr: 1.000e-03, eta: 4:08:26, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3488 2022-12-31 22:54:29,599 - mmcls - INFO - Epoch [67][1900/5005] lr: 1.000e-03, eta: 4:08:17, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3448 2022-12-31 22:54:38,365 - mmcls - INFO - Epoch [67][2000/5005] lr: 1.000e-03, eta: 4:08:08, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3410 2022-12-31 22:54:47,030 - mmcls - INFO - Epoch [67][2100/5005] lr: 1.000e-03, eta: 4:07:59, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3372 2022-12-31 22:54:55,794 - mmcls - INFO - Epoch [67][2200/5005] lr: 1.000e-03, eta: 4:07:50, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3556 2022-12-31 22:55:04,496 - mmcls - INFO - Epoch [67][2300/5005] lr: 1.000e-03, eta: 4:07:41, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3355 2022-12-31 22:55:13,326 - mmcls - INFO - Epoch [67][2400/5005] lr: 1.000e-03, eta: 4:07:32, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3478 2022-12-31 22:55:21,936 - mmcls - INFO - Epoch [67][2500/5005] lr: 1.000e-03, eta: 4:07:23, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3401 2022-12-31 22:55:30,521 - mmcls - INFO - Epoch [67][2600/5005] lr: 1.000e-03, eta: 4:07:14, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.3704 2022-12-31 22:55:39,110 - mmcls - INFO - Epoch [67][2700/5005] lr: 1.000e-03, eta: 4:07:05, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.3523 2022-12-31 22:55:47,966 - mmcls - INFO - Epoch [67][2800/5005] lr: 1.000e-03, eta: 4:06:56, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3411 2022-12-31 22:55:56,793 - mmcls - INFO - Epoch [67][2900/5005] lr: 1.000e-03, eta: 4:06:48, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.3426 2022-12-31 22:56:05,477 - mmcls - INFO - Epoch [67][3000/5005] lr: 1.000e-03, eta: 4:06:39, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3315 2022-12-31 22:56:14,226 - mmcls - INFO - Epoch [67][3100/5005] lr: 1.000e-03, eta: 4:06:30, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3520 2022-12-31 22:56:23,061 - mmcls - INFO - Epoch [67][3200/5005] lr: 1.000e-03, eta: 4:06:21, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3428 2022-12-31 22:56:31,790 - mmcls - INFO - Epoch [67][3300/5005] lr: 1.000e-03, eta: 4:06:12, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3219 2022-12-31 22:56:40,892 - mmcls - INFO - Epoch [67][3400/5005] lr: 1.000e-03, eta: 4:06:03, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.3635 2022-12-31 22:56:49,362 - mmcls - INFO - Epoch [67][3500/5005] lr: 1.000e-03, eta: 4:05:54, time: 0.085, data_time: 0.030, memory: 1224, loss: 1.3679 2022-12-31 22:56:58,255 - mmcls - INFO - Epoch [67][3600/5005] lr: 1.000e-03, eta: 4:05:45, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3563 2022-12-31 22:57:06,932 - mmcls - INFO - Epoch [67][3700/5005] lr: 1.000e-03, eta: 4:05:36, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3498 2022-12-31 22:57:15,672 - mmcls - INFO - Epoch [67][3800/5005] lr: 1.000e-03, eta: 4:05:28, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3327 2022-12-31 22:57:24,356 - mmcls - INFO - Epoch [67][3900/5005] lr: 1.000e-03, eta: 4:05:19, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3510 2022-12-31 22:57:33,436 - mmcls - INFO - Epoch [67][4000/5005] lr: 1.000e-03, eta: 4:05:10, time: 0.091, data_time: 0.041, memory: 1224, loss: 1.3575 2022-12-31 22:57:42,517 - mmcls - INFO - Epoch [67][4100/5005] lr: 1.000e-03, eta: 4:05:01, time: 0.091, data_time: 0.041, memory: 1224, loss: 1.3353 2022-12-31 22:57:51,251 - mmcls - INFO - Epoch [67][4200/5005] lr: 1.000e-03, eta: 4:04:52, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3368 2022-12-31 22:57:59,976 - mmcls - INFO - Epoch [67][4300/5005] lr: 1.000e-03, eta: 4:04:43, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3592 2022-12-31 22:58:09,433 - mmcls - INFO - Epoch [67][4400/5005] lr: 1.000e-03, eta: 4:04:35, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.3691 2022-12-31 22:58:18,076 - mmcls - INFO - Epoch [67][4500/5005] lr: 1.000e-03, eta: 4:04:26, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3395 2022-12-31 22:58:27,059 - mmcls - INFO - Epoch [67][4600/5005] lr: 1.000e-03, eta: 4:04:17, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3608 2022-12-31 22:58:35,777 - mmcls - INFO - Epoch [67][4700/5005] lr: 1.000e-03, eta: 4:04:08, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3571 2022-12-31 22:58:44,564 - mmcls - INFO - Epoch [67][4800/5005] lr: 1.000e-03, eta: 4:03:59, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3703 2022-12-31 22:58:53,133 - mmcls - INFO - Epoch [67][4900/5005] lr: 1.000e-03, eta: 4:03:50, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3625 2022-12-31 22:59:01,729 - mmcls - INFO - Epoch [67][5000/5005] lr: 1.000e-03, eta: 4:03:41, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.3446 2022-12-31 22:59:24,328 - mmcls - INFO - Epoch(val) [67][196] accuracy_top-1: 70.4220, accuracy_top-5: 89.6000 2022-12-31 22:59:35,942 - mmcls - INFO - Epoch [68][100/5005] lr: 1.000e-03, eta: 4:03:33, time: 0.116, data_time: 0.062, memory: 1224, loss: 1.3478 2022-12-31 22:59:44,828 - mmcls - INFO - Epoch [68][200/5005] lr: 1.000e-03, eta: 4:03:24, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.3504 2022-12-31 22:59:53,374 - mmcls - INFO - Epoch [68][300/5005] lr: 1.000e-03, eta: 4:03:15, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.3433 2022-12-31 23:00:02,464 - mmcls - INFO - Epoch [68][400/5005] lr: 1.000e-03, eta: 4:03:06, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3488 2022-12-31 23:00:11,106 - mmcls - INFO - Epoch [68][500/5005] lr: 1.000e-03, eta: 4:02:57, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3439 2022-12-31 23:00:19,839 - mmcls - INFO - Epoch [68][600/5005] lr: 1.000e-03, eta: 4:02:49, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3427 2022-12-31 23:00:28,569 - mmcls - INFO - Epoch [68][700/5005] lr: 1.000e-03, eta: 4:02:40, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.3330 2022-12-31 23:00:37,208 - mmcls - INFO - Epoch [68][800/5005] lr: 1.000e-03, eta: 4:02:31, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3357 2022-12-31 23:00:45,871 - mmcls - INFO - Epoch [68][900/5005] lr: 1.000e-03, eta: 4:02:22, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3397 2022-12-31 23:00:54,744 - mmcls - INFO - Epoch [68][1000/5005] lr: 1.000e-03, eta: 4:02:13, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3509 2022-12-31 23:01:03,296 - mmcls - INFO - Epoch [68][1100/5005] lr: 1.000e-03, eta: 4:02:04, time: 0.086, data_time: 0.026, memory: 1224, loss: 1.3325 2022-12-31 23:01:11,864 - mmcls - INFO - Epoch [68][1200/5005] lr: 1.000e-03, eta: 4:01:55, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3470 2022-12-31 23:01:20,603 - mmcls - INFO - Epoch [68][1300/5005] lr: 1.000e-03, eta: 4:01:46, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3500 2022-12-31 23:01:29,515 - mmcls - INFO - Epoch [68][1400/5005] lr: 1.000e-03, eta: 4:01:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3536 2022-12-31 23:01:38,216 - mmcls - INFO - Epoch [68][1500/5005] lr: 1.000e-03, eta: 4:01:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3427 2022-12-31 23:01:47,174 - mmcls - INFO - Epoch [68][1600/5005] lr: 1.000e-03, eta: 4:01:19, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.3439 2022-12-31 23:01:56,017 - mmcls - INFO - Epoch [68][1700/5005] lr: 1.000e-03, eta: 4:01:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3318 2022-12-31 23:02:04,477 - mmcls - INFO - Epoch [68][1800/5005] lr: 1.000e-03, eta: 4:01:02, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.3414 2022-12-31 23:02:13,366 - mmcls - INFO - Epoch [68][1900/5005] lr: 1.000e-03, eta: 4:00:53, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3267 2022-12-31 23:02:22,011 - mmcls - INFO - Epoch [68][2000/5005] lr: 1.000e-03, eta: 4:00:44, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3284 2022-12-31 23:02:30,805 - mmcls - INFO - Epoch [68][2100/5005] lr: 1.000e-03, eta: 4:00:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3461 2022-12-31 23:02:39,459 - mmcls - INFO - Epoch [68][2200/5005] lr: 1.000e-03, eta: 4:00:26, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3488 2022-12-31 23:02:48,250 - mmcls - INFO - Epoch [68][2300/5005] lr: 1.000e-03, eta: 4:00:17, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3440 2022-12-31 23:02:57,083 - mmcls - INFO - Epoch [68][2400/5005] lr: 1.000e-03, eta: 4:00:08, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3624 2022-12-31 23:03:05,916 - mmcls - INFO - Epoch [68][2500/5005] lr: 1.000e-03, eta: 3:59:59, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3335 2022-12-31 23:03:15,114 - mmcls - INFO - Epoch [68][2600/5005] lr: 1.000e-03, eta: 3:59:51, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.3619 2022-12-31 23:03:24,089 - mmcls - INFO - Epoch [68][2700/5005] lr: 1.000e-03, eta: 3:59:42, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.3404 2022-12-31 23:03:32,758 - mmcls - INFO - Epoch [68][2800/5005] lr: 1.000e-03, eta: 3:59:33, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3436 2022-12-31 23:03:41,459 - mmcls - INFO - Epoch [68][2900/5005] lr: 1.000e-03, eta: 3:59:24, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3400 2022-12-31 23:03:50,361 - mmcls - INFO - Epoch [68][3000/5005] lr: 1.000e-03, eta: 3:59:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3572 2022-12-31 23:03:58,989 - mmcls - INFO - Epoch [68][3100/5005] lr: 1.000e-03, eta: 3:59:06, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3413 2022-12-31 23:04:07,437 - mmcls - INFO - Epoch [68][3200/5005] lr: 1.000e-03, eta: 3:58:57, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.3457 2022-12-31 23:04:16,212 - mmcls - INFO - Epoch [68][3300/5005] lr: 1.000e-03, eta: 3:58:48, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3377 2022-12-31 23:04:25,070 - mmcls - INFO - Epoch [68][3400/5005] lr: 1.000e-03, eta: 3:58:39, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3462 2022-12-31 23:04:33,810 - mmcls - INFO - Epoch [68][3500/5005] lr: 1.000e-03, eta: 3:58:31, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.3439 2022-12-31 23:04:42,724 - mmcls - INFO - Epoch [68][3600/5005] lr: 1.000e-03, eta: 3:58:22, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3339 2022-12-31 23:04:51,665 - mmcls - INFO - Epoch [68][3700/5005] lr: 1.000e-03, eta: 3:58:13, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3336 2022-12-31 23:05:00,497 - mmcls - INFO - Epoch [68][3800/5005] lr: 1.000e-03, eta: 3:58:04, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3304 2022-12-31 23:05:09,307 - mmcls - INFO - Epoch [68][3900/5005] lr: 1.000e-03, eta: 3:57:55, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3452 2022-12-31 23:05:18,218 - mmcls - INFO - Epoch [68][4000/5005] lr: 1.000e-03, eta: 3:57:46, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3382 2022-12-31 23:05:27,242 - mmcls - INFO - Epoch [68][4100/5005] lr: 1.000e-03, eta: 3:57:38, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.3375 2022-12-31 23:05:35,823 - mmcls - INFO - Epoch [68][4200/5005] lr: 1.000e-03, eta: 3:57:29, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.3294 2022-12-31 23:05:44,923 - mmcls - INFO - Epoch [68][4300/5005] lr: 1.000e-03, eta: 3:57:20, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.3447 2022-12-31 23:05:53,578 - mmcls - INFO - Epoch [68][4400/5005] lr: 1.000e-03, eta: 3:57:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3336 2022-12-31 23:06:02,275 - mmcls - INFO - Epoch [68][4500/5005] lr: 1.000e-03, eta: 3:57:02, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3323 2022-12-31 23:06:11,008 - mmcls - INFO - Epoch [68][4600/5005] lr: 1.000e-03, eta: 3:56:53, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3412 2022-12-31 23:06:19,765 - mmcls - INFO - Epoch [68][4700/5005] lr: 1.000e-03, eta: 3:56:44, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.3308 2022-12-31 23:06:29,205 - mmcls - INFO - Epoch [68][4800/5005] lr: 1.000e-03, eta: 3:56:36, time: 0.094, data_time: 0.013, memory: 1224, loss: 1.3520 2022-12-31 23:06:37,946 - mmcls - INFO - Epoch [68][4900/5005] lr: 1.000e-03, eta: 3:56:27, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.3507 2022-12-31 23:06:46,630 - mmcls - INFO - Epoch [68][5000/5005] lr: 1.000e-03, eta: 3:56:18, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3455 2022-12-31 23:07:09,494 - mmcls - INFO - Epoch(val) [68][196] accuracy_top-1: 70.6840, accuracy_top-5: 89.6440 2022-12-31 23:07:20,468 - mmcls - INFO - Epoch [69][100/5005] lr: 1.000e-03, eta: 3:56:09, time: 0.109, data_time: 0.039, memory: 1224, loss: 1.3329 2022-12-31 23:07:29,049 - mmcls - INFO - Epoch [69][200/5005] lr: 1.000e-03, eta: 3:56:00, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3281 2022-12-31 23:07:37,720 - mmcls - INFO - Epoch [69][300/5005] lr: 1.000e-03, eta: 3:55:51, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3405 2022-12-31 23:07:46,747 - mmcls - INFO - Epoch [69][400/5005] lr: 1.000e-03, eta: 3:55:43, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.3292 2022-12-31 23:07:55,725 - mmcls - INFO - Epoch [69][500/5005] lr: 1.000e-03, eta: 3:55:34, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3085 2022-12-31 23:08:04,501 - mmcls - INFO - Epoch [69][600/5005] lr: 1.000e-03, eta: 3:55:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3458 2022-12-31 23:08:13,096 - mmcls - INFO - Epoch [69][700/5005] lr: 1.000e-03, eta: 3:55:16, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3371 2022-12-31 23:08:21,869 - mmcls - INFO - Epoch [69][800/5005] lr: 1.000e-03, eta: 3:55:07, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3466 2022-12-31 23:08:30,315 - mmcls - INFO - Epoch [69][900/5005] lr: 1.000e-03, eta: 3:54:58, time: 0.084, data_time: 0.016, memory: 1224, loss: 1.3093 2022-12-31 23:08:39,385 - mmcls - INFO - Epoch [69][1000/5005] lr: 1.000e-03, eta: 3:54:49, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.3521 2022-12-31 23:08:48,093 - mmcls - INFO - Epoch [69][1100/5005] lr: 1.000e-03, eta: 3:54:40, time: 0.087, data_time: 0.038, memory: 1224, loss: 1.3360 2022-12-31 23:08:57,097 - mmcls - INFO - Epoch [69][1200/5005] lr: 1.000e-03, eta: 3:54:31, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3378 2022-12-31 23:09:06,222 - mmcls - INFO - Epoch [69][1300/5005] lr: 1.000e-03, eta: 3:54:23, time: 0.091, data_time: 0.014, memory: 1224, loss: 1.3342 2022-12-31 23:09:15,282 - mmcls - INFO - Epoch [69][1400/5005] lr: 1.000e-03, eta: 3:54:14, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3387 2022-12-31 23:09:23,879 - mmcls - INFO - Epoch [69][1500/5005] lr: 1.000e-03, eta: 3:54:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3379 2022-12-31 23:09:32,981 - mmcls - INFO - Epoch [69][1600/5005] lr: 1.000e-03, eta: 3:53:56, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3418 2022-12-31 23:09:41,897 - mmcls - INFO - Epoch [69][1700/5005] lr: 1.000e-03, eta: 3:53:47, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3390 2022-12-31 23:09:50,626 - mmcls - INFO - Epoch [69][1800/5005] lr: 1.000e-03, eta: 3:53:39, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.3189 2022-12-31 23:09:59,415 - mmcls - INFO - Epoch [69][1900/5005] lr: 1.000e-03, eta: 3:53:30, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3436 2022-12-31 23:10:08,434 - mmcls - INFO - Epoch [69][2000/5005] lr: 1.000e-03, eta: 3:53:21, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.3427 2022-12-31 23:10:17,178 - mmcls - INFO - Epoch [69][2100/5005] lr: 1.000e-03, eta: 3:53:12, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3440 2022-12-31 23:10:25,953 - mmcls - INFO - Epoch [69][2200/5005] lr: 1.000e-03, eta: 3:53:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3202 2022-12-31 23:10:34,894 - mmcls - INFO - Epoch [69][2300/5005] lr: 1.000e-03, eta: 3:52:54, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3501 2022-12-31 23:10:43,619 - mmcls - INFO - Epoch [69][2400/5005] lr: 1.000e-03, eta: 3:52:45, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3505 2022-12-31 23:10:52,288 - mmcls - INFO - Epoch [69][2500/5005] lr: 1.000e-03, eta: 3:52:36, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3570 2022-12-31 23:11:01,503 - mmcls - INFO - Epoch [69][2600/5005] lr: 1.000e-03, eta: 3:52:28, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.3358 2022-12-31 23:11:10,147 - mmcls - INFO - Epoch [69][2700/5005] lr: 1.000e-03, eta: 3:52:19, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.3284 2022-12-31 23:11:18,973 - mmcls - INFO - Epoch [69][2800/5005] lr: 1.000e-03, eta: 3:52:10, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3293 2022-12-31 23:11:27,654 - mmcls - INFO - Epoch [69][2900/5005] lr: 1.000e-03, eta: 3:52:01, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3580 2022-12-31 23:11:36,497 - mmcls - INFO - Epoch [69][3000/5005] lr: 1.000e-03, eta: 3:51:52, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3457 2022-12-31 23:11:45,502 - mmcls - INFO - Epoch [69][3100/5005] lr: 1.000e-03, eta: 3:51:43, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3453 2022-12-31 23:11:54,515 - mmcls - INFO - Epoch [69][3200/5005] lr: 1.000e-03, eta: 3:51:35, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.3171 2022-12-31 23:12:03,476 - mmcls - INFO - Epoch [69][3300/5005] lr: 1.000e-03, eta: 3:51:26, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3383 2022-12-31 23:12:12,393 - mmcls - INFO - Epoch [69][3400/5005] lr: 1.000e-03, eta: 3:51:17, time: 0.089, data_time: 0.038, memory: 1224, loss: 1.3176 2022-12-31 23:12:21,256 - mmcls - INFO - Epoch [69][3500/5005] lr: 1.000e-03, eta: 3:51:08, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3320 2022-12-31 23:12:30,203 - mmcls - INFO - Epoch [69][3600/5005] lr: 1.000e-03, eta: 3:50:59, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3456 2022-12-31 23:12:39,063 - mmcls - INFO - Epoch [69][3700/5005] lr: 1.000e-03, eta: 3:50:50, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3501 2022-12-31 23:12:47,675 - mmcls - INFO - Epoch [69][3800/5005] lr: 1.000e-03, eta: 3:50:41, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3391 2022-12-31 23:12:56,606 - mmcls - INFO - Epoch [69][3900/5005] lr: 1.000e-03, eta: 3:50:33, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3232 2022-12-31 23:13:05,382 - mmcls - INFO - Epoch [69][4000/5005] lr: 1.000e-03, eta: 3:50:24, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3383 2022-12-31 23:13:14,279 - mmcls - INFO - Epoch [69][4100/5005] lr: 1.000e-03, eta: 3:50:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3232 2022-12-31 23:13:22,982 - mmcls - INFO - Epoch [69][4200/5005] lr: 1.000e-03, eta: 3:50:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3500 2022-12-31 23:13:31,541 - mmcls - INFO - Epoch [69][4300/5005] lr: 1.000e-03, eta: 3:49:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3245 2022-12-31 23:13:40,103 - mmcls - INFO - Epoch [69][4400/5005] lr: 1.000e-03, eta: 3:49:48, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.3571 2022-12-31 23:13:49,134 - mmcls - INFO - Epoch [69][4500/5005] lr: 1.000e-03, eta: 3:49:39, time: 0.090, data_time: 0.022, memory: 1224, loss: 1.3372 2022-12-31 23:13:57,891 - mmcls - INFO - Epoch [69][4600/5005] lr: 1.000e-03, eta: 3:49:30, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3347 2022-12-31 23:14:06,611 - mmcls - INFO - Epoch [69][4700/5005] lr: 1.000e-03, eta: 3:49:21, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3432 2022-12-31 23:14:15,203 - mmcls - INFO - Epoch [69][4800/5005] lr: 1.000e-03, eta: 3:49:13, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.3372 2022-12-31 23:14:24,009 - mmcls - INFO - Epoch [69][4900/5005] lr: 1.000e-03, eta: 3:49:04, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3431 2022-12-31 23:14:32,607 - mmcls - INFO - Epoch [69][5000/5005] lr: 1.000e-03, eta: 3:48:55, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3465 2022-12-31 23:14:55,341 - mmcls - INFO - Epoch(val) [69][196] accuracy_top-1: 70.7200, accuracy_top-5: 89.6600 2022-12-31 23:15:06,288 - mmcls - INFO - Epoch [70][100/5005] lr: 1.000e-03, eta: 3:48:46, time: 0.109, data_time: 0.061, memory: 1224, loss: 1.3194 2022-12-31 23:15:15,010 - mmcls - INFO - Epoch [70][200/5005] lr: 1.000e-03, eta: 3:48:37, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3099 2022-12-31 23:15:23,618 - mmcls - INFO - Epoch [70][300/5005] lr: 1.000e-03, eta: 3:48:28, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.3049 2022-12-31 23:15:32,776 - mmcls - INFO - Epoch [70][400/5005] lr: 1.000e-03, eta: 3:48:20, time: 0.092, data_time: 0.020, memory: 1224, loss: 1.3230 2022-12-31 23:15:41,589 - mmcls - INFO - Epoch [70][500/5005] lr: 1.000e-03, eta: 3:48:11, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3236 2022-12-31 23:15:50,353 - mmcls - INFO - Epoch [70][600/5005] lr: 1.000e-03, eta: 3:48:02, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.3271 2022-12-31 23:15:59,152 - mmcls - INFO - Epoch [70][700/5005] lr: 1.000e-03, eta: 3:47:53, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.3273 2022-12-31 23:16:07,814 - mmcls - INFO - Epoch [70][800/5005] lr: 1.000e-03, eta: 3:47:44, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.3423 2022-12-31 23:16:16,697 - mmcls - INFO - Epoch [70][900/5005] lr: 1.000e-03, eta: 3:47:35, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.3217 2022-12-31 23:16:25,481 - mmcls - INFO - Epoch [70][1000/5005] lr: 1.000e-03, eta: 3:47:26, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3387 2022-12-31 23:16:34,242 - mmcls - INFO - Epoch [70][1100/5005] lr: 1.000e-03, eta: 3:47:17, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3389 2022-12-31 23:16:42,740 - mmcls - INFO - Epoch [70][1200/5005] lr: 1.000e-03, eta: 3:47:08, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3344 2022-12-31 23:16:51,499 - mmcls - INFO - Epoch [70][1300/5005] lr: 1.000e-03, eta: 3:46:59, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3224 2022-12-31 23:17:00,169 - mmcls - INFO - Epoch [70][1400/5005] lr: 1.000e-03, eta: 3:46:50, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3271 2022-12-31 23:17:09,258 - mmcls - INFO - Epoch [70][1500/5005] lr: 1.000e-03, eta: 3:46:42, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.3252 2022-12-31 23:17:17,865 - mmcls - INFO - Epoch [70][1600/5005] lr: 1.000e-03, eta: 3:46:33, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3271 2022-12-31 23:17:26,789 - mmcls - INFO - Epoch [70][1700/5005] lr: 1.000e-03, eta: 3:46:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3256 2022-12-31 23:17:35,413 - mmcls - INFO - Epoch [70][1800/5005] lr: 1.000e-03, eta: 3:46:15, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3475 2022-12-31 23:17:44,227 - mmcls - INFO - Epoch [70][1900/5005] lr: 1.000e-03, eta: 3:46:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3236 2022-12-31 23:17:53,513 - mmcls - INFO - Epoch [70][2000/5005] lr: 1.000e-03, eta: 3:45:57, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.3308 2022-12-31 23:18:02,214 - mmcls - INFO - Epoch [70][2100/5005] lr: 1.000e-03, eta: 3:45:49, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3594 2022-12-31 23:18:11,051 - mmcls - INFO - Epoch [70][2200/5005] lr: 1.000e-03, eta: 3:45:40, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3361 2022-12-31 23:18:19,786 - mmcls - INFO - Epoch [70][2300/5005] lr: 1.000e-03, eta: 3:45:31, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.3306 2022-12-31 23:18:28,574 - mmcls - INFO - Epoch [70][2400/5005] lr: 1.000e-03, eta: 3:45:22, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3282 2022-12-31 23:18:37,496 - mmcls - INFO - Epoch [70][2500/5005] lr: 1.000e-03, eta: 3:45:13, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.3352 2022-12-31 23:18:46,490 - mmcls - INFO - Epoch [70][2600/5005] lr: 1.000e-03, eta: 3:45:04, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3364 2022-12-31 23:18:55,243 - mmcls - INFO - Epoch [70][2700/5005] lr: 1.000e-03, eta: 3:44:55, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3164 2022-12-31 23:19:04,075 - mmcls - INFO - Epoch [70][2800/5005] lr: 1.000e-03, eta: 3:44:47, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.3379 2022-12-31 23:19:12,877 - mmcls - INFO - Epoch [70][2900/5005] lr: 1.000e-03, eta: 3:44:38, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3250 2022-12-31 23:19:21,507 - mmcls - INFO - Epoch [70][3000/5005] lr: 1.000e-03, eta: 3:44:29, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.3156 2022-12-31 23:19:30,285 - mmcls - INFO - Epoch [70][3100/5005] lr: 1.000e-03, eta: 3:44:20, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3207 2022-12-31 23:19:39,014 - mmcls - INFO - Epoch [70][3200/5005] lr: 1.000e-03, eta: 3:44:11, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.3313 2022-12-31 23:19:47,742 - mmcls - INFO - Epoch [70][3300/5005] lr: 1.000e-03, eta: 3:44:02, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3386 2022-12-31 23:19:56,809 - mmcls - INFO - Epoch [70][3400/5005] lr: 1.000e-03, eta: 3:43:53, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3435 2022-12-31 23:20:05,544 - mmcls - INFO - Epoch [70][3500/5005] lr: 1.000e-03, eta: 3:43:44, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3224 2022-12-31 23:20:13,972 - mmcls - INFO - Epoch [70][3600/5005] lr: 1.000e-03, eta: 3:43:35, time: 0.084, data_time: 0.009, memory: 1224, loss: 1.3333 2022-12-31 23:20:22,802 - mmcls - INFO - Epoch [70][3700/5005] lr: 1.000e-03, eta: 3:43:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3663 2022-12-31 23:20:31,739 - mmcls - INFO - Epoch [70][3800/5005] lr: 1.000e-03, eta: 3:43:18, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3423 2022-12-31 23:20:40,402 - mmcls - INFO - Epoch [70][3900/5005] lr: 1.000e-03, eta: 3:43:09, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3220 2022-12-31 23:20:49,195 - mmcls - INFO - Epoch [70][4000/5005] lr: 1.000e-03, eta: 3:43:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3353 2022-12-31 23:20:57,907 - mmcls - INFO - Epoch [70][4100/5005] lr: 1.000e-03, eta: 3:42:51, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3118 2022-12-31 23:21:06,861 - mmcls - INFO - Epoch [70][4200/5005] lr: 1.000e-03, eta: 3:42:42, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.3717 2022-12-31 23:21:15,721 - mmcls - INFO - Epoch [70][4300/5005] lr: 1.000e-03, eta: 3:42:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3174 2022-12-31 23:21:24,431 - mmcls - INFO - Epoch [70][4400/5005] lr: 1.000e-03, eta: 3:42:24, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3416 2022-12-31 23:21:33,206 - mmcls - INFO - Epoch [70][4500/5005] lr: 1.000e-03, eta: 3:42:15, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3437 2022-12-31 23:21:42,167 - mmcls - INFO - Epoch [70][4600/5005] lr: 1.000e-03, eta: 3:42:07, time: 0.090, data_time: 0.030, memory: 1224, loss: 1.3196 2022-12-31 23:21:51,080 - mmcls - INFO - Epoch [70][4700/5005] lr: 1.000e-03, eta: 3:41:58, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3293 2022-12-31 23:21:59,904 - mmcls - INFO - Epoch [70][4800/5005] lr: 1.000e-03, eta: 3:41:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3493 2022-12-31 23:22:08,875 - mmcls - INFO - Epoch [70][4900/5005] lr: 1.000e-03, eta: 3:41:40, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.3287 2022-12-31 23:22:17,590 - mmcls - INFO - Epoch [70][5000/5005] lr: 1.000e-03, eta: 3:41:31, time: 0.087, data_time: 0.034, memory: 1224, loss: 1.3245 2022-12-31 23:22:40,253 - mmcls - INFO - Epoch(val) [70][196] accuracy_top-1: 70.8500, accuracy_top-5: 89.6880 2022-12-31 23:22:51,311 - mmcls - INFO - Epoch [71][100/5005] lr: 1.000e-03, eta: 3:41:23, time: 0.110, data_time: 0.032, memory: 1224, loss: 1.3259 2022-12-31 23:23:00,281 - mmcls - INFO - Epoch [71][200/5005] lr: 1.000e-03, eta: 3:41:14, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.3298 2022-12-31 23:23:09,007 - mmcls - INFO - Epoch [71][300/5005] lr: 1.000e-03, eta: 3:41:05, time: 0.087, data_time: 0.038, memory: 1224, loss: 1.3195 2022-12-31 23:23:17,707 - mmcls - INFO - Epoch [71][400/5005] lr: 1.000e-03, eta: 3:40:56, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3387 2022-12-31 23:23:26,592 - mmcls - INFO - Epoch [71][500/5005] lr: 1.000e-03, eta: 3:40:47, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3162 2022-12-31 23:23:35,668 - mmcls - INFO - Epoch [71][600/5005] lr: 1.000e-03, eta: 3:40:38, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3407 2022-12-31 23:23:44,503 - mmcls - INFO - Epoch [71][700/5005] lr: 1.000e-03, eta: 3:40:30, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3168 2022-12-31 23:23:52,962 - mmcls - INFO - Epoch [71][800/5005] lr: 1.000e-03, eta: 3:40:21, time: 0.084, data_time: 0.008, memory: 1224, loss: 1.3212 2022-12-31 23:24:01,754 - mmcls - INFO - Epoch [71][900/5005] lr: 1.000e-03, eta: 3:40:12, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3201 2022-12-31 23:24:10,509 - mmcls - INFO - Epoch [71][1000/5005] lr: 1.000e-03, eta: 3:40:03, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3173 2022-12-31 23:24:19,251 - mmcls - INFO - Epoch [71][1100/5005] lr: 1.000e-03, eta: 3:39:54, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2936 2022-12-31 23:24:27,865 - mmcls - INFO - Epoch [71][1200/5005] lr: 1.000e-03, eta: 3:39:45, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3146 2022-12-31 23:24:36,590 - mmcls - INFO - Epoch [71][1300/5005] lr: 1.000e-03, eta: 3:39:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3171 2022-12-31 23:24:45,359 - mmcls - INFO - Epoch [71][1400/5005] lr: 1.000e-03, eta: 3:39:27, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3289 2022-12-31 23:24:54,136 - mmcls - INFO - Epoch [71][1500/5005] lr: 1.000e-03, eta: 3:39:18, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.3339 2022-12-31 23:25:03,011 - mmcls - INFO - Epoch [71][1600/5005] lr: 1.000e-03, eta: 3:39:09, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.3424 2022-12-31 23:25:11,751 - mmcls - INFO - Epoch [71][1700/5005] lr: 1.000e-03, eta: 3:39:01, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3229 2022-12-31 23:25:20,590 - mmcls - INFO - Epoch [71][1800/5005] lr: 1.000e-03, eta: 3:38:52, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3157 2022-12-31 23:25:29,494 - mmcls - INFO - Epoch [71][1900/5005] lr: 1.000e-03, eta: 3:38:43, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3182 2022-12-31 23:25:38,214 - mmcls - INFO - Epoch [71][2000/5005] lr: 1.000e-03, eta: 3:38:34, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3221 2022-12-31 23:25:46,922 - mmcls - INFO - Epoch [71][2100/5005] lr: 1.000e-03, eta: 3:38:25, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3359 2022-12-31 23:25:55,709 - mmcls - INFO - Epoch [71][2200/5005] lr: 1.000e-03, eta: 3:38:16, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.3200 2022-12-31 23:26:04,083 - mmcls - INFO - Epoch [71][2300/5005] lr: 1.000e-03, eta: 3:38:07, time: 0.084, data_time: 0.006, memory: 1224, loss: 1.3268 2022-12-31 23:26:12,841 - mmcls - INFO - Epoch [71][2400/5005] lr: 1.000e-03, eta: 3:37:58, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3188 2022-12-31 23:26:21,656 - mmcls - INFO - Epoch [71][2500/5005] lr: 1.000e-03, eta: 3:37:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3294 2022-12-31 23:26:30,361 - mmcls - INFO - Epoch [71][2600/5005] lr: 1.000e-03, eta: 3:37:40, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3261 2022-12-31 23:26:38,989 - mmcls - INFO - Epoch [71][2700/5005] lr: 1.000e-03, eta: 3:37:31, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.3463 2022-12-31 23:26:47,981 - mmcls - INFO - Epoch [71][2800/5005] lr: 1.000e-03, eta: 3:37:23, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.3138 2022-12-31 23:26:56,575 - mmcls - INFO - Epoch [71][2900/5005] lr: 1.000e-03, eta: 3:37:14, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3086 2022-12-31 23:27:05,408 - mmcls - INFO - Epoch [71][3000/5005] lr: 1.000e-03, eta: 3:37:05, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3297 2022-12-31 23:27:14,230 - mmcls - INFO - Epoch [71][3100/5005] lr: 1.000e-03, eta: 3:36:56, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3352 2022-12-31 23:27:22,931 - mmcls - INFO - Epoch [71][3200/5005] lr: 1.000e-03, eta: 3:36:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3473 2022-12-31 23:27:31,722 - mmcls - INFO - Epoch [71][3300/5005] lr: 1.000e-03, eta: 3:36:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2955 2022-12-31 23:27:40,833 - mmcls - INFO - Epoch [71][3400/5005] lr: 1.000e-03, eta: 3:36:29, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.3284 2022-12-31 23:27:49,698 - mmcls - INFO - Epoch [71][3500/5005] lr: 1.000e-03, eta: 3:36:21, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.3112 2022-12-31 23:27:58,232 - mmcls - INFO - Epoch [71][3600/5005] lr: 1.000e-03, eta: 3:36:12, time: 0.085, data_time: 0.023, memory: 1224, loss: 1.3421 2022-12-31 23:28:07,339 - mmcls - INFO - Epoch [71][3700/5005] lr: 1.000e-03, eta: 3:36:03, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.3161 2022-12-31 23:28:16,077 - mmcls - INFO - Epoch [71][3800/5005] lr: 1.000e-03, eta: 3:35:54, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3206 2022-12-31 23:28:24,731 - mmcls - INFO - Epoch [71][3900/5005] lr: 1.000e-03, eta: 3:35:45, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3227 2022-12-31 23:28:33,663 - mmcls - INFO - Epoch [71][4000/5005] lr: 1.000e-03, eta: 3:35:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3294 2022-12-31 23:28:42,581 - mmcls - INFO - Epoch [71][4100/5005] lr: 1.000e-03, eta: 3:35:27, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3057 2022-12-31 23:28:51,680 - mmcls - INFO - Epoch [71][4200/5005] lr: 1.000e-03, eta: 3:35:19, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.3256 2022-12-31 23:29:00,576 - mmcls - INFO - Epoch [71][4300/5005] lr: 1.000e-03, eta: 3:35:10, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.3182 2022-12-31 23:29:09,945 - mmcls - INFO - Epoch [71][4400/5005] lr: 1.000e-03, eta: 3:35:01, time: 0.094, data_time: 0.012, memory: 1224, loss: 1.3344 2022-12-31 23:29:18,779 - mmcls - INFO - Epoch [71][4500/5005] lr: 1.000e-03, eta: 3:34:52, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3517 2022-12-31 23:29:27,750 - mmcls - INFO - Epoch [71][4600/5005] lr: 1.000e-03, eta: 3:34:44, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.3375 2022-12-31 23:29:36,378 - mmcls - INFO - Epoch [71][4700/5005] lr: 1.000e-03, eta: 3:34:35, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.3493 2022-12-31 23:29:45,214 - mmcls - INFO - Epoch [71][4800/5005] lr: 1.000e-03, eta: 3:34:26, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3229 2022-12-31 23:29:53,875 - mmcls - INFO - Epoch [71][4900/5005] lr: 1.000e-03, eta: 3:34:17, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3449 2022-12-31 23:30:02,800 - mmcls - INFO - Epoch [71][5000/5005] lr: 1.000e-03, eta: 3:34:08, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3500 2022-12-31 23:30:25,541 - mmcls - INFO - Epoch(val) [71][196] accuracy_top-1: 70.7460, accuracy_top-5: 89.7800 2022-12-31 23:30:36,312 - mmcls - INFO - Epoch [72][100/5005] lr: 1.000e-03, eta: 3:33:59, time: 0.107, data_time: 0.048, memory: 1224, loss: 1.3252 2022-12-31 23:30:44,878 - mmcls - INFO - Epoch [72][200/5005] lr: 1.000e-03, eta: 3:33:50, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.3451 2022-12-31 23:30:53,692 - mmcls - INFO - Epoch [72][300/5005] lr: 1.000e-03, eta: 3:33:41, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3291 2022-12-31 23:31:02,740 - mmcls - INFO - Epoch [72][400/5005] lr: 1.000e-03, eta: 3:33:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3321 2022-12-31 23:31:11,687 - mmcls - INFO - Epoch [72][500/5005] lr: 1.000e-03, eta: 3:33:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3227 2022-12-31 23:31:20,326 - mmcls - INFO - Epoch [72][600/5005] lr: 1.000e-03, eta: 3:33:15, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3279 2022-12-31 23:31:29,044 - mmcls - INFO - Epoch [72][700/5005] lr: 1.000e-03, eta: 3:33:06, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3161 2022-12-31 23:31:37,981 - mmcls - INFO - Epoch [72][800/5005] lr: 1.000e-03, eta: 3:32:57, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.3265 2022-12-31 23:31:46,831 - mmcls - INFO - Epoch [72][900/5005] lr: 1.000e-03, eta: 3:32:48, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3097 2022-12-31 23:31:56,052 - mmcls - INFO - Epoch [72][1000/5005] lr: 1.000e-03, eta: 3:32:40, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.3357 2022-12-31 23:32:05,238 - mmcls - INFO - Epoch [72][1100/5005] lr: 1.000e-03, eta: 3:32:31, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.3173 2022-12-31 23:32:14,188 - mmcls - INFO - Epoch [72][1200/5005] lr: 1.000e-03, eta: 3:32:22, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3140 2022-12-31 23:32:22,819 - mmcls - INFO - Epoch [72][1300/5005] lr: 1.000e-03, eta: 3:32:13, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3051 2022-12-31 23:32:31,797 - mmcls - INFO - Epoch [72][1400/5005] lr: 1.000e-03, eta: 3:32:04, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3384 2022-12-31 23:32:40,528 - mmcls - INFO - Epoch [72][1500/5005] lr: 1.000e-03, eta: 3:31:55, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3129 2022-12-31 23:32:49,145 - mmcls - INFO - Epoch [72][1600/5005] lr: 1.000e-03, eta: 3:31:46, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.3355 2022-12-31 23:32:57,933 - mmcls - INFO - Epoch [72][1700/5005] lr: 1.000e-03, eta: 3:31:38, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3248 2022-12-31 23:33:06,626 - mmcls - INFO - Epoch [72][1800/5005] lr: 1.000e-03, eta: 3:31:29, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.3311 2022-12-31 23:33:15,425 - mmcls - INFO - Epoch [72][1900/5005] lr: 1.000e-03, eta: 3:31:20, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3103 2022-12-31 23:33:24,270 - mmcls - INFO - Epoch [72][2000/5005] lr: 1.000e-03, eta: 3:31:11, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3096 2022-12-31 23:33:33,321 - mmcls - INFO - Epoch [72][2100/5005] lr: 1.000e-03, eta: 3:31:02, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3252 2022-12-31 23:33:41,961 - mmcls - INFO - Epoch [72][2200/5005] lr: 1.000e-03, eta: 3:30:53, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3183 2022-12-31 23:33:50,667 - mmcls - INFO - Epoch [72][2300/5005] lr: 1.000e-03, eta: 3:30:44, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3202 2022-12-31 23:33:59,432 - mmcls - INFO - Epoch [72][2400/5005] lr: 1.000e-03, eta: 3:30:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3344 2022-12-31 23:34:08,164 - mmcls - INFO - Epoch [72][2500/5005] lr: 1.000e-03, eta: 3:30:27, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3035 2022-12-31 23:34:17,108 - mmcls - INFO - Epoch [72][2600/5005] lr: 1.000e-03, eta: 3:30:18, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.3057 2022-12-31 23:34:25,980 - mmcls - INFO - Epoch [72][2700/5005] lr: 1.000e-03, eta: 3:30:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3367 2022-12-31 23:34:34,946 - mmcls - INFO - Epoch [72][2800/5005] lr: 1.000e-03, eta: 3:30:00, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3086 2022-12-31 23:34:43,758 - mmcls - INFO - Epoch [72][2900/5005] lr: 1.000e-03, eta: 3:29:51, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3263 2022-12-31 23:34:52,685 - mmcls - INFO - Epoch [72][3000/5005] lr: 1.000e-03, eta: 3:29:42, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3060 2022-12-31 23:35:01,502 - mmcls - INFO - Epoch [72][3100/5005] lr: 1.000e-03, eta: 3:29:34, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3168 2022-12-31 23:35:10,256 - mmcls - INFO - Epoch [72][3200/5005] lr: 1.000e-03, eta: 3:29:25, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3162 2022-12-31 23:35:19,196 - mmcls - INFO - Epoch [72][3300/5005] lr: 1.000e-03, eta: 3:29:16, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.3113 2022-12-31 23:35:28,244 - mmcls - INFO - Epoch [72][3400/5005] lr: 1.000e-03, eta: 3:29:07, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3248 2022-12-31 23:35:37,029 - mmcls - INFO - Epoch [72][3500/5005] lr: 1.000e-03, eta: 3:28:58, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3188 2022-12-31 23:35:46,009 - mmcls - INFO - Epoch [72][3600/5005] lr: 1.000e-03, eta: 3:28:49, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3192 2022-12-31 23:35:54,872 - mmcls - INFO - Epoch [72][3700/5005] lr: 1.000e-03, eta: 3:28:41, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3264 2022-12-31 23:36:03,813 - mmcls - INFO - Epoch [72][3800/5005] lr: 1.000e-03, eta: 3:28:32, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3349 2022-12-31 23:36:12,438 - mmcls - INFO - Epoch [72][3900/5005] lr: 1.000e-03, eta: 3:28:23, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3342 2022-12-31 23:36:21,176 - mmcls - INFO - Epoch [72][4000/5005] lr: 1.000e-03, eta: 3:28:14, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3168 2022-12-31 23:36:30,188 - mmcls - INFO - Epoch [72][4100/5005] lr: 1.000e-03, eta: 3:28:05, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3380 2022-12-31 23:36:38,973 - mmcls - INFO - Epoch [72][4200/5005] lr: 1.000e-03, eta: 3:27:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3178 2022-12-31 23:36:47,641 - mmcls - INFO - Epoch [72][4300/5005] lr: 1.000e-03, eta: 3:27:47, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3418 2022-12-31 23:36:56,455 - mmcls - INFO - Epoch [72][4400/5005] lr: 1.000e-03, eta: 3:27:38, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3338 2022-12-31 23:37:05,018 - mmcls - INFO - Epoch [72][4500/5005] lr: 1.000e-03, eta: 3:27:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3241 2022-12-31 23:37:13,888 - mmcls - INFO - Epoch [72][4600/5005] lr: 1.000e-03, eta: 3:27:21, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3411 2022-12-31 23:37:22,650 - mmcls - INFO - Epoch [72][4700/5005] lr: 1.000e-03, eta: 3:27:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3211 2022-12-31 23:37:31,589 - mmcls - INFO - Epoch [72][4800/5005] lr: 1.000e-03, eta: 3:27:03, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3203 2022-12-31 23:37:40,621 - mmcls - INFO - Epoch [72][4900/5005] lr: 1.000e-03, eta: 3:26:54, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3196 2022-12-31 23:37:49,094 - mmcls - INFO - Epoch [72][5000/5005] lr: 1.000e-03, eta: 3:26:45, time: 0.085, data_time: 0.023, memory: 1224, loss: 1.3489 2022-12-31 23:38:11,851 - mmcls - INFO - Epoch(val) [72][196] accuracy_top-1: 70.7920, accuracy_top-5: 89.7060 2022-12-31 23:38:23,085 - mmcls - INFO - Epoch [73][100/5005] lr: 1.000e-03, eta: 3:26:37, time: 0.112, data_time: 0.036, memory: 1224, loss: 1.3193 2022-12-31 23:38:32,143 - mmcls - INFO - Epoch [73][200/5005] lr: 1.000e-03, eta: 3:26:28, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3135 2022-12-31 23:38:40,669 - mmcls - INFO - Epoch [73][300/5005] lr: 1.000e-03, eta: 3:26:19, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.3077 2022-12-31 23:38:49,605 - mmcls - INFO - Epoch [73][400/5005] lr: 1.000e-03, eta: 3:26:10, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3181 2022-12-31 23:38:58,633 - mmcls - INFO - Epoch [73][500/5005] lr: 1.000e-03, eta: 3:26:01, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3058 2022-12-31 23:39:07,400 - mmcls - INFO - Epoch [73][600/5005] lr: 1.000e-03, eta: 3:25:52, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3213 2022-12-31 23:39:16,668 - mmcls - INFO - Epoch [73][700/5005] lr: 1.000e-03, eta: 3:25:44, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.3335 2022-12-31 23:39:25,634 - mmcls - INFO - Epoch [73][800/5005] lr: 1.000e-03, eta: 3:25:35, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.3100 2022-12-31 23:39:34,408 - mmcls - INFO - Epoch [73][900/5005] lr: 1.000e-03, eta: 3:25:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3061 2022-12-31 23:39:43,560 - mmcls - INFO - Epoch [73][1000/5005] lr: 1.000e-03, eta: 3:25:17, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.3145 2022-12-31 23:39:52,576 - mmcls - INFO - Epoch [73][1100/5005] lr: 1.000e-03, eta: 3:25:08, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3246 2022-12-31 23:40:01,402 - mmcls - INFO - Epoch [73][1200/5005] lr: 1.000e-03, eta: 3:25:00, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3299 2022-12-31 23:40:10,106 - mmcls - INFO - Epoch [73][1300/5005] lr: 1.000e-03, eta: 3:24:51, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3042 2022-12-31 23:40:18,721 - mmcls - INFO - Epoch [73][1400/5005] lr: 1.000e-03, eta: 3:24:42, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.3111 2022-12-31 23:40:27,735 - mmcls - INFO - Epoch [73][1500/5005] lr: 1.000e-03, eta: 3:24:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3312 2022-12-31 23:40:36,346 - mmcls - INFO - Epoch [73][1600/5005] lr: 1.000e-03, eta: 3:24:24, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3182 2022-12-31 23:40:45,186 - mmcls - INFO - Epoch [73][1700/5005] lr: 1.000e-03, eta: 3:24:15, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3333 2022-12-31 23:40:54,207 - mmcls - INFO - Epoch [73][1800/5005] lr: 1.000e-03, eta: 3:24:06, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3085 2022-12-31 23:41:03,326 - mmcls - INFO - Epoch [73][1900/5005] lr: 1.000e-03, eta: 3:23:58, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3018 2022-12-31 23:41:11,994 - mmcls - INFO - Epoch [73][2000/5005] lr: 1.000e-03, eta: 3:23:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3136 2022-12-31 23:41:21,001 - mmcls - INFO - Epoch [73][2100/5005] lr: 1.000e-03, eta: 3:23:40, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.3384 2022-12-31 23:41:29,786 - mmcls - INFO - Epoch [73][2200/5005] lr: 1.000e-03, eta: 3:23:31, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3231 2022-12-31 23:41:38,851 - mmcls - INFO - Epoch [73][2300/5005] lr: 1.000e-03, eta: 3:23:22, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.3115 2022-12-31 23:41:47,489 - mmcls - INFO - Epoch [73][2400/5005] lr: 1.000e-03, eta: 3:23:13, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3163 2022-12-31 23:41:56,380 - mmcls - INFO - Epoch [73][2500/5005] lr: 1.000e-03, eta: 3:23:04, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3087 2022-12-31 23:42:05,710 - mmcls - INFO - Epoch [73][2600/5005] lr: 1.000e-03, eta: 3:22:56, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.3040 2022-12-31 23:42:14,536 - mmcls - INFO - Epoch [73][2700/5005] lr: 1.000e-03, eta: 3:22:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3059 2022-12-31 23:42:23,346 - mmcls - INFO - Epoch [73][2800/5005] lr: 1.000e-03, eta: 3:22:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3102 2022-12-31 23:42:31,933 - mmcls - INFO - Epoch [73][2900/5005] lr: 1.000e-03, eta: 3:22:29, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.3234 2022-12-31 23:42:40,856 - mmcls - INFO - Epoch [73][3000/5005] lr: 1.000e-03, eta: 3:22:20, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.3034 2022-12-31 23:42:49,710 - mmcls - INFO - Epoch [73][3100/5005] lr: 1.000e-03, eta: 3:22:11, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.2843 2022-12-31 23:42:58,333 - mmcls - INFO - Epoch [73][3200/5005] lr: 1.000e-03, eta: 3:22:02, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3054 2022-12-31 23:43:07,176 - mmcls - INFO - Epoch [73][3300/5005] lr: 1.000e-03, eta: 3:21:54, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3155 2022-12-31 23:43:15,907 - mmcls - INFO - Epoch [73][3400/5005] lr: 1.000e-03, eta: 3:21:45, time: 0.087, data_time: 0.038, memory: 1224, loss: 1.3416 2022-12-31 23:43:24,637 - mmcls - INFO - Epoch [73][3500/5005] lr: 1.000e-03, eta: 3:21:36, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3368 2022-12-31 23:43:33,302 - mmcls - INFO - Epoch [73][3600/5005] lr: 1.000e-03, eta: 3:21:27, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3372 2022-12-31 23:43:42,317 - mmcls - INFO - Epoch [73][3700/5005] lr: 1.000e-03, eta: 3:21:18, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3483 2022-12-31 23:43:51,106 - mmcls - INFO - Epoch [73][3800/5005] lr: 1.000e-03, eta: 3:21:09, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3189 2022-12-31 23:43:59,980 - mmcls - INFO - Epoch [73][3900/5005] lr: 1.000e-03, eta: 3:21:00, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3106 2022-12-31 23:44:08,737 - mmcls - INFO - Epoch [73][4000/5005] lr: 1.000e-03, eta: 3:20:51, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3249 2022-12-31 23:44:17,654 - mmcls - INFO - Epoch [73][4100/5005] lr: 1.000e-03, eta: 3:20:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3305 2022-12-31 23:44:26,532 - mmcls - INFO - Epoch [73][4200/5005] lr: 1.000e-03, eta: 3:20:34, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3166 2022-12-31 23:44:34,926 - mmcls - INFO - Epoch [73][4300/5005] lr: 1.000e-03, eta: 3:20:25, time: 0.084, data_time: 0.006, memory: 1224, loss: 1.3008 2022-12-31 23:44:43,601 - mmcls - INFO - Epoch [73][4400/5005] lr: 1.000e-03, eta: 3:20:16, time: 0.087, data_time: 0.036, memory: 1224, loss: 1.3272 2022-12-31 23:44:52,283 - mmcls - INFO - Epoch [73][4500/5005] lr: 1.000e-03, eta: 3:20:07, time: 0.087, data_time: 0.033, memory: 1224, loss: 1.3248 2022-12-31 23:45:00,962 - mmcls - INFO - Epoch [73][4600/5005] lr: 1.000e-03, eta: 3:19:58, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.3255 2022-12-31 23:45:09,644 - mmcls - INFO - Epoch [73][4700/5005] lr: 1.000e-03, eta: 3:19:49, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.3454 2022-12-31 23:45:18,463 - mmcls - INFO - Epoch [73][4800/5005] lr: 1.000e-03, eta: 3:19:40, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2998 2022-12-31 23:45:27,221 - mmcls - INFO - Epoch [73][4900/5005] lr: 1.000e-03, eta: 3:19:31, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.3254 2022-12-31 23:45:36,067 - mmcls - INFO - Epoch [73][5000/5005] lr: 1.000e-03, eta: 3:19:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3311 2022-12-31 23:45:58,926 - mmcls - INFO - Epoch(val) [73][196] accuracy_top-1: 70.7760, accuracy_top-5: 89.6520 2022-12-31 23:46:09,850 - mmcls - INFO - Epoch [74][100/5005] lr: 1.000e-03, eta: 3:19:14, time: 0.109, data_time: 0.047, memory: 1224, loss: 1.3014 2022-12-31 23:46:18,749 - mmcls - INFO - Epoch [74][200/5005] lr: 1.000e-03, eta: 3:19:05, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3119 2022-12-31 23:46:27,607 - mmcls - INFO - Epoch [74][300/5005] lr: 1.000e-03, eta: 3:18:56, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3177 2022-12-31 23:46:36,554 - mmcls - INFO - Epoch [74][400/5005] lr: 1.000e-03, eta: 3:18:47, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3055 2022-12-31 23:46:45,024 - mmcls - INFO - Epoch [74][500/5005] lr: 1.000e-03, eta: 3:18:38, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.2970 2022-12-31 23:46:53,602 - mmcls - INFO - Epoch [74][600/5005] lr: 1.000e-03, eta: 3:18:29, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3164 2022-12-31 23:47:02,479 - mmcls - INFO - Epoch [74][700/5005] lr: 1.000e-03, eta: 3:18:20, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3008 2022-12-31 23:47:11,046 - mmcls - INFO - Epoch [74][800/5005] lr: 1.000e-03, eta: 3:18:12, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3427 2022-12-31 23:47:19,976 - mmcls - INFO - Epoch [74][900/5005] lr: 1.000e-03, eta: 3:18:03, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3054 2022-12-31 23:47:28,714 - mmcls - INFO - Epoch [74][1000/5005] lr: 1.000e-03, eta: 3:17:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3273 2022-12-31 23:47:37,241 - mmcls - INFO - Epoch [74][1100/5005] lr: 1.000e-03, eta: 3:17:45, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.3021 2022-12-31 23:47:46,522 - mmcls - INFO - Epoch [74][1200/5005] lr: 1.000e-03, eta: 3:17:36, time: 0.093, data_time: 0.027, memory: 1224, loss: 1.3088 2022-12-31 23:47:55,300 - mmcls - INFO - Epoch [74][1300/5005] lr: 1.000e-03, eta: 3:17:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2897 2022-12-31 23:48:04,127 - mmcls - INFO - Epoch [74][1400/5005] lr: 1.000e-03, eta: 3:17:18, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2908 2022-12-31 23:48:13,018 - mmcls - INFO - Epoch [74][1500/5005] lr: 1.000e-03, eta: 3:17:10, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3087 2022-12-31 23:48:22,095 - mmcls - INFO - Epoch [74][1600/5005] lr: 1.000e-03, eta: 3:17:01, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.3121 2022-12-31 23:48:31,356 - mmcls - INFO - Epoch [74][1700/5005] lr: 1.000e-03, eta: 3:16:52, time: 0.093, data_time: 0.008, memory: 1224, loss: 1.3056 2022-12-31 23:48:40,152 - mmcls - INFO - Epoch [74][1800/5005] lr: 1.000e-03, eta: 3:16:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2961 2022-12-31 23:48:48,926 - mmcls - INFO - Epoch [74][1900/5005] lr: 1.000e-03, eta: 3:16:34, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.2947 2022-12-31 23:48:57,551 - mmcls - INFO - Epoch [74][2000/5005] lr: 1.000e-03, eta: 3:16:25, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3349 2022-12-31 23:49:06,603 - mmcls - INFO - Epoch [74][2100/5005] lr: 1.000e-03, eta: 3:16:17, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3522 2022-12-31 23:49:15,138 - mmcls - INFO - Epoch [74][2200/5005] lr: 1.000e-03, eta: 3:16:08, time: 0.085, data_time: 0.020, memory: 1224, loss: 1.3075 2022-12-31 23:49:23,679 - mmcls - INFO - Epoch [74][2300/5005] lr: 1.000e-03, eta: 3:15:59, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.3001 2022-12-31 23:49:32,202 - mmcls - INFO - Epoch [74][2400/5005] lr: 1.000e-03, eta: 3:15:50, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.3032 2022-12-31 23:49:40,902 - mmcls - INFO - Epoch [74][2500/5005] lr: 1.000e-03, eta: 3:15:41, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.3106 2022-12-31 23:49:49,646 - mmcls - INFO - Epoch [74][2600/5005] lr: 1.000e-03, eta: 3:15:32, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3210 2022-12-31 23:49:58,621 - mmcls - INFO - Epoch [74][2700/5005] lr: 1.000e-03, eta: 3:15:23, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3235 2022-12-31 23:50:07,593 - mmcls - INFO - Epoch [74][2800/5005] lr: 1.000e-03, eta: 3:15:14, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3146 2022-12-31 23:50:16,210 - mmcls - INFO - Epoch [74][2900/5005] lr: 1.000e-03, eta: 3:15:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3043 2022-12-31 23:50:24,880 - mmcls - INFO - Epoch [74][3000/5005] lr: 1.000e-03, eta: 3:14:56, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3140 2022-12-31 23:50:34,074 - mmcls - INFO - Epoch [74][3100/5005] lr: 1.000e-03, eta: 3:14:48, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.3155 2022-12-31 23:50:43,086 - mmcls - INFO - Epoch [74][3200/5005] lr: 1.000e-03, eta: 3:14:39, time: 0.090, data_time: 0.023, memory: 1224, loss: 1.3071 2022-12-31 23:50:51,926 - mmcls - INFO - Epoch [74][3300/5005] lr: 1.000e-03, eta: 3:14:30, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3160 2022-12-31 23:51:00,628 - mmcls - INFO - Epoch [74][3400/5005] lr: 1.000e-03, eta: 3:14:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3165 2022-12-31 23:51:09,719 - mmcls - INFO - Epoch [74][3500/5005] lr: 1.000e-03, eta: 3:14:12, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.3235 2022-12-31 23:51:18,325 - mmcls - INFO - Epoch [74][3600/5005] lr: 1.000e-03, eta: 3:14:03, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3391 2022-12-31 23:51:27,134 - mmcls - INFO - Epoch [74][3700/5005] lr: 1.000e-03, eta: 3:13:55, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3092 2022-12-31 23:51:35,867 - mmcls - INFO - Epoch [74][3800/5005] lr: 1.000e-03, eta: 3:13:46, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3129 2022-12-31 23:51:44,334 - mmcls - INFO - Epoch [74][3900/5005] lr: 1.000e-03, eta: 3:13:37, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.3230 2022-12-31 23:51:53,421 - mmcls - INFO - Epoch [74][4000/5005] lr: 1.000e-03, eta: 3:13:28, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3227 2022-12-31 23:52:02,599 - mmcls - INFO - Epoch [74][4100/5005] lr: 1.000e-03, eta: 3:13:19, time: 0.092, data_time: 0.007, memory: 1224, loss: 1.3237 2022-12-31 23:52:11,519 - mmcls - INFO - Epoch [74][4200/5005] lr: 1.000e-03, eta: 3:13:10, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3200 2022-12-31 23:52:20,750 - mmcls - INFO - Epoch [74][4300/5005] lr: 1.000e-03, eta: 3:13:02, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.3174 2022-12-31 23:52:29,521 - mmcls - INFO - Epoch [74][4400/5005] lr: 1.000e-03, eta: 3:12:53, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3308 2022-12-31 23:52:38,205 - mmcls - INFO - Epoch [74][4500/5005] lr: 1.000e-03, eta: 3:12:44, time: 0.087, data_time: 0.029, memory: 1224, loss: 1.3064 2022-12-31 23:52:47,001 - mmcls - INFO - Epoch [74][4600/5005] lr: 1.000e-03, eta: 3:12:35, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3234 2022-12-31 23:52:55,733 - mmcls - INFO - Epoch [74][4700/5005] lr: 1.000e-03, eta: 3:12:26, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3080 2022-12-31 23:53:04,516 - mmcls - INFO - Epoch [74][4800/5005] lr: 1.000e-03, eta: 3:12:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3241 2022-12-31 23:53:13,422 - mmcls - INFO - Epoch [74][4900/5005] lr: 1.000e-03, eta: 3:12:08, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3190 2022-12-31 23:53:21,980 - mmcls - INFO - Epoch [74][5000/5005] lr: 1.000e-03, eta: 3:11:59, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.2951 2022-12-31 23:53:44,619 - mmcls - INFO - Epoch(val) [74][196] accuracy_top-1: 70.9300, accuracy_top-5: 89.7420 2022-12-31 23:53:55,705 - mmcls - INFO - Epoch [75][100/5005] lr: 1.000e-03, eta: 3:11:51, time: 0.111, data_time: 0.064, memory: 1224, loss: 1.3078 2022-12-31 23:54:04,274 - mmcls - INFO - Epoch [75][200/5005] lr: 1.000e-03, eta: 3:11:42, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3172 2022-12-31 23:54:13,592 - mmcls - INFO - Epoch [75][300/5005] lr: 1.000e-03, eta: 3:11:33, time: 0.093, data_time: 0.020, memory: 1224, loss: 1.2919 2022-12-31 23:54:22,502 - mmcls - INFO - Epoch [75][400/5005] lr: 1.000e-03, eta: 3:11:24, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2839 2022-12-31 23:54:31,108 - mmcls - INFO - Epoch [75][500/5005] lr: 1.000e-03, eta: 3:11:15, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3017 2022-12-31 23:54:39,845 - mmcls - INFO - Epoch [75][600/5005] lr: 1.000e-03, eta: 3:11:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3150 2022-12-31 23:54:48,737 - mmcls - INFO - Epoch [75][700/5005] lr: 1.000e-03, eta: 3:10:58, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.2933 2022-12-31 23:54:57,538 - mmcls - INFO - Epoch [75][800/5005] lr: 1.000e-03, eta: 3:10:49, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2944 2022-12-31 23:55:06,288 - mmcls - INFO - Epoch [75][900/5005] lr: 1.000e-03, eta: 3:10:40, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3293 2022-12-31 23:55:14,859 - mmcls - INFO - Epoch [75][1000/5005] lr: 1.000e-03, eta: 3:10:31, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2939 2022-12-31 23:55:23,849 - mmcls - INFO - Epoch [75][1100/5005] lr: 1.000e-03, eta: 3:10:22, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3169 2022-12-31 23:55:32,879 - mmcls - INFO - Epoch [75][1200/5005] lr: 1.000e-03, eta: 3:10:13, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.2920 2022-12-31 23:55:41,674 - mmcls - INFO - Epoch [75][1300/5005] lr: 1.000e-03, eta: 3:10:04, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.3173 2022-12-31 23:55:50,602 - mmcls - INFO - Epoch [75][1400/5005] lr: 1.000e-03, eta: 3:09:56, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3148 2022-12-31 23:55:59,589 - mmcls - INFO - Epoch [75][1500/5005] lr: 1.000e-03, eta: 3:09:47, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3211 2022-12-31 23:56:08,369 - mmcls - INFO - Epoch [75][1600/5005] lr: 1.000e-03, eta: 3:09:38, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3008 2022-12-31 23:56:17,255 - mmcls - INFO - Epoch [75][1700/5005] lr: 1.000e-03, eta: 3:09:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3059 2022-12-31 23:56:26,253 - mmcls - INFO - Epoch [75][1800/5005] lr: 1.000e-03, eta: 3:09:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3075 2022-12-31 23:56:34,922 - mmcls - INFO - Epoch [75][1900/5005] lr: 1.000e-03, eta: 3:09:11, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.3105 2022-12-31 23:56:43,610 - mmcls - INFO - Epoch [75][2000/5005] lr: 1.000e-03, eta: 3:09:02, time: 0.087, data_time: 0.036, memory: 1224, loss: 1.3049 2022-12-31 23:56:52,582 - mmcls - INFO - Epoch [75][2100/5005] lr: 1.000e-03, eta: 3:08:54, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3198 2022-12-31 23:57:01,300 - mmcls - INFO - Epoch [75][2200/5005] lr: 1.000e-03, eta: 3:08:45, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.3367 2022-12-31 23:57:09,911 - mmcls - INFO - Epoch [75][2300/5005] lr: 1.000e-03, eta: 3:08:36, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3189 2022-12-31 23:57:18,983 - mmcls - INFO - Epoch [75][2400/5005] lr: 1.000e-03, eta: 3:08:27, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2956 2022-12-31 23:57:27,644 - mmcls - INFO - Epoch [75][2500/5005] lr: 1.000e-03, eta: 3:08:18, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3171 2022-12-31 23:57:36,229 - mmcls - INFO - Epoch [75][2600/5005] lr: 1.000e-03, eta: 3:08:09, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.2870 2022-12-31 23:57:45,138 - mmcls - INFO - Epoch [75][2700/5005] lr: 1.000e-03, eta: 3:08:00, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3103 2022-12-31 23:57:53,804 - mmcls - INFO - Epoch [75][2800/5005] lr: 1.000e-03, eta: 3:07:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3074 2022-12-31 23:58:02,563 - mmcls - INFO - Epoch [75][2900/5005] lr: 1.000e-03, eta: 3:07:42, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3031 2022-12-31 23:58:11,585 - mmcls - INFO - Epoch [75][3000/5005] lr: 1.000e-03, eta: 3:07:34, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3114 2022-12-31 23:58:20,427 - mmcls - INFO - Epoch [75][3100/5005] lr: 1.000e-03, eta: 3:07:25, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2911 2022-12-31 23:58:29,062 - mmcls - INFO - Epoch [75][3200/5005] lr: 1.000e-03, eta: 3:07:16, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3295 2022-12-31 23:58:38,097 - mmcls - INFO - Epoch [75][3300/5005] lr: 1.000e-03, eta: 3:07:07, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3111 2022-12-31 23:58:46,821 - mmcls - INFO - Epoch [75][3400/5005] lr: 1.000e-03, eta: 3:06:58, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.3133 2022-12-31 23:58:55,634 - mmcls - INFO - Epoch [75][3500/5005] lr: 1.000e-03, eta: 3:06:49, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3095 2022-12-31 23:59:04,916 - mmcls - INFO - Epoch [75][3600/5005] lr: 1.000e-03, eta: 3:06:41, time: 0.093, data_time: 0.015, memory: 1224, loss: 1.3036 2022-12-31 23:59:13,553 - mmcls - INFO - Epoch [75][3700/5005] lr: 1.000e-03, eta: 3:06:32, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3204 2022-12-31 23:59:22,141 - mmcls - INFO - Epoch [75][3800/5005] lr: 1.000e-03, eta: 3:06:23, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.3287 2022-12-31 23:59:30,852 - mmcls - INFO - Epoch [75][3900/5005] lr: 1.000e-03, eta: 3:06:14, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.3078 2022-12-31 23:59:39,460 - mmcls - INFO - Epoch [75][4000/5005] lr: 1.000e-03, eta: 3:06:05, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.3180 2022-12-31 23:59:48,173 - mmcls - INFO - Epoch [75][4100/5005] lr: 1.000e-03, eta: 3:05:56, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3031 2022-12-31 23:59:56,840 - mmcls - INFO - Epoch [75][4200/5005] lr: 1.000e-03, eta: 3:05:47, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3408 2023-01-01 00:00:05,582 - mmcls - INFO - Epoch [75][4300/5005] lr: 1.000e-03, eta: 3:05:38, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.3209 2023-01-01 00:00:14,465 - mmcls - INFO - Epoch [75][4400/5005] lr: 1.000e-03, eta: 3:05:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3175 2023-01-01 00:00:23,371 - mmcls - INFO - Epoch [75][4500/5005] lr: 1.000e-03, eta: 3:05:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3061 2023-01-01 00:00:32,434 - mmcls - INFO - Epoch [75][4600/5005] lr: 1.000e-03, eta: 3:05:12, time: 0.091, data_time: 0.017, memory: 1224, loss: 1.3217 2023-01-01 00:00:41,098 - mmcls - INFO - Epoch [75][4700/5005] lr: 1.000e-03, eta: 3:05:03, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3197 2023-01-01 00:00:49,874 - mmcls - INFO - Epoch [75][4800/5005] lr: 1.000e-03, eta: 3:04:54, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3153 2023-01-01 00:00:58,453 - mmcls - INFO - Epoch [75][4900/5005] lr: 1.000e-03, eta: 3:04:45, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3522 2023-01-01 00:01:07,366 - mmcls - INFO - Epoch [75][5000/5005] lr: 1.000e-03, eta: 3:04:36, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.3133 2023-01-01 00:01:29,986 - mmcls - INFO - Epoch(val) [75][196] accuracy_top-1: 70.7880, accuracy_top-5: 89.8980 2023-01-01 00:01:41,381 - mmcls - INFO - Epoch [76][100/5005] lr: 1.000e-03, eta: 3:04:28, time: 0.114, data_time: 0.045, memory: 1224, loss: 1.3082 2023-01-01 00:01:50,075 - mmcls - INFO - Epoch [76][200/5005] lr: 1.000e-03, eta: 3:04:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3059 2023-01-01 00:01:59,153 - mmcls - INFO - Epoch [76][300/5005] lr: 1.000e-03, eta: 3:04:10, time: 0.091, data_time: 0.032, memory: 1224, loss: 1.3050 2023-01-01 00:02:08,243 - mmcls - INFO - Epoch [76][400/5005] lr: 1.000e-03, eta: 3:04:01, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.3209 2023-01-01 00:02:17,286 - mmcls - INFO - Epoch [76][500/5005] lr: 1.000e-03, eta: 3:03:52, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3198 2023-01-01 00:02:26,061 - mmcls - INFO - Epoch [76][600/5005] lr: 1.000e-03, eta: 3:03:43, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3098 2023-01-01 00:02:34,855 - mmcls - INFO - Epoch [76][700/5005] lr: 1.000e-03, eta: 3:03:35, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2880 2023-01-01 00:02:43,872 - mmcls - INFO - Epoch [76][800/5005] lr: 1.000e-03, eta: 3:03:26, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.3116 2023-01-01 00:02:52,713 - mmcls - INFO - Epoch [76][900/5005] lr: 1.000e-03, eta: 3:03:17, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3150 2023-01-01 00:03:01,513 - mmcls - INFO - Epoch [76][1000/5005] lr: 1.000e-03, eta: 3:03:08, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3195 2023-01-01 00:03:10,201 - mmcls - INFO - Epoch [76][1100/5005] lr: 1.000e-03, eta: 3:02:59, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.3090 2023-01-01 00:03:19,426 - mmcls - INFO - Epoch [76][1200/5005] lr: 1.000e-03, eta: 3:02:50, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.3215 2023-01-01 00:03:28,278 - mmcls - INFO - Epoch [76][1300/5005] lr: 1.000e-03, eta: 3:02:42, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3086 2023-01-01 00:03:37,236 - mmcls - INFO - Epoch [76][1400/5005] lr: 1.000e-03, eta: 3:02:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3286 2023-01-01 00:03:45,913 - mmcls - INFO - Epoch [76][1500/5005] lr: 1.000e-03, eta: 3:02:24, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2935 2023-01-01 00:03:54,908 - mmcls - INFO - Epoch [76][1600/5005] lr: 1.000e-03, eta: 3:02:15, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3080 2023-01-01 00:04:04,011 - mmcls - INFO - Epoch [76][1700/5005] lr: 1.000e-03, eta: 3:02:06, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.3001 2023-01-01 00:04:13,203 - mmcls - INFO - Epoch [76][1800/5005] lr: 1.000e-03, eta: 3:01:58, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.2970 2023-01-01 00:04:21,792 - mmcls - INFO - Epoch [76][1900/5005] lr: 1.000e-03, eta: 3:01:49, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.3048 2023-01-01 00:04:30,778 - mmcls - INFO - Epoch [76][2000/5005] lr: 1.000e-03, eta: 3:01:40, time: 0.090, data_time: 0.032, memory: 1224, loss: 1.3109 2023-01-01 00:04:39,643 - mmcls - INFO - Epoch [76][2100/5005] lr: 1.000e-03, eta: 3:01:31, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2949 2023-01-01 00:04:48,469 - mmcls - INFO - Epoch [76][2200/5005] lr: 1.000e-03, eta: 3:01:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2870 2023-01-01 00:04:57,172 - mmcls - INFO - Epoch [76][2300/5005] lr: 1.000e-03, eta: 3:01:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3163 2023-01-01 00:05:05,894 - mmcls - INFO - Epoch [76][2400/5005] lr: 1.000e-03, eta: 3:01:04, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.3024 2023-01-01 00:05:14,495 - mmcls - INFO - Epoch [76][2500/5005] lr: 1.000e-03, eta: 3:00:55, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.3052 2023-01-01 00:05:23,542 - mmcls - INFO - Epoch [76][2600/5005] lr: 1.000e-03, eta: 3:00:47, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.3047 2023-01-01 00:05:32,503 - mmcls - INFO - Epoch [76][2700/5005] lr: 1.000e-03, eta: 3:00:38, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.3182 2023-01-01 00:05:41,326 - mmcls - INFO - Epoch [76][2800/5005] lr: 1.000e-03, eta: 3:00:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3069 2023-01-01 00:05:50,131 - mmcls - INFO - Epoch [76][2900/5005] lr: 1.000e-03, eta: 3:00:20, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2949 2023-01-01 00:05:58,624 - mmcls - INFO - Epoch [76][3000/5005] lr: 1.000e-03, eta: 3:00:11, time: 0.085, data_time: 0.018, memory: 1224, loss: 1.2927 2023-01-01 00:06:07,580 - mmcls - INFO - Epoch [76][3100/5005] lr: 1.000e-03, eta: 3:00:02, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3305 2023-01-01 00:06:16,158 - mmcls - INFO - Epoch [76][3200/5005] lr: 1.000e-03, eta: 2:59:53, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3074 2023-01-01 00:06:24,677 - mmcls - INFO - Epoch [76][3300/5005] lr: 1.000e-03, eta: 2:59:44, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.3127 2023-01-01 00:06:33,442 - mmcls - INFO - Epoch [76][3400/5005] lr: 1.000e-03, eta: 2:59:35, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3178 2023-01-01 00:06:42,428 - mmcls - INFO - Epoch [76][3500/5005] lr: 1.000e-03, eta: 2:59:27, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.3264 2023-01-01 00:06:51,237 - mmcls - INFO - Epoch [76][3600/5005] lr: 1.000e-03, eta: 2:59:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3133 2023-01-01 00:06:59,893 - mmcls - INFO - Epoch [76][3700/5005] lr: 1.000e-03, eta: 2:59:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2870 2023-01-01 00:07:08,702 - mmcls - INFO - Epoch [76][3800/5005] lr: 1.000e-03, eta: 2:59:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3089 2023-01-01 00:07:17,510 - mmcls - INFO - Epoch [76][3900/5005] lr: 1.000e-03, eta: 2:58:51, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3060 2023-01-01 00:07:26,226 - mmcls - INFO - Epoch [76][4000/5005] lr: 1.000e-03, eta: 2:58:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3119 2023-01-01 00:07:34,831 - mmcls - INFO - Epoch [76][4100/5005] lr: 1.000e-03, eta: 2:58:33, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3027 2023-01-01 00:07:43,725 - mmcls - INFO - Epoch [76][4200/5005] lr: 1.000e-03, eta: 2:58:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3229 2023-01-01 00:07:52,434 - mmcls - INFO - Epoch [76][4300/5005] lr: 1.000e-03, eta: 2:58:16, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3095 2023-01-01 00:08:01,100 - mmcls - INFO - Epoch [76][4400/5005] lr: 1.000e-03, eta: 2:58:07, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.3110 2023-01-01 00:08:09,720 - mmcls - INFO - Epoch [76][4500/5005] lr: 1.000e-03, eta: 2:57:58, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.3251 2023-01-01 00:08:18,478 - mmcls - INFO - Epoch [76][4600/5005] lr: 1.000e-03, eta: 2:57:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3127 2023-01-01 00:08:27,318 - mmcls - INFO - Epoch [76][4700/5005] lr: 1.000e-03, eta: 2:57:40, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.3160 2023-01-01 00:08:36,383 - mmcls - INFO - Epoch [76][4800/5005] lr: 1.000e-03, eta: 2:57:31, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3159 2023-01-01 00:08:45,323 - mmcls - INFO - Epoch [76][4900/5005] lr: 1.000e-03, eta: 2:57:22, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3030 2023-01-01 00:08:54,001 - mmcls - INFO - Epoch [76][5000/5005] lr: 1.000e-03, eta: 2:57:13, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3080 2023-01-01 00:09:16,650 - mmcls - INFO - Epoch(val) [76][196] accuracy_top-1: 70.9440, accuracy_top-5: 89.8660 2023-01-01 00:09:27,377 - mmcls - INFO - Epoch [77][100/5005] lr: 1.000e-03, eta: 2:57:05, time: 0.107, data_time: 0.048, memory: 1224, loss: 1.3065 2023-01-01 00:09:36,342 - mmcls - INFO - Epoch [77][200/5005] lr: 1.000e-03, eta: 2:56:56, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.2947 2023-01-01 00:09:45,032 - mmcls - INFO - Epoch [77][300/5005] lr: 1.000e-03, eta: 2:56:47, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2938 2023-01-01 00:09:53,892 - mmcls - INFO - Epoch [77][400/5005] lr: 1.000e-03, eta: 2:56:38, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3075 2023-01-01 00:10:02,833 - mmcls - INFO - Epoch [77][500/5005] lr: 1.000e-03, eta: 2:56:29, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3001 2023-01-01 00:10:11,703 - mmcls - INFO - Epoch [77][600/5005] lr: 1.000e-03, eta: 2:56:20, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2984 2023-01-01 00:10:20,233 - mmcls - INFO - Epoch [77][700/5005] lr: 1.000e-03, eta: 2:56:11, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.3249 2023-01-01 00:10:28,905 - mmcls - INFO - Epoch [77][800/5005] lr: 1.000e-03, eta: 2:56:02, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.3162 2023-01-01 00:10:37,882 - mmcls - INFO - Epoch [77][900/5005] lr: 1.000e-03, eta: 2:55:54, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2896 2023-01-01 00:10:46,710 - mmcls - INFO - Epoch [77][1000/5005] lr: 1.000e-03, eta: 2:55:45, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2987 2023-01-01 00:10:55,274 - mmcls - INFO - Epoch [77][1100/5005] lr: 1.000e-03, eta: 2:55:36, time: 0.086, data_time: 0.028, memory: 1224, loss: 1.2997 2023-01-01 00:11:04,072 - mmcls - INFO - Epoch [77][1200/5005] lr: 1.000e-03, eta: 2:55:27, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2937 2023-01-01 00:11:12,915 - mmcls - INFO - Epoch [77][1300/5005] lr: 1.000e-03, eta: 2:55:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2945 2023-01-01 00:11:21,582 - mmcls - INFO - Epoch [77][1400/5005] lr: 1.000e-03, eta: 2:55:09, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2874 2023-01-01 00:11:30,291 - mmcls - INFO - Epoch [77][1500/5005] lr: 1.000e-03, eta: 2:55:00, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3273 2023-01-01 00:11:38,869 - mmcls - INFO - Epoch [77][1600/5005] lr: 1.000e-03, eta: 2:54:51, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.3031 2023-01-01 00:11:47,607 - mmcls - INFO - Epoch [77][1700/5005] lr: 1.000e-03, eta: 2:54:42, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.2937 2023-01-01 00:11:56,508 - mmcls - INFO - Epoch [77][1800/5005] lr: 1.000e-03, eta: 2:54:34, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3020 2023-01-01 00:12:05,247 - mmcls - INFO - Epoch [77][1900/5005] lr: 1.000e-03, eta: 2:54:25, time: 0.087, data_time: 0.019, memory: 1224, loss: 1.3022 2023-01-01 00:12:13,823 - mmcls - INFO - Epoch [77][2000/5005] lr: 1.000e-03, eta: 2:54:16, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.2893 2023-01-01 00:12:22,576 - mmcls - INFO - Epoch [77][2100/5005] lr: 1.000e-03, eta: 2:54:07, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2970 2023-01-01 00:12:31,562 - mmcls - INFO - Epoch [77][2200/5005] lr: 1.000e-03, eta: 2:53:58, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.3163 2023-01-01 00:12:40,656 - mmcls - INFO - Epoch [77][2300/5005] lr: 1.000e-03, eta: 2:53:49, time: 0.091, data_time: 0.038, memory: 1224, loss: 1.3072 2023-01-01 00:12:49,417 - mmcls - INFO - Epoch [77][2400/5005] lr: 1.000e-03, eta: 2:53:40, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3011 2023-01-01 00:12:58,192 - mmcls - INFO - Epoch [77][2500/5005] lr: 1.000e-03, eta: 2:53:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3109 2023-01-01 00:13:07,314 - mmcls - INFO - Epoch [77][2600/5005] lr: 1.000e-03, eta: 2:53:23, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3002 2023-01-01 00:13:15,897 - mmcls - INFO - Epoch [77][2700/5005] lr: 1.000e-03, eta: 2:53:14, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3037 2023-01-01 00:13:24,695 - mmcls - INFO - Epoch [77][2800/5005] lr: 1.000e-03, eta: 2:53:05, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2876 2023-01-01 00:13:33,338 - mmcls - INFO - Epoch [77][2900/5005] lr: 1.000e-03, eta: 2:52:56, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.2962 2023-01-01 00:13:42,385 - mmcls - INFO - Epoch [77][3000/5005] lr: 1.000e-03, eta: 2:52:47, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.3169 2023-01-01 00:13:51,154 - mmcls - INFO - Epoch [77][3100/5005] lr: 1.000e-03, eta: 2:52:38, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3070 2023-01-01 00:14:00,177 - mmcls - INFO - Epoch [77][3200/5005] lr: 1.000e-03, eta: 2:52:30, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3193 2023-01-01 00:14:08,880 - mmcls - INFO - Epoch [77][3300/5005] lr: 1.000e-03, eta: 2:52:21, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3168 2023-01-01 00:14:17,866 - mmcls - INFO - Epoch [77][3400/5005] lr: 1.000e-03, eta: 2:52:12, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.2967 2023-01-01 00:14:26,486 - mmcls - INFO - Epoch [77][3500/5005] lr: 1.000e-03, eta: 2:52:03, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3048 2023-01-01 00:14:34,916 - mmcls - INFO - Epoch [77][3600/5005] lr: 1.000e-03, eta: 2:51:54, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.3163 2023-01-01 00:14:43,612 - mmcls - INFO - Epoch [77][3700/5005] lr: 1.000e-03, eta: 2:51:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3113 2023-01-01 00:14:52,623 - mmcls - INFO - Epoch [77][3800/5005] lr: 1.000e-03, eta: 2:51:36, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3169 2023-01-01 00:15:01,252 - mmcls - INFO - Epoch [77][3900/5005] lr: 1.000e-03, eta: 2:51:27, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2975 2023-01-01 00:15:10,046 - mmcls - INFO - Epoch [77][4000/5005] lr: 1.000e-03, eta: 2:51:19, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.3123 2023-01-01 00:15:19,049 - mmcls - INFO - Epoch [77][4100/5005] lr: 1.000e-03, eta: 2:51:10, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.3244 2023-01-01 00:15:27,601 - mmcls - INFO - Epoch [77][4200/5005] lr: 1.000e-03, eta: 2:51:01, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.3140 2023-01-01 00:15:36,877 - mmcls - INFO - Epoch [77][4300/5005] lr: 1.000e-03, eta: 2:50:52, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.3098 2023-01-01 00:15:45,386 - mmcls - INFO - Epoch [77][4400/5005] lr: 1.000e-03, eta: 2:50:43, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.3165 2023-01-01 00:15:54,333 - mmcls - INFO - Epoch [77][4500/5005] lr: 1.000e-03, eta: 2:50:34, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.3086 2023-01-01 00:16:03,614 - mmcls - INFO - Epoch [77][4600/5005] lr: 1.000e-03, eta: 2:50:26, time: 0.093, data_time: 0.006, memory: 1224, loss: 1.3232 2023-01-01 00:16:12,449 - mmcls - INFO - Epoch [77][4700/5005] lr: 1.000e-03, eta: 2:50:17, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2943 2023-01-01 00:16:21,160 - mmcls - INFO - Epoch [77][4800/5005] lr: 1.000e-03, eta: 2:50:08, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3104 2023-01-01 00:16:29,781 - mmcls - INFO - Epoch [77][4900/5005] lr: 1.000e-03, eta: 2:49:59, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.3113 2023-01-01 00:16:38,721 - mmcls - INFO - Epoch [77][5000/5005] lr: 1.000e-03, eta: 2:49:50, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.3196 2023-01-01 00:17:01,272 - mmcls - INFO - Epoch(val) [77][196] accuracy_top-1: 70.8320, accuracy_top-5: 89.9060 2023-01-01 00:17:12,214 - mmcls - INFO - Epoch [78][100/5005] lr: 1.000e-03, eta: 2:49:41, time: 0.109, data_time: 0.054, memory: 1224, loss: 1.2829 2023-01-01 00:17:20,909 - mmcls - INFO - Epoch [78][200/5005] lr: 1.000e-03, eta: 2:49:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2961 2023-01-01 00:17:29,828 - mmcls - INFO - Epoch [78][300/5005] lr: 1.000e-03, eta: 2:49:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3014 2023-01-01 00:17:38,660 - mmcls - INFO - Epoch [78][400/5005] lr: 1.000e-03, eta: 2:49:15, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2814 2023-01-01 00:17:47,595 - mmcls - INFO - Epoch [78][500/5005] lr: 1.000e-03, eta: 2:49:06, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.3028 2023-01-01 00:17:56,141 - mmcls - INFO - Epoch [78][600/5005] lr: 1.000e-03, eta: 2:48:57, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2921 2023-01-01 00:18:05,299 - mmcls - INFO - Epoch [78][700/5005] lr: 1.000e-03, eta: 2:48:48, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.3095 2023-01-01 00:18:14,078 - mmcls - INFO - Epoch [78][800/5005] lr: 1.000e-03, eta: 2:48:39, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2919 2023-01-01 00:18:23,026 - mmcls - INFO - Epoch [78][900/5005] lr: 1.000e-03, eta: 2:48:30, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2879 2023-01-01 00:18:31,660 - mmcls - INFO - Epoch [78][1000/5005] lr: 1.000e-03, eta: 2:48:21, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2995 2023-01-01 00:18:40,581 - mmcls - INFO - Epoch [78][1100/5005] lr: 1.000e-03, eta: 2:48:13, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2977 2023-01-01 00:18:49,280 - mmcls - INFO - Epoch [78][1200/5005] lr: 1.000e-03, eta: 2:48:04, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2964 2023-01-01 00:18:57,913 - mmcls - INFO - Epoch [78][1300/5005] lr: 1.000e-03, eta: 2:47:55, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2928 2023-01-01 00:19:06,880 - mmcls - INFO - Epoch [78][1400/5005] lr: 1.000e-03, eta: 2:47:46, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3078 2023-01-01 00:19:16,143 - mmcls - INFO - Epoch [78][1500/5005] lr: 1.000e-03, eta: 2:47:37, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2998 2023-01-01 00:19:25,001 - mmcls - INFO - Epoch [78][1600/5005] lr: 1.000e-03, eta: 2:47:28, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.3132 2023-01-01 00:19:33,750 - mmcls - INFO - Epoch [78][1700/5005] lr: 1.000e-03, eta: 2:47:20, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3230 2023-01-01 00:19:42,821 - mmcls - INFO - Epoch [78][1800/5005] lr: 1.000e-03, eta: 2:47:11, time: 0.091, data_time: 0.036, memory: 1224, loss: 1.2753 2023-01-01 00:19:51,721 - mmcls - INFO - Epoch [78][1900/5005] lr: 1.000e-03, eta: 2:47:02, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2958 2023-01-01 00:20:00,617 - mmcls - INFO - Epoch [78][2000/5005] lr: 1.000e-03, eta: 2:46:53, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3168 2023-01-01 00:20:09,592 - mmcls - INFO - Epoch [78][2100/5005] lr: 1.000e-03, eta: 2:46:44, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3026 2023-01-01 00:20:18,362 - mmcls - INFO - Epoch [78][2200/5005] lr: 1.000e-03, eta: 2:46:35, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2881 2023-01-01 00:20:26,886 - mmcls - INFO - Epoch [78][2300/5005] lr: 1.000e-03, eta: 2:46:26, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.3093 2023-01-01 00:20:35,616 - mmcls - INFO - Epoch [78][2400/5005] lr: 1.000e-03, eta: 2:46:18, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2845 2023-01-01 00:20:44,413 - mmcls - INFO - Epoch [78][2500/5005] lr: 1.000e-03, eta: 2:46:09, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2930 2023-01-01 00:20:53,234 - mmcls - INFO - Epoch [78][2600/5005] lr: 1.000e-03, eta: 2:46:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2912 2023-01-01 00:21:02,040 - mmcls - INFO - Epoch [78][2700/5005] lr: 1.000e-03, eta: 2:45:51, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.3015 2023-01-01 00:21:10,691 - mmcls - INFO - Epoch [78][2800/5005] lr: 1.000e-03, eta: 2:45:42, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3126 2023-01-01 00:21:19,803 - mmcls - INFO - Epoch [78][2900/5005] lr: 1.000e-03, eta: 2:45:33, time: 0.091, data_time: 0.023, memory: 1224, loss: 1.2819 2023-01-01 00:21:28,554 - mmcls - INFO - Epoch [78][3000/5005] lr: 1.000e-03, eta: 2:45:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3061 2023-01-01 00:21:37,449 - mmcls - INFO - Epoch [78][3100/5005] lr: 1.000e-03, eta: 2:45:16, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2889 2023-01-01 00:21:46,158 - mmcls - INFO - Epoch [78][3200/5005] lr: 1.000e-03, eta: 2:45:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3008 2023-01-01 00:21:54,694 - mmcls - INFO - Epoch [78][3300/5005] lr: 1.000e-03, eta: 2:44:58, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.3192 2023-01-01 00:22:03,941 - mmcls - INFO - Epoch [78][3400/5005] lr: 1.000e-03, eta: 2:44:49, time: 0.092, data_time: 0.011, memory: 1224, loss: 1.3005 2023-01-01 00:22:12,619 - mmcls - INFO - Epoch [78][3500/5005] lr: 1.000e-03, eta: 2:44:40, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2997 2023-01-01 00:22:21,844 - mmcls - INFO - Epoch [78][3600/5005] lr: 1.000e-03, eta: 2:44:31, time: 0.092, data_time: 0.006, memory: 1224, loss: 1.3108 2023-01-01 00:22:30,596 - mmcls - INFO - Epoch [78][3700/5005] lr: 1.000e-03, eta: 2:44:22, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3219 2023-01-01 00:22:39,208 - mmcls - INFO - Epoch [78][3800/5005] lr: 1.000e-03, eta: 2:44:14, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2987 2023-01-01 00:22:48,034 - mmcls - INFO - Epoch [78][3900/5005] lr: 1.000e-03, eta: 2:44:05, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.3052 2023-01-01 00:22:56,768 - mmcls - INFO - Epoch [78][4000/5005] lr: 1.000e-03, eta: 2:43:56, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3028 2023-01-01 00:23:05,822 - mmcls - INFO - Epoch [78][4100/5005] lr: 1.000e-03, eta: 2:43:47, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.2885 2023-01-01 00:23:14,694 - mmcls - INFO - Epoch [78][4200/5005] lr: 1.000e-03, eta: 2:43:38, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.3164 2023-01-01 00:23:23,615 - mmcls - INFO - Epoch [78][4300/5005] lr: 1.000e-03, eta: 2:43:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3057 2023-01-01 00:23:32,403 - mmcls - INFO - Epoch [78][4400/5005] lr: 1.000e-03, eta: 2:43:20, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2902 2023-01-01 00:23:41,625 - mmcls - INFO - Epoch [78][4500/5005] lr: 1.000e-03, eta: 2:43:12, time: 0.092, data_time: 0.014, memory: 1224, loss: 1.3237 2023-01-01 00:23:50,569 - mmcls - INFO - Epoch [78][4600/5005] lr: 1.000e-03, eta: 2:43:03, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2999 2023-01-01 00:23:59,335 - mmcls - INFO - Epoch [78][4700/5005] lr: 1.000e-03, eta: 2:42:54, time: 0.088, data_time: 0.033, memory: 1224, loss: 1.3047 2023-01-01 00:24:08,162 - mmcls - INFO - Epoch [78][4800/5005] lr: 1.000e-03, eta: 2:42:45, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3174 2023-01-01 00:24:16,908 - mmcls - INFO - Epoch [78][4900/5005] lr: 1.000e-03, eta: 2:42:36, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3119 2023-01-01 00:24:25,763 - mmcls - INFO - Epoch [78][5000/5005] lr: 1.000e-03, eta: 2:42:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2989 2023-01-01 00:24:48,558 - mmcls - INFO - Epoch(val) [78][196] accuracy_top-1: 71.0000, accuracy_top-5: 89.8480 2023-01-01 00:24:59,815 - mmcls - INFO - Epoch [79][100/5005] lr: 1.000e-03, eta: 2:42:19, time: 0.112, data_time: 0.031, memory: 1224, loss: 1.2893 2023-01-01 00:25:08,691 - mmcls - INFO - Epoch [79][200/5005] lr: 1.000e-03, eta: 2:42:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2951 2023-01-01 00:25:17,559 - mmcls - INFO - Epoch [79][300/5005] lr: 1.000e-03, eta: 2:42:01, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2902 2023-01-01 00:25:26,770 - mmcls - INFO - Epoch [79][400/5005] lr: 1.000e-03, eta: 2:41:52, time: 0.092, data_time: 0.025, memory: 1224, loss: 1.2893 2023-01-01 00:25:35,674 - mmcls - INFO - Epoch [79][500/5005] lr: 1.000e-03, eta: 2:41:43, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.3006 2023-01-01 00:25:44,611 - mmcls - INFO - Epoch [79][600/5005] lr: 1.000e-03, eta: 2:41:35, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2970 2023-01-01 00:25:53,415 - mmcls - INFO - Epoch [79][700/5005] lr: 1.000e-03, eta: 2:41:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2629 2023-01-01 00:26:02,297 - mmcls - INFO - Epoch [79][800/5005] lr: 1.000e-03, eta: 2:41:17, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2993 2023-01-01 00:26:10,918 - mmcls - INFO - Epoch [79][900/5005] lr: 1.000e-03, eta: 2:41:08, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.3081 2023-01-01 00:26:19,825 - mmcls - INFO - Epoch [79][1000/5005] lr: 1.000e-03, eta: 2:40:59, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3022 2023-01-01 00:26:28,528 - mmcls - INFO - Epoch [79][1100/5005] lr: 1.000e-03, eta: 2:40:50, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.2824 2023-01-01 00:26:37,346 - mmcls - INFO - Epoch [79][1200/5005] lr: 1.000e-03, eta: 2:40:41, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2900 2023-01-01 00:26:46,161 - mmcls - INFO - Epoch [79][1300/5005] lr: 1.000e-03, eta: 2:40:32, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.3031 2023-01-01 00:26:55,010 - mmcls - INFO - Epoch [79][1400/5005] lr: 1.000e-03, eta: 2:40:24, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2951 2023-01-01 00:27:04,104 - mmcls - INFO - Epoch [79][1500/5005] lr: 1.000e-03, eta: 2:40:15, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.2777 2023-01-01 00:27:12,927 - mmcls - INFO - Epoch [79][1600/5005] lr: 1.000e-03, eta: 2:40:06, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2879 2023-01-01 00:27:21,574 - mmcls - INFO - Epoch [79][1700/5005] lr: 1.000e-03, eta: 2:39:57, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3004 2023-01-01 00:27:30,726 - mmcls - INFO - Epoch [79][1800/5005] lr: 1.000e-03, eta: 2:39:48, time: 0.092, data_time: 0.018, memory: 1224, loss: 1.2778 2023-01-01 00:27:39,784 - mmcls - INFO - Epoch [79][1900/5005] lr: 1.000e-03, eta: 2:39:39, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.3041 2023-01-01 00:27:48,384 - mmcls - INFO - Epoch [79][2000/5005] lr: 1.000e-03, eta: 2:39:31, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3142 2023-01-01 00:27:57,431 - mmcls - INFO - Epoch [79][2100/5005] lr: 1.000e-03, eta: 2:39:22, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2987 2023-01-01 00:28:06,066 - mmcls - INFO - Epoch [79][2200/5005] lr: 1.000e-03, eta: 2:39:13, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3009 2023-01-01 00:28:14,970 - mmcls - INFO - Epoch [79][2300/5005] lr: 1.000e-03, eta: 2:39:04, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2893 2023-01-01 00:28:23,759 - mmcls - INFO - Epoch [79][2400/5005] lr: 1.000e-03, eta: 2:38:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.3021 2023-01-01 00:28:32,647 - mmcls - INFO - Epoch [79][2500/5005] lr: 1.000e-03, eta: 2:38:46, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.3047 2023-01-01 00:28:41,325 - mmcls - INFO - Epoch [79][2600/5005] lr: 1.000e-03, eta: 2:38:37, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.3011 2023-01-01 00:28:50,066 - mmcls - INFO - Epoch [79][2700/5005] lr: 1.000e-03, eta: 2:38:28, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.2937 2023-01-01 00:28:58,931 - mmcls - INFO - Epoch [79][2800/5005] lr: 1.000e-03, eta: 2:38:20, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.3275 2023-01-01 00:29:08,057 - mmcls - INFO - Epoch [79][2900/5005] lr: 1.000e-03, eta: 2:38:11, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2968 2023-01-01 00:29:16,835 - mmcls - INFO - Epoch [79][3000/5005] lr: 1.000e-03, eta: 2:38:02, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2995 2023-01-01 00:29:25,702 - mmcls - INFO - Epoch [79][3100/5005] lr: 1.000e-03, eta: 2:37:53, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2994 2023-01-01 00:29:34,489 - mmcls - INFO - Epoch [79][3200/5005] lr: 1.000e-03, eta: 2:37:44, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.3077 2023-01-01 00:29:43,581 - mmcls - INFO - Epoch [79][3300/5005] lr: 1.000e-03, eta: 2:37:35, time: 0.091, data_time: 0.025, memory: 1224, loss: 1.3142 2023-01-01 00:29:52,403 - mmcls - INFO - Epoch [79][3400/5005] lr: 1.000e-03, eta: 2:37:27, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.3127 2023-01-01 00:30:01,072 - mmcls - INFO - Epoch [79][3500/5005] lr: 1.000e-03, eta: 2:37:18, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.3232 2023-01-01 00:30:09,937 - mmcls - INFO - Epoch [79][3600/5005] lr: 1.000e-03, eta: 2:37:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3163 2023-01-01 00:30:18,682 - mmcls - INFO - Epoch [79][3700/5005] lr: 1.000e-03, eta: 2:37:00, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3136 2023-01-01 00:30:27,275 - mmcls - INFO - Epoch [79][3800/5005] lr: 1.000e-03, eta: 2:36:51, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2831 2023-01-01 00:30:36,280 - mmcls - INFO - Epoch [79][3900/5005] lr: 1.000e-03, eta: 2:36:42, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.2930 2023-01-01 00:30:45,136 - mmcls - INFO - Epoch [79][4000/5005] lr: 1.000e-03, eta: 2:36:33, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2900 2023-01-01 00:30:53,925 - mmcls - INFO - Epoch [79][4100/5005] lr: 1.000e-03, eta: 2:36:25, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3092 2023-01-01 00:31:02,676 - mmcls - INFO - Epoch [79][4200/5005] lr: 1.000e-03, eta: 2:36:16, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.3183 2023-01-01 00:31:11,496 - mmcls - INFO - Epoch [79][4300/5005] lr: 1.000e-03, eta: 2:36:07, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3062 2023-01-01 00:31:20,269 - mmcls - INFO - Epoch [79][4400/5005] lr: 1.000e-03, eta: 2:35:58, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.3120 2023-01-01 00:31:29,170 - mmcls - INFO - Epoch [79][4500/5005] lr: 1.000e-03, eta: 2:35:49, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2844 2023-01-01 00:31:37,842 - mmcls - INFO - Epoch [79][4600/5005] lr: 1.000e-03, eta: 2:35:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3209 2023-01-01 00:31:46,676 - mmcls - INFO - Epoch [79][4700/5005] lr: 1.000e-03, eta: 2:35:31, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2798 2023-01-01 00:31:55,674 - mmcls - INFO - Epoch [79][4800/5005] lr: 1.000e-03, eta: 2:35:22, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3105 2023-01-01 00:32:04,125 - mmcls - INFO - Epoch [79][4900/5005] lr: 1.000e-03, eta: 2:35:14, time: 0.084, data_time: 0.001, memory: 1224, loss: 1.3036 2023-01-01 00:32:13,130 - mmcls - INFO - Epoch [79][5000/5005] lr: 1.000e-03, eta: 2:35:05, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2972 2023-01-01 00:32:35,662 - mmcls - INFO - Epoch(val) [79][196] accuracy_top-1: 70.8720, accuracy_top-5: 89.8440 2023-01-01 00:32:46,835 - mmcls - INFO - Epoch [80][100/5005] lr: 1.000e-03, eta: 2:34:56, time: 0.111, data_time: 0.036, memory: 1224, loss: 1.3141 2023-01-01 00:32:55,612 - mmcls - INFO - Epoch [80][200/5005] lr: 1.000e-03, eta: 2:34:47, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3048 2023-01-01 00:33:04,561 - mmcls - INFO - Epoch [80][300/5005] lr: 1.000e-03, eta: 2:34:38, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2886 2023-01-01 00:33:13,193 - mmcls - INFO - Epoch [80][400/5005] lr: 1.000e-03, eta: 2:34:29, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2773 2023-01-01 00:33:22,095 - mmcls - INFO - Epoch [80][500/5005] lr: 1.000e-03, eta: 2:34:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2810 2023-01-01 00:33:31,096 - mmcls - INFO - Epoch [80][600/5005] lr: 1.000e-03, eta: 2:34:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2821 2023-01-01 00:33:40,193 - mmcls - INFO - Epoch [80][700/5005] lr: 1.000e-03, eta: 2:34:03, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.2897 2023-01-01 00:33:48,908 - mmcls - INFO - Epoch [80][800/5005] lr: 1.000e-03, eta: 2:33:54, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.3056 2023-01-01 00:33:57,756 - mmcls - INFO - Epoch [80][900/5005] lr: 1.000e-03, eta: 2:33:45, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.3023 2023-01-01 00:34:06,237 - mmcls - INFO - Epoch [80][1000/5005] lr: 1.000e-03, eta: 2:33:36, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.2800 2023-01-01 00:34:15,428 - mmcls - INFO - Epoch [80][1100/5005] lr: 1.000e-03, eta: 2:33:27, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2942 2023-01-01 00:34:24,006 - mmcls - INFO - Epoch [80][1200/5005] lr: 1.000e-03, eta: 2:33:18, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2819 2023-01-01 00:34:32,843 - mmcls - INFO - Epoch [80][1300/5005] lr: 1.000e-03, eta: 2:33:10, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2939 2023-01-01 00:34:41,553 - mmcls - INFO - Epoch [80][1400/5005] lr: 1.000e-03, eta: 2:33:01, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.2898 2023-01-01 00:34:50,173 - mmcls - INFO - Epoch [80][1500/5005] lr: 1.000e-03, eta: 2:32:52, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2871 2023-01-01 00:34:59,160 - mmcls - INFO - Epoch [80][1600/5005] lr: 1.000e-03, eta: 2:32:43, time: 0.090, data_time: 0.025, memory: 1224, loss: 1.2850 2023-01-01 00:35:07,836 - mmcls - INFO - Epoch [80][1700/5005] lr: 1.000e-03, eta: 2:32:34, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2795 2023-01-01 00:35:16,905 - mmcls - INFO - Epoch [80][1800/5005] lr: 1.000e-03, eta: 2:32:25, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.3082 2023-01-01 00:35:25,510 - mmcls - INFO - Epoch [80][1900/5005] lr: 1.000e-03, eta: 2:32:16, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2849 2023-01-01 00:35:34,361 - mmcls - INFO - Epoch [80][2000/5005] lr: 1.000e-03, eta: 2:32:08, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2886 2023-01-01 00:35:43,014 - mmcls - INFO - Epoch [80][2100/5005] lr: 1.000e-03, eta: 2:31:59, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2865 2023-01-01 00:35:52,056 - mmcls - INFO - Epoch [80][2200/5005] lr: 1.000e-03, eta: 2:31:50, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.3099 2023-01-01 00:36:00,784 - mmcls - INFO - Epoch [80][2300/5005] lr: 1.000e-03, eta: 2:31:41, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2879 2023-01-01 00:36:09,373 - mmcls - INFO - Epoch [80][2400/5005] lr: 1.000e-03, eta: 2:31:32, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3045 2023-01-01 00:36:18,370 - mmcls - INFO - Epoch [80][2500/5005] lr: 1.000e-03, eta: 2:31:23, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2736 2023-01-01 00:36:26,949 - mmcls - INFO - Epoch [80][2600/5005] lr: 1.000e-03, eta: 2:31:14, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2990 2023-01-01 00:36:35,554 - mmcls - INFO - Epoch [80][2700/5005] lr: 1.000e-03, eta: 2:31:05, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.2946 2023-01-01 00:36:44,163 - mmcls - INFO - Epoch [80][2800/5005] lr: 1.000e-03, eta: 2:30:56, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.3148 2023-01-01 00:36:52,836 - mmcls - INFO - Epoch [80][2900/5005] lr: 1.000e-03, eta: 2:30:48, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2886 2023-01-01 00:37:01,609 - mmcls - INFO - Epoch [80][3000/5005] lr: 1.000e-03, eta: 2:30:39, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3011 2023-01-01 00:37:10,160 - mmcls - INFO - Epoch [80][3100/5005] lr: 1.000e-03, eta: 2:30:30, time: 0.086, data_time: 0.027, memory: 1224, loss: 1.3041 2023-01-01 00:37:19,292 - mmcls - INFO - Epoch [80][3200/5005] lr: 1.000e-03, eta: 2:30:21, time: 0.091, data_time: 0.034, memory: 1224, loss: 1.3308 2023-01-01 00:37:28,082 - mmcls - INFO - Epoch [80][3300/5005] lr: 1.000e-03, eta: 2:30:12, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2721 2023-01-01 00:37:36,724 - mmcls - INFO - Epoch [80][3400/5005] lr: 1.000e-03, eta: 2:30:03, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2986 2023-01-01 00:37:45,469 - mmcls - INFO - Epoch [80][3500/5005] lr: 1.000e-03, eta: 2:29:54, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.3231 2023-01-01 00:37:54,176 - mmcls - INFO - Epoch [80][3600/5005] lr: 1.000e-03, eta: 2:29:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.3109 2023-01-01 00:38:02,724 - mmcls - INFO - Epoch [80][3700/5005] lr: 1.000e-03, eta: 2:29:36, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.3084 2023-01-01 00:38:11,401 - mmcls - INFO - Epoch [80][3800/5005] lr: 1.000e-03, eta: 2:29:28, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3036 2023-01-01 00:38:20,134 - mmcls - INFO - Epoch [80][3900/5005] lr: 1.000e-03, eta: 2:29:19, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2956 2023-01-01 00:38:29,504 - mmcls - INFO - Epoch [80][4000/5005] lr: 1.000e-03, eta: 2:29:10, time: 0.094, data_time: 0.004, memory: 1224, loss: 1.3044 2023-01-01 00:38:38,352 - mmcls - INFO - Epoch [80][4100/5005] lr: 1.000e-03, eta: 2:29:01, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.2895 2023-01-01 00:38:46,848 - mmcls - INFO - Epoch [80][4200/5005] lr: 1.000e-03, eta: 2:28:52, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.3085 2023-01-01 00:38:55,898 - mmcls - INFO - Epoch [80][4300/5005] lr: 1.000e-03, eta: 2:28:43, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3175 2023-01-01 00:39:04,955 - mmcls - INFO - Epoch [80][4400/5005] lr: 1.000e-03, eta: 2:28:35, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.3032 2023-01-01 00:39:13,758 - mmcls - INFO - Epoch [80][4500/5005] lr: 1.000e-03, eta: 2:28:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2877 2023-01-01 00:39:22,721 - mmcls - INFO - Epoch [80][4600/5005] lr: 1.000e-03, eta: 2:28:17, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2962 2023-01-01 00:39:31,575 - mmcls - INFO - Epoch [80][4700/5005] lr: 1.000e-03, eta: 2:28:08, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.3089 2023-01-01 00:39:40,527 - mmcls - INFO - Epoch [80][4800/5005] lr: 1.000e-03, eta: 2:27:59, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3244 2023-01-01 00:39:49,564 - mmcls - INFO - Epoch [80][4900/5005] lr: 1.000e-03, eta: 2:27:50, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2871 2023-01-01 00:39:58,264 - mmcls - INFO - Epoch [80][5000/5005] lr: 1.000e-03, eta: 2:27:41, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3107 2023-01-01 00:40:21,095 - mmcls - INFO - Epoch(val) [80][196] accuracy_top-1: 70.8120, accuracy_top-5: 89.8560 2023-01-01 00:40:31,956 - mmcls - INFO - Epoch [81][100/5005] lr: 1.000e-03, eta: 2:27:33, time: 0.108, data_time: 0.050, memory: 1224, loss: 1.2983 2023-01-01 00:40:41,106 - mmcls - INFO - Epoch [81][200/5005] lr: 1.000e-03, eta: 2:27:24, time: 0.091, data_time: 0.037, memory: 1224, loss: 1.2828 2023-01-01 00:40:50,266 - mmcls - INFO - Epoch [81][300/5005] lr: 1.000e-03, eta: 2:27:15, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.2888 2023-01-01 00:40:59,180 - mmcls - INFO - Epoch [81][400/5005] lr: 1.000e-03, eta: 2:27:06, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.3094 2023-01-01 00:41:08,110 - mmcls - INFO - Epoch [81][500/5005] lr: 1.000e-03, eta: 2:26:57, time: 0.089, data_time: 0.036, memory: 1224, loss: 1.2818 2023-01-01 00:41:16,933 - mmcls - INFO - Epoch [81][600/5005] lr: 1.000e-03, eta: 2:26:48, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.2824 2023-01-01 00:41:25,583 - mmcls - INFO - Epoch [81][700/5005] lr: 1.000e-03, eta: 2:26:40, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.2877 2023-01-01 00:41:34,524 - mmcls - INFO - Epoch [81][800/5005] lr: 1.000e-03, eta: 2:26:31, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.2852 2023-01-01 00:41:43,393 - mmcls - INFO - Epoch [81][900/5005] lr: 1.000e-03, eta: 2:26:22, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.3032 2023-01-01 00:41:52,158 - mmcls - INFO - Epoch [81][1000/5005] lr: 1.000e-03, eta: 2:26:13, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2860 2023-01-01 00:42:01,046 - mmcls - INFO - Epoch [81][1100/5005] lr: 1.000e-03, eta: 2:26:04, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2938 2023-01-01 00:42:10,104 - mmcls - INFO - Epoch [81][1200/5005] lr: 1.000e-03, eta: 2:25:55, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2951 2023-01-01 00:42:18,954 - mmcls - INFO - Epoch [81][1300/5005] lr: 1.000e-03, eta: 2:25:47, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2849 2023-01-01 00:42:27,734 - mmcls - INFO - Epoch [81][1400/5005] lr: 1.000e-03, eta: 2:25:38, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2818 2023-01-01 00:42:36,584 - mmcls - INFO - Epoch [81][1500/5005] lr: 1.000e-03, eta: 2:25:29, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3066 2023-01-01 00:42:45,426 - mmcls - INFO - Epoch [81][1600/5005] lr: 1.000e-03, eta: 2:25:20, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2743 2023-01-01 00:42:54,229 - mmcls - INFO - Epoch [81][1700/5005] lr: 1.000e-03, eta: 2:25:11, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.2878 2023-01-01 00:43:02,900 - mmcls - INFO - Epoch [81][1800/5005] lr: 1.000e-03, eta: 2:25:02, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.3188 2023-01-01 00:43:11,704 - mmcls - INFO - Epoch [81][1900/5005] lr: 1.000e-03, eta: 2:24:53, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2842 2023-01-01 00:43:20,364 - mmcls - INFO - Epoch [81][2000/5005] lr: 1.000e-03, eta: 2:24:44, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2795 2023-01-01 00:43:29,254 - mmcls - INFO - Epoch [81][2100/5005] lr: 1.000e-03, eta: 2:24:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2781 2023-01-01 00:43:38,098 - mmcls - INFO - Epoch [81][2200/5005] lr: 1.000e-03, eta: 2:24:27, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3047 2023-01-01 00:43:46,832 - mmcls - INFO - Epoch [81][2300/5005] lr: 1.000e-03, eta: 2:24:18, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2829 2023-01-01 00:43:55,584 - mmcls - INFO - Epoch [81][2400/5005] lr: 1.000e-03, eta: 2:24:09, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.3008 2023-01-01 00:44:04,048 - mmcls - INFO - Epoch [81][2500/5005] lr: 1.000e-03, eta: 2:24:00, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.2849 2023-01-01 00:44:12,760 - mmcls - INFO - Epoch [81][2600/5005] lr: 1.000e-03, eta: 2:23:51, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.2912 2023-01-01 00:44:21,473 - mmcls - INFO - Epoch [81][2700/5005] lr: 1.000e-03, eta: 2:23:42, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2861 2023-01-01 00:44:30,274 - mmcls - INFO - Epoch [81][2800/5005] lr: 1.000e-03, eta: 2:23:33, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2875 2023-01-01 00:44:39,169 - mmcls - INFO - Epoch [81][2900/5005] lr: 1.000e-03, eta: 2:23:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2989 2023-01-01 00:44:47,808 - mmcls - INFO - Epoch [81][3000/5005] lr: 1.000e-03, eta: 2:23:16, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.2879 2023-01-01 00:44:56,503 - mmcls - INFO - Epoch [81][3100/5005] lr: 1.000e-03, eta: 2:23:07, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.3019 2023-01-01 00:45:05,408 - mmcls - INFO - Epoch [81][3200/5005] lr: 1.000e-03, eta: 2:22:58, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.2877 2023-01-01 00:45:14,175 - mmcls - INFO - Epoch [81][3300/5005] lr: 1.000e-03, eta: 2:22:49, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.2807 2023-01-01 00:45:23,626 - mmcls - INFO - Epoch [81][3400/5005] lr: 1.000e-03, eta: 2:22:40, time: 0.094, data_time: 0.031, memory: 1224, loss: 1.2908 2023-01-01 00:45:32,588 - mmcls - INFO - Epoch [81][3500/5005] lr: 1.000e-03, eta: 2:22:31, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.3014 2023-01-01 00:45:41,258 - mmcls - INFO - Epoch [81][3600/5005] lr: 1.000e-03, eta: 2:22:23, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2978 2023-01-01 00:45:50,165 - mmcls - INFO - Epoch [81][3700/5005] lr: 1.000e-03, eta: 2:22:14, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2921 2023-01-01 00:45:59,078 - mmcls - INFO - Epoch [81][3800/5005] lr: 1.000e-03, eta: 2:22:05, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2983 2023-01-01 00:46:07,920 - mmcls - INFO - Epoch [81][3900/5005] lr: 1.000e-03, eta: 2:21:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2900 2023-01-01 00:46:17,024 - mmcls - INFO - Epoch [81][4000/5005] lr: 1.000e-03, eta: 2:21:47, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2788 2023-01-01 00:46:25,806 - mmcls - INFO - Epoch [81][4100/5005] lr: 1.000e-03, eta: 2:21:38, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2878 2023-01-01 00:46:34,773 - mmcls - INFO - Epoch [81][4200/5005] lr: 1.000e-03, eta: 2:21:30, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.3129 2023-01-01 00:46:43,551 - mmcls - INFO - Epoch [81][4300/5005] lr: 1.000e-03, eta: 2:21:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2799 2023-01-01 00:46:52,349 - mmcls - INFO - Epoch [81][4400/5005] lr: 1.000e-03, eta: 2:21:12, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2992 2023-01-01 00:47:01,039 - mmcls - INFO - Epoch [81][4500/5005] lr: 1.000e-03, eta: 2:21:03, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2932 2023-01-01 00:47:09,840 - mmcls - INFO - Epoch [81][4600/5005] lr: 1.000e-03, eta: 2:20:54, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2722 2023-01-01 00:47:18,574 - mmcls - INFO - Epoch [81][4700/5005] lr: 1.000e-03, eta: 2:20:45, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2962 2023-01-01 00:47:27,093 - mmcls - INFO - Epoch [81][4800/5005] lr: 1.000e-03, eta: 2:20:36, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.3120 2023-01-01 00:47:36,177 - mmcls - INFO - Epoch [81][4900/5005] lr: 1.000e-03, eta: 2:20:27, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.3039 2023-01-01 00:47:44,700 - mmcls - INFO - Epoch [81][5000/5005] lr: 1.000e-03, eta: 2:20:19, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.3071 2023-01-01 00:48:07,370 - mmcls - INFO - Epoch(val) [81][196] accuracy_top-1: 70.9600, accuracy_top-5: 89.8140 2023-01-01 00:48:18,282 - mmcls - INFO - Epoch [82][100/5005] lr: 1.000e-03, eta: 2:20:10, time: 0.109, data_time: 0.041, memory: 1224, loss: 1.2730 2023-01-01 00:48:27,046 - mmcls - INFO - Epoch [82][200/5005] lr: 1.000e-03, eta: 2:20:01, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.3126 2023-01-01 00:48:36,110 - mmcls - INFO - Epoch [82][300/5005] lr: 1.000e-03, eta: 2:19:52, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2974 2023-01-01 00:48:44,751 - mmcls - INFO - Epoch [82][400/5005] lr: 1.000e-03, eta: 2:19:43, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2873 2023-01-01 00:48:53,480 - mmcls - INFO - Epoch [82][500/5005] lr: 1.000e-03, eta: 2:19:34, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2987 2023-01-01 00:49:02,564 - mmcls - INFO - Epoch [82][600/5005] lr: 1.000e-03, eta: 2:19:25, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.3060 2023-01-01 00:49:11,246 - mmcls - INFO - Epoch [82][700/5005] lr: 1.000e-03, eta: 2:19:16, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2958 2023-01-01 00:49:20,046 - mmcls - INFO - Epoch [82][800/5005] lr: 1.000e-03, eta: 2:19:08, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.3001 2023-01-01 00:49:28,765 - mmcls - INFO - Epoch [82][900/5005] lr: 1.000e-03, eta: 2:18:59, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2774 2023-01-01 00:49:37,962 - mmcls - INFO - Epoch [82][1000/5005] lr: 1.000e-03, eta: 2:18:50, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.2871 2023-01-01 00:49:46,634 - mmcls - INFO - Epoch [82][1100/5005] lr: 1.000e-03, eta: 2:18:41, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.2862 2023-01-01 00:49:55,435 - mmcls - INFO - Epoch [82][1200/5005] lr: 1.000e-03, eta: 2:18:32, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2895 2023-01-01 00:50:04,458 - mmcls - INFO - Epoch [82][1300/5005] lr: 1.000e-03, eta: 2:18:23, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2945 2023-01-01 00:50:13,415 - mmcls - INFO - Epoch [82][1400/5005] lr: 1.000e-03, eta: 2:18:15, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.3015 2023-01-01 00:50:21,993 - mmcls - INFO - Epoch [82][1500/5005] lr: 1.000e-03, eta: 2:18:06, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2938 2023-01-01 00:50:30,621 - mmcls - INFO - Epoch [82][1600/5005] lr: 1.000e-03, eta: 2:17:57, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2721 2023-01-01 00:50:39,441 - mmcls - INFO - Epoch [82][1700/5005] lr: 1.000e-03, eta: 2:17:48, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2834 2023-01-01 00:50:48,453 - mmcls - INFO - Epoch [82][1800/5005] lr: 1.000e-03, eta: 2:17:39, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.2878 2023-01-01 00:50:57,169 - mmcls - INFO - Epoch [82][1900/5005] lr: 1.000e-03, eta: 2:17:30, time: 0.087, data_time: 0.030, memory: 1224, loss: 1.3010 2023-01-01 00:51:06,325 - mmcls - INFO - Epoch [82][2000/5005] lr: 1.000e-03, eta: 2:17:21, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.2895 2023-01-01 00:51:14,732 - mmcls - INFO - Epoch [82][2100/5005] lr: 1.000e-03, eta: 2:17:12, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.2899 2023-01-01 00:51:23,709 - mmcls - INFO - Epoch [82][2200/5005] lr: 1.000e-03, eta: 2:17:04, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2963 2023-01-01 00:51:32,646 - mmcls - INFO - Epoch [82][2300/5005] lr: 1.000e-03, eta: 2:16:55, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2974 2023-01-01 00:51:41,493 - mmcls - INFO - Epoch [82][2400/5005] lr: 1.000e-03, eta: 2:16:46, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2739 2023-01-01 00:51:50,487 - mmcls - INFO - Epoch [82][2500/5005] lr: 1.000e-03, eta: 2:16:37, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3045 2023-01-01 00:51:59,052 - mmcls - INFO - Epoch [82][2600/5005] lr: 1.000e-03, eta: 2:16:28, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2940 2023-01-01 00:52:07,664 - mmcls - INFO - Epoch [82][2700/5005] lr: 1.000e-03, eta: 2:16:19, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3043 2023-01-01 00:52:16,546 - mmcls - INFO - Epoch [82][2800/5005] lr: 1.000e-03, eta: 2:16:10, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.3021 2023-01-01 00:52:25,205 - mmcls - INFO - Epoch [82][2900/5005] lr: 1.000e-03, eta: 2:16:01, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2918 2023-01-01 00:52:34,045 - mmcls - INFO - Epoch [82][3000/5005] lr: 1.000e-03, eta: 2:15:53, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2677 2023-01-01 00:52:42,702 - mmcls - INFO - Epoch [82][3100/5005] lr: 1.000e-03, eta: 2:15:44, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2935 2023-01-01 00:52:51,446 - mmcls - INFO - Epoch [82][3200/5005] lr: 1.000e-03, eta: 2:15:35, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2918 2023-01-01 00:53:00,300 - mmcls - INFO - Epoch [82][3300/5005] lr: 1.000e-03, eta: 2:15:26, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2744 2023-01-01 00:53:09,090 - mmcls - INFO - Epoch [82][3400/5005] lr: 1.000e-03, eta: 2:15:17, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2925 2023-01-01 00:53:17,698 - mmcls - INFO - Epoch [82][3500/5005] lr: 1.000e-03, eta: 2:15:08, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.3016 2023-01-01 00:53:26,574 - mmcls - INFO - Epoch [82][3600/5005] lr: 1.000e-03, eta: 2:14:59, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.2831 2023-01-01 00:53:35,156 - mmcls - INFO - Epoch [82][3700/5005] lr: 1.000e-03, eta: 2:14:50, time: 0.086, data_time: 0.031, memory: 1224, loss: 1.3006 2023-01-01 00:53:44,091 - mmcls - INFO - Epoch [82][3800/5005] lr: 1.000e-03, eta: 2:14:42, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.2782 2023-01-01 00:53:52,747 - mmcls - INFO - Epoch [82][3900/5005] lr: 1.000e-03, eta: 2:14:33, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2758 2023-01-01 00:54:01,491 - mmcls - INFO - Epoch [82][4000/5005] lr: 1.000e-03, eta: 2:14:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2847 2023-01-01 00:54:10,363 - mmcls - INFO - Epoch [82][4100/5005] lr: 1.000e-03, eta: 2:14:15, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2831 2023-01-01 00:54:19,080 - mmcls - INFO - Epoch [82][4200/5005] lr: 1.000e-03, eta: 2:14:06, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2811 2023-01-01 00:54:28,035 - mmcls - INFO - Epoch [82][4300/5005] lr: 1.000e-03, eta: 2:13:57, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3008 2023-01-01 00:54:37,130 - mmcls - INFO - Epoch [82][4400/5005] lr: 1.000e-03, eta: 2:13:48, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2813 2023-01-01 00:54:46,159 - mmcls - INFO - Epoch [82][4500/5005] lr: 1.000e-03, eta: 2:13:40, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3124 2023-01-01 00:54:55,005 - mmcls - INFO - Epoch [82][4600/5005] lr: 1.000e-03, eta: 2:13:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2967 2023-01-01 00:55:03,787 - mmcls - INFO - Epoch [82][4700/5005] lr: 1.000e-03, eta: 2:13:22, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.3040 2023-01-01 00:55:12,524 - mmcls - INFO - Epoch [82][4800/5005] lr: 1.000e-03, eta: 2:13:13, time: 0.087, data_time: 0.010, memory: 1224, loss: 1.3220 2023-01-01 00:55:21,494 - mmcls - INFO - Epoch [82][4900/5005] lr: 1.000e-03, eta: 2:13:04, time: 0.090, data_time: 0.020, memory: 1224, loss: 1.3056 2023-01-01 00:55:30,468 - mmcls - INFO - Epoch [82][5000/5005] lr: 1.000e-03, eta: 2:12:55, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.3092 2023-01-01 00:55:53,188 - mmcls - INFO - Epoch(val) [82][196] accuracy_top-1: 71.0820, accuracy_top-5: 89.9260 2023-01-01 00:56:04,320 - mmcls - INFO - Epoch [83][100/5005] lr: 1.000e-03, eta: 2:12:47, time: 0.111, data_time: 0.057, memory: 1224, loss: 1.2851 2023-01-01 00:56:12,883 - mmcls - INFO - Epoch [83][200/5005] lr: 1.000e-03, eta: 2:12:38, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3035 2023-01-01 00:56:21,822 - mmcls - INFO - Epoch [83][300/5005] lr: 1.000e-03, eta: 2:12:29, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2717 2023-01-01 00:56:30,567 - mmcls - INFO - Epoch [83][400/5005] lr: 1.000e-03, eta: 2:12:20, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2804 2023-01-01 00:56:39,197 - mmcls - INFO - Epoch [83][500/5005] lr: 1.000e-03, eta: 2:12:11, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2509 2023-01-01 00:56:48,269 - mmcls - INFO - Epoch [83][600/5005] lr: 1.000e-03, eta: 2:12:02, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2801 2023-01-01 00:56:56,860 - mmcls - INFO - Epoch [83][700/5005] lr: 1.000e-03, eta: 2:11:53, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.2589 2023-01-01 00:57:05,760 - mmcls - INFO - Epoch [83][800/5005] lr: 1.000e-03, eta: 2:11:44, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2735 2023-01-01 00:57:14,551 - mmcls - INFO - Epoch [83][900/5005] lr: 1.000e-03, eta: 2:11:36, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2896 2023-01-01 00:57:23,259 - mmcls - INFO - Epoch [83][1000/5005] lr: 1.000e-03, eta: 2:11:27, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2804 2023-01-01 00:57:32,178 - mmcls - INFO - Epoch [83][1100/5005] lr: 1.000e-03, eta: 2:11:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2860 2023-01-01 00:57:41,331 - mmcls - INFO - Epoch [83][1200/5005] lr: 1.000e-03, eta: 2:11:09, time: 0.092, data_time: 0.020, memory: 1224, loss: 1.3012 2023-01-01 00:57:50,041 - mmcls - INFO - Epoch [83][1300/5005] lr: 1.000e-03, eta: 2:11:00, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2683 2023-01-01 00:57:59,026 - mmcls - INFO - Epoch [83][1400/5005] lr: 1.000e-03, eta: 2:10:51, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.2939 2023-01-01 00:58:07,798 - mmcls - INFO - Epoch [83][1500/5005] lr: 1.000e-03, eta: 2:10:42, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2981 2023-01-01 00:58:16,733 - mmcls - INFO - Epoch [83][1600/5005] lr: 1.000e-03, eta: 2:10:34, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2792 2023-01-01 00:58:25,634 - mmcls - INFO - Epoch [83][1700/5005] lr: 1.000e-03, eta: 2:10:25, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2844 2023-01-01 00:58:34,315 - mmcls - INFO - Epoch [83][1800/5005] lr: 1.000e-03, eta: 2:10:16, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2756 2023-01-01 00:58:43,168 - mmcls - INFO - Epoch [83][1900/5005] lr: 1.000e-03, eta: 2:10:07, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2809 2023-01-01 00:58:52,015 - mmcls - INFO - Epoch [83][2000/5005] lr: 1.000e-03, eta: 2:09:58, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2690 2023-01-01 00:59:00,843 - mmcls - INFO - Epoch [83][2100/5005] lr: 1.000e-03, eta: 2:09:49, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2972 2023-01-01 00:59:09,379 - mmcls - INFO - Epoch [83][2200/5005] lr: 1.000e-03, eta: 2:09:40, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.3035 2023-01-01 00:59:18,214 - mmcls - INFO - Epoch [83][2300/5005] lr: 1.000e-03, eta: 2:09:32, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.2941 2023-01-01 00:59:27,434 - mmcls - INFO - Epoch [83][2400/5005] lr: 1.000e-03, eta: 2:09:23, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.2883 2023-01-01 00:59:35,898 - mmcls - INFO - Epoch [83][2500/5005] lr: 1.000e-03, eta: 2:09:14, time: 0.085, data_time: 0.030, memory: 1224, loss: 1.2925 2023-01-01 00:59:44,623 - mmcls - INFO - Epoch [83][2600/5005] lr: 1.000e-03, eta: 2:09:05, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.2862 2023-01-01 00:59:53,211 - mmcls - INFO - Epoch [83][2700/5005] lr: 1.000e-03, eta: 2:08:56, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.3044 2023-01-01 01:00:01,698 - mmcls - INFO - Epoch [83][2800/5005] lr: 1.000e-03, eta: 2:08:47, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.2890 2023-01-01 01:00:10,718 - mmcls - INFO - Epoch [83][2900/5005] lr: 1.000e-03, eta: 2:08:38, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2819 2023-01-01 01:00:19,750 - mmcls - INFO - Epoch [83][3000/5005] lr: 1.000e-03, eta: 2:08:29, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.3019 2023-01-01 01:00:28,355 - mmcls - INFO - Epoch [83][3100/5005] lr: 1.000e-03, eta: 2:08:21, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.2893 2023-01-01 01:00:37,125 - mmcls - INFO - Epoch [83][3200/5005] lr: 1.000e-03, eta: 2:08:12, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2851 2023-01-01 01:00:45,913 - mmcls - INFO - Epoch [83][3300/5005] lr: 1.000e-03, eta: 2:08:03, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.3029 2023-01-01 01:00:55,026 - mmcls - INFO - Epoch [83][3400/5005] lr: 1.000e-03, eta: 2:07:54, time: 0.091, data_time: 0.030, memory: 1224, loss: 1.3108 2023-01-01 01:01:03,700 - mmcls - INFO - Epoch [83][3500/5005] lr: 1.000e-03, eta: 2:07:45, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.3104 2023-01-01 01:01:12,595 - mmcls - INFO - Epoch [83][3600/5005] lr: 1.000e-03, eta: 2:07:36, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2883 2023-01-01 01:01:21,561 - mmcls - INFO - Epoch [83][3700/5005] lr: 1.000e-03, eta: 2:07:27, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.3123 2023-01-01 01:01:30,172 - mmcls - INFO - Epoch [83][3800/5005] lr: 1.000e-03, eta: 2:07:19, time: 0.086, data_time: 0.030, memory: 1224, loss: 1.2895 2023-01-01 01:01:38,777 - mmcls - INFO - Epoch [83][3900/5005] lr: 1.000e-03, eta: 2:07:10, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.2712 2023-01-01 01:01:47,423 - mmcls - INFO - Epoch [83][4000/5005] lr: 1.000e-03, eta: 2:07:01, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2919 2023-01-01 01:01:56,302 - mmcls - INFO - Epoch [83][4100/5005] lr: 1.000e-03, eta: 2:06:52, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.2910 2023-01-01 01:02:05,056 - mmcls - INFO - Epoch [83][4200/5005] lr: 1.000e-03, eta: 2:06:43, time: 0.088, data_time: 0.025, memory: 1224, loss: 1.2830 2023-01-01 01:02:13,685 - mmcls - INFO - Epoch [83][4300/5005] lr: 1.000e-03, eta: 2:06:34, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.3035 2023-01-01 01:02:22,293 - mmcls - INFO - Epoch [83][4400/5005] lr: 1.000e-03, eta: 2:06:25, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.2990 2023-01-01 01:02:31,288 - mmcls - INFO - Epoch [83][4500/5005] lr: 1.000e-03, eta: 2:06:16, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.2826 2023-01-01 01:02:40,032 - mmcls - INFO - Epoch [83][4600/5005] lr: 1.000e-03, eta: 2:06:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2977 2023-01-01 01:02:48,723 - mmcls - INFO - Epoch [83][4700/5005] lr: 1.000e-03, eta: 2:05:59, time: 0.087, data_time: 0.034, memory: 1224, loss: 1.2798 2023-01-01 01:02:57,779 - mmcls - INFO - Epoch [83][4800/5005] lr: 1.000e-03, eta: 2:05:50, time: 0.091, data_time: 0.020, memory: 1224, loss: 1.3106 2023-01-01 01:03:06,546 - mmcls - INFO - Epoch [83][4900/5005] lr: 1.000e-03, eta: 2:05:41, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2874 2023-01-01 01:03:15,471 - mmcls - INFO - Epoch [83][5000/5005] lr: 1.000e-03, eta: 2:05:32, time: 0.089, data_time: 0.019, memory: 1224, loss: 1.2951 2023-01-01 01:03:38,052 - mmcls - INFO - Epoch(val) [83][196] accuracy_top-1: 71.0400, accuracy_top-5: 89.8040 2023-01-01 01:03:49,270 - mmcls - INFO - Epoch [84][100/5005] lr: 1.000e-03, eta: 2:05:23, time: 0.112, data_time: 0.033, memory: 1224, loss: 1.2824 2023-01-01 01:03:58,133 - mmcls - INFO - Epoch [84][200/5005] lr: 1.000e-03, eta: 2:05:14, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.2707 2023-01-01 01:04:06,954 - mmcls - INFO - Epoch [84][300/5005] lr: 1.000e-03, eta: 2:05:06, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2750 2023-01-01 01:04:15,532 - mmcls - INFO - Epoch [84][400/5005] lr: 1.000e-03, eta: 2:04:57, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2698 2023-01-01 01:04:24,579 - mmcls - INFO - Epoch [84][500/5005] lr: 1.000e-03, eta: 2:04:48, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.2743 2023-01-01 01:04:33,604 - mmcls - INFO - Epoch [84][600/5005] lr: 1.000e-03, eta: 2:04:39, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2959 2023-01-01 01:04:42,630 - mmcls - INFO - Epoch [84][700/5005] lr: 1.000e-03, eta: 2:04:30, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.2790 2023-01-01 01:04:51,133 - mmcls - INFO - Epoch [84][800/5005] lr: 1.000e-03, eta: 2:04:21, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.2762 2023-01-01 01:04:59,962 - mmcls - INFO - Epoch [84][900/5005] lr: 1.000e-03, eta: 2:04:12, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.2684 2023-01-01 01:05:08,513 - mmcls - INFO - Epoch [84][1000/5005] lr: 1.000e-03, eta: 2:04:03, time: 0.085, data_time: 0.015, memory: 1224, loss: 1.2734 2023-01-01 01:05:17,174 - mmcls - INFO - Epoch [84][1100/5005] lr: 1.000e-03, eta: 2:03:55, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2869 2023-01-01 01:05:26,246 - mmcls - INFO - Epoch [84][1200/5005] lr: 1.000e-03, eta: 2:03:46, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.2898 2023-01-01 01:05:35,179 - mmcls - INFO - Epoch [84][1300/5005] lr: 1.000e-03, eta: 2:03:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2870 2023-01-01 01:05:44,339 - mmcls - INFO - Epoch [84][1400/5005] lr: 1.000e-03, eta: 2:03:28, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.2938 2023-01-01 01:05:53,042 - mmcls - INFO - Epoch [84][1500/5005] lr: 1.000e-03, eta: 2:03:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2607 2023-01-01 01:06:01,946 - mmcls - INFO - Epoch [84][1600/5005] lr: 1.000e-03, eta: 2:03:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2794 2023-01-01 01:06:10,834 - mmcls - INFO - Epoch [84][1700/5005] lr: 1.000e-03, eta: 2:03:02, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2906 2023-01-01 01:06:19,796 - mmcls - INFO - Epoch [84][1800/5005] lr: 1.000e-03, eta: 2:02:53, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2810 2023-01-01 01:06:28,669 - mmcls - INFO - Epoch [84][1900/5005] lr: 1.000e-03, eta: 2:02:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2835 2023-01-01 01:06:37,518 - mmcls - INFO - Epoch [84][2000/5005] lr: 1.000e-03, eta: 2:02:35, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2598 2023-01-01 01:06:46,312 - mmcls - INFO - Epoch [84][2100/5005] lr: 1.000e-03, eta: 2:02:26, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2724 2023-01-01 01:06:55,185 - mmcls - INFO - Epoch [84][2200/5005] lr: 1.000e-03, eta: 2:02:17, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2808 2023-01-01 01:07:03,868 - mmcls - INFO - Epoch [84][2300/5005] lr: 1.000e-03, eta: 2:02:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2836 2023-01-01 01:07:12,517 - mmcls - INFO - Epoch [84][2400/5005] lr: 1.000e-03, eta: 2:02:00, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2846 2023-01-01 01:07:21,242 - mmcls - INFO - Epoch [84][2500/5005] lr: 1.000e-03, eta: 2:01:51, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.2789 2023-01-01 01:07:30,008 - mmcls - INFO - Epoch [84][2600/5005] lr: 1.000e-03, eta: 2:01:42, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2974 2023-01-01 01:07:38,991 - mmcls - INFO - Epoch [84][2700/5005] lr: 1.000e-03, eta: 2:01:33, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2999 2023-01-01 01:07:47,880 - mmcls - INFO - Epoch [84][2800/5005] lr: 1.000e-03, eta: 2:01:24, time: 0.089, data_time: 0.028, memory: 1224, loss: 1.2779 2023-01-01 01:07:56,754 - mmcls - INFO - Epoch [84][2900/5005] lr: 1.000e-03, eta: 2:01:15, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2900 2023-01-01 01:08:05,608 - mmcls - INFO - Epoch [84][3000/5005] lr: 1.000e-03, eta: 2:01:06, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2846 2023-01-01 01:08:14,697 - mmcls - INFO - Epoch [84][3100/5005] lr: 1.000e-03, eta: 2:00:58, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.3015 2023-01-01 01:08:23,368 - mmcls - INFO - Epoch [84][3200/5005] lr: 1.000e-03, eta: 2:00:49, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.3140 2023-01-01 01:08:32,310 - mmcls - INFO - Epoch [84][3300/5005] lr: 1.000e-03, eta: 2:00:40, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2662 2023-01-01 01:08:41,188 - mmcls - INFO - Epoch [84][3400/5005] lr: 1.000e-03, eta: 2:00:31, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.3041 2023-01-01 01:08:50,037 - mmcls - INFO - Epoch [84][3500/5005] lr: 1.000e-03, eta: 2:00:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2718 2023-01-01 01:08:58,771 - mmcls - INFO - Epoch [84][3600/5005] lr: 1.000e-03, eta: 2:00:13, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2778 2023-01-01 01:09:07,450 - mmcls - INFO - Epoch [84][3700/5005] lr: 1.000e-03, eta: 2:00:04, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.3163 2023-01-01 01:09:16,086 - mmcls - INFO - Epoch [84][3800/5005] lr: 1.000e-03, eta: 1:59:56, time: 0.086, data_time: 0.014, memory: 1224, loss: 1.3188 2023-01-01 01:09:25,067 - mmcls - INFO - Epoch [84][3900/5005] lr: 1.000e-03, eta: 1:59:47, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2827 2023-01-01 01:09:34,098 - mmcls - INFO - Epoch [84][4000/5005] lr: 1.000e-03, eta: 1:59:38, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.2985 2023-01-01 01:09:42,716 - mmcls - INFO - Epoch [84][4100/5005] lr: 1.000e-03, eta: 1:59:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2982 2023-01-01 01:09:51,665 - mmcls - INFO - Epoch [84][4200/5005] lr: 1.000e-03, eta: 1:59:20, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2778 2023-01-01 01:10:00,437 - mmcls - INFO - Epoch [84][4300/5005] lr: 1.000e-03, eta: 1:59:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.3051 2023-01-01 01:10:08,971 - mmcls - INFO - Epoch [84][4400/5005] lr: 1.000e-03, eta: 1:59:02, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2918 2023-01-01 01:10:17,615 - mmcls - INFO - Epoch [84][4500/5005] lr: 1.000e-03, eta: 1:58:53, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.2959 2023-01-01 01:10:26,233 - mmcls - INFO - Epoch [84][4600/5005] lr: 1.000e-03, eta: 1:58:45, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2955 2023-01-01 01:10:35,010 - mmcls - INFO - Epoch [84][4700/5005] lr: 1.000e-03, eta: 1:58:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2938 2023-01-01 01:10:43,818 - mmcls - INFO - Epoch [84][4800/5005] lr: 1.000e-03, eta: 1:58:27, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.3134 2023-01-01 01:10:52,542 - mmcls - INFO - Epoch [84][4900/5005] lr: 1.000e-03, eta: 1:58:18, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2823 2023-01-01 01:11:01,259 - mmcls - INFO - Epoch [84][5000/5005] lr: 1.000e-03, eta: 1:58:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.3082 2023-01-01 01:11:24,002 - mmcls - INFO - Epoch(val) [84][196] accuracy_top-1: 70.9780, accuracy_top-5: 89.8520 2023-01-01 01:11:35,364 - mmcls - INFO - Epoch [85][100/5005] lr: 1.000e-03, eta: 1:58:00, time: 0.113, data_time: 0.044, memory: 1224, loss: 1.2856 2023-01-01 01:11:44,203 - mmcls - INFO - Epoch [85][200/5005] lr: 1.000e-03, eta: 1:57:51, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.2766 2023-01-01 01:11:53,030 - mmcls - INFO - Epoch [85][300/5005] lr: 1.000e-03, eta: 1:57:42, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2819 2023-01-01 01:12:01,846 - mmcls - INFO - Epoch [85][400/5005] lr: 1.000e-03, eta: 1:57:34, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.2694 2023-01-01 01:12:10,509 - mmcls - INFO - Epoch [85][500/5005] lr: 1.000e-03, eta: 1:57:25, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2782 2023-01-01 01:12:19,799 - mmcls - INFO - Epoch [85][600/5005] lr: 1.000e-03, eta: 1:57:16, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.2691 2023-01-01 01:12:28,396 - mmcls - INFO - Epoch [85][700/5005] lr: 1.000e-03, eta: 1:57:07, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.3021 2023-01-01 01:12:37,345 - mmcls - INFO - Epoch [85][800/5005] lr: 1.000e-03, eta: 1:56:58, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2866 2023-01-01 01:12:46,102 - mmcls - INFO - Epoch [85][900/5005] lr: 1.000e-03, eta: 1:56:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2738 2023-01-01 01:12:54,805 - mmcls - INFO - Epoch [85][1000/5005] lr: 1.000e-03, eta: 1:56:40, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2733 2023-01-01 01:13:03,815 - mmcls - INFO - Epoch [85][1100/5005] lr: 1.000e-03, eta: 1:56:32, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2930 2023-01-01 01:13:13,042 - mmcls - INFO - Epoch [85][1200/5005] lr: 1.000e-03, eta: 1:56:23, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.2859 2023-01-01 01:13:21,733 - mmcls - INFO - Epoch [85][1300/5005] lr: 1.000e-03, eta: 1:56:14, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2638 2023-01-01 01:13:30,534 - mmcls - INFO - Epoch [85][1400/5005] lr: 1.000e-03, eta: 1:56:05, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2885 2023-01-01 01:13:39,449 - mmcls - INFO - Epoch [85][1500/5005] lr: 1.000e-03, eta: 1:55:56, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2716 2023-01-01 01:13:48,127 - mmcls - INFO - Epoch [85][1600/5005] lr: 1.000e-03, eta: 1:55:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2826 2023-01-01 01:13:57,059 - mmcls - INFO - Epoch [85][1700/5005] lr: 1.000e-03, eta: 1:55:39, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.2855 2023-01-01 01:14:06,106 - mmcls - INFO - Epoch [85][1800/5005] lr: 1.000e-03, eta: 1:55:30, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2940 2023-01-01 01:14:14,917 - mmcls - INFO - Epoch [85][1900/5005] lr: 1.000e-03, eta: 1:55:21, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2825 2023-01-01 01:14:23,539 - mmcls - INFO - Epoch [85][2000/5005] lr: 1.000e-03, eta: 1:55:12, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2754 2023-01-01 01:14:32,402 - mmcls - INFO - Epoch [85][2100/5005] lr: 1.000e-03, eta: 1:55:03, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2850 2023-01-01 01:14:40,945 - mmcls - INFO - Epoch [85][2200/5005] lr: 1.000e-03, eta: 1:54:54, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2802 2023-01-01 01:14:49,981 - mmcls - INFO - Epoch [85][2300/5005] lr: 1.000e-03, eta: 1:54:45, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.2769 2023-01-01 01:14:58,959 - mmcls - INFO - Epoch [85][2400/5005] lr: 1.000e-03, eta: 1:54:37, time: 0.090, data_time: 0.035, memory: 1224, loss: 1.2784 2023-01-01 01:15:07,671 - mmcls - INFO - Epoch [85][2500/5005] lr: 1.000e-03, eta: 1:54:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2938 2023-01-01 01:15:16,607 - mmcls - INFO - Epoch [85][2600/5005] lr: 1.000e-03, eta: 1:54:19, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2875 2023-01-01 01:15:25,313 - mmcls - INFO - Epoch [85][2700/5005] lr: 1.000e-03, eta: 1:54:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2830 2023-01-01 01:15:34,075 - mmcls - INFO - Epoch [85][2800/5005] lr: 1.000e-03, eta: 1:54:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2957 2023-01-01 01:15:42,550 - mmcls - INFO - Epoch [85][2900/5005] lr: 1.000e-03, eta: 1:53:52, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.2799 2023-01-01 01:15:51,452 - mmcls - INFO - Epoch [85][3000/5005] lr: 1.000e-03, eta: 1:53:43, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.3005 2023-01-01 01:16:00,406 - mmcls - INFO - Epoch [85][3100/5005] lr: 1.000e-03, eta: 1:53:34, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.2718 2023-01-01 01:16:09,233 - mmcls - INFO - Epoch [85][3200/5005] lr: 1.000e-03, eta: 1:53:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2734 2023-01-01 01:16:17,954 - mmcls - INFO - Epoch [85][3300/5005] lr: 1.000e-03, eta: 1:53:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2756 2023-01-01 01:16:26,676 - mmcls - INFO - Epoch [85][3400/5005] lr: 1.000e-03, eta: 1:53:08, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2902 2023-01-01 01:16:35,280 - mmcls - INFO - Epoch [85][3500/5005] lr: 1.000e-03, eta: 1:52:59, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2947 2023-01-01 01:16:44,065 - mmcls - INFO - Epoch [85][3600/5005] lr: 1.000e-03, eta: 1:52:50, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.3015 2023-01-01 01:16:53,274 - mmcls - INFO - Epoch [85][3700/5005] lr: 1.000e-03, eta: 1:52:41, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2902 2023-01-01 01:17:01,938 - mmcls - INFO - Epoch [85][3800/5005] lr: 1.000e-03, eta: 1:52:32, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2739 2023-01-01 01:17:11,092 - mmcls - INFO - Epoch [85][3900/5005] lr: 1.000e-03, eta: 1:52:24, time: 0.091, data_time: 0.004, memory: 1224, loss: 1.2858 2023-01-01 01:17:19,962 - mmcls - INFO - Epoch [85][4000/5005] lr: 1.000e-03, eta: 1:52:15, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2845 2023-01-01 01:17:28,658 - mmcls - INFO - Epoch [85][4100/5005] lr: 1.000e-03, eta: 1:52:06, time: 0.087, data_time: 0.009, memory: 1224, loss: 1.2826 2023-01-01 01:17:37,498 - mmcls - INFO - Epoch [85][4200/5005] lr: 1.000e-03, eta: 1:51:57, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2864 2023-01-01 01:17:46,205 - mmcls - INFO - Epoch [85][4300/5005] lr: 1.000e-03, eta: 1:51:48, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.2880 2023-01-01 01:17:55,332 - mmcls - INFO - Epoch [85][4400/5005] lr: 1.000e-03, eta: 1:51:39, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.2827 2023-01-01 01:18:04,179 - mmcls - INFO - Epoch [85][4500/5005] lr: 1.000e-03, eta: 1:51:31, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2744 2023-01-01 01:18:13,256 - mmcls - INFO - Epoch [85][4600/5005] lr: 1.000e-03, eta: 1:51:22, time: 0.091, data_time: 0.009, memory: 1224, loss: 1.2894 2023-01-01 01:18:21,955 - mmcls - INFO - Epoch [85][4700/5005] lr: 1.000e-03, eta: 1:51:13, time: 0.087, data_time: 0.043, memory: 1224, loss: 1.3038 2023-01-01 01:18:30,673 - mmcls - INFO - Epoch [85][4800/5005] lr: 1.000e-03, eta: 1:51:04, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.2985 2023-01-01 01:18:39,599 - mmcls - INFO - Epoch [85][4900/5005] lr: 1.000e-03, eta: 1:50:55, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2717 2023-01-01 01:18:48,104 - mmcls - INFO - Epoch [85][5000/5005] lr: 1.000e-03, eta: 1:50:46, time: 0.085, data_time: 0.011, memory: 1224, loss: 1.2970 2023-01-01 01:19:10,698 - mmcls - INFO - Epoch(val) [85][196] accuracy_top-1: 70.9540, accuracy_top-5: 89.8720 2023-01-01 01:19:21,518 - mmcls - INFO - Epoch [86][100/5005] lr: 1.000e-03, eta: 1:50:37, time: 0.108, data_time: 0.037, memory: 1224, loss: 1.2708 2023-01-01 01:19:30,489 - mmcls - INFO - Epoch [86][200/5005] lr: 1.000e-03, eta: 1:50:28, time: 0.090, data_time: 0.034, memory: 1224, loss: 1.2815 2023-01-01 01:19:39,140 - mmcls - INFO - Epoch [86][300/5005] lr: 1.000e-03, eta: 1:50:19, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2638 2023-01-01 01:19:47,854 - mmcls - INFO - Epoch [86][400/5005] lr: 1.000e-03, eta: 1:50:11, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2629 2023-01-01 01:19:56,661 - mmcls - INFO - Epoch [86][500/5005] lr: 1.000e-03, eta: 1:50:02, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2863 2023-01-01 01:20:05,549 - mmcls - INFO - Epoch [86][600/5005] lr: 1.000e-03, eta: 1:49:53, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2655 2023-01-01 01:20:14,470 - mmcls - INFO - Epoch [86][700/5005] lr: 1.000e-03, eta: 1:49:44, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2810 2023-01-01 01:20:23,340 - mmcls - INFO - Epoch [86][800/5005] lr: 1.000e-03, eta: 1:49:35, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.2767 2023-01-01 01:20:32,114 - mmcls - INFO - Epoch [86][900/5005] lr: 1.000e-03, eta: 1:49:26, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2689 2023-01-01 01:20:41,657 - mmcls - INFO - Epoch [86][1000/5005] lr: 1.000e-03, eta: 1:49:18, time: 0.095, data_time: 0.005, memory: 1224, loss: 1.3059 2023-01-01 01:20:50,431 - mmcls - INFO - Epoch [86][1100/5005] lr: 1.000e-03, eta: 1:49:09, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2729 2023-01-01 01:20:59,229 - mmcls - INFO - Epoch [86][1200/5005] lr: 1.000e-03, eta: 1:49:00, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2617 2023-01-01 01:21:07,813 - mmcls - INFO - Epoch [86][1300/5005] lr: 1.000e-03, eta: 1:48:51, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2662 2023-01-01 01:21:16,371 - mmcls - INFO - Epoch [86][1400/5005] lr: 1.000e-03, eta: 1:48:42, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2783 2023-01-01 01:21:25,096 - mmcls - INFO - Epoch [86][1500/5005] lr: 1.000e-03, eta: 1:48:33, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2595 2023-01-01 01:21:33,864 - mmcls - INFO - Epoch [86][1600/5005] lr: 1.000e-03, eta: 1:48:24, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2882 2023-01-01 01:21:42,628 - mmcls - INFO - Epoch [86][1700/5005] lr: 1.000e-03, eta: 1:48:15, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2813 2023-01-01 01:21:51,579 - mmcls - INFO - Epoch [86][1800/5005] lr: 1.000e-03, eta: 1:48:07, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.2765 2023-01-01 01:22:00,347 - mmcls - INFO - Epoch [86][1900/5005] lr: 1.000e-03, eta: 1:47:58, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2691 2023-01-01 01:22:09,060 - mmcls - INFO - Epoch [86][2000/5005] lr: 1.000e-03, eta: 1:47:49, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2733 2023-01-01 01:22:17,816 - mmcls - INFO - Epoch [86][2100/5005] lr: 1.000e-03, eta: 1:47:40, time: 0.088, data_time: 0.016, memory: 1224, loss: 1.2849 2023-01-01 01:22:26,458 - mmcls - INFO - Epoch [86][2200/5005] lr: 1.000e-03, eta: 1:47:31, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2952 2023-01-01 01:22:35,084 - mmcls - INFO - Epoch [86][2300/5005] lr: 1.000e-03, eta: 1:47:22, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2911 2023-01-01 01:22:43,856 - mmcls - INFO - Epoch [86][2400/5005] lr: 1.000e-03, eta: 1:47:13, time: 0.088, data_time: 0.028, memory: 1224, loss: 1.2814 2023-01-01 01:22:52,837 - mmcls - INFO - Epoch [86][2500/5005] lr: 1.000e-03, eta: 1:47:04, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.2935 2023-01-01 01:23:01,642 - mmcls - INFO - Epoch [86][2600/5005] lr: 1.000e-03, eta: 1:46:56, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2802 2023-01-01 01:23:10,522 - mmcls - INFO - Epoch [86][2700/5005] lr: 1.000e-03, eta: 1:46:47, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2898 2023-01-01 01:23:19,091 - mmcls - INFO - Epoch [86][2800/5005] lr: 1.000e-03, eta: 1:46:38, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.3070 2023-01-01 01:23:27,773 - mmcls - INFO - Epoch [86][2900/5005] lr: 1.000e-03, eta: 1:46:29, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2827 2023-01-01 01:23:37,137 - mmcls - INFO - Epoch [86][3000/5005] lr: 1.000e-03, eta: 1:46:20, time: 0.094, data_time: 0.017, memory: 1224, loss: 1.2926 2023-01-01 01:23:46,146 - mmcls - INFO - Epoch [86][3100/5005] lr: 1.000e-03, eta: 1:46:11, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.3014 2023-01-01 01:23:54,852 - mmcls - INFO - Epoch [86][3200/5005] lr: 1.000e-03, eta: 1:46:03, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2815 2023-01-01 01:24:03,793 - mmcls - INFO - Epoch [86][3300/5005] lr: 1.000e-03, eta: 1:45:54, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.2794 2023-01-01 01:24:12,393 - mmcls - INFO - Epoch [86][3400/5005] lr: 1.000e-03, eta: 1:45:45, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2709 2023-01-01 01:24:21,314 - mmcls - INFO - Epoch [86][3500/5005] lr: 1.000e-03, eta: 1:45:36, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.3008 2023-01-01 01:24:30,115 - mmcls - INFO - Epoch [86][3600/5005] lr: 1.000e-03, eta: 1:45:27, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2713 2023-01-01 01:24:39,003 - mmcls - INFO - Epoch [86][3700/5005] lr: 1.000e-03, eta: 1:45:18, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2851 2023-01-01 01:24:47,704 - mmcls - INFO - Epoch [86][3800/5005] lr: 1.000e-03, eta: 1:45:09, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2720 2023-01-01 01:24:57,237 - mmcls - INFO - Epoch [86][3900/5005] lr: 1.000e-03, eta: 1:45:01, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.3062 2023-01-01 01:25:06,341 - mmcls - INFO - Epoch [86][4000/5005] lr: 1.000e-03, eta: 1:44:52, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.2945 2023-01-01 01:25:15,033 - mmcls - INFO - Epoch [86][4100/5005] lr: 1.000e-03, eta: 1:44:43, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.2879 2023-01-01 01:25:23,692 - mmcls - INFO - Epoch [86][4200/5005] lr: 1.000e-03, eta: 1:44:34, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2985 2023-01-01 01:25:32,305 - mmcls - INFO - Epoch [86][4300/5005] lr: 1.000e-03, eta: 1:44:25, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2830 2023-01-01 01:25:40,962 - mmcls - INFO - Epoch [86][4400/5005] lr: 1.000e-03, eta: 1:44:16, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2899 2023-01-01 01:25:49,712 - mmcls - INFO - Epoch [86][4500/5005] lr: 1.000e-03, eta: 1:44:07, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2898 2023-01-01 01:25:58,639 - mmcls - INFO - Epoch [86][4600/5005] lr: 1.000e-03, eta: 1:43:59, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2780 2023-01-01 01:26:07,604 - mmcls - INFO - Epoch [86][4700/5005] lr: 1.000e-03, eta: 1:43:50, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.2835 2023-01-01 01:26:16,303 - mmcls - INFO - Epoch [86][4800/5005] lr: 1.000e-03, eta: 1:43:41, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2711 2023-01-01 01:26:25,008 - mmcls - INFO - Epoch [86][4900/5005] lr: 1.000e-03, eta: 1:43:32, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2981 2023-01-01 01:26:33,537 - mmcls - INFO - Epoch [86][5000/5005] lr: 1.000e-03, eta: 1:43:23, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2920 2023-01-01 01:28:47,399 - mmcls - INFO - Epoch(val) [86][196] accuracy_top-1: 71.1700, accuracy_top-5: 89.8860 2023-01-01 01:28:58,477 - mmcls - INFO - Epoch [87][100/5005] lr: 1.000e-03, eta: 1:43:14, time: 0.110, data_time: 0.047, memory: 1224, loss: 1.2761 2023-01-01 01:29:07,149 - mmcls - INFO - Epoch [87][200/5005] lr: 1.000e-03, eta: 1:43:05, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2642 2023-01-01 01:29:15,978 - mmcls - INFO - Epoch [87][300/5005] lr: 1.000e-03, eta: 1:42:56, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2820 2023-01-01 01:29:24,923 - mmcls - INFO - Epoch [87][400/5005] lr: 1.000e-03, eta: 1:42:47, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2617 2023-01-01 01:29:33,653 - mmcls - INFO - Epoch [87][500/5005] lr: 1.000e-03, eta: 1:42:39, time: 0.087, data_time: 0.033, memory: 1224, loss: 1.2728 2023-01-01 01:29:42,692 - mmcls - INFO - Epoch [87][600/5005] lr: 1.000e-03, eta: 1:42:30, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.2617 2023-01-01 01:29:51,583 - mmcls - INFO - Epoch [87][700/5005] lr: 1.000e-03, eta: 1:42:21, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2930 2023-01-01 01:30:00,386 - mmcls - INFO - Epoch [87][800/5005] lr: 1.000e-03, eta: 1:42:12, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2949 2023-01-01 01:30:09,089 - mmcls - INFO - Epoch [87][900/5005] lr: 1.000e-03, eta: 1:42:03, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2633 2023-01-01 01:30:17,848 - mmcls - INFO - Epoch [87][1000/5005] lr: 1.000e-03, eta: 1:41:54, time: 0.088, data_time: 0.011, memory: 1224, loss: 1.2929 2023-01-01 01:30:27,039 - mmcls - INFO - Epoch [87][1100/5005] lr: 1.000e-03, eta: 1:41:46, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2744 2023-01-01 01:30:35,800 - mmcls - INFO - Epoch [87][1200/5005] lr: 1.000e-03, eta: 1:41:37, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2815 2023-01-01 01:30:44,361 - mmcls - INFO - Epoch [87][1300/5005] lr: 1.000e-03, eta: 1:41:28, time: 0.085, data_time: 0.008, memory: 1224, loss: 1.2768 2023-01-01 01:30:53,226 - mmcls - INFO - Epoch [87][1400/5005] lr: 1.000e-03, eta: 1:41:19, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2875 2023-01-01 01:31:01,999 - mmcls - INFO - Epoch [87][1500/5005] lr: 1.000e-03, eta: 1:41:10, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2617 2023-01-01 01:31:10,867 - mmcls - INFO - Epoch [87][1600/5005] lr: 1.000e-03, eta: 1:41:01, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.2634 2023-01-01 01:31:19,798 - mmcls - INFO - Epoch [87][1700/5005] lr: 1.000e-03, eta: 1:40:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2829 2023-01-01 01:31:28,774 - mmcls - INFO - Epoch [87][1800/5005] lr: 1.000e-03, eta: 1:40:44, time: 0.090, data_time: 0.034, memory: 1224, loss: 1.2802 2023-01-01 01:31:37,733 - mmcls - INFO - Epoch [87][1900/5005] lr: 1.000e-03, eta: 1:40:35, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2630 2023-01-01 01:31:46,326 - mmcls - INFO - Epoch [87][2000/5005] lr: 1.000e-03, eta: 1:40:26, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.3037 2023-01-01 01:31:55,161 - mmcls - INFO - Epoch [87][2100/5005] lr: 1.000e-03, eta: 1:40:17, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2783 2023-01-01 01:32:04,070 - mmcls - INFO - Epoch [87][2200/5005] lr: 1.000e-03, eta: 1:40:08, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2636 2023-01-01 01:32:13,120 - mmcls - INFO - Epoch [87][2300/5005] lr: 1.000e-03, eta: 1:39:59, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.2880 2023-01-01 01:32:22,361 - mmcls - INFO - Epoch [87][2400/5005] lr: 1.000e-03, eta: 1:39:50, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.2767 2023-01-01 01:32:31,331 - mmcls - INFO - Epoch [87][2500/5005] lr: 1.000e-03, eta: 1:39:42, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.2761 2023-01-01 01:32:40,226 - mmcls - INFO - Epoch [87][2600/5005] lr: 1.000e-03, eta: 1:39:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3008 2023-01-01 01:32:49,190 - mmcls - INFO - Epoch [87][2700/5005] lr: 1.000e-03, eta: 1:39:24, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2730 2023-01-01 01:32:57,921 - mmcls - INFO - Epoch [87][2800/5005] lr: 1.000e-03, eta: 1:39:15, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2806 2023-01-01 01:33:06,631 - mmcls - INFO - Epoch [87][2900/5005] lr: 1.000e-03, eta: 1:39:06, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2821 2023-01-01 01:33:15,394 - mmcls - INFO - Epoch [87][3000/5005] lr: 1.000e-03, eta: 1:38:57, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2996 2023-01-01 01:33:24,179 - mmcls - INFO - Epoch [87][3100/5005] lr: 1.000e-03, eta: 1:38:48, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2612 2023-01-01 01:33:33,278 - mmcls - INFO - Epoch [87][3200/5005] lr: 1.000e-03, eta: 1:38:40, time: 0.091, data_time: 0.021, memory: 1224, loss: 1.2955 2023-01-01 01:33:41,886 - mmcls - INFO - Epoch [87][3300/5005] lr: 1.000e-03, eta: 1:38:31, time: 0.086, data_time: 0.028, memory: 1224, loss: 1.2976 2023-01-01 01:33:50,344 - mmcls - INFO - Epoch [87][3400/5005] lr: 1.000e-03, eta: 1:38:22, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.2673 2023-01-01 01:33:59,051 - mmcls - INFO - Epoch [87][3500/5005] lr: 1.000e-03, eta: 1:38:13, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2718 2023-01-01 01:34:07,837 - mmcls - INFO - Epoch [87][3600/5005] lr: 1.000e-03, eta: 1:38:04, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2988 2023-01-01 01:34:16,638 - mmcls - INFO - Epoch [87][3700/5005] lr: 1.000e-03, eta: 1:37:55, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2930 2023-01-01 01:34:25,495 - mmcls - INFO - Epoch [87][3800/5005] lr: 1.000e-03, eta: 1:37:46, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2861 2023-01-01 01:34:34,317 - mmcls - INFO - Epoch [87][3900/5005] lr: 1.000e-03, eta: 1:37:38, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2840 2023-01-01 01:34:43,118 - mmcls - INFO - Epoch [87][4000/5005] lr: 1.000e-03, eta: 1:37:29, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2952 2023-01-01 01:34:51,803 - mmcls - INFO - Epoch [87][4100/5005] lr: 1.000e-03, eta: 1:37:20, time: 0.087, data_time: 0.024, memory: 1224, loss: 1.2908 2023-01-01 01:35:00,456 - mmcls - INFO - Epoch [87][4200/5005] lr: 1.000e-03, eta: 1:37:11, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2728 2023-01-01 01:35:09,109 - mmcls - INFO - Epoch [87][4300/5005] lr: 1.000e-03, eta: 1:37:02, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2766 2023-01-01 01:35:18,092 - mmcls - INFO - Epoch [87][4400/5005] lr: 1.000e-03, eta: 1:36:53, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.2922 2023-01-01 01:35:26,661 - mmcls - INFO - Epoch [87][4500/5005] lr: 1.000e-03, eta: 1:36:44, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.3032 2023-01-01 01:35:35,349 - mmcls - INFO - Epoch [87][4600/5005] lr: 1.000e-03, eta: 1:36:35, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.2808 2023-01-01 01:35:43,947 - mmcls - INFO - Epoch [87][4700/5005] lr: 1.000e-03, eta: 1:36:27, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.2534 2023-01-01 01:35:52,691 - mmcls - INFO - Epoch [87][4800/5005] lr: 1.000e-03, eta: 1:36:18, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2895 2023-01-01 01:36:01,503 - mmcls - INFO - Epoch [87][4900/5005] lr: 1.000e-03, eta: 1:36:09, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2984 2023-01-01 01:36:10,487 - mmcls - INFO - Epoch [87][5000/5005] lr: 1.000e-03, eta: 1:36:00, time: 0.090, data_time: 0.016, memory: 1224, loss: 1.2941 2023-01-01 01:36:33,231 - mmcls - INFO - Epoch(val) [87][196] accuracy_top-1: 70.9100, accuracy_top-5: 89.8600 2023-01-01 01:36:44,473 - mmcls - INFO - Epoch [88][100/5005] lr: 1.000e-03, eta: 1:35:51, time: 0.112, data_time: 0.032, memory: 1224, loss: 1.2761 2023-01-01 01:36:53,422 - mmcls - INFO - Epoch [88][200/5005] lr: 1.000e-03, eta: 1:35:42, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2545 2023-01-01 01:37:02,569 - mmcls - INFO - Epoch [88][300/5005] lr: 1.000e-03, eta: 1:35:33, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.2694 2023-01-01 01:37:11,249 - mmcls - INFO - Epoch [88][400/5005] lr: 1.000e-03, eta: 1:35:24, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2663 2023-01-01 01:37:20,034 - mmcls - INFO - Epoch [88][500/5005] lr: 1.000e-03, eta: 1:35:16, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2672 2023-01-01 01:37:28,955 - mmcls - INFO - Epoch [88][600/5005] lr: 1.000e-03, eta: 1:35:07, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2765 2023-01-01 01:37:37,864 - mmcls - INFO - Epoch [88][700/5005] lr: 1.000e-03, eta: 1:34:58, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2720 2023-01-01 01:37:46,516 - mmcls - INFO - Epoch [88][800/5005] lr: 1.000e-03, eta: 1:34:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2557 2023-01-01 01:37:55,264 - mmcls - INFO - Epoch [88][900/5005] lr: 1.000e-03, eta: 1:34:40, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2646 2023-01-01 01:38:03,906 - mmcls - INFO - Epoch [88][1000/5005] lr: 1.000e-03, eta: 1:34:31, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2691 2023-01-01 01:38:12,674 - mmcls - INFO - Epoch [88][1100/5005] lr: 1.000e-03, eta: 1:34:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2711 2023-01-01 01:38:21,274 - mmcls - INFO - Epoch [88][1200/5005] lr: 1.000e-03, eta: 1:34:13, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2703 2023-01-01 01:38:30,089 - mmcls - INFO - Epoch [88][1300/5005] lr: 1.000e-03, eta: 1:34:05, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2650 2023-01-01 01:38:38,895 - mmcls - INFO - Epoch [88][1400/5005] lr: 1.000e-03, eta: 1:33:56, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2642 2023-01-01 01:38:47,524 - mmcls - INFO - Epoch [88][1500/5005] lr: 1.000e-03, eta: 1:33:47, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.2896 2023-01-01 01:38:56,410 - mmcls - INFO - Epoch [88][1600/5005] lr: 1.000e-03, eta: 1:33:38, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.2746 2023-01-01 01:39:05,035 - mmcls - INFO - Epoch [88][1700/5005] lr: 1.000e-03, eta: 1:33:29, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2968 2023-01-01 01:39:14,382 - mmcls - INFO - Epoch [88][1800/5005] lr: 1.000e-03, eta: 1:33:20, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.2855 2023-01-01 01:39:23,081 - mmcls - INFO - Epoch [88][1900/5005] lr: 1.000e-03, eta: 1:33:11, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2809 2023-01-01 01:39:32,045 - mmcls - INFO - Epoch [88][2000/5005] lr: 1.000e-03, eta: 1:33:03, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.2807 2023-01-01 01:39:40,952 - mmcls - INFO - Epoch [88][2100/5005] lr: 1.000e-03, eta: 1:32:54, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.2771 2023-01-01 01:39:49,410 - mmcls - INFO - Epoch [88][2200/5005] lr: 1.000e-03, eta: 1:32:45, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.2794 2023-01-01 01:39:58,131 - mmcls - INFO - Epoch [88][2300/5005] lr: 1.000e-03, eta: 1:32:36, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2885 2023-01-01 01:40:06,880 - mmcls - INFO - Epoch [88][2400/5005] lr: 1.000e-03, eta: 1:32:27, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.2956 2023-01-01 01:40:15,481 - mmcls - INFO - Epoch [88][2500/5005] lr: 1.000e-03, eta: 1:32:18, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2794 2023-01-01 01:40:24,220 - mmcls - INFO - Epoch [88][2600/5005] lr: 1.000e-03, eta: 1:32:09, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.2720 2023-01-01 01:40:32,943 - mmcls - INFO - Epoch [88][2700/5005] lr: 1.000e-03, eta: 1:32:01, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2774 2023-01-01 01:40:41,505 - mmcls - INFO - Epoch [88][2800/5005] lr: 1.000e-03, eta: 1:31:52, time: 0.086, data_time: 0.017, memory: 1224, loss: 1.2761 2023-01-01 01:40:50,326 - mmcls - INFO - Epoch [88][2900/5005] lr: 1.000e-03, eta: 1:31:43, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2753 2023-01-01 01:40:58,994 - mmcls - INFO - Epoch [88][3000/5005] lr: 1.000e-03, eta: 1:31:34, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2841 2023-01-01 01:41:07,718 - mmcls - INFO - Epoch [88][3100/5005] lr: 1.000e-03, eta: 1:31:25, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2762 2023-01-01 01:41:16,648 - mmcls - INFO - Epoch [88][3200/5005] lr: 1.000e-03, eta: 1:31:16, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2946 2023-01-01 01:41:25,618 - mmcls - INFO - Epoch [88][3300/5005] lr: 1.000e-03, eta: 1:31:07, time: 0.090, data_time: 0.017, memory: 1224, loss: 1.2821 2023-01-01 01:41:34,437 - mmcls - INFO - Epoch [88][3400/5005] lr: 1.000e-03, eta: 1:30:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2948 2023-01-01 01:41:43,324 - mmcls - INFO - Epoch [88][3500/5005] lr: 1.000e-03, eta: 1:30:50, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2832 2023-01-01 01:41:52,307 - mmcls - INFO - Epoch [88][3600/5005] lr: 1.000e-03, eta: 1:30:41, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2776 2023-01-01 01:42:01,199 - mmcls - INFO - Epoch [88][3700/5005] lr: 1.000e-03, eta: 1:30:32, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.2684 2023-01-01 01:42:10,107 - mmcls - INFO - Epoch [88][3800/5005] lr: 1.000e-03, eta: 1:30:23, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2485 2023-01-01 01:42:18,735 - mmcls - INFO - Epoch [88][3900/5005] lr: 1.000e-03, eta: 1:30:14, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2665 2023-01-01 01:42:27,977 - mmcls - INFO - Epoch [88][4000/5005] lr: 1.000e-03, eta: 1:30:05, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.2732 2023-01-01 01:42:36,835 - mmcls - INFO - Epoch [88][4100/5005] lr: 1.000e-03, eta: 1:29:57, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2880 2023-01-01 01:42:45,437 - mmcls - INFO - Epoch [88][4200/5005] lr: 1.000e-03, eta: 1:29:48, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.3018 2023-01-01 01:42:54,296 - mmcls - INFO - Epoch [88][4300/5005] lr: 1.000e-03, eta: 1:29:39, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.2886 2023-01-01 01:43:03,122 - mmcls - INFO - Epoch [88][4400/5005] lr: 1.000e-03, eta: 1:29:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2786 2023-01-01 01:43:11,763 - mmcls - INFO - Epoch [88][4500/5005] lr: 1.000e-03, eta: 1:29:21, time: 0.086, data_time: 0.022, memory: 1224, loss: 1.2774 2023-01-01 01:43:20,906 - mmcls - INFO - Epoch [88][4600/5005] lr: 1.000e-03, eta: 1:29:12, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.2871 2023-01-01 01:43:29,560 - mmcls - INFO - Epoch [88][4700/5005] lr: 1.000e-03, eta: 1:29:03, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2622 2023-01-01 01:43:38,215 - mmcls - INFO - Epoch [88][4800/5005] lr: 1.000e-03, eta: 1:28:55, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.3072 2023-01-01 01:43:47,212 - mmcls - INFO - Epoch [88][4900/5005] lr: 1.000e-03, eta: 1:28:46, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.2661 2023-01-01 01:43:55,937 - mmcls - INFO - Epoch [88][5000/5005] lr: 1.000e-03, eta: 1:28:37, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2888 2023-01-01 01:44:18,527 - mmcls - INFO - Epoch(val) [88][196] accuracy_top-1: 71.0680, accuracy_top-5: 89.8800 2023-01-01 01:44:29,600 - mmcls - INFO - Epoch [89][100/5005] lr: 1.000e-03, eta: 1:28:28, time: 0.111, data_time: 0.062, memory: 1224, loss: 1.2769 2023-01-01 01:44:38,219 - mmcls - INFO - Epoch [89][200/5005] lr: 1.000e-03, eta: 1:28:19, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2625 2023-01-01 01:44:46,955 - mmcls - INFO - Epoch [89][300/5005] lr: 1.000e-03, eta: 1:28:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2824 2023-01-01 01:44:55,812 - mmcls - INFO - Epoch [89][400/5005] lr: 1.000e-03, eta: 1:28:01, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2479 2023-01-01 01:45:04,960 - mmcls - INFO - Epoch [89][500/5005] lr: 1.000e-03, eta: 1:27:52, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.2844 2023-01-01 01:45:13,991 - mmcls - INFO - Epoch [89][600/5005] lr: 1.000e-03, eta: 1:27:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2664 2023-01-01 01:45:22,832 - mmcls - INFO - Epoch [89][700/5005] lr: 1.000e-03, eta: 1:27:35, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2622 2023-01-01 01:45:32,043 - mmcls - INFO - Epoch [89][800/5005] lr: 1.000e-03, eta: 1:27:26, time: 0.092, data_time: 0.043, memory: 1224, loss: 1.2707 2023-01-01 01:45:41,101 - mmcls - INFO - Epoch [89][900/5005] lr: 1.000e-03, eta: 1:27:17, time: 0.091, data_time: 0.019, memory: 1224, loss: 1.2562 2023-01-01 01:45:49,827 - mmcls - INFO - Epoch [89][1000/5005] lr: 1.000e-03, eta: 1:27:08, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2736 2023-01-01 01:45:58,771 - mmcls - INFO - Epoch [89][1100/5005] lr: 1.000e-03, eta: 1:26:59, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2557 2023-01-01 01:46:07,563 - mmcls - INFO - Epoch [89][1200/5005] lr: 1.000e-03, eta: 1:26:50, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2726 2023-01-01 01:46:16,399 - mmcls - INFO - Epoch [89][1300/5005] lr: 1.000e-03, eta: 1:26:42, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2945 2023-01-01 01:46:25,264 - mmcls - INFO - Epoch [89][1400/5005] lr: 1.000e-03, eta: 1:26:33, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2565 2023-01-01 01:46:33,948 - mmcls - INFO - Epoch [89][1500/5005] lr: 1.000e-03, eta: 1:26:24, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2822 2023-01-01 01:46:42,613 - mmcls - INFO - Epoch [89][1600/5005] lr: 1.000e-03, eta: 1:26:15, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2769 2023-01-01 01:46:51,231 - mmcls - INFO - Epoch [89][1700/5005] lr: 1.000e-03, eta: 1:26:06, time: 0.086, data_time: 0.019, memory: 1224, loss: 1.2812 2023-01-01 01:47:00,089 - mmcls - INFO - Epoch [89][1800/5005] lr: 1.000e-03, eta: 1:25:57, time: 0.089, data_time: 0.024, memory: 1224, loss: 1.2826 2023-01-01 01:47:09,578 - mmcls - INFO - Epoch [89][1900/5005] lr: 1.000e-03, eta: 1:25:48, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.2781 2023-01-01 01:47:18,240 - mmcls - INFO - Epoch [89][2000/5005] lr: 1.000e-03, eta: 1:25:40, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2593 2023-01-01 01:47:27,257 - mmcls - INFO - Epoch [89][2100/5005] lr: 1.000e-03, eta: 1:25:31, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2818 2023-01-01 01:47:36,313 - mmcls - INFO - Epoch [89][2200/5005] lr: 1.000e-03, eta: 1:25:22, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2567 2023-01-01 01:47:45,234 - mmcls - INFO - Epoch [89][2300/5005] lr: 1.000e-03, eta: 1:25:13, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2823 2023-01-01 01:47:54,091 - mmcls - INFO - Epoch [89][2400/5005] lr: 1.000e-03, eta: 1:25:04, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2920 2023-01-01 01:48:03,155 - mmcls - INFO - Epoch [89][2500/5005] lr: 1.000e-03, eta: 1:24:55, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.2887 2023-01-01 01:48:11,747 - mmcls - INFO - Epoch [89][2600/5005] lr: 1.000e-03, eta: 1:24:47, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.2739 2023-01-01 01:48:20,489 - mmcls - INFO - Epoch [89][2700/5005] lr: 1.000e-03, eta: 1:24:38, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.2564 2023-01-01 01:48:29,158 - mmcls - INFO - Epoch [89][2800/5005] lr: 1.000e-03, eta: 1:24:29, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2779 2023-01-01 01:48:37,810 - mmcls - INFO - Epoch [89][2900/5005] lr: 1.000e-03, eta: 1:24:20, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2828 2023-01-01 01:48:46,506 - mmcls - INFO - Epoch [89][3000/5005] lr: 1.000e-03, eta: 1:24:11, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2805 2023-01-01 01:48:55,015 - mmcls - INFO - Epoch [89][3100/5005] lr: 1.000e-03, eta: 1:24:02, time: 0.085, data_time: 0.012, memory: 1224, loss: 1.2896 2023-01-01 01:49:03,452 - mmcls - INFO - Epoch [89][3200/5005] lr: 1.000e-03, eta: 1:23:53, time: 0.084, data_time: 0.013, memory: 1224, loss: 1.2726 2023-01-01 01:49:12,244 - mmcls - INFO - Epoch [89][3300/5005] lr: 1.000e-03, eta: 1:23:44, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2916 2023-01-01 01:49:20,962 - mmcls - INFO - Epoch [89][3400/5005] lr: 1.000e-03, eta: 1:23:36, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2857 2023-01-01 01:49:29,840 - mmcls - INFO - Epoch [89][3500/5005] lr: 1.000e-03, eta: 1:23:27, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2705 2023-01-01 01:49:38,710 - mmcls - INFO - Epoch [89][3600/5005] lr: 1.000e-03, eta: 1:23:18, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2780 2023-01-01 01:49:47,435 - mmcls - INFO - Epoch [89][3700/5005] lr: 1.000e-03, eta: 1:23:09, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2863 2023-01-01 01:49:56,207 - mmcls - INFO - Epoch [89][3800/5005] lr: 1.000e-03, eta: 1:23:00, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2804 2023-01-01 01:50:05,070 - mmcls - INFO - Epoch [89][3900/5005] lr: 1.000e-03, eta: 1:22:51, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2806 2023-01-01 01:50:13,650 - mmcls - INFO - Epoch [89][4000/5005] lr: 1.000e-03, eta: 1:22:42, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.3034 2023-01-01 01:50:22,428 - mmcls - INFO - Epoch [89][4100/5005] lr: 1.000e-03, eta: 1:22:33, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2929 2023-01-01 01:50:31,125 - mmcls - INFO - Epoch [89][4200/5005] lr: 1.000e-03, eta: 1:22:25, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2803 2023-01-01 01:50:39,562 - mmcls - INFO - Epoch [89][4300/5005] lr: 1.000e-03, eta: 1:22:16, time: 0.084, data_time: 0.005, memory: 1224, loss: 1.2758 2023-01-01 01:50:48,412 - mmcls - INFO - Epoch [89][4400/5005] lr: 1.000e-03, eta: 1:22:07, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2783 2023-01-01 01:50:57,678 - mmcls - INFO - Epoch [89][4500/5005] lr: 1.000e-03, eta: 1:21:58, time: 0.093, data_time: 0.022, memory: 1224, loss: 1.2896 2023-01-01 01:51:06,508 - mmcls - INFO - Epoch [89][4600/5005] lr: 1.000e-03, eta: 1:21:49, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2810 2023-01-01 01:51:15,301 - mmcls - INFO - Epoch [89][4700/5005] lr: 1.000e-03, eta: 1:21:40, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2766 2023-01-01 01:51:24,307 - mmcls - INFO - Epoch [89][4800/5005] lr: 1.000e-03, eta: 1:21:31, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2687 2023-01-01 01:51:32,922 - mmcls - INFO - Epoch [89][4900/5005] lr: 1.000e-03, eta: 1:21:23, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2689 2023-01-01 01:51:41,856 - mmcls - INFO - Epoch [89][5000/5005] lr: 1.000e-03, eta: 1:21:14, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2807 2023-01-01 01:52:04,736 - mmcls - INFO - Epoch(val) [89][196] accuracy_top-1: 71.0760, accuracy_top-5: 89.9500 2023-01-01 01:52:15,864 - mmcls - INFO - Epoch [90][100/5005] lr: 1.000e-03, eta: 1:21:05, time: 0.111, data_time: 0.064, memory: 1224, loss: 1.2517 2023-01-01 01:52:25,028 - mmcls - INFO - Epoch [90][200/5005] lr: 1.000e-03, eta: 1:20:56, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.2510 2023-01-01 01:52:33,595 - mmcls - INFO - Epoch [90][300/5005] lr: 1.000e-03, eta: 1:20:47, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2792 2023-01-01 01:52:42,270 - mmcls - INFO - Epoch [90][400/5005] lr: 1.000e-03, eta: 1:20:38, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.2709 2023-01-01 01:52:51,067 - mmcls - INFO - Epoch [90][500/5005] lr: 1.000e-03, eta: 1:20:29, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2680 2023-01-01 01:53:00,049 - mmcls - INFO - Epoch [90][600/5005] lr: 1.000e-03, eta: 1:20:20, time: 0.090, data_time: 0.012, memory: 1224, loss: 1.2829 2023-01-01 01:53:08,738 - mmcls - INFO - Epoch [90][700/5005] lr: 1.000e-03, eta: 1:20:12, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2779 2023-01-01 01:53:17,900 - mmcls - INFO - Epoch [90][800/5005] lr: 1.000e-03, eta: 1:20:03, time: 0.092, data_time: 0.014, memory: 1224, loss: 1.2567 2023-01-01 01:53:26,679 - mmcls - INFO - Epoch [90][900/5005] lr: 1.000e-03, eta: 1:19:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2775 2023-01-01 01:53:35,187 - mmcls - INFO - Epoch [90][1000/5005] lr: 1.000e-03, eta: 1:19:45, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.2599 2023-01-01 01:53:43,849 - mmcls - INFO - Epoch [90][1100/5005] lr: 1.000e-03, eta: 1:19:36, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.2426 2023-01-01 01:53:52,441 - mmcls - INFO - Epoch [90][1200/5005] lr: 1.000e-03, eta: 1:19:27, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.2880 2023-01-01 01:54:01,547 - mmcls - INFO - Epoch [90][1300/5005] lr: 1.000e-03, eta: 1:19:18, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2675 2023-01-01 01:54:10,669 - mmcls - INFO - Epoch [90][1400/5005] lr: 1.000e-03, eta: 1:19:10, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2585 2023-01-01 01:54:19,760 - mmcls - INFO - Epoch [90][1500/5005] lr: 1.000e-03, eta: 1:19:01, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.2993 2023-01-01 01:54:28,769 - mmcls - INFO - Epoch [90][1600/5005] lr: 1.000e-03, eta: 1:18:52, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2694 2023-01-01 01:54:37,501 - mmcls - INFO - Epoch [90][1700/5005] lr: 1.000e-03, eta: 1:18:43, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2606 2023-01-01 01:54:46,537 - mmcls - INFO - Epoch [90][1800/5005] lr: 1.000e-03, eta: 1:18:34, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2781 2023-01-01 01:54:54,897 - mmcls - INFO - Epoch [90][1900/5005] lr: 1.000e-03, eta: 1:18:25, time: 0.084, data_time: 0.002, memory: 1224, loss: 1.2629 2023-01-01 01:55:03,781 - mmcls - INFO - Epoch [90][2000/5005] lr: 1.000e-03, eta: 1:18:16, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2944 2023-01-01 01:55:12,532 - mmcls - INFO - Epoch [90][2100/5005] lr: 1.000e-03, eta: 1:18:08, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2641 2023-01-01 01:55:21,598 - mmcls - INFO - Epoch [90][2200/5005] lr: 1.000e-03, eta: 1:17:59, time: 0.091, data_time: 0.010, memory: 1224, loss: 1.2699 2023-01-01 01:55:30,764 - mmcls - INFO - Epoch [90][2300/5005] lr: 1.000e-03, eta: 1:17:50, time: 0.092, data_time: 0.009, memory: 1224, loss: 1.2809 2023-01-01 01:55:39,365 - mmcls - INFO - Epoch [90][2400/5005] lr: 1.000e-03, eta: 1:17:41, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2815 2023-01-01 01:55:48,362 - mmcls - INFO - Epoch [90][2500/5005] lr: 1.000e-03, eta: 1:17:32, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2828 2023-01-01 01:55:57,412 - mmcls - INFO - Epoch [90][2600/5005] lr: 1.000e-03, eta: 1:17:23, time: 0.091, data_time: 0.008, memory: 1224, loss: 1.2953 2023-01-01 01:56:05,999 - mmcls - INFO - Epoch [90][2700/5005] lr: 1.000e-03, eta: 1:17:15, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.2620 2023-01-01 01:56:14,726 - mmcls - INFO - Epoch [90][2800/5005] lr: 1.000e-03, eta: 1:17:06, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.2580 2023-01-01 01:56:23,570 - mmcls - INFO - Epoch [90][2900/5005] lr: 1.000e-03, eta: 1:16:57, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2724 2023-01-01 01:56:32,886 - mmcls - INFO - Epoch [90][3000/5005] lr: 1.000e-03, eta: 1:16:48, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.2700 2023-01-01 01:56:41,403 - mmcls - INFO - Epoch [90][3100/5005] lr: 1.000e-03, eta: 1:16:39, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2732 2023-01-01 01:56:50,203 - mmcls - INFO - Epoch [90][3200/5005] lr: 1.000e-03, eta: 1:16:30, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2733 2023-01-01 01:56:58,890 - mmcls - INFO - Epoch [90][3300/5005] lr: 1.000e-03, eta: 1:16:21, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2491 2023-01-01 01:57:07,437 - mmcls - INFO - Epoch [90][3400/5005] lr: 1.000e-03, eta: 1:16:12, time: 0.085, data_time: 0.010, memory: 1224, loss: 1.2849 2023-01-01 01:57:16,462 - mmcls - INFO - Epoch [90][3500/5005] lr: 1.000e-03, eta: 1:16:04, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.2931 2023-01-01 01:57:25,172 - mmcls - INFO - Epoch [90][3600/5005] lr: 1.000e-03, eta: 1:15:55, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2762 2023-01-01 01:57:33,904 - mmcls - INFO - Epoch [90][3700/5005] lr: 1.000e-03, eta: 1:15:46, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2769 2023-01-01 01:57:42,990 - mmcls - INFO - Epoch [90][3800/5005] lr: 1.000e-03, eta: 1:15:37, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.2759 2023-01-01 01:57:51,715 - mmcls - INFO - Epoch [90][3900/5005] lr: 1.000e-03, eta: 1:15:28, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2803 2023-01-01 01:58:00,456 - mmcls - INFO - Epoch [90][4000/5005] lr: 1.000e-03, eta: 1:15:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2547 2023-01-01 01:58:09,901 - mmcls - INFO - Epoch [90][4100/5005] lr: 1.000e-03, eta: 1:15:11, time: 0.095, data_time: 0.001, memory: 1224, loss: 1.2809 2023-01-01 01:58:18,550 - mmcls - INFO - Epoch [90][4200/5005] lr: 1.000e-03, eta: 1:15:02, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2914 2023-01-01 01:58:27,208 - mmcls - INFO - Epoch [90][4300/5005] lr: 1.000e-03, eta: 1:14:53, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2629 2023-01-01 01:58:35,773 - mmcls - INFO - Epoch [90][4400/5005] lr: 1.000e-03, eta: 1:14:44, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2721 2023-01-01 01:58:44,691 - mmcls - INFO - Epoch [90][4500/5005] lr: 1.000e-03, eta: 1:14:35, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.3016 2023-01-01 01:58:53,367 - mmcls - INFO - Epoch [90][4600/5005] lr: 1.000e-03, eta: 1:14:26, time: 0.087, data_time: 0.023, memory: 1224, loss: 1.2897 2023-01-01 01:59:02,072 - mmcls - INFO - Epoch [90][4700/5005] lr: 1.000e-03, eta: 1:14:17, time: 0.087, data_time: 0.020, memory: 1224, loss: 1.2914 2023-01-01 01:59:11,003 - mmcls - INFO - Epoch [90][4800/5005] lr: 1.000e-03, eta: 1:14:08, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.2819 2023-01-01 01:59:19,843 - mmcls - INFO - Epoch [90][4900/5005] lr: 1.000e-03, eta: 1:14:00, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2689 2023-01-01 01:59:28,873 - mmcls - INFO - Epoch [90][5000/5005] lr: 1.000e-03, eta: 1:13:51, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2769 2023-01-01 01:59:51,424 - mmcls - INFO - Epoch(val) [90][196] accuracy_top-1: 70.9520, accuracy_top-5: 89.8720 2023-01-01 02:00:02,404 - mmcls - INFO - Epoch [91][100/5005] lr: 1.000e-04, eta: 1:13:42, time: 0.110, data_time: 0.049, memory: 1224, loss: 1.2593 2023-01-01 02:00:11,326 - mmcls - INFO - Epoch [91][200/5005] lr: 1.000e-04, eta: 1:13:33, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2482 2023-01-01 02:00:20,149 - mmcls - INFO - Epoch [91][300/5005] lr: 1.000e-04, eta: 1:13:24, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2556 2023-01-01 02:00:29,223 - mmcls - INFO - Epoch [91][400/5005] lr: 1.000e-04, eta: 1:13:15, time: 0.091, data_time: 0.013, memory: 1224, loss: 1.2425 2023-01-01 02:00:38,083 - mmcls - INFO - Epoch [91][500/5005] lr: 1.000e-04, eta: 1:13:06, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2561 2023-01-01 02:00:46,761 - mmcls - INFO - Epoch [91][600/5005] lr: 1.000e-04, eta: 1:12:57, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2513 2023-01-01 02:00:55,398 - mmcls - INFO - Epoch [91][700/5005] lr: 1.000e-04, eta: 1:12:49, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2426 2023-01-01 02:01:04,264 - mmcls - INFO - Epoch [91][800/5005] lr: 1.000e-04, eta: 1:12:40, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2318 2023-01-01 02:01:12,922 - mmcls - INFO - Epoch [91][900/5005] lr: 1.000e-04, eta: 1:12:31, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2373 2023-01-01 02:01:21,694 - mmcls - INFO - Epoch [91][1000/5005] lr: 1.000e-04, eta: 1:12:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2388 2023-01-01 02:01:30,438 - mmcls - INFO - Epoch [91][1100/5005] lr: 1.000e-04, eta: 1:12:13, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2603 2023-01-01 02:01:39,390 - mmcls - INFO - Epoch [91][1200/5005] lr: 1.000e-04, eta: 1:12:04, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2582 2023-01-01 02:01:48,096 - mmcls - INFO - Epoch [91][1300/5005] lr: 1.000e-04, eta: 1:11:55, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.2639 2023-01-01 02:01:57,049 - mmcls - INFO - Epoch [91][1400/5005] lr: 1.000e-04, eta: 1:11:47, time: 0.090, data_time: 0.021, memory: 1224, loss: 1.2435 2023-01-01 02:02:05,666 - mmcls - INFO - Epoch [91][1500/5005] lr: 1.000e-04, eta: 1:11:38, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2423 2023-01-01 02:02:14,511 - mmcls - INFO - Epoch [91][1600/5005] lr: 1.000e-04, eta: 1:11:29, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2513 2023-01-01 02:02:23,406 - mmcls - INFO - Epoch [91][1700/5005] lr: 1.000e-04, eta: 1:11:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2490 2023-01-01 02:02:32,177 - mmcls - INFO - Epoch [91][1800/5005] lr: 1.000e-04, eta: 1:11:11, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2375 2023-01-01 02:02:41,089 - mmcls - INFO - Epoch [91][1900/5005] lr: 1.000e-04, eta: 1:11:02, time: 0.089, data_time: 0.030, memory: 1224, loss: 1.2562 2023-01-01 02:02:49,995 - mmcls - INFO - Epoch [91][2000/5005] lr: 1.000e-04, eta: 1:10:53, time: 0.089, data_time: 0.023, memory: 1224, loss: 1.2396 2023-01-01 02:02:58,670 - mmcls - INFO - Epoch [91][2100/5005] lr: 1.000e-04, eta: 1:10:45, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2397 2023-01-01 02:03:07,225 - mmcls - INFO - Epoch [91][2200/5005] lr: 1.000e-04, eta: 1:10:36, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2591 2023-01-01 02:03:15,973 - mmcls - INFO - Epoch [91][2300/5005] lr: 1.000e-04, eta: 1:10:27, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2316 2023-01-01 02:03:24,736 - mmcls - INFO - Epoch [91][2400/5005] lr: 1.000e-04, eta: 1:10:18, time: 0.088, data_time: 0.044, memory: 1224, loss: 1.2210 2023-01-01 02:03:33,694 - mmcls - INFO - Epoch [91][2500/5005] lr: 1.000e-04, eta: 1:10:09, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2473 2023-01-01 02:03:42,479 - mmcls - INFO - Epoch [91][2600/5005] lr: 1.000e-04, eta: 1:10:00, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2614 2023-01-01 02:03:51,136 - mmcls - INFO - Epoch [91][2700/5005] lr: 1.000e-04, eta: 1:09:51, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.2454 2023-01-01 02:03:59,971 - mmcls - INFO - Epoch [91][2800/5005] lr: 1.000e-04, eta: 1:09:43, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2515 2023-01-01 02:04:08,717 - mmcls - INFO - Epoch [91][2900/5005] lr: 1.000e-04, eta: 1:09:34, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2383 2023-01-01 02:04:17,532 - mmcls - INFO - Epoch [91][3000/5005] lr: 1.000e-04, eta: 1:09:25, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2641 2023-01-01 02:04:26,741 - mmcls - INFO - Epoch [91][3100/5005] lr: 1.000e-04, eta: 1:09:16, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.2565 2023-01-01 02:04:35,435 - mmcls - INFO - Epoch [91][3200/5005] lr: 1.000e-04, eta: 1:09:07, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2344 2023-01-01 02:04:44,342 - mmcls - INFO - Epoch [91][3300/5005] lr: 1.000e-04, eta: 1:08:58, time: 0.089, data_time: 0.035, memory: 1224, loss: 1.2363 2023-01-01 02:04:52,938 - mmcls - INFO - Epoch [91][3400/5005] lr: 1.000e-04, eta: 1:08:49, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2328 2023-01-01 02:05:01,370 - mmcls - INFO - Epoch [91][3500/5005] lr: 1.000e-04, eta: 1:08:40, time: 0.084, data_time: 0.005, memory: 1224, loss: 1.2448 2023-01-01 02:05:10,200 - mmcls - INFO - Epoch [91][3600/5005] lr: 1.000e-04, eta: 1:08:32, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2267 2023-01-01 02:05:18,957 - mmcls - INFO - Epoch [91][3700/5005] lr: 1.000e-04, eta: 1:08:23, time: 0.087, data_time: 0.022, memory: 1224, loss: 1.2500 2023-01-01 02:05:27,613 - mmcls - INFO - Epoch [91][3800/5005] lr: 1.000e-04, eta: 1:08:14, time: 0.087, data_time: 0.015, memory: 1224, loss: 1.2356 2023-01-01 02:05:36,837 - mmcls - INFO - Epoch [91][3900/5005] lr: 1.000e-04, eta: 1:08:05, time: 0.092, data_time: 0.005, memory: 1224, loss: 1.2369 2023-01-01 02:05:46,000 - mmcls - INFO - Epoch [91][4000/5005] lr: 1.000e-04, eta: 1:07:56, time: 0.092, data_time: 0.001, memory: 1224, loss: 1.2350 2023-01-01 02:05:54,710 - mmcls - INFO - Epoch [91][4100/5005] lr: 1.000e-04, eta: 1:07:47, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2243 2023-01-01 02:06:03,635 - mmcls - INFO - Epoch [91][4200/5005] lr: 1.000e-04, eta: 1:07:39, time: 0.089, data_time: 0.007, memory: 1224, loss: 1.2559 2023-01-01 02:06:12,478 - mmcls - INFO - Epoch [91][4300/5005] lr: 1.000e-04, eta: 1:07:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2381 2023-01-01 02:06:21,449 - mmcls - INFO - Epoch [91][4400/5005] lr: 1.000e-04, eta: 1:07:21, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2501 2023-01-01 02:06:30,404 - mmcls - INFO - Epoch [91][4500/5005] lr: 1.000e-04, eta: 1:07:12, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2383 2023-01-01 02:06:39,341 - mmcls - INFO - Epoch [91][4600/5005] lr: 1.000e-04, eta: 1:07:03, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.2397 2023-01-01 02:06:48,056 - mmcls - INFO - Epoch [91][4700/5005] lr: 1.000e-04, eta: 1:06:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2739 2023-01-01 02:06:56,734 - mmcls - INFO - Epoch [91][4800/5005] lr: 1.000e-04, eta: 1:06:45, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2629 2023-01-01 02:07:05,531 - mmcls - INFO - Epoch [91][4900/5005] lr: 1.000e-04, eta: 1:06:37, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2391 2023-01-01 02:07:14,426 - mmcls - INFO - Epoch [91][5000/5005] lr: 1.000e-04, eta: 1:06:28, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2403 2023-01-01 02:07:36,961 - mmcls - INFO - Epoch(val) [91][196] accuracy_top-1: 71.3920, accuracy_top-5: 90.1500 2023-01-01 02:07:47,861 - mmcls - INFO - Epoch [92][100/5005] lr: 1.000e-04, eta: 1:06:19, time: 0.109, data_time: 0.036, memory: 1224, loss: 1.2221 2023-01-01 02:07:56,764 - mmcls - INFO - Epoch [92][200/5005] lr: 1.000e-04, eta: 1:06:10, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2454 2023-01-01 02:08:05,544 - mmcls - INFO - Epoch [92][300/5005] lr: 1.000e-04, eta: 1:06:01, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2372 2023-01-01 02:08:14,241 - mmcls - INFO - Epoch [92][400/5005] lr: 1.000e-04, eta: 1:05:52, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2293 2023-01-01 02:08:22,942 - mmcls - INFO - Epoch [92][500/5005] lr: 1.000e-04, eta: 1:05:43, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2180 2023-01-01 02:08:31,641 - mmcls - INFO - Epoch [92][600/5005] lr: 1.000e-04, eta: 1:05:34, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2193 2023-01-01 02:08:40,408 - mmcls - INFO - Epoch [92][700/5005] lr: 1.000e-04, eta: 1:05:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2297 2023-01-01 02:08:49,157 - mmcls - INFO - Epoch [92][800/5005] lr: 1.000e-04, eta: 1:05:17, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2375 2023-01-01 02:08:58,152 - mmcls - INFO - Epoch [92][900/5005] lr: 1.000e-04, eta: 1:05:08, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2607 2023-01-01 02:09:06,738 - mmcls - INFO - Epoch [92][1000/5005] lr: 1.000e-04, eta: 1:04:59, time: 0.086, data_time: 0.009, memory: 1224, loss: 1.2393 2023-01-01 02:09:15,622 - mmcls - INFO - Epoch [92][1100/5005] lr: 1.000e-04, eta: 1:04:50, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2622 2023-01-01 02:09:24,342 - mmcls - INFO - Epoch [92][1200/5005] lr: 1.000e-04, eta: 1:04:41, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.2489 2023-01-01 02:09:33,245 - mmcls - INFO - Epoch [92][1300/5005] lr: 1.000e-04, eta: 1:04:32, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2403 2023-01-01 02:09:41,989 - mmcls - INFO - Epoch [92][1400/5005] lr: 1.000e-04, eta: 1:04:23, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2166 2023-01-01 02:09:50,424 - mmcls - INFO - Epoch [92][1500/5005] lr: 1.000e-04, eta: 1:04:14, time: 0.084, data_time: 0.005, memory: 1224, loss: 1.2442 2023-01-01 02:09:59,266 - mmcls - INFO - Epoch [92][1600/5005] lr: 1.000e-04, eta: 1:04:06, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2280 2023-01-01 02:10:07,891 - mmcls - INFO - Epoch [92][1700/5005] lr: 1.000e-04, eta: 1:03:57, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2564 2023-01-01 02:10:16,926 - mmcls - INFO - Epoch [92][1800/5005] lr: 1.000e-04, eta: 1:03:48, time: 0.090, data_time: 0.011, memory: 1224, loss: 1.2369 2023-01-01 02:10:25,828 - mmcls - INFO - Epoch [92][1900/5005] lr: 1.000e-04, eta: 1:03:39, time: 0.089, data_time: 0.026, memory: 1224, loss: 1.2334 2023-01-01 02:10:34,702 - mmcls - INFO - Epoch [92][2000/5005] lr: 1.000e-04, eta: 1:03:30, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2396 2023-01-01 02:10:43,665 - mmcls - INFO - Epoch [92][2100/5005] lr: 1.000e-04, eta: 1:03:21, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2264 2023-01-01 02:10:52,443 - mmcls - INFO - Epoch [92][2200/5005] lr: 1.000e-04, eta: 1:03:13, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.2573 2023-01-01 02:11:01,709 - mmcls - INFO - Epoch [92][2300/5005] lr: 1.000e-04, eta: 1:03:04, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.2390 2023-01-01 02:11:10,483 - mmcls - INFO - Epoch [92][2400/5005] lr: 1.000e-04, eta: 1:02:55, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2557 2023-01-01 02:11:18,951 - mmcls - INFO - Epoch [92][2500/5005] lr: 1.000e-04, eta: 1:02:46, time: 0.085, data_time: 0.009, memory: 1224, loss: 1.2408 2023-01-01 02:11:27,913 - mmcls - INFO - Epoch [92][2600/5005] lr: 1.000e-04, eta: 1:02:37, time: 0.090, data_time: 0.000, memory: 1224, loss: 1.2391 2023-01-01 02:11:36,425 - mmcls - INFO - Epoch [92][2700/5005] lr: 1.000e-04, eta: 1:02:28, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2432 2023-01-01 02:11:45,640 - mmcls - INFO - Epoch [92][2800/5005] lr: 1.000e-04, eta: 1:02:19, time: 0.092, data_time: 0.004, memory: 1224, loss: 1.2240 2023-01-01 02:11:54,524 - mmcls - INFO - Epoch [92][2900/5005] lr: 1.000e-04, eta: 1:02:11, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2250 2023-01-01 02:12:03,326 - mmcls - INFO - Epoch [92][3000/5005] lr: 1.000e-04, eta: 1:02:02, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2354 2023-01-01 02:12:12,224 - mmcls - INFO - Epoch [92][3100/5005] lr: 1.000e-04, eta: 1:01:53, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2471 2023-01-01 02:12:21,184 - mmcls - INFO - Epoch [92][3200/5005] lr: 1.000e-04, eta: 1:01:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2268 2023-01-01 02:12:29,974 - mmcls - INFO - Epoch [92][3300/5005] lr: 1.000e-04, eta: 1:01:35, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2516 2023-01-01 02:12:38,839 - mmcls - INFO - Epoch [92][3400/5005] lr: 1.000e-04, eta: 1:01:26, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2219 2023-01-01 02:12:47,559 - mmcls - INFO - Epoch [92][3500/5005] lr: 1.000e-04, eta: 1:01:17, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2557 2023-01-01 02:12:56,464 - mmcls - INFO - Epoch [92][3600/5005] lr: 1.000e-04, eta: 1:01:09, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2319 2023-01-01 02:13:05,232 - mmcls - INFO - Epoch [92][3700/5005] lr: 1.000e-04, eta: 1:01:00, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2497 2023-01-01 02:13:14,107 - mmcls - INFO - Epoch [92][3800/5005] lr: 1.000e-04, eta: 1:00:51, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.2347 2023-01-01 02:13:22,766 - mmcls - INFO - Epoch [92][3900/5005] lr: 1.000e-04, eta: 1:00:42, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2414 2023-01-01 02:13:31,278 - mmcls - INFO - Epoch [92][4000/5005] lr: 1.000e-04, eta: 1:00:33, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.2320 2023-01-01 02:13:40,115 - mmcls - INFO - Epoch [92][4100/5005] lr: 1.000e-04, eta: 1:00:24, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2608 2023-01-01 02:13:48,885 - mmcls - INFO - Epoch [92][4200/5005] lr: 1.000e-04, eta: 1:00:15, time: 0.088, data_time: 0.023, memory: 1224, loss: 1.2207 2023-01-01 02:13:58,056 - mmcls - INFO - Epoch [92][4300/5005] lr: 1.000e-04, eta: 1:00:07, time: 0.092, data_time: 0.014, memory: 1224, loss: 1.2383 2023-01-01 02:14:06,897 - mmcls - INFO - Epoch [92][4400/5005] lr: 1.000e-04, eta: 0:59:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2420 2023-01-01 02:14:15,659 - mmcls - INFO - Epoch [92][4500/5005] lr: 1.000e-04, eta: 0:59:49, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2457 2023-01-01 02:14:24,552 - mmcls - INFO - Epoch [92][4600/5005] lr: 1.000e-04, eta: 0:59:40, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2302 2023-01-01 02:14:33,300 - mmcls - INFO - Epoch [92][4700/5005] lr: 1.000e-04, eta: 0:59:31, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2333 2023-01-01 02:14:42,269 - mmcls - INFO - Epoch [92][4800/5005] lr: 1.000e-04, eta: 0:59:22, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2604 2023-01-01 02:14:51,373 - mmcls - INFO - Epoch [92][4900/5005] lr: 1.000e-04, eta: 0:59:13, time: 0.091, data_time: 0.026, memory: 1224, loss: 1.2319 2023-01-01 02:15:00,173 - mmcls - INFO - Epoch [92][5000/5005] lr: 1.000e-04, eta: 0:59:05, time: 0.088, data_time: 0.037, memory: 1224, loss: 1.2458 2023-01-01 02:15:22,780 - mmcls - INFO - Epoch(val) [92][196] accuracy_top-1: 71.4800, accuracy_top-5: 90.1240 2023-01-01 02:15:33,907 - mmcls - INFO - Epoch [93][100/5005] lr: 1.000e-04, eta: 0:58:55, time: 0.111, data_time: 0.069, memory: 1224, loss: 1.2322 2023-01-01 02:15:42,555 - mmcls - INFO - Epoch [93][200/5005] lr: 1.000e-04, eta: 0:58:47, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2203 2023-01-01 02:15:51,470 - mmcls - INFO - Epoch [93][300/5005] lr: 1.000e-04, eta: 0:58:38, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2550 2023-01-01 02:16:00,397 - mmcls - INFO - Epoch [93][400/5005] lr: 1.000e-04, eta: 0:58:29, time: 0.089, data_time: 0.011, memory: 1224, loss: 1.2211 2023-01-01 02:16:09,142 - mmcls - INFO - Epoch [93][500/5005] lr: 1.000e-04, eta: 0:58:20, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2154 2023-01-01 02:16:18,204 - mmcls - INFO - Epoch [93][600/5005] lr: 1.000e-04, eta: 0:58:11, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2191 2023-01-01 02:16:27,126 - mmcls - INFO - Epoch [93][700/5005] lr: 1.000e-04, eta: 0:58:02, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2394 2023-01-01 02:16:35,958 - mmcls - INFO - Epoch [93][800/5005] lr: 1.000e-04, eta: 0:57:54, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2354 2023-01-01 02:16:44,988 - mmcls - INFO - Epoch [93][900/5005] lr: 1.000e-04, eta: 0:57:45, time: 0.090, data_time: 0.013, memory: 1224, loss: 1.2557 2023-01-01 02:16:53,510 - mmcls - INFO - Epoch [93][1000/5005] lr: 1.000e-04, eta: 0:57:36, time: 0.085, data_time: 0.014, memory: 1224, loss: 1.2329 2023-01-01 02:17:02,250 - mmcls - INFO - Epoch [93][1100/5005] lr: 1.000e-04, eta: 0:57:27, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.2210 2023-01-01 02:17:10,887 - mmcls - INFO - Epoch [93][1200/5005] lr: 1.000e-04, eta: 0:57:18, time: 0.086, data_time: 0.016, memory: 1224, loss: 1.2241 2023-01-01 02:17:19,529 - mmcls - INFO - Epoch [93][1300/5005] lr: 1.000e-04, eta: 0:57:09, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2548 2023-01-01 02:17:28,187 - mmcls - INFO - Epoch [93][1400/5005] lr: 1.000e-04, eta: 0:57:00, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2458 2023-01-01 02:17:37,065 - mmcls - INFO - Epoch [93][1500/5005] lr: 1.000e-04, eta: 0:56:51, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2156 2023-01-01 02:17:45,952 - mmcls - INFO - Epoch [93][1600/5005] lr: 1.000e-04, eta: 0:56:43, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2472 2023-01-01 02:17:54,626 - mmcls - INFO - Epoch [93][1700/5005] lr: 1.000e-04, eta: 0:56:34, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2553 2023-01-01 02:18:03,410 - mmcls - INFO - Epoch [93][1800/5005] lr: 1.000e-04, eta: 0:56:25, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2171 2023-01-01 02:18:11,887 - mmcls - INFO - Epoch [93][1900/5005] lr: 1.000e-04, eta: 0:56:16, time: 0.085, data_time: 0.001, memory: 1224, loss: 1.2351 2023-01-01 02:18:20,805 - mmcls - INFO - Epoch [93][2000/5005] lr: 1.000e-04, eta: 0:56:07, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2468 2023-01-01 02:18:29,520 - mmcls - INFO - Epoch [93][2100/5005] lr: 1.000e-04, eta: 0:55:58, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2440 2023-01-01 02:18:38,702 - mmcls - INFO - Epoch [93][2200/5005] lr: 1.000e-04, eta: 0:55:49, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2418 2023-01-01 02:18:47,461 - mmcls - INFO - Epoch [93][2300/5005] lr: 1.000e-04, eta: 0:55:41, time: 0.087, data_time: 0.000, memory: 1224, loss: 1.2414 2023-01-01 02:18:56,266 - mmcls - INFO - Epoch [93][2400/5005] lr: 1.000e-04, eta: 0:55:32, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2655 2023-01-01 02:19:05,031 - mmcls - INFO - Epoch [93][2500/5005] lr: 1.000e-04, eta: 0:55:23, time: 0.088, data_time: 0.018, memory: 1224, loss: 1.2619 2023-01-01 02:19:13,834 - mmcls - INFO - Epoch [93][2600/5005] lr: 1.000e-04, eta: 0:55:14, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2314 2023-01-01 02:19:22,587 - mmcls - INFO - Epoch [93][2700/5005] lr: 1.000e-04, eta: 0:55:05, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2297 2023-01-01 02:19:31,699 - mmcls - INFO - Epoch [93][2800/5005] lr: 1.000e-04, eta: 0:54:56, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2392 2023-01-01 02:19:40,570 - mmcls - INFO - Epoch [93][2900/5005] lr: 1.000e-04, eta: 0:54:47, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.2360 2023-01-01 02:19:49,184 - mmcls - INFO - Epoch [93][3000/5005] lr: 1.000e-04, eta: 0:54:39, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2317 2023-01-01 02:19:58,253 - mmcls - INFO - Epoch [93][3100/5005] lr: 1.000e-04, eta: 0:54:30, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.2208 2023-01-01 02:20:07,028 - mmcls - INFO - Epoch [93][3200/5005] lr: 1.000e-04, eta: 0:54:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2227 2023-01-01 02:20:15,934 - mmcls - INFO - Epoch [93][3300/5005] lr: 1.000e-04, eta: 0:54:12, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2399 2023-01-01 02:20:24,961 - mmcls - INFO - Epoch [93][3400/5005] lr: 1.000e-04, eta: 0:54:03, time: 0.090, data_time: 0.014, memory: 1224, loss: 1.2283 2023-01-01 02:20:33,758 - mmcls - INFO - Epoch [93][3500/5005] lr: 1.000e-04, eta: 0:53:54, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2488 2023-01-01 02:20:42,639 - mmcls - INFO - Epoch [93][3600/5005] lr: 1.000e-04, eta: 0:53:46, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.2252 2023-01-01 02:20:51,629 - mmcls - INFO - Epoch [93][3700/5005] lr: 1.000e-04, eta: 0:53:37, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2571 2023-01-01 02:21:00,318 - mmcls - INFO - Epoch [93][3800/5005] lr: 1.000e-04, eta: 0:53:28, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2337 2023-01-01 02:21:09,232 - mmcls - INFO - Epoch [93][3900/5005] lr: 1.000e-04, eta: 0:53:19, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.2258 2023-01-01 02:21:17,825 - mmcls - INFO - Epoch [93][4000/5005] lr: 1.000e-04, eta: 0:53:10, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2435 2023-01-01 02:21:26,764 - mmcls - INFO - Epoch [93][4100/5005] lr: 1.000e-04, eta: 0:53:01, time: 0.089, data_time: 0.021, memory: 1224, loss: 1.2205 2023-01-01 02:21:35,838 - mmcls - INFO - Epoch [93][4200/5005] lr: 1.000e-04, eta: 0:52:52, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2256 2023-01-01 02:21:44,390 - mmcls - INFO - Epoch [93][4300/5005] lr: 1.000e-04, eta: 0:52:44, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.2352 2023-01-01 02:21:53,572 - mmcls - INFO - Epoch [93][4400/5005] lr: 1.000e-04, eta: 0:52:35, time: 0.092, data_time: 0.016, memory: 1224, loss: 1.2434 2023-01-01 02:22:02,060 - mmcls - INFO - Epoch [93][4500/5005] lr: 1.000e-04, eta: 0:52:26, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2436 2023-01-01 02:22:10,562 - mmcls - INFO - Epoch [93][4600/5005] lr: 1.000e-04, eta: 0:52:17, time: 0.085, data_time: 0.006, memory: 1224, loss: 1.2375 2023-01-01 02:22:19,403 - mmcls - INFO - Epoch [93][4700/5005] lr: 1.000e-04, eta: 0:52:08, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2653 2023-01-01 02:22:28,086 - mmcls - INFO - Epoch [93][4800/5005] lr: 1.000e-04, eta: 0:51:59, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2337 2023-01-01 02:22:36,847 - mmcls - INFO - Epoch [93][4900/5005] lr: 1.000e-04, eta: 0:51:50, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2379 2023-01-01 02:22:45,499 - mmcls - INFO - Epoch [93][5000/5005] lr: 1.000e-04, eta: 0:51:42, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2175 2023-01-01 02:23:08,155 - mmcls - INFO - Epoch(val) [93][196] accuracy_top-1: 71.5360, accuracy_top-5: 90.1280 2023-01-01 02:23:19,115 - mmcls - INFO - Epoch [94][100/5005] lr: 1.000e-04, eta: 0:51:32, time: 0.109, data_time: 0.040, memory: 1224, loss: 1.2231 2023-01-01 02:23:27,988 - mmcls - INFO - Epoch [94][200/5005] lr: 1.000e-04, eta: 0:51:23, time: 0.089, data_time: 0.015, memory: 1224, loss: 1.2338 2023-01-01 02:23:36,903 - mmcls - INFO - Epoch [94][300/5005] lr: 1.000e-04, eta: 0:51:15, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2309 2023-01-01 02:23:45,406 - mmcls - INFO - Epoch [94][400/5005] lr: 1.000e-04, eta: 0:51:06, time: 0.085, data_time: 0.022, memory: 1224, loss: 1.2268 2023-01-01 02:23:54,368 - mmcls - INFO - Epoch [94][500/5005] lr: 1.000e-04, eta: 0:50:57, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2188 2023-01-01 02:24:03,463 - mmcls - INFO - Epoch [94][600/5005] lr: 1.000e-04, eta: 0:50:48, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2402 2023-01-01 02:24:12,236 - mmcls - INFO - Epoch [94][700/5005] lr: 1.000e-04, eta: 0:50:39, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2232 2023-01-01 02:24:21,071 - mmcls - INFO - Epoch [94][800/5005] lr: 1.000e-04, eta: 0:50:30, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2467 2023-01-01 02:24:29,605 - mmcls - INFO - Epoch [94][900/5005] lr: 1.000e-04, eta: 0:50:21, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.2226 2023-01-01 02:24:38,542 - mmcls - INFO - Epoch [94][1000/5005] lr: 1.000e-04, eta: 0:50:13, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.2185 2023-01-01 02:24:47,374 - mmcls - INFO - Epoch [94][1100/5005] lr: 1.000e-04, eta: 0:50:04, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2104 2023-01-01 02:24:56,174 - mmcls - INFO - Epoch [94][1200/5005] lr: 1.000e-04, eta: 0:49:55, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2529 2023-01-01 02:25:04,835 - mmcls - INFO - Epoch [94][1300/5005] lr: 1.000e-04, eta: 0:49:46, time: 0.087, data_time: 0.032, memory: 1224, loss: 1.2343 2023-01-01 02:25:13,708 - mmcls - INFO - Epoch [94][1400/5005] lr: 1.000e-04, eta: 0:49:37, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2252 2023-01-01 02:25:22,418 - mmcls - INFO - Epoch [94][1500/5005] lr: 1.000e-04, eta: 0:49:28, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.2372 2023-01-01 02:25:31,121 - mmcls - INFO - Epoch [94][1600/5005] lr: 1.000e-04, eta: 0:49:19, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2249 2023-01-01 02:25:39,849 - mmcls - INFO - Epoch [94][1700/5005] lr: 1.000e-04, eta: 0:49:11, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2114 2023-01-01 02:25:48,629 - mmcls - INFO - Epoch [94][1800/5005] lr: 1.000e-04, eta: 0:49:02, time: 0.088, data_time: 0.000, memory: 1224, loss: 1.2313 2023-01-01 02:25:57,179 - mmcls - INFO - Epoch [94][1900/5005] lr: 1.000e-04, eta: 0:48:53, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2321 2023-01-01 02:26:05,926 - mmcls - INFO - Epoch [94][2000/5005] lr: 1.000e-04, eta: 0:48:44, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2612 2023-01-01 02:26:15,000 - mmcls - INFO - Epoch [94][2100/5005] lr: 1.000e-04, eta: 0:48:35, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2405 2023-01-01 02:26:24,288 - mmcls - INFO - Epoch [94][2200/5005] lr: 1.000e-04, eta: 0:48:26, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2324 2023-01-01 02:26:33,157 - mmcls - INFO - Epoch [94][2300/5005] lr: 1.000e-04, eta: 0:48:18, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2392 2023-01-01 02:26:42,067 - mmcls - INFO - Epoch [94][2400/5005] lr: 1.000e-04, eta: 0:48:09, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2265 2023-01-01 02:26:50,780 - mmcls - INFO - Epoch [94][2500/5005] lr: 1.000e-04, eta: 0:48:00, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2153 2023-01-01 02:26:59,483 - mmcls - INFO - Epoch [94][2600/5005] lr: 1.000e-04, eta: 0:47:51, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2220 2023-01-01 02:27:08,075 - mmcls - INFO - Epoch [94][2700/5005] lr: 1.000e-04, eta: 0:47:42, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2158 2023-01-01 02:27:17,414 - mmcls - INFO - Epoch [94][2800/5005] lr: 1.000e-04, eta: 0:47:33, time: 0.093, data_time: 0.002, memory: 1224, loss: 1.2244 2023-01-01 02:27:26,259 - mmcls - INFO - Epoch [94][2900/5005] lr: 1.000e-04, eta: 0:47:24, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2105 2023-01-01 02:27:35,162 - mmcls - INFO - Epoch [94][3000/5005] lr: 1.000e-04, eta: 0:47:16, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2200 2023-01-01 02:27:43,934 - mmcls - INFO - Epoch [94][3100/5005] lr: 1.000e-04, eta: 0:47:07, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2470 2023-01-01 02:27:52,840 - mmcls - INFO - Epoch [94][3200/5005] lr: 1.000e-04, eta: 0:46:58, time: 0.089, data_time: 0.006, memory: 1224, loss: 1.2430 2023-01-01 02:28:01,743 - mmcls - INFO - Epoch [94][3300/5005] lr: 1.000e-04, eta: 0:46:49, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2281 2023-01-01 02:28:10,925 - mmcls - INFO - Epoch [94][3400/5005] lr: 1.000e-04, eta: 0:46:40, time: 0.092, data_time: 0.038, memory: 1224, loss: 1.2344 2023-01-01 02:28:19,742 - mmcls - INFO - Epoch [94][3500/5005] lr: 1.000e-04, eta: 0:46:31, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2322 2023-01-01 02:28:28,692 - mmcls - INFO - Epoch [94][3600/5005] lr: 1.000e-04, eta: 0:46:22, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2526 2023-01-01 02:28:37,428 - mmcls - INFO - Epoch [94][3700/5005] lr: 1.000e-04, eta: 0:46:14, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2237 2023-01-01 02:28:46,062 - mmcls - INFO - Epoch [94][3800/5005] lr: 1.000e-04, eta: 0:46:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2270 2023-01-01 02:28:54,819 - mmcls - INFO - Epoch [94][3900/5005] lr: 1.000e-04, eta: 0:45:56, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2226 2023-01-01 02:29:03,596 - mmcls - INFO - Epoch [94][4000/5005] lr: 1.000e-04, eta: 0:45:47, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2415 2023-01-01 02:29:12,301 - mmcls - INFO - Epoch [94][4100/5005] lr: 1.000e-04, eta: 0:45:38, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2141 2023-01-01 02:29:20,967 - mmcls - INFO - Epoch [94][4200/5005] lr: 1.000e-04, eta: 0:45:29, time: 0.087, data_time: 0.021, memory: 1224, loss: 1.2332 2023-01-01 02:29:29,940 - mmcls - INFO - Epoch [94][4300/5005] lr: 1.000e-04, eta: 0:45:20, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2397 2023-01-01 02:29:38,843 - mmcls - INFO - Epoch [94][4400/5005] lr: 1.000e-04, eta: 0:45:12, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2487 2023-01-01 02:29:47,817 - mmcls - INFO - Epoch [94][4500/5005] lr: 1.000e-04, eta: 0:45:03, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2330 2023-01-01 02:29:56,762 - mmcls - INFO - Epoch [94][4600/5005] lr: 1.000e-04, eta: 0:44:54, time: 0.089, data_time: 0.000, memory: 1224, loss: 1.2448 2023-01-01 02:30:05,577 - mmcls - INFO - Epoch [94][4700/5005] lr: 1.000e-04, eta: 0:44:45, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2149 2023-01-01 02:30:14,126 - mmcls - INFO - Epoch [94][4800/5005] lr: 1.000e-04, eta: 0:44:36, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2510 2023-01-01 02:30:22,941 - mmcls - INFO - Epoch [94][4900/5005] lr: 1.000e-04, eta: 0:44:27, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2387 2023-01-01 02:30:31,698 - mmcls - INFO - Epoch [94][5000/5005] lr: 1.000e-04, eta: 0:44:18, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.2505 2023-01-01 02:30:54,330 - mmcls - INFO - Epoch(val) [94][196] accuracy_top-1: 71.5540, accuracy_top-5: 90.2140 2023-01-01 02:31:05,421 - mmcls - INFO - Epoch [95][100/5005] lr: 1.000e-04, eta: 0:44:09, time: 0.111, data_time: 0.061, memory: 1224, loss: 1.2457 2023-01-01 02:31:14,275 - mmcls - INFO - Epoch [95][200/5005] lr: 1.000e-04, eta: 0:44:00, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2312 2023-01-01 02:31:22,894 - mmcls - INFO - Epoch [95][300/5005] lr: 1.000e-04, eta: 0:43:52, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2208 2023-01-01 02:31:31,650 - mmcls - INFO - Epoch [95][400/5005] lr: 1.000e-04, eta: 0:43:43, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2210 2023-01-01 02:31:40,839 - mmcls - INFO - Epoch [95][500/5005] lr: 1.000e-04, eta: 0:43:34, time: 0.092, data_time: 0.017, memory: 1224, loss: 1.2192 2023-01-01 02:31:49,660 - mmcls - INFO - Epoch [95][600/5005] lr: 1.000e-04, eta: 0:43:25, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2204 2023-01-01 02:31:58,529 - mmcls - INFO - Epoch [95][700/5005] lr: 1.000e-04, eta: 0:43:16, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2115 2023-01-01 02:32:07,202 - mmcls - INFO - Epoch [95][800/5005] lr: 1.000e-04, eta: 0:43:07, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2260 2023-01-01 02:32:16,034 - mmcls - INFO - Epoch [95][900/5005] lr: 1.000e-04, eta: 0:42:58, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2403 2023-01-01 02:32:24,750 - mmcls - INFO - Epoch [95][1000/5005] lr: 1.000e-04, eta: 0:42:50, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2350 2023-01-01 02:32:33,772 - mmcls - INFO - Epoch [95][1100/5005] lr: 1.000e-04, eta: 0:42:41, time: 0.090, data_time: 0.007, memory: 1224, loss: 1.2268 2023-01-01 02:32:42,953 - mmcls - INFO - Epoch [95][1200/5005] lr: 1.000e-04, eta: 0:42:32, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.2448 2023-01-01 02:32:51,664 - mmcls - INFO - Epoch [95][1300/5005] lr: 1.000e-04, eta: 0:42:23, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2320 2023-01-01 02:33:00,833 - mmcls - INFO - Epoch [95][1400/5005] lr: 1.000e-04, eta: 0:42:14, time: 0.092, data_time: 0.002, memory: 1224, loss: 1.2295 2023-01-01 02:33:09,454 - mmcls - INFO - Epoch [95][1500/5005] lr: 1.000e-04, eta: 0:42:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2487 2023-01-01 02:33:18,453 - mmcls - INFO - Epoch [95][1600/5005] lr: 1.000e-04, eta: 0:41:56, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2379 2023-01-01 02:33:27,163 - mmcls - INFO - Epoch [95][1700/5005] lr: 1.000e-04, eta: 0:41:48, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2317 2023-01-01 02:33:36,054 - mmcls - INFO - Epoch [95][1800/5005] lr: 1.000e-04, eta: 0:41:39, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2035 2023-01-01 02:33:44,809 - mmcls - INFO - Epoch [95][1900/5005] lr: 1.000e-04, eta: 0:41:30, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2266 2023-01-01 02:33:53,642 - mmcls - INFO - Epoch [95][2000/5005] lr: 1.000e-04, eta: 0:41:21, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2154 2023-01-01 02:34:02,421 - mmcls - INFO - Epoch [95][2100/5005] lr: 1.000e-04, eta: 0:41:12, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2251 2023-01-01 02:34:10,804 - mmcls - INFO - Epoch [95][2200/5005] lr: 1.000e-04, eta: 0:41:03, time: 0.084, data_time: 0.004, memory: 1224, loss: 1.2318 2023-01-01 02:34:19,670 - mmcls - INFO - Epoch [95][2300/5005] lr: 1.000e-04, eta: 0:40:54, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2391 2023-01-01 02:34:28,408 - mmcls - INFO - Epoch [95][2400/5005] lr: 1.000e-04, eta: 0:40:46, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2312 2023-01-01 02:34:37,170 - mmcls - INFO - Epoch [95][2500/5005] lr: 1.000e-04, eta: 0:40:37, time: 0.088, data_time: 0.026, memory: 1224, loss: 1.2017 2023-01-01 02:34:46,138 - mmcls - INFO - Epoch [95][2600/5005] lr: 1.000e-04, eta: 0:40:28, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.2482 2023-01-01 02:34:55,148 - mmcls - INFO - Epoch [95][2700/5005] lr: 1.000e-04, eta: 0:40:19, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.2308 2023-01-01 02:35:03,948 - mmcls - INFO - Epoch [95][2800/5005] lr: 1.000e-04, eta: 0:40:10, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2544 2023-01-01 02:35:12,745 - mmcls - INFO - Epoch [95][2900/5005] lr: 1.000e-04, eta: 0:40:01, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2462 2023-01-01 02:35:21,439 - mmcls - INFO - Epoch [95][3000/5005] lr: 1.000e-04, eta: 0:39:52, time: 0.087, data_time: 0.028, memory: 1224, loss: 1.2178 2023-01-01 02:35:30,417 - mmcls - INFO - Epoch [95][3100/5005] lr: 1.000e-04, eta: 0:39:44, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2195 2023-01-01 02:35:39,669 - mmcls - INFO - Epoch [95][3200/5005] lr: 1.000e-04, eta: 0:39:35, time: 0.093, data_time: 0.003, memory: 1224, loss: 1.2412 2023-01-01 02:35:48,433 - mmcls - INFO - Epoch [95][3300/5005] lr: 1.000e-04, eta: 0:39:26, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2268 2023-01-01 02:35:57,407 - mmcls - INFO - Epoch [95][3400/5005] lr: 1.000e-04, eta: 0:39:17, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2301 2023-01-01 02:36:06,455 - mmcls - INFO - Epoch [95][3500/5005] lr: 1.000e-04, eta: 0:39:08, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2342 2023-01-01 02:36:15,229 - mmcls - INFO - Epoch [95][3600/5005] lr: 1.000e-04, eta: 0:38:59, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2188 2023-01-01 02:36:23,775 - mmcls - INFO - Epoch [95][3700/5005] lr: 1.000e-04, eta: 0:38:51, time: 0.085, data_time: 0.017, memory: 1224, loss: 1.2277 2023-01-01 02:36:32,374 - mmcls - INFO - Epoch [95][3800/5005] lr: 1.000e-04, eta: 0:38:42, time: 0.086, data_time: 0.035, memory: 1224, loss: 1.2262 2023-01-01 02:36:40,970 - mmcls - INFO - Epoch [95][3900/5005] lr: 1.000e-04, eta: 0:38:33, time: 0.086, data_time: 0.024, memory: 1224, loss: 1.2564 2023-01-01 02:36:49,472 - mmcls - INFO - Epoch [95][4000/5005] lr: 1.000e-04, eta: 0:38:24, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2336 2023-01-01 02:36:58,451 - mmcls - INFO - Epoch [95][4100/5005] lr: 1.000e-04, eta: 0:38:15, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2062 2023-01-01 02:37:07,053 - mmcls - INFO - Epoch [95][4200/5005] lr: 1.000e-04, eta: 0:38:06, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2086 2023-01-01 02:37:15,885 - mmcls - INFO - Epoch [95][4300/5005] lr: 1.000e-04, eta: 0:37:57, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2490 2023-01-01 02:37:24,477 - mmcls - INFO - Epoch [95][4400/5005] lr: 1.000e-04, eta: 0:37:48, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2342 2023-01-01 02:37:33,244 - mmcls - INFO - Epoch [95][4500/5005] lr: 1.000e-04, eta: 0:37:40, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2297 2023-01-01 02:37:42,190 - mmcls - INFO - Epoch [95][4600/5005] lr: 1.000e-04, eta: 0:37:31, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2172 2023-01-01 02:37:50,993 - mmcls - INFO - Epoch [95][4700/5005] lr: 1.000e-04, eta: 0:37:22, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2337 2023-01-01 02:37:59,856 - mmcls - INFO - Epoch [95][4800/5005] lr: 1.000e-04, eta: 0:37:13, time: 0.089, data_time: 0.034, memory: 1224, loss: 1.2354 2023-01-01 02:38:08,681 - mmcls - INFO - Epoch [95][4900/5005] lr: 1.000e-04, eta: 0:37:04, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2431 2023-01-01 02:38:17,495 - mmcls - INFO - Epoch [95][5000/5005] lr: 1.000e-04, eta: 0:36:55, time: 0.088, data_time: 0.009, memory: 1224, loss: 1.2244 2023-01-01 02:38:40,214 - mmcls - INFO - Epoch(val) [95][196] accuracy_top-1: 71.6720, accuracy_top-5: 90.1620 2023-01-01 02:38:51,203 - mmcls - INFO - Epoch [96][100/5005] lr: 1.000e-04, eta: 0:36:46, time: 0.110, data_time: 0.049, memory: 1224, loss: 1.1938 2023-01-01 02:39:00,084 - mmcls - INFO - Epoch [96][200/5005] lr: 1.000e-04, eta: 0:36:37, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2391 2023-01-01 02:39:08,674 - mmcls - INFO - Epoch [96][300/5005] lr: 1.000e-04, eta: 0:36:28, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.2236 2023-01-01 02:39:17,542 - mmcls - INFO - Epoch [96][400/5005] lr: 1.000e-04, eta: 0:36:20, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2475 2023-01-01 02:39:26,318 - mmcls - INFO - Epoch [96][500/5005] lr: 1.000e-04, eta: 0:36:11, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2362 2023-01-01 02:39:35,093 - mmcls - INFO - Epoch [96][600/5005] lr: 1.000e-04, eta: 0:36:02, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2188 2023-01-01 02:39:43,802 - mmcls - INFO - Epoch [96][700/5005] lr: 1.000e-04, eta: 0:35:53, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2319 2023-01-01 02:39:52,599 - mmcls - INFO - Epoch [96][800/5005] lr: 1.000e-04, eta: 0:35:44, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2197 2023-01-01 02:40:01,739 - mmcls - INFO - Epoch [96][900/5005] lr: 1.000e-04, eta: 0:35:35, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2361 2023-01-01 02:40:10,495 - mmcls - INFO - Epoch [96][1000/5005] lr: 1.000e-04, eta: 0:35:26, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2394 2023-01-01 02:40:19,276 - mmcls - INFO - Epoch [96][1100/5005] lr: 1.000e-04, eta: 0:35:18, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2415 2023-01-01 02:40:28,116 - mmcls - INFO - Epoch [96][1200/5005] lr: 1.000e-04, eta: 0:35:09, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2218 2023-01-01 02:40:36,711 - mmcls - INFO - Epoch [96][1300/5005] lr: 1.000e-04, eta: 0:35:00, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2445 2023-01-01 02:40:45,341 - mmcls - INFO - Epoch [96][1400/5005] lr: 1.000e-04, eta: 0:34:51, time: 0.086, data_time: 0.023, memory: 1224, loss: 1.2291 2023-01-01 02:40:54,027 - mmcls - INFO - Epoch [96][1500/5005] lr: 1.000e-04, eta: 0:34:42, time: 0.087, data_time: 0.025, memory: 1224, loss: 1.2371 2023-01-01 02:41:02,778 - mmcls - INFO - Epoch [96][1600/5005] lr: 1.000e-04, eta: 0:34:33, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2212 2023-01-01 02:41:11,623 - mmcls - INFO - Epoch [96][1700/5005] lr: 1.000e-04, eta: 0:34:24, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2221 2023-01-01 02:41:20,373 - mmcls - INFO - Epoch [96][1800/5005] lr: 1.000e-04, eta: 0:34:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2136 2023-01-01 02:41:29,084 - mmcls - INFO - Epoch [96][1900/5005] lr: 1.000e-04, eta: 0:34:07, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2423 2023-01-01 02:41:37,882 - mmcls - INFO - Epoch [96][2000/5005] lr: 1.000e-04, eta: 0:33:58, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2338 2023-01-01 02:41:47,149 - mmcls - INFO - Epoch [96][2100/5005] lr: 1.000e-04, eta: 0:33:49, time: 0.093, data_time: 0.019, memory: 1224, loss: 1.2283 2023-01-01 02:41:55,981 - mmcls - INFO - Epoch [96][2200/5005] lr: 1.000e-04, eta: 0:33:40, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2140 2023-01-01 02:42:05,044 - mmcls - INFO - Epoch [96][2300/5005] lr: 1.000e-04, eta: 0:33:31, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2263 2023-01-01 02:42:13,982 - mmcls - INFO - Epoch [96][2400/5005] lr: 1.000e-04, eta: 0:33:22, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.2256 2023-01-01 02:42:22,613 - mmcls - INFO - Epoch [96][2500/5005] lr: 1.000e-04, eta: 0:33:14, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2271 2023-01-01 02:42:31,206 - mmcls - INFO - Epoch [96][2600/5005] lr: 1.000e-04, eta: 0:33:05, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2138 2023-01-01 02:42:39,996 - mmcls - INFO - Epoch [96][2700/5005] lr: 1.000e-04, eta: 0:32:56, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2261 2023-01-01 02:42:48,674 - mmcls - INFO - Epoch [96][2800/5005] lr: 1.000e-04, eta: 0:32:47, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2367 2023-01-01 02:42:57,654 - mmcls - INFO - Epoch [96][2900/5005] lr: 1.000e-04, eta: 0:32:38, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.2223 2023-01-01 02:43:06,253 - mmcls - INFO - Epoch [96][3000/5005] lr: 1.000e-04, eta: 0:32:29, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2379 2023-01-01 02:43:15,084 - mmcls - INFO - Epoch [96][3100/5005] lr: 1.000e-04, eta: 0:32:20, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2453 2023-01-01 02:43:23,928 - mmcls - INFO - Epoch [96][3200/5005] lr: 1.000e-04, eta: 0:32:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2352 2023-01-01 02:43:32,417 - mmcls - INFO - Epoch [96][3300/5005] lr: 1.000e-04, eta: 0:32:03, time: 0.085, data_time: 0.013, memory: 1224, loss: 1.2399 2023-01-01 02:43:41,478 - mmcls - INFO - Epoch [96][3400/5005] lr: 1.000e-04, eta: 0:31:54, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.2244 2023-01-01 02:43:49,801 - mmcls - INFO - Epoch [96][3500/5005] lr: 1.000e-04, eta: 0:31:45, time: 0.083, data_time: 0.004, memory: 1224, loss: 1.2431 2023-01-01 02:43:58,598 - mmcls - INFO - Epoch [96][3600/5005] lr: 1.000e-04, eta: 0:31:36, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2244 2023-01-01 02:44:07,115 - mmcls - INFO - Epoch [96][3700/5005] lr: 1.000e-04, eta: 0:31:27, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2318 2023-01-01 02:44:15,864 - mmcls - INFO - Epoch [96][3800/5005] lr: 1.000e-04, eta: 0:31:18, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2453 2023-01-01 02:44:24,524 - mmcls - INFO - Epoch [96][3900/5005] lr: 1.000e-04, eta: 0:31:10, time: 0.087, data_time: 0.026, memory: 1224, loss: 1.2192 2023-01-01 02:44:33,264 - mmcls - INFO - Epoch [96][4000/5005] lr: 1.000e-04, eta: 0:31:01, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2317 2023-01-01 02:44:41,887 - mmcls - INFO - Epoch [96][4100/5005] lr: 1.000e-04, eta: 0:30:52, time: 0.086, data_time: 0.010, memory: 1224, loss: 1.2298 2023-01-01 02:44:50,799 - mmcls - INFO - Epoch [96][4200/5005] lr: 1.000e-04, eta: 0:30:43, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2458 2023-01-01 02:44:59,805 - mmcls - INFO - Epoch [96][4300/5005] lr: 1.000e-04, eta: 0:30:34, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2296 2023-01-01 02:45:08,634 - mmcls - INFO - Epoch [96][4400/5005] lr: 1.000e-04, eta: 0:30:25, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2354 2023-01-01 02:45:17,363 - mmcls - INFO - Epoch [96][4500/5005] lr: 1.000e-04, eta: 0:30:16, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2244 2023-01-01 02:45:26,000 - mmcls - INFO - Epoch [96][4600/5005] lr: 1.000e-04, eta: 0:30:08, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2309 2023-01-01 02:45:34,758 - mmcls - INFO - Epoch [96][4700/5005] lr: 1.000e-04, eta: 0:29:59, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.2288 2023-01-01 02:45:43,623 - mmcls - INFO - Epoch [96][4800/5005] lr: 1.000e-04, eta: 0:29:50, time: 0.089, data_time: 0.008, memory: 1224, loss: 1.2457 2023-01-01 02:45:52,684 - mmcls - INFO - Epoch [96][4900/5005] lr: 1.000e-04, eta: 0:29:41, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.2386 2023-01-01 02:46:01,805 - mmcls - INFO - Epoch [96][5000/5005] lr: 1.000e-04, eta: 0:29:32, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2523 2023-01-01 02:46:24,377 - mmcls - INFO - Epoch(val) [96][196] accuracy_top-1: 71.6240, accuracy_top-5: 90.1460 2023-01-01 02:46:35,241 - mmcls - INFO - Epoch [97][100/5005] lr: 1.000e-04, eta: 0:29:23, time: 0.108, data_time: 0.064, memory: 1224, loss: 1.2338 2023-01-01 02:46:44,063 - mmcls - INFO - Epoch [97][200/5005] lr: 1.000e-04, eta: 0:29:14, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2229 2023-01-01 02:46:53,102 - mmcls - INFO - Epoch [97][300/5005] lr: 1.000e-04, eta: 0:29:05, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2287 2023-01-01 02:47:01,673 - mmcls - INFO - Epoch [97][400/5005] lr: 1.000e-04, eta: 0:28:56, time: 0.086, data_time: 0.008, memory: 1224, loss: 1.2285 2023-01-01 02:47:10,575 - mmcls - INFO - Epoch [97][500/5005] lr: 1.000e-04, eta: 0:28:48, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2308 2023-01-01 02:47:19,270 - mmcls - INFO - Epoch [97][600/5005] lr: 1.000e-04, eta: 0:28:39, time: 0.087, data_time: 0.018, memory: 1224, loss: 1.2200 2023-01-01 02:47:27,826 - mmcls - INFO - Epoch [97][700/5005] lr: 1.000e-04, eta: 0:28:30, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2221 2023-01-01 02:47:36,457 - mmcls - INFO - Epoch [97][800/5005] lr: 1.000e-04, eta: 0:28:21, time: 0.086, data_time: 0.012, memory: 1224, loss: 1.2344 2023-01-01 02:47:45,519 - mmcls - INFO - Epoch [97][900/5005] lr: 1.000e-04, eta: 0:28:12, time: 0.091, data_time: 0.015, memory: 1224, loss: 1.1948 2023-01-01 02:47:54,629 - mmcls - INFO - Epoch [97][1000/5005] lr: 1.000e-04, eta: 0:28:03, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2146 2023-01-01 02:48:03,377 - mmcls - INFO - Epoch [97][1100/5005] lr: 1.000e-04, eta: 0:27:54, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2324 2023-01-01 02:48:12,318 - mmcls - INFO - Epoch [97][1200/5005] lr: 1.000e-04, eta: 0:27:46, time: 0.089, data_time: 0.031, memory: 1224, loss: 1.2014 2023-01-01 02:48:21,055 - mmcls - INFO - Epoch [97][1300/5005] lr: 1.000e-04, eta: 0:27:37, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2248 2023-01-01 02:48:29,867 - mmcls - INFO - Epoch [97][1400/5005] lr: 1.000e-04, eta: 0:27:28, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2194 2023-01-01 02:48:38,578 - mmcls - INFO - Epoch [97][1500/5005] lr: 1.000e-04, eta: 0:27:19, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2405 2023-01-01 02:48:47,286 - mmcls - INFO - Epoch [97][1600/5005] lr: 1.000e-04, eta: 0:27:10, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2400 2023-01-01 02:48:56,055 - mmcls - INFO - Epoch [97][1700/5005] lr: 1.000e-04, eta: 0:27:01, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2407 2023-01-01 02:49:04,958 - mmcls - INFO - Epoch [97][1800/5005] lr: 1.000e-04, eta: 0:26:52, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2181 2023-01-01 02:49:14,068 - mmcls - INFO - Epoch [97][1900/5005] lr: 1.000e-04, eta: 0:26:44, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2202 2023-01-01 02:49:23,097 - mmcls - INFO - Epoch [97][2000/5005] lr: 1.000e-04, eta: 0:26:35, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.2338 2023-01-01 02:49:31,727 - mmcls - INFO - Epoch [97][2100/5005] lr: 1.000e-04, eta: 0:26:26, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2183 2023-01-01 02:49:41,034 - mmcls - INFO - Epoch [97][2200/5005] lr: 1.000e-04, eta: 0:26:17, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2292 2023-01-01 02:49:50,027 - mmcls - INFO - Epoch [97][2300/5005] lr: 1.000e-04, eta: 0:26:08, time: 0.090, data_time: 0.005, memory: 1224, loss: 1.2285 2023-01-01 02:49:58,681 - mmcls - INFO - Epoch [97][2400/5005] lr: 1.000e-04, eta: 0:25:59, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2102 2023-01-01 02:50:07,406 - mmcls - INFO - Epoch [97][2500/5005] lr: 1.000e-04, eta: 0:25:51, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2180 2023-01-01 02:50:15,952 - mmcls - INFO - Epoch [97][2600/5005] lr: 1.000e-04, eta: 0:25:42, time: 0.086, data_time: 0.002, memory: 1224, loss: 1.2347 2023-01-01 02:50:25,267 - mmcls - INFO - Epoch [97][2700/5005] lr: 1.000e-04, eta: 0:25:33, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2376 2023-01-01 02:50:33,975 - mmcls - INFO - Epoch [97][2800/5005] lr: 1.000e-04, eta: 0:25:24, time: 0.087, data_time: 0.004, memory: 1224, loss: 1.2309 2023-01-01 02:50:43,010 - mmcls - INFO - Epoch [97][2900/5005] lr: 1.000e-04, eta: 0:25:15, time: 0.090, data_time: 0.003, memory: 1224, loss: 1.2407 2023-01-01 02:50:51,947 - mmcls - INFO - Epoch [97][3000/5005] lr: 1.000e-04, eta: 0:25:06, time: 0.089, data_time: 0.020, memory: 1224, loss: 1.2268 2023-01-01 02:51:01,036 - mmcls - INFO - Epoch [97][3100/5005] lr: 1.000e-04, eta: 0:24:57, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2325 2023-01-01 02:51:09,705 - mmcls - INFO - Epoch [97][3200/5005] lr: 1.000e-04, eta: 0:24:49, time: 0.087, data_time: 0.011, memory: 1224, loss: 1.2276 2023-01-01 02:51:18,740 - mmcls - INFO - Epoch [97][3300/5005] lr: 1.000e-04, eta: 0:24:40, time: 0.090, data_time: 0.009, memory: 1224, loss: 1.2317 2023-01-01 02:51:27,448 - mmcls - INFO - Epoch [97][3400/5005] lr: 1.000e-04, eta: 0:24:31, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2425 2023-01-01 02:51:36,312 - mmcls - INFO - Epoch [97][3500/5005] lr: 1.000e-04, eta: 0:24:22, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2353 2023-01-01 02:51:45,399 - mmcls - INFO - Epoch [97][3600/5005] lr: 1.000e-04, eta: 0:24:13, time: 0.091, data_time: 0.000, memory: 1224, loss: 1.2308 2023-01-01 02:51:54,260 - mmcls - INFO - Epoch [97][3700/5005] lr: 1.000e-04, eta: 0:24:04, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2311 2023-01-01 02:52:02,937 - mmcls - INFO - Epoch [97][3800/5005] lr: 1.000e-04, eta: 0:23:55, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2239 2023-01-01 02:52:11,658 - mmcls - INFO - Epoch [97][3900/5005] lr: 1.000e-04, eta: 0:23:47, time: 0.087, data_time: 0.005, memory: 1224, loss: 1.2220 2023-01-01 02:52:20,233 - mmcls - INFO - Epoch [97][4000/5005] lr: 1.000e-04, eta: 0:23:38, time: 0.086, data_time: 0.020, memory: 1224, loss: 1.2256 2023-01-01 02:52:28,830 - mmcls - INFO - Epoch [97][4100/5005] lr: 1.000e-04, eta: 0:23:29, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2438 2023-01-01 02:52:37,404 - mmcls - INFO - Epoch [97][4200/5005] lr: 1.000e-04, eta: 0:23:20, time: 0.086, data_time: 0.005, memory: 1224, loss: 1.2236 2023-01-01 02:52:46,458 - mmcls - INFO - Epoch [97][4300/5005] lr: 1.000e-04, eta: 0:23:11, time: 0.091, data_time: 0.005, memory: 1224, loss: 1.2398 2023-01-01 02:52:55,366 - mmcls - INFO - Epoch [97][4400/5005] lr: 1.000e-04, eta: 0:23:02, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.2186 2023-01-01 02:53:04,254 - mmcls - INFO - Epoch [97][4500/5005] lr: 1.000e-04, eta: 0:22:53, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2273 2023-01-01 02:53:13,293 - mmcls - INFO - Epoch [97][4600/5005] lr: 1.000e-04, eta: 0:22:45, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2426 2023-01-01 02:53:22,134 - mmcls - INFO - Epoch [97][4700/5005] lr: 1.000e-04, eta: 0:22:36, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.2328 2023-01-01 02:53:30,915 - mmcls - INFO - Epoch [97][4800/5005] lr: 1.000e-04, eta: 0:22:27, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2497 2023-01-01 02:53:40,011 - mmcls - INFO - Epoch [97][4900/5005] lr: 1.000e-04, eta: 0:22:18, time: 0.091, data_time: 0.006, memory: 1224, loss: 1.2172 2023-01-01 02:53:49,155 - mmcls - INFO - Epoch [97][5000/5005] lr: 1.000e-04, eta: 0:22:09, time: 0.091, data_time: 0.018, memory: 1224, loss: 1.2549 2023-01-01 02:54:11,786 - mmcls - INFO - Epoch(val) [97][196] accuracy_top-1: 71.6400, accuracy_top-5: 90.2020 2023-01-01 02:54:22,804 - mmcls - INFO - Epoch [98][100/5005] lr: 1.000e-04, eta: 0:22:00, time: 0.110, data_time: 0.053, memory: 1224, loss: 1.2417 2023-01-01 02:54:31,613 - mmcls - INFO - Epoch [98][200/5005] lr: 1.000e-04, eta: 0:21:51, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2436 2023-01-01 02:54:40,894 - mmcls - INFO - Epoch [98][300/5005] lr: 1.000e-04, eta: 0:21:42, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2246 2023-01-01 02:54:49,523 - mmcls - INFO - Epoch [98][400/5005] lr: 1.000e-04, eta: 0:21:33, time: 0.086, data_time: 0.004, memory: 1224, loss: 1.2162 2023-01-01 02:54:58,347 - mmcls - INFO - Epoch [98][500/5005] lr: 1.000e-04, eta: 0:21:25, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2400 2023-01-01 02:55:06,951 - mmcls - INFO - Epoch [98][600/5005] lr: 1.000e-04, eta: 0:21:16, time: 0.086, data_time: 0.006, memory: 1224, loss: 1.2232 2023-01-01 02:55:15,639 - mmcls - INFO - Epoch [98][700/5005] lr: 1.000e-04, eta: 0:21:07, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2212 2023-01-01 02:55:24,651 - mmcls - INFO - Epoch [98][800/5005] lr: 1.000e-04, eta: 0:20:58, time: 0.090, data_time: 0.019, memory: 1224, loss: 1.2223 2023-01-01 02:55:33,530 - mmcls - INFO - Epoch [98][900/5005] lr: 1.000e-04, eta: 0:20:49, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2084 2023-01-01 02:55:42,150 - mmcls - INFO - Epoch [98][1000/5005] lr: 1.000e-04, eta: 0:20:40, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2321 2023-01-01 02:55:50,961 - mmcls - INFO - Epoch [98][1100/5005] lr: 1.000e-04, eta: 0:20:31, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.1992 2023-01-01 02:55:59,765 - mmcls - INFO - Epoch [98][1200/5005] lr: 1.000e-04, eta: 0:20:23, time: 0.088, data_time: 0.003, memory: 1224, loss: 1.2266 2023-01-01 02:56:08,779 - mmcls - INFO - Epoch [98][1300/5005] lr: 1.000e-04, eta: 0:20:14, time: 0.090, data_time: 0.004, memory: 1224, loss: 1.2411 2023-01-01 02:56:17,495 - mmcls - INFO - Epoch [98][1400/5005] lr: 1.000e-04, eta: 0:20:05, time: 0.087, data_time: 0.013, memory: 1224, loss: 1.2236 2023-01-01 02:56:26,154 - mmcls - INFO - Epoch [98][1500/5005] lr: 1.000e-04, eta: 0:19:56, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2258 2023-01-01 02:56:35,066 - mmcls - INFO - Epoch [98][1600/5005] lr: 1.000e-04, eta: 0:19:47, time: 0.089, data_time: 0.012, memory: 1224, loss: 1.2184 2023-01-01 02:56:43,826 - mmcls - INFO - Epoch [98][1700/5005] lr: 1.000e-04, eta: 0:19:38, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2363 2023-01-01 02:56:52,863 - mmcls - INFO - Epoch [98][1800/5005] lr: 1.000e-04, eta: 0:19:29, time: 0.090, data_time: 0.015, memory: 1224, loss: 1.2142 2023-01-01 02:57:01,693 - mmcls - INFO - Epoch [98][1900/5005] lr: 1.000e-04, eta: 0:19:21, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2073 2023-01-01 02:57:11,035 - mmcls - INFO - Epoch [98][2000/5005] lr: 1.000e-04, eta: 0:19:12, time: 0.093, data_time: 0.001, memory: 1224, loss: 1.2343 2023-01-01 02:57:20,539 - mmcls - INFO - Epoch [98][2100/5005] lr: 1.000e-04, eta: 0:19:03, time: 0.095, data_time: 0.005, memory: 1224, loss: 1.2308 2023-01-01 02:57:29,327 - mmcls - INFO - Epoch [98][2200/5005] lr: 1.000e-04, eta: 0:18:54, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2436 2023-01-01 02:57:37,960 - mmcls - INFO - Epoch [98][2300/5005] lr: 1.000e-04, eta: 0:18:45, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2227 2023-01-01 02:57:46,914 - mmcls - INFO - Epoch [98][2400/5005] lr: 1.000e-04, eta: 0:18:36, time: 0.090, data_time: 0.018, memory: 1224, loss: 1.2464 2023-01-01 02:57:55,612 - mmcls - INFO - Epoch [98][2500/5005] lr: 1.000e-04, eta: 0:18:27, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2323 2023-01-01 02:58:04,311 - mmcls - INFO - Epoch [98][2600/5005] lr: 1.000e-04, eta: 0:18:19, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2349 2023-01-01 02:58:12,737 - mmcls - INFO - Epoch [98][2700/5005] lr: 1.000e-04, eta: 0:18:10, time: 0.084, data_time: 0.003, memory: 1224, loss: 1.2159 2023-01-01 02:58:21,736 - mmcls - INFO - Epoch [98][2800/5005] lr: 1.000e-04, eta: 0:18:01, time: 0.090, data_time: 0.010, memory: 1224, loss: 1.2330 2023-01-01 02:58:30,589 - mmcls - INFO - Epoch [98][2900/5005] lr: 1.000e-04, eta: 0:17:52, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2358 2023-01-01 02:58:39,266 - mmcls - INFO - Epoch [98][3000/5005] lr: 1.000e-04, eta: 0:17:43, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2363 2023-01-01 02:58:48,206 - mmcls - INFO - Epoch [98][3100/5005] lr: 1.000e-04, eta: 0:17:34, time: 0.089, data_time: 0.013, memory: 1224, loss: 1.2124 2023-01-01 02:58:56,996 - mmcls - INFO - Epoch [98][3200/5005] lr: 1.000e-04, eta: 0:17:25, time: 0.088, data_time: 0.013, memory: 1224, loss: 1.2046 2023-01-01 02:59:05,733 - mmcls - INFO - Epoch [98][3300/5005] lr: 1.000e-04, eta: 0:17:17, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2256 2023-01-01 02:59:14,471 - mmcls - INFO - Epoch [98][3400/5005] lr: 1.000e-04, eta: 0:17:08, time: 0.087, data_time: 0.041, memory: 1224, loss: 1.2313 2023-01-01 02:59:23,445 - mmcls - INFO - Epoch [98][3500/5005] lr: 1.000e-04, eta: 0:16:59, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2473 2023-01-01 02:59:32,241 - mmcls - INFO - Epoch [98][3600/5005] lr: 1.000e-04, eta: 0:16:50, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2433 2023-01-01 02:59:41,114 - mmcls - INFO - Epoch [98][3700/5005] lr: 1.000e-04, eta: 0:16:41, time: 0.089, data_time: 0.025, memory: 1224, loss: 1.2314 2023-01-01 02:59:50,070 - mmcls - INFO - Epoch [98][3800/5005] lr: 1.000e-04, eta: 0:16:32, time: 0.090, data_time: 0.002, memory: 1224, loss: 1.2322 2023-01-01 02:59:58,716 - mmcls - INFO - Epoch [98][3900/5005] lr: 1.000e-04, eta: 0:16:24, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.2311 2023-01-01 03:00:07,574 - mmcls - INFO - Epoch [98][4000/5005] lr: 1.000e-04, eta: 0:16:15, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2278 2023-01-01 03:00:16,349 - mmcls - INFO - Epoch [98][4100/5005] lr: 1.000e-04, eta: 0:16:06, time: 0.088, data_time: 0.014, memory: 1224, loss: 1.2321 2023-01-01 03:00:25,069 - mmcls - INFO - Epoch [98][4200/5005] lr: 1.000e-04, eta: 0:15:57, time: 0.087, data_time: 0.040, memory: 1224, loss: 1.2368 2023-01-01 03:00:33,756 - mmcls - INFO - Epoch [98][4300/5005] lr: 1.000e-04, eta: 0:15:48, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2218 2023-01-01 03:00:42,706 - mmcls - INFO - Epoch [98][4400/5005] lr: 1.000e-04, eta: 0:15:39, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2348 2023-01-01 03:00:51,586 - mmcls - INFO - Epoch [98][4500/5005] lr: 1.000e-04, eta: 0:15:30, time: 0.089, data_time: 0.004, memory: 1224, loss: 1.2303 2023-01-01 03:01:00,302 - mmcls - INFO - Epoch [98][4600/5005] lr: 1.000e-04, eta: 0:15:22, time: 0.087, data_time: 0.003, memory: 1224, loss: 1.2204 2023-01-01 03:01:09,061 - mmcls - INFO - Epoch [98][4700/5005] lr: 1.000e-04, eta: 0:15:13, time: 0.088, data_time: 0.015, memory: 1224, loss: 1.2420 2023-01-01 03:01:17,795 - mmcls - INFO - Epoch [98][4800/5005] lr: 1.000e-04, eta: 0:15:04, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2286 2023-01-01 03:01:26,583 - mmcls - INFO - Epoch [98][4900/5005] lr: 1.000e-04, eta: 0:14:55, time: 0.088, data_time: 0.005, memory: 1224, loss: 1.2158 2023-01-01 03:01:35,429 - mmcls - INFO - Epoch [98][5000/5005] lr: 1.000e-04, eta: 0:14:46, time: 0.088, data_time: 0.007, memory: 1224, loss: 1.2404 2023-01-01 03:01:58,226 - mmcls - INFO - Epoch(val) [98][196] accuracy_top-1: 71.6960, accuracy_top-5: 90.1540 2023-01-01 03:02:09,094 - mmcls - INFO - Epoch [99][100/5005] lr: 1.000e-04, eta: 0:14:37, time: 0.108, data_time: 0.058, memory: 1224, loss: 1.2313 2023-01-01 03:02:17,979 - mmcls - INFO - Epoch [99][200/5005] lr: 1.000e-04, eta: 0:14:28, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2384 2023-01-01 03:02:26,664 - mmcls - INFO - Epoch [99][300/5005] lr: 1.000e-04, eta: 0:14:19, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2412 2023-01-01 03:02:35,766 - mmcls - INFO - Epoch [99][400/5005] lr: 1.000e-04, eta: 0:14:10, time: 0.091, data_time: 0.011, memory: 1224, loss: 1.2398 2023-01-01 03:02:44,347 - mmcls - INFO - Epoch [99][500/5005] lr: 1.000e-04, eta: 0:14:01, time: 0.086, data_time: 0.028, memory: 1224, loss: 1.2172 2023-01-01 03:02:52,944 - mmcls - INFO - Epoch [99][600/5005] lr: 1.000e-04, eta: 0:13:53, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2151 2023-01-01 03:03:01,959 - mmcls - INFO - Epoch [99][700/5005] lr: 1.000e-04, eta: 0:13:44, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2073 2023-01-01 03:03:11,154 - mmcls - INFO - Epoch [99][800/5005] lr: 1.000e-04, eta: 0:13:35, time: 0.092, data_time: 0.019, memory: 1224, loss: 1.2160 2023-01-01 03:03:19,894 - mmcls - INFO - Epoch [99][900/5005] lr: 1.000e-04, eta: 0:13:26, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2329 2023-01-01 03:03:28,530 - mmcls - INFO - Epoch [99][1000/5005] lr: 1.000e-04, eta: 0:13:17, time: 0.086, data_time: 0.000, memory: 1224, loss: 1.2243 2023-01-01 03:03:37,648 - mmcls - INFO - Epoch [99][1100/5005] lr: 1.000e-04, eta: 0:13:08, time: 0.091, data_time: 0.003, memory: 1224, loss: 1.2324 2023-01-01 03:03:46,731 - mmcls - INFO - Epoch [99][1200/5005] lr: 1.000e-04, eta: 0:12:59, time: 0.091, data_time: 0.007, memory: 1224, loss: 1.2252 2023-01-01 03:03:55,513 - mmcls - INFO - Epoch [99][1300/5005] lr: 1.000e-04, eta: 0:12:51, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.1974 2023-01-01 03:04:04,304 - mmcls - INFO - Epoch [99][1400/5005] lr: 1.000e-04, eta: 0:12:42, time: 0.088, data_time: 0.012, memory: 1224, loss: 1.2254 2023-01-01 03:04:13,009 - mmcls - INFO - Epoch [99][1500/5005] lr: 1.000e-04, eta: 0:12:33, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2252 2023-01-01 03:04:21,883 - mmcls - INFO - Epoch [99][1600/5005] lr: 1.000e-04, eta: 0:12:24, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2314 2023-01-01 03:04:30,533 - mmcls - INFO - Epoch [99][1700/5005] lr: 1.000e-04, eta: 0:12:15, time: 0.086, data_time: 0.021, memory: 1224, loss: 1.2387 2023-01-01 03:04:39,054 - mmcls - INFO - Epoch [99][1800/5005] lr: 1.000e-04, eta: 0:12:06, time: 0.085, data_time: 0.005, memory: 1224, loss: 1.2167 2023-01-01 03:04:47,507 - mmcls - INFO - Epoch [99][1900/5005] lr: 1.000e-04, eta: 0:11:57, time: 0.085, data_time: 0.002, memory: 1224, loss: 1.2335 2023-01-01 03:04:56,177 - mmcls - INFO - Epoch [99][2000/5005] lr: 1.000e-04, eta: 0:11:49, time: 0.087, data_time: 0.014, memory: 1224, loss: 1.2326 2023-01-01 03:05:04,747 - mmcls - INFO - Epoch [99][2100/5005] lr: 1.000e-04, eta: 0:11:40, time: 0.086, data_time: 0.018, memory: 1224, loss: 1.2180 2023-01-01 03:05:13,592 - mmcls - INFO - Epoch [99][2200/5005] lr: 1.000e-04, eta: 0:11:31, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2544 2023-01-01 03:05:22,390 - mmcls - INFO - Epoch [99][2300/5005] lr: 1.000e-04, eta: 0:11:22, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2365 2023-01-01 03:05:31,514 - mmcls - INFO - Epoch [99][2400/5005] lr: 1.000e-04, eta: 0:11:13, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2217 2023-01-01 03:05:40,344 - mmcls - INFO - Epoch [99][2500/5005] lr: 1.000e-04, eta: 0:11:04, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2192 2023-01-01 03:05:49,195 - mmcls - INFO - Epoch [99][2600/5005] lr: 1.000e-04, eta: 0:10:56, time: 0.089, data_time: 0.014, memory: 1224, loss: 1.2203 2023-01-01 03:05:58,023 - mmcls - INFO - Epoch [99][2700/5005] lr: 1.000e-04, eta: 0:10:47, time: 0.088, data_time: 0.021, memory: 1224, loss: 1.2493 2023-01-01 03:06:06,798 - mmcls - INFO - Epoch [99][2800/5005] lr: 1.000e-04, eta: 0:10:38, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2274 2023-01-01 03:06:15,590 - mmcls - INFO - Epoch [99][2900/5005] lr: 1.000e-04, eta: 0:10:29, time: 0.088, data_time: 0.006, memory: 1224, loss: 1.2380 2023-01-01 03:06:24,144 - mmcls - INFO - Epoch [99][3000/5005] lr: 1.000e-04, eta: 0:10:20, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2417 2023-01-01 03:06:32,940 - mmcls - INFO - Epoch [99][3100/5005] lr: 1.000e-04, eta: 0:10:11, time: 0.088, data_time: 0.024, memory: 1224, loss: 1.2068 2023-01-01 03:06:42,077 - mmcls - INFO - Epoch [99][3200/5005] lr: 1.000e-04, eta: 0:10:02, time: 0.091, data_time: 0.022, memory: 1224, loss: 1.2200 2023-01-01 03:06:50,869 - mmcls - INFO - Epoch [99][3300/5005] lr: 1.000e-04, eta: 0:09:54, time: 0.088, data_time: 0.027, memory: 1224, loss: 1.2268 2023-01-01 03:07:00,041 - mmcls - INFO - Epoch [99][3400/5005] lr: 1.000e-04, eta: 0:09:45, time: 0.092, data_time: 0.003, memory: 1224, loss: 1.2254 2023-01-01 03:07:08,687 - mmcls - INFO - Epoch [99][3500/5005] lr: 1.000e-04, eta: 0:09:36, time: 0.086, data_time: 0.007, memory: 1224, loss: 1.2396 2023-01-01 03:07:17,419 - mmcls - INFO - Epoch [99][3600/5005] lr: 1.000e-04, eta: 0:09:27, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2280 2023-01-01 03:07:26,124 - mmcls - INFO - Epoch [99][3700/5005] lr: 1.000e-04, eta: 0:09:18, time: 0.087, data_time: 0.006, memory: 1224, loss: 1.2300 2023-01-01 03:07:35,007 - mmcls - INFO - Epoch [99][3800/5005] lr: 1.000e-04, eta: 0:09:09, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2300 2023-01-01 03:07:43,868 - mmcls - INFO - Epoch [99][3900/5005] lr: 1.000e-04, eta: 0:09:00, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2278 2023-01-01 03:07:52,585 - mmcls - INFO - Epoch [99][4000/5005] lr: 1.000e-04, eta: 0:08:52, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2112 2023-01-01 03:08:01,663 - mmcls - INFO - Epoch [99][4100/5005] lr: 1.000e-04, eta: 0:08:43, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2278 2023-01-01 03:08:10,519 - mmcls - INFO - Epoch [99][4200/5005] lr: 1.000e-04, eta: 0:08:34, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2130 2023-01-01 03:08:19,526 - mmcls - INFO - Epoch [99][4300/5005] lr: 1.000e-04, eta: 0:08:25, time: 0.090, data_time: 0.008, memory: 1224, loss: 1.2146 2023-01-01 03:08:28,176 - mmcls - INFO - Epoch [99][4400/5005] lr: 1.000e-04, eta: 0:08:16, time: 0.087, data_time: 0.012, memory: 1224, loss: 1.2229 2023-01-01 03:08:37,006 - mmcls - INFO - Epoch [99][4500/5005] lr: 1.000e-04, eta: 0:08:07, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2451 2023-01-01 03:08:45,654 - mmcls - INFO - Epoch [99][4600/5005] lr: 1.000e-04, eta: 0:07:58, time: 0.086, data_time: 0.013, memory: 1224, loss: 1.2240 2023-01-01 03:08:54,752 - mmcls - INFO - Epoch [99][4700/5005] lr: 1.000e-04, eta: 0:07:50, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2335 2023-01-01 03:09:03,320 - mmcls - INFO - Epoch [99][4800/5005] lr: 1.000e-04, eta: 0:07:41, time: 0.086, data_time: 0.011, memory: 1224, loss: 1.2231 2023-01-01 03:09:12,196 - mmcls - INFO - Epoch [99][4900/5005] lr: 1.000e-04, eta: 0:07:32, time: 0.089, data_time: 0.010, memory: 1224, loss: 1.2261 2023-01-01 03:09:20,957 - mmcls - INFO - Epoch [99][5000/5005] lr: 1.000e-04, eta: 0:07:23, time: 0.088, data_time: 0.017, memory: 1224, loss: 1.2232 2023-01-01 03:09:43,603 - mmcls - INFO - Epoch(val) [99][196] accuracy_top-1: 71.5960, accuracy_top-5: 90.1140 2023-01-01 03:09:54,559 - mmcls - INFO - Epoch [100][100/5005] lr: 1.000e-04, eta: 0:07:14, time: 0.109, data_time: 0.030, memory: 1224, loss: 1.2175 2023-01-01 03:10:03,307 - mmcls - INFO - Epoch [100][200/5005] lr: 1.000e-04, eta: 0:07:05, time: 0.087, data_time: 0.017, memory: 1224, loss: 1.2294 2023-01-01 03:10:11,968 - mmcls - INFO - Epoch [100][300/5005] lr: 1.000e-04, eta: 0:06:56, time: 0.087, data_time: 0.016, memory: 1224, loss: 1.2100 2023-01-01 03:10:21,057 - mmcls - INFO - Epoch [100][400/5005] lr: 1.000e-04, eta: 0:06:47, time: 0.091, data_time: 0.012, memory: 1224, loss: 1.2207 2023-01-01 03:10:29,534 - mmcls - INFO - Epoch [100][500/5005] lr: 1.000e-04, eta: 0:06:38, time: 0.085, data_time: 0.003, memory: 1224, loss: 1.2498 2023-01-01 03:10:38,605 - mmcls - INFO - Epoch [100][600/5005] lr: 1.000e-04, eta: 0:06:29, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2211 2023-01-01 03:10:47,346 - mmcls - INFO - Epoch [100][700/5005] lr: 1.000e-04, eta: 0:06:21, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2161 2023-01-01 03:10:56,100 - mmcls - INFO - Epoch [100][800/5005] lr: 1.000e-04, eta: 0:06:12, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2212 2023-01-01 03:11:05,582 - mmcls - INFO - Epoch [100][900/5005] lr: 1.000e-04, eta: 0:06:03, time: 0.095, data_time: 0.006, memory: 1224, loss: 1.2465 2023-01-01 03:11:14,262 - mmcls - INFO - Epoch [100][1000/5005] lr: 1.000e-04, eta: 0:05:54, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2416 2023-01-01 03:11:23,132 - mmcls - INFO - Epoch [100][1100/5005] lr: 1.000e-04, eta: 0:05:45, time: 0.089, data_time: 0.016, memory: 1224, loss: 1.1948 2023-01-01 03:11:31,809 - mmcls - INFO - Epoch [100][1200/5005] lr: 1.000e-04, eta: 0:05:36, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2341 2023-01-01 03:11:40,464 - mmcls - INFO - Epoch [100][1300/5005] lr: 1.000e-04, eta: 0:05:28, time: 0.087, data_time: 0.008, memory: 1224, loss: 1.2149 2023-01-01 03:11:49,265 - mmcls - INFO - Epoch [100][1400/5005] lr: 1.000e-04, eta: 0:05:19, time: 0.088, data_time: 0.010, memory: 1224, loss: 1.2349 2023-01-01 03:11:58,294 - mmcls - INFO - Epoch [100][1500/5005] lr: 1.000e-04, eta: 0:05:10, time: 0.090, data_time: 0.006, memory: 1224, loss: 1.2265 2023-01-01 03:12:07,186 - mmcls - INFO - Epoch [100][1600/5005] lr: 1.000e-04, eta: 0:05:01, time: 0.089, data_time: 0.027, memory: 1224, loss: 1.2456 2023-01-01 03:12:16,093 - mmcls - INFO - Epoch [100][1700/5005] lr: 1.000e-04, eta: 0:04:52, time: 0.089, data_time: 0.009, memory: 1224, loss: 1.2285 2023-01-01 03:12:25,032 - mmcls - INFO - Epoch [100][1800/5005] lr: 1.000e-04, eta: 0:04:43, time: 0.089, data_time: 0.003, memory: 1224, loss: 1.2184 2023-01-01 03:12:34,190 - mmcls - INFO - Epoch [100][1900/5005] lr: 1.000e-04, eta: 0:04:34, time: 0.092, data_time: 0.012, memory: 1224, loss: 1.2283 2023-01-01 03:12:42,992 - mmcls - INFO - Epoch [100][2000/5005] lr: 1.000e-04, eta: 0:04:26, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2500 2023-01-01 03:12:51,812 - mmcls - INFO - Epoch [100][2100/5005] lr: 1.000e-04, eta: 0:04:17, time: 0.088, data_time: 0.022, memory: 1224, loss: 1.2379 2023-01-01 03:13:00,517 - mmcls - INFO - Epoch [100][2200/5005] lr: 1.000e-04, eta: 0:04:08, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2180 2023-01-01 03:13:09,284 - mmcls - INFO - Epoch [100][2300/5005] lr: 1.000e-04, eta: 0:03:59, time: 0.088, data_time: 0.001, memory: 1224, loss: 1.2147 2023-01-01 03:13:18,582 - mmcls - INFO - Epoch [100][2400/5005] lr: 1.000e-04, eta: 0:03:50, time: 0.093, data_time: 0.031, memory: 1224, loss: 1.2010 2023-01-01 03:13:27,488 - mmcls - INFO - Epoch [100][2500/5005] lr: 1.000e-04, eta: 0:03:41, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2219 2023-01-01 03:13:36,287 - mmcls - INFO - Epoch [100][2600/5005] lr: 1.000e-04, eta: 0:03:32, time: 0.088, data_time: 0.020, memory: 1224, loss: 1.2233 2023-01-01 03:13:44,875 - mmcls - INFO - Epoch [100][2700/5005] lr: 1.000e-04, eta: 0:03:24, time: 0.086, data_time: 0.003, memory: 1224, loss: 1.2213 2023-01-01 03:13:53,941 - mmcls - INFO - Epoch [100][2800/5005] lr: 1.000e-04, eta: 0:03:15, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2028 2023-01-01 03:14:02,844 - mmcls - INFO - Epoch [100][2900/5005] lr: 1.000e-04, eta: 0:03:06, time: 0.089, data_time: 0.005, memory: 1224, loss: 1.2282 2023-01-01 03:14:11,478 - mmcls - INFO - Epoch [100][3000/5005] lr: 1.000e-04, eta: 0:02:57, time: 0.086, data_time: 0.015, memory: 1224, loss: 1.2239 2023-01-01 03:14:20,549 - mmcls - INFO - Epoch [100][3100/5005] lr: 1.000e-04, eta: 0:02:48, time: 0.091, data_time: 0.001, memory: 1224, loss: 1.2161 2023-01-01 03:14:29,435 - mmcls - INFO - Epoch [100][3200/5005] lr: 1.000e-04, eta: 0:02:39, time: 0.089, data_time: 0.002, memory: 1224, loss: 1.2328 2023-01-01 03:14:38,097 - mmcls - INFO - Epoch [100][3300/5005] lr: 1.000e-04, eta: 0:02:30, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2263 2023-01-01 03:14:46,794 - mmcls - INFO - Epoch [100][3400/5005] lr: 1.000e-04, eta: 0:02:22, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2487 2023-01-01 03:14:55,876 - mmcls - INFO - Epoch [100][3500/5005] lr: 1.000e-04, eta: 0:02:13, time: 0.091, data_time: 0.002, memory: 1224, loss: 1.2514 2023-01-01 03:15:04,671 - mmcls - INFO - Epoch [100][3600/5005] lr: 1.000e-04, eta: 0:02:04, time: 0.088, data_time: 0.002, memory: 1224, loss: 1.2297 2023-01-01 03:15:13,961 - mmcls - INFO - Epoch [100][3700/5005] lr: 1.000e-04, eta: 0:01:55, time: 0.093, data_time: 0.005, memory: 1224, loss: 1.2171 2023-01-01 03:15:22,796 - mmcls - INFO - Epoch [100][3800/5005] lr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.004, memory: 1224, loss: 1.2539 2023-01-01 03:15:31,476 - mmcls - INFO - Epoch [100][3900/5005] lr: 1.000e-04, eta: 0:01:37, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2212 2023-01-01 03:15:40,229 - mmcls - INFO - Epoch [100][4000/5005] lr: 1.000e-04, eta: 0:01:28, time: 0.087, data_time: 0.001, memory: 1224, loss: 1.2156 2023-01-01 03:15:49,078 - mmcls - INFO - Epoch [100][4100/5005] lr: 1.000e-04, eta: 0:01:20, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2416 2023-01-01 03:15:58,031 - mmcls - INFO - Epoch [100][4200/5005] lr: 1.000e-04, eta: 0:01:11, time: 0.090, data_time: 0.001, memory: 1224, loss: 1.2284 2023-01-01 03:16:06,916 - mmcls - INFO - Epoch [100][4300/5005] lr: 1.000e-04, eta: 0:01:02, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2206 2023-01-01 03:16:15,744 - mmcls - INFO - Epoch [100][4400/5005] lr: 1.000e-04, eta: 0:00:53, time: 0.088, data_time: 0.008, memory: 1224, loss: 1.2132 2023-01-01 03:16:24,684 - mmcls - INFO - Epoch [100][4500/5005] lr: 1.000e-04, eta: 0:00:44, time: 0.089, data_time: 0.017, memory: 1224, loss: 1.2234 2023-01-01 03:16:33,245 - mmcls - INFO - Epoch [100][4600/5005] lr: 1.000e-04, eta: 0:00:35, time: 0.086, data_time: 0.001, memory: 1224, loss: 1.2242 2023-01-01 03:16:41,944 - mmcls - INFO - Epoch [100][4700/5005] lr: 1.000e-04, eta: 0:00:27, time: 0.087, data_time: 0.007, memory: 1224, loss: 1.2359 2023-01-01 03:16:50,822 - mmcls - INFO - Epoch [100][4800/5005] lr: 1.000e-04, eta: 0:00:18, time: 0.089, data_time: 0.001, memory: 1224, loss: 1.2263 2023-01-01 03:16:59,554 - mmcls - INFO - Epoch [100][4900/5005] lr: 1.000e-04, eta: 0:00:09, time: 0.087, data_time: 0.002, memory: 1224, loss: 1.2047 2023-01-01 03:17:08,062 - mmcls - INFO - Epoch [100][5000/5005] lr: 1.000e-04, eta: 0:00:00, time: 0.085, data_time: 0.007, memory: 1224, loss: 1.2166 2023-01-01 03:17:08,412 - mmcls - INFO - Saving checkpoint at 100 epochs 2023-01-01 03:17:30,821 - mmcls - INFO - Epoch(val) [100][196] accuracy_top-1: 71.6440, accuracy_top-5: 90.2320 ================================================ FILE: classification-NO-activation-function/logs/pinet_norelu.log ================================================ 2023-01-02 13:57:26,413 - mmcls - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.6.12 |Anaconda, Inc.| (default, Sep 8 2020, 23:10:56) [GCC 7.3.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 10.2, V10.2.8 GCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0 PyTorch: 1.9.0+cu102 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 10.2 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70 - CuDNN 7.6.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu102 OpenCV: 4.5.5 MMCV: 1.5.1 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler: 10.2 MMClassification: 0.24.0+91b85bb ------------------------------------------------------------ 2023-01-02 13:57:26,414 - mmcls - INFO - Distributed training: True 2023-01-02 13:57:26,577 - mmcls - INFO - Config: model = dict( type='ImageClassifier', backbone=dict( type='ResNet', depth=18, num_stages=4, out_indices=(3, ), style='pytorch'), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=1000, in_channels=512, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5))) dataset_type = 'ImageNet' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ] data = dict( samples_per_gpu=32, workers_per_gpu=2, train=dict( type='ImageNet', data_prefix='data/imagenet/train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ]), val=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]), test=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ])) evaluation = dict(interval=1, metric='accuracy') optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict(policy='step', step=[30, 60, 90]) runner = dict(type='EpochBasedRunner', max_epochs=100) checkpoint_config = dict(interval=50) log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] work_dir = './work_dirs/resnet18_8xb32_in1k_pi' gpu_ids = range(0, 8) 2023-01-02 13:57:32,376 - mmcls - INFO - Set random seed to 560605285, deterministic: False 2023-01-02 13:57:32,636 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}] 2023-01-02 13:57:32,722 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01} Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn3.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn3.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn3.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn3.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.downsample.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier head.fc.weight - torch.Size([1000, 512]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([1000]): NormalInit: mean=0, std=0.01, bias=0 2023-01-02 13:58:01,844 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi 2023-01-02 13:58:01,845 - mmcls - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook -------------------- after_train_iter: (ABOVE_NORMAL) DistOptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (VERY_LOW ) TextLoggerHook -------------------- before_val_iter: (LOW ) IterTimerHook -------------------- after_val_iter: (LOW ) IterTimerHook -------------------- after_val_epoch: (VERY_LOW ) TextLoggerHook -------------------- after_run: (VERY_LOW ) TextLoggerHook -------------------- 2023-01-02 13:58:01,845 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs 2023-01-02 13:58:01,845 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend. 2023-01-02 13:58:13,661 - mmcls - INFO - Epoch [1][100/5005] lr: 1.000e-01, eta: 16:23:29, time: 0.118, data_time: 0.032, memory: 1125, loss: 6.7216 2023-01-02 13:58:22,752 - mmcls - INFO - Epoch [1][200/5005] lr: 1.000e-01, eta: 14:30:45, time: 0.091, data_time: 0.001, memory: 1125, loss: 6.4537 2023-01-02 13:58:31,383 - mmcls - INFO - Epoch [1][300/5005] lr: 1.000e-01, eta: 13:40:06, time: 0.086, data_time: 0.013, memory: 1125, loss: 6.2304 2023-01-02 13:58:40,451 - mmcls - INFO - Epoch [1][400/5005] lr: 1.000e-01, eta: 13:24:02, time: 0.091, data_time: 0.012, memory: 1125, loss: 6.0236 2023-01-02 13:58:49,221 - mmcls - INFO - Epoch [1][500/5005] lr: 1.000e-01, eta: 13:09:08, time: 0.088, data_time: 0.000, memory: 1125, loss: 5.8518 2023-01-02 13:58:58,027 - mmcls - INFO - Epoch [1][600/5005] lr: 1.000e-01, eta: 12:59:42, time: 0.088, data_time: 0.026, memory: 1125, loss: 5.7128 2023-01-02 13:59:06,656 - mmcls - INFO - Epoch [1][700/5005] lr: 1.000e-01, eta: 12:50:52, time: 0.086, data_time: 0.008, memory: 1125, loss: 5.6081 2023-01-02 13:59:15,609 - mmcls - INFO - Epoch [1][800/5005] lr: 1.000e-01, eta: 12:47:35, time: 0.090, data_time: 0.005, memory: 1125, loss: 5.4917 2023-01-02 13:59:24,197 - mmcls - INFO - Epoch [1][900/5005] lr: 1.000e-01, eta: 12:41:37, time: 0.086, data_time: 0.003, memory: 1125, loss: 5.4012 2023-01-02 13:59:32,980 - mmcls - INFO - Epoch [1][1000/5005] lr: 1.000e-01, eta: 12:38:29, time: 0.088, data_time: 0.013, memory: 1125, loss: 5.3144 2023-01-02 13:59:42,330 - mmcls - INFO - Epoch [1][1100/5005] lr: 1.000e-01, eta: 12:40:08, time: 0.093, data_time: 0.024, memory: 1125, loss: 5.2574 2023-01-02 13:59:51,017 - mmcls - INFO - Epoch [1][1200/5005] lr: 1.000e-01, eta: 12:36:50, time: 0.087, data_time: 0.022, memory: 1125, loss: 5.1409 2023-01-02 14:00:00,044 - mmcls - INFO - Epoch [1][1300/5005] lr: 1.000e-01, eta: 12:36:17, time: 0.090, data_time: 0.005, memory: 1125, loss: 5.0996 2023-01-02 14:00:08,613 - mmcls - INFO - Epoch [1][1400/5005] lr: 1.000e-01, eta: 12:33:01, time: 0.086, data_time: 0.028, memory: 1125, loss: 5.0061 2023-01-02 14:00:17,572 - mmcls - INFO - Epoch [1][1500/5005] lr: 1.000e-01, eta: 12:32:20, time: 0.090, data_time: 0.012, memory: 1125, loss: 4.9650 2023-01-02 14:00:26,345 - mmcls - INFO - Epoch [1][1600/5005] lr: 1.000e-01, eta: 12:30:44, time: 0.088, data_time: 0.014, memory: 1125, loss: 4.8796 2023-01-02 14:00:35,367 - mmcls - INFO - Epoch [1][1700/5005] lr: 1.000e-01, eta: 12:30:33, time: 0.090, data_time: 0.003, memory: 1125, loss: 4.8402 2023-01-02 14:00:44,208 - mmcls - INFO - Epoch [1][1800/5005] lr: 1.000e-01, eta: 12:29:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 4.7744 2023-01-02 14:00:52,827 - mmcls - INFO - Epoch [1][1900/5005] lr: 1.000e-01, eta: 12:27:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 4.7170 2023-01-02 14:01:01,377 - mmcls - INFO - Epoch [1][2000/5005] lr: 1.000e-01, eta: 12:25:38, time: 0.085, data_time: 0.007, memory: 1125, loss: 4.6737 2023-01-02 14:01:09,982 - mmcls - INFO - Epoch [1][2100/5005] lr: 1.000e-01, eta: 12:24:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 4.6141 2023-01-02 14:01:18,844 - mmcls - INFO - Epoch [1][2200/5005] lr: 1.000e-01, eta: 12:23:30, time: 0.089, data_time: 0.009, memory: 1125, loss: 4.5775 2023-01-02 14:01:27,551 - mmcls - INFO - Epoch [1][2300/5005] lr: 1.000e-01, eta: 12:22:28, time: 0.087, data_time: 0.013, memory: 1125, loss: 4.5183 2023-01-02 14:01:36,521 - mmcls - INFO - Epoch [1][2400/5005] lr: 1.000e-01, eta: 12:22:25, time: 0.090, data_time: 0.009, memory: 1125, loss: 4.5045 2023-01-02 14:01:45,233 - mmcls - INFO - Epoch [1][2500/5005] lr: 1.000e-01, eta: 12:21:30, time: 0.087, data_time: 0.011, memory: 1125, loss: 4.4384 2023-01-02 14:01:54,011 - mmcls - INFO - Epoch [1][2600/5005] lr: 1.000e-01, eta: 12:20:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 4.3905 2023-01-02 14:02:02,589 - mmcls - INFO - Epoch [1][2700/5005] lr: 1.000e-01, eta: 12:19:36, time: 0.086, data_time: 0.003, memory: 1125, loss: 4.3860 2023-01-02 14:02:11,415 - mmcls - INFO - Epoch [1][2800/5005] lr: 1.000e-01, eta: 12:19:12, time: 0.088, data_time: 0.027, memory: 1125, loss: 4.3300 2023-01-02 14:02:20,399 - mmcls - INFO - Epoch [1][2900/5005] lr: 1.000e-01, eta: 12:19:16, time: 0.090, data_time: 0.007, memory: 1125, loss: 4.3115 2023-01-02 14:02:29,372 - mmcls - INFO - Epoch [1][3000/5005] lr: 1.000e-01, eta: 12:19:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 4.2887 2023-01-02 14:02:38,053 - mmcls - INFO - Epoch [1][3100/5005] lr: 1.000e-01, eta: 12:18:30, time: 0.087, data_time: 0.004, memory: 1125, loss: 4.2473 2023-01-02 14:02:46,800 - mmcls - INFO - Epoch [1][3200/5005] lr: 1.000e-01, eta: 12:17:55, time: 0.087, data_time: 0.032, memory: 1125, loss: 4.2131 2023-01-02 14:02:55,629 - mmcls - INFO - Epoch [1][3300/5005] lr: 1.000e-01, eta: 12:17:35, time: 0.088, data_time: 0.008, memory: 1125, loss: 4.1839 2023-01-02 14:03:04,240 - mmcls - INFO - Epoch [1][3400/5005] lr: 1.000e-01, eta: 12:16:44, time: 0.086, data_time: 0.012, memory: 1125, loss: 4.1642 2023-01-02 14:03:12,852 - mmcls - INFO - Epoch [1][3500/5005] lr: 1.000e-01, eta: 12:15:54, time: 0.086, data_time: 0.006, memory: 1125, loss: 4.1469 2023-01-02 14:03:21,618 - mmcls - INFO - Epoch [1][3600/5005] lr: 1.000e-01, eta: 12:15:29, time: 0.088, data_time: 0.012, memory: 1125, loss: 4.0707 2023-01-02 14:03:30,326 - mmcls - INFO - Epoch [1][3700/5005] lr: 1.000e-01, eta: 12:14:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 4.1024 2023-01-02 14:03:38,995 - mmcls - INFO - Epoch [1][3800/5005] lr: 1.000e-01, eta: 12:14:21, time: 0.087, data_time: 0.028, memory: 1125, loss: 4.1037 2023-01-02 14:03:47,723 - mmcls - INFO - Epoch [1][3900/5005] lr: 1.000e-01, eta: 12:13:55, time: 0.087, data_time: 0.004, memory: 1125, loss: 4.0537 2023-01-02 14:03:56,519 - mmcls - INFO - Epoch [1][4000/5005] lr: 1.000e-01, eta: 12:13:37, time: 0.088, data_time: 0.002, memory: 1125, loss: 4.0364 2023-01-02 14:04:05,209 - mmcls - INFO - Epoch [1][4100/5005] lr: 1.000e-01, eta: 12:13:06, time: 0.087, data_time: 0.010, memory: 1125, loss: 4.0029 2023-01-02 14:04:13,998 - mmcls - INFO - Epoch [1][4200/5005] lr: 1.000e-01, eta: 12:12:49, time: 0.088, data_time: 0.010, memory: 1125, loss: 3.9831 2023-01-02 14:04:22,992 - mmcls - INFO - Epoch [1][4300/5005] lr: 1.000e-01, eta: 12:12:55, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.9957 2023-01-02 14:04:31,727 - mmcls - INFO - Epoch [1][4400/5005] lr: 1.000e-01, eta: 12:12:32, time: 0.087, data_time: 0.006, memory: 1125, loss: 3.9515 2023-01-02 14:04:40,854 - mmcls - INFO - Epoch [1][4500/5005] lr: 1.000e-01, eta: 12:12:52, time: 0.091, data_time: 0.013, memory: 1125, loss: 3.9311 2023-01-02 14:04:49,813 - mmcls - INFO - Epoch [1][4600/5005] lr: 1.000e-01, eta: 12:12:53, time: 0.090, data_time: 0.015, memory: 1125, loss: 3.9065 2023-01-02 14:04:58,510 - mmcls - INFO - Epoch [1][4700/5005] lr: 1.000e-01, eta: 12:12:26, time: 0.087, data_time: 0.004, memory: 1125, loss: 3.9106 2023-01-02 14:05:07,002 - mmcls - INFO - Epoch [1][4800/5005] lr: 1.000e-01, eta: 12:11:39, time: 0.085, data_time: 0.004, memory: 1125, loss: 3.8843 2023-01-02 14:05:15,925 - mmcls - INFO - Epoch [1][4900/5005] lr: 1.000e-01, eta: 12:11:37, time: 0.089, data_time: 0.012, memory: 1125, loss: 3.8728 2023-01-02 14:05:24,482 - mmcls - INFO - Epoch [1][5000/5005] lr: 1.000e-01, eta: 12:10:58, time: 0.086, data_time: 0.022, memory: 1125, loss: 3.8471 2023-01-02 14:05:47,770 - mmcls - INFO - Epoch(val) [1][196] accuracy_top-1: 29.1120, accuracy_top-5: 52.7260 2023-01-02 14:05:58,565 - mmcls - INFO - Epoch [2][100/5005] lr: 1.000e-01, eta: 12:13:13, time: 0.108, data_time: 0.038, memory: 1125, loss: 3.7960 2023-01-02 14:06:07,253 - mmcls - INFO - Epoch [2][200/5005] lr: 1.000e-01, eta: 12:12:45, time: 0.087, data_time: 0.008, memory: 1125, loss: 3.7810 2023-01-02 14:06:16,265 - mmcls - INFO - Epoch [2][300/5005] lr: 1.000e-01, eta: 12:12:49, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.7801 2023-01-02 14:06:24,971 - mmcls - INFO - Epoch [2][400/5005] lr: 1.000e-01, eta: 12:12:24, time: 0.087, data_time: 0.015, memory: 1125, loss: 3.7731 2023-01-02 14:06:33,748 - mmcls - INFO - Epoch [2][500/5005] lr: 1.000e-01, eta: 12:12:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.7721 2023-01-02 14:06:42,846 - mmcls - INFO - Epoch [2][600/5005] lr: 1.000e-01, eta: 12:12:17, time: 0.091, data_time: 0.016, memory: 1125, loss: 3.7409 2023-01-02 14:06:51,836 - mmcls - INFO - Epoch [2][700/5005] lr: 1.000e-01, eta: 12:12:18, time: 0.090, data_time: 0.012, memory: 1125, loss: 3.7093 2023-01-02 14:07:00,533 - mmcls - INFO - Epoch [2][800/5005] lr: 1.000e-01, eta: 12:11:53, time: 0.087, data_time: 0.025, memory: 1125, loss: 3.7298 2023-01-02 14:07:09,499 - mmcls - INFO - Epoch [2][900/5005] lr: 1.000e-01, eta: 12:11:52, time: 0.090, data_time: 0.024, memory: 1125, loss: 3.7059 2023-01-02 14:07:18,236 - mmcls - INFO - Epoch [2][1000/5005] lr: 1.000e-01, eta: 12:11:32, time: 0.087, data_time: 0.005, memory: 1125, loss: 3.7281 2023-01-02 14:07:26,912 - mmcls - INFO - Epoch [2][1100/5005] lr: 1.000e-01, eta: 12:11:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.6853 2023-01-02 14:07:35,761 - mmcls - INFO - Epoch [2][1200/5005] lr: 1.000e-01, eta: 12:10:55, time: 0.089, data_time: 0.020, memory: 1125, loss: 3.6787 2023-01-02 14:07:44,409 - mmcls - INFO - Epoch [2][1300/5005] lr: 1.000e-01, eta: 12:10:28, time: 0.086, data_time: 0.004, memory: 1125, loss: 3.6545 2023-01-02 14:07:53,074 - mmcls - INFO - Epoch [2][1400/5005] lr: 1.000e-01, eta: 12:10:04, time: 0.087, data_time: 0.036, memory: 1125, loss: 3.6267 2023-01-02 14:08:01,813 - mmcls - INFO - Epoch [2][1500/5005] lr: 1.000e-01, eta: 12:09:46, time: 0.087, data_time: 0.015, memory: 1125, loss: 3.6460 2023-01-02 14:08:10,612 - mmcls - INFO - Epoch [2][1600/5005] lr: 1.000e-01, eta: 12:09:31, time: 0.088, data_time: 0.013, memory: 1125, loss: 3.6666 2023-01-02 14:08:19,369 - mmcls - INFO - Epoch [2][1700/5005] lr: 1.000e-01, eta: 12:09:15, time: 0.088, data_time: 0.018, memory: 1125, loss: 3.6377 2023-01-02 14:08:28,054 - mmcls - INFO - Epoch [2][1800/5005] lr: 1.000e-01, eta: 12:08:53, time: 0.087, data_time: 0.031, memory: 1125, loss: 3.6239 2023-01-02 14:08:36,991 - mmcls - INFO - Epoch [2][1900/5005] lr: 1.000e-01, eta: 12:08:50, time: 0.089, data_time: 0.003, memory: 1125, loss: 3.5787 2023-01-02 14:08:45,414 - mmcls - INFO - Epoch [2][2000/5005] lr: 1.000e-01, eta: 12:08:11, time: 0.084, data_time: 0.014, memory: 1125, loss: 3.6004 2023-01-02 14:08:54,214 - mmcls - INFO - Epoch [2][2100/5005] lr: 1.000e-01, eta: 12:07:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.5631 2023-01-02 14:09:03,016 - mmcls - INFO - Epoch [2][2200/5005] lr: 1.000e-01, eta: 12:07:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 3.5865 2023-01-02 14:09:11,855 - mmcls - INFO - Epoch [2][2300/5005] lr: 1.000e-01, eta: 12:07:35, time: 0.088, data_time: 0.009, memory: 1125, loss: 3.5694 2023-01-02 14:09:20,894 - mmcls - INFO - Epoch [2][2400/5005] lr: 1.000e-01, eta: 12:07:39, time: 0.090, data_time: 0.022, memory: 1125, loss: 3.5889 2023-01-02 14:09:29,452 - mmcls - INFO - Epoch [2][2500/5005] lr: 1.000e-01, eta: 12:07:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.5807 2023-01-02 14:09:38,015 - mmcls - INFO - Epoch [2][2600/5005] lr: 1.000e-01, eta: 12:06:43, time: 0.086, data_time: 0.011, memory: 1125, loss: 3.5289 2023-01-02 14:09:46,761 - mmcls - INFO - Epoch [2][2700/5005] lr: 1.000e-01, eta: 12:06:28, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.5261 2023-01-02 14:09:55,712 - mmcls - INFO - Epoch [2][2800/5005] lr: 1.000e-01, eta: 12:06:26, time: 0.090, data_time: 0.001, memory: 1125, loss: 3.5424 2023-01-02 14:10:04,506 - mmcls - INFO - Epoch [2][2900/5005] lr: 1.000e-01, eta: 12:06:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.5257 2023-01-02 14:10:13,263 - mmcls - INFO - Epoch [2][3000/5005] lr: 1.000e-01, eta: 12:05:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 3.5201 2023-01-02 14:10:21,738 - mmcls - INFO - Epoch [2][3100/5005] lr: 1.000e-01, eta: 12:05:28, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.5033 2023-01-02 14:10:30,333 - mmcls - INFO - Epoch [2][3200/5005] lr: 1.000e-01, eta: 12:05:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.4899 2023-01-02 14:10:39,239 - mmcls - INFO - Epoch [2][3300/5005] lr: 1.000e-01, eta: 12:05:00, time: 0.089, data_time: 0.046, memory: 1125, loss: 3.4838 2023-01-02 14:10:48,194 - mmcls - INFO - Epoch [2][3400/5005] lr: 1.000e-01, eta: 12:04:58, time: 0.090, data_time: 0.005, memory: 1125, loss: 3.4990 2023-01-02 14:10:56,644 - mmcls - INFO - Epoch [2][3500/5005] lr: 1.000e-01, eta: 12:04:26, time: 0.084, data_time: 0.001, memory: 1125, loss: 3.4438 2023-01-02 14:11:05,505 - mmcls - INFO - Epoch [2][3600/5005] lr: 1.000e-01, eta: 12:04:18, time: 0.089, data_time: 0.003, memory: 1125, loss: 3.4726 2023-01-02 14:11:14,017 - mmcls - INFO - Epoch [2][3700/5005] lr: 1.000e-01, eta: 12:03:51, time: 0.085, data_time: 0.003, memory: 1125, loss: 3.4733 2023-01-02 14:11:22,368 - mmcls - INFO - Epoch [2][3800/5005] lr: 1.000e-01, eta: 12:03:16, time: 0.084, data_time: 0.001, memory: 1125, loss: 3.4658 2023-01-02 14:11:31,211 - mmcls - INFO - Epoch [2][3900/5005] lr: 1.000e-01, eta: 12:03:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.4773 2023-01-02 14:11:40,104 - mmcls - INFO - Epoch [2][4000/5005] lr: 1.000e-01, eta: 12:03:02, time: 0.089, data_time: 0.038, memory: 1125, loss: 3.4446 2023-01-02 14:11:48,736 - mmcls - INFO - Epoch [2][4100/5005] lr: 1.000e-01, eta: 12:02:44, time: 0.086, data_time: 0.008, memory: 1125, loss: 3.4515 2023-01-02 14:11:57,541 - mmcls - INFO - Epoch [2][4200/5005] lr: 1.000e-01, eta: 12:02:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.4241 2023-01-02 14:12:06,352 - mmcls - INFO - Epoch [2][4300/5005] lr: 1.000e-01, eta: 12:02:23, time: 0.088, data_time: 0.009, memory: 1125, loss: 3.4420 2023-01-02 14:12:15,033 - mmcls - INFO - Epoch [2][4400/5005] lr: 1.000e-01, eta: 12:02:07, time: 0.087, data_time: 0.020, memory: 1125, loss: 3.4339 2023-01-02 14:12:24,044 - mmcls - INFO - Epoch [2][4500/5005] lr: 1.000e-01, eta: 12:02:08, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.4188 2023-01-02 14:12:32,719 - mmcls - INFO - Epoch [2][4600/5005] lr: 1.000e-01, eta: 12:01:51, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.4073 2023-01-02 14:12:41,802 - mmcls - INFO - Epoch [2][4700/5005] lr: 1.000e-01, eta: 12:01:56, time: 0.091, data_time: 0.007, memory: 1125, loss: 3.3928 2023-01-02 14:12:50,572 - mmcls - INFO - Epoch [2][4800/5005] lr: 1.000e-01, eta: 12:01:44, time: 0.088, data_time: 0.049, memory: 1125, loss: 3.4004 2023-01-02 14:12:58,868 - mmcls - INFO - Epoch [2][4900/5005] lr: 1.000e-01, eta: 12:01:10, time: 0.083, data_time: 0.017, memory: 1125, loss: 3.3770 2023-01-02 14:13:07,709 - mmcls - INFO - Epoch [2][5000/5005] lr: 1.000e-01, eta: 12:01:01, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.4078 2023-01-02 14:13:30,849 - mmcls - INFO - Epoch(val) [2][196] accuracy_top-1: 35.1040, accuracy_top-5: 60.1120 2023-01-02 14:13:42,036 - mmcls - INFO - Epoch [3][100/5005] lr: 1.000e-01, eta: 12:02:24, time: 0.112, data_time: 0.062, memory: 1125, loss: 3.3333 2023-01-02 14:13:50,667 - mmcls - INFO - Epoch [3][200/5005] lr: 1.000e-01, eta: 12:02:06, time: 0.086, data_time: 0.025, memory: 1125, loss: 3.3084 2023-01-02 14:13:59,392 - mmcls - INFO - Epoch [3][300/5005] lr: 1.000e-01, eta: 12:01:52, time: 0.087, data_time: 0.004, memory: 1125, loss: 3.3154 2023-01-02 14:14:08,040 - mmcls - INFO - Epoch [3][400/5005] lr: 1.000e-01, eta: 12:01:34, time: 0.086, data_time: 0.012, memory: 1125, loss: 3.3643 2023-01-02 14:14:16,748 - mmcls - INFO - Epoch [3][500/5005] lr: 1.000e-01, eta: 12:01:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.3228 2023-01-02 14:14:25,662 - mmcls - INFO - Epoch [3][600/5005] lr: 1.000e-01, eta: 12:01:14, time: 0.089, data_time: 0.011, memory: 1125, loss: 3.3300 2023-01-02 14:14:34,451 - mmcls - INFO - Epoch [3][700/5005] lr: 1.000e-01, eta: 12:01:03, time: 0.088, data_time: 0.007, memory: 1125, loss: 3.3287 2023-01-02 14:14:43,063 - mmcls - INFO - Epoch [3][800/5005] lr: 1.000e-01, eta: 12:00:44, time: 0.086, data_time: 0.010, memory: 1125, loss: 3.3256 2023-01-02 14:14:51,639 - mmcls - INFO - Epoch [3][900/5005] lr: 1.000e-01, eta: 12:00:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.3459 2023-01-02 14:15:00,366 - mmcls - INFO - Epoch [3][1000/5005] lr: 1.000e-01, eta: 12:00:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.3520 2023-01-02 14:15:09,084 - mmcls - INFO - Epoch [3][1100/5005] lr: 1.000e-01, eta: 11:59:57, time: 0.087, data_time: 0.005, memory: 1125, loss: 3.3345 2023-01-02 14:15:17,565 - mmcls - INFO - Epoch [3][1200/5005] lr: 1.000e-01, eta: 11:59:32, time: 0.085, data_time: 0.032, memory: 1125, loss: 3.3008 2023-01-02 14:15:26,207 - mmcls - INFO - Epoch [3][1300/5005] lr: 1.000e-01, eta: 11:59:16, time: 0.086, data_time: 0.017, memory: 1125, loss: 3.3020 2023-01-02 14:15:35,254 - mmcls - INFO - Epoch [3][1400/5005] lr: 1.000e-01, eta: 11:59:17, time: 0.091, data_time: 0.001, memory: 1125, loss: 3.3133 2023-01-02 14:15:43,896 - mmcls - INFO - Epoch [3][1500/5005] lr: 1.000e-01, eta: 11:59:00, time: 0.086, data_time: 0.005, memory: 1125, loss: 3.3350 2023-01-02 14:15:52,667 - mmcls - INFO - Epoch [3][1600/5005] lr: 1.000e-01, eta: 11:58:49, time: 0.088, data_time: 0.014, memory: 1125, loss: 3.3185 2023-01-02 14:16:01,282 - mmcls - INFO - Epoch [3][1700/5005] lr: 1.000e-01, eta: 11:58:31, time: 0.086, data_time: 0.007, memory: 1125, loss: 3.2907 2023-01-02 14:16:09,877 - mmcls - INFO - Epoch [3][1800/5005] lr: 1.000e-01, eta: 11:58:14, time: 0.086, data_time: 0.026, memory: 1125, loss: 3.3159 2023-01-02 14:16:18,741 - mmcls - INFO - Epoch [3][1900/5005] lr: 1.000e-01, eta: 11:58:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.3320 2023-01-02 14:16:27,390 - mmcls - INFO - Epoch [3][2000/5005] lr: 1.000e-01, eta: 11:57:51, time: 0.086, data_time: 0.005, memory: 1125, loss: 3.3032 2023-01-02 14:16:36,396 - mmcls - INFO - Epoch [3][2100/5005] lr: 1.000e-01, eta: 11:57:49, time: 0.090, data_time: 0.001, memory: 1125, loss: 3.2939 2023-01-02 14:16:45,509 - mmcls - INFO - Epoch [3][2200/5005] lr: 1.000e-01, eta: 11:57:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 3.2853 2023-01-02 14:16:54,424 - mmcls - INFO - Epoch [3][2300/5005] lr: 1.000e-01, eta: 11:57:47, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.2435 2023-01-02 14:17:03,152 - mmcls - INFO - Epoch [3][2400/5005] lr: 1.000e-01, eta: 11:57:34, time: 0.087, data_time: 0.020, memory: 1125, loss: 3.2653 2023-01-02 14:17:12,147 - mmcls - INFO - Epoch [3][2500/5005] lr: 1.000e-01, eta: 11:57:32, time: 0.090, data_time: 0.029, memory: 1125, loss: 3.2438 2023-01-02 14:17:20,655 - mmcls - INFO - Epoch [3][2600/5005] lr: 1.000e-01, eta: 11:57:11, time: 0.085, data_time: 0.018, memory: 1125, loss: 3.2889 2023-01-02 14:17:29,368 - mmcls - INFO - Epoch [3][2700/5005] lr: 1.000e-01, eta: 11:56:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.2767 2023-01-02 14:17:38,297 - mmcls - INFO - Epoch [3][2800/5005] lr: 1.000e-01, eta: 11:56:54, time: 0.089, data_time: 0.003, memory: 1125, loss: 3.2733 2023-01-02 14:17:46,934 - mmcls - INFO - Epoch [3][2900/5005] lr: 1.000e-01, eta: 11:56:38, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.2407 2023-01-02 14:17:55,447 - mmcls - INFO - Epoch [3][3000/5005] lr: 1.000e-01, eta: 11:56:18, time: 0.085, data_time: 0.009, memory: 1125, loss: 3.2583 2023-01-02 14:18:04,385 - mmcls - INFO - Epoch [3][3100/5005] lr: 1.000e-01, eta: 11:56:14, time: 0.089, data_time: 0.024, memory: 1125, loss: 3.2377 2023-01-02 14:18:13,247 - mmcls - INFO - Epoch [3][3200/5005] lr: 1.000e-01, eta: 11:56:07, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.2548 2023-01-02 14:18:22,002 - mmcls - INFO - Epoch [3][3300/5005] lr: 1.000e-01, eta: 11:55:55, time: 0.087, data_time: 0.018, memory: 1125, loss: 3.2715 2023-01-02 14:18:30,720 - mmcls - INFO - Epoch [3][3400/5005] lr: 1.000e-01, eta: 11:55:43, time: 0.087, data_time: 0.024, memory: 1125, loss: 3.2439 2023-01-02 14:18:39,147 - mmcls - INFO - Epoch [3][3500/5005] lr: 1.000e-01, eta: 11:55:20, time: 0.084, data_time: 0.006, memory: 1125, loss: 3.2191 2023-01-02 14:18:48,091 - mmcls - INFO - Epoch [3][3600/5005] lr: 1.000e-01, eta: 11:55:16, time: 0.089, data_time: 0.020, memory: 1125, loss: 3.2309 2023-01-02 14:18:56,758 - mmcls - INFO - Epoch [3][3700/5005] lr: 1.000e-01, eta: 11:55:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.2302 2023-01-02 14:19:05,482 - mmcls - INFO - Epoch [3][3800/5005] lr: 1.000e-01, eta: 11:54:50, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.2237 2023-01-02 14:19:14,094 - mmcls - INFO - Epoch [3][3900/5005] lr: 1.000e-01, eta: 11:54:34, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.2400 2023-01-02 14:19:22,819 - mmcls - INFO - Epoch [3][4000/5005] lr: 1.000e-01, eta: 11:54:22, time: 0.087, data_time: 0.004, memory: 1125, loss: 3.2361 2023-01-02 14:19:31,348 - mmcls - INFO - Epoch [3][4100/5005] lr: 1.000e-01, eta: 11:54:03, time: 0.085, data_time: 0.003, memory: 1125, loss: 3.2289 2023-01-02 14:19:40,123 - mmcls - INFO - Epoch [3][4200/5005] lr: 1.000e-01, eta: 11:53:53, time: 0.088, data_time: 0.014, memory: 1125, loss: 3.2093 2023-01-02 14:19:49,347 - mmcls - INFO - Epoch [3][4300/5005] lr: 1.000e-01, eta: 11:53:59, time: 0.092, data_time: 0.003, memory: 1125, loss: 3.2403 2023-01-02 14:19:58,034 - mmcls - INFO - Epoch [3][4400/5005] lr: 1.000e-01, eta: 11:53:46, time: 0.087, data_time: 0.002, memory: 1125, loss: 3.2210 2023-01-02 14:20:06,662 - mmcls - INFO - Epoch [3][4500/5005] lr: 1.000e-01, eta: 11:53:31, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.2159 2023-01-02 14:20:15,476 - mmcls - INFO - Epoch [3][4600/5005] lr: 1.000e-01, eta: 11:53:22, time: 0.088, data_time: 0.007, memory: 1125, loss: 3.2036 2023-01-02 14:20:24,158 - mmcls - INFO - Epoch [3][4700/5005] lr: 1.000e-01, eta: 11:53:09, time: 0.087, data_time: 0.006, memory: 1125, loss: 3.1943 2023-01-02 14:20:32,922 - mmcls - INFO - Epoch [3][4800/5005] lr: 1.000e-01, eta: 11:52:58, time: 0.088, data_time: 0.014, memory: 1125, loss: 3.2171 2023-01-02 14:20:41,482 - mmcls - INFO - Epoch [3][4900/5005] lr: 1.000e-01, eta: 11:52:42, time: 0.086, data_time: 0.004, memory: 1125, loss: 3.1994 2023-01-02 14:20:50,302 - mmcls - INFO - Epoch [3][5000/5005] lr: 1.000e-01, eta: 11:52:33, time: 0.088, data_time: 0.026, memory: 1125, loss: 3.2051 2023-01-02 14:21:12,943 - mmcls - INFO - Epoch(val) [3][196] accuracy_top-1: 39.9600, accuracy_top-5: 64.9560 2023-01-02 14:21:24,136 - mmcls - INFO - Epoch [4][100/5005] lr: 1.000e-01, eta: 11:53:26, time: 0.112, data_time: 0.051, memory: 1125, loss: 3.1131 2023-01-02 14:21:33,058 - mmcls - INFO - Epoch [4][200/5005] lr: 1.000e-01, eta: 11:53:20, time: 0.089, data_time: 0.018, memory: 1125, loss: 3.1158 2023-01-02 14:21:41,933 - mmcls - INFO - Epoch [4][300/5005] lr: 1.000e-01, eta: 11:53:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.1188 2023-01-02 14:21:50,972 - mmcls - INFO - Epoch [4][400/5005] lr: 1.000e-01, eta: 11:53:11, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.1672 2023-01-02 14:21:59,826 - mmcls - INFO - Epoch [4][500/5005] lr: 1.000e-01, eta: 11:53:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.1210 2023-01-02 14:22:08,511 - mmcls - INFO - Epoch [4][600/5005] lr: 1.000e-01, eta: 11:52:50, time: 0.087, data_time: 0.009, memory: 1125, loss: 3.1879 2023-01-02 14:22:17,119 - mmcls - INFO - Epoch [4][700/5005] lr: 1.000e-01, eta: 11:52:35, time: 0.086, data_time: 0.005, memory: 1125, loss: 3.1573 2023-01-02 14:22:25,893 - mmcls - INFO - Epoch [4][800/5005] lr: 1.000e-01, eta: 11:52:25, time: 0.088, data_time: 0.012, memory: 1125, loss: 3.1364 2023-01-02 14:22:34,469 - mmcls - INFO - Epoch [4][900/5005] lr: 1.000e-01, eta: 11:52:08, time: 0.086, data_time: 0.008, memory: 1125, loss: 3.1412 2023-01-02 14:22:43,141 - mmcls - INFO - Epoch [4][1000/5005] lr: 1.000e-01, eta: 11:51:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.1448 2023-01-02 14:22:52,136 - mmcls - INFO - Epoch [4][1100/5005] lr: 1.000e-01, eta: 11:51:52, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.1381 2023-01-02 14:23:00,875 - mmcls - INFO - Epoch [4][1200/5005] lr: 1.000e-01, eta: 11:51:41, time: 0.087, data_time: 0.017, memory: 1125, loss: 3.1426 2023-01-02 14:23:10,024 - mmcls - INFO - Epoch [4][1300/5005] lr: 1.000e-01, eta: 11:51:41, time: 0.091, data_time: 0.002, memory: 1125, loss: 3.1770 2023-01-02 14:23:18,791 - mmcls - INFO - Epoch [4][1400/5005] lr: 1.000e-01, eta: 11:51:31, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.1552 2023-01-02 14:23:27,285 - mmcls - INFO - Epoch [4][1500/5005] lr: 1.000e-01, eta: 11:51:13, time: 0.085, data_time: 0.008, memory: 1125, loss: 3.1531 2023-01-02 14:23:36,110 - mmcls - INFO - Epoch [4][1600/5005] lr: 1.000e-01, eta: 11:51:04, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.1557 2023-01-02 14:23:44,715 - mmcls - INFO - Epoch [4][1700/5005] lr: 1.000e-01, eta: 11:50:49, time: 0.086, data_time: 0.025, memory: 1125, loss: 3.1353 2023-01-02 14:23:53,747 - mmcls - INFO - Epoch [4][1800/5005] lr: 1.000e-01, eta: 11:50:46, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.1173 2023-01-02 14:24:02,416 - mmcls - INFO - Epoch [4][1900/5005] lr: 1.000e-01, eta: 11:50:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 3.1344 2023-01-02 14:24:10,999 - mmcls - INFO - Epoch [4][2000/5005] lr: 1.000e-01, eta: 11:50:18, time: 0.086, data_time: 0.003, memory: 1125, loss: 3.1405 2023-01-02 14:24:19,884 - mmcls - INFO - Epoch [4][2100/5005] lr: 1.000e-01, eta: 11:50:11, time: 0.089, data_time: 0.011, memory: 1125, loss: 3.1367 2023-01-02 14:24:28,570 - mmcls - INFO - Epoch [4][2200/5005] lr: 1.000e-01, eta: 11:49:58, time: 0.087, data_time: 0.025, memory: 1125, loss: 3.1424 2023-01-02 14:24:37,188 - mmcls - INFO - Epoch [4][2300/5005] lr: 1.000e-01, eta: 11:49:44, time: 0.086, data_time: 0.011, memory: 1125, loss: 3.1286 2023-01-02 14:24:45,749 - mmcls - INFO - Epoch [4][2400/5005] lr: 1.000e-01, eta: 11:49:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.1214 2023-01-02 14:24:54,626 - mmcls - INFO - Epoch [4][2500/5005] lr: 1.000e-01, eta: 11:49:21, time: 0.089, data_time: 0.027, memory: 1125, loss: 3.1218 2023-01-02 14:25:03,117 - mmcls - INFO - Epoch [4][2600/5005] lr: 1.000e-01, eta: 11:49:04, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.1824 2023-01-02 14:25:11,910 - mmcls - INFO - Epoch [4][2700/5005] lr: 1.000e-01, eta: 11:48:54, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.1256 2023-01-02 14:25:21,118 - mmcls - INFO - Epoch [4][2800/5005] lr: 1.000e-01, eta: 11:48:57, time: 0.092, data_time: 0.002, memory: 1125, loss: 3.0892 2023-01-02 14:25:30,034 - mmcls - INFO - Epoch [4][2900/5005] lr: 1.000e-01, eta: 11:48:50, time: 0.089, data_time: 0.013, memory: 1125, loss: 3.1399 2023-01-02 14:25:39,021 - mmcls - INFO - Epoch [4][3000/5005] lr: 1.000e-01, eta: 11:48:46, time: 0.090, data_time: 0.030, memory: 1125, loss: 3.1751 2023-01-02 14:25:47,556 - mmcls - INFO - Epoch [4][3100/5005] lr: 1.000e-01, eta: 11:48:30, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.1041 2023-01-02 14:25:56,513 - mmcls - INFO - Epoch [4][3200/5005] lr: 1.000e-01, eta: 11:48:25, time: 0.090, data_time: 0.016, memory: 1125, loss: 3.1026 2023-01-02 14:26:05,507 - mmcls - INFO - Epoch [4][3300/5005] lr: 1.000e-01, eta: 11:48:21, time: 0.090, data_time: 0.032, memory: 1125, loss: 3.1057 2023-01-02 14:26:14,322 - mmcls - INFO - Epoch [4][3400/5005] lr: 1.000e-01, eta: 11:48:12, time: 0.088, data_time: 0.007, memory: 1125, loss: 3.1114 2023-01-02 14:26:23,127 - mmcls - INFO - Epoch [4][3500/5005] lr: 1.000e-01, eta: 11:48:03, time: 0.088, data_time: 0.013, memory: 1125, loss: 3.1241 2023-01-02 14:26:31,856 - mmcls - INFO - Epoch [4][3600/5005] lr: 1.000e-01, eta: 11:47:52, time: 0.087, data_time: 0.016, memory: 1125, loss: 3.1183 2023-01-02 14:26:40,590 - mmcls - INFO - Epoch [4][3700/5005] lr: 1.000e-01, eta: 11:47:41, time: 0.087, data_time: 0.028, memory: 1125, loss: 3.0828 2023-01-02 14:26:49,221 - mmcls - INFO - Epoch [4][3800/5005] lr: 1.000e-01, eta: 11:47:27, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.0934 2023-01-02 14:26:58,061 - mmcls - INFO - Epoch [4][3900/5005] lr: 1.000e-01, eta: 11:47:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.1240 2023-01-02 14:27:06,768 - mmcls - INFO - Epoch [4][4000/5005] lr: 1.000e-01, eta: 11:47:08, time: 0.087, data_time: 0.025, memory: 1125, loss: 3.1182 2023-01-02 14:27:15,436 - mmcls - INFO - Epoch [4][4100/5005] lr: 1.000e-01, eta: 11:46:55, time: 0.087, data_time: 0.005, memory: 1125, loss: 3.1000 2023-01-02 14:27:24,187 - mmcls - INFO - Epoch [4][4200/5005] lr: 1.000e-01, eta: 11:46:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 3.0869 2023-01-02 14:27:32,903 - mmcls - INFO - Epoch [4][4300/5005] lr: 1.000e-01, eta: 11:46:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 3.1074 2023-01-02 14:27:41,807 - mmcls - INFO - Epoch [4][4400/5005] lr: 1.000e-01, eta: 11:46:27, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.1028 2023-01-02 14:27:50,531 - mmcls - INFO - Epoch [4][4500/5005] lr: 1.000e-01, eta: 11:46:16, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.1017 2023-01-02 14:27:59,505 - mmcls - INFO - Epoch [4][4600/5005] lr: 1.000e-01, eta: 11:46:11, time: 0.090, data_time: 0.001, memory: 1125, loss: 3.1245 2023-01-02 14:28:08,402 - mmcls - INFO - Epoch [4][4700/5005] lr: 1.000e-01, eta: 11:46:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.0789 2023-01-02 14:28:16,991 - mmcls - INFO - Epoch [4][4800/5005] lr: 1.000e-01, eta: 11:45:50, time: 0.086, data_time: 0.008, memory: 1125, loss: 3.1092 2023-01-02 14:28:25,910 - mmcls - INFO - Epoch [4][4900/5005] lr: 1.000e-01, eta: 11:45:44, time: 0.089, data_time: 0.003, memory: 1125, loss: 3.1001 2023-01-02 14:28:35,064 - mmcls - INFO - Epoch [4][5000/5005] lr: 1.000e-01, eta: 11:45:44, time: 0.092, data_time: 0.002, memory: 1125, loss: 3.1240 2023-01-02 14:28:57,792 - mmcls - INFO - Epoch(val) [4][196] accuracy_top-1: 42.0100, accuracy_top-5: 66.9020 2023-01-02 14:29:08,720 - mmcls - INFO - Epoch [5][100/5005] lr: 1.000e-01, eta: 11:46:14, time: 0.109, data_time: 0.049, memory: 1125, loss: 3.0220 2023-01-02 14:29:17,491 - mmcls - INFO - Epoch [5][200/5005] lr: 1.000e-01, eta: 11:46:04, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.0346 2023-01-02 14:29:26,591 - mmcls - INFO - Epoch [5][300/5005] lr: 1.000e-01, eta: 11:46:02, time: 0.091, data_time: 0.000, memory: 1125, loss: 3.0180 2023-01-02 14:29:35,389 - mmcls - INFO - Epoch [5][400/5005] lr: 1.000e-01, eta: 11:45:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.0381 2023-01-02 14:29:43,788 - mmcls - INFO - Epoch [5][500/5005] lr: 1.000e-01, eta: 11:45:33, time: 0.084, data_time: 0.012, memory: 1125, loss: 3.0530 2023-01-02 14:29:52,673 - mmcls - INFO - Epoch [5][600/5005] lr: 1.000e-01, eta: 11:45:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.0275 2023-01-02 14:30:01,575 - mmcls - INFO - Epoch [5][700/5005] lr: 1.000e-01, eta: 11:45:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.0422 2023-01-02 14:30:10,162 - mmcls - INFO - Epoch [5][800/5005] lr: 1.000e-01, eta: 11:45:05, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.0433 2023-01-02 14:30:19,126 - mmcls - INFO - Epoch [5][900/5005] lr: 1.000e-01, eta: 11:45:00, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.0309 2023-01-02 14:30:27,736 - mmcls - INFO - Epoch [5][1000/5005] lr: 1.000e-01, eta: 11:44:46, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.0654 2023-01-02 14:30:36,258 - mmcls - INFO - Epoch [5][1100/5005] lr: 1.000e-01, eta: 11:44:30, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.0620 2023-01-02 14:30:45,232 - mmcls - INFO - Epoch [5][1200/5005] lr: 1.000e-01, eta: 11:44:25, time: 0.090, data_time: 0.010, memory: 1125, loss: 3.0736 2023-01-02 14:30:53,850 - mmcls - INFO - Epoch [5][1300/5005] lr: 1.000e-01, eta: 11:44:12, time: 0.086, data_time: 0.018, memory: 1125, loss: 3.0687 2023-01-02 14:31:02,728 - mmcls - INFO - Epoch [5][1400/5005] lr: 1.000e-01, eta: 11:44:04, time: 0.089, data_time: 0.023, memory: 1125, loss: 3.0512 2023-01-02 14:31:11,651 - mmcls - INFO - Epoch [5][1500/5005] lr: 1.000e-01, eta: 11:43:58, time: 0.089, data_time: 0.000, memory: 1125, loss: 3.0553 2023-01-02 14:31:20,642 - mmcls - INFO - Epoch [5][1600/5005] lr: 1.000e-01, eta: 11:43:53, time: 0.090, data_time: 0.004, memory: 1125, loss: 3.0283 2023-01-02 14:31:29,112 - mmcls - INFO - Epoch [5][1700/5005] lr: 1.000e-01, eta: 11:43:36, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.0577 2023-01-02 14:31:37,559 - mmcls - INFO - Epoch [5][1800/5005] lr: 1.000e-01, eta: 11:43:19, time: 0.085, data_time: 0.023, memory: 1125, loss: 3.0478 2023-01-02 14:31:46,286 - mmcls - INFO - Epoch [5][1900/5005] lr: 1.000e-01, eta: 11:43:09, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.0519 2023-01-02 14:31:54,785 - mmcls - INFO - Epoch [5][2000/5005] lr: 1.000e-01, eta: 11:42:53, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.0372 2023-01-02 14:32:03,363 - mmcls - INFO - Epoch [5][2100/5005] lr: 1.000e-01, eta: 11:42:39, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.0422 2023-01-02 14:32:12,351 - mmcls - INFO - Epoch [5][2200/5005] lr: 1.000e-01, eta: 11:42:34, time: 0.090, data_time: 0.020, memory: 1125, loss: 3.0502 2023-01-02 14:32:21,213 - mmcls - INFO - Epoch [5][2300/5005] lr: 1.000e-01, eta: 11:42:26, time: 0.089, data_time: 0.016, memory: 1125, loss: 3.0721 2023-01-02 14:32:29,991 - mmcls - INFO - Epoch [5][2400/5005] lr: 1.000e-01, eta: 11:42:16, time: 0.088, data_time: 0.004, memory: 1125, loss: 3.0511 2023-01-02 14:32:38,582 - mmcls - INFO - Epoch [5][2500/5005] lr: 1.000e-01, eta: 11:42:03, time: 0.086, data_time: 0.003, memory: 1125, loss: 3.0831 2023-01-02 14:32:47,335 - mmcls - INFO - Epoch [5][2600/5005] lr: 1.000e-01, eta: 11:41:53, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.0097 2023-01-02 14:32:56,043 - mmcls - INFO - Epoch [5][2700/5005] lr: 1.000e-01, eta: 11:41:41, time: 0.087, data_time: 0.018, memory: 1125, loss: 3.0608 2023-01-02 14:33:04,633 - mmcls - INFO - Epoch [5][2800/5005] lr: 1.000e-01, eta: 11:41:28, time: 0.086, data_time: 0.014, memory: 1125, loss: 3.0170 2023-01-02 14:33:13,275 - mmcls - INFO - Epoch [5][2900/5005] lr: 1.000e-01, eta: 11:41:16, time: 0.086, data_time: 0.024, memory: 1125, loss: 3.0466 2023-01-02 14:33:22,222 - mmcls - INFO - Epoch [5][3000/5005] lr: 1.000e-01, eta: 11:41:10, time: 0.090, data_time: 0.030, memory: 1125, loss: 3.0204 2023-01-02 14:33:30,961 - mmcls - INFO - Epoch [5][3100/5005] lr: 1.000e-01, eta: 11:40:59, time: 0.087, data_time: 0.005, memory: 1125, loss: 3.0622 2023-01-02 14:33:39,557 - mmcls - INFO - Epoch [5][3200/5005] lr: 1.000e-01, eta: 11:40:46, time: 0.086, data_time: 0.015, memory: 1125, loss: 3.0448 2023-01-02 14:33:48,025 - mmcls - INFO - Epoch [5][3300/5005] lr: 1.000e-01, eta: 11:40:30, time: 0.085, data_time: 0.008, memory: 1125, loss: 3.0254 2023-01-02 14:33:56,887 - mmcls - INFO - Epoch [5][3400/5005] lr: 1.000e-01, eta: 11:40:23, time: 0.089, data_time: 0.004, memory: 1125, loss: 3.0540 2023-01-02 14:34:05,954 - mmcls - INFO - Epoch [5][3500/5005] lr: 1.000e-01, eta: 11:40:19, time: 0.091, data_time: 0.012, memory: 1125, loss: 3.0490 2023-01-02 14:34:14,786 - mmcls - INFO - Epoch [5][3600/5005] lr: 1.000e-01, eta: 11:40:11, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.0444 2023-01-02 14:34:23,823 - mmcls - INFO - Epoch [5][3700/5005] lr: 1.000e-01, eta: 11:40:06, time: 0.090, data_time: 0.001, memory: 1125, loss: 3.0414 2023-01-02 14:34:32,460 - mmcls - INFO - Epoch [5][3800/5005] lr: 1.000e-01, eta: 11:39:54, time: 0.086, data_time: 0.009, memory: 1125, loss: 3.0614 2023-01-02 14:34:41,312 - mmcls - INFO - Epoch [5][3900/5005] lr: 1.000e-01, eta: 11:39:46, time: 0.089, data_time: 0.015, memory: 1125, loss: 3.0436 2023-01-02 14:34:49,944 - mmcls - INFO - Epoch [5][4000/5005] lr: 1.000e-01, eta: 11:39:34, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.0514 2023-01-02 14:34:58,920 - mmcls - INFO - Epoch [5][4100/5005] lr: 1.000e-01, eta: 11:39:28, time: 0.090, data_time: 0.017, memory: 1125, loss: 3.0210 2023-01-02 14:35:07,509 - mmcls - INFO - Epoch [5][4200/5005] lr: 1.000e-01, eta: 11:39:15, time: 0.086, data_time: 0.019, memory: 1125, loss: 3.0356 2023-01-02 14:35:16,456 - mmcls - INFO - Epoch [5][4300/5005] lr: 1.000e-01, eta: 11:39:09, time: 0.089, data_time: 0.032, memory: 1125, loss: 3.0307 2023-01-02 14:35:25,247 - mmcls - INFO - Epoch [5][4400/5005] lr: 1.000e-01, eta: 11:39:00, time: 0.088, data_time: 0.045, memory: 1125, loss: 3.0226 2023-01-02 14:35:34,075 - mmcls - INFO - Epoch [5][4500/5005] lr: 1.000e-01, eta: 11:38:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.0161 2023-01-02 14:35:42,708 - mmcls - INFO - Epoch [5][4600/5005] lr: 1.000e-01, eta: 11:38:39, time: 0.086, data_time: 0.031, memory: 1125, loss: 3.0251 2023-01-02 14:35:51,823 - mmcls - INFO - Epoch [5][4700/5005] lr: 1.000e-01, eta: 11:38:36, time: 0.091, data_time: 0.023, memory: 1125, loss: 3.0200 2023-01-02 14:36:00,554 - mmcls - INFO - Epoch [5][4800/5005] lr: 1.000e-01, eta: 11:38:26, time: 0.087, data_time: 0.007, memory: 1125, loss: 3.0380 2023-01-02 14:36:09,254 - mmcls - INFO - Epoch [5][4900/5005] lr: 1.000e-01, eta: 11:38:15, time: 0.087, data_time: 0.022, memory: 1125, loss: 3.0229 2023-01-02 14:36:17,823 - mmcls - INFO - Epoch [5][5000/5005] lr: 1.000e-01, eta: 11:38:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 3.0262 2023-01-02 14:36:40,518 - mmcls - INFO - Epoch(val) [5][196] accuracy_top-1: 43.3380, accuracy_top-5: 68.1840 2023-01-02 14:36:51,299 - mmcls - INFO - Epoch [6][100/5005] lr: 1.000e-01, eta: 11:38:21, time: 0.108, data_time: 0.035, memory: 1125, loss: 2.9812 2023-01-02 14:37:00,163 - mmcls - INFO - Epoch [6][200/5005] lr: 1.000e-01, eta: 11:38:13, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.9813 2023-01-02 14:37:08,769 - mmcls - INFO - Epoch [6][300/5005] lr: 1.000e-01, eta: 11:38:00, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.9888 2023-01-02 14:37:17,620 - mmcls - INFO - Epoch [6][400/5005] lr: 1.000e-01, eta: 11:37:52, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.9919 2023-01-02 14:37:26,445 - mmcls - INFO - Epoch [6][500/5005] lr: 1.000e-01, eta: 11:37:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9567 2023-01-02 14:37:35,114 - mmcls - INFO - Epoch [6][600/5005] lr: 1.000e-01, eta: 11:37:32, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.9761 2023-01-02 14:37:43,789 - mmcls - INFO - Epoch [6][700/5005] lr: 1.000e-01, eta: 11:37:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9974 2023-01-02 14:37:52,546 - mmcls - INFO - Epoch [6][800/5005] lr: 1.000e-01, eta: 11:37:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.9946 2023-01-02 14:38:01,109 - mmcls - INFO - Epoch [6][900/5005] lr: 1.000e-01, eta: 11:36:57, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.9493 2023-01-02 14:38:09,948 - mmcls - INFO - Epoch [6][1000/5005] lr: 1.000e-01, eta: 11:36:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9926 2023-01-02 14:38:18,694 - mmcls - INFO - Epoch [6][1100/5005] lr: 1.000e-01, eta: 11:36:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.9675 2023-01-02 14:38:27,340 - mmcls - INFO - Epoch [6][1200/5005] lr: 1.000e-01, eta: 11:36:27, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9929 2023-01-02 14:38:36,214 - mmcls - INFO - Epoch [6][1300/5005] lr: 1.000e-01, eta: 11:36:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9758 2023-01-02 14:38:45,534 - mmcls - INFO - Epoch [6][1400/5005] lr: 1.000e-01, eta: 11:36:20, time: 0.093, data_time: 0.013, memory: 1125, loss: 3.0134 2023-01-02 14:38:54,356 - mmcls - INFO - Epoch [6][1500/5005] lr: 1.000e-01, eta: 11:36:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.9883 2023-01-02 14:39:03,085 - mmcls - INFO - Epoch [6][1600/5005] lr: 1.000e-01, eta: 11:36:01, time: 0.087, data_time: 0.010, memory: 1125, loss: 3.0147 2023-01-02 14:39:11,820 - mmcls - INFO - Epoch [6][1700/5005] lr: 1.000e-01, eta: 11:35:51, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.9864 2023-01-02 14:39:20,336 - mmcls - INFO - Epoch [6][1800/5005] lr: 1.000e-01, eta: 11:35:36, time: 0.085, data_time: 0.011, memory: 1125, loss: 3.0101 2023-01-02 14:39:29,232 - mmcls - INFO - Epoch [6][1900/5005] lr: 1.000e-01, eta: 11:35:29, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.9994 2023-01-02 14:39:38,068 - mmcls - INFO - Epoch [6][2000/5005] lr: 1.000e-01, eta: 11:35:21, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.9782 2023-01-02 14:39:46,773 - mmcls - INFO - Epoch [6][2100/5005] lr: 1.000e-01, eta: 11:35:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9974 2023-01-02 14:39:55,237 - mmcls - INFO - Epoch [6][2200/5005] lr: 1.000e-01, eta: 11:34:55, time: 0.085, data_time: 0.003, memory: 1125, loss: 3.0088 2023-01-02 14:40:04,123 - mmcls - INFO - Epoch [6][2300/5005] lr: 1.000e-01, eta: 11:34:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9771 2023-01-02 14:40:12,683 - mmcls - INFO - Epoch [6][2400/5005] lr: 1.000e-01, eta: 11:34:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.9843 2023-01-02 14:40:21,541 - mmcls - INFO - Epoch [6][2500/5005] lr: 1.000e-01, eta: 11:34:27, time: 0.089, data_time: 0.002, memory: 1125, loss: 3.0082 2023-01-02 14:40:30,313 - mmcls - INFO - Epoch [6][2600/5005] lr: 1.000e-01, eta: 11:34:17, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.9878 2023-01-02 14:40:38,806 - mmcls - INFO - Epoch [6][2700/5005] lr: 1.000e-01, eta: 11:34:03, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.0128 2023-01-02 14:40:47,432 - mmcls - INFO - Epoch [6][2800/5005] lr: 1.000e-01, eta: 11:33:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9815 2023-01-02 14:40:56,114 - mmcls - INFO - Epoch [6][2900/5005] lr: 1.000e-01, eta: 11:33:40, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.9812 2023-01-02 14:41:04,835 - mmcls - INFO - Epoch [6][3000/5005] lr: 1.000e-01, eta: 11:33:30, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.9583 2023-01-02 14:41:13,376 - mmcls - INFO - Epoch [6][3100/5005] lr: 1.000e-01, eta: 11:33:16, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.0103 2023-01-02 14:41:22,243 - mmcls - INFO - Epoch [6][3200/5005] lr: 1.000e-01, eta: 11:33:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.9967 2023-01-02 14:41:30,865 - mmcls - INFO - Epoch [6][3300/5005] lr: 1.000e-01, eta: 11:32:57, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9908 2023-01-02 14:41:39,666 - mmcls - INFO - Epoch [6][3400/5005] lr: 1.000e-01, eta: 11:32:48, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.9883 2023-01-02 14:41:48,448 - mmcls - INFO - Epoch [6][3500/5005] lr: 1.000e-01, eta: 11:32:39, time: 0.088, data_time: 0.013, memory: 1125, loss: 3.0260 2023-01-02 14:41:57,253 - mmcls - INFO - Epoch [6][3600/5005] lr: 1.000e-01, eta: 11:32:30, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.9699 2023-01-02 14:42:06,384 - mmcls - INFO - Epoch [6][3700/5005] lr: 1.000e-01, eta: 11:32:26, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.9972 2023-01-02 14:42:14,798 - mmcls - INFO - Epoch [6][3800/5005] lr: 1.000e-01, eta: 11:32:11, time: 0.084, data_time: 0.012, memory: 1125, loss: 2.9861 2023-01-02 14:42:23,742 - mmcls - INFO - Epoch [6][3900/5005] lr: 1.000e-01, eta: 11:32:04, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.9820 2023-01-02 14:42:32,326 - mmcls - INFO - Epoch [6][4000/5005] lr: 1.000e-01, eta: 11:31:52, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9716 2023-01-02 14:42:40,843 - mmcls - INFO - Epoch [6][4100/5005] lr: 1.000e-01, eta: 11:31:39, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.9680 2023-01-02 14:42:49,987 - mmcls - INFO - Epoch [6][4200/5005] lr: 1.000e-01, eta: 11:31:35, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.9913 2023-01-02 14:42:58,737 - mmcls - INFO - Epoch [6][4300/5005] lr: 1.000e-01, eta: 11:31:26, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.9889 2023-01-02 14:43:07,313 - mmcls - INFO - Epoch [6][4400/5005] lr: 1.000e-01, eta: 11:31:13, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.9891 2023-01-02 14:43:16,272 - mmcls - INFO - Epoch [6][4500/5005] lr: 1.000e-01, eta: 11:31:07, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.9842 2023-01-02 14:43:25,265 - mmcls - INFO - Epoch [6][4600/5005] lr: 1.000e-01, eta: 11:31:01, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.0015 2023-01-02 14:43:33,826 - mmcls - INFO - Epoch [6][4700/5005] lr: 1.000e-01, eta: 11:30:48, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.9929 2023-01-02 14:43:43,085 - mmcls - INFO - Epoch [6][4800/5005] lr: 1.000e-01, eta: 11:30:47, time: 0.093, data_time: 0.007, memory: 1125, loss: 2.9976 2023-01-02 14:43:51,745 - mmcls - INFO - Epoch [6][4900/5005] lr: 1.000e-01, eta: 11:30:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.0232 2023-01-02 14:44:00,331 - mmcls - INFO - Epoch [6][5000/5005] lr: 1.000e-01, eta: 11:30:23, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.9889 2023-01-02 14:44:23,192 - mmcls - INFO - Epoch(val) [6][196] accuracy_top-1: 43.4060, accuracy_top-5: 68.6280 2023-01-02 14:44:34,192 - mmcls - INFO - Epoch [7][100/5005] lr: 1.000e-01, eta: 11:30:41, time: 0.110, data_time: 0.040, memory: 1125, loss: 2.8982 2023-01-02 14:44:43,084 - mmcls - INFO - Epoch [7][200/5005] lr: 1.000e-01, eta: 11:30:34, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9316 2023-01-02 14:44:51,681 - mmcls - INFO - Epoch [7][300/5005] lr: 1.000e-01, eta: 11:30:21, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.9112 2023-01-02 14:45:00,949 - mmcls - INFO - Epoch [7][400/5005] lr: 1.000e-01, eta: 11:30:20, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.9378 2023-01-02 14:45:09,466 - mmcls - INFO - Epoch [7][500/5005] lr: 1.000e-01, eta: 11:30:06, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.9281 2023-01-02 14:45:18,025 - mmcls - INFO - Epoch [7][600/5005] lr: 1.000e-01, eta: 11:29:54, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.9406 2023-01-02 14:45:26,799 - mmcls - INFO - Epoch [7][700/5005] lr: 1.000e-01, eta: 11:29:44, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9263 2023-01-02 14:45:35,429 - mmcls - INFO - Epoch [7][800/5005] lr: 1.000e-01, eta: 11:29:33, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.9317 2023-01-02 14:45:44,809 - mmcls - INFO - Epoch [7][900/5005] lr: 1.000e-01, eta: 11:29:32, time: 0.094, data_time: 0.022, memory: 1125, loss: 2.9477 2023-01-02 14:45:53,401 - mmcls - INFO - Epoch [7][1000/5005] lr: 1.000e-01, eta: 11:29:20, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.9676 2023-01-02 14:46:02,158 - mmcls - INFO - Epoch [7][1100/5005] lr: 1.000e-01, eta: 11:29:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.9621 2023-01-02 14:46:10,796 - mmcls - INFO - Epoch [7][1200/5005] lr: 1.000e-01, eta: 11:28:59, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9785 2023-01-02 14:46:19,517 - mmcls - INFO - Epoch [7][1300/5005] lr: 1.000e-01, eta: 11:28:49, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.9845 2023-01-02 14:46:28,232 - mmcls - INFO - Epoch [7][1400/5005] lr: 1.000e-01, eta: 11:28:39, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.9535 2023-01-02 14:46:36,804 - mmcls - INFO - Epoch [7][1500/5005] lr: 1.000e-01, eta: 11:28:27, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9542 2023-01-02 14:46:45,791 - mmcls - INFO - Epoch [7][1600/5005] lr: 1.000e-01, eta: 11:28:20, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.9665 2023-01-02 14:46:54,355 - mmcls - INFO - Epoch [7][1700/5005] lr: 1.000e-01, eta: 11:28:08, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9690 2023-01-02 14:47:03,022 - mmcls - INFO - Epoch [7][1800/5005] lr: 1.000e-01, eta: 11:27:57, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.9735 2023-01-02 14:47:11,617 - mmcls - INFO - Epoch [7][1900/5005] lr: 1.000e-01, eta: 11:27:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9356 2023-01-02 14:47:20,389 - mmcls - INFO - Epoch [7][2000/5005] lr: 1.000e-01, eta: 11:27:36, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9586 2023-01-02 14:47:29,160 - mmcls - INFO - Epoch [7][2100/5005] lr: 1.000e-01, eta: 11:27:27, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.9668 2023-01-02 14:47:38,120 - mmcls - INFO - Epoch [7][2200/5005] lr: 1.000e-01, eta: 11:27:20, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.9429 2023-01-02 14:47:46,687 - mmcls - INFO - Epoch [7][2300/5005] lr: 1.000e-01, eta: 11:27:08, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.9575 2023-01-02 14:47:55,281 - mmcls - INFO - Epoch [7][2400/5005] lr: 1.000e-01, eta: 11:26:56, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.9677 2023-01-02 14:48:04,214 - mmcls - INFO - Epoch [7][2500/5005] lr: 1.000e-01, eta: 11:26:49, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.9668 2023-01-02 14:48:12,908 - mmcls - INFO - Epoch [7][2600/5005] lr: 1.000e-01, eta: 11:26:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9425 2023-01-02 14:48:21,666 - mmcls - INFO - Epoch [7][2700/5005] lr: 1.000e-01, eta: 11:26:29, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.9252 2023-01-02 14:48:30,600 - mmcls - INFO - Epoch [7][2800/5005] lr: 1.000e-01, eta: 11:26:22, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.9272 2023-01-02 14:48:38,896 - mmcls - INFO - Epoch [7][2900/5005] lr: 1.000e-01, eta: 11:26:06, time: 0.083, data_time: 0.034, memory: 1125, loss: 2.9466 2023-01-02 14:48:47,740 - mmcls - INFO - Epoch [7][3000/5005] lr: 1.000e-01, eta: 11:25:57, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.9503 2023-01-02 14:48:56,433 - mmcls - INFO - Epoch [7][3100/5005] lr: 1.000e-01, eta: 11:25:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.9654 2023-01-02 14:49:05,060 - mmcls - INFO - Epoch [7][3200/5005] lr: 1.000e-01, eta: 11:25:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9160 2023-01-02 14:49:13,845 - mmcls - INFO - Epoch [7][3300/5005] lr: 1.000e-01, eta: 11:25:27, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.9491 2023-01-02 14:49:22,741 - mmcls - INFO - Epoch [7][3400/5005] lr: 1.000e-01, eta: 11:25:19, time: 0.089, data_time: 0.037, memory: 1125, loss: 2.9285 2023-01-02 14:49:31,409 - mmcls - INFO - Epoch [7][3500/5005] lr: 1.000e-01, eta: 11:25:08, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.9407 2023-01-02 14:49:40,112 - mmcls - INFO - Epoch [7][3600/5005] lr: 1.000e-01, eta: 11:24:58, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.9414 2023-01-02 14:49:49,009 - mmcls - INFO - Epoch [7][3700/5005] lr: 1.000e-01, eta: 11:24:51, time: 0.089, data_time: 0.036, memory: 1125, loss: 2.9300 2023-01-02 14:49:57,705 - mmcls - INFO - Epoch [7][3800/5005] lr: 1.000e-01, eta: 11:24:41, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.9396 2023-01-02 14:50:06,389 - mmcls - INFO - Epoch [7][3900/5005] lr: 1.000e-01, eta: 11:24:30, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.9478 2023-01-02 14:50:14,943 - mmcls - INFO - Epoch [7][4000/5005] lr: 1.000e-01, eta: 11:24:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9620 2023-01-02 14:50:23,668 - mmcls - INFO - Epoch [7][4100/5005] lr: 1.000e-01, eta: 11:24:08, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.9389 2023-01-02 14:50:32,818 - mmcls - INFO - Epoch [7][4200/5005] lr: 1.000e-01, eta: 11:24:04, time: 0.092, data_time: 0.014, memory: 1125, loss: 2.9474 2023-01-02 14:50:41,351 - mmcls - INFO - Epoch [7][4300/5005] lr: 1.000e-01, eta: 11:23:51, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.9531 2023-01-02 14:50:50,224 - mmcls - INFO - Epoch [7][4400/5005] lr: 1.000e-01, eta: 11:23:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9252 2023-01-02 14:50:58,831 - mmcls - INFO - Epoch [7][4500/5005] lr: 1.000e-01, eta: 11:23:32, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9607 2023-01-02 14:51:07,428 - mmcls - INFO - Epoch [7][4600/5005] lr: 1.000e-01, eta: 11:23:21, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.9620 2023-01-02 14:51:16,083 - mmcls - INFO - Epoch [7][4700/5005] lr: 1.000e-01, eta: 11:23:10, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.9383 2023-01-02 14:51:25,077 - mmcls - INFO - Epoch [7][4800/5005] lr: 1.000e-01, eta: 11:23:04, time: 0.090, data_time: 0.032, memory: 1125, loss: 2.9515 2023-01-02 14:51:34,040 - mmcls - INFO - Epoch [7][4900/5005] lr: 1.000e-01, eta: 11:22:57, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.9258 2023-01-02 14:51:42,714 - mmcls - INFO - Epoch [7][5000/5005] lr: 1.000e-01, eta: 11:22:46, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.9416 2023-01-02 14:52:05,523 - mmcls - INFO - Epoch(val) [7][196] accuracy_top-1: 44.4660, accuracy_top-5: 69.6020 2023-01-02 14:52:16,662 - mmcls - INFO - Epoch [8][100/5005] lr: 1.000e-01, eta: 11:23:02, time: 0.111, data_time: 0.051, memory: 1125, loss: 2.8917 2023-01-02 14:52:25,346 - mmcls - INFO - Epoch [8][200/5005] lr: 1.000e-01, eta: 11:22:52, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.8463 2023-01-02 14:52:33,993 - mmcls - INFO - Epoch [8][300/5005] lr: 1.000e-01, eta: 11:22:41, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8774 2023-01-02 14:52:43,117 - mmcls - INFO - Epoch [8][400/5005] lr: 1.000e-01, eta: 11:22:36, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.8875 2023-01-02 14:52:51,900 - mmcls - INFO - Epoch [8][500/5005] lr: 1.000e-01, eta: 11:22:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9092 2023-01-02 14:53:00,809 - mmcls - INFO - Epoch [8][600/5005] lr: 1.000e-01, eta: 11:22:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9100 2023-01-02 14:53:09,316 - mmcls - INFO - Epoch [8][700/5005] lr: 1.000e-01, eta: 11:22:07, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.9134 2023-01-02 14:53:17,967 - mmcls - INFO - Epoch [8][800/5005] lr: 1.000e-01, eta: 11:21:56, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.9127 2023-01-02 14:53:26,750 - mmcls - INFO - Epoch [8][900/5005] lr: 1.000e-01, eta: 11:21:47, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.9212 2023-01-02 14:53:35,393 - mmcls - INFO - Epoch [8][1000/5005] lr: 1.000e-01, eta: 11:21:36, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.9212 2023-01-02 14:53:44,106 - mmcls - INFO - Epoch [8][1100/5005] lr: 1.000e-01, eta: 11:21:26, time: 0.087, data_time: 0.033, memory: 1125, loss: 2.9003 2023-01-02 14:53:52,961 - mmcls - INFO - Epoch [8][1200/5005] lr: 1.000e-01, eta: 11:21:18, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.9114 2023-01-02 14:54:02,026 - mmcls - INFO - Epoch [8][1300/5005] lr: 1.000e-01, eta: 11:21:12, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.9166 2023-01-02 14:54:10,681 - mmcls - INFO - Epoch [8][1400/5005] lr: 1.000e-01, eta: 11:21:01, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.9165 2023-01-02 14:54:19,444 - mmcls - INFO - Epoch [8][1500/5005] lr: 1.000e-01, eta: 11:20:52, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.9322 2023-01-02 14:54:28,079 - mmcls - INFO - Epoch [8][1600/5005] lr: 1.000e-01, eta: 11:20:41, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.9327 2023-01-02 14:54:36,990 - mmcls - INFO - Epoch [8][1700/5005] lr: 1.000e-01, eta: 11:20:34, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.9079 2023-01-02 14:54:45,617 - mmcls - INFO - Epoch [8][1800/5005] lr: 1.000e-01, eta: 11:20:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9249 2023-01-02 14:54:54,446 - mmcls - INFO - Epoch [8][1900/5005] lr: 1.000e-01, eta: 11:20:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9142 2023-01-02 14:55:03,171 - mmcls - INFO - Epoch [8][2000/5005] lr: 1.000e-01, eta: 11:20:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9108 2023-01-02 14:55:11,889 - mmcls - INFO - Epoch [8][2100/5005] lr: 1.000e-01, eta: 11:19:54, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.9250 2023-01-02 14:55:20,711 - mmcls - INFO - Epoch [8][2200/5005] lr: 1.000e-01, eta: 11:19:46, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.9275 2023-01-02 14:55:29,306 - mmcls - INFO - Epoch [8][2300/5005] lr: 1.000e-01, eta: 11:19:34, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.9232 2023-01-02 14:55:38,160 - mmcls - INFO - Epoch [8][2400/5005] lr: 1.000e-01, eta: 11:19:26, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9106 2023-01-02 14:55:46,737 - mmcls - INFO - Epoch [8][2500/5005] lr: 1.000e-01, eta: 11:19:15, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.9214 2023-01-02 14:55:55,300 - mmcls - INFO - Epoch [8][2600/5005] lr: 1.000e-01, eta: 11:19:03, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9100 2023-01-02 14:56:04,125 - mmcls - INFO - Epoch [8][2700/5005] lr: 1.000e-01, eta: 11:18:54, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.9653 2023-01-02 14:56:12,975 - mmcls - INFO - Epoch [8][2800/5005] lr: 1.000e-01, eta: 11:18:46, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9345 2023-01-02 14:56:21,880 - mmcls - INFO - Epoch [8][2900/5005] lr: 1.000e-01, eta: 11:18:38, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9417 2023-01-02 14:56:30,520 - mmcls - INFO - Epoch [8][3000/5005] lr: 1.000e-01, eta: 11:18:28, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.9047 2023-01-02 14:56:39,379 - mmcls - INFO - Epoch [8][3100/5005] lr: 1.000e-01, eta: 11:18:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9306 2023-01-02 14:56:48,271 - mmcls - INFO - Epoch [8][3200/5005] lr: 1.000e-01, eta: 11:18:12, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.9522 2023-01-02 14:56:57,144 - mmcls - INFO - Epoch [8][3300/5005] lr: 1.000e-01, eta: 11:18:04, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.9391 2023-01-02 14:57:05,685 - mmcls - INFO - Epoch [8][3400/5005] lr: 1.000e-01, eta: 11:17:52, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.9543 2023-01-02 14:57:14,522 - mmcls - INFO - Epoch [8][3500/5005] lr: 1.000e-01, eta: 11:17:44, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8975 2023-01-02 14:57:23,149 - mmcls - INFO - Epoch [8][3600/5005] lr: 1.000e-01, eta: 11:17:33, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9194 2023-01-02 14:57:31,978 - mmcls - INFO - Epoch [8][3700/5005] lr: 1.000e-01, eta: 11:17:24, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.9088 2023-01-02 14:57:40,841 - mmcls - INFO - Epoch [8][3800/5005] lr: 1.000e-01, eta: 11:17:16, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9259 2023-01-02 14:57:49,580 - mmcls - INFO - Epoch [8][3900/5005] lr: 1.000e-01, eta: 11:17:06, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.9184 2023-01-02 14:57:58,559 - mmcls - INFO - Epoch [8][4000/5005] lr: 1.000e-01, eta: 11:17:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8786 2023-01-02 14:58:07,611 - mmcls - INFO - Epoch [8][4100/5005] lr: 1.000e-01, eta: 11:16:54, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.9180 2023-01-02 14:58:16,169 - mmcls - INFO - Epoch [8][4200/5005] lr: 1.000e-01, eta: 11:16:42, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.9164 2023-01-02 14:58:24,786 - mmcls - INFO - Epoch [8][4300/5005] lr: 1.000e-01, eta: 11:16:31, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.9439 2023-01-02 14:58:33,593 - mmcls - INFO - Epoch [8][4400/5005] lr: 1.000e-01, eta: 11:16:22, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.9145 2023-01-02 14:58:41,983 - mmcls - INFO - Epoch [8][4500/5005] lr: 1.000e-01, eta: 11:16:09, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.9169 2023-01-02 14:58:50,663 - mmcls - INFO - Epoch [8][4600/5005] lr: 1.000e-01, eta: 11:15:59, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.9205 2023-01-02 14:58:59,629 - mmcls - INFO - Epoch [8][4700/5005] lr: 1.000e-01, eta: 11:15:52, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.9356 2023-01-02 14:59:08,543 - mmcls - INFO - Epoch [8][4800/5005] lr: 1.000e-01, eta: 11:15:44, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.9031 2023-01-02 14:59:17,381 - mmcls - INFO - Epoch [8][4900/5005] lr: 1.000e-01, eta: 11:15:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9374 2023-01-02 14:59:26,237 - mmcls - INFO - Epoch [8][5000/5005] lr: 1.000e-01, eta: 11:15:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9000 2023-01-02 14:59:48,926 - mmcls - INFO - Epoch(val) [8][196] accuracy_top-1: 44.0820, accuracy_top-5: 68.5000 2023-01-02 14:59:59,667 - mmcls - INFO - Epoch [9][100/5005] lr: 1.000e-01, eta: 11:15:35, time: 0.107, data_time: 0.044, memory: 1125, loss: 2.8681 2023-01-02 15:00:08,351 - mmcls - INFO - Epoch [9][200/5005] lr: 1.000e-01, eta: 11:15:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8638 2023-01-02 15:00:17,061 - mmcls - INFO - Epoch [9][300/5005] lr: 1.000e-01, eta: 11:15:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8543 2023-01-02 15:00:25,967 - mmcls - INFO - Epoch [9][400/5005] lr: 1.000e-01, eta: 11:15:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8557 2023-01-02 15:00:34,510 - mmcls - INFO - Epoch [9][500/5005] lr: 1.000e-01, eta: 11:14:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8779 2023-01-02 15:00:43,091 - mmcls - INFO - Epoch [9][600/5005] lr: 1.000e-01, eta: 11:14:44, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8857 2023-01-02 15:00:51,631 - mmcls - INFO - Epoch [9][700/5005] lr: 1.000e-01, eta: 11:14:33, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.8659 2023-01-02 15:01:00,329 - mmcls - INFO - Epoch [9][800/5005] lr: 1.000e-01, eta: 11:14:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8702 2023-01-02 15:01:09,219 - mmcls - INFO - Epoch [9][900/5005] lr: 1.000e-01, eta: 11:14:15, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.8643 2023-01-02 15:01:17,727 - mmcls - INFO - Epoch [9][1000/5005] lr: 1.000e-01, eta: 11:14:03, time: 0.085, data_time: 0.027, memory: 1125, loss: 2.8700 2023-01-02 15:01:26,378 - mmcls - INFO - Epoch [9][1100/5005] lr: 1.000e-01, eta: 11:13:52, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8889 2023-01-02 15:01:34,981 - mmcls - INFO - Epoch [9][1200/5005] lr: 1.000e-01, eta: 11:13:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9077 2023-01-02 15:01:43,768 - mmcls - INFO - Epoch [9][1300/5005] lr: 1.000e-01, eta: 11:13:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8749 2023-01-02 15:01:52,893 - mmcls - INFO - Epoch [9][1400/5005] lr: 1.000e-01, eta: 11:13:27, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.9098 2023-01-02 15:02:01,638 - mmcls - INFO - Epoch [9][1500/5005] lr: 1.000e-01, eta: 11:13:17, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.8912 2023-01-02 15:02:10,360 - mmcls - INFO - Epoch [9][1600/5005] lr: 1.000e-01, eta: 11:13:08, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.9100 2023-01-02 15:02:19,007 - mmcls - INFO - Epoch [9][1700/5005] lr: 1.000e-01, eta: 11:12:57, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8978 2023-01-02 15:02:28,096 - mmcls - INFO - Epoch [9][1800/5005] lr: 1.000e-01, eta: 11:12:52, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.9194 2023-01-02 15:02:36,938 - mmcls - INFO - Epoch [9][1900/5005] lr: 1.000e-01, eta: 11:12:43, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.8765 2023-01-02 15:02:45,803 - mmcls - INFO - Epoch [9][2000/5005] lr: 1.000e-01, eta: 11:12:35, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.9110 2023-01-02 15:02:54,377 - mmcls - INFO - Epoch [9][2100/5005] lr: 1.000e-01, eta: 11:12:24, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.8996 2023-01-02 15:03:03,050 - mmcls - INFO - Epoch [9][2200/5005] lr: 1.000e-01, eta: 11:12:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8832 2023-01-02 15:03:11,803 - mmcls - INFO - Epoch [9][2300/5005] lr: 1.000e-01, eta: 11:12:04, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.9103 2023-01-02 15:03:20,509 - mmcls - INFO - Epoch [9][2400/5005] lr: 1.000e-01, eta: 11:11:54, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.8904 2023-01-02 15:03:29,006 - mmcls - INFO - Epoch [9][2500/5005] lr: 1.000e-01, eta: 11:11:42, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.9118 2023-01-02 15:03:37,679 - mmcls - INFO - Epoch [9][2600/5005] lr: 1.000e-01, eta: 11:11:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9047 2023-01-02 15:03:46,680 - mmcls - INFO - Epoch [9][2700/5005] lr: 1.000e-01, eta: 11:11:25, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8788 2023-01-02 15:03:55,559 - mmcls - INFO - Epoch [9][2800/5005] lr: 1.000e-01, eta: 11:11:17, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.9125 2023-01-02 15:04:04,279 - mmcls - INFO - Epoch [9][2900/5005] lr: 1.000e-01, eta: 11:11:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9118 2023-01-02 15:04:13,080 - mmcls - INFO - Epoch [9][3000/5005] lr: 1.000e-01, eta: 11:10:59, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.9252 2023-01-02 15:04:22,098 - mmcls - INFO - Epoch [9][3100/5005] lr: 1.000e-01, eta: 11:10:52, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.8873 2023-01-02 15:04:30,772 - mmcls - INFO - Epoch [9][3200/5005] lr: 1.000e-01, eta: 11:10:42, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8843 2023-01-02 15:04:39,580 - mmcls - INFO - Epoch [9][3300/5005] lr: 1.000e-01, eta: 11:10:33, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.9042 2023-01-02 15:04:48,411 - mmcls - INFO - Epoch [9][3400/5005] lr: 1.000e-01, eta: 11:10:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9070 2023-01-02 15:04:56,978 - mmcls - INFO - Epoch [9][3500/5005] lr: 1.000e-01, eta: 11:10:14, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9071 2023-01-02 15:05:05,616 - mmcls - INFO - Epoch [9][3600/5005] lr: 1.000e-01, eta: 11:10:03, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.9190 2023-01-02 15:05:14,395 - mmcls - INFO - Epoch [9][3700/5005] lr: 1.000e-01, eta: 11:09:54, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9216 2023-01-02 15:05:23,294 - mmcls - INFO - Epoch [9][3800/5005] lr: 1.000e-01, eta: 11:09:47, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8925 2023-01-02 15:05:32,319 - mmcls - INFO - Epoch [9][3900/5005] lr: 1.000e-01, eta: 11:09:40, time: 0.090, data_time: 0.000, memory: 1125, loss: 2.8981 2023-01-02 15:05:40,956 - mmcls - INFO - Epoch [9][4000/5005] lr: 1.000e-01, eta: 11:09:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9000 2023-01-02 15:05:49,595 - mmcls - INFO - Epoch [9][4100/5005] lr: 1.000e-01, eta: 11:09:19, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.9096 2023-01-02 15:05:58,227 - mmcls - INFO - Epoch [9][4200/5005] lr: 1.000e-01, eta: 11:09:08, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.9059 2023-01-02 15:06:07,174 - mmcls - INFO - Epoch [9][4300/5005] lr: 1.000e-01, eta: 11:09:01, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.8552 2023-01-02 15:06:15,645 - mmcls - INFO - Epoch [9][4400/5005] lr: 1.000e-01, eta: 11:08:49, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8857 2023-01-02 15:06:24,452 - mmcls - INFO - Epoch [9][4500/5005] lr: 1.000e-01, eta: 11:08:40, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.8717 2023-01-02 15:06:33,432 - mmcls - INFO - Epoch [9][4600/5005] lr: 1.000e-01, eta: 11:08:33, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.8903 2023-01-02 15:06:42,421 - mmcls - INFO - Epoch [9][4700/5005] lr: 1.000e-01, eta: 11:08:27, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8963 2023-01-02 15:06:51,712 - mmcls - INFO - Epoch [9][4800/5005] lr: 1.000e-01, eta: 11:08:23, time: 0.093, data_time: 0.002, memory: 1125, loss: 2.9031 2023-01-02 15:07:00,495 - mmcls - INFO - Epoch [9][4900/5005] lr: 1.000e-01, eta: 11:08:14, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.9023 2023-01-02 15:07:09,118 - mmcls - INFO - Epoch [9][5000/5005] lr: 1.000e-01, eta: 11:08:03, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.9074 2023-01-02 15:07:31,928 - mmcls - INFO - Epoch(val) [9][196] accuracy_top-1: 44.4040, accuracy_top-5: 69.1900 2023-01-02 15:07:42,662 - mmcls - INFO - Epoch [10][100/5005] lr: 1.000e-01, eta: 11:08:09, time: 0.107, data_time: 0.042, memory: 1125, loss: 2.8080 2023-01-02 15:07:51,454 - mmcls - INFO - Epoch [10][200/5005] lr: 1.000e-01, eta: 11:08:00, time: 0.088, data_time: 0.042, memory: 1125, loss: 2.8723 2023-01-02 15:08:00,312 - mmcls - INFO - Epoch [10][300/5005] lr: 1.000e-01, eta: 11:07:51, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.8538 2023-01-02 15:08:08,926 - mmcls - INFO - Epoch [10][400/5005] lr: 1.000e-01, eta: 11:07:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8423 2023-01-02 15:08:17,703 - mmcls - INFO - Epoch [10][500/5005] lr: 1.000e-01, eta: 11:07:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8541 2023-01-02 15:08:26,429 - mmcls - INFO - Epoch [10][600/5005] lr: 1.000e-01, eta: 11:07:22, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8341 2023-01-02 15:08:35,063 - mmcls - INFO - Epoch [10][700/5005] lr: 1.000e-01, eta: 11:07:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8561 2023-01-02 15:08:44,166 - mmcls - INFO - Epoch [10][800/5005] lr: 1.000e-01, eta: 11:07:06, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.8593 2023-01-02 15:08:52,808 - mmcls - INFO - Epoch [10][900/5005] lr: 1.000e-01, eta: 11:06:55, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8786 2023-01-02 15:09:01,494 - mmcls - INFO - Epoch [10][1000/5005] lr: 1.000e-01, eta: 11:06:45, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8873 2023-01-02 15:09:10,260 - mmcls - INFO - Epoch [10][1100/5005] lr: 1.000e-01, eta: 11:06:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8601 2023-01-02 15:09:18,790 - mmcls - INFO - Epoch [10][1200/5005] lr: 1.000e-01, eta: 11:06:25, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.8584 2023-01-02 15:09:27,772 - mmcls - INFO - Epoch [10][1300/5005] lr: 1.000e-01, eta: 11:06:18, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.8934 2023-01-02 15:09:36,557 - mmcls - INFO - Epoch [10][1400/5005] lr: 1.000e-01, eta: 11:06:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8705 2023-01-02 15:09:45,413 - mmcls - INFO - Epoch [10][1500/5005] lr: 1.000e-01, eta: 11:06:00, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8633 2023-01-02 15:09:54,364 - mmcls - INFO - Epoch [10][1600/5005] lr: 1.000e-01, eta: 11:05:53, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.8748 2023-01-02 15:10:03,246 - mmcls - INFO - Epoch [10][1700/5005] lr: 1.000e-01, eta: 11:05:45, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8849 2023-01-02 15:10:12,302 - mmcls - INFO - Epoch [10][1800/5005] lr: 1.000e-01, eta: 11:05:39, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.8873 2023-01-02 15:10:21,033 - mmcls - INFO - Epoch [10][1900/5005] lr: 1.000e-01, eta: 11:05:29, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.8961 2023-01-02 15:10:29,741 - mmcls - INFO - Epoch [10][2000/5005] lr: 1.000e-01, eta: 11:05:19, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.9094 2023-01-02 15:10:38,735 - mmcls - INFO - Epoch [10][2100/5005] lr: 1.000e-01, eta: 11:05:12, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8846 2023-01-02 15:10:47,373 - mmcls - INFO - Epoch [10][2200/5005] lr: 1.000e-01, eta: 11:05:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8684 2023-01-02 15:10:56,009 - mmcls - INFO - Epoch [10][2300/5005] lr: 1.000e-01, eta: 11:04:51, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.9021 2023-01-02 15:11:04,641 - mmcls - INFO - Epoch [10][2400/5005] lr: 1.000e-01, eta: 11:04:41, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8475 2023-01-02 15:11:13,332 - mmcls - INFO - Epoch [10][2500/5005] lr: 1.000e-01, eta: 11:04:31, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.9088 2023-01-02 15:11:22,341 - mmcls - INFO - Epoch [10][2600/5005] lr: 1.000e-01, eta: 11:04:24, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.8661 2023-01-02 15:11:31,072 - mmcls - INFO - Epoch [10][2700/5005] lr: 1.000e-01, eta: 11:04:15, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.8996 2023-01-02 15:11:39,914 - mmcls - INFO - Epoch [10][2800/5005] lr: 1.000e-01, eta: 11:04:06, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8829 2023-01-02 15:11:48,460 - mmcls - INFO - Epoch [10][2900/5005] lr: 1.000e-01, eta: 11:03:55, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9022 2023-01-02 15:11:56,975 - mmcls - INFO - Epoch [10][3000/5005] lr: 1.000e-01, eta: 11:03:44, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8918 2023-01-02 15:12:06,135 - mmcls - INFO - Epoch [10][3100/5005] lr: 1.000e-01, eta: 11:03:38, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.8734 2023-01-02 15:12:14,830 - mmcls - INFO - Epoch [10][3200/5005] lr: 1.000e-01, eta: 11:03:28, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8930 2023-01-02 15:12:23,684 - mmcls - INFO - Epoch [10][3300/5005] lr: 1.000e-01, eta: 11:03:20, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.8959 2023-01-02 15:12:32,276 - mmcls - INFO - Epoch [10][3400/5005] lr: 1.000e-01, eta: 11:03:09, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.9245 2023-01-02 15:12:40,788 - mmcls - INFO - Epoch [10][3500/5005] lr: 1.000e-01, eta: 11:02:58, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8817 2023-01-02 15:12:49,495 - mmcls - INFO - Epoch [10][3600/5005] lr: 1.000e-01, eta: 11:02:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8829 2023-01-02 15:12:58,239 - mmcls - INFO - Epoch [10][3700/5005] lr: 1.000e-01, eta: 11:02:39, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8802 2023-01-02 15:13:06,847 - mmcls - INFO - Epoch [10][3800/5005] lr: 1.000e-01, eta: 11:02:28, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8921 2023-01-02 15:13:15,454 - mmcls - INFO - Epoch [10][3900/5005] lr: 1.000e-01, eta: 11:02:18, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8959 2023-01-02 15:13:24,172 - mmcls - INFO - Epoch [10][4000/5005] lr: 1.000e-01, eta: 11:02:08, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8567 2023-01-02 15:13:32,865 - mmcls - INFO - Epoch [10][4100/5005] lr: 1.000e-01, eta: 11:01:58, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.8804 2023-01-02 15:13:41,530 - mmcls - INFO - Epoch [10][4200/5005] lr: 1.000e-01, eta: 11:01:48, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8793 2023-01-02 15:13:50,270 - mmcls - INFO - Epoch [10][4300/5005] lr: 1.000e-01, eta: 11:01:39, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8873 2023-01-02 15:13:58,956 - mmcls - INFO - Epoch [10][4400/5005] lr: 1.000e-01, eta: 11:01:29, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.8777 2023-01-02 15:14:07,605 - mmcls - INFO - Epoch [10][4500/5005] lr: 1.000e-01, eta: 11:01:19, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8554 2023-01-02 15:14:16,631 - mmcls - INFO - Epoch [10][4600/5005] lr: 1.000e-01, eta: 11:01:12, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.8894 2023-01-02 15:14:25,241 - mmcls - INFO - Epoch [10][4700/5005] lr: 1.000e-01, eta: 11:01:02, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8480 2023-01-02 15:14:33,862 - mmcls - INFO - Epoch [10][4800/5005] lr: 1.000e-01, eta: 11:00:51, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8714 2023-01-02 15:14:42,897 - mmcls - INFO - Epoch [10][4900/5005] lr: 1.000e-01, eta: 11:00:44, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.8829 2023-01-02 15:14:51,843 - mmcls - INFO - Epoch [10][5000/5005] lr: 1.000e-01, eta: 11:00:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8873 2023-01-02 15:15:14,621 - mmcls - INFO - Epoch(val) [10][196] accuracy_top-1: 44.3000, accuracy_top-5: 68.5680 2023-01-02 15:15:25,431 - mmcls - INFO - Epoch [11][100/5005] lr: 1.000e-01, eta: 11:00:42, time: 0.108, data_time: 0.043, memory: 1125, loss: 2.8049 2023-01-02 15:15:33,864 - mmcls - INFO - Epoch [11][200/5005] lr: 1.000e-01, eta: 11:00:29, time: 0.084, data_time: 0.014, memory: 1125, loss: 2.8014 2023-01-02 15:15:42,437 - mmcls - INFO - Epoch [11][300/5005] lr: 1.000e-01, eta: 11:00:19, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.7949 2023-01-02 15:15:51,029 - mmcls - INFO - Epoch [11][400/5005] lr: 1.000e-01, eta: 11:00:08, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.8783 2023-01-02 15:16:00,101 - mmcls - INFO - Epoch [11][500/5005] lr: 1.000e-01, eta: 11:00:02, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.8452 2023-01-02 15:16:08,707 - mmcls - INFO - Epoch [11][600/5005] lr: 1.000e-01, eta: 10:59:51, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.8330 2023-01-02 15:16:17,595 - mmcls - INFO - Epoch [11][700/5005] lr: 1.000e-01, eta: 10:59:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8399 2023-01-02 15:16:26,323 - mmcls - INFO - Epoch [11][800/5005] lr: 1.000e-01, eta: 10:59:33, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.8346 2023-01-02 15:16:35,111 - mmcls - INFO - Epoch [11][900/5005] lr: 1.000e-01, eta: 10:59:25, time: 0.088, data_time: 0.039, memory: 1125, loss: 2.8341 2023-01-02 15:16:43,631 - mmcls - INFO - Epoch [11][1000/5005] lr: 1.000e-01, eta: 10:59:13, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.8394 2023-01-02 15:16:52,267 - mmcls - INFO - Epoch [11][1100/5005] lr: 1.000e-01, eta: 10:59:03, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8384 2023-01-02 15:17:00,974 - mmcls - INFO - Epoch [11][1200/5005] lr: 1.000e-01, eta: 10:58:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8608 2023-01-02 15:17:10,007 - mmcls - INFO - Epoch [11][1300/5005] lr: 1.000e-01, eta: 10:58:47, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8850 2023-01-02 15:17:18,655 - mmcls - INFO - Epoch [11][1400/5005] lr: 1.000e-01, eta: 10:58:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8755 2023-01-02 15:17:27,383 - mmcls - INFO - Epoch [11][1500/5005] lr: 1.000e-01, eta: 10:58:27, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.8608 2023-01-02 15:17:36,265 - mmcls - INFO - Epoch [11][1600/5005] lr: 1.000e-01, eta: 10:58:19, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.8735 2023-01-02 15:17:45,007 - mmcls - INFO - Epoch [11][1700/5005] lr: 1.000e-01, eta: 10:58:10, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8749 2023-01-02 15:17:53,883 - mmcls - INFO - Epoch [11][1800/5005] lr: 1.000e-01, eta: 10:58:01, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.8345 2023-01-02 15:18:02,650 - mmcls - INFO - Epoch [11][1900/5005] lr: 1.000e-01, eta: 10:57:52, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.8835 2023-01-02 15:18:11,289 - mmcls - INFO - Epoch [11][2000/5005] lr: 1.000e-01, eta: 10:57:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8670 2023-01-02 15:18:19,832 - mmcls - INFO - Epoch [11][2100/5005] lr: 1.000e-01, eta: 10:57:31, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.8625 2023-01-02 15:18:28,562 - mmcls - INFO - Epoch [11][2200/5005] lr: 1.000e-01, eta: 10:57:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8745 2023-01-02 15:18:37,403 - mmcls - INFO - Epoch [11][2300/5005] lr: 1.000e-01, eta: 10:57:13, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.8973 2023-01-02 15:18:46,133 - mmcls - INFO - Epoch [11][2400/5005] lr: 1.000e-01, eta: 10:57:04, time: 0.087, data_time: 0.042, memory: 1125, loss: 2.8745 2023-01-02 15:18:54,772 - mmcls - INFO - Epoch [11][2500/5005] lr: 1.000e-01, eta: 10:56:54, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.8773 2023-01-02 15:19:03,947 - mmcls - INFO - Epoch [11][2600/5005] lr: 1.000e-01, eta: 10:56:48, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.8614 2023-01-02 15:19:12,420 - mmcls - INFO - Epoch [11][2700/5005] lr: 1.000e-01, eta: 10:56:37, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.8640 2023-01-02 15:19:21,084 - mmcls - INFO - Epoch [11][2800/5005] lr: 1.000e-01, eta: 10:56:27, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8487 2023-01-02 15:19:30,073 - mmcls - INFO - Epoch [11][2900/5005] lr: 1.000e-01, eta: 10:56:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8559 2023-01-02 15:19:38,610 - mmcls - INFO - Epoch [11][3000/5005] lr: 1.000e-01, eta: 10:56:08, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.8854 2023-01-02 15:19:47,519 - mmcls - INFO - Epoch [11][3100/5005] lr: 1.000e-01, eta: 10:56:00, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8459 2023-01-02 15:19:56,279 - mmcls - INFO - Epoch [11][3200/5005] lr: 1.000e-01, eta: 10:55:51, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.8410 2023-01-02 15:20:04,939 - mmcls - INFO - Epoch [11][3300/5005] lr: 1.000e-01, eta: 10:55:41, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8938 2023-01-02 15:20:13,897 - mmcls - INFO - Epoch [11][3400/5005] lr: 1.000e-01, eta: 10:55:34, time: 0.090, data_time: 0.024, memory: 1125, loss: 2.8543 2023-01-02 15:20:22,464 - mmcls - INFO - Epoch [11][3500/5005] lr: 1.000e-01, eta: 10:55:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8942 2023-01-02 15:20:31,275 - mmcls - INFO - Epoch [11][3600/5005] lr: 1.000e-01, eta: 10:55:15, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8859 2023-01-02 15:20:39,972 - mmcls - INFO - Epoch [11][3700/5005] lr: 1.000e-01, eta: 10:55:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8594 2023-01-02 15:20:48,699 - mmcls - INFO - Epoch [11][3800/5005] lr: 1.000e-01, eta: 10:54:55, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.8558 2023-01-02 15:20:57,348 - mmcls - INFO - Epoch [11][3900/5005] lr: 1.000e-01, eta: 10:54:45, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.8733 2023-01-02 15:21:05,911 - mmcls - INFO - Epoch [11][4000/5005] lr: 1.000e-01, eta: 10:54:35, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.8723 2023-01-02 15:21:14,864 - mmcls - INFO - Epoch [11][4100/5005] lr: 1.000e-01, eta: 10:54:27, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.8729 2023-01-02 15:21:23,443 - mmcls - INFO - Epoch [11][4200/5005] lr: 1.000e-01, eta: 10:54:17, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8508 2023-01-02 15:21:32,318 - mmcls - INFO - Epoch [11][4300/5005] lr: 1.000e-01, eta: 10:54:08, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.8776 2023-01-02 15:21:41,557 - mmcls - INFO - Epoch [11][4400/5005] lr: 1.000e-01, eta: 10:54:03, time: 0.092, data_time: 0.003, memory: 1125, loss: 2.8977 2023-01-02 15:21:50,364 - mmcls - INFO - Epoch [11][4500/5005] lr: 1.000e-01, eta: 10:53:55, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.8407 2023-01-02 15:21:58,980 - mmcls - INFO - Epoch [11][4600/5005] lr: 1.000e-01, eta: 10:53:44, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.8652 2023-01-02 15:22:07,782 - mmcls - INFO - Epoch [11][4700/5005] lr: 1.000e-01, eta: 10:53:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8974 2023-01-02 15:22:16,470 - mmcls - INFO - Epoch [11][4800/5005] lr: 1.000e-01, eta: 10:53:26, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8437 2023-01-02 15:22:25,268 - mmcls - INFO - Epoch [11][4900/5005] lr: 1.000e-01, eta: 10:53:17, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8457 2023-01-02 15:22:33,706 - mmcls - INFO - Epoch [11][5000/5005] lr: 1.000e-01, eta: 10:53:05, time: 0.084, data_time: 0.021, memory: 1125, loss: 2.8968 2023-01-02 15:22:56,610 - mmcls - INFO - Epoch(val) [11][196] accuracy_top-1: 43.8000, accuracy_top-5: 68.8280 2023-01-02 15:23:07,490 - mmcls - INFO - Epoch [12][100/5005] lr: 1.000e-01, eta: 10:53:09, time: 0.109, data_time: 0.030, memory: 1125, loss: 2.8319 2023-01-02 15:23:16,064 - mmcls - INFO - Epoch [12][200/5005] lr: 1.000e-01, eta: 10:52:58, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.8342 2023-01-02 15:23:25,428 - mmcls - INFO - Epoch [12][300/5005] lr: 1.000e-01, eta: 10:52:54, time: 0.094, data_time: 0.029, memory: 1125, loss: 2.8235 2023-01-02 15:23:34,026 - mmcls - INFO - Epoch [12][400/5005] lr: 1.000e-01, eta: 10:52:44, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8387 2023-01-02 15:23:42,525 - mmcls - INFO - Epoch [12][500/5005] lr: 1.000e-01, eta: 10:52:33, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.8199 2023-01-02 15:23:50,960 - mmcls - INFO - Epoch [12][600/5005] lr: 1.000e-01, eta: 10:52:21, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.8213 2023-01-02 15:23:59,953 - mmcls - INFO - Epoch [12][700/5005] lr: 1.000e-01, eta: 10:52:14, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8082 2023-01-02 15:24:08,435 - mmcls - INFO - Epoch [12][800/5005] lr: 1.000e-01, eta: 10:52:02, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.8230 2023-01-02 15:24:17,301 - mmcls - INFO - Epoch [12][900/5005] lr: 1.000e-01, eta: 10:51:54, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.8144 2023-01-02 15:24:25,922 - mmcls - INFO - Epoch [12][1000/5005] lr: 1.000e-01, eta: 10:51:44, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.8632 2023-01-02 15:24:34,643 - mmcls - INFO - Epoch [12][1100/5005] lr: 1.000e-01, eta: 10:51:34, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.8528 2023-01-02 15:24:43,160 - mmcls - INFO - Epoch [12][1200/5005] lr: 1.000e-01, eta: 10:51:23, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.8230 2023-01-02 15:24:51,890 - mmcls - INFO - Epoch [12][1300/5005] lr: 1.000e-01, eta: 10:51:14, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.8256 2023-01-02 15:25:00,821 - mmcls - INFO - Epoch [12][1400/5005] lr: 1.000e-01, eta: 10:51:06, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8362 2023-01-02 15:25:10,170 - mmcls - INFO - Epoch [12][1500/5005] lr: 1.000e-01, eta: 10:51:02, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.8218 2023-01-02 15:25:19,127 - mmcls - INFO - Epoch [12][1600/5005] lr: 1.000e-01, eta: 10:50:54, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.8465 2023-01-02 15:25:27,749 - mmcls - INFO - Epoch [12][1700/5005] lr: 1.000e-01, eta: 10:50:44, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8378 2023-01-02 15:25:36,732 - mmcls - INFO - Epoch [12][1800/5005] lr: 1.000e-01, eta: 10:50:37, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.8408 2023-01-02 15:25:45,239 - mmcls - INFO - Epoch [12][1900/5005] lr: 1.000e-01, eta: 10:50:26, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8299 2023-01-02 15:25:54,268 - mmcls - INFO - Epoch [12][2000/5005] lr: 1.000e-01, eta: 10:50:18, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8127 2023-01-02 15:26:03,026 - mmcls - INFO - Epoch [12][2100/5005] lr: 1.000e-01, eta: 10:50:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8597 2023-01-02 15:26:11,766 - mmcls - INFO - Epoch [12][2200/5005] lr: 1.000e-01, eta: 10:50:00, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8575 2023-01-02 15:26:20,688 - mmcls - INFO - Epoch [12][2300/5005] lr: 1.000e-01, eta: 10:49:52, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.8431 2023-01-02 15:26:29,252 - mmcls - INFO - Epoch [12][2400/5005] lr: 1.000e-01, eta: 10:49:42, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.8687 2023-01-02 15:26:37,897 - mmcls - INFO - Epoch [12][2500/5005] lr: 1.000e-01, eta: 10:49:32, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8393 2023-01-02 15:26:46,464 - mmcls - INFO - Epoch [12][2600/5005] lr: 1.000e-01, eta: 10:49:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8500 2023-01-02 15:26:54,968 - mmcls - INFO - Epoch [12][2700/5005] lr: 1.000e-01, eta: 10:49:10, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.8715 2023-01-02 15:27:03,907 - mmcls - INFO - Epoch [12][2800/5005] lr: 1.000e-01, eta: 10:49:02, time: 0.089, data_time: 0.033, memory: 1125, loss: 2.8389 2023-01-02 15:27:12,540 - mmcls - INFO - Epoch [12][2900/5005] lr: 1.000e-01, eta: 10:48:52, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8558 2023-01-02 15:27:21,584 - mmcls - INFO - Epoch [12][3000/5005] lr: 1.000e-01, eta: 10:48:45, time: 0.090, data_time: 0.000, memory: 1125, loss: 2.8518 2023-01-02 15:27:30,126 - mmcls - INFO - Epoch [12][3100/5005] lr: 1.000e-01, eta: 10:48:35, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8681 2023-01-02 15:27:38,791 - mmcls - INFO - Epoch [12][3200/5005] lr: 1.000e-01, eta: 10:48:25, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.8621 2023-01-02 15:27:47,626 - mmcls - INFO - Epoch [12][3300/5005] lr: 1.000e-01, eta: 10:48:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8559 2023-01-02 15:27:56,924 - mmcls - INFO - Epoch [12][3400/5005] lr: 1.000e-01, eta: 10:48:11, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.8396 2023-01-02 15:28:05,528 - mmcls - INFO - Epoch [12][3500/5005] lr: 1.000e-01, eta: 10:48:01, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8228 2023-01-02 15:28:14,175 - mmcls - INFO - Epoch [12][3600/5005] lr: 1.000e-01, eta: 10:47:51, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.8173 2023-01-02 15:28:23,127 - mmcls - INFO - Epoch [12][3700/5005] lr: 1.000e-01, eta: 10:47:43, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.8347 2023-01-02 15:28:31,938 - mmcls - INFO - Epoch [12][3800/5005] lr: 1.000e-01, eta: 10:47:35, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.8309 2023-01-02 15:28:40,727 - mmcls - INFO - Epoch [12][3900/5005] lr: 1.000e-01, eta: 10:47:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8404 2023-01-02 15:28:49,831 - mmcls - INFO - Epoch [12][4000/5005] lr: 1.000e-01, eta: 10:47:19, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.8249 2023-01-02 15:28:58,684 - mmcls - INFO - Epoch [12][4100/5005] lr: 1.000e-01, eta: 10:47:11, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.8550 2023-01-02 15:29:07,201 - mmcls - INFO - Epoch [12][4200/5005] lr: 1.000e-01, eta: 10:47:00, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.8375 2023-01-02 15:29:16,031 - mmcls - INFO - Epoch [12][4300/5005] lr: 1.000e-01, eta: 10:46:51, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8127 2023-01-02 15:29:24,914 - mmcls - INFO - Epoch [12][4400/5005] lr: 1.000e-01, eta: 10:46:43, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.8561 2023-01-02 15:29:33,920 - mmcls - INFO - Epoch [12][4500/5005] lr: 1.000e-01, eta: 10:46:36, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.8537 2023-01-02 15:29:42,844 - mmcls - INFO - Epoch [12][4600/5005] lr: 1.000e-01, eta: 10:46:28, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8550 2023-01-02 15:29:51,461 - mmcls - INFO - Epoch [12][4700/5005] lr: 1.000e-01, eta: 10:46:18, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8433 2023-01-02 15:30:00,010 - mmcls - INFO - Epoch [12][4800/5005] lr: 1.000e-01, eta: 10:46:07, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.8520 2023-01-02 15:30:08,502 - mmcls - INFO - Epoch [12][4900/5005] lr: 1.000e-01, eta: 10:45:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8440 2023-01-02 15:30:17,230 - mmcls - INFO - Epoch [12][5000/5005] lr: 1.000e-01, eta: 10:45:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8278 2023-01-02 15:30:39,913 - mmcls - INFO - Epoch(val) [12][196] accuracy_top-1: 45.5140, accuracy_top-5: 70.3220 2023-01-02 15:30:50,926 - mmcls - INFO - Epoch [13][100/5005] lr: 1.000e-01, eta: 10:45:51, time: 0.110, data_time: 0.052, memory: 1125, loss: 2.7919 2023-01-02 15:30:59,822 - mmcls - INFO - Epoch [13][200/5005] lr: 1.000e-01, eta: 10:45:42, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.7866 2023-01-02 15:31:08,685 - mmcls - INFO - Epoch [13][300/5005] lr: 1.000e-01, eta: 10:45:34, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.7919 2023-01-02 15:31:17,372 - mmcls - INFO - Epoch [13][400/5005] lr: 1.000e-01, eta: 10:45:25, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7930 2023-01-02 15:31:26,115 - mmcls - INFO - Epoch [13][500/5005] lr: 1.000e-01, eta: 10:45:15, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8024 2023-01-02 15:31:34,771 - mmcls - INFO - Epoch [13][600/5005] lr: 1.000e-01, eta: 10:45:05, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7903 2023-01-02 15:31:43,585 - mmcls - INFO - Epoch [13][700/5005] lr: 1.000e-01, eta: 10:44:57, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.8082 2023-01-02 15:31:52,249 - mmcls - INFO - Epoch [13][800/5005] lr: 1.000e-01, eta: 10:44:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8311 2023-01-02 15:32:00,845 - mmcls - INFO - Epoch [13][900/5005] lr: 1.000e-01, eta: 10:44:37, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.7889 2023-01-02 15:32:09,967 - mmcls - INFO - Epoch [13][1000/5005] lr: 1.000e-01, eta: 10:44:30, time: 0.091, data_time: 0.008, memory: 1125, loss: 2.8203 2023-01-02 15:32:18,576 - mmcls - INFO - Epoch [13][1100/5005] lr: 1.000e-01, eta: 10:44:20, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8076 2023-01-02 15:32:27,407 - mmcls - INFO - Epoch [13][1200/5005] lr: 1.000e-01, eta: 10:44:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8021 2023-01-02 15:32:35,851 - mmcls - INFO - Epoch [13][1300/5005] lr: 1.000e-01, eta: 10:44:00, time: 0.084, data_time: 0.015, memory: 1125, loss: 2.8230 2023-01-02 15:32:44,496 - mmcls - INFO - Epoch [13][1400/5005] lr: 1.000e-01, eta: 10:43:50, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8188 2023-01-02 15:32:53,424 - mmcls - INFO - Epoch [13][1500/5005] lr: 1.000e-01, eta: 10:43:42, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8038 2023-01-02 15:33:02,061 - mmcls - INFO - Epoch [13][1600/5005] lr: 1.000e-01, eta: 10:43:32, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.8457 2023-01-02 15:33:11,035 - mmcls - INFO - Epoch [13][1700/5005] lr: 1.000e-01, eta: 10:43:25, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.8238 2023-01-02 15:33:19,908 - mmcls - INFO - Epoch [13][1800/5005] lr: 1.000e-01, eta: 10:43:16, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.8380 2023-01-02 15:33:29,239 - mmcls - INFO - Epoch [13][1900/5005] lr: 1.000e-01, eta: 10:43:11, time: 0.093, data_time: 0.036, memory: 1125, loss: 2.8231 2023-01-02 15:33:38,215 - mmcls - INFO - Epoch [13][2000/5005] lr: 1.000e-01, eta: 10:43:04, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8445 2023-01-02 15:33:46,923 - mmcls - INFO - Epoch [13][2100/5005] lr: 1.000e-01, eta: 10:42:54, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.8248 2023-01-02 15:33:55,685 - mmcls - INFO - Epoch [13][2200/5005] lr: 1.000e-01, eta: 10:42:45, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.8107 2023-01-02 15:34:04,288 - mmcls - INFO - Epoch [13][2300/5005] lr: 1.000e-01, eta: 10:42:35, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.8320 2023-01-02 15:34:12,906 - mmcls - INFO - Epoch [13][2400/5005] lr: 1.000e-01, eta: 10:42:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8427 2023-01-02 15:34:21,614 - mmcls - INFO - Epoch [13][2500/5005] lr: 1.000e-01, eta: 10:42:16, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8519 2023-01-02 15:34:30,359 - mmcls - INFO - Epoch [13][2600/5005] lr: 1.000e-01, eta: 10:42:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8345 2023-01-02 15:34:39,172 - mmcls - INFO - Epoch [13][2700/5005] lr: 1.000e-01, eta: 10:41:58, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.8199 2023-01-02 15:34:47,990 - mmcls - INFO - Epoch [13][2800/5005] lr: 1.000e-01, eta: 10:41:49, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8481 2023-01-02 15:34:56,758 - mmcls - INFO - Epoch [13][2900/5005] lr: 1.000e-01, eta: 10:41:40, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8379 2023-01-02 15:35:05,603 - mmcls - INFO - Epoch [13][3000/5005] lr: 1.000e-01, eta: 10:41:32, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.8616 2023-01-02 15:35:14,372 - mmcls - INFO - Epoch [13][3100/5005] lr: 1.000e-01, eta: 10:41:22, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.8474 2023-01-02 15:35:22,954 - mmcls - INFO - Epoch [13][3200/5005] lr: 1.000e-01, eta: 10:41:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8446 2023-01-02 15:35:31,571 - mmcls - INFO - Epoch [13][3300/5005] lr: 1.000e-01, eta: 10:41:02, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.8285 2023-01-02 15:35:40,226 - mmcls - INFO - Epoch [13][3400/5005] lr: 1.000e-01, eta: 10:40:52, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.8883 2023-01-02 15:35:48,766 - mmcls - INFO - Epoch [13][3500/5005] lr: 1.000e-01, eta: 10:40:42, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8557 2023-01-02 15:35:57,256 - mmcls - INFO - Epoch [13][3600/5005] lr: 1.000e-01, eta: 10:40:31, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.8227 2023-01-02 15:36:06,410 - mmcls - INFO - Epoch [13][3700/5005] lr: 1.000e-01, eta: 10:40:24, time: 0.092, data_time: 0.014, memory: 1125, loss: 2.8310 2023-01-02 15:36:15,276 - mmcls - INFO - Epoch [13][3800/5005] lr: 1.000e-01, eta: 10:40:16, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8282 2023-01-02 15:36:24,256 - mmcls - INFO - Epoch [13][3900/5005] lr: 1.000e-01, eta: 10:40:09, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.8449 2023-01-02 15:36:32,753 - mmcls - INFO - Epoch [13][4000/5005] lr: 1.000e-01, eta: 10:39:58, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8511 2023-01-02 15:36:41,349 - mmcls - INFO - Epoch [13][4100/5005] lr: 1.000e-01, eta: 10:39:47, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.8300 2023-01-02 15:36:50,237 - mmcls - INFO - Epoch [13][4200/5005] lr: 1.000e-01, eta: 10:39:39, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.8113 2023-01-02 15:36:58,812 - mmcls - INFO - Epoch [13][4300/5005] lr: 1.000e-01, eta: 10:39:29, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.8256 2023-01-02 15:37:07,624 - mmcls - INFO - Epoch [13][4400/5005] lr: 1.000e-01, eta: 10:39:20, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.8323 2023-01-02 15:37:16,467 - mmcls - INFO - Epoch [13][4500/5005] lr: 1.000e-01, eta: 10:39:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8583 2023-01-02 15:37:25,233 - mmcls - INFO - Epoch [13][4600/5005] lr: 1.000e-01, eta: 10:39:03, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8182 2023-01-02 15:37:33,830 - mmcls - INFO - Epoch [13][4700/5005] lr: 1.000e-01, eta: 10:38:53, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8311 2023-01-02 15:37:42,479 - mmcls - INFO - Epoch [13][4800/5005] lr: 1.000e-01, eta: 10:38:43, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.8253 2023-01-02 15:37:51,378 - mmcls - INFO - Epoch [13][4900/5005] lr: 1.000e-01, eta: 10:38:35, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.8438 2023-01-02 15:38:00,445 - mmcls - INFO - Epoch [13][5000/5005] lr: 1.000e-01, eta: 10:38:28, time: 0.091, data_time: 0.013, memory: 1125, loss: 2.8601 2023-01-02 15:38:23,241 - mmcls - INFO - Epoch(val) [13][196] accuracy_top-1: 45.5280, accuracy_top-5: 70.2580 2023-01-02 15:38:34,290 - mmcls - INFO - Epoch [14][100/5005] lr: 1.000e-01, eta: 10:38:30, time: 0.110, data_time: 0.055, memory: 1125, loss: 2.7851 2023-01-02 15:38:42,954 - mmcls - INFO - Epoch [14][200/5005] lr: 1.000e-01, eta: 10:38:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7928 2023-01-02 15:38:51,784 - mmcls - INFO - Epoch [14][300/5005] lr: 1.000e-01, eta: 10:38:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7973 2023-01-02 15:39:00,637 - mmcls - INFO - Epoch [14][400/5005] lr: 1.000e-01, eta: 10:38:04, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7954 2023-01-02 15:39:09,333 - mmcls - INFO - Epoch [14][500/5005] lr: 1.000e-01, eta: 10:37:54, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7836 2023-01-02 15:39:18,092 - mmcls - INFO - Epoch [14][600/5005] lr: 1.000e-01, eta: 10:37:45, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.8192 2023-01-02 15:39:26,976 - mmcls - INFO - Epoch [14][700/5005] lr: 1.000e-01, eta: 10:37:37, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7702 2023-01-02 15:39:35,789 - mmcls - INFO - Epoch [14][800/5005] lr: 1.000e-01, eta: 10:37:28, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8164 2023-01-02 15:39:44,291 - mmcls - INFO - Epoch [14][900/5005] lr: 1.000e-01, eta: 10:37:17, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.8162 2023-01-02 15:39:53,039 - mmcls - INFO - Epoch [14][1000/5005] lr: 1.000e-01, eta: 10:37:08, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8091 2023-01-02 15:40:01,705 - mmcls - INFO - Epoch [14][1100/5005] lr: 1.000e-01, eta: 10:36:58, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8093 2023-01-02 15:40:10,550 - mmcls - INFO - Epoch [14][1200/5005] lr: 1.000e-01, eta: 10:36:50, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.8269 2023-01-02 15:40:19,294 - mmcls - INFO - Epoch [14][1300/5005] lr: 1.000e-01, eta: 10:36:41, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.8027 2023-01-02 15:40:27,971 - mmcls - INFO - Epoch [14][1400/5005] lr: 1.000e-01, eta: 10:36:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8114 2023-01-02 15:40:36,710 - mmcls - INFO - Epoch [14][1500/5005] lr: 1.000e-01, eta: 10:36:22, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.8077 2023-01-02 15:40:45,310 - mmcls - INFO - Epoch [14][1600/5005] lr: 1.000e-01, eta: 10:36:12, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.8291 2023-01-02 15:40:54,367 - mmcls - INFO - Epoch [14][1700/5005] lr: 1.000e-01, eta: 10:36:05, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.8201 2023-01-02 15:41:02,930 - mmcls - INFO - Epoch [14][1800/5005] lr: 1.000e-01, eta: 10:35:54, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.8354 2023-01-02 15:41:11,749 - mmcls - INFO - Epoch [14][1900/5005] lr: 1.000e-01, eta: 10:35:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8334 2023-01-02 15:41:20,416 - mmcls - INFO - Epoch [14][2000/5005] lr: 1.000e-01, eta: 10:35:36, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7979 2023-01-02 15:41:29,144 - mmcls - INFO - Epoch [14][2100/5005] lr: 1.000e-01, eta: 10:35:27, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8297 2023-01-02 15:41:37,706 - mmcls - INFO - Epoch [14][2200/5005] lr: 1.000e-01, eta: 10:35:17, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8299 2023-01-02 15:41:46,645 - mmcls - INFO - Epoch [14][2300/5005] lr: 1.000e-01, eta: 10:35:09, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.8110 2023-01-02 15:41:55,242 - mmcls - INFO - Epoch [14][2400/5005] lr: 1.000e-01, eta: 10:34:59, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.8316 2023-01-02 15:42:04,071 - mmcls - INFO - Epoch [14][2500/5005] lr: 1.000e-01, eta: 10:34:50, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.8370 2023-01-02 15:42:12,793 - mmcls - INFO - Epoch [14][2600/5005] lr: 1.000e-01, eta: 10:34:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8126 2023-01-02 15:42:21,386 - mmcls - INFO - Epoch [14][2700/5005] lr: 1.000e-01, eta: 10:34:31, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.8056 2023-01-02 15:42:30,486 - mmcls - INFO - Epoch [14][2800/5005] lr: 1.000e-01, eta: 10:34:24, time: 0.091, data_time: 0.011, memory: 1125, loss: 2.8510 2023-01-02 15:42:39,046 - mmcls - INFO - Epoch [14][2900/5005] lr: 1.000e-01, eta: 10:34:13, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.8607 2023-01-02 15:42:47,833 - mmcls - INFO - Epoch [14][3000/5005] lr: 1.000e-01, eta: 10:34:04, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8353 2023-01-02 15:42:56,843 - mmcls - INFO - Epoch [14][3100/5005] lr: 1.000e-01, eta: 10:33:57, time: 0.090, data_time: 0.020, memory: 1125, loss: 2.8037 2023-01-02 15:43:05,408 - mmcls - INFO - Epoch [14][3200/5005] lr: 1.000e-01, eta: 10:33:47, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.8073 2023-01-02 15:43:14,577 - mmcls - INFO - Epoch [14][3300/5005] lr: 1.000e-01, eta: 10:33:40, time: 0.092, data_time: 0.016, memory: 1125, loss: 2.8480 2023-01-02 15:43:23,548 - mmcls - INFO - Epoch [14][3400/5005] lr: 1.000e-01, eta: 10:33:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8372 2023-01-02 15:43:32,476 - mmcls - INFO - Epoch [14][3500/5005] lr: 1.000e-01, eta: 10:33:25, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.8387 2023-01-02 15:43:41,110 - mmcls - INFO - Epoch [14][3600/5005] lr: 1.000e-01, eta: 10:33:15, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.8335 2023-01-02 15:43:49,636 - mmcls - INFO - Epoch [14][3700/5005] lr: 1.000e-01, eta: 10:33:04, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8394 2023-01-02 15:43:58,593 - mmcls - INFO - Epoch [14][3800/5005] lr: 1.000e-01, eta: 10:32:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8074 2023-01-02 15:44:07,324 - mmcls - INFO - Epoch [14][3900/5005] lr: 1.000e-01, eta: 10:32:47, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.8394 2023-01-02 15:44:15,967 - mmcls - INFO - Epoch [14][4000/5005] lr: 1.000e-01, eta: 10:32:37, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.8444 2023-01-02 15:44:24,558 - mmcls - INFO - Epoch [14][4100/5005] lr: 1.000e-01, eta: 10:32:27, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8276 2023-01-02 15:44:33,510 - mmcls - INFO - Epoch [14][4200/5005] lr: 1.000e-01, eta: 10:32:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8333 2023-01-02 15:44:42,464 - mmcls - INFO - Epoch [14][4300/5005] lr: 1.000e-01, eta: 10:32:12, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8289 2023-01-02 15:44:51,447 - mmcls - INFO - Epoch [14][4400/5005] lr: 1.000e-01, eta: 10:32:04, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.7879 2023-01-02 15:45:00,387 - mmcls - INFO - Epoch [14][4500/5005] lr: 1.000e-01, eta: 10:31:56, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8038 2023-01-02 15:45:09,365 - mmcls - INFO - Epoch [14][4600/5005] lr: 1.000e-01, eta: 10:31:48, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.8335 2023-01-02 15:45:18,051 - mmcls - INFO - Epoch [14][4700/5005] lr: 1.000e-01, eta: 10:31:39, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.8420 2023-01-02 15:45:26,747 - mmcls - INFO - Epoch [14][4800/5005] lr: 1.000e-01, eta: 10:31:29, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7850 2023-01-02 15:45:35,574 - mmcls - INFO - Epoch [14][4900/5005] lr: 1.000e-01, eta: 10:31:21, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.8421 2023-01-02 15:45:44,084 - mmcls - INFO - Epoch [14][5000/5005] lr: 1.000e-01, eta: 10:31:10, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.8244 2023-01-02 15:46:06,878 - mmcls - INFO - Epoch(val) [14][196] accuracy_top-1: 44.0180, accuracy_top-5: 68.6460 2023-01-02 15:46:17,772 - mmcls - INFO - Epoch [15][100/5005] lr: 1.000e-01, eta: 10:31:11, time: 0.109, data_time: 0.043, memory: 1125, loss: 2.7601 2023-01-02 15:46:26,407 - mmcls - INFO - Epoch [15][200/5005] lr: 1.000e-01, eta: 10:31:01, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.7402 2023-01-02 15:46:35,085 - mmcls - INFO - Epoch [15][300/5005] lr: 1.000e-01, eta: 10:30:52, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7804 2023-01-02 15:46:43,827 - mmcls - INFO - Epoch [15][400/5005] lr: 1.000e-01, eta: 10:30:42, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.8031 2023-01-02 15:46:52,854 - mmcls - INFO - Epoch [15][500/5005] lr: 1.000e-01, eta: 10:30:35, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7692 2023-01-02 15:47:01,559 - mmcls - INFO - Epoch [15][600/5005] lr: 1.000e-01, eta: 10:30:26, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.7888 2023-01-02 15:47:10,477 - mmcls - INFO - Epoch [15][700/5005] lr: 1.000e-01, eta: 10:30:18, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7781 2023-01-02 15:47:19,117 - mmcls - INFO - Epoch [15][800/5005] lr: 1.000e-01, eta: 10:30:08, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7776 2023-01-02 15:47:27,808 - mmcls - INFO - Epoch [15][900/5005] lr: 1.000e-01, eta: 10:29:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7948 2023-01-02 15:47:36,371 - mmcls - INFO - Epoch [15][1000/5005] lr: 1.000e-01, eta: 10:29:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.8284 2023-01-02 15:47:45,313 - mmcls - INFO - Epoch [15][1100/5005] lr: 1.000e-01, eta: 10:29:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8063 2023-01-02 15:47:53,886 - mmcls - INFO - Epoch [15][1200/5005] lr: 1.000e-01, eta: 10:29:30, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.7908 2023-01-02 15:48:03,093 - mmcls - INFO - Epoch [15][1300/5005] lr: 1.000e-01, eta: 10:29:24, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.8253 2023-01-02 15:48:11,810 - mmcls - INFO - Epoch [15][1400/5005] lr: 1.000e-01, eta: 10:29:14, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8231 2023-01-02 15:48:20,840 - mmcls - INFO - Epoch [15][1500/5005] lr: 1.000e-01, eta: 10:29:07, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.7962 2023-01-02 15:48:29,484 - mmcls - INFO - Epoch [15][1600/5005] lr: 1.000e-01, eta: 10:28:57, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.7879 2023-01-02 15:48:38,212 - mmcls - INFO - Epoch [15][1700/5005] lr: 1.000e-01, eta: 10:28:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7886 2023-01-02 15:48:46,805 - mmcls - INFO - Epoch [15][1800/5005] lr: 1.000e-01, eta: 10:28:38, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8105 2023-01-02 15:48:55,440 - mmcls - INFO - Epoch [15][1900/5005] lr: 1.000e-01, eta: 10:28:28, time: 0.086, data_time: 0.030, memory: 1125, loss: 2.8194 2023-01-02 15:49:03,897 - mmcls - INFO - Epoch [15][2000/5005] lr: 1.000e-01, eta: 10:28:17, time: 0.084, data_time: 0.004, memory: 1125, loss: 2.8245 2023-01-02 15:49:12,346 - mmcls - INFO - Epoch [15][2100/5005] lr: 1.000e-01, eta: 10:28:06, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.7962 2023-01-02 15:49:21,604 - mmcls - INFO - Epoch [15][2200/5005] lr: 1.000e-01, eta: 10:28:00, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.8053 2023-01-02 15:49:30,464 - mmcls - INFO - Epoch [15][2300/5005] lr: 1.000e-01, eta: 10:27:52, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.7935 2023-01-02 15:49:39,657 - mmcls - INFO - Epoch [15][2400/5005] lr: 1.000e-01, eta: 10:27:45, time: 0.092, data_time: 0.021, memory: 1125, loss: 2.8622 2023-01-02 15:49:48,220 - mmcls - INFO - Epoch [15][2500/5005] lr: 1.000e-01, eta: 10:27:35, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.8578 2023-01-02 15:49:56,824 - mmcls - INFO - Epoch [15][2600/5005] lr: 1.000e-01, eta: 10:27:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.8465 2023-01-02 15:50:05,511 - mmcls - INFO - Epoch [15][2700/5005] lr: 1.000e-01, eta: 10:27:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8097 2023-01-02 15:50:14,445 - mmcls - INFO - Epoch [15][2800/5005] lr: 1.000e-01, eta: 10:27:08, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.8356 2023-01-02 15:50:23,215 - mmcls - INFO - Epoch [15][2900/5005] lr: 1.000e-01, eta: 10:26:59, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8263 2023-01-02 15:50:31,794 - mmcls - INFO - Epoch [15][3000/5005] lr: 1.000e-01, eta: 10:26:49, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8068 2023-01-02 15:50:40,686 - mmcls - INFO - Epoch [15][3100/5005] lr: 1.000e-01, eta: 10:26:40, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.8365 2023-01-02 15:50:49,420 - mmcls - INFO - Epoch [15][3200/5005] lr: 1.000e-01, eta: 10:26:31, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.8419 2023-01-02 15:50:58,190 - mmcls - INFO - Epoch [15][3300/5005] lr: 1.000e-01, eta: 10:26:22, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.7967 2023-01-02 15:51:06,873 - mmcls - INFO - Epoch [15][3400/5005] lr: 1.000e-01, eta: 10:26:13, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.8437 2023-01-02 15:51:15,543 - mmcls - INFO - Epoch [15][3500/5005] lr: 1.000e-01, eta: 10:26:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8124 2023-01-02 15:51:24,314 - mmcls - INFO - Epoch [15][3600/5005] lr: 1.000e-01, eta: 10:25:54, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8236 2023-01-02 15:51:33,230 - mmcls - INFO - Epoch [15][3700/5005] lr: 1.000e-01, eta: 10:25:46, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8173 2023-01-02 15:51:42,252 - mmcls - INFO - Epoch [15][3800/5005] lr: 1.000e-01, eta: 10:25:39, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8150 2023-01-02 15:51:51,014 - mmcls - INFO - Epoch [15][3900/5005] lr: 1.000e-01, eta: 10:25:30, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8306 2023-01-02 15:51:59,653 - mmcls - INFO - Epoch [15][4000/5005] lr: 1.000e-01, eta: 10:25:20, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8235 2023-01-02 15:52:08,450 - mmcls - INFO - Epoch [15][4100/5005] lr: 1.000e-01, eta: 10:25:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7919 2023-01-02 15:52:17,193 - mmcls - INFO - Epoch [15][4200/5005] lr: 1.000e-01, eta: 10:25:02, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8015 2023-01-02 15:52:26,030 - mmcls - INFO - Epoch [15][4300/5005] lr: 1.000e-01, eta: 10:24:53, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.8177 2023-01-02 15:52:34,771 - mmcls - INFO - Epoch [15][4400/5005] lr: 1.000e-01, eta: 10:24:44, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.8342 2023-01-02 15:52:43,464 - mmcls - INFO - Epoch [15][4500/5005] lr: 1.000e-01, eta: 10:24:35, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8008 2023-01-02 15:52:52,592 - mmcls - INFO - Epoch [15][4600/5005] lr: 1.000e-01, eta: 10:24:28, time: 0.091, data_time: 0.037, memory: 1125, loss: 2.8188 2023-01-02 15:53:01,479 - mmcls - INFO - Epoch [15][4700/5005] lr: 1.000e-01, eta: 10:24:20, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.8348 2023-01-02 15:53:10,272 - mmcls - INFO - Epoch [15][4800/5005] lr: 1.000e-01, eta: 10:24:11, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.8186 2023-01-02 15:53:19,066 - mmcls - INFO - Epoch [15][4900/5005] lr: 1.000e-01, eta: 10:24:02, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.7787 2023-01-02 15:53:27,740 - mmcls - INFO - Epoch [15][5000/5005] lr: 1.000e-01, eta: 10:23:52, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8110 2023-01-02 15:53:50,549 - mmcls - INFO - Epoch(val) [15][196] accuracy_top-1: 46.5100, accuracy_top-5: 71.0960 2023-01-02 15:54:01,288 - mmcls - INFO - Epoch [16][100/5005] lr: 1.000e-01, eta: 10:23:52, time: 0.107, data_time: 0.044, memory: 1125, loss: 2.7535 2023-01-02 15:54:09,998 - mmcls - INFO - Epoch [16][200/5005] lr: 1.000e-01, eta: 10:23:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7987 2023-01-02 15:54:18,651 - mmcls - INFO - Epoch [16][300/5005] lr: 1.000e-01, eta: 10:23:33, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7810 2023-01-02 15:54:27,500 - mmcls - INFO - Epoch [16][400/5005] lr: 1.000e-01, eta: 10:23:24, time: 0.088, data_time: 0.026, memory: 1125, loss: 2.7654 2023-01-02 15:54:36,121 - mmcls - INFO - Epoch [16][500/5005] lr: 1.000e-01, eta: 10:23:14, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.7680 2023-01-02 15:54:44,969 - mmcls - INFO - Epoch [16][600/5005] lr: 1.000e-01, eta: 10:23:06, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.7982 2023-01-02 15:54:53,694 - mmcls - INFO - Epoch [16][700/5005] lr: 1.000e-01, eta: 10:22:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7752 2023-01-02 15:55:02,342 - mmcls - INFO - Epoch [16][800/5005] lr: 1.000e-01, eta: 10:22:47, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7816 2023-01-02 15:55:11,309 - mmcls - INFO - Epoch [16][900/5005] lr: 1.000e-01, eta: 10:22:39, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.7903 2023-01-02 15:55:20,295 - mmcls - INFO - Epoch [16][1000/5005] lr: 1.000e-01, eta: 10:22:31, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7754 2023-01-02 15:55:28,798 - mmcls - INFO - Epoch [16][1100/5005] lr: 1.000e-01, eta: 10:22:21, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7716 2023-01-02 15:55:37,396 - mmcls - INFO - Epoch [16][1200/5005] lr: 1.000e-01, eta: 10:22:11, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7807 2023-01-02 15:55:46,199 - mmcls - INFO - Epoch [16][1300/5005] lr: 1.000e-01, eta: 10:22:02, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7993 2023-01-02 15:55:54,849 - mmcls - INFO - Epoch [16][1400/5005] lr: 1.000e-01, eta: 10:21:52, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.7783 2023-01-02 15:56:03,562 - mmcls - INFO - Epoch [16][1500/5005] lr: 1.000e-01, eta: 10:21:43, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7939 2023-01-02 15:56:12,326 - mmcls - INFO - Epoch [16][1600/5005] lr: 1.000e-01, eta: 10:21:34, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.7879 2023-01-02 15:56:20,796 - mmcls - INFO - Epoch [16][1700/5005] lr: 1.000e-01, eta: 10:21:23, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.7913 2023-01-02 15:56:29,625 - mmcls - INFO - Epoch [16][1800/5005] lr: 1.000e-01, eta: 10:21:15, time: 0.088, data_time: 0.025, memory: 1125, loss: 2.7944 2023-01-02 15:56:38,405 - mmcls - INFO - Epoch [16][1900/5005] lr: 1.000e-01, eta: 10:21:06, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.7747 2023-01-02 15:56:47,161 - mmcls - INFO - Epoch [16][2000/5005] lr: 1.000e-01, eta: 10:20:57, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.7947 2023-01-02 15:56:55,832 - mmcls - INFO - Epoch [16][2100/5005] lr: 1.000e-01, eta: 10:20:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7971 2023-01-02 15:57:04,382 - mmcls - INFO - Epoch [16][2200/5005] lr: 1.000e-01, eta: 10:20:37, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.7921 2023-01-02 15:57:12,975 - mmcls - INFO - Epoch [16][2300/5005] lr: 1.000e-01, eta: 10:20:27, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.8254 2023-01-02 15:57:21,660 - mmcls - INFO - Epoch [16][2400/5005] lr: 1.000e-01, eta: 10:20:18, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.8223 2023-01-02 15:57:30,691 - mmcls - INFO - Epoch [16][2500/5005] lr: 1.000e-01, eta: 10:20:10, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.8299 2023-01-02 15:57:39,446 - mmcls - INFO - Epoch [16][2600/5005] lr: 1.000e-01, eta: 10:20:01, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8037 2023-01-02 15:57:47,970 - mmcls - INFO - Epoch [16][2700/5005] lr: 1.000e-01, eta: 10:19:51, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7635 2023-01-02 15:57:56,933 - mmcls - INFO - Epoch [16][2800/5005] lr: 1.000e-01, eta: 10:19:43, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.8261 2023-01-02 15:58:05,479 - mmcls - INFO - Epoch [16][2900/5005] lr: 1.000e-01, eta: 10:19:33, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8039 2023-01-02 15:58:14,098 - mmcls - INFO - Epoch [16][3000/5005] lr: 1.000e-01, eta: 10:19:23, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.7998 2023-01-02 15:58:22,758 - mmcls - INFO - Epoch [16][3100/5005] lr: 1.000e-01, eta: 10:19:13, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8152 2023-01-02 15:58:31,518 - mmcls - INFO - Epoch [16][3200/5005] lr: 1.000e-01, eta: 10:19:05, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.7807 2023-01-02 15:58:40,437 - mmcls - INFO - Epoch [16][3300/5005] lr: 1.000e-01, eta: 10:18:56, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.8538 2023-01-02 15:58:49,421 - mmcls - INFO - Epoch [16][3400/5005] lr: 1.000e-01, eta: 10:18:49, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.8157 2023-01-02 15:58:58,367 - mmcls - INFO - Epoch [16][3500/5005] lr: 1.000e-01, eta: 10:18:41, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.8170 2023-01-02 15:59:07,020 - mmcls - INFO - Epoch [16][3600/5005] lr: 1.000e-01, eta: 10:18:31, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.8215 2023-01-02 15:59:15,788 - mmcls - INFO - Epoch [16][3700/5005] lr: 1.000e-01, eta: 10:18:22, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.8072 2023-01-02 15:59:24,496 - mmcls - INFO - Epoch [16][3800/5005] lr: 1.000e-01, eta: 10:18:13, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8132 2023-01-02 15:59:33,098 - mmcls - INFO - Epoch [16][3900/5005] lr: 1.000e-01, eta: 10:18:03, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8216 2023-01-02 15:59:42,284 - mmcls - INFO - Epoch [16][4000/5005] lr: 1.000e-01, eta: 10:17:56, time: 0.092, data_time: 0.000, memory: 1125, loss: 2.8169 2023-01-02 15:59:50,751 - mmcls - INFO - Epoch [16][4100/5005] lr: 1.000e-01, eta: 10:17:46, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.8069 2023-01-02 15:59:59,582 - mmcls - INFO - Epoch [16][4200/5005] lr: 1.000e-01, eta: 10:17:37, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.8187 2023-01-02 16:00:08,383 - mmcls - INFO - Epoch [16][4300/5005] lr: 1.000e-01, eta: 10:17:28, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.8338 2023-01-02 16:00:16,947 - mmcls - INFO - Epoch [16][4400/5005] lr: 1.000e-01, eta: 10:17:18, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.8240 2023-01-02 16:00:25,632 - mmcls - INFO - Epoch [16][4500/5005] lr: 1.000e-01, eta: 10:17:09, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8542 2023-01-02 16:00:34,571 - mmcls - INFO - Epoch [16][4600/5005] lr: 1.000e-01, eta: 10:17:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8106 2023-01-02 16:00:43,378 - mmcls - INFO - Epoch [16][4700/5005] lr: 1.000e-01, eta: 10:16:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7778 2023-01-02 16:00:52,285 - mmcls - INFO - Epoch [16][4800/5005] lr: 1.000e-01, eta: 10:16:44, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8106 2023-01-02 16:01:00,978 - mmcls - INFO - Epoch [16][4900/5005] lr: 1.000e-01, eta: 10:16:34, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.8030 2023-01-02 16:01:09,875 - mmcls - INFO - Epoch [16][5000/5005] lr: 1.000e-01, eta: 10:16:26, time: 0.089, data_time: 0.030, memory: 1125, loss: 2.8186 2023-01-02 16:01:32,657 - mmcls - INFO - Epoch(val) [16][196] accuracy_top-1: 46.7060, accuracy_top-5: 71.5340 2023-01-02 16:01:44,032 - mmcls - INFO - Epoch [17][100/5005] lr: 1.000e-01, eta: 10:16:28, time: 0.114, data_time: 0.052, memory: 1125, loss: 2.7266 2023-01-02 16:01:52,684 - mmcls - INFO - Epoch [17][200/5005] lr: 1.000e-01, eta: 10:16:18, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7334 2023-01-02 16:02:01,317 - mmcls - INFO - Epoch [17][300/5005] lr: 1.000e-01, eta: 10:16:09, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7498 2023-01-02 16:02:10,053 - mmcls - INFO - Epoch [17][400/5005] lr: 1.000e-01, eta: 10:16:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7684 2023-01-02 16:02:19,003 - mmcls - INFO - Epoch [17][500/5005] lr: 1.000e-01, eta: 10:15:52, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7475 2023-01-02 16:02:27,781 - mmcls - INFO - Epoch [17][600/5005] lr: 1.000e-01, eta: 10:15:43, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7497 2023-01-02 16:02:36,776 - mmcls - INFO - Epoch [17][700/5005] lr: 1.000e-01, eta: 10:15:35, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.7900 2023-01-02 16:02:45,476 - mmcls - INFO - Epoch [17][800/5005] lr: 1.000e-01, eta: 10:15:25, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.8031 2023-01-02 16:02:54,066 - mmcls - INFO - Epoch [17][900/5005] lr: 1.000e-01, eta: 10:15:16, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.7547 2023-01-02 16:03:02,794 - mmcls - INFO - Epoch [17][1000/5005] lr: 1.000e-01, eta: 10:15:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7858 2023-01-02 16:03:11,401 - mmcls - INFO - Epoch [17][1100/5005] lr: 1.000e-01, eta: 10:14:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7902 2023-01-02 16:03:20,260 - mmcls - INFO - Epoch [17][1200/5005] lr: 1.000e-01, eta: 10:14:48, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7698 2023-01-02 16:03:29,537 - mmcls - INFO - Epoch [17][1300/5005] lr: 1.000e-01, eta: 10:14:42, time: 0.093, data_time: 0.003, memory: 1125, loss: 2.8103 2023-01-02 16:03:38,131 - mmcls - INFO - Epoch [17][1400/5005] lr: 1.000e-01, eta: 10:14:32, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.8049 2023-01-02 16:03:46,911 - mmcls - INFO - Epoch [17][1500/5005] lr: 1.000e-01, eta: 10:14:23, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.7882 2023-01-02 16:03:55,656 - mmcls - INFO - Epoch [17][1600/5005] lr: 1.000e-01, eta: 10:14:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8091 2023-01-02 16:04:04,511 - mmcls - INFO - Epoch [17][1700/5005] lr: 1.000e-01, eta: 10:14:05, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7598 2023-01-02 16:04:13,216 - mmcls - INFO - Epoch [17][1800/5005] lr: 1.000e-01, eta: 10:13:56, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.7738 2023-01-02 16:04:21,920 - mmcls - INFO - Epoch [17][1900/5005] lr: 1.000e-01, eta: 10:13:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7924 2023-01-02 16:04:30,764 - mmcls - INFO - Epoch [17][2000/5005] lr: 1.000e-01, eta: 10:13:38, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7674 2023-01-02 16:04:39,560 - mmcls - INFO - Epoch [17][2100/5005] lr: 1.000e-01, eta: 10:13:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8211 2023-01-02 16:04:48,071 - mmcls - INFO - Epoch [17][2200/5005] lr: 1.000e-01, eta: 10:13:19, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7878 2023-01-02 16:04:57,114 - mmcls - INFO - Epoch [17][2300/5005] lr: 1.000e-01, eta: 10:13:12, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.7657 2023-01-02 16:05:05,887 - mmcls - INFO - Epoch [17][2400/5005] lr: 1.000e-01, eta: 10:13:03, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.7794 2023-01-02 16:05:14,652 - mmcls - INFO - Epoch [17][2500/5005] lr: 1.000e-01, eta: 10:12:54, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.8314 2023-01-02 16:05:23,509 - mmcls - INFO - Epoch [17][2600/5005] lr: 1.000e-01, eta: 10:12:45, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8234 2023-01-02 16:05:32,258 - mmcls - INFO - Epoch [17][2700/5005] lr: 1.000e-01, eta: 10:12:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7898 2023-01-02 16:05:40,831 - mmcls - INFO - Epoch [17][2800/5005] lr: 1.000e-01, eta: 10:12:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7911 2023-01-02 16:05:49,590 - mmcls - INFO - Epoch [17][2900/5005] lr: 1.000e-01, eta: 10:12:17, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7865 2023-01-02 16:05:58,406 - mmcls - INFO - Epoch [17][3000/5005] lr: 1.000e-01, eta: 10:12:08, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.8145 2023-01-02 16:06:07,347 - mmcls - INFO - Epoch [17][3100/5005] lr: 1.000e-01, eta: 10:12:00, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7865 2023-01-02 16:06:16,171 - mmcls - INFO - Epoch [17][3200/5005] lr: 1.000e-01, eta: 10:11:52, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.8018 2023-01-02 16:06:24,873 - mmcls - INFO - Epoch [17][3300/5005] lr: 1.000e-01, eta: 10:11:42, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.8026 2023-01-02 16:06:33,647 - mmcls - INFO - Epoch [17][3400/5005] lr: 1.000e-01, eta: 10:11:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8199 2023-01-02 16:06:42,320 - mmcls - INFO - Epoch [17][3500/5005] lr: 1.000e-01, eta: 10:11:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7994 2023-01-02 16:06:51,098 - mmcls - INFO - Epoch [17][3600/5005] lr: 1.000e-01, eta: 10:11:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8069 2023-01-02 16:06:59,615 - mmcls - INFO - Epoch [17][3700/5005] lr: 1.000e-01, eta: 10:11:05, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.8155 2023-01-02 16:07:08,789 - mmcls - INFO - Epoch [17][3800/5005] lr: 1.000e-01, eta: 10:10:58, time: 0.092, data_time: 0.013, memory: 1125, loss: 2.7992 2023-01-02 16:07:17,497 - mmcls - INFO - Epoch [17][3900/5005] lr: 1.000e-01, eta: 10:10:49, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.8417 2023-01-02 16:07:26,394 - mmcls - INFO - Epoch [17][4000/5005] lr: 1.000e-01, eta: 10:10:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8048 2023-01-02 16:07:35,094 - mmcls - INFO - Epoch [17][4100/5005] lr: 1.000e-01, eta: 10:10:31, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.7980 2023-01-02 16:07:43,856 - mmcls - INFO - Epoch [17][4200/5005] lr: 1.000e-01, eta: 10:10:22, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.8072 2023-01-02 16:07:52,973 - mmcls - INFO - Epoch [17][4300/5005] lr: 1.000e-01, eta: 10:10:15, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.7836 2023-01-02 16:08:01,686 - mmcls - INFO - Epoch [17][4400/5005] lr: 1.000e-01, eta: 10:10:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7852 2023-01-02 16:08:10,133 - mmcls - INFO - Epoch [17][4500/5005] lr: 1.000e-01, eta: 10:09:55, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.8473 2023-01-02 16:08:19,189 - mmcls - INFO - Epoch [17][4600/5005] lr: 1.000e-01, eta: 10:09:48, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7892 2023-01-02 16:08:28,086 - mmcls - INFO - Epoch [17][4700/5005] lr: 1.000e-01, eta: 10:09:39, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.8265 2023-01-02 16:08:36,814 - mmcls - INFO - Epoch [17][4800/5005] lr: 1.000e-01, eta: 10:09:30, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7952 2023-01-02 16:08:45,552 - mmcls - INFO - Epoch [17][4900/5005] lr: 1.000e-01, eta: 10:09:21, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7943 2023-01-02 16:08:54,151 - mmcls - INFO - Epoch [17][5000/5005] lr: 1.000e-01, eta: 10:09:11, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.8049 2023-01-02 16:09:16,680 - mmcls - INFO - Epoch(val) [17][196] accuracy_top-1: 45.1320, accuracy_top-5: 70.1660 2023-01-02 16:09:27,427 - mmcls - INFO - Epoch [18][100/5005] lr: 1.000e-01, eta: 10:09:09, time: 0.107, data_time: 0.041, memory: 1125, loss: 2.7560 2023-01-02 16:09:36,437 - mmcls - INFO - Epoch [18][200/5005] lr: 1.000e-01, eta: 10:09:01, time: 0.090, data_time: 0.020, memory: 1125, loss: 2.7118 2023-01-02 16:09:45,298 - mmcls - INFO - Epoch [18][300/5005] lr: 1.000e-01, eta: 10:08:53, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7448 2023-01-02 16:09:54,160 - mmcls - INFO - Epoch [18][400/5005] lr: 1.000e-01, eta: 10:08:44, time: 0.089, data_time: 0.028, memory: 1125, loss: 2.7430 2023-01-02 16:10:02,914 - mmcls - INFO - Epoch [18][500/5005] lr: 1.000e-01, eta: 10:08:35, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.7601 2023-01-02 16:10:11,384 - mmcls - INFO - Epoch [18][600/5005] lr: 1.000e-01, eta: 10:08:25, time: 0.085, data_time: 0.028, memory: 1125, loss: 2.7810 2023-01-02 16:10:20,087 - mmcls - INFO - Epoch [18][700/5005] lr: 1.000e-01, eta: 10:08:16, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7384 2023-01-02 16:10:28,784 - mmcls - INFO - Epoch [18][800/5005] lr: 1.000e-01, eta: 10:08:06, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.7582 2023-01-02 16:10:37,396 - mmcls - INFO - Epoch [18][900/5005] lr: 1.000e-01, eta: 10:07:57, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.7512 2023-01-02 16:10:45,981 - mmcls - INFO - Epoch [18][1000/5005] lr: 1.000e-01, eta: 10:07:47, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.7906 2023-01-02 16:10:54,674 - mmcls - INFO - Epoch [18][1100/5005] lr: 1.000e-01, eta: 10:07:38, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7748 2023-01-02 16:11:03,302 - mmcls - INFO - Epoch [18][1200/5005] lr: 1.000e-01, eta: 10:07:28, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7663 2023-01-02 16:11:11,838 - mmcls - INFO - Epoch [18][1300/5005] lr: 1.000e-01, eta: 10:07:18, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.7653 2023-01-02 16:11:20,718 - mmcls - INFO - Epoch [18][1400/5005] lr: 1.000e-01, eta: 10:07:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7735 2023-01-02 16:11:29,629 - mmcls - INFO - Epoch [18][1500/5005] lr: 1.000e-01, eta: 10:07:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8341 2023-01-02 16:11:38,497 - mmcls - INFO - Epoch [18][1600/5005] lr: 1.000e-01, eta: 10:06:53, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.8104 2023-01-02 16:11:47,203 - mmcls - INFO - Epoch [18][1700/5005] lr: 1.000e-01, eta: 10:06:43, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8084 2023-01-02 16:11:55,723 - mmcls - INFO - Epoch [18][1800/5005] lr: 1.000e-01, eta: 10:06:33, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.8056 2023-01-02 16:12:04,480 - mmcls - INFO - Epoch [18][1900/5005] lr: 1.000e-01, eta: 10:06:24, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.8080 2023-01-02 16:12:13,095 - mmcls - INFO - Epoch [18][2000/5005] lr: 1.000e-01, eta: 10:06:15, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7865 2023-01-02 16:12:21,725 - mmcls - INFO - Epoch [18][2100/5005] lr: 1.000e-01, eta: 10:06:05, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8241 2023-01-02 16:12:30,665 - mmcls - INFO - Epoch [18][2200/5005] lr: 1.000e-01, eta: 10:05:57, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.7630 2023-01-02 16:12:39,187 - mmcls - INFO - Epoch [18][2300/5005] lr: 1.000e-01, eta: 10:05:47, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.7914 2023-01-02 16:12:48,050 - mmcls - INFO - Epoch [18][2400/5005] lr: 1.000e-01, eta: 10:05:38, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.7917 2023-01-02 16:12:56,510 - mmcls - INFO - Epoch [18][2500/5005] lr: 1.000e-01, eta: 10:05:28, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7829 2023-01-02 16:13:05,249 - mmcls - INFO - Epoch [18][2600/5005] lr: 1.000e-01, eta: 10:05:19, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7610 2023-01-02 16:13:14,319 - mmcls - INFO - Epoch [18][2700/5005] lr: 1.000e-01, eta: 10:05:11, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.8070 2023-01-02 16:13:23,060 - mmcls - INFO - Epoch [18][2800/5005] lr: 1.000e-01, eta: 10:05:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8036 2023-01-02 16:13:31,713 - mmcls - INFO - Epoch [18][2900/5005] lr: 1.000e-01, eta: 10:04:53, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7975 2023-01-02 16:13:40,318 - mmcls - INFO - Epoch [18][3000/5005] lr: 1.000e-01, eta: 10:04:43, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.7852 2023-01-02 16:13:49,203 - mmcls - INFO - Epoch [18][3100/5005] lr: 1.000e-01, eta: 10:04:35, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.7771 2023-01-02 16:13:58,046 - mmcls - INFO - Epoch [18][3200/5005] lr: 1.000e-01, eta: 10:04:26, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.7710 2023-01-02 16:14:06,666 - mmcls - INFO - Epoch [18][3300/5005] lr: 1.000e-01, eta: 10:04:16, time: 0.086, data_time: 0.034, memory: 1125, loss: 2.8006 2023-01-02 16:14:15,223 - mmcls - INFO - Epoch [18][3400/5005] lr: 1.000e-01, eta: 10:04:06, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.8391 2023-01-02 16:14:23,817 - mmcls - INFO - Epoch [18][3500/5005] lr: 1.000e-01, eta: 10:03:57, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.8042 2023-01-02 16:14:32,691 - mmcls - INFO - Epoch [18][3600/5005] lr: 1.000e-01, eta: 10:03:48, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.7870 2023-01-02 16:14:41,395 - mmcls - INFO - Epoch [18][3700/5005] lr: 1.000e-01, eta: 10:03:39, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.8071 2023-01-02 16:14:50,196 - mmcls - INFO - Epoch [18][3800/5005] lr: 1.000e-01, eta: 10:03:30, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.8203 2023-01-02 16:14:59,053 - mmcls - INFO - Epoch [18][3900/5005] lr: 1.000e-01, eta: 10:03:22, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.7658 2023-01-02 16:15:07,955 - mmcls - INFO - Epoch [18][4000/5005] lr: 1.000e-01, eta: 10:03:13, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7816 2023-01-02 16:15:16,624 - mmcls - INFO - Epoch [18][4100/5005] lr: 1.000e-01, eta: 10:03:04, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7720 2023-01-02 16:15:25,653 - mmcls - INFO - Epoch [18][4200/5005] lr: 1.000e-01, eta: 10:02:56, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.8065 2023-01-02 16:15:34,443 - mmcls - INFO - Epoch [18][4300/5005] lr: 1.000e-01, eta: 10:02:47, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7763 2023-01-02 16:15:42,800 - mmcls - INFO - Epoch [18][4400/5005] lr: 1.000e-01, eta: 10:02:37, time: 0.084, data_time: 0.017, memory: 1125, loss: 2.7919 2023-01-02 16:15:51,822 - mmcls - INFO - Epoch [18][4500/5005] lr: 1.000e-01, eta: 10:02:29, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.8191 2023-01-02 16:16:00,630 - mmcls - INFO - Epoch [18][4600/5005] lr: 1.000e-01, eta: 10:02:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.8064 2023-01-02 16:16:09,504 - mmcls - INFO - Epoch [18][4700/5005] lr: 1.000e-01, eta: 10:02:12, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.8100 2023-01-02 16:16:18,351 - mmcls - INFO - Epoch [18][4800/5005] lr: 1.000e-01, eta: 10:02:03, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.8051 2023-01-02 16:16:27,284 - mmcls - INFO - Epoch [18][4900/5005] lr: 1.000e-01, eta: 10:01:55, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.8049 2023-01-02 16:16:35,851 - mmcls - INFO - Epoch [18][5000/5005] lr: 1.000e-01, eta: 10:01:45, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7751 2023-01-02 16:16:58,419 - mmcls - INFO - Epoch(val) [18][196] accuracy_top-1: 46.1460, accuracy_top-5: 70.9880 2023-01-02 16:17:09,439 - mmcls - INFO - Epoch [19][100/5005] lr: 1.000e-01, eta: 10:01:44, time: 0.110, data_time: 0.060, memory: 1125, loss: 2.7016 2023-01-02 16:17:18,084 - mmcls - INFO - Epoch [19][200/5005] lr: 1.000e-01, eta: 10:01:34, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7263 2023-01-02 16:17:26,723 - mmcls - INFO - Epoch [19][300/5005] lr: 1.000e-01, eta: 10:01:25, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7233 2023-01-02 16:17:35,679 - mmcls - INFO - Epoch [19][400/5005] lr: 1.000e-01, eta: 10:01:17, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7556 2023-01-02 16:17:44,254 - mmcls - INFO - Epoch [19][500/5005] lr: 1.000e-01, eta: 10:01:07, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.7231 2023-01-02 16:17:52,694 - mmcls - INFO - Epoch [19][600/5005] lr: 1.000e-01, eta: 10:00:56, time: 0.084, data_time: 0.022, memory: 1125, loss: 2.7724 2023-01-02 16:18:01,350 - mmcls - INFO - Epoch [19][700/5005] lr: 1.000e-01, eta: 10:00:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7384 2023-01-02 16:18:10,144 - mmcls - INFO - Epoch [19][800/5005] lr: 1.000e-01, eta: 10:00:38, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7471 2023-01-02 16:18:18,887 - mmcls - INFO - Epoch [19][900/5005] lr: 1.000e-01, eta: 10:00:29, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7589 2023-01-02 16:18:27,637 - mmcls - INFO - Epoch [19][1000/5005] lr: 1.000e-01, eta: 10:00:20, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7482 2023-01-02 16:18:36,731 - mmcls - INFO - Epoch [19][1100/5005] lr: 1.000e-01, eta: 10:00:13, time: 0.091, data_time: 0.031, memory: 1125, loss: 2.7771 2023-01-02 16:18:45,330 - mmcls - INFO - Epoch [19][1200/5005] lr: 1.000e-01, eta: 10:00:03, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7295 2023-01-02 16:18:54,236 - mmcls - INFO - Epoch [19][1300/5005] lr: 1.000e-01, eta: 9:59:55, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7648 2023-01-02 16:19:03,315 - mmcls - INFO - Epoch [19][1400/5005] lr: 1.000e-01, eta: 9:59:47, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7788 2023-01-02 16:19:11,856 - mmcls - INFO - Epoch [19][1500/5005] lr: 1.000e-01, eta: 9:59:37, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7618 2023-01-02 16:19:20,721 - mmcls - INFO - Epoch [19][1600/5005] lr: 1.000e-01, eta: 9:59:29, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.7830 2023-01-02 16:19:29,421 - mmcls - INFO - Epoch [19][1700/5005] lr: 1.000e-01, eta: 9:59:19, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7678 2023-01-02 16:19:38,109 - mmcls - INFO - Epoch [19][1800/5005] lr: 1.000e-01, eta: 9:59:10, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7869 2023-01-02 16:19:46,820 - mmcls - INFO - Epoch [19][1900/5005] lr: 1.000e-01, eta: 9:59:01, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7610 2023-01-02 16:19:55,363 - mmcls - INFO - Epoch [19][2000/5005] lr: 1.000e-01, eta: 9:58:51, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.8016 2023-01-02 16:20:03,906 - mmcls - INFO - Epoch [19][2100/5005] lr: 1.000e-01, eta: 9:58:41, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7849 2023-01-02 16:20:12,825 - mmcls - INFO - Epoch [19][2200/5005] lr: 1.000e-01, eta: 9:58:33, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.7876 2023-01-02 16:20:21,565 - mmcls - INFO - Epoch [19][2300/5005] lr: 1.000e-01, eta: 9:58:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7840 2023-01-02 16:20:30,473 - mmcls - INFO - Epoch [19][2400/5005] lr: 1.000e-01, eta: 9:58:15, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7932 2023-01-02 16:20:39,300 - mmcls - INFO - Epoch [19][2500/5005] lr: 1.000e-01, eta: 9:58:07, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7783 2023-01-02 16:20:48,002 - mmcls - INFO - Epoch [19][2600/5005] lr: 1.000e-01, eta: 9:57:58, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7832 2023-01-02 16:20:56,703 - mmcls - INFO - Epoch [19][2700/5005] lr: 1.000e-01, eta: 9:57:48, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.7829 2023-01-02 16:21:05,177 - mmcls - INFO - Epoch [19][2800/5005] lr: 1.000e-01, eta: 9:57:38, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.7919 2023-01-02 16:21:14,332 - mmcls - INFO - Epoch [19][2900/5005] lr: 1.000e-01, eta: 9:57:31, time: 0.092, data_time: 0.004, memory: 1125, loss: 2.7757 2023-01-02 16:21:23,187 - mmcls - INFO - Epoch [19][3000/5005] lr: 1.000e-01, eta: 9:57:22, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7631 2023-01-02 16:21:31,922 - mmcls - INFO - Epoch [19][3100/5005] lr: 1.000e-01, eta: 9:57:13, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7979 2023-01-02 16:21:40,823 - mmcls - INFO - Epoch [19][3200/5005] lr: 1.000e-01, eta: 9:57:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7806 2023-01-02 16:21:49,428 - mmcls - INFO - Epoch [19][3300/5005] lr: 1.000e-01, eta: 9:56:55, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.7982 2023-01-02 16:21:58,059 - mmcls - INFO - Epoch [19][3400/5005] lr: 1.000e-01, eta: 9:56:46, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.7901 2023-01-02 16:22:06,759 - mmcls - INFO - Epoch [19][3500/5005] lr: 1.000e-01, eta: 9:56:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7914 2023-01-02 16:22:15,659 - mmcls - INFO - Epoch [19][3600/5005] lr: 1.000e-01, eta: 9:56:28, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7920 2023-01-02 16:22:24,394 - mmcls - INFO - Epoch [19][3700/5005] lr: 1.000e-01, eta: 9:56:19, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7701 2023-01-02 16:22:33,051 - mmcls - INFO - Epoch [19][3800/5005] lr: 1.000e-01, eta: 9:56:10, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7778 2023-01-02 16:22:41,885 - mmcls - INFO - Epoch [19][3900/5005] lr: 1.000e-01, eta: 9:56:01, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.7833 2023-01-02 16:22:50,734 - mmcls - INFO - Epoch [19][4000/5005] lr: 1.000e-01, eta: 9:55:52, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7763 2023-01-02 16:22:59,644 - mmcls - INFO - Epoch [19][4100/5005] lr: 1.000e-01, eta: 9:55:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7999 2023-01-02 16:23:08,728 - mmcls - INFO - Epoch [19][4200/5005] lr: 1.000e-01, eta: 9:55:37, time: 0.091, data_time: 0.008, memory: 1125, loss: 2.7961 2023-01-02 16:23:17,259 - mmcls - INFO - Epoch [19][4300/5005] lr: 1.000e-01, eta: 9:55:27, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.7900 2023-01-02 16:23:26,218 - mmcls - INFO - Epoch [19][4400/5005] lr: 1.000e-01, eta: 9:55:18, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7876 2023-01-02 16:23:34,715 - mmcls - INFO - Epoch [19][4500/5005] lr: 1.000e-01, eta: 9:55:08, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.8017 2023-01-02 16:23:43,445 - mmcls - INFO - Epoch [19][4600/5005] lr: 1.000e-01, eta: 9:54:59, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.8049 2023-01-02 16:23:52,111 - mmcls - INFO - Epoch [19][4700/5005] lr: 1.000e-01, eta: 9:54:50, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7942 2023-01-02 16:24:00,829 - mmcls - INFO - Epoch [19][4800/5005] lr: 1.000e-01, eta: 9:54:41, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7905 2023-01-02 16:24:10,086 - mmcls - INFO - Epoch [19][4900/5005] lr: 1.000e-01, eta: 9:54:34, time: 0.093, data_time: 0.009, memory: 1125, loss: 2.8235 2023-01-02 16:24:18,780 - mmcls - INFO - Epoch [19][5000/5005] lr: 1.000e-01, eta: 9:54:25, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7934 2023-01-02 16:24:41,404 - mmcls - INFO - Epoch(val) [19][196] accuracy_top-1: 45.8820, accuracy_top-5: 70.6780 2023-01-02 16:24:52,416 - mmcls - INFO - Epoch [20][100/5005] lr: 1.000e-01, eta: 9:54:23, time: 0.110, data_time: 0.042, memory: 1125, loss: 2.7377 2023-01-02 16:25:01,121 - mmcls - INFO - Epoch [20][200/5005] lr: 1.000e-01, eta: 9:54:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7249 2023-01-02 16:25:09,878 - mmcls - INFO - Epoch [20][300/5005] lr: 1.000e-01, eta: 9:54:05, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7322 2023-01-02 16:25:18,945 - mmcls - INFO - Epoch [20][400/5005] lr: 1.000e-01, eta: 9:53:57, time: 0.091, data_time: 0.008, memory: 1125, loss: 2.7664 2023-01-02 16:25:27,603 - mmcls - INFO - Epoch [20][500/5005] lr: 1.000e-01, eta: 9:53:48, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7499 2023-01-02 16:25:36,264 - mmcls - INFO - Epoch [20][600/5005] lr: 1.000e-01, eta: 9:53:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7580 2023-01-02 16:25:44,912 - mmcls - INFO - Epoch [20][700/5005] lr: 1.000e-01, eta: 9:53:29, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.7387 2023-01-02 16:25:53,701 - mmcls - INFO - Epoch [20][800/5005] lr: 1.000e-01, eta: 9:53:20, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7511 2023-01-02 16:26:02,547 - mmcls - INFO - Epoch [20][900/5005] lr: 1.000e-01, eta: 9:53:11, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7449 2023-01-02 16:26:11,388 - mmcls - INFO - Epoch [20][1000/5005] lr: 1.000e-01, eta: 9:53:03, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.7484 2023-01-02 16:26:20,379 - mmcls - INFO - Epoch [20][1100/5005] lr: 1.000e-01, eta: 9:52:55, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7461 2023-01-02 16:26:29,078 - mmcls - INFO - Epoch [20][1200/5005] lr: 1.000e-01, eta: 9:52:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7734 2023-01-02 16:26:37,659 - mmcls - INFO - Epoch [20][1300/5005] lr: 1.000e-01, eta: 9:52:36, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8020 2023-01-02 16:26:46,485 - mmcls - INFO - Epoch [20][1400/5005] lr: 1.000e-01, eta: 9:52:27, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8181 2023-01-02 16:26:55,505 - mmcls - INFO - Epoch [20][1500/5005] lr: 1.000e-01, eta: 9:52:19, time: 0.090, data_time: 0.029, memory: 1125, loss: 2.7784 2023-01-02 16:27:04,330 - mmcls - INFO - Epoch [20][1600/5005] lr: 1.000e-01, eta: 9:52:11, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.8000 2023-01-02 16:27:13,094 - mmcls - INFO - Epoch [20][1700/5005] lr: 1.000e-01, eta: 9:52:02, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.7577 2023-01-02 16:27:21,852 - mmcls - INFO - Epoch [20][1800/5005] lr: 1.000e-01, eta: 9:51:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7956 2023-01-02 16:27:30,667 - mmcls - INFO - Epoch [20][1900/5005] lr: 1.000e-01, eta: 9:51:44, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.7665 2023-01-02 16:27:39,366 - mmcls - INFO - Epoch [20][2000/5005] lr: 1.000e-01, eta: 9:51:35, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7718 2023-01-02 16:27:48,030 - mmcls - INFO - Epoch [20][2100/5005] lr: 1.000e-01, eta: 9:51:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7967 2023-01-02 16:27:56,822 - mmcls - INFO - Epoch [20][2200/5005] lr: 1.000e-01, eta: 9:51:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7578 2023-01-02 16:28:05,545 - mmcls - INFO - Epoch [20][2300/5005] lr: 1.000e-01, eta: 9:51:07, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7671 2023-01-02 16:28:14,087 - mmcls - INFO - Epoch [20][2400/5005] lr: 1.000e-01, eta: 9:50:58, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7670 2023-01-02 16:28:22,978 - mmcls - INFO - Epoch [20][2500/5005] lr: 1.000e-01, eta: 9:50:49, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7825 2023-01-02 16:28:31,849 - mmcls - INFO - Epoch [20][2600/5005] lr: 1.000e-01, eta: 9:50:41, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7743 2023-01-02 16:28:40,606 - mmcls - INFO - Epoch [20][2700/5005] lr: 1.000e-01, eta: 9:50:32, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7753 2023-01-02 16:28:49,591 - mmcls - INFO - Epoch [20][2800/5005] lr: 1.000e-01, eta: 9:50:24, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7780 2023-01-02 16:28:57,995 - mmcls - INFO - Epoch [20][2900/5005] lr: 1.000e-01, eta: 9:50:13, time: 0.084, data_time: 0.008, memory: 1125, loss: 2.7856 2023-01-02 16:29:06,818 - mmcls - INFO - Epoch [20][3000/5005] lr: 1.000e-01, eta: 9:50:04, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7867 2023-01-02 16:29:15,386 - mmcls - INFO - Epoch [20][3100/5005] lr: 1.000e-01, eta: 9:49:55, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.7677 2023-01-02 16:29:24,154 - mmcls - INFO - Epoch [20][3200/5005] lr: 1.000e-01, eta: 9:49:46, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.8104 2023-01-02 16:29:32,797 - mmcls - INFO - Epoch [20][3300/5005] lr: 1.000e-01, eta: 9:49:36, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.7921 2023-01-02 16:29:41,535 - mmcls - INFO - Epoch [20][3400/5005] lr: 1.000e-01, eta: 9:49:27, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.7876 2023-01-02 16:29:50,537 - mmcls - INFO - Epoch [20][3500/5005] lr: 1.000e-01, eta: 9:49:19, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7976 2023-01-02 16:29:59,427 - mmcls - INFO - Epoch [20][3600/5005] lr: 1.000e-01, eta: 9:49:11, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.8053 2023-01-02 16:30:08,017 - mmcls - INFO - Epoch [20][3700/5005] lr: 1.000e-01, eta: 9:49:01, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.7672 2023-01-02 16:30:16,899 - mmcls - INFO - Epoch [20][3800/5005] lr: 1.000e-01, eta: 9:48:53, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7651 2023-01-02 16:30:25,778 - mmcls - INFO - Epoch [20][3900/5005] lr: 1.000e-01, eta: 9:48:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7959 2023-01-02 16:30:34,433 - mmcls - INFO - Epoch [20][4000/5005] lr: 1.000e-01, eta: 9:48:35, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.8087 2023-01-02 16:30:42,907 - mmcls - INFO - Epoch [20][4100/5005] lr: 1.000e-01, eta: 9:48:25, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7782 2023-01-02 16:30:51,701 - mmcls - INFO - Epoch [20][4200/5005] lr: 1.000e-01, eta: 9:48:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7764 2023-01-02 16:31:00,670 - mmcls - INFO - Epoch [20][4300/5005] lr: 1.000e-01, eta: 9:48:08, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7874 2023-01-02 16:31:09,177 - mmcls - INFO - Epoch [20][4400/5005] lr: 1.000e-01, eta: 9:47:58, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.7531 2023-01-02 16:31:17,941 - mmcls - INFO - Epoch [20][4500/5005] lr: 1.000e-01, eta: 9:47:49, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.8207 2023-01-02 16:31:26,675 - mmcls - INFO - Epoch [20][4600/5005] lr: 1.000e-01, eta: 9:47:40, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.7703 2023-01-02 16:31:35,392 - mmcls - INFO - Epoch [20][4700/5005] lr: 1.000e-01, eta: 9:47:31, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7905 2023-01-02 16:31:44,295 - mmcls - INFO - Epoch [20][4800/5005] lr: 1.000e-01, eta: 9:47:22, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.7810 2023-01-02 16:31:53,156 - mmcls - INFO - Epoch [20][4900/5005] lr: 1.000e-01, eta: 9:47:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7717 2023-01-02 16:32:01,691 - mmcls - INFO - Epoch [20][5000/5005] lr: 1.000e-01, eta: 9:47:04, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.8088 2023-01-02 16:32:24,482 - mmcls - INFO - Epoch(val) [20][196] accuracy_top-1: 47.1480, accuracy_top-5: 71.9200 2023-01-02 16:32:35,524 - mmcls - INFO - Epoch [21][100/5005] lr: 1.000e-01, eta: 9:47:02, time: 0.110, data_time: 0.042, memory: 1125, loss: 2.7202 2023-01-02 16:32:44,422 - mmcls - INFO - Epoch [21][200/5005] lr: 1.000e-01, eta: 9:46:54, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7283 2023-01-02 16:32:53,236 - mmcls - INFO - Epoch [21][300/5005] lr: 1.000e-01, eta: 9:46:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7296 2023-01-02 16:33:01,905 - mmcls - INFO - Epoch [21][400/5005] lr: 1.000e-01, eta: 9:46:36, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.7511 2023-01-02 16:33:10,479 - mmcls - INFO - Epoch [21][500/5005] lr: 1.000e-01, eta: 9:46:26, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7615 2023-01-02 16:33:19,467 - mmcls - INFO - Epoch [21][600/5005] lr: 1.000e-01, eta: 9:46:18, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7538 2023-01-02 16:33:28,157 - mmcls - INFO - Epoch [21][700/5005] lr: 1.000e-01, eta: 9:46:09, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.7420 2023-01-02 16:33:36,828 - mmcls - INFO - Epoch [21][800/5005] lr: 1.000e-01, eta: 9:45:59, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.7315 2023-01-02 16:33:45,760 - mmcls - INFO - Epoch [21][900/5005] lr: 1.000e-01, eta: 9:45:51, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7455 2023-01-02 16:33:54,490 - mmcls - INFO - Epoch [21][1000/5005] lr: 1.000e-01, eta: 9:45:42, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.7705 2023-01-02 16:34:03,153 - mmcls - INFO - Epoch [21][1100/5005] lr: 1.000e-01, eta: 9:45:33, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7757 2023-01-02 16:34:11,772 - mmcls - INFO - Epoch [21][1200/5005] lr: 1.000e-01, eta: 9:45:23, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.7664 2023-01-02 16:34:20,697 - mmcls - INFO - Epoch [21][1300/5005] lr: 1.000e-01, eta: 9:45:15, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7494 2023-01-02 16:34:29,231 - mmcls - INFO - Epoch [21][1400/5005] lr: 1.000e-01, eta: 9:45:05, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.7545 2023-01-02 16:34:37,939 - mmcls - INFO - Epoch [21][1500/5005] lr: 1.000e-01, eta: 9:44:56, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7713 2023-01-02 16:34:46,352 - mmcls - INFO - Epoch [21][1600/5005] lr: 1.000e-01, eta: 9:44:45, time: 0.084, data_time: 0.004, memory: 1125, loss: 2.7684 2023-01-02 16:34:55,238 - mmcls - INFO - Epoch [21][1700/5005] lr: 1.000e-01, eta: 9:44:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7853 2023-01-02 16:35:04,270 - mmcls - INFO - Epoch [21][1800/5005] lr: 1.000e-01, eta: 9:44:29, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7778 2023-01-02 16:35:13,387 - mmcls - INFO - Epoch [21][1900/5005] lr: 1.000e-01, eta: 9:44:22, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.7820 2023-01-02 16:35:22,383 - mmcls - INFO - Epoch [21][2000/5005] lr: 1.000e-01, eta: 9:44:13, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7899 2023-01-02 16:35:31,204 - mmcls - INFO - Epoch [21][2100/5005] lr: 1.000e-01, eta: 9:44:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7618 2023-01-02 16:35:39,858 - mmcls - INFO - Epoch [21][2200/5005] lr: 1.000e-01, eta: 9:43:55, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.7865 2023-01-02 16:35:48,539 - mmcls - INFO - Epoch [21][2300/5005] lr: 1.000e-01, eta: 9:43:46, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7749 2023-01-02 16:35:57,484 - mmcls - INFO - Epoch [21][2400/5005] lr: 1.000e-01, eta: 9:43:38, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.7774 2023-01-02 16:36:06,415 - mmcls - INFO - Epoch [21][2500/5005] lr: 1.000e-01, eta: 9:43:30, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.7835 2023-01-02 16:36:15,161 - mmcls - INFO - Epoch [21][2600/5005] lr: 1.000e-01, eta: 9:43:21, time: 0.088, data_time: 0.026, memory: 1125, loss: 2.7764 2023-01-02 16:36:23,905 - mmcls - INFO - Epoch [21][2700/5005] lr: 1.000e-01, eta: 9:43:12, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7840 2023-01-02 16:36:32,887 - mmcls - INFO - Epoch [21][2800/5005] lr: 1.000e-01, eta: 9:43:03, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.7621 2023-01-02 16:36:41,631 - mmcls - INFO - Epoch [21][2900/5005] lr: 1.000e-01, eta: 9:42:54, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7648 2023-01-02 16:36:50,371 - mmcls - INFO - Epoch [21][3000/5005] lr: 1.000e-01, eta: 9:42:45, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.8159 2023-01-02 16:36:59,322 - mmcls - INFO - Epoch [21][3100/5005] lr: 1.000e-01, eta: 9:42:37, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.7705 2023-01-02 16:37:08,153 - mmcls - INFO - Epoch [21][3200/5005] lr: 1.000e-01, eta: 9:42:29, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7846 2023-01-02 16:37:17,076 - mmcls - INFO - Epoch [21][3300/5005] lr: 1.000e-01, eta: 9:42:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7640 2023-01-02 16:37:25,743 - mmcls - INFO - Epoch [21][3400/5005] lr: 1.000e-01, eta: 9:42:11, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.8216 2023-01-02 16:37:34,564 - mmcls - INFO - Epoch [21][3500/5005] lr: 1.000e-01, eta: 9:42:02, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7816 2023-01-02 16:37:43,634 - mmcls - INFO - Epoch [21][3600/5005] lr: 1.000e-01, eta: 9:41:54, time: 0.091, data_time: 0.027, memory: 1125, loss: 2.7871 2023-01-02 16:37:52,311 - mmcls - INFO - Epoch [21][3700/5005] lr: 1.000e-01, eta: 9:41:45, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7757 2023-01-02 16:38:00,966 - mmcls - INFO - Epoch [21][3800/5005] lr: 1.000e-01, eta: 9:41:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7715 2023-01-02 16:38:09,745 - mmcls - INFO - Epoch [21][3900/5005] lr: 1.000e-01, eta: 9:41:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7872 2023-01-02 16:38:18,507 - mmcls - INFO - Epoch [21][4000/5005] lr: 1.000e-01, eta: 9:41:18, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.7662 2023-01-02 16:38:27,380 - mmcls - INFO - Epoch [21][4100/5005] lr: 1.000e-01, eta: 9:41:09, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7885 2023-01-02 16:38:36,264 - mmcls - INFO - Epoch [21][4200/5005] lr: 1.000e-01, eta: 9:41:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7742 2023-01-02 16:38:45,058 - mmcls - INFO - Epoch [21][4300/5005] lr: 1.000e-01, eta: 9:40:52, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.7760 2023-01-02 16:38:53,905 - mmcls - INFO - Epoch [21][4400/5005] lr: 1.000e-01, eta: 9:40:44, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7757 2023-01-02 16:39:02,581 - mmcls - INFO - Epoch [21][4500/5005] lr: 1.000e-01, eta: 9:40:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7804 2023-01-02 16:39:11,393 - mmcls - INFO - Epoch [21][4600/5005] lr: 1.000e-01, eta: 9:40:26, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7535 2023-01-02 16:39:20,091 - mmcls - INFO - Epoch [21][4700/5005] lr: 1.000e-01, eta: 9:40:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7920 2023-01-02 16:39:29,006 - mmcls - INFO - Epoch [21][4800/5005] lr: 1.000e-01, eta: 9:40:08, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7869 2023-01-02 16:39:37,951 - mmcls - INFO - Epoch [21][4900/5005] lr: 1.000e-01, eta: 9:40:00, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7950 2023-01-02 16:39:46,651 - mmcls - INFO - Epoch [21][5000/5005] lr: 1.000e-01, eta: 9:39:51, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7603 2023-01-02 16:40:10,152 - mmcls - INFO - Epoch(val) [21][196] accuracy_top-1: 45.5680, accuracy_top-5: 70.2900 2023-01-02 16:40:21,284 - mmcls - INFO - Epoch [22][100/5005] lr: 1.000e-01, eta: 9:39:48, time: 0.111, data_time: 0.033, memory: 1125, loss: 2.7087 2023-01-02 16:40:30,131 - mmcls - INFO - Epoch [22][200/5005] lr: 1.000e-01, eta: 9:39:40, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7337 2023-01-02 16:40:38,881 - mmcls - INFO - Epoch [22][300/5005] lr: 1.000e-01, eta: 9:39:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7114 2023-01-02 16:40:47,731 - mmcls - INFO - Epoch [22][400/5005] lr: 1.000e-01, eta: 9:39:22, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7192 2023-01-02 16:40:56,420 - mmcls - INFO - Epoch [22][500/5005] lr: 1.000e-01, eta: 9:39:13, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7290 2023-01-02 16:41:05,195 - mmcls - INFO - Epoch [22][600/5005] lr: 1.000e-01, eta: 9:39:04, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7307 2023-01-02 16:41:13,922 - mmcls - INFO - Epoch [22][700/5005] lr: 1.000e-01, eta: 9:38:55, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7515 2023-01-02 16:41:22,910 - mmcls - INFO - Epoch [22][800/5005] lr: 1.000e-01, eta: 9:38:47, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.7344 2023-01-02 16:41:31,710 - mmcls - INFO - Epoch [22][900/5005] lr: 1.000e-01, eta: 9:38:38, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7479 2023-01-02 16:41:40,754 - mmcls - INFO - Epoch [22][1000/5005] lr: 1.000e-01, eta: 9:38:30, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.7269 2023-01-02 16:41:49,616 - mmcls - INFO - Epoch [22][1100/5005] lr: 1.000e-01, eta: 9:38:22, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.7594 2023-01-02 16:41:58,192 - mmcls - INFO - Epoch [22][1200/5005] lr: 1.000e-01, eta: 9:38:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7550 2023-01-02 16:42:06,896 - mmcls - INFO - Epoch [22][1300/5005] lr: 1.000e-01, eta: 9:38:03, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7714 2023-01-02 16:42:16,348 - mmcls - INFO - Epoch [22][1400/5005] lr: 1.000e-01, eta: 9:37:56, time: 0.095, data_time: 0.012, memory: 1125, loss: 2.7659 2023-01-02 16:42:25,072 - mmcls - INFO - Epoch [22][1500/5005] lr: 1.000e-01, eta: 9:37:47, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7423 2023-01-02 16:42:34,014 - mmcls - INFO - Epoch [22][1600/5005] lr: 1.000e-01, eta: 9:37:39, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7309 2023-01-02 16:42:43,092 - mmcls - INFO - Epoch [22][1700/5005] lr: 1.000e-01, eta: 9:37:31, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.7757 2023-01-02 16:42:51,759 - mmcls - INFO - Epoch [22][1800/5005] lr: 1.000e-01, eta: 9:37:22, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7625 2023-01-02 16:43:00,668 - mmcls - INFO - Epoch [22][1900/5005] lr: 1.000e-01, eta: 9:37:13, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7598 2023-01-02 16:43:09,586 - mmcls - INFO - Epoch [22][2000/5005] lr: 1.000e-01, eta: 9:37:05, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.7675 2023-01-02 16:43:18,501 - mmcls - INFO - Epoch [22][2100/5005] lr: 1.000e-01, eta: 9:36:57, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7878 2023-01-02 16:43:27,353 - mmcls - INFO - Epoch [22][2200/5005] lr: 1.000e-01, eta: 9:36:48, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7511 2023-01-02 16:43:36,865 - mmcls - INFO - Epoch [22][2300/5005] lr: 1.000e-01, eta: 9:36:42, time: 0.095, data_time: 0.014, memory: 1125, loss: 2.7509 2023-01-02 16:43:45,592 - mmcls - INFO - Epoch [22][2400/5005] lr: 1.000e-01, eta: 9:36:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7728 2023-01-02 16:43:54,812 - mmcls - INFO - Epoch [22][2500/5005] lr: 1.000e-01, eta: 9:36:25, time: 0.092, data_time: 0.011, memory: 1125, loss: 2.7645 2023-01-02 16:44:03,469 - mmcls - INFO - Epoch [22][2600/5005] lr: 1.000e-01, eta: 9:36:16, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.8062 2023-01-02 16:44:12,207 - mmcls - INFO - Epoch [22][2700/5005] lr: 1.000e-01, eta: 9:36:07, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7324 2023-01-02 16:44:20,957 - mmcls - INFO - Epoch [22][2800/5005] lr: 1.000e-01, eta: 9:35:58, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.7621 2023-01-02 16:44:29,571 - mmcls - INFO - Epoch [22][2900/5005] lr: 1.000e-01, eta: 9:35:49, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.7573 2023-01-02 16:44:38,247 - mmcls - INFO - Epoch [22][3000/5005] lr: 1.000e-01, eta: 9:35:39, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.7625 2023-01-02 16:44:47,361 - mmcls - INFO - Epoch [22][3100/5005] lr: 1.000e-01, eta: 9:35:32, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.7559 2023-01-02 16:44:56,088 - mmcls - INFO - Epoch [22][3200/5005] lr: 1.000e-01, eta: 9:35:23, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7817 2023-01-02 16:45:04,938 - mmcls - INFO - Epoch [22][3300/5005] lr: 1.000e-01, eta: 9:35:14, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7811 2023-01-02 16:45:13,820 - mmcls - INFO - Epoch [22][3400/5005] lr: 1.000e-01, eta: 9:35:05, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.7592 2023-01-02 16:45:22,860 - mmcls - INFO - Epoch [22][3500/5005] lr: 1.000e-01, eta: 9:34:58, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7334 2023-01-02 16:45:31,297 - mmcls - INFO - Epoch [22][3600/5005] lr: 1.000e-01, eta: 9:34:47, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.7626 2023-01-02 16:45:39,987 - mmcls - INFO - Epoch [22][3700/5005] lr: 1.000e-01, eta: 9:34:38, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7581 2023-01-02 16:45:48,616 - mmcls - INFO - Epoch [22][3800/5005] lr: 1.000e-01, eta: 9:34:29, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8084 2023-01-02 16:45:57,189 - mmcls - INFO - Epoch [22][3900/5005] lr: 1.000e-01, eta: 9:34:19, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7778 2023-01-02 16:46:06,059 - mmcls - INFO - Epoch [22][4000/5005] lr: 1.000e-01, eta: 9:34:11, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7777 2023-01-02 16:46:15,440 - mmcls - INFO - Epoch [22][4100/5005] lr: 1.000e-01, eta: 9:34:04, time: 0.094, data_time: 0.002, memory: 1125, loss: 2.7576 2023-01-02 16:46:24,528 - mmcls - INFO - Epoch [22][4200/5005] lr: 1.000e-01, eta: 9:33:56, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.7504 2023-01-02 16:46:33,206 - mmcls - INFO - Epoch [22][4300/5005] lr: 1.000e-01, eta: 9:33:47, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7693 2023-01-02 16:46:41,875 - mmcls - INFO - Epoch [22][4400/5005] lr: 1.000e-01, eta: 9:33:37, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7637 2023-01-02 16:46:50,792 - mmcls - INFO - Epoch [22][4500/5005] lr: 1.000e-01, eta: 9:33:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7646 2023-01-02 16:46:59,422 - mmcls - INFO - Epoch [22][4600/5005] lr: 1.000e-01, eta: 9:33:20, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7849 2023-01-02 16:47:08,370 - mmcls - INFO - Epoch [22][4700/5005] lr: 1.000e-01, eta: 9:33:11, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.7837 2023-01-02 16:47:17,086 - mmcls - INFO - Epoch [22][4800/5005] lr: 1.000e-01, eta: 9:33:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8141 2023-01-02 16:47:25,969 - mmcls - INFO - Epoch [22][4900/5005] lr: 1.000e-01, eta: 9:32:54, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7754 2023-01-02 16:47:34,677 - mmcls - INFO - Epoch [22][5000/5005] lr: 1.000e-01, eta: 9:32:45, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8034 2023-01-02 16:47:57,170 - mmcls - INFO - Epoch(val) [22][196] accuracy_top-1: 46.9400, accuracy_top-5: 71.9000 2023-01-02 16:48:08,165 - mmcls - INFO - Epoch [23][100/5005] lr: 1.000e-01, eta: 9:32:42, time: 0.110, data_time: 0.039, memory: 1125, loss: 2.6823 2023-01-02 16:48:16,888 - mmcls - INFO - Epoch [23][200/5005] lr: 1.000e-01, eta: 9:32:32, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7295 2023-01-02 16:48:25,898 - mmcls - INFO - Epoch [23][300/5005] lr: 1.000e-01, eta: 9:32:24, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7057 2023-01-02 16:48:34,408 - mmcls - INFO - Epoch [23][400/5005] lr: 1.000e-01, eta: 9:32:14, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7217 2023-01-02 16:48:42,973 - mmcls - INFO - Epoch [23][500/5005] lr: 1.000e-01, eta: 9:32:05, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7200 2023-01-02 16:48:51,804 - mmcls - INFO - Epoch [23][600/5005] lr: 1.000e-01, eta: 9:31:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7282 2023-01-02 16:49:00,315 - mmcls - INFO - Epoch [23][700/5005] lr: 1.000e-01, eta: 9:31:46, time: 0.085, data_time: 0.025, memory: 1125, loss: 2.7217 2023-01-02 16:49:09,486 - mmcls - INFO - Epoch [23][800/5005] lr: 1.000e-01, eta: 9:31:39, time: 0.092, data_time: 0.013, memory: 1125, loss: 2.7366 2023-01-02 16:49:18,217 - mmcls - INFO - Epoch [23][900/5005] lr: 1.000e-01, eta: 9:31:30, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.7625 2023-01-02 16:49:27,020 - mmcls - INFO - Epoch [23][1000/5005] lr: 1.000e-01, eta: 9:31:21, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.7514 2023-01-02 16:49:35,723 - mmcls - INFO - Epoch [23][1100/5005] lr: 1.000e-01, eta: 9:31:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7424 2023-01-02 16:49:44,714 - mmcls - INFO - Epoch [23][1200/5005] lr: 1.000e-01, eta: 9:31:04, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7591 2023-01-02 16:49:53,278 - mmcls - INFO - Epoch [23][1300/5005] lr: 1.000e-01, eta: 9:30:54, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7528 2023-01-02 16:50:02,214 - mmcls - INFO - Epoch [23][1400/5005] lr: 1.000e-01, eta: 9:30:46, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7473 2023-01-02 16:50:11,007 - mmcls - INFO - Epoch [23][1500/5005] lr: 1.000e-01, eta: 9:30:37, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7565 2023-01-02 16:50:20,024 - mmcls - INFO - Epoch [23][1600/5005] lr: 1.000e-01, eta: 9:30:29, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.7190 2023-01-02 16:50:28,859 - mmcls - INFO - Epoch [23][1700/5005] lr: 1.000e-01, eta: 9:30:20, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7704 2023-01-02 16:50:37,764 - mmcls - INFO - Epoch [23][1800/5005] lr: 1.000e-01, eta: 9:30:12, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7545 2023-01-02 16:50:46,239 - mmcls - INFO - Epoch [23][1900/5005] lr: 1.000e-01, eta: 9:30:02, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7761 2023-01-02 16:50:55,001 - mmcls - INFO - Epoch [23][2000/5005] lr: 1.000e-01, eta: 9:29:53, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7869 2023-01-02 16:51:03,976 - mmcls - INFO - Epoch [23][2100/5005] lr: 1.000e-01, eta: 9:29:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7418 2023-01-02 16:51:12,868 - mmcls - INFO - Epoch [23][2200/5005] lr: 1.000e-01, eta: 9:29:36, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7564 2023-01-02 16:51:21,408 - mmcls - INFO - Epoch [23][2300/5005] lr: 1.000e-01, eta: 9:29:26, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7503 2023-01-02 16:51:29,953 - mmcls - INFO - Epoch [23][2400/5005] lr: 1.000e-01, eta: 9:29:16, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7633 2023-01-02 16:51:38,546 - mmcls - INFO - Epoch [23][2500/5005] lr: 1.000e-01, eta: 9:29:07, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7809 2023-01-02 16:51:47,270 - mmcls - INFO - Epoch [23][2600/5005] lr: 1.000e-01, eta: 9:28:58, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7603 2023-01-02 16:51:55,867 - mmcls - INFO - Epoch [23][2700/5005] lr: 1.000e-01, eta: 9:28:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.7621 2023-01-02 16:52:04,790 - mmcls - INFO - Epoch [23][2800/5005] lr: 1.000e-01, eta: 9:28:40, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.7735 2023-01-02 16:52:13,646 - mmcls - INFO - Epoch [23][2900/5005] lr: 1.000e-01, eta: 9:28:31, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7672 2023-01-02 16:52:22,503 - mmcls - INFO - Epoch [23][3000/5005] lr: 1.000e-01, eta: 9:28:23, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7382 2023-01-02 16:52:31,133 - mmcls - INFO - Epoch [23][3100/5005] lr: 1.000e-01, eta: 9:28:13, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7717 2023-01-02 16:52:40,086 - mmcls - INFO - Epoch [23][3200/5005] lr: 1.000e-01, eta: 9:28:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7494 2023-01-02 16:52:49,542 - mmcls - INFO - Epoch [23][3300/5005] lr: 1.000e-01, eta: 9:27:58, time: 0.094, data_time: 0.002, memory: 1125, loss: 2.7590 2023-01-02 16:52:58,248 - mmcls - INFO - Epoch [23][3400/5005] lr: 1.000e-01, eta: 9:27:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7751 2023-01-02 16:53:06,788 - mmcls - INFO - Epoch [23][3500/5005] lr: 1.000e-01, eta: 9:27:40, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.7795 2023-01-02 16:53:15,706 - mmcls - INFO - Epoch [23][3600/5005] lr: 1.000e-01, eta: 9:27:31, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7856 2023-01-02 16:53:24,339 - mmcls - INFO - Epoch [23][3700/5005] lr: 1.000e-01, eta: 9:27:22, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.7907 2023-01-02 16:53:32,685 - mmcls - INFO - Epoch [23][3800/5005] lr: 1.000e-01, eta: 9:27:11, time: 0.083, data_time: 0.001, memory: 1125, loss: 2.7658 2023-01-02 16:53:41,524 - mmcls - INFO - Epoch [23][3900/5005] lr: 1.000e-01, eta: 9:27:03, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7824 2023-01-02 16:53:50,213 - mmcls - INFO - Epoch [23][4000/5005] lr: 1.000e-01, eta: 9:26:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7700 2023-01-02 16:53:59,168 - mmcls - INFO - Epoch [23][4100/5005] lr: 1.000e-01, eta: 9:26:45, time: 0.090, data_time: 0.042, memory: 1125, loss: 2.7720 2023-01-02 16:54:07,586 - mmcls - INFO - Epoch [23][4200/5005] lr: 1.000e-01, eta: 9:26:35, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.7795 2023-01-02 16:54:16,441 - mmcls - INFO - Epoch [23][4300/5005] lr: 1.000e-01, eta: 9:26:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7695 2023-01-02 16:54:25,203 - mmcls - INFO - Epoch [23][4400/5005] lr: 1.000e-01, eta: 9:26:18, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7625 2023-01-02 16:54:33,590 - mmcls - INFO - Epoch [23][4500/5005] lr: 1.000e-01, eta: 9:26:07, time: 0.084, data_time: 0.029, memory: 1125, loss: 2.7719 2023-01-02 16:54:42,401 - mmcls - INFO - Epoch [23][4600/5005] lr: 1.000e-01, eta: 9:25:59, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7878 2023-01-02 16:54:51,592 - mmcls - INFO - Epoch [23][4700/5005] lr: 1.000e-01, eta: 9:25:51, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.7616 2023-01-02 16:55:00,255 - mmcls - INFO - Epoch [23][4800/5005] lr: 1.000e-01, eta: 9:25:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7821 2023-01-02 16:55:09,041 - mmcls - INFO - Epoch [23][4900/5005] lr: 1.000e-01, eta: 9:25:33, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7434 2023-01-02 16:55:17,652 - mmcls - INFO - Epoch [23][5000/5005] lr: 1.000e-01, eta: 9:25:24, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.7795 2023-01-02 16:55:40,229 - mmcls - INFO - Epoch(val) [23][196] accuracy_top-1: 38.4400, accuracy_top-5: 61.7120 2023-01-02 16:55:51,162 - mmcls - INFO - Epoch [24][100/5005] lr: 1.000e-01, eta: 9:25:20, time: 0.109, data_time: 0.029, memory: 1125, loss: 2.7405 2023-01-02 16:55:59,931 - mmcls - INFO - Epoch [24][200/5005] lr: 1.000e-01, eta: 9:25:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7084 2023-01-02 16:56:09,001 - mmcls - INFO - Epoch [24][300/5005] lr: 1.000e-01, eta: 9:25:03, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7281 2023-01-02 16:56:17,777 - mmcls - INFO - Epoch [24][400/5005] lr: 1.000e-01, eta: 9:24:54, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.7185 2023-01-02 16:56:26,547 - mmcls - INFO - Epoch [24][500/5005] lr: 1.000e-01, eta: 9:24:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7236 2023-01-02 16:56:35,651 - mmcls - INFO - Epoch [24][600/5005] lr: 1.000e-01, eta: 9:24:37, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.7302 2023-01-02 16:56:44,269 - mmcls - INFO - Epoch [24][700/5005] lr: 1.000e-01, eta: 9:24:28, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.7839 2023-01-02 16:56:52,750 - mmcls - INFO - Epoch [24][800/5005] lr: 1.000e-01, eta: 9:24:18, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7702 2023-01-02 16:57:01,245 - mmcls - INFO - Epoch [24][900/5005] lr: 1.000e-01, eta: 9:24:08, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7672 2023-01-02 16:57:10,021 - mmcls - INFO - Epoch [24][1000/5005] lr: 1.000e-01, eta: 9:23:59, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7634 2023-01-02 16:57:18,868 - mmcls - INFO - Epoch [24][1100/5005] lr: 1.000e-01, eta: 9:23:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7622 2023-01-02 16:57:27,814 - mmcls - INFO - Epoch [24][1200/5005] lr: 1.000e-01, eta: 9:23:42, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.7479 2023-01-02 16:57:36,737 - mmcls - INFO - Epoch [24][1300/5005] lr: 1.000e-01, eta: 9:23:34, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.7305 2023-01-02 16:57:45,487 - mmcls - INFO - Epoch [24][1400/5005] lr: 1.000e-01, eta: 9:23:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7446 2023-01-02 16:57:54,092 - mmcls - INFO - Epoch [24][1500/5005] lr: 1.000e-01, eta: 9:23:16, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.7467 2023-01-02 16:58:02,967 - mmcls - INFO - Epoch [24][1600/5005] lr: 1.000e-01, eta: 9:23:07, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7460 2023-01-02 16:58:11,750 - mmcls - INFO - Epoch [24][1700/5005] lr: 1.000e-01, eta: 9:22:58, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.7933 2023-01-02 16:58:20,448 - mmcls - INFO - Epoch [24][1800/5005] lr: 1.000e-01, eta: 9:22:49, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7300 2023-01-02 16:58:29,364 - mmcls - INFO - Epoch [24][1900/5005] lr: 1.000e-01, eta: 9:22:40, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.7983 2023-01-02 16:58:38,058 - mmcls - INFO - Epoch [24][2000/5005] lr: 1.000e-01, eta: 9:22:31, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8262 2023-01-02 16:58:46,689 - mmcls - INFO - Epoch [24][2100/5005] lr: 1.000e-01, eta: 9:22:22, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7750 2023-01-02 16:58:55,643 - mmcls - INFO - Epoch [24][2200/5005] lr: 1.000e-01, eta: 9:22:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7659 2023-01-02 16:59:04,426 - mmcls - INFO - Epoch [24][2300/5005] lr: 1.000e-01, eta: 9:22:05, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7483 2023-01-02 16:59:13,300 - mmcls - INFO - Epoch [24][2400/5005] lr: 1.000e-01, eta: 9:21:56, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7722 2023-01-02 16:59:22,019 - mmcls - INFO - Epoch [24][2500/5005] lr: 1.000e-01, eta: 9:21:47, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.7575 2023-01-02 16:59:30,714 - mmcls - INFO - Epoch [24][2600/5005] lr: 1.000e-01, eta: 9:21:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7821 2023-01-02 16:59:39,587 - mmcls - INFO - Epoch [24][2700/5005] lr: 1.000e-01, eta: 9:21:29, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7649 2023-01-02 16:59:48,285 - mmcls - INFO - Epoch [24][2800/5005] lr: 1.000e-01, eta: 9:21:20, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7773 2023-01-02 16:59:56,931 - mmcls - INFO - Epoch [24][2900/5005] lr: 1.000e-01, eta: 9:21:11, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7679 2023-01-02 17:00:05,437 - mmcls - INFO - Epoch [24][3000/5005] lr: 1.000e-01, eta: 9:21:01, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7436 2023-01-02 17:00:14,276 - mmcls - INFO - Epoch [24][3100/5005] lr: 1.000e-01, eta: 9:20:52, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.7863 2023-01-02 17:00:22,819 - mmcls - INFO - Epoch [24][3200/5005] lr: 1.000e-01, eta: 9:20:43, time: 0.085, data_time: 0.033, memory: 1125, loss: 2.7452 2023-01-02 17:00:31,620 - mmcls - INFO - Epoch [24][3300/5005] lr: 1.000e-01, eta: 9:20:34, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.7565 2023-01-02 17:00:40,333 - mmcls - INFO - Epoch [24][3400/5005] lr: 1.000e-01, eta: 9:20:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7446 2023-01-02 17:00:49,004 - mmcls - INFO - Epoch [24][3500/5005] lr: 1.000e-01, eta: 9:20:16, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7769 2023-01-02 17:00:58,106 - mmcls - INFO - Epoch [24][3600/5005] lr: 1.000e-01, eta: 9:20:08, time: 0.091, data_time: 0.020, memory: 1125, loss: 2.7617 2023-01-02 17:01:06,823 - mmcls - INFO - Epoch [24][3700/5005] lr: 1.000e-01, eta: 9:19:59, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7589 2023-01-02 17:01:15,924 - mmcls - INFO - Epoch [24][3800/5005] lr: 1.000e-01, eta: 9:19:51, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7520 2023-01-02 17:01:24,659 - mmcls - INFO - Epoch [24][3900/5005] lr: 1.000e-01, eta: 9:19:42, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7666 2023-01-02 17:01:32,976 - mmcls - INFO - Epoch [24][4000/5005] lr: 1.000e-01, eta: 9:19:31, time: 0.083, data_time: 0.017, memory: 1125, loss: 2.7404 2023-01-02 17:01:42,187 - mmcls - INFO - Epoch [24][4100/5005] lr: 1.000e-01, eta: 9:19:24, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.7800 2023-01-02 17:01:51,179 - mmcls - INFO - Epoch [24][4200/5005] lr: 1.000e-01, eta: 9:19:16, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7701 2023-01-02 17:01:59,891 - mmcls - INFO - Epoch [24][4300/5005] lr: 1.000e-01, eta: 9:19:07, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7763 2023-01-02 17:02:08,490 - mmcls - INFO - Epoch [24][4400/5005] lr: 1.000e-01, eta: 9:18:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7453 2023-01-02 17:02:17,505 - mmcls - INFO - Epoch [24][4500/5005] lr: 1.000e-01, eta: 9:18:49, time: 0.090, data_time: 0.021, memory: 1125, loss: 2.7831 2023-01-02 17:02:25,884 - mmcls - INFO - Epoch [24][4600/5005] lr: 1.000e-01, eta: 9:18:39, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.7473 2023-01-02 17:02:34,390 - mmcls - INFO - Epoch [24][4700/5005] lr: 1.000e-01, eta: 9:18:29, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.7456 2023-01-02 17:02:43,176 - mmcls - INFO - Epoch [24][4800/5005] lr: 1.000e-01, eta: 9:18:20, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7461 2023-01-02 17:02:51,894 - mmcls - INFO - Epoch [24][4900/5005] lr: 1.000e-01, eta: 9:18:11, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7560 2023-01-02 17:03:00,438 - mmcls - INFO - Epoch [24][5000/5005] lr: 1.000e-01, eta: 9:18:02, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7659 2023-01-02 17:03:23,030 - mmcls - INFO - Epoch(val) [24][196] accuracy_top-1: 44.6100, accuracy_top-5: 69.5100 2023-01-02 17:03:33,842 - mmcls - INFO - Epoch [25][100/5005] lr: 1.000e-01, eta: 9:17:57, time: 0.108, data_time: 0.053, memory: 1125, loss: 2.6890 2023-01-02 17:03:42,676 - mmcls - INFO - Epoch [25][200/5005] lr: 1.000e-01, eta: 9:17:49, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.6972 2023-01-02 17:03:51,255 - mmcls - INFO - Epoch [25][300/5005] lr: 1.000e-01, eta: 9:17:39, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7029 2023-01-02 17:03:59,836 - mmcls - INFO - Epoch [25][400/5005] lr: 1.000e-01, eta: 9:17:30, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7300 2023-01-02 17:04:08,434 - mmcls - INFO - Epoch [25][500/5005] lr: 1.000e-01, eta: 9:17:20, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.7200 2023-01-02 17:04:17,080 - mmcls - INFO - Epoch [25][600/5005] lr: 1.000e-01, eta: 9:17:11, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.7134 2023-01-02 17:04:25,727 - mmcls - INFO - Epoch [25][700/5005] lr: 1.000e-01, eta: 9:17:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7469 2023-01-02 17:04:34,436 - mmcls - INFO - Epoch [25][800/5005] lr: 1.000e-01, eta: 9:16:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7378 2023-01-02 17:04:43,462 - mmcls - INFO - Epoch [25][900/5005] lr: 1.000e-01, eta: 9:16:44, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.7148 2023-01-02 17:04:52,165 - mmcls - INFO - Epoch [25][1000/5005] lr: 1.000e-01, eta: 9:16:35, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7670 2023-01-02 17:05:01,138 - mmcls - INFO - Epoch [25][1100/5005] lr: 1.000e-01, eta: 9:16:27, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7486 2023-01-02 17:05:10,017 - mmcls - INFO - Epoch [25][1200/5005] lr: 1.000e-01, eta: 9:16:18, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.7490 2023-01-02 17:05:18,655 - mmcls - INFO - Epoch [25][1300/5005] lr: 1.000e-01, eta: 9:16:09, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.7382 2023-01-02 17:05:27,347 - mmcls - INFO - Epoch [25][1400/5005] lr: 1.000e-01, eta: 9:16:00, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.7492 2023-01-02 17:05:36,064 - mmcls - INFO - Epoch [25][1500/5005] lr: 1.000e-01, eta: 9:15:51, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7475 2023-01-02 17:05:44,838 - mmcls - INFO - Epoch [25][1600/5005] lr: 1.000e-01, eta: 9:15:42, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7207 2023-01-02 17:05:53,403 - mmcls - INFO - Epoch [25][1700/5005] lr: 1.000e-01, eta: 9:15:32, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7547 2023-01-02 17:06:02,182 - mmcls - INFO - Epoch [25][1800/5005] lr: 1.000e-01, eta: 9:15:23, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7615 2023-01-02 17:06:10,797 - mmcls - INFO - Epoch [25][1900/5005] lr: 1.000e-01, eta: 9:15:14, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.7529 2023-01-02 17:06:19,623 - mmcls - INFO - Epoch [25][2000/5005] lr: 1.000e-01, eta: 9:15:05, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.7852 2023-01-02 17:06:28,460 - mmcls - INFO - Epoch [25][2100/5005] lr: 1.000e-01, eta: 9:14:57, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.7766 2023-01-02 17:06:37,422 - mmcls - INFO - Epoch [25][2200/5005] lr: 1.000e-01, eta: 9:14:48, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7956 2023-01-02 17:06:46,099 - mmcls - INFO - Epoch [25][2300/5005] lr: 1.000e-01, eta: 9:14:39, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.7900 2023-01-02 17:06:54,971 - mmcls - INFO - Epoch [25][2400/5005] lr: 1.000e-01, eta: 9:14:31, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.7651 2023-01-02 17:07:03,996 - mmcls - INFO - Epoch [25][2500/5005] lr: 1.000e-01, eta: 9:14:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7417 2023-01-02 17:07:12,760 - mmcls - INFO - Epoch [25][2600/5005] lr: 1.000e-01, eta: 9:14:14, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7426 2023-01-02 17:07:21,696 - mmcls - INFO - Epoch [25][2700/5005] lr: 1.000e-01, eta: 9:14:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7448 2023-01-02 17:07:30,442 - mmcls - INFO - Epoch [25][2800/5005] lr: 1.000e-01, eta: 9:13:56, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7657 2023-01-02 17:07:39,000 - mmcls - INFO - Epoch [25][2900/5005] lr: 1.000e-01, eta: 9:13:47, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.7641 2023-01-02 17:07:47,587 - mmcls - INFO - Epoch [25][3000/5005] lr: 1.000e-01, eta: 9:13:37, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.7847 2023-01-02 17:07:56,296 - mmcls - INFO - Epoch [25][3100/5005] lr: 1.000e-01, eta: 9:13:28, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.7492 2023-01-02 17:08:05,250 - mmcls - INFO - Epoch [25][3200/5005] lr: 1.000e-01, eta: 9:13:20, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.7494 2023-01-02 17:08:13,850 - mmcls - INFO - Epoch [25][3300/5005] lr: 1.000e-01, eta: 9:13:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7394 2023-01-02 17:08:22,477 - mmcls - INFO - Epoch [25][3400/5005] lr: 1.000e-01, eta: 9:13:01, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7637 2023-01-02 17:08:31,019 - mmcls - INFO - Epoch [25][3500/5005] lr: 1.000e-01, eta: 9:12:51, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.7408 2023-01-02 17:08:39,693 - mmcls - INFO - Epoch [25][3600/5005] lr: 1.000e-01, eta: 9:12:42, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.7334 2023-01-02 17:08:48,573 - mmcls - INFO - Epoch [25][3700/5005] lr: 1.000e-01, eta: 9:12:34, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7633 2023-01-02 17:08:57,271 - mmcls - INFO - Epoch [25][3800/5005] lr: 1.000e-01, eta: 9:12:24, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7848 2023-01-02 17:09:06,185 - mmcls - INFO - Epoch [25][3900/5005] lr: 1.000e-01, eta: 9:12:16, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7429 2023-01-02 17:09:14,898 - mmcls - INFO - Epoch [25][4000/5005] lr: 1.000e-01, eta: 9:12:07, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7833 2023-01-02 17:09:23,787 - mmcls - INFO - Epoch [25][4100/5005] lr: 1.000e-01, eta: 9:11:58, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7964 2023-01-02 17:09:32,460 - mmcls - INFO - Epoch [25][4200/5005] lr: 1.000e-01, eta: 9:11:49, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7809 2023-01-02 17:09:41,367 - mmcls - INFO - Epoch [25][4300/5005] lr: 1.000e-01, eta: 9:11:41, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7529 2023-01-02 17:09:50,253 - mmcls - INFO - Epoch [25][4400/5005] lr: 1.000e-01, eta: 9:11:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7554 2023-01-02 17:09:59,105 - mmcls - INFO - Epoch [25][4500/5005] lr: 1.000e-01, eta: 9:11:23, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7613 2023-01-02 17:10:08,084 - mmcls - INFO - Epoch [25][4600/5005] lr: 1.000e-01, eta: 9:11:15, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.7520 2023-01-02 17:10:16,701 - mmcls - INFO - Epoch [25][4700/5005] lr: 1.000e-01, eta: 9:11:06, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.7774 2023-01-02 17:10:25,180 - mmcls - INFO - Epoch [25][4800/5005] lr: 1.000e-01, eta: 9:10:56, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.7808 2023-01-02 17:10:34,177 - mmcls - INFO - Epoch [25][4900/5005] lr: 1.000e-01, eta: 9:10:48, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7444 2023-01-02 17:10:42,704 - mmcls - INFO - Epoch [25][5000/5005] lr: 1.000e-01, eta: 9:10:38, time: 0.085, data_time: 0.022, memory: 1125, loss: 2.7700 2023-01-02 17:11:05,612 - mmcls - INFO - Epoch(val) [25][196] accuracy_top-1: 46.8820, accuracy_top-5: 71.5980 2023-01-02 17:11:16,682 - mmcls - INFO - Epoch [26][100/5005] lr: 1.000e-01, eta: 9:10:34, time: 0.111, data_time: 0.036, memory: 1125, loss: 2.7039 2023-01-02 17:11:25,394 - mmcls - INFO - Epoch [26][200/5005] lr: 1.000e-01, eta: 9:10:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7221 2023-01-02 17:11:34,368 - mmcls - INFO - Epoch [26][300/5005] lr: 1.000e-01, eta: 9:10:17, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7090 2023-01-02 17:11:43,155 - mmcls - INFO - Epoch [26][400/5005] lr: 1.000e-01, eta: 9:10:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7192 2023-01-02 17:11:51,813 - mmcls - INFO - Epoch [26][500/5005] lr: 1.000e-01, eta: 9:09:59, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7009 2023-01-02 17:12:00,533 - mmcls - INFO - Epoch [26][600/5005] lr: 1.000e-01, eta: 9:09:50, time: 0.087, data_time: 0.041, memory: 1125, loss: 2.7057 2023-01-02 17:12:09,601 - mmcls - INFO - Epoch [26][700/5005] lr: 1.000e-01, eta: 9:09:42, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.7428 2023-01-02 17:12:18,206 - mmcls - INFO - Epoch [26][800/5005] lr: 1.000e-01, eta: 9:09:32, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7324 2023-01-02 17:12:27,048 - mmcls - INFO - Epoch [26][900/5005] lr: 1.000e-01, eta: 9:09:24, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.7224 2023-01-02 17:12:35,854 - mmcls - INFO - Epoch [26][1000/5005] lr: 1.000e-01, eta: 9:09:15, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7656 2023-01-02 17:12:44,365 - mmcls - INFO - Epoch [26][1100/5005] lr: 1.000e-01, eta: 9:09:05, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7757 2023-01-02 17:12:53,166 - mmcls - INFO - Epoch [26][1200/5005] lr: 1.000e-01, eta: 9:08:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7314 2023-01-02 17:13:01,719 - mmcls - INFO - Epoch [26][1300/5005] lr: 1.000e-01, eta: 9:08:47, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7461 2023-01-02 17:13:10,358 - mmcls - INFO - Epoch [26][1400/5005] lr: 1.000e-01, eta: 9:08:38, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7227 2023-01-02 17:13:19,162 - mmcls - INFO - Epoch [26][1500/5005] lr: 1.000e-01, eta: 9:08:29, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7536 2023-01-02 17:13:28,077 - mmcls - INFO - Epoch [26][1600/5005] lr: 1.000e-01, eta: 9:08:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7508 2023-01-02 17:13:36,680 - mmcls - INFO - Epoch [26][1700/5005] lr: 1.000e-01, eta: 9:08:11, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7450 2023-01-02 17:13:45,582 - mmcls - INFO - Epoch [26][1800/5005] lr: 1.000e-01, eta: 9:08:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7017 2023-01-02 17:13:54,208 - mmcls - INFO - Epoch [26][1900/5005] lr: 1.000e-01, eta: 9:07:53, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.7210 2023-01-02 17:14:02,887 - mmcls - INFO - Epoch [26][2000/5005] lr: 1.000e-01, eta: 9:07:44, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7324 2023-01-02 17:14:11,665 - mmcls - INFO - Epoch [26][2100/5005] lr: 1.000e-01, eta: 9:07:35, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7330 2023-01-02 17:14:20,185 - mmcls - INFO - Epoch [26][2200/5005] lr: 1.000e-01, eta: 9:07:26, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.7539 2023-01-02 17:14:29,136 - mmcls - INFO - Epoch [26][2300/5005] lr: 1.000e-01, eta: 9:07:17, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.7489 2023-01-02 17:14:37,506 - mmcls - INFO - Epoch [26][2400/5005] lr: 1.000e-01, eta: 9:07:07, time: 0.084, data_time: 0.014, memory: 1125, loss: 2.7745 2023-01-02 17:14:46,311 - mmcls - INFO - Epoch [26][2500/5005] lr: 1.000e-01, eta: 9:06:58, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7411 2023-01-02 17:14:55,105 - mmcls - INFO - Epoch [26][2600/5005] lr: 1.000e-01, eta: 9:06:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7634 2023-01-02 17:15:03,803 - mmcls - INFO - Epoch [26][2700/5005] lr: 1.000e-01, eta: 9:06:40, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.7524 2023-01-02 17:15:12,534 - mmcls - INFO - Epoch [26][2800/5005] lr: 1.000e-01, eta: 9:06:31, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7714 2023-01-02 17:15:21,116 - mmcls - INFO - Epoch [26][2900/5005] lr: 1.000e-01, eta: 9:06:22, time: 0.086, data_time: 0.031, memory: 1125, loss: 2.7548 2023-01-02 17:15:30,115 - mmcls - INFO - Epoch [26][3000/5005] lr: 1.000e-01, eta: 9:06:14, time: 0.090, data_time: 0.023, memory: 1125, loss: 2.7475 2023-01-02 17:15:38,869 - mmcls - INFO - Epoch [26][3100/5005] lr: 1.000e-01, eta: 9:06:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7752 2023-01-02 17:15:47,445 - mmcls - INFO - Epoch [26][3200/5005] lr: 1.000e-01, eta: 9:05:55, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7656 2023-01-02 17:15:56,320 - mmcls - INFO - Epoch [26][3300/5005] lr: 1.000e-01, eta: 9:05:47, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7713 2023-01-02 17:16:04,896 - mmcls - INFO - Epoch [26][3400/5005] lr: 1.000e-01, eta: 9:05:37, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7271 2023-01-02 17:16:13,397 - mmcls - INFO - Epoch [26][3500/5005] lr: 1.000e-01, eta: 9:05:28, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.7338 2023-01-02 17:16:22,249 - mmcls - INFO - Epoch [26][3600/5005] lr: 1.000e-01, eta: 9:05:19, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7590 2023-01-02 17:16:31,027 - mmcls - INFO - Epoch [26][3700/5005] lr: 1.000e-01, eta: 9:05:10, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.7817 2023-01-02 17:16:39,908 - mmcls - INFO - Epoch [26][3800/5005] lr: 1.000e-01, eta: 9:05:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7806 2023-01-02 17:16:48,655 - mmcls - INFO - Epoch [26][3900/5005] lr: 1.000e-01, eta: 9:04:53, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7492 2023-01-02 17:16:57,418 - mmcls - INFO - Epoch [26][4000/5005] lr: 1.000e-01, eta: 9:04:44, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.7744 2023-01-02 17:17:05,861 - mmcls - INFO - Epoch [26][4100/5005] lr: 1.000e-01, eta: 9:04:34, time: 0.084, data_time: 0.012, memory: 1125, loss: 2.7942 2023-01-02 17:17:14,697 - mmcls - INFO - Epoch [26][4200/5005] lr: 1.000e-01, eta: 9:04:25, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7880 2023-01-02 17:17:23,202 - mmcls - INFO - Epoch [26][4300/5005] lr: 1.000e-01, eta: 9:04:15, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7588 2023-01-02 17:17:32,016 - mmcls - INFO - Epoch [26][4400/5005] lr: 1.000e-01, eta: 9:04:07, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7864 2023-01-02 17:17:40,862 - mmcls - INFO - Epoch [26][4500/5005] lr: 1.000e-01, eta: 9:03:58, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.7655 2023-01-02 17:17:49,795 - mmcls - INFO - Epoch [26][4600/5005] lr: 1.000e-01, eta: 9:03:50, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7460 2023-01-02 17:17:58,723 - mmcls - INFO - Epoch [26][4700/5005] lr: 1.000e-01, eta: 9:03:41, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7828 2023-01-02 17:18:07,609 - mmcls - INFO - Epoch [26][4800/5005] lr: 1.000e-01, eta: 9:03:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7800 2023-01-02 17:18:16,286 - mmcls - INFO - Epoch [26][4900/5005] lr: 1.000e-01, eta: 9:03:23, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7294 2023-01-02 17:18:25,133 - mmcls - INFO - Epoch [26][5000/5005] lr: 1.000e-01, eta: 9:03:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7590 2023-01-02 17:18:47,854 - mmcls - INFO - Epoch(val) [26][196] accuracy_top-1: 47.6060, accuracy_top-5: 72.2680 2023-01-02 17:18:59,039 - mmcls - INFO - Epoch [27][100/5005] lr: 1.000e-01, eta: 9:03:11, time: 0.112, data_time: 0.035, memory: 1125, loss: 2.6789 2023-01-02 17:19:07,607 - mmcls - INFO - Epoch [27][200/5005] lr: 1.000e-01, eta: 9:03:01, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.6783 2023-01-02 17:19:16,432 - mmcls - INFO - Epoch [27][300/5005] lr: 1.000e-01, eta: 9:02:53, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7165 2023-01-02 17:19:25,353 - mmcls - INFO - Epoch [27][400/5005] lr: 1.000e-01, eta: 9:02:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7196 2023-01-02 17:19:33,987 - mmcls - INFO - Epoch [27][500/5005] lr: 1.000e-01, eta: 9:02:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.6999 2023-01-02 17:19:43,140 - mmcls - INFO - Epoch [27][600/5005] lr: 1.000e-01, eta: 9:02:27, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.6883 2023-01-02 17:19:51,769 - mmcls - INFO - Epoch [27][700/5005] lr: 1.000e-01, eta: 9:02:18, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7368 2023-01-02 17:20:00,650 - mmcls - INFO - Epoch [27][800/5005] lr: 1.000e-01, eta: 9:02:09, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.6997 2023-01-02 17:20:09,821 - mmcls - INFO - Epoch [27][900/5005] lr: 1.000e-01, eta: 9:02:02, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.7469 2023-01-02 17:20:18,477 - mmcls - INFO - Epoch [27][1000/5005] lr: 1.000e-01, eta: 9:01:52, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.7228 2023-01-02 17:20:27,305 - mmcls - INFO - Epoch [27][1100/5005] lr: 1.000e-01, eta: 9:01:44, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.7277 2023-01-02 17:20:35,920 - mmcls - INFO - Epoch [27][1200/5005] lr: 1.000e-01, eta: 9:01:34, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7226 2023-01-02 17:20:44,708 - mmcls - INFO - Epoch [27][1300/5005] lr: 1.000e-01, eta: 9:01:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7017 2023-01-02 17:20:53,333 - mmcls - INFO - Epoch [27][1400/5005] lr: 1.000e-01, eta: 9:01:16, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.7282 2023-01-02 17:21:02,038 - mmcls - INFO - Epoch [27][1500/5005] lr: 1.000e-01, eta: 9:01:07, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.7536 2023-01-02 17:21:11,020 - mmcls - INFO - Epoch [27][1600/5005] lr: 1.000e-01, eta: 9:00:59, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7609 2023-01-02 17:21:19,761 - mmcls - INFO - Epoch [27][1700/5005] lr: 1.000e-01, eta: 9:00:50, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7746 2023-01-02 17:21:28,322 - mmcls - INFO - Epoch [27][1800/5005] lr: 1.000e-01, eta: 9:00:40, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7629 2023-01-02 17:21:36,855 - mmcls - INFO - Epoch [27][1900/5005] lr: 1.000e-01, eta: 9:00:31, time: 0.085, data_time: 0.027, memory: 1125, loss: 2.7557 2023-01-02 17:21:45,479 - mmcls - INFO - Epoch [27][2000/5005] lr: 1.000e-01, eta: 9:00:21, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.7755 2023-01-02 17:21:54,120 - mmcls - INFO - Epoch [27][2100/5005] lr: 1.000e-01, eta: 9:00:12, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7660 2023-01-02 17:22:03,187 - mmcls - INFO - Epoch [27][2200/5005] lr: 1.000e-01, eta: 9:00:04, time: 0.091, data_time: 0.031, memory: 1125, loss: 2.7295 2023-01-02 17:22:12,204 - mmcls - INFO - Epoch [27][2300/5005] lr: 1.000e-01, eta: 8:59:56, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7710 2023-01-02 17:22:20,826 - mmcls - INFO - Epoch [27][2400/5005] lr: 1.000e-01, eta: 8:59:47, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.7556 2023-01-02 17:22:29,583 - mmcls - INFO - Epoch [27][2500/5005] lr: 1.000e-01, eta: 8:59:38, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7576 2023-01-02 17:22:38,393 - mmcls - INFO - Epoch [27][2600/5005] lr: 1.000e-01, eta: 8:59:29, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.7386 2023-01-02 17:22:47,145 - mmcls - INFO - Epoch [27][2700/5005] lr: 1.000e-01, eta: 8:59:20, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7638 2023-01-02 17:22:55,989 - mmcls - INFO - Epoch [27][2800/5005] lr: 1.000e-01, eta: 8:59:11, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7459 2023-01-02 17:23:04,762 - mmcls - INFO - Epoch [27][2900/5005] lr: 1.000e-01, eta: 8:59:02, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7402 2023-01-02 17:23:13,832 - mmcls - INFO - Epoch [27][3000/5005] lr: 1.000e-01, eta: 8:58:54, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7439 2023-01-02 17:23:22,340 - mmcls - INFO - Epoch [27][3100/5005] lr: 1.000e-01, eta: 8:58:45, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7318 2023-01-02 17:23:30,945 - mmcls - INFO - Epoch [27][3200/5005] lr: 1.000e-01, eta: 8:58:35, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7613 2023-01-02 17:23:39,650 - mmcls - INFO - Epoch [27][3300/5005] lr: 1.000e-01, eta: 8:58:26, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7503 2023-01-02 17:23:48,405 - mmcls - INFO - Epoch [27][3400/5005] lr: 1.000e-01, eta: 8:58:17, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.7451 2023-01-02 17:23:57,020 - mmcls - INFO - Epoch [27][3500/5005] lr: 1.000e-01, eta: 8:58:08, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7799 2023-01-02 17:24:05,574 - mmcls - INFO - Epoch [27][3600/5005] lr: 1.000e-01, eta: 8:57:59, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7804 2023-01-02 17:24:14,403 - mmcls - INFO - Epoch [27][3700/5005] lr: 1.000e-01, eta: 8:57:50, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.7396 2023-01-02 17:24:23,166 - mmcls - INFO - Epoch [27][3800/5005] lr: 1.000e-01, eta: 8:57:41, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7596 2023-01-02 17:24:31,966 - mmcls - INFO - Epoch [27][3900/5005] lr: 1.000e-01, eta: 8:57:32, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7467 2023-01-02 17:24:40,821 - mmcls - INFO - Epoch [27][4000/5005] lr: 1.000e-01, eta: 8:57:24, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7502 2023-01-02 17:24:49,494 - mmcls - INFO - Epoch [27][4100/5005] lr: 1.000e-01, eta: 8:57:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.7663 2023-01-02 17:24:58,238 - mmcls - INFO - Epoch [27][4200/5005] lr: 1.000e-01, eta: 8:57:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7602 2023-01-02 17:25:06,950 - mmcls - INFO - Epoch [27][4300/5005] lr: 1.000e-01, eta: 8:56:56, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7490 2023-01-02 17:25:15,739 - mmcls - INFO - Epoch [27][4400/5005] lr: 1.000e-01, eta: 8:56:47, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7623 2023-01-02 17:25:24,526 - mmcls - INFO - Epoch [27][4500/5005] lr: 1.000e-01, eta: 8:56:39, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7656 2023-01-02 17:25:33,328 - mmcls - INFO - Epoch [27][4600/5005] lr: 1.000e-01, eta: 8:56:30, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7590 2023-01-02 17:25:42,254 - mmcls - INFO - Epoch [27][4700/5005] lr: 1.000e-01, eta: 8:56:21, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.7486 2023-01-02 17:25:50,898 - mmcls - INFO - Epoch [27][4800/5005] lr: 1.000e-01, eta: 8:56:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7750 2023-01-02 17:25:59,758 - mmcls - INFO - Epoch [27][4900/5005] lr: 1.000e-01, eta: 8:56:04, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7674 2023-01-02 17:26:08,913 - mmcls - INFO - Epoch [27][5000/5005] lr: 1.000e-01, eta: 8:55:56, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.7601 2023-01-02 17:26:31,670 - mmcls - INFO - Epoch(val) [27][196] accuracy_top-1: 46.7160, accuracy_top-5: 71.7220 2023-01-02 17:26:42,585 - mmcls - INFO - Epoch [28][100/5005] lr: 1.000e-01, eta: 8:55:51, time: 0.109, data_time: 0.058, memory: 1125, loss: 2.6768 2023-01-02 17:26:51,173 - mmcls - INFO - Epoch [28][200/5005] lr: 1.000e-01, eta: 8:55:42, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.6970 2023-01-02 17:26:59,655 - mmcls - INFO - Epoch [28][300/5005] lr: 1.000e-01, eta: 8:55:32, time: 0.085, data_time: 0.000, memory: 1125, loss: 2.7182 2023-01-02 17:27:08,336 - mmcls - INFO - Epoch [28][400/5005] lr: 1.000e-01, eta: 8:55:23, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.7071 2023-01-02 17:27:17,037 - mmcls - INFO - Epoch [28][500/5005] lr: 1.000e-01, eta: 8:55:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7312 2023-01-02 17:27:25,923 - mmcls - INFO - Epoch [28][600/5005] lr: 1.000e-01, eta: 8:55:05, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7054 2023-01-02 17:27:34,738 - mmcls - INFO - Epoch [28][700/5005] lr: 1.000e-01, eta: 8:54:56, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7404 2023-01-02 17:27:43,535 - mmcls - INFO - Epoch [28][800/5005] lr: 1.000e-01, eta: 8:54:47, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7538 2023-01-02 17:27:51,933 - mmcls - INFO - Epoch [28][900/5005] lr: 1.000e-01, eta: 8:54:38, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.7431 2023-01-02 17:28:00,803 - mmcls - INFO - Epoch [28][1000/5005] lr: 1.000e-01, eta: 8:54:29, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.7410 2023-01-02 17:28:09,833 - mmcls - INFO - Epoch [28][1100/5005] lr: 1.000e-01, eta: 8:54:21, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7576 2023-01-02 17:28:18,815 - mmcls - INFO - Epoch [28][1200/5005] lr: 1.000e-01, eta: 8:54:12, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.7419 2023-01-02 17:28:27,876 - mmcls - INFO - Epoch [28][1300/5005] lr: 1.000e-01, eta: 8:54:04, time: 0.091, data_time: 0.015, memory: 1125, loss: 2.7500 2023-01-02 17:28:36,520 - mmcls - INFO - Epoch [28][1400/5005] lr: 1.000e-01, eta: 8:53:55, time: 0.087, data_time: 0.028, memory: 1125, loss: 2.7465 2023-01-02 17:28:45,136 - mmcls - INFO - Epoch [28][1500/5005] lr: 1.000e-01, eta: 8:53:46, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.7270 2023-01-02 17:28:54,380 - mmcls - INFO - Epoch [28][1600/5005] lr: 1.000e-01, eta: 8:53:38, time: 0.092, data_time: 0.007, memory: 1125, loss: 2.7400 2023-01-02 17:29:03,274 - mmcls - INFO - Epoch [28][1700/5005] lr: 1.000e-01, eta: 8:53:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7522 2023-01-02 17:29:12,056 - mmcls - INFO - Epoch [28][1800/5005] lr: 1.000e-01, eta: 8:53:21, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.7326 2023-01-02 17:29:21,159 - mmcls - INFO - Epoch [28][1900/5005] lr: 1.000e-01, eta: 8:53:13, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.7471 2023-01-02 17:29:30,016 - mmcls - INFO - Epoch [28][2000/5005] lr: 1.000e-01, eta: 8:53:04, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7591 2023-01-02 17:29:38,789 - mmcls - INFO - Epoch [28][2100/5005] lr: 1.000e-01, eta: 8:52:55, time: 0.088, data_time: 0.034, memory: 1125, loss: 2.7524 2023-01-02 17:29:47,425 - mmcls - INFO - Epoch [28][2200/5005] lr: 1.000e-01, eta: 8:52:46, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7494 2023-01-02 17:29:56,247 - mmcls - INFO - Epoch [28][2300/5005] lr: 1.000e-01, eta: 8:52:37, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7241 2023-01-02 17:30:05,187 - mmcls - INFO - Epoch [28][2400/5005] lr: 1.000e-01, eta: 8:52:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7607 2023-01-02 17:30:14,057 - mmcls - INFO - Epoch [28][2500/5005] lr: 1.000e-01, eta: 8:52:20, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7283 2023-01-02 17:30:22,691 - mmcls - INFO - Epoch [28][2600/5005] lr: 1.000e-01, eta: 8:52:11, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.7226 2023-01-02 17:30:32,050 - mmcls - INFO - Epoch [28][2700/5005] lr: 1.000e-01, eta: 8:52:04, time: 0.094, data_time: 0.001, memory: 1125, loss: 2.7384 2023-01-02 17:30:40,640 - mmcls - INFO - Epoch [28][2800/5005] lr: 1.000e-01, eta: 8:51:54, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7495 2023-01-02 17:30:49,659 - mmcls - INFO - Epoch [28][2900/5005] lr: 1.000e-01, eta: 8:51:46, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.7258 2023-01-02 17:30:58,256 - mmcls - INFO - Epoch [28][3000/5005] lr: 1.000e-01, eta: 8:51:37, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.7826 2023-01-02 17:31:06,806 - mmcls - INFO - Epoch [28][3100/5005] lr: 1.000e-01, eta: 8:51:27, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7716 2023-01-02 17:31:15,518 - mmcls - INFO - Epoch [28][3200/5005] lr: 1.000e-01, eta: 8:51:18, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7560 2023-01-02 17:31:24,046 - mmcls - INFO - Epoch [28][3300/5005] lr: 1.000e-01, eta: 8:51:09, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.7356 2023-01-02 17:31:32,629 - mmcls - INFO - Epoch [28][3400/5005] lr: 1.000e-01, eta: 8:50:59, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7275 2023-01-02 17:31:41,249 - mmcls - INFO - Epoch [28][3500/5005] lr: 1.000e-01, eta: 8:50:50, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7596 2023-01-02 17:31:49,986 - mmcls - INFO - Epoch [28][3600/5005] lr: 1.000e-01, eta: 8:50:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7689 2023-01-02 17:31:58,822 - mmcls - INFO - Epoch [28][3700/5005] lr: 1.000e-01, eta: 8:50:32, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.7575 2023-01-02 17:32:07,641 - mmcls - INFO - Epoch [28][3800/5005] lr: 1.000e-01, eta: 8:50:24, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7607 2023-01-02 17:32:16,299 - mmcls - INFO - Epoch [28][3900/5005] lr: 1.000e-01, eta: 8:50:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.7408 2023-01-02 17:32:25,312 - mmcls - INFO - Epoch [28][4000/5005] lr: 1.000e-01, eta: 8:50:06, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7570 2023-01-02 17:32:33,696 - mmcls - INFO - Epoch [28][4100/5005] lr: 1.000e-01, eta: 8:49:56, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.7453 2023-01-02 17:32:42,374 - mmcls - INFO - Epoch [28][4200/5005] lr: 1.000e-01, eta: 8:49:47, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7780 2023-01-02 17:32:51,019 - mmcls - INFO - Epoch [28][4300/5005] lr: 1.000e-01, eta: 8:49:38, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7489 2023-01-02 17:32:59,717 - mmcls - INFO - Epoch [28][4400/5005] lr: 1.000e-01, eta: 8:49:29, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.7445 2023-01-02 17:33:08,222 - mmcls - INFO - Epoch [28][4500/5005] lr: 1.000e-01, eta: 8:49:19, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7774 2023-01-02 17:33:16,654 - mmcls - INFO - Epoch [28][4600/5005] lr: 1.000e-01, eta: 8:49:09, time: 0.084, data_time: 0.014, memory: 1125, loss: 2.7723 2023-01-02 17:33:25,483 - mmcls - INFO - Epoch [28][4700/5005] lr: 1.000e-01, eta: 8:49:01, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.7714 2023-01-02 17:33:34,155 - mmcls - INFO - Epoch [28][4800/5005] lr: 1.000e-01, eta: 8:48:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7557 2023-01-02 17:33:42,813 - mmcls - INFO - Epoch [28][4900/5005] lr: 1.000e-01, eta: 8:48:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7771 2023-01-02 17:33:51,722 - mmcls - INFO - Epoch [28][5000/5005] lr: 1.000e-01, eta: 8:48:34, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.7566 2023-01-02 17:34:14,322 - mmcls - INFO - Epoch(val) [28][196] accuracy_top-1: 46.8700, accuracy_top-5: 71.5360 2023-01-02 17:34:25,416 - mmcls - INFO - Epoch [29][100/5005] lr: 1.000e-01, eta: 8:48:29, time: 0.111, data_time: 0.060, memory: 1125, loss: 2.6934 2023-01-02 17:34:34,223 - mmcls - INFO - Epoch [29][200/5005] lr: 1.000e-01, eta: 8:48:21, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7018 2023-01-02 17:34:43,089 - mmcls - INFO - Epoch [29][300/5005] lr: 1.000e-01, eta: 8:48:12, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7138 2023-01-02 17:34:51,899 - mmcls - INFO - Epoch [29][400/5005] lr: 1.000e-01, eta: 8:48:03, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.7241 2023-01-02 17:35:00,612 - mmcls - INFO - Epoch [29][500/5005] lr: 1.000e-01, eta: 8:47:54, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.7043 2023-01-02 17:35:09,256 - mmcls - INFO - Epoch [29][600/5005] lr: 1.000e-01, eta: 8:47:45, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.6991 2023-01-02 17:35:17,899 - mmcls - INFO - Epoch [29][700/5005] lr: 1.000e-01, eta: 8:47:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7458 2023-01-02 17:35:26,702 - mmcls - INFO - Epoch [29][800/5005] lr: 1.000e-01, eta: 8:47:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7183 2023-01-02 17:35:35,525 - mmcls - INFO - Epoch [29][900/5005] lr: 1.000e-01, eta: 8:47:18, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.7089 2023-01-02 17:35:44,255 - mmcls - INFO - Epoch [29][1000/5005] lr: 1.000e-01, eta: 8:47:09, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7213 2023-01-02 17:35:52,983 - mmcls - INFO - Epoch [29][1100/5005] lr: 1.000e-01, eta: 8:47:00, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.7538 2023-01-02 17:36:01,719 - mmcls - INFO - Epoch [29][1200/5005] lr: 1.000e-01, eta: 8:46:51, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7252 2023-01-02 17:36:10,573 - mmcls - INFO - Epoch [29][1300/5005] lr: 1.000e-01, eta: 8:46:43, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7415 2023-01-02 17:36:19,296 - mmcls - INFO - Epoch [29][1400/5005] lr: 1.000e-01, eta: 8:46:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7019 2023-01-02 17:36:28,348 - mmcls - INFO - Epoch [29][1500/5005] lr: 1.000e-01, eta: 8:46:25, time: 0.090, data_time: 0.030, memory: 1125, loss: 2.7214 2023-01-02 17:36:37,132 - mmcls - INFO - Epoch [29][1600/5005] lr: 1.000e-01, eta: 8:46:17, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7333 2023-01-02 17:36:46,044 - mmcls - INFO - Epoch [29][1700/5005] lr: 1.000e-01, eta: 8:46:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7382 2023-01-02 17:36:54,596 - mmcls - INFO - Epoch [29][1800/5005] lr: 1.000e-01, eta: 8:45:59, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7244 2023-01-02 17:37:03,150 - mmcls - INFO - Epoch [29][1900/5005] lr: 1.000e-01, eta: 8:45:49, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.7241 2023-01-02 17:37:12,067 - mmcls - INFO - Epoch [29][2000/5005] lr: 1.000e-01, eta: 8:45:41, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.7238 2023-01-02 17:37:20,947 - mmcls - INFO - Epoch [29][2100/5005] lr: 1.000e-01, eta: 8:45:32, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.7298 2023-01-02 17:37:29,721 - mmcls - INFO - Epoch [29][2200/5005] lr: 1.000e-01, eta: 8:45:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7351 2023-01-02 17:37:38,543 - mmcls - INFO - Epoch [29][2300/5005] lr: 1.000e-01, eta: 8:45:14, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7379 2023-01-02 17:37:47,203 - mmcls - INFO - Epoch [29][2400/5005] lr: 1.000e-01, eta: 8:45:05, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7632 2023-01-02 17:37:55,995 - mmcls - INFO - Epoch [29][2500/5005] lr: 1.000e-01, eta: 8:44:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7415 2023-01-02 17:38:04,638 - mmcls - INFO - Epoch [29][2600/5005] lr: 1.000e-01, eta: 8:44:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7327 2023-01-02 17:38:13,091 - mmcls - INFO - Epoch [29][2700/5005] lr: 1.000e-01, eta: 8:44:38, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7457 2023-01-02 17:38:21,766 - mmcls - INFO - Epoch [29][2800/5005] lr: 1.000e-01, eta: 8:44:28, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.7503 2023-01-02 17:38:30,513 - mmcls - INFO - Epoch [29][2900/5005] lr: 1.000e-01, eta: 8:44:19, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7442 2023-01-02 17:38:39,107 - mmcls - INFO - Epoch [29][3000/5005] lr: 1.000e-01, eta: 8:44:10, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7464 2023-01-02 17:38:47,835 - mmcls - INFO - Epoch [29][3100/5005] lr: 1.000e-01, eta: 8:44:01, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7507 2023-01-02 17:38:56,555 - mmcls - INFO - Epoch [29][3200/5005] lr: 1.000e-01, eta: 8:43:52, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7945 2023-01-02 17:39:05,116 - mmcls - INFO - Epoch [29][3300/5005] lr: 1.000e-01, eta: 8:43:43, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.7176 2023-01-02 17:39:13,849 - mmcls - INFO - Epoch [29][3400/5005] lr: 1.000e-01, eta: 8:43:34, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7623 2023-01-02 17:39:22,380 - mmcls - INFO - Epoch [29][3500/5005] lr: 1.000e-01, eta: 8:43:24, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.7663 2023-01-02 17:39:31,018 - mmcls - INFO - Epoch [29][3600/5005] lr: 1.000e-01, eta: 8:43:15, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7172 2023-01-02 17:39:40,263 - mmcls - INFO - Epoch [29][3700/5005] lr: 1.000e-01, eta: 8:43:07, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.7354 2023-01-02 17:39:48,642 - mmcls - INFO - Epoch [29][3800/5005] lr: 1.000e-01, eta: 8:42:58, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.7483 2023-01-02 17:39:57,436 - mmcls - INFO - Epoch [29][3900/5005] lr: 1.000e-01, eta: 8:42:49, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.7464 2023-01-02 17:40:06,169 - mmcls - INFO - Epoch [29][4000/5005] lr: 1.000e-01, eta: 8:42:40, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7859 2023-01-02 17:40:14,836 - mmcls - INFO - Epoch [29][4100/5005] lr: 1.000e-01, eta: 8:42:31, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.7396 2023-01-02 17:40:23,570 - mmcls - INFO - Epoch [29][4200/5005] lr: 1.000e-01, eta: 8:42:22, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.7610 2023-01-02 17:40:32,017 - mmcls - INFO - Epoch [29][4300/5005] lr: 1.000e-01, eta: 8:42:12, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.7586 2023-01-02 17:40:40,955 - mmcls - INFO - Epoch [29][4400/5005] lr: 1.000e-01, eta: 8:42:04, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.7628 2023-01-02 17:40:49,964 - mmcls - INFO - Epoch [29][4500/5005] lr: 1.000e-01, eta: 8:41:55, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7093 2023-01-02 17:40:58,423 - mmcls - INFO - Epoch [29][4600/5005] lr: 1.000e-01, eta: 8:41:46, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.7423 2023-01-02 17:41:07,259 - mmcls - INFO - Epoch [29][4700/5005] lr: 1.000e-01, eta: 8:41:37, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.7344 2023-01-02 17:41:16,490 - mmcls - INFO - Epoch [29][4800/5005] lr: 1.000e-01, eta: 8:41:29, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.7441 2023-01-02 17:41:25,238 - mmcls - INFO - Epoch [29][4900/5005] lr: 1.000e-01, eta: 8:41:20, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.7309 2023-01-02 17:41:33,767 - mmcls - INFO - Epoch [29][5000/5005] lr: 1.000e-01, eta: 8:41:11, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7415 2023-01-02 17:41:56,650 - mmcls - INFO - Epoch(val) [29][196] accuracy_top-1: 46.5840, accuracy_top-5: 71.5840 2023-01-02 17:42:07,584 - mmcls - INFO - Epoch [30][100/5005] lr: 1.000e-01, eta: 8:41:06, time: 0.109, data_time: 0.043, memory: 1125, loss: 2.6688 2023-01-02 17:42:16,117 - mmcls - INFO - Epoch [30][200/5005] lr: 1.000e-01, eta: 8:40:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.6735 2023-01-02 17:42:24,996 - mmcls - INFO - Epoch [30][300/5005] lr: 1.000e-01, eta: 8:40:48, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.6809 2023-01-02 17:42:33,581 - mmcls - INFO - Epoch [30][400/5005] lr: 1.000e-01, eta: 8:40:38, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.6810 2023-01-02 17:42:42,588 - mmcls - INFO - Epoch [30][500/5005] lr: 1.000e-01, eta: 8:40:30, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7232 2023-01-02 17:42:51,457 - mmcls - INFO - Epoch [30][600/5005] lr: 1.000e-01, eta: 8:40:21, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7288 2023-01-02 17:43:00,062 - mmcls - INFO - Epoch [30][700/5005] lr: 1.000e-01, eta: 8:40:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.6969 2023-01-02 17:43:08,826 - mmcls - INFO - Epoch [30][800/5005] lr: 1.000e-01, eta: 8:40:03, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.7253 2023-01-02 17:43:17,681 - mmcls - INFO - Epoch [30][900/5005] lr: 1.000e-01, eta: 8:39:54, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.7297 2023-01-02 17:43:26,428 - mmcls - INFO - Epoch [30][1000/5005] lr: 1.000e-01, eta: 8:39:46, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7405 2023-01-02 17:43:34,962 - mmcls - INFO - Epoch [30][1100/5005] lr: 1.000e-01, eta: 8:39:36, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7503 2023-01-02 17:43:43,998 - mmcls - INFO - Epoch [30][1200/5005] lr: 1.000e-01, eta: 8:39:28, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7273 2023-01-02 17:43:52,680 - mmcls - INFO - Epoch [30][1300/5005] lr: 1.000e-01, eta: 8:39:19, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7409 2023-01-02 17:44:01,498 - mmcls - INFO - Epoch [30][1400/5005] lr: 1.000e-01, eta: 8:39:10, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7325 2023-01-02 17:44:10,338 - mmcls - INFO - Epoch [30][1500/5005] lr: 1.000e-01, eta: 8:39:01, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7325 2023-01-02 17:44:19,062 - mmcls - INFO - Epoch [30][1600/5005] lr: 1.000e-01, eta: 8:38:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7440 2023-01-02 17:44:27,718 - mmcls - INFO - Epoch [30][1700/5005] lr: 1.000e-01, eta: 8:38:43, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7221 2023-01-02 17:44:36,351 - mmcls - INFO - Epoch [30][1800/5005] lr: 1.000e-01, eta: 8:38:34, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7422 2023-01-02 17:44:44,944 - mmcls - INFO - Epoch [30][1900/5005] lr: 1.000e-01, eta: 8:38:25, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7530 2023-01-02 17:44:53,631 - mmcls - INFO - Epoch [30][2000/5005] lr: 1.000e-01, eta: 8:38:16, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7329 2023-01-02 17:45:02,241 - mmcls - INFO - Epoch [30][2100/5005] lr: 1.000e-01, eta: 8:38:06, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7285 2023-01-02 17:45:10,818 - mmcls - INFO - Epoch [30][2200/5005] lr: 1.000e-01, eta: 8:37:57, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.7356 2023-01-02 17:45:19,841 - mmcls - INFO - Epoch [30][2300/5005] lr: 1.000e-01, eta: 8:37:49, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7488 2023-01-02 17:45:28,435 - mmcls - INFO - Epoch [30][2400/5005] lr: 1.000e-01, eta: 8:37:39, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7392 2023-01-02 17:45:36,952 - mmcls - INFO - Epoch [30][2500/5005] lr: 1.000e-01, eta: 8:37:30, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7307 2023-01-02 17:45:45,740 - mmcls - INFO - Epoch [30][2600/5005] lr: 1.000e-01, eta: 8:37:21, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.7282 2023-01-02 17:45:54,290 - mmcls - INFO - Epoch [30][2700/5005] lr: 1.000e-01, eta: 8:37:12, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7124 2023-01-02 17:46:02,962 - mmcls - INFO - Epoch [30][2800/5005] lr: 1.000e-01, eta: 8:37:03, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.7612 2023-01-02 17:46:11,676 - mmcls - INFO - Epoch [30][2900/5005] lr: 1.000e-01, eta: 8:36:54, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.7756 2023-01-02 17:46:20,688 - mmcls - INFO - Epoch [30][3000/5005] lr: 1.000e-01, eta: 8:36:45, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.7655 2023-01-02 17:46:29,297 - mmcls - INFO - Epoch [30][3100/5005] lr: 1.000e-01, eta: 8:36:36, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.7438 2023-01-02 17:46:38,346 - mmcls - INFO - Epoch [30][3200/5005] lr: 1.000e-01, eta: 8:36:28, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.7240 2023-01-02 17:46:47,132 - mmcls - INFO - Epoch [30][3300/5005] lr: 1.000e-01, eta: 8:36:19, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.7358 2023-01-02 17:46:56,021 - mmcls - INFO - Epoch [30][3400/5005] lr: 1.000e-01, eta: 8:36:10, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7255 2023-01-02 17:47:04,789 - mmcls - INFO - Epoch [30][3500/5005] lr: 1.000e-01, eta: 8:36:02, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.7347 2023-01-02 17:47:13,531 - mmcls - INFO - Epoch [30][3600/5005] lr: 1.000e-01, eta: 8:35:53, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.7208 2023-01-02 17:47:22,211 - mmcls - INFO - Epoch [30][3700/5005] lr: 1.000e-01, eta: 8:35:43, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.7401 2023-01-02 17:47:30,781 - mmcls - INFO - Epoch [30][3800/5005] lr: 1.000e-01, eta: 8:35:34, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.7512 2023-01-02 17:47:39,488 - mmcls - INFO - Epoch [30][3900/5005] lr: 1.000e-01, eta: 8:35:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.7480 2023-01-02 17:47:48,225 - mmcls - INFO - Epoch [30][4000/5005] lr: 1.000e-01, eta: 8:35:16, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.7583 2023-01-02 17:47:57,075 - mmcls - INFO - Epoch [30][4100/5005] lr: 1.000e-01, eta: 8:35:07, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.7553 2023-01-02 17:48:06,034 - mmcls - INFO - Epoch [30][4200/5005] lr: 1.000e-01, eta: 8:34:59, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.7895 2023-01-02 17:48:14,655 - mmcls - INFO - Epoch [30][4300/5005] lr: 1.000e-01, eta: 8:34:50, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7481 2023-01-02 17:48:23,558 - mmcls - INFO - Epoch [30][4400/5005] lr: 1.000e-01, eta: 8:34:41, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7295 2023-01-02 17:48:32,101 - mmcls - INFO - Epoch [30][4500/5005] lr: 1.000e-01, eta: 8:34:32, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.7428 2023-01-02 17:48:40,795 - mmcls - INFO - Epoch [30][4600/5005] lr: 1.000e-01, eta: 8:34:23, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.7354 2023-01-02 17:48:49,525 - mmcls - INFO - Epoch [30][4700/5005] lr: 1.000e-01, eta: 8:34:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7695 2023-01-02 17:48:58,363 - mmcls - INFO - Epoch [30][4800/5005] lr: 1.000e-01, eta: 8:34:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7839 2023-01-02 17:49:07,001 - mmcls - INFO - Epoch [30][4900/5005] lr: 1.000e-01, eta: 8:33:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7489 2023-01-02 17:49:15,461 - mmcls - INFO - Epoch [30][5000/5005] lr: 1.000e-01, eta: 8:33:46, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.7697 2023-01-02 17:49:38,023 - mmcls - INFO - Epoch(val) [30][196] accuracy_top-1: 47.3160, accuracy_top-5: 72.0300 2023-01-02 17:49:49,123 - mmcls - INFO - Epoch [31][100/5005] lr: 1.000e-02, eta: 8:33:41, time: 0.111, data_time: 0.035, memory: 1125, loss: 2.5818 2023-01-02 17:49:57,720 - mmcls - INFO - Epoch [31][200/5005] lr: 1.000e-02, eta: 8:33:32, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.4888 2023-01-02 17:50:06,360 - mmcls - INFO - Epoch [31][300/5005] lr: 1.000e-02, eta: 8:33:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4139 2023-01-02 17:50:15,292 - mmcls - INFO - Epoch [31][400/5005] lr: 1.000e-02, eta: 8:33:14, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4022 2023-01-02 17:50:23,904 - mmcls - INFO - Epoch [31][500/5005] lr: 1.000e-02, eta: 8:33:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4123 2023-01-02 17:50:32,679 - mmcls - INFO - Epoch [31][600/5005] lr: 1.000e-02, eta: 8:32:56, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4339 2023-01-02 17:50:41,290 - mmcls - INFO - Epoch [31][700/5005] lr: 1.000e-02, eta: 8:32:47, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.3671 2023-01-02 17:50:50,186 - mmcls - INFO - Epoch [31][800/5005] lr: 1.000e-02, eta: 8:32:39, time: 0.089, data_time: 0.035, memory: 1125, loss: 2.3817 2023-01-02 17:50:59,065 - mmcls - INFO - Epoch [31][900/5005] lr: 1.000e-02, eta: 8:32:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.3710 2023-01-02 17:51:08,076 - mmcls - INFO - Epoch [31][1000/5005] lr: 1.000e-02, eta: 8:32:22, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.3543 2023-01-02 17:51:16,887 - mmcls - INFO - Epoch [31][1100/5005] lr: 1.000e-02, eta: 8:32:13, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.3347 2023-01-02 17:51:25,713 - mmcls - INFO - Epoch [31][1200/5005] lr: 1.000e-02, eta: 8:32:04, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.3386 2023-01-02 17:51:34,836 - mmcls - INFO - Epoch [31][1300/5005] lr: 1.000e-02, eta: 8:31:56, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.3301 2023-01-02 17:51:43,799 - mmcls - INFO - Epoch [31][1400/5005] lr: 1.000e-02, eta: 8:31:48, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.3304 2023-01-02 17:51:52,627 - mmcls - INFO - Epoch [31][1500/5005] lr: 1.000e-02, eta: 8:31:39, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.3149 2023-01-02 17:52:01,453 - mmcls - INFO - Epoch [31][1600/5005] lr: 1.000e-02, eta: 8:31:30, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.3279 2023-01-02 17:52:10,080 - mmcls - INFO - Epoch [31][1700/5005] lr: 1.000e-02, eta: 8:31:21, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.3223 2023-01-02 17:52:19,157 - mmcls - INFO - Epoch [31][1800/5005] lr: 1.000e-02, eta: 8:31:13, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.3137 2023-01-02 17:52:27,884 - mmcls - INFO - Epoch [31][1900/5005] lr: 1.000e-02, eta: 8:31:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.3230 2023-01-02 17:52:36,552 - mmcls - INFO - Epoch [31][2000/5005] lr: 1.000e-02, eta: 8:30:55, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.3035 2023-01-02 17:52:45,061 - mmcls - INFO - Epoch [31][2100/5005] lr: 1.000e-02, eta: 8:30:45, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.3211 2023-01-02 17:52:53,620 - mmcls - INFO - Epoch [31][2200/5005] lr: 1.000e-02, eta: 8:30:36, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.2944 2023-01-02 17:53:02,522 - mmcls - INFO - Epoch [31][2300/5005] lr: 1.000e-02, eta: 8:30:27, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.3048 2023-01-02 17:53:11,495 - mmcls - INFO - Epoch [31][2400/5005] lr: 1.000e-02, eta: 8:30:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.2837 2023-01-02 17:53:20,334 - mmcls - INFO - Epoch [31][2500/5005] lr: 1.000e-02, eta: 8:30:10, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.2895 2023-01-02 17:53:28,942 - mmcls - INFO - Epoch [31][2600/5005] lr: 1.000e-02, eta: 8:30:01, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.2585 2023-01-02 17:53:37,561 - mmcls - INFO - Epoch [31][2700/5005] lr: 1.000e-02, eta: 8:29:52, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.2543 2023-01-02 17:53:46,267 - mmcls - INFO - Epoch [31][2800/5005] lr: 1.000e-02, eta: 8:29:43, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.2756 2023-01-02 17:53:54,836 - mmcls - INFO - Epoch [31][2900/5005] lr: 1.000e-02, eta: 8:29:33, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.2734 2023-01-02 17:54:03,733 - mmcls - INFO - Epoch [31][3000/5005] lr: 1.000e-02, eta: 8:29:25, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.2619 2023-01-02 17:54:12,292 - mmcls - INFO - Epoch [31][3100/5005] lr: 1.000e-02, eta: 8:29:15, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.2780 2023-01-02 17:54:20,824 - mmcls - INFO - Epoch [31][3200/5005] lr: 1.000e-02, eta: 8:29:06, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.2480 2023-01-02 17:54:29,544 - mmcls - INFO - Epoch [31][3300/5005] lr: 1.000e-02, eta: 8:28:57, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.2660 2023-01-02 17:54:38,773 - mmcls - INFO - Epoch [31][3400/5005] lr: 1.000e-02, eta: 8:28:49, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.2643 2023-01-02 17:54:47,447 - mmcls - INFO - Epoch [31][3500/5005] lr: 1.000e-02, eta: 8:28:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.2765 2023-01-02 17:54:56,176 - mmcls - INFO - Epoch [31][3600/5005] lr: 1.000e-02, eta: 8:28:31, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.2523 2023-01-02 17:55:04,856 - mmcls - INFO - Epoch [31][3700/5005] lr: 1.000e-02, eta: 8:28:22, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.2758 2023-01-02 17:55:13,706 - mmcls - INFO - Epoch [31][3800/5005] lr: 1.000e-02, eta: 8:28:13, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.2534 2023-01-02 17:55:22,575 - mmcls - INFO - Epoch [31][3900/5005] lr: 1.000e-02, eta: 8:28:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.2445 2023-01-02 17:55:31,097 - mmcls - INFO - Epoch [31][4000/5005] lr: 1.000e-02, eta: 8:27:55, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.2589 2023-01-02 17:55:40,195 - mmcls - INFO - Epoch [31][4100/5005] lr: 1.000e-02, eta: 8:27:47, time: 0.091, data_time: 0.000, memory: 1125, loss: 2.2367 2023-01-02 17:55:48,858 - mmcls - INFO - Epoch [31][4200/5005] lr: 1.000e-02, eta: 8:27:38, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.2451 2023-01-02 17:55:57,680 - mmcls - INFO - Epoch [31][4300/5005] lr: 1.000e-02, eta: 8:27:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.2508 2023-01-02 17:56:06,529 - mmcls - INFO - Epoch [31][4400/5005] lr: 1.000e-02, eta: 8:27:21, time: 0.088, data_time: 0.026, memory: 1125, loss: 2.2257 2023-01-02 17:56:15,317 - mmcls - INFO - Epoch [31][4500/5005] lr: 1.000e-02, eta: 8:27:12, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.2705 2023-01-02 17:56:24,129 - mmcls - INFO - Epoch [31][4600/5005] lr: 1.000e-02, eta: 8:27:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.2471 2023-01-02 17:56:32,897 - mmcls - INFO - Epoch [31][4700/5005] lr: 1.000e-02, eta: 8:26:54, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.2241 2023-01-02 17:56:41,774 - mmcls - INFO - Epoch [31][4800/5005] lr: 1.000e-02, eta: 8:26:46, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.2195 2023-01-02 17:56:50,443 - mmcls - INFO - Epoch [31][4900/5005] lr: 1.000e-02, eta: 8:26:37, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.2416 2023-01-02 17:56:59,194 - mmcls - INFO - Epoch [31][5000/5005] lr: 1.000e-02, eta: 8:26:28, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.2529 2023-01-02 17:57:21,931 - mmcls - INFO - Epoch(val) [31][196] accuracy_top-1: 56.4000, accuracy_top-5: 79.0200 2023-01-02 17:57:33,437 - mmcls - INFO - Epoch [32][100/5005] lr: 1.000e-02, eta: 8:26:23, time: 0.115, data_time: 0.035, memory: 1125, loss: 2.2348 2023-01-02 17:57:42,194 - mmcls - INFO - Epoch [32][200/5005] lr: 1.000e-02, eta: 8:26:14, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.2084 2023-01-02 17:57:50,617 - mmcls - INFO - Epoch [32][300/5005] lr: 1.000e-02, eta: 8:26:05, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.1896 2023-01-02 17:57:59,561 - mmcls - INFO - Epoch [32][400/5005] lr: 1.000e-02, eta: 8:25:56, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.2139 2023-01-02 17:58:08,311 - mmcls - INFO - Epoch [32][500/5005] lr: 1.000e-02, eta: 8:25:47, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.2086 2023-01-02 17:58:17,110 - mmcls - INFO - Epoch [32][600/5005] lr: 1.000e-02, eta: 8:25:39, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.2002 2023-01-02 17:58:25,612 - mmcls - INFO - Epoch [32][700/5005] lr: 1.000e-02, eta: 8:25:29, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.2029 2023-01-02 17:58:34,142 - mmcls - INFO - Epoch [32][800/5005] lr: 1.000e-02, eta: 8:25:20, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.2074 2023-01-02 17:58:42,754 - mmcls - INFO - Epoch [32][900/5005] lr: 1.000e-02, eta: 8:25:11, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.1826 2023-01-02 17:58:51,575 - mmcls - INFO - Epoch [32][1000/5005] lr: 1.000e-02, eta: 8:25:02, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.1793 2023-01-02 17:59:00,144 - mmcls - INFO - Epoch [32][1100/5005] lr: 1.000e-02, eta: 8:24:52, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.1860 2023-01-02 17:59:09,137 - mmcls - INFO - Epoch [32][1200/5005] lr: 1.000e-02, eta: 8:24:44, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.2236 2023-01-02 17:59:17,993 - mmcls - INFO - Epoch [32][1300/5005] lr: 1.000e-02, eta: 8:24:35, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1923 2023-01-02 17:59:27,064 - mmcls - INFO - Epoch [32][1400/5005] lr: 1.000e-02, eta: 8:24:27, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.1909 2023-01-02 17:59:35,980 - mmcls - INFO - Epoch [32][1500/5005] lr: 1.000e-02, eta: 8:24:19, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.1936 2023-01-02 17:59:44,708 - mmcls - INFO - Epoch [32][1600/5005] lr: 1.000e-02, eta: 8:24:10, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.1822 2023-01-02 17:59:53,549 - mmcls - INFO - Epoch [32][1700/5005] lr: 1.000e-02, eta: 8:24:01, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.2039 2023-01-02 18:00:02,402 - mmcls - INFO - Epoch [32][1800/5005] lr: 1.000e-02, eta: 8:23:52, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.1784 2023-01-02 18:00:10,948 - mmcls - INFO - Epoch [32][1900/5005] lr: 1.000e-02, eta: 8:23:43, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1895 2023-01-02 18:00:19,614 - mmcls - INFO - Epoch [32][2000/5005] lr: 1.000e-02, eta: 8:23:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1872 2023-01-02 18:00:28,505 - mmcls - INFO - Epoch [32][2100/5005] lr: 1.000e-02, eta: 8:23:25, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.1619 2023-01-02 18:00:36,985 - mmcls - INFO - Epoch [32][2200/5005] lr: 1.000e-02, eta: 8:23:16, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.2023 2023-01-02 18:00:45,910 - mmcls - INFO - Epoch [32][2300/5005] lr: 1.000e-02, eta: 8:23:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1882 2023-01-02 18:00:54,844 - mmcls - INFO - Epoch [32][2400/5005] lr: 1.000e-02, eta: 8:22:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1801 2023-01-02 18:01:03,878 - mmcls - INFO - Epoch [32][2500/5005] lr: 1.000e-02, eta: 8:22:50, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.1952 2023-01-02 18:01:12,450 - mmcls - INFO - Epoch [32][2600/5005] lr: 1.000e-02, eta: 8:22:41, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.1713 2023-01-02 18:01:21,499 - mmcls - INFO - Epoch [32][2700/5005] lr: 1.000e-02, eta: 8:22:33, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.1727 2023-01-02 18:01:30,264 - mmcls - INFO - Epoch [32][2800/5005] lr: 1.000e-02, eta: 8:22:24, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1728 2023-01-02 18:01:38,959 - mmcls - INFO - Epoch [32][2900/5005] lr: 1.000e-02, eta: 8:22:15, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.1931 2023-01-02 18:01:47,516 - mmcls - INFO - Epoch [32][3000/5005] lr: 1.000e-02, eta: 8:22:06, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.1662 2023-01-02 18:01:56,515 - mmcls - INFO - Epoch [32][3100/5005] lr: 1.000e-02, eta: 8:21:57, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.1935 2023-01-02 18:02:05,091 - mmcls - INFO - Epoch [32][3200/5005] lr: 1.000e-02, eta: 8:21:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.2100 2023-01-02 18:02:13,737 - mmcls - INFO - Epoch [32][3300/5005] lr: 1.000e-02, eta: 8:21:39, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.1774 2023-01-02 18:02:22,297 - mmcls - INFO - Epoch [32][3400/5005] lr: 1.000e-02, eta: 8:21:30, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.2123 2023-01-02 18:02:30,957 - mmcls - INFO - Epoch [32][3500/5005] lr: 1.000e-02, eta: 8:21:20, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.1760 2023-01-02 18:02:39,545 - mmcls - INFO - Epoch [32][3600/5005] lr: 1.000e-02, eta: 8:21:11, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.1779 2023-01-02 18:02:48,479 - mmcls - INFO - Epoch [32][3700/5005] lr: 1.000e-02, eta: 8:21:03, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.1647 2023-01-02 18:02:57,048 - mmcls - INFO - Epoch [32][3800/5005] lr: 1.000e-02, eta: 8:20:53, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.1975 2023-01-02 18:03:05,567 - mmcls - INFO - Epoch [32][3900/5005] lr: 1.000e-02, eta: 8:20:44, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.1678 2023-01-02 18:03:14,208 - mmcls - INFO - Epoch [32][4000/5005] lr: 1.000e-02, eta: 8:20:35, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.1720 2023-01-02 18:03:22,975 - mmcls - INFO - Epoch [32][4100/5005] lr: 1.000e-02, eta: 8:20:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.1803 2023-01-02 18:03:31,540 - mmcls - INFO - Epoch [32][4200/5005] lr: 1.000e-02, eta: 8:20:17, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.1688 2023-01-02 18:03:40,173 - mmcls - INFO - Epoch [32][4300/5005] lr: 1.000e-02, eta: 8:20:07, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.1697 2023-01-02 18:03:48,732 - mmcls - INFO - Epoch [32][4400/5005] lr: 1.000e-02, eta: 8:19:58, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.1938 2023-01-02 18:03:57,798 - mmcls - INFO - Epoch [32][4500/5005] lr: 1.000e-02, eta: 8:19:50, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.1671 2023-01-02 18:04:06,373 - mmcls - INFO - Epoch [32][4600/5005] lr: 1.000e-02, eta: 8:19:41, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.1847 2023-01-02 18:04:15,174 - mmcls - INFO - Epoch [32][4700/5005] lr: 1.000e-02, eta: 8:19:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.1696 2023-01-02 18:04:23,847 - mmcls - INFO - Epoch [32][4800/5005] lr: 1.000e-02, eta: 8:19:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1672 2023-01-02 18:04:32,691 - mmcls - INFO - Epoch [32][4900/5005] lr: 1.000e-02, eta: 8:19:14, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.1670 2023-01-02 18:04:41,308 - mmcls - INFO - Epoch [32][5000/5005] lr: 1.000e-02, eta: 8:19:05, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.1764 2023-01-02 18:05:04,160 - mmcls - INFO - Epoch(val) [32][196] accuracy_top-1: 57.8620, accuracy_top-5: 80.3340 2023-01-02 18:05:15,213 - mmcls - INFO - Epoch [33][100/5005] lr: 1.000e-02, eta: 8:18:59, time: 0.110, data_time: 0.044, memory: 1125, loss: 2.1562 2023-01-02 18:05:23,963 - mmcls - INFO - Epoch [33][200/5005] lr: 1.000e-02, eta: 8:18:51, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.1644 2023-01-02 18:05:33,090 - mmcls - INFO - Epoch [33][300/5005] lr: 1.000e-02, eta: 8:18:42, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.1324 2023-01-02 18:05:41,747 - mmcls - INFO - Epoch [33][400/5005] lr: 1.000e-02, eta: 8:18:33, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.1398 2023-01-02 18:05:50,171 - mmcls - INFO - Epoch [33][500/5005] lr: 1.000e-02, eta: 8:18:24, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.1404 2023-01-02 18:05:58,783 - mmcls - INFO - Epoch [33][600/5005] lr: 1.000e-02, eta: 8:18:15, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.1005 2023-01-02 18:06:07,712 - mmcls - INFO - Epoch [33][700/5005] lr: 1.000e-02, eta: 8:18:06, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.1157 2023-01-02 18:06:16,338 - mmcls - INFO - Epoch [33][800/5005] lr: 1.000e-02, eta: 8:17:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.1578 2023-01-02 18:06:25,060 - mmcls - INFO - Epoch [33][900/5005] lr: 1.000e-02, eta: 8:17:48, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.1505 2023-01-02 18:06:34,281 - mmcls - INFO - Epoch [33][1000/5005] lr: 1.000e-02, eta: 8:17:40, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.1319 2023-01-02 18:06:42,717 - mmcls - INFO - Epoch [33][1100/5005] lr: 1.000e-02, eta: 8:17:30, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.1228 2023-01-02 18:06:51,533 - mmcls - INFO - Epoch [33][1200/5005] lr: 1.000e-02, eta: 8:17:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1454 2023-01-02 18:07:00,038 - mmcls - INFO - Epoch [33][1300/5005] lr: 1.000e-02, eta: 8:17:12, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1237 2023-01-02 18:07:08,576 - mmcls - INFO - Epoch [33][1400/5005] lr: 1.000e-02, eta: 8:17:03, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.1492 2023-01-02 18:07:17,376 - mmcls - INFO - Epoch [33][1500/5005] lr: 1.000e-02, eta: 8:16:54, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.1134 2023-01-02 18:07:26,244 - mmcls - INFO - Epoch [33][1600/5005] lr: 1.000e-02, eta: 8:16:45, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.1457 2023-01-02 18:07:35,028 - mmcls - INFO - Epoch [33][1700/5005] lr: 1.000e-02, eta: 8:16:37, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.1331 2023-01-02 18:07:43,656 - mmcls - INFO - Epoch [33][1800/5005] lr: 1.000e-02, eta: 8:16:27, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.1490 2023-01-02 18:07:52,278 - mmcls - INFO - Epoch [33][1900/5005] lr: 1.000e-02, eta: 8:16:18, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.1543 2023-01-02 18:08:01,058 - mmcls - INFO - Epoch [33][2000/5005] lr: 1.000e-02, eta: 8:16:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1363 2023-01-02 18:08:10,076 - mmcls - INFO - Epoch [33][2100/5005] lr: 1.000e-02, eta: 8:16:01, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.1449 2023-01-02 18:08:18,917 - mmcls - INFO - Epoch [33][2200/5005] lr: 1.000e-02, eta: 8:15:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1240 2023-01-02 18:08:27,658 - mmcls - INFO - Epoch [33][2300/5005] lr: 1.000e-02, eta: 8:15:43, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.1386 2023-01-02 18:08:36,399 - mmcls - INFO - Epoch [33][2400/5005] lr: 1.000e-02, eta: 8:15:35, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.1224 2023-01-02 18:08:45,199 - mmcls - INFO - Epoch [33][2500/5005] lr: 1.000e-02, eta: 8:15:26, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.1552 2023-01-02 18:08:54,084 - mmcls - INFO - Epoch [33][2600/5005] lr: 1.000e-02, eta: 8:15:17, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1403 2023-01-02 18:09:02,833 - mmcls - INFO - Epoch [33][2700/5005] lr: 1.000e-02, eta: 8:15:08, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.1393 2023-01-02 18:09:11,413 - mmcls - INFO - Epoch [33][2800/5005] lr: 1.000e-02, eta: 8:14:59, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.1371 2023-01-02 18:09:20,223 - mmcls - INFO - Epoch [33][2900/5005] lr: 1.000e-02, eta: 8:14:50, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.1243 2023-01-02 18:09:28,976 - mmcls - INFO - Epoch [33][3000/5005] lr: 1.000e-02, eta: 8:14:41, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.1432 2023-01-02 18:09:37,944 - mmcls - INFO - Epoch [33][3100/5005] lr: 1.000e-02, eta: 8:14:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.1219 2023-01-02 18:09:46,492 - mmcls - INFO - Epoch [33][3200/5005] lr: 1.000e-02, eta: 8:14:23, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.1307 2023-01-02 18:09:55,123 - mmcls - INFO - Epoch [33][3300/5005] lr: 1.000e-02, eta: 8:14:14, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.1223 2023-01-02 18:10:03,747 - mmcls - INFO - Epoch [33][3400/5005] lr: 1.000e-02, eta: 8:14:05, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.1413 2023-01-02 18:10:12,328 - mmcls - INFO - Epoch [33][3500/5005] lr: 1.000e-02, eta: 8:13:56, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.1219 2023-01-02 18:10:21,021 - mmcls - INFO - Epoch [33][3600/5005] lr: 1.000e-02, eta: 8:13:47, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.1397 2023-01-02 18:10:29,730 - mmcls - INFO - Epoch [33][3700/5005] lr: 1.000e-02, eta: 8:13:38, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.1571 2023-01-02 18:10:38,452 - mmcls - INFO - Epoch [33][3800/5005] lr: 1.000e-02, eta: 8:13:29, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.1479 2023-01-02 18:10:47,223 - mmcls - INFO - Epoch [33][3900/5005] lr: 1.000e-02, eta: 8:13:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1485 2023-01-02 18:10:56,449 - mmcls - INFO - Epoch [33][4000/5005] lr: 1.000e-02, eta: 8:13:12, time: 0.092, data_time: 0.024, memory: 1125, loss: 2.1363 2023-01-02 18:11:04,900 - mmcls - INFO - Epoch [33][4100/5005] lr: 1.000e-02, eta: 8:13:03, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1260 2023-01-02 18:11:13,549 - mmcls - INFO - Epoch [33][4200/5005] lr: 1.000e-02, eta: 8:12:54, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.1298 2023-01-02 18:11:22,293 - mmcls - INFO - Epoch [33][4300/5005] lr: 1.000e-02, eta: 8:12:45, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.1277 2023-01-02 18:11:30,792 - mmcls - INFO - Epoch [33][4400/5005] lr: 1.000e-02, eta: 8:12:35, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1444 2023-01-02 18:11:39,432 - mmcls - INFO - Epoch [33][4500/5005] lr: 1.000e-02, eta: 8:12:26, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.1711 2023-01-02 18:11:47,970 - mmcls - INFO - Epoch [33][4600/5005] lr: 1.000e-02, eta: 8:12:17, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1482 2023-01-02 18:11:56,684 - mmcls - INFO - Epoch [33][4700/5005] lr: 1.000e-02, eta: 8:12:08, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.1467 2023-01-02 18:12:05,218 - mmcls - INFO - Epoch [33][4800/5005] lr: 1.000e-02, eta: 8:11:58, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.1412 2023-01-02 18:12:13,772 - mmcls - INFO - Epoch [33][4900/5005] lr: 1.000e-02, eta: 8:11:49, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.1504 2023-01-02 18:12:22,361 - mmcls - INFO - Epoch [33][5000/5005] lr: 1.000e-02, eta: 8:11:40, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.1544 2023-01-02 18:12:45,239 - mmcls - INFO - Epoch(val) [33][196] accuracy_top-1: 58.0160, accuracy_top-5: 80.4460 2023-01-02 18:12:56,155 - mmcls - INFO - Epoch [34][100/5005] lr: 1.000e-02, eta: 8:11:34, time: 0.109, data_time: 0.056, memory: 1125, loss: 2.0925 2023-01-02 18:13:04,863 - mmcls - INFO - Epoch [34][200/5005] lr: 1.000e-02, eta: 8:11:25, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.1087 2023-01-02 18:13:13,539 - mmcls - INFO - Epoch [34][300/5005] lr: 1.000e-02, eta: 8:11:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0778 2023-01-02 18:13:22,474 - mmcls - INFO - Epoch [34][400/5005] lr: 1.000e-02, eta: 8:11:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0873 2023-01-02 18:13:31,328 - mmcls - INFO - Epoch [34][500/5005] lr: 1.000e-02, eta: 8:10:59, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.1122 2023-01-02 18:13:40,129 - mmcls - INFO - Epoch [34][600/5005] lr: 1.000e-02, eta: 8:10:50, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.1211 2023-01-02 18:13:48,902 - mmcls - INFO - Epoch [34][700/5005] lr: 1.000e-02, eta: 8:10:41, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.1079 2023-01-02 18:13:57,772 - mmcls - INFO - Epoch [34][800/5005] lr: 1.000e-02, eta: 8:10:33, time: 0.089, data_time: 0.028, memory: 1125, loss: 2.1178 2023-01-02 18:14:06,311 - mmcls - INFO - Epoch [34][900/5005] lr: 1.000e-02, eta: 8:10:23, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1305 2023-01-02 18:14:15,073 - mmcls - INFO - Epoch [34][1000/5005] lr: 1.000e-02, eta: 8:10:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1128 2023-01-02 18:14:23,733 - mmcls - INFO - Epoch [34][1100/5005] lr: 1.000e-02, eta: 8:10:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0866 2023-01-02 18:14:32,459 - mmcls - INFO - Epoch [34][1200/5005] lr: 1.000e-02, eta: 8:09:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.1124 2023-01-02 18:14:41,162 - mmcls - INFO - Epoch [34][1300/5005] lr: 1.000e-02, eta: 8:09:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1085 2023-01-02 18:14:49,677 - mmcls - INFO - Epoch [34][1400/5005] lr: 1.000e-02, eta: 8:09:38, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0785 2023-01-02 18:14:58,380 - mmcls - INFO - Epoch [34][1500/5005] lr: 1.000e-02, eta: 8:09:29, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0933 2023-01-02 18:15:06,951 - mmcls - INFO - Epoch [34][1600/5005] lr: 1.000e-02, eta: 8:09:20, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.1120 2023-01-02 18:15:15,611 - mmcls - INFO - Epoch [34][1700/5005] lr: 1.000e-02, eta: 8:09:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1190 2023-01-02 18:15:24,318 - mmcls - INFO - Epoch [34][1800/5005] lr: 1.000e-02, eta: 8:09:02, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.1080 2023-01-02 18:15:33,183 - mmcls - INFO - Epoch [34][1900/5005] lr: 1.000e-02, eta: 8:08:53, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.1173 2023-01-02 18:15:41,828 - mmcls - INFO - Epoch [34][2000/5005] lr: 1.000e-02, eta: 8:08:44, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.1299 2023-01-02 18:15:50,215 - mmcls - INFO - Epoch [34][2100/5005] lr: 1.000e-02, eta: 8:08:34, time: 0.084, data_time: 0.011, memory: 1125, loss: 2.0976 2023-01-02 18:15:58,903 - mmcls - INFO - Epoch [34][2200/5005] lr: 1.000e-02, eta: 8:08:25, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.1339 2023-01-02 18:16:07,878 - mmcls - INFO - Epoch [34][2300/5005] lr: 1.000e-02, eta: 8:08:17, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0977 2023-01-02 18:16:16,946 - mmcls - INFO - Epoch [34][2400/5005] lr: 1.000e-02, eta: 8:08:09, time: 0.091, data_time: 0.014, memory: 1125, loss: 2.1125 2023-01-02 18:16:25,510 - mmcls - INFO - Epoch [34][2500/5005] lr: 1.000e-02, eta: 8:07:59, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.1289 2023-01-02 18:16:34,324 - mmcls - INFO - Epoch [34][2600/5005] lr: 1.000e-02, eta: 8:07:51, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.1009 2023-01-02 18:16:43,272 - mmcls - INFO - Epoch [34][2700/5005] lr: 1.000e-02, eta: 8:07:42, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0993 2023-01-02 18:16:51,932 - mmcls - INFO - Epoch [34][2800/5005] lr: 1.000e-02, eta: 8:07:33, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.1046 2023-01-02 18:17:00,442 - mmcls - INFO - Epoch [34][2900/5005] lr: 1.000e-02, eta: 8:07:24, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1241 2023-01-02 18:17:09,338 - mmcls - INFO - Epoch [34][3000/5005] lr: 1.000e-02, eta: 8:07:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1173 2023-01-02 18:17:18,242 - mmcls - INFO - Epoch [34][3100/5005] lr: 1.000e-02, eta: 8:07:06, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.1357 2023-01-02 18:17:26,847 - mmcls - INFO - Epoch [34][3200/5005] lr: 1.000e-02, eta: 8:06:57, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.1328 2023-01-02 18:17:35,350 - mmcls - INFO - Epoch [34][3300/5005] lr: 1.000e-02, eta: 8:06:48, time: 0.085, data_time: 0.031, memory: 1125, loss: 2.1099 2023-01-02 18:17:44,034 - mmcls - INFO - Epoch [34][3400/5005] lr: 1.000e-02, eta: 8:06:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.1022 2023-01-02 18:17:52,839 - mmcls - INFO - Epoch [34][3500/5005] lr: 1.000e-02, eta: 8:06:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1066 2023-01-02 18:18:01,557 - mmcls - INFO - Epoch [34][3600/5005] lr: 1.000e-02, eta: 8:06:21, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.1318 2023-01-02 18:18:10,401 - mmcls - INFO - Epoch [34][3700/5005] lr: 1.000e-02, eta: 8:06:12, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.1070 2023-01-02 18:18:19,216 - mmcls - INFO - Epoch [34][3800/5005] lr: 1.000e-02, eta: 8:06:04, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.1105 2023-01-02 18:18:28,011 - mmcls - INFO - Epoch [34][3900/5005] lr: 1.000e-02, eta: 8:05:55, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.1047 2023-01-02 18:18:36,712 - mmcls - INFO - Epoch [34][4000/5005] lr: 1.000e-02, eta: 8:05:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.1036 2023-01-02 18:18:45,446 - mmcls - INFO - Epoch [34][4100/5005] lr: 1.000e-02, eta: 8:05:37, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.1059 2023-01-02 18:18:53,896 - mmcls - INFO - Epoch [34][4200/5005] lr: 1.000e-02, eta: 8:05:27, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1126 2023-01-02 18:19:02,584 - mmcls - INFO - Epoch [34][4300/5005] lr: 1.000e-02, eta: 8:05:18, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.1049 2023-01-02 18:19:11,342 - mmcls - INFO - Epoch [34][4400/5005] lr: 1.000e-02, eta: 8:05:10, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.1157 2023-01-02 18:19:20,354 - mmcls - INFO - Epoch [34][4500/5005] lr: 1.000e-02, eta: 8:05:01, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.1143 2023-01-02 18:19:29,708 - mmcls - INFO - Epoch [34][4600/5005] lr: 1.000e-02, eta: 8:04:54, time: 0.094, data_time: 0.001, memory: 1125, loss: 2.1231 2023-01-02 18:19:38,611 - mmcls - INFO - Epoch [34][4700/5005] lr: 1.000e-02, eta: 8:04:45, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.1119 2023-01-02 18:19:47,502 - mmcls - INFO - Epoch [34][4800/5005] lr: 1.000e-02, eta: 8:04:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1119 2023-01-02 18:19:56,405 - mmcls - INFO - Epoch [34][4900/5005] lr: 1.000e-02, eta: 8:04:28, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1232 2023-01-02 18:20:05,182 - mmcls - INFO - Epoch [34][5000/5005] lr: 1.000e-02, eta: 8:04:19, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.1078 2023-01-02 18:20:27,986 - mmcls - INFO - Epoch(val) [34][196] accuracy_top-1: 58.6180, accuracy_top-5: 80.8720 2023-01-02 18:20:39,018 - mmcls - INFO - Epoch [35][100/5005] lr: 1.000e-02, eta: 8:04:13, time: 0.110, data_time: 0.045, memory: 1125, loss: 2.0632 2023-01-02 18:20:47,757 - mmcls - INFO - Epoch [35][200/5005] lr: 1.000e-02, eta: 8:04:04, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0763 2023-01-02 18:20:56,210 - mmcls - INFO - Epoch [35][300/5005] lr: 1.000e-02, eta: 8:03:55, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.0768 2023-01-02 18:21:04,937 - mmcls - INFO - Epoch [35][400/5005] lr: 1.000e-02, eta: 8:03:46, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0889 2023-01-02 18:21:13,628 - mmcls - INFO - Epoch [35][500/5005] lr: 1.000e-02, eta: 8:03:37, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0954 2023-01-02 18:21:22,471 - mmcls - INFO - Epoch [35][600/5005] lr: 1.000e-02, eta: 8:03:28, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0911 2023-01-02 18:21:31,121 - mmcls - INFO - Epoch [35][700/5005] lr: 1.000e-02, eta: 8:03:19, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.0646 2023-01-02 18:21:39,707 - mmcls - INFO - Epoch [35][800/5005] lr: 1.000e-02, eta: 8:03:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0751 2023-01-02 18:21:48,473 - mmcls - INFO - Epoch [35][900/5005] lr: 1.000e-02, eta: 8:03:01, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0861 2023-01-02 18:21:57,302 - mmcls - INFO - Epoch [35][1000/5005] lr: 1.000e-02, eta: 8:02:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0692 2023-01-02 18:22:06,127 - mmcls - INFO - Epoch [35][1100/5005] lr: 1.000e-02, eta: 8:02:43, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0840 2023-01-02 18:22:14,689 - mmcls - INFO - Epoch [35][1200/5005] lr: 1.000e-02, eta: 8:02:34, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.1125 2023-01-02 18:22:23,350 - mmcls - INFO - Epoch [35][1300/5005] lr: 1.000e-02, eta: 8:02:25, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0746 2023-01-02 18:22:31,797 - mmcls - INFO - Epoch [35][1400/5005] lr: 1.000e-02, eta: 8:02:16, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.0808 2023-01-02 18:22:40,754 - mmcls - INFO - Epoch [35][1500/5005] lr: 1.000e-02, eta: 8:02:07, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0915 2023-01-02 18:22:49,476 - mmcls - INFO - Epoch [35][1600/5005] lr: 1.000e-02, eta: 8:01:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0870 2023-01-02 18:22:57,989 - mmcls - INFO - Epoch [35][1700/5005] lr: 1.000e-02, eta: 8:01:49, time: 0.085, data_time: 0.000, memory: 1125, loss: 2.0973 2023-01-02 18:23:06,730 - mmcls - INFO - Epoch [35][1800/5005] lr: 1.000e-02, eta: 8:01:40, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.1016 2023-01-02 18:23:15,689 - mmcls - INFO - Epoch [35][1900/5005] lr: 1.000e-02, eta: 8:01:31, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.1011 2023-01-02 18:23:24,103 - mmcls - INFO - Epoch [35][2000/5005] lr: 1.000e-02, eta: 8:01:22, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.0790 2023-01-02 18:23:33,010 - mmcls - INFO - Epoch [35][2100/5005] lr: 1.000e-02, eta: 8:01:13, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0945 2023-01-02 18:23:41,911 - mmcls - INFO - Epoch [35][2200/5005] lr: 1.000e-02, eta: 8:01:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.1031 2023-01-02 18:23:50,372 - mmcls - INFO - Epoch [35][2300/5005] lr: 1.000e-02, eta: 8:00:55, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0716 2023-01-02 18:23:59,024 - mmcls - INFO - Epoch [35][2400/5005] lr: 1.000e-02, eta: 8:00:46, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0852 2023-01-02 18:24:08,011 - mmcls - INFO - Epoch [35][2500/5005] lr: 1.000e-02, eta: 8:00:38, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0978 2023-01-02 18:24:16,782 - mmcls - INFO - Epoch [35][2600/5005] lr: 1.000e-02, eta: 8:00:29, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.1069 2023-01-02 18:24:25,628 - mmcls - INFO - Epoch [35][2700/5005] lr: 1.000e-02, eta: 8:00:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1047 2023-01-02 18:24:34,404 - mmcls - INFO - Epoch [35][2800/5005] lr: 1.000e-02, eta: 8:00:11, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.1118 2023-01-02 18:24:43,168 - mmcls - INFO - Epoch [35][2900/5005] lr: 1.000e-02, eta: 8:00:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.1153 2023-01-02 18:24:51,837 - mmcls - INFO - Epoch [35][3000/5005] lr: 1.000e-02, eta: 7:59:54, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0989 2023-01-02 18:25:00,340 - mmcls - INFO - Epoch [35][3100/5005] lr: 1.000e-02, eta: 7:59:44, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1038 2023-01-02 18:25:09,582 - mmcls - INFO - Epoch [35][3200/5005] lr: 1.000e-02, eta: 7:59:36, time: 0.092, data_time: 0.012, memory: 1125, loss: 2.0990 2023-01-02 18:25:18,047 - mmcls - INFO - Epoch [35][3300/5005] lr: 1.000e-02, eta: 7:59:27, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.1118 2023-01-02 18:25:27,095 - mmcls - INFO - Epoch [35][3400/5005] lr: 1.000e-02, eta: 7:59:18, time: 0.091, data_time: 0.000, memory: 1125, loss: 2.1203 2023-01-02 18:25:35,758 - mmcls - INFO - Epoch [35][3500/5005] lr: 1.000e-02, eta: 7:59:09, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0851 2023-01-02 18:25:44,464 - mmcls - INFO - Epoch [35][3600/5005] lr: 1.000e-02, eta: 7:59:00, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.1016 2023-01-02 18:25:53,184 - mmcls - INFO - Epoch [35][3700/5005] lr: 1.000e-02, eta: 7:58:51, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.1010 2023-01-02 18:26:01,713 - mmcls - INFO - Epoch [35][3800/5005] lr: 1.000e-02, eta: 7:58:42, time: 0.085, data_time: 0.026, memory: 1125, loss: 2.0774 2023-01-02 18:26:10,439 - mmcls - INFO - Epoch [35][3900/5005] lr: 1.000e-02, eta: 7:58:33, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0888 2023-01-02 18:26:19,011 - mmcls - INFO - Epoch [35][4000/5005] lr: 1.000e-02, eta: 7:58:24, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.1025 2023-01-02 18:26:27,668 - mmcls - INFO - Epoch [35][4100/5005] lr: 1.000e-02, eta: 7:58:15, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0900 2023-01-02 18:26:36,646 - mmcls - INFO - Epoch [35][4200/5005] lr: 1.000e-02, eta: 7:58:07, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0958 2023-01-02 18:26:45,543 - mmcls - INFO - Epoch [35][4300/5005] lr: 1.000e-02, eta: 7:57:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0941 2023-01-02 18:26:54,186 - mmcls - INFO - Epoch [35][4400/5005] lr: 1.000e-02, eta: 7:57:49, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.1008 2023-01-02 18:27:03,146 - mmcls - INFO - Epoch [35][4500/5005] lr: 1.000e-02, eta: 7:57:40, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.1075 2023-01-02 18:27:11,734 - mmcls - INFO - Epoch [35][4600/5005] lr: 1.000e-02, eta: 7:57:31, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0893 2023-01-02 18:27:20,484 - mmcls - INFO - Epoch [35][4700/5005] lr: 1.000e-02, eta: 7:57:22, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0938 2023-01-02 18:27:29,005 - mmcls - INFO - Epoch [35][4800/5005] lr: 1.000e-02, eta: 7:57:13, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0890 2023-01-02 18:27:37,897 - mmcls - INFO - Epoch [35][4900/5005] lr: 1.000e-02, eta: 7:57:04, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.1118 2023-01-02 18:27:46,897 - mmcls - INFO - Epoch [35][5000/5005] lr: 1.000e-02, eta: 7:56:56, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.1089 2023-01-02 18:28:09,634 - mmcls - INFO - Epoch(val) [35][196] accuracy_top-1: 58.4460, accuracy_top-5: 80.6280 2023-01-02 18:28:20,695 - mmcls - INFO - Epoch [36][100/5005] lr: 1.000e-02, eta: 7:56:50, time: 0.110, data_time: 0.048, memory: 1125, loss: 2.0296 2023-01-02 18:28:29,199 - mmcls - INFO - Epoch [36][200/5005] lr: 1.000e-02, eta: 7:56:41, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.0817 2023-01-02 18:28:38,181 - mmcls - INFO - Epoch [36][300/5005] lr: 1.000e-02, eta: 7:56:32, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.0515 2023-01-02 18:28:46,750 - mmcls - INFO - Epoch [36][400/5005] lr: 1.000e-02, eta: 7:56:23, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0496 2023-01-02 18:28:55,436 - mmcls - INFO - Epoch [36][500/5005] lr: 1.000e-02, eta: 7:56:14, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0721 2023-01-02 18:29:04,204 - mmcls - INFO - Epoch [36][600/5005] lr: 1.000e-02, eta: 7:56:05, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0567 2023-01-02 18:29:12,761 - mmcls - INFO - Epoch [36][700/5005] lr: 1.000e-02, eta: 7:55:56, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0598 2023-01-02 18:29:21,826 - mmcls - INFO - Epoch [36][800/5005] lr: 1.000e-02, eta: 7:55:48, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0459 2023-01-02 18:29:30,082 - mmcls - INFO - Epoch [36][900/5005] lr: 1.000e-02, eta: 7:55:38, time: 0.082, data_time: 0.003, memory: 1125, loss: 2.0744 2023-01-02 18:29:38,723 - mmcls - INFO - Epoch [36][1000/5005] lr: 1.000e-02, eta: 7:55:29, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.0461 2023-01-02 18:29:47,532 - mmcls - INFO - Epoch [36][1100/5005] lr: 1.000e-02, eta: 7:55:20, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.0916 2023-01-02 18:29:55,970 - mmcls - INFO - Epoch [36][1200/5005] lr: 1.000e-02, eta: 7:55:10, time: 0.084, data_time: 0.008, memory: 1125, loss: 2.0380 2023-01-02 18:30:04,752 - mmcls - INFO - Epoch [36][1300/5005] lr: 1.000e-02, eta: 7:55:02, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0807 2023-01-02 18:30:13,530 - mmcls - INFO - Epoch [36][1400/5005] lr: 1.000e-02, eta: 7:54:53, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0577 2023-01-02 18:30:22,315 - mmcls - INFO - Epoch [36][1500/5005] lr: 1.000e-02, eta: 7:54:44, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0879 2023-01-02 18:30:31,358 - mmcls - INFO - Epoch [36][1600/5005] lr: 1.000e-02, eta: 7:54:36, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.0812 2023-01-02 18:30:39,931 - mmcls - INFO - Epoch [36][1700/5005] lr: 1.000e-02, eta: 7:54:26, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.0736 2023-01-02 18:30:48,713 - mmcls - INFO - Epoch [36][1800/5005] lr: 1.000e-02, eta: 7:54:18, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0870 2023-01-02 18:30:57,501 - mmcls - INFO - Epoch [36][1900/5005] lr: 1.000e-02, eta: 7:54:09, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0910 2023-01-02 18:31:06,175 - mmcls - INFO - Epoch [36][2000/5005] lr: 1.000e-02, eta: 7:54:00, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0878 2023-01-02 18:31:14,839 - mmcls - INFO - Epoch [36][2100/5005] lr: 1.000e-02, eta: 7:53:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0696 2023-01-02 18:31:23,455 - mmcls - INFO - Epoch [36][2200/5005] lr: 1.000e-02, eta: 7:53:42, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.1052 2023-01-02 18:31:32,047 - mmcls - INFO - Epoch [36][2300/5005] lr: 1.000e-02, eta: 7:53:32, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.0608 2023-01-02 18:31:40,588 - mmcls - INFO - Epoch [36][2400/5005] lr: 1.000e-02, eta: 7:53:23, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.0756 2023-01-02 18:31:49,573 - mmcls - INFO - Epoch [36][2500/5005] lr: 1.000e-02, eta: 7:53:15, time: 0.090, data_time: 0.023, memory: 1125, loss: 2.0625 2023-01-02 18:31:58,312 - mmcls - INFO - Epoch [36][2600/5005] lr: 1.000e-02, eta: 7:53:06, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0382 2023-01-02 18:32:06,798 - mmcls - INFO - Epoch [36][2700/5005] lr: 1.000e-02, eta: 7:52:56, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.0794 2023-01-02 18:32:15,515 - mmcls - INFO - Epoch [36][2800/5005] lr: 1.000e-02, eta: 7:52:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0958 2023-01-02 18:32:24,187 - mmcls - INFO - Epoch [36][2900/5005] lr: 1.000e-02, eta: 7:52:39, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0667 2023-01-02 18:32:32,827 - mmcls - INFO - Epoch [36][3000/5005] lr: 1.000e-02, eta: 7:52:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0963 2023-01-02 18:32:41,341 - mmcls - INFO - Epoch [36][3100/5005] lr: 1.000e-02, eta: 7:52:20, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0632 2023-01-02 18:32:50,107 - mmcls - INFO - Epoch [36][3200/5005] lr: 1.000e-02, eta: 7:52:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0850 2023-01-02 18:32:58,805 - mmcls - INFO - Epoch [36][3300/5005] lr: 1.000e-02, eta: 7:52:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0718 2023-01-02 18:33:07,587 - mmcls - INFO - Epoch [36][3400/5005] lr: 1.000e-02, eta: 7:51:53, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0895 2023-01-02 18:33:16,581 - mmcls - INFO - Epoch [36][3500/5005] lr: 1.000e-02, eta: 7:51:45, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0881 2023-01-02 18:33:25,205 - mmcls - INFO - Epoch [36][3600/5005] lr: 1.000e-02, eta: 7:51:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.1207 2023-01-02 18:33:34,050 - mmcls - INFO - Epoch [36][3700/5005] lr: 1.000e-02, eta: 7:51:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0753 2023-01-02 18:33:43,280 - mmcls - INFO - Epoch [36][3800/5005] lr: 1.000e-02, eta: 7:51:19, time: 0.092, data_time: 0.037, memory: 1125, loss: 2.0886 2023-01-02 18:33:52,240 - mmcls - INFO - Epoch [36][3900/5005] lr: 1.000e-02, eta: 7:51:11, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.0774 2023-01-02 18:34:00,897 - mmcls - INFO - Epoch [36][4000/5005] lr: 1.000e-02, eta: 7:51:02, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.0767 2023-01-02 18:34:10,293 - mmcls - INFO - Epoch [36][4100/5005] lr: 1.000e-02, eta: 7:50:54, time: 0.094, data_time: 0.025, memory: 1125, loss: 2.1083 2023-01-02 18:34:18,928 - mmcls - INFO - Epoch [36][4200/5005] lr: 1.000e-02, eta: 7:50:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0796 2023-01-02 18:34:27,744 - mmcls - INFO - Epoch [36][4300/5005] lr: 1.000e-02, eta: 7:50:36, time: 0.088, data_time: 0.032, memory: 1125, loss: 2.0784 2023-01-02 18:34:36,742 - mmcls - INFO - Epoch [36][4400/5005] lr: 1.000e-02, eta: 7:50:28, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0542 2023-01-02 18:34:45,715 - mmcls - INFO - Epoch [36][4500/5005] lr: 1.000e-02, eta: 7:50:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.1211 2023-01-02 18:34:54,830 - mmcls - INFO - Epoch [36][4600/5005] lr: 1.000e-02, eta: 7:50:11, time: 0.091, data_time: 0.000, memory: 1125, loss: 2.0615 2023-01-02 18:35:03,692 - mmcls - INFO - Epoch [36][4700/5005] lr: 1.000e-02, eta: 7:50:02, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.1128 2023-01-02 18:35:12,453 - mmcls - INFO - Epoch [36][4800/5005] lr: 1.000e-02, eta: 7:49:53, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0801 2023-01-02 18:35:21,576 - mmcls - INFO - Epoch [36][4900/5005] lr: 1.000e-02, eta: 7:49:45, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.0590 2023-01-02 18:35:30,458 - mmcls - INFO - Epoch [36][5000/5005] lr: 1.000e-02, eta: 7:49:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0927 2023-01-02 18:35:53,199 - mmcls - INFO - Epoch(val) [36][196] accuracy_top-1: 58.8720, accuracy_top-5: 81.0720 2023-01-02 18:36:04,322 - mmcls - INFO - Epoch [37][100/5005] lr: 1.000e-02, eta: 7:49:31, time: 0.111, data_time: 0.044, memory: 1125, loss: 2.0431 2023-01-02 18:36:13,130 - mmcls - INFO - Epoch [37][200/5005] lr: 1.000e-02, eta: 7:49:22, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0586 2023-01-02 18:36:21,983 - mmcls - INFO - Epoch [37][300/5005] lr: 1.000e-02, eta: 7:49:13, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0457 2023-01-02 18:36:31,323 - mmcls - INFO - Epoch [37][400/5005] lr: 1.000e-02, eta: 7:49:05, time: 0.093, data_time: 0.008, memory: 1125, loss: 2.0416 2023-01-02 18:36:39,917 - mmcls - INFO - Epoch [37][500/5005] lr: 1.000e-02, eta: 7:48:56, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0411 2023-01-02 18:36:49,366 - mmcls - INFO - Epoch [37][600/5005] lr: 1.000e-02, eta: 7:48:49, time: 0.095, data_time: 0.009, memory: 1125, loss: 2.0542 2023-01-02 18:36:58,182 - mmcls - INFO - Epoch [37][700/5005] lr: 1.000e-02, eta: 7:48:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0681 2023-01-02 18:37:06,788 - mmcls - INFO - Epoch [37][800/5005] lr: 1.000e-02, eta: 7:48:31, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0343 2023-01-02 18:37:16,080 - mmcls - INFO - Epoch [37][900/5005] lr: 1.000e-02, eta: 7:48:23, time: 0.093, data_time: 0.004, memory: 1125, loss: 2.0396 2023-01-02 18:37:24,471 - mmcls - INFO - Epoch [37][1000/5005] lr: 1.000e-02, eta: 7:48:13, time: 0.084, data_time: 0.015, memory: 1125, loss: 2.0746 2023-01-02 18:37:33,200 - mmcls - INFO - Epoch [37][1100/5005] lr: 1.000e-02, eta: 7:48:04, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0758 2023-01-02 18:37:41,835 - mmcls - INFO - Epoch [37][1200/5005] lr: 1.000e-02, eta: 7:47:55, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0717 2023-01-02 18:37:50,377 - mmcls - INFO - Epoch [37][1300/5005] lr: 1.000e-02, eta: 7:47:46, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0605 2023-01-02 18:37:59,091 - mmcls - INFO - Epoch [37][1400/5005] lr: 1.000e-02, eta: 7:47:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0767 2023-01-02 18:38:07,766 - mmcls - INFO - Epoch [37][1500/5005] lr: 1.000e-02, eta: 7:47:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0655 2023-01-02 18:38:16,395 - mmcls - INFO - Epoch [37][1600/5005] lr: 1.000e-02, eta: 7:47:19, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.0648 2023-01-02 18:38:25,185 - mmcls - INFO - Epoch [37][1700/5005] lr: 1.000e-02, eta: 7:47:10, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0526 2023-01-02 18:38:33,697 - mmcls - INFO - Epoch [37][1800/5005] lr: 1.000e-02, eta: 7:47:01, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0584 2023-01-02 18:38:42,367 - mmcls - INFO - Epoch [37][1900/5005] lr: 1.000e-02, eta: 7:46:52, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0684 2023-01-02 18:38:51,423 - mmcls - INFO - Epoch [37][2000/5005] lr: 1.000e-02, eta: 7:46:43, time: 0.091, data_time: 0.039, memory: 1125, loss: 2.0791 2023-01-02 18:39:00,177 - mmcls - INFO - Epoch [37][2100/5005] lr: 1.000e-02, eta: 7:46:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0638 2023-01-02 18:39:08,932 - mmcls - INFO - Epoch [37][2200/5005] lr: 1.000e-02, eta: 7:46:26, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0745 2023-01-02 18:39:17,831 - mmcls - INFO - Epoch [37][2300/5005] lr: 1.000e-02, eta: 7:46:17, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0728 2023-01-02 18:39:27,047 - mmcls - INFO - Epoch [37][2400/5005] lr: 1.000e-02, eta: 7:46:09, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.0670 2023-01-02 18:39:35,708 - mmcls - INFO - Epoch [37][2500/5005] lr: 1.000e-02, eta: 7:46:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0581 2023-01-02 18:39:44,328 - mmcls - INFO - Epoch [37][2600/5005] lr: 1.000e-02, eta: 7:45:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0817 2023-01-02 18:39:53,202 - mmcls - INFO - Epoch [37][2700/5005] lr: 1.000e-02, eta: 7:45:42, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0739 2023-01-02 18:40:01,933 - mmcls - INFO - Epoch [37][2800/5005] lr: 1.000e-02, eta: 7:45:33, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0544 2023-01-02 18:40:10,587 - mmcls - INFO - Epoch [37][2900/5005] lr: 1.000e-02, eta: 7:45:24, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.0697 2023-01-02 18:40:18,864 - mmcls - INFO - Epoch [37][3000/5005] lr: 1.000e-02, eta: 7:45:14, time: 0.083, data_time: 0.001, memory: 1125, loss: 2.0796 2023-01-02 18:40:28,000 - mmcls - INFO - Epoch [37][3100/5005] lr: 1.000e-02, eta: 7:45:06, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.0749 2023-01-02 18:40:36,824 - mmcls - INFO - Epoch [37][3200/5005] lr: 1.000e-02, eta: 7:44:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0766 2023-01-02 18:40:45,662 - mmcls - INFO - Epoch [37][3300/5005] lr: 1.000e-02, eta: 7:44:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0901 2023-01-02 18:40:54,470 - mmcls - INFO - Epoch [37][3400/5005] lr: 1.000e-02, eta: 7:44:40, time: 0.088, data_time: 0.029, memory: 1125, loss: 2.0648 2023-01-02 18:41:02,900 - mmcls - INFO - Epoch [37][3500/5005] lr: 1.000e-02, eta: 7:44:31, time: 0.084, data_time: 0.006, memory: 1125, loss: 2.0630 2023-01-02 18:41:11,553 - mmcls - INFO - Epoch [37][3600/5005] lr: 1.000e-02, eta: 7:44:22, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0854 2023-01-02 18:41:20,697 - mmcls - INFO - Epoch [37][3700/5005] lr: 1.000e-02, eta: 7:44:13, time: 0.091, data_time: 0.028, memory: 1125, loss: 2.0832 2023-01-02 18:41:29,482 - mmcls - INFO - Epoch [37][3800/5005] lr: 1.000e-02, eta: 7:44:04, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0734 2023-01-02 18:41:38,495 - mmcls - INFO - Epoch [37][3900/5005] lr: 1.000e-02, eta: 7:43:56, time: 0.090, data_time: 0.019, memory: 1125, loss: 2.0790 2023-01-02 18:41:47,071 - mmcls - INFO - Epoch [37][4000/5005] lr: 1.000e-02, eta: 7:43:47, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0606 2023-01-02 18:41:55,840 - mmcls - INFO - Epoch [37][4100/5005] lr: 1.000e-02, eta: 7:43:38, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.0746 2023-01-02 18:42:04,882 - mmcls - INFO - Epoch [37][4200/5005] lr: 1.000e-02, eta: 7:43:30, time: 0.090, data_time: 0.021, memory: 1125, loss: 2.0817 2023-01-02 18:42:13,487 - mmcls - INFO - Epoch [37][4300/5005] lr: 1.000e-02, eta: 7:43:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0635 2023-01-02 18:42:22,279 - mmcls - INFO - Epoch [37][4400/5005] lr: 1.000e-02, eta: 7:43:12, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0770 2023-01-02 18:42:31,091 - mmcls - INFO - Epoch [37][4500/5005] lr: 1.000e-02, eta: 7:43:03, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0693 2023-01-02 18:42:39,512 - mmcls - INFO - Epoch [37][4600/5005] lr: 1.000e-02, eta: 7:42:54, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0751 2023-01-02 18:42:48,300 - mmcls - INFO - Epoch [37][4700/5005] lr: 1.000e-02, eta: 7:42:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0923 2023-01-02 18:42:57,006 - mmcls - INFO - Epoch [37][4800/5005] lr: 1.000e-02, eta: 7:42:36, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0539 2023-01-02 18:43:05,517 - mmcls - INFO - Epoch [37][4900/5005] lr: 1.000e-02, eta: 7:42:26, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.1091 2023-01-02 18:43:14,660 - mmcls - INFO - Epoch [37][5000/5005] lr: 1.000e-02, eta: 7:42:18, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0655 2023-01-02 18:43:37,473 - mmcls - INFO - Epoch(val) [37][196] accuracy_top-1: 58.9380, accuracy_top-5: 81.2060 2023-01-02 18:43:48,265 - mmcls - INFO - Epoch [38][100/5005] lr: 1.000e-02, eta: 7:42:12, time: 0.108, data_time: 0.031, memory: 1125, loss: 2.0445 2023-01-02 18:43:56,992 - mmcls - INFO - Epoch [38][200/5005] lr: 1.000e-02, eta: 7:42:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0324 2023-01-02 18:44:05,487 - mmcls - INFO - Epoch [38][300/5005] lr: 1.000e-02, eta: 7:41:53, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.0464 2023-01-02 18:44:14,132 - mmcls - INFO - Epoch [38][400/5005] lr: 1.000e-02, eta: 7:41:44, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0149 2023-01-02 18:44:22,722 - mmcls - INFO - Epoch [38][500/5005] lr: 1.000e-02, eta: 7:41:35, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0627 2023-01-02 18:44:31,479 - mmcls - INFO - Epoch [38][600/5005] lr: 1.000e-02, eta: 7:41:26, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0490 2023-01-02 18:44:40,304 - mmcls - INFO - Epoch [38][700/5005] lr: 1.000e-02, eta: 7:41:18, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0401 2023-01-02 18:44:49,102 - mmcls - INFO - Epoch [38][800/5005] lr: 1.000e-02, eta: 7:41:09, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0555 2023-01-02 18:44:57,727 - mmcls - INFO - Epoch [38][900/5005] lr: 1.000e-02, eta: 7:41:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0340 2023-01-02 18:45:06,592 - mmcls - INFO - Epoch [38][1000/5005] lr: 1.000e-02, eta: 7:40:51, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0522 2023-01-02 18:45:15,288 - mmcls - INFO - Epoch [38][1100/5005] lr: 1.000e-02, eta: 7:40:42, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.0360 2023-01-02 18:45:24,027 - mmcls - INFO - Epoch [38][1200/5005] lr: 1.000e-02, eta: 7:40:33, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0383 2023-01-02 18:45:32,659 - mmcls - INFO - Epoch [38][1300/5005] lr: 1.000e-02, eta: 7:40:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0591 2023-01-02 18:45:41,541 - mmcls - INFO - Epoch [38][1400/5005] lr: 1.000e-02, eta: 7:40:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0596 2023-01-02 18:45:50,480 - mmcls - INFO - Epoch [38][1500/5005] lr: 1.000e-02, eta: 7:40:07, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0487 2023-01-02 18:45:59,206 - mmcls - INFO - Epoch [38][1600/5005] lr: 1.000e-02, eta: 7:39:58, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0550 2023-01-02 18:46:07,937 - mmcls - INFO - Epoch [38][1700/5005] lr: 1.000e-02, eta: 7:39:49, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0637 2023-01-02 18:46:16,568 - mmcls - INFO - Epoch [38][1800/5005] lr: 1.000e-02, eta: 7:39:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0509 2023-01-02 18:46:25,355 - mmcls - INFO - Epoch [38][1900/5005] lr: 1.000e-02, eta: 7:39:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0618 2023-01-02 18:46:34,329 - mmcls - INFO - Epoch [38][2000/5005] lr: 1.000e-02, eta: 7:39:23, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0724 2023-01-02 18:46:43,114 - mmcls - INFO - Epoch [38][2100/5005] lr: 1.000e-02, eta: 7:39:14, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0654 2023-01-02 18:46:51,845 - mmcls - INFO - Epoch [38][2200/5005] lr: 1.000e-02, eta: 7:39:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0551 2023-01-02 18:47:00,573 - mmcls - INFO - Epoch [38][2300/5005] lr: 1.000e-02, eta: 7:38:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0687 2023-01-02 18:47:09,156 - mmcls - INFO - Epoch [38][2400/5005] lr: 1.000e-02, eta: 7:38:47, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.0673 2023-01-02 18:47:17,865 - mmcls - INFO - Epoch [38][2500/5005] lr: 1.000e-02, eta: 7:38:38, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0775 2023-01-02 18:47:26,974 - mmcls - INFO - Epoch [38][2600/5005] lr: 1.000e-02, eta: 7:38:30, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.0749 2023-01-02 18:47:35,641 - mmcls - INFO - Epoch [38][2700/5005] lr: 1.000e-02, eta: 7:38:21, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0426 2023-01-02 18:47:44,259 - mmcls - INFO - Epoch [38][2800/5005] lr: 1.000e-02, eta: 7:38:11, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0634 2023-01-02 18:47:53,455 - mmcls - INFO - Epoch [38][2900/5005] lr: 1.000e-02, eta: 7:38:03, time: 0.092, data_time: 0.005, memory: 1125, loss: 2.0609 2023-01-02 18:48:02,323 - mmcls - INFO - Epoch [38][3000/5005] lr: 1.000e-02, eta: 7:37:55, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0569 2023-01-02 18:48:11,260 - mmcls - INFO - Epoch [38][3100/5005] lr: 1.000e-02, eta: 7:37:46, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0747 2023-01-02 18:48:20,447 - mmcls - INFO - Epoch [38][3200/5005] lr: 1.000e-02, eta: 7:37:38, time: 0.092, data_time: 0.007, memory: 1125, loss: 2.0481 2023-01-02 18:48:29,016 - mmcls - INFO - Epoch [38][3300/5005] lr: 1.000e-02, eta: 7:37:29, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.0742 2023-01-02 18:48:38,326 - mmcls - INFO - Epoch [38][3400/5005] lr: 1.000e-02, eta: 7:37:21, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.0835 2023-01-02 18:48:46,940 - mmcls - INFO - Epoch [38][3500/5005] lr: 1.000e-02, eta: 7:37:12, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0706 2023-01-02 18:48:55,547 - mmcls - INFO - Epoch [38][3600/5005] lr: 1.000e-02, eta: 7:37:03, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.0529 2023-01-02 18:49:04,317 - mmcls - INFO - Epoch [38][3700/5005] lr: 1.000e-02, eta: 7:36:54, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0796 2023-01-02 18:49:13,184 - mmcls - INFO - Epoch [38][3800/5005] lr: 1.000e-02, eta: 7:36:45, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0756 2023-01-02 18:49:21,973 - mmcls - INFO - Epoch [38][3900/5005] lr: 1.000e-02, eta: 7:36:36, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0753 2023-01-02 18:49:30,580 - mmcls - INFO - Epoch [38][4000/5005] lr: 1.000e-02, eta: 7:36:27, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0753 2023-01-02 18:49:39,136 - mmcls - INFO - Epoch [38][4100/5005] lr: 1.000e-02, eta: 7:36:18, time: 0.086, data_time: 0.036, memory: 1125, loss: 2.0526 2023-01-02 18:49:47,855 - mmcls - INFO - Epoch [38][4200/5005] lr: 1.000e-02, eta: 7:36:09, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0540 2023-01-02 18:49:56,168 - mmcls - INFO - Epoch [38][4300/5005] lr: 1.000e-02, eta: 7:35:59, time: 0.083, data_time: 0.011, memory: 1125, loss: 2.0617 2023-01-02 18:50:04,760 - mmcls - INFO - Epoch [38][4400/5005] lr: 1.000e-02, eta: 7:35:50, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0671 2023-01-02 18:50:13,401 - mmcls - INFO - Epoch [38][4500/5005] lr: 1.000e-02, eta: 7:35:41, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0820 2023-01-02 18:50:22,109 - mmcls - INFO - Epoch [38][4600/5005] lr: 1.000e-02, eta: 7:35:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0755 2023-01-02 18:50:31,063 - mmcls - INFO - Epoch [38][4700/5005] lr: 1.000e-02, eta: 7:35:24, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.0664 2023-01-02 18:50:39,565 - mmcls - INFO - Epoch [38][4800/5005] lr: 1.000e-02, eta: 7:35:14, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.0736 2023-01-02 18:50:48,441 - mmcls - INFO - Epoch [38][4900/5005] lr: 1.000e-02, eta: 7:35:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0619 2023-01-02 18:50:57,137 - mmcls - INFO - Epoch [38][5000/5005] lr: 1.000e-02, eta: 7:34:57, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0705 2023-01-02 18:51:19,842 - mmcls - INFO - Epoch(val) [38][196] accuracy_top-1: 58.7960, accuracy_top-5: 81.2400 2023-01-02 18:51:30,589 - mmcls - INFO - Epoch [39][100/5005] lr: 1.000e-02, eta: 7:34:50, time: 0.107, data_time: 0.037, memory: 1125, loss: 2.0226 2023-01-02 18:51:39,634 - mmcls - INFO - Epoch [39][200/5005] lr: 1.000e-02, eta: 7:34:42, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0322 2023-01-02 18:51:48,268 - mmcls - INFO - Epoch [39][300/5005] lr: 1.000e-02, eta: 7:34:33, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0261 2023-01-02 18:51:57,299 - mmcls - INFO - Epoch [39][400/5005] lr: 1.000e-02, eta: 7:34:24, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0504 2023-01-02 18:52:06,473 - mmcls - INFO - Epoch [39][500/5005] lr: 1.000e-02, eta: 7:34:16, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.0315 2023-01-02 18:52:15,216 - mmcls - INFO - Epoch [39][600/5005] lr: 1.000e-02, eta: 7:34:07, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0239 2023-01-02 18:52:23,805 - mmcls - INFO - Epoch [39][700/5005] lr: 1.000e-02, eta: 7:33:58, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0334 2023-01-02 18:52:32,566 - mmcls - INFO - Epoch [39][800/5005] lr: 1.000e-02, eta: 7:33:49, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0381 2023-01-02 18:52:41,118 - mmcls - INFO - Epoch [39][900/5005] lr: 1.000e-02, eta: 7:33:40, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0391 2023-01-02 18:52:49,656 - mmcls - INFO - Epoch [39][1000/5005] lr: 1.000e-02, eta: 7:33:31, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0427 2023-01-02 18:52:58,396 - mmcls - INFO - Epoch [39][1100/5005] lr: 1.000e-02, eta: 7:33:22, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0389 2023-01-02 18:53:07,273 - mmcls - INFO - Epoch [39][1200/5005] lr: 1.000e-02, eta: 7:33:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0575 2023-01-02 18:53:15,872 - mmcls - INFO - Epoch [39][1300/5005] lr: 1.000e-02, eta: 7:33:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0435 2023-01-02 18:53:24,698 - mmcls - INFO - Epoch [39][1400/5005] lr: 1.000e-02, eta: 7:32:55, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0758 2023-01-02 18:53:33,671 - mmcls - INFO - Epoch [39][1500/5005] lr: 1.000e-02, eta: 7:32:47, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.0444 2023-01-02 18:53:42,520 - mmcls - INFO - Epoch [39][1600/5005] lr: 1.000e-02, eta: 7:32:38, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0735 2023-01-02 18:53:51,054 - mmcls - INFO - Epoch [39][1700/5005] lr: 1.000e-02, eta: 7:32:29, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0431 2023-01-02 18:53:59,828 - mmcls - INFO - Epoch [39][1800/5005] lr: 1.000e-02, eta: 7:32:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0319 2023-01-02 18:54:08,477 - mmcls - INFO - Epoch [39][1900/5005] lr: 1.000e-02, eta: 7:32:11, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0413 2023-01-02 18:54:17,289 - mmcls - INFO - Epoch [39][2000/5005] lr: 1.000e-02, eta: 7:32:02, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0585 2023-01-02 18:54:26,147 - mmcls - INFO - Epoch [39][2100/5005] lr: 1.000e-02, eta: 7:31:53, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0829 2023-01-02 18:54:34,981 - mmcls - INFO - Epoch [39][2200/5005] lr: 1.000e-02, eta: 7:31:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0595 2023-01-02 18:54:43,816 - mmcls - INFO - Epoch [39][2300/5005] lr: 1.000e-02, eta: 7:31:36, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0686 2023-01-02 18:54:52,582 - mmcls - INFO - Epoch [39][2400/5005] lr: 1.000e-02, eta: 7:31:27, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0754 2023-01-02 18:55:01,172 - mmcls - INFO - Epoch [39][2500/5005] lr: 1.000e-02, eta: 7:31:18, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0815 2023-01-02 18:55:09,811 - mmcls - INFO - Epoch [39][2600/5005] lr: 1.000e-02, eta: 7:31:09, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0710 2023-01-02 18:55:18,516 - mmcls - INFO - Epoch [39][2700/5005] lr: 1.000e-02, eta: 7:31:00, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0511 2023-01-02 18:55:27,052 - mmcls - INFO - Epoch [39][2800/5005] lr: 1.000e-02, eta: 7:30:51, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0423 2023-01-02 18:55:35,895 - mmcls - INFO - Epoch [39][2900/5005] lr: 1.000e-02, eta: 7:30:42, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0803 2023-01-02 18:55:45,021 - mmcls - INFO - Epoch [39][3000/5005] lr: 1.000e-02, eta: 7:30:34, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0542 2023-01-02 18:55:53,897 - mmcls - INFO - Epoch [39][3100/5005] lr: 1.000e-02, eta: 7:30:25, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0474 2023-01-02 18:56:02,829 - mmcls - INFO - Epoch [39][3200/5005] lr: 1.000e-02, eta: 7:30:16, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0503 2023-01-02 18:56:11,292 - mmcls - INFO - Epoch [39][3300/5005] lr: 1.000e-02, eta: 7:30:07, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0767 2023-01-02 18:56:19,966 - mmcls - INFO - Epoch [39][3400/5005] lr: 1.000e-02, eta: 7:29:58, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0792 2023-01-02 18:56:28,824 - mmcls - INFO - Epoch [39][3500/5005] lr: 1.000e-02, eta: 7:29:49, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0661 2023-01-02 18:56:37,635 - mmcls - INFO - Epoch [39][3600/5005] lr: 1.000e-02, eta: 7:29:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0889 2023-01-02 18:56:46,477 - mmcls - INFO - Epoch [39][3700/5005] lr: 1.000e-02, eta: 7:29:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0711 2023-01-02 18:56:55,185 - mmcls - INFO - Epoch [39][3800/5005] lr: 1.000e-02, eta: 7:29:23, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0520 2023-01-02 18:57:03,642 - mmcls - INFO - Epoch [39][3900/5005] lr: 1.000e-02, eta: 7:29:14, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.0539 2023-01-02 18:57:12,546 - mmcls - INFO - Epoch [39][4000/5005] lr: 1.000e-02, eta: 7:29:05, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.0568 2023-01-02 18:57:21,351 - mmcls - INFO - Epoch [39][4100/5005] lr: 1.000e-02, eta: 7:28:56, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0594 2023-01-02 18:57:30,221 - mmcls - INFO - Epoch [39][4200/5005] lr: 1.000e-02, eta: 7:28:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0751 2023-01-02 18:57:39,170 - mmcls - INFO - Epoch [39][4300/5005] lr: 1.000e-02, eta: 7:28:39, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0411 2023-01-02 18:57:48,304 - mmcls - INFO - Epoch [39][4400/5005] lr: 1.000e-02, eta: 7:28:31, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0461 2023-01-02 18:57:57,185 - mmcls - INFO - Epoch [39][4500/5005] lr: 1.000e-02, eta: 7:28:22, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.0931 2023-01-02 18:58:05,839 - mmcls - INFO - Epoch [39][4600/5005] lr: 1.000e-02, eta: 7:28:13, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0650 2023-01-02 18:58:14,701 - mmcls - INFO - Epoch [39][4700/5005] lr: 1.000e-02, eta: 7:28:04, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.0638 2023-01-02 18:58:23,214 - mmcls - INFO - Epoch [39][4800/5005] lr: 1.000e-02, eta: 7:27:55, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0911 2023-01-02 18:58:31,784 - mmcls - INFO - Epoch [39][4900/5005] lr: 1.000e-02, eta: 7:27:46, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0683 2023-01-02 18:58:40,653 - mmcls - INFO - Epoch [39][5000/5005] lr: 1.000e-02, eta: 7:27:37, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0486 2023-01-02 18:59:03,432 - mmcls - INFO - Epoch(val) [39][196] accuracy_top-1: 59.1960, accuracy_top-5: 80.9420 2023-01-02 18:59:14,307 - mmcls - INFO - Epoch [40][100/5005] lr: 1.000e-02, eta: 7:27:31, time: 0.109, data_time: 0.044, memory: 1125, loss: 2.0355 2023-01-02 18:59:23,132 - mmcls - INFO - Epoch [40][200/5005] lr: 1.000e-02, eta: 7:27:22, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0295 2023-01-02 18:59:31,844 - mmcls - INFO - Epoch [40][300/5005] lr: 1.000e-02, eta: 7:27:13, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0420 2023-01-02 18:59:40,661 - mmcls - INFO - Epoch [40][400/5005] lr: 1.000e-02, eta: 7:27:04, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0599 2023-01-02 18:59:49,307 - mmcls - INFO - Epoch [40][500/5005] lr: 1.000e-02, eta: 7:26:55, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.0488 2023-01-02 18:59:58,063 - mmcls - INFO - Epoch [40][600/5005] lr: 1.000e-02, eta: 7:26:46, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.0273 2023-01-02 19:00:07,008 - mmcls - INFO - Epoch [40][700/5005] lr: 1.000e-02, eta: 7:26:38, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0478 2023-01-02 19:00:15,565 - mmcls - INFO - Epoch [40][800/5005] lr: 1.000e-02, eta: 7:26:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0160 2023-01-02 19:00:24,193 - mmcls - INFO - Epoch [40][900/5005] lr: 1.000e-02, eta: 7:26:19, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0689 2023-01-02 19:00:32,860 - mmcls - INFO - Epoch [40][1000/5005] lr: 1.000e-02, eta: 7:26:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0518 2023-01-02 19:00:41,218 - mmcls - INFO - Epoch [40][1100/5005] lr: 1.000e-02, eta: 7:26:01, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0487 2023-01-02 19:00:49,874 - mmcls - INFO - Epoch [40][1200/5005] lr: 1.000e-02, eta: 7:25:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0548 2023-01-02 19:00:58,577 - mmcls - INFO - Epoch [40][1300/5005] lr: 1.000e-02, eta: 7:25:43, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0291 2023-01-02 19:01:07,439 - mmcls - INFO - Epoch [40][1400/5005] lr: 1.000e-02, eta: 7:25:34, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0580 2023-01-02 19:01:16,345 - mmcls - INFO - Epoch [40][1500/5005] lr: 1.000e-02, eta: 7:25:26, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0444 2023-01-02 19:01:24,746 - mmcls - INFO - Epoch [40][1600/5005] lr: 1.000e-02, eta: 7:25:16, time: 0.084, data_time: 0.002, memory: 1125, loss: 2.0535 2023-01-02 19:01:34,208 - mmcls - INFO - Epoch [40][1700/5005] lr: 1.000e-02, eta: 7:25:08, time: 0.095, data_time: 0.010, memory: 1125, loss: 2.0217 2023-01-02 19:01:42,803 - mmcls - INFO - Epoch [40][1800/5005] lr: 1.000e-02, eta: 7:24:59, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0707 2023-01-02 19:01:51,433 - mmcls - INFO - Epoch [40][1900/5005] lr: 1.000e-02, eta: 7:24:50, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.0558 2023-01-02 19:02:00,160 - mmcls - INFO - Epoch [40][2000/5005] lr: 1.000e-02, eta: 7:24:41, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0537 2023-01-02 19:02:08,855 - mmcls - INFO - Epoch [40][2100/5005] lr: 1.000e-02, eta: 7:24:32, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0492 2023-01-02 19:02:17,521 - mmcls - INFO - Epoch [40][2200/5005] lr: 1.000e-02, eta: 7:24:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0513 2023-01-02 19:02:26,102 - mmcls - INFO - Epoch [40][2300/5005] lr: 1.000e-02, eta: 7:24:14, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0686 2023-01-02 19:02:34,527 - mmcls - INFO - Epoch [40][2400/5005] lr: 1.000e-02, eta: 7:24:05, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.0691 2023-01-02 19:02:43,276 - mmcls - INFO - Epoch [40][2500/5005] lr: 1.000e-02, eta: 7:23:56, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0537 2023-01-02 19:02:52,350 - mmcls - INFO - Epoch [40][2600/5005] lr: 1.000e-02, eta: 7:23:48, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.0470 2023-01-02 19:03:01,053 - mmcls - INFO - Epoch [40][2700/5005] lr: 1.000e-02, eta: 7:23:39, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0394 2023-01-02 19:03:09,584 - mmcls - INFO - Epoch [40][2800/5005] lr: 1.000e-02, eta: 7:23:30, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.0484 2023-01-02 19:03:18,320 - mmcls - INFO - Epoch [40][2900/5005] lr: 1.000e-02, eta: 7:23:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0699 2023-01-02 19:03:26,904 - mmcls - INFO - Epoch [40][3000/5005] lr: 1.000e-02, eta: 7:23:12, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0958 2023-01-02 19:03:35,631 - mmcls - INFO - Epoch [40][3100/5005] lr: 1.000e-02, eta: 7:23:03, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0650 2023-01-02 19:03:44,323 - mmcls - INFO - Epoch [40][3200/5005] lr: 1.000e-02, eta: 7:22:54, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0698 2023-01-02 19:03:53,026 - mmcls - INFO - Epoch [40][3300/5005] lr: 1.000e-02, eta: 7:22:45, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0492 2023-01-02 19:04:01,793 - mmcls - INFO - Epoch [40][3400/5005] lr: 1.000e-02, eta: 7:22:36, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0664 2023-01-02 19:04:10,274 - mmcls - INFO - Epoch [40][3500/5005] lr: 1.000e-02, eta: 7:22:27, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0584 2023-01-02 19:04:18,936 - mmcls - INFO - Epoch [40][3600/5005] lr: 1.000e-02, eta: 7:22:18, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0411 2023-01-02 19:04:27,578 - mmcls - INFO - Epoch [40][3700/5005] lr: 1.000e-02, eta: 7:22:09, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0729 2023-01-02 19:04:36,163 - mmcls - INFO - Epoch [40][3800/5005] lr: 1.000e-02, eta: 7:21:59, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0601 2023-01-02 19:04:45,130 - mmcls - INFO - Epoch [40][3900/5005] lr: 1.000e-02, eta: 7:21:51, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0609 2023-01-02 19:04:53,824 - mmcls - INFO - Epoch [40][4000/5005] lr: 1.000e-02, eta: 7:21:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0927 2023-01-02 19:05:02,239 - mmcls - INFO - Epoch [40][4100/5005] lr: 1.000e-02, eta: 7:21:33, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.0721 2023-01-02 19:05:10,851 - mmcls - INFO - Epoch [40][4200/5005] lr: 1.000e-02, eta: 7:21:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0539 2023-01-02 19:05:19,315 - mmcls - INFO - Epoch [40][4300/5005] lr: 1.000e-02, eta: 7:21:14, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0572 2023-01-02 19:05:28,762 - mmcls - INFO - Epoch [40][4400/5005] lr: 1.000e-02, eta: 7:21:06, time: 0.094, data_time: 0.013, memory: 1125, loss: 2.0562 2023-01-02 19:05:37,913 - mmcls - INFO - Epoch [40][4500/5005] lr: 1.000e-02, eta: 7:20:58, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0483 2023-01-02 19:05:46,671 - mmcls - INFO - Epoch [40][4600/5005] lr: 1.000e-02, eta: 7:20:49, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0915 2023-01-02 19:05:55,361 - mmcls - INFO - Epoch [40][4700/5005] lr: 1.000e-02, eta: 7:20:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0636 2023-01-02 19:06:04,219 - mmcls - INFO - Epoch [40][4800/5005] lr: 1.000e-02, eta: 7:20:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0723 2023-01-02 19:06:13,150 - mmcls - INFO - Epoch [40][4900/5005] lr: 1.000e-02, eta: 7:20:23, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0658 2023-01-02 19:06:21,559 - mmcls - INFO - Epoch [40][5000/5005] lr: 1.000e-02, eta: 7:20:14, time: 0.084, data_time: 0.011, memory: 1125, loss: 2.0304 2023-01-02 19:06:44,091 - mmcls - INFO - Epoch(val) [40][196] accuracy_top-1: 59.2180, accuracy_top-5: 81.6340 2023-01-02 19:06:55,035 - mmcls - INFO - Epoch [41][100/5005] lr: 1.000e-02, eta: 7:20:07, time: 0.109, data_time: 0.037, memory: 1125, loss: 2.0206 2023-01-02 19:07:03,855 - mmcls - INFO - Epoch [41][200/5005] lr: 1.000e-02, eta: 7:19:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0410 2023-01-02 19:07:12,423 - mmcls - INFO - Epoch [41][300/5005] lr: 1.000e-02, eta: 7:19:49, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0550 2023-01-02 19:07:21,312 - mmcls - INFO - Epoch [41][400/5005] lr: 1.000e-02, eta: 7:19:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0275 2023-01-02 19:07:30,049 - mmcls - INFO - Epoch [41][500/5005] lr: 1.000e-02, eta: 7:19:32, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0277 2023-01-02 19:07:38,766 - mmcls - INFO - Epoch [41][600/5005] lr: 1.000e-02, eta: 7:19:23, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0472 2023-01-02 19:07:47,421 - mmcls - INFO - Epoch [41][700/5005] lr: 1.000e-02, eta: 7:19:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0393 2023-01-02 19:07:55,973 - mmcls - INFO - Epoch [41][800/5005] lr: 1.000e-02, eta: 7:19:04, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.0328 2023-01-02 19:08:04,777 - mmcls - INFO - Epoch [41][900/5005] lr: 1.000e-02, eta: 7:18:56, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0388 2023-01-02 19:08:13,583 - mmcls - INFO - Epoch [41][1000/5005] lr: 1.000e-02, eta: 7:18:47, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.0324 2023-01-02 19:08:22,277 - mmcls - INFO - Epoch [41][1100/5005] lr: 1.000e-02, eta: 7:18:38, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0051 2023-01-02 19:08:30,940 - mmcls - INFO - Epoch [41][1200/5005] lr: 1.000e-02, eta: 7:18:29, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0474 2023-01-02 19:08:39,458 - mmcls - INFO - Epoch [41][1300/5005] lr: 1.000e-02, eta: 7:18:20, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.0622 2023-01-02 19:08:48,050 - mmcls - INFO - Epoch [41][1400/5005] lr: 1.000e-02, eta: 7:18:11, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0499 2023-01-02 19:08:56,769 - mmcls - INFO - Epoch [41][1500/5005] lr: 1.000e-02, eta: 7:18:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0262 2023-01-02 19:09:05,547 - mmcls - INFO - Epoch [41][1600/5005] lr: 1.000e-02, eta: 7:17:53, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0568 2023-01-02 19:09:14,140 - mmcls - INFO - Epoch [41][1700/5005] lr: 1.000e-02, eta: 7:17:44, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0637 2023-01-02 19:09:22,967 - mmcls - INFO - Epoch [41][1800/5005] lr: 1.000e-02, eta: 7:17:35, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0690 2023-01-02 19:09:31,753 - mmcls - INFO - Epoch [41][1900/5005] lr: 1.000e-02, eta: 7:17:26, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0488 2023-01-02 19:09:40,408 - mmcls - INFO - Epoch [41][2000/5005] lr: 1.000e-02, eta: 7:17:17, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0353 2023-01-02 19:09:48,978 - mmcls - INFO - Epoch [41][2100/5005] lr: 1.000e-02, eta: 7:17:08, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0460 2023-01-02 19:09:57,471 - mmcls - INFO - Epoch [41][2200/5005] lr: 1.000e-02, eta: 7:16:59, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0665 2023-01-02 19:10:05,994 - mmcls - INFO - Epoch [41][2300/5005] lr: 1.000e-02, eta: 7:16:50, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0430 2023-01-02 19:10:14,603 - mmcls - INFO - Epoch [41][2400/5005] lr: 1.000e-02, eta: 7:16:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0228 2023-01-02 19:10:23,295 - mmcls - INFO - Epoch [41][2500/5005] lr: 1.000e-02, eta: 7:16:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0593 2023-01-02 19:10:32,408 - mmcls - INFO - Epoch [41][2600/5005] lr: 1.000e-02, eta: 7:16:23, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0586 2023-01-02 19:10:41,011 - mmcls - INFO - Epoch [41][2700/5005] lr: 1.000e-02, eta: 7:16:14, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0804 2023-01-02 19:10:49,610 - mmcls - INFO - Epoch [41][2800/5005] lr: 1.000e-02, eta: 7:16:05, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.0679 2023-01-02 19:10:58,339 - mmcls - INFO - Epoch [41][2900/5005] lr: 1.000e-02, eta: 7:15:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0679 2023-01-02 19:11:07,106 - mmcls - INFO - Epoch [41][3000/5005] lr: 1.000e-02, eta: 7:15:47, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0683 2023-01-02 19:11:15,596 - mmcls - INFO - Epoch [41][3100/5005] lr: 1.000e-02, eta: 7:15:38, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.0547 2023-01-02 19:11:24,896 - mmcls - INFO - Epoch [41][3200/5005] lr: 1.000e-02, eta: 7:15:30, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.0576 2023-01-02 19:11:33,446 - mmcls - INFO - Epoch [41][3300/5005] lr: 1.000e-02, eta: 7:15:21, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0601 2023-01-02 19:11:42,218 - mmcls - INFO - Epoch [41][3400/5005] lr: 1.000e-02, eta: 7:15:12, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.0559 2023-01-02 19:11:50,881 - mmcls - INFO - Epoch [41][3500/5005] lr: 1.000e-02, eta: 7:15:03, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0569 2023-01-02 19:11:59,729 - mmcls - INFO - Epoch [41][3600/5005] lr: 1.000e-02, eta: 7:14:54, time: 0.088, data_time: 0.024, memory: 1125, loss: 2.0590 2023-01-02 19:12:08,532 - mmcls - INFO - Epoch [41][3700/5005] lr: 1.000e-02, eta: 7:14:46, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.0708 2023-01-02 19:12:17,217 - mmcls - INFO - Epoch [41][3800/5005] lr: 1.000e-02, eta: 7:14:37, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0531 2023-01-02 19:12:25,921 - mmcls - INFO - Epoch [41][3900/5005] lr: 1.000e-02, eta: 7:14:28, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0418 2023-01-02 19:12:34,725 - mmcls - INFO - Epoch [41][4000/5005] lr: 1.000e-02, eta: 7:14:19, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.0732 2023-01-02 19:12:43,566 - mmcls - INFO - Epoch [41][4100/5005] lr: 1.000e-02, eta: 7:14:10, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0670 2023-01-02 19:12:52,422 - mmcls - INFO - Epoch [41][4200/5005] lr: 1.000e-02, eta: 7:14:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0552 2023-01-02 19:13:01,531 - mmcls - INFO - Epoch [41][4300/5005] lr: 1.000e-02, eta: 7:13:53, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0599 2023-01-02 19:13:10,107 - mmcls - INFO - Epoch [41][4400/5005] lr: 1.000e-02, eta: 7:13:44, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0668 2023-01-02 19:13:18,870 - mmcls - INFO - Epoch [41][4500/5005] lr: 1.000e-02, eta: 7:13:35, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0704 2023-01-02 19:13:28,022 - mmcls - INFO - Epoch [41][4600/5005] lr: 1.000e-02, eta: 7:13:27, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0771 2023-01-02 19:13:36,925 - mmcls - INFO - Epoch [41][4700/5005] lr: 1.000e-02, eta: 7:13:18, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0380 2023-01-02 19:13:45,726 - mmcls - INFO - Epoch [41][4800/5005] lr: 1.000e-02, eta: 7:13:09, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0707 2023-01-02 19:13:54,644 - mmcls - INFO - Epoch [41][4900/5005] lr: 1.000e-02, eta: 7:13:01, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0827 2023-01-02 19:14:03,528 - mmcls - INFO - Epoch [41][5000/5005] lr: 1.000e-02, eta: 7:12:52, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0528 2023-01-02 19:14:26,336 - mmcls - INFO - Epoch(val) [41][196] accuracy_top-1: 59.2860, accuracy_top-5: 81.3060 2023-01-02 19:14:37,263 - mmcls - INFO - Epoch [42][100/5005] lr: 1.000e-02, eta: 7:12:45, time: 0.109, data_time: 0.039, memory: 1125, loss: 2.0201 2023-01-02 19:14:46,120 - mmcls - INFO - Epoch [42][200/5005] lr: 1.000e-02, eta: 7:12:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0359 2023-01-02 19:14:55,169 - mmcls - INFO - Epoch [42][300/5005] lr: 1.000e-02, eta: 7:12:28, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.0276 2023-01-02 19:15:03,891 - mmcls - INFO - Epoch [42][400/5005] lr: 1.000e-02, eta: 7:12:19, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.0330 2023-01-02 19:15:12,520 - mmcls - INFO - Epoch [42][500/5005] lr: 1.000e-02, eta: 7:12:10, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0245 2023-01-02 19:15:21,345 - mmcls - INFO - Epoch [42][600/5005] lr: 1.000e-02, eta: 7:12:02, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0241 2023-01-02 19:15:29,953 - mmcls - INFO - Epoch [42][700/5005] lr: 1.000e-02, eta: 7:11:52, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0413 2023-01-02 19:15:38,627 - mmcls - INFO - Epoch [42][800/5005] lr: 1.000e-02, eta: 7:11:43, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0398 2023-01-02 19:15:47,203 - mmcls - INFO - Epoch [42][900/5005] lr: 1.000e-02, eta: 7:11:34, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.0349 2023-01-02 19:15:56,049 - mmcls - INFO - Epoch [42][1000/5005] lr: 1.000e-02, eta: 7:11:26, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0093 2023-01-02 19:16:04,752 - mmcls - INFO - Epoch [42][1100/5005] lr: 1.000e-02, eta: 7:11:17, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0230 2023-01-02 19:16:13,841 - mmcls - INFO - Epoch [42][1200/5005] lr: 1.000e-02, eta: 7:11:08, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.0399 2023-01-02 19:16:22,728 - mmcls - INFO - Epoch [42][1300/5005] lr: 1.000e-02, eta: 7:11:00, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0180 2023-01-02 19:16:31,671 - mmcls - INFO - Epoch [42][1400/5005] lr: 1.000e-02, eta: 7:10:51, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.0505 2023-01-02 19:16:40,217 - mmcls - INFO - Epoch [42][1500/5005] lr: 1.000e-02, eta: 7:10:42, time: 0.085, data_time: 0.028, memory: 1125, loss: 2.0487 2023-01-02 19:16:48,802 - mmcls - INFO - Epoch [42][1600/5005] lr: 1.000e-02, eta: 7:10:33, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0430 2023-01-02 19:16:57,523 - mmcls - INFO - Epoch [42][1700/5005] lr: 1.000e-02, eta: 7:10:24, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.0591 2023-01-02 19:17:06,396 - mmcls - INFO - Epoch [42][1800/5005] lr: 1.000e-02, eta: 7:10:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0412 2023-01-02 19:17:14,871 - mmcls - INFO - Epoch [42][1900/5005] lr: 1.000e-02, eta: 7:10:06, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.0590 2023-01-02 19:17:23,740 - mmcls - INFO - Epoch [42][2000/5005] lr: 1.000e-02, eta: 7:09:57, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0734 2023-01-02 19:17:32,502 - mmcls - INFO - Epoch [42][2100/5005] lr: 1.000e-02, eta: 7:09:48, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0546 2023-01-02 19:17:41,195 - mmcls - INFO - Epoch [42][2200/5005] lr: 1.000e-02, eta: 7:09:40, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0467 2023-01-02 19:17:50,057 - mmcls - INFO - Epoch [42][2300/5005] lr: 1.000e-02, eta: 7:09:31, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0598 2023-01-02 19:17:59,144 - mmcls - INFO - Epoch [42][2400/5005] lr: 1.000e-02, eta: 7:09:22, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0711 2023-01-02 19:18:08,347 - mmcls - INFO - Epoch [42][2500/5005] lr: 1.000e-02, eta: 7:09:14, time: 0.092, data_time: 0.029, memory: 1125, loss: 2.0438 2023-01-02 19:18:17,270 - mmcls - INFO - Epoch [42][2600/5005] lr: 1.000e-02, eta: 7:09:06, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.0659 2023-01-02 19:18:25,984 - mmcls - INFO - Epoch [42][2700/5005] lr: 1.000e-02, eta: 7:08:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0652 2023-01-02 19:18:34,670 - mmcls - INFO - Epoch [42][2800/5005] lr: 1.000e-02, eta: 7:08:48, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0534 2023-01-02 19:18:43,472 - mmcls - INFO - Epoch [42][2900/5005] lr: 1.000e-02, eta: 7:08:39, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.0411 2023-01-02 19:18:52,311 - mmcls - INFO - Epoch [42][3000/5005] lr: 1.000e-02, eta: 7:08:30, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0561 2023-01-02 19:19:00,715 - mmcls - INFO - Epoch [42][3100/5005] lr: 1.000e-02, eta: 7:08:21, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.0561 2023-01-02 19:19:09,666 - mmcls - INFO - Epoch [42][3200/5005] lr: 1.000e-02, eta: 7:08:12, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0707 2023-01-02 19:19:18,550 - mmcls - INFO - Epoch [42][3300/5005] lr: 1.000e-02, eta: 7:08:04, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0470 2023-01-02 19:19:27,144 - mmcls - INFO - Epoch [42][3400/5005] lr: 1.000e-02, eta: 7:07:55, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.0452 2023-01-02 19:19:35,905 - mmcls - INFO - Epoch [42][3500/5005] lr: 1.000e-02, eta: 7:07:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0331 2023-01-02 19:19:44,472 - mmcls - INFO - Epoch [42][3600/5005] lr: 1.000e-02, eta: 7:07:37, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0612 2023-01-02 19:19:53,138 - mmcls - INFO - Epoch [42][3700/5005] lr: 1.000e-02, eta: 7:07:28, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0736 2023-01-02 19:20:01,727 - mmcls - INFO - Epoch [42][3800/5005] lr: 1.000e-02, eta: 7:07:18, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0680 2023-01-02 19:20:10,766 - mmcls - INFO - Epoch [42][3900/5005] lr: 1.000e-02, eta: 7:07:10, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0908 2023-01-02 19:20:19,687 - mmcls - INFO - Epoch [42][4000/5005] lr: 1.000e-02, eta: 7:07:01, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0831 2023-01-02 19:20:28,749 - mmcls - INFO - Epoch [42][4100/5005] lr: 1.000e-02, eta: 7:06:53, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.0994 2023-01-02 19:20:37,653 - mmcls - INFO - Epoch [42][4200/5005] lr: 1.000e-02, eta: 7:06:44, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0420 2023-01-02 19:20:46,234 - mmcls - INFO - Epoch [42][4300/5005] lr: 1.000e-02, eta: 7:06:35, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0897 2023-01-02 19:20:55,018 - mmcls - INFO - Epoch [42][4400/5005] lr: 1.000e-02, eta: 7:06:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0532 2023-01-02 19:21:03,666 - mmcls - INFO - Epoch [42][4500/5005] lr: 1.000e-02, eta: 7:06:17, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0636 2023-01-02 19:21:12,559 - mmcls - INFO - Epoch [42][4600/5005] lr: 1.000e-02, eta: 7:06:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0605 2023-01-02 19:21:21,089 - mmcls - INFO - Epoch [42][4700/5005] lr: 1.000e-02, eta: 7:06:00, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.0729 2023-01-02 19:21:29,954 - mmcls - INFO - Epoch [42][4800/5005] lr: 1.000e-02, eta: 7:05:51, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0899 2023-01-02 19:21:38,660 - mmcls - INFO - Epoch [42][4900/5005] lr: 1.000e-02, eta: 7:05:42, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0623 2023-01-02 19:21:47,373 - mmcls - INFO - Epoch [42][5000/5005] lr: 1.000e-02, eta: 7:05:33, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0848 2023-01-02 19:22:09,975 - mmcls - INFO - Epoch(val) [42][196] accuracy_top-1: 58.8560, accuracy_top-5: 81.1120 2023-01-02 19:22:20,966 - mmcls - INFO - Epoch [43][100/5005] lr: 1.000e-02, eta: 7:05:26, time: 0.110, data_time: 0.035, memory: 1125, loss: 2.0031 2023-01-02 19:22:29,648 - mmcls - INFO - Epoch [43][200/5005] lr: 1.000e-02, eta: 7:05:17, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0094 2023-01-02 19:22:38,284 - mmcls - INFO - Epoch [43][300/5005] lr: 1.000e-02, eta: 7:05:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0286 2023-01-02 19:22:46,923 - mmcls - INFO - Epoch [43][400/5005] lr: 1.000e-02, eta: 7:04:59, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.0053 2023-01-02 19:22:55,828 - mmcls - INFO - Epoch [43][500/5005] lr: 1.000e-02, eta: 7:04:51, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.0237 2023-01-02 19:23:04,530 - mmcls - INFO - Epoch [43][600/5005] lr: 1.000e-02, eta: 7:04:42, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0413 2023-01-02 19:23:14,361 - mmcls - INFO - Epoch [43][700/5005] lr: 1.000e-02, eta: 7:04:34, time: 0.098, data_time: 0.007, memory: 1125, loss: 2.0514 2023-01-02 19:23:23,055 - mmcls - INFO - Epoch [43][800/5005] lr: 1.000e-02, eta: 7:04:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0255 2023-01-02 19:23:32,005 - mmcls - INFO - Epoch [43][900/5005] lr: 1.000e-02, eta: 7:04:17, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.0356 2023-01-02 19:23:40,981 - mmcls - INFO - Epoch [43][1000/5005] lr: 1.000e-02, eta: 7:04:08, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.0292 2023-01-02 19:23:49,965 - mmcls - INFO - Epoch [43][1100/5005] lr: 1.000e-02, eta: 7:04:00, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.0171 2023-01-02 19:23:58,724 - mmcls - INFO - Epoch [43][1200/5005] lr: 1.000e-02, eta: 7:03:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0468 2023-01-02 19:24:07,554 - mmcls - INFO - Epoch [43][1300/5005] lr: 1.000e-02, eta: 7:03:42, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0623 2023-01-02 19:24:17,236 - mmcls - INFO - Epoch [43][1400/5005] lr: 1.000e-02, eta: 7:03:34, time: 0.097, data_time: 0.001, memory: 1125, loss: 2.0479 2023-01-02 19:24:25,942 - mmcls - INFO - Epoch [43][1500/5005] lr: 1.000e-02, eta: 7:03:26, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0295 2023-01-02 19:24:34,943 - mmcls - INFO - Epoch [43][1600/5005] lr: 1.000e-02, eta: 7:03:17, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.0380 2023-01-02 19:24:44,027 - mmcls - INFO - Epoch [43][1700/5005] lr: 1.000e-02, eta: 7:03:09, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0525 2023-01-02 19:24:53,018 - mmcls - INFO - Epoch [43][1800/5005] lr: 1.000e-02, eta: 7:03:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0218 2023-01-02 19:25:01,683 - mmcls - INFO - Epoch [43][1900/5005] lr: 1.000e-02, eta: 7:02:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0575 2023-01-02 19:25:10,416 - mmcls - INFO - Epoch [43][2000/5005] lr: 1.000e-02, eta: 7:02:42, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0311 2023-01-02 19:25:19,121 - mmcls - INFO - Epoch [43][2100/5005] lr: 1.000e-02, eta: 7:02:33, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0610 2023-01-02 19:25:27,997 - mmcls - INFO - Epoch [43][2200/5005] lr: 1.000e-02, eta: 7:02:25, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.0495 2023-01-02 19:25:36,604 - mmcls - INFO - Epoch [43][2300/5005] lr: 1.000e-02, eta: 7:02:16, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0472 2023-01-02 19:25:45,434 - mmcls - INFO - Epoch [43][2400/5005] lr: 1.000e-02, eta: 7:02:07, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0713 2023-01-02 19:25:54,138 - mmcls - INFO - Epoch [43][2500/5005] lr: 1.000e-02, eta: 7:01:58, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0467 2023-01-02 19:26:02,925 - mmcls - INFO - Epoch [43][2600/5005] lr: 1.000e-02, eta: 7:01:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0601 2023-01-02 19:26:11,576 - mmcls - INFO - Epoch [43][2700/5005] lr: 1.000e-02, eta: 7:01:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0469 2023-01-02 19:26:20,298 - mmcls - INFO - Epoch [43][2800/5005] lr: 1.000e-02, eta: 7:01:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0539 2023-01-02 19:26:28,912 - mmcls - INFO - Epoch [43][2900/5005] lr: 1.000e-02, eta: 7:01:22, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.0857 2023-01-02 19:26:37,719 - mmcls - INFO - Epoch [43][3000/5005] lr: 1.000e-02, eta: 7:01:13, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.0759 2023-01-02 19:26:46,506 - mmcls - INFO - Epoch [43][3100/5005] lr: 1.000e-02, eta: 7:01:04, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0348 2023-01-02 19:26:54,988 - mmcls - INFO - Epoch [43][3200/5005] lr: 1.000e-02, eta: 7:00:55, time: 0.085, data_time: 0.022, memory: 1125, loss: 2.0667 2023-01-02 19:27:03,563 - mmcls - INFO - Epoch [43][3300/5005] lr: 1.000e-02, eta: 7:00:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0591 2023-01-02 19:27:12,367 - mmcls - INFO - Epoch [43][3400/5005] lr: 1.000e-02, eta: 7:00:37, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0729 2023-01-02 19:27:20,977 - mmcls - INFO - Epoch [43][3500/5005] lr: 1.000e-02, eta: 7:00:28, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0449 2023-01-02 19:27:29,758 - mmcls - INFO - Epoch [43][3600/5005] lr: 1.000e-02, eta: 7:00:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0702 2023-01-02 19:27:38,660 - mmcls - INFO - Epoch [43][3700/5005] lr: 1.000e-02, eta: 7:00:11, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0670 2023-01-02 19:27:47,216 - mmcls - INFO - Epoch [43][3800/5005] lr: 1.000e-02, eta: 7:00:02, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0778 2023-01-02 19:27:55,879 - mmcls - INFO - Epoch [43][3900/5005] lr: 1.000e-02, eta: 6:59:53, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0840 2023-01-02 19:28:04,848 - mmcls - INFO - Epoch [43][4000/5005] lr: 1.000e-02, eta: 6:59:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0543 2023-01-02 19:28:13,651 - mmcls - INFO - Epoch [43][4100/5005] lr: 1.000e-02, eta: 6:59:35, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0592 2023-01-02 19:28:22,332 - mmcls - INFO - Epoch [43][4200/5005] lr: 1.000e-02, eta: 6:59:26, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0636 2023-01-02 19:28:30,809 - mmcls - INFO - Epoch [43][4300/5005] lr: 1.000e-02, eta: 6:59:17, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0740 2023-01-02 19:28:39,330 - mmcls - INFO - Epoch [43][4400/5005] lr: 1.000e-02, eta: 6:59:08, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.0485 2023-01-02 19:28:47,911 - mmcls - INFO - Epoch [43][4500/5005] lr: 1.000e-02, eta: 6:58:59, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.0637 2023-01-02 19:28:57,288 - mmcls - INFO - Epoch [43][4600/5005] lr: 1.000e-02, eta: 6:58:51, time: 0.094, data_time: 0.002, memory: 1125, loss: 2.0945 2023-01-02 19:29:06,209 - mmcls - INFO - Epoch [43][4700/5005] lr: 1.000e-02, eta: 6:58:42, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.0635 2023-01-02 19:29:15,128 - mmcls - INFO - Epoch [43][4800/5005] lr: 1.000e-02, eta: 6:58:34, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.0715 2023-01-02 19:29:24,009 - mmcls - INFO - Epoch [43][4900/5005] lr: 1.000e-02, eta: 6:58:25, time: 0.089, data_time: 0.031, memory: 1125, loss: 2.0785 2023-01-02 19:29:32,502 - mmcls - INFO - Epoch [43][5000/5005] lr: 1.000e-02, eta: 6:58:16, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0666 2023-01-02 19:29:55,163 - mmcls - INFO - Epoch(val) [43][196] accuracy_top-1: 58.8480, accuracy_top-5: 81.3240 2023-01-02 19:30:06,658 - mmcls - INFO - Epoch [44][100/5005] lr: 1.000e-02, eta: 6:58:10, time: 0.115, data_time: 0.050, memory: 1125, loss: 2.0243 2023-01-02 19:30:15,277 - mmcls - INFO - Epoch [44][200/5005] lr: 1.000e-02, eta: 6:58:00, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.9881 2023-01-02 19:30:24,236 - mmcls - INFO - Epoch [44][300/5005] lr: 1.000e-02, eta: 6:57:52, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0104 2023-01-02 19:30:33,060 - mmcls - INFO - Epoch [44][400/5005] lr: 1.000e-02, eta: 6:57:43, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.0223 2023-01-02 19:30:41,680 - mmcls - INFO - Epoch [44][500/5005] lr: 1.000e-02, eta: 6:57:34, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0276 2023-01-02 19:30:50,541 - mmcls - INFO - Epoch [44][600/5005] lr: 1.000e-02, eta: 6:57:25, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0231 2023-01-02 19:30:59,255 - mmcls - INFO - Epoch [44][700/5005] lr: 1.000e-02, eta: 6:57:16, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0363 2023-01-02 19:31:08,014 - mmcls - INFO - Epoch [44][800/5005] lr: 1.000e-02, eta: 6:57:08, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0514 2023-01-02 19:31:16,413 - mmcls - INFO - Epoch [44][900/5005] lr: 1.000e-02, eta: 6:56:58, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.0130 2023-01-02 19:31:25,217 - mmcls - INFO - Epoch [44][1000/5005] lr: 1.000e-02, eta: 6:56:50, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0413 2023-01-02 19:31:33,767 - mmcls - INFO - Epoch [44][1100/5005] lr: 1.000e-02, eta: 6:56:40, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0430 2023-01-02 19:31:42,563 - mmcls - INFO - Epoch [44][1200/5005] lr: 1.000e-02, eta: 6:56:32, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.0730 2023-01-02 19:31:51,211 - mmcls - INFO - Epoch [44][1300/5005] lr: 1.000e-02, eta: 6:56:23, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0586 2023-01-02 19:32:00,298 - mmcls - INFO - Epoch [44][1400/5005] lr: 1.000e-02, eta: 6:56:14, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0206 2023-01-02 19:32:09,513 - mmcls - INFO - Epoch [44][1500/5005] lr: 1.000e-02, eta: 6:56:06, time: 0.092, data_time: 0.013, memory: 1125, loss: 2.0475 2023-01-02 19:32:18,030 - mmcls - INFO - Epoch [44][1600/5005] lr: 1.000e-02, eta: 6:55:57, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.0439 2023-01-02 19:32:26,684 - mmcls - INFO - Epoch [44][1700/5005] lr: 1.000e-02, eta: 6:55:48, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0348 2023-01-02 19:32:35,175 - mmcls - INFO - Epoch [44][1800/5005] lr: 1.000e-02, eta: 6:55:39, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0526 2023-01-02 19:32:43,883 - mmcls - INFO - Epoch [44][1900/5005] lr: 1.000e-02, eta: 6:55:30, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0498 2023-01-02 19:32:52,702 - mmcls - INFO - Epoch [44][2000/5005] lr: 1.000e-02, eta: 6:55:21, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0560 2023-01-02 19:33:01,367 - mmcls - INFO - Epoch [44][2100/5005] lr: 1.000e-02, eta: 6:55:12, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0760 2023-01-02 19:33:10,210 - mmcls - INFO - Epoch [44][2200/5005] lr: 1.000e-02, eta: 6:55:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0520 2023-01-02 19:33:18,882 - mmcls - INFO - Epoch [44][2300/5005] lr: 1.000e-02, eta: 6:54:54, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0826 2023-01-02 19:33:27,462 - mmcls - INFO - Epoch [44][2400/5005] lr: 1.000e-02, eta: 6:54:45, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.0505 2023-01-02 19:33:36,412 - mmcls - INFO - Epoch [44][2500/5005] lr: 1.000e-02, eta: 6:54:37, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0472 2023-01-02 19:33:45,053 - mmcls - INFO - Epoch [44][2600/5005] lr: 1.000e-02, eta: 6:54:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0342 2023-01-02 19:33:54,092 - mmcls - INFO - Epoch [44][2700/5005] lr: 1.000e-02, eta: 6:54:19, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0515 2023-01-02 19:34:02,664 - mmcls - INFO - Epoch [44][2800/5005] lr: 1.000e-02, eta: 6:54:10, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.0493 2023-01-02 19:34:11,652 - mmcls - INFO - Epoch [44][2900/5005] lr: 1.000e-02, eta: 6:54:01, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.0526 2023-01-02 19:34:20,401 - mmcls - INFO - Epoch [44][3000/5005] lr: 1.000e-02, eta: 6:53:53, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0714 2023-01-02 19:34:29,408 - mmcls - INFO - Epoch [44][3100/5005] lr: 1.000e-02, eta: 6:53:44, time: 0.090, data_time: 0.038, memory: 1125, loss: 2.0642 2023-01-02 19:34:38,179 - mmcls - INFO - Epoch [44][3200/5005] lr: 1.000e-02, eta: 6:53:35, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0664 2023-01-02 19:34:46,775 - mmcls - INFO - Epoch [44][3300/5005] lr: 1.000e-02, eta: 6:53:26, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0311 2023-01-02 19:34:55,765 - mmcls - INFO - Epoch [44][3400/5005] lr: 1.000e-02, eta: 6:53:18, time: 0.090, data_time: 0.018, memory: 1125, loss: 2.0469 2023-01-02 19:35:04,418 - mmcls - INFO - Epoch [44][3500/5005] lr: 1.000e-02, eta: 6:53:09, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0471 2023-01-02 19:35:13,194 - mmcls - INFO - Epoch [44][3600/5005] lr: 1.000e-02, eta: 6:53:00, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0577 2023-01-02 19:35:21,971 - mmcls - INFO - Epoch [44][3700/5005] lr: 1.000e-02, eta: 6:52:51, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0416 2023-01-02 19:35:30,859 - mmcls - INFO - Epoch [44][3800/5005] lr: 1.000e-02, eta: 6:52:42, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0387 2023-01-02 19:35:39,525 - mmcls - INFO - Epoch [44][3900/5005] lr: 1.000e-02, eta: 6:52:33, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.0577 2023-01-02 19:35:48,032 - mmcls - INFO - Epoch [44][4000/5005] lr: 1.000e-02, eta: 6:52:24, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0653 2023-01-02 19:35:56,702 - mmcls - INFO - Epoch [44][4100/5005] lr: 1.000e-02, eta: 6:52:15, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0706 2023-01-02 19:36:05,488 - mmcls - INFO - Epoch [44][4200/5005] lr: 1.000e-02, eta: 6:52:06, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.0574 2023-01-02 19:36:13,986 - mmcls - INFO - Epoch [44][4300/5005] lr: 1.000e-02, eta: 6:51:57, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0704 2023-01-02 19:36:22,595 - mmcls - INFO - Epoch [44][4400/5005] lr: 1.000e-02, eta: 6:51:48, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0868 2023-01-02 19:36:31,189 - mmcls - INFO - Epoch [44][4500/5005] lr: 1.000e-02, eta: 6:51:39, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.0511 2023-01-02 19:36:40,109 - mmcls - INFO - Epoch [44][4600/5005] lr: 1.000e-02, eta: 6:51:30, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0741 2023-01-02 19:36:48,988 - mmcls - INFO - Epoch [44][4700/5005] lr: 1.000e-02, eta: 6:51:22, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.0772 2023-01-02 19:36:57,756 - mmcls - INFO - Epoch [44][4800/5005] lr: 1.000e-02, eta: 6:51:13, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0461 2023-01-02 19:37:06,704 - mmcls - INFO - Epoch [44][4900/5005] lr: 1.000e-02, eta: 6:51:04, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.0539 2023-01-02 19:37:15,578 - mmcls - INFO - Epoch [44][5000/5005] lr: 1.000e-02, eta: 6:50:56, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0761 2023-01-02 19:37:38,180 - mmcls - INFO - Epoch(val) [44][196] accuracy_top-1: 58.5340, accuracy_top-5: 81.0160 2023-01-02 19:37:49,354 - mmcls - INFO - Epoch [45][100/5005] lr: 1.000e-02, eta: 6:50:49, time: 0.112, data_time: 0.036, memory: 1125, loss: 2.0059 2023-01-02 19:37:58,097 - mmcls - INFO - Epoch [45][200/5005] lr: 1.000e-02, eta: 6:50:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0260 2023-01-02 19:38:06,673 - mmcls - INFO - Epoch [45][300/5005] lr: 1.000e-02, eta: 6:50:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0154 2023-01-02 19:38:15,559 - mmcls - INFO - Epoch [45][400/5005] lr: 1.000e-02, eta: 6:50:22, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.0222 2023-01-02 19:38:24,368 - mmcls - INFO - Epoch [45][500/5005] lr: 1.000e-02, eta: 6:50:13, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.0237 2023-01-02 19:38:33,178 - mmcls - INFO - Epoch [45][600/5005] lr: 1.000e-02, eta: 6:50:05, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0233 2023-01-02 19:38:41,996 - mmcls - INFO - Epoch [45][700/5005] lr: 1.000e-02, eta: 6:49:56, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0005 2023-01-02 19:38:50,485 - mmcls - INFO - Epoch [45][800/5005] lr: 1.000e-02, eta: 6:49:47, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0364 2023-01-02 19:38:59,256 - mmcls - INFO - Epoch [45][900/5005] lr: 1.000e-02, eta: 6:49:38, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0112 2023-01-02 19:39:08,052 - mmcls - INFO - Epoch [45][1000/5005] lr: 1.000e-02, eta: 6:49:29, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0315 2023-01-02 19:39:16,738 - mmcls - INFO - Epoch [45][1100/5005] lr: 1.000e-02, eta: 6:49:20, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.0500 2023-01-02 19:39:25,430 - mmcls - INFO - Epoch [45][1200/5005] lr: 1.000e-02, eta: 6:49:11, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0523 2023-01-02 19:39:34,149 - mmcls - INFO - Epoch [45][1300/5005] lr: 1.000e-02, eta: 6:49:02, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0378 2023-01-02 19:39:42,921 - mmcls - INFO - Epoch [45][1400/5005] lr: 1.000e-02, eta: 6:48:53, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0336 2023-01-02 19:39:51,618 - mmcls - INFO - Epoch [45][1500/5005] lr: 1.000e-02, eta: 6:48:44, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0515 2023-01-02 19:40:00,466 - mmcls - INFO - Epoch [45][1600/5005] lr: 1.000e-02, eta: 6:48:36, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0550 2023-01-02 19:40:09,136 - mmcls - INFO - Epoch [45][1700/5005] lr: 1.000e-02, eta: 6:48:27, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0569 2023-01-02 19:40:17,789 - mmcls - INFO - Epoch [45][1800/5005] lr: 1.000e-02, eta: 6:48:18, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0555 2023-01-02 19:40:26,567 - mmcls - INFO - Epoch [45][1900/5005] lr: 1.000e-02, eta: 6:48:09, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0521 2023-01-02 19:40:35,275 - mmcls - INFO - Epoch [45][2000/5005] lr: 1.000e-02, eta: 6:48:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0436 2023-01-02 19:40:44,071 - mmcls - INFO - Epoch [45][2100/5005] lr: 1.000e-02, eta: 6:47:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0468 2023-01-02 19:40:52,571 - mmcls - INFO - Epoch [45][2200/5005] lr: 1.000e-02, eta: 6:47:42, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0588 2023-01-02 19:41:01,381 - mmcls - INFO - Epoch [45][2300/5005] lr: 1.000e-02, eta: 6:47:33, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0342 2023-01-02 19:41:10,213 - mmcls - INFO - Epoch [45][2400/5005] lr: 1.000e-02, eta: 6:47:25, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0482 2023-01-02 19:41:18,964 - mmcls - INFO - Epoch [45][2500/5005] lr: 1.000e-02, eta: 6:47:16, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0369 2023-01-02 19:41:27,831 - mmcls - INFO - Epoch [45][2600/5005] lr: 1.000e-02, eta: 6:47:07, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0470 2023-01-02 19:41:36,643 - mmcls - INFO - Epoch [45][2700/5005] lr: 1.000e-02, eta: 6:46:58, time: 0.088, data_time: 0.040, memory: 1125, loss: 2.0759 2023-01-02 19:41:45,112 - mmcls - INFO - Epoch [45][2800/5005] lr: 1.000e-02, eta: 6:46:49, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.0339 2023-01-02 19:41:54,425 - mmcls - INFO - Epoch [45][2900/5005] lr: 1.000e-02, eta: 6:46:41, time: 0.093, data_time: 0.011, memory: 1125, loss: 2.0249 2023-01-02 19:42:03,381 - mmcls - INFO - Epoch [45][3000/5005] lr: 1.000e-02, eta: 6:46:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0596 2023-01-02 19:42:12,046 - mmcls - INFO - Epoch [45][3100/5005] lr: 1.000e-02, eta: 6:46:23, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0521 2023-01-02 19:42:20,643 - mmcls - INFO - Epoch [45][3200/5005] lr: 1.000e-02, eta: 6:46:14, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0582 2023-01-02 19:42:29,338 - mmcls - INFO - Epoch [45][3300/5005] lr: 1.000e-02, eta: 6:46:05, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.0574 2023-01-02 19:42:37,775 - mmcls - INFO - Epoch [45][3400/5005] lr: 1.000e-02, eta: 6:45:56, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.0601 2023-01-02 19:42:46,241 - mmcls - INFO - Epoch [45][3500/5005] lr: 1.000e-02, eta: 6:45:47, time: 0.085, data_time: 0.045, memory: 1125, loss: 2.0870 2023-01-02 19:42:54,913 - mmcls - INFO - Epoch [45][3600/5005] lr: 1.000e-02, eta: 6:45:38, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0561 2023-01-02 19:43:03,754 - mmcls - INFO - Epoch [45][3700/5005] lr: 1.000e-02, eta: 6:45:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0808 2023-01-02 19:43:12,579 - mmcls - INFO - Epoch [45][3800/5005] lr: 1.000e-02, eta: 6:45:20, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0732 2023-01-02 19:43:21,339 - mmcls - INFO - Epoch [45][3900/5005] lr: 1.000e-02, eta: 6:45:12, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0520 2023-01-02 19:43:29,923 - mmcls - INFO - Epoch [45][4000/5005] lr: 1.000e-02, eta: 6:45:03, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0327 2023-01-02 19:43:38,650 - mmcls - INFO - Epoch [45][4100/5005] lr: 1.000e-02, eta: 6:44:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0783 2023-01-02 19:43:47,439 - mmcls - INFO - Epoch [45][4200/5005] lr: 1.000e-02, eta: 6:44:45, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0665 2023-01-02 19:43:56,042 - mmcls - INFO - Epoch [45][4300/5005] lr: 1.000e-02, eta: 6:44:36, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.0671 2023-01-02 19:44:04,920 - mmcls - INFO - Epoch [45][4400/5005] lr: 1.000e-02, eta: 6:44:27, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0739 2023-01-02 19:44:13,921 - mmcls - INFO - Epoch [45][4500/5005] lr: 1.000e-02, eta: 6:44:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0542 2023-01-02 19:44:22,748 - mmcls - INFO - Epoch [45][4600/5005] lr: 1.000e-02, eta: 6:44:10, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0818 2023-01-02 19:44:31,365 - mmcls - INFO - Epoch [45][4700/5005] lr: 1.000e-02, eta: 6:44:01, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0550 2023-01-02 19:44:39,936 - mmcls - INFO - Epoch [45][4800/5005] lr: 1.000e-02, eta: 6:43:52, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0506 2023-01-02 19:44:48,809 - mmcls - INFO - Epoch [45][4900/5005] lr: 1.000e-02, eta: 6:43:43, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0842 2023-01-02 19:44:57,569 - mmcls - INFO - Epoch [45][5000/5005] lr: 1.000e-02, eta: 6:43:34, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0895 2023-01-02 19:45:20,091 - mmcls - INFO - Epoch(val) [45][196] accuracy_top-1: 58.7160, accuracy_top-5: 80.8700 2023-01-02 19:45:31,087 - mmcls - INFO - Epoch [46][100/5005] lr: 1.000e-02, eta: 6:43:27, time: 0.110, data_time: 0.038, memory: 1125, loss: 2.0291 2023-01-02 19:45:39,673 - mmcls - INFO - Epoch [46][200/5005] lr: 1.000e-02, eta: 6:43:18, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0355 2023-01-02 19:45:48,541 - mmcls - INFO - Epoch [46][300/5005] lr: 1.000e-02, eta: 6:43:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0070 2023-01-02 19:45:57,148 - mmcls - INFO - Epoch [46][400/5005] lr: 1.000e-02, eta: 6:43:00, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.9885 2023-01-02 19:46:06,015 - mmcls - INFO - Epoch [46][500/5005] lr: 1.000e-02, eta: 6:42:52, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0161 2023-01-02 19:46:14,653 - mmcls - INFO - Epoch [46][600/5005] lr: 1.000e-02, eta: 6:42:43, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.0257 2023-01-02 19:46:23,630 - mmcls - INFO - Epoch [46][700/5005] lr: 1.000e-02, eta: 6:42:34, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0297 2023-01-02 19:46:32,667 - mmcls - INFO - Epoch [46][800/5005] lr: 1.000e-02, eta: 6:42:25, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0248 2023-01-02 19:46:41,527 - mmcls - INFO - Epoch [46][900/5005] lr: 1.000e-02, eta: 6:42:17, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0451 2023-01-02 19:46:50,253 - mmcls - INFO - Epoch [46][1000/5005] lr: 1.000e-02, eta: 6:42:08, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.9996 2023-01-02 19:46:59,030 - mmcls - INFO - Epoch [46][1100/5005] lr: 1.000e-02, eta: 6:41:59, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0339 2023-01-02 19:47:07,622 - mmcls - INFO - Epoch [46][1200/5005] lr: 1.000e-02, eta: 6:41:50, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0334 2023-01-02 19:47:16,844 - mmcls - INFO - Epoch [46][1300/5005] lr: 1.000e-02, eta: 6:41:42, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.0331 2023-01-02 19:47:25,523 - mmcls - INFO - Epoch [46][1400/5005] lr: 1.000e-02, eta: 6:41:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0402 2023-01-02 19:47:34,273 - mmcls - INFO - Epoch [46][1500/5005] lr: 1.000e-02, eta: 6:41:24, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0767 2023-01-02 19:47:43,604 - mmcls - INFO - Epoch [46][1600/5005] lr: 1.000e-02, eta: 6:41:16, time: 0.093, data_time: 0.000, memory: 1125, loss: 2.0344 2023-01-02 19:47:52,127 - mmcls - INFO - Epoch [46][1700/5005] lr: 1.000e-02, eta: 6:41:07, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.0641 2023-01-02 19:48:01,041 - mmcls - INFO - Epoch [46][1800/5005] lr: 1.000e-02, eta: 6:40:58, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0558 2023-01-02 19:48:09,914 - mmcls - INFO - Epoch [46][1900/5005] lr: 1.000e-02, eta: 6:40:49, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0484 2023-01-02 19:48:18,392 - mmcls - INFO - Epoch [46][2000/5005] lr: 1.000e-02, eta: 6:40:40, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0642 2023-01-02 19:48:27,230 - mmcls - INFO - Epoch [46][2100/5005] lr: 1.000e-02, eta: 6:40:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0484 2023-01-02 19:48:36,105 - mmcls - INFO - Epoch [46][2200/5005] lr: 1.000e-02, eta: 6:40:23, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.0560 2023-01-02 19:48:44,664 - mmcls - INFO - Epoch [46][2300/5005] lr: 1.000e-02, eta: 6:40:14, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0552 2023-01-02 19:48:53,513 - mmcls - INFO - Epoch [46][2400/5005] lr: 1.000e-02, eta: 6:40:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0382 2023-01-02 19:49:02,452 - mmcls - INFO - Epoch [46][2500/5005] lr: 1.000e-02, eta: 6:39:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0472 2023-01-02 19:49:11,187 - mmcls - INFO - Epoch [46][2600/5005] lr: 1.000e-02, eta: 6:39:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0441 2023-01-02 19:49:19,632 - mmcls - INFO - Epoch [46][2700/5005] lr: 1.000e-02, eta: 6:39:38, time: 0.084, data_time: 0.003, memory: 1125, loss: 2.0585 2023-01-02 19:49:28,382 - mmcls - INFO - Epoch [46][2800/5005] lr: 1.000e-02, eta: 6:39:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0681 2023-01-02 19:49:36,888 - mmcls - INFO - Epoch [46][2900/5005] lr: 1.000e-02, eta: 6:39:20, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0407 2023-01-02 19:49:45,501 - mmcls - INFO - Epoch [46][3000/5005] lr: 1.000e-02, eta: 6:39:11, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0454 2023-01-02 19:49:54,264 - mmcls - INFO - Epoch [46][3100/5005] lr: 1.000e-02, eta: 6:39:02, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.0620 2023-01-02 19:50:02,942 - mmcls - INFO - Epoch [46][3200/5005] lr: 1.000e-02, eta: 6:38:53, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0608 2023-01-02 19:50:11,678 - mmcls - INFO - Epoch [46][3300/5005] lr: 1.000e-02, eta: 6:38:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0462 2023-01-02 19:50:20,661 - mmcls - INFO - Epoch [46][3400/5005] lr: 1.000e-02, eta: 6:38:36, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0721 2023-01-02 19:50:29,625 - mmcls - INFO - Epoch [46][3500/5005] lr: 1.000e-02, eta: 6:38:27, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0735 2023-01-02 19:50:38,488 - mmcls - INFO - Epoch [46][3600/5005] lr: 1.000e-02, eta: 6:38:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0662 2023-01-02 19:50:47,297 - mmcls - INFO - Epoch [46][3700/5005] lr: 1.000e-02, eta: 6:38:10, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0656 2023-01-02 19:50:55,898 - mmcls - INFO - Epoch [46][3800/5005] lr: 1.000e-02, eta: 6:38:01, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0804 2023-01-02 19:51:04,449 - mmcls - INFO - Epoch [46][3900/5005] lr: 1.000e-02, eta: 6:37:52, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0656 2023-01-02 19:51:13,226 - mmcls - INFO - Epoch [46][4000/5005] lr: 1.000e-02, eta: 6:37:43, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0655 2023-01-02 19:51:21,756 - mmcls - INFO - Epoch [46][4100/5005] lr: 1.000e-02, eta: 6:37:34, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0651 2023-01-02 19:51:30,647 - mmcls - INFO - Epoch [46][4200/5005] lr: 1.000e-02, eta: 6:37:25, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.0813 2023-01-02 19:51:39,644 - mmcls - INFO - Epoch [46][4300/5005] lr: 1.000e-02, eta: 6:37:16, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0629 2023-01-02 19:51:48,557 - mmcls - INFO - Epoch [46][4400/5005] lr: 1.000e-02, eta: 6:37:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0983 2023-01-02 19:51:57,323 - mmcls - INFO - Epoch [46][4500/5005] lr: 1.000e-02, eta: 6:36:59, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0692 2023-01-02 19:52:06,034 - mmcls - INFO - Epoch [46][4600/5005] lr: 1.000e-02, eta: 6:36:50, time: 0.087, data_time: 0.033, memory: 1125, loss: 2.0624 2023-01-02 19:52:14,993 - mmcls - INFO - Epoch [46][4700/5005] lr: 1.000e-02, eta: 6:36:41, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.0610 2023-01-02 19:52:24,224 - mmcls - INFO - Epoch [46][4800/5005] lr: 1.000e-02, eta: 6:36:33, time: 0.092, data_time: 0.007, memory: 1125, loss: 2.0754 2023-01-02 19:52:33,311 - mmcls - INFO - Epoch [46][4900/5005] lr: 1.000e-02, eta: 6:36:25, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0488 2023-01-02 19:52:42,039 - mmcls - INFO - Epoch [46][5000/5005] lr: 1.000e-02, eta: 6:36:16, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0762 2023-01-02 19:53:04,739 - mmcls - INFO - Epoch(val) [46][196] accuracy_top-1: 58.7600, accuracy_top-5: 80.9620 2023-01-02 19:53:15,862 - mmcls - INFO - Epoch [47][100/5005] lr: 1.000e-02, eta: 6:36:09, time: 0.111, data_time: 0.043, memory: 1125, loss: 2.0173 2023-01-02 19:53:24,677 - mmcls - INFO - Epoch [47][200/5005] lr: 1.000e-02, eta: 6:36:00, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0309 2023-01-02 19:53:33,220 - mmcls - INFO - Epoch [47][300/5005] lr: 1.000e-02, eta: 6:35:51, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.0221 2023-01-02 19:53:42,306 - mmcls - INFO - Epoch [47][400/5005] lr: 1.000e-02, eta: 6:35:42, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0371 2023-01-02 19:53:50,877 - mmcls - INFO - Epoch [47][500/5005] lr: 1.000e-02, eta: 6:35:33, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0050 2023-01-02 19:53:59,462 - mmcls - INFO - Epoch [47][600/5005] lr: 1.000e-02, eta: 6:35:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0260 2023-01-02 19:54:08,142 - mmcls - INFO - Epoch [47][700/5005] lr: 1.000e-02, eta: 6:35:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0326 2023-01-02 19:54:16,901 - mmcls - INFO - Epoch [47][800/5005] lr: 1.000e-02, eta: 6:35:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0219 2023-01-02 19:54:25,831 - mmcls - INFO - Epoch [47][900/5005] lr: 1.000e-02, eta: 6:34:58, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0107 2023-01-02 19:54:34,626 - mmcls - INFO - Epoch [47][1000/5005] lr: 1.000e-02, eta: 6:34:49, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0292 2023-01-02 19:54:43,375 - mmcls - INFO - Epoch [47][1100/5005] lr: 1.000e-02, eta: 6:34:40, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0235 2023-01-02 19:54:52,583 - mmcls - INFO - Epoch [47][1200/5005] lr: 1.000e-02, eta: 6:34:32, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.0373 2023-01-02 19:55:01,521 - mmcls - INFO - Epoch [47][1300/5005] lr: 1.000e-02, eta: 6:34:23, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0507 2023-01-02 19:55:10,114 - mmcls - INFO - Epoch [47][1400/5005] lr: 1.000e-02, eta: 6:34:14, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.0215 2023-01-02 19:55:18,750 - mmcls - INFO - Epoch [47][1500/5005] lr: 1.000e-02, eta: 6:34:05, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0447 2023-01-02 19:55:27,738 - mmcls - INFO - Epoch [47][1600/5005] lr: 1.000e-02, eta: 6:33:57, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0272 2023-01-02 19:55:36,638 - mmcls - INFO - Epoch [47][1700/5005] lr: 1.000e-02, eta: 6:33:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0437 2023-01-02 19:55:45,894 - mmcls - INFO - Epoch [47][1800/5005] lr: 1.000e-02, eta: 6:33:40, time: 0.093, data_time: 0.025, memory: 1125, loss: 2.0520 2023-01-02 19:55:54,840 - mmcls - INFO - Epoch [47][1900/5005] lr: 1.000e-02, eta: 6:33:31, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0531 2023-01-02 19:56:03,610 - mmcls - INFO - Epoch [47][2000/5005] lr: 1.000e-02, eta: 6:33:22, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0276 2023-01-02 19:56:12,109 - mmcls - INFO - Epoch [47][2100/5005] lr: 1.000e-02, eta: 6:33:13, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.0344 2023-01-02 19:56:20,705 - mmcls - INFO - Epoch [47][2200/5005] lr: 1.000e-02, eta: 6:33:04, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0568 2023-01-02 19:56:29,461 - mmcls - INFO - Epoch [47][2300/5005] lr: 1.000e-02, eta: 6:32:55, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0333 2023-01-02 19:56:38,180 - mmcls - INFO - Epoch [47][2400/5005] lr: 1.000e-02, eta: 6:32:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0439 2023-01-02 19:56:46,991 - mmcls - INFO - Epoch [47][2500/5005] lr: 1.000e-02, eta: 6:32:38, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0483 2023-01-02 19:56:55,713 - mmcls - INFO - Epoch [47][2600/5005] lr: 1.000e-02, eta: 6:32:29, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0328 2023-01-02 19:57:04,151 - mmcls - INFO - Epoch [47][2700/5005] lr: 1.000e-02, eta: 6:32:19, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0637 2023-01-02 19:57:13,078 - mmcls - INFO - Epoch [47][2800/5005] lr: 1.000e-02, eta: 6:32:11, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0466 2023-01-02 19:57:21,640 - mmcls - INFO - Epoch [47][2900/5005] lr: 1.000e-02, eta: 6:32:02, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.0506 2023-01-02 19:57:30,618 - mmcls - INFO - Epoch [47][3000/5005] lr: 1.000e-02, eta: 6:31:53, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.0671 2023-01-02 19:57:39,499 - mmcls - INFO - Epoch [47][3100/5005] lr: 1.000e-02, eta: 6:31:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0524 2023-01-02 19:57:48,085 - mmcls - INFO - Epoch [47][3200/5005] lr: 1.000e-02, eta: 6:31:35, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0480 2023-01-02 19:57:56,589 - mmcls - INFO - Epoch [47][3300/5005] lr: 1.000e-02, eta: 6:31:26, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0775 2023-01-02 19:58:05,378 - mmcls - INFO - Epoch [47][3400/5005] lr: 1.000e-02, eta: 6:31:17, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0447 2023-01-02 19:58:13,971 - mmcls - INFO - Epoch [47][3500/5005] lr: 1.000e-02, eta: 6:31:08, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.0516 2023-01-02 19:58:22,849 - mmcls - INFO - Epoch [47][3600/5005] lr: 1.000e-02, eta: 6:31:00, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0464 2023-01-02 19:58:31,401 - mmcls - INFO - Epoch [47][3700/5005] lr: 1.000e-02, eta: 6:30:51, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.0702 2023-01-02 19:58:40,311 - mmcls - INFO - Epoch [47][3800/5005] lr: 1.000e-02, eta: 6:30:42, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0524 2023-01-02 19:58:49,055 - mmcls - INFO - Epoch [47][3900/5005] lr: 1.000e-02, eta: 6:30:33, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.0440 2023-01-02 19:58:57,910 - mmcls - INFO - Epoch [47][4000/5005] lr: 1.000e-02, eta: 6:30:24, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0596 2023-01-02 19:59:06,494 - mmcls - INFO - Epoch [47][4100/5005] lr: 1.000e-02, eta: 6:30:15, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0423 2023-01-02 19:59:15,201 - mmcls - INFO - Epoch [47][4200/5005] lr: 1.000e-02, eta: 6:30:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0723 2023-01-02 19:59:23,990 - mmcls - INFO - Epoch [47][4300/5005] lr: 1.000e-02, eta: 6:29:58, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0652 2023-01-02 19:59:32,817 - mmcls - INFO - Epoch [47][4400/5005] lr: 1.000e-02, eta: 6:29:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0801 2023-01-02 19:59:41,777 - mmcls - INFO - Epoch [47][4500/5005] lr: 1.000e-02, eta: 6:29:40, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.0764 2023-01-02 19:59:50,254 - mmcls - INFO - Epoch [47][4600/5005] lr: 1.000e-02, eta: 6:29:31, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0596 2023-01-02 19:59:59,018 - mmcls - INFO - Epoch [47][4700/5005] lr: 1.000e-02, eta: 6:29:22, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0587 2023-01-02 20:00:07,650 - mmcls - INFO - Epoch [47][4800/5005] lr: 1.000e-02, eta: 6:29:13, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0608 2023-01-02 20:00:16,314 - mmcls - INFO - Epoch [47][4900/5005] lr: 1.000e-02, eta: 6:29:04, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0825 2023-01-02 20:00:24,991 - mmcls - INFO - Epoch [47][5000/5005] lr: 1.000e-02, eta: 6:28:55, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0843 2023-01-02 20:00:47,628 - mmcls - INFO - Epoch(val) [47][196] accuracy_top-1: 58.9060, accuracy_top-5: 81.0780 2023-01-02 20:00:58,718 - mmcls - INFO - Epoch [48][100/5005] lr: 1.000e-02, eta: 6:28:48, time: 0.111, data_time: 0.038, memory: 1125, loss: 2.0280 2023-01-02 20:01:07,781 - mmcls - INFO - Epoch [48][200/5005] lr: 1.000e-02, eta: 6:28:40, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.0136 2023-01-02 20:01:16,947 - mmcls - INFO - Epoch [48][300/5005] lr: 1.000e-02, eta: 6:28:31, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.0228 2023-01-02 20:01:25,442 - mmcls - INFO - Epoch [48][400/5005] lr: 1.000e-02, eta: 6:28:22, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0383 2023-01-02 20:01:34,343 - mmcls - INFO - Epoch [48][500/5005] lr: 1.000e-02, eta: 6:28:14, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.0386 2023-01-02 20:01:42,813 - mmcls - INFO - Epoch [48][600/5005] lr: 1.000e-02, eta: 6:28:04, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.0259 2023-01-02 20:01:51,708 - mmcls - INFO - Epoch [48][700/5005] lr: 1.000e-02, eta: 6:27:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0248 2023-01-02 20:02:00,387 - mmcls - INFO - Epoch [48][800/5005] lr: 1.000e-02, eta: 6:27:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0566 2023-01-02 20:02:09,220 - mmcls - INFO - Epoch [48][900/5005] lr: 1.000e-02, eta: 6:27:38, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0264 2023-01-02 20:02:17,938 - mmcls - INFO - Epoch [48][1000/5005] lr: 1.000e-02, eta: 6:27:29, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.0447 2023-01-02 20:02:26,766 - mmcls - INFO - Epoch [48][1100/5005] lr: 1.000e-02, eta: 6:27:20, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0287 2023-01-02 20:02:35,459 - mmcls - INFO - Epoch [48][1200/5005] lr: 1.000e-02, eta: 6:27:11, time: 0.087, data_time: 0.030, memory: 1125, loss: 2.0424 2023-01-02 20:02:44,179 - mmcls - INFO - Epoch [48][1300/5005] lr: 1.000e-02, eta: 6:27:03, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.0400 2023-01-02 20:02:52,541 - mmcls - INFO - Epoch [48][1400/5005] lr: 1.000e-02, eta: 6:26:53, time: 0.084, data_time: 0.004, memory: 1125, loss: 2.0323 2023-01-02 20:03:01,290 - mmcls - INFO - Epoch [48][1500/5005] lr: 1.000e-02, eta: 6:26:44, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0742 2023-01-02 20:03:10,392 - mmcls - INFO - Epoch [48][1600/5005] lr: 1.000e-02, eta: 6:26:36, time: 0.091, data_time: 0.016, memory: 1125, loss: 2.0388 2023-01-02 20:03:18,888 - mmcls - INFO - Epoch [48][1700/5005] lr: 1.000e-02, eta: 6:26:27, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.0322 2023-01-02 20:03:27,420 - mmcls - INFO - Epoch [48][1800/5005] lr: 1.000e-02, eta: 6:26:18, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0491 2023-01-02 20:03:36,312 - mmcls - INFO - Epoch [48][1900/5005] lr: 1.000e-02, eta: 6:26:09, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.0612 2023-01-02 20:03:44,775 - mmcls - INFO - Epoch [48][2000/5005] lr: 1.000e-02, eta: 6:26:00, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0574 2023-01-02 20:03:53,511 - mmcls - INFO - Epoch [48][2100/5005] lr: 1.000e-02, eta: 6:25:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0468 2023-01-02 20:04:02,228 - mmcls - INFO - Epoch [48][2200/5005] lr: 1.000e-02, eta: 6:25:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0521 2023-01-02 20:04:10,703 - mmcls - INFO - Epoch [48][2300/5005] lr: 1.000e-02, eta: 6:25:33, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0513 2023-01-02 20:04:19,545 - mmcls - INFO - Epoch [48][2400/5005] lr: 1.000e-02, eta: 6:25:24, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0610 2023-01-02 20:04:28,396 - mmcls - INFO - Epoch [48][2500/5005] lr: 1.000e-02, eta: 6:25:15, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0479 2023-01-02 20:04:36,922 - mmcls - INFO - Epoch [48][2600/5005] lr: 1.000e-02, eta: 6:25:06, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.0430 2023-01-02 20:04:45,967 - mmcls - INFO - Epoch [48][2700/5005] lr: 1.000e-02, eta: 6:24:58, time: 0.091, data_time: 0.014, memory: 1125, loss: 2.0304 2023-01-02 20:04:54,890 - mmcls - INFO - Epoch [48][2800/5005] lr: 1.000e-02, eta: 6:24:49, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0479 2023-01-02 20:05:04,109 - mmcls - INFO - Epoch [48][2900/5005] lr: 1.000e-02, eta: 6:24:41, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.0643 2023-01-02 20:05:12,961 - mmcls - INFO - Epoch [48][3000/5005] lr: 1.000e-02, eta: 6:24:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0285 2023-01-02 20:05:21,655 - mmcls - INFO - Epoch [48][3100/5005] lr: 1.000e-02, eta: 6:24:23, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0716 2023-01-02 20:05:30,431 - mmcls - INFO - Epoch [48][3200/5005] lr: 1.000e-02, eta: 6:24:14, time: 0.088, data_time: 0.034, memory: 1125, loss: 2.0585 2023-01-02 20:05:38,980 - mmcls - INFO - Epoch [48][3300/5005] lr: 1.000e-02, eta: 6:24:05, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.0467 2023-01-02 20:05:47,859 - mmcls - INFO - Epoch [48][3400/5005] lr: 1.000e-02, eta: 6:23:57, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0615 2023-01-02 20:05:56,543 - mmcls - INFO - Epoch [48][3500/5005] lr: 1.000e-02, eta: 6:23:48, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0729 2023-01-02 20:06:05,212 - mmcls - INFO - Epoch [48][3600/5005] lr: 1.000e-02, eta: 6:23:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0368 2023-01-02 20:06:13,615 - mmcls - INFO - Epoch [48][3700/5005] lr: 1.000e-02, eta: 6:23:29, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.0650 2023-01-02 20:06:22,176 - mmcls - INFO - Epoch [48][3800/5005] lr: 1.000e-02, eta: 6:23:20, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0474 2023-01-02 20:06:30,919 - mmcls - INFO - Epoch [48][3900/5005] lr: 1.000e-02, eta: 6:23:12, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0278 2023-01-02 20:06:39,462 - mmcls - INFO - Epoch [48][4000/5005] lr: 1.000e-02, eta: 6:23:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0561 2023-01-02 20:06:47,954 - mmcls - INFO - Epoch [48][4100/5005] lr: 1.000e-02, eta: 6:22:53, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0526 2023-01-02 20:06:56,866 - mmcls - INFO - Epoch [48][4200/5005] lr: 1.000e-02, eta: 6:22:45, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.0495 2023-01-02 20:07:05,615 - mmcls - INFO - Epoch [48][4300/5005] lr: 1.000e-02, eta: 6:22:36, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0771 2023-01-02 20:07:14,202 - mmcls - INFO - Epoch [48][4400/5005] lr: 1.000e-02, eta: 6:22:27, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.0555 2023-01-02 20:07:22,868 - mmcls - INFO - Epoch [48][4500/5005] lr: 1.000e-02, eta: 6:22:18, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0882 2023-01-02 20:07:31,772 - mmcls - INFO - Epoch [48][4600/5005] lr: 1.000e-02, eta: 6:22:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0521 2023-01-02 20:07:40,812 - mmcls - INFO - Epoch [48][4700/5005] lr: 1.000e-02, eta: 6:22:01, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0575 2023-01-02 20:07:49,384 - mmcls - INFO - Epoch [48][4800/5005] lr: 1.000e-02, eta: 6:21:52, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0488 2023-01-02 20:07:57,892 - mmcls - INFO - Epoch [48][4900/5005] lr: 1.000e-02, eta: 6:21:43, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0620 2023-01-02 20:08:06,711 - mmcls - INFO - Epoch [48][5000/5005] lr: 1.000e-02, eta: 6:21:34, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0390 2023-01-02 20:08:29,373 - mmcls - INFO - Epoch(val) [48][196] accuracy_top-1: 58.8160, accuracy_top-5: 81.1040 2023-01-02 20:08:40,440 - mmcls - INFO - Epoch [49][100/5005] lr: 1.000e-02, eta: 6:21:26, time: 0.110, data_time: 0.058, memory: 1125, loss: 1.9938 2023-01-02 20:08:49,497 - mmcls - INFO - Epoch [49][200/5005] lr: 1.000e-02, eta: 6:21:18, time: 0.091, data_time: 0.021, memory: 1125, loss: 2.0216 2023-01-02 20:08:57,939 - mmcls - INFO - Epoch [49][300/5005] lr: 1.000e-02, eta: 6:21:09, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.0060 2023-01-02 20:09:06,847 - mmcls - INFO - Epoch [49][400/5005] lr: 1.000e-02, eta: 6:21:00, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.0042 2023-01-02 20:09:15,679 - mmcls - INFO - Epoch [49][500/5005] lr: 1.000e-02, eta: 6:20:51, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.9961 2023-01-02 20:09:24,300 - mmcls - INFO - Epoch [49][600/5005] lr: 1.000e-02, eta: 6:20:42, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0174 2023-01-02 20:09:32,996 - mmcls - INFO - Epoch [49][700/5005] lr: 1.000e-02, eta: 6:20:33, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0193 2023-01-02 20:09:41,726 - mmcls - INFO - Epoch [49][800/5005] lr: 1.000e-02, eta: 6:20:25, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.0332 2023-01-02 20:09:50,293 - mmcls - INFO - Epoch [49][900/5005] lr: 1.000e-02, eta: 6:20:15, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.0350 2023-01-02 20:09:59,293 - mmcls - INFO - Epoch [49][1000/5005] lr: 1.000e-02, eta: 6:20:07, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0615 2023-01-02 20:10:07,831 - mmcls - INFO - Epoch [49][1100/5005] lr: 1.000e-02, eta: 6:19:58, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0221 2023-01-02 20:10:16,598 - mmcls - INFO - Epoch [49][1200/5005] lr: 1.000e-02, eta: 6:19:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0400 2023-01-02 20:10:25,494 - mmcls - INFO - Epoch [49][1300/5005] lr: 1.000e-02, eta: 6:19:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0477 2023-01-02 20:10:34,192 - mmcls - INFO - Epoch [49][1400/5005] lr: 1.000e-02, eta: 6:19:31, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0385 2023-01-02 20:10:42,950 - mmcls - INFO - Epoch [49][1500/5005] lr: 1.000e-02, eta: 6:19:23, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0476 2023-01-02 20:10:51,635 - mmcls - INFO - Epoch [49][1600/5005] lr: 1.000e-02, eta: 6:19:14, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0417 2023-01-02 20:11:00,649 - mmcls - INFO - Epoch [49][1700/5005] lr: 1.000e-02, eta: 6:19:05, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.0610 2023-01-02 20:11:09,170 - mmcls - INFO - Epoch [49][1800/5005] lr: 1.000e-02, eta: 6:18:56, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.0329 2023-01-02 20:11:18,036 - mmcls - INFO - Epoch [49][1900/5005] lr: 1.000e-02, eta: 6:18:47, time: 0.089, data_time: 0.034, memory: 1125, loss: 2.0647 2023-01-02 20:11:26,724 - mmcls - INFO - Epoch [49][2000/5005] lr: 1.000e-02, eta: 6:18:38, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0547 2023-01-02 20:11:35,495 - mmcls - INFO - Epoch [49][2100/5005] lr: 1.000e-02, eta: 6:18:30, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0473 2023-01-02 20:11:44,112 - mmcls - INFO - Epoch [49][2200/5005] lr: 1.000e-02, eta: 6:18:21, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0629 2023-01-02 20:11:52,758 - mmcls - INFO - Epoch [49][2300/5005] lr: 1.000e-02, eta: 6:18:12, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0414 2023-01-02 20:12:01,402 - mmcls - INFO - Epoch [49][2400/5005] lr: 1.000e-02, eta: 6:18:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0598 2023-01-02 20:12:10,208 - mmcls - INFO - Epoch [49][2500/5005] lr: 1.000e-02, eta: 6:17:54, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0357 2023-01-02 20:12:18,887 - mmcls - INFO - Epoch [49][2600/5005] lr: 1.000e-02, eta: 6:17:45, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.0617 2023-01-02 20:12:27,668 - mmcls - INFO - Epoch [49][2700/5005] lr: 1.000e-02, eta: 6:17:36, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0452 2023-01-02 20:12:36,727 - mmcls - INFO - Epoch [49][2800/5005] lr: 1.000e-02, eta: 6:17:28, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0728 2023-01-02 20:12:45,790 - mmcls - INFO - Epoch [49][2900/5005] lr: 1.000e-02, eta: 6:17:19, time: 0.091, data_time: 0.018, memory: 1125, loss: 2.0668 2023-01-02 20:12:54,791 - mmcls - INFO - Epoch [49][3000/5005] lr: 1.000e-02, eta: 6:17:10, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.0530 2023-01-02 20:13:03,736 - mmcls - INFO - Epoch [49][3100/5005] lr: 1.000e-02, eta: 6:17:02, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0481 2023-01-02 20:13:12,442 - mmcls - INFO - Epoch [49][3200/5005] lr: 1.000e-02, eta: 6:16:53, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0466 2023-01-02 20:13:21,146 - mmcls - INFO - Epoch [49][3300/5005] lr: 1.000e-02, eta: 6:16:44, time: 0.087, data_time: 0.038, memory: 1125, loss: 2.0582 2023-01-02 20:13:30,311 - mmcls - INFO - Epoch [49][3400/5005] lr: 1.000e-02, eta: 6:16:36, time: 0.092, data_time: 0.035, memory: 1125, loss: 2.0464 2023-01-02 20:13:39,188 - mmcls - INFO - Epoch [49][3500/5005] lr: 1.000e-02, eta: 6:16:27, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0548 2023-01-02 20:13:47,733 - mmcls - INFO - Epoch [49][3600/5005] lr: 1.000e-02, eta: 6:16:18, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0743 2023-01-02 20:13:56,136 - mmcls - INFO - Epoch [49][3700/5005] lr: 1.000e-02, eta: 6:16:09, time: 0.084, data_time: 0.026, memory: 1125, loss: 2.0599 2023-01-02 20:14:05,072 - mmcls - INFO - Epoch [49][3800/5005] lr: 1.000e-02, eta: 6:16:00, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0683 2023-01-02 20:14:13,770 - mmcls - INFO - Epoch [49][3900/5005] lr: 1.000e-02, eta: 6:15:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0561 2023-01-02 20:14:22,694 - mmcls - INFO - Epoch [49][4000/5005] lr: 1.000e-02, eta: 6:15:42, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0504 2023-01-02 20:14:31,454 - mmcls - INFO - Epoch [49][4100/5005] lr: 1.000e-02, eta: 6:15:34, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.0428 2023-01-02 20:14:40,173 - mmcls - INFO - Epoch [49][4200/5005] lr: 1.000e-02, eta: 6:15:25, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0613 2023-01-02 20:14:48,817 - mmcls - INFO - Epoch [49][4300/5005] lr: 1.000e-02, eta: 6:15:16, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0548 2023-01-02 20:14:57,762 - mmcls - INFO - Epoch [49][4400/5005] lr: 1.000e-02, eta: 6:15:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0530 2023-01-02 20:15:06,401 - mmcls - INFO - Epoch [49][4500/5005] lr: 1.000e-02, eta: 6:14:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0454 2023-01-02 20:15:15,155 - mmcls - INFO - Epoch [49][4600/5005] lr: 1.000e-02, eta: 6:14:49, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0620 2023-01-02 20:15:23,872 - mmcls - INFO - Epoch [49][4700/5005] lr: 1.000e-02, eta: 6:14:40, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.0588 2023-01-02 20:15:32,743 - mmcls - INFO - Epoch [49][4800/5005] lr: 1.000e-02, eta: 6:14:32, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.0708 2023-01-02 20:15:41,443 - mmcls - INFO - Epoch [49][4900/5005] lr: 1.000e-02, eta: 6:14:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0434 2023-01-02 20:15:50,252 - mmcls - INFO - Epoch [49][5000/5005] lr: 1.000e-02, eta: 6:14:14, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0576 2023-01-02 20:17:12,193 - mmcls - INFO - Epoch(val) [49][196] accuracy_top-1: 58.5780, accuracy_top-5: 80.8760 2023-01-02 20:17:23,264 - mmcls - INFO - Epoch [50][100/5005] lr: 1.000e-02, eta: 6:14:07, time: 0.110, data_time: 0.036, memory: 1125, loss: 2.0171 2023-01-02 20:17:32,194 - mmcls - INFO - Epoch [50][200/5005] lr: 1.000e-02, eta: 6:13:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.9811 2023-01-02 20:17:40,696 - mmcls - INFO - Epoch [50][300/5005] lr: 1.000e-02, eta: 6:13:49, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0240 2023-01-02 20:17:49,224 - mmcls - INFO - Epoch [50][400/5005] lr: 1.000e-02, eta: 6:13:40, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0109 2023-01-02 20:17:58,339 - mmcls - INFO - Epoch [50][500/5005] lr: 1.000e-02, eta: 6:13:31, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.0040 2023-01-02 20:18:07,093 - mmcls - INFO - Epoch [50][600/5005] lr: 1.000e-02, eta: 6:13:23, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.0313 2023-01-02 20:18:15,912 - mmcls - INFO - Epoch [50][700/5005] lr: 1.000e-02, eta: 6:13:14, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.9965 2023-01-02 20:18:24,270 - mmcls - INFO - Epoch [50][800/5005] lr: 1.000e-02, eta: 6:13:04, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0051 2023-01-02 20:18:33,098 - mmcls - INFO - Epoch [50][900/5005] lr: 1.000e-02, eta: 6:12:56, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0237 2023-01-02 20:18:42,067 - mmcls - INFO - Epoch [50][1000/5005] lr: 1.000e-02, eta: 6:12:47, time: 0.090, data_time: 0.031, memory: 1125, loss: 2.0250 2023-01-02 20:18:50,735 - mmcls - INFO - Epoch [50][1100/5005] lr: 1.000e-02, eta: 6:12:38, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.0120 2023-01-02 20:18:59,707 - mmcls - INFO - Epoch [50][1200/5005] lr: 1.000e-02, eta: 6:12:30, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0564 2023-01-02 20:19:08,595 - mmcls - INFO - Epoch [50][1300/5005] lr: 1.000e-02, eta: 6:12:21, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0251 2023-01-02 20:19:17,302 - mmcls - INFO - Epoch [50][1400/5005] lr: 1.000e-02, eta: 6:12:12, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0415 2023-01-02 20:19:26,057 - mmcls - INFO - Epoch [50][1500/5005] lr: 1.000e-02, eta: 6:12:03, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0378 2023-01-02 20:19:34,495 - mmcls - INFO - Epoch [50][1600/5005] lr: 1.000e-02, eta: 6:11:54, time: 0.084, data_time: 0.016, memory: 1125, loss: 2.0351 2023-01-02 20:19:43,232 - mmcls - INFO - Epoch [50][1700/5005] lr: 1.000e-02, eta: 6:11:45, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.0331 2023-01-02 20:19:51,989 - mmcls - INFO - Epoch [50][1800/5005] lr: 1.000e-02, eta: 6:11:36, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0354 2023-01-02 20:20:01,139 - mmcls - INFO - Epoch [50][1900/5005] lr: 1.000e-02, eta: 6:11:28, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.0500 2023-01-02 20:20:09,670 - mmcls - INFO - Epoch [50][2000/5005] lr: 1.000e-02, eta: 6:11:19, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0447 2023-01-02 20:20:18,340 - mmcls - INFO - Epoch [50][2100/5005] lr: 1.000e-02, eta: 6:11:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0557 2023-01-02 20:20:26,862 - mmcls - INFO - Epoch [50][2200/5005] lr: 1.000e-02, eta: 6:11:01, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.0658 2023-01-02 20:20:35,501 - mmcls - INFO - Epoch [50][2300/5005] lr: 1.000e-02, eta: 6:10:52, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0626 2023-01-02 20:20:44,428 - mmcls - INFO - Epoch [50][2400/5005] lr: 1.000e-02, eta: 6:10:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0711 2023-01-02 20:20:53,183 - mmcls - INFO - Epoch [50][2500/5005] lr: 1.000e-02, eta: 6:10:34, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0465 2023-01-02 20:21:01,790 - mmcls - INFO - Epoch [50][2600/5005] lr: 1.000e-02, eta: 6:10:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0397 2023-01-02 20:21:10,459 - mmcls - INFO - Epoch [50][2700/5005] lr: 1.000e-02, eta: 6:10:16, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0380 2023-01-02 20:21:19,035 - mmcls - INFO - Epoch [50][2800/5005] lr: 1.000e-02, eta: 6:10:07, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0460 2023-01-02 20:21:27,835 - mmcls - INFO - Epoch [50][2900/5005] lr: 1.000e-02, eta: 6:09:59, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0430 2023-01-02 20:21:36,703 - mmcls - INFO - Epoch [50][3000/5005] lr: 1.000e-02, eta: 6:09:50, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0425 2023-01-02 20:21:45,637 - mmcls - INFO - Epoch [50][3100/5005] lr: 1.000e-02, eta: 6:09:41, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0573 2023-01-02 20:21:54,331 - mmcls - INFO - Epoch [50][3200/5005] lr: 1.000e-02, eta: 6:09:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0674 2023-01-02 20:22:03,101 - mmcls - INFO - Epoch [50][3300/5005] lr: 1.000e-02, eta: 6:09:23, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0250 2023-01-02 20:22:12,063 - mmcls - INFO - Epoch [50][3400/5005] lr: 1.000e-02, eta: 6:09:15, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.0748 2023-01-02 20:22:20,748 - mmcls - INFO - Epoch [50][3500/5005] lr: 1.000e-02, eta: 6:09:06, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0726 2023-01-02 20:22:29,599 - mmcls - INFO - Epoch [50][3600/5005] lr: 1.000e-02, eta: 6:08:57, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0609 2023-01-02 20:22:38,221 - mmcls - INFO - Epoch [50][3700/5005] lr: 1.000e-02, eta: 6:08:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0826 2023-01-02 20:22:47,244 - mmcls - INFO - Epoch [50][3800/5005] lr: 1.000e-02, eta: 6:08:40, time: 0.090, data_time: 0.024, memory: 1125, loss: 2.0673 2023-01-02 20:22:56,063 - mmcls - INFO - Epoch [50][3900/5005] lr: 1.000e-02, eta: 6:08:31, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.0670 2023-01-02 20:23:04,491 - mmcls - INFO - Epoch [50][4000/5005] lr: 1.000e-02, eta: 6:08:22, time: 0.084, data_time: 0.023, memory: 1125, loss: 2.0532 2023-01-02 20:23:13,251 - mmcls - INFO - Epoch [50][4100/5005] lr: 1.000e-02, eta: 6:08:13, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.0460 2023-01-02 20:23:21,858 - mmcls - INFO - Epoch [50][4200/5005] lr: 1.000e-02, eta: 6:08:04, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0324 2023-01-02 20:23:30,673 - mmcls - INFO - Epoch [50][4300/5005] lr: 1.000e-02, eta: 6:07:55, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0579 2023-01-02 20:23:39,448 - mmcls - INFO - Epoch [50][4400/5005] lr: 1.000e-02, eta: 6:07:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0630 2023-01-02 20:23:48,453 - mmcls - INFO - Epoch [50][4500/5005] lr: 1.000e-02, eta: 6:07:38, time: 0.090, data_time: 0.024, memory: 1125, loss: 2.0781 2023-01-02 20:23:57,075 - mmcls - INFO - Epoch [50][4600/5005] lr: 1.000e-02, eta: 6:07:29, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0718 2023-01-02 20:24:06,119 - mmcls - INFO - Epoch [50][4700/5005] lr: 1.000e-02, eta: 6:07:20, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0584 2023-01-02 20:24:14,818 - mmcls - INFO - Epoch [50][4800/5005] lr: 1.000e-02, eta: 6:07:11, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0305 2023-01-02 20:24:23,728 - mmcls - INFO - Epoch [50][4900/5005] lr: 1.000e-02, eta: 6:07:03, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0790 2023-01-02 20:24:32,133 - mmcls - INFO - Epoch [50][5000/5005] lr: 1.000e-02, eta: 6:06:53, time: 0.084, data_time: 0.014, memory: 1125, loss: 2.0692 2023-01-02 20:24:32,492 - mmcls - INFO - Saving checkpoint at 50 epochs 2023-01-02 20:24:54,818 - mmcls - INFO - Epoch(val) [50][196] accuracy_top-1: 58.2560, accuracy_top-5: 80.6980 2023-01-02 20:25:06,238 - mmcls - INFO - Epoch [51][100/5005] lr: 1.000e-02, eta: 6:06:46, time: 0.114, data_time: 0.043, memory: 1125, loss: 2.0007 2023-01-02 20:25:14,889 - mmcls - INFO - Epoch [51][200/5005] lr: 1.000e-02, eta: 6:06:37, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0108 2023-01-02 20:25:23,402 - mmcls - INFO - Epoch [51][300/5005] lr: 1.000e-02, eta: 6:06:28, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.0070 2023-01-02 20:25:31,958 - mmcls - INFO - Epoch [51][400/5005] lr: 1.000e-02, eta: 6:06:19, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.0263 2023-01-02 20:25:40,645 - mmcls - INFO - Epoch [51][500/5005] lr: 1.000e-02, eta: 6:06:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9903 2023-01-02 20:25:49,449 - mmcls - INFO - Epoch [51][600/5005] lr: 1.000e-02, eta: 6:06:02, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0080 2023-01-02 20:25:57,933 - mmcls - INFO - Epoch [51][700/5005] lr: 1.000e-02, eta: 6:05:52, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.0458 2023-01-02 20:26:07,063 - mmcls - INFO - Epoch [51][800/5005] lr: 1.000e-02, eta: 6:05:44, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.0176 2023-01-02 20:26:15,820 - mmcls - INFO - Epoch [51][900/5005] lr: 1.000e-02, eta: 6:05:35, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.0134 2023-01-02 20:26:24,813 - mmcls - INFO - Epoch [51][1000/5005] lr: 1.000e-02, eta: 6:05:26, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.0402 2023-01-02 20:26:33,256 - mmcls - INFO - Epoch [51][1100/5005] lr: 1.000e-02, eta: 6:05:17, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.9989 2023-01-02 20:26:41,910 - mmcls - INFO - Epoch [51][1200/5005] lr: 1.000e-02, eta: 6:05:08, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0248 2023-01-02 20:26:50,577 - mmcls - INFO - Epoch [51][1300/5005] lr: 1.000e-02, eta: 6:04:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0253 2023-01-02 20:26:59,847 - mmcls - INFO - Epoch [51][1400/5005] lr: 1.000e-02, eta: 6:04:51, time: 0.093, data_time: 0.002, memory: 1125, loss: 2.0162 2023-01-02 20:27:08,579 - mmcls - INFO - Epoch [51][1500/5005] lr: 1.000e-02, eta: 6:04:42, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0566 2023-01-02 20:27:17,038 - mmcls - INFO - Epoch [51][1600/5005] lr: 1.000e-02, eta: 6:04:33, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.0506 2023-01-02 20:27:25,671 - mmcls - INFO - Epoch [51][1700/5005] lr: 1.000e-02, eta: 6:04:24, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.0709 2023-01-02 20:27:34,275 - mmcls - INFO - Epoch [51][1800/5005] lr: 1.000e-02, eta: 6:04:15, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0165 2023-01-02 20:27:42,995 - mmcls - INFO - Epoch [51][1900/5005] lr: 1.000e-02, eta: 6:04:06, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0495 2023-01-02 20:27:51,794 - mmcls - INFO - Epoch [51][2000/5005] lr: 1.000e-02, eta: 6:03:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0470 2023-01-02 20:28:00,520 - mmcls - INFO - Epoch [51][2100/5005] lr: 1.000e-02, eta: 6:03:49, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0384 2023-01-02 20:28:09,401 - mmcls - INFO - Epoch [51][2200/5005] lr: 1.000e-02, eta: 6:03:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0498 2023-01-02 20:28:18,210 - mmcls - INFO - Epoch [51][2300/5005] lr: 1.000e-02, eta: 6:03:31, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0412 2023-01-02 20:28:27,161 - mmcls - INFO - Epoch [51][2400/5005] lr: 1.000e-02, eta: 6:03:23, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0397 2023-01-02 20:28:35,803 - mmcls - INFO - Epoch [51][2500/5005] lr: 1.000e-02, eta: 6:03:14, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.0211 2023-01-02 20:28:44,544 - mmcls - INFO - Epoch [51][2600/5005] lr: 1.000e-02, eta: 6:03:05, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0407 2023-01-02 20:28:53,213 - mmcls - INFO - Epoch [51][2700/5005] lr: 1.000e-02, eta: 6:02:56, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0419 2023-01-02 20:29:02,132 - mmcls - INFO - Epoch [51][2800/5005] lr: 1.000e-02, eta: 6:02:47, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0470 2023-01-02 20:29:11,484 - mmcls - INFO - Epoch [51][2900/5005] lr: 1.000e-02, eta: 6:02:39, time: 0.094, data_time: 0.002, memory: 1125, loss: 2.0455 2023-01-02 20:29:20,842 - mmcls - INFO - Epoch [51][3000/5005] lr: 1.000e-02, eta: 6:02:31, time: 0.094, data_time: 0.003, memory: 1125, loss: 2.0586 2023-01-02 20:29:29,572 - mmcls - INFO - Epoch [51][3100/5005] lr: 1.000e-02, eta: 6:02:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0528 2023-01-02 20:29:38,331 - mmcls - INFO - Epoch [51][3200/5005] lr: 1.000e-02, eta: 6:02:13, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0762 2023-01-02 20:29:47,280 - mmcls - INFO - Epoch [51][3300/5005] lr: 1.000e-02, eta: 6:02:04, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.0642 2023-01-02 20:29:55,909 - mmcls - INFO - Epoch [51][3400/5005] lr: 1.000e-02, eta: 6:01:55, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0838 2023-01-02 20:30:04,799 - mmcls - INFO - Epoch [51][3500/5005] lr: 1.000e-02, eta: 6:01:47, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.0525 2023-01-02 20:30:13,760 - mmcls - INFO - Epoch [51][3600/5005] lr: 1.000e-02, eta: 6:01:38, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.0439 2023-01-02 20:30:22,605 - mmcls - INFO - Epoch [51][3700/5005] lr: 1.000e-02, eta: 6:01:29, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0621 2023-01-02 20:30:31,579 - mmcls - INFO - Epoch [51][3800/5005] lr: 1.000e-02, eta: 6:01:21, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0749 2023-01-02 20:30:40,065 - mmcls - INFO - Epoch [51][3900/5005] lr: 1.000e-02, eta: 6:01:11, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0303 2023-01-02 20:30:48,664 - mmcls - INFO - Epoch [51][4000/5005] lr: 1.000e-02, eta: 6:01:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0462 2023-01-02 20:30:57,863 - mmcls - INFO - Epoch [51][4100/5005] lr: 1.000e-02, eta: 6:00:54, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0575 2023-01-02 20:31:06,542 - mmcls - INFO - Epoch [51][4200/5005] lr: 1.000e-02, eta: 6:00:45, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0307 2023-01-02 20:31:15,336 - mmcls - INFO - Epoch [51][4300/5005] lr: 1.000e-02, eta: 6:00:36, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0550 2023-01-02 20:31:23,954 - mmcls - INFO - Epoch [51][4400/5005] lr: 1.000e-02, eta: 6:00:27, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0502 2023-01-02 20:31:32,863 - mmcls - INFO - Epoch [51][4500/5005] lr: 1.000e-02, eta: 6:00:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0715 2023-01-02 20:31:41,829 - mmcls - INFO - Epoch [51][4600/5005] lr: 1.000e-02, eta: 6:00:10, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0381 2023-01-02 20:31:50,273 - mmcls - INFO - Epoch [51][4700/5005] lr: 1.000e-02, eta: 6:00:01, time: 0.084, data_time: 0.002, memory: 1125, loss: 2.0549 2023-01-02 20:31:59,049 - mmcls - INFO - Epoch [51][4800/5005] lr: 1.000e-02, eta: 5:59:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0674 2023-01-02 20:32:07,780 - mmcls - INFO - Epoch [51][4900/5005] lr: 1.000e-02, eta: 5:59:43, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0712 2023-01-02 20:32:16,409 - mmcls - INFO - Epoch [51][5000/5005] lr: 1.000e-02, eta: 5:59:34, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0679 2023-01-02 20:32:38,922 - mmcls - INFO - Epoch(val) [51][196] accuracy_top-1: 58.9520, accuracy_top-5: 81.1940 2023-01-02 20:32:50,241 - mmcls - INFO - Epoch [52][100/5005] lr: 1.000e-02, eta: 5:59:27, time: 0.113, data_time: 0.044, memory: 1125, loss: 1.9965 2023-01-02 20:32:59,307 - mmcls - INFO - Epoch [52][200/5005] lr: 1.000e-02, eta: 5:59:18, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.9934 2023-01-02 20:33:07,958 - mmcls - INFO - Epoch [52][300/5005] lr: 1.000e-02, eta: 5:59:10, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0050 2023-01-02 20:33:16,895 - mmcls - INFO - Epoch [52][400/5005] lr: 1.000e-02, eta: 5:59:01, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.9964 2023-01-02 20:33:25,659 - mmcls - INFO - Epoch [52][500/5005] lr: 1.000e-02, eta: 5:58:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.9885 2023-01-02 20:33:34,508 - mmcls - INFO - Epoch [52][600/5005] lr: 1.000e-02, eta: 5:58:43, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0242 2023-01-02 20:33:43,246 - mmcls - INFO - Epoch [52][700/5005] lr: 1.000e-02, eta: 5:58:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0388 2023-01-02 20:33:51,870 - mmcls - INFO - Epoch [52][800/5005] lr: 1.000e-02, eta: 5:58:25, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0205 2023-01-02 20:34:00,428 - mmcls - INFO - Epoch [52][900/5005] lr: 1.000e-02, eta: 5:58:16, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0318 2023-01-02 20:34:09,319 - mmcls - INFO - Epoch [52][1000/5005] lr: 1.000e-02, eta: 5:58:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0435 2023-01-02 20:34:17,734 - mmcls - INFO - Epoch [52][1100/5005] lr: 1.000e-02, eta: 5:57:59, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.0015 2023-01-02 20:34:26,778 - mmcls - INFO - Epoch [52][1200/5005] lr: 1.000e-02, eta: 5:57:50, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0271 2023-01-02 20:34:35,574 - mmcls - INFO - Epoch [52][1300/5005] lr: 1.000e-02, eta: 5:57:41, time: 0.088, data_time: 0.044, memory: 1125, loss: 2.0330 2023-01-02 20:34:44,130 - mmcls - INFO - Epoch [52][1400/5005] lr: 1.000e-02, eta: 5:57:32, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.0266 2023-01-02 20:34:52,936 - mmcls - INFO - Epoch [52][1500/5005] lr: 1.000e-02, eta: 5:57:23, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0197 2023-01-02 20:35:01,715 - mmcls - INFO - Epoch [52][1600/5005] lr: 1.000e-02, eta: 5:57:15, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0489 2023-01-02 20:35:10,844 - mmcls - INFO - Epoch [52][1700/5005] lr: 1.000e-02, eta: 5:57:06, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0452 2023-01-02 20:35:19,852 - mmcls - INFO - Epoch [52][1800/5005] lr: 1.000e-02, eta: 5:56:58, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0359 2023-01-02 20:35:28,496 - mmcls - INFO - Epoch [52][1900/5005] lr: 1.000e-02, eta: 5:56:49, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.0417 2023-01-02 20:35:37,049 - mmcls - INFO - Epoch [52][2000/5005] lr: 1.000e-02, eta: 5:56:40, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0220 2023-01-02 20:35:46,194 - mmcls - INFO - Epoch [52][2100/5005] lr: 1.000e-02, eta: 5:56:31, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0536 2023-01-02 20:35:54,916 - mmcls - INFO - Epoch [52][2200/5005] lr: 1.000e-02, eta: 5:56:22, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0585 2023-01-02 20:36:04,022 - mmcls - INFO - Epoch [52][2300/5005] lr: 1.000e-02, eta: 5:56:14, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0500 2023-01-02 20:36:12,643 - mmcls - INFO - Epoch [52][2400/5005] lr: 1.000e-02, eta: 5:56:05, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0419 2023-01-02 20:36:21,700 - mmcls - INFO - Epoch [52][2500/5005] lr: 1.000e-02, eta: 5:55:56, time: 0.091, data_time: 0.021, memory: 1125, loss: 2.0607 2023-01-02 20:36:30,365 - mmcls - INFO - Epoch [52][2600/5005] lr: 1.000e-02, eta: 5:55:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0692 2023-01-02 20:36:39,021 - mmcls - INFO - Epoch [52][2700/5005] lr: 1.000e-02, eta: 5:55:38, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0521 2023-01-02 20:36:47,851 - mmcls - INFO - Epoch [52][2800/5005] lr: 1.000e-02, eta: 5:55:30, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0588 2023-01-02 20:36:56,683 - mmcls - INFO - Epoch [52][2900/5005] lr: 1.000e-02, eta: 5:55:21, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0579 2023-01-02 20:37:05,705 - mmcls - INFO - Epoch [52][3000/5005] lr: 1.000e-02, eta: 5:55:12, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0449 2023-01-02 20:37:14,308 - mmcls - INFO - Epoch [52][3100/5005] lr: 1.000e-02, eta: 5:55:03, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0342 2023-01-02 20:37:22,770 - mmcls - INFO - Epoch [52][3200/5005] lr: 1.000e-02, eta: 5:54:54, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0430 2023-01-02 20:37:31,416 - mmcls - INFO - Epoch [52][3300/5005] lr: 1.000e-02, eta: 5:54:45, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0509 2023-01-02 20:37:40,190 - mmcls - INFO - Epoch [52][3400/5005] lr: 1.000e-02, eta: 5:54:36, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0421 2023-01-02 20:37:48,738 - mmcls - INFO - Epoch [52][3500/5005] lr: 1.000e-02, eta: 5:54:27, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.0401 2023-01-02 20:37:57,258 - mmcls - INFO - Epoch [52][3600/5005] lr: 1.000e-02, eta: 5:54:18, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.0629 2023-01-02 20:38:06,022 - mmcls - INFO - Epoch [52][3700/5005] lr: 1.000e-02, eta: 5:54:09, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0671 2023-01-02 20:38:14,772 - mmcls - INFO - Epoch [52][3800/5005] lr: 1.000e-02, eta: 5:54:01, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0742 2023-01-02 20:38:23,994 - mmcls - INFO - Epoch [52][3900/5005] lr: 1.000e-02, eta: 5:53:52, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0736 2023-01-02 20:38:32,805 - mmcls - INFO - Epoch [52][4000/5005] lr: 1.000e-02, eta: 5:53:43, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0439 2023-01-02 20:38:41,335 - mmcls - INFO - Epoch [52][4100/5005] lr: 1.000e-02, eta: 5:53:34, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.0494 2023-01-02 20:38:50,237 - mmcls - INFO - Epoch [52][4200/5005] lr: 1.000e-02, eta: 5:53:26, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0483 2023-01-02 20:38:59,389 - mmcls - INFO - Epoch [52][4300/5005] lr: 1.000e-02, eta: 5:53:17, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0591 2023-01-02 20:39:08,160 - mmcls - INFO - Epoch [52][4400/5005] lr: 1.000e-02, eta: 5:53:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0596 2023-01-02 20:39:16,737 - mmcls - INFO - Epoch [52][4500/5005] lr: 1.000e-02, eta: 5:52:59, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0435 2023-01-02 20:39:25,650 - mmcls - INFO - Epoch [52][4600/5005] lr: 1.000e-02, eta: 5:52:51, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.0624 2023-01-02 20:39:34,258 - mmcls - INFO - Epoch [52][4700/5005] lr: 1.000e-02, eta: 5:52:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0650 2023-01-02 20:39:42,910 - mmcls - INFO - Epoch [52][4800/5005] lr: 1.000e-02, eta: 5:52:33, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0582 2023-01-02 20:39:51,767 - mmcls - INFO - Epoch [52][4900/5005] lr: 1.000e-02, eta: 5:52:24, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0704 2023-01-02 20:40:00,322 - mmcls - INFO - Epoch [52][5000/5005] lr: 1.000e-02, eta: 5:52:15, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0712 2023-01-02 20:40:23,120 - mmcls - INFO - Epoch(val) [52][196] accuracy_top-1: 59.2600, accuracy_top-5: 81.5180 2023-01-02 20:40:34,252 - mmcls - INFO - Epoch [53][100/5005] lr: 1.000e-02, eta: 5:52:07, time: 0.111, data_time: 0.070, memory: 1125, loss: 1.9983 2023-01-02 20:40:42,599 - mmcls - INFO - Epoch [53][200/5005] lr: 1.000e-02, eta: 5:51:58, time: 0.084, data_time: 0.023, memory: 1125, loss: 2.0188 2023-01-02 20:40:51,839 - mmcls - INFO - Epoch [53][300/5005] lr: 1.000e-02, eta: 5:51:50, time: 0.092, data_time: 0.024, memory: 1125, loss: 2.0199 2023-01-02 20:41:00,441 - mmcls - INFO - Epoch [53][400/5005] lr: 1.000e-02, eta: 5:51:41, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.0195 2023-01-02 20:41:09,000 - mmcls - INFO - Epoch [53][500/5005] lr: 1.000e-02, eta: 5:51:32, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0084 2023-01-02 20:41:17,956 - mmcls - INFO - Epoch [53][600/5005] lr: 1.000e-02, eta: 5:51:23, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0291 2023-01-02 20:41:27,030 - mmcls - INFO - Epoch [53][700/5005] lr: 1.000e-02, eta: 5:51:15, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.0123 2023-01-02 20:41:35,749 - mmcls - INFO - Epoch [53][800/5005] lr: 1.000e-02, eta: 5:51:06, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.0324 2023-01-02 20:41:44,669 - mmcls - INFO - Epoch [53][900/5005] lr: 1.000e-02, eta: 5:50:57, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0214 2023-01-02 20:41:53,355 - mmcls - INFO - Epoch [53][1000/5005] lr: 1.000e-02, eta: 5:50:48, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0274 2023-01-02 20:42:02,060 - mmcls - INFO - Epoch [53][1100/5005] lr: 1.000e-02, eta: 5:50:39, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0192 2023-01-02 20:42:10,590 - mmcls - INFO - Epoch [53][1200/5005] lr: 1.000e-02, eta: 5:50:30, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0304 2023-01-02 20:42:19,750 - mmcls - INFO - Epoch [53][1300/5005] lr: 1.000e-02, eta: 5:50:22, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0245 2023-01-02 20:42:28,443 - mmcls - INFO - Epoch [53][1400/5005] lr: 1.000e-02, eta: 5:50:13, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0342 2023-01-02 20:42:37,013 - mmcls - INFO - Epoch [53][1500/5005] lr: 1.000e-02, eta: 5:50:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0098 2023-01-02 20:42:45,938 - mmcls - INFO - Epoch [53][1600/5005] lr: 1.000e-02, eta: 5:49:55, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0235 2023-01-02 20:42:54,554 - mmcls - INFO - Epoch [53][1700/5005] lr: 1.000e-02, eta: 5:49:46, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.0677 2023-01-02 20:43:03,314 - mmcls - INFO - Epoch [53][1800/5005] lr: 1.000e-02, eta: 5:49:37, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0160 2023-01-02 20:43:12,001 - mmcls - INFO - Epoch [53][1900/5005] lr: 1.000e-02, eta: 5:49:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0170 2023-01-02 20:43:20,585 - mmcls - INFO - Epoch [53][2000/5005] lr: 1.000e-02, eta: 5:49:20, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0225 2023-01-02 20:43:29,593 - mmcls - INFO - Epoch [53][2100/5005] lr: 1.000e-02, eta: 5:49:11, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0327 2023-01-02 20:43:38,202 - mmcls - INFO - Epoch [53][2200/5005] lr: 1.000e-02, eta: 5:49:02, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.0244 2023-01-02 20:43:46,925 - mmcls - INFO - Epoch [53][2300/5005] lr: 1.000e-02, eta: 5:48:53, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0239 2023-01-02 20:43:55,653 - mmcls - INFO - Epoch [53][2400/5005] lr: 1.000e-02, eta: 5:48:44, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0495 2023-01-02 20:44:04,365 - mmcls - INFO - Epoch [53][2500/5005] lr: 1.000e-02, eta: 5:48:35, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0622 2023-01-02 20:44:13,117 - mmcls - INFO - Epoch [53][2600/5005] lr: 1.000e-02, eta: 5:48:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0505 2023-01-02 20:44:22,041 - mmcls - INFO - Epoch [53][2700/5005] lr: 1.000e-02, eta: 5:48:18, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0325 2023-01-02 20:44:30,861 - mmcls - INFO - Epoch [53][2800/5005] lr: 1.000e-02, eta: 5:48:09, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0454 2023-01-02 20:44:39,811 - mmcls - INFO - Epoch [53][2900/5005] lr: 1.000e-02, eta: 5:48:00, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.0747 2023-01-02 20:44:48,739 - mmcls - INFO - Epoch [53][3000/5005] lr: 1.000e-02, eta: 5:47:52, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.0514 2023-01-02 20:44:57,712 - mmcls - INFO - Epoch [53][3100/5005] lr: 1.000e-02, eta: 5:47:43, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0557 2023-01-02 20:45:06,383 - mmcls - INFO - Epoch [53][3200/5005] lr: 1.000e-02, eta: 5:47:34, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0512 2023-01-02 20:45:15,168 - mmcls - INFO - Epoch [53][3300/5005] lr: 1.000e-02, eta: 5:47:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0368 2023-01-02 20:45:23,656 - mmcls - INFO - Epoch [53][3400/5005] lr: 1.000e-02, eta: 5:47:16, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.0504 2023-01-02 20:45:32,348 - mmcls - INFO - Epoch [53][3500/5005] lr: 1.000e-02, eta: 5:47:07, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0129 2023-01-02 20:45:41,116 - mmcls - INFO - Epoch [53][3600/5005] lr: 1.000e-02, eta: 5:46:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0591 2023-01-02 20:45:49,877 - mmcls - INFO - Epoch [53][3700/5005] lr: 1.000e-02, eta: 5:46:50, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0446 2023-01-02 20:45:58,570 - mmcls - INFO - Epoch [53][3800/5005] lr: 1.000e-02, eta: 5:46:41, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.0608 2023-01-02 20:46:07,207 - mmcls - INFO - Epoch [53][3900/5005] lr: 1.000e-02, eta: 5:46:32, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.0431 2023-01-02 20:46:15,761 - mmcls - INFO - Epoch [53][4000/5005] lr: 1.000e-02, eta: 5:46:23, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0560 2023-01-02 20:46:24,445 - mmcls - INFO - Epoch [53][4100/5005] lr: 1.000e-02, eta: 5:46:14, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0544 2023-01-02 20:46:32,954 - mmcls - INFO - Epoch [53][4200/5005] lr: 1.000e-02, eta: 5:46:05, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.0426 2023-01-02 20:46:41,878 - mmcls - INFO - Epoch [53][4300/5005] lr: 1.000e-02, eta: 5:45:56, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.0635 2023-01-02 20:46:50,725 - mmcls - INFO - Epoch [53][4400/5005] lr: 1.000e-02, eta: 5:45:47, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.0377 2023-01-02 20:46:59,314 - mmcls - INFO - Epoch [53][4500/5005] lr: 1.000e-02, eta: 5:45:38, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0614 2023-01-02 20:47:08,161 - mmcls - INFO - Epoch [53][4600/5005] lr: 1.000e-02, eta: 5:45:30, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0600 2023-01-02 20:47:17,309 - mmcls - INFO - Epoch [53][4700/5005] lr: 1.000e-02, eta: 5:45:21, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.0831 2023-01-02 20:47:26,057 - mmcls - INFO - Epoch [53][4800/5005] lr: 1.000e-02, eta: 5:45:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0694 2023-01-02 20:47:34,738 - mmcls - INFO - Epoch [53][4900/5005] lr: 1.000e-02, eta: 5:45:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0316 2023-01-02 20:47:43,444 - mmcls - INFO - Epoch [53][5000/5005] lr: 1.000e-02, eta: 5:44:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0641 2023-01-02 20:48:06,005 - mmcls - INFO - Epoch(val) [53][196] accuracy_top-1: 59.2100, accuracy_top-5: 81.3380 2023-01-02 20:48:17,099 - mmcls - INFO - Epoch [54][100/5005] lr: 1.000e-02, eta: 5:44:47, time: 0.111, data_time: 0.053, memory: 1125, loss: 2.0110 2023-01-02 20:48:25,741 - mmcls - INFO - Epoch [54][200/5005] lr: 1.000e-02, eta: 5:44:38, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.9945 2023-01-02 20:48:34,141 - mmcls - INFO - Epoch [54][300/5005] lr: 1.000e-02, eta: 5:44:29, time: 0.084, data_time: 0.001, memory: 1125, loss: 2.0133 2023-01-02 20:48:42,850 - mmcls - INFO - Epoch [54][400/5005] lr: 1.000e-02, eta: 5:44:20, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0300 2023-01-02 20:48:51,782 - mmcls - INFO - Epoch [54][500/5005] lr: 1.000e-02, eta: 5:44:11, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.0187 2023-01-02 20:49:00,391 - mmcls - INFO - Epoch [54][600/5005] lr: 1.000e-02, eta: 5:44:02, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.0015 2023-01-02 20:49:09,146 - mmcls - INFO - Epoch [54][700/5005] lr: 1.000e-02, eta: 5:43:54, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.0122 2023-01-02 20:49:17,592 - mmcls - INFO - Epoch [54][800/5005] lr: 1.000e-02, eta: 5:43:44, time: 0.084, data_time: 0.023, memory: 1125, loss: 2.0248 2023-01-02 20:49:26,261 - mmcls - INFO - Epoch [54][900/5005] lr: 1.000e-02, eta: 5:43:36, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.0306 2023-01-02 20:49:35,051 - mmcls - INFO - Epoch [54][1000/5005] lr: 1.000e-02, eta: 5:43:27, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0347 2023-01-02 20:49:43,662 - mmcls - INFO - Epoch [54][1100/5005] lr: 1.000e-02, eta: 5:43:18, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0469 2023-01-02 20:49:52,259 - mmcls - INFO - Epoch [54][1200/5005] lr: 1.000e-02, eta: 5:43:09, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0182 2023-01-02 20:50:01,000 - mmcls - INFO - Epoch [54][1300/5005] lr: 1.000e-02, eta: 5:43:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0477 2023-01-02 20:50:09,839 - mmcls - INFO - Epoch [54][1400/5005] lr: 1.000e-02, eta: 5:42:51, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0396 2023-01-02 20:50:18,529 - mmcls - INFO - Epoch [54][1500/5005] lr: 1.000e-02, eta: 5:42:42, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0193 2023-01-02 20:50:27,727 - mmcls - INFO - Epoch [54][1600/5005] lr: 1.000e-02, eta: 5:42:34, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.0190 2023-01-02 20:50:36,480 - mmcls - INFO - Epoch [54][1700/5005] lr: 1.000e-02, eta: 5:42:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0183 2023-01-02 20:50:45,241 - mmcls - INFO - Epoch [54][1800/5005] lr: 1.000e-02, eta: 5:42:16, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0212 2023-01-02 20:50:54,562 - mmcls - INFO - Epoch [54][1900/5005] lr: 1.000e-02, eta: 5:42:08, time: 0.093, data_time: 0.029, memory: 1125, loss: 2.0389 2023-01-02 20:51:02,995 - mmcls - INFO - Epoch [54][2000/5005] lr: 1.000e-02, eta: 5:41:59, time: 0.084, data_time: 0.026, memory: 1125, loss: 2.0238 2023-01-02 20:51:11,972 - mmcls - INFO - Epoch [54][2100/5005] lr: 1.000e-02, eta: 5:41:50, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0153 2023-01-02 20:51:20,858 - mmcls - INFO - Epoch [54][2200/5005] lr: 1.000e-02, eta: 5:41:41, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0478 2023-01-02 20:51:29,727 - mmcls - INFO - Epoch [54][2300/5005] lr: 1.000e-02, eta: 5:41:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0310 2023-01-02 20:51:38,364 - mmcls - INFO - Epoch [54][2400/5005] lr: 1.000e-02, eta: 5:41:24, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0371 2023-01-02 20:51:47,317 - mmcls - INFO - Epoch [54][2500/5005] lr: 1.000e-02, eta: 5:41:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0524 2023-01-02 20:51:56,118 - mmcls - INFO - Epoch [54][2600/5005] lr: 1.000e-02, eta: 5:41:06, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0188 2023-01-02 20:52:04,667 - mmcls - INFO - Epoch [54][2700/5005] lr: 1.000e-02, eta: 5:40:57, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0307 2023-01-02 20:52:13,174 - mmcls - INFO - Epoch [54][2800/5005] lr: 1.000e-02, eta: 5:40:48, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0318 2023-01-02 20:52:21,723 - mmcls - INFO - Epoch [54][2900/5005] lr: 1.000e-02, eta: 5:40:39, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0272 2023-01-02 20:52:30,405 - mmcls - INFO - Epoch [54][3000/5005] lr: 1.000e-02, eta: 5:40:30, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0463 2023-01-02 20:52:39,231 - mmcls - INFO - Epoch [54][3100/5005] lr: 1.000e-02, eta: 5:40:21, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0425 2023-01-02 20:52:48,203 - mmcls - INFO - Epoch [54][3200/5005] lr: 1.000e-02, eta: 5:40:13, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0262 2023-01-02 20:52:57,066 - mmcls - INFO - Epoch [54][3300/5005] lr: 1.000e-02, eta: 5:40:04, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0550 2023-01-02 20:53:05,734 - mmcls - INFO - Epoch [54][3400/5005] lr: 1.000e-02, eta: 5:39:55, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.0690 2023-01-02 20:53:14,338 - mmcls - INFO - Epoch [54][3500/5005] lr: 1.000e-02, eta: 5:39:46, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0526 2023-01-02 20:53:22,994 - mmcls - INFO - Epoch [54][3600/5005] lr: 1.000e-02, eta: 5:39:37, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0766 2023-01-02 20:53:31,872 - mmcls - INFO - Epoch [54][3700/5005] lr: 1.000e-02, eta: 5:39:29, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0575 2023-01-02 20:53:40,806 - mmcls - INFO - Epoch [54][3800/5005] lr: 1.000e-02, eta: 5:39:20, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.0680 2023-01-02 20:53:49,563 - mmcls - INFO - Epoch [54][3900/5005] lr: 1.000e-02, eta: 5:39:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0677 2023-01-02 20:53:58,250 - mmcls - INFO - Epoch [54][4000/5005] lr: 1.000e-02, eta: 5:39:02, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0440 2023-01-02 20:54:06,770 - mmcls - INFO - Epoch [54][4100/5005] lr: 1.000e-02, eta: 5:38:53, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0522 2023-01-02 20:54:15,425 - mmcls - INFO - Epoch [54][4200/5005] lr: 1.000e-02, eta: 5:38:44, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0466 2023-01-02 20:54:24,247 - mmcls - INFO - Epoch [54][4300/5005] lr: 1.000e-02, eta: 5:38:35, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0718 2023-01-02 20:54:32,999 - mmcls - INFO - Epoch [54][4400/5005] lr: 1.000e-02, eta: 5:38:27, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0497 2023-01-02 20:54:41,536 - mmcls - INFO - Epoch [54][4500/5005] lr: 1.000e-02, eta: 5:38:18, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.0492 2023-01-02 20:54:50,312 - mmcls - INFO - Epoch [54][4600/5005] lr: 1.000e-02, eta: 5:38:09, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0327 2023-01-02 20:54:59,083 - mmcls - INFO - Epoch [54][4700/5005] lr: 1.000e-02, eta: 5:38:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0540 2023-01-02 20:55:07,854 - mmcls - INFO - Epoch [54][4800/5005] lr: 1.000e-02, eta: 5:37:51, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0719 2023-01-02 20:55:16,984 - mmcls - INFO - Epoch [54][4900/5005] lr: 1.000e-02, eta: 5:37:43, time: 0.091, data_time: 0.013, memory: 1125, loss: 2.0558 2023-01-02 20:55:25,734 - mmcls - INFO - Epoch [54][5000/5005] lr: 1.000e-02, eta: 5:37:34, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0452 2023-01-02 20:55:48,293 - mmcls - INFO - Epoch(val) [54][196] accuracy_top-1: 59.4420, accuracy_top-5: 81.6360 2023-01-02 20:55:59,264 - mmcls - INFO - Epoch [55][100/5005] lr: 1.000e-02, eta: 5:37:26, time: 0.109, data_time: 0.039, memory: 1125, loss: 2.0058 2023-01-02 20:56:08,124 - mmcls - INFO - Epoch [55][200/5005] lr: 1.000e-02, eta: 5:37:17, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0088 2023-01-02 20:56:16,664 - mmcls - INFO - Epoch [55][300/5005] lr: 1.000e-02, eta: 5:37:08, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0151 2023-01-02 20:56:25,650 - mmcls - INFO - Epoch [55][400/5005] lr: 1.000e-02, eta: 5:37:00, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.0168 2023-01-02 20:56:34,243 - mmcls - INFO - Epoch [55][500/5005] lr: 1.000e-02, eta: 5:36:51, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0146 2023-01-02 20:56:42,921 - mmcls - INFO - Epoch [55][600/5005] lr: 1.000e-02, eta: 5:36:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0195 2023-01-02 20:56:51,805 - mmcls - INFO - Epoch [55][700/5005] lr: 1.000e-02, eta: 5:36:33, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.0142 2023-01-02 20:57:00,739 - mmcls - INFO - Epoch [55][800/5005] lr: 1.000e-02, eta: 5:36:24, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.0322 2023-01-02 20:57:09,261 - mmcls - INFO - Epoch [55][900/5005] lr: 1.000e-02, eta: 5:36:15, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0202 2023-01-02 20:57:18,225 - mmcls - INFO - Epoch [55][1000/5005] lr: 1.000e-02, eta: 5:36:07, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.0298 2023-01-02 20:57:27,029 - mmcls - INFO - Epoch [55][1100/5005] lr: 1.000e-02, eta: 5:35:58, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0107 2023-01-02 20:57:35,831 - mmcls - INFO - Epoch [55][1200/5005] lr: 1.000e-02, eta: 5:35:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0335 2023-01-02 20:57:44,483 - mmcls - INFO - Epoch [55][1300/5005] lr: 1.000e-02, eta: 5:35:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0334 2023-01-02 20:57:53,377 - mmcls - INFO - Epoch [55][1400/5005] lr: 1.000e-02, eta: 5:35:31, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0305 2023-01-02 20:58:02,127 - mmcls - INFO - Epoch [55][1500/5005] lr: 1.000e-02, eta: 5:35:23, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0509 2023-01-02 20:58:10,735 - mmcls - INFO - Epoch [55][1600/5005] lr: 1.000e-02, eta: 5:35:14, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0176 2023-01-02 20:58:19,640 - mmcls - INFO - Epoch [55][1700/5005] lr: 1.000e-02, eta: 5:35:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0239 2023-01-02 20:58:28,324 - mmcls - INFO - Epoch [55][1800/5005] lr: 1.000e-02, eta: 5:34:56, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0051 2023-01-02 20:58:37,062 - mmcls - INFO - Epoch [55][1900/5005] lr: 1.000e-02, eta: 5:34:47, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.0043 2023-01-02 20:58:45,824 - mmcls - INFO - Epoch [55][2000/5005] lr: 1.000e-02, eta: 5:34:38, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0374 2023-01-02 20:58:54,872 - mmcls - INFO - Epoch [55][2100/5005] lr: 1.000e-02, eta: 5:34:30, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.0074 2023-01-02 20:59:03,802 - mmcls - INFO - Epoch [55][2200/5005] lr: 1.000e-02, eta: 5:34:21, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.0409 2023-01-02 20:59:12,683 - mmcls - INFO - Epoch [55][2300/5005] lr: 1.000e-02, eta: 5:34:12, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0517 2023-01-02 20:59:21,148 - mmcls - INFO - Epoch [55][2400/5005] lr: 1.000e-02, eta: 5:34:03, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.0020 2023-01-02 20:59:29,873 - mmcls - INFO - Epoch [55][2500/5005] lr: 1.000e-02, eta: 5:33:54, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0411 2023-01-02 20:59:38,884 - mmcls - INFO - Epoch [55][2600/5005] lr: 1.000e-02, eta: 5:33:46, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.0302 2023-01-02 20:59:47,530 - mmcls - INFO - Epoch [55][2700/5005] lr: 1.000e-02, eta: 5:33:37, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.0420 2023-01-02 20:59:56,574 - mmcls - INFO - Epoch [55][2800/5005] lr: 1.000e-02, eta: 5:33:28, time: 0.090, data_time: 0.053, memory: 1125, loss: 2.0400 2023-01-02 21:00:05,264 - mmcls - INFO - Epoch [55][2900/5005] lr: 1.000e-02, eta: 5:33:19, time: 0.087, data_time: 0.036, memory: 1125, loss: 2.0586 2023-01-02 21:00:14,277 - mmcls - INFO - Epoch [55][3000/5005] lr: 1.000e-02, eta: 5:33:11, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0351 2023-01-02 21:00:23,060 - mmcls - INFO - Epoch [55][3100/5005] lr: 1.000e-02, eta: 5:33:02, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.0709 2023-01-02 21:00:31,809 - mmcls - INFO - Epoch [55][3200/5005] lr: 1.000e-02, eta: 5:32:53, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0467 2023-01-02 21:00:40,360 - mmcls - INFO - Epoch [55][3300/5005] lr: 1.000e-02, eta: 5:32:44, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.0310 2023-01-02 21:00:48,871 - mmcls - INFO - Epoch [55][3400/5005] lr: 1.000e-02, eta: 5:32:35, time: 0.085, data_time: 0.028, memory: 1125, loss: 2.0634 2023-01-02 21:00:57,749 - mmcls - INFO - Epoch [55][3500/5005] lr: 1.000e-02, eta: 5:32:26, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.0536 2023-01-02 21:01:06,573 - mmcls - INFO - Epoch [55][3600/5005] lr: 1.000e-02, eta: 5:32:18, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0407 2023-01-02 21:01:15,550 - mmcls - INFO - Epoch [55][3700/5005] lr: 1.000e-02, eta: 5:32:09, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0469 2023-01-02 21:01:24,065 - mmcls - INFO - Epoch [55][3800/5005] lr: 1.000e-02, eta: 5:32:00, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0473 2023-01-02 21:01:32,848 - mmcls - INFO - Epoch [55][3900/5005] lr: 1.000e-02, eta: 5:31:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0248 2023-01-02 21:01:41,422 - mmcls - INFO - Epoch [55][4000/5005] lr: 1.000e-02, eta: 5:31:42, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.0545 2023-01-02 21:01:50,273 - mmcls - INFO - Epoch [55][4100/5005] lr: 1.000e-02, eta: 5:31:33, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.0381 2023-01-02 21:01:58,718 - mmcls - INFO - Epoch [55][4200/5005] lr: 1.000e-02, eta: 5:31:24, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.0555 2023-01-02 21:02:07,563 - mmcls - INFO - Epoch [55][4300/5005] lr: 1.000e-02, eta: 5:31:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0396 2023-01-02 21:02:16,322 - mmcls - INFO - Epoch [55][4400/5005] lr: 1.000e-02, eta: 5:31:07, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0727 2023-01-02 21:02:25,017 - mmcls - INFO - Epoch [55][4500/5005] lr: 1.000e-02, eta: 5:30:58, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0430 2023-01-02 21:02:33,493 - mmcls - INFO - Epoch [55][4600/5005] lr: 1.000e-02, eta: 5:30:49, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0565 2023-01-02 21:02:42,051 - mmcls - INFO - Epoch [55][4700/5005] lr: 1.000e-02, eta: 5:30:40, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0481 2023-01-02 21:02:50,662 - mmcls - INFO - Epoch [55][4800/5005] lr: 1.000e-02, eta: 5:30:31, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0715 2023-01-02 21:02:59,444 - mmcls - INFO - Epoch [55][4900/5005] lr: 1.000e-02, eta: 5:30:22, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0709 2023-01-02 21:03:08,600 - mmcls - INFO - Epoch [55][5000/5005] lr: 1.000e-02, eta: 5:30:13, time: 0.092, data_time: 0.003, memory: 1125, loss: 2.0496 2023-01-02 21:03:31,362 - mmcls - INFO - Epoch(val) [55][196] accuracy_top-1: 58.8380, accuracy_top-5: 81.3180 2023-01-02 21:03:42,336 - mmcls - INFO - Epoch [56][100/5005] lr: 1.000e-02, eta: 5:30:06, time: 0.109, data_time: 0.038, memory: 1125, loss: 1.9866 2023-01-02 21:03:51,240 - mmcls - INFO - Epoch [56][200/5005] lr: 1.000e-02, eta: 5:29:57, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0061 2023-01-02 21:03:59,933 - mmcls - INFO - Epoch [56][300/5005] lr: 1.000e-02, eta: 5:29:48, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.0117 2023-01-02 21:04:08,662 - mmcls - INFO - Epoch [56][400/5005] lr: 1.000e-02, eta: 5:29:39, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.9922 2023-01-02 21:04:17,537 - mmcls - INFO - Epoch [56][500/5005] lr: 1.000e-02, eta: 5:29:30, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.0020 2023-01-02 21:04:26,178 - mmcls - INFO - Epoch [56][600/5005] lr: 1.000e-02, eta: 5:29:21, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.9990 2023-01-02 21:04:35,099 - mmcls - INFO - Epoch [56][700/5005] lr: 1.000e-02, eta: 5:29:13, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0085 2023-01-02 21:04:43,829 - mmcls - INFO - Epoch [56][800/5005] lr: 1.000e-02, eta: 5:29:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0311 2023-01-02 21:04:52,661 - mmcls - INFO - Epoch [56][900/5005] lr: 1.000e-02, eta: 5:28:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0102 2023-01-02 21:05:01,331 - mmcls - INFO - Epoch [56][1000/5005] lr: 1.000e-02, eta: 5:28:46, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.0206 2023-01-02 21:05:10,086 - mmcls - INFO - Epoch [56][1100/5005] lr: 1.000e-02, eta: 5:28:37, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0134 2023-01-02 21:05:18,778 - mmcls - INFO - Epoch [56][1200/5005] lr: 1.000e-02, eta: 5:28:29, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0370 2023-01-02 21:05:27,656 - mmcls - INFO - Epoch [56][1300/5005] lr: 1.000e-02, eta: 5:28:20, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0199 2023-01-02 21:05:36,464 - mmcls - INFO - Epoch [56][1400/5005] lr: 1.000e-02, eta: 5:28:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0205 2023-01-02 21:05:44,921 - mmcls - INFO - Epoch [56][1500/5005] lr: 1.000e-02, eta: 5:28:02, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0243 2023-01-02 21:05:54,024 - mmcls - INFO - Epoch [56][1600/5005] lr: 1.000e-02, eta: 5:27:53, time: 0.091, data_time: 0.017, memory: 1125, loss: 2.0266 2023-01-02 21:06:02,617 - mmcls - INFO - Epoch [56][1700/5005] lr: 1.000e-02, eta: 5:27:44, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.0167 2023-01-02 21:06:11,152 - mmcls - INFO - Epoch [56][1800/5005] lr: 1.000e-02, eta: 5:27:35, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.0329 2023-01-02 21:06:20,001 - mmcls - INFO - Epoch [56][1900/5005] lr: 1.000e-02, eta: 5:27:27, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0269 2023-01-02 21:06:28,548 - mmcls - INFO - Epoch [56][2000/5005] lr: 1.000e-02, eta: 5:27:18, time: 0.085, data_time: 0.000, memory: 1125, loss: 2.0355 2023-01-02 21:06:37,408 - mmcls - INFO - Epoch [56][2100/5005] lr: 1.000e-02, eta: 5:27:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0250 2023-01-02 21:06:46,130 - mmcls - INFO - Epoch [56][2200/5005] lr: 1.000e-02, eta: 5:27:00, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0197 2023-01-02 21:06:54,920 - mmcls - INFO - Epoch [56][2300/5005] lr: 1.000e-02, eta: 5:26:51, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0107 2023-01-02 21:07:03,690 - mmcls - INFO - Epoch [56][2400/5005] lr: 1.000e-02, eta: 5:26:42, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0185 2023-01-02 21:07:12,734 - mmcls - INFO - Epoch [56][2500/5005] lr: 1.000e-02, eta: 5:26:34, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0453 2023-01-02 21:07:21,229 - mmcls - INFO - Epoch [56][2600/5005] lr: 1.000e-02, eta: 5:26:25, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.0346 2023-01-02 21:07:29,789 - mmcls - INFO - Epoch [56][2700/5005] lr: 1.000e-02, eta: 5:26:16, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.0408 2023-01-02 21:07:38,563 - mmcls - INFO - Epoch [56][2800/5005] lr: 1.000e-02, eta: 5:26:07, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.0282 2023-01-02 21:07:47,178 - mmcls - INFO - Epoch [56][2900/5005] lr: 1.000e-02, eta: 5:25:58, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0564 2023-01-02 21:07:55,884 - mmcls - INFO - Epoch [56][3000/5005] lr: 1.000e-02, eta: 5:25:49, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0475 2023-01-02 21:08:04,652 - mmcls - INFO - Epoch [56][3100/5005] lr: 1.000e-02, eta: 5:25:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0268 2023-01-02 21:08:13,321 - mmcls - INFO - Epoch [56][3200/5005] lr: 1.000e-02, eta: 5:25:31, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0361 2023-01-02 21:08:22,285 - mmcls - INFO - Epoch [56][3300/5005] lr: 1.000e-02, eta: 5:25:23, time: 0.090, data_time: 0.017, memory: 1125, loss: 2.0400 2023-01-02 21:08:30,956 - mmcls - INFO - Epoch [56][3400/5005] lr: 1.000e-02, eta: 5:25:14, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.0634 2023-01-02 21:08:39,342 - mmcls - INFO - Epoch [56][3500/5005] lr: 1.000e-02, eta: 5:25:05, time: 0.084, data_time: 0.002, memory: 1125, loss: 2.0280 2023-01-02 21:08:47,912 - mmcls - INFO - Epoch [56][3600/5005] lr: 1.000e-02, eta: 5:24:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0491 2023-01-02 21:08:56,467 - mmcls - INFO - Epoch [56][3700/5005] lr: 1.000e-02, eta: 5:24:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0567 2023-01-02 21:09:05,120 - mmcls - INFO - Epoch [56][3800/5005] lr: 1.000e-02, eta: 5:24:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0573 2023-01-02 21:09:13,702 - mmcls - INFO - Epoch [56][3900/5005] lr: 1.000e-02, eta: 5:24:29, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0516 2023-01-02 21:09:22,686 - mmcls - INFO - Epoch [56][4000/5005] lr: 1.000e-02, eta: 5:24:20, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.0506 2023-01-02 21:09:31,506 - mmcls - INFO - Epoch [56][4100/5005] lr: 1.000e-02, eta: 5:24:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0283 2023-01-02 21:09:40,209 - mmcls - INFO - Epoch [56][4200/5005] lr: 1.000e-02, eta: 5:24:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0416 2023-01-02 21:09:48,708 - mmcls - INFO - Epoch [56][4300/5005] lr: 1.000e-02, eta: 5:23:54, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.0622 2023-01-02 21:09:57,509 - mmcls - INFO - Epoch [56][4400/5005] lr: 1.000e-02, eta: 5:23:45, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.0730 2023-01-02 21:10:06,359 - mmcls - INFO - Epoch [56][4500/5005] lr: 1.000e-02, eta: 5:23:36, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0591 2023-01-02 21:10:15,130 - mmcls - INFO - Epoch [56][4600/5005] lr: 1.000e-02, eta: 5:23:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0330 2023-01-02 21:10:24,121 - mmcls - INFO - Epoch [56][4700/5005] lr: 1.000e-02, eta: 5:23:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0448 2023-01-02 21:10:32,755 - mmcls - INFO - Epoch [56][4800/5005] lr: 1.000e-02, eta: 5:23:10, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0425 2023-01-02 21:10:41,712 - mmcls - INFO - Epoch [56][4900/5005] lr: 1.000e-02, eta: 5:23:01, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.0422 2023-01-02 21:10:50,093 - mmcls - INFO - Epoch [56][5000/5005] lr: 1.000e-02, eta: 5:22:52, time: 0.084, data_time: 0.027, memory: 1125, loss: 2.0565 2023-01-02 21:11:12,853 - mmcls - INFO - Epoch(val) [56][196] accuracy_top-1: 58.8200, accuracy_top-5: 81.2680 2023-01-02 21:11:23,883 - mmcls - INFO - Epoch [57][100/5005] lr: 1.000e-02, eta: 5:22:44, time: 0.110, data_time: 0.056, memory: 1125, loss: 1.9954 2023-01-02 21:11:32,628 - mmcls - INFO - Epoch [57][200/5005] lr: 1.000e-02, eta: 5:22:35, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.9724 2023-01-02 21:11:41,179 - mmcls - INFO - Epoch [57][300/5005] lr: 1.000e-02, eta: 5:22:26, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.0083 2023-01-02 21:11:50,112 - mmcls - INFO - Epoch [57][400/5005] lr: 1.000e-02, eta: 5:22:17, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0198 2023-01-02 21:11:59,038 - mmcls - INFO - Epoch [57][500/5005] lr: 1.000e-02, eta: 5:22:09, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.0064 2023-01-02 21:12:07,951 - mmcls - INFO - Epoch [57][600/5005] lr: 1.000e-02, eta: 5:22:00, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0162 2023-01-02 21:12:17,130 - mmcls - INFO - Epoch [57][700/5005] lr: 1.000e-02, eta: 5:21:52, time: 0.092, data_time: 0.009, memory: 1125, loss: 2.0224 2023-01-02 21:12:25,667 - mmcls - INFO - Epoch [57][800/5005] lr: 1.000e-02, eta: 5:21:43, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.0059 2023-01-02 21:12:34,398 - mmcls - INFO - Epoch [57][900/5005] lr: 1.000e-02, eta: 5:21:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0128 2023-01-02 21:12:43,058 - mmcls - INFO - Epoch [57][1000/5005] lr: 1.000e-02, eta: 5:21:25, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0387 2023-01-02 21:12:52,151 - mmcls - INFO - Epoch [57][1100/5005] lr: 1.000e-02, eta: 5:21:16, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.0282 2023-01-02 21:13:00,897 - mmcls - INFO - Epoch [57][1200/5005] lr: 1.000e-02, eta: 5:21:07, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.0435 2023-01-02 21:13:09,707 - mmcls - INFO - Epoch [57][1300/5005] lr: 1.000e-02, eta: 5:20:59, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0173 2023-01-02 21:13:18,513 - mmcls - INFO - Epoch [57][1400/5005] lr: 1.000e-02, eta: 5:20:50, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.0360 2023-01-02 21:13:27,208 - mmcls - INFO - Epoch [57][1500/5005] lr: 1.000e-02, eta: 5:20:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0393 2023-01-02 21:13:35,995 - mmcls - INFO - Epoch [57][1600/5005] lr: 1.000e-02, eta: 5:20:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0129 2023-01-02 21:13:44,730 - mmcls - INFO - Epoch [57][1700/5005] lr: 1.000e-02, eta: 5:20:23, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0180 2023-01-02 21:13:53,495 - mmcls - INFO - Epoch [57][1800/5005] lr: 1.000e-02, eta: 5:20:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0501 2023-01-02 21:14:02,382 - mmcls - INFO - Epoch [57][1900/5005] lr: 1.000e-02, eta: 5:20:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0305 2023-01-02 21:14:11,067 - mmcls - INFO - Epoch [57][2000/5005] lr: 1.000e-02, eta: 5:19:57, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0421 2023-01-02 21:14:19,691 - mmcls - INFO - Epoch [57][2100/5005] lr: 1.000e-02, eta: 5:19:48, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.0183 2023-01-02 21:14:28,956 - mmcls - INFO - Epoch [57][2200/5005] lr: 1.000e-02, eta: 5:19:39, time: 0.093, data_time: 0.022, memory: 1125, loss: 2.0265 2023-01-02 21:14:37,699 - mmcls - INFO - Epoch [57][2300/5005] lr: 1.000e-02, eta: 5:19:31, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.0334 2023-01-02 21:14:46,617 - mmcls - INFO - Epoch [57][2400/5005] lr: 1.000e-02, eta: 5:19:22, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.0376 2023-01-02 21:14:55,243 - mmcls - INFO - Epoch [57][2500/5005] lr: 1.000e-02, eta: 5:19:13, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.0017 2023-01-02 21:15:04,304 - mmcls - INFO - Epoch [57][2600/5005] lr: 1.000e-02, eta: 5:19:04, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0305 2023-01-02 21:15:13,341 - mmcls - INFO - Epoch [57][2700/5005] lr: 1.000e-02, eta: 5:18:56, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0173 2023-01-02 21:15:21,932 - mmcls - INFO - Epoch [57][2800/5005] lr: 1.000e-02, eta: 5:18:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0416 2023-01-02 21:15:30,654 - mmcls - INFO - Epoch [57][2900/5005] lr: 1.000e-02, eta: 5:18:38, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.0427 2023-01-02 21:15:39,641 - mmcls - INFO - Epoch [57][3000/5005] lr: 1.000e-02, eta: 5:18:29, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.0354 2023-01-02 21:15:48,288 - mmcls - INFO - Epoch [57][3100/5005] lr: 1.000e-02, eta: 5:18:20, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0066 2023-01-02 21:15:57,049 - mmcls - INFO - Epoch [57][3200/5005] lr: 1.000e-02, eta: 5:18:12, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.0541 2023-01-02 21:16:05,864 - mmcls - INFO - Epoch [57][3300/5005] lr: 1.000e-02, eta: 5:18:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0631 2023-01-02 21:16:14,611 - mmcls - INFO - Epoch [57][3400/5005] lr: 1.000e-02, eta: 5:17:54, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.0459 2023-01-02 21:16:23,290 - mmcls - INFO - Epoch [57][3500/5005] lr: 1.000e-02, eta: 5:17:45, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0481 2023-01-02 21:16:31,827 - mmcls - INFO - Epoch [57][3600/5005] lr: 1.000e-02, eta: 5:17:36, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.0476 2023-01-02 21:16:40,642 - mmcls - INFO - Epoch [57][3700/5005] lr: 1.000e-02, eta: 5:17:27, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0393 2023-01-02 21:16:49,080 - mmcls - INFO - Epoch [57][3800/5005] lr: 1.000e-02, eta: 5:17:18, time: 0.084, data_time: 0.007, memory: 1125, loss: 2.0305 2023-01-02 21:16:58,058 - mmcls - INFO - Epoch [57][3900/5005] lr: 1.000e-02, eta: 5:17:10, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.0327 2023-01-02 21:17:06,625 - mmcls - INFO - Epoch [57][4000/5005] lr: 1.000e-02, eta: 5:17:01, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.0445 2023-01-02 21:17:15,694 - mmcls - INFO - Epoch [57][4100/5005] lr: 1.000e-02, eta: 5:16:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0510 2023-01-02 21:17:24,649 - mmcls - INFO - Epoch [57][4200/5005] lr: 1.000e-02, eta: 5:16:43, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0421 2023-01-02 21:17:33,611 - mmcls - INFO - Epoch [57][4300/5005] lr: 1.000e-02, eta: 5:16:35, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0448 2023-01-02 21:17:42,445 - mmcls - INFO - Epoch [57][4400/5005] lr: 1.000e-02, eta: 5:16:26, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0540 2023-01-02 21:17:50,961 - mmcls - INFO - Epoch [57][4500/5005] lr: 1.000e-02, eta: 5:16:17, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.0398 2023-01-02 21:17:59,359 - mmcls - INFO - Epoch [57][4600/5005] lr: 1.000e-02, eta: 5:16:08, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.0704 2023-01-02 21:18:08,214 - mmcls - INFO - Epoch [57][4700/5005] lr: 1.000e-02, eta: 5:15:59, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0564 2023-01-02 21:18:16,949 - mmcls - INFO - Epoch [57][4800/5005] lr: 1.000e-02, eta: 5:15:50, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.0506 2023-01-02 21:18:25,788 - mmcls - INFO - Epoch [57][4900/5005] lr: 1.000e-02, eta: 5:15:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0213 2023-01-02 21:18:34,431 - mmcls - INFO - Epoch [57][5000/5005] lr: 1.000e-02, eta: 5:15:32, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0334 2023-01-02 21:18:57,100 - mmcls - INFO - Epoch(val) [57][196] accuracy_top-1: 58.9540, accuracy_top-5: 81.7580 2023-01-02 21:19:07,952 - mmcls - INFO - Epoch [58][100/5005] lr: 1.000e-02, eta: 5:15:24, time: 0.108, data_time: 0.044, memory: 1125, loss: 2.0079 2023-01-02 21:19:16,589 - mmcls - INFO - Epoch [58][200/5005] lr: 1.000e-02, eta: 5:15:16, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.9998 2023-01-02 21:19:25,496 - mmcls - INFO - Epoch [58][300/5005] lr: 1.000e-02, eta: 5:15:07, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.9916 2023-01-02 21:19:34,014 - mmcls - INFO - Epoch [58][400/5005] lr: 1.000e-02, eta: 5:14:58, time: 0.085, data_time: 0.009, memory: 1125, loss: 2.0089 2023-01-02 21:19:42,920 - mmcls - INFO - Epoch [58][500/5005] lr: 1.000e-02, eta: 5:14:49, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0026 2023-01-02 21:19:51,678 - mmcls - INFO - Epoch [58][600/5005] lr: 1.000e-02, eta: 5:14:40, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.0027 2023-01-02 21:20:00,258 - mmcls - INFO - Epoch [58][700/5005] lr: 1.000e-02, eta: 5:14:31, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.0261 2023-01-02 21:20:08,898 - mmcls - INFO - Epoch [58][800/5005] lr: 1.000e-02, eta: 5:14:22, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.0172 2023-01-02 21:20:17,612 - mmcls - INFO - Epoch [58][900/5005] lr: 1.000e-02, eta: 5:14:14, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.0143 2023-01-02 21:20:26,528 - mmcls - INFO - Epoch [58][1000/5005] lr: 1.000e-02, eta: 5:14:05, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.0158 2023-01-02 21:20:34,926 - mmcls - INFO - Epoch [58][1100/5005] lr: 1.000e-02, eta: 5:13:56, time: 0.084, data_time: 0.008, memory: 1125, loss: 2.0338 2023-01-02 21:20:43,851 - mmcls - INFO - Epoch [58][1200/5005] lr: 1.000e-02, eta: 5:13:47, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.0531 2023-01-02 21:20:52,372 - mmcls - INFO - Epoch [58][1300/5005] lr: 1.000e-02, eta: 5:13:38, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.0444 2023-01-02 21:21:01,600 - mmcls - INFO - Epoch [58][1400/5005] lr: 1.000e-02, eta: 5:13:30, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.0003 2023-01-02 21:21:10,163 - mmcls - INFO - Epoch [58][1500/5005] lr: 1.000e-02, eta: 5:13:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0323 2023-01-02 21:21:19,039 - mmcls - INFO - Epoch [58][1600/5005] lr: 1.000e-02, eta: 5:13:12, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.0388 2023-01-02 21:21:27,819 - mmcls - INFO - Epoch [58][1700/5005] lr: 1.000e-02, eta: 5:13:03, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.0501 2023-01-02 21:21:36,604 - mmcls - INFO - Epoch [58][1800/5005] lr: 1.000e-02, eta: 5:12:54, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0144 2023-01-02 21:21:45,327 - mmcls - INFO - Epoch [58][1900/5005] lr: 1.000e-02, eta: 5:12:45, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0330 2023-01-02 21:21:54,439 - mmcls - INFO - Epoch [58][2000/5005] lr: 1.000e-02, eta: 5:12:37, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.0511 2023-01-02 21:22:03,392 - mmcls - INFO - Epoch [58][2100/5005] lr: 1.000e-02, eta: 5:12:28, time: 0.090, data_time: 0.020, memory: 1125, loss: 2.0115 2023-01-02 21:22:12,127 - mmcls - INFO - Epoch [58][2200/5005] lr: 1.000e-02, eta: 5:12:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0345 2023-01-02 21:22:21,043 - mmcls - INFO - Epoch [58][2300/5005] lr: 1.000e-02, eta: 5:12:11, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.0006 2023-01-02 21:22:29,950 - mmcls - INFO - Epoch [58][2400/5005] lr: 1.000e-02, eta: 5:12:02, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0373 2023-01-02 21:22:38,638 - mmcls - INFO - Epoch [58][2500/5005] lr: 1.000e-02, eta: 5:11:53, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0332 2023-01-02 21:22:47,547 - mmcls - INFO - Epoch [58][2600/5005] lr: 1.000e-02, eta: 5:11:44, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.0228 2023-01-02 21:22:56,182 - mmcls - INFO - Epoch [58][2700/5005] lr: 1.000e-02, eta: 5:11:35, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.0259 2023-01-02 21:23:04,788 - mmcls - INFO - Epoch [58][2800/5005] lr: 1.000e-02, eta: 5:11:26, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0153 2023-01-02 21:23:13,452 - mmcls - INFO - Epoch [58][2900/5005] lr: 1.000e-02, eta: 5:11:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0494 2023-01-02 21:23:22,319 - mmcls - INFO - Epoch [58][3000/5005] lr: 1.000e-02, eta: 5:11:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0274 2023-01-02 21:23:31,319 - mmcls - INFO - Epoch [58][3100/5005] lr: 1.000e-02, eta: 5:11:00, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.0469 2023-01-02 21:23:39,889 - mmcls - INFO - Epoch [58][3200/5005] lr: 1.000e-02, eta: 5:10:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0241 2023-01-02 21:23:48,830 - mmcls - INFO - Epoch [58][3300/5005] lr: 1.000e-02, eta: 5:10:42, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0442 2023-01-02 21:23:57,632 - mmcls - INFO - Epoch [58][3400/5005] lr: 1.000e-02, eta: 5:10:34, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.0456 2023-01-02 21:24:06,666 - mmcls - INFO - Epoch [58][3500/5005] lr: 1.000e-02, eta: 5:10:25, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0422 2023-01-02 21:24:15,096 - mmcls - INFO - Epoch [58][3600/5005] lr: 1.000e-02, eta: 5:10:16, time: 0.084, data_time: 0.007, memory: 1125, loss: 2.0363 2023-01-02 21:24:23,809 - mmcls - INFO - Epoch [58][3700/5005] lr: 1.000e-02, eta: 5:10:07, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0509 2023-01-02 21:24:32,465 - mmcls - INFO - Epoch [58][3800/5005] lr: 1.000e-02, eta: 5:09:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.0270 2023-01-02 21:24:41,228 - mmcls - INFO - Epoch [58][3900/5005] lr: 1.000e-02, eta: 5:09:49, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0318 2023-01-02 21:24:49,925 - mmcls - INFO - Epoch [58][4000/5005] lr: 1.000e-02, eta: 5:09:40, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.0569 2023-01-02 21:24:58,771 - mmcls - INFO - Epoch [58][4100/5005] lr: 1.000e-02, eta: 5:09:32, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.0440 2023-01-02 21:25:07,274 - mmcls - INFO - Epoch [58][4200/5005] lr: 1.000e-02, eta: 5:09:23, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.0113 2023-01-02 21:25:16,169 - mmcls - INFO - Epoch [58][4300/5005] lr: 1.000e-02, eta: 5:09:14, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.0700 2023-01-02 21:25:24,868 - mmcls - INFO - Epoch [58][4400/5005] lr: 1.000e-02, eta: 5:09:05, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.0502 2023-01-02 21:25:33,631 - mmcls - INFO - Epoch [58][4500/5005] lr: 1.000e-02, eta: 5:08:56, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0626 2023-01-02 21:25:42,471 - mmcls - INFO - Epoch [58][4600/5005] lr: 1.000e-02, eta: 5:08:48, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.0460 2023-01-02 21:25:51,290 - mmcls - INFO - Epoch [58][4700/5005] lr: 1.000e-02, eta: 5:08:39, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.0342 2023-01-02 21:25:59,894 - mmcls - INFO - Epoch [58][4800/5005] lr: 1.000e-02, eta: 5:08:30, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0518 2023-01-02 21:26:08,647 - mmcls - INFO - Epoch [58][4900/5005] lr: 1.000e-02, eta: 5:08:21, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0420 2023-01-02 21:26:17,476 - mmcls - INFO - Epoch [58][5000/5005] lr: 1.000e-02, eta: 5:08:12, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.0465 2023-01-02 21:26:40,180 - mmcls - INFO - Epoch(val) [58][196] accuracy_top-1: 58.9340, accuracy_top-5: 81.2680 2023-01-02 21:26:51,111 - mmcls - INFO - Epoch [59][100/5005] lr: 1.000e-02, eta: 5:08:04, time: 0.109, data_time: 0.060, memory: 1125, loss: 1.9875 2023-01-02 21:26:59,773 - mmcls - INFO - Epoch [59][200/5005] lr: 1.000e-02, eta: 5:07:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9994 2023-01-02 21:27:08,498 - mmcls - INFO - Epoch [59][300/5005] lr: 1.000e-02, eta: 5:07:46, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.9894 2023-01-02 21:27:17,235 - mmcls - INFO - Epoch [59][400/5005] lr: 1.000e-02, eta: 5:07:38, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.9993 2023-01-02 21:27:25,894 - mmcls - INFO - Epoch [59][500/5005] lr: 1.000e-02, eta: 5:07:29, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0186 2023-01-02 21:27:34,680 - mmcls - INFO - Epoch [59][600/5005] lr: 1.000e-02, eta: 5:07:20, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0069 2023-01-02 21:27:43,486 - mmcls - INFO - Epoch [59][700/5005] lr: 1.000e-02, eta: 5:07:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0178 2023-01-02 21:27:52,512 - mmcls - INFO - Epoch [59][800/5005] lr: 1.000e-02, eta: 5:07:02, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0080 2023-01-02 21:28:01,232 - mmcls - INFO - Epoch [59][900/5005] lr: 1.000e-02, eta: 5:06:54, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0058 2023-01-02 21:28:10,245 - mmcls - INFO - Epoch [59][1000/5005] lr: 1.000e-02, eta: 5:06:45, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.0206 2023-01-02 21:28:18,927 - mmcls - INFO - Epoch [59][1100/5005] lr: 1.000e-02, eta: 5:06:36, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.0054 2023-01-02 21:28:27,527 - mmcls - INFO - Epoch [59][1200/5005] lr: 1.000e-02, eta: 5:06:27, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0041 2023-01-02 21:28:36,531 - mmcls - INFO - Epoch [59][1300/5005] lr: 1.000e-02, eta: 5:06:18, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.0198 2023-01-02 21:28:45,249 - mmcls - INFO - Epoch [59][1400/5005] lr: 1.000e-02, eta: 5:06:10, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.0001 2023-01-02 21:28:53,743 - mmcls - INFO - Epoch [59][1500/5005] lr: 1.000e-02, eta: 5:06:01, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0192 2023-01-02 21:29:02,944 - mmcls - INFO - Epoch [59][1600/5005] lr: 1.000e-02, eta: 5:05:52, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.0229 2023-01-02 21:29:11,742 - mmcls - INFO - Epoch [59][1700/5005] lr: 1.000e-02, eta: 5:05:43, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.0254 2023-01-02 21:29:20,755 - mmcls - INFO - Epoch [59][1800/5005] lr: 1.000e-02, eta: 5:05:35, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.0143 2023-01-02 21:29:29,301 - mmcls - INFO - Epoch [59][1900/5005] lr: 1.000e-02, eta: 5:05:26, time: 0.085, data_time: 0.034, memory: 1125, loss: 2.0206 2023-01-02 21:29:38,031 - mmcls - INFO - Epoch [59][2000/5005] lr: 1.000e-02, eta: 5:05:17, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0220 2023-01-02 21:29:47,173 - mmcls - INFO - Epoch [59][2100/5005] lr: 1.000e-02, eta: 5:05:08, time: 0.091, data_time: 0.014, memory: 1125, loss: 2.0378 2023-01-02 21:29:56,029 - mmcls - INFO - Epoch [59][2200/5005] lr: 1.000e-02, eta: 5:05:00, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.0278 2023-01-02 21:30:04,956 - mmcls - INFO - Epoch [59][2300/5005] lr: 1.000e-02, eta: 5:04:51, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.9976 2023-01-02 21:30:13,686 - mmcls - INFO - Epoch [59][2400/5005] lr: 1.000e-02, eta: 5:04:42, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.9974 2023-01-02 21:30:22,816 - mmcls - INFO - Epoch [59][2500/5005] lr: 1.000e-02, eta: 5:04:33, time: 0.091, data_time: 0.011, memory: 1125, loss: 2.0246 2023-01-02 21:30:31,336 - mmcls - INFO - Epoch [59][2600/5005] lr: 1.000e-02, eta: 5:04:24, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.0323 2023-01-02 21:30:40,317 - mmcls - INFO - Epoch [59][2700/5005] lr: 1.000e-02, eta: 5:04:16, time: 0.090, data_time: 0.045, memory: 1125, loss: 2.0425 2023-01-02 21:30:49,034 - mmcls - INFO - Epoch [59][2800/5005] lr: 1.000e-02, eta: 5:04:07, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.0214 2023-01-02 21:30:57,778 - mmcls - INFO - Epoch [59][2900/5005] lr: 1.000e-02, eta: 5:03:58, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0354 2023-01-02 21:31:06,763 - mmcls - INFO - Epoch [59][3000/5005] lr: 1.000e-02, eta: 5:03:49, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0254 2023-01-02 21:31:15,696 - mmcls - INFO - Epoch [59][3100/5005] lr: 1.000e-02, eta: 5:03:41, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.0248 2023-01-02 21:31:24,366 - mmcls - INFO - Epoch [59][3200/5005] lr: 1.000e-02, eta: 5:03:32, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0487 2023-01-02 21:31:33,305 - mmcls - INFO - Epoch [59][3300/5005] lr: 1.000e-02, eta: 5:03:23, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0274 2023-01-02 21:31:42,221 - mmcls - INFO - Epoch [59][3400/5005] lr: 1.000e-02, eta: 5:03:14, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.0365 2023-01-02 21:31:50,916 - mmcls - INFO - Epoch [59][3500/5005] lr: 1.000e-02, eta: 5:03:06, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.0742 2023-01-02 21:31:59,441 - mmcls - INFO - Epoch [59][3600/5005] lr: 1.000e-02, eta: 5:02:57, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0550 2023-01-02 21:32:08,065 - mmcls - INFO - Epoch [59][3700/5005] lr: 1.000e-02, eta: 5:02:48, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0455 2023-01-02 21:32:16,647 - mmcls - INFO - Epoch [59][3800/5005] lr: 1.000e-02, eta: 5:02:39, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0476 2023-01-02 21:32:25,213 - mmcls - INFO - Epoch [59][3900/5005] lr: 1.000e-02, eta: 5:02:30, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.0558 2023-01-02 21:32:33,742 - mmcls - INFO - Epoch [59][4000/5005] lr: 1.000e-02, eta: 5:02:21, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.0284 2023-01-02 21:32:42,457 - mmcls - INFO - Epoch [59][4100/5005] lr: 1.000e-02, eta: 5:02:12, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.0474 2023-01-02 21:32:51,328 - mmcls - INFO - Epoch [59][4200/5005] lr: 1.000e-02, eta: 5:02:03, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0423 2023-01-02 21:33:00,135 - mmcls - INFO - Epoch [59][4300/5005] lr: 1.000e-02, eta: 5:01:54, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.0679 2023-01-02 21:33:08,592 - mmcls - INFO - Epoch [59][4400/5005] lr: 1.000e-02, eta: 5:01:45, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.0390 2023-01-02 21:33:17,517 - mmcls - INFO - Epoch [59][4500/5005] lr: 1.000e-02, eta: 5:01:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.0407 2023-01-02 21:33:26,591 - mmcls - INFO - Epoch [59][4600/5005] lr: 1.000e-02, eta: 5:01:28, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.0485 2023-01-02 21:33:35,591 - mmcls - INFO - Epoch [59][4700/5005] lr: 1.000e-02, eta: 5:01:19, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.0655 2023-01-02 21:33:44,700 - mmcls - INFO - Epoch [59][4800/5005] lr: 1.000e-02, eta: 5:01:11, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0599 2023-01-02 21:33:53,844 - mmcls - INFO - Epoch [59][4900/5005] lr: 1.000e-02, eta: 5:01:02, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.0476 2023-01-02 21:34:02,745 - mmcls - INFO - Epoch [59][5000/5005] lr: 1.000e-02, eta: 5:00:53, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.0559 2023-01-02 21:34:25,385 - mmcls - INFO - Epoch(val) [59][196] accuracy_top-1: 58.9060, accuracy_top-5: 81.2580 2023-01-02 21:34:36,054 - mmcls - INFO - Epoch [60][100/5005] lr: 1.000e-02, eta: 5:00:45, time: 0.107, data_time: 0.062, memory: 1125, loss: 2.0107 2023-01-02 21:34:44,849 - mmcls - INFO - Epoch [60][200/5005] lr: 1.000e-02, eta: 5:00:36, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.0069 2023-01-02 21:34:53,486 - mmcls - INFO - Epoch [60][300/5005] lr: 1.000e-02, eta: 5:00:27, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.9866 2023-01-02 21:35:02,289 - mmcls - INFO - Epoch [60][400/5005] lr: 1.000e-02, eta: 5:00:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0177 2023-01-02 21:35:10,985 - mmcls - INFO - Epoch [60][500/5005] lr: 1.000e-02, eta: 5:00:10, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0122 2023-01-02 21:35:20,108 - mmcls - INFO - Epoch [60][600/5005] lr: 1.000e-02, eta: 5:00:01, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.9937 2023-01-02 21:35:29,137 - mmcls - INFO - Epoch [60][700/5005] lr: 1.000e-02, eta: 4:59:53, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.0133 2023-01-02 21:35:37,907 - mmcls - INFO - Epoch [60][800/5005] lr: 1.000e-02, eta: 4:59:44, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0261 2023-01-02 21:35:46,518 - mmcls - INFO - Epoch [60][900/5005] lr: 1.000e-02, eta: 4:59:35, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.9930 2023-01-02 21:35:55,478 - mmcls - INFO - Epoch [60][1000/5005] lr: 1.000e-02, eta: 4:59:26, time: 0.090, data_time: 0.026, memory: 1125, loss: 1.9928 2023-01-02 21:36:04,135 - mmcls - INFO - Epoch [60][1100/5005] lr: 1.000e-02, eta: 4:59:17, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.9818 2023-01-02 21:36:13,031 - mmcls - INFO - Epoch [60][1200/5005] lr: 1.000e-02, eta: 4:59:09, time: 0.089, data_time: 0.038, memory: 1125, loss: 2.0457 2023-01-02 21:36:21,977 - mmcls - INFO - Epoch [60][1300/5005] lr: 1.000e-02, eta: 4:59:00, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.0179 2023-01-02 21:36:30,835 - mmcls - INFO - Epoch [60][1400/5005] lr: 1.000e-02, eta: 4:58:51, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.0220 2023-01-02 21:36:39,670 - mmcls - INFO - Epoch [60][1500/5005] lr: 1.000e-02, eta: 4:58:42, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0144 2023-01-02 21:36:48,272 - mmcls - INFO - Epoch [60][1600/5005] lr: 1.000e-02, eta: 4:58:33, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.0036 2023-01-02 21:36:57,056 - mmcls - INFO - Epoch [60][1700/5005] lr: 1.000e-02, eta: 4:58:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0279 2023-01-02 21:37:06,107 - mmcls - INFO - Epoch [60][1800/5005] lr: 1.000e-02, eta: 4:58:16, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0076 2023-01-02 21:37:14,705 - mmcls - INFO - Epoch [60][1900/5005] lr: 1.000e-02, eta: 4:58:07, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0249 2023-01-02 21:37:23,772 - mmcls - INFO - Epoch [60][2000/5005] lr: 1.000e-02, eta: 4:57:58, time: 0.091, data_time: 0.002, memory: 1125, loss: 2.0023 2023-01-02 21:37:32,490 - mmcls - INFO - Epoch [60][2100/5005] lr: 1.000e-02, eta: 4:57:50, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.0553 2023-01-02 21:37:41,093 - mmcls - INFO - Epoch [60][2200/5005] lr: 1.000e-02, eta: 4:57:41, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.0543 2023-01-02 21:37:49,705 - mmcls - INFO - Epoch [60][2300/5005] lr: 1.000e-02, eta: 4:57:32, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.0275 2023-01-02 21:37:58,105 - mmcls - INFO - Epoch [60][2400/5005] lr: 1.000e-02, eta: 4:57:23, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.0325 2023-01-02 21:38:07,003 - mmcls - INFO - Epoch [60][2500/5005] lr: 1.000e-02, eta: 4:57:14, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.0034 2023-01-02 21:38:15,988 - mmcls - INFO - Epoch [60][2600/5005] lr: 1.000e-02, eta: 4:57:05, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.0468 2023-01-02 21:38:24,626 - mmcls - INFO - Epoch [60][2700/5005] lr: 1.000e-02, eta: 4:56:56, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.0342 2023-01-02 21:38:33,313 - mmcls - INFO - Epoch [60][2800/5005] lr: 1.000e-02, eta: 4:56:47, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0279 2023-01-02 21:38:41,996 - mmcls - INFO - Epoch [60][2900/5005] lr: 1.000e-02, eta: 4:56:39, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.0305 2023-01-02 21:38:51,049 - mmcls - INFO - Epoch [60][3000/5005] lr: 1.000e-02, eta: 4:56:30, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.0364 2023-01-02 21:38:59,829 - mmcls - INFO - Epoch [60][3100/5005] lr: 1.000e-02, eta: 4:56:21, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0049 2023-01-02 21:39:08,588 - mmcls - INFO - Epoch [60][3200/5005] lr: 1.000e-02, eta: 4:56:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.0395 2023-01-02 21:39:17,155 - mmcls - INFO - Epoch [60][3300/5005] lr: 1.000e-02, eta: 4:56:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.0200 2023-01-02 21:39:25,931 - mmcls - INFO - Epoch [60][3400/5005] lr: 1.000e-02, eta: 4:55:55, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.0385 2023-01-02 21:39:34,586 - mmcls - INFO - Epoch [60][3500/5005] lr: 1.000e-02, eta: 4:55:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0365 2023-01-02 21:39:43,256 - mmcls - INFO - Epoch [60][3600/5005] lr: 1.000e-02, eta: 4:55:37, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.0380 2023-01-02 21:39:52,006 - mmcls - INFO - Epoch [60][3700/5005] lr: 1.000e-02, eta: 4:55:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.0353 2023-01-02 21:40:00,798 - mmcls - INFO - Epoch [60][3800/5005] lr: 1.000e-02, eta: 4:55:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.0774 2023-01-02 21:40:09,603 - mmcls - INFO - Epoch [60][3900/5005] lr: 1.000e-02, eta: 4:55:10, time: 0.088, data_time: 0.025, memory: 1125, loss: 2.0621 2023-01-02 21:40:18,153 - mmcls - INFO - Epoch [60][4000/5005] lr: 1.000e-02, eta: 4:55:01, time: 0.085, data_time: 0.036, memory: 1125, loss: 2.0318 2023-01-02 21:40:26,689 - mmcls - INFO - Epoch [60][4100/5005] lr: 1.000e-02, eta: 4:54:52, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.0251 2023-01-02 21:40:35,856 - mmcls - INFO - Epoch [60][4200/5005] lr: 1.000e-02, eta: 4:54:44, time: 0.092, data_time: 0.016, memory: 1125, loss: 2.0324 2023-01-02 21:40:44,535 - mmcls - INFO - Epoch [60][4300/5005] lr: 1.000e-02, eta: 4:54:35, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.0320 2023-01-02 21:40:53,152 - mmcls - INFO - Epoch [60][4400/5005] lr: 1.000e-02, eta: 4:54:26, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.0453 2023-01-02 21:41:01,711 - mmcls - INFO - Epoch [60][4500/5005] lr: 1.000e-02, eta: 4:54:17, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.0048 2023-01-02 21:41:10,372 - mmcls - INFO - Epoch [60][4600/5005] lr: 1.000e-02, eta: 4:54:08, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.0292 2023-01-02 21:41:19,131 - mmcls - INFO - Epoch [60][4700/5005] lr: 1.000e-02, eta: 4:53:59, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.0691 2023-01-02 21:41:27,775 - mmcls - INFO - Epoch [60][4800/5005] lr: 1.000e-02, eta: 4:53:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.0611 2023-01-02 21:41:36,594 - mmcls - INFO - Epoch [60][4900/5005] lr: 1.000e-02, eta: 4:53:42, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.0419 2023-01-02 21:41:45,479 - mmcls - INFO - Epoch [60][5000/5005] lr: 1.000e-02, eta: 4:53:33, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.0372 2023-01-02 21:42:08,419 - mmcls - INFO - Epoch(val) [60][196] accuracy_top-1: 58.5800, accuracy_top-5: 80.9500 2023-01-02 21:42:18,981 - mmcls - INFO - Epoch [61][100/5005] lr: 1.000e-03, eta: 4:53:25, time: 0.105, data_time: 0.039, memory: 1125, loss: 1.9652 2023-01-02 21:42:28,051 - mmcls - INFO - Epoch [61][200/5005] lr: 1.000e-03, eta: 4:53:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.9211 2023-01-02 21:42:36,756 - mmcls - INFO - Epoch [61][300/5005] lr: 1.000e-03, eta: 4:53:07, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8979 2023-01-02 21:42:45,406 - mmcls - INFO - Epoch [61][400/5005] lr: 1.000e-03, eta: 4:52:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.9082 2023-01-02 21:42:54,111 - mmcls - INFO - Epoch [61][500/5005] lr: 1.000e-03, eta: 4:52:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9108 2023-01-02 21:43:02,740 - mmcls - INFO - Epoch [61][600/5005] lr: 1.000e-03, eta: 4:52:40, time: 0.086, data_time: 0.038, memory: 1125, loss: 1.9084 2023-01-02 21:43:11,519 - mmcls - INFO - Epoch [61][700/5005] lr: 1.000e-03, eta: 4:52:32, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.8764 2023-01-02 21:43:20,218 - mmcls - INFO - Epoch [61][800/5005] lr: 1.000e-03, eta: 4:52:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9080 2023-01-02 21:43:28,835 - mmcls - INFO - Epoch [61][900/5005] lr: 1.000e-03, eta: 4:52:14, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8828 2023-01-02 21:43:37,724 - mmcls - INFO - Epoch [61][1000/5005] lr: 1.000e-03, eta: 4:52:05, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.8972 2023-01-02 21:43:46,517 - mmcls - INFO - Epoch [61][1100/5005] lr: 1.000e-03, eta: 4:51:56, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.8813 2023-01-02 21:43:55,264 - mmcls - INFO - Epoch [61][1200/5005] lr: 1.000e-03, eta: 4:51:47, time: 0.088, data_time: 0.045, memory: 1125, loss: 1.8929 2023-01-02 21:44:04,063 - mmcls - INFO - Epoch [61][1300/5005] lr: 1.000e-03, eta: 4:51:39, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8833 2023-01-02 21:44:12,985 - mmcls - INFO - Epoch [61][1400/5005] lr: 1.000e-03, eta: 4:51:30, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.8705 2023-01-02 21:44:21,571 - mmcls - INFO - Epoch [61][1500/5005] lr: 1.000e-03, eta: 4:51:21, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.8890 2023-01-02 21:44:30,118 - mmcls - INFO - Epoch [61][1600/5005] lr: 1.000e-03, eta: 4:51:12, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.8620 2023-01-02 21:44:38,706 - mmcls - INFO - Epoch [61][1700/5005] lr: 1.000e-03, eta: 4:51:03, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.8852 2023-01-02 21:44:47,330 - mmcls - INFO - Epoch [61][1800/5005] lr: 1.000e-03, eta: 4:50:54, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8556 2023-01-02 21:44:56,032 - mmcls - INFO - Epoch [61][1900/5005] lr: 1.000e-03, eta: 4:50:45, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.8808 2023-01-02 21:45:04,656 - mmcls - INFO - Epoch [61][2000/5005] lr: 1.000e-03, eta: 4:50:36, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8755 2023-01-02 21:45:13,452 - mmcls - INFO - Epoch [61][2100/5005] lr: 1.000e-03, eta: 4:50:28, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.8727 2023-01-02 21:45:22,553 - mmcls - INFO - Epoch [61][2200/5005] lr: 1.000e-03, eta: 4:50:19, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.8767 2023-01-02 21:45:31,154 - mmcls - INFO - Epoch [61][2300/5005] lr: 1.000e-03, eta: 4:50:10, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8652 2023-01-02 21:45:39,817 - mmcls - INFO - Epoch [61][2400/5005] lr: 1.000e-03, eta: 4:50:01, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.8627 2023-01-02 21:45:48,659 - mmcls - INFO - Epoch [61][2500/5005] lr: 1.000e-03, eta: 4:49:52, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.8795 2023-01-02 21:45:57,341 - mmcls - INFO - Epoch [61][2600/5005] lr: 1.000e-03, eta: 4:49:44, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8467 2023-01-02 21:46:05,973 - mmcls - INFO - Epoch [61][2700/5005] lr: 1.000e-03, eta: 4:49:35, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8587 2023-01-02 21:46:14,474 - mmcls - INFO - Epoch [61][2800/5005] lr: 1.000e-03, eta: 4:49:26, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.8786 2023-01-02 21:46:23,168 - mmcls - INFO - Epoch [61][2900/5005] lr: 1.000e-03, eta: 4:49:17, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8607 2023-01-02 21:46:31,879 - mmcls - INFO - Epoch [61][3000/5005] lr: 1.000e-03, eta: 4:49:08, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.8601 2023-01-02 21:46:40,739 - mmcls - INFO - Epoch [61][3100/5005] lr: 1.000e-03, eta: 4:48:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.8737 2023-01-02 21:46:49,234 - mmcls - INFO - Epoch [61][3200/5005] lr: 1.000e-03, eta: 4:48:50, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.8727 2023-01-02 21:46:58,017 - mmcls - INFO - Epoch [61][3300/5005] lr: 1.000e-03, eta: 4:48:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8458 2023-01-02 21:47:06,817 - mmcls - INFO - Epoch [61][3400/5005] lr: 1.000e-03, eta: 4:48:33, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8573 2023-01-02 21:47:15,394 - mmcls - INFO - Epoch [61][3500/5005] lr: 1.000e-03, eta: 4:48:24, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.8593 2023-01-02 21:47:24,260 - mmcls - INFO - Epoch [61][3600/5005] lr: 1.000e-03, eta: 4:48:15, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.8848 2023-01-02 21:47:33,100 - mmcls - INFO - Epoch [61][3700/5005] lr: 1.000e-03, eta: 4:48:06, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8811 2023-01-02 21:47:42,023 - mmcls - INFO - Epoch [61][3800/5005] lr: 1.000e-03, eta: 4:47:57, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.8560 2023-01-02 21:47:50,713 - mmcls - INFO - Epoch [61][3900/5005] lr: 1.000e-03, eta: 4:47:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8565 2023-01-02 21:47:59,425 - mmcls - INFO - Epoch [61][4000/5005] lr: 1.000e-03, eta: 4:47:40, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.8362 2023-01-02 21:48:08,011 - mmcls - INFO - Epoch [61][4100/5005] lr: 1.000e-03, eta: 4:47:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8646 2023-01-02 21:48:16,623 - mmcls - INFO - Epoch [61][4200/5005] lr: 1.000e-03, eta: 4:47:22, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8273 2023-01-02 21:48:25,559 - mmcls - INFO - Epoch [61][4300/5005] lr: 1.000e-03, eta: 4:47:13, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.8344 2023-01-02 21:48:34,776 - mmcls - INFO - Epoch [61][4400/5005] lr: 1.000e-03, eta: 4:47:05, time: 0.092, data_time: 0.009, memory: 1125, loss: 1.8491 2023-01-02 21:48:43,465 - mmcls - INFO - Epoch [61][4500/5005] lr: 1.000e-03, eta: 4:46:56, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.8562 2023-01-02 21:48:52,213 - mmcls - INFO - Epoch [61][4600/5005] lr: 1.000e-03, eta: 4:46:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8508 2023-01-02 21:49:00,969 - mmcls - INFO - Epoch [61][4700/5005] lr: 1.000e-03, eta: 4:46:38, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8635 2023-01-02 21:49:09,415 - mmcls - INFO - Epoch [61][4800/5005] lr: 1.000e-03, eta: 4:46:29, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.8310 2023-01-02 21:49:18,595 - mmcls - INFO - Epoch [61][4900/5005] lr: 1.000e-03, eta: 4:46:20, time: 0.092, data_time: 0.011, memory: 1125, loss: 1.8452 2023-01-02 21:49:27,167 - mmcls - INFO - Epoch [61][5000/5005] lr: 1.000e-03, eta: 4:46:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8552 2023-01-02 21:49:50,085 - mmcls - INFO - Epoch(val) [61][196] accuracy_top-1: 61.8940, accuracy_top-5: 83.3100 2023-01-02 21:50:01,191 - mmcls - INFO - Epoch [62][100/5005] lr: 1.000e-03, eta: 4:46:03, time: 0.111, data_time: 0.029, memory: 1125, loss: 1.8341 2023-01-02 21:50:10,077 - mmcls - INFO - Epoch [62][200/5005] lr: 1.000e-03, eta: 4:45:55, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.8329 2023-01-02 21:50:19,076 - mmcls - INFO - Epoch [62][300/5005] lr: 1.000e-03, eta: 4:45:46, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.8621 2023-01-02 21:50:27,780 - mmcls - INFO - Epoch [62][400/5005] lr: 1.000e-03, eta: 4:45:37, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.8362 2023-01-02 21:50:36,117 - mmcls - INFO - Epoch [62][500/5005] lr: 1.000e-03, eta: 4:45:28, time: 0.083, data_time: 0.013, memory: 1125, loss: 1.8261 2023-01-02 21:50:44,780 - mmcls - INFO - Epoch [62][600/5005] lr: 1.000e-03, eta: 4:45:19, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.8056 2023-01-02 21:50:53,463 - mmcls - INFO - Epoch [62][700/5005] lr: 1.000e-03, eta: 4:45:10, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.8544 2023-01-02 21:51:02,157 - mmcls - INFO - Epoch [62][800/5005] lr: 1.000e-03, eta: 4:45:01, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.8563 2023-01-02 21:51:10,909 - mmcls - INFO - Epoch [62][900/5005] lr: 1.000e-03, eta: 4:44:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8360 2023-01-02 21:51:19,768 - mmcls - INFO - Epoch [62][1000/5005] lr: 1.000e-03, eta: 4:44:44, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.8471 2023-01-02 21:51:28,582 - mmcls - INFO - Epoch [62][1100/5005] lr: 1.000e-03, eta: 4:44:35, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8098 2023-01-02 21:51:37,340 - mmcls - INFO - Epoch [62][1200/5005] lr: 1.000e-03, eta: 4:44:26, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.8417 2023-01-02 21:51:45,911 - mmcls - INFO - Epoch [62][1300/5005] lr: 1.000e-03, eta: 4:44:17, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.8396 2023-01-02 21:51:54,598 - mmcls - INFO - Epoch [62][1400/5005] lr: 1.000e-03, eta: 4:44:08, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.8321 2023-01-02 21:52:03,784 - mmcls - INFO - Epoch [62][1500/5005] lr: 1.000e-03, eta: 4:44:00, time: 0.092, data_time: 0.015, memory: 1125, loss: 1.8242 2023-01-02 21:52:12,234 - mmcls - INFO - Epoch [62][1600/5005] lr: 1.000e-03, eta: 4:43:51, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.8384 2023-01-02 21:52:21,067 - mmcls - INFO - Epoch [62][1700/5005] lr: 1.000e-03, eta: 4:43:42, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8250 2023-01-02 21:52:29,955 - mmcls - INFO - Epoch [62][1800/5005] lr: 1.000e-03, eta: 4:43:33, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.8491 2023-01-02 21:52:38,578 - mmcls - INFO - Epoch [62][1900/5005] lr: 1.000e-03, eta: 4:43:24, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.8450 2023-01-02 21:52:47,237 - mmcls - INFO - Epoch [62][2000/5005] lr: 1.000e-03, eta: 4:43:16, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.8369 2023-01-02 21:52:56,396 - mmcls - INFO - Epoch [62][2100/5005] lr: 1.000e-03, eta: 4:43:07, time: 0.092, data_time: 0.003, memory: 1125, loss: 1.8453 2023-01-02 21:53:05,177 - mmcls - INFO - Epoch [62][2200/5005] lr: 1.000e-03, eta: 4:42:58, time: 0.088, data_time: 0.039, memory: 1125, loss: 1.8369 2023-01-02 21:53:13,709 - mmcls - INFO - Epoch [62][2300/5005] lr: 1.000e-03, eta: 4:42:49, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8259 2023-01-02 21:53:22,802 - mmcls - INFO - Epoch [62][2400/5005] lr: 1.000e-03, eta: 4:42:41, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.8330 2023-01-02 21:53:31,993 - mmcls - INFO - Epoch [62][2500/5005] lr: 1.000e-03, eta: 4:42:32, time: 0.092, data_time: 0.017, memory: 1125, loss: 1.8475 2023-01-02 21:53:40,942 - mmcls - INFO - Epoch [62][2600/5005] lr: 1.000e-03, eta: 4:42:23, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.8353 2023-01-02 21:53:49,504 - mmcls - INFO - Epoch [62][2700/5005] lr: 1.000e-03, eta: 4:42:14, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8403 2023-01-02 21:53:58,358 - mmcls - INFO - Epoch [62][2800/5005] lr: 1.000e-03, eta: 4:42:06, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.8222 2023-01-02 21:54:07,320 - mmcls - INFO - Epoch [62][2900/5005] lr: 1.000e-03, eta: 4:41:57, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.8219 2023-01-02 21:54:16,016 - mmcls - INFO - Epoch [62][3000/5005] lr: 1.000e-03, eta: 4:41:48, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.8374 2023-01-02 21:54:24,629 - mmcls - INFO - Epoch [62][3100/5005] lr: 1.000e-03, eta: 4:41:39, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7985 2023-01-02 21:54:33,346 - mmcls - INFO - Epoch [62][3200/5005] lr: 1.000e-03, eta: 4:41:30, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.8470 2023-01-02 21:54:42,168 - mmcls - INFO - Epoch [62][3300/5005] lr: 1.000e-03, eta: 4:41:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8387 2023-01-02 21:54:51,056 - mmcls - INFO - Epoch [62][3400/5005] lr: 1.000e-03, eta: 4:41:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.8465 2023-01-02 21:54:59,618 - mmcls - INFO - Epoch [62][3500/5005] lr: 1.000e-03, eta: 4:41:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8312 2023-01-02 21:55:08,420 - mmcls - INFO - Epoch [62][3600/5005] lr: 1.000e-03, eta: 4:40:55, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.8282 2023-01-02 21:55:17,011 - mmcls - INFO - Epoch [62][3700/5005] lr: 1.000e-03, eta: 4:40:46, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.8284 2023-01-02 21:55:25,840 - mmcls - INFO - Epoch [62][3800/5005] lr: 1.000e-03, eta: 4:40:37, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8274 2023-01-02 21:55:34,577 - mmcls - INFO - Epoch [62][3900/5005] lr: 1.000e-03, eta: 4:40:29, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.8478 2023-01-02 21:55:43,118 - mmcls - INFO - Epoch [62][4000/5005] lr: 1.000e-03, eta: 4:40:20, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.8279 2023-01-02 21:55:51,856 - mmcls - INFO - Epoch [62][4100/5005] lr: 1.000e-03, eta: 4:40:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8363 2023-01-02 21:56:00,445 - mmcls - INFO - Epoch [62][4200/5005] lr: 1.000e-03, eta: 4:40:02, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.8346 2023-01-02 21:56:09,192 - mmcls - INFO - Epoch [62][4300/5005] lr: 1.000e-03, eta: 4:39:53, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8280 2023-01-02 21:56:17,997 - mmcls - INFO - Epoch [62][4400/5005] lr: 1.000e-03, eta: 4:39:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8297 2023-01-02 21:56:26,446 - mmcls - INFO - Epoch [62][4500/5005] lr: 1.000e-03, eta: 4:39:35, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.8657 2023-01-02 21:56:35,121 - mmcls - INFO - Epoch [62][4600/5005] lr: 1.000e-03, eta: 4:39:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8375 2023-01-02 21:56:43,629 - mmcls - INFO - Epoch [62][4700/5005] lr: 1.000e-03, eta: 4:39:17, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8380 2023-01-02 21:56:52,456 - mmcls - INFO - Epoch [62][4800/5005] lr: 1.000e-03, eta: 4:39:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8272 2023-01-02 21:57:01,238 - mmcls - INFO - Epoch [62][4900/5005] lr: 1.000e-03, eta: 4:39:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8293 2023-01-02 21:57:09,756 - mmcls - INFO - Epoch [62][5000/5005] lr: 1.000e-03, eta: 4:38:51, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.8253 2023-01-02 21:57:32,482 - mmcls - INFO - Epoch(val) [62][196] accuracy_top-1: 62.2640, accuracy_top-5: 83.5400 2023-01-02 21:57:43,346 - mmcls - INFO - Epoch [63][100/5005] lr: 1.000e-03, eta: 4:38:43, time: 0.108, data_time: 0.045, memory: 1125, loss: 1.8143 2023-01-02 21:57:52,054 - mmcls - INFO - Epoch [63][200/5005] lr: 1.000e-03, eta: 4:38:34, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.8152 2023-01-02 21:58:00,550 - mmcls - INFO - Epoch [63][300/5005] lr: 1.000e-03, eta: 4:38:25, time: 0.085, data_time: 0.000, memory: 1125, loss: 1.8172 2023-01-02 21:58:09,111 - mmcls - INFO - Epoch [63][400/5005] lr: 1.000e-03, eta: 4:38:16, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8233 2023-01-02 21:58:17,942 - mmcls - INFO - Epoch [63][500/5005] lr: 1.000e-03, eta: 4:38:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8160 2023-01-02 21:58:26,664 - mmcls - INFO - Epoch [63][600/5005] lr: 1.000e-03, eta: 4:37:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7919 2023-01-02 21:58:35,790 - mmcls - INFO - Epoch [63][700/5005] lr: 1.000e-03, eta: 4:37:50, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.8211 2023-01-02 21:58:44,180 - mmcls - INFO - Epoch [63][800/5005] lr: 1.000e-03, eta: 4:37:41, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.8173 2023-01-02 21:58:53,074 - mmcls - INFO - Epoch [63][900/5005] lr: 1.000e-03, eta: 4:37:32, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.8325 2023-01-02 21:59:01,602 - mmcls - INFO - Epoch [63][1000/5005] lr: 1.000e-03, eta: 4:37:23, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.8195 2023-01-02 21:59:10,106 - mmcls - INFO - Epoch [63][1100/5005] lr: 1.000e-03, eta: 4:37:14, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.8342 2023-01-02 21:59:18,954 - mmcls - INFO - Epoch [63][1200/5005] lr: 1.000e-03, eta: 4:37:05, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.8399 2023-01-02 21:59:28,293 - mmcls - INFO - Epoch [63][1300/5005] lr: 1.000e-03, eta: 4:36:57, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.8180 2023-01-02 21:59:36,929 - mmcls - INFO - Epoch [63][1400/5005] lr: 1.000e-03, eta: 4:36:48, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.8308 2023-01-02 21:59:45,608 - mmcls - INFO - Epoch [63][1500/5005] lr: 1.000e-03, eta: 4:36:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8310 2023-01-02 21:59:54,516 - mmcls - INFO - Epoch [63][1600/5005] lr: 1.000e-03, eta: 4:36:30, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.8217 2023-01-02 22:00:03,294 - mmcls - INFO - Epoch [63][1700/5005] lr: 1.000e-03, eta: 4:36:21, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.8122 2023-01-02 22:00:12,352 - mmcls - INFO - Epoch [63][1800/5005] lr: 1.000e-03, eta: 4:36:13, time: 0.091, data_time: 0.008, memory: 1125, loss: 1.7951 2023-01-02 22:00:21,054 - mmcls - INFO - Epoch [63][1900/5005] lr: 1.000e-03, eta: 4:36:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8327 2023-01-02 22:00:29,613 - mmcls - INFO - Epoch [63][2000/5005] lr: 1.000e-03, eta: 4:35:55, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.8094 2023-01-02 22:00:38,734 - mmcls - INFO - Epoch [63][2100/5005] lr: 1.000e-03, eta: 4:35:46, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.7998 2023-01-02 22:00:47,361 - mmcls - INFO - Epoch [63][2200/5005] lr: 1.000e-03, eta: 4:35:37, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.8085 2023-01-02 22:00:56,089 - mmcls - INFO - Epoch [63][2300/5005] lr: 1.000e-03, eta: 4:35:29, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8040 2023-01-02 22:01:05,085 - mmcls - INFO - Epoch [63][2400/5005] lr: 1.000e-03, eta: 4:35:20, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8541 2023-01-02 22:01:13,719 - mmcls - INFO - Epoch [63][2500/5005] lr: 1.000e-03, eta: 4:35:11, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.8154 2023-01-02 22:01:22,282 - mmcls - INFO - Epoch [63][2600/5005] lr: 1.000e-03, eta: 4:35:02, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.8057 2023-01-02 22:01:31,108 - mmcls - INFO - Epoch [63][2700/5005] lr: 1.000e-03, eta: 4:34:53, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.8231 2023-01-02 22:01:40,232 - mmcls - INFO - Epoch [63][2800/5005] lr: 1.000e-03, eta: 4:34:45, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.8262 2023-01-02 22:01:48,687 - mmcls - INFO - Epoch [63][2900/5005] lr: 1.000e-03, eta: 4:34:36, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.8242 2023-01-02 22:01:57,763 - mmcls - INFO - Epoch [63][3000/5005] lr: 1.000e-03, eta: 4:34:27, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.8163 2023-01-02 22:02:06,552 - mmcls - INFO - Epoch [63][3100/5005] lr: 1.000e-03, eta: 4:34:18, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.8040 2023-01-02 22:02:15,312 - mmcls - INFO - Epoch [63][3200/5005] lr: 1.000e-03, eta: 4:34:09, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8274 2023-01-02 22:02:24,053 - mmcls - INFO - Epoch [63][3300/5005] lr: 1.000e-03, eta: 4:34:01, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.8188 2023-01-02 22:02:32,768 - mmcls - INFO - Epoch [63][3400/5005] lr: 1.000e-03, eta: 4:33:52, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.8001 2023-01-02 22:02:41,577 - mmcls - INFO - Epoch [63][3500/5005] lr: 1.000e-03, eta: 4:33:43, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8226 2023-01-02 22:02:50,384 - mmcls - INFO - Epoch [63][3600/5005] lr: 1.000e-03, eta: 4:33:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8216 2023-01-02 22:02:59,174 - mmcls - INFO - Epoch [63][3700/5005] lr: 1.000e-03, eta: 4:33:25, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.8072 2023-01-02 22:03:07,800 - mmcls - INFO - Epoch [63][3800/5005] lr: 1.000e-03, eta: 4:33:16, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.8219 2023-01-02 22:03:16,730 - mmcls - INFO - Epoch [63][3900/5005] lr: 1.000e-03, eta: 4:33:08, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.8109 2023-01-02 22:03:25,346 - mmcls - INFO - Epoch [63][4000/5005] lr: 1.000e-03, eta: 4:32:59, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.8031 2023-01-02 22:03:34,150 - mmcls - INFO - Epoch [63][4100/5005] lr: 1.000e-03, eta: 4:32:50, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.8297 2023-01-02 22:03:43,244 - mmcls - INFO - Epoch [63][4200/5005] lr: 1.000e-03, eta: 4:32:41, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.8062 2023-01-02 22:03:51,857 - mmcls - INFO - Epoch [63][4300/5005] lr: 1.000e-03, eta: 4:32:32, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.8196 2023-01-02 22:04:00,586 - mmcls - INFO - Epoch [63][4400/5005] lr: 1.000e-03, eta: 4:32:24, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8092 2023-01-02 22:04:09,213 - mmcls - INFO - Epoch [63][4500/5005] lr: 1.000e-03, eta: 4:32:15, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.8172 2023-01-02 22:04:17,870 - mmcls - INFO - Epoch [63][4600/5005] lr: 1.000e-03, eta: 4:32:06, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.8171 2023-01-02 22:04:27,126 - mmcls - INFO - Epoch [63][4700/5005] lr: 1.000e-03, eta: 4:31:57, time: 0.092, data_time: 0.038, memory: 1125, loss: 1.8033 2023-01-02 22:04:35,933 - mmcls - INFO - Epoch [63][4800/5005] lr: 1.000e-03, eta: 4:31:49, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8151 2023-01-02 22:04:45,036 - mmcls - INFO - Epoch [63][4900/5005] lr: 1.000e-03, eta: 4:31:40, time: 0.091, data_time: 0.000, memory: 1125, loss: 1.8303 2023-01-02 22:04:53,877 - mmcls - INFO - Epoch [63][5000/5005] lr: 1.000e-03, eta: 4:31:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8312 2023-01-02 22:05:16,548 - mmcls - INFO - Epoch(val) [63][196] accuracy_top-1: 62.1700, accuracy_top-5: 83.5240 2023-01-02 22:05:27,627 - mmcls - INFO - Epoch [64][100/5005] lr: 1.000e-03, eta: 4:31:23, time: 0.111, data_time: 0.035, memory: 1125, loss: 1.7874 2023-01-02 22:05:36,477 - mmcls - INFO - Epoch [64][200/5005] lr: 1.000e-03, eta: 4:31:14, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.8018 2023-01-02 22:05:45,205 - mmcls - INFO - Epoch [64][300/5005] lr: 1.000e-03, eta: 4:31:05, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.8119 2023-01-02 22:05:53,878 - mmcls - INFO - Epoch [64][400/5005] lr: 1.000e-03, eta: 4:30:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8253 2023-01-02 22:06:02,552 - mmcls - INFO - Epoch [64][500/5005] lr: 1.000e-03, eta: 4:30:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8248 2023-01-02 22:06:11,247 - mmcls - INFO - Epoch [64][600/5005] lr: 1.000e-03, eta: 4:30:39, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8129 2023-01-02 22:06:20,088 - mmcls - INFO - Epoch [64][700/5005] lr: 1.000e-03, eta: 4:30:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8143 2023-01-02 22:06:29,100 - mmcls - INFO - Epoch [64][800/5005] lr: 1.000e-03, eta: 4:30:21, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7928 2023-01-02 22:06:37,818 - mmcls - INFO - Epoch [64][900/5005] lr: 1.000e-03, eta: 4:30:12, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8071 2023-01-02 22:06:46,468 - mmcls - INFO - Epoch [64][1000/5005] lr: 1.000e-03, eta: 4:30:04, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7954 2023-01-02 22:06:55,232 - mmcls - INFO - Epoch [64][1100/5005] lr: 1.000e-03, eta: 4:29:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8111 2023-01-02 22:07:03,841 - mmcls - INFO - Epoch [64][1200/5005] lr: 1.000e-03, eta: 4:29:46, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.8028 2023-01-02 22:07:12,595 - mmcls - INFO - Epoch [64][1300/5005] lr: 1.000e-03, eta: 4:29:37, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.8233 2023-01-02 22:07:21,323 - mmcls - INFO - Epoch [64][1400/5005] lr: 1.000e-03, eta: 4:29:28, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.8172 2023-01-02 22:07:29,978 - mmcls - INFO - Epoch [64][1500/5005] lr: 1.000e-03, eta: 4:29:19, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7940 2023-01-02 22:07:38,813 - mmcls - INFO - Epoch [64][1600/5005] lr: 1.000e-03, eta: 4:29:11, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7975 2023-01-02 22:07:47,369 - mmcls - INFO - Epoch [64][1700/5005] lr: 1.000e-03, eta: 4:29:02, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.8092 2023-01-02 22:07:56,068 - mmcls - INFO - Epoch [64][1800/5005] lr: 1.000e-03, eta: 4:28:53, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.8088 2023-01-02 22:08:04,995 - mmcls - INFO - Epoch [64][1900/5005] lr: 1.000e-03, eta: 4:28:44, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.7875 2023-01-02 22:08:13,521 - mmcls - INFO - Epoch [64][2000/5005] lr: 1.000e-03, eta: 4:28:35, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7978 2023-01-02 22:08:22,230 - mmcls - INFO - Epoch [64][2100/5005] lr: 1.000e-03, eta: 4:28:26, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.8198 2023-01-02 22:08:31,053 - mmcls - INFO - Epoch [64][2200/5005] lr: 1.000e-03, eta: 4:28:17, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.8126 2023-01-02 22:08:39,850 - mmcls - INFO - Epoch [64][2300/5005] lr: 1.000e-03, eta: 4:28:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7789 2023-01-02 22:08:48,416 - mmcls - INFO - Epoch [64][2400/5005] lr: 1.000e-03, eta: 4:28:00, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.8229 2023-01-02 22:08:56,972 - mmcls - INFO - Epoch [64][2500/5005] lr: 1.000e-03, eta: 4:27:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8055 2023-01-02 22:09:05,731 - mmcls - INFO - Epoch [64][2600/5005] lr: 1.000e-03, eta: 4:27:42, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8137 2023-01-02 22:09:14,620 - mmcls - INFO - Epoch [64][2700/5005] lr: 1.000e-03, eta: 4:27:33, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.8243 2023-01-02 22:09:23,060 - mmcls - INFO - Epoch [64][2800/5005] lr: 1.000e-03, eta: 4:27:24, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.8246 2023-01-02 22:09:31,529 - mmcls - INFO - Epoch [64][2900/5005] lr: 1.000e-03, eta: 4:27:15, time: 0.085, data_time: 0.038, memory: 1125, loss: 1.7989 2023-01-02 22:09:40,189 - mmcls - INFO - Epoch [64][3000/5005] lr: 1.000e-03, eta: 4:27:06, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.8013 2023-01-02 22:09:48,947 - mmcls - INFO - Epoch [64][3100/5005] lr: 1.000e-03, eta: 4:26:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8045 2023-01-02 22:09:57,344 - mmcls - INFO - Epoch [64][3200/5005] lr: 1.000e-03, eta: 4:26:49, time: 0.084, data_time: 0.021, memory: 1125, loss: 1.7843 2023-01-02 22:10:06,171 - mmcls - INFO - Epoch [64][3300/5005] lr: 1.000e-03, eta: 4:26:40, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8047 2023-01-02 22:10:14,916 - mmcls - INFO - Epoch [64][3400/5005] lr: 1.000e-03, eta: 4:26:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8150 2023-01-02 22:10:23,664 - mmcls - INFO - Epoch [64][3500/5005] lr: 1.000e-03, eta: 4:26:22, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7964 2023-01-02 22:10:32,693 - mmcls - INFO - Epoch [64][3600/5005] lr: 1.000e-03, eta: 4:26:13, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7984 2023-01-02 22:10:41,147 - mmcls - INFO - Epoch [64][3700/5005] lr: 1.000e-03, eta: 4:26:04, time: 0.085, data_time: 0.000, memory: 1125, loss: 1.8228 2023-01-02 22:10:50,246 - mmcls - INFO - Epoch [64][3800/5005] lr: 1.000e-03, eta: 4:25:56, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.8270 2023-01-02 22:10:58,953 - mmcls - INFO - Epoch [64][3900/5005] lr: 1.000e-03, eta: 4:25:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7950 2023-01-02 22:11:07,549 - mmcls - INFO - Epoch [64][4000/5005] lr: 1.000e-03, eta: 4:25:38, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.8182 2023-01-02 22:11:16,381 - mmcls - INFO - Epoch [64][4100/5005] lr: 1.000e-03, eta: 4:25:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8024 2023-01-02 22:11:24,998 - mmcls - INFO - Epoch [64][4200/5005] lr: 1.000e-03, eta: 4:25:20, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7892 2023-01-02 22:11:33,874 - mmcls - INFO - Epoch [64][4300/5005] lr: 1.000e-03, eta: 4:25:12, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.8183 2023-01-02 22:11:43,083 - mmcls - INFO - Epoch [64][4400/5005] lr: 1.000e-03, eta: 4:25:03, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.8066 2023-01-02 22:11:51,742 - mmcls - INFO - Epoch [64][4500/5005] lr: 1.000e-03, eta: 4:24:54, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.8072 2023-01-02 22:12:00,522 - mmcls - INFO - Epoch [64][4600/5005] lr: 1.000e-03, eta: 4:24:45, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7924 2023-01-02 22:12:09,110 - mmcls - INFO - Epoch [64][4700/5005] lr: 1.000e-03, eta: 4:24:36, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.8174 2023-01-02 22:12:17,828 - mmcls - INFO - Epoch [64][4800/5005] lr: 1.000e-03, eta: 4:24:28, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.8275 2023-01-02 22:12:27,004 - mmcls - INFO - Epoch [64][4900/5005] lr: 1.000e-03, eta: 4:24:19, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.8071 2023-01-02 22:12:35,612 - mmcls - INFO - Epoch [64][5000/5005] lr: 1.000e-03, eta: 4:24:10, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.8250 2023-01-02 22:12:58,435 - mmcls - INFO - Epoch(val) [64][196] accuracy_top-1: 62.5880, accuracy_top-5: 83.6740 2023-01-02 22:13:09,155 - mmcls - INFO - Epoch [65][100/5005] lr: 1.000e-03, eta: 4:24:02, time: 0.107, data_time: 0.039, memory: 1125, loss: 1.7930 2023-01-02 22:13:18,043 - mmcls - INFO - Epoch [65][200/5005] lr: 1.000e-03, eta: 4:23:53, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7752 2023-01-02 22:13:26,989 - mmcls - INFO - Epoch [65][300/5005] lr: 1.000e-03, eta: 4:23:44, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7861 2023-01-02 22:13:35,726 - mmcls - INFO - Epoch [65][400/5005] lr: 1.000e-03, eta: 4:23:35, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.8075 2023-01-02 22:13:44,299 - mmcls - INFO - Epoch [65][500/5005] lr: 1.000e-03, eta: 4:23:26, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7760 2023-01-02 22:13:53,226 - mmcls - INFO - Epoch [65][600/5005] lr: 1.000e-03, eta: 4:23:18, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7985 2023-01-02 22:14:01,871 - mmcls - INFO - Epoch [65][700/5005] lr: 1.000e-03, eta: 4:23:09, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7938 2023-01-02 22:14:10,497 - mmcls - INFO - Epoch [65][800/5005] lr: 1.000e-03, eta: 4:23:00, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7914 2023-01-02 22:14:19,410 - mmcls - INFO - Epoch [65][900/5005] lr: 1.000e-03, eta: 4:22:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7970 2023-01-02 22:14:28,013 - mmcls - INFO - Epoch [65][1000/5005] lr: 1.000e-03, eta: 4:22:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7994 2023-01-02 22:14:36,927 - mmcls - INFO - Epoch [65][1100/5005] lr: 1.000e-03, eta: 4:22:34, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.8219 2023-01-02 22:14:46,026 - mmcls - INFO - Epoch [65][1200/5005] lr: 1.000e-03, eta: 4:22:25, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7978 2023-01-02 22:14:54,878 - mmcls - INFO - Epoch [65][1300/5005] lr: 1.000e-03, eta: 4:22:16, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8048 2023-01-02 22:15:03,731 - mmcls - INFO - Epoch [65][1400/5005] lr: 1.000e-03, eta: 4:22:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.8074 2023-01-02 22:15:12,745 - mmcls - INFO - Epoch [65][1500/5005] lr: 1.000e-03, eta: 4:21:59, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7850 2023-01-02 22:15:21,622 - mmcls - INFO - Epoch [65][1600/5005] lr: 1.000e-03, eta: 4:21:50, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7975 2023-01-02 22:15:30,301 - mmcls - INFO - Epoch [65][1700/5005] lr: 1.000e-03, eta: 4:21:41, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8093 2023-01-02 22:15:39,362 - mmcls - INFO - Epoch [65][1800/5005] lr: 1.000e-03, eta: 4:21:32, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.7929 2023-01-02 22:15:48,272 - mmcls - INFO - Epoch [65][1900/5005] lr: 1.000e-03, eta: 4:21:24, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.8227 2023-01-02 22:15:56,817 - mmcls - INFO - Epoch [65][2000/5005] lr: 1.000e-03, eta: 4:21:15, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7949 2023-01-02 22:16:05,615 - mmcls - INFO - Epoch [65][2100/5005] lr: 1.000e-03, eta: 4:21:06, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.8032 2023-01-02 22:16:14,187 - mmcls - INFO - Epoch [65][2200/5005] lr: 1.000e-03, eta: 4:20:57, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.8033 2023-01-02 22:16:22,980 - mmcls - INFO - Epoch [65][2300/5005] lr: 1.000e-03, eta: 4:20:48, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.8218 2023-01-02 22:16:31,900 - mmcls - INFO - Epoch [65][2400/5005] lr: 1.000e-03, eta: 4:20:40, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.8032 2023-01-02 22:16:40,668 - mmcls - INFO - Epoch [65][2500/5005] lr: 1.000e-03, eta: 4:20:31, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7926 2023-01-02 22:16:49,245 - mmcls - INFO - Epoch [65][2600/5005] lr: 1.000e-03, eta: 4:20:22, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7900 2023-01-02 22:16:58,292 - mmcls - INFO - Epoch [65][2700/5005] lr: 1.000e-03, eta: 4:20:13, time: 0.090, data_time: 0.020, memory: 1125, loss: 1.7912 2023-01-02 22:17:06,919 - mmcls - INFO - Epoch [65][2800/5005] lr: 1.000e-03, eta: 4:20:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7982 2023-01-02 22:17:15,852 - mmcls - INFO - Epoch [65][2900/5005] lr: 1.000e-03, eta: 4:19:56, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.8014 2023-01-02 22:17:24,496 - mmcls - INFO - Epoch [65][3000/5005] lr: 1.000e-03, eta: 4:19:47, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.8020 2023-01-02 22:17:33,311 - mmcls - INFO - Epoch [65][3100/5005] lr: 1.000e-03, eta: 4:19:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8249 2023-01-02 22:17:42,244 - mmcls - INFO - Epoch [65][3200/5005] lr: 1.000e-03, eta: 4:19:29, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.8001 2023-01-02 22:17:51,284 - mmcls - INFO - Epoch [65][3300/5005] lr: 1.000e-03, eta: 4:19:21, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.8089 2023-01-02 22:17:59,862 - mmcls - INFO - Epoch [65][3400/5005] lr: 1.000e-03, eta: 4:19:12, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7910 2023-01-02 22:18:08,510 - mmcls - INFO - Epoch [65][3500/5005] lr: 1.000e-03, eta: 4:19:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8082 2023-01-02 22:18:17,614 - mmcls - INFO - Epoch [65][3600/5005] lr: 1.000e-03, eta: 4:18:54, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.8011 2023-01-02 22:18:26,517 - mmcls - INFO - Epoch [65][3700/5005] lr: 1.000e-03, eta: 4:18:45, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.8130 2023-01-02 22:18:35,551 - mmcls - INFO - Epoch [65][3800/5005] lr: 1.000e-03, eta: 4:18:37, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.8175 2023-01-02 22:18:44,424 - mmcls - INFO - Epoch [65][3900/5005] lr: 1.000e-03, eta: 4:18:28, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.8097 2023-01-02 22:18:53,388 - mmcls - INFO - Epoch [65][4000/5005] lr: 1.000e-03, eta: 4:18:19, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.8081 2023-01-02 22:19:01,866 - mmcls - INFO - Epoch [65][4100/5005] lr: 1.000e-03, eta: 4:18:10, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7829 2023-01-02 22:19:10,588 - mmcls - INFO - Epoch [65][4200/5005] lr: 1.000e-03, eta: 4:18:01, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7939 2023-01-02 22:19:18,973 - mmcls - INFO - Epoch [65][4300/5005] lr: 1.000e-03, eta: 4:17:52, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.7972 2023-01-02 22:19:27,970 - mmcls - INFO - Epoch [65][4400/5005] lr: 1.000e-03, eta: 4:17:44, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.8000 2023-01-02 22:19:36,958 - mmcls - INFO - Epoch [65][4500/5005] lr: 1.000e-03, eta: 4:17:35, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7767 2023-01-02 22:19:45,576 - mmcls - INFO - Epoch [65][4600/5005] lr: 1.000e-03, eta: 4:17:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7862 2023-01-02 22:19:54,402 - mmcls - INFO - Epoch [65][4700/5005] lr: 1.000e-03, eta: 4:17:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7839 2023-01-02 22:20:03,006 - mmcls - INFO - Epoch [65][4800/5005] lr: 1.000e-03, eta: 4:17:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7920 2023-01-02 22:20:11,994 - mmcls - INFO - Epoch [65][4900/5005] lr: 1.000e-03, eta: 4:17:00, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7852 2023-01-02 22:20:20,941 - mmcls - INFO - Epoch [65][5000/5005] lr: 1.000e-03, eta: 4:16:51, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.8156 2023-01-02 22:20:43,601 - mmcls - INFO - Epoch(val) [65][196] accuracy_top-1: 62.6320, accuracy_top-5: 83.7080 2023-01-02 22:20:54,503 - mmcls - INFO - Epoch [66][100/5005] lr: 1.000e-03, eta: 4:16:43, time: 0.109, data_time: 0.041, memory: 1125, loss: 1.7912 2023-01-02 22:21:03,889 - mmcls - INFO - Epoch [66][200/5005] lr: 1.000e-03, eta: 4:16:34, time: 0.094, data_time: 0.012, memory: 1125, loss: 1.8160 2023-01-02 22:21:12,344 - mmcls - INFO - Epoch [66][300/5005] lr: 1.000e-03, eta: 4:16:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7760 2023-01-02 22:21:21,768 - mmcls - INFO - Epoch [66][400/5005] lr: 1.000e-03, eta: 4:16:17, time: 0.094, data_time: 0.004, memory: 1125, loss: 1.7805 2023-01-02 22:21:30,582 - mmcls - INFO - Epoch [66][500/5005] lr: 1.000e-03, eta: 4:16:08, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7738 2023-01-02 22:21:39,517 - mmcls - INFO - Epoch [66][600/5005] lr: 1.000e-03, eta: 4:15:59, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7803 2023-01-02 22:21:48,411 - mmcls - INFO - Epoch [66][700/5005] lr: 1.000e-03, eta: 4:15:50, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7723 2023-01-02 22:21:56,891 - mmcls - INFO - Epoch [66][800/5005] lr: 1.000e-03, eta: 4:15:41, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7863 2023-01-02 22:22:05,827 - mmcls - INFO - Epoch [66][900/5005] lr: 1.000e-03, eta: 4:15:33, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.8146 2023-01-02 22:22:14,562 - mmcls - INFO - Epoch [66][1000/5005] lr: 1.000e-03, eta: 4:15:24, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.7901 2023-01-02 22:22:23,302 - mmcls - INFO - Epoch [66][1100/5005] lr: 1.000e-03, eta: 4:15:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7841 2023-01-02 22:22:32,406 - mmcls - INFO - Epoch [66][1200/5005] lr: 1.000e-03, eta: 4:15:06, time: 0.091, data_time: 0.008, memory: 1125, loss: 1.8000 2023-01-02 22:22:40,998 - mmcls - INFO - Epoch [66][1300/5005] lr: 1.000e-03, eta: 4:14:58, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7669 2023-01-02 22:22:49,645 - mmcls - INFO - Epoch [66][1400/5005] lr: 1.000e-03, eta: 4:14:49, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7669 2023-01-02 22:22:58,246 - mmcls - INFO - Epoch [66][1500/5005] lr: 1.000e-03, eta: 4:14:40, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7880 2023-01-02 22:23:06,967 - mmcls - INFO - Epoch [66][1600/5005] lr: 1.000e-03, eta: 4:14:31, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7752 2023-01-02 22:23:15,628 - mmcls - INFO - Epoch [66][1700/5005] lr: 1.000e-03, eta: 4:14:22, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7887 2023-01-02 22:23:24,510 - mmcls - INFO - Epoch [66][1800/5005] lr: 1.000e-03, eta: 4:14:13, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7767 2023-01-02 22:23:33,042 - mmcls - INFO - Epoch [66][1900/5005] lr: 1.000e-03, eta: 4:14:04, time: 0.085, data_time: 0.047, memory: 1125, loss: 1.7948 2023-01-02 22:23:41,755 - mmcls - INFO - Epoch [66][2000/5005] lr: 1.000e-03, eta: 4:13:56, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7963 2023-01-02 22:23:50,290 - mmcls - INFO - Epoch [66][2100/5005] lr: 1.000e-03, eta: 4:13:47, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7995 2023-01-02 22:23:58,997 - mmcls - INFO - Epoch [66][2200/5005] lr: 1.000e-03, eta: 4:13:38, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.8123 2023-01-02 22:24:07,898 - mmcls - INFO - Epoch [66][2300/5005] lr: 1.000e-03, eta: 4:13:29, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7972 2023-01-02 22:24:16,512 - mmcls - INFO - Epoch [66][2400/5005] lr: 1.000e-03, eta: 4:13:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7566 2023-01-02 22:24:25,084 - mmcls - INFO - Epoch [66][2500/5005] lr: 1.000e-03, eta: 4:13:11, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7912 2023-01-02 22:24:33,891 - mmcls - INFO - Epoch [66][2600/5005] lr: 1.000e-03, eta: 4:13:02, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8072 2023-01-02 22:24:42,506 - mmcls - INFO - Epoch [66][2700/5005] lr: 1.000e-03, eta: 4:12:53, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.7881 2023-01-02 22:24:51,184 - mmcls - INFO - Epoch [66][2800/5005] lr: 1.000e-03, eta: 4:12:45, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7906 2023-01-02 22:25:00,133 - mmcls - INFO - Epoch [66][2900/5005] lr: 1.000e-03, eta: 4:12:36, time: 0.089, data_time: 0.032, memory: 1125, loss: 1.8072 2023-01-02 22:25:08,846 - mmcls - INFO - Epoch [66][3000/5005] lr: 1.000e-03, eta: 4:12:27, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8021 2023-01-02 22:25:17,540 - mmcls - INFO - Epoch [66][3100/5005] lr: 1.000e-03, eta: 4:12:18, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8029 2023-01-02 22:25:26,158 - mmcls - INFO - Epoch [66][3200/5005] lr: 1.000e-03, eta: 4:12:09, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7690 2023-01-02 22:25:34,853 - mmcls - INFO - Epoch [66][3300/5005] lr: 1.000e-03, eta: 4:12:00, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.7924 2023-01-02 22:25:43,521 - mmcls - INFO - Epoch [66][3400/5005] lr: 1.000e-03, eta: 4:11:52, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7825 2023-01-02 22:25:52,505 - mmcls - INFO - Epoch [66][3500/5005] lr: 1.000e-03, eta: 4:11:43, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7679 2023-01-02 22:26:01,504 - mmcls - INFO - Epoch [66][3600/5005] lr: 1.000e-03, eta: 4:11:34, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.8048 2023-01-02 22:26:10,308 - mmcls - INFO - Epoch [66][3700/5005] lr: 1.000e-03, eta: 4:11:25, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7868 2023-01-02 22:26:19,027 - mmcls - INFO - Epoch [66][3800/5005] lr: 1.000e-03, eta: 4:11:17, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7853 2023-01-02 22:26:27,846 - mmcls - INFO - Epoch [66][3900/5005] lr: 1.000e-03, eta: 4:11:08, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7979 2023-01-02 22:26:36,740 - mmcls - INFO - Epoch [66][4000/5005] lr: 1.000e-03, eta: 4:10:59, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7921 2023-01-02 22:26:45,370 - mmcls - INFO - Epoch [66][4100/5005] lr: 1.000e-03, eta: 4:10:50, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.8058 2023-01-02 22:26:53,732 - mmcls - INFO - Epoch [66][4200/5005] lr: 1.000e-03, eta: 4:10:41, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.8000 2023-01-02 22:27:02,682 - mmcls - INFO - Epoch [66][4300/5005] lr: 1.000e-03, eta: 4:10:32, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.8140 2023-01-02 22:27:11,294 - mmcls - INFO - Epoch [66][4400/5005] lr: 1.000e-03, eta: 4:10:24, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7791 2023-01-02 22:27:19,902 - mmcls - INFO - Epoch [66][4500/5005] lr: 1.000e-03, eta: 4:10:15, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7746 2023-01-02 22:27:28,506 - mmcls - INFO - Epoch [66][4600/5005] lr: 1.000e-03, eta: 4:10:06, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.8259 2023-01-02 22:27:37,550 - mmcls - INFO - Epoch [66][4700/5005] lr: 1.000e-03, eta: 4:09:57, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7891 2023-01-02 22:27:46,185 - mmcls - INFO - Epoch [66][4800/5005] lr: 1.000e-03, eta: 4:09:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.8120 2023-01-02 22:27:55,035 - mmcls - INFO - Epoch [66][4900/5005] lr: 1.000e-03, eta: 4:09:39, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.8052 2023-01-02 22:28:04,138 - mmcls - INFO - Epoch [66][5000/5005] lr: 1.000e-03, eta: 4:09:31, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.8014 2023-01-02 22:28:26,821 - mmcls - INFO - Epoch(val) [66][196] accuracy_top-1: 62.5700, accuracy_top-5: 83.5600 2023-01-02 22:28:37,913 - mmcls - INFO - Epoch [67][100/5005] lr: 1.000e-03, eta: 4:09:22, time: 0.111, data_time: 0.031, memory: 1125, loss: 1.7807 2023-01-02 22:28:46,425 - mmcls - INFO - Epoch [67][200/5005] lr: 1.000e-03, eta: 4:09:14, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7920 2023-01-02 22:28:55,133 - mmcls - INFO - Epoch [67][300/5005] lr: 1.000e-03, eta: 4:09:05, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.8262 2023-01-02 22:29:03,584 - mmcls - INFO - Epoch [67][400/5005] lr: 1.000e-03, eta: 4:08:56, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.7777 2023-01-02 22:29:12,632 - mmcls - INFO - Epoch [67][500/5005] lr: 1.000e-03, eta: 4:08:47, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.7687 2023-01-02 22:29:21,357 - mmcls - INFO - Epoch [67][600/5005] lr: 1.000e-03, eta: 4:08:38, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7743 2023-01-02 22:29:30,522 - mmcls - INFO - Epoch [67][700/5005] lr: 1.000e-03, eta: 4:08:30, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7759 2023-01-02 22:29:39,220 - mmcls - INFO - Epoch [67][800/5005] lr: 1.000e-03, eta: 4:08:21, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7892 2023-01-02 22:29:47,927 - mmcls - INFO - Epoch [67][900/5005] lr: 1.000e-03, eta: 4:08:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7807 2023-01-02 22:29:56,765 - mmcls - INFO - Epoch [67][1000/5005] lr: 1.000e-03, eta: 4:08:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.8116 2023-01-02 22:30:05,627 - mmcls - INFO - Epoch [67][1100/5005] lr: 1.000e-03, eta: 4:07:54, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7926 2023-01-02 22:30:14,287 - mmcls - INFO - Epoch [67][1200/5005] lr: 1.000e-03, eta: 4:07:45, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.8031 2023-01-02 22:30:22,937 - mmcls - INFO - Epoch [67][1300/5005] lr: 1.000e-03, eta: 4:07:37, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7795 2023-01-02 22:30:31,554 - mmcls - INFO - Epoch [67][1400/5005] lr: 1.000e-03, eta: 4:07:28, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7691 2023-01-02 22:30:40,577 - mmcls - INFO - Epoch [67][1500/5005] lr: 1.000e-03, eta: 4:07:19, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7867 2023-01-02 22:30:49,153 - mmcls - INFO - Epoch [67][1600/5005] lr: 1.000e-03, eta: 4:07:10, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7923 2023-01-02 22:30:58,173 - mmcls - INFO - Epoch [67][1700/5005] lr: 1.000e-03, eta: 4:07:01, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7753 2023-01-02 22:31:06,963 - mmcls - INFO - Epoch [67][1800/5005] lr: 1.000e-03, eta: 4:06:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7847 2023-01-02 22:31:15,926 - mmcls - INFO - Epoch [67][1900/5005] lr: 1.000e-03, eta: 4:06:44, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.7612 2023-01-02 22:31:24,679 - mmcls - INFO - Epoch [67][2000/5005] lr: 1.000e-03, eta: 4:06:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7965 2023-01-02 22:31:33,402 - mmcls - INFO - Epoch [67][2100/5005] lr: 1.000e-03, eta: 4:06:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7882 2023-01-02 22:31:42,099 - mmcls - INFO - Epoch [67][2200/5005] lr: 1.000e-03, eta: 4:06:17, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7912 2023-01-02 22:31:50,965 - mmcls - INFO - Epoch [67][2300/5005] lr: 1.000e-03, eta: 4:06:09, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7994 2023-01-02 22:31:59,522 - mmcls - INFO - Epoch [67][2400/5005] lr: 1.000e-03, eta: 4:06:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7921 2023-01-02 22:32:08,466 - mmcls - INFO - Epoch [67][2500/5005] lr: 1.000e-03, eta: 4:05:51, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7818 2023-01-02 22:32:17,158 - mmcls - INFO - Epoch [67][2600/5005] lr: 1.000e-03, eta: 4:05:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8064 2023-01-02 22:32:25,804 - mmcls - INFO - Epoch [67][2700/5005] lr: 1.000e-03, eta: 4:05:33, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.8094 2023-01-02 22:32:34,279 - mmcls - INFO - Epoch [67][2800/5005] lr: 1.000e-03, eta: 4:05:24, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7949 2023-01-02 22:32:43,258 - mmcls - INFO - Epoch [67][2900/5005] lr: 1.000e-03, eta: 4:05:16, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.8009 2023-01-02 22:32:51,849 - mmcls - INFO - Epoch [67][3000/5005] lr: 1.000e-03, eta: 4:05:07, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7687 2023-01-02 22:33:00,586 - mmcls - INFO - Epoch [67][3100/5005] lr: 1.000e-03, eta: 4:04:58, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7819 2023-01-02 22:33:09,460 - mmcls - INFO - Epoch [67][3200/5005] lr: 1.000e-03, eta: 4:04:49, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7726 2023-01-02 22:33:18,358 - mmcls - INFO - Epoch [67][3300/5005] lr: 1.000e-03, eta: 4:04:40, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7817 2023-01-02 22:33:27,054 - mmcls - INFO - Epoch [67][3400/5005] lr: 1.000e-03, eta: 4:04:32, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7803 2023-01-02 22:33:35,959 - mmcls - INFO - Epoch [67][3500/5005] lr: 1.000e-03, eta: 4:04:23, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7690 2023-01-02 22:33:44,994 - mmcls - INFO - Epoch [67][3600/5005] lr: 1.000e-03, eta: 4:04:14, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7872 2023-01-02 22:33:53,758 - mmcls - INFO - Epoch [67][3700/5005] lr: 1.000e-03, eta: 4:04:05, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7903 2023-01-02 22:34:02,434 - mmcls - INFO - Epoch [67][3800/5005] lr: 1.000e-03, eta: 4:03:56, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7882 2023-01-02 22:34:11,088 - mmcls - INFO - Epoch [67][3900/5005] lr: 1.000e-03, eta: 4:03:48, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7828 2023-01-02 22:34:19,861 - mmcls - INFO - Epoch [67][4000/5005] lr: 1.000e-03, eta: 4:03:39, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.8140 2023-01-02 22:34:28,609 - mmcls - INFO - Epoch [67][4100/5005] lr: 1.000e-03, eta: 4:03:30, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7938 2023-01-02 22:34:37,325 - mmcls - INFO - Epoch [67][4200/5005] lr: 1.000e-03, eta: 4:03:21, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.8141 2023-01-02 22:34:45,921 - mmcls - INFO - Epoch [67][4300/5005] lr: 1.000e-03, eta: 4:03:12, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7934 2023-01-02 22:34:54,626 - mmcls - INFO - Epoch [67][4400/5005] lr: 1.000e-03, eta: 4:03:03, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7897 2023-01-02 22:35:03,031 - mmcls - INFO - Epoch [67][4500/5005] lr: 1.000e-03, eta: 4:02:54, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.7899 2023-01-02 22:35:11,540 - mmcls - INFO - Epoch [67][4600/5005] lr: 1.000e-03, eta: 4:02:45, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7903 2023-01-02 22:35:20,057 - mmcls - INFO - Epoch [67][4700/5005] lr: 1.000e-03, eta: 4:02:36, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7794 2023-01-02 22:35:28,784 - mmcls - INFO - Epoch [67][4800/5005] lr: 1.000e-03, eta: 4:02:28, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7852 2023-01-02 22:35:37,568 - mmcls - INFO - Epoch [67][4900/5005] lr: 1.000e-03, eta: 4:02:19, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7788 2023-01-02 22:35:46,161 - mmcls - INFO - Epoch [67][5000/5005] lr: 1.000e-03, eta: 4:02:10, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7931 2023-01-02 22:36:08,896 - mmcls - INFO - Epoch(val) [67][196] accuracy_top-1: 62.7560, accuracy_top-5: 83.7000 2023-01-02 22:36:19,793 - mmcls - INFO - Epoch [68][100/5005] lr: 1.000e-03, eta: 4:02:01, time: 0.109, data_time: 0.040, memory: 1125, loss: 1.7695 2023-01-02 22:36:28,445 - mmcls - INFO - Epoch [68][200/5005] lr: 1.000e-03, eta: 4:01:53, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7762 2023-01-02 22:36:36,893 - mmcls - INFO - Epoch [68][300/5005] lr: 1.000e-03, eta: 4:01:44, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.7678 2023-01-02 22:36:45,556 - mmcls - INFO - Epoch [68][400/5005] lr: 1.000e-03, eta: 4:01:35, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7854 2023-01-02 22:36:54,465 - mmcls - INFO - Epoch [68][500/5005] lr: 1.000e-03, eta: 4:01:26, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7620 2023-01-02 22:37:03,185 - mmcls - INFO - Epoch [68][600/5005] lr: 1.000e-03, eta: 4:01:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7990 2023-01-02 22:37:11,814 - mmcls - INFO - Epoch [68][700/5005] lr: 1.000e-03, eta: 4:01:08, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7924 2023-01-02 22:37:20,720 - mmcls - INFO - Epoch [68][800/5005] lr: 1.000e-03, eta: 4:01:00, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7847 2023-01-02 22:37:29,461 - mmcls - INFO - Epoch [68][900/5005] lr: 1.000e-03, eta: 4:00:51, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7724 2023-01-02 22:37:38,196 - mmcls - INFO - Epoch [68][1000/5005] lr: 1.000e-03, eta: 4:00:42, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7867 2023-01-02 22:37:46,982 - mmcls - INFO - Epoch [68][1100/5005] lr: 1.000e-03, eta: 4:00:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7769 2023-01-02 22:37:55,531 - mmcls - INFO - Epoch [68][1200/5005] lr: 1.000e-03, eta: 4:00:24, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7503 2023-01-02 22:38:04,589 - mmcls - INFO - Epoch [68][1300/5005] lr: 1.000e-03, eta: 4:00:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7724 2023-01-02 22:38:13,401 - mmcls - INFO - Epoch [68][1400/5005] lr: 1.000e-03, eta: 4:00:07, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7670 2023-01-02 22:38:21,883 - mmcls - INFO - Epoch [68][1500/5005] lr: 1.000e-03, eta: 3:59:58, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7923 2023-01-02 22:38:30,558 - mmcls - INFO - Epoch [68][1600/5005] lr: 1.000e-03, eta: 3:59:49, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7892 2023-01-02 22:38:39,770 - mmcls - INFO - Epoch [68][1700/5005] lr: 1.000e-03, eta: 3:59:40, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7817 2023-01-02 22:38:48,793 - mmcls - INFO - Epoch [68][1800/5005] lr: 1.000e-03, eta: 3:59:32, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7874 2023-01-02 22:38:57,391 - mmcls - INFO - Epoch [68][1900/5005] lr: 1.000e-03, eta: 3:59:23, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7915 2023-01-02 22:39:06,188 - mmcls - INFO - Epoch [68][2000/5005] lr: 1.000e-03, eta: 3:59:14, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7769 2023-01-02 22:39:15,080 - mmcls - INFO - Epoch [68][2100/5005] lr: 1.000e-03, eta: 3:59:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7930 2023-01-02 22:39:24,069 - mmcls - INFO - Epoch [68][2200/5005] lr: 1.000e-03, eta: 3:58:56, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7912 2023-01-02 22:39:32,699 - mmcls - INFO - Epoch [68][2300/5005] lr: 1.000e-03, eta: 3:58:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7754 2023-01-02 22:39:41,473 - mmcls - INFO - Epoch [68][2400/5005] lr: 1.000e-03, eta: 3:58:39, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7725 2023-01-02 22:39:50,218 - mmcls - INFO - Epoch [68][2500/5005] lr: 1.000e-03, eta: 3:58:30, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7839 2023-01-02 22:39:58,884 - mmcls - INFO - Epoch [68][2600/5005] lr: 1.000e-03, eta: 3:58:21, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7794 2023-01-02 22:40:07,595 - mmcls - INFO - Epoch [68][2700/5005] lr: 1.000e-03, eta: 3:58:12, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7689 2023-01-02 22:40:16,550 - mmcls - INFO - Epoch [68][2800/5005] lr: 1.000e-03, eta: 3:58:04, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7820 2023-01-02 22:40:25,131 - mmcls - INFO - Epoch [68][2900/5005] lr: 1.000e-03, eta: 3:57:55, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7608 2023-01-02 22:40:33,956 - mmcls - INFO - Epoch [68][3000/5005] lr: 1.000e-03, eta: 3:57:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7718 2023-01-02 22:40:42,769 - mmcls - INFO - Epoch [68][3100/5005] lr: 1.000e-03, eta: 3:57:37, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7583 2023-01-02 22:40:51,815 - mmcls - INFO - Epoch [68][3200/5005] lr: 1.000e-03, eta: 3:57:28, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.8122 2023-01-02 22:41:00,462 - mmcls - INFO - Epoch [68][3300/5005] lr: 1.000e-03, eta: 3:57:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7680 2023-01-02 22:41:08,919 - mmcls - INFO - Epoch [68][3400/5005] lr: 1.000e-03, eta: 3:57:11, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7833 2023-01-02 22:41:17,765 - mmcls - INFO - Epoch [68][3500/5005] lr: 1.000e-03, eta: 3:57:02, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7737 2023-01-02 22:41:26,414 - mmcls - INFO - Epoch [68][3600/5005] lr: 1.000e-03, eta: 3:56:53, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7859 2023-01-02 22:41:35,539 - mmcls - INFO - Epoch [68][3700/5005] lr: 1.000e-03, eta: 3:56:44, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.8051 2023-01-02 22:41:43,976 - mmcls - INFO - Epoch [68][3800/5005] lr: 1.000e-03, eta: 3:56:35, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.7783 2023-01-02 22:41:52,756 - mmcls - INFO - Epoch [68][3900/5005] lr: 1.000e-03, eta: 3:56:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7794 2023-01-02 22:42:01,623 - mmcls - INFO - Epoch [68][4000/5005] lr: 1.000e-03, eta: 3:56:18, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7912 2023-01-02 22:42:10,185 - mmcls - INFO - Epoch [68][4100/5005] lr: 1.000e-03, eta: 3:56:09, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7939 2023-01-02 22:42:18,761 - mmcls - INFO - Epoch [68][4200/5005] lr: 1.000e-03, eta: 3:56:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7960 2023-01-02 22:42:27,842 - mmcls - INFO - Epoch [68][4300/5005] lr: 1.000e-03, eta: 3:55:51, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7652 2023-01-02 22:42:36,547 - mmcls - INFO - Epoch [68][4400/5005] lr: 1.000e-03, eta: 3:55:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7619 2023-01-02 22:42:45,405 - mmcls - INFO - Epoch [68][4500/5005] lr: 1.000e-03, eta: 3:55:34, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7681 2023-01-02 22:42:54,437 - mmcls - INFO - Epoch [68][4600/5005] lr: 1.000e-03, eta: 3:55:25, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7910 2023-01-02 22:43:03,127 - mmcls - INFO - Epoch [68][4700/5005] lr: 1.000e-03, eta: 3:55:16, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7864 2023-01-02 22:43:11,863 - mmcls - INFO - Epoch [68][4800/5005] lr: 1.000e-03, eta: 3:55:07, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7858 2023-01-02 22:43:20,876 - mmcls - INFO - Epoch [68][4900/5005] lr: 1.000e-03, eta: 3:54:59, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7869 2023-01-02 22:43:29,920 - mmcls - INFO - Epoch [68][5000/5005] lr: 1.000e-03, eta: 3:54:50, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7863 2023-01-02 22:43:52,536 - mmcls - INFO - Epoch(val) [68][196] accuracy_top-1: 62.8000, accuracy_top-5: 83.8520 2023-01-02 22:44:03,573 - mmcls - INFO - Epoch [69][100/5005] lr: 1.000e-03, eta: 3:54:42, time: 0.110, data_time: 0.033, memory: 1125, loss: 1.7917 2023-01-02 22:44:12,200 - mmcls - INFO - Epoch [69][200/5005] lr: 1.000e-03, eta: 3:54:33, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7605 2023-01-02 22:44:21,122 - mmcls - INFO - Epoch [69][300/5005] lr: 1.000e-03, eta: 3:54:24, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7816 2023-01-02 22:44:30,023 - mmcls - INFO - Epoch [69][400/5005] lr: 1.000e-03, eta: 3:54:15, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7613 2023-01-02 22:44:38,783 - mmcls - INFO - Epoch [69][500/5005] lr: 1.000e-03, eta: 3:54:06, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7821 2023-01-02 22:44:47,565 - mmcls - INFO - Epoch [69][600/5005] lr: 1.000e-03, eta: 3:53:58, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7774 2023-01-02 22:44:56,535 - mmcls - INFO - Epoch [69][700/5005] lr: 1.000e-03, eta: 3:53:49, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7600 2023-01-02 22:45:05,243 - mmcls - INFO - Epoch [69][800/5005] lr: 1.000e-03, eta: 3:53:40, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7811 2023-01-02 22:45:14,138 - mmcls - INFO - Epoch [69][900/5005] lr: 1.000e-03, eta: 3:53:31, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.7853 2023-01-02 22:45:22,616 - mmcls - INFO - Epoch [69][1000/5005] lr: 1.000e-03, eta: 3:53:22, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.7707 2023-01-02 22:45:31,301 - mmcls - INFO - Epoch [69][1100/5005] lr: 1.000e-03, eta: 3:53:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7808 2023-01-02 22:45:40,297 - mmcls - INFO - Epoch [69][1200/5005] lr: 1.000e-03, eta: 3:53:05, time: 0.090, data_time: 0.000, memory: 1125, loss: 1.7677 2023-01-02 22:45:49,161 - mmcls - INFO - Epoch [69][1300/5005] lr: 1.000e-03, eta: 3:52:56, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7735 2023-01-02 22:45:57,828 - mmcls - INFO - Epoch [69][1400/5005] lr: 1.000e-03, eta: 3:52:47, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7866 2023-01-02 22:46:06,608 - mmcls - INFO - Epoch [69][1500/5005] lr: 1.000e-03, eta: 3:52:38, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7836 2023-01-02 22:46:15,302 - mmcls - INFO - Epoch [69][1600/5005] lr: 1.000e-03, eta: 3:52:29, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7771 2023-01-02 22:46:24,046 - mmcls - INFO - Epoch [69][1700/5005] lr: 1.000e-03, eta: 3:52:21, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7659 2023-01-02 22:46:32,658 - mmcls - INFO - Epoch [69][1800/5005] lr: 1.000e-03, eta: 3:52:12, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7702 2023-01-02 22:46:41,635 - mmcls - INFO - Epoch [69][1900/5005] lr: 1.000e-03, eta: 3:52:03, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7900 2023-01-02 22:46:50,395 - mmcls - INFO - Epoch [69][2000/5005] lr: 1.000e-03, eta: 3:51:54, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7660 2023-01-02 22:46:59,225 - mmcls - INFO - Epoch [69][2100/5005] lr: 1.000e-03, eta: 3:51:45, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7560 2023-01-02 22:47:07,834 - mmcls - INFO - Epoch [69][2200/5005] lr: 1.000e-03, eta: 3:51:37, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7884 2023-01-02 22:47:16,684 - mmcls - INFO - Epoch [69][2300/5005] lr: 1.000e-03, eta: 3:51:28, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7781 2023-01-02 22:47:25,377 - mmcls - INFO - Epoch [69][2400/5005] lr: 1.000e-03, eta: 3:51:19, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7639 2023-01-02 22:47:33,974 - mmcls - INFO - Epoch [69][2500/5005] lr: 1.000e-03, eta: 3:51:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7793 2023-01-02 22:47:42,675 - mmcls - INFO - Epoch [69][2600/5005] lr: 1.000e-03, eta: 3:51:01, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7620 2023-01-02 22:47:51,233 - mmcls - INFO - Epoch [69][2700/5005] lr: 1.000e-03, eta: 3:50:52, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7869 2023-01-02 22:48:00,060 - mmcls - INFO - Epoch [69][2800/5005] lr: 1.000e-03, eta: 3:50:43, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.8169 2023-01-02 22:48:08,620 - mmcls - INFO - Epoch [69][2900/5005] lr: 1.000e-03, eta: 3:50:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7808 2023-01-02 22:48:17,769 - mmcls - INFO - Epoch [69][3000/5005] lr: 1.000e-03, eta: 3:50:26, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7812 2023-01-02 22:48:26,291 - mmcls - INFO - Epoch [69][3100/5005] lr: 1.000e-03, eta: 3:50:17, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7609 2023-01-02 22:48:35,115 - mmcls - INFO - Epoch [69][3200/5005] lr: 1.000e-03, eta: 3:50:08, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7591 2023-01-02 22:48:43,782 - mmcls - INFO - Epoch [69][3300/5005] lr: 1.000e-03, eta: 3:49:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7616 2023-01-02 22:48:52,561 - mmcls - INFO - Epoch [69][3400/5005] lr: 1.000e-03, eta: 3:49:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7736 2023-01-02 22:49:01,575 - mmcls - INFO - Epoch [69][3500/5005] lr: 1.000e-03, eta: 3:49:42, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7539 2023-01-02 22:49:10,426 - mmcls - INFO - Epoch [69][3600/5005] lr: 1.000e-03, eta: 3:49:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7725 2023-01-02 22:49:19,352 - mmcls - INFO - Epoch [69][3700/5005] lr: 1.000e-03, eta: 3:49:24, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7652 2023-01-02 22:49:27,924 - mmcls - INFO - Epoch [69][3800/5005] lr: 1.000e-03, eta: 3:49:15, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7585 2023-01-02 22:49:36,760 - mmcls - INFO - Epoch [69][3900/5005] lr: 1.000e-03, eta: 3:49:07, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7845 2023-01-02 22:49:45,375 - mmcls - INFO - Epoch [69][4000/5005] lr: 1.000e-03, eta: 3:48:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7823 2023-01-02 22:49:53,788 - mmcls - INFO - Epoch [69][4100/5005] lr: 1.000e-03, eta: 3:48:49, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7717 2023-01-02 22:50:02,639 - mmcls - INFO - Epoch [69][4200/5005] lr: 1.000e-03, eta: 3:48:40, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7710 2023-01-02 22:50:11,187 - mmcls - INFO - Epoch [69][4300/5005] lr: 1.000e-03, eta: 3:48:31, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7787 2023-01-02 22:50:19,946 - mmcls - INFO - Epoch [69][4400/5005] lr: 1.000e-03, eta: 3:48:22, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7794 2023-01-02 22:50:28,913 - mmcls - INFO - Epoch [69][4500/5005] lr: 1.000e-03, eta: 3:48:14, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7732 2023-01-02 22:50:37,756 - mmcls - INFO - Epoch [69][4600/5005] lr: 1.000e-03, eta: 3:48:05, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7944 2023-01-02 22:50:46,375 - mmcls - INFO - Epoch [69][4700/5005] lr: 1.000e-03, eta: 3:47:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7648 2023-01-02 22:50:55,046 - mmcls - INFO - Epoch [69][4800/5005] lr: 1.000e-03, eta: 3:47:47, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7608 2023-01-02 22:51:03,746 - mmcls - INFO - Epoch [69][4900/5005] lr: 1.000e-03, eta: 3:47:38, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7835 2023-01-02 22:51:12,553 - mmcls - INFO - Epoch [69][5000/5005] lr: 1.000e-03, eta: 3:47:29, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7934 2023-01-02 22:51:35,231 - mmcls - INFO - Epoch(val) [69][196] accuracy_top-1: 62.6220, accuracy_top-5: 83.8120 2023-01-02 22:51:46,003 - mmcls - INFO - Epoch [70][100/5005] lr: 1.000e-03, eta: 3:47:21, time: 0.108, data_time: 0.034, memory: 1125, loss: 1.7690 2023-01-02 22:51:54,899 - mmcls - INFO - Epoch [70][200/5005] lr: 1.000e-03, eta: 3:47:12, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7589 2023-01-02 22:52:03,741 - mmcls - INFO - Epoch [70][300/5005] lr: 1.000e-03, eta: 3:47:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7585 2023-01-02 22:52:12,500 - mmcls - INFO - Epoch [70][400/5005] lr: 1.000e-03, eta: 3:46:55, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7675 2023-01-02 22:52:21,150 - mmcls - INFO - Epoch [70][500/5005] lr: 1.000e-03, eta: 3:46:46, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7646 2023-01-02 22:52:29,907 - mmcls - INFO - Epoch [70][600/5005] lr: 1.000e-03, eta: 3:46:37, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7881 2023-01-02 22:52:38,823 - mmcls - INFO - Epoch [70][700/5005] lr: 1.000e-03, eta: 3:46:28, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7577 2023-01-02 22:52:47,571 - mmcls - INFO - Epoch [70][800/5005] lr: 1.000e-03, eta: 3:46:19, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7770 2023-01-02 22:52:56,447 - mmcls - INFO - Epoch [70][900/5005] lr: 1.000e-03, eta: 3:46:11, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7727 2023-01-02 22:53:04,892 - mmcls - INFO - Epoch [70][1000/5005] lr: 1.000e-03, eta: 3:46:02, time: 0.084, data_time: 0.022, memory: 1125, loss: 1.7678 2023-01-02 22:53:13,361 - mmcls - INFO - Epoch [70][1100/5005] lr: 1.000e-03, eta: 3:45:53, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7596 2023-01-02 22:53:22,574 - mmcls - INFO - Epoch [70][1200/5005] lr: 1.000e-03, eta: 3:45:44, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7420 2023-01-02 22:53:31,511 - mmcls - INFO - Epoch [70][1300/5005] lr: 1.000e-03, eta: 3:45:35, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7777 2023-01-02 22:53:40,687 - mmcls - INFO - Epoch [70][1400/5005] lr: 1.000e-03, eta: 3:45:27, time: 0.092, data_time: 0.006, memory: 1125, loss: 1.7715 2023-01-02 22:53:49,171 - mmcls - INFO - Epoch [70][1500/5005] lr: 1.000e-03, eta: 3:45:18, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7631 2023-01-02 22:53:57,880 - mmcls - INFO - Epoch [70][1600/5005] lr: 1.000e-03, eta: 3:45:09, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7616 2023-01-02 22:54:06,715 - mmcls - INFO - Epoch [70][1700/5005] lr: 1.000e-03, eta: 3:45:00, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7656 2023-01-02 22:54:15,506 - mmcls - INFO - Epoch [70][1800/5005] lr: 1.000e-03, eta: 3:44:51, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7589 2023-01-02 22:54:24,461 - mmcls - INFO - Epoch [70][1900/5005] lr: 1.000e-03, eta: 3:44:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7978 2023-01-02 22:54:33,207 - mmcls - INFO - Epoch [70][2000/5005] lr: 1.000e-03, eta: 3:44:34, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7607 2023-01-02 22:54:42,112 - mmcls - INFO - Epoch [70][2100/5005] lr: 1.000e-03, eta: 3:44:25, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7972 2023-01-02 22:54:50,556 - mmcls - INFO - Epoch [70][2200/5005] lr: 1.000e-03, eta: 3:44:16, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.7778 2023-01-02 22:54:59,378 - mmcls - INFO - Epoch [70][2300/5005] lr: 1.000e-03, eta: 3:44:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7678 2023-01-02 22:55:08,086 - mmcls - INFO - Epoch [70][2400/5005] lr: 1.000e-03, eta: 3:43:58, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7463 2023-01-02 22:55:16,957 - mmcls - INFO - Epoch [70][2500/5005] lr: 1.000e-03, eta: 3:43:50, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7498 2023-01-02 22:55:25,748 - mmcls - INFO - Epoch [70][2600/5005] lr: 1.000e-03, eta: 3:43:41, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7716 2023-01-02 22:55:34,383 - mmcls - INFO - Epoch [70][2700/5005] lr: 1.000e-03, eta: 3:43:32, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7673 2023-01-02 22:55:43,135 - mmcls - INFO - Epoch [70][2800/5005] lr: 1.000e-03, eta: 3:43:23, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7757 2023-01-02 22:55:51,991 - mmcls - INFO - Epoch [70][2900/5005] lr: 1.000e-03, eta: 3:43:14, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7677 2023-01-02 22:56:00,756 - mmcls - INFO - Epoch [70][3000/5005] lr: 1.000e-03, eta: 3:43:06, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7685 2023-01-02 22:56:09,467 - mmcls - INFO - Epoch [70][3100/5005] lr: 1.000e-03, eta: 3:42:57, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7586 2023-01-02 22:56:18,287 - mmcls - INFO - Epoch [70][3200/5005] lr: 1.000e-03, eta: 3:42:48, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7702 2023-01-02 22:56:26,811 - mmcls - INFO - Epoch [70][3300/5005] lr: 1.000e-03, eta: 3:42:39, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7468 2023-01-02 22:56:35,489 - mmcls - INFO - Epoch [70][3400/5005] lr: 1.000e-03, eta: 3:42:30, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7764 2023-01-02 22:56:44,226 - mmcls - INFO - Epoch [70][3500/5005] lr: 1.000e-03, eta: 3:42:21, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7666 2023-01-02 22:56:52,893 - mmcls - INFO - Epoch [70][3600/5005] lr: 1.000e-03, eta: 3:42:13, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7858 2023-01-02 22:57:02,210 - mmcls - INFO - Epoch [70][3700/5005] lr: 1.000e-03, eta: 3:42:04, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7740 2023-01-02 22:57:11,189 - mmcls - INFO - Epoch [70][3800/5005] lr: 1.000e-03, eta: 3:41:55, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7655 2023-01-02 22:57:20,164 - mmcls - INFO - Epoch [70][3900/5005] lr: 1.000e-03, eta: 3:41:46, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7750 2023-01-02 22:57:28,866 - mmcls - INFO - Epoch [70][4000/5005] lr: 1.000e-03, eta: 3:41:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7795 2023-01-02 22:57:37,658 - mmcls - INFO - Epoch [70][4100/5005] lr: 1.000e-03, eta: 3:41:29, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7590 2023-01-02 22:57:46,224 - mmcls - INFO - Epoch [70][4200/5005] lr: 1.000e-03, eta: 3:41:20, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7980 2023-01-02 22:57:54,915 - mmcls - INFO - Epoch [70][4300/5005] lr: 1.000e-03, eta: 3:41:11, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7947 2023-01-02 22:58:03,428 - mmcls - INFO - Epoch [70][4400/5005] lr: 1.000e-03, eta: 3:41:02, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7712 2023-01-02 22:58:12,124 - mmcls - INFO - Epoch [70][4500/5005] lr: 1.000e-03, eta: 3:40:53, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7720 2023-01-02 22:58:20,899 - mmcls - INFO - Epoch [70][4600/5005] lr: 1.000e-03, eta: 3:40:45, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7963 2023-01-02 22:58:29,807 - mmcls - INFO - Epoch [70][4700/5005] lr: 1.000e-03, eta: 3:40:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7810 2023-01-02 22:58:38,445 - mmcls - INFO - Epoch [70][4800/5005] lr: 1.000e-03, eta: 3:40:27, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7823 2023-01-02 22:58:47,018 - mmcls - INFO - Epoch [70][4900/5005] lr: 1.000e-03, eta: 3:40:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7854 2023-01-02 22:58:56,087 - mmcls - INFO - Epoch [70][5000/5005] lr: 1.000e-03, eta: 3:40:09, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7901 2023-01-02 22:59:18,724 - mmcls - INFO - Epoch(val) [70][196] accuracy_top-1: 62.7640, accuracy_top-5: 83.8820 2023-01-02 22:59:29,623 - mmcls - INFO - Epoch [71][100/5005] lr: 1.000e-03, eta: 3:40:01, time: 0.109, data_time: 0.032, memory: 1125, loss: 1.7590 2023-01-02 22:59:38,165 - mmcls - INFO - Epoch [71][200/5005] lr: 1.000e-03, eta: 3:39:52, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7460 2023-01-02 22:59:47,052 - mmcls - INFO - Epoch [71][300/5005] lr: 1.000e-03, eta: 3:39:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7796 2023-01-02 22:59:55,725 - mmcls - INFO - Epoch [71][400/5005] lr: 1.000e-03, eta: 3:39:34, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7453 2023-01-02 23:00:04,647 - mmcls - INFO - Epoch [71][500/5005] lr: 1.000e-03, eta: 3:39:26, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7549 2023-01-02 23:00:13,158 - mmcls - INFO - Epoch [71][600/5005] lr: 1.000e-03, eta: 3:39:17, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7490 2023-01-02 23:00:21,827 - mmcls - INFO - Epoch [71][700/5005] lr: 1.000e-03, eta: 3:39:08, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7535 2023-01-02 23:00:30,610 - mmcls - INFO - Epoch [71][800/5005] lr: 1.000e-03, eta: 3:38:59, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7577 2023-01-02 23:00:39,258 - mmcls - INFO - Epoch [71][900/5005] lr: 1.000e-03, eta: 3:38:50, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7674 2023-01-02 23:00:47,949 - mmcls - INFO - Epoch [71][1000/5005] lr: 1.000e-03, eta: 3:38:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7530 2023-01-02 23:00:56,908 - mmcls - INFO - Epoch [71][1100/5005] lr: 1.000e-03, eta: 3:38:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7699 2023-01-02 23:01:05,539 - mmcls - INFO - Epoch [71][1200/5005] lr: 1.000e-03, eta: 3:38:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7648 2023-01-02 23:01:14,332 - mmcls - INFO - Epoch [71][1300/5005] lr: 1.000e-03, eta: 3:38:15, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7733 2023-01-02 23:01:23,258 - mmcls - INFO - Epoch [71][1400/5005] lr: 1.000e-03, eta: 3:38:06, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7662 2023-01-02 23:01:32,431 - mmcls - INFO - Epoch [71][1500/5005] lr: 1.000e-03, eta: 3:37:57, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7803 2023-01-02 23:01:41,090 - mmcls - INFO - Epoch [71][1600/5005] lr: 1.000e-03, eta: 3:37:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7462 2023-01-02 23:01:49,992 - mmcls - INFO - Epoch [71][1700/5005] lr: 1.000e-03, eta: 3:37:40, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7693 2023-01-02 23:01:58,590 - mmcls - INFO - Epoch [71][1800/5005] lr: 1.000e-03, eta: 3:37:31, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7742 2023-01-02 23:02:07,191 - mmcls - INFO - Epoch [71][1900/5005] lr: 1.000e-03, eta: 3:37:22, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7612 2023-01-02 23:02:16,074 - mmcls - INFO - Epoch [71][2000/5005] lr: 1.000e-03, eta: 3:37:13, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7940 2023-01-02 23:02:24,924 - mmcls - INFO - Epoch [71][2100/5005] lr: 1.000e-03, eta: 3:37:05, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7921 2023-01-02 23:02:33,970 - mmcls - INFO - Epoch [71][2200/5005] lr: 1.000e-03, eta: 3:36:56, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7680 2023-01-02 23:02:43,082 - mmcls - INFO - Epoch [71][2300/5005] lr: 1.000e-03, eta: 3:36:47, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7548 2023-01-02 23:02:51,732 - mmcls - INFO - Epoch [71][2400/5005] lr: 1.000e-03, eta: 3:36:38, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7641 2023-01-02 23:03:00,393 - mmcls - INFO - Epoch [71][2500/5005] lr: 1.000e-03, eta: 3:36:29, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.7779 2023-01-02 23:03:09,115 - mmcls - INFO - Epoch [71][2600/5005] lr: 1.000e-03, eta: 3:36:21, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7607 2023-01-02 23:03:17,943 - mmcls - INFO - Epoch [71][2700/5005] lr: 1.000e-03, eta: 3:36:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7521 2023-01-02 23:03:26,897 - mmcls - INFO - Epoch [71][2800/5005] lr: 1.000e-03, eta: 3:36:03, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7727 2023-01-02 23:03:35,917 - mmcls - INFO - Epoch [71][2900/5005] lr: 1.000e-03, eta: 3:35:54, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7762 2023-01-02 23:03:44,467 - mmcls - INFO - Epoch [71][3000/5005] lr: 1.000e-03, eta: 3:35:46, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.7752 2023-01-02 23:03:53,328 - mmcls - INFO - Epoch [71][3100/5005] lr: 1.000e-03, eta: 3:35:37, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7724 2023-01-02 23:04:02,124 - mmcls - INFO - Epoch [71][3200/5005] lr: 1.000e-03, eta: 3:35:28, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7680 2023-01-02 23:04:10,865 - mmcls - INFO - Epoch [71][3300/5005] lr: 1.000e-03, eta: 3:35:19, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7694 2023-01-02 23:04:19,431 - mmcls - INFO - Epoch [71][3400/5005] lr: 1.000e-03, eta: 3:35:10, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7713 2023-01-02 23:04:28,116 - mmcls - INFO - Epoch [71][3500/5005] lr: 1.000e-03, eta: 3:35:01, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7792 2023-01-02 23:04:36,886 - mmcls - INFO - Epoch [71][3600/5005] lr: 1.000e-03, eta: 3:34:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7755 2023-01-02 23:04:45,642 - mmcls - INFO - Epoch [71][3700/5005] lr: 1.000e-03, eta: 3:34:44, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7777 2023-01-02 23:04:54,086 - mmcls - INFO - Epoch [71][3800/5005] lr: 1.000e-03, eta: 3:34:35, time: 0.084, data_time: 0.017, memory: 1125, loss: 1.7885 2023-01-02 23:05:02,607 - mmcls - INFO - Epoch [71][3900/5005] lr: 1.000e-03, eta: 3:34:26, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7892 2023-01-02 23:05:11,530 - mmcls - INFO - Epoch [71][4000/5005] lr: 1.000e-03, eta: 3:34:17, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7646 2023-01-02 23:05:20,134 - mmcls - INFO - Epoch [71][4100/5005] lr: 1.000e-03, eta: 3:34:08, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7746 2023-01-02 23:05:28,921 - mmcls - INFO - Epoch [71][4200/5005] lr: 1.000e-03, eta: 3:33:59, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7870 2023-01-02 23:05:37,569 - mmcls - INFO - Epoch [71][4300/5005] lr: 1.000e-03, eta: 3:33:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7694 2023-01-02 23:05:46,290 - mmcls - INFO - Epoch [71][4400/5005] lr: 1.000e-03, eta: 3:33:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7811 2023-01-02 23:05:55,007 - mmcls - INFO - Epoch [71][4500/5005] lr: 1.000e-03, eta: 3:33:33, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7667 2023-01-02 23:06:03,646 - mmcls - INFO - Epoch [71][4600/5005] lr: 1.000e-03, eta: 3:33:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7890 2023-01-02 23:06:12,578 - mmcls - INFO - Epoch [71][4700/5005] lr: 1.000e-03, eta: 3:33:15, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7673 2023-01-02 23:06:21,355 - mmcls - INFO - Epoch [71][4800/5005] lr: 1.000e-03, eta: 3:33:07, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7789 2023-01-02 23:06:30,035 - mmcls - INFO - Epoch [71][4900/5005] lr: 1.000e-03, eta: 3:32:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7519 2023-01-02 23:06:38,738 - mmcls - INFO - Epoch [71][5000/5005] lr: 1.000e-03, eta: 3:32:49, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.7705 2023-01-02 23:07:01,480 - mmcls - INFO - Epoch(val) [71][196] accuracy_top-1: 62.7220, accuracy_top-5: 83.8600 2023-01-02 23:07:12,593 - mmcls - INFO - Epoch [72][100/5005] lr: 1.000e-03, eta: 3:32:40, time: 0.111, data_time: 0.035, memory: 1125, loss: 1.7214 2023-01-02 23:07:21,666 - mmcls - INFO - Epoch [72][200/5005] lr: 1.000e-03, eta: 3:32:32, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7564 2023-01-02 23:07:30,351 - mmcls - INFO - Epoch [72][300/5005] lr: 1.000e-03, eta: 3:32:23, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7832 2023-01-02 23:07:39,152 - mmcls - INFO - Epoch [72][400/5005] lr: 1.000e-03, eta: 3:32:14, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.7502 2023-01-02 23:07:47,728 - mmcls - INFO - Epoch [72][500/5005] lr: 1.000e-03, eta: 3:32:05, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7670 2023-01-02 23:07:56,642 - mmcls - INFO - Epoch [72][600/5005] lr: 1.000e-03, eta: 3:31:56, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7298 2023-01-02 23:08:05,281 - mmcls - INFO - Epoch [72][700/5005] lr: 1.000e-03, eta: 3:31:48, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7819 2023-01-02 23:08:14,579 - mmcls - INFO - Epoch [72][800/5005] lr: 1.000e-03, eta: 3:31:39, time: 0.093, data_time: 0.015, memory: 1125, loss: 1.7524 2023-01-02 23:08:23,545 - mmcls - INFO - Epoch [72][900/5005] lr: 1.000e-03, eta: 3:31:30, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7527 2023-01-02 23:08:32,158 - mmcls - INFO - Epoch [72][1000/5005] lr: 1.000e-03, eta: 3:31:21, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7577 2023-01-02 23:08:41,217 - mmcls - INFO - Epoch [72][1100/5005] lr: 1.000e-03, eta: 3:31:13, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7638 2023-01-02 23:08:49,814 - mmcls - INFO - Epoch [72][1200/5005] lr: 1.000e-03, eta: 3:31:04, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7797 2023-01-02 23:08:58,484 - mmcls - INFO - Epoch [72][1300/5005] lr: 1.000e-03, eta: 3:30:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7612 2023-01-02 23:09:07,168 - mmcls - INFO - Epoch [72][1400/5005] lr: 1.000e-03, eta: 3:30:46, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7597 2023-01-02 23:09:16,266 - mmcls - INFO - Epoch [72][1500/5005] lr: 1.000e-03, eta: 3:30:37, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.7641 2023-01-02 23:09:25,183 - mmcls - INFO - Epoch [72][1600/5005] lr: 1.000e-03, eta: 3:30:29, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7380 2023-01-02 23:09:33,970 - mmcls - INFO - Epoch [72][1700/5005] lr: 1.000e-03, eta: 3:30:20, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7649 2023-01-02 23:09:42,769 - mmcls - INFO - Epoch [72][1800/5005] lr: 1.000e-03, eta: 3:30:11, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.7757 2023-01-02 23:09:51,542 - mmcls - INFO - Epoch [72][1900/5005] lr: 1.000e-03, eta: 3:30:02, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7666 2023-01-02 23:10:00,616 - mmcls - INFO - Epoch [72][2000/5005] lr: 1.000e-03, eta: 3:29:54, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.7646 2023-01-02 23:10:09,264 - mmcls - INFO - Epoch [72][2100/5005] lr: 1.000e-03, eta: 3:29:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7563 2023-01-02 23:10:18,460 - mmcls - INFO - Epoch [72][2200/5005] lr: 1.000e-03, eta: 3:29:36, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7813 2023-01-02 23:10:27,276 - mmcls - INFO - Epoch [72][2300/5005] lr: 1.000e-03, eta: 3:29:27, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7715 2023-01-02 23:10:36,111 - mmcls - INFO - Epoch [72][2400/5005] lr: 1.000e-03, eta: 3:29:19, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7785 2023-01-02 23:10:44,870 - mmcls - INFO - Epoch [72][2500/5005] lr: 1.000e-03, eta: 3:29:10, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7624 2023-01-02 23:10:53,589 - mmcls - INFO - Epoch [72][2600/5005] lr: 1.000e-03, eta: 3:29:01, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7515 2023-01-02 23:11:02,527 - mmcls - INFO - Epoch [72][2700/5005] lr: 1.000e-03, eta: 3:28:52, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.8053 2023-01-02 23:11:11,241 - mmcls - INFO - Epoch [72][2800/5005] lr: 1.000e-03, eta: 3:28:43, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7673 2023-01-02 23:11:19,922 - mmcls - INFO - Epoch [72][2900/5005] lr: 1.000e-03, eta: 3:28:34, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7697 2023-01-02 23:11:28,702 - mmcls - INFO - Epoch [72][3000/5005] lr: 1.000e-03, eta: 3:28:26, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7698 2023-01-02 23:11:37,349 - mmcls - INFO - Epoch [72][3100/5005] lr: 1.000e-03, eta: 3:28:17, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7657 2023-01-02 23:11:46,118 - mmcls - INFO - Epoch [72][3200/5005] lr: 1.000e-03, eta: 3:28:08, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7649 2023-01-02 23:11:55,029 - mmcls - INFO - Epoch [72][3300/5005] lr: 1.000e-03, eta: 3:27:59, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.7846 2023-01-02 23:12:03,473 - mmcls - INFO - Epoch [72][3400/5005] lr: 1.000e-03, eta: 3:27:50, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7621 2023-01-02 23:12:12,110 - mmcls - INFO - Epoch [72][3500/5005] lr: 1.000e-03, eta: 3:27:41, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7690 2023-01-02 23:12:20,598 - mmcls - INFO - Epoch [72][3600/5005] lr: 1.000e-03, eta: 3:27:32, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7796 2023-01-02 23:12:29,101 - mmcls - INFO - Epoch [72][3700/5005] lr: 1.000e-03, eta: 3:27:24, time: 0.085, data_time: 0.025, memory: 1125, loss: 1.7640 2023-01-02 23:12:37,949 - mmcls - INFO - Epoch [72][3800/5005] lr: 1.000e-03, eta: 3:27:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7578 2023-01-02 23:12:46,588 - mmcls - INFO - Epoch [72][3900/5005] lr: 1.000e-03, eta: 3:27:06, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7555 2023-01-02 23:12:55,194 - mmcls - INFO - Epoch [72][4000/5005] lr: 1.000e-03, eta: 3:26:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7756 2023-01-02 23:13:04,096 - mmcls - INFO - Epoch [72][4100/5005] lr: 1.000e-03, eta: 3:26:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7537 2023-01-02 23:13:12,878 - mmcls - INFO - Epoch [72][4200/5005] lr: 1.000e-03, eta: 3:26:40, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7668 2023-01-02 23:13:21,420 - mmcls - INFO - Epoch [72][4300/5005] lr: 1.000e-03, eta: 3:26:31, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7497 2023-01-02 23:13:30,360 - mmcls - INFO - Epoch [72][4400/5005] lr: 1.000e-03, eta: 3:26:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7846 2023-01-02 23:13:39,101 - mmcls - INFO - Epoch [72][4500/5005] lr: 1.000e-03, eta: 3:26:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7806 2023-01-02 23:13:47,864 - mmcls - INFO - Epoch [72][4600/5005] lr: 1.000e-03, eta: 3:26:04, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7738 2023-01-02 23:13:56,638 - mmcls - INFO - Epoch [72][4700/5005] lr: 1.000e-03, eta: 3:25:55, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7495 2023-01-02 23:14:05,276 - mmcls - INFO - Epoch [72][4800/5005] lr: 1.000e-03, eta: 3:25:47, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7494 2023-01-02 23:14:14,416 - mmcls - INFO - Epoch [72][4900/5005] lr: 1.000e-03, eta: 3:25:38, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7526 2023-01-02 23:14:23,198 - mmcls - INFO - Epoch [72][5000/5005] lr: 1.000e-03, eta: 3:25:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7818 2023-01-02 23:14:45,791 - mmcls - INFO - Epoch(val) [72][196] accuracy_top-1: 62.9620, accuracy_top-5: 83.9620 2023-01-02 23:14:56,824 - mmcls - INFO - Epoch [73][100/5005] lr: 1.000e-03, eta: 3:25:21, time: 0.110, data_time: 0.051, memory: 1125, loss: 1.7471 2023-01-02 23:15:05,294 - mmcls - INFO - Epoch [73][200/5005] lr: 1.000e-03, eta: 3:25:12, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7756 2023-01-02 23:15:14,027 - mmcls - INFO - Epoch [73][300/5005] lr: 1.000e-03, eta: 3:25:03, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7711 2023-01-02 23:15:23,076 - mmcls - INFO - Epoch [73][400/5005] lr: 1.000e-03, eta: 3:24:54, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7390 2023-01-02 23:15:32,028 - mmcls - INFO - Epoch [73][500/5005] lr: 1.000e-03, eta: 3:24:45, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7747 2023-01-02 23:15:40,704 - mmcls - INFO - Epoch [73][600/5005] lr: 1.000e-03, eta: 3:24:37, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7796 2023-01-02 23:15:49,622 - mmcls - INFO - Epoch [73][700/5005] lr: 1.000e-03, eta: 3:24:28, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7818 2023-01-02 23:15:58,264 - mmcls - INFO - Epoch [73][800/5005] lr: 1.000e-03, eta: 3:24:19, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7589 2023-01-02 23:16:07,024 - mmcls - INFO - Epoch [73][900/5005] lr: 1.000e-03, eta: 3:24:10, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7713 2023-01-02 23:16:15,671 - mmcls - INFO - Epoch [73][1000/5005] lr: 1.000e-03, eta: 3:24:01, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7408 2023-01-02 23:16:24,607 - mmcls - INFO - Epoch [73][1100/5005] lr: 1.000e-03, eta: 3:23:52, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7507 2023-01-02 23:16:33,298 - mmcls - INFO - Epoch [73][1200/5005] lr: 1.000e-03, eta: 3:23:44, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7509 2023-01-02 23:16:41,820 - mmcls - INFO - Epoch [73][1300/5005] lr: 1.000e-03, eta: 3:23:35, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7521 2023-01-02 23:16:50,812 - mmcls - INFO - Epoch [73][1400/5005] lr: 1.000e-03, eta: 3:23:26, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7910 2023-01-02 23:16:59,776 - mmcls - INFO - Epoch [73][1500/5005] lr: 1.000e-03, eta: 3:23:17, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.7795 2023-01-02 23:17:08,207 - mmcls - INFO - Epoch [73][1600/5005] lr: 1.000e-03, eta: 3:23:08, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.7574 2023-01-02 23:17:17,023 - mmcls - INFO - Epoch [73][1700/5005] lr: 1.000e-03, eta: 3:23:00, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7489 2023-01-02 23:17:25,515 - mmcls - INFO - Epoch [73][1800/5005] lr: 1.000e-03, eta: 3:22:51, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7526 2023-01-02 23:17:34,241 - mmcls - INFO - Epoch [73][1900/5005] lr: 1.000e-03, eta: 3:22:42, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7579 2023-01-02 23:17:42,739 - mmcls - INFO - Epoch [73][2000/5005] lr: 1.000e-03, eta: 3:22:33, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.7806 2023-01-02 23:17:51,462 - mmcls - INFO - Epoch [73][2100/5005] lr: 1.000e-03, eta: 3:22:24, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7584 2023-01-02 23:18:00,441 - mmcls - INFO - Epoch [73][2200/5005] lr: 1.000e-03, eta: 3:22:15, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7652 2023-01-02 23:18:08,979 - mmcls - INFO - Epoch [73][2300/5005] lr: 1.000e-03, eta: 3:22:06, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7770 2023-01-02 23:18:17,985 - mmcls - INFO - Epoch [73][2400/5005] lr: 1.000e-03, eta: 3:21:58, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7446 2023-01-02 23:18:26,595 - mmcls - INFO - Epoch [73][2500/5005] lr: 1.000e-03, eta: 3:21:49, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7598 2023-01-02 23:18:35,425 - mmcls - INFO - Epoch [73][2600/5005] lr: 1.000e-03, eta: 3:21:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7438 2023-01-02 23:18:44,126 - mmcls - INFO - Epoch [73][2700/5005] lr: 1.000e-03, eta: 3:21:31, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7495 2023-01-02 23:18:52,902 - mmcls - INFO - Epoch [73][2800/5005] lr: 1.000e-03, eta: 3:21:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7682 2023-01-02 23:19:01,751 - mmcls - INFO - Epoch [73][2900/5005] lr: 1.000e-03, eta: 3:21:14, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7545 2023-01-02 23:19:10,526 - mmcls - INFO - Epoch [73][3000/5005] lr: 1.000e-03, eta: 3:21:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7728 2023-01-02 23:19:19,335 - mmcls - INFO - Epoch [73][3100/5005] lr: 1.000e-03, eta: 3:20:56, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.7479 2023-01-02 23:19:28,044 - mmcls - INFO - Epoch [73][3200/5005] lr: 1.000e-03, eta: 3:20:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7769 2023-01-02 23:19:36,832 - mmcls - INFO - Epoch [73][3300/5005] lr: 1.000e-03, eta: 3:20:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7694 2023-01-02 23:19:45,819 - mmcls - INFO - Epoch [73][3400/5005] lr: 1.000e-03, eta: 3:20:30, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7440 2023-01-02 23:19:54,934 - mmcls - INFO - Epoch [73][3500/5005] lr: 1.000e-03, eta: 3:20:21, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7523 2023-01-02 23:20:03,619 - mmcls - INFO - Epoch [73][3600/5005] lr: 1.000e-03, eta: 3:20:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7668 2023-01-02 23:20:12,414 - mmcls - INFO - Epoch [73][3700/5005] lr: 1.000e-03, eta: 3:20:03, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7664 2023-01-02 23:20:21,109 - mmcls - INFO - Epoch [73][3800/5005] lr: 1.000e-03, eta: 3:19:55, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7560 2023-01-02 23:20:29,834 - mmcls - INFO - Epoch [73][3900/5005] lr: 1.000e-03, eta: 3:19:46, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7584 2023-01-02 23:20:38,537 - mmcls - INFO - Epoch [73][4000/5005] lr: 1.000e-03, eta: 3:19:37, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7747 2023-01-02 23:20:47,388 - mmcls - INFO - Epoch [73][4100/5005] lr: 1.000e-03, eta: 3:19:28, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7709 2023-01-02 23:20:55,874 - mmcls - INFO - Epoch [73][4200/5005] lr: 1.000e-03, eta: 3:19:19, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7647 2023-01-02 23:21:04,585 - mmcls - INFO - Epoch [73][4300/5005] lr: 1.000e-03, eta: 3:19:10, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7819 2023-01-02 23:21:13,301 - mmcls - INFO - Epoch [73][4400/5005] lr: 1.000e-03, eta: 3:19:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7579 2023-01-02 23:21:22,010 - mmcls - INFO - Epoch [73][4500/5005] lr: 1.000e-03, eta: 3:18:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7653 2023-01-02 23:21:30,756 - mmcls - INFO - Epoch [73][4600/5005] lr: 1.000e-03, eta: 3:18:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7691 2023-01-02 23:21:39,881 - mmcls - INFO - Epoch [73][4700/5005] lr: 1.000e-03, eta: 3:18:35, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.7644 2023-01-02 23:21:48,636 - mmcls - INFO - Epoch [73][4800/5005] lr: 1.000e-03, eta: 3:18:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7688 2023-01-02 23:21:57,595 - mmcls - INFO - Epoch [73][4900/5005] lr: 1.000e-03, eta: 3:18:18, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7723 2023-01-02 23:22:06,194 - mmcls - INFO - Epoch [73][5000/5005] lr: 1.000e-03, eta: 3:18:09, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7752 2023-01-02 23:22:28,729 - mmcls - INFO - Epoch(val) [73][196] accuracy_top-1: 62.8440, accuracy_top-5: 83.7000 2023-01-02 23:22:39,769 - mmcls - INFO - Epoch [74][100/5005] lr: 1.000e-03, eta: 3:18:00, time: 0.110, data_time: 0.045, memory: 1125, loss: 1.7377 2023-01-02 23:22:48,879 - mmcls - INFO - Epoch [74][200/5005] lr: 1.000e-03, eta: 3:17:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7510 2023-01-02 23:22:57,877 - mmcls - INFO - Epoch [74][300/5005] lr: 1.000e-03, eta: 3:17:43, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7456 2023-01-02 23:23:06,420 - mmcls - INFO - Epoch [74][400/5005] lr: 1.000e-03, eta: 3:17:34, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7601 2023-01-02 23:23:15,057 - mmcls - INFO - Epoch [74][500/5005] lr: 1.000e-03, eta: 3:17:25, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7366 2023-01-02 23:23:24,039 - mmcls - INFO - Epoch [74][600/5005] lr: 1.000e-03, eta: 3:17:16, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.7640 2023-01-02 23:23:32,915 - mmcls - INFO - Epoch [74][700/5005] lr: 1.000e-03, eta: 3:17:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7518 2023-01-02 23:23:41,657 - mmcls - INFO - Epoch [74][800/5005] lr: 1.000e-03, eta: 3:16:59, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7639 2023-01-02 23:23:50,146 - mmcls - INFO - Epoch [74][900/5005] lr: 1.000e-03, eta: 3:16:50, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7257 2023-01-02 23:23:58,771 - mmcls - INFO - Epoch [74][1000/5005] lr: 1.000e-03, eta: 3:16:41, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7644 2023-01-02 23:24:07,620 - mmcls - INFO - Epoch [74][1100/5005] lr: 1.000e-03, eta: 3:16:32, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7618 2023-01-02 23:24:16,442 - mmcls - INFO - Epoch [74][1200/5005] lr: 1.000e-03, eta: 3:16:23, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7527 2023-01-02 23:24:25,193 - mmcls - INFO - Epoch [74][1300/5005] lr: 1.000e-03, eta: 3:16:15, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7679 2023-01-02 23:24:34,012 - mmcls - INFO - Epoch [74][1400/5005] lr: 1.000e-03, eta: 3:16:06, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7539 2023-01-02 23:24:42,564 - mmcls - INFO - Epoch [74][1500/5005] lr: 1.000e-03, eta: 3:15:57, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7514 2023-01-02 23:24:51,817 - mmcls - INFO - Epoch [74][1600/5005] lr: 1.000e-03, eta: 3:15:48, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7736 2023-01-02 23:25:00,518 - mmcls - INFO - Epoch [74][1700/5005] lr: 1.000e-03, eta: 3:15:39, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7530 2023-01-02 23:25:09,369 - mmcls - INFO - Epoch [74][1800/5005] lr: 1.000e-03, eta: 3:15:31, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7682 2023-01-02 23:25:18,191 - mmcls - INFO - Epoch [74][1900/5005] lr: 1.000e-03, eta: 3:15:22, time: 0.088, data_time: 0.000, memory: 1125, loss: 1.7459 2023-01-02 23:25:26,846 - mmcls - INFO - Epoch [74][2000/5005] lr: 1.000e-03, eta: 3:15:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7449 2023-01-02 23:25:35,350 - mmcls - INFO - Epoch [74][2100/5005] lr: 1.000e-03, eta: 3:15:04, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7716 2023-01-02 23:25:44,113 - mmcls - INFO - Epoch [74][2200/5005] lr: 1.000e-03, eta: 3:14:55, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7217 2023-01-02 23:25:52,841 - mmcls - INFO - Epoch [74][2300/5005] lr: 1.000e-03, eta: 3:14:46, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7631 2023-01-02 23:26:01,593 - mmcls - INFO - Epoch [74][2400/5005] lr: 1.000e-03, eta: 3:14:38, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7384 2023-01-02 23:26:10,311 - mmcls - INFO - Epoch [74][2500/5005] lr: 1.000e-03, eta: 3:14:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7777 2023-01-02 23:26:18,924 - mmcls - INFO - Epoch [74][2600/5005] lr: 1.000e-03, eta: 3:14:20, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7397 2023-01-02 23:26:27,659 - mmcls - INFO - Epoch [74][2700/5005] lr: 1.000e-03, eta: 3:14:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7505 2023-01-02 23:26:36,438 - mmcls - INFO - Epoch [74][2800/5005] lr: 1.000e-03, eta: 3:14:02, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7801 2023-01-02 23:26:45,241 - mmcls - INFO - Epoch [74][2900/5005] lr: 1.000e-03, eta: 3:13:54, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7509 2023-01-02 23:26:53,944 - mmcls - INFO - Epoch [74][3000/5005] lr: 1.000e-03, eta: 3:13:45, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7530 2023-01-02 23:27:02,392 - mmcls - INFO - Epoch [74][3100/5005] lr: 1.000e-03, eta: 3:13:36, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7540 2023-01-02 23:27:11,325 - mmcls - INFO - Epoch [74][3200/5005] lr: 1.000e-03, eta: 3:13:27, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7809 2023-01-02 23:27:19,949 - mmcls - INFO - Epoch [74][3300/5005] lr: 1.000e-03, eta: 3:13:18, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7552 2023-01-02 23:27:28,575 - mmcls - INFO - Epoch [74][3400/5005] lr: 1.000e-03, eta: 3:13:09, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7789 2023-01-02 23:27:37,488 - mmcls - INFO - Epoch [74][3500/5005] lr: 1.000e-03, eta: 3:13:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7448 2023-01-02 23:27:46,020 - mmcls - INFO - Epoch [74][3600/5005] lr: 1.000e-03, eta: 3:12:52, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7845 2023-01-02 23:27:54,758 - mmcls - INFO - Epoch [74][3700/5005] lr: 1.000e-03, eta: 3:12:43, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7438 2023-01-02 23:28:03,511 - mmcls - INFO - Epoch [74][3800/5005] lr: 1.000e-03, eta: 3:12:34, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7603 2023-01-02 23:28:12,215 - mmcls - INFO - Epoch [74][3900/5005] lr: 1.000e-03, eta: 3:12:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7566 2023-01-02 23:28:21,230 - mmcls - INFO - Epoch [74][4000/5005] lr: 1.000e-03, eta: 3:12:16, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.7811 2023-01-02 23:28:29,822 - mmcls - INFO - Epoch [74][4100/5005] lr: 1.000e-03, eta: 3:12:08, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7623 2023-01-02 23:28:38,773 - mmcls - INFO - Epoch [74][4200/5005] lr: 1.000e-03, eta: 3:11:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7671 2023-01-02 23:28:47,589 - mmcls - INFO - Epoch [74][4300/5005] lr: 1.000e-03, eta: 3:11:50, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7659 2023-01-02 23:28:56,139 - mmcls - INFO - Epoch [74][4400/5005] lr: 1.000e-03, eta: 3:11:41, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.7914 2023-01-02 23:29:05,042 - mmcls - INFO - Epoch [74][4500/5005] lr: 1.000e-03, eta: 3:11:32, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.7757 2023-01-02 23:29:13,654 - mmcls - INFO - Epoch [74][4600/5005] lr: 1.000e-03, eta: 3:11:24, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7649 2023-01-02 23:29:22,286 - mmcls - INFO - Epoch [74][4700/5005] lr: 1.000e-03, eta: 3:11:15, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7858 2023-01-02 23:29:30,919 - mmcls - INFO - Epoch [74][4800/5005] lr: 1.000e-03, eta: 3:11:06, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7577 2023-01-02 23:29:39,894 - mmcls - INFO - Epoch [74][4900/5005] lr: 1.000e-03, eta: 3:10:57, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7786 2023-01-02 23:29:48,506 - mmcls - INFO - Epoch [74][5000/5005] lr: 1.000e-03, eta: 3:10:48, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7610 2023-01-02 23:30:11,245 - mmcls - INFO - Epoch(val) [74][196] accuracy_top-1: 63.0460, accuracy_top-5: 84.0300 2023-01-02 23:30:22,032 - mmcls - INFO - Epoch [75][100/5005] lr: 1.000e-03, eta: 3:10:40, time: 0.108, data_time: 0.053, memory: 1125, loss: 1.7590 2023-01-02 23:30:30,997 - mmcls - INFO - Epoch [75][200/5005] lr: 1.000e-03, eta: 3:10:31, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7335 2023-01-02 23:30:39,547 - mmcls - INFO - Epoch [75][300/5005] lr: 1.000e-03, eta: 3:10:22, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7534 2023-01-02 23:30:48,393 - mmcls - INFO - Epoch [75][400/5005] lr: 1.000e-03, eta: 3:10:13, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7456 2023-01-02 23:30:57,074 - mmcls - INFO - Epoch [75][500/5005] lr: 1.000e-03, eta: 3:10:04, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7608 2023-01-02 23:31:05,811 - mmcls - INFO - Epoch [75][600/5005] lr: 1.000e-03, eta: 3:09:55, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7531 2023-01-02 23:31:14,714 - mmcls - INFO - Epoch [75][700/5005] lr: 1.000e-03, eta: 3:09:47, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7513 2023-01-02 23:31:23,440 - mmcls - INFO - Epoch [75][800/5005] lr: 1.000e-03, eta: 3:09:38, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7596 2023-01-02 23:31:32,463 - mmcls - INFO - Epoch [75][900/5005] lr: 1.000e-03, eta: 3:09:29, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7440 2023-01-02 23:31:41,128 - mmcls - INFO - Epoch [75][1000/5005] lr: 1.000e-03, eta: 3:09:20, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7456 2023-01-02 23:31:49,841 - mmcls - INFO - Epoch [75][1100/5005] lr: 1.000e-03, eta: 3:09:11, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7523 2023-01-02 23:31:58,623 - mmcls - INFO - Epoch [75][1200/5005] lr: 1.000e-03, eta: 3:09:03, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7589 2023-01-02 23:32:07,351 - mmcls - INFO - Epoch [75][1300/5005] lr: 1.000e-03, eta: 3:08:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7438 2023-01-02 23:32:16,267 - mmcls - INFO - Epoch [75][1400/5005] lr: 1.000e-03, eta: 3:08:45, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7631 2023-01-02 23:32:24,868 - mmcls - INFO - Epoch [75][1500/5005] lr: 1.000e-03, eta: 3:08:36, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7549 2023-01-02 23:32:33,583 - mmcls - INFO - Epoch [75][1600/5005] lr: 1.000e-03, eta: 3:08:27, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7601 2023-01-02 23:32:42,347 - mmcls - INFO - Epoch [75][1700/5005] lr: 1.000e-03, eta: 3:08:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7639 2023-01-02 23:32:51,005 - mmcls - INFO - Epoch [75][1800/5005] lr: 1.000e-03, eta: 3:08:10, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7675 2023-01-02 23:32:59,641 - mmcls - INFO - Epoch [75][1900/5005] lr: 1.000e-03, eta: 3:08:01, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7716 2023-01-02 23:33:08,388 - mmcls - INFO - Epoch [75][2000/5005] lr: 1.000e-03, eta: 3:07:52, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7494 2023-01-02 23:33:17,494 - mmcls - INFO - Epoch [75][2100/5005] lr: 1.000e-03, eta: 3:07:43, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7509 2023-01-02 23:33:26,279 - mmcls - INFO - Epoch [75][2200/5005] lr: 1.000e-03, eta: 3:07:35, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7554 2023-01-02 23:33:35,219 - mmcls - INFO - Epoch [75][2300/5005] lr: 1.000e-03, eta: 3:07:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7627 2023-01-02 23:33:43,867 - mmcls - INFO - Epoch [75][2400/5005] lr: 1.000e-03, eta: 3:07:17, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7322 2023-01-02 23:33:52,557 - mmcls - INFO - Epoch [75][2500/5005] lr: 1.000e-03, eta: 3:07:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7877 2023-01-02 23:34:01,215 - mmcls - INFO - Epoch [75][2600/5005] lr: 1.000e-03, eta: 3:06:59, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7495 2023-01-02 23:34:09,858 - mmcls - INFO - Epoch [75][2700/5005] lr: 1.000e-03, eta: 3:06:50, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7607 2023-01-02 23:34:18,750 - mmcls - INFO - Epoch [75][2800/5005] lr: 1.000e-03, eta: 3:06:42, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7708 2023-01-02 23:34:27,355 - mmcls - INFO - Epoch [75][2900/5005] lr: 1.000e-03, eta: 3:06:33, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7619 2023-01-02 23:34:35,820 - mmcls - INFO - Epoch [75][3000/5005] lr: 1.000e-03, eta: 3:06:24, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7535 2023-01-02 23:34:44,435 - mmcls - INFO - Epoch [75][3100/5005] lr: 1.000e-03, eta: 3:06:15, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7398 2023-01-02 23:34:53,177 - mmcls - INFO - Epoch [75][3200/5005] lr: 1.000e-03, eta: 3:06:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7551 2023-01-02 23:35:02,019 - mmcls - INFO - Epoch [75][3300/5005] lr: 1.000e-03, eta: 3:05:57, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7627 2023-01-02 23:35:10,804 - mmcls - INFO - Epoch [75][3400/5005] lr: 1.000e-03, eta: 3:05:49, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7498 2023-01-02 23:35:19,562 - mmcls - INFO - Epoch [75][3500/5005] lr: 1.000e-03, eta: 3:05:40, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7439 2023-01-02 23:35:28,313 - mmcls - INFO - Epoch [75][3600/5005] lr: 1.000e-03, eta: 3:05:31, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.7538 2023-01-02 23:35:37,023 - mmcls - INFO - Epoch [75][3700/5005] lr: 1.000e-03, eta: 3:05:22, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7634 2023-01-02 23:35:45,627 - mmcls - INFO - Epoch [75][3800/5005] lr: 1.000e-03, eta: 3:05:13, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7577 2023-01-02 23:35:54,357 - mmcls - INFO - Epoch [75][3900/5005] lr: 1.000e-03, eta: 3:05:05, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7616 2023-01-02 23:36:03,249 - mmcls - INFO - Epoch [75][4000/5005] lr: 1.000e-03, eta: 3:04:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7565 2023-01-02 23:36:11,924 - mmcls - INFO - Epoch [75][4100/5005] lr: 1.000e-03, eta: 3:04:47, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7389 2023-01-02 23:36:20,659 - mmcls - INFO - Epoch [75][4200/5005] lr: 1.000e-03, eta: 3:04:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7428 2023-01-02 23:36:29,481 - mmcls - INFO - Epoch [75][4300/5005] lr: 1.000e-03, eta: 3:04:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7517 2023-01-02 23:36:38,131 - mmcls - INFO - Epoch [75][4400/5005] lr: 1.000e-03, eta: 3:04:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7455 2023-01-02 23:36:46,903 - mmcls - INFO - Epoch [75][4500/5005] lr: 1.000e-03, eta: 3:04:12, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7634 2023-01-02 23:36:55,804 - mmcls - INFO - Epoch [75][4600/5005] lr: 1.000e-03, eta: 3:04:03, time: 0.089, data_time: 0.034, memory: 1125, loss: 1.7654 2023-01-02 23:37:04,655 - mmcls - INFO - Epoch [75][4700/5005] lr: 1.000e-03, eta: 3:03:54, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7528 2023-01-02 23:37:13,399 - mmcls - INFO - Epoch [75][4800/5005] lr: 1.000e-03, eta: 3:03:45, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.7571 2023-01-02 23:37:22,407 - mmcls - INFO - Epoch [75][4900/5005] lr: 1.000e-03, eta: 3:03:37, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7500 2023-01-02 23:37:31,083 - mmcls - INFO - Epoch [75][5000/5005] lr: 1.000e-03, eta: 3:03:28, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7685 2023-01-02 23:37:53,646 - mmcls - INFO - Epoch(val) [75][196] accuracy_top-1: 62.9480, accuracy_top-5: 83.8380 2023-01-02 23:38:04,747 - mmcls - INFO - Epoch [76][100/5005] lr: 1.000e-03, eta: 3:03:19, time: 0.111, data_time: 0.047, memory: 1125, loss: 1.7450 2023-01-02 23:38:13,250 - mmcls - INFO - Epoch [76][200/5005] lr: 1.000e-03, eta: 3:03:10, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7592 2023-01-02 23:38:22,184 - mmcls - INFO - Epoch [76][300/5005] lr: 1.000e-03, eta: 3:03:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7504 2023-01-02 23:38:30,838 - mmcls - INFO - Epoch [76][400/5005] lr: 1.000e-03, eta: 3:02:53, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7396 2023-01-02 23:38:39,647 - mmcls - INFO - Epoch [76][500/5005] lr: 1.000e-03, eta: 3:02:44, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7469 2023-01-02 23:38:48,507 - mmcls - INFO - Epoch [76][600/5005] lr: 1.000e-03, eta: 3:02:35, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7322 2023-01-02 23:38:57,127 - mmcls - INFO - Epoch [76][700/5005] lr: 1.000e-03, eta: 3:02:26, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7474 2023-01-02 23:39:06,166 - mmcls - INFO - Epoch [76][800/5005] lr: 1.000e-03, eta: 3:02:17, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7611 2023-01-02 23:39:15,134 - mmcls - INFO - Epoch [76][900/5005] lr: 1.000e-03, eta: 3:02:09, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7589 2023-01-02 23:39:23,790 - mmcls - INFO - Epoch [76][1000/5005] lr: 1.000e-03, eta: 3:02:00, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7340 2023-01-02 23:39:32,468 - mmcls - INFO - Epoch [76][1100/5005] lr: 1.000e-03, eta: 3:01:51, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7453 2023-01-02 23:39:41,036 - mmcls - INFO - Epoch [76][1200/5005] lr: 1.000e-03, eta: 3:01:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7491 2023-01-02 23:39:49,776 - mmcls - INFO - Epoch [76][1300/5005] lr: 1.000e-03, eta: 3:01:33, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7604 2023-01-02 23:39:58,416 - mmcls - INFO - Epoch [76][1400/5005] lr: 1.000e-03, eta: 3:01:24, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7517 2023-01-02 23:40:07,491 - mmcls - INFO - Epoch [76][1500/5005] lr: 1.000e-03, eta: 3:01:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7490 2023-01-02 23:40:16,499 - mmcls - INFO - Epoch [76][1600/5005] lr: 1.000e-03, eta: 3:01:07, time: 0.090, data_time: 0.024, memory: 1125, loss: 1.7513 2023-01-02 23:40:25,292 - mmcls - INFO - Epoch [76][1700/5005] lr: 1.000e-03, eta: 3:00:58, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7626 2023-01-02 23:40:34,127 - mmcls - INFO - Epoch [76][1800/5005] lr: 1.000e-03, eta: 3:00:49, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7377 2023-01-02 23:40:42,529 - mmcls - INFO - Epoch [76][1900/5005] lr: 1.000e-03, eta: 3:00:41, time: 0.084, data_time: 0.009, memory: 1125, loss: 1.7562 2023-01-02 23:40:51,390 - mmcls - INFO - Epoch [76][2000/5005] lr: 1.000e-03, eta: 3:00:32, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7376 2023-01-02 23:41:00,073 - mmcls - INFO - Epoch [76][2100/5005] lr: 1.000e-03, eta: 3:00:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7305 2023-01-02 23:41:09,183 - mmcls - INFO - Epoch [76][2200/5005] lr: 1.000e-03, eta: 3:00:14, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.7531 2023-01-02 23:41:18,392 - mmcls - INFO - Epoch [76][2300/5005] lr: 1.000e-03, eta: 3:00:06, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.7468 2023-01-02 23:41:27,104 - mmcls - INFO - Epoch [76][2400/5005] lr: 1.000e-03, eta: 2:59:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7766 2023-01-02 23:41:36,185 - mmcls - INFO - Epoch [76][2500/5005] lr: 1.000e-03, eta: 2:59:48, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.7470 2023-01-02 23:41:45,156 - mmcls - INFO - Epoch [76][2600/5005] lr: 1.000e-03, eta: 2:59:39, time: 0.090, data_time: 0.042, memory: 1125, loss: 1.7531 2023-01-02 23:41:53,723 - mmcls - INFO - Epoch [76][2700/5005] lr: 1.000e-03, eta: 2:59:30, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7517 2023-01-02 23:42:02,508 - mmcls - INFO - Epoch [76][2800/5005] lr: 1.000e-03, eta: 2:59:22, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7280 2023-01-02 23:42:11,346 - mmcls - INFO - Epoch [76][2900/5005] lr: 1.000e-03, eta: 2:59:13, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7625 2023-01-02 23:42:20,030 - mmcls - INFO - Epoch [76][3000/5005] lr: 1.000e-03, eta: 2:59:04, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7524 2023-01-02 23:42:28,675 - mmcls - INFO - Epoch [76][3100/5005] lr: 1.000e-03, eta: 2:58:55, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7647 2023-01-02 23:42:37,625 - mmcls - INFO - Epoch [76][3200/5005] lr: 1.000e-03, eta: 2:58:46, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7485 2023-01-02 23:42:46,549 - mmcls - INFO - Epoch [76][3300/5005] lr: 1.000e-03, eta: 2:58:38, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7216 2023-01-02 23:42:55,406 - mmcls - INFO - Epoch [76][3400/5005] lr: 1.000e-03, eta: 2:58:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7515 2023-01-02 23:43:04,158 - mmcls - INFO - Epoch [76][3500/5005] lr: 1.000e-03, eta: 2:58:20, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7587 2023-01-02 23:43:13,279 - mmcls - INFO - Epoch [76][3600/5005] lr: 1.000e-03, eta: 2:58:11, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.7652 2023-01-02 23:43:22,207 - mmcls - INFO - Epoch [76][3700/5005] lr: 1.000e-03, eta: 2:58:03, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.7646 2023-01-02 23:43:30,906 - mmcls - INFO - Epoch [76][3800/5005] lr: 1.000e-03, eta: 2:57:54, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7481 2023-01-02 23:43:39,692 - mmcls - INFO - Epoch [76][3900/5005] lr: 1.000e-03, eta: 2:57:45, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7589 2023-01-02 23:43:48,509 - mmcls - INFO - Epoch [76][4000/5005] lr: 1.000e-03, eta: 2:57:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7748 2023-01-02 23:43:57,347 - mmcls - INFO - Epoch [76][4100/5005] lr: 1.000e-03, eta: 2:57:27, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7592 2023-01-02 23:44:06,032 - mmcls - INFO - Epoch [76][4200/5005] lr: 1.000e-03, eta: 2:57:19, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7617 2023-01-02 23:44:14,451 - mmcls - INFO - Epoch [76][4300/5005] lr: 1.000e-03, eta: 2:57:10, time: 0.084, data_time: 0.017, memory: 1125, loss: 1.7606 2023-01-02 23:44:23,051 - mmcls - INFO - Epoch [76][4400/5005] lr: 1.000e-03, eta: 2:57:01, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7851 2023-01-02 23:44:31,867 - mmcls - INFO - Epoch [76][4500/5005] lr: 1.000e-03, eta: 2:56:52, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7349 2023-01-02 23:44:40,649 - mmcls - INFO - Epoch [76][4600/5005] lr: 1.000e-03, eta: 2:56:43, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7707 2023-01-02 23:44:49,181 - mmcls - INFO - Epoch [76][4700/5005] lr: 1.000e-03, eta: 2:56:34, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7568 2023-01-02 23:44:58,476 - mmcls - INFO - Epoch [76][4800/5005] lr: 1.000e-03, eta: 2:56:26, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7649 2023-01-02 23:45:07,487 - mmcls - INFO - Epoch [76][4900/5005] lr: 1.000e-03, eta: 2:56:17, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7821 2023-01-02 23:45:16,233 - mmcls - INFO - Epoch [76][5000/5005] lr: 1.000e-03, eta: 2:56:08, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7532 2023-01-02 23:45:38,854 - mmcls - INFO - Epoch(val) [76][196] accuracy_top-1: 62.9720, accuracy_top-5: 84.0080 2023-01-02 23:45:49,881 - mmcls - INFO - Epoch [77][100/5005] lr: 1.000e-03, eta: 2:55:59, time: 0.110, data_time: 0.047, memory: 1125, loss: 1.7383 2023-01-02 23:45:58,776 - mmcls - INFO - Epoch [77][200/5005] lr: 1.000e-03, eta: 2:55:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7478 2023-01-02 23:46:07,468 - mmcls - INFO - Epoch [77][300/5005] lr: 1.000e-03, eta: 2:55:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7306 2023-01-02 23:46:16,519 - mmcls - INFO - Epoch [77][400/5005] lr: 1.000e-03, eta: 2:55:33, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7387 2023-01-02 23:46:25,161 - mmcls - INFO - Epoch [77][500/5005] lr: 1.000e-03, eta: 2:55:24, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7211 2023-01-02 23:46:33,955 - mmcls - INFO - Epoch [77][600/5005] lr: 1.000e-03, eta: 2:55:15, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7674 2023-01-02 23:46:42,765 - mmcls - INFO - Epoch [77][700/5005] lr: 1.000e-03, eta: 2:55:07, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7549 2023-01-02 23:46:51,607 - mmcls - INFO - Epoch [77][800/5005] lr: 1.000e-03, eta: 2:54:58, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7678 2023-01-02 23:47:00,361 - mmcls - INFO - Epoch [77][900/5005] lr: 1.000e-03, eta: 2:54:49, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7592 2023-01-02 23:47:08,908 - mmcls - INFO - Epoch [77][1000/5005] lr: 1.000e-03, eta: 2:54:40, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7413 2023-01-02 23:47:17,881 - mmcls - INFO - Epoch [77][1100/5005] lr: 1.000e-03, eta: 2:54:31, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7363 2023-01-02 23:47:26,581 - mmcls - INFO - Epoch [77][1200/5005] lr: 1.000e-03, eta: 2:54:23, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7583 2023-01-02 23:47:35,377 - mmcls - INFO - Epoch [77][1300/5005] lr: 1.000e-03, eta: 2:54:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7492 2023-01-02 23:47:44,249 - mmcls - INFO - Epoch [77][1400/5005] lr: 1.000e-03, eta: 2:54:05, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.7449 2023-01-02 23:47:52,633 - mmcls - INFO - Epoch [77][1500/5005] lr: 1.000e-03, eta: 2:53:56, time: 0.084, data_time: 0.012, memory: 1125, loss: 1.7373 2023-01-02 23:48:01,171 - mmcls - INFO - Epoch [77][1600/5005] lr: 1.000e-03, eta: 2:53:47, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7533 2023-01-02 23:48:09,624 - mmcls - INFO - Epoch [77][1700/5005] lr: 1.000e-03, eta: 2:53:38, time: 0.084, data_time: 0.016, memory: 1125, loss: 1.7521 2023-01-02 23:48:18,244 - mmcls - INFO - Epoch [77][1800/5005] lr: 1.000e-03, eta: 2:53:29, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7704 2023-01-02 23:48:26,772 - mmcls - INFO - Epoch [77][1900/5005] lr: 1.000e-03, eta: 2:53:21, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7631 2023-01-02 23:48:35,595 - mmcls - INFO - Epoch [77][2000/5005] lr: 1.000e-03, eta: 2:53:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7460 2023-01-02 23:48:44,112 - mmcls - INFO - Epoch [77][2100/5005] lr: 1.000e-03, eta: 2:53:03, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7466 2023-01-02 23:48:52,913 - mmcls - INFO - Epoch [77][2200/5005] lr: 1.000e-03, eta: 2:52:54, time: 0.088, data_time: 0.040, memory: 1125, loss: 1.7690 2023-01-02 23:49:01,546 - mmcls - INFO - Epoch [77][2300/5005] lr: 1.000e-03, eta: 2:52:45, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7404 2023-01-02 23:49:10,353 - mmcls - INFO - Epoch [77][2400/5005] lr: 1.000e-03, eta: 2:52:36, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7522 2023-01-02 23:49:19,182 - mmcls - INFO - Epoch [77][2500/5005] lr: 1.000e-03, eta: 2:52:28, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7486 2023-01-02 23:49:27,777 - mmcls - INFO - Epoch [77][2600/5005] lr: 1.000e-03, eta: 2:52:19, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.7372 2023-01-02 23:49:36,443 - mmcls - INFO - Epoch [77][2700/5005] lr: 1.000e-03, eta: 2:52:10, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7520 2023-01-02 23:49:45,103 - mmcls - INFO - Epoch [77][2800/5005] lr: 1.000e-03, eta: 2:52:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7461 2023-01-02 23:49:53,849 - mmcls - INFO - Epoch [77][2900/5005] lr: 1.000e-03, eta: 2:51:52, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7608 2023-01-02 23:50:02,566 - mmcls - INFO - Epoch [77][3000/5005] lr: 1.000e-03, eta: 2:51:44, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7512 2023-01-02 23:50:11,227 - mmcls - INFO - Epoch [77][3100/5005] lr: 1.000e-03, eta: 2:51:35, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7572 2023-01-02 23:50:20,233 - mmcls - INFO - Epoch [77][3200/5005] lr: 1.000e-03, eta: 2:51:26, time: 0.090, data_time: 0.020, memory: 1125, loss: 1.7637 2023-01-02 23:50:29,055 - mmcls - INFO - Epoch [77][3300/5005] lr: 1.000e-03, eta: 2:51:17, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.7483 2023-01-02 23:50:37,826 - mmcls - INFO - Epoch [77][3400/5005] lr: 1.000e-03, eta: 2:51:08, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.7593 2023-01-02 23:50:46,695 - mmcls - INFO - Epoch [77][3500/5005] lr: 1.000e-03, eta: 2:51:00, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7452 2023-01-02 23:50:55,268 - mmcls - INFO - Epoch [77][3600/5005] lr: 1.000e-03, eta: 2:50:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7458 2023-01-02 23:51:04,058 - mmcls - INFO - Epoch [77][3700/5005] lr: 1.000e-03, eta: 2:50:42, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7621 2023-01-02 23:51:12,736 - mmcls - INFO - Epoch [77][3800/5005] lr: 1.000e-03, eta: 2:50:33, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7643 2023-01-02 23:51:21,383 - mmcls - INFO - Epoch [77][3900/5005] lr: 1.000e-03, eta: 2:50:24, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7654 2023-01-02 23:51:30,478 - mmcls - INFO - Epoch [77][4000/5005] lr: 1.000e-03, eta: 2:50:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7430 2023-01-02 23:51:39,371 - mmcls - INFO - Epoch [77][4100/5005] lr: 1.000e-03, eta: 2:50:07, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7655 2023-01-02 23:51:47,949 - mmcls - INFO - Epoch [77][4200/5005] lr: 1.000e-03, eta: 2:49:58, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7508 2023-01-02 23:51:56,848 - mmcls - INFO - Epoch [77][4300/5005] lr: 1.000e-03, eta: 2:49:49, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7452 2023-01-02 23:52:05,789 - mmcls - INFO - Epoch [77][4400/5005] lr: 1.000e-03, eta: 2:49:40, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7439 2023-01-02 23:52:14,367 - mmcls - INFO - Epoch [77][4500/5005] lr: 1.000e-03, eta: 2:49:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7711 2023-01-02 23:52:23,099 - mmcls - INFO - Epoch [77][4600/5005] lr: 1.000e-03, eta: 2:49:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7563 2023-01-02 23:52:31,881 - mmcls - INFO - Epoch [77][4700/5005] lr: 1.000e-03, eta: 2:49:14, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7487 2023-01-02 23:52:40,588 - mmcls - INFO - Epoch [77][4800/5005] lr: 1.000e-03, eta: 2:49:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7834 2023-01-02 23:52:49,544 - mmcls - INFO - Epoch [77][4900/5005] lr: 1.000e-03, eta: 2:48:56, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7790 2023-01-02 23:52:58,517 - mmcls - INFO - Epoch [77][5000/5005] lr: 1.000e-03, eta: 2:48:48, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7687 2023-01-02 23:53:21,218 - mmcls - INFO - Epoch(val) [77][196] accuracy_top-1: 63.0620, accuracy_top-5: 84.0200 2023-01-02 23:53:31,956 - mmcls - INFO - Epoch [78][100/5005] lr: 1.000e-03, eta: 2:48:39, time: 0.107, data_time: 0.036, memory: 1125, loss: 1.7646 2023-01-02 23:53:40,678 - mmcls - INFO - Epoch [78][200/5005] lr: 1.000e-03, eta: 2:48:30, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7272 2023-01-02 23:53:49,511 - mmcls - INFO - Epoch [78][300/5005] lr: 1.000e-03, eta: 2:48:21, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7302 2023-01-02 23:53:58,127 - mmcls - INFO - Epoch [78][400/5005] lr: 1.000e-03, eta: 2:48:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7427 2023-01-02 23:54:06,708 - mmcls - INFO - Epoch [78][500/5005] lr: 1.000e-03, eta: 2:48:03, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7714 2023-01-02 23:54:15,520 - mmcls - INFO - Epoch [78][600/5005] lr: 1.000e-03, eta: 2:47:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7399 2023-01-02 23:54:24,271 - mmcls - INFO - Epoch [78][700/5005] lr: 1.000e-03, eta: 2:47:46, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7433 2023-01-02 23:54:32,915 - mmcls - INFO - Epoch [78][800/5005] lr: 1.000e-03, eta: 2:47:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7578 2023-01-02 23:54:41,570 - mmcls - INFO - Epoch [78][900/5005] lr: 1.000e-03, eta: 2:47:28, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7600 2023-01-02 23:54:50,068 - mmcls - INFO - Epoch [78][1000/5005] lr: 1.000e-03, eta: 2:47:19, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7368 2023-01-02 23:54:58,773 - mmcls - INFO - Epoch [78][1100/5005] lr: 1.000e-03, eta: 2:47:10, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7431 2023-01-02 23:55:07,415 - mmcls - INFO - Epoch [78][1200/5005] lr: 1.000e-03, eta: 2:47:02, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7176 2023-01-02 23:55:16,544 - mmcls - INFO - Epoch [78][1300/5005] lr: 1.000e-03, eta: 2:46:53, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7579 2023-01-02 23:55:25,476 - mmcls - INFO - Epoch [78][1400/5005] lr: 1.000e-03, eta: 2:46:44, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7499 2023-01-02 23:55:34,040 - mmcls - INFO - Epoch [78][1500/5005] lr: 1.000e-03, eta: 2:46:35, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7423 2023-01-02 23:55:42,778 - mmcls - INFO - Epoch [78][1600/5005] lr: 1.000e-03, eta: 2:46:26, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7470 2023-01-02 23:55:51,863 - mmcls - INFO - Epoch [78][1700/5005] lr: 1.000e-03, eta: 2:46:18, time: 0.091, data_time: 0.021, memory: 1125, loss: 1.7379 2023-01-02 23:56:00,797 - mmcls - INFO - Epoch [78][1800/5005] lr: 1.000e-03, eta: 2:46:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7468 2023-01-02 23:56:09,531 - mmcls - INFO - Epoch [78][1900/5005] lr: 1.000e-03, eta: 2:46:00, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7450 2023-01-02 23:56:18,209 - mmcls - INFO - Epoch [78][2000/5005] lr: 1.000e-03, eta: 2:45:51, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7496 2023-01-02 23:56:27,050 - mmcls - INFO - Epoch [78][2100/5005] lr: 1.000e-03, eta: 2:45:43, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7300 2023-01-02 23:56:36,017 - mmcls - INFO - Epoch [78][2200/5005] lr: 1.000e-03, eta: 2:45:34, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7453 2023-01-02 23:56:44,854 - mmcls - INFO - Epoch [78][2300/5005] lr: 1.000e-03, eta: 2:45:25, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7455 2023-01-02 23:56:54,107 - mmcls - INFO - Epoch [78][2400/5005] lr: 1.000e-03, eta: 2:45:16, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7272 2023-01-02 23:57:02,584 - mmcls - INFO - Epoch [78][2500/5005] lr: 1.000e-03, eta: 2:45:07, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7569 2023-01-02 23:57:11,326 - mmcls - INFO - Epoch [78][2600/5005] lr: 1.000e-03, eta: 2:44:59, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7505 2023-01-02 23:57:20,343 - mmcls - INFO - Epoch [78][2700/5005] lr: 1.000e-03, eta: 2:44:50, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7434 2023-01-02 23:57:28,868 - mmcls - INFO - Epoch [78][2800/5005] lr: 1.000e-03, eta: 2:44:41, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7732 2023-01-02 23:57:37,698 - mmcls - INFO - Epoch [78][2900/5005] lr: 1.000e-03, eta: 2:44:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7519 2023-01-02 23:57:46,661 - mmcls - INFO - Epoch [78][3000/5005] lr: 1.000e-03, eta: 2:44:23, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7387 2023-01-02 23:57:55,392 - mmcls - INFO - Epoch [78][3100/5005] lr: 1.000e-03, eta: 2:44:15, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7385 2023-01-02 23:58:04,555 - mmcls - INFO - Epoch [78][3200/5005] lr: 1.000e-03, eta: 2:44:06, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7464 2023-01-02 23:58:13,383 - mmcls - INFO - Epoch [78][3300/5005] lr: 1.000e-03, eta: 2:43:57, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7337 2023-01-02 23:58:22,026 - mmcls - INFO - Epoch [78][3400/5005] lr: 1.000e-03, eta: 2:43:48, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7427 2023-01-02 23:58:30,698 - mmcls - INFO - Epoch [78][3500/5005] lr: 1.000e-03, eta: 2:43:39, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7438 2023-01-02 23:58:39,689 - mmcls - INFO - Epoch [78][3600/5005] lr: 1.000e-03, eta: 2:43:31, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7567 2023-01-02 23:58:48,512 - mmcls - INFO - Epoch [78][3700/5005] lr: 1.000e-03, eta: 2:43:22, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.7548 2023-01-02 23:58:57,147 - mmcls - INFO - Epoch [78][3800/5005] lr: 1.000e-03, eta: 2:43:13, time: 0.086, data_time: 0.034, memory: 1125, loss: 1.7697 2023-01-02 23:59:06,091 - mmcls - INFO - Epoch [78][3900/5005] lr: 1.000e-03, eta: 2:43:04, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.7527 2023-01-02 23:59:14,602 - mmcls - INFO - Epoch [78][4000/5005] lr: 1.000e-03, eta: 2:42:55, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7532 2023-01-02 23:59:23,228 - mmcls - INFO - Epoch [78][4100/5005] lr: 1.000e-03, eta: 2:42:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7305 2023-01-02 23:59:32,294 - mmcls - INFO - Epoch [78][4200/5005] lr: 1.000e-03, eta: 2:42:38, time: 0.091, data_time: 0.023, memory: 1125, loss: 1.7571 2023-01-02 23:59:41,170 - mmcls - INFO - Epoch [78][4300/5005] lr: 1.000e-03, eta: 2:42:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7739 2023-01-02 23:59:50,052 - mmcls - INFO - Epoch [78][4400/5005] lr: 1.000e-03, eta: 2:42:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7605 2023-01-02 23:59:58,653 - mmcls - INFO - Epoch [78][4500/5005] lr: 1.000e-03, eta: 2:42:12, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7714 2023-01-03 00:00:07,485 - mmcls - INFO - Epoch [78][4600/5005] lr: 1.000e-03, eta: 2:42:03, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7547 2023-01-03 00:00:16,112 - mmcls - INFO - Epoch [78][4700/5005] lr: 1.000e-03, eta: 2:41:54, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7462 2023-01-03 00:00:24,726 - mmcls - INFO - Epoch [78][4800/5005] lr: 1.000e-03, eta: 2:41:45, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7486 2023-01-03 00:00:33,437 - mmcls - INFO - Epoch [78][4900/5005] lr: 1.000e-03, eta: 2:41:36, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7555 2023-01-03 00:00:42,524 - mmcls - INFO - Epoch [78][5000/5005] lr: 1.000e-03, eta: 2:41:27, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7564 2023-01-03 00:01:05,081 - mmcls - INFO - Epoch(val) [78][196] accuracy_top-1: 63.2260, accuracy_top-5: 83.9840 2023-01-03 00:01:16,184 - mmcls - INFO - Epoch [79][100/5005] lr: 1.000e-03, eta: 2:41:19, time: 0.111, data_time: 0.041, memory: 1125, loss: 1.7701 2023-01-03 00:01:25,058 - mmcls - INFO - Epoch [79][200/5005] lr: 1.000e-03, eta: 2:41:10, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7443 2023-01-03 00:01:33,809 - mmcls - INFO - Epoch [79][300/5005] lr: 1.000e-03, eta: 2:41:01, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7266 2023-01-03 00:01:42,502 - mmcls - INFO - Epoch [79][400/5005] lr: 1.000e-03, eta: 2:40:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7104 2023-01-03 00:01:51,560 - mmcls - INFO - Epoch [79][500/5005] lr: 1.000e-03, eta: 2:40:44, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7482 2023-01-03 00:02:00,261 - mmcls - INFO - Epoch [79][600/5005] lr: 1.000e-03, eta: 2:40:35, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7327 2023-01-03 00:02:09,076 - mmcls - INFO - Epoch [79][700/5005] lr: 1.000e-03, eta: 2:40:26, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7371 2023-01-03 00:02:17,765 - mmcls - INFO - Epoch [79][800/5005] lr: 1.000e-03, eta: 2:40:17, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7416 2023-01-03 00:02:26,623 - mmcls - INFO - Epoch [79][900/5005] lr: 1.000e-03, eta: 2:40:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7328 2023-01-03 00:02:35,166 - mmcls - INFO - Epoch [79][1000/5005] lr: 1.000e-03, eta: 2:40:00, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7510 2023-01-03 00:02:44,011 - mmcls - INFO - Epoch [79][1100/5005] lr: 1.000e-03, eta: 2:39:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7680 2023-01-03 00:02:52,686 - mmcls - INFO - Epoch [79][1200/5005] lr: 1.000e-03, eta: 2:39:42, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7630 2023-01-03 00:03:01,564 - mmcls - INFO - Epoch [79][1300/5005] lr: 1.000e-03, eta: 2:39:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7406 2023-01-03 00:03:10,590 - mmcls - INFO - Epoch [79][1400/5005] lr: 1.000e-03, eta: 2:39:24, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7422 2023-01-03 00:03:19,090 - mmcls - INFO - Epoch [79][1500/5005] lr: 1.000e-03, eta: 2:39:16, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7360 2023-01-03 00:03:27,699 - mmcls - INFO - Epoch [79][1600/5005] lr: 1.000e-03, eta: 2:39:07, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7649 2023-01-03 00:03:36,637 - mmcls - INFO - Epoch [79][1700/5005] lr: 1.000e-03, eta: 2:38:58, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7502 2023-01-03 00:03:45,195 - mmcls - INFO - Epoch [79][1800/5005] lr: 1.000e-03, eta: 2:38:49, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7608 2023-01-03 00:03:53,851 - mmcls - INFO - Epoch [79][1900/5005] lr: 1.000e-03, eta: 2:38:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7382 2023-01-03 00:04:02,558 - mmcls - INFO - Epoch [79][2000/5005] lr: 1.000e-03, eta: 2:38:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7382 2023-01-03 00:04:11,184 - mmcls - INFO - Epoch [79][2100/5005] lr: 1.000e-03, eta: 2:38:23, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7658 2023-01-03 00:04:19,831 - mmcls - INFO - Epoch [79][2200/5005] lr: 1.000e-03, eta: 2:38:14, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.7484 2023-01-03 00:04:28,520 - mmcls - INFO - Epoch [79][2300/5005] lr: 1.000e-03, eta: 2:38:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7464 2023-01-03 00:04:37,324 - mmcls - INFO - Epoch [79][2400/5005] lr: 1.000e-03, eta: 2:37:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7543 2023-01-03 00:04:46,005 - mmcls - INFO - Epoch [79][2500/5005] lr: 1.000e-03, eta: 2:37:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7426 2023-01-03 00:04:54,720 - mmcls - INFO - Epoch [79][2600/5005] lr: 1.000e-03, eta: 2:37:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7423 2023-01-03 00:05:03,599 - mmcls - INFO - Epoch [79][2700/5005] lr: 1.000e-03, eta: 2:37:30, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7296 2023-01-03 00:05:12,191 - mmcls - INFO - Epoch [79][2800/5005] lr: 1.000e-03, eta: 2:37:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7513 2023-01-03 00:05:20,990 - mmcls - INFO - Epoch [79][2900/5005] lr: 1.000e-03, eta: 2:37:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7635 2023-01-03 00:05:29,709 - mmcls - INFO - Epoch [79][3000/5005] lr: 1.000e-03, eta: 2:37:03, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7448 2023-01-03 00:05:38,286 - mmcls - INFO - Epoch [79][3100/5005] lr: 1.000e-03, eta: 2:36:54, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7394 2023-01-03 00:05:47,272 - mmcls - INFO - Epoch [79][3200/5005] lr: 1.000e-03, eta: 2:36:46, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7603 2023-01-03 00:05:55,832 - mmcls - INFO - Epoch [79][3300/5005] lr: 1.000e-03, eta: 2:36:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7587 2023-01-03 00:06:04,684 - mmcls - INFO - Epoch [79][3400/5005] lr: 1.000e-03, eta: 2:36:28, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7547 2023-01-03 00:06:13,431 - mmcls - INFO - Epoch [79][3500/5005] lr: 1.000e-03, eta: 2:36:19, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7576 2023-01-03 00:06:21,811 - mmcls - INFO - Epoch [79][3600/5005] lr: 1.000e-03, eta: 2:36:10, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7522 2023-01-03 00:06:30,986 - mmcls - INFO - Epoch [79][3700/5005] lr: 1.000e-03, eta: 2:36:01, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7591 2023-01-03 00:06:39,889 - mmcls - INFO - Epoch [79][3800/5005] lr: 1.000e-03, eta: 2:35:53, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7318 2023-01-03 00:06:48,519 - mmcls - INFO - Epoch [79][3900/5005] lr: 1.000e-03, eta: 2:35:44, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7447 2023-01-03 00:06:57,145 - mmcls - INFO - Epoch [79][4000/5005] lr: 1.000e-03, eta: 2:35:35, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.7591 2023-01-03 00:07:05,721 - mmcls - INFO - Epoch [79][4100/5005] lr: 1.000e-03, eta: 2:35:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7472 2023-01-03 00:07:14,428 - mmcls - INFO - Epoch [79][4200/5005] lr: 1.000e-03, eta: 2:35:17, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7434 2023-01-03 00:07:23,380 - mmcls - INFO - Epoch [79][4300/5005] lr: 1.000e-03, eta: 2:35:09, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.7372 2023-01-03 00:07:31,961 - mmcls - INFO - Epoch [79][4400/5005] lr: 1.000e-03, eta: 2:35:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7374 2023-01-03 00:07:41,138 - mmcls - INFO - Epoch [79][4500/5005] lr: 1.000e-03, eta: 2:34:51, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7389 2023-01-03 00:07:49,810 - mmcls - INFO - Epoch [79][4600/5005] lr: 1.000e-03, eta: 2:34:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7690 2023-01-03 00:07:58,503 - mmcls - INFO - Epoch [79][4700/5005] lr: 1.000e-03, eta: 2:34:33, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7490 2023-01-03 00:08:07,254 - mmcls - INFO - Epoch [79][4800/5005] lr: 1.000e-03, eta: 2:34:25, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7448 2023-01-03 00:08:15,920 - mmcls - INFO - Epoch [79][4900/5005] lr: 1.000e-03, eta: 2:34:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7676 2023-01-03 00:08:24,756 - mmcls - INFO - Epoch [79][5000/5005] lr: 1.000e-03, eta: 2:34:07, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7508 2023-01-03 00:08:47,271 - mmcls - INFO - Epoch(val) [79][196] accuracy_top-1: 62.9860, accuracy_top-5: 84.0600 2023-01-03 00:08:58,078 - mmcls - INFO - Epoch [80][100/5005] lr: 1.000e-03, eta: 2:33:58, time: 0.108, data_time: 0.037, memory: 1125, loss: 1.7225 2023-01-03 00:09:06,590 - mmcls - INFO - Epoch [80][200/5005] lr: 1.000e-03, eta: 2:33:49, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7079 2023-01-03 00:09:15,148 - mmcls - INFO - Epoch [80][300/5005] lr: 1.000e-03, eta: 2:33:40, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7235 2023-01-03 00:09:23,778 - mmcls - INFO - Epoch [80][400/5005] lr: 1.000e-03, eta: 2:33:32, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.7348 2023-01-03 00:09:32,531 - mmcls - INFO - Epoch [80][500/5005] lr: 1.000e-03, eta: 2:33:23, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7417 2023-01-03 00:09:41,247 - mmcls - INFO - Epoch [80][600/5005] lr: 1.000e-03, eta: 2:33:14, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7374 2023-01-03 00:09:49,759 - mmcls - INFO - Epoch [80][700/5005] lr: 1.000e-03, eta: 2:33:05, time: 0.085, data_time: 0.042, memory: 1125, loss: 1.7381 2023-01-03 00:09:58,423 - mmcls - INFO - Epoch [80][800/5005] lr: 1.000e-03, eta: 2:32:56, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7384 2023-01-03 00:10:07,151 - mmcls - INFO - Epoch [80][900/5005] lr: 1.000e-03, eta: 2:32:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7485 2023-01-03 00:10:15,778 - mmcls - INFO - Epoch [80][1000/5005] lr: 1.000e-03, eta: 2:32:39, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.7119 2023-01-03 00:10:24,487 - mmcls - INFO - Epoch [80][1100/5005] lr: 1.000e-03, eta: 2:32:30, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7425 2023-01-03 00:10:32,866 - mmcls - INFO - Epoch [80][1200/5005] lr: 1.000e-03, eta: 2:32:21, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.7356 2023-01-03 00:10:41,647 - mmcls - INFO - Epoch [80][1300/5005] lr: 1.000e-03, eta: 2:32:12, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7383 2023-01-03 00:10:50,470 - mmcls - INFO - Epoch [80][1400/5005] lr: 1.000e-03, eta: 2:32:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7490 2023-01-03 00:10:59,326 - mmcls - INFO - Epoch [80][1500/5005] lr: 1.000e-03, eta: 2:31:54, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7250 2023-01-03 00:11:08,093 - mmcls - INFO - Epoch [80][1600/5005] lr: 1.000e-03, eta: 2:31:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7314 2023-01-03 00:11:16,932 - mmcls - INFO - Epoch [80][1700/5005] lr: 1.000e-03, eta: 2:31:37, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7253 2023-01-03 00:11:25,367 - mmcls - INFO - Epoch [80][1800/5005] lr: 1.000e-03, eta: 2:31:28, time: 0.084, data_time: 0.033, memory: 1125, loss: 1.7586 2023-01-03 00:11:34,583 - mmcls - INFO - Epoch [80][1900/5005] lr: 1.000e-03, eta: 2:31:19, time: 0.092, data_time: 0.022, memory: 1125, loss: 1.7397 2023-01-03 00:11:43,367 - mmcls - INFO - Epoch [80][2000/5005] lr: 1.000e-03, eta: 2:31:10, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7359 2023-01-03 00:11:52,597 - mmcls - INFO - Epoch [80][2100/5005] lr: 1.000e-03, eta: 2:31:02, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7608 2023-01-03 00:12:01,784 - mmcls - INFO - Epoch [80][2200/5005] lr: 1.000e-03, eta: 2:30:53, time: 0.092, data_time: 0.022, memory: 1125, loss: 1.7482 2023-01-03 00:12:10,400 - mmcls - INFO - Epoch [80][2300/5005] lr: 1.000e-03, eta: 2:30:44, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7699 2023-01-03 00:12:18,956 - mmcls - INFO - Epoch [80][2400/5005] lr: 1.000e-03, eta: 2:30:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7508 2023-01-03 00:12:27,531 - mmcls - INFO - Epoch [80][2500/5005] lr: 1.000e-03, eta: 2:30:27, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7634 2023-01-03 00:12:36,272 - mmcls - INFO - Epoch [80][2600/5005] lr: 1.000e-03, eta: 2:30:18, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7525 2023-01-03 00:12:44,865 - mmcls - INFO - Epoch [80][2700/5005] lr: 1.000e-03, eta: 2:30:09, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7315 2023-01-03 00:12:53,570 - mmcls - INFO - Epoch [80][2800/5005] lr: 1.000e-03, eta: 2:30:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7633 2023-01-03 00:13:02,387 - mmcls - INFO - Epoch [80][2900/5005] lr: 1.000e-03, eta: 2:29:51, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7510 2023-01-03 00:13:11,141 - mmcls - INFO - Epoch [80][3000/5005] lr: 1.000e-03, eta: 2:29:42, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7537 2023-01-03 00:13:19,914 - mmcls - INFO - Epoch [80][3100/5005] lr: 1.000e-03, eta: 2:29:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7597 2023-01-03 00:13:28,808 - mmcls - INFO - Epoch [80][3200/5005] lr: 1.000e-03, eta: 2:29:25, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7581 2023-01-03 00:13:37,612 - mmcls - INFO - Epoch [80][3300/5005] lr: 1.000e-03, eta: 2:29:16, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7505 2023-01-03 00:13:46,702 - mmcls - INFO - Epoch [80][3400/5005] lr: 1.000e-03, eta: 2:29:07, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7371 2023-01-03 00:13:55,690 - mmcls - INFO - Epoch [80][3500/5005] lr: 1.000e-03, eta: 2:28:59, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7579 2023-01-03 00:14:04,544 - mmcls - INFO - Epoch [80][3600/5005] lr: 1.000e-03, eta: 2:28:50, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7570 2023-01-03 00:14:13,591 - mmcls - INFO - Epoch [80][3700/5005] lr: 1.000e-03, eta: 2:28:41, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.7618 2023-01-03 00:14:22,218 - mmcls - INFO - Epoch [80][3800/5005] lr: 1.000e-03, eta: 2:28:32, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7563 2023-01-03 00:14:31,077 - mmcls - INFO - Epoch [80][3900/5005] lr: 1.000e-03, eta: 2:28:23, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7513 2023-01-03 00:14:39,645 - mmcls - INFO - Epoch [80][4000/5005] lr: 1.000e-03, eta: 2:28:15, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7534 2023-01-03 00:14:48,246 - mmcls - INFO - Epoch [80][4100/5005] lr: 1.000e-03, eta: 2:28:06, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7335 2023-01-03 00:14:56,948 - mmcls - INFO - Epoch [80][4200/5005] lr: 1.000e-03, eta: 2:27:57, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7509 2023-01-03 00:15:05,806 - mmcls - INFO - Epoch [80][4300/5005] lr: 1.000e-03, eta: 2:27:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7600 2023-01-03 00:15:14,594 - mmcls - INFO - Epoch [80][4400/5005] lr: 1.000e-03, eta: 2:27:39, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7366 2023-01-03 00:15:23,514 - mmcls - INFO - Epoch [80][4500/5005] lr: 1.000e-03, eta: 2:27:31, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7744 2023-01-03 00:15:32,129 - mmcls - INFO - Epoch [80][4600/5005] lr: 1.000e-03, eta: 2:27:22, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7189 2023-01-03 00:15:40,908 - mmcls - INFO - Epoch [80][4700/5005] lr: 1.000e-03, eta: 2:27:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7476 2023-01-03 00:15:49,695 - mmcls - INFO - Epoch [80][4800/5005] lr: 1.000e-03, eta: 2:27:04, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7494 2023-01-03 00:15:58,426 - mmcls - INFO - Epoch [80][4900/5005] lr: 1.000e-03, eta: 2:26:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7691 2023-01-03 00:16:07,527 - mmcls - INFO - Epoch [80][5000/5005] lr: 1.000e-03, eta: 2:26:47, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.7618 2023-01-03 00:16:30,128 - mmcls - INFO - Epoch(val) [80][196] accuracy_top-1: 63.1860, accuracy_top-5: 84.1320 2023-01-03 00:16:41,284 - mmcls - INFO - Epoch [81][100/5005] lr: 1.000e-03, eta: 2:26:38, time: 0.111, data_time: 0.045, memory: 1125, loss: 1.7145 2023-01-03 00:16:50,014 - mmcls - INFO - Epoch [81][200/5005] lr: 1.000e-03, eta: 2:26:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7159 2023-01-03 00:16:58,442 - mmcls - INFO - Epoch [81][300/5005] lr: 1.000e-03, eta: 2:26:20, time: 0.084, data_time: 0.012, memory: 1125, loss: 1.7319 2023-01-03 00:17:07,018 - mmcls - INFO - Epoch [81][400/5005] lr: 1.000e-03, eta: 2:26:11, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7198 2023-01-03 00:17:15,573 - mmcls - INFO - Epoch [81][500/5005] lr: 1.000e-03, eta: 2:26:02, time: 0.085, data_time: 0.031, memory: 1125, loss: 1.7145 2023-01-03 00:17:24,494 - mmcls - INFO - Epoch [81][600/5005] lr: 1.000e-03, eta: 2:25:54, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7558 2023-01-03 00:17:33,241 - mmcls - INFO - Epoch [81][700/5005] lr: 1.000e-03, eta: 2:25:45, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7160 2023-01-03 00:17:41,870 - mmcls - INFO - Epoch [81][800/5005] lr: 1.000e-03, eta: 2:25:36, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7394 2023-01-03 00:17:50,401 - mmcls - INFO - Epoch [81][900/5005] lr: 1.000e-03, eta: 2:25:27, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7362 2023-01-03 00:17:59,629 - mmcls - INFO - Epoch [81][1000/5005] lr: 1.000e-03, eta: 2:25:18, time: 0.092, data_time: 0.008, memory: 1125, loss: 1.7333 2023-01-03 00:18:08,060 - mmcls - INFO - Epoch [81][1100/5005] lr: 1.000e-03, eta: 2:25:10, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7325 2023-01-03 00:18:16,677 - mmcls - INFO - Epoch [81][1200/5005] lr: 1.000e-03, eta: 2:25:01, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7505 2023-01-03 00:18:25,636 - mmcls - INFO - Epoch [81][1300/5005] lr: 1.000e-03, eta: 2:24:52, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7388 2023-01-03 00:18:34,275 - mmcls - INFO - Epoch [81][1400/5005] lr: 1.000e-03, eta: 2:24:43, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7320 2023-01-03 00:18:42,986 - mmcls - INFO - Epoch [81][1500/5005] lr: 1.000e-03, eta: 2:24:34, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7369 2023-01-03 00:18:51,970 - mmcls - INFO - Epoch [81][1600/5005] lr: 1.000e-03, eta: 2:24:26, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7701 2023-01-03 00:19:00,555 - mmcls - INFO - Epoch [81][1700/5005] lr: 1.000e-03, eta: 2:24:17, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7442 2023-01-03 00:19:09,471 - mmcls - INFO - Epoch [81][1800/5005] lr: 1.000e-03, eta: 2:24:08, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7378 2023-01-03 00:19:18,123 - mmcls - INFO - Epoch [81][1900/5005] lr: 1.000e-03, eta: 2:23:59, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7479 2023-01-03 00:19:27,129 - mmcls - INFO - Epoch [81][2000/5005] lr: 1.000e-03, eta: 2:23:50, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7442 2023-01-03 00:19:35,789 - mmcls - INFO - Epoch [81][2100/5005] lr: 1.000e-03, eta: 2:23:41, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.7655 2023-01-03 00:19:44,682 - mmcls - INFO - Epoch [81][2200/5005] lr: 1.000e-03, eta: 2:23:33, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7522 2023-01-03 00:19:53,234 - mmcls - INFO - Epoch [81][2300/5005] lr: 1.000e-03, eta: 2:23:24, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7572 2023-01-03 00:20:02,015 - mmcls - INFO - Epoch [81][2400/5005] lr: 1.000e-03, eta: 2:23:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7511 2023-01-03 00:20:10,669 - mmcls - INFO - Epoch [81][2500/5005] lr: 1.000e-03, eta: 2:23:06, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7349 2023-01-03 00:20:19,454 - mmcls - INFO - Epoch [81][2600/5005] lr: 1.000e-03, eta: 2:22:57, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7154 2023-01-03 00:20:28,268 - mmcls - INFO - Epoch [81][2700/5005] lr: 1.000e-03, eta: 2:22:49, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7390 2023-01-03 00:20:36,645 - mmcls - INFO - Epoch [81][2800/5005] lr: 1.000e-03, eta: 2:22:40, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7447 2023-01-03 00:20:45,339 - mmcls - INFO - Epoch [81][2900/5005] lr: 1.000e-03, eta: 2:22:31, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7278 2023-01-03 00:20:54,297 - mmcls - INFO - Epoch [81][3000/5005] lr: 1.000e-03, eta: 2:22:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7362 2023-01-03 00:21:03,070 - mmcls - INFO - Epoch [81][3100/5005] lr: 1.000e-03, eta: 2:22:13, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7437 2023-01-03 00:21:11,723 - mmcls - INFO - Epoch [81][3200/5005] lr: 1.000e-03, eta: 2:22:05, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7507 2023-01-03 00:21:20,576 - mmcls - INFO - Epoch [81][3300/5005] lr: 1.000e-03, eta: 2:21:56, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7526 2023-01-03 00:21:29,660 - mmcls - INFO - Epoch [81][3400/5005] lr: 1.000e-03, eta: 2:21:47, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.7477 2023-01-03 00:21:38,269 - mmcls - INFO - Epoch [81][3500/5005] lr: 1.000e-03, eta: 2:21:38, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7374 2023-01-03 00:21:46,851 - mmcls - INFO - Epoch [81][3600/5005] lr: 1.000e-03, eta: 2:21:29, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7253 2023-01-03 00:21:55,696 - mmcls - INFO - Epoch [81][3700/5005] lr: 1.000e-03, eta: 2:21:21, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7460 2023-01-03 00:22:04,337 - mmcls - INFO - Epoch [81][3800/5005] lr: 1.000e-03, eta: 2:21:12, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7468 2023-01-03 00:22:13,151 - mmcls - INFO - Epoch [81][3900/5005] lr: 1.000e-03, eta: 2:21:03, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7551 2023-01-03 00:22:22,079 - mmcls - INFO - Epoch [81][4000/5005] lr: 1.000e-03, eta: 2:20:54, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7439 2023-01-03 00:22:30,795 - mmcls - INFO - Epoch [81][4100/5005] lr: 1.000e-03, eta: 2:20:45, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7511 2023-01-03 00:22:39,469 - mmcls - INFO - Epoch [81][4200/5005] lr: 1.000e-03, eta: 2:20:36, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7462 2023-01-03 00:22:48,002 - mmcls - INFO - Epoch [81][4300/5005] lr: 1.000e-03, eta: 2:20:28, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7470 2023-01-03 00:22:56,910 - mmcls - INFO - Epoch [81][4400/5005] lr: 1.000e-03, eta: 2:20:19, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7798 2023-01-03 00:23:05,585 - mmcls - INFO - Epoch [81][4500/5005] lr: 1.000e-03, eta: 2:20:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7560 2023-01-03 00:23:14,448 - mmcls - INFO - Epoch [81][4600/5005] lr: 1.000e-03, eta: 2:20:01, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7688 2023-01-03 00:23:23,117 - mmcls - INFO - Epoch [81][4700/5005] lr: 1.000e-03, eta: 2:19:52, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7477 2023-01-03 00:23:32,094 - mmcls - INFO - Epoch [81][4800/5005] lr: 1.000e-03, eta: 2:19:44, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.7476 2023-01-03 00:23:40,671 - mmcls - INFO - Epoch [81][4900/5005] lr: 1.000e-03, eta: 2:19:35, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7515 2023-01-03 00:23:49,592 - mmcls - INFO - Epoch [81][5000/5005] lr: 1.000e-03, eta: 2:19:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7343 2023-01-03 00:24:12,398 - mmcls - INFO - Epoch(val) [81][196] accuracy_top-1: 63.0440, accuracy_top-5: 83.9820 2023-01-03 00:24:23,254 - mmcls - INFO - Epoch [82][100/5005] lr: 1.000e-03, eta: 2:19:17, time: 0.108, data_time: 0.051, memory: 1125, loss: 1.7414 2023-01-03 00:24:32,128 - mmcls - INFO - Epoch [82][200/5005] lr: 1.000e-03, eta: 2:19:08, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7385 2023-01-03 00:24:41,286 - mmcls - INFO - Epoch [82][300/5005] lr: 1.000e-03, eta: 2:19:00, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7606 2023-01-03 00:24:49,834 - mmcls - INFO - Epoch [82][400/5005] lr: 1.000e-03, eta: 2:18:51, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7288 2023-01-03 00:24:58,668 - mmcls - INFO - Epoch [82][500/5005] lr: 1.000e-03, eta: 2:18:42, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7429 2023-01-03 00:25:07,535 - mmcls - INFO - Epoch [82][600/5005] lr: 1.000e-03, eta: 2:18:33, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7300 2023-01-03 00:25:16,114 - mmcls - INFO - Epoch [82][700/5005] lr: 1.000e-03, eta: 2:18:24, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7190 2023-01-03 00:25:25,423 - mmcls - INFO - Epoch [82][800/5005] lr: 1.000e-03, eta: 2:18:16, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7190 2023-01-03 00:25:34,315 - mmcls - INFO - Epoch [82][900/5005] lr: 1.000e-03, eta: 2:18:07, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7478 2023-01-03 00:25:42,992 - mmcls - INFO - Epoch [82][1000/5005] lr: 1.000e-03, eta: 2:17:58, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7067 2023-01-03 00:25:51,432 - mmcls - INFO - Epoch [82][1100/5005] lr: 1.000e-03, eta: 2:17:49, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.7315 2023-01-03 00:26:00,331 - mmcls - INFO - Epoch [82][1200/5005] lr: 1.000e-03, eta: 2:17:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7437 2023-01-03 00:26:09,299 - mmcls - INFO - Epoch [82][1300/5005] lr: 1.000e-03, eta: 2:17:32, time: 0.090, data_time: 0.020, memory: 1125, loss: 1.7295 2023-01-03 00:26:17,826 - mmcls - INFO - Epoch [82][1400/5005] lr: 1.000e-03, eta: 2:17:23, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.7222 2023-01-03 00:26:26,750 - mmcls - INFO - Epoch [82][1500/5005] lr: 1.000e-03, eta: 2:17:14, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7435 2023-01-03 00:26:35,496 - mmcls - INFO - Epoch [82][1600/5005] lr: 1.000e-03, eta: 2:17:05, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7590 2023-01-03 00:26:44,039 - mmcls - INFO - Epoch [82][1700/5005] lr: 1.000e-03, eta: 2:16:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7430 2023-01-03 00:26:52,737 - mmcls - INFO - Epoch [82][1800/5005] lr: 1.000e-03, eta: 2:16:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7390 2023-01-03 00:27:01,399 - mmcls - INFO - Epoch [82][1900/5005] lr: 1.000e-03, eta: 2:16:39, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7444 2023-01-03 00:27:10,080 - mmcls - INFO - Epoch [82][2000/5005] lr: 1.000e-03, eta: 2:16:30, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7472 2023-01-03 00:27:18,720 - mmcls - INFO - Epoch [82][2100/5005] lr: 1.000e-03, eta: 2:16:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7213 2023-01-03 00:27:27,346 - mmcls - INFO - Epoch [82][2200/5005] lr: 1.000e-03, eta: 2:16:12, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7261 2023-01-03 00:27:36,000 - mmcls - INFO - Epoch [82][2300/5005] lr: 1.000e-03, eta: 2:16:03, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7383 2023-01-03 00:27:44,850 - mmcls - INFO - Epoch [82][2400/5005] lr: 1.000e-03, eta: 2:15:55, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7457 2023-01-03 00:27:53,475 - mmcls - INFO - Epoch [82][2500/5005] lr: 1.000e-03, eta: 2:15:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7537 2023-01-03 00:28:02,265 - mmcls - INFO - Epoch [82][2600/5005] lr: 1.000e-03, eta: 2:15:37, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7584 2023-01-03 00:28:11,268 - mmcls - INFO - Epoch [82][2700/5005] lr: 1.000e-03, eta: 2:15:28, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7230 2023-01-03 00:28:19,720 - mmcls - INFO - Epoch [82][2800/5005] lr: 1.000e-03, eta: 2:15:19, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.7384 2023-01-03 00:28:28,597 - mmcls - INFO - Epoch [82][2900/5005] lr: 1.000e-03, eta: 2:15:11, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7446 2023-01-03 00:28:37,386 - mmcls - INFO - Epoch [82][3000/5005] lr: 1.000e-03, eta: 2:15:02, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7308 2023-01-03 00:28:46,010 - mmcls - INFO - Epoch [82][3100/5005] lr: 1.000e-03, eta: 2:14:53, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7288 2023-01-03 00:28:54,648 - mmcls - INFO - Epoch [82][3200/5005] lr: 1.000e-03, eta: 2:14:44, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7551 2023-01-03 00:29:03,414 - mmcls - INFO - Epoch [82][3300/5005] lr: 1.000e-03, eta: 2:14:35, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7398 2023-01-03 00:29:12,217 - mmcls - INFO - Epoch [82][3400/5005] lr: 1.000e-03, eta: 2:14:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7338 2023-01-03 00:29:20,844 - mmcls - INFO - Epoch [82][3500/5005] lr: 1.000e-03, eta: 2:14:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7602 2023-01-03 00:29:29,300 - mmcls - INFO - Epoch [82][3600/5005] lr: 1.000e-03, eta: 2:14:09, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7453 2023-01-03 00:29:37,990 - mmcls - INFO - Epoch [82][3700/5005] lr: 1.000e-03, eta: 2:14:00, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7597 2023-01-03 00:29:46,902 - mmcls - INFO - Epoch [82][3800/5005] lr: 1.000e-03, eta: 2:13:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7550 2023-01-03 00:29:55,568 - mmcls - INFO - Epoch [82][3900/5005] lr: 1.000e-03, eta: 2:13:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7486 2023-01-03 00:30:04,066 - mmcls - INFO - Epoch [82][4000/5005] lr: 1.000e-03, eta: 2:13:34, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7282 2023-01-03 00:30:13,075 - mmcls - INFO - Epoch [82][4100/5005] lr: 1.000e-03, eta: 2:13:25, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7572 2023-01-03 00:30:21,776 - mmcls - INFO - Epoch [82][4200/5005] lr: 1.000e-03, eta: 2:13:16, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7561 2023-01-03 00:30:30,497 - mmcls - INFO - Epoch [82][4300/5005] lr: 1.000e-03, eta: 2:13:07, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7624 2023-01-03 00:30:39,503 - mmcls - INFO - Epoch [82][4400/5005] lr: 1.000e-03, eta: 2:12:58, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7360 2023-01-03 00:30:48,491 - mmcls - INFO - Epoch [82][4500/5005] lr: 1.000e-03, eta: 2:12:50, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.7339 2023-01-03 00:30:56,994 - mmcls - INFO - Epoch [82][4600/5005] lr: 1.000e-03, eta: 2:12:41, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7418 2023-01-03 00:31:05,696 - mmcls - INFO - Epoch [82][4700/5005] lr: 1.000e-03, eta: 2:12:32, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.7528 2023-01-03 00:31:14,610 - mmcls - INFO - Epoch [82][4800/5005] lr: 1.000e-03, eta: 2:12:23, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7628 2023-01-03 00:31:23,265 - mmcls - INFO - Epoch [82][4900/5005] lr: 1.000e-03, eta: 2:12:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7401 2023-01-03 00:31:32,067 - mmcls - INFO - Epoch [82][5000/5005] lr: 1.000e-03, eta: 2:12:06, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7556 2023-01-03 00:31:54,671 - mmcls - INFO - Epoch(val) [82][196] accuracy_top-1: 63.0600, accuracy_top-5: 83.9920 2023-01-03 00:32:05,527 - mmcls - INFO - Epoch [83][100/5005] lr: 1.000e-03, eta: 2:11:57, time: 0.108, data_time: 0.042, memory: 1125, loss: 1.7171 2023-01-03 00:32:14,387 - mmcls - INFO - Epoch [83][200/5005] lr: 1.000e-03, eta: 2:11:48, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.7249 2023-01-03 00:32:23,339 - mmcls - INFO - Epoch [83][300/5005] lr: 1.000e-03, eta: 2:11:39, time: 0.089, data_time: 0.037, memory: 1125, loss: 1.7246 2023-01-03 00:32:32,101 - mmcls - INFO - Epoch [83][400/5005] lr: 1.000e-03, eta: 2:11:30, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7267 2023-01-03 00:32:40,756 - mmcls - INFO - Epoch [83][500/5005] lr: 1.000e-03, eta: 2:11:21, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7451 2023-01-03 00:32:49,588 - mmcls - INFO - Epoch [83][600/5005] lr: 1.000e-03, eta: 2:11:13, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7403 2023-01-03 00:32:58,256 - mmcls - INFO - Epoch [83][700/5005] lr: 1.000e-03, eta: 2:11:04, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7390 2023-01-03 00:33:07,218 - mmcls - INFO - Epoch [83][800/5005] lr: 1.000e-03, eta: 2:10:55, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7207 2023-01-03 00:33:15,978 - mmcls - INFO - Epoch [83][900/5005] lr: 1.000e-03, eta: 2:10:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7620 2023-01-03 00:33:24,571 - mmcls - INFO - Epoch [83][1000/5005] lr: 1.000e-03, eta: 2:10:37, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7391 2023-01-03 00:33:33,308 - mmcls - INFO - Epoch [83][1100/5005] lr: 1.000e-03, eta: 2:10:29, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.7306 2023-01-03 00:33:41,961 - mmcls - INFO - Epoch [83][1200/5005] lr: 1.000e-03, eta: 2:10:20, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7399 2023-01-03 00:33:50,758 - mmcls - INFO - Epoch [83][1300/5005] lr: 1.000e-03, eta: 2:10:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7273 2023-01-03 00:33:59,504 - mmcls - INFO - Epoch [83][1400/5005] lr: 1.000e-03, eta: 2:10:02, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7288 2023-01-03 00:34:08,243 - mmcls - INFO - Epoch [83][1500/5005] lr: 1.000e-03, eta: 2:09:53, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7394 2023-01-03 00:34:17,074 - mmcls - INFO - Epoch [83][1600/5005] lr: 1.000e-03, eta: 2:09:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7668 2023-01-03 00:34:25,976 - mmcls - INFO - Epoch [83][1700/5005] lr: 1.000e-03, eta: 2:09:36, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7093 2023-01-03 00:34:34,789 - mmcls - INFO - Epoch [83][1800/5005] lr: 1.000e-03, eta: 2:09:27, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7498 2023-01-03 00:34:44,793 - mmcls - INFO - Epoch [83][1900/5005] lr: 1.000e-03, eta: 2:09:18, time: 0.100, data_time: 0.006, memory: 1125, loss: 1.7511 2023-01-03 00:34:53,511 - mmcls - INFO - Epoch [83][2000/5005] lr: 1.000e-03, eta: 2:09:10, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7425 2023-01-03 00:35:02,268 - mmcls - INFO - Epoch [83][2100/5005] lr: 1.000e-03, eta: 2:09:01, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7316 2023-01-03 00:35:11,329 - mmcls - INFO - Epoch [83][2200/5005] lr: 1.000e-03, eta: 2:08:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7381 2023-01-03 00:35:20,000 - mmcls - INFO - Epoch [83][2300/5005] lr: 1.000e-03, eta: 2:08:43, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7370 2023-01-03 00:35:28,764 - mmcls - INFO - Epoch [83][2400/5005] lr: 1.000e-03, eta: 2:08:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7483 2023-01-03 00:35:37,301 - mmcls - INFO - Epoch [83][2500/5005] lr: 1.000e-03, eta: 2:08:26, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7502 2023-01-03 00:35:45,956 - mmcls - INFO - Epoch [83][2600/5005] lr: 1.000e-03, eta: 2:08:17, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7253 2023-01-03 00:35:54,730 - mmcls - INFO - Epoch [83][2700/5005] lr: 1.000e-03, eta: 2:08:08, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7557 2023-01-03 00:36:03,951 - mmcls - INFO - Epoch [83][2800/5005] lr: 1.000e-03, eta: 2:07:59, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7484 2023-01-03 00:36:12,783 - mmcls - INFO - Epoch [83][2900/5005] lr: 1.000e-03, eta: 2:07:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7411 2023-01-03 00:36:21,448 - mmcls - INFO - Epoch [83][3000/5005] lr: 1.000e-03, eta: 2:07:42, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7750 2023-01-03 00:36:30,025 - mmcls - INFO - Epoch [83][3100/5005] lr: 1.000e-03, eta: 2:07:33, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7245 2023-01-03 00:36:38,549 - mmcls - INFO - Epoch [83][3200/5005] lr: 1.000e-03, eta: 2:07:24, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.7509 2023-01-03 00:36:47,326 - mmcls - INFO - Epoch [83][3300/5005] lr: 1.000e-03, eta: 2:07:15, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7378 2023-01-03 00:36:56,091 - mmcls - INFO - Epoch [83][3400/5005] lr: 1.000e-03, eta: 2:07:06, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7546 2023-01-03 00:37:04,856 - mmcls - INFO - Epoch [83][3500/5005] lr: 1.000e-03, eta: 2:06:58, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7397 2023-01-03 00:37:13,533 - mmcls - INFO - Epoch [83][3600/5005] lr: 1.000e-03, eta: 2:06:49, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7430 2023-01-03 00:37:22,257 - mmcls - INFO - Epoch [83][3700/5005] lr: 1.000e-03, eta: 2:06:40, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7530 2023-01-03 00:37:30,822 - mmcls - INFO - Epoch [83][3800/5005] lr: 1.000e-03, eta: 2:06:31, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7407 2023-01-03 00:37:39,602 - mmcls - INFO - Epoch [83][3900/5005] lr: 1.000e-03, eta: 2:06:22, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7486 2023-01-03 00:37:48,635 - mmcls - INFO - Epoch [83][4000/5005] lr: 1.000e-03, eta: 2:06:14, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7603 2023-01-03 00:37:57,237 - mmcls - INFO - Epoch [83][4100/5005] lr: 1.000e-03, eta: 2:06:05, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7393 2023-01-03 00:38:05,808 - mmcls - INFO - Epoch [83][4200/5005] lr: 1.000e-03, eta: 2:05:56, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7625 2023-01-03 00:38:14,908 - mmcls - INFO - Epoch [83][4300/5005] lr: 1.000e-03, eta: 2:05:47, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7396 2023-01-03 00:38:23,553 - mmcls - INFO - Epoch [83][4400/5005] lr: 1.000e-03, eta: 2:05:38, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7554 2023-01-03 00:38:32,197 - mmcls - INFO - Epoch [83][4500/5005] lr: 1.000e-03, eta: 2:05:29, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7307 2023-01-03 00:38:41,012 - mmcls - INFO - Epoch [83][4600/5005] lr: 1.000e-03, eta: 2:05:21, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7511 2023-01-03 00:38:49,745 - mmcls - INFO - Epoch [83][4700/5005] lr: 1.000e-03, eta: 2:05:12, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7653 2023-01-03 00:38:58,535 - mmcls - INFO - Epoch [83][4800/5005] lr: 1.000e-03, eta: 2:05:03, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7214 2023-01-03 00:39:07,148 - mmcls - INFO - Epoch [83][4900/5005] lr: 1.000e-03, eta: 2:04:54, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7509 2023-01-03 00:39:16,077 - mmcls - INFO - Epoch [83][5000/5005] lr: 1.000e-03, eta: 2:04:45, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7317 2023-01-03 00:39:38,706 - mmcls - INFO - Epoch(val) [83][196] accuracy_top-1: 63.1780, accuracy_top-5: 84.1160 2023-01-03 00:39:49,561 - mmcls - INFO - Epoch [84][100/5005] lr: 1.000e-03, eta: 2:04:37, time: 0.108, data_time: 0.064, memory: 1125, loss: 1.7267 2023-01-03 00:39:58,338 - mmcls - INFO - Epoch [84][200/5005] lr: 1.000e-03, eta: 2:04:28, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7071 2023-01-03 00:40:07,474 - mmcls - INFO - Epoch [84][300/5005] lr: 1.000e-03, eta: 2:04:19, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7427 2023-01-03 00:40:16,271 - mmcls - INFO - Epoch [84][400/5005] lr: 1.000e-03, eta: 2:04:10, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7301 2023-01-03 00:40:25,509 - mmcls - INFO - Epoch [84][500/5005] lr: 1.000e-03, eta: 2:04:01, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7083 2023-01-03 00:40:34,236 - mmcls - INFO - Epoch [84][600/5005] lr: 1.000e-03, eta: 2:03:53, time: 0.087, data_time: 0.040, memory: 1125, loss: 1.7347 2023-01-03 00:40:42,989 - mmcls - INFO - Epoch [84][700/5005] lr: 1.000e-03, eta: 2:03:44, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7290 2023-01-03 00:40:51,800 - mmcls - INFO - Epoch [84][800/5005] lr: 1.000e-03, eta: 2:03:35, time: 0.088, data_time: 0.042, memory: 1125, loss: 1.7467 2023-01-03 00:41:00,626 - mmcls - INFO - Epoch [84][900/5005] lr: 1.000e-03, eta: 2:03:26, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7192 2023-01-03 00:41:09,517 - mmcls - INFO - Epoch [84][1000/5005] lr: 1.000e-03, eta: 2:03:18, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.7403 2023-01-03 00:41:18,284 - mmcls - INFO - Epoch [84][1100/5005] lr: 1.000e-03, eta: 2:03:09, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.7325 2023-01-03 00:41:26,894 - mmcls - INFO - Epoch [84][1200/5005] lr: 1.000e-03, eta: 2:03:00, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7385 2023-01-03 00:41:35,751 - mmcls - INFO - Epoch [84][1300/5005] lr: 1.000e-03, eta: 2:02:51, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7306 2023-01-03 00:41:44,795 - mmcls - INFO - Epoch [84][1400/5005] lr: 1.000e-03, eta: 2:02:42, time: 0.090, data_time: 0.029, memory: 1125, loss: 1.7372 2023-01-03 00:41:53,410 - mmcls - INFO - Epoch [84][1500/5005] lr: 1.000e-03, eta: 2:02:33, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7347 2023-01-03 00:42:02,398 - mmcls - INFO - Epoch [84][1600/5005] lr: 1.000e-03, eta: 2:02:25, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7407 2023-01-03 00:42:11,198 - mmcls - INFO - Epoch [84][1700/5005] lr: 1.000e-03, eta: 2:02:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7408 2023-01-03 00:42:20,040 - mmcls - INFO - Epoch [84][1800/5005] lr: 1.000e-03, eta: 2:02:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7458 2023-01-03 00:42:28,833 - mmcls - INFO - Epoch [84][1900/5005] lr: 1.000e-03, eta: 2:01:58, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7224 2023-01-03 00:42:37,550 - mmcls - INFO - Epoch [84][2000/5005] lr: 1.000e-03, eta: 2:01:50, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7338 2023-01-03 00:42:46,201 - mmcls - INFO - Epoch [84][2100/5005] lr: 1.000e-03, eta: 2:01:41, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7187 2023-01-03 00:42:55,290 - mmcls - INFO - Epoch [84][2200/5005] lr: 1.000e-03, eta: 2:01:32, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7246 2023-01-03 00:43:04,071 - mmcls - INFO - Epoch [84][2300/5005] lr: 1.000e-03, eta: 2:01:23, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7504 2023-01-03 00:43:12,716 - mmcls - INFO - Epoch [84][2400/5005] lr: 1.000e-03, eta: 2:01:14, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7515 2023-01-03 00:43:21,294 - mmcls - INFO - Epoch [84][2500/5005] lr: 1.000e-03, eta: 2:01:05, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7343 2023-01-03 00:43:30,006 - mmcls - INFO - Epoch [84][2600/5005] lr: 1.000e-03, eta: 2:00:57, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7357 2023-01-03 00:43:39,061 - mmcls - INFO - Epoch [84][2700/5005] lr: 1.000e-03, eta: 2:00:48, time: 0.091, data_time: 0.021, memory: 1125, loss: 1.7546 2023-01-03 00:43:47,826 - mmcls - INFO - Epoch [84][2800/5005] lr: 1.000e-03, eta: 2:00:39, time: 0.088, data_time: 0.042, memory: 1125, loss: 1.7619 2023-01-03 00:43:56,624 - mmcls - INFO - Epoch [84][2900/5005] lr: 1.000e-03, eta: 2:00:30, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7292 2023-01-03 00:44:05,378 - mmcls - INFO - Epoch [84][3000/5005] lr: 1.000e-03, eta: 2:00:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7210 2023-01-03 00:44:14,187 - mmcls - INFO - Epoch [84][3100/5005] lr: 1.000e-03, eta: 2:00:13, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7494 2023-01-03 00:44:22,736 - mmcls - INFO - Epoch [84][3200/5005] lr: 1.000e-03, eta: 2:00:04, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7324 2023-01-03 00:44:31,158 - mmcls - INFO - Epoch [84][3300/5005] lr: 1.000e-03, eta: 1:59:55, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7398 2023-01-03 00:44:39,937 - mmcls - INFO - Epoch [84][3400/5005] lr: 1.000e-03, eta: 1:59:46, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7355 2023-01-03 00:44:48,630 - mmcls - INFO - Epoch [84][3500/5005] lr: 1.000e-03, eta: 1:59:37, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.7481 2023-01-03 00:44:57,419 - mmcls - INFO - Epoch [84][3600/5005] lr: 1.000e-03, eta: 1:59:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7222 2023-01-03 00:45:06,059 - mmcls - INFO - Epoch [84][3700/5005] lr: 1.000e-03, eta: 1:59:20, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7262 2023-01-03 00:45:14,736 - mmcls - INFO - Epoch [84][3800/5005] lr: 1.000e-03, eta: 1:59:11, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7325 2023-01-03 00:45:23,422 - mmcls - INFO - Epoch [84][3900/5005] lr: 1.000e-03, eta: 1:59:02, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7405 2023-01-03 00:45:32,454 - mmcls - INFO - Epoch [84][4000/5005] lr: 1.000e-03, eta: 1:58:53, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7354 2023-01-03 00:45:41,445 - mmcls - INFO - Epoch [84][4100/5005] lr: 1.000e-03, eta: 1:58:45, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7487 2023-01-03 00:45:50,250 - mmcls - INFO - Epoch [84][4200/5005] lr: 1.000e-03, eta: 1:58:36, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7441 2023-01-03 00:45:59,378 - mmcls - INFO - Epoch [84][4300/5005] lr: 1.000e-03, eta: 1:58:27, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7373 2023-01-03 00:46:07,762 - mmcls - INFO - Epoch [84][4400/5005] lr: 1.000e-03, eta: 1:58:18, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.7598 2023-01-03 00:46:16,580 - mmcls - INFO - Epoch [84][4500/5005] lr: 1.000e-03, eta: 1:58:09, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7119 2023-01-03 00:46:24,905 - mmcls - INFO - Epoch [84][4600/5005] lr: 1.000e-03, eta: 1:58:00, time: 0.083, data_time: 0.017, memory: 1125, loss: 1.7593 2023-01-03 00:46:33,656 - mmcls - INFO - Epoch [84][4700/5005] lr: 1.000e-03, eta: 1:57:52, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7476 2023-01-03 00:46:42,397 - mmcls - INFO - Epoch [84][4800/5005] lr: 1.000e-03, eta: 1:57:43, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7464 2023-01-03 00:46:51,099 - mmcls - INFO - Epoch [84][4900/5005] lr: 1.000e-03, eta: 1:57:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7322 2023-01-03 00:46:59,974 - mmcls - INFO - Epoch [84][5000/5005] lr: 1.000e-03, eta: 1:57:25, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7637 2023-01-03 00:47:22,970 - mmcls - INFO - Epoch(val) [84][196] accuracy_top-1: 63.1160, accuracy_top-5: 84.0320 2023-01-03 00:47:33,874 - mmcls - INFO - Epoch [85][100/5005] lr: 1.000e-03, eta: 1:57:16, time: 0.109, data_time: 0.032, memory: 1125, loss: 1.7181 2023-01-03 00:47:42,841 - mmcls - INFO - Epoch [85][200/5005] lr: 1.000e-03, eta: 1:57:08, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7298 2023-01-03 00:47:51,914 - mmcls - INFO - Epoch [85][300/5005] lr: 1.000e-03, eta: 1:56:59, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.7174 2023-01-03 00:48:00,629 - mmcls - INFO - Epoch [85][400/5005] lr: 1.000e-03, eta: 1:56:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7504 2023-01-03 00:48:09,409 - mmcls - INFO - Epoch [85][500/5005] lr: 1.000e-03, eta: 1:56:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7228 2023-01-03 00:48:18,040 - mmcls - INFO - Epoch [85][600/5005] lr: 1.000e-03, eta: 1:56:32, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.6925 2023-01-03 00:48:26,790 - mmcls - INFO - Epoch [85][700/5005] lr: 1.000e-03, eta: 1:56:24, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7447 2023-01-03 00:48:35,469 - mmcls - INFO - Epoch [85][800/5005] lr: 1.000e-03, eta: 1:56:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7476 2023-01-03 00:48:44,348 - mmcls - INFO - Epoch [85][900/5005] lr: 1.000e-03, eta: 1:56:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7258 2023-01-03 00:48:52,878 - mmcls - INFO - Epoch [85][1000/5005] lr: 1.000e-03, eta: 1:55:57, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7403 2023-01-03 00:49:01,589 - mmcls - INFO - Epoch [85][1100/5005] lr: 1.000e-03, eta: 1:55:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7356 2023-01-03 00:49:10,305 - mmcls - INFO - Epoch [85][1200/5005] lr: 1.000e-03, eta: 1:55:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7201 2023-01-03 00:49:18,874 - mmcls - INFO - Epoch [85][1300/5005] lr: 1.000e-03, eta: 1:55:31, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7412 2023-01-03 00:49:27,805 - mmcls - INFO - Epoch [85][1400/5005] lr: 1.000e-03, eta: 1:55:22, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7329 2023-01-03 00:49:36,544 - mmcls - INFO - Epoch [85][1500/5005] lr: 1.000e-03, eta: 1:55:13, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7220 2023-01-03 00:49:45,937 - mmcls - INFO - Epoch [85][1600/5005] lr: 1.000e-03, eta: 1:55:04, time: 0.094, data_time: 0.013, memory: 1125, loss: 1.7227 2023-01-03 00:49:54,631 - mmcls - INFO - Epoch [85][1700/5005] lr: 1.000e-03, eta: 1:54:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7251 2023-01-03 00:50:03,391 - mmcls - INFO - Epoch [85][1800/5005] lr: 1.000e-03, eta: 1:54:47, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7198 2023-01-03 00:50:11,952 - mmcls - INFO - Epoch [85][1900/5005] lr: 1.000e-03, eta: 1:54:38, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.7393 2023-01-03 00:50:20,550 - mmcls - INFO - Epoch [85][2000/5005] lr: 1.000e-03, eta: 1:54:29, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7269 2023-01-03 00:50:29,247 - mmcls - INFO - Epoch [85][2100/5005] lr: 1.000e-03, eta: 1:54:20, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7302 2023-01-03 00:50:38,096 - mmcls - INFO - Epoch [85][2200/5005] lr: 1.000e-03, eta: 1:54:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7326 2023-01-03 00:50:46,712 - mmcls - INFO - Epoch [85][2300/5005] lr: 1.000e-03, eta: 1:54:03, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7379 2023-01-03 00:50:55,406 - mmcls - INFO - Epoch [85][2400/5005] lr: 1.000e-03, eta: 1:53:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7590 2023-01-03 00:51:04,186 - mmcls - INFO - Epoch [85][2500/5005] lr: 1.000e-03, eta: 1:53:45, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7437 2023-01-03 00:51:12,692 - mmcls - INFO - Epoch [85][2600/5005] lr: 1.000e-03, eta: 1:53:36, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7597 2023-01-03 00:51:21,328 - mmcls - INFO - Epoch [85][2700/5005] lr: 1.000e-03, eta: 1:53:27, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7186 2023-01-03 00:51:30,077 - mmcls - INFO - Epoch [85][2800/5005] lr: 1.000e-03, eta: 1:53:19, time: 0.087, data_time: 0.042, memory: 1125, loss: 1.7282 2023-01-03 00:51:38,786 - mmcls - INFO - Epoch [85][2900/5005] lr: 1.000e-03, eta: 1:53:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7231 2023-01-03 00:51:47,953 - mmcls - INFO - Epoch [85][3000/5005] lr: 1.000e-03, eta: 1:53:01, time: 0.092, data_time: 0.007, memory: 1125, loss: 1.7303 2023-01-03 00:51:56,781 - mmcls - INFO - Epoch [85][3100/5005] lr: 1.000e-03, eta: 1:52:52, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7156 2023-01-03 00:52:05,458 - mmcls - INFO - Epoch [85][3200/5005] lr: 1.000e-03, eta: 1:52:43, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7320 2023-01-03 00:52:14,275 - mmcls - INFO - Epoch [85][3300/5005] lr: 1.000e-03, eta: 1:52:35, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7759 2023-01-03 00:52:22,993 - mmcls - INFO - Epoch [85][3400/5005] lr: 1.000e-03, eta: 1:52:26, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7197 2023-01-03 00:52:31,909 - mmcls - INFO - Epoch [85][3500/5005] lr: 1.000e-03, eta: 1:52:17, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7305 2023-01-03 00:52:40,584 - mmcls - INFO - Epoch [85][3600/5005] lr: 1.000e-03, eta: 1:52:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7510 2023-01-03 00:52:49,211 - mmcls - INFO - Epoch [85][3700/5005] lr: 1.000e-03, eta: 1:51:59, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7545 2023-01-03 00:52:57,780 - mmcls - INFO - Epoch [85][3800/5005] lr: 1.000e-03, eta: 1:51:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7315 2023-01-03 00:53:06,714 - mmcls - INFO - Epoch [85][3900/5005] lr: 1.000e-03, eta: 1:51:42, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7671 2023-01-03 00:53:15,297 - mmcls - INFO - Epoch [85][4000/5005] lr: 1.000e-03, eta: 1:51:33, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7523 2023-01-03 00:53:24,223 - mmcls - INFO - Epoch [85][4100/5005] lr: 1.000e-03, eta: 1:51:24, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7386 2023-01-03 00:53:32,760 - mmcls - INFO - Epoch [85][4200/5005] lr: 1.000e-03, eta: 1:51:15, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7487 2023-01-03 00:53:41,341 - mmcls - INFO - Epoch [85][4300/5005] lr: 1.000e-03, eta: 1:51:06, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7409 2023-01-03 00:53:49,872 - mmcls - INFO - Epoch [85][4400/5005] lr: 1.000e-03, eta: 1:50:58, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7494 2023-01-03 00:53:58,354 - mmcls - INFO - Epoch [85][4500/5005] lr: 1.000e-03, eta: 1:50:49, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7540 2023-01-03 00:54:07,198 - mmcls - INFO - Epoch [85][4600/5005] lr: 1.000e-03, eta: 1:50:40, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7714 2023-01-03 00:54:16,123 - mmcls - INFO - Epoch [85][4700/5005] lr: 1.000e-03, eta: 1:50:31, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7380 2023-01-03 00:54:24,921 - mmcls - INFO - Epoch [85][4800/5005] lr: 1.000e-03, eta: 1:50:22, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7235 2023-01-03 00:54:33,411 - mmcls - INFO - Epoch [85][4900/5005] lr: 1.000e-03, eta: 1:50:14, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7473 2023-01-03 00:54:42,560 - mmcls - INFO - Epoch [85][5000/5005] lr: 1.000e-03, eta: 1:50:05, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7447 2023-01-03 00:55:05,164 - mmcls - INFO - Epoch(val) [85][196] accuracy_top-1: 63.1960, accuracy_top-5: 83.8420 2023-01-03 00:55:16,169 - mmcls - INFO - Epoch [86][100/5005] lr: 1.000e-03, eta: 1:49:56, time: 0.110, data_time: 0.033, memory: 1125, loss: 1.7208 2023-01-03 00:55:24,889 - mmcls - INFO - Epoch [86][200/5005] lr: 1.000e-03, eta: 1:49:47, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7211 2023-01-03 00:55:33,507 - mmcls - INFO - Epoch [86][300/5005] lr: 1.000e-03, eta: 1:49:38, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7278 2023-01-03 00:55:42,086 - mmcls - INFO - Epoch [86][400/5005] lr: 1.000e-03, eta: 1:49:29, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7239 2023-01-03 00:55:51,048 - mmcls - INFO - Epoch [86][500/5005] lr: 1.000e-03, eta: 1:49:21, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.7160 2023-01-03 00:55:59,740 - mmcls - INFO - Epoch [86][600/5005] lr: 1.000e-03, eta: 1:49:12, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7497 2023-01-03 00:56:08,467 - mmcls - INFO - Epoch [86][700/5005] lr: 1.000e-03, eta: 1:49:03, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7163 2023-01-03 00:56:17,305 - mmcls - INFO - Epoch [86][800/5005] lr: 1.000e-03, eta: 1:48:54, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7296 2023-01-03 00:56:26,040 - mmcls - INFO - Epoch [86][900/5005] lr: 1.000e-03, eta: 1:48:45, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7131 2023-01-03 00:56:34,902 - mmcls - INFO - Epoch [86][1000/5005] lr: 1.000e-03, eta: 1:48:37, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7430 2023-01-03 00:56:43,518 - mmcls - INFO - Epoch [86][1100/5005] lr: 1.000e-03, eta: 1:48:28, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7388 2023-01-03 00:56:52,501 - mmcls - INFO - Epoch [86][1200/5005] lr: 1.000e-03, eta: 1:48:19, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7209 2023-01-03 00:57:01,186 - mmcls - INFO - Epoch [86][1300/5005] lr: 1.000e-03, eta: 1:48:10, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.7336 2023-01-03 00:57:09,853 - mmcls - INFO - Epoch [86][1400/5005] lr: 1.000e-03, eta: 1:48:01, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7201 2023-01-03 00:57:18,473 - mmcls - INFO - Epoch [86][1500/5005] lr: 1.000e-03, eta: 1:47:53, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7354 2023-01-03 00:57:27,256 - mmcls - INFO - Epoch [86][1600/5005] lr: 1.000e-03, eta: 1:47:44, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7121 2023-01-03 00:57:35,888 - mmcls - INFO - Epoch [86][1700/5005] lr: 1.000e-03, eta: 1:47:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7508 2023-01-03 00:57:44,744 - mmcls - INFO - Epoch [86][1800/5005] lr: 1.000e-03, eta: 1:47:26, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7353 2023-01-03 00:57:53,752 - mmcls - INFO - Epoch [86][1900/5005] lr: 1.000e-03, eta: 1:47:17, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7315 2023-01-03 00:58:02,508 - mmcls - INFO - Epoch [86][2000/5005] lr: 1.000e-03, eta: 1:47:09, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7348 2023-01-03 00:58:11,131 - mmcls - INFO - Epoch [86][2100/5005] lr: 1.000e-03, eta: 1:47:00, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7243 2023-01-03 00:58:19,943 - mmcls - INFO - Epoch [86][2200/5005] lr: 1.000e-03, eta: 1:46:51, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7310 2023-01-03 00:58:28,716 - mmcls - INFO - Epoch [86][2300/5005] lr: 1.000e-03, eta: 1:46:42, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7126 2023-01-03 00:58:37,400 - mmcls - INFO - Epoch [86][2400/5005] lr: 1.000e-03, eta: 1:46:33, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7217 2023-01-03 00:58:46,159 - mmcls - INFO - Epoch [86][2500/5005] lr: 1.000e-03, eta: 1:46:25, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7446 2023-01-03 00:58:55,078 - mmcls - INFO - Epoch [86][2600/5005] lr: 1.000e-03, eta: 1:46:16, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7251 2023-01-03 00:59:03,929 - mmcls - INFO - Epoch [86][2700/5005] lr: 1.000e-03, eta: 1:46:07, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7422 2023-01-03 00:59:12,921 - mmcls - INFO - Epoch [86][2800/5005] lr: 1.000e-03, eta: 1:45:58, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7194 2023-01-03 00:59:21,670 - mmcls - INFO - Epoch [86][2900/5005] lr: 1.000e-03, eta: 1:45:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7161 2023-01-03 00:59:30,625 - mmcls - INFO - Epoch [86][3000/5005] lr: 1.000e-03, eta: 1:45:41, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7165 2023-01-03 00:59:39,343 - mmcls - INFO - Epoch [86][3100/5005] lr: 1.000e-03, eta: 1:45:32, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7312 2023-01-03 00:59:48,065 - mmcls - INFO - Epoch [86][3200/5005] lr: 1.000e-03, eta: 1:45:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7517 2023-01-03 00:59:56,992 - mmcls - INFO - Epoch [86][3300/5005] lr: 1.000e-03, eta: 1:45:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7347 2023-01-03 01:00:05,985 - mmcls - INFO - Epoch [86][3400/5005] lr: 1.000e-03, eta: 1:45:05, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7366 2023-01-03 01:00:14,692 - mmcls - INFO - Epoch [86][3500/5005] lr: 1.000e-03, eta: 1:44:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7496 2023-01-03 01:00:23,405 - mmcls - INFO - Epoch [86][3600/5005] lr: 1.000e-03, eta: 1:44:48, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7170 2023-01-03 01:00:32,020 - mmcls - INFO - Epoch [86][3700/5005] lr: 1.000e-03, eta: 1:44:39, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7378 2023-01-03 01:00:40,790 - mmcls - INFO - Epoch [86][3800/5005] lr: 1.000e-03, eta: 1:44:30, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7453 2023-01-03 01:00:49,459 - mmcls - INFO - Epoch [86][3900/5005] lr: 1.000e-03, eta: 1:44:21, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7447 2023-01-03 01:00:57,949 - mmcls - INFO - Epoch [86][4000/5005] lr: 1.000e-03, eta: 1:44:13, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7309 2023-01-03 01:01:06,624 - mmcls - INFO - Epoch [86][4100/5005] lr: 1.000e-03, eta: 1:44:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7313 2023-01-03 01:01:15,380 - mmcls - INFO - Epoch [86][4200/5005] lr: 1.000e-03, eta: 1:43:55, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7244 2023-01-03 01:01:24,420 - mmcls - INFO - Epoch [86][4300/5005] lr: 1.000e-03, eta: 1:43:46, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7321 2023-01-03 01:01:32,935 - mmcls - INFO - Epoch [86][4400/5005] lr: 1.000e-03, eta: 1:43:37, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7572 2023-01-03 01:01:41,525 - mmcls - INFO - Epoch [86][4500/5005] lr: 1.000e-03, eta: 1:43:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7512 2023-01-03 01:01:50,286 - mmcls - INFO - Epoch [86][4600/5005] lr: 1.000e-03, eta: 1:43:20, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7397 2023-01-03 01:01:59,236 - mmcls - INFO - Epoch [86][4700/5005] lr: 1.000e-03, eta: 1:43:11, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7426 2023-01-03 01:02:08,112 - mmcls - INFO - Epoch [86][4800/5005] lr: 1.000e-03, eta: 1:43:02, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7473 2023-01-03 01:02:16,892 - mmcls - INFO - Epoch [86][4900/5005] lr: 1.000e-03, eta: 1:42:53, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7593 2023-01-03 01:02:25,770 - mmcls - INFO - Epoch [86][5000/5005] lr: 1.000e-03, eta: 1:42:45, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7524 2023-01-03 01:02:48,362 - mmcls - INFO - Epoch(val) [86][196] accuracy_top-1: 63.2620, accuracy_top-5: 84.0420 2023-01-03 01:02:59,134 - mmcls - INFO - Epoch [87][100/5005] lr: 1.000e-03, eta: 1:42:36, time: 0.107, data_time: 0.048, memory: 1125, loss: 1.7362 2023-01-03 01:03:07,795 - mmcls - INFO - Epoch [87][200/5005] lr: 1.000e-03, eta: 1:42:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7063 2023-01-03 01:03:16,531 - mmcls - INFO - Epoch [87][300/5005] lr: 1.000e-03, eta: 1:42:18, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7236 2023-01-03 01:03:25,172 - mmcls - INFO - Epoch [87][400/5005] lr: 1.000e-03, eta: 1:42:09, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7131 2023-01-03 01:03:33,753 - mmcls - INFO - Epoch [87][500/5005] lr: 1.000e-03, eta: 1:42:00, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7266 2023-01-03 01:03:42,638 - mmcls - INFO - Epoch [87][600/5005] lr: 1.000e-03, eta: 1:41:51, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7176 2023-01-03 01:03:51,534 - mmcls - INFO - Epoch [87][700/5005] lr: 1.000e-03, eta: 1:41:43, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7306 2023-01-03 01:04:00,065 - mmcls - INFO - Epoch [87][800/5005] lr: 1.000e-03, eta: 1:41:34, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7071 2023-01-03 01:04:09,158 - mmcls - INFO - Epoch [87][900/5005] lr: 1.000e-03, eta: 1:41:25, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.7326 2023-01-03 01:04:18,233 - mmcls - INFO - Epoch [87][1000/5005] lr: 1.000e-03, eta: 1:41:16, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.7288 2023-01-03 01:04:26,790 - mmcls - INFO - Epoch [87][1100/5005] lr: 1.000e-03, eta: 1:41:07, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7151 2023-01-03 01:04:35,592 - mmcls - INFO - Epoch [87][1200/5005] lr: 1.000e-03, eta: 1:40:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7191 2023-01-03 01:04:44,434 - mmcls - INFO - Epoch [87][1300/5005] lr: 1.000e-03, eta: 1:40:50, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7365 2023-01-03 01:04:53,234 - mmcls - INFO - Epoch [87][1400/5005] lr: 1.000e-03, eta: 1:40:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7182 2023-01-03 01:05:02,157 - mmcls - INFO - Epoch [87][1500/5005] lr: 1.000e-03, eta: 1:40:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7210 2023-01-03 01:05:10,681 - mmcls - INFO - Epoch [87][1600/5005] lr: 1.000e-03, eta: 1:40:23, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.7444 2023-01-03 01:05:19,339 - mmcls - INFO - Epoch [87][1700/5005] lr: 1.000e-03, eta: 1:40:15, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7478 2023-01-03 01:05:28,225 - mmcls - INFO - Epoch [87][1800/5005] lr: 1.000e-03, eta: 1:40:06, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7180 2023-01-03 01:05:37,105 - mmcls - INFO - Epoch [87][1900/5005] lr: 1.000e-03, eta: 1:39:57, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7082 2023-01-03 01:05:45,960 - mmcls - INFO - Epoch [87][2000/5005] lr: 1.000e-03, eta: 1:39:48, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.7233 2023-01-03 01:05:54,629 - mmcls - INFO - Epoch [87][2100/5005] lr: 1.000e-03, eta: 1:39:39, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7445 2023-01-03 01:06:03,233 - mmcls - INFO - Epoch [87][2200/5005] lr: 1.000e-03, eta: 1:39:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7110 2023-01-03 01:06:11,534 - mmcls - INFO - Epoch [87][2300/5005] lr: 1.000e-03, eta: 1:39:22, time: 0.083, data_time: 0.001, memory: 1125, loss: 1.7380 2023-01-03 01:06:20,306 - mmcls - INFO - Epoch [87][2400/5005] lr: 1.000e-03, eta: 1:39:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7299 2023-01-03 01:06:29,189 - mmcls - INFO - Epoch [87][2500/5005] lr: 1.000e-03, eta: 1:39:04, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7331 2023-01-03 01:06:38,133 - mmcls - INFO - Epoch [87][2600/5005] lr: 1.000e-03, eta: 1:38:55, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7346 2023-01-03 01:06:46,997 - mmcls - INFO - Epoch [87][2700/5005] lr: 1.000e-03, eta: 1:38:47, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7399 2023-01-03 01:06:55,662 - mmcls - INFO - Epoch [87][2800/5005] lr: 1.000e-03, eta: 1:38:38, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7416 2023-01-03 01:07:04,166 - mmcls - INFO - Epoch [87][2900/5005] lr: 1.000e-03, eta: 1:38:29, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7453 2023-01-03 01:07:13,106 - mmcls - INFO - Epoch [87][3000/5005] lr: 1.000e-03, eta: 1:38:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7289 2023-01-03 01:07:21,509 - mmcls - INFO - Epoch [87][3100/5005] lr: 1.000e-03, eta: 1:38:11, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.7199 2023-01-03 01:07:30,193 - mmcls - INFO - Epoch [87][3200/5005] lr: 1.000e-03, eta: 1:38:03, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7366 2023-01-03 01:07:38,720 - mmcls - INFO - Epoch [87][3300/5005] lr: 1.000e-03, eta: 1:37:54, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7529 2023-01-03 01:07:47,663 - mmcls - INFO - Epoch [87][3400/5005] lr: 1.000e-03, eta: 1:37:45, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7415 2023-01-03 01:07:56,383 - mmcls - INFO - Epoch [87][3500/5005] lr: 1.000e-03, eta: 1:37:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7210 2023-01-03 01:08:05,472 - mmcls - INFO - Epoch [87][3600/5005] lr: 1.000e-03, eta: 1:37:27, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7549 2023-01-03 01:08:14,507 - mmcls - INFO - Epoch [87][3700/5005] lr: 1.000e-03, eta: 1:37:19, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7373 2023-01-03 01:08:23,274 - mmcls - INFO - Epoch [87][3800/5005] lr: 1.000e-03, eta: 1:37:10, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7517 2023-01-03 01:08:32,483 - mmcls - INFO - Epoch [87][3900/5005] lr: 1.000e-03, eta: 1:37:01, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.7453 2023-01-03 01:08:41,047 - mmcls - INFO - Epoch [87][4000/5005] lr: 1.000e-03, eta: 1:36:52, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7114 2023-01-03 01:08:49,672 - mmcls - INFO - Epoch [87][4100/5005] lr: 1.000e-03, eta: 1:36:43, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7546 2023-01-03 01:08:58,285 - mmcls - INFO - Epoch [87][4200/5005] lr: 1.000e-03, eta: 1:36:35, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7331 2023-01-03 01:09:07,186 - mmcls - INFO - Epoch [87][4300/5005] lr: 1.000e-03, eta: 1:36:26, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7642 2023-01-03 01:09:16,039 - mmcls - INFO - Epoch [87][4400/5005] lr: 1.000e-03, eta: 1:36:17, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7415 2023-01-03 01:09:24,621 - mmcls - INFO - Epoch [87][4500/5005] lr: 1.000e-03, eta: 1:36:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7430 2023-01-03 01:09:33,080 - mmcls - INFO - Epoch [87][4600/5005] lr: 1.000e-03, eta: 1:35:59, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7415 2023-01-03 01:09:41,825 - mmcls - INFO - Epoch [87][4700/5005] lr: 1.000e-03, eta: 1:35:51, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7317 2023-01-03 01:09:50,713 - mmcls - INFO - Epoch [87][4800/5005] lr: 1.000e-03, eta: 1:35:42, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7325 2023-01-03 01:09:59,296 - mmcls - INFO - Epoch [87][4900/5005] lr: 1.000e-03, eta: 1:35:33, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7556 2023-01-03 01:10:08,179 - mmcls - INFO - Epoch [87][5000/5005] lr: 1.000e-03, eta: 1:35:24, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7471 2023-01-03 01:10:30,704 - mmcls - INFO - Epoch(val) [87][196] accuracy_top-1: 63.1700, accuracy_top-5: 84.1560 2023-01-03 01:10:41,921 - mmcls - INFO - Epoch [88][100/5005] lr: 1.000e-03, eta: 1:35:15, time: 0.112, data_time: 0.051, memory: 1125, loss: 1.7392 2023-01-03 01:10:50,327 - mmcls - INFO - Epoch [88][200/5005] lr: 1.000e-03, eta: 1:35:06, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7278 2023-01-03 01:10:59,132 - mmcls - INFO - Epoch [88][300/5005] lr: 1.000e-03, eta: 1:34:58, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7082 2023-01-03 01:11:08,237 - mmcls - INFO - Epoch [88][400/5005] lr: 1.000e-03, eta: 1:34:49, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7159 2023-01-03 01:11:16,971 - mmcls - INFO - Epoch [88][500/5005] lr: 1.000e-03, eta: 1:34:40, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7406 2023-01-03 01:11:25,423 - mmcls - INFO - Epoch [88][600/5005] lr: 1.000e-03, eta: 1:34:31, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7021 2023-01-03 01:11:34,176 - mmcls - INFO - Epoch [88][700/5005] lr: 1.000e-03, eta: 1:34:22, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7450 2023-01-03 01:11:43,169 - mmcls - INFO - Epoch [88][800/5005] lr: 1.000e-03, eta: 1:34:14, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7423 2023-01-03 01:11:51,875 - mmcls - INFO - Epoch [88][900/5005] lr: 1.000e-03, eta: 1:34:05, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7382 2023-01-03 01:12:00,410 - mmcls - INFO - Epoch [88][1000/5005] lr: 1.000e-03, eta: 1:33:56, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7163 2023-01-03 01:12:09,077 - mmcls - INFO - Epoch [88][1100/5005] lr: 1.000e-03, eta: 1:33:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7299 2023-01-03 01:12:18,268 - mmcls - INFO - Epoch [88][1200/5005] lr: 1.000e-03, eta: 1:33:38, time: 0.092, data_time: 0.027, memory: 1125, loss: 1.7278 2023-01-03 01:12:26,936 - mmcls - INFO - Epoch [88][1300/5005] lr: 1.000e-03, eta: 1:33:30, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7272 2023-01-03 01:12:35,956 - mmcls - INFO - Epoch [88][1400/5005] lr: 1.000e-03, eta: 1:33:21, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7087 2023-01-03 01:12:44,430 - mmcls - INFO - Epoch [88][1500/5005] lr: 1.000e-03, eta: 1:33:12, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7365 2023-01-03 01:12:53,292 - mmcls - INFO - Epoch [88][1600/5005] lr: 1.000e-03, eta: 1:33:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7383 2023-01-03 01:13:02,009 - mmcls - INFO - Epoch [88][1700/5005] lr: 1.000e-03, eta: 1:32:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7379 2023-01-03 01:13:10,645 - mmcls - INFO - Epoch [88][1800/5005] lr: 1.000e-03, eta: 1:32:45, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7460 2023-01-03 01:13:19,322 - mmcls - INFO - Epoch [88][1900/5005] lr: 1.000e-03, eta: 1:32:37, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7512 2023-01-03 01:13:28,017 - mmcls - INFO - Epoch [88][2000/5005] lr: 1.000e-03, eta: 1:32:28, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7197 2023-01-03 01:13:36,862 - mmcls - INFO - Epoch [88][2100/5005] lr: 1.000e-03, eta: 1:32:19, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7069 2023-01-03 01:13:45,487 - mmcls - INFO - Epoch [88][2200/5005] lr: 1.000e-03, eta: 1:32:10, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7289 2023-01-03 01:13:54,683 - mmcls - INFO - Epoch [88][2300/5005] lr: 1.000e-03, eta: 1:32:01, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7426 2023-01-03 01:14:03,288 - mmcls - INFO - Epoch [88][2400/5005] lr: 1.000e-03, eta: 1:31:53, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7361 2023-01-03 01:14:12,231 - mmcls - INFO - Epoch [88][2500/5005] lr: 1.000e-03, eta: 1:31:44, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7238 2023-01-03 01:14:21,003 - mmcls - INFO - Epoch [88][2600/5005] lr: 1.000e-03, eta: 1:31:35, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7197 2023-01-03 01:14:29,758 - mmcls - INFO - Epoch [88][2700/5005] lr: 1.000e-03, eta: 1:31:26, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7594 2023-01-03 01:14:38,423 - mmcls - INFO - Epoch [88][2800/5005] lr: 1.000e-03, eta: 1:31:17, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7375 2023-01-03 01:14:47,187 - mmcls - INFO - Epoch [88][2900/5005] lr: 1.000e-03, eta: 1:31:09, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7278 2023-01-03 01:14:55,927 - mmcls - INFO - Epoch [88][3000/5005] lr: 1.000e-03, eta: 1:31:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7172 2023-01-03 01:15:04,513 - mmcls - INFO - Epoch [88][3100/5005] lr: 1.000e-03, eta: 1:30:51, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7134 2023-01-03 01:15:13,290 - mmcls - INFO - Epoch [88][3200/5005] lr: 1.000e-03, eta: 1:30:42, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7318 2023-01-03 01:15:22,011 - mmcls - INFO - Epoch [88][3300/5005] lr: 1.000e-03, eta: 1:30:33, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7118 2023-01-03 01:15:30,627 - mmcls - INFO - Epoch [88][3400/5005] lr: 1.000e-03, eta: 1:30:25, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7612 2023-01-03 01:15:39,460 - mmcls - INFO - Epoch [88][3500/5005] lr: 1.000e-03, eta: 1:30:16, time: 0.088, data_time: 0.038, memory: 1125, loss: 1.7288 2023-01-03 01:15:48,205 - mmcls - INFO - Epoch [88][3600/5005] lr: 1.000e-03, eta: 1:30:07, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7293 2023-01-03 01:15:57,201 - mmcls - INFO - Epoch [88][3700/5005] lr: 1.000e-03, eta: 1:29:58, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7400 2023-01-03 01:16:05,866 - mmcls - INFO - Epoch [88][3800/5005] lr: 1.000e-03, eta: 1:29:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7295 2023-01-03 01:16:14,611 - mmcls - INFO - Epoch [88][3900/5005] lr: 1.000e-03, eta: 1:29:41, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7554 2023-01-03 01:16:23,791 - mmcls - INFO - Epoch [88][4000/5005] lr: 1.000e-03, eta: 1:29:32, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7364 2023-01-03 01:16:32,588 - mmcls - INFO - Epoch [88][4100/5005] lr: 1.000e-03, eta: 1:29:23, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7445 2023-01-03 01:16:41,193 - mmcls - INFO - Epoch [88][4200/5005] lr: 1.000e-03, eta: 1:29:14, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7377 2023-01-03 01:16:49,840 - mmcls - INFO - Epoch [88][4300/5005] lr: 1.000e-03, eta: 1:29:05, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7310 2023-01-03 01:16:58,500 - mmcls - INFO - Epoch [88][4400/5005] lr: 1.000e-03, eta: 1:28:57, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7451 2023-01-03 01:17:07,537 - mmcls - INFO - Epoch [88][4500/5005] lr: 1.000e-03, eta: 1:28:48, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7120 2023-01-03 01:17:16,362 - mmcls - INFO - Epoch [88][4600/5005] lr: 1.000e-03, eta: 1:28:39, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7353 2023-01-03 01:17:24,987 - mmcls - INFO - Epoch [88][4700/5005] lr: 1.000e-03, eta: 1:28:30, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7436 2023-01-03 01:17:33,709 - mmcls - INFO - Epoch [88][4800/5005] lr: 1.000e-03, eta: 1:28:21, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7512 2023-01-03 01:17:42,407 - mmcls - INFO - Epoch [88][4900/5005] lr: 1.000e-03, eta: 1:28:13, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7276 2023-01-03 01:17:51,015 - mmcls - INFO - Epoch [88][5000/5005] lr: 1.000e-03, eta: 1:28:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7105 2023-01-03 01:18:13,480 - mmcls - INFO - Epoch(val) [88][196] accuracy_top-1: 62.9860, accuracy_top-5: 83.9660 2023-01-03 01:18:24,435 - mmcls - INFO - Epoch [89][100/5005] lr: 1.000e-03, eta: 1:27:55, time: 0.109, data_time: 0.046, memory: 1125, loss: 1.7025 2023-01-03 01:18:33,337 - mmcls - INFO - Epoch [89][200/5005] lr: 1.000e-03, eta: 1:27:46, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7214 2023-01-03 01:18:41,909 - mmcls - INFO - Epoch [89][300/5005] lr: 1.000e-03, eta: 1:27:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7345 2023-01-03 01:18:50,875 - mmcls - INFO - Epoch [89][400/5005] lr: 1.000e-03, eta: 1:27:28, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7194 2023-01-03 01:18:59,782 - mmcls - INFO - Epoch [89][500/5005] lr: 1.000e-03, eta: 1:27:20, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7049 2023-01-03 01:19:09,068 - mmcls - INFO - Epoch [89][600/5005] lr: 1.000e-03, eta: 1:27:11, time: 0.093, data_time: 0.008, memory: 1125, loss: 1.7336 2023-01-03 01:19:17,752 - mmcls - INFO - Epoch [89][700/5005] lr: 1.000e-03, eta: 1:27:02, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7126 2023-01-03 01:19:26,633 - mmcls - INFO - Epoch [89][800/5005] lr: 1.000e-03, eta: 1:26:53, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7252 2023-01-03 01:19:35,686 - mmcls - INFO - Epoch [89][900/5005] lr: 1.000e-03, eta: 1:26:45, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7156 2023-01-03 01:19:44,353 - mmcls - INFO - Epoch [89][1000/5005] lr: 1.000e-03, eta: 1:26:36, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.6973 2023-01-03 01:19:53,271 - mmcls - INFO - Epoch [89][1100/5005] lr: 1.000e-03, eta: 1:26:27, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7425 2023-01-03 01:20:01,827 - mmcls - INFO - Epoch [89][1200/5005] lr: 1.000e-03, eta: 1:26:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7224 2023-01-03 01:20:10,540 - mmcls - INFO - Epoch [89][1300/5005] lr: 1.000e-03, eta: 1:26:09, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7328 2023-01-03 01:20:19,256 - mmcls - INFO - Epoch [89][1400/5005] lr: 1.000e-03, eta: 1:26:00, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7283 2023-01-03 01:20:27,833 - mmcls - INFO - Epoch [89][1500/5005] lr: 1.000e-03, eta: 1:25:52, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.7363 2023-01-03 01:20:36,448 - mmcls - INFO - Epoch [89][1600/5005] lr: 1.000e-03, eta: 1:25:43, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7350 2023-01-03 01:20:44,981 - mmcls - INFO - Epoch [89][1700/5005] lr: 1.000e-03, eta: 1:25:34, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7467 2023-01-03 01:20:53,675 - mmcls - INFO - Epoch [89][1800/5005] lr: 1.000e-03, eta: 1:25:25, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7274 2023-01-03 01:21:02,339 - mmcls - INFO - Epoch [89][1900/5005] lr: 1.000e-03, eta: 1:25:16, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7513 2023-01-03 01:21:10,806 - mmcls - INFO - Epoch [89][2000/5005] lr: 1.000e-03, eta: 1:25:08, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7440 2023-01-03 01:21:19,506 - mmcls - INFO - Epoch [89][2100/5005] lr: 1.000e-03, eta: 1:24:59, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7018 2023-01-03 01:21:28,238 - mmcls - INFO - Epoch [89][2200/5005] lr: 1.000e-03, eta: 1:24:50, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7499 2023-01-03 01:21:37,023 - mmcls - INFO - Epoch [89][2300/5005] lr: 1.000e-03, eta: 1:24:41, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7254 2023-01-03 01:21:45,839 - mmcls - INFO - Epoch [89][2400/5005] lr: 1.000e-03, eta: 1:24:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7375 2023-01-03 01:21:54,622 - mmcls - INFO - Epoch [89][2500/5005] lr: 1.000e-03, eta: 1:24:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7375 2023-01-03 01:22:03,353 - mmcls - INFO - Epoch [89][2600/5005] lr: 1.000e-03, eta: 1:24:15, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7564 2023-01-03 01:22:12,190 - mmcls - INFO - Epoch [89][2700/5005] lr: 1.000e-03, eta: 1:24:06, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.7262 2023-01-03 01:22:21,280 - mmcls - INFO - Epoch [89][2800/5005] lr: 1.000e-03, eta: 1:23:57, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7313 2023-01-03 01:22:29,874 - mmcls - INFO - Epoch [89][2900/5005] lr: 1.000e-03, eta: 1:23:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7482 2023-01-03 01:22:38,508 - mmcls - INFO - Epoch [89][3000/5005] lr: 1.000e-03, eta: 1:23:39, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7245 2023-01-03 01:22:47,176 - mmcls - INFO - Epoch [89][3100/5005] lr: 1.000e-03, eta: 1:23:31, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7463 2023-01-03 01:22:55,891 - mmcls - INFO - Epoch [89][3200/5005] lr: 1.000e-03, eta: 1:23:22, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7258 2023-01-03 01:23:04,819 - mmcls - INFO - Epoch [89][3300/5005] lr: 1.000e-03, eta: 1:23:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7237 2023-01-03 01:23:13,628 - mmcls - INFO - Epoch [89][3400/5005] lr: 1.000e-03, eta: 1:23:04, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7142 2023-01-03 01:23:22,426 - mmcls - INFO - Epoch [89][3500/5005] lr: 1.000e-03, eta: 1:22:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7287 2023-01-03 01:23:31,449 - mmcls - INFO - Epoch [89][3600/5005] lr: 1.000e-03, eta: 1:22:47, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7216 2023-01-03 01:23:39,799 - mmcls - INFO - Epoch [89][3700/5005] lr: 1.000e-03, eta: 1:22:38, time: 0.083, data_time: 0.030, memory: 1125, loss: 1.7217 2023-01-03 01:23:48,582 - mmcls - INFO - Epoch [89][3800/5005] lr: 1.000e-03, eta: 1:22:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7447 2023-01-03 01:23:57,372 - mmcls - INFO - Epoch [89][3900/5005] lr: 1.000e-03, eta: 1:22:20, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7362 2023-01-03 01:24:06,284 - mmcls - INFO - Epoch [89][4000/5005] lr: 1.000e-03, eta: 1:22:11, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.7507 2023-01-03 01:24:14,762 - mmcls - INFO - Epoch [89][4100/5005] lr: 1.000e-03, eta: 1:22:03, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.7293 2023-01-03 01:24:23,446 - mmcls - INFO - Epoch [89][4200/5005] lr: 1.000e-03, eta: 1:21:54, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7457 2023-01-03 01:24:32,174 - mmcls - INFO - Epoch [89][4300/5005] lr: 1.000e-03, eta: 1:21:45, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7242 2023-01-03 01:24:40,981 - mmcls - INFO - Epoch [89][4400/5005] lr: 1.000e-03, eta: 1:21:36, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6991 2023-01-03 01:24:49,608 - mmcls - INFO - Epoch [89][4500/5005] lr: 1.000e-03, eta: 1:21:27, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7463 2023-01-03 01:24:58,406 - mmcls - INFO - Epoch [89][4600/5005] lr: 1.000e-03, eta: 1:21:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7423 2023-01-03 01:25:06,892 - mmcls - INFO - Epoch [89][4700/5005] lr: 1.000e-03, eta: 1:21:10, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7233 2023-01-03 01:25:15,635 - mmcls - INFO - Epoch [89][4800/5005] lr: 1.000e-03, eta: 1:21:01, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7359 2023-01-03 01:25:24,333 - mmcls - INFO - Epoch [89][4900/5005] lr: 1.000e-03, eta: 1:20:52, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7100 2023-01-03 01:25:33,104 - mmcls - INFO - Epoch [89][5000/5005] lr: 1.000e-03, eta: 1:20:43, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7414 2023-01-03 01:25:55,678 - mmcls - INFO - Epoch(val) [89][196] accuracy_top-1: 63.1020, accuracy_top-5: 83.9760 2023-01-03 01:26:06,373 - mmcls - INFO - Epoch [90][100/5005] lr: 1.000e-03, eta: 1:20:34, time: 0.107, data_time: 0.061, memory: 1125, loss: 1.7329 2023-01-03 01:26:14,987 - mmcls - INFO - Epoch [90][200/5005] lr: 1.000e-03, eta: 1:20:25, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7104 2023-01-03 01:26:23,718 - mmcls - INFO - Epoch [90][300/5005] lr: 1.000e-03, eta: 1:20:17, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7435 2023-01-03 01:26:32,312 - mmcls - INFO - Epoch [90][400/5005] lr: 1.000e-03, eta: 1:20:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7163 2023-01-03 01:26:40,974 - mmcls - INFO - Epoch [90][500/5005] lr: 1.000e-03, eta: 1:19:59, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7299 2023-01-03 01:26:49,902 - mmcls - INFO - Epoch [90][600/5005] lr: 1.000e-03, eta: 1:19:50, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7321 2023-01-03 01:26:58,475 - mmcls - INFO - Epoch [90][700/5005] lr: 1.000e-03, eta: 1:19:41, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7164 2023-01-03 01:27:07,041 - mmcls - INFO - Epoch [90][800/5005] lr: 1.000e-03, eta: 1:19:33, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7111 2023-01-03 01:27:15,741 - mmcls - INFO - Epoch [90][900/5005] lr: 1.000e-03, eta: 1:19:24, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.7269 2023-01-03 01:27:24,476 - mmcls - INFO - Epoch [90][1000/5005] lr: 1.000e-03, eta: 1:19:15, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.6981 2023-01-03 01:27:33,007 - mmcls - INFO - Epoch [90][1100/5005] lr: 1.000e-03, eta: 1:19:06, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7227 2023-01-03 01:27:41,872 - mmcls - INFO - Epoch [90][1200/5005] lr: 1.000e-03, eta: 1:18:57, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7141 2023-01-03 01:27:50,447 - mmcls - INFO - Epoch [90][1300/5005] lr: 1.000e-03, eta: 1:18:49, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7461 2023-01-03 01:27:59,240 - mmcls - INFO - Epoch [90][1400/5005] lr: 1.000e-03, eta: 1:18:40, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7280 2023-01-03 01:28:08,035 - mmcls - INFO - Epoch [90][1500/5005] lr: 1.000e-03, eta: 1:18:31, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7308 2023-01-03 01:28:16,807 - mmcls - INFO - Epoch [90][1600/5005] lr: 1.000e-03, eta: 1:18:22, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7218 2023-01-03 01:28:25,716 - mmcls - INFO - Epoch [90][1700/5005] lr: 1.000e-03, eta: 1:18:13, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7590 2023-01-03 01:28:34,652 - mmcls - INFO - Epoch [90][1800/5005] lr: 1.000e-03, eta: 1:18:05, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7233 2023-01-03 01:28:43,195 - mmcls - INFO - Epoch [90][1900/5005] lr: 1.000e-03, eta: 1:17:56, time: 0.085, data_time: 0.024, memory: 1125, loss: 1.7216 2023-01-03 01:28:52,201 - mmcls - INFO - Epoch [90][2000/5005] lr: 1.000e-03, eta: 1:17:47, time: 0.090, data_time: 0.037, memory: 1125, loss: 1.7398 2023-01-03 01:29:00,789 - mmcls - INFO - Epoch [90][2100/5005] lr: 1.000e-03, eta: 1:17:38, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.7343 2023-01-03 01:29:09,796 - mmcls - INFO - Epoch [90][2200/5005] lr: 1.000e-03, eta: 1:17:29, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7301 2023-01-03 01:29:18,545 - mmcls - INFO - Epoch [90][2300/5005] lr: 1.000e-03, eta: 1:17:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7293 2023-01-03 01:29:27,211 - mmcls - INFO - Epoch [90][2400/5005] lr: 1.000e-03, eta: 1:17:12, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7374 2023-01-03 01:29:35,952 - mmcls - INFO - Epoch [90][2500/5005] lr: 1.000e-03, eta: 1:17:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7194 2023-01-03 01:29:44,799 - mmcls - INFO - Epoch [90][2600/5005] lr: 1.000e-03, eta: 1:16:54, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7337 2023-01-03 01:29:53,517 - mmcls - INFO - Epoch [90][2700/5005] lr: 1.000e-03, eta: 1:16:45, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7299 2023-01-03 01:30:02,306 - mmcls - INFO - Epoch [90][2800/5005] lr: 1.000e-03, eta: 1:16:37, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7257 2023-01-03 01:30:11,263 - mmcls - INFO - Epoch [90][2900/5005] lr: 1.000e-03, eta: 1:16:28, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7163 2023-01-03 01:30:19,985 - mmcls - INFO - Epoch [90][3000/5005] lr: 1.000e-03, eta: 1:16:19, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.7595 2023-01-03 01:30:28,540 - mmcls - INFO - Epoch [90][3100/5005] lr: 1.000e-03, eta: 1:16:10, time: 0.086, data_time: 0.040, memory: 1125, loss: 1.7226 2023-01-03 01:30:36,982 - mmcls - INFO - Epoch [90][3200/5005] lr: 1.000e-03, eta: 1:16:01, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.7233 2023-01-03 01:30:45,733 - mmcls - INFO - Epoch [90][3300/5005] lr: 1.000e-03, eta: 1:15:53, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7322 2023-01-03 01:30:54,758 - mmcls - INFO - Epoch [90][3400/5005] lr: 1.000e-03, eta: 1:15:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7235 2023-01-03 01:31:03,556 - mmcls - INFO - Epoch [90][3500/5005] lr: 1.000e-03, eta: 1:15:35, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7201 2023-01-03 01:31:12,114 - mmcls - INFO - Epoch [90][3600/5005] lr: 1.000e-03, eta: 1:15:26, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7360 2023-01-03 01:31:20,644 - mmcls - INFO - Epoch [90][3700/5005] lr: 1.000e-03, eta: 1:15:17, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7302 2023-01-03 01:31:29,350 - mmcls - INFO - Epoch [90][3800/5005] lr: 1.000e-03, eta: 1:15:08, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7246 2023-01-03 01:31:38,410 - mmcls - INFO - Epoch [90][3900/5005] lr: 1.000e-03, eta: 1:15:00, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7375 2023-01-03 01:31:47,350 - mmcls - INFO - Epoch [90][4000/5005] lr: 1.000e-03, eta: 1:14:51, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7311 2023-01-03 01:31:56,367 - mmcls - INFO - Epoch [90][4100/5005] lr: 1.000e-03, eta: 1:14:42, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.7430 2023-01-03 01:32:05,167 - mmcls - INFO - Epoch [90][4200/5005] lr: 1.000e-03, eta: 1:14:33, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7329 2023-01-03 01:32:13,809 - mmcls - INFO - Epoch [90][4300/5005] lr: 1.000e-03, eta: 1:14:25, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.7424 2023-01-03 01:32:22,503 - mmcls - INFO - Epoch [90][4400/5005] lr: 1.000e-03, eta: 1:14:16, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7170 2023-01-03 01:32:31,244 - mmcls - INFO - Epoch [90][4500/5005] lr: 1.000e-03, eta: 1:14:07, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7372 2023-01-03 01:32:39,954 - mmcls - INFO - Epoch [90][4600/5005] lr: 1.000e-03, eta: 1:13:58, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7216 2023-01-03 01:32:48,853 - mmcls - INFO - Epoch [90][4700/5005] lr: 1.000e-03, eta: 1:13:49, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7563 2023-01-03 01:32:57,626 - mmcls - INFO - Epoch [90][4800/5005] lr: 1.000e-03, eta: 1:13:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7349 2023-01-03 01:33:06,336 - mmcls - INFO - Epoch [90][4900/5005] lr: 1.000e-03, eta: 1:13:32, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7407 2023-01-03 01:33:14,933 - mmcls - INFO - Epoch [90][5000/5005] lr: 1.000e-03, eta: 1:13:23, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7507 2023-01-03 01:33:37,558 - mmcls - INFO - Epoch(val) [90][196] accuracy_top-1: 63.2260, accuracy_top-5: 84.1020 2023-01-03 01:33:48,341 - mmcls - INFO - Epoch [91][100/5005] lr: 1.000e-04, eta: 1:13:14, time: 0.108, data_time: 0.053, memory: 1125, loss: 1.7012 2023-01-03 01:33:57,017 - mmcls - INFO - Epoch [91][200/5005] lr: 1.000e-04, eta: 1:13:05, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6877 2023-01-03 01:34:05,950 - mmcls - INFO - Epoch [91][300/5005] lr: 1.000e-04, eta: 1:12:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7058 2023-01-03 01:34:14,491 - mmcls - INFO - Epoch [91][400/5005] lr: 1.000e-04, eta: 1:12:47, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6914 2023-01-03 01:34:23,185 - mmcls - INFO - Epoch [91][500/5005] lr: 1.000e-04, eta: 1:12:39, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7183 2023-01-03 01:34:32,119 - mmcls - INFO - Epoch [91][600/5005] lr: 1.000e-04, eta: 1:12:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7279 2023-01-03 01:34:40,987 - mmcls - INFO - Epoch [91][700/5005] lr: 1.000e-04, eta: 1:12:21, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7001 2023-01-03 01:34:49,743 - mmcls - INFO - Epoch [91][800/5005] lr: 1.000e-04, eta: 1:12:12, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7057 2023-01-03 01:34:58,708 - mmcls - INFO - Epoch [91][900/5005] lr: 1.000e-04, eta: 1:12:03, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7296 2023-01-03 01:35:07,297 - mmcls - INFO - Epoch [91][1000/5005] lr: 1.000e-04, eta: 1:11:55, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.6856 2023-01-03 01:35:15,859 - mmcls - INFO - Epoch [91][1100/5005] lr: 1.000e-04, eta: 1:11:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7155 2023-01-03 01:35:24,581 - mmcls - INFO - Epoch [91][1200/5005] lr: 1.000e-04, eta: 1:11:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6945 2023-01-03 01:35:33,130 - mmcls - INFO - Epoch [91][1300/5005] lr: 1.000e-04, eta: 1:11:28, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7180 2023-01-03 01:35:41,856 - mmcls - INFO - Epoch [91][1400/5005] lr: 1.000e-04, eta: 1:11:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6956 2023-01-03 01:35:50,604 - mmcls - INFO - Epoch [91][1500/5005] lr: 1.000e-04, eta: 1:11:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6934 2023-01-03 01:35:59,333 - mmcls - INFO - Epoch [91][1600/5005] lr: 1.000e-04, eta: 1:11:02, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7170 2023-01-03 01:36:08,042 - mmcls - INFO - Epoch [91][1700/5005] lr: 1.000e-04, eta: 1:10:53, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7154 2023-01-03 01:36:16,557 - mmcls - INFO - Epoch [91][1800/5005] lr: 1.000e-04, eta: 1:10:44, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6991 2023-01-03 01:36:25,148 - mmcls - INFO - Epoch [91][1900/5005] lr: 1.000e-04, eta: 1:10:35, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.6871 2023-01-03 01:36:33,941 - mmcls - INFO - Epoch [91][2000/5005] lr: 1.000e-04, eta: 1:10:27, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7037 2023-01-03 01:36:42,928 - mmcls - INFO - Epoch [91][2100/5005] lr: 1.000e-04, eta: 1:10:18, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.6937 2023-01-03 01:36:51,778 - mmcls - INFO - Epoch [91][2200/5005] lr: 1.000e-04, eta: 1:10:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7015 2023-01-03 01:37:00,452 - mmcls - INFO - Epoch [91][2300/5005] lr: 1.000e-04, eta: 1:10:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7062 2023-01-03 01:37:09,512 - mmcls - INFO - Epoch [91][2400/5005] lr: 1.000e-04, eta: 1:09:51, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.6687 2023-01-03 01:37:18,052 - mmcls - INFO - Epoch [91][2500/5005] lr: 1.000e-04, eta: 1:09:43, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.6990 2023-01-03 01:37:26,841 - mmcls - INFO - Epoch [91][2600/5005] lr: 1.000e-04, eta: 1:09:34, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7100 2023-01-03 01:37:35,541 - mmcls - INFO - Epoch [91][2700/5005] lr: 1.000e-04, eta: 1:09:25, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6909 2023-01-03 01:37:44,996 - mmcls - INFO - Epoch [91][2800/5005] lr: 1.000e-04, eta: 1:09:16, time: 0.095, data_time: 0.003, memory: 1125, loss: 1.7106 2023-01-03 01:37:53,462 - mmcls - INFO - Epoch [91][2900/5005] lr: 1.000e-04, eta: 1:09:07, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7080 2023-01-03 01:38:02,664 - mmcls - INFO - Epoch [91][3000/5005] lr: 1.000e-04, eta: 1:08:59, time: 0.092, data_time: 0.016, memory: 1125, loss: 1.7024 2023-01-03 01:38:11,062 - mmcls - INFO - Epoch [91][3100/5005] lr: 1.000e-04, eta: 1:08:50, time: 0.084, data_time: 0.024, memory: 1125, loss: 1.7033 2023-01-03 01:38:19,786 - mmcls - INFO - Epoch [91][3200/5005] lr: 1.000e-04, eta: 1:08:41, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.6905 2023-01-03 01:38:28,373 - mmcls - INFO - Epoch [91][3300/5005] lr: 1.000e-04, eta: 1:08:32, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6996 2023-01-03 01:38:37,164 - mmcls - INFO - Epoch [91][3400/5005] lr: 1.000e-04, eta: 1:08:23, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6875 2023-01-03 01:38:45,866 - mmcls - INFO - Epoch [91][3500/5005] lr: 1.000e-04, eta: 1:08:15, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7032 2023-01-03 01:38:54,636 - mmcls - INFO - Epoch [91][3600/5005] lr: 1.000e-04, eta: 1:08:06, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.6923 2023-01-03 01:39:03,413 - mmcls - INFO - Epoch [91][3700/5005] lr: 1.000e-04, eta: 1:07:57, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7099 2023-01-03 01:39:12,034 - mmcls - INFO - Epoch [91][3800/5005] lr: 1.000e-04, eta: 1:07:48, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6918 2023-01-03 01:39:21,244 - mmcls - INFO - Epoch [91][3900/5005] lr: 1.000e-04, eta: 1:07:39, time: 0.092, data_time: 0.010, memory: 1125, loss: 1.6822 2023-01-03 01:39:30,043 - mmcls - INFO - Epoch [91][4000/5005] lr: 1.000e-04, eta: 1:07:31, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6903 2023-01-03 01:39:38,687 - mmcls - INFO - Epoch [91][4100/5005] lr: 1.000e-04, eta: 1:07:22, time: 0.086, data_time: 0.046, memory: 1125, loss: 1.6972 2023-01-03 01:39:47,309 - mmcls - INFO - Epoch [91][4200/5005] lr: 1.000e-04, eta: 1:07:13, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7015 2023-01-03 01:39:56,122 - mmcls - INFO - Epoch [91][4300/5005] lr: 1.000e-04, eta: 1:07:04, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.6924 2023-01-03 01:40:05,198 - mmcls - INFO - Epoch [91][4400/5005] lr: 1.000e-04, eta: 1:06:55, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7057 2023-01-03 01:40:14,218 - mmcls - INFO - Epoch [91][4500/5005] lr: 1.000e-04, eta: 1:06:47, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7113 2023-01-03 01:40:23,120 - mmcls - INFO - Epoch [91][4600/5005] lr: 1.000e-04, eta: 1:06:38, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7073 2023-01-03 01:40:31,924 - mmcls - INFO - Epoch [91][4700/5005] lr: 1.000e-04, eta: 1:06:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7011 2023-01-03 01:40:40,475 - mmcls - INFO - Epoch [91][4800/5005] lr: 1.000e-04, eta: 1:06:20, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7255 2023-01-03 01:40:49,250 - mmcls - INFO - Epoch [91][4900/5005] lr: 1.000e-04, eta: 1:06:11, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.6913 2023-01-03 01:40:57,915 - mmcls - INFO - Epoch [91][5000/5005] lr: 1.000e-04, eta: 1:06:03, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6937 2023-01-03 01:41:20,648 - mmcls - INFO - Epoch(val) [91][196] accuracy_top-1: 63.4760, accuracy_top-5: 84.2940 2023-01-03 01:41:31,684 - mmcls - INFO - Epoch [92][100/5005] lr: 1.000e-04, eta: 1:05:54, time: 0.110, data_time: 0.058, memory: 1125, loss: 1.6944 2023-01-03 01:41:40,526 - mmcls - INFO - Epoch [92][200/5005] lr: 1.000e-04, eta: 1:05:45, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7090 2023-01-03 01:41:49,282 - mmcls - INFO - Epoch [92][300/5005] lr: 1.000e-04, eta: 1:05:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6862 2023-01-03 01:41:58,059 - mmcls - INFO - Epoch [92][400/5005] lr: 1.000e-04, eta: 1:05:27, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7106 2023-01-03 01:42:06,613 - mmcls - INFO - Epoch [92][500/5005] lr: 1.000e-04, eta: 1:05:18, time: 0.086, data_time: 0.041, memory: 1125, loss: 1.6793 2023-01-03 01:42:15,250 - mmcls - INFO - Epoch [92][600/5005] lr: 1.000e-04, eta: 1:05:10, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7058 2023-01-03 01:42:23,929 - mmcls - INFO - Epoch [92][700/5005] lr: 1.000e-04, eta: 1:05:01, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7036 2023-01-03 01:42:32,830 - mmcls - INFO - Epoch [92][800/5005] lr: 1.000e-04, eta: 1:04:52, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6918 2023-01-03 01:42:41,404 - mmcls - INFO - Epoch [92][900/5005] lr: 1.000e-04, eta: 1:04:43, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6907 2023-01-03 01:42:49,908 - mmcls - INFO - Epoch [92][1000/5005] lr: 1.000e-04, eta: 1:04:34, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.6861 2023-01-03 01:42:58,359 - mmcls - INFO - Epoch [92][1100/5005] lr: 1.000e-04, eta: 1:04:25, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.6765 2023-01-03 01:43:06,998 - mmcls - INFO - Epoch [92][1200/5005] lr: 1.000e-04, eta: 1:04:17, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7046 2023-01-03 01:43:16,047 - mmcls - INFO - Epoch [92][1300/5005] lr: 1.000e-04, eta: 1:04:08, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.6839 2023-01-03 01:43:24,955 - mmcls - INFO - Epoch [92][1400/5005] lr: 1.000e-04, eta: 1:03:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6924 2023-01-03 01:43:34,227 - mmcls - INFO - Epoch [92][1500/5005] lr: 1.000e-04, eta: 1:03:50, time: 0.093, data_time: 0.005, memory: 1125, loss: 1.6891 2023-01-03 01:43:43,103 - mmcls - INFO - Epoch [92][1600/5005] lr: 1.000e-04, eta: 1:03:42, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7057 2023-01-03 01:43:51,747 - mmcls - INFO - Epoch [92][1700/5005] lr: 1.000e-04, eta: 1:03:33, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7164 2023-01-03 01:44:00,563 - mmcls - INFO - Epoch [92][1800/5005] lr: 1.000e-04, eta: 1:03:24, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6929 2023-01-03 01:44:09,524 - mmcls - INFO - Epoch [92][1900/5005] lr: 1.000e-04, eta: 1:03:15, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6804 2023-01-03 01:44:18,225 - mmcls - INFO - Epoch [92][2000/5005] lr: 1.000e-04, eta: 1:03:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6754 2023-01-03 01:44:27,078 - mmcls - INFO - Epoch [92][2100/5005] lr: 1.000e-04, eta: 1:02:58, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.6950 2023-01-03 01:44:35,651 - mmcls - INFO - Epoch [92][2200/5005] lr: 1.000e-04, eta: 1:02:49, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7076 2023-01-03 01:44:44,299 - mmcls - INFO - Epoch [92][2300/5005] lr: 1.000e-04, eta: 1:02:40, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6963 2023-01-03 01:44:53,556 - mmcls - INFO - Epoch [92][2400/5005] lr: 1.000e-04, eta: 1:02:31, time: 0.093, data_time: 0.020, memory: 1125, loss: 1.6975 2023-01-03 01:45:02,234 - mmcls - INFO - Epoch [92][2500/5005] lr: 1.000e-04, eta: 1:02:22, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6798 2023-01-03 01:45:10,742 - mmcls - INFO - Epoch [92][2600/5005] lr: 1.000e-04, eta: 1:02:14, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.7143 2023-01-03 01:45:19,425 - mmcls - INFO - Epoch [92][2700/5005] lr: 1.000e-04, eta: 1:02:05, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.6922 2023-01-03 01:45:28,203 - mmcls - INFO - Epoch [92][2800/5005] lr: 1.000e-04, eta: 1:01:56, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7142 2023-01-03 01:45:37,103 - mmcls - INFO - Epoch [92][2900/5005] lr: 1.000e-04, eta: 1:01:47, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.6918 2023-01-03 01:45:45,842 - mmcls - INFO - Epoch [92][3000/5005] lr: 1.000e-04, eta: 1:01:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6697 2023-01-03 01:45:54,557 - mmcls - INFO - Epoch [92][3100/5005] lr: 1.000e-04, eta: 1:01:30, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7180 2023-01-03 01:46:02,950 - mmcls - INFO - Epoch [92][3200/5005] lr: 1.000e-04, eta: 1:01:21, time: 0.084, data_time: 0.000, memory: 1125, loss: 1.6752 2023-01-03 01:46:11,622 - mmcls - INFO - Epoch [92][3300/5005] lr: 1.000e-04, eta: 1:01:12, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7038 2023-01-03 01:46:20,083 - mmcls - INFO - Epoch [92][3400/5005] lr: 1.000e-04, eta: 1:01:03, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6924 2023-01-03 01:46:28,602 - mmcls - INFO - Epoch [92][3500/5005] lr: 1.000e-04, eta: 1:00:54, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6915 2023-01-03 01:46:37,362 - mmcls - INFO - Epoch [92][3600/5005] lr: 1.000e-04, eta: 1:00:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6747 2023-01-03 01:46:46,288 - mmcls - INFO - Epoch [92][3700/5005] lr: 1.000e-04, eta: 1:00:37, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6920 2023-01-03 01:46:54,910 - mmcls - INFO - Epoch [92][3800/5005] lr: 1.000e-04, eta: 1:00:28, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7065 2023-01-03 01:47:03,765 - mmcls - INFO - Epoch [92][3900/5005] lr: 1.000e-04, eta: 1:00:19, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6899 2023-01-03 01:47:13,095 - mmcls - INFO - Epoch [92][4000/5005] lr: 1.000e-04, eta: 1:00:10, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7154 2023-01-03 01:47:21,666 - mmcls - INFO - Epoch [92][4100/5005] lr: 1.000e-04, eta: 1:00:01, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.6879 2023-01-03 01:47:30,562 - mmcls - INFO - Epoch [92][4200/5005] lr: 1.000e-04, eta: 0:59:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7020 2023-01-03 01:47:39,370 - mmcls - INFO - Epoch [92][4300/5005] lr: 1.000e-04, eta: 0:59:44, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7053 2023-01-03 01:47:48,079 - mmcls - INFO - Epoch [92][4400/5005] lr: 1.000e-04, eta: 0:59:35, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.6922 2023-01-03 01:47:56,742 - mmcls - INFO - Epoch [92][4500/5005] lr: 1.000e-04, eta: 0:59:26, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6740 2023-01-03 01:48:05,443 - mmcls - INFO - Epoch [92][4600/5005] lr: 1.000e-04, eta: 0:59:17, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6952 2023-01-03 01:48:14,067 - mmcls - INFO - Epoch [92][4700/5005] lr: 1.000e-04, eta: 0:59:09, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6852 2023-01-03 01:48:22,716 - mmcls - INFO - Epoch [92][4800/5005] lr: 1.000e-04, eta: 0:59:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6902 2023-01-03 01:48:31,710 - mmcls - INFO - Epoch [92][4900/5005] lr: 1.000e-04, eta: 0:58:51, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.6873 2023-01-03 01:48:40,688 - mmcls - INFO - Epoch [92][5000/5005] lr: 1.000e-04, eta: 0:58:42, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7019 2023-01-03 01:49:03,397 - mmcls - INFO - Epoch(val) [92][196] accuracy_top-1: 63.6180, accuracy_top-5: 84.3060 2023-01-03 01:49:14,360 - mmcls - INFO - Epoch [93][100/5005] lr: 1.000e-04, eta: 0:58:33, time: 0.110, data_time: 0.029, memory: 1125, loss: 1.6968 2023-01-03 01:49:23,065 - mmcls - INFO - Epoch [93][200/5005] lr: 1.000e-04, eta: 0:58:24, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6873 2023-01-03 01:49:31,760 - mmcls - INFO - Epoch [93][300/5005] lr: 1.000e-04, eta: 0:58:16, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6936 2023-01-03 01:49:40,395 - mmcls - INFO - Epoch [93][400/5005] lr: 1.000e-04, eta: 0:58:07, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7013 2023-01-03 01:49:49,006 - mmcls - INFO - Epoch [93][500/5005] lr: 1.000e-04, eta: 0:57:58, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7129 2023-01-03 01:49:57,831 - mmcls - INFO - Epoch [93][600/5005] lr: 1.000e-04, eta: 0:57:49, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6798 2023-01-03 01:50:06,574 - mmcls - INFO - Epoch [93][700/5005] lr: 1.000e-04, eta: 0:57:40, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.6868 2023-01-03 01:50:15,470 - mmcls - INFO - Epoch [93][800/5005] lr: 1.000e-04, eta: 0:57:32, time: 0.089, data_time: 0.046, memory: 1125, loss: 1.6784 2023-01-03 01:50:24,350 - mmcls - INFO - Epoch [93][900/5005] lr: 1.000e-04, eta: 0:57:23, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.6989 2023-01-03 01:50:33,414 - mmcls - INFO - Epoch [93][1000/5005] lr: 1.000e-04, eta: 0:57:14, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.6899 2023-01-03 01:50:42,261 - mmcls - INFO - Epoch [93][1100/5005] lr: 1.000e-04, eta: 0:57:05, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7069 2023-01-03 01:50:51,117 - mmcls - INFO - Epoch [93][1200/5005] lr: 1.000e-04, eta: 0:56:56, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6999 2023-01-03 01:50:59,886 - mmcls - INFO - Epoch [93][1300/5005] lr: 1.000e-04, eta: 0:56:48, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6721 2023-01-03 01:51:09,039 - mmcls - INFO - Epoch [93][1400/5005] lr: 1.000e-04, eta: 0:56:39, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.6813 2023-01-03 01:51:17,705 - mmcls - INFO - Epoch [93][1500/5005] lr: 1.000e-04, eta: 0:56:30, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.6758 2023-01-03 01:51:26,811 - mmcls - INFO - Epoch [93][1600/5005] lr: 1.000e-04, eta: 0:56:21, time: 0.091, data_time: 0.020, memory: 1125, loss: 1.6944 2023-01-03 01:51:35,248 - mmcls - INFO - Epoch [93][1700/5005] lr: 1.000e-04, eta: 0:56:12, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.7144 2023-01-03 01:51:44,086 - mmcls - INFO - Epoch [93][1800/5005] lr: 1.000e-04, eta: 0:56:04, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.6906 2023-01-03 01:51:52,902 - mmcls - INFO - Epoch [93][1900/5005] lr: 1.000e-04, eta: 0:55:55, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.6640 2023-01-03 01:52:01,392 - mmcls - INFO - Epoch [93][2000/5005] lr: 1.000e-04, eta: 0:55:46, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.7113 2023-01-03 01:52:09,894 - mmcls - INFO - Epoch [93][2100/5005] lr: 1.000e-04, eta: 0:55:37, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.6911 2023-01-03 01:52:18,820 - mmcls - INFO - Epoch [93][2200/5005] lr: 1.000e-04, eta: 0:55:28, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6822 2023-01-03 01:52:27,702 - mmcls - INFO - Epoch [93][2300/5005] lr: 1.000e-04, eta: 0:55:20, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6813 2023-01-03 01:52:36,566 - mmcls - INFO - Epoch [93][2400/5005] lr: 1.000e-04, eta: 0:55:11, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6701 2023-01-03 01:52:45,163 - mmcls - INFO - Epoch [93][2500/5005] lr: 1.000e-04, eta: 0:55:02, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7031 2023-01-03 01:52:53,890 - mmcls - INFO - Epoch [93][2600/5005] lr: 1.000e-04, eta: 0:54:53, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7050 2023-01-03 01:53:02,610 - mmcls - INFO - Epoch [93][2700/5005] lr: 1.000e-04, eta: 0:54:44, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.6648 2023-01-03 01:53:11,264 - mmcls - INFO - Epoch [93][2800/5005] lr: 1.000e-04, eta: 0:54:36, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.6818 2023-01-03 01:53:20,364 - mmcls - INFO - Epoch [93][2900/5005] lr: 1.000e-04, eta: 0:54:27, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.6924 2023-01-03 01:53:29,407 - mmcls - INFO - Epoch [93][3000/5005] lr: 1.000e-04, eta: 0:54:18, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.6957 2023-01-03 01:53:38,060 - mmcls - INFO - Epoch [93][3100/5005] lr: 1.000e-04, eta: 0:54:09, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6956 2023-01-03 01:53:46,787 - mmcls - INFO - Epoch [93][3200/5005] lr: 1.000e-04, eta: 0:54:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7071 2023-01-03 01:53:55,674 - mmcls - INFO - Epoch [93][3300/5005] lr: 1.000e-04, eta: 0:53:52, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7105 2023-01-03 01:54:04,723 - mmcls - INFO - Epoch [93][3400/5005] lr: 1.000e-04, eta: 0:53:43, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7033 2023-01-03 01:54:13,257 - mmcls - INFO - Epoch [93][3500/5005] lr: 1.000e-04, eta: 0:53:34, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.6716 2023-01-03 01:54:22,224 - mmcls - INFO - Epoch [93][3600/5005] lr: 1.000e-04, eta: 0:53:25, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.6863 2023-01-03 01:54:30,946 - mmcls - INFO - Epoch [93][3700/5005] lr: 1.000e-04, eta: 0:53:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6596 2023-01-03 01:54:39,708 - mmcls - INFO - Epoch [93][3800/5005] lr: 1.000e-04, eta: 0:53:08, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7098 2023-01-03 01:54:48,406 - mmcls - INFO - Epoch [93][3900/5005] lr: 1.000e-04, eta: 0:52:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6963 2023-01-03 01:54:56,918 - mmcls - INFO - Epoch [93][4000/5005] lr: 1.000e-04, eta: 0:52:50, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.6976 2023-01-03 01:55:05,584 - mmcls - INFO - Epoch [93][4100/5005] lr: 1.000e-04, eta: 0:52:41, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7116 2023-01-03 01:55:14,588 - mmcls - INFO - Epoch [93][4200/5005] lr: 1.000e-04, eta: 0:52:32, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.6899 2023-01-03 01:55:23,179 - mmcls - INFO - Epoch [93][4300/5005] lr: 1.000e-04, eta: 0:52:24, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.6858 2023-01-03 01:55:31,917 - mmcls - INFO - Epoch [93][4400/5005] lr: 1.000e-04, eta: 0:52:15, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6727 2023-01-03 01:55:40,715 - mmcls - INFO - Epoch [93][4500/5005] lr: 1.000e-04, eta: 0:52:06, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6813 2023-01-03 01:55:49,433 - mmcls - INFO - Epoch [93][4600/5005] lr: 1.000e-04, eta: 0:51:57, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.6805 2023-01-03 01:55:58,159 - mmcls - INFO - Epoch [93][4700/5005] lr: 1.000e-04, eta: 0:51:48, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.6736 2023-01-03 01:56:06,721 - mmcls - INFO - Epoch [93][4800/5005] lr: 1.000e-04, eta: 0:51:40, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.7189 2023-01-03 01:56:15,490 - mmcls - INFO - Epoch [93][4900/5005] lr: 1.000e-04, eta: 0:51:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7171 2023-01-03 01:56:24,071 - mmcls - INFO - Epoch [93][5000/5005] lr: 1.000e-04, eta: 0:51:22, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6795 2023-01-03 01:56:46,861 - mmcls - INFO - Epoch(val) [93][196] accuracy_top-1: 63.6640, accuracy_top-5: 84.3360 2023-01-03 01:56:58,132 - mmcls - INFO - Epoch [94][100/5005] lr: 1.000e-04, eta: 0:51:13, time: 0.112, data_time: 0.045, memory: 1125, loss: 1.6792 2023-01-03 01:57:06,759 - mmcls - INFO - Epoch [94][200/5005] lr: 1.000e-04, eta: 0:51:04, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.6814 2023-01-03 01:57:15,553 - mmcls - INFO - Epoch [94][300/5005] lr: 1.000e-04, eta: 0:50:55, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6866 2023-01-03 01:57:24,595 - mmcls - INFO - Epoch [94][400/5005] lr: 1.000e-04, eta: 0:50:47, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6802 2023-01-03 01:57:33,266 - mmcls - INFO - Epoch [94][500/5005] lr: 1.000e-04, eta: 0:50:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6949 2023-01-03 01:57:41,933 - mmcls - INFO - Epoch [94][600/5005] lr: 1.000e-04, eta: 0:50:29, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6812 2023-01-03 01:57:50,418 - mmcls - INFO - Epoch [94][700/5005] lr: 1.000e-04, eta: 0:50:20, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6841 2023-01-03 01:57:59,199 - mmcls - INFO - Epoch [94][800/5005] lr: 1.000e-04, eta: 0:50:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7055 2023-01-03 01:58:07,860 - mmcls - INFO - Epoch [94][900/5005] lr: 1.000e-04, eta: 0:50:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6779 2023-01-03 01:58:16,755 - mmcls - INFO - Epoch [94][1000/5005] lr: 1.000e-04, eta: 0:49:54, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.6768 2023-01-03 01:58:25,462 - mmcls - INFO - Epoch [94][1100/5005] lr: 1.000e-04, eta: 0:49:45, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7079 2023-01-03 01:58:34,398 - mmcls - INFO - Epoch [94][1200/5005] lr: 1.000e-04, eta: 0:49:36, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.7017 2023-01-03 01:58:43,069 - mmcls - INFO - Epoch [94][1300/5005] lr: 1.000e-04, eta: 0:49:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7031 2023-01-03 01:58:52,220 - mmcls - INFO - Epoch [94][1400/5005] lr: 1.000e-04, eta: 0:49:19, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.7144 2023-01-03 01:59:00,701 - mmcls - INFO - Epoch [94][1500/5005] lr: 1.000e-04, eta: 0:49:10, time: 0.085, data_time: 0.024, memory: 1125, loss: 1.7034 2023-01-03 01:59:09,459 - mmcls - INFO - Epoch [94][1600/5005] lr: 1.000e-04, eta: 0:49:01, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.6872 2023-01-03 01:59:18,200 - mmcls - INFO - Epoch [94][1700/5005] lr: 1.000e-04, eta: 0:48:52, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6910 2023-01-03 01:59:27,279 - mmcls - INFO - Epoch [94][1800/5005] lr: 1.000e-04, eta: 0:48:43, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.6924 2023-01-03 01:59:35,815 - mmcls - INFO - Epoch [94][1900/5005] lr: 1.000e-04, eta: 0:48:35, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.6922 2023-01-03 01:59:44,426 - mmcls - INFO - Epoch [94][2000/5005] lr: 1.000e-04, eta: 0:48:26, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7008 2023-01-03 01:59:53,135 - mmcls - INFO - Epoch [94][2100/5005] lr: 1.000e-04, eta: 0:48:17, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6695 2023-01-03 02:00:01,939 - mmcls - INFO - Epoch [94][2200/5005] lr: 1.000e-04, eta: 0:48:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6896 2023-01-03 02:00:11,126 - mmcls - INFO - Epoch [94][2300/5005] lr: 1.000e-04, eta: 0:47:59, time: 0.092, data_time: 0.004, memory: 1125, loss: 1.7032 2023-01-03 02:00:19,881 - mmcls - INFO - Epoch [94][2400/5005] lr: 1.000e-04, eta: 0:47:51, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6957 2023-01-03 02:00:28,348 - mmcls - INFO - Epoch [94][2500/5005] lr: 1.000e-04, eta: 0:47:42, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6895 2023-01-03 02:00:36,930 - mmcls - INFO - Epoch [94][2600/5005] lr: 1.000e-04, eta: 0:47:33, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.7118 2023-01-03 02:00:45,494 - mmcls - INFO - Epoch [94][2700/5005] lr: 1.000e-04, eta: 0:47:24, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6663 2023-01-03 02:00:53,956 - mmcls - INFO - Epoch [94][2800/5005] lr: 1.000e-04, eta: 0:47:15, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.7031 2023-01-03 02:01:02,764 - mmcls - INFO - Epoch [94][2900/5005] lr: 1.000e-04, eta: 0:47:06, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.6771 2023-01-03 02:01:11,495 - mmcls - INFO - Epoch [94][3000/5005] lr: 1.000e-04, eta: 0:46:58, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.6994 2023-01-03 02:01:20,173 - mmcls - INFO - Epoch [94][3100/5005] lr: 1.000e-04, eta: 0:46:49, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6957 2023-01-03 02:01:29,042 - mmcls - INFO - Epoch [94][3200/5005] lr: 1.000e-04, eta: 0:46:40, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6875 2023-01-03 02:01:37,618 - mmcls - INFO - Epoch [94][3300/5005] lr: 1.000e-04, eta: 0:46:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7009 2023-01-03 02:01:46,347 - mmcls - INFO - Epoch [94][3400/5005] lr: 1.000e-04, eta: 0:46:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6750 2023-01-03 02:01:55,536 - mmcls - INFO - Epoch [94][3500/5005] lr: 1.000e-04, eta: 0:46:14, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.6876 2023-01-03 02:02:04,768 - mmcls - INFO - Epoch [94][3600/5005] lr: 1.000e-04, eta: 0:46:05, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.6699 2023-01-03 02:02:13,428 - mmcls - INFO - Epoch [94][3700/5005] lr: 1.000e-04, eta: 0:45:56, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.6905 2023-01-03 02:02:22,208 - mmcls - INFO - Epoch [94][3800/5005] lr: 1.000e-04, eta: 0:45:47, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.6741 2023-01-03 02:02:31,169 - mmcls - INFO - Epoch [94][3900/5005] lr: 1.000e-04, eta: 0:45:39, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6786 2023-01-03 02:02:40,047 - mmcls - INFO - Epoch [94][4000/5005] lr: 1.000e-04, eta: 0:45:30, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6926 2023-01-03 02:02:48,586 - mmcls - INFO - Epoch [94][4100/5005] lr: 1.000e-04, eta: 0:45:21, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.6487 2023-01-03 02:02:57,579 - mmcls - INFO - Epoch [94][4200/5005] lr: 1.000e-04, eta: 0:45:12, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.6870 2023-01-03 02:03:06,363 - mmcls - INFO - Epoch [94][4300/5005] lr: 1.000e-04, eta: 0:45:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7038 2023-01-03 02:03:15,371 - mmcls - INFO - Epoch [94][4400/5005] lr: 1.000e-04, eta: 0:44:55, time: 0.090, data_time: 0.040, memory: 1125, loss: 1.7010 2023-01-03 02:03:24,175 - mmcls - INFO - Epoch [94][4500/5005] lr: 1.000e-04, eta: 0:44:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6944 2023-01-03 02:03:32,818 - mmcls - INFO - Epoch [94][4600/5005] lr: 1.000e-04, eta: 0:44:37, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6863 2023-01-03 02:03:42,029 - mmcls - INFO - Epoch [94][4700/5005] lr: 1.000e-04, eta: 0:44:28, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.6943 2023-01-03 02:03:50,589 - mmcls - INFO - Epoch [94][4800/5005] lr: 1.000e-04, eta: 0:44:19, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6964 2023-01-03 02:03:59,370 - mmcls - INFO - Epoch [94][4900/5005] lr: 1.000e-04, eta: 0:44:11, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6843 2023-01-03 02:04:08,405 - mmcls - INFO - Epoch [94][5000/5005] lr: 1.000e-04, eta: 0:44:02, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7006 2023-01-03 02:04:30,906 - mmcls - INFO - Epoch(val) [94][196] accuracy_top-1: 63.5280, accuracy_top-5: 84.1540 2023-01-03 02:04:41,856 - mmcls - INFO - Epoch [95][100/5005] lr: 1.000e-04, eta: 0:43:53, time: 0.109, data_time: 0.050, memory: 1125, loss: 1.6851 2023-01-03 02:04:50,790 - mmcls - INFO - Epoch [95][200/5005] lr: 1.000e-04, eta: 0:43:44, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.6811 2023-01-03 02:04:59,455 - mmcls - INFO - Epoch [95][300/5005] lr: 1.000e-04, eta: 0:43:35, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7138 2023-01-03 02:05:07,983 - mmcls - INFO - Epoch [95][400/5005] lr: 1.000e-04, eta: 0:43:26, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6757 2023-01-03 02:05:16,748 - mmcls - INFO - Epoch [95][500/5005] lr: 1.000e-04, eta: 0:43:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6782 2023-01-03 02:05:25,789 - mmcls - INFO - Epoch [95][600/5005] lr: 1.000e-04, eta: 0:43:09, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6881 2023-01-03 02:05:34,294 - mmcls - INFO - Epoch [95][700/5005] lr: 1.000e-04, eta: 0:43:00, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6974 2023-01-03 02:05:42,899 - mmcls - INFO - Epoch [95][800/5005] lr: 1.000e-04, eta: 0:42:51, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.6723 2023-01-03 02:05:51,604 - mmcls - INFO - Epoch [95][900/5005] lr: 1.000e-04, eta: 0:42:42, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6787 2023-01-03 02:06:00,468 - mmcls - INFO - Epoch [95][1000/5005] lr: 1.000e-04, eta: 0:42:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7076 2023-01-03 02:06:09,170 - mmcls - INFO - Epoch [95][1100/5005] lr: 1.000e-04, eta: 0:42:25, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6704 2023-01-03 02:06:17,894 - mmcls - INFO - Epoch [95][1200/5005] lr: 1.000e-04, eta: 0:42:16, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6876 2023-01-03 02:06:26,984 - mmcls - INFO - Epoch [95][1300/5005] lr: 1.000e-04, eta: 0:42:07, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6815 2023-01-03 02:06:36,004 - mmcls - INFO - Epoch [95][1400/5005] lr: 1.000e-04, eta: 0:41:58, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6816 2023-01-03 02:06:44,549 - mmcls - INFO - Epoch [95][1500/5005] lr: 1.000e-04, eta: 0:41:49, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6815 2023-01-03 02:06:53,080 - mmcls - INFO - Epoch [95][1600/5005] lr: 1.000e-04, eta: 0:41:41, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6739 2023-01-03 02:07:01,952 - mmcls - INFO - Epoch [95][1700/5005] lr: 1.000e-04, eta: 0:41:32, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.6917 2023-01-03 02:07:10,914 - mmcls - INFO - Epoch [95][1800/5005] lr: 1.000e-04, eta: 0:41:23, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6968 2023-01-03 02:07:19,698 - mmcls - INFO - Epoch [95][1900/5005] lr: 1.000e-04, eta: 0:41:14, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7058 2023-01-03 02:07:28,745 - mmcls - INFO - Epoch [95][2000/5005] lr: 1.000e-04, eta: 0:41:06, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7054 2023-01-03 02:07:37,597 - mmcls - INFO - Epoch [95][2100/5005] lr: 1.000e-04, eta: 0:40:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6848 2023-01-03 02:07:46,541 - mmcls - INFO - Epoch [95][2200/5005] lr: 1.000e-04, eta: 0:40:48, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7131 2023-01-03 02:07:55,113 - mmcls - INFO - Epoch [95][2300/5005] lr: 1.000e-04, eta: 0:40:39, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6965 2023-01-03 02:08:03,978 - mmcls - INFO - Epoch [95][2400/5005] lr: 1.000e-04, eta: 0:40:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6817 2023-01-03 02:08:12,557 - mmcls - INFO - Epoch [95][2500/5005] lr: 1.000e-04, eta: 0:40:22, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7002 2023-01-03 02:08:21,489 - mmcls - INFO - Epoch [95][2600/5005] lr: 1.000e-04, eta: 0:40:13, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.6864 2023-01-03 02:08:30,352 - mmcls - INFO - Epoch [95][2700/5005] lr: 1.000e-04, eta: 0:40:04, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.6860 2023-01-03 02:08:39,098 - mmcls - INFO - Epoch [95][2800/5005] lr: 1.000e-04, eta: 0:39:55, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7112 2023-01-03 02:08:47,759 - mmcls - INFO - Epoch [95][2900/5005] lr: 1.000e-04, eta: 0:39:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6996 2023-01-03 02:08:56,612 - mmcls - INFO - Epoch [95][3000/5005] lr: 1.000e-04, eta: 0:39:38, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7053 2023-01-03 02:09:05,271 - mmcls - INFO - Epoch [95][3100/5005] lr: 1.000e-04, eta: 0:39:29, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6769 2023-01-03 02:09:14,407 - mmcls - INFO - Epoch [95][3200/5005] lr: 1.000e-04, eta: 0:39:20, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.6987 2023-01-03 02:09:23,171 - mmcls - INFO - Epoch [95][3300/5005] lr: 1.000e-04, eta: 0:39:11, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6822 2023-01-03 02:09:31,990 - mmcls - INFO - Epoch [95][3400/5005] lr: 1.000e-04, eta: 0:39:02, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7098 2023-01-03 02:09:40,560 - mmcls - INFO - Epoch [95][3500/5005] lr: 1.000e-04, eta: 0:38:54, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6928 2023-01-03 02:09:49,678 - mmcls - INFO - Epoch [95][3600/5005] lr: 1.000e-04, eta: 0:38:45, time: 0.091, data_time: 0.020, memory: 1125, loss: 1.6911 2023-01-03 02:09:58,726 - mmcls - INFO - Epoch [95][3700/5005] lr: 1.000e-04, eta: 0:38:36, time: 0.090, data_time: 0.038, memory: 1125, loss: 1.6872 2023-01-03 02:10:07,446 - mmcls - INFO - Epoch [95][3800/5005] lr: 1.000e-04, eta: 0:38:27, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6989 2023-01-03 02:10:16,151 - mmcls - INFO - Epoch [95][3900/5005] lr: 1.000e-04, eta: 0:38:18, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.6809 2023-01-03 02:10:24,820 - mmcls - INFO - Epoch [95][4000/5005] lr: 1.000e-04, eta: 0:38:10, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6895 2023-01-03 02:10:33,631 - mmcls - INFO - Epoch [95][4100/5005] lr: 1.000e-04, eta: 0:38:01, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6836 2023-01-03 02:10:42,067 - mmcls - INFO - Epoch [95][4200/5005] lr: 1.000e-04, eta: 0:37:52, time: 0.084, data_time: 0.000, memory: 1125, loss: 1.6847 2023-01-03 02:10:50,897 - mmcls - INFO - Epoch [95][4300/5005] lr: 1.000e-04, eta: 0:37:43, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.7125 2023-01-03 02:10:59,657 - mmcls - INFO - Epoch [95][4400/5005] lr: 1.000e-04, eta: 0:37:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6777 2023-01-03 02:11:08,452 - mmcls - INFO - Epoch [95][4500/5005] lr: 1.000e-04, eta: 0:37:26, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7047 2023-01-03 02:11:17,070 - mmcls - INFO - Epoch [95][4600/5005] lr: 1.000e-04, eta: 0:37:17, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.6837 2023-01-03 02:11:25,712 - mmcls - INFO - Epoch [95][4700/5005] lr: 1.000e-04, eta: 0:37:08, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7087 2023-01-03 02:11:34,851 - mmcls - INFO - Epoch [95][4800/5005] lr: 1.000e-04, eta: 0:36:59, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6881 2023-01-03 02:11:44,100 - mmcls - INFO - Epoch [95][4900/5005] lr: 1.000e-04, eta: 0:36:50, time: 0.092, data_time: 0.007, memory: 1125, loss: 1.6896 2023-01-03 02:11:52,453 - mmcls - INFO - Epoch [95][5000/5005] lr: 1.000e-04, eta: 0:36:42, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.6842 2023-01-03 02:12:15,260 - mmcls - INFO - Epoch(val) [95][196] accuracy_top-1: 63.6340, accuracy_top-5: 84.2080 2023-01-03 02:12:26,335 - mmcls - INFO - Epoch [96][100/5005] lr: 1.000e-04, eta: 0:36:32, time: 0.111, data_time: 0.044, memory: 1125, loss: 1.6958 2023-01-03 02:12:34,885 - mmcls - INFO - Epoch [96][200/5005] lr: 1.000e-04, eta: 0:36:24, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6892 2023-01-03 02:12:43,293 - mmcls - INFO - Epoch [96][300/5005] lr: 1.000e-04, eta: 0:36:15, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.6937 2023-01-03 02:12:52,401 - mmcls - INFO - Epoch [96][400/5005] lr: 1.000e-04, eta: 0:36:06, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.6735 2023-01-03 02:13:01,145 - mmcls - INFO - Epoch [96][500/5005] lr: 1.000e-04, eta: 0:35:57, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6577 2023-01-03 02:13:09,705 - mmcls - INFO - Epoch [96][600/5005] lr: 1.000e-04, eta: 0:35:48, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.6803 2023-01-03 02:13:18,404 - mmcls - INFO - Epoch [96][700/5005] lr: 1.000e-04, eta: 0:35:40, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.6681 2023-01-03 02:13:27,186 - mmcls - INFO - Epoch [96][800/5005] lr: 1.000e-04, eta: 0:35:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7010 2023-01-03 02:13:35,658 - mmcls - INFO - Epoch [96][900/5005] lr: 1.000e-04, eta: 0:35:22, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7067 2023-01-03 02:13:44,104 - mmcls - INFO - Epoch [96][1000/5005] lr: 1.000e-04, eta: 0:35:13, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.6863 2023-01-03 02:13:52,792 - mmcls - INFO - Epoch [96][1100/5005] lr: 1.000e-04, eta: 0:35:04, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.6610 2023-01-03 02:14:01,372 - mmcls - INFO - Epoch [96][1200/5005] lr: 1.000e-04, eta: 0:34:56, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6952 2023-01-03 02:14:10,045 - mmcls - INFO - Epoch [96][1300/5005] lr: 1.000e-04, eta: 0:34:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6953 2023-01-03 02:14:18,701 - mmcls - INFO - Epoch [96][1400/5005] lr: 1.000e-04, eta: 0:34:38, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6883 2023-01-03 02:14:27,671 - mmcls - INFO - Epoch [96][1500/5005] lr: 1.000e-04, eta: 0:34:29, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6909 2023-01-03 02:14:36,312 - mmcls - INFO - Epoch [96][1600/5005] lr: 1.000e-04, eta: 0:34:20, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7144 2023-01-03 02:14:45,219 - mmcls - INFO - Epoch [96][1700/5005] lr: 1.000e-04, eta: 0:34:12, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6852 2023-01-03 02:14:53,990 - mmcls - INFO - Epoch [96][1800/5005] lr: 1.000e-04, eta: 0:34:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6798 2023-01-03 02:15:03,205 - mmcls - INFO - Epoch [96][1900/5005] lr: 1.000e-04, eta: 0:33:54, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7201 2023-01-03 02:15:12,068 - mmcls - INFO - Epoch [96][2000/5005] lr: 1.000e-04, eta: 0:33:45, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.6749 2023-01-03 02:15:20,910 - mmcls - INFO - Epoch [96][2100/5005] lr: 1.000e-04, eta: 0:33:36, time: 0.088, data_time: 0.034, memory: 1125, loss: 1.6785 2023-01-03 02:15:29,892 - mmcls - INFO - Epoch [96][2200/5005] lr: 1.000e-04, eta: 0:33:28, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.6836 2023-01-03 02:15:38,588 - mmcls - INFO - Epoch [96][2300/5005] lr: 1.000e-04, eta: 0:33:19, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7128 2023-01-03 02:15:47,296 - mmcls - INFO - Epoch [96][2400/5005] lr: 1.000e-04, eta: 0:33:10, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6978 2023-01-03 02:15:55,872 - mmcls - INFO - Epoch [96][2500/5005] lr: 1.000e-04, eta: 0:33:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6831 2023-01-03 02:16:04,542 - mmcls - INFO - Epoch [96][2600/5005] lr: 1.000e-04, eta: 0:32:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6774 2023-01-03 02:16:13,409 - mmcls - INFO - Epoch [96][2700/5005] lr: 1.000e-04, eta: 0:32:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6882 2023-01-03 02:16:21,912 - mmcls - INFO - Epoch [96][2800/5005] lr: 1.000e-04, eta: 0:32:35, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.6935 2023-01-03 02:16:30,648 - mmcls - INFO - Epoch [96][2900/5005] lr: 1.000e-04, eta: 0:32:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6883 2023-01-03 02:16:39,661 - mmcls - INFO - Epoch [96][3000/5005] lr: 1.000e-04, eta: 0:32:17, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7065 2023-01-03 02:16:48,348 - mmcls - INFO - Epoch [96][3100/5005] lr: 1.000e-04, eta: 0:32:08, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.6736 2023-01-03 02:16:57,337 - mmcls - INFO - Epoch [96][3200/5005] lr: 1.000e-04, eta: 0:32:00, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.6852 2023-01-03 02:17:06,211 - mmcls - INFO - Epoch [96][3300/5005] lr: 1.000e-04, eta: 0:31:51, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.6713 2023-01-03 02:17:14,956 - mmcls - INFO - Epoch [96][3400/5005] lr: 1.000e-04, eta: 0:31:42, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.6964 2023-01-03 02:17:23,807 - mmcls - INFO - Epoch [96][3500/5005] lr: 1.000e-04, eta: 0:31:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6828 2023-01-03 02:17:32,757 - mmcls - INFO - Epoch [96][3600/5005] lr: 1.000e-04, eta: 0:31:24, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.6919 2023-01-03 02:17:41,408 - mmcls - INFO - Epoch [96][3700/5005] lr: 1.000e-04, eta: 0:31:16, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.6966 2023-01-03 02:17:50,160 - mmcls - INFO - Epoch [96][3800/5005] lr: 1.000e-04, eta: 0:31:07, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.6792 2023-01-03 02:17:58,783 - mmcls - INFO - Epoch [96][3900/5005] lr: 1.000e-04, eta: 0:30:58, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7083 2023-01-03 02:18:07,738 - mmcls - INFO - Epoch [96][4000/5005] lr: 1.000e-04, eta: 0:30:49, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6779 2023-01-03 02:18:16,407 - mmcls - INFO - Epoch [96][4100/5005] lr: 1.000e-04, eta: 0:30:40, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6905 2023-01-03 02:18:24,975 - mmcls - INFO - Epoch [96][4200/5005] lr: 1.000e-04, eta: 0:30:32, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6992 2023-01-03 02:18:33,656 - mmcls - INFO - Epoch [96][4300/5005] lr: 1.000e-04, eta: 0:30:23, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6972 2023-01-03 02:18:42,457 - mmcls - INFO - Epoch [96][4400/5005] lr: 1.000e-04, eta: 0:30:14, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6706 2023-01-03 02:18:51,564 - mmcls - INFO - Epoch [96][4500/5005] lr: 1.000e-04, eta: 0:30:05, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6953 2023-01-03 02:19:00,332 - mmcls - INFO - Epoch [96][4600/5005] lr: 1.000e-04, eta: 0:29:56, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.6915 2023-01-03 02:19:08,983 - mmcls - INFO - Epoch [96][4700/5005] lr: 1.000e-04, eta: 0:29:48, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6759 2023-01-03 02:19:17,580 - mmcls - INFO - Epoch [96][4800/5005] lr: 1.000e-04, eta: 0:29:39, time: 0.086, data_time: 0.037, memory: 1125, loss: 1.6843 2023-01-03 02:19:26,214 - mmcls - INFO - Epoch [96][4900/5005] lr: 1.000e-04, eta: 0:29:30, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6742 2023-01-03 02:19:35,040 - mmcls - INFO - Epoch [96][5000/5005] lr: 1.000e-04, eta: 0:29:21, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.6705 2023-01-03 02:19:57,749 - mmcls - INFO - Epoch(val) [96][196] accuracy_top-1: 63.7640, accuracy_top-5: 84.2920 2023-01-03 02:20:08,886 - mmcls - INFO - Epoch [97][100/5005] lr: 1.000e-04, eta: 0:29:12, time: 0.111, data_time: 0.054, memory: 1125, loss: 1.7024 2023-01-03 02:20:17,458 - mmcls - INFO - Epoch [97][200/5005] lr: 1.000e-04, eta: 0:29:03, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6677 2023-01-03 02:20:25,906 - mmcls - INFO - Epoch [97][300/5005] lr: 1.000e-04, eta: 0:28:54, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.6843 2023-01-03 02:20:34,600 - mmcls - INFO - Epoch [97][400/5005] lr: 1.000e-04, eta: 0:28:46, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7039 2023-01-03 02:20:43,560 - mmcls - INFO - Epoch [97][500/5005] lr: 1.000e-04, eta: 0:28:37, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.6835 2023-01-03 02:20:52,211 - mmcls - INFO - Epoch [97][600/5005] lr: 1.000e-04, eta: 0:28:28, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6898 2023-01-03 02:21:00,821 - mmcls - INFO - Epoch [97][700/5005] lr: 1.000e-04, eta: 0:28:19, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.6835 2023-01-03 02:21:09,476 - mmcls - INFO - Epoch [97][800/5005] lr: 1.000e-04, eta: 0:28:10, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.7074 2023-01-03 02:21:18,033 - mmcls - INFO - Epoch [97][900/5005] lr: 1.000e-04, eta: 0:28:02, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6881 2023-01-03 02:21:26,676 - mmcls - INFO - Epoch [97][1000/5005] lr: 1.000e-04, eta: 0:27:53, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.6820 2023-01-03 02:21:35,502 - mmcls - INFO - Epoch [97][1100/5005] lr: 1.000e-04, eta: 0:27:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6591 2023-01-03 02:21:44,262 - mmcls - INFO - Epoch [97][1200/5005] lr: 1.000e-04, eta: 0:27:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6876 2023-01-03 02:21:52,933 - mmcls - INFO - Epoch [97][1300/5005] lr: 1.000e-04, eta: 0:27:26, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7092 2023-01-03 02:22:01,738 - mmcls - INFO - Epoch [97][1400/5005] lr: 1.000e-04, eta: 0:27:18, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6696 2023-01-03 02:22:10,488 - mmcls - INFO - Epoch [97][1500/5005] lr: 1.000e-04, eta: 0:27:09, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.6880 2023-01-03 02:22:19,049 - mmcls - INFO - Epoch [97][1600/5005] lr: 1.000e-04, eta: 0:27:00, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6954 2023-01-03 02:22:27,815 - mmcls - INFO - Epoch [97][1700/5005] lr: 1.000e-04, eta: 0:26:51, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.6897 2023-01-03 02:22:36,689 - mmcls - INFO - Epoch [97][1800/5005] lr: 1.000e-04, eta: 0:26:42, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.6931 2023-01-03 02:22:45,384 - mmcls - INFO - Epoch [97][1900/5005] lr: 1.000e-04, eta: 0:26:34, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6825 2023-01-03 02:22:54,018 - mmcls - INFO - Epoch [97][2000/5005] lr: 1.000e-04, eta: 0:26:25, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6952 2023-01-03 02:23:02,973 - mmcls - INFO - Epoch [97][2100/5005] lr: 1.000e-04, eta: 0:26:16, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6950 2023-01-03 02:23:11,493 - mmcls - INFO - Epoch [97][2200/5005] lr: 1.000e-04, eta: 0:26:07, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7080 2023-01-03 02:23:20,299 - mmcls - INFO - Epoch [97][2300/5005] lr: 1.000e-04, eta: 0:25:58, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.6822 2023-01-03 02:23:29,121 - mmcls - INFO - Epoch [97][2400/5005] lr: 1.000e-04, eta: 0:25:50, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6784 2023-01-03 02:23:37,752 - mmcls - INFO - Epoch [97][2500/5005] lr: 1.000e-04, eta: 0:25:41, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.6916 2023-01-03 02:23:46,789 - mmcls - INFO - Epoch [97][2600/5005] lr: 1.000e-04, eta: 0:25:32, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6750 2023-01-03 02:23:55,618 - mmcls - INFO - Epoch [97][2700/5005] lr: 1.000e-04, eta: 0:25:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6911 2023-01-03 02:24:04,693 - mmcls - INFO - Epoch [97][2800/5005] lr: 1.000e-04, eta: 0:25:14, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6998 2023-01-03 02:24:13,551 - mmcls - INFO - Epoch [97][2900/5005] lr: 1.000e-04, eta: 0:25:06, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.6802 2023-01-03 02:24:22,225 - mmcls - INFO - Epoch [97][3000/5005] lr: 1.000e-04, eta: 0:24:57, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.6661 2023-01-03 02:24:31,141 - mmcls - INFO - Epoch [97][3100/5005] lr: 1.000e-04, eta: 0:24:48, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.6800 2023-01-03 02:24:39,749 - mmcls - INFO - Epoch [97][3200/5005] lr: 1.000e-04, eta: 0:24:39, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7104 2023-01-03 02:24:48,655 - mmcls - INFO - Epoch [97][3300/5005] lr: 1.000e-04, eta: 0:24:30, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.6990 2023-01-03 02:24:57,343 - mmcls - INFO - Epoch [97][3400/5005] lr: 1.000e-04, eta: 0:24:22, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.6784 2023-01-03 02:25:06,047 - mmcls - INFO - Epoch [97][3500/5005] lr: 1.000e-04, eta: 0:24:13, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7029 2023-01-03 02:25:14,771 - mmcls - INFO - Epoch [97][3600/5005] lr: 1.000e-04, eta: 0:24:04, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7032 2023-01-03 02:25:23,380 - mmcls - INFO - Epoch [97][3700/5005] lr: 1.000e-04, eta: 0:23:55, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7054 2023-01-03 02:25:32,443 - mmcls - INFO - Epoch [97][3800/5005] lr: 1.000e-04, eta: 0:23:46, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6876 2023-01-03 02:25:41,284 - mmcls - INFO - Epoch [97][3900/5005] lr: 1.000e-04, eta: 0:23:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6774 2023-01-03 02:25:49,868 - mmcls - INFO - Epoch [97][4000/5005] lr: 1.000e-04, eta: 0:23:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.6944 2023-01-03 02:25:59,056 - mmcls - INFO - Epoch [97][4100/5005] lr: 1.000e-04, eta: 0:23:20, time: 0.092, data_time: 0.000, memory: 1125, loss: 1.6693 2023-01-03 02:26:08,038 - mmcls - INFO - Epoch [97][4200/5005] lr: 1.000e-04, eta: 0:23:11, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.6891 2023-01-03 02:26:16,755 - mmcls - INFO - Epoch [97][4300/5005] lr: 1.000e-04, eta: 0:23:02, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6712 2023-01-03 02:26:25,174 - mmcls - INFO - Epoch [97][4400/5005] lr: 1.000e-04, eta: 0:22:54, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.7026 2023-01-03 02:26:33,761 - mmcls - INFO - Epoch [97][4500/5005] lr: 1.000e-04, eta: 0:22:45, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6885 2023-01-03 02:26:42,268 - mmcls - INFO - Epoch [97][4600/5005] lr: 1.000e-04, eta: 0:22:36, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7094 2023-01-03 02:26:51,370 - mmcls - INFO - Epoch [97][4700/5005] lr: 1.000e-04, eta: 0:22:27, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.6959 2023-01-03 02:27:00,445 - mmcls - INFO - Epoch [97][4800/5005] lr: 1.000e-04, eta: 0:22:18, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.6801 2023-01-03 02:27:09,000 - mmcls - INFO - Epoch [97][4900/5005] lr: 1.000e-04, eta: 0:22:10, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6978 2023-01-03 02:27:18,163 - mmcls - INFO - Epoch [97][5000/5005] lr: 1.000e-04, eta: 0:22:01, time: 0.092, data_time: 0.004, memory: 1125, loss: 1.7025 2023-01-03 02:27:40,818 - mmcls - INFO - Epoch(val) [97][196] accuracy_top-1: 63.7580, accuracy_top-5: 84.4780 2023-01-03 02:27:51,918 - mmcls - INFO - Epoch [98][100/5005] lr: 1.000e-04, eta: 0:21:52, time: 0.111, data_time: 0.033, memory: 1125, loss: 1.7087 2023-01-03 02:28:00,936 - mmcls - INFO - Epoch [98][200/5005] lr: 1.000e-04, eta: 0:21:43, time: 0.090, data_time: 0.043, memory: 1125, loss: 1.7063 2023-01-03 02:28:09,953 - mmcls - INFO - Epoch [98][300/5005] lr: 1.000e-04, eta: 0:21:34, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.6886 2023-01-03 02:28:18,535 - mmcls - INFO - Epoch [98][400/5005] lr: 1.000e-04, eta: 0:21:25, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6890 2023-01-03 02:28:26,997 - mmcls - INFO - Epoch [98][500/5005] lr: 1.000e-04, eta: 0:21:17, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6864 2023-01-03 02:28:35,578 - mmcls - INFO - Epoch [98][600/5005] lr: 1.000e-04, eta: 0:21:08, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.6803 2023-01-03 02:28:44,000 - mmcls - INFO - Epoch [98][700/5005] lr: 1.000e-04, eta: 0:20:59, time: 0.084, data_time: 0.012, memory: 1125, loss: 1.6980 2023-01-03 02:28:53,280 - mmcls - INFO - Epoch [98][800/5005] lr: 1.000e-04, eta: 0:20:50, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.6765 2023-01-03 02:29:02,130 - mmcls - INFO - Epoch [98][900/5005] lr: 1.000e-04, eta: 0:20:41, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7047 2023-01-03 02:29:10,914 - mmcls - INFO - Epoch [98][1000/5005] lr: 1.000e-04, eta: 0:20:33, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6788 2023-01-03 02:29:19,420 - mmcls - INFO - Epoch [98][1100/5005] lr: 1.000e-04, eta: 0:20:24, time: 0.085, data_time: 0.000, memory: 1125, loss: 1.7008 2023-01-03 02:29:28,179 - mmcls - INFO - Epoch [98][1200/5005] lr: 1.000e-04, eta: 0:20:15, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.6994 2023-01-03 02:29:37,077 - mmcls - INFO - Epoch [98][1300/5005] lr: 1.000e-04, eta: 0:20:06, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.6931 2023-01-03 02:29:45,871 - mmcls - INFO - Epoch [98][1400/5005] lr: 1.000e-04, eta: 0:19:57, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6671 2023-01-03 02:29:54,303 - mmcls - INFO - Epoch [98][1500/5005] lr: 1.000e-04, eta: 0:19:49, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.6609 2023-01-03 02:30:03,225 - mmcls - INFO - Epoch [98][1600/5005] lr: 1.000e-04, eta: 0:19:40, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.6857 2023-01-03 02:30:11,786 - mmcls - INFO - Epoch [98][1700/5005] lr: 1.000e-04, eta: 0:19:31, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6782 2023-01-03 02:30:20,555 - mmcls - INFO - Epoch [98][1800/5005] lr: 1.000e-04, eta: 0:19:22, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.6720 2023-01-03 02:30:29,266 - mmcls - INFO - Epoch [98][1900/5005] lr: 1.000e-04, eta: 0:19:13, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6713 2023-01-03 02:30:38,585 - mmcls - INFO - Epoch [98][2000/5005] lr: 1.000e-04, eta: 0:19:05, time: 0.093, data_time: 0.002, memory: 1125, loss: 1.6904 2023-01-03 02:30:47,224 - mmcls - INFO - Epoch [98][2100/5005] lr: 1.000e-04, eta: 0:18:56, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6884 2023-01-03 02:30:55,844 - mmcls - INFO - Epoch [98][2200/5005] lr: 1.000e-04, eta: 0:18:47, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6685 2023-01-03 02:31:04,700 - mmcls - INFO - Epoch [98][2300/5005] lr: 1.000e-04, eta: 0:18:38, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.6833 2023-01-03 02:31:13,453 - mmcls - INFO - Epoch [98][2400/5005] lr: 1.000e-04, eta: 0:18:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6988 2023-01-03 02:31:22,498 - mmcls - INFO - Epoch [98][2500/5005] lr: 1.000e-04, eta: 0:18:21, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6977 2023-01-03 02:31:30,730 - mmcls - INFO - Epoch [98][2600/5005] lr: 1.000e-04, eta: 0:18:12, time: 0.082, data_time: 0.004, memory: 1125, loss: 1.6824 2023-01-03 02:31:39,569 - mmcls - INFO - Epoch [98][2700/5005] lr: 1.000e-04, eta: 0:18:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6880 2023-01-03 02:31:48,206 - mmcls - INFO - Epoch [98][2800/5005] lr: 1.000e-04, eta: 0:17:54, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6770 2023-01-03 02:31:56,567 - mmcls - INFO - Epoch [98][2900/5005] lr: 1.000e-04, eta: 0:17:45, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.6612 2023-01-03 02:32:05,170 - mmcls - INFO - Epoch [98][3000/5005] lr: 1.000e-04, eta: 0:17:37, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.6671 2023-01-03 02:32:13,689 - mmcls - INFO - Epoch [98][3100/5005] lr: 1.000e-04, eta: 0:17:28, time: 0.085, data_time: 0.030, memory: 1125, loss: 1.6798 2023-01-03 02:32:22,451 - mmcls - INFO - Epoch [98][3200/5005] lr: 1.000e-04, eta: 0:17:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6865 2023-01-03 02:32:31,328 - mmcls - INFO - Epoch [98][3300/5005] lr: 1.000e-04, eta: 0:17:10, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7042 2023-01-03 02:32:40,036 - mmcls - INFO - Epoch [98][3400/5005] lr: 1.000e-04, eta: 0:17:01, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6810 2023-01-03 02:32:49,235 - mmcls - INFO - Epoch [98][3500/5005] lr: 1.000e-04, eta: 0:16:53, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.6574 2023-01-03 02:32:58,064 - mmcls - INFO - Epoch [98][3600/5005] lr: 1.000e-04, eta: 0:16:44, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.6864 2023-01-03 02:33:06,895 - mmcls - INFO - Epoch [98][3700/5005] lr: 1.000e-04, eta: 0:16:35, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6982 2023-01-03 02:33:15,400 - mmcls - INFO - Epoch [98][3800/5005] lr: 1.000e-04, eta: 0:16:26, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.6712 2023-01-03 02:33:24,037 - mmcls - INFO - Epoch [98][3900/5005] lr: 1.000e-04, eta: 0:16:17, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6808 2023-01-03 02:33:32,740 - mmcls - INFO - Epoch [98][4000/5005] lr: 1.000e-04, eta: 0:16:09, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6900 2023-01-03 02:33:41,322 - mmcls - INFO - Epoch [98][4100/5005] lr: 1.000e-04, eta: 0:16:00, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6962 2023-01-03 02:33:50,115 - mmcls - INFO - Epoch [98][4200/5005] lr: 1.000e-04, eta: 0:15:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6924 2023-01-03 02:33:58,891 - mmcls - INFO - Epoch [98][4300/5005] lr: 1.000e-04, eta: 0:15:42, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6742 2023-01-03 02:34:08,250 - mmcls - INFO - Epoch [98][4400/5005] lr: 1.000e-04, eta: 0:15:33, time: 0.093, data_time: 0.007, memory: 1125, loss: 1.6754 2023-01-03 02:34:17,235 - mmcls - INFO - Epoch [98][4500/5005] lr: 1.000e-04, eta: 0:15:25, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6969 2023-01-03 02:34:25,699 - mmcls - INFO - Epoch [98][4600/5005] lr: 1.000e-04, eta: 0:15:16, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.6973 2023-01-03 02:34:34,338 - mmcls - INFO - Epoch [98][4700/5005] lr: 1.000e-04, eta: 0:15:07, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6973 2023-01-03 02:34:43,049 - mmcls - INFO - Epoch [98][4800/5005] lr: 1.000e-04, eta: 0:14:58, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.6795 2023-01-03 02:34:51,600 - mmcls - INFO - Epoch [98][4900/5005] lr: 1.000e-04, eta: 0:14:49, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7002 2023-01-03 02:35:00,516 - mmcls - INFO - Epoch [98][5000/5005] lr: 1.000e-04, eta: 0:14:41, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.6871 2023-01-03 02:35:23,545 - mmcls - INFO - Epoch(val) [98][196] accuracy_top-1: 63.6820, accuracy_top-5: 84.4020 2023-01-03 02:35:34,700 - mmcls - INFO - Epoch [99][100/5005] lr: 1.000e-04, eta: 0:14:31, time: 0.111, data_time: 0.031, memory: 1125, loss: 1.6862 2023-01-03 02:35:43,251 - mmcls - INFO - Epoch [99][200/5005] lr: 1.000e-04, eta: 0:14:23, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.6923 2023-01-03 02:35:51,807 - mmcls - INFO - Epoch [99][300/5005] lr: 1.000e-04, eta: 0:14:14, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6815 2023-01-03 02:36:00,958 - mmcls - INFO - Epoch [99][400/5005] lr: 1.000e-04, eta: 0:14:05, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.6847 2023-01-03 02:36:09,540 - mmcls - INFO - Epoch [99][500/5005] lr: 1.000e-04, eta: 0:13:56, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6842 2023-01-03 02:36:18,697 - mmcls - INFO - Epoch [99][600/5005] lr: 1.000e-04, eta: 0:13:47, time: 0.092, data_time: 0.023, memory: 1125, loss: 1.6816 2023-01-03 02:36:27,612 - mmcls - INFO - Epoch [99][700/5005] lr: 1.000e-04, eta: 0:13:39, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.6849 2023-01-03 02:36:36,238 - mmcls - INFO - Epoch [99][800/5005] lr: 1.000e-04, eta: 0:13:30, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.6949 2023-01-03 02:36:44,824 - mmcls - INFO - Epoch [99][900/5005] lr: 1.000e-04, eta: 0:13:21, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.6774 2023-01-03 02:36:53,507 - mmcls - INFO - Epoch [99][1000/5005] lr: 1.000e-04, eta: 0:13:12, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6957 2023-01-03 02:37:02,172 - mmcls - INFO - Epoch [99][1100/5005] lr: 1.000e-04, eta: 0:13:03, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6712 2023-01-03 02:37:11,055 - mmcls - INFO - Epoch [99][1200/5005] lr: 1.000e-04, eta: 0:12:55, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6931 2023-01-03 02:37:19,931 - mmcls - INFO - Epoch [99][1300/5005] lr: 1.000e-04, eta: 0:12:46, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7034 2023-01-03 02:37:28,695 - mmcls - INFO - Epoch [99][1400/5005] lr: 1.000e-04, eta: 0:12:37, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6829 2023-01-03 02:37:37,402 - mmcls - INFO - Epoch [99][1500/5005] lr: 1.000e-04, eta: 0:12:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6837 2023-01-03 02:37:45,835 - mmcls - INFO - Epoch [99][1600/5005] lr: 1.000e-04, eta: 0:12:19, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.6825 2023-01-03 02:37:54,488 - mmcls - INFO - Epoch [99][1700/5005] lr: 1.000e-04, eta: 0:12:11, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6799 2023-01-03 02:38:03,407 - mmcls - INFO - Epoch [99][1800/5005] lr: 1.000e-04, eta: 0:12:02, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6863 2023-01-03 02:38:12,123 - mmcls - INFO - Epoch [99][1900/5005] lr: 1.000e-04, eta: 0:11:53, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6782 2023-01-03 02:38:21,052 - mmcls - INFO - Epoch [99][2000/5005] lr: 1.000e-04, eta: 0:11:44, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6640 2023-01-03 02:38:29,696 - mmcls - INFO - Epoch [99][2100/5005] lr: 1.000e-04, eta: 0:11:35, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6685 2023-01-03 02:38:38,506 - mmcls - INFO - Epoch [99][2200/5005] lr: 1.000e-04, eta: 0:11:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6660 2023-01-03 02:38:47,428 - mmcls - INFO - Epoch [99][2300/5005] lr: 1.000e-04, eta: 0:11:18, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.6831 2023-01-03 02:38:56,224 - mmcls - INFO - Epoch [99][2400/5005] lr: 1.000e-04, eta: 0:11:09, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.6704 2023-01-03 02:39:04,875 - mmcls - INFO - Epoch [99][2500/5005] lr: 1.000e-04, eta: 0:11:00, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6981 2023-01-03 02:39:13,704 - mmcls - INFO - Epoch [99][2600/5005] lr: 1.000e-04, eta: 0:10:51, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6937 2023-01-03 02:39:22,556 - mmcls - INFO - Epoch [99][2700/5005] lr: 1.000e-04, eta: 0:10:43, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7004 2023-01-03 02:39:31,207 - mmcls - INFO - Epoch [99][2800/5005] lr: 1.000e-04, eta: 0:10:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6808 2023-01-03 02:39:39,829 - mmcls - INFO - Epoch [99][2900/5005] lr: 1.000e-04, eta: 0:10:25, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6643 2023-01-03 02:39:48,362 - mmcls - INFO - Epoch [99][3000/5005] lr: 1.000e-04, eta: 0:10:16, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.6922 2023-01-03 02:39:57,310 - mmcls - INFO - Epoch [99][3100/5005] lr: 1.000e-04, eta: 0:10:07, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.6887 2023-01-03 02:40:06,137 - mmcls - INFO - Epoch [99][3200/5005] lr: 1.000e-04, eta: 0:09:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6752 2023-01-03 02:40:14,842 - mmcls - INFO - Epoch [99][3300/5005] lr: 1.000e-04, eta: 0:09:50, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6768 2023-01-03 02:40:23,373 - mmcls - INFO - Epoch [99][3400/5005] lr: 1.000e-04, eta: 0:09:41, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.6731 2023-01-03 02:40:31,773 - mmcls - INFO - Epoch [99][3500/5005] lr: 1.000e-04, eta: 0:09:32, time: 0.084, data_time: 0.009, memory: 1125, loss: 1.7051 2023-01-03 02:40:40,542 - mmcls - INFO - Epoch [99][3600/5005] lr: 1.000e-04, eta: 0:09:23, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6964 2023-01-03 02:40:49,505 - mmcls - INFO - Epoch [99][3700/5005] lr: 1.000e-04, eta: 0:09:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6656 2023-01-03 02:40:58,261 - mmcls - INFO - Epoch [99][3800/5005] lr: 1.000e-04, eta: 0:09:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6825 2023-01-03 02:41:07,090 - mmcls - INFO - Epoch [99][3900/5005] lr: 1.000e-04, eta: 0:08:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6988 2023-01-03 02:41:15,693 - mmcls - INFO - Epoch [99][4000/5005] lr: 1.000e-04, eta: 0:08:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6853 2023-01-03 02:41:24,499 - mmcls - INFO - Epoch [99][4100/5005] lr: 1.000e-04, eta: 0:08:39, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6851 2023-01-03 02:41:33,190 - mmcls - INFO - Epoch [99][4200/5005] lr: 1.000e-04, eta: 0:08:31, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6889 2023-01-03 02:41:42,078 - mmcls - INFO - Epoch [99][4300/5005] lr: 1.000e-04, eta: 0:08:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6996 2023-01-03 02:41:51,006 - mmcls - INFO - Epoch [99][4400/5005] lr: 1.000e-04, eta: 0:08:13, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.6755 2023-01-03 02:41:59,652 - mmcls - INFO - Epoch [99][4500/5005] lr: 1.000e-04, eta: 0:08:04, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6955 2023-01-03 02:42:08,636 - mmcls - INFO - Epoch [99][4600/5005] lr: 1.000e-04, eta: 0:07:55, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7071 2023-01-03 02:42:17,438 - mmcls - INFO - Epoch [99][4700/5005] lr: 1.000e-04, eta: 0:07:47, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7034 2023-01-03 02:42:26,313 - mmcls - INFO - Epoch [99][4800/5005] lr: 1.000e-04, eta: 0:07:38, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7006 2023-01-03 02:42:35,154 - mmcls - INFO - Epoch [99][4900/5005] lr: 1.000e-04, eta: 0:07:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6813 2023-01-03 02:42:43,954 - mmcls - INFO - Epoch [99][5000/5005] lr: 1.000e-04, eta: 0:07:20, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6696 2023-01-03 02:43:06,635 - mmcls - INFO - Epoch(val) [99][196] accuracy_top-1: 63.8040, accuracy_top-5: 84.4540 2023-01-03 02:43:17,566 - mmcls - INFO - Epoch [100][100/5005] lr: 1.000e-04, eta: 0:07:11, time: 0.109, data_time: 0.053, memory: 1125, loss: 1.6779 2023-01-03 02:43:26,398 - mmcls - INFO - Epoch [100][200/5005] lr: 1.000e-04, eta: 0:07:02, time: 0.088, data_time: 0.032, memory: 1125, loss: 1.6805 2023-01-03 02:43:35,387 - mmcls - INFO - Epoch [100][300/5005] lr: 1.000e-04, eta: 0:06:53, time: 0.090, data_time: 0.000, memory: 1125, loss: 1.6904 2023-01-03 02:43:44,008 - mmcls - INFO - Epoch [100][400/5005] lr: 1.000e-04, eta: 0:06:45, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.6909 2023-01-03 02:43:52,788 - mmcls - INFO - Epoch [100][500/5005] lr: 1.000e-04, eta: 0:06:36, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6876 2023-01-03 02:44:01,686 - mmcls - INFO - Epoch [100][600/5005] lr: 1.000e-04, eta: 0:06:27, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6883 2023-01-03 02:44:10,408 - mmcls - INFO - Epoch [100][700/5005] lr: 1.000e-04, eta: 0:06:18, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6804 2023-01-03 02:44:19,340 - mmcls - INFO - Epoch [100][800/5005] lr: 1.000e-04, eta: 0:06:09, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.6706 2023-01-03 02:44:27,847 - mmcls - INFO - Epoch [100][900/5005] lr: 1.000e-04, eta: 0:06:01, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.6950 2023-01-03 02:44:36,895 - mmcls - INFO - Epoch [100][1000/5005] lr: 1.000e-04, eta: 0:05:52, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7045 2023-01-03 02:44:45,807 - mmcls - INFO - Epoch [100][1100/5005] lr: 1.000e-04, eta: 0:05:43, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.6766 2023-01-03 02:44:54,551 - mmcls - INFO - Epoch [100][1200/5005] lr: 1.000e-04, eta: 0:05:34, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6840 2023-01-03 02:45:03,535 - mmcls - INFO - Epoch [100][1300/5005] lr: 1.000e-04, eta: 0:05:25, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.6925 2023-01-03 02:45:12,512 - mmcls - INFO - Epoch [100][1400/5005] lr: 1.000e-04, eta: 0:05:17, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.6746 2023-01-03 02:45:20,981 - mmcls - INFO - Epoch [100][1500/5005] lr: 1.000e-04, eta: 0:05:08, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.6781 2023-01-03 02:45:29,964 - mmcls - INFO - Epoch [100][1600/5005] lr: 1.000e-04, eta: 0:04:59, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6980 2023-01-03 02:45:38,479 - mmcls - INFO - Epoch [100][1700/5005] lr: 1.000e-04, eta: 0:04:50, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.6976 2023-01-03 02:45:47,177 - mmcls - INFO - Epoch [100][1800/5005] lr: 1.000e-04, eta: 0:04:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6805 2023-01-03 02:45:55,865 - mmcls - INFO - Epoch [100][1900/5005] lr: 1.000e-04, eta: 0:04:33, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6945 2023-01-03 02:46:04,441 - mmcls - INFO - Epoch [100][2000/5005] lr: 1.000e-04, eta: 0:04:24, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7072 2023-01-03 02:46:13,167 - mmcls - INFO - Epoch [100][2100/5005] lr: 1.000e-04, eta: 0:04:15, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6832 2023-01-03 02:46:22,054 - mmcls - INFO - Epoch [100][2200/5005] lr: 1.000e-04, eta: 0:04:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6751 2023-01-03 02:46:30,846 - mmcls - INFO - Epoch [100][2300/5005] lr: 1.000e-04, eta: 0:03:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6701 2023-01-03 02:46:39,433 - mmcls - INFO - Epoch [100][2400/5005] lr: 1.000e-04, eta: 0:03:49, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6927 2023-01-03 02:46:48,165 - mmcls - INFO - Epoch [100][2500/5005] lr: 1.000e-04, eta: 0:03:40, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6653 2023-01-03 02:46:56,957 - mmcls - INFO - Epoch [100][2600/5005] lr: 1.000e-04, eta: 0:03:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6978 2023-01-03 02:47:05,896 - mmcls - INFO - Epoch [100][2700/5005] lr: 1.000e-04, eta: 0:03:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6745 2023-01-03 02:47:14,732 - mmcls - INFO - Epoch [100][2800/5005] lr: 1.000e-04, eta: 0:03:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6697 2023-01-03 02:47:23,692 - mmcls - INFO - Epoch [100][2900/5005] lr: 1.000e-04, eta: 0:03:05, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6831 2023-01-03 02:47:32,522 - mmcls - INFO - Epoch [100][3000/5005] lr: 1.000e-04, eta: 0:02:56, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6955 2023-01-03 02:47:41,337 - mmcls - INFO - Epoch [100][3100/5005] lr: 1.000e-04, eta: 0:02:47, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6996 2023-01-03 02:47:49,830 - mmcls - INFO - Epoch [100][3200/5005] lr: 1.000e-04, eta: 0:02:38, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7043 2023-01-03 02:47:58,788 - mmcls - INFO - Epoch [100][3300/5005] lr: 1.000e-04, eta: 0:02:30, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6863 2023-01-03 02:48:07,702 - mmcls - INFO - Epoch [100][3400/5005] lr: 1.000e-04, eta: 0:02:21, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7145 2023-01-03 02:48:16,360 - mmcls - INFO - Epoch [100][3500/5005] lr: 1.000e-04, eta: 0:02:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6950 2023-01-03 02:48:25,099 - mmcls - INFO - Epoch [100][3600/5005] lr: 1.000e-04, eta: 0:02:03, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6933 2023-01-03 02:48:33,888 - mmcls - INFO - Epoch [100][3700/5005] lr: 1.000e-04, eta: 0:01:54, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.6865 2023-01-03 02:48:42,699 - mmcls - INFO - Epoch [100][3800/5005] lr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6914 2023-01-03 02:48:51,346 - mmcls - INFO - Epoch [100][3900/5005] lr: 1.000e-04, eta: 0:01:37, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6628 2023-01-03 02:49:00,044 - mmcls - INFO - Epoch [100][4000/5005] lr: 1.000e-04, eta: 0:01:28, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6716 2023-01-03 02:49:08,637 - mmcls - INFO - Epoch [100][4100/5005] lr: 1.000e-04, eta: 0:01:19, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6865 2023-01-03 02:49:17,407 - mmcls - INFO - Epoch [100][4200/5005] lr: 1.000e-04, eta: 0:01:10, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.6872 2023-01-03 02:49:26,404 - mmcls - INFO - Epoch [100][4300/5005] lr: 1.000e-04, eta: 0:01:02, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.6791 2023-01-03 02:49:35,142 - mmcls - INFO - Epoch [100][4400/5005] lr: 1.000e-04, eta: 0:00:53, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6953 2023-01-03 02:49:43,687 - mmcls - INFO - Epoch [100][4500/5005] lr: 1.000e-04, eta: 0:00:44, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6830 2023-01-03 02:49:52,276 - mmcls - INFO - Epoch [100][4600/5005] lr: 1.000e-04, eta: 0:00:35, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7026 2023-01-03 02:50:00,925 - mmcls - INFO - Epoch [100][4700/5005] lr: 1.000e-04, eta: 0:00:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6973 2023-01-03 02:50:09,481 - mmcls - INFO - Epoch [100][4800/5005] lr: 1.000e-04, eta: 0:00:18, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6781 2023-01-03 02:50:18,557 - mmcls - INFO - Epoch [100][4900/5005] lr: 1.000e-04, eta: 0:00:09, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.6820 2023-01-03 02:50:27,240 - mmcls - INFO - Epoch [100][5000/5005] lr: 1.000e-04, eta: 0:00:00, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6544 2023-01-03 02:50:27,568 - mmcls - INFO - Saving checkpoint at 100 epochs 2023-01-03 02:50:50,069 - mmcls - INFO - Epoch(val) [100][196] accuracy_top-1: 63.6660, accuracy_top-5: 84.3400 ================================================ FILE: classification-NO-activation-function/logs/pinet_relu.log ================================================ 2023-01-03 15:18:24,147 - mmcls - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.6.12 |Anaconda, Inc.| (default, Sep 8 2020, 23:10:56) [GCC 7.3.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 10.2, V10.2.8 GCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0 PyTorch: 1.9.0+cu102 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 10.2 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70 - CuDNN 7.6.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu102 OpenCV: 4.5.5 MMCV: 1.5.1 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler: 10.2 MMClassification: 0.24.0+91b85bb ------------------------------------------------------------ 2023-01-03 15:18:24,147 - mmcls - INFO - Distributed training: True 2023-01-03 15:18:24,311 - mmcls - INFO - Config: model = dict( type='ImageClassifier', backbone=dict( type='ResNet', depth=18, num_stages=4, out_indices=(3, ), style='pytorch'), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=1000, in_channels=512, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5))) dataset_type = 'ImageNet' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ] data = dict( samples_per_gpu=32, workers_per_gpu=2, train=dict( type='ImageNet', data_prefix='data/imagenet/train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ]), val=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]), test=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ])) evaluation = dict(interval=1, metric='accuracy') optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict(policy='step', step=[30, 60, 90]) runner = dict(type='EpochBasedRunner', max_epochs=100) checkpoint_config = dict(interval=50) log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] work_dir = './work_dirs/resnet18_8xb32_in1k_pi' gpu_ids = range(0, 8) 2023-01-03 15:18:29,763 - mmcls - INFO - Set random seed to 1668662542, deterministic: False 2023-01-03 15:18:29,974 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}] 2023-01-03 15:18:30,059 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01} Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn3.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn3.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn3.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn3.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn3.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn3.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn3.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn3.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.downsample.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn3.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn3.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier head.fc.weight - torch.Size([1000, 512]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([1000]): NormalInit: mean=0, std=0.01, bias=0 2023-01-03 15:18:58,454 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi 2023-01-03 15:18:58,454 - mmcls - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook -------------------- after_train_iter: (ABOVE_NORMAL) DistOptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (VERY_LOW ) TextLoggerHook -------------------- before_val_iter: (LOW ) IterTimerHook -------------------- after_val_iter: (LOW ) IterTimerHook -------------------- after_val_epoch: (VERY_LOW ) TextLoggerHook -------------------- after_run: (VERY_LOW ) TextLoggerHook -------------------- 2023-01-03 15:18:58,455 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs 2023-01-03 15:18:58,455 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend. 2023-01-03 15:19:10,547 - mmcls - INFO - Epoch [1][100/5005] lr: 1.000e-01, eta: 16:46:24, time: 0.121, data_time: 0.038, memory: 1125, loss: 6.8861 2023-01-03 15:19:19,638 - mmcls - INFO - Epoch [1][200/5005] lr: 1.000e-01, eta: 14:42:08, time: 0.091, data_time: 0.017, memory: 1125, loss: 6.7358 2023-01-03 15:19:28,497 - mmcls - INFO - Epoch [1][300/5005] lr: 1.000e-01, eta: 13:54:09, time: 0.089, data_time: 0.034, memory: 1125, loss: 6.5839 2023-01-03 15:19:37,552 - mmcls - INFO - Epoch [1][400/5005] lr: 1.000e-01, eta: 13:34:05, time: 0.091, data_time: 0.026, memory: 1125, loss: 6.4154 2023-01-03 15:19:46,252 - mmcls - INFO - Epoch [1][500/5005] lr: 1.000e-01, eta: 13:16:00, time: 0.087, data_time: 0.040, memory: 1125, loss: 6.2515 2023-01-03 15:19:54,938 - mmcls - INFO - Epoch [1][600/5005] lr: 1.000e-01, eta: 13:03:57, time: 0.087, data_time: 0.013, memory: 1125, loss: 6.1224 2023-01-03 15:20:03,842 - mmcls - INFO - Epoch [1][700/5005] lr: 1.000e-01, eta: 12:57:42, time: 0.089, data_time: 0.033, memory: 1125, loss: 5.9919 2023-01-03 15:20:13,331 - mmcls - INFO - Epoch [1][800/5005] lr: 1.000e-01, eta: 12:59:12, time: 0.095, data_time: 0.008, memory: 1125, loss: 5.8911 2023-01-03 15:20:21,998 - mmcls - INFO - Epoch [1][900/5005] lr: 1.000e-01, eta: 12:52:37, time: 0.087, data_time: 0.011, memory: 1125, loss: 5.7362 2023-01-03 15:20:30,562 - mmcls - INFO - Epoch [1][1000/5005] lr: 1.000e-01, eta: 12:46:26, time: 0.086, data_time: 0.018, memory: 1125, loss: 5.6388 2023-01-03 15:20:39,173 - mmcls - INFO - Epoch [1][1100/5005] lr: 1.000e-01, eta: 12:41:50, time: 0.086, data_time: 0.028, memory: 1125, loss: 5.5561 2023-01-03 15:20:47,798 - mmcls - INFO - Epoch [1][1200/5005] lr: 1.000e-01, eta: 12:38:01, time: 0.086, data_time: 0.015, memory: 1125, loss: 5.4535 2023-01-03 15:20:56,687 - mmcls - INFO - Epoch [1][1300/5005] lr: 1.000e-01, eta: 12:36:31, time: 0.089, data_time: 0.020, memory: 1125, loss: 5.3708 2023-01-03 15:21:05,539 - mmcls - INFO - Epoch [1][1400/5005] lr: 1.000e-01, eta: 12:34:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 5.3070 2023-01-03 15:21:14,544 - mmcls - INFO - Epoch [1][1500/5005] lr: 1.000e-01, eta: 12:34:18, time: 0.090, data_time: 0.007, memory: 1125, loss: 5.2293 2023-01-03 15:21:23,635 - mmcls - INFO - Epoch [1][1600/5005] lr: 1.000e-01, eta: 12:34:14, time: 0.091, data_time: 0.023, memory: 1125, loss: 5.1703 2023-01-03 15:21:32,674 - mmcls - INFO - Epoch [1][1700/5005] lr: 1.000e-01, eta: 12:33:55, time: 0.090, data_time: 0.036, memory: 1125, loss: 5.1147 2023-01-03 15:21:41,822 - mmcls - INFO - Epoch [1][1800/5005] lr: 1.000e-01, eta: 12:34:09, time: 0.092, data_time: 0.052, memory: 1125, loss: 5.0406 2023-01-03 15:21:50,808 - mmcls - INFO - Epoch [1][1900/5005] lr: 1.000e-01, eta: 12:33:39, time: 0.090, data_time: 0.038, memory: 1125, loss: 4.9875 2023-01-03 15:21:59,710 - mmcls - INFO - Epoch [1][2000/5005] lr: 1.000e-01, eta: 12:32:46, time: 0.089, data_time: 0.022, memory: 1125, loss: 4.9228 2023-01-03 15:22:08,434 - mmcls - INFO - Epoch [1][2100/5005] lr: 1.000e-01, eta: 12:31:16, time: 0.087, data_time: 0.019, memory: 1125, loss: 4.8593 2023-01-03 15:22:17,296 - mmcls - INFO - Epoch [1][2200/5005] lr: 1.000e-01, eta: 12:30:28, time: 0.089, data_time: 0.038, memory: 1125, loss: 4.8095 2023-01-03 15:22:26,245 - mmcls - INFO - Epoch [1][2300/5005] lr: 1.000e-01, eta: 12:29:57, time: 0.089, data_time: 0.033, memory: 1125, loss: 4.7575 2023-01-03 15:22:34,982 - mmcls - INFO - Epoch [1][2400/5005] lr: 1.000e-01, eta: 12:28:48, time: 0.087, data_time: 0.010, memory: 1125, loss: 4.7292 2023-01-03 15:22:43,903 - mmcls - INFO - Epoch [1][2500/5005] lr: 1.000e-01, eta: 12:28:20, time: 0.089, data_time: 0.002, memory: 1125, loss: 4.6630 2023-01-03 15:22:52,693 - mmcls - INFO - Epoch [1][2600/5005] lr: 1.000e-01, eta: 12:27:26, time: 0.088, data_time: 0.011, memory: 1125, loss: 4.6083 2023-01-03 15:23:01,492 - mmcls - INFO - Epoch [1][2700/5005] lr: 1.000e-01, eta: 12:26:38, time: 0.088, data_time: 0.007, memory: 1125, loss: 4.5430 2023-01-03 15:23:10,562 - mmcls - INFO - Epoch [1][2800/5005] lr: 1.000e-01, eta: 12:26:43, time: 0.091, data_time: 0.012, memory: 1125, loss: 4.5334 2023-01-03 15:23:19,665 - mmcls - INFO - Epoch [1][2900/5005] lr: 1.000e-01, eta: 12:26:50, time: 0.091, data_time: 0.040, memory: 1125, loss: 4.4810 2023-01-03 15:23:28,409 - mmcls - INFO - Epoch [1][3000/5005] lr: 1.000e-01, eta: 12:25:57, time: 0.087, data_time: 0.015, memory: 1125, loss: 4.4313 2023-01-03 15:23:37,023 - mmcls - INFO - Epoch [1][3100/5005] lr: 1.000e-01, eta: 12:24:47, time: 0.086, data_time: 0.012, memory: 1125, loss: 4.4097 2023-01-03 15:23:45,652 - mmcls - INFO - Epoch [1][3200/5005] lr: 1.000e-01, eta: 12:23:43, time: 0.086, data_time: 0.010, memory: 1125, loss: 4.3585 2023-01-03 15:23:54,674 - mmcls - INFO - Epoch [1][3300/5005] lr: 1.000e-01, eta: 12:23:41, time: 0.090, data_time: 0.021, memory: 1125, loss: 4.3516 2023-01-03 15:24:03,561 - mmcls - INFO - Epoch [1][3400/5005] lr: 1.000e-01, eta: 12:23:18, time: 0.089, data_time: 0.007, memory: 1125, loss: 4.2911 2023-01-03 15:24:12,140 - mmcls - INFO - Epoch [1][3500/5005] lr: 1.000e-01, eta: 12:22:14, time: 0.086, data_time: 0.024, memory: 1125, loss: 4.2691 2023-01-03 15:24:20,656 - mmcls - INFO - Epoch [1][3600/5005] lr: 1.000e-01, eta: 12:21:04, time: 0.085, data_time: 0.031, memory: 1125, loss: 4.2172 2023-01-03 15:24:29,396 - mmcls - INFO - Epoch [1][3700/5005] lr: 1.000e-01, eta: 12:20:27, time: 0.087, data_time: 0.012, memory: 1125, loss: 4.2102 2023-01-03 15:24:38,129 - mmcls - INFO - Epoch [1][3800/5005] lr: 1.000e-01, eta: 12:19:50, time: 0.087, data_time: 0.015, memory: 1125, loss: 4.1719 2023-01-03 15:24:46,654 - mmcls - INFO - Epoch [1][3900/5005] lr: 1.000e-01, eta: 12:18:49, time: 0.085, data_time: 0.034, memory: 1125, loss: 4.1208 2023-01-03 15:24:55,282 - mmcls - INFO - Epoch [1][4000/5005] lr: 1.000e-01, eta: 12:18:01, time: 0.086, data_time: 0.013, memory: 1125, loss: 4.1009 2023-01-03 15:25:04,226 - mmcls - INFO - Epoch [1][4100/5005] lr: 1.000e-01, eta: 12:17:57, time: 0.090, data_time: 0.007, memory: 1125, loss: 4.0630 2023-01-03 15:25:12,889 - mmcls - INFO - Epoch [1][4200/5005] lr: 1.000e-01, eta: 12:17:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 4.0263 2023-01-03 15:25:21,532 - mmcls - INFO - Epoch [1][4300/5005] lr: 1.000e-01, eta: 12:16:37, time: 0.086, data_time: 0.034, memory: 1125, loss: 3.9998 2023-01-03 15:25:30,500 - mmcls - INFO - Epoch [1][4400/5005] lr: 1.000e-01, eta: 12:16:34, time: 0.090, data_time: 0.007, memory: 1125, loss: 4.0183 2023-01-03 15:25:39,207 - mmcls - INFO - Epoch [1][4500/5005] lr: 1.000e-01, eta: 12:16:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.9670 2023-01-03 15:25:48,035 - mmcls - INFO - Epoch [1][4600/5005] lr: 1.000e-01, eta: 12:15:47, time: 0.088, data_time: 0.004, memory: 1125, loss: 3.9385 2023-01-03 15:25:56,659 - mmcls - INFO - Epoch [1][4700/5005] lr: 1.000e-01, eta: 12:15:07, time: 0.086, data_time: 0.016, memory: 1125, loss: 3.9028 2023-01-03 15:26:05,493 - mmcls - INFO - Epoch [1][4800/5005] lr: 1.000e-01, eta: 12:14:51, time: 0.088, data_time: 0.039, memory: 1125, loss: 3.9059 2023-01-03 15:26:14,265 - mmcls - INFO - Epoch [1][4900/5005] lr: 1.000e-01, eta: 12:14:31, time: 0.088, data_time: 0.014, memory: 1125, loss: 3.8832 2023-01-03 15:26:23,130 - mmcls - INFO - Epoch [1][5000/5005] lr: 1.000e-01, eta: 12:14:19, time: 0.089, data_time: 0.004, memory: 1125, loss: 3.8454 2023-01-03 15:26:46,332 - mmcls - INFO - Epoch(val) [1][196] accuracy_top-1: 26.6600, accuracy_top-5: 50.6340 2023-01-03 15:26:57,526 - mmcls - INFO - Epoch [2][100/5005] lr: 1.000e-01, eta: 12:17:08, time: 0.112, data_time: 0.071, memory: 1125, loss: 3.7910 2023-01-03 15:27:06,109 - mmcls - INFO - Epoch [2][200/5005] lr: 1.000e-01, eta: 12:16:27, time: 0.086, data_time: 0.010, memory: 1125, loss: 3.8017 2023-01-03 15:27:14,809 - mmcls - INFO - Epoch [2][300/5005] lr: 1.000e-01, eta: 12:15:56, time: 0.087, data_time: 0.014, memory: 1125, loss: 3.7630 2023-01-03 15:27:23,545 - mmcls - INFO - Epoch [2][400/5005] lr: 1.000e-01, eta: 12:15:31, time: 0.087, data_time: 0.021, memory: 1125, loss: 3.7589 2023-01-03 15:27:32,471 - mmcls - INFO - Epoch [2][500/5005] lr: 1.000e-01, eta: 12:15:23, time: 0.089, data_time: 0.006, memory: 1125, loss: 3.7228 2023-01-03 15:27:41,160 - mmcls - INFO - Epoch [2][600/5005] lr: 1.000e-01, eta: 12:14:54, time: 0.087, data_time: 0.003, memory: 1125, loss: 3.7156 2023-01-03 15:27:50,085 - mmcls - INFO - Epoch [2][700/5005] lr: 1.000e-01, eta: 12:14:46, time: 0.089, data_time: 0.012, memory: 1125, loss: 3.7089 2023-01-03 15:27:58,642 - mmcls - INFO - Epoch [2][800/5005] lr: 1.000e-01, eta: 12:14:08, time: 0.086, data_time: 0.005, memory: 1125, loss: 3.6864 2023-01-03 15:28:07,716 - mmcls - INFO - Epoch [2][900/5005] lr: 1.000e-01, eta: 12:14:13, time: 0.091, data_time: 0.013, memory: 1125, loss: 3.6658 2023-01-03 15:28:16,484 - mmcls - INFO - Epoch [2][1000/5005] lr: 1.000e-01, eta: 12:13:53, time: 0.088, data_time: 0.004, memory: 1125, loss: 3.6299 2023-01-03 15:28:25,007 - mmcls - INFO - Epoch [2][1100/5005] lr: 1.000e-01, eta: 12:13:12, time: 0.085, data_time: 0.002, memory: 1125, loss: 3.6258 2023-01-03 15:28:33,515 - mmcls - INFO - Epoch [2][1200/5005] lr: 1.000e-01, eta: 12:12:32, time: 0.085, data_time: 0.001, memory: 1125, loss: 3.6390 2023-01-03 15:28:42,283 - mmcls - INFO - Epoch [2][1300/5005] lr: 1.000e-01, eta: 12:12:13, time: 0.088, data_time: 0.007, memory: 1125, loss: 3.6106 2023-01-03 15:28:51,050 - mmcls - INFO - Epoch [2][1400/5005] lr: 1.000e-01, eta: 12:11:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.5996 2023-01-03 15:28:59,647 - mmcls - INFO - Epoch [2][1500/5005] lr: 1.000e-01, eta: 12:11:24, time: 0.086, data_time: 0.017, memory: 1125, loss: 3.5724 2023-01-03 15:29:08,238 - mmcls - INFO - Epoch [2][1600/5005] lr: 1.000e-01, eta: 12:10:53, time: 0.086, data_time: 0.018, memory: 1125, loss: 3.5616 2023-01-03 15:29:17,123 - mmcls - INFO - Epoch [2][1700/5005] lr: 1.000e-01, eta: 12:10:45, time: 0.089, data_time: 0.012, memory: 1125, loss: 3.5777 2023-01-03 15:29:25,777 - mmcls - INFO - Epoch [2][1800/5005] lr: 1.000e-01, eta: 12:10:20, time: 0.087, data_time: 0.010, memory: 1125, loss: 3.5460 2023-01-03 15:29:34,310 - mmcls - INFO - Epoch [2][1900/5005] lr: 1.000e-01, eta: 12:09:46, time: 0.085, data_time: 0.011, memory: 1125, loss: 3.5145 2023-01-03 15:29:43,258 - mmcls - INFO - Epoch [2][2000/5005] lr: 1.000e-01, eta: 12:09:43, time: 0.090, data_time: 0.003, memory: 1125, loss: 3.5156 2023-01-03 15:29:51,739 - mmcls - INFO - Epoch [2][2100/5005] lr: 1.000e-01, eta: 12:09:06, time: 0.085, data_time: 0.018, memory: 1125, loss: 3.4966 2023-01-03 15:30:00,382 - mmcls - INFO - Epoch [2][2200/5005] lr: 1.000e-01, eta: 12:08:42, time: 0.086, data_time: 0.006, memory: 1125, loss: 3.4741 2023-01-03 15:30:09,261 - mmcls - INFO - Epoch [2][2300/5005] lr: 1.000e-01, eta: 12:08:35, time: 0.089, data_time: 0.010, memory: 1125, loss: 3.4618 2023-01-03 15:30:18,124 - mmcls - INFO - Epoch [2][2400/5005] lr: 1.000e-01, eta: 12:08:25, time: 0.088, data_time: 0.038, memory: 1125, loss: 3.4485 2023-01-03 15:30:27,173 - mmcls - INFO - Epoch [2][2500/5005] lr: 1.000e-01, eta: 12:08:29, time: 0.091, data_time: 0.002, memory: 1125, loss: 3.4584 2023-01-03 15:30:36,328 - mmcls - INFO - Epoch [2][2600/5005] lr: 1.000e-01, eta: 12:08:38, time: 0.091, data_time: 0.006, memory: 1125, loss: 3.4459 2023-01-03 15:30:45,538 - mmcls - INFO - Epoch [2][2700/5005] lr: 1.000e-01, eta: 12:08:51, time: 0.092, data_time: 0.009, memory: 1125, loss: 3.4440 2023-01-03 15:30:54,387 - mmcls - INFO - Epoch [2][2800/5005] lr: 1.000e-01, eta: 12:08:40, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.4240 2023-01-03 15:31:03,196 - mmcls - INFO - Epoch [2][2900/5005] lr: 1.000e-01, eta: 12:08:27, time: 0.088, data_time: 0.019, memory: 1125, loss: 3.4440 2023-01-03 15:31:12,485 - mmcls - INFO - Epoch [2][3000/5005] lr: 1.000e-01, eta: 12:08:44, time: 0.093, data_time: 0.009, memory: 1125, loss: 3.4074 2023-01-03 15:31:21,164 - mmcls - INFO - Epoch [2][3100/5005] lr: 1.000e-01, eta: 12:08:23, time: 0.087, data_time: 0.012, memory: 1125, loss: 3.3897 2023-01-03 15:31:29,945 - mmcls - INFO - Epoch [2][3200/5005] lr: 1.000e-01, eta: 12:08:09, time: 0.088, data_time: 0.011, memory: 1125, loss: 3.3759 2023-01-03 15:31:38,538 - mmcls - INFO - Epoch [2][3300/5005] lr: 1.000e-01, eta: 12:07:43, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.3747 2023-01-03 15:31:47,611 - mmcls - INFO - Epoch [2][3400/5005] lr: 1.000e-01, eta: 12:07:46, time: 0.091, data_time: 0.015, memory: 1125, loss: 3.3758 2023-01-03 15:31:56,274 - mmcls - INFO - Epoch [2][3500/5005] lr: 1.000e-01, eta: 12:07:25, time: 0.087, data_time: 0.008, memory: 1125, loss: 3.3746 2023-01-03 15:32:04,916 - mmcls - INFO - Epoch [2][3600/5005] lr: 1.000e-01, eta: 12:07:03, time: 0.086, data_time: 0.003, memory: 1125, loss: 3.3526 2023-01-03 15:32:13,911 - mmcls - INFO - Epoch [2][3700/5005] lr: 1.000e-01, eta: 12:07:01, time: 0.090, data_time: 0.002, memory: 1125, loss: 3.3560 2023-01-03 15:32:22,491 - mmcls - INFO - Epoch [2][3800/5005] lr: 1.000e-01, eta: 12:06:36, time: 0.086, data_time: 0.017, memory: 1125, loss: 3.3498 2023-01-03 15:32:31,666 - mmcls - INFO - Epoch [2][3900/5005] lr: 1.000e-01, eta: 12:06:44, time: 0.092, data_time: 0.006, memory: 1125, loss: 3.3383 2023-01-03 15:32:40,320 - mmcls - INFO - Epoch [2][4000/5005] lr: 1.000e-01, eta: 12:06:24, time: 0.087, data_time: 0.028, memory: 1125, loss: 3.3252 2023-01-03 15:32:49,268 - mmcls - INFO - Epoch [2][4100/5005] lr: 1.000e-01, eta: 12:06:19, time: 0.089, data_time: 0.009, memory: 1125, loss: 3.3006 2023-01-03 15:32:58,071 - mmcls - INFO - Epoch [2][4200/5005] lr: 1.000e-01, eta: 12:06:06, time: 0.088, data_time: 0.020, memory: 1125, loss: 3.2968 2023-01-03 15:33:06,651 - mmcls - INFO - Epoch [2][4300/5005] lr: 1.000e-01, eta: 12:05:42, time: 0.086, data_time: 0.006, memory: 1125, loss: 3.2907 2023-01-03 15:33:15,499 - mmcls - INFO - Epoch [2][4400/5005] lr: 1.000e-01, eta: 12:05:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.2737 2023-01-03 15:33:24,361 - mmcls - INFO - Epoch [2][4500/5005] lr: 1.000e-01, eta: 12:05:23, time: 0.089, data_time: 0.025, memory: 1125, loss: 3.2848 2023-01-03 15:33:32,922 - mmcls - INFO - Epoch [2][4600/5005] lr: 1.000e-01, eta: 12:04:59, time: 0.086, data_time: 0.003, memory: 1125, loss: 3.2837 2023-01-03 15:33:41,643 - mmcls - INFO - Epoch [2][4700/5005] lr: 1.000e-01, eta: 12:04:43, time: 0.087, data_time: 0.011, memory: 1125, loss: 3.2658 2023-01-03 15:33:50,468 - mmcls - INFO - Epoch [2][4800/5005] lr: 1.000e-01, eta: 12:04:32, time: 0.088, data_time: 0.021, memory: 1125, loss: 3.2736 2023-01-03 15:33:59,221 - mmcls - INFO - Epoch [2][4900/5005] lr: 1.000e-01, eta: 12:04:18, time: 0.088, data_time: 0.006, memory: 1125, loss: 3.2593 2023-01-03 15:34:07,820 - mmcls - INFO - Epoch [2][5000/5005] lr: 1.000e-01, eta: 12:03:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 3.2728 2023-01-03 15:34:30,821 - mmcls - INFO - Epoch(val) [2][196] accuracy_top-1: 35.4840, accuracy_top-5: 61.0780 2023-01-03 15:34:41,956 - mmcls - INFO - Epoch [3][100/5005] lr: 1.000e-01, eta: 12:05:15, time: 0.111, data_time: 0.038, memory: 1125, loss: 3.1983 2023-01-03 15:34:50,837 - mmcls - INFO - Epoch [3][200/5005] lr: 1.000e-01, eta: 12:05:06, time: 0.089, data_time: 0.025, memory: 1125, loss: 3.1895 2023-01-03 15:34:59,597 - mmcls - INFO - Epoch [3][300/5005] lr: 1.000e-01, eta: 12:04:52, time: 0.088, data_time: 0.022, memory: 1125, loss: 3.1871 2023-01-03 15:35:08,195 - mmcls - INFO - Epoch [3][400/5005] lr: 1.000e-01, eta: 12:04:30, time: 0.086, data_time: 0.009, memory: 1125, loss: 3.2168 2023-01-03 15:35:16,867 - mmcls - INFO - Epoch [3][500/5005] lr: 1.000e-01, eta: 12:04:12, time: 0.087, data_time: 0.010, memory: 1125, loss: 3.1907 2023-01-03 15:35:25,807 - mmcls - INFO - Epoch [3][600/5005] lr: 1.000e-01, eta: 12:04:06, time: 0.089, data_time: 0.007, memory: 1125, loss: 3.1476 2023-01-03 15:35:34,325 - mmcls - INFO - Epoch [3][700/5005] lr: 1.000e-01, eta: 12:03:41, time: 0.085, data_time: 0.018, memory: 1125, loss: 3.1764 2023-01-03 15:35:42,914 - mmcls - INFO - Epoch [3][800/5005] lr: 1.000e-01, eta: 12:03:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 3.1573 2023-01-03 15:35:51,483 - mmcls - INFO - Epoch [3][900/5005] lr: 1.000e-01, eta: 12:02:58, time: 0.086, data_time: 0.023, memory: 1125, loss: 3.1756 2023-01-03 15:36:00,125 - mmcls - INFO - Epoch [3][1000/5005] lr: 1.000e-01, eta: 12:02:39, time: 0.086, data_time: 0.015, memory: 1125, loss: 3.1709 2023-01-03 15:36:08,834 - mmcls - INFO - Epoch [3][1100/5005] lr: 1.000e-01, eta: 12:02:24, time: 0.087, data_time: 0.006, memory: 1125, loss: 3.1598 2023-01-03 15:36:17,666 - mmcls - INFO - Epoch [3][1200/5005] lr: 1.000e-01, eta: 12:02:14, time: 0.088, data_time: 0.012, memory: 1125, loss: 3.1340 2023-01-03 15:36:26,389 - mmcls - INFO - Epoch [3][1300/5005] lr: 1.000e-01, eta: 12:01:59, time: 0.087, data_time: 0.015, memory: 1125, loss: 3.1802 2023-01-03 15:36:35,224 - mmcls - INFO - Epoch [3][1400/5005] lr: 1.000e-01, eta: 12:01:49, time: 0.088, data_time: 0.020, memory: 1125, loss: 3.1507 2023-01-03 15:36:43,903 - mmcls - INFO - Epoch [3][1500/5005] lr: 1.000e-01, eta: 12:01:33, time: 0.087, data_time: 0.029, memory: 1125, loss: 3.1183 2023-01-03 15:36:52,511 - mmcls - INFO - Epoch [3][1600/5005] lr: 1.000e-01, eta: 12:01:14, time: 0.086, data_time: 0.030, memory: 1125, loss: 3.1390 2023-01-03 15:37:01,029 - mmcls - INFO - Epoch [3][1700/5005] lr: 1.000e-01, eta: 12:00:51, time: 0.085, data_time: 0.033, memory: 1125, loss: 3.1151 2023-01-03 15:37:09,885 - mmcls - INFO - Epoch [3][1800/5005] lr: 1.000e-01, eta: 12:00:42, time: 0.089, data_time: 0.022, memory: 1125, loss: 3.1254 2023-01-03 15:37:18,426 - mmcls - INFO - Epoch [3][1900/5005] lr: 1.000e-01, eta: 12:00:21, time: 0.085, data_time: 0.011, memory: 1125, loss: 3.1233 2023-01-03 15:37:27,318 - mmcls - INFO - Epoch [3][2000/5005] lr: 1.000e-01, eta: 12:00:14, time: 0.089, data_time: 0.029, memory: 1125, loss: 3.1377 2023-01-03 15:37:36,024 - mmcls - INFO - Epoch [3][2100/5005] lr: 1.000e-01, eta: 11:59:59, time: 0.087, data_time: 0.004, memory: 1125, loss: 3.0860 2023-01-03 15:37:44,704 - mmcls - INFO - Epoch [3][2200/5005] lr: 1.000e-01, eta: 11:59:44, time: 0.087, data_time: 0.033, memory: 1125, loss: 3.1365 2023-01-03 15:37:53,558 - mmcls - INFO - Epoch [3][2300/5005] lr: 1.000e-01, eta: 11:59:35, time: 0.088, data_time: 0.005, memory: 1125, loss: 3.1126 2023-01-03 15:38:02,627 - mmcls - INFO - Epoch [3][2400/5005] lr: 1.000e-01, eta: 11:59:35, time: 0.091, data_time: 0.002, memory: 1125, loss: 3.0613 2023-01-03 15:38:11,240 - mmcls - INFO - Epoch [3][2500/5005] lr: 1.000e-01, eta: 11:59:17, time: 0.086, data_time: 0.008, memory: 1125, loss: 3.1118 2023-01-03 15:38:20,051 - mmcls - INFO - Epoch [3][2600/5005] lr: 1.000e-01, eta: 11:59:07, time: 0.088, data_time: 0.029, memory: 1125, loss: 3.0687 2023-01-03 15:38:28,804 - mmcls - INFO - Epoch [3][2700/5005] lr: 1.000e-01, eta: 11:58:55, time: 0.088, data_time: 0.011, memory: 1125, loss: 3.1012 2023-01-03 15:38:37,605 - mmcls - INFO - Epoch [3][2800/5005] lr: 1.000e-01, eta: 11:58:44, time: 0.088, data_time: 0.011, memory: 1125, loss: 3.0932 2023-01-03 15:38:46,148 - mmcls - INFO - Epoch [3][2900/5005] lr: 1.000e-01, eta: 11:58:24, time: 0.085, data_time: 0.030, memory: 1125, loss: 3.0778 2023-01-03 15:38:54,981 - mmcls - INFO - Epoch [3][3000/5005] lr: 1.000e-01, eta: 11:58:14, time: 0.088, data_time: 0.010, memory: 1125, loss: 3.0865 2023-01-03 15:39:03,509 - mmcls - INFO - Epoch [3][3100/5005] lr: 1.000e-01, eta: 11:57:54, time: 0.085, data_time: 0.018, memory: 1125, loss: 3.0816 2023-01-03 15:39:12,330 - mmcls - INFO - Epoch [3][3200/5005] lr: 1.000e-01, eta: 11:57:45, time: 0.088, data_time: 0.002, memory: 1125, loss: 3.0725 2023-01-03 15:39:21,130 - mmcls - INFO - Epoch [3][3300/5005] lr: 1.000e-01, eta: 11:57:35, time: 0.088, data_time: 0.001, memory: 1125, loss: 3.0673 2023-01-03 15:39:29,683 - mmcls - INFO - Epoch [3][3400/5005] lr: 1.000e-01, eta: 11:57:15, time: 0.085, data_time: 0.007, memory: 1125, loss: 3.0762 2023-01-03 15:39:38,690 - mmcls - INFO - Epoch [3][3500/5005] lr: 1.000e-01, eta: 11:57:13, time: 0.090, data_time: 0.008, memory: 1125, loss: 3.0792 2023-01-03 15:39:47,289 - mmcls - INFO - Epoch [3][3600/5005] lr: 1.000e-01, eta: 11:56:55, time: 0.086, data_time: 0.033, memory: 1125, loss: 3.0612 2023-01-03 15:39:56,180 - mmcls - INFO - Epoch [3][3700/5005] lr: 1.000e-01, eta: 11:56:48, time: 0.089, data_time: 0.006, memory: 1125, loss: 3.0442 2023-01-03 15:40:05,069 - mmcls - INFO - Epoch [3][3800/5005] lr: 1.000e-01, eta: 11:56:42, time: 0.089, data_time: 0.007, memory: 1125, loss: 3.0363 2023-01-03 15:40:14,011 - mmcls - INFO - Epoch [3][3900/5005] lr: 1.000e-01, eta: 11:56:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.0170 2023-01-03 15:40:22,924 - mmcls - INFO - Epoch [3][4000/5005] lr: 1.000e-01, eta: 11:56:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 3.0327 2023-01-03 15:40:31,606 - mmcls - INFO - Epoch [3][4100/5005] lr: 1.000e-01, eta: 11:56:16, time: 0.087, data_time: 0.001, memory: 1125, loss: 3.0476 2023-01-03 15:40:40,797 - mmcls - INFO - Epoch [3][4200/5005] lr: 1.000e-01, eta: 11:56:19, time: 0.092, data_time: 0.002, memory: 1125, loss: 3.0443 2023-01-03 15:40:49,431 - mmcls - INFO - Epoch [3][4300/5005] lr: 1.000e-01, eta: 11:56:03, time: 0.086, data_time: 0.009, memory: 1125, loss: 3.0229 2023-01-03 15:40:58,262 - mmcls - INFO - Epoch [3][4400/5005] lr: 1.000e-01, eta: 11:55:54, time: 0.088, data_time: 0.020, memory: 1125, loss: 3.0156 2023-01-03 15:41:06,907 - mmcls - INFO - Epoch [3][4500/5005] lr: 1.000e-01, eta: 11:55:39, time: 0.086, data_time: 0.009, memory: 1125, loss: 3.0322 2023-01-03 15:41:15,972 - mmcls - INFO - Epoch [3][4600/5005] lr: 1.000e-01, eta: 11:55:38, time: 0.091, data_time: 0.027, memory: 1125, loss: 2.9978 2023-01-03 15:41:24,734 - mmcls - INFO - Epoch [3][4700/5005] lr: 1.000e-01, eta: 11:55:26, time: 0.088, data_time: 0.009, memory: 1125, loss: 3.0369 2023-01-03 15:41:33,652 - mmcls - INFO - Epoch [3][4800/5005] lr: 1.000e-01, eta: 11:55:20, time: 0.089, data_time: 0.016, memory: 1125, loss: 3.0155 2023-01-03 15:41:42,786 - mmcls - INFO - Epoch [3][4900/5005] lr: 1.000e-01, eta: 11:55:21, time: 0.091, data_time: 0.026, memory: 1125, loss: 3.0293 2023-01-03 15:41:51,486 - mmcls - INFO - Epoch [3][5000/5005] lr: 1.000e-01, eta: 11:55:07, time: 0.087, data_time: 0.029, memory: 1125, loss: 3.0136 2023-01-03 15:42:14,146 - mmcls - INFO - Epoch(val) [3][196] accuracy_top-1: 41.0100, accuracy_top-5: 67.1080 2023-01-03 15:42:25,032 - mmcls - INFO - Epoch [4][100/5005] lr: 1.000e-01, eta: 11:55:49, time: 0.109, data_time: 0.048, memory: 1125, loss: 2.9524 2023-01-03 15:42:34,102 - mmcls - INFO - Epoch [4][200/5005] lr: 1.000e-01, eta: 11:55:47, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.9433 2023-01-03 15:42:43,180 - mmcls - INFO - Epoch [4][300/5005] lr: 1.000e-01, eta: 11:55:46, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.9584 2023-01-03 15:42:51,993 - mmcls - INFO - Epoch [4][400/5005] lr: 1.000e-01, eta: 11:55:35, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.9773 2023-01-03 15:43:00,914 - mmcls - INFO - Epoch [4][500/5005] lr: 1.000e-01, eta: 11:55:29, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.9513 2023-01-03 15:43:09,612 - mmcls - INFO - Epoch [4][600/5005] lr: 1.000e-01, eta: 11:55:15, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.9697 2023-01-03 15:43:18,502 - mmcls - INFO - Epoch [4][700/5005] lr: 1.000e-01, eta: 11:55:07, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.9516 2023-01-03 15:43:27,081 - mmcls - INFO - Epoch [4][800/5005] lr: 1.000e-01, eta: 11:54:50, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.9589 2023-01-03 15:43:36,129 - mmcls - INFO - Epoch [4][900/5005] lr: 1.000e-01, eta: 11:54:47, time: 0.090, data_time: 0.034, memory: 1125, loss: 2.9083 2023-01-03 15:43:45,069 - mmcls - INFO - Epoch [4][1000/5005] lr: 1.000e-01, eta: 11:54:41, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.9740 2023-01-03 15:43:53,729 - mmcls - INFO - Epoch [4][1100/5005] lr: 1.000e-01, eta: 11:54:27, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.9804 2023-01-03 15:44:02,621 - mmcls - INFO - Epoch [4][1200/5005] lr: 1.000e-01, eta: 11:54:19, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.9395 2023-01-03 15:44:11,590 - mmcls - INFO - Epoch [4][1300/5005] lr: 1.000e-01, eta: 11:54:14, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.9476 2023-01-03 15:44:20,625 - mmcls - INFO - Epoch [4][1400/5005] lr: 1.000e-01, eta: 11:54:10, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.9523 2023-01-03 15:44:29,544 - mmcls - INFO - Epoch [4][1500/5005] lr: 1.000e-01, eta: 11:54:03, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.9428 2023-01-03 15:44:38,487 - mmcls - INFO - Epoch [4][1600/5005] lr: 1.000e-01, eta: 11:53:57, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.9393 2023-01-03 15:44:47,117 - mmcls - INFO - Epoch [4][1700/5005] lr: 1.000e-01, eta: 11:53:42, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9422 2023-01-03 15:44:55,770 - mmcls - INFO - Epoch [4][1800/5005] lr: 1.000e-01, eta: 11:53:27, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.9309 2023-01-03 15:45:04,258 - mmcls - INFO - Epoch [4][1900/5005] lr: 1.000e-01, eta: 11:53:08, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.9073 2023-01-03 15:45:12,933 - mmcls - INFO - Epoch [4][2000/5005] lr: 1.000e-01, eta: 11:52:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9317 2023-01-03 15:45:21,508 - mmcls - INFO - Epoch [4][2100/5005] lr: 1.000e-01, eta: 11:52:37, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.8988 2023-01-03 15:45:30,106 - mmcls - INFO - Epoch [4][2200/5005] lr: 1.000e-01, eta: 11:52:22, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.9402 2023-01-03 15:45:38,890 - mmcls - INFO - Epoch [4][2300/5005] lr: 1.000e-01, eta: 11:52:11, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.9377 2023-01-03 15:45:47,698 - mmcls - INFO - Epoch [4][2400/5005] lr: 1.000e-01, eta: 11:52:01, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.9206 2023-01-03 15:45:56,891 - mmcls - INFO - Epoch [4][2500/5005] lr: 1.000e-01, eta: 11:52:02, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.9342 2023-01-03 15:46:05,599 - mmcls - INFO - Epoch [4][2600/5005] lr: 1.000e-01, eta: 11:51:50, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.9168 2023-01-03 15:46:14,296 - mmcls - INFO - Epoch [4][2700/5005] lr: 1.000e-01, eta: 11:51:37, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.9171 2023-01-03 15:46:22,978 - mmcls - INFO - Epoch [4][2800/5005] lr: 1.000e-01, eta: 11:51:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.9262 2023-01-03 15:46:31,805 - mmcls - INFO - Epoch [4][2900/5005] lr: 1.000e-01, eta: 11:51:14, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.9406 2023-01-03 15:46:40,744 - mmcls - INFO - Epoch [4][3000/5005] lr: 1.000e-01, eta: 11:51:08, time: 0.089, data_time: 0.048, memory: 1125, loss: 2.9370 2023-01-03 15:46:49,471 - mmcls - INFO - Epoch [4][3100/5005] lr: 1.000e-01, eta: 11:50:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.9111 2023-01-03 15:46:58,124 - mmcls - INFO - Epoch [4][3200/5005] lr: 1.000e-01, eta: 11:50:42, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.9217 2023-01-03 15:47:06,898 - mmcls - INFO - Epoch [4][3300/5005] lr: 1.000e-01, eta: 11:50:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.9190 2023-01-03 15:47:15,577 - mmcls - INFO - Epoch [4][3400/5005] lr: 1.000e-01, eta: 11:50:18, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.9357 2023-01-03 15:47:24,562 - mmcls - INFO - Epoch [4][3500/5005] lr: 1.000e-01, eta: 11:50:13, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.8989 2023-01-03 15:47:33,165 - mmcls - INFO - Epoch [4][3600/5005] lr: 1.000e-01, eta: 11:49:58, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.9365 2023-01-03 15:47:41,890 - mmcls - INFO - Epoch [4][3700/5005] lr: 1.000e-01, eta: 11:49:46, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.9031 2023-01-03 15:47:50,495 - mmcls - INFO - Epoch [4][3800/5005] lr: 1.000e-01, eta: 11:49:31, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.9280 2023-01-03 15:47:59,327 - mmcls - INFO - Epoch [4][3900/5005] lr: 1.000e-01, eta: 11:49:22, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.8919 2023-01-03 15:48:07,788 - mmcls - INFO - Epoch [4][4000/5005] lr: 1.000e-01, eta: 11:49:04, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.9052 2023-01-03 15:48:16,793 - mmcls - INFO - Epoch [4][4100/5005] lr: 1.000e-01, eta: 11:48:59, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.8841 2023-01-03 15:48:25,701 - mmcls - INFO - Epoch [4][4200/5005] lr: 1.000e-01, eta: 11:48:52, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.8946 2023-01-03 15:48:34,610 - mmcls - INFO - Epoch [4][4300/5005] lr: 1.000e-01, eta: 11:48:45, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8977 2023-01-03 15:48:43,381 - mmcls - INFO - Epoch [4][4400/5005] lr: 1.000e-01, eta: 11:48:35, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.8860 2023-01-03 15:48:52,369 - mmcls - INFO - Epoch [4][4500/5005] lr: 1.000e-01, eta: 11:48:29, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.8708 2023-01-03 15:49:01,206 - mmcls - INFO - Epoch [4][4600/5005] lr: 1.000e-01, eta: 11:48:20, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.8940 2023-01-03 15:49:10,133 - mmcls - INFO - Epoch [4][4700/5005] lr: 1.000e-01, eta: 11:48:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.9077 2023-01-03 15:49:19,157 - mmcls - INFO - Epoch [4][4800/5005] lr: 1.000e-01, eta: 11:48:09, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.8987 2023-01-03 15:49:27,994 - mmcls - INFO - Epoch [4][4900/5005] lr: 1.000e-01, eta: 11:48:00, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.8931 2023-01-03 15:49:37,092 - mmcls - INFO - Epoch [4][5000/5005] lr: 1.000e-01, eta: 11:47:58, time: 0.091, data_time: 0.035, memory: 1125, loss: 2.8569 2023-01-03 15:49:59,931 - mmcls - INFO - Epoch(val) [4][196] accuracy_top-1: 43.4940, accuracy_top-5: 69.8200 2023-01-03 15:50:10,849 - mmcls - INFO - Epoch [5][100/5005] lr: 1.000e-01, eta: 11:48:27, time: 0.109, data_time: 0.052, memory: 1125, loss: 2.8349 2023-01-03 15:50:19,702 - mmcls - INFO - Epoch [5][200/5005] lr: 1.000e-01, eta: 11:48:18, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.8035 2023-01-03 15:50:28,694 - mmcls - INFO - Epoch [5][300/5005] lr: 1.000e-01, eta: 11:48:13, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.8564 2023-01-03 15:50:37,329 - mmcls - INFO - Epoch [5][400/5005] lr: 1.000e-01, eta: 11:47:59, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.8557 2023-01-03 15:50:46,065 - mmcls - INFO - Epoch [5][500/5005] lr: 1.000e-01, eta: 11:47:47, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.8324 2023-01-03 15:50:54,876 - mmcls - INFO - Epoch [5][600/5005] lr: 1.000e-01, eta: 11:47:37, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8073 2023-01-03 15:51:03,312 - mmcls - INFO - Epoch [5][700/5005] lr: 1.000e-01, eta: 11:47:19, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.8404 2023-01-03 15:51:12,081 - mmcls - INFO - Epoch [5][800/5005] lr: 1.000e-01, eta: 11:47:09, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.8232 2023-01-03 15:51:20,673 - mmcls - INFO - Epoch [5][900/5005] lr: 1.000e-01, eta: 11:46:54, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.8333 2023-01-03 15:51:29,681 - mmcls - INFO - Epoch [5][1000/5005] lr: 1.000e-01, eta: 11:46:49, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.8413 2023-01-03 15:51:38,415 - mmcls - INFO - Epoch [5][1100/5005] lr: 1.000e-01, eta: 11:46:37, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.8251 2023-01-03 15:51:47,106 - mmcls - INFO - Epoch [5][1200/5005] lr: 1.000e-01, eta: 11:46:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.8240 2023-01-03 15:51:55,837 - mmcls - INFO - Epoch [5][1300/5005] lr: 1.000e-01, eta: 11:46:14, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.8205 2023-01-03 15:52:04,561 - mmcls - INFO - Epoch [5][1400/5005] lr: 1.000e-01, eta: 11:46:02, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.8289 2023-01-03 15:52:13,358 - mmcls - INFO - Epoch [5][1500/5005] lr: 1.000e-01, eta: 11:45:52, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.8048 2023-01-03 15:52:22,071 - mmcls - INFO - Epoch [5][1600/5005] lr: 1.000e-01, eta: 11:45:40, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.8426 2023-01-03 15:52:30,671 - mmcls - INFO - Epoch [5][1700/5005] lr: 1.000e-01, eta: 11:45:26, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.8386 2023-01-03 15:52:39,250 - mmcls - INFO - Epoch [5][1800/5005] lr: 1.000e-01, eta: 11:45:12, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.8075 2023-01-03 15:52:47,998 - mmcls - INFO - Epoch [5][1900/5005] lr: 1.000e-01, eta: 11:45:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8372 2023-01-03 15:52:56,587 - mmcls - INFO - Epoch [5][2000/5005] lr: 1.000e-01, eta: 11:44:46, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.8068 2023-01-03 15:53:05,430 - mmcls - INFO - Epoch [5][2100/5005] lr: 1.000e-01, eta: 11:44:38, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.8410 2023-01-03 15:53:14,222 - mmcls - INFO - Epoch [5][2200/5005] lr: 1.000e-01, eta: 11:44:28, time: 0.088, data_time: 0.048, memory: 1125, loss: 2.8278 2023-01-03 15:53:23,096 - mmcls - INFO - Epoch [5][2300/5005] lr: 1.000e-01, eta: 11:44:20, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.8503 2023-01-03 15:53:31,593 - mmcls - INFO - Epoch [5][2400/5005] lr: 1.000e-01, eta: 11:44:04, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.8163 2023-01-03 15:53:40,562 - mmcls - INFO - Epoch [5][2500/5005] lr: 1.000e-01, eta: 11:43:58, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.8316 2023-01-03 15:53:49,173 - mmcls - INFO - Epoch [5][2600/5005] lr: 1.000e-01, eta: 11:43:44, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.8203 2023-01-03 15:53:57,994 - mmcls - INFO - Epoch [5][2700/5005] lr: 1.000e-01, eta: 11:43:35, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.8392 2023-01-03 15:54:07,016 - mmcls - INFO - Epoch [5][2800/5005] lr: 1.000e-01, eta: 11:43:30, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.8401 2023-01-03 15:54:15,709 - mmcls - INFO - Epoch [5][2900/5005] lr: 1.000e-01, eta: 11:43:18, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.8627 2023-01-03 15:54:24,798 - mmcls - INFO - Epoch [5][3000/5005] lr: 1.000e-01, eta: 11:43:14, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.8207 2023-01-03 15:54:33,531 - mmcls - INFO - Epoch [5][3100/5005] lr: 1.000e-01, eta: 11:43:04, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.7959 2023-01-03 15:54:42,221 - mmcls - INFO - Epoch [5][3200/5005] lr: 1.000e-01, eta: 11:42:52, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7929 2023-01-03 15:54:51,242 - mmcls - INFO - Epoch [5][3300/5005] lr: 1.000e-01, eta: 11:42:47, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.8216 2023-01-03 15:55:00,094 - mmcls - INFO - Epoch [5][3400/5005] lr: 1.000e-01, eta: 11:42:38, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.7983 2023-01-03 15:55:08,921 - mmcls - INFO - Epoch [5][3500/5005] lr: 1.000e-01, eta: 11:42:29, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.8278 2023-01-03 15:55:17,788 - mmcls - INFO - Epoch [5][3600/5005] lr: 1.000e-01, eta: 11:42:21, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.8511 2023-01-03 15:55:26,364 - mmcls - INFO - Epoch [5][3700/5005] lr: 1.000e-01, eta: 11:42:07, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.8386 2023-01-03 15:55:34,961 - mmcls - INFO - Epoch [5][3800/5005] lr: 1.000e-01, eta: 11:41:53, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.8179 2023-01-03 15:55:43,632 - mmcls - INFO - Epoch [5][3900/5005] lr: 1.000e-01, eta: 11:41:41, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.8292 2023-01-03 15:55:52,915 - mmcls - INFO - Epoch [5][4000/5005] lr: 1.000e-01, eta: 11:41:41, time: 0.093, data_time: 0.003, memory: 1125, loss: 2.8158 2023-01-03 15:56:01,580 - mmcls - INFO - Epoch [5][4100/5005] lr: 1.000e-01, eta: 11:41:29, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.8072 2023-01-03 15:56:10,241 - mmcls - INFO - Epoch [5][4200/5005] lr: 1.000e-01, eta: 11:41:16, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.8145 2023-01-03 15:56:19,078 - mmcls - INFO - Epoch [5][4300/5005] lr: 1.000e-01, eta: 11:41:08, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.8239 2023-01-03 15:56:27,815 - mmcls - INFO - Epoch [5][4400/5005] lr: 1.000e-01, eta: 11:40:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.8089 2023-01-03 15:56:36,358 - mmcls - INFO - Epoch [5][4500/5005] lr: 1.000e-01, eta: 11:40:42, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.7913 2023-01-03 15:56:45,112 - mmcls - INFO - Epoch [5][4600/5005] lr: 1.000e-01, eta: 11:40:32, time: 0.087, data_time: 0.029, memory: 1125, loss: 2.7802 2023-01-03 15:56:53,659 - mmcls - INFO - Epoch [5][4700/5005] lr: 1.000e-01, eta: 11:40:18, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.7832 2023-01-03 15:57:02,454 - mmcls - INFO - Epoch [5][4800/5005] lr: 1.000e-01, eta: 11:40:08, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.7884 2023-01-03 15:57:11,210 - mmcls - INFO - Epoch [5][4900/5005] lr: 1.000e-01, eta: 11:39:58, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.7935 2023-01-03 15:57:20,353 - mmcls - INFO - Epoch [5][5000/5005] lr: 1.000e-01, eta: 11:39:55, time: 0.091, data_time: 0.030, memory: 1125, loss: 2.7992 2023-01-03 15:57:43,010 - mmcls - INFO - Epoch(val) [5][196] accuracy_top-1: 44.4220, accuracy_top-5: 70.4080 2023-01-03 15:57:54,420 - mmcls - INFO - Epoch [6][100/5005] lr: 1.000e-01, eta: 11:40:25, time: 0.114, data_time: 0.041, memory: 1125, loss: 2.7526 2023-01-03 15:58:03,301 - mmcls - INFO - Epoch [6][200/5005] lr: 1.000e-01, eta: 11:40:17, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.7741 2023-01-03 15:58:11,994 - mmcls - INFO - Epoch [6][300/5005] lr: 1.000e-01, eta: 11:40:05, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7651 2023-01-03 15:58:20,835 - mmcls - INFO - Epoch [6][400/5005] lr: 1.000e-01, eta: 11:39:57, time: 0.088, data_time: 0.029, memory: 1125, loss: 2.7616 2023-01-03 15:58:29,356 - mmcls - INFO - Epoch [6][500/5005] lr: 1.000e-01, eta: 11:39:42, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.7531 2023-01-03 15:58:38,340 - mmcls - INFO - Epoch [6][600/5005] lr: 1.000e-01, eta: 11:39:36, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.7398 2023-01-03 15:58:47,110 - mmcls - INFO - Epoch [6][700/5005] lr: 1.000e-01, eta: 11:39:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7650 2023-01-03 15:58:55,937 - mmcls - INFO - Epoch [6][800/5005] lr: 1.000e-01, eta: 11:39:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7432 2023-01-03 15:59:04,845 - mmcls - INFO - Epoch [6][900/5005] lr: 1.000e-01, eta: 11:39:09, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7584 2023-01-03 15:59:13,548 - mmcls - INFO - Epoch [6][1000/5005] lr: 1.000e-01, eta: 11:38:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7862 2023-01-03 15:59:22,545 - mmcls - INFO - Epoch [6][1100/5005] lr: 1.000e-01, eta: 11:38:52, time: 0.090, data_time: 0.006, memory: 1125, loss: 2.7674 2023-01-03 15:59:31,219 - mmcls - INFO - Epoch [6][1200/5005] lr: 1.000e-01, eta: 11:38:40, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7839 2023-01-03 15:59:39,916 - mmcls - INFO - Epoch [6][1300/5005] lr: 1.000e-01, eta: 11:38:28, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.7966 2023-01-03 15:59:49,192 - mmcls - INFO - Epoch [6][1400/5005] lr: 1.000e-01, eta: 11:38:27, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.7524 2023-01-03 15:59:58,563 - mmcls - INFO - Epoch [6][1500/5005] lr: 1.000e-01, eta: 11:38:28, time: 0.094, data_time: 0.025, memory: 1125, loss: 2.7729 2023-01-03 16:00:07,498 - mmcls - INFO - Epoch [6][1600/5005] lr: 1.000e-01, eta: 11:38:21, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.7527 2023-01-03 16:00:16,175 - mmcls - INFO - Epoch [6][1700/5005] lr: 1.000e-01, eta: 11:38:09, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7683 2023-01-03 16:00:24,862 - mmcls - INFO - Epoch [6][1800/5005] lr: 1.000e-01, eta: 11:37:57, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.7809 2023-01-03 16:00:33,468 - mmcls - INFO - Epoch [6][1900/5005] lr: 1.000e-01, eta: 11:37:44, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.7685 2023-01-03 16:00:42,380 - mmcls - INFO - Epoch [6][2000/5005] lr: 1.000e-01, eta: 11:37:37, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.7513 2023-01-03 16:00:51,329 - mmcls - INFO - Epoch [6][2100/5005] lr: 1.000e-01, eta: 11:37:30, time: 0.090, data_time: 0.027, memory: 1125, loss: 2.7411 2023-01-03 16:01:00,205 - mmcls - INFO - Epoch [6][2200/5005] lr: 1.000e-01, eta: 11:37:21, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.7658 2023-01-03 16:01:08,833 - mmcls - INFO - Epoch [6][2300/5005] lr: 1.000e-01, eta: 11:37:09, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7526 2023-01-03 16:01:18,064 - mmcls - INFO - Epoch [6][2400/5005] lr: 1.000e-01, eta: 11:37:07, time: 0.092, data_time: 0.013, memory: 1125, loss: 2.7653 2023-01-03 16:01:26,606 - mmcls - INFO - Epoch [6][2500/5005] lr: 1.000e-01, eta: 11:36:53, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.7744 2023-01-03 16:01:35,330 - mmcls - INFO - Epoch [6][2600/5005] lr: 1.000e-01, eta: 11:36:42, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7556 2023-01-03 16:01:43,967 - mmcls - INFO - Epoch [6][2700/5005] lr: 1.000e-01, eta: 11:36:30, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.7828 2023-01-03 16:01:52,753 - mmcls - INFO - Epoch [6][2800/5005] lr: 1.000e-01, eta: 11:36:20, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.7650 2023-01-03 16:02:01,342 - mmcls - INFO - Epoch [6][2900/5005] lr: 1.000e-01, eta: 11:36:07, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.7545 2023-01-03 16:02:10,130 - mmcls - INFO - Epoch [6][3000/5005] lr: 1.000e-01, eta: 11:35:57, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.7498 2023-01-03 16:02:18,961 - mmcls - INFO - Epoch [6][3100/5005] lr: 1.000e-01, eta: 11:35:48, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.7573 2023-01-03 16:02:27,717 - mmcls - INFO - Epoch [6][3200/5005] lr: 1.000e-01, eta: 11:35:38, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7340 2023-01-03 16:02:36,389 - mmcls - INFO - Epoch [6][3300/5005] lr: 1.000e-01, eta: 11:35:26, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.7580 2023-01-03 16:02:45,150 - mmcls - INFO - Epoch [6][3400/5005] lr: 1.000e-01, eta: 11:35:16, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.7548 2023-01-03 16:02:53,851 - mmcls - INFO - Epoch [6][3500/5005] lr: 1.000e-01, eta: 11:35:05, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.7397 2023-01-03 16:03:02,451 - mmcls - INFO - Epoch [6][3600/5005] lr: 1.000e-01, eta: 11:34:52, time: 0.086, data_time: 0.039, memory: 1125, loss: 2.7477 2023-01-03 16:03:11,373 - mmcls - INFO - Epoch [6][3700/5005] lr: 1.000e-01, eta: 11:34:45, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.7720 2023-01-03 16:03:20,174 - mmcls - INFO - Epoch [6][3800/5005] lr: 1.000e-01, eta: 11:34:35, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.7651 2023-01-03 16:03:28,858 - mmcls - INFO - Epoch [6][3900/5005] lr: 1.000e-01, eta: 11:34:24, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.7842 2023-01-03 16:03:37,560 - mmcls - INFO - Epoch [6][4000/5005] lr: 1.000e-01, eta: 11:34:13, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.7246 2023-01-03 16:03:46,557 - mmcls - INFO - Epoch [6][4100/5005] lr: 1.000e-01, eta: 11:34:07, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.7571 2023-01-03 16:03:55,378 - mmcls - INFO - Epoch [6][4200/5005] lr: 1.000e-01, eta: 11:33:58, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7526 2023-01-03 16:04:03,903 - mmcls - INFO - Epoch [6][4300/5005] lr: 1.000e-01, eta: 11:33:44, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.7691 2023-01-03 16:04:13,039 - mmcls - INFO - Epoch [6][4400/5005] lr: 1.000e-01, eta: 11:33:40, time: 0.091, data_time: 0.011, memory: 1125, loss: 2.7380 2023-01-03 16:04:21,925 - mmcls - INFO - Epoch [6][4500/5005] lr: 1.000e-01, eta: 11:33:32, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.7314 2023-01-03 16:04:31,039 - mmcls - INFO - Epoch [6][4600/5005] lr: 1.000e-01, eta: 11:33:28, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.7479 2023-01-03 16:04:39,741 - mmcls - INFO - Epoch [6][4700/5005] lr: 1.000e-01, eta: 11:33:17, time: 0.087, data_time: 0.037, memory: 1125, loss: 2.7504 2023-01-03 16:04:48,562 - mmcls - INFO - Epoch [6][4800/5005] lr: 1.000e-01, eta: 11:33:07, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.7432 2023-01-03 16:04:57,658 - mmcls - INFO - Epoch [6][4900/5005] lr: 1.000e-01, eta: 11:33:03, time: 0.091, data_time: 0.034, memory: 1125, loss: 2.7310 2023-01-03 16:05:06,459 - mmcls - INFO - Epoch [6][5000/5005] lr: 1.000e-01, eta: 11:32:53, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.7430 2023-01-03 16:05:28,978 - mmcls - INFO - Epoch(val) [6][196] accuracy_top-1: 44.0160, accuracy_top-5: 70.7660 2023-01-03 16:05:39,926 - mmcls - INFO - Epoch [7][100/5005] lr: 1.000e-01, eta: 11:33:10, time: 0.109, data_time: 0.040, memory: 1125, loss: 2.7128 2023-01-03 16:05:48,494 - mmcls - INFO - Epoch [7][200/5005] lr: 1.000e-01, eta: 11:32:57, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.6944 2023-01-03 16:05:57,374 - mmcls - INFO - Epoch [7][300/5005] lr: 1.000e-01, eta: 11:32:48, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7195 2023-01-03 16:06:06,066 - mmcls - INFO - Epoch [7][400/5005] lr: 1.000e-01, eta: 11:32:37, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.6993 2023-01-03 16:06:14,653 - mmcls - INFO - Epoch [7][500/5005] lr: 1.000e-01, eta: 11:32:24, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.6877 2023-01-03 16:06:23,505 - mmcls - INFO - Epoch [7][600/5005] lr: 1.000e-01, eta: 11:32:16, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.6918 2023-01-03 16:06:31,976 - mmcls - INFO - Epoch [7][700/5005] lr: 1.000e-01, eta: 11:32:01, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.7069 2023-01-03 16:06:41,036 - mmcls - INFO - Epoch [7][800/5005] lr: 1.000e-01, eta: 11:31:56, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.7133 2023-01-03 16:06:49,738 - mmcls - INFO - Epoch [7][900/5005] lr: 1.000e-01, eta: 11:31:45, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.7005 2023-01-03 16:06:58,453 - mmcls - INFO - Epoch [7][1000/5005] lr: 1.000e-01, eta: 11:31:34, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.7099 2023-01-03 16:07:07,262 - mmcls - INFO - Epoch [7][1100/5005] lr: 1.000e-01, eta: 11:31:25, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.7471 2023-01-03 16:07:16,279 - mmcls - INFO - Epoch [7][1200/5005] lr: 1.000e-01, eta: 11:31:19, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.6998 2023-01-03 16:07:24,819 - mmcls - INFO - Epoch [7][1300/5005] lr: 1.000e-01, eta: 11:31:05, time: 0.085, data_time: 0.027, memory: 1125, loss: 2.7193 2023-01-03 16:07:33,957 - mmcls - INFO - Epoch [7][1400/5005] lr: 1.000e-01, eta: 11:31:01, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.7208 2023-01-03 16:07:42,837 - mmcls - INFO - Epoch [7][1500/5005] lr: 1.000e-01, eta: 11:30:53, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.7228 2023-01-03 16:07:51,437 - mmcls - INFO - Epoch [7][1600/5005] lr: 1.000e-01, eta: 11:30:40, time: 0.086, data_time: 0.030, memory: 1125, loss: 2.7159 2023-01-03 16:08:00,534 - mmcls - INFO - Epoch [7][1700/5005] lr: 1.000e-01, eta: 11:30:35, time: 0.091, data_time: 0.016, memory: 1125, loss: 2.6782 2023-01-03 16:08:09,224 - mmcls - INFO - Epoch [7][1800/5005] lr: 1.000e-01, eta: 11:30:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7194 2023-01-03 16:08:18,041 - mmcls - INFO - Epoch [7][1900/5005] lr: 1.000e-01, eta: 11:30:15, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.7155 2023-01-03 16:08:26,875 - mmcls - INFO - Epoch [7][2000/5005] lr: 1.000e-01, eta: 11:30:06, time: 0.088, data_time: 0.039, memory: 1125, loss: 2.7177 2023-01-03 16:08:35,496 - mmcls - INFO - Epoch [7][2100/5005] lr: 1.000e-01, eta: 11:29:54, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7275 2023-01-03 16:08:44,293 - mmcls - INFO - Epoch [7][2200/5005] lr: 1.000e-01, eta: 11:29:45, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.7524 2023-01-03 16:08:53,084 - mmcls - INFO - Epoch [7][2300/5005] lr: 1.000e-01, eta: 11:29:35, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.7250 2023-01-03 16:09:01,739 - mmcls - INFO - Epoch [7][2400/5005] lr: 1.000e-01, eta: 11:29:24, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.6961 2023-01-03 16:09:10,361 - mmcls - INFO - Epoch [7][2500/5005] lr: 1.000e-01, eta: 11:29:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.7207 2023-01-03 16:09:18,754 - mmcls - INFO - Epoch [7][2600/5005] lr: 1.000e-01, eta: 11:28:56, time: 0.084, data_time: 0.025, memory: 1125, loss: 2.7360 2023-01-03 16:09:27,809 - mmcls - INFO - Epoch [7][2700/5005] lr: 1.000e-01, eta: 11:28:51, time: 0.091, data_time: 0.026, memory: 1125, loss: 2.7016 2023-01-03 16:09:36,582 - mmcls - INFO - Epoch [7][2800/5005] lr: 1.000e-01, eta: 11:28:41, time: 0.088, data_time: 0.029, memory: 1125, loss: 2.7370 2023-01-03 16:09:45,142 - mmcls - INFO - Epoch [7][2900/5005] lr: 1.000e-01, eta: 11:28:28, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.7147 2023-01-03 16:09:54,062 - mmcls - INFO - Epoch [7][3000/5005] lr: 1.000e-01, eta: 11:28:21, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.7133 2023-01-03 16:10:02,961 - mmcls - INFO - Epoch [7][3100/5005] lr: 1.000e-01, eta: 11:28:13, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.7160 2023-01-03 16:10:11,684 - mmcls - INFO - Epoch [7][3200/5005] lr: 1.000e-01, eta: 11:28:02, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.6959 2023-01-03 16:10:20,297 - mmcls - INFO - Epoch [7][3300/5005] lr: 1.000e-01, eta: 11:27:50, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.7229 2023-01-03 16:10:28,981 - mmcls - INFO - Epoch [7][3400/5005] lr: 1.000e-01, eta: 11:27:39, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.7229 2023-01-03 16:10:37,854 - mmcls - INFO - Epoch [7][3500/5005] lr: 1.000e-01, eta: 11:27:31, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.7181 2023-01-03 16:10:46,884 - mmcls - INFO - Epoch [7][3600/5005] lr: 1.000e-01, eta: 11:27:25, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.6851 2023-01-03 16:10:55,761 - mmcls - INFO - Epoch [7][3700/5005] lr: 1.000e-01, eta: 11:27:17, time: 0.089, data_time: 0.035, memory: 1125, loss: 2.7074 2023-01-03 16:11:04,513 - mmcls - INFO - Epoch [7][3800/5005] lr: 1.000e-01, eta: 11:27:07, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.7011 2023-01-03 16:11:13,449 - mmcls - INFO - Epoch [7][3900/5005] lr: 1.000e-01, eta: 11:26:59, time: 0.089, data_time: 0.028, memory: 1125, loss: 2.7132 2023-01-03 16:11:22,329 - mmcls - INFO - Epoch [7][4000/5005] lr: 1.000e-01, eta: 11:26:51, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.7299 2023-01-03 16:11:31,113 - mmcls - INFO - Epoch [7][4100/5005] lr: 1.000e-01, eta: 11:26:41, time: 0.088, data_time: 0.047, memory: 1125, loss: 2.7286 2023-01-03 16:11:39,605 - mmcls - INFO - Epoch [7][4200/5005] lr: 1.000e-01, eta: 11:26:28, time: 0.085, data_time: 0.041, memory: 1125, loss: 2.7065 2023-01-03 16:11:48,320 - mmcls - INFO - Epoch [7][4300/5005] lr: 1.000e-01, eta: 11:26:17, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.7015 2023-01-03 16:11:57,392 - mmcls - INFO - Epoch [7][4400/5005] lr: 1.000e-01, eta: 11:26:12, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.7023 2023-01-03 16:12:06,075 - mmcls - INFO - Epoch [7][4500/5005] lr: 1.000e-01, eta: 11:26:01, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.7052 2023-01-03 16:12:14,887 - mmcls - INFO - Epoch [7][4600/5005] lr: 1.000e-01, eta: 11:25:52, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.6985 2023-01-03 16:12:23,871 - mmcls - INFO - Epoch [7][4700/5005] lr: 1.000e-01, eta: 11:25:45, time: 0.090, data_time: 0.040, memory: 1125, loss: 2.7030 2023-01-03 16:12:32,893 - mmcls - INFO - Epoch [7][4800/5005] lr: 1.000e-01, eta: 11:25:39, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.7198 2023-01-03 16:12:41,683 - mmcls - INFO - Epoch [7][4900/5005] lr: 1.000e-01, eta: 11:25:29, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.6914 2023-01-03 16:12:50,393 - mmcls - INFO - Epoch [7][5000/5005] lr: 1.000e-01, eta: 11:25:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.7296 2023-01-03 16:13:13,128 - mmcls - INFO - Epoch(val) [7][196] accuracy_top-1: 46.7100, accuracy_top-5: 72.5680 2023-01-03 16:13:24,154 - mmcls - INFO - Epoch [8][100/5005] lr: 1.000e-01, eta: 11:25:32, time: 0.110, data_time: 0.037, memory: 1125, loss: 2.6507 2023-01-03 16:13:32,934 - mmcls - INFO - Epoch [8][200/5005] lr: 1.000e-01, eta: 11:25:23, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.6699 2023-01-03 16:13:41,571 - mmcls - INFO - Epoch [8][300/5005] lr: 1.000e-01, eta: 11:25:11, time: 0.086, data_time: 0.030, memory: 1125, loss: 2.6612 2023-01-03 16:13:50,512 - mmcls - INFO - Epoch [8][400/5005] lr: 1.000e-01, eta: 11:25:04, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.6721 2023-01-03 16:13:59,262 - mmcls - INFO - Epoch [8][500/5005] lr: 1.000e-01, eta: 11:24:54, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.6534 2023-01-03 16:14:07,780 - mmcls - INFO - Epoch [8][600/5005] lr: 1.000e-01, eta: 11:24:41, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.6587 2023-01-03 16:14:16,594 - mmcls - INFO - Epoch [8][700/5005] lr: 1.000e-01, eta: 11:24:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.6935 2023-01-03 16:14:25,488 - mmcls - INFO - Epoch [8][800/5005] lr: 1.000e-01, eta: 11:24:23, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.6666 2023-01-03 16:14:33,977 - mmcls - INFO - Epoch [8][900/5005] lr: 1.000e-01, eta: 11:24:10, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.6889 2023-01-03 16:14:42,494 - mmcls - INFO - Epoch [8][1000/5005] lr: 1.000e-01, eta: 11:23:57, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.6657 2023-01-03 16:14:51,381 - mmcls - INFO - Epoch [8][1100/5005] lr: 1.000e-01, eta: 11:23:49, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.6696 2023-01-03 16:15:00,110 - mmcls - INFO - Epoch [8][1200/5005] lr: 1.000e-01, eta: 11:23:39, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.6875 2023-01-03 16:15:08,923 - mmcls - INFO - Epoch [8][1300/5005] lr: 1.000e-01, eta: 11:23:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.7078 2023-01-03 16:15:17,858 - mmcls - INFO - Epoch [8][1400/5005] lr: 1.000e-01, eta: 11:23:22, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.6690 2023-01-03 16:15:26,382 - mmcls - INFO - Epoch [8][1500/5005] lr: 1.000e-01, eta: 11:23:09, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.6688 2023-01-03 16:15:35,211 - mmcls - INFO - Epoch [8][1600/5005] lr: 1.000e-01, eta: 11:23:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.6945 2023-01-03 16:15:44,031 - mmcls - INFO - Epoch [8][1700/5005] lr: 1.000e-01, eta: 11:22:51, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.6738 2023-01-03 16:15:52,904 - mmcls - INFO - Epoch [8][1800/5005] lr: 1.000e-01, eta: 11:22:43, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.6709 2023-01-03 16:16:01,896 - mmcls - INFO - Epoch [8][1900/5005] lr: 1.000e-01, eta: 11:22:36, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.6949 2023-01-03 16:16:10,500 - mmcls - INFO - Epoch [8][2000/5005] lr: 1.000e-01, eta: 11:22:24, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.6637 2023-01-03 16:16:19,019 - mmcls - INFO - Epoch [8][2100/5005] lr: 1.000e-01, eta: 11:22:12, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.6592 2023-01-03 16:16:27,546 - mmcls - INFO - Epoch [8][2200/5005] lr: 1.000e-01, eta: 11:21:59, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.6983 2023-01-03 16:16:36,133 - mmcls - INFO - Epoch [8][2300/5005] lr: 1.000e-01, eta: 11:21:47, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.6541 2023-01-03 16:16:44,778 - mmcls - INFO - Epoch [8][2400/5005] lr: 1.000e-01, eta: 11:21:36, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.6927 2023-01-03 16:16:53,703 - mmcls - INFO - Epoch [8][2500/5005] lr: 1.000e-01, eta: 11:21:28, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.6876 2023-01-03 16:17:02,356 - mmcls - INFO - Epoch [8][2600/5005] lr: 1.000e-01, eta: 11:21:17, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.6946 2023-01-03 16:17:11,122 - mmcls - INFO - Epoch [8][2700/5005] lr: 1.000e-01, eta: 11:21:07, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.7066 2023-01-03 16:17:20,433 - mmcls - INFO - Epoch [8][2800/5005] lr: 1.000e-01, eta: 11:21:04, time: 0.093, data_time: 0.008, memory: 1125, loss: 2.6756 2023-01-03 16:17:29,189 - mmcls - INFO - Epoch [8][2900/5005] lr: 1.000e-01, eta: 11:20:55, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.6669 2023-01-03 16:17:38,147 - mmcls - INFO - Epoch [8][3000/5005] lr: 1.000e-01, eta: 11:20:47, time: 0.090, data_time: 0.026, memory: 1125, loss: 2.6775 2023-01-03 16:17:46,753 - mmcls - INFO - Epoch [8][3100/5005] lr: 1.000e-01, eta: 11:20:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.6831 2023-01-03 16:17:55,182 - mmcls - INFO - Epoch [8][3200/5005] lr: 1.000e-01, eta: 11:20:22, time: 0.084, data_time: 0.007, memory: 1125, loss: 2.6914 2023-01-03 16:18:04,094 - mmcls - INFO - Epoch [8][3300/5005] lr: 1.000e-01, eta: 11:20:14, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.6446 2023-01-03 16:18:12,967 - mmcls - INFO - Epoch [8][3400/5005] lr: 1.000e-01, eta: 11:20:06, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.6825 2023-01-03 16:18:21,532 - mmcls - INFO - Epoch [8][3500/5005] lr: 1.000e-01, eta: 11:19:54, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.6664 2023-01-03 16:18:30,449 - mmcls - INFO - Epoch [8][3600/5005] lr: 1.000e-01, eta: 11:19:46, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.6764 2023-01-03 16:18:39,287 - mmcls - INFO - Epoch [8][3700/5005] lr: 1.000e-01, eta: 11:19:37, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.6769 2023-01-03 16:18:47,991 - mmcls - INFO - Epoch [8][3800/5005] lr: 1.000e-01, eta: 11:19:27, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.6822 2023-01-03 16:18:56,802 - mmcls - INFO - Epoch [8][3900/5005] lr: 1.000e-01, eta: 11:19:18, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.6877 2023-01-03 16:19:05,687 - mmcls - INFO - Epoch [8][4000/5005] lr: 1.000e-01, eta: 11:19:10, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6935 2023-01-03 16:19:14,719 - mmcls - INFO - Epoch [8][4100/5005] lr: 1.000e-01, eta: 11:19:03, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.6538 2023-01-03 16:19:23,410 - mmcls - INFO - Epoch [8][4200/5005] lr: 1.000e-01, eta: 11:18:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.6592 2023-01-03 16:19:31,910 - mmcls - INFO - Epoch [8][4300/5005] lr: 1.000e-01, eta: 11:18:40, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.6868 2023-01-03 16:19:40,619 - mmcls - INFO - Epoch [8][4400/5005] lr: 1.000e-01, eta: 11:18:30, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.6806 2023-01-03 16:19:49,508 - mmcls - INFO - Epoch [8][4500/5005] lr: 1.000e-01, eta: 11:18:21, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.6743 2023-01-03 16:19:58,615 - mmcls - INFO - Epoch [8][4600/5005] lr: 1.000e-01, eta: 11:18:16, time: 0.091, data_time: 0.016, memory: 1125, loss: 2.6954 2023-01-03 16:20:07,543 - mmcls - INFO - Epoch [8][4700/5005] lr: 1.000e-01, eta: 11:18:08, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6731 2023-01-03 16:20:16,042 - mmcls - INFO - Epoch [8][4800/5005] lr: 1.000e-01, eta: 11:17:55, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.6893 2023-01-03 16:20:25,110 - mmcls - INFO - Epoch [8][4900/5005] lr: 1.000e-01, eta: 11:17:49, time: 0.091, data_time: 0.005, memory: 1125, loss: 2.6769 2023-01-03 16:20:33,840 - mmcls - INFO - Epoch [8][5000/5005] lr: 1.000e-01, eta: 11:17:39, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.6838 2023-01-03 16:20:56,622 - mmcls - INFO - Epoch(val) [8][196] accuracy_top-1: 45.5300, accuracy_top-5: 71.7540 2023-01-03 16:21:07,595 - mmcls - INFO - Epoch [9][100/5005] lr: 1.000e-01, eta: 11:17:49, time: 0.109, data_time: 0.038, memory: 1125, loss: 2.6299 2023-01-03 16:21:16,407 - mmcls - INFO - Epoch [9][200/5005] lr: 1.000e-01, eta: 11:17:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.6156 2023-01-03 16:21:24,916 - mmcls - INFO - Epoch [9][300/5005] lr: 1.000e-01, eta: 11:17:28, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.6425 2023-01-03 16:21:33,560 - mmcls - INFO - Epoch [9][400/5005] lr: 1.000e-01, eta: 11:17:17, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.6543 2023-01-03 16:21:42,169 - mmcls - INFO - Epoch [9][500/5005] lr: 1.000e-01, eta: 11:17:05, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.6298 2023-01-03 16:21:50,871 - mmcls - INFO - Epoch [9][600/5005] lr: 1.000e-01, eta: 11:16:55, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.6507 2023-01-03 16:21:59,330 - mmcls - INFO - Epoch [9][700/5005] lr: 1.000e-01, eta: 11:16:42, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.6526 2023-01-03 16:22:07,985 - mmcls - INFO - Epoch [9][800/5005] lr: 1.000e-01, eta: 11:16:31, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.6183 2023-01-03 16:22:16,733 - mmcls - INFO - Epoch [9][900/5005] lr: 1.000e-01, eta: 11:16:21, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.6456 2023-01-03 16:22:25,728 - mmcls - INFO - Epoch [9][1000/5005] lr: 1.000e-01, eta: 11:16:14, time: 0.090, data_time: 0.035, memory: 1125, loss: 2.6258 2023-01-03 16:22:34,567 - mmcls - INFO - Epoch [9][1100/5005] lr: 1.000e-01, eta: 11:16:06, time: 0.088, data_time: 0.024, memory: 1125, loss: 2.6411 2023-01-03 16:22:43,262 - mmcls - INFO - Epoch [9][1200/5005] lr: 1.000e-01, eta: 11:15:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.6596 2023-01-03 16:22:51,992 - mmcls - INFO - Epoch [9][1300/5005] lr: 1.000e-01, eta: 11:15:45, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.6807 2023-01-03 16:23:00,925 - mmcls - INFO - Epoch [9][1400/5005] lr: 1.000e-01, eta: 11:15:38, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.6502 2023-01-03 16:23:09,644 - mmcls - INFO - Epoch [9][1500/5005] lr: 1.000e-01, eta: 11:15:27, time: 0.087, data_time: 0.037, memory: 1125, loss: 2.6315 2023-01-03 16:23:18,298 - mmcls - INFO - Epoch [9][1600/5005] lr: 1.000e-01, eta: 11:15:17, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.6434 2023-01-03 16:23:26,987 - mmcls - INFO - Epoch [9][1700/5005] lr: 1.000e-01, eta: 11:15:06, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.6408 2023-01-03 16:23:36,117 - mmcls - INFO - Epoch [9][1800/5005] lr: 1.000e-01, eta: 11:15:01, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.6351 2023-01-03 16:23:44,613 - mmcls - INFO - Epoch [9][1900/5005] lr: 1.000e-01, eta: 11:14:48, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.6598 2023-01-03 16:23:53,664 - mmcls - INFO - Epoch [9][2000/5005] lr: 1.000e-01, eta: 11:14:42, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.6395 2023-01-03 16:24:02,707 - mmcls - INFO - Epoch [9][2100/5005] lr: 1.000e-01, eta: 11:14:35, time: 0.090, data_time: 0.023, memory: 1125, loss: 2.6474 2023-01-03 16:24:11,405 - mmcls - INFO - Epoch [9][2200/5005] lr: 1.000e-01, eta: 11:14:25, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.6438 2023-01-03 16:24:20,192 - mmcls - INFO - Epoch [9][2300/5005] lr: 1.000e-01, eta: 11:14:16, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6250 2023-01-03 16:24:29,063 - mmcls - INFO - Epoch [9][2400/5005] lr: 1.000e-01, eta: 11:14:07, time: 0.089, data_time: 0.038, memory: 1125, loss: 2.6557 2023-01-03 16:24:37,780 - mmcls - INFO - Epoch [9][2500/5005] lr: 1.000e-01, eta: 11:13:57, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.6437 2023-01-03 16:24:46,396 - mmcls - INFO - Epoch [9][2600/5005] lr: 1.000e-01, eta: 11:13:46, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.6453 2023-01-03 16:24:55,054 - mmcls - INFO - Epoch [9][2700/5005] lr: 1.000e-01, eta: 11:13:35, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.6343 2023-01-03 16:25:03,727 - mmcls - INFO - Epoch [9][2800/5005] lr: 1.000e-01, eta: 11:13:25, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.6383 2023-01-03 16:25:12,369 - mmcls - INFO - Epoch [9][2900/5005] lr: 1.000e-01, eta: 11:13:14, time: 0.086, data_time: 0.037, memory: 1125, loss: 2.6506 2023-01-03 16:25:21,112 - mmcls - INFO - Epoch [9][3000/5005] lr: 1.000e-01, eta: 11:13:04, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.6527 2023-01-03 16:25:29,593 - mmcls - INFO - Epoch [9][3100/5005] lr: 1.000e-01, eta: 11:12:52, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.6718 2023-01-03 16:25:38,549 - mmcls - INFO - Epoch [9][3200/5005] lr: 1.000e-01, eta: 11:12:44, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.6633 2023-01-03 16:25:47,514 - mmcls - INFO - Epoch [9][3300/5005] lr: 1.000e-01, eta: 11:12:37, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.6374 2023-01-03 16:25:56,074 - mmcls - INFO - Epoch [9][3400/5005] lr: 1.000e-01, eta: 11:12:25, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.6768 2023-01-03 16:26:05,015 - mmcls - INFO - Epoch [9][3500/5005] lr: 1.000e-01, eta: 11:12:18, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.6665 2023-01-03 16:26:14,254 - mmcls - INFO - Epoch [9][3600/5005] lr: 1.000e-01, eta: 11:12:13, time: 0.092, data_time: 0.002, memory: 1125, loss: 2.6323 2023-01-03 16:26:23,268 - mmcls - INFO - Epoch [9][3700/5005] lr: 1.000e-01, eta: 11:12:06, time: 0.090, data_time: 0.030, memory: 1125, loss: 2.6450 2023-01-03 16:26:32,289 - mmcls - INFO - Epoch [9][3800/5005] lr: 1.000e-01, eta: 11:11:59, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.6795 2023-01-03 16:26:40,835 - mmcls - INFO - Epoch [9][3900/5005] lr: 1.000e-01, eta: 11:11:48, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.6294 2023-01-03 16:26:49,568 - mmcls - INFO - Epoch [9][4000/5005] lr: 1.000e-01, eta: 11:11:38, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.6336 2023-01-03 16:26:58,244 - mmcls - INFO - Epoch [9][4100/5005] lr: 1.000e-01, eta: 11:11:27, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.6369 2023-01-03 16:27:07,032 - mmcls - INFO - Epoch [9][4200/5005] lr: 1.000e-01, eta: 11:11:18, time: 0.088, data_time: 0.040, memory: 1125, loss: 2.6801 2023-01-03 16:27:15,691 - mmcls - INFO - Epoch [9][4300/5005] lr: 1.000e-01, eta: 11:11:08, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.6137 2023-01-03 16:27:24,273 - mmcls - INFO - Epoch [9][4400/5005] lr: 1.000e-01, eta: 11:10:56, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.6357 2023-01-03 16:27:32,979 - mmcls - INFO - Epoch [9][4500/5005] lr: 1.000e-01, eta: 11:10:46, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.6431 2023-01-03 16:27:41,927 - mmcls - INFO - Epoch [9][4600/5005] lr: 1.000e-01, eta: 11:10:38, time: 0.089, data_time: 0.030, memory: 1125, loss: 2.6606 2023-01-03 16:27:50,904 - mmcls - INFO - Epoch [9][4700/5005] lr: 1.000e-01, eta: 11:10:31, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.6576 2023-01-03 16:27:59,846 - mmcls - INFO - Epoch [9][4800/5005] lr: 1.000e-01, eta: 11:10:24, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.6452 2023-01-03 16:28:08,635 - mmcls - INFO - Epoch [9][4900/5005] lr: 1.000e-01, eta: 11:10:14, time: 0.088, data_time: 0.044, memory: 1125, loss: 2.6461 2023-01-03 16:28:17,249 - mmcls - INFO - Epoch [9][5000/5005] lr: 1.000e-01, eta: 11:10:03, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.6521 2023-01-03 16:28:40,196 - mmcls - INFO - Epoch(val) [9][196] accuracy_top-1: 46.3660, accuracy_top-5: 72.4300 2023-01-03 16:28:51,092 - mmcls - INFO - Epoch [10][100/5005] lr: 1.000e-01, eta: 11:10:10, time: 0.109, data_time: 0.031, memory: 1125, loss: 2.6027 2023-01-03 16:28:59,747 - mmcls - INFO - Epoch [10][200/5005] lr: 1.000e-01, eta: 11:10:00, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.6276 2023-01-03 16:29:08,382 - mmcls - INFO - Epoch [10][300/5005] lr: 1.000e-01, eta: 11:09:49, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5809 2023-01-03 16:29:17,222 - mmcls - INFO - Epoch [10][400/5005] lr: 1.000e-01, eta: 11:09:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.5983 2023-01-03 16:29:26,276 - mmcls - INFO - Epoch [10][500/5005] lr: 1.000e-01, eta: 11:09:33, time: 0.090, data_time: 0.023, memory: 1125, loss: 2.6043 2023-01-03 16:29:35,003 - mmcls - INFO - Epoch [10][600/5005] lr: 1.000e-01, eta: 11:09:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.6181 2023-01-03 16:29:43,738 - mmcls - INFO - Epoch [10][700/5005] lr: 1.000e-01, eta: 11:09:14, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5932 2023-01-03 16:29:52,709 - mmcls - INFO - Epoch [10][800/5005] lr: 1.000e-01, eta: 11:09:06, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.6374 2023-01-03 16:30:01,662 - mmcls - INFO - Epoch [10][900/5005] lr: 1.000e-01, eta: 11:08:59, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.6166 2023-01-03 16:30:10,333 - mmcls - INFO - Epoch [10][1000/5005] lr: 1.000e-01, eta: 11:08:48, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.5883 2023-01-03 16:30:18,888 - mmcls - INFO - Epoch [10][1100/5005] lr: 1.000e-01, eta: 11:08:37, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.6196 2023-01-03 16:30:27,564 - mmcls - INFO - Epoch [10][1200/5005] lr: 1.000e-01, eta: 11:08:26, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.5982 2023-01-03 16:30:36,406 - mmcls - INFO - Epoch [10][1300/5005] lr: 1.000e-01, eta: 11:08:18, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.6395 2023-01-03 16:30:45,135 - mmcls - INFO - Epoch [10][1400/5005] lr: 1.000e-01, eta: 11:08:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.6136 2023-01-03 16:30:53,927 - mmcls - INFO - Epoch [10][1500/5005] lr: 1.000e-01, eta: 11:07:59, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.6355 2023-01-03 16:31:02,612 - mmcls - INFO - Epoch [10][1600/5005] lr: 1.000e-01, eta: 11:07:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.6475 2023-01-03 16:31:11,556 - mmcls - INFO - Epoch [10][1700/5005] lr: 1.000e-01, eta: 11:07:41, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.6057 2023-01-03 16:31:20,122 - mmcls - INFO - Epoch [10][1800/5005] lr: 1.000e-01, eta: 11:07:29, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.6324 2023-01-03 16:31:28,962 - mmcls - INFO - Epoch [10][1900/5005] lr: 1.000e-01, eta: 11:07:21, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6472 2023-01-03 16:31:37,991 - mmcls - INFO - Epoch [10][2000/5005] lr: 1.000e-01, eta: 11:07:14, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.6168 2023-01-03 16:31:46,535 - mmcls - INFO - Epoch [10][2100/5005] lr: 1.000e-01, eta: 11:07:02, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.6150 2023-01-03 16:31:55,611 - mmcls - INFO - Epoch [10][2200/5005] lr: 1.000e-01, eta: 11:06:56, time: 0.091, data_time: 0.020, memory: 1125, loss: 2.6022 2023-01-03 16:32:04,119 - mmcls - INFO - Epoch [10][2300/5005] lr: 1.000e-01, eta: 11:06:44, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.6382 2023-01-03 16:32:13,221 - mmcls - INFO - Epoch [10][2400/5005] lr: 1.000e-01, eta: 11:06:38, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.6241 2023-01-03 16:32:22,021 - mmcls - INFO - Epoch [10][2500/5005] lr: 1.000e-01, eta: 11:06:28, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.6272 2023-01-03 16:32:30,611 - mmcls - INFO - Epoch [10][2600/5005] lr: 1.000e-01, eta: 11:06:17, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.6385 2023-01-03 16:32:39,493 - mmcls - INFO - Epoch [10][2700/5005] lr: 1.000e-01, eta: 11:06:09, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.6159 2023-01-03 16:32:48,016 - mmcls - INFO - Epoch [10][2800/5005] lr: 1.000e-01, eta: 11:05:57, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.6338 2023-01-03 16:32:56,602 - mmcls - INFO - Epoch [10][2900/5005] lr: 1.000e-01, eta: 11:05:46, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.6137 2023-01-03 16:33:05,418 - mmcls - INFO - Epoch [10][3000/5005] lr: 1.000e-01, eta: 11:05:37, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.6313 2023-01-03 16:33:14,362 - mmcls - INFO - Epoch [10][3100/5005] lr: 1.000e-01, eta: 11:05:30, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.6090 2023-01-03 16:33:23,255 - mmcls - INFO - Epoch [10][3200/5005] lr: 1.000e-01, eta: 11:05:21, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.6534 2023-01-03 16:33:32,057 - mmcls - INFO - Epoch [10][3300/5005] lr: 1.000e-01, eta: 11:05:12, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6277 2023-01-03 16:33:40,568 - mmcls - INFO - Epoch [10][3400/5005] lr: 1.000e-01, eta: 11:05:00, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.6406 2023-01-03 16:33:49,693 - mmcls - INFO - Epoch [10][3500/5005] lr: 1.000e-01, eta: 11:04:54, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.6288 2023-01-03 16:33:58,513 - mmcls - INFO - Epoch [10][3600/5005] lr: 1.000e-01, eta: 11:04:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.6613 2023-01-03 16:34:06,981 - mmcls - INFO - Epoch [10][3700/5005] lr: 1.000e-01, eta: 11:04:33, time: 0.085, data_time: 0.003, memory: 1125, loss: 2.6105 2023-01-03 16:34:15,755 - mmcls - INFO - Epoch [10][3800/5005] lr: 1.000e-01, eta: 11:04:24, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.6381 2023-01-03 16:34:24,617 - mmcls - INFO - Epoch [10][3900/5005] lr: 1.000e-01, eta: 11:04:15, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.6182 2023-01-03 16:34:33,252 - mmcls - INFO - Epoch [10][4000/5005] lr: 1.000e-01, eta: 11:04:05, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.6087 2023-01-03 16:34:42,221 - mmcls - INFO - Epoch [10][4100/5005] lr: 1.000e-01, eta: 11:03:57, time: 0.090, data_time: 0.037, memory: 1125, loss: 2.6480 2023-01-03 16:34:50,935 - mmcls - INFO - Epoch [10][4200/5005] lr: 1.000e-01, eta: 11:03:47, time: 0.087, data_time: 0.027, memory: 1125, loss: 2.6403 2023-01-03 16:34:59,886 - mmcls - INFO - Epoch [10][4300/5005] lr: 1.000e-01, eta: 11:03:40, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.6531 2023-01-03 16:35:08,890 - mmcls - INFO - Epoch [10][4400/5005] lr: 1.000e-01, eta: 11:03:33, time: 0.090, data_time: 0.032, memory: 1125, loss: 2.6548 2023-01-03 16:35:17,886 - mmcls - INFO - Epoch [10][4500/5005] lr: 1.000e-01, eta: 11:03:25, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.6138 2023-01-03 16:35:26,523 - mmcls - INFO - Epoch [10][4600/5005] lr: 1.000e-01, eta: 11:03:15, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.6142 2023-01-03 16:35:35,193 - mmcls - INFO - Epoch [10][4700/5005] lr: 1.000e-01, eta: 11:03:04, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.6173 2023-01-03 16:35:43,750 - mmcls - INFO - Epoch [10][4800/5005] lr: 1.000e-01, eta: 11:02:53, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.6315 2023-01-03 16:35:52,497 - mmcls - INFO - Epoch [10][4900/5005] lr: 1.000e-01, eta: 11:02:44, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.6207 2023-01-03 16:36:01,417 - mmcls - INFO - Epoch [10][5000/5005] lr: 1.000e-01, eta: 11:02:36, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.6449 2023-01-03 16:36:24,017 - mmcls - INFO - Epoch(val) [10][196] accuracy_top-1: 47.1720, accuracy_top-5: 73.4980 2023-01-03 16:36:34,825 - mmcls - INFO - Epoch [11][100/5005] lr: 1.000e-01, eta: 11:02:40, time: 0.108, data_time: 0.061, memory: 1125, loss: 2.5712 2023-01-03 16:36:43,317 - mmcls - INFO - Epoch [11][200/5005] lr: 1.000e-01, eta: 11:02:28, time: 0.085, data_time: 0.023, memory: 1125, loss: 2.5676 2023-01-03 16:36:52,206 - mmcls - INFO - Epoch [11][300/5005] lr: 1.000e-01, eta: 11:02:20, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.5800 2023-01-03 16:37:00,595 - mmcls - INFO - Epoch [11][400/5005] lr: 1.000e-01, eta: 11:02:07, time: 0.084, data_time: 0.011, memory: 1125, loss: 2.5926 2023-01-03 16:37:09,637 - mmcls - INFO - Epoch [11][500/5005] lr: 1.000e-01, eta: 11:02:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5609 2023-01-03 16:37:18,295 - mmcls - INFO - Epoch [11][600/5005] lr: 1.000e-01, eta: 11:01:50, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.6096 2023-01-03 16:37:26,765 - mmcls - INFO - Epoch [11][700/5005] lr: 1.000e-01, eta: 11:01:38, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.5962 2023-01-03 16:37:35,401 - mmcls - INFO - Epoch [11][800/5005] lr: 1.000e-01, eta: 11:01:27, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.5938 2023-01-03 16:37:44,040 - mmcls - INFO - Epoch [11][900/5005] lr: 1.000e-01, eta: 11:01:17, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.6052 2023-01-03 16:37:52,678 - mmcls - INFO - Epoch [11][1000/5005] lr: 1.000e-01, eta: 11:01:06, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.6059 2023-01-03 16:38:01,659 - mmcls - INFO - Epoch [11][1100/5005] lr: 1.000e-01, eta: 11:00:59, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.5805 2023-01-03 16:38:10,361 - mmcls - INFO - Epoch [11][1200/5005] lr: 1.000e-01, eta: 11:00:49, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.5866 2023-01-03 16:38:19,049 - mmcls - INFO - Epoch [11][1300/5005] lr: 1.000e-01, eta: 11:00:39, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.6187 2023-01-03 16:38:27,803 - mmcls - INFO - Epoch [11][1400/5005] lr: 1.000e-01, eta: 11:00:29, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.6221 2023-01-03 16:38:36,417 - mmcls - INFO - Epoch [11][1500/5005] lr: 1.000e-01, eta: 11:00:19, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.6244 2023-01-03 16:38:45,338 - mmcls - INFO - Epoch [11][1600/5005] lr: 1.000e-01, eta: 11:00:11, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6339 2023-01-03 16:38:54,181 - mmcls - INFO - Epoch [11][1700/5005] lr: 1.000e-01, eta: 11:00:02, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6037 2023-01-03 16:39:02,634 - mmcls - INFO - Epoch [11][1800/5005] lr: 1.000e-01, eta: 10:59:50, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.5895 2023-01-03 16:39:11,363 - mmcls - INFO - Epoch [11][1900/5005] lr: 1.000e-01, eta: 10:59:40, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.5957 2023-01-03 16:39:20,084 - mmcls - INFO - Epoch [11][2000/5005] lr: 1.000e-01, eta: 10:59:31, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.6142 2023-01-03 16:39:29,048 - mmcls - INFO - Epoch [11][2100/5005] lr: 1.000e-01, eta: 10:59:23, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.6024 2023-01-03 16:39:37,938 - mmcls - INFO - Epoch [11][2200/5005] lr: 1.000e-01, eta: 10:59:15, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6090 2023-01-03 16:39:46,926 - mmcls - INFO - Epoch [11][2300/5005] lr: 1.000e-01, eta: 10:59:07, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.5957 2023-01-03 16:39:55,637 - mmcls - INFO - Epoch [11][2400/5005] lr: 1.000e-01, eta: 10:58:58, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.6168 2023-01-03 16:40:04,562 - mmcls - INFO - Epoch [11][2500/5005] lr: 1.000e-01, eta: 10:58:50, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5991 2023-01-03 16:40:13,454 - mmcls - INFO - Epoch [11][2600/5005] lr: 1.000e-01, eta: 10:58:41, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.5964 2023-01-03 16:40:22,243 - mmcls - INFO - Epoch [11][2700/5005] lr: 1.000e-01, eta: 10:58:32, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.6097 2023-01-03 16:40:31,075 - mmcls - INFO - Epoch [11][2800/5005] lr: 1.000e-01, eta: 10:58:23, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.6229 2023-01-03 16:40:40,071 - mmcls - INFO - Epoch [11][2900/5005] lr: 1.000e-01, eta: 10:58:16, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.6455 2023-01-03 16:40:49,047 - mmcls - INFO - Epoch [11][3000/5005] lr: 1.000e-01, eta: 10:58:08, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.6136 2023-01-03 16:40:58,175 - mmcls - INFO - Epoch [11][3100/5005] lr: 1.000e-01, eta: 10:58:02, time: 0.091, data_time: 0.019, memory: 1125, loss: 2.5987 2023-01-03 16:41:06,956 - mmcls - INFO - Epoch [11][3200/5005] lr: 1.000e-01, eta: 10:57:53, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.6236 2023-01-03 16:41:15,639 - mmcls - INFO - Epoch [11][3300/5005] lr: 1.000e-01, eta: 10:57:43, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.5932 2023-01-03 16:41:24,595 - mmcls - INFO - Epoch [11][3400/5005] lr: 1.000e-01, eta: 10:57:35, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.6171 2023-01-03 16:41:33,438 - mmcls - INFO - Epoch [11][3500/5005] lr: 1.000e-01, eta: 10:57:27, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.6292 2023-01-03 16:41:42,672 - mmcls - INFO - Epoch [11][3600/5005] lr: 1.000e-01, eta: 10:57:21, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.6277 2023-01-03 16:41:51,374 - mmcls - INFO - Epoch [11][3700/5005] lr: 1.000e-01, eta: 10:57:11, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.6283 2023-01-03 16:42:00,277 - mmcls - INFO - Epoch [11][3800/5005] lr: 1.000e-01, eta: 10:57:03, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.5890 2023-01-03 16:42:09,397 - mmcls - INFO - Epoch [11][3900/5005] lr: 1.000e-01, eta: 10:56:57, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.6114 2023-01-03 16:42:18,104 - mmcls - INFO - Epoch [11][4000/5005] lr: 1.000e-01, eta: 10:56:47, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.6151 2023-01-03 16:42:27,259 - mmcls - INFO - Epoch [11][4100/5005] lr: 1.000e-01, eta: 10:56:41, time: 0.092, data_time: 0.024, memory: 1125, loss: 2.5854 2023-01-03 16:42:36,028 - mmcls - INFO - Epoch [11][4200/5005] lr: 1.000e-01, eta: 10:56:31, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.6171 2023-01-03 16:42:44,984 - mmcls - INFO - Epoch [11][4300/5005] lr: 1.000e-01, eta: 10:56:24, time: 0.090, data_time: 0.019, memory: 1125, loss: 2.6150 2023-01-03 16:42:53,616 - mmcls - INFO - Epoch [11][4400/5005] lr: 1.000e-01, eta: 10:56:13, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.5974 2023-01-03 16:43:02,687 - mmcls - INFO - Epoch [11][4500/5005] lr: 1.000e-01, eta: 10:56:06, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.6064 2023-01-03 16:43:11,478 - mmcls - INFO - Epoch [11][4600/5005] lr: 1.000e-01, eta: 10:55:57, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.6112 2023-01-03 16:43:20,205 - mmcls - INFO - Epoch [11][4700/5005] lr: 1.000e-01, eta: 10:55:47, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.5873 2023-01-03 16:43:29,324 - mmcls - INFO - Epoch [11][4800/5005] lr: 1.000e-01, eta: 10:55:41, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.6095 2023-01-03 16:43:38,221 - mmcls - INFO - Epoch [11][4900/5005] lr: 1.000e-01, eta: 10:55:33, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.5990 2023-01-03 16:43:47,346 - mmcls - INFO - Epoch [11][5000/5005] lr: 1.000e-01, eta: 10:55:26, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.6335 2023-01-03 16:44:10,240 - mmcls - INFO - Epoch(val) [11][196] accuracy_top-1: 48.3720, accuracy_top-5: 73.9280 2023-01-03 16:44:21,125 - mmcls - INFO - Epoch [12][100/5005] lr: 1.000e-01, eta: 10:55:30, time: 0.109, data_time: 0.036, memory: 1125, loss: 2.5498 2023-01-03 16:44:29,798 - mmcls - INFO - Epoch [12][200/5005] lr: 1.000e-01, eta: 10:55:20, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.5588 2023-01-03 16:44:39,127 - mmcls - INFO - Epoch [12][300/5005] lr: 1.000e-01, eta: 10:55:15, time: 0.093, data_time: 0.034, memory: 1125, loss: 2.5591 2023-01-03 16:44:48,004 - mmcls - INFO - Epoch [12][400/5005] lr: 1.000e-01, eta: 10:55:06, time: 0.089, data_time: 0.038, memory: 1125, loss: 2.5958 2023-01-03 16:44:56,640 - mmcls - INFO - Epoch [12][500/5005] lr: 1.000e-01, eta: 10:54:56, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5451 2023-01-03 16:45:05,691 - mmcls - INFO - Epoch [12][600/5005] lr: 1.000e-01, eta: 10:54:49, time: 0.090, data_time: 0.029, memory: 1125, loss: 2.5965 2023-01-03 16:45:14,379 - mmcls - INFO - Epoch [12][700/5005] lr: 1.000e-01, eta: 10:54:39, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.5766 2023-01-03 16:45:23,163 - mmcls - INFO - Epoch [12][800/5005] lr: 1.000e-01, eta: 10:54:30, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5778 2023-01-03 16:45:32,221 - mmcls - INFO - Epoch [12][900/5005] lr: 1.000e-01, eta: 10:54:23, time: 0.091, data_time: 0.008, memory: 1125, loss: 2.5803 2023-01-03 16:45:40,717 - mmcls - INFO - Epoch [12][1000/5005] lr: 1.000e-01, eta: 10:54:11, time: 0.085, data_time: 0.013, memory: 1125, loss: 2.5763 2023-01-03 16:45:49,672 - mmcls - INFO - Epoch [12][1100/5005] lr: 1.000e-01, eta: 10:54:03, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5972 2023-01-03 16:45:58,482 - mmcls - INFO - Epoch [12][1200/5005] lr: 1.000e-01, eta: 10:53:54, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.5618 2023-01-03 16:46:07,490 - mmcls - INFO - Epoch [12][1300/5005] lr: 1.000e-01, eta: 10:53:47, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.6014 2023-01-03 16:46:16,391 - mmcls - INFO - Epoch [12][1400/5005] lr: 1.000e-01, eta: 10:53:39, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.5761 2023-01-03 16:46:25,277 - mmcls - INFO - Epoch [12][1500/5005] lr: 1.000e-01, eta: 10:53:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5813 2023-01-03 16:46:34,013 - mmcls - INFO - Epoch [12][1600/5005] lr: 1.000e-01, eta: 10:53:21, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.5935 2023-01-03 16:46:42,675 - mmcls - INFO - Epoch [12][1700/5005] lr: 1.000e-01, eta: 10:53:10, time: 0.087, data_time: 0.039, memory: 1125, loss: 2.5764 2023-01-03 16:46:52,043 - mmcls - INFO - Epoch [12][1800/5005] lr: 1.000e-01, eta: 10:53:06, time: 0.094, data_time: 0.003, memory: 1125, loss: 2.6027 2023-01-03 16:47:00,641 - mmcls - INFO - Epoch [12][1900/5005] lr: 1.000e-01, eta: 10:52:55, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.6076 2023-01-03 16:47:09,555 - mmcls - INFO - Epoch [12][2000/5005] lr: 1.000e-01, eta: 10:52:47, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5961 2023-01-03 16:47:18,377 - mmcls - INFO - Epoch [12][2100/5005] lr: 1.000e-01, eta: 10:52:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5979 2023-01-03 16:47:27,342 - mmcls - INFO - Epoch [12][2200/5005] lr: 1.000e-01, eta: 10:52:30, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.5765 2023-01-03 16:47:36,076 - mmcls - INFO - Epoch [12][2300/5005] lr: 1.000e-01, eta: 10:52:20, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.6145 2023-01-03 16:47:44,695 - mmcls - INFO - Epoch [12][2400/5005] lr: 1.000e-01, eta: 10:52:10, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.5929 2023-01-03 16:47:53,690 - mmcls - INFO - Epoch [12][2500/5005] lr: 1.000e-01, eta: 10:52:02, time: 0.090, data_time: 0.031, memory: 1125, loss: 2.5936 2023-01-03 16:48:02,306 - mmcls - INFO - Epoch [12][2600/5005] lr: 1.000e-01, eta: 10:51:52, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.6007 2023-01-03 16:48:11,428 - mmcls - INFO - Epoch [12][2700/5005] lr: 1.000e-01, eta: 10:51:45, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.6073 2023-01-03 16:48:19,960 - mmcls - INFO - Epoch [12][2800/5005] lr: 1.000e-01, eta: 10:51:34, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.5808 2023-01-03 16:48:28,631 - mmcls - INFO - Epoch [12][2900/5005] lr: 1.000e-01, eta: 10:51:24, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.6005 2023-01-03 16:48:37,249 - mmcls - INFO - Epoch [12][3000/5005] lr: 1.000e-01, eta: 10:51:14, time: 0.086, data_time: 0.040, memory: 1125, loss: 2.5816 2023-01-03 16:48:46,076 - mmcls - INFO - Epoch [12][3100/5005] lr: 1.000e-01, eta: 10:51:05, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.5967 2023-01-03 16:48:54,964 - mmcls - INFO - Epoch [12][3200/5005] lr: 1.000e-01, eta: 10:50:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.6057 2023-01-03 16:49:03,804 - mmcls - INFO - Epoch [12][3300/5005] lr: 1.000e-01, eta: 10:50:48, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.6072 2023-01-03 16:49:12,560 - mmcls - INFO - Epoch [12][3400/5005] lr: 1.000e-01, eta: 10:50:38, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5946 2023-01-03 16:49:21,317 - mmcls - INFO - Epoch [12][3500/5005] lr: 1.000e-01, eta: 10:50:29, time: 0.088, data_time: 0.039, memory: 1125, loss: 2.5906 2023-01-03 16:49:30,091 - mmcls - INFO - Epoch [12][3600/5005] lr: 1.000e-01, eta: 10:50:19, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5885 2023-01-03 16:49:38,832 - mmcls - INFO - Epoch [12][3700/5005] lr: 1.000e-01, eta: 10:50:10, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.6114 2023-01-03 16:49:47,778 - mmcls - INFO - Epoch [12][3800/5005] lr: 1.000e-01, eta: 10:50:02, time: 0.090, data_time: 0.029, memory: 1125, loss: 2.5896 2023-01-03 16:49:56,600 - mmcls - INFO - Epoch [12][3900/5005] lr: 1.000e-01, eta: 10:49:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5781 2023-01-03 16:50:05,660 - mmcls - INFO - Epoch [12][4000/5005] lr: 1.000e-01, eta: 10:49:46, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.5769 2023-01-03 16:50:14,418 - mmcls - INFO - Epoch [12][4100/5005] lr: 1.000e-01, eta: 10:49:37, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.5975 2023-01-03 16:50:23,053 - mmcls - INFO - Epoch [12][4200/5005] lr: 1.000e-01, eta: 10:49:26, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.5867 2023-01-03 16:50:31,806 - mmcls - INFO - Epoch [12][4300/5005] lr: 1.000e-01, eta: 10:49:17, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.5968 2023-01-03 16:50:40,553 - mmcls - INFO - Epoch [12][4400/5005] lr: 1.000e-01, eta: 10:49:07, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.6266 2023-01-03 16:50:49,233 - mmcls - INFO - Epoch [12][4500/5005] lr: 1.000e-01, eta: 10:48:57, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5995 2023-01-03 16:50:58,465 - mmcls - INFO - Epoch [12][4600/5005] lr: 1.000e-01, eta: 10:48:52, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.5978 2023-01-03 16:51:07,318 - mmcls - INFO - Epoch [12][4700/5005] lr: 1.000e-01, eta: 10:48:43, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.5962 2023-01-03 16:51:15,934 - mmcls - INFO - Epoch [12][4800/5005] lr: 1.000e-01, eta: 10:48:32, time: 0.086, data_time: 0.033, memory: 1125, loss: 2.5826 2023-01-03 16:51:24,559 - mmcls - INFO - Epoch [12][4900/5005] lr: 1.000e-01, eta: 10:48:22, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.6016 2023-01-03 16:51:33,344 - mmcls - INFO - Epoch [12][5000/5005] lr: 1.000e-01, eta: 10:48:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5841 2023-01-03 16:51:55,941 - mmcls - INFO - Epoch(val) [12][196] accuracy_top-1: 47.0480, accuracy_top-5: 73.1620 2023-01-03 16:52:06,761 - mmcls - INFO - Epoch [13][100/5005] lr: 1.000e-01, eta: 10:48:15, time: 0.108, data_time: 0.040, memory: 1125, loss: 2.5431 2023-01-03 16:52:15,280 - mmcls - INFO - Epoch [13][200/5005] lr: 1.000e-01, eta: 10:48:04, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.5674 2023-01-03 16:52:23,849 - mmcls - INFO - Epoch [13][300/5005] lr: 1.000e-01, eta: 10:47:53, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.5353 2023-01-03 16:52:32,425 - mmcls - INFO - Epoch [13][400/5005] lr: 1.000e-01, eta: 10:47:42, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5142 2023-01-03 16:52:41,209 - mmcls - INFO - Epoch [13][500/5005] lr: 1.000e-01, eta: 10:47:33, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5520 2023-01-03 16:52:49,911 - mmcls - INFO - Epoch [13][600/5005] lr: 1.000e-01, eta: 10:47:23, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5595 2023-01-03 16:52:58,758 - mmcls - INFO - Epoch [13][700/5005] lr: 1.000e-01, eta: 10:47:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5419 2023-01-03 16:53:07,277 - mmcls - INFO - Epoch [13][800/5005] lr: 1.000e-01, eta: 10:47:03, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.5656 2023-01-03 16:53:15,931 - mmcls - INFO - Epoch [13][900/5005] lr: 1.000e-01, eta: 10:46:53, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5569 2023-01-03 16:53:24,835 - mmcls - INFO - Epoch [13][1000/5005] lr: 1.000e-01, eta: 10:46:45, time: 0.089, data_time: 0.036, memory: 1125, loss: 2.5462 2023-01-03 16:53:33,662 - mmcls - INFO - Epoch [13][1100/5005] lr: 1.000e-01, eta: 10:46:36, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.5789 2023-01-03 16:53:42,242 - mmcls - INFO - Epoch [13][1200/5005] lr: 1.000e-01, eta: 10:46:25, time: 0.086, data_time: 0.042, memory: 1125, loss: 2.5714 2023-01-03 16:53:51,266 - mmcls - INFO - Epoch [13][1300/5005] lr: 1.000e-01, eta: 10:46:18, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.5763 2023-01-03 16:54:00,074 - mmcls - INFO - Epoch [13][1400/5005] lr: 1.000e-01, eta: 10:46:09, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.5639 2023-01-03 16:54:09,086 - mmcls - INFO - Epoch [13][1500/5005] lr: 1.000e-01, eta: 10:46:01, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.5854 2023-01-03 16:54:18,146 - mmcls - INFO - Epoch [13][1600/5005] lr: 1.000e-01, eta: 10:45:54, time: 0.091, data_time: 0.028, memory: 1125, loss: 2.5609 2023-01-03 16:54:27,031 - mmcls - INFO - Epoch [13][1700/5005] lr: 1.000e-01, eta: 10:45:46, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5816 2023-01-03 16:54:35,795 - mmcls - INFO - Epoch [13][1800/5005] lr: 1.000e-01, eta: 10:45:36, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5780 2023-01-03 16:54:44,604 - mmcls - INFO - Epoch [13][1900/5005] lr: 1.000e-01, eta: 10:45:27, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.5592 2023-01-03 16:54:53,433 - mmcls - INFO - Epoch [13][2000/5005] lr: 1.000e-01, eta: 10:45:19, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.5791 2023-01-03 16:55:02,149 - mmcls - INFO - Epoch [13][2100/5005] lr: 1.000e-01, eta: 10:45:09, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.6032 2023-01-03 16:55:10,769 - mmcls - INFO - Epoch [13][2200/5005] lr: 1.000e-01, eta: 10:44:59, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5689 2023-01-03 16:55:19,361 - mmcls - INFO - Epoch [13][2300/5005] lr: 1.000e-01, eta: 10:44:48, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.5813 2023-01-03 16:55:28,277 - mmcls - INFO - Epoch [13][2400/5005] lr: 1.000e-01, eta: 10:44:40, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.5783 2023-01-03 16:55:37,023 - mmcls - INFO - Epoch [13][2500/5005] lr: 1.000e-01, eta: 10:44:30, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.5807 2023-01-03 16:55:45,567 - mmcls - INFO - Epoch [13][2600/5005] lr: 1.000e-01, eta: 10:44:20, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.5885 2023-01-03 16:55:54,374 - mmcls - INFO - Epoch [13][2700/5005] lr: 1.000e-01, eta: 10:44:11, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.5994 2023-01-03 16:56:02,931 - mmcls - INFO - Epoch [13][2800/5005] lr: 1.000e-01, eta: 10:44:00, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.5897 2023-01-03 16:56:11,668 - mmcls - INFO - Epoch [13][2900/5005] lr: 1.000e-01, eta: 10:43:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5748 2023-01-03 16:56:20,316 - mmcls - INFO - Epoch [13][3000/5005] lr: 1.000e-01, eta: 10:43:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5843 2023-01-03 16:56:29,246 - mmcls - INFO - Epoch [13][3100/5005] lr: 1.000e-01, eta: 10:43:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5651 2023-01-03 16:56:37,939 - mmcls - INFO - Epoch [13][3200/5005] lr: 1.000e-01, eta: 10:43:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5790 2023-01-03 16:56:46,806 - mmcls - INFO - Epoch [13][3300/5005] lr: 1.000e-01, eta: 10:43:14, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5809 2023-01-03 16:56:55,408 - mmcls - INFO - Epoch [13][3400/5005] lr: 1.000e-01, eta: 10:43:03, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.5844 2023-01-03 16:57:03,966 - mmcls - INFO - Epoch [13][3500/5005] lr: 1.000e-01, eta: 10:42:53, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.5749 2023-01-03 16:57:12,794 - mmcls - INFO - Epoch [13][3600/5005] lr: 1.000e-01, eta: 10:42:44, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.5700 2023-01-03 16:57:21,572 - mmcls - INFO - Epoch [13][3700/5005] lr: 1.000e-01, eta: 10:42:35, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5711 2023-01-03 16:57:30,100 - mmcls - INFO - Epoch [13][3800/5005] lr: 1.000e-01, eta: 10:42:24, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.5479 2023-01-03 16:57:39,143 - mmcls - INFO - Epoch [13][3900/5005] lr: 1.000e-01, eta: 10:42:17, time: 0.091, data_time: 0.019, memory: 1125, loss: 2.5781 2023-01-03 16:57:48,125 - mmcls - INFO - Epoch [13][4000/5005] lr: 1.000e-01, eta: 10:42:09, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5953 2023-01-03 16:57:57,158 - mmcls - INFO - Epoch [13][4100/5005] lr: 1.000e-01, eta: 10:42:01, time: 0.090, data_time: 0.021, memory: 1125, loss: 2.5915 2023-01-03 16:58:05,959 - mmcls - INFO - Epoch [13][4200/5005] lr: 1.000e-01, eta: 10:41:52, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5958 2023-01-03 16:58:14,557 - mmcls - INFO - Epoch [13][4300/5005] lr: 1.000e-01, eta: 10:41:42, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.5825 2023-01-03 16:58:23,482 - mmcls - INFO - Epoch [13][4400/5005] lr: 1.000e-01, eta: 10:41:34, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.5761 2023-01-03 16:58:32,252 - mmcls - INFO - Epoch [13][4500/5005] lr: 1.000e-01, eta: 10:41:24, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.5753 2023-01-03 16:58:41,172 - mmcls - INFO - Epoch [13][4600/5005] lr: 1.000e-01, eta: 10:41:16, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5774 2023-01-03 16:58:49,526 - mmcls - INFO - Epoch [13][4700/5005] lr: 1.000e-01, eta: 10:41:04, time: 0.083, data_time: 0.009, memory: 1125, loss: 2.6008 2023-01-03 16:58:58,489 - mmcls - INFO - Epoch [13][4800/5005] lr: 1.000e-01, eta: 10:40:56, time: 0.090, data_time: 0.029, memory: 1125, loss: 2.5778 2023-01-03 16:59:07,222 - mmcls - INFO - Epoch [13][4900/5005] lr: 1.000e-01, eta: 10:40:47, time: 0.087, data_time: 0.037, memory: 1125, loss: 2.5831 2023-01-03 16:59:16,202 - mmcls - INFO - Epoch [13][5000/5005] lr: 1.000e-01, eta: 10:40:39, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.5547 2023-01-03 16:59:38,884 - mmcls - INFO - Epoch(val) [13][196] accuracy_top-1: 48.3680, accuracy_top-5: 74.2320 2023-01-03 16:59:49,916 - mmcls - INFO - Epoch [14][100/5005] lr: 1.000e-01, eta: 10:40:41, time: 0.110, data_time: 0.059, memory: 1125, loss: 2.5312 2023-01-03 16:59:59,377 - mmcls - INFO - Epoch [14][200/5005] lr: 1.000e-01, eta: 10:40:37, time: 0.095, data_time: 0.012, memory: 1125, loss: 2.5460 2023-01-03 17:00:08,018 - mmcls - INFO - Epoch [14][300/5005] lr: 1.000e-01, eta: 10:40:27, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5286 2023-01-03 17:00:16,987 - mmcls - INFO - Epoch [14][400/5005] lr: 1.000e-01, eta: 10:40:19, time: 0.090, data_time: 0.003, memory: 1125, loss: 2.5242 2023-01-03 17:00:25,634 - mmcls - INFO - Epoch [14][500/5005] lr: 1.000e-01, eta: 10:40:09, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.5494 2023-01-03 17:00:34,491 - mmcls - INFO - Epoch [14][600/5005] lr: 1.000e-01, eta: 10:40:00, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.5302 2023-01-03 17:00:43,379 - mmcls - INFO - Epoch [14][700/5005] lr: 1.000e-01, eta: 10:39:51, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.5678 2023-01-03 17:00:51,801 - mmcls - INFO - Epoch [14][800/5005] lr: 1.000e-01, eta: 10:39:40, time: 0.084, data_time: 0.013, memory: 1125, loss: 2.5367 2023-01-03 17:01:01,130 - mmcls - INFO - Epoch [14][900/5005] lr: 1.000e-01, eta: 10:39:34, time: 0.093, data_time: 0.012, memory: 1125, loss: 2.5874 2023-01-03 17:01:09,936 - mmcls - INFO - Epoch [14][1000/5005] lr: 1.000e-01, eta: 10:39:25, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.5757 2023-01-03 17:01:18,522 - mmcls - INFO - Epoch [14][1100/5005] lr: 1.000e-01, eta: 10:39:15, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5730 2023-01-03 17:01:27,073 - mmcls - INFO - Epoch [14][1200/5005] lr: 1.000e-01, eta: 10:39:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.5545 2023-01-03 17:01:35,886 - mmcls - INFO - Epoch [14][1300/5005] lr: 1.000e-01, eta: 10:38:55, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5566 2023-01-03 17:01:44,534 - mmcls - INFO - Epoch [14][1400/5005] lr: 1.000e-01, eta: 10:38:45, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.5551 2023-01-03 17:01:53,233 - mmcls - INFO - Epoch [14][1500/5005] lr: 1.000e-01, eta: 10:38:36, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5712 2023-01-03 17:02:01,646 - mmcls - INFO - Epoch [14][1600/5005] lr: 1.000e-01, eta: 10:38:24, time: 0.084, data_time: 0.009, memory: 1125, loss: 2.5793 2023-01-03 17:02:10,514 - mmcls - INFO - Epoch [14][1700/5005] lr: 1.000e-01, eta: 10:38:16, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5459 2023-01-03 17:02:19,322 - mmcls - INFO - Epoch [14][1800/5005] lr: 1.000e-01, eta: 10:38:07, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5984 2023-01-03 17:02:27,959 - mmcls - INFO - Epoch [14][1900/5005] lr: 1.000e-01, eta: 10:37:56, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5631 2023-01-03 17:02:36,683 - mmcls - INFO - Epoch [14][2000/5005] lr: 1.000e-01, eta: 10:37:47, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5897 2023-01-03 17:02:45,547 - mmcls - INFO - Epoch [14][2100/5005] lr: 1.000e-01, eta: 10:37:38, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.5611 2023-01-03 17:02:54,367 - mmcls - INFO - Epoch [14][2200/5005] lr: 1.000e-01, eta: 10:37:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5602 2023-01-03 17:03:03,231 - mmcls - INFO - Epoch [14][2300/5005] lr: 1.000e-01, eta: 10:37:21, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.5393 2023-01-03 17:03:11,919 - mmcls - INFO - Epoch [14][2400/5005] lr: 1.000e-01, eta: 10:37:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5683 2023-01-03 17:03:20,478 - mmcls - INFO - Epoch [14][2500/5005] lr: 1.000e-01, eta: 10:37:00, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.5583 2023-01-03 17:03:29,129 - mmcls - INFO - Epoch [14][2600/5005] lr: 1.000e-01, eta: 10:36:50, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.5570 2023-01-03 17:03:37,882 - mmcls - INFO - Epoch [14][2700/5005] lr: 1.000e-01, eta: 10:36:41, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.5555 2023-01-03 17:03:46,500 - mmcls - INFO - Epoch [14][2800/5005] lr: 1.000e-01, eta: 10:36:31, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.5458 2023-01-03 17:03:55,371 - mmcls - INFO - Epoch [14][2900/5005] lr: 1.000e-01, eta: 10:36:22, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.5659 2023-01-03 17:04:04,358 - mmcls - INFO - Epoch [14][3000/5005] lr: 1.000e-01, eta: 10:36:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5642 2023-01-03 17:04:12,925 - mmcls - INFO - Epoch [14][3100/5005] lr: 1.000e-01, eta: 10:36:04, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5541 2023-01-03 17:04:21,541 - mmcls - INFO - Epoch [14][3200/5005] lr: 1.000e-01, eta: 10:35:54, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5332 2023-01-03 17:04:30,375 - mmcls - INFO - Epoch [14][3300/5005] lr: 1.000e-01, eta: 10:35:45, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.5599 2023-01-03 17:04:39,235 - mmcls - INFO - Epoch [14][3400/5005] lr: 1.000e-01, eta: 10:35:37, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.5605 2023-01-03 17:04:47,815 - mmcls - INFO - Epoch [14][3500/5005] lr: 1.000e-01, eta: 10:35:26, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5305 2023-01-03 17:04:56,608 - mmcls - INFO - Epoch [14][3600/5005] lr: 1.000e-01, eta: 10:35:17, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.5897 2023-01-03 17:05:05,431 - mmcls - INFO - Epoch [14][3700/5005] lr: 1.000e-01, eta: 10:35:08, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.5935 2023-01-03 17:05:14,185 - mmcls - INFO - Epoch [14][3800/5005] lr: 1.000e-01, eta: 10:34:59, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5882 2023-01-03 17:05:23,017 - mmcls - INFO - Epoch [14][3900/5005] lr: 1.000e-01, eta: 10:34:50, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.5675 2023-01-03 17:05:31,519 - mmcls - INFO - Epoch [14][4000/5005] lr: 1.000e-01, eta: 10:34:39, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.5772 2023-01-03 17:05:40,528 - mmcls - INFO - Epoch [14][4100/5005] lr: 1.000e-01, eta: 10:34:32, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.5594 2023-01-03 17:05:49,417 - mmcls - INFO - Epoch [14][4200/5005] lr: 1.000e-01, eta: 10:34:23, time: 0.089, data_time: 0.011, memory: 1125, loss: 2.5483 2023-01-03 17:05:58,051 - mmcls - INFO - Epoch [14][4300/5005] lr: 1.000e-01, eta: 10:34:13, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.5690 2023-01-03 17:06:07,125 - mmcls - INFO - Epoch [14][4400/5005] lr: 1.000e-01, eta: 10:34:06, time: 0.091, data_time: 0.031, memory: 1125, loss: 2.5626 2023-01-03 17:06:16,050 - mmcls - INFO - Epoch [14][4500/5005] lr: 1.000e-01, eta: 10:33:58, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5985 2023-01-03 17:06:24,847 - mmcls - INFO - Epoch [14][4600/5005] lr: 1.000e-01, eta: 10:33:49, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5664 2023-01-03 17:06:33,372 - mmcls - INFO - Epoch [14][4700/5005] lr: 1.000e-01, eta: 10:33:38, time: 0.085, data_time: 0.028, memory: 1125, loss: 2.5969 2023-01-03 17:06:42,404 - mmcls - INFO - Epoch [14][4800/5005] lr: 1.000e-01, eta: 10:33:30, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.5566 2023-01-03 17:06:51,044 - mmcls - INFO - Epoch [14][4900/5005] lr: 1.000e-01, eta: 10:33:20, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5908 2023-01-03 17:06:59,641 - mmcls - INFO - Epoch [14][5000/5005] lr: 1.000e-01, eta: 10:33:10, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5567 2023-01-03 17:07:22,235 - mmcls - INFO - Epoch(val) [14][196] accuracy_top-1: 48.7580, accuracy_top-5: 74.3940 2023-01-03 17:07:33,311 - mmcls - INFO - Epoch [15][100/5005] lr: 1.000e-01, eta: 10:33:12, time: 0.111, data_time: 0.064, memory: 1125, loss: 2.5225 2023-01-03 17:07:42,384 - mmcls - INFO - Epoch [15][200/5005] lr: 1.000e-01, eta: 10:33:04, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.5285 2023-01-03 17:07:51,085 - mmcls - INFO - Epoch [15][300/5005] lr: 1.000e-01, eta: 10:32:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4920 2023-01-03 17:08:00,157 - mmcls - INFO - Epoch [15][400/5005] lr: 1.000e-01, eta: 10:32:48, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.5218 2023-01-03 17:08:08,919 - mmcls - INFO - Epoch [15][500/5005] lr: 1.000e-01, eta: 10:32:38, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.5355 2023-01-03 17:08:17,671 - mmcls - INFO - Epoch [15][600/5005] lr: 1.000e-01, eta: 10:32:29, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5498 2023-01-03 17:08:27,247 - mmcls - INFO - Epoch [15][700/5005] lr: 1.000e-01, eta: 10:32:25, time: 0.096, data_time: 0.012, memory: 1125, loss: 2.5154 2023-01-03 17:08:36,476 - mmcls - INFO - Epoch [15][800/5005] lr: 1.000e-01, eta: 10:32:18, time: 0.092, data_time: 0.032, memory: 1125, loss: 2.5216 2023-01-03 17:08:45,059 - mmcls - INFO - Epoch [15][900/5005] lr: 1.000e-01, eta: 10:32:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5596 2023-01-03 17:08:53,949 - mmcls - INFO - Epoch [15][1000/5005] lr: 1.000e-01, eta: 10:31:59, time: 0.089, data_time: 0.024, memory: 1125, loss: 2.5598 2023-01-03 17:09:02,916 - mmcls - INFO - Epoch [15][1100/5005] lr: 1.000e-01, eta: 10:31:51, time: 0.090, data_time: 0.040, memory: 1125, loss: 2.5478 2023-01-03 17:09:11,508 - mmcls - INFO - Epoch [15][1200/5005] lr: 1.000e-01, eta: 10:31:41, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.5347 2023-01-03 17:09:20,380 - mmcls - INFO - Epoch [15][1300/5005] lr: 1.000e-01, eta: 10:31:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5539 2023-01-03 17:09:29,315 - mmcls - INFO - Epoch [15][1400/5005] lr: 1.000e-01, eta: 10:31:24, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.5666 2023-01-03 17:09:37,994 - mmcls - INFO - Epoch [15][1500/5005] lr: 1.000e-01, eta: 10:31:15, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.5309 2023-01-03 17:09:46,661 - mmcls - INFO - Epoch [15][1600/5005] lr: 1.000e-01, eta: 10:31:05, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5400 2023-01-03 17:09:55,467 - mmcls - INFO - Epoch [15][1700/5005] lr: 1.000e-01, eta: 10:30:56, time: 0.088, data_time: 0.027, memory: 1125, loss: 2.5454 2023-01-03 17:10:04,073 - mmcls - INFO - Epoch [15][1800/5005] lr: 1.000e-01, eta: 10:30:46, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.5504 2023-01-03 17:10:12,892 - mmcls - INFO - Epoch [15][1900/5005] lr: 1.000e-01, eta: 10:30:37, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.5488 2023-01-03 17:10:21,641 - mmcls - INFO - Epoch [15][2000/5005] lr: 1.000e-01, eta: 10:30:27, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.5643 2023-01-03 17:10:30,517 - mmcls - INFO - Epoch [15][2100/5005] lr: 1.000e-01, eta: 10:30:19, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.5452 2023-01-03 17:10:39,536 - mmcls - INFO - Epoch [15][2200/5005] lr: 1.000e-01, eta: 10:30:11, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.5606 2023-01-03 17:10:48,410 - mmcls - INFO - Epoch [15][2300/5005] lr: 1.000e-01, eta: 10:30:03, time: 0.089, data_time: 0.010, memory: 1125, loss: 2.5617 2023-01-03 17:10:57,106 - mmcls - INFO - Epoch [15][2400/5005] lr: 1.000e-01, eta: 10:29:53, time: 0.087, data_time: 0.043, memory: 1125, loss: 2.5581 2023-01-03 17:11:05,927 - mmcls - INFO - Epoch [15][2500/5005] lr: 1.000e-01, eta: 10:29:44, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.5588 2023-01-03 17:11:15,103 - mmcls - INFO - Epoch [15][2600/5005] lr: 1.000e-01, eta: 10:29:37, time: 0.092, data_time: 0.011, memory: 1125, loss: 2.5286 2023-01-03 17:11:23,676 - mmcls - INFO - Epoch [15][2700/5005] lr: 1.000e-01, eta: 10:29:27, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.5651 2023-01-03 17:11:32,357 - mmcls - INFO - Epoch [15][2800/5005] lr: 1.000e-01, eta: 10:29:17, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.5624 2023-01-03 17:11:41,168 - mmcls - INFO - Epoch [15][2900/5005] lr: 1.000e-01, eta: 10:29:08, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.5500 2023-01-03 17:11:50,097 - mmcls - INFO - Epoch [15][3000/5005] lr: 1.000e-01, eta: 10:29:00, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.5401 2023-01-03 17:11:58,911 - mmcls - INFO - Epoch [15][3100/5005] lr: 1.000e-01, eta: 10:28:51, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.5669 2023-01-03 17:12:07,663 - mmcls - INFO - Epoch [15][3200/5005] lr: 1.000e-01, eta: 10:28:42, time: 0.088, data_time: 0.041, memory: 1125, loss: 2.5574 2023-01-03 17:12:16,322 - mmcls - INFO - Epoch [15][3300/5005] lr: 1.000e-01, eta: 10:28:32, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.5520 2023-01-03 17:12:24,974 - mmcls - INFO - Epoch [15][3400/5005] lr: 1.000e-01, eta: 10:28:22, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.5374 2023-01-03 17:12:33,750 - mmcls - INFO - Epoch [15][3500/5005] lr: 1.000e-01, eta: 10:28:13, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5436 2023-01-03 17:12:42,253 - mmcls - INFO - Epoch [15][3600/5005] lr: 1.000e-01, eta: 10:28:02, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.5544 2023-01-03 17:12:51,389 - mmcls - INFO - Epoch [15][3700/5005] lr: 1.000e-01, eta: 10:27:55, time: 0.091, data_time: 0.049, memory: 1125, loss: 2.5489 2023-01-03 17:12:59,903 - mmcls - INFO - Epoch [15][3800/5005] lr: 1.000e-01, eta: 10:27:45, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.5535 2023-01-03 17:13:08,517 - mmcls - INFO - Epoch [15][3900/5005] lr: 1.000e-01, eta: 10:27:34, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5689 2023-01-03 17:13:17,536 - mmcls - INFO - Epoch [15][4000/5005] lr: 1.000e-01, eta: 10:27:27, time: 0.090, data_time: 0.043, memory: 1125, loss: 2.5539 2023-01-03 17:13:26,227 - mmcls - INFO - Epoch [15][4100/5005] lr: 1.000e-01, eta: 10:27:17, time: 0.087, data_time: 0.034, memory: 1125, loss: 2.5852 2023-01-03 17:13:35,360 - mmcls - INFO - Epoch [15][4200/5005] lr: 1.000e-01, eta: 10:27:10, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.5228 2023-01-03 17:13:44,156 - mmcls - INFO - Epoch [15][4300/5005] lr: 1.000e-01, eta: 10:27:01, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.5906 2023-01-03 17:13:52,863 - mmcls - INFO - Epoch [15][4400/5005] lr: 1.000e-01, eta: 10:26:51, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5627 2023-01-03 17:14:01,512 - mmcls - INFO - Epoch [15][4500/5005] lr: 1.000e-01, eta: 10:26:42, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.5623 2023-01-03 17:14:10,257 - mmcls - INFO - Epoch [15][4600/5005] lr: 1.000e-01, eta: 10:26:32, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5434 2023-01-03 17:14:19,084 - mmcls - INFO - Epoch [15][4700/5005] lr: 1.000e-01, eta: 10:26:23, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5791 2023-01-03 17:14:27,921 - mmcls - INFO - Epoch [15][4800/5005] lr: 1.000e-01, eta: 10:26:15, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5484 2023-01-03 17:14:36,403 - mmcls - INFO - Epoch [15][4900/5005] lr: 1.000e-01, eta: 10:26:04, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.5470 2023-01-03 17:14:45,017 - mmcls - INFO - Epoch [15][5000/5005] lr: 1.000e-01, eta: 10:25:54, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.5674 2023-01-03 17:15:07,604 - mmcls - INFO - Epoch(val) [15][196] accuracy_top-1: 47.4920, accuracy_top-5: 73.5260 2023-01-03 17:15:18,513 - mmcls - INFO - Epoch [16][100/5005] lr: 1.000e-01, eta: 10:25:54, time: 0.109, data_time: 0.046, memory: 1125, loss: 2.4889 2023-01-03 17:15:27,373 - mmcls - INFO - Epoch [16][200/5005] lr: 1.000e-01, eta: 10:25:45, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.4954 2023-01-03 17:15:36,146 - mmcls - INFO - Epoch [16][300/5005] lr: 1.000e-01, eta: 10:25:36, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.5112 2023-01-03 17:15:44,778 - mmcls - INFO - Epoch [16][400/5005] lr: 1.000e-01, eta: 10:25:26, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5407 2023-01-03 17:15:53,603 - mmcls - INFO - Epoch [16][500/5005] lr: 1.000e-01, eta: 10:25:17, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5093 2023-01-03 17:16:02,530 - mmcls - INFO - Epoch [16][600/5005] lr: 1.000e-01, eta: 10:25:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5153 2023-01-03 17:16:11,348 - mmcls - INFO - Epoch [16][700/5005] lr: 1.000e-01, eta: 10:25:00, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.5004 2023-01-03 17:16:19,964 - mmcls - INFO - Epoch [16][800/5005] lr: 1.000e-01, eta: 10:24:50, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.4996 2023-01-03 17:16:28,638 - mmcls - INFO - Epoch [16][900/5005] lr: 1.000e-01, eta: 10:24:40, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.5450 2023-01-03 17:16:37,348 - mmcls - INFO - Epoch [16][1000/5005] lr: 1.000e-01, eta: 10:24:31, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.5120 2023-01-03 17:16:46,017 - mmcls - INFO - Epoch [16][1100/5005] lr: 1.000e-01, eta: 10:24:21, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5518 2023-01-03 17:16:54,838 - mmcls - INFO - Epoch [16][1200/5005] lr: 1.000e-01, eta: 10:24:12, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.5509 2023-01-03 17:17:03,596 - mmcls - INFO - Epoch [16][1300/5005] lr: 1.000e-01, eta: 10:24:03, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.5316 2023-01-03 17:17:12,347 - mmcls - INFO - Epoch [16][1400/5005] lr: 1.000e-01, eta: 10:23:54, time: 0.087, data_time: 0.038, memory: 1125, loss: 2.5425 2023-01-03 17:17:21,420 - mmcls - INFO - Epoch [16][1500/5005] lr: 1.000e-01, eta: 10:23:46, time: 0.091, data_time: 0.015, memory: 1125, loss: 2.5496 2023-01-03 17:17:30,044 - mmcls - INFO - Epoch [16][1600/5005] lr: 1.000e-01, eta: 10:23:36, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.5414 2023-01-03 17:17:38,885 - mmcls - INFO - Epoch [16][1700/5005] lr: 1.000e-01, eta: 10:23:27, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.5349 2023-01-03 17:17:47,823 - mmcls - INFO - Epoch [16][1800/5005] lr: 1.000e-01, eta: 10:23:19, time: 0.089, data_time: 0.040, memory: 1125, loss: 2.5585 2023-01-03 17:17:56,503 - mmcls - INFO - Epoch [16][1900/5005] lr: 1.000e-01, eta: 10:23:09, time: 0.087, data_time: 0.043, memory: 1125, loss: 2.5296 2023-01-03 17:18:05,134 - mmcls - INFO - Epoch [16][2000/5005] lr: 1.000e-01, eta: 10:23:00, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5451 2023-01-03 17:18:13,694 - mmcls - INFO - Epoch [16][2100/5005] lr: 1.000e-01, eta: 10:22:49, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.5493 2023-01-03 17:18:22,826 - mmcls - INFO - Epoch [16][2200/5005] lr: 1.000e-01, eta: 10:22:42, time: 0.091, data_time: 0.013, memory: 1125, loss: 2.5553 2023-01-03 17:18:31,389 - mmcls - INFO - Epoch [16][2300/5005] lr: 1.000e-01, eta: 10:22:32, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.5483 2023-01-03 17:18:40,085 - mmcls - INFO - Epoch [16][2400/5005] lr: 1.000e-01, eta: 10:22:22, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.5533 2023-01-03 17:18:48,767 - mmcls - INFO - Epoch [16][2500/5005] lr: 1.000e-01, eta: 10:22:13, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.5545 2023-01-03 17:18:58,029 - mmcls - INFO - Epoch [16][2600/5005] lr: 1.000e-01, eta: 10:22:06, time: 0.093, data_time: 0.030, memory: 1125, loss: 2.5485 2023-01-03 17:19:06,966 - mmcls - INFO - Epoch [16][2700/5005] lr: 1.000e-01, eta: 10:21:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.5332 2023-01-03 17:19:15,451 - mmcls - INFO - Epoch [16][2800/5005] lr: 1.000e-01, eta: 10:21:47, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.5665 2023-01-03 17:19:24,214 - mmcls - INFO - Epoch [16][2900/5005] lr: 1.000e-01, eta: 10:21:38, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.5204 2023-01-03 17:19:32,895 - mmcls - INFO - Epoch [16][3000/5005] lr: 1.000e-01, eta: 10:21:28, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.5250 2023-01-03 17:19:41,649 - mmcls - INFO - Epoch [16][3100/5005] lr: 1.000e-01, eta: 10:21:19, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5398 2023-01-03 17:19:50,228 - mmcls - INFO - Epoch [16][3200/5005] lr: 1.000e-01, eta: 10:21:09, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.5071 2023-01-03 17:19:59,111 - mmcls - INFO - Epoch [16][3300/5005] lr: 1.000e-01, eta: 10:21:00, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.5658 2023-01-03 17:20:07,815 - mmcls - INFO - Epoch [16][3400/5005] lr: 1.000e-01, eta: 10:20:51, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.5130 2023-01-03 17:20:16,597 - mmcls - INFO - Epoch [16][3500/5005] lr: 1.000e-01, eta: 10:20:42, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.5622 2023-01-03 17:20:25,495 - mmcls - INFO - Epoch [16][3600/5005] lr: 1.000e-01, eta: 10:20:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5596 2023-01-03 17:20:34,467 - mmcls - INFO - Epoch [16][3700/5005] lr: 1.000e-01, eta: 10:20:25, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.5494 2023-01-03 17:20:43,164 - mmcls - INFO - Epoch [16][3800/5005] lr: 1.000e-01, eta: 10:20:16, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.5525 2023-01-03 17:20:51,535 - mmcls - INFO - Epoch [16][3900/5005] lr: 1.000e-01, eta: 10:20:05, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.5430 2023-01-03 17:21:00,055 - mmcls - INFO - Epoch [16][4000/5005] lr: 1.000e-01, eta: 10:19:54, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.5457 2023-01-03 17:21:08,845 - mmcls - INFO - Epoch [16][4100/5005] lr: 1.000e-01, eta: 10:19:45, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.5614 2023-01-03 17:21:18,110 - mmcls - INFO - Epoch [16][4200/5005] lr: 1.000e-01, eta: 10:19:39, time: 0.093, data_time: 0.007, memory: 1125, loss: 2.5472 2023-01-03 17:21:26,628 - mmcls - INFO - Epoch [16][4300/5005] lr: 1.000e-01, eta: 10:19:28, time: 0.085, data_time: 0.020, memory: 1125, loss: 2.5241 2023-01-03 17:21:35,474 - mmcls - INFO - Epoch [16][4400/5005] lr: 1.000e-01, eta: 10:19:19, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.5229 2023-01-03 17:21:44,298 - mmcls - INFO - Epoch [16][4500/5005] lr: 1.000e-01, eta: 10:19:11, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.5866 2023-01-03 17:21:53,041 - mmcls - INFO - Epoch [16][4600/5005] lr: 1.000e-01, eta: 10:19:01, time: 0.087, data_time: 0.029, memory: 1125, loss: 2.5573 2023-01-03 17:22:01,945 - mmcls - INFO - Epoch [16][4700/5005] lr: 1.000e-01, eta: 10:18:53, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5492 2023-01-03 17:22:10,659 - mmcls - INFO - Epoch [16][4800/5005] lr: 1.000e-01, eta: 10:18:43, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.5615 2023-01-03 17:22:19,841 - mmcls - INFO - Epoch [16][4900/5005] lr: 1.000e-01, eta: 10:18:37, time: 0.092, data_time: 0.007, memory: 1125, loss: 2.5421 2023-01-03 17:22:28,559 - mmcls - INFO - Epoch [16][5000/5005] lr: 1.000e-01, eta: 10:18:27, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.5454 2023-01-03 17:22:51,370 - mmcls - INFO - Epoch(val) [16][196] accuracy_top-1: 47.9200, accuracy_top-5: 74.1040 2023-01-03 17:23:02,087 - mmcls - INFO - Epoch [17][100/5005] lr: 1.000e-01, eta: 10:18:25, time: 0.107, data_time: 0.047, memory: 1125, loss: 2.5069 2023-01-03 17:23:10,838 - mmcls - INFO - Epoch [17][200/5005] lr: 1.000e-01, eta: 10:18:16, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5141 2023-01-03 17:23:19,480 - mmcls - INFO - Epoch [17][300/5005] lr: 1.000e-01, eta: 10:18:06, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.4727 2023-01-03 17:23:28,096 - mmcls - INFO - Epoch [17][400/5005] lr: 1.000e-01, eta: 10:17:56, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.5135 2023-01-03 17:23:37,140 - mmcls - INFO - Epoch [17][500/5005] lr: 1.000e-01, eta: 10:17:49, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.5067 2023-01-03 17:23:45,930 - mmcls - INFO - Epoch [17][600/5005] lr: 1.000e-01, eta: 10:17:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.5294 2023-01-03 17:23:54,631 - mmcls - INFO - Epoch [17][700/5005] lr: 1.000e-01, eta: 10:17:30, time: 0.087, data_time: 0.030, memory: 1125, loss: 2.5320 2023-01-03 17:24:03,506 - mmcls - INFO - Epoch [17][800/5005] lr: 1.000e-01, eta: 10:17:22, time: 0.089, data_time: 0.019, memory: 1125, loss: 2.5231 2023-01-03 17:24:12,177 - mmcls - INFO - Epoch [17][900/5005] lr: 1.000e-01, eta: 10:17:12, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.5195 2023-01-03 17:24:20,895 - mmcls - INFO - Epoch [17][1000/5005] lr: 1.000e-01, eta: 10:17:02, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.5340 2023-01-03 17:24:30,079 - mmcls - INFO - Epoch [17][1100/5005] lr: 1.000e-01, eta: 10:16:56, time: 0.092, data_time: 0.027, memory: 1125, loss: 2.5233 2023-01-03 17:24:38,594 - mmcls - INFO - Epoch [17][1200/5005] lr: 1.000e-01, eta: 10:16:45, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.4994 2023-01-03 17:24:47,277 - mmcls - INFO - Epoch [17][1300/5005] lr: 1.000e-01, eta: 10:16:35, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5382 2023-01-03 17:24:55,918 - mmcls - INFO - Epoch [17][1400/5005] lr: 1.000e-01, eta: 10:16:26, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.5373 2023-01-03 17:25:04,689 - mmcls - INFO - Epoch [17][1500/5005] lr: 1.000e-01, eta: 10:16:17, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.4976 2023-01-03 17:25:13,343 - mmcls - INFO - Epoch [17][1600/5005] lr: 1.000e-01, eta: 10:16:07, time: 0.086, data_time: 0.044, memory: 1125, loss: 2.5370 2023-01-03 17:25:22,338 - mmcls - INFO - Epoch [17][1700/5005] lr: 1.000e-01, eta: 10:15:59, time: 0.090, data_time: 0.014, memory: 1125, loss: 2.5272 2023-01-03 17:25:30,949 - mmcls - INFO - Epoch [17][1800/5005] lr: 1.000e-01, eta: 10:15:49, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5186 2023-01-03 17:25:39,883 - mmcls - INFO - Epoch [17][1900/5005] lr: 1.000e-01, eta: 10:15:41, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5262 2023-01-03 17:25:48,509 - mmcls - INFO - Epoch [17][2000/5005] lr: 1.000e-01, eta: 10:15:31, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5137 2023-01-03 17:25:57,314 - mmcls - INFO - Epoch [17][2100/5005] lr: 1.000e-01, eta: 10:15:22, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5227 2023-01-03 17:26:05,947 - mmcls - INFO - Epoch [17][2200/5005] lr: 1.000e-01, eta: 10:15:12, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5140 2023-01-03 17:26:14,602 - mmcls - INFO - Epoch [17][2300/5005] lr: 1.000e-01, eta: 10:15:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.5181 2023-01-03 17:26:23,285 - mmcls - INFO - Epoch [17][2400/5005] lr: 1.000e-01, eta: 10:14:53, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5374 2023-01-03 17:26:32,003 - mmcls - INFO - Epoch [17][2500/5005] lr: 1.000e-01, eta: 10:14:43, time: 0.087, data_time: 0.036, memory: 1125, loss: 2.5357 2023-01-03 17:26:40,951 - mmcls - INFO - Epoch [17][2600/5005] lr: 1.000e-01, eta: 10:14:35, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5697 2023-01-03 17:26:49,918 - mmcls - INFO - Epoch [17][2700/5005] lr: 1.000e-01, eta: 10:14:27, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.5304 2023-01-03 17:26:58,819 - mmcls - INFO - Epoch [17][2800/5005] lr: 1.000e-01, eta: 10:14:19, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.5441 2023-01-03 17:27:07,853 - mmcls - INFO - Epoch [17][2900/5005] lr: 1.000e-01, eta: 10:14:11, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.5316 2023-01-03 17:27:16,574 - mmcls - INFO - Epoch [17][3000/5005] lr: 1.000e-01, eta: 10:14:02, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.5314 2023-01-03 17:27:25,549 - mmcls - INFO - Epoch [17][3100/5005] lr: 1.000e-01, eta: 10:13:53, time: 0.090, data_time: 0.020, memory: 1125, loss: 2.5252 2023-01-03 17:27:34,390 - mmcls - INFO - Epoch [17][3200/5005] lr: 1.000e-01, eta: 10:13:45, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5217 2023-01-03 17:27:43,120 - mmcls - INFO - Epoch [17][3300/5005] lr: 1.000e-01, eta: 10:13:35, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.5275 2023-01-03 17:27:52,029 - mmcls - INFO - Epoch [17][3400/5005] lr: 1.000e-01, eta: 10:13:27, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.5239 2023-01-03 17:28:01,190 - mmcls - INFO - Epoch [17][3500/5005] lr: 1.000e-01, eta: 10:13:20, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.5434 2023-01-03 17:28:09,740 - mmcls - INFO - Epoch [17][3600/5005] lr: 1.000e-01, eta: 10:13:10, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.5491 2023-01-03 17:28:18,346 - mmcls - INFO - Epoch [17][3700/5005] lr: 1.000e-01, eta: 10:13:00, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5532 2023-01-03 17:28:27,209 - mmcls - INFO - Epoch [17][3800/5005] lr: 1.000e-01, eta: 10:12:51, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5699 2023-01-03 17:28:36,094 - mmcls - INFO - Epoch [17][3900/5005] lr: 1.000e-01, eta: 10:12:42, time: 0.089, data_time: 0.030, memory: 1125, loss: 2.5372 2023-01-03 17:28:44,860 - mmcls - INFO - Epoch [17][4000/5005] lr: 1.000e-01, eta: 10:12:33, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5386 2023-01-03 17:28:53,541 - mmcls - INFO - Epoch [17][4100/5005] lr: 1.000e-01, eta: 10:12:24, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.5270 2023-01-03 17:29:02,282 - mmcls - INFO - Epoch [17][4200/5005] lr: 1.000e-01, eta: 10:12:15, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.5371 2023-01-03 17:29:10,900 - mmcls - INFO - Epoch [17][4300/5005] lr: 1.000e-01, eta: 10:12:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5393 2023-01-03 17:29:19,635 - mmcls - INFO - Epoch [17][4400/5005] lr: 1.000e-01, eta: 10:11:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5315 2023-01-03 17:29:28,482 - mmcls - INFO - Epoch [17][4500/5005] lr: 1.000e-01, eta: 10:11:47, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5283 2023-01-03 17:29:37,374 - mmcls - INFO - Epoch [17][4600/5005] lr: 1.000e-01, eta: 10:11:38, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5665 2023-01-03 17:29:46,523 - mmcls - INFO - Epoch [17][4700/5005] lr: 1.000e-01, eta: 10:11:31, time: 0.092, data_time: 0.003, memory: 1125, loss: 2.5196 2023-01-03 17:29:55,192 - mmcls - INFO - Epoch [17][4800/5005] lr: 1.000e-01, eta: 10:11:21, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5328 2023-01-03 17:30:03,990 - mmcls - INFO - Epoch [17][4900/5005] lr: 1.000e-01, eta: 10:11:12, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.5348 2023-01-03 17:30:12,700 - mmcls - INFO - Epoch [17][5000/5005] lr: 1.000e-01, eta: 10:11:03, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.5491 2023-01-03 17:30:35,397 - mmcls - INFO - Epoch(val) [17][196] accuracy_top-1: 49.7740, accuracy_top-5: 75.5680 2023-01-03 17:30:46,151 - mmcls - INFO - Epoch [18][100/5005] lr: 1.000e-01, eta: 10:11:01, time: 0.107, data_time: 0.060, memory: 1125, loss: 2.4843 2023-01-03 17:30:55,063 - mmcls - INFO - Epoch [18][200/5005] lr: 1.000e-01, eta: 10:10:52, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.4970 2023-01-03 17:31:04,047 - mmcls - INFO - Epoch [18][300/5005] lr: 1.000e-01, eta: 10:10:44, time: 0.090, data_time: 0.024, memory: 1125, loss: 2.4932 2023-01-03 17:31:12,699 - mmcls - INFO - Epoch [18][400/5005] lr: 1.000e-01, eta: 10:10:35, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.4998 2023-01-03 17:31:21,445 - mmcls - INFO - Epoch [18][500/5005] lr: 1.000e-01, eta: 10:10:25, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.5124 2023-01-03 17:31:30,263 - mmcls - INFO - Epoch [18][600/5005] lr: 1.000e-01, eta: 10:10:17, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5247 2023-01-03 17:31:39,175 - mmcls - INFO - Epoch [18][700/5005] lr: 1.000e-01, eta: 10:10:08, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.4798 2023-01-03 17:31:48,105 - mmcls - INFO - Epoch [18][800/5005] lr: 1.000e-01, eta: 10:10:00, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.4789 2023-01-03 17:31:57,174 - mmcls - INFO - Epoch [18][900/5005] lr: 1.000e-01, eta: 10:09:52, time: 0.091, data_time: 0.026, memory: 1125, loss: 2.5098 2023-01-03 17:32:05,713 - mmcls - INFO - Epoch [18][1000/5005] lr: 1.000e-01, eta: 10:09:42, time: 0.085, data_time: 0.031, memory: 1125, loss: 2.5020 2023-01-03 17:32:14,469 - mmcls - INFO - Epoch [18][1100/5005] lr: 1.000e-01, eta: 10:09:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5327 2023-01-03 17:32:22,993 - mmcls - INFO - Epoch [18][1200/5005] lr: 1.000e-01, eta: 10:09:22, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.5354 2023-01-03 17:32:32,073 - mmcls - INFO - Epoch [18][1300/5005] lr: 1.000e-01, eta: 10:09:15, time: 0.091, data_time: 0.026, memory: 1125, loss: 2.5186 2023-01-03 17:32:40,612 - mmcls - INFO - Epoch [18][1400/5005] lr: 1.000e-01, eta: 10:09:05, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.5309 2023-01-03 17:32:49,437 - mmcls - INFO - Epoch [18][1500/5005] lr: 1.000e-01, eta: 10:08:56, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.5012 2023-01-03 17:32:58,298 - mmcls - INFO - Epoch [18][1600/5005] lr: 1.000e-01, eta: 10:08:47, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5319 2023-01-03 17:33:07,151 - mmcls - INFO - Epoch [18][1700/5005] lr: 1.000e-01, eta: 10:08:38, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5188 2023-01-03 17:33:16,078 - mmcls - INFO - Epoch [18][1800/5005] lr: 1.000e-01, eta: 10:08:30, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5203 2023-01-03 17:33:24,929 - mmcls - INFO - Epoch [18][1900/5005] lr: 1.000e-01, eta: 10:08:21, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5200 2023-01-03 17:33:33,763 - mmcls - INFO - Epoch [18][2000/5005] lr: 1.000e-01, eta: 10:08:13, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.5088 2023-01-03 17:33:42,518 - mmcls - INFO - Epoch [18][2100/5005] lr: 1.000e-01, eta: 10:08:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5158 2023-01-03 17:33:51,048 - mmcls - INFO - Epoch [18][2200/5005] lr: 1.000e-01, eta: 10:07:53, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.5466 2023-01-03 17:33:59,830 - mmcls - INFO - Epoch [18][2300/5005] lr: 1.000e-01, eta: 10:07:44, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.5006 2023-01-03 17:34:08,756 - mmcls - INFO - Epoch [18][2400/5005] lr: 1.000e-01, eta: 10:07:36, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.5201 2023-01-03 17:34:17,278 - mmcls - INFO - Epoch [18][2500/5005] lr: 1.000e-01, eta: 10:07:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.5298 2023-01-03 17:34:26,199 - mmcls - INFO - Epoch [18][2600/5005] lr: 1.000e-01, eta: 10:07:17, time: 0.089, data_time: 0.043, memory: 1125, loss: 2.5447 2023-01-03 17:34:34,954 - mmcls - INFO - Epoch [18][2700/5005] lr: 1.000e-01, eta: 10:07:08, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.5343 2023-01-03 17:34:43,485 - mmcls - INFO - Epoch [18][2800/5005] lr: 1.000e-01, eta: 10:06:58, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.4876 2023-01-03 17:34:52,345 - mmcls - INFO - Epoch [18][2900/5005] lr: 1.000e-01, eta: 10:06:49, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.5133 2023-01-03 17:35:00,977 - mmcls - INFO - Epoch [18][3000/5005] lr: 1.000e-01, eta: 10:06:39, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.5208 2023-01-03 17:35:09,716 - mmcls - INFO - Epoch [18][3100/5005] lr: 1.000e-01, eta: 10:06:30, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.5168 2023-01-03 17:35:18,585 - mmcls - INFO - Epoch [18][3200/5005] lr: 1.000e-01, eta: 10:06:21, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5095 2023-01-03 17:35:27,307 - mmcls - INFO - Epoch [18][3300/5005] lr: 1.000e-01, eta: 10:06:12, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.5385 2023-01-03 17:35:36,017 - mmcls - INFO - Epoch [18][3400/5005] lr: 1.000e-01, eta: 10:06:03, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.5201 2023-01-03 17:35:44,816 - mmcls - INFO - Epoch [18][3500/5005] lr: 1.000e-01, eta: 10:05:54, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.5241 2023-01-03 17:35:53,526 - mmcls - INFO - Epoch [18][3600/5005] lr: 1.000e-01, eta: 10:05:44, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.5185 2023-01-03 17:36:02,298 - mmcls - INFO - Epoch [18][3700/5005] lr: 1.000e-01, eta: 10:05:35, time: 0.088, data_time: 0.041, memory: 1125, loss: 2.5527 2023-01-03 17:36:10,956 - mmcls - INFO - Epoch [18][3800/5005] lr: 1.000e-01, eta: 10:05:26, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.5370 2023-01-03 17:36:19,669 - mmcls - INFO - Epoch [18][3900/5005] lr: 1.000e-01, eta: 10:05:16, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.5342 2023-01-03 17:36:28,558 - mmcls - INFO - Epoch [18][4000/5005] lr: 1.000e-01, eta: 10:05:08, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.5186 2023-01-03 17:36:37,242 - mmcls - INFO - Epoch [18][4100/5005] lr: 1.000e-01, eta: 10:04:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.5354 2023-01-03 17:36:46,184 - mmcls - INFO - Epoch [18][4200/5005] lr: 1.000e-01, eta: 10:04:50, time: 0.089, data_time: 0.013, memory: 1125, loss: 2.5506 2023-01-03 17:36:55,045 - mmcls - INFO - Epoch [18][4300/5005] lr: 1.000e-01, eta: 10:04:41, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.5157 2023-01-03 17:37:03,816 - mmcls - INFO - Epoch [18][4400/5005] lr: 1.000e-01, eta: 10:04:32, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.5500 2023-01-03 17:37:12,316 - mmcls - INFO - Epoch [18][4500/5005] lr: 1.000e-01, eta: 10:04:22, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.5153 2023-01-03 17:37:21,415 - mmcls - INFO - Epoch [18][4600/5005] lr: 1.000e-01, eta: 10:04:14, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.5452 2023-01-03 17:37:30,112 - mmcls - INFO - Epoch [18][4700/5005] lr: 1.000e-01, eta: 10:04:05, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.5506 2023-01-03 17:37:39,092 - mmcls - INFO - Epoch [18][4800/5005] lr: 1.000e-01, eta: 10:03:57, time: 0.090, data_time: 0.017, memory: 1125, loss: 2.5042 2023-01-03 17:37:47,951 - mmcls - INFO - Epoch [18][4900/5005] lr: 1.000e-01, eta: 10:03:48, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.5521 2023-01-03 17:37:56,665 - mmcls - INFO - Epoch [18][5000/5005] lr: 1.000e-01, eta: 10:03:39, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.5304 2023-01-03 17:38:19,270 - mmcls - INFO - Epoch(val) [18][196] accuracy_top-1: 48.8620, accuracy_top-5: 74.6160 2023-01-03 17:38:30,141 - mmcls - INFO - Epoch [19][100/5005] lr: 1.000e-01, eta: 10:03:37, time: 0.109, data_time: 0.041, memory: 1125, loss: 2.4707 2023-01-03 17:38:38,874 - mmcls - INFO - Epoch [19][200/5005] lr: 1.000e-01, eta: 10:03:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4771 2023-01-03 17:38:48,089 - mmcls - INFO - Epoch [19][300/5005] lr: 1.000e-01, eta: 10:03:20, time: 0.092, data_time: 0.004, memory: 1125, loss: 2.4861 2023-01-03 17:38:56,742 - mmcls - INFO - Epoch [19][400/5005] lr: 1.000e-01, eta: 10:03:11, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5079 2023-01-03 17:39:05,357 - mmcls - INFO - Epoch [19][500/5005] lr: 1.000e-01, eta: 10:03:01, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.4876 2023-01-03 17:39:14,113 - mmcls - INFO - Epoch [19][600/5005] lr: 1.000e-01, eta: 10:02:52, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.5190 2023-01-03 17:39:22,925 - mmcls - INFO - Epoch [19][700/5005] lr: 1.000e-01, eta: 10:02:43, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4887 2023-01-03 17:39:31,739 - mmcls - INFO - Epoch [19][800/5005] lr: 1.000e-01, eta: 10:02:34, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4846 2023-01-03 17:39:40,301 - mmcls - INFO - Epoch [19][900/5005] lr: 1.000e-01, eta: 10:02:24, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.4904 2023-01-03 17:39:49,023 - mmcls - INFO - Epoch [19][1000/5005] lr: 1.000e-01, eta: 10:02:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5144 2023-01-03 17:39:57,717 - mmcls - INFO - Epoch [19][1100/5005] lr: 1.000e-01, eta: 10:02:05, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.5110 2023-01-03 17:40:06,492 - mmcls - INFO - Epoch [19][1200/5005] lr: 1.000e-01, eta: 10:01:56, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5075 2023-01-03 17:40:15,106 - mmcls - INFO - Epoch [19][1300/5005] lr: 1.000e-01, eta: 10:01:46, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.5036 2023-01-03 17:40:23,790 - mmcls - INFO - Epoch [19][1400/5005] lr: 1.000e-01, eta: 10:01:37, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4986 2023-01-03 17:40:32,400 - mmcls - INFO - Epoch [19][1500/5005] lr: 1.000e-01, eta: 10:01:27, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.4973 2023-01-03 17:40:41,276 - mmcls - INFO - Epoch [19][1600/5005] lr: 1.000e-01, eta: 10:01:19, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.5371 2023-01-03 17:40:49,936 - mmcls - INFO - Epoch [19][1700/5005] lr: 1.000e-01, eta: 10:01:09, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.5210 2023-01-03 17:40:59,035 - mmcls - INFO - Epoch [19][1800/5005] lr: 1.000e-01, eta: 10:01:01, time: 0.091, data_time: 0.013, memory: 1125, loss: 2.4950 2023-01-03 17:41:07,865 - mmcls - INFO - Epoch [19][1900/5005] lr: 1.000e-01, eta: 10:00:53, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5077 2023-01-03 17:41:16,708 - mmcls - INFO - Epoch [19][2000/5005] lr: 1.000e-01, eta: 10:00:44, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4882 2023-01-03 17:41:25,266 - mmcls - INFO - Epoch [19][2100/5005] lr: 1.000e-01, eta: 10:00:34, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4995 2023-01-03 17:41:34,078 - mmcls - INFO - Epoch [19][2200/5005] lr: 1.000e-01, eta: 10:00:25, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.5402 2023-01-03 17:41:42,623 - mmcls - INFO - Epoch [19][2300/5005] lr: 1.000e-01, eta: 10:00:15, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5050 2023-01-03 17:41:51,272 - mmcls - INFO - Epoch [19][2400/5005] lr: 1.000e-01, eta: 10:00:05, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5127 2023-01-03 17:42:00,342 - mmcls - INFO - Epoch [19][2500/5005] lr: 1.000e-01, eta: 9:59:58, time: 0.091, data_time: 0.012, memory: 1125, loss: 2.5271 2023-01-03 17:42:09,043 - mmcls - INFO - Epoch [19][2600/5005] lr: 1.000e-01, eta: 9:59:48, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.4995 2023-01-03 17:42:17,771 - mmcls - INFO - Epoch [19][2700/5005] lr: 1.000e-01, eta: 9:59:39, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5255 2023-01-03 17:42:27,128 - mmcls - INFO - Epoch [19][2800/5005] lr: 1.000e-01, eta: 9:59:32, time: 0.094, data_time: 0.016, memory: 1125, loss: 2.5362 2023-01-03 17:42:35,554 - mmcls - INFO - Epoch [19][2900/5005] lr: 1.000e-01, eta: 9:59:22, time: 0.084, data_time: 0.002, memory: 1125, loss: 2.5273 2023-01-03 17:42:44,228 - mmcls - INFO - Epoch [19][3000/5005] lr: 1.000e-01, eta: 9:59:12, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5070 2023-01-03 17:42:53,157 - mmcls - INFO - Epoch [19][3100/5005] lr: 1.000e-01, eta: 9:59:04, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.5304 2023-01-03 17:43:01,877 - mmcls - INFO - Epoch [19][3200/5005] lr: 1.000e-01, eta: 9:58:55, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.5170 2023-01-03 17:43:11,045 - mmcls - INFO - Epoch [19][3300/5005] lr: 1.000e-01, eta: 9:58:47, time: 0.092, data_time: 0.015, memory: 1125, loss: 2.5371 2023-01-03 17:43:19,752 - mmcls - INFO - Epoch [19][3400/5005] lr: 1.000e-01, eta: 9:58:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.5241 2023-01-03 17:43:28,567 - mmcls - INFO - Epoch [19][3500/5005] lr: 1.000e-01, eta: 9:58:29, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.5156 2023-01-03 17:43:37,206 - mmcls - INFO - Epoch [19][3600/5005] lr: 1.000e-01, eta: 9:58:20, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.5135 2023-01-03 17:43:46,070 - mmcls - INFO - Epoch [19][3700/5005] lr: 1.000e-01, eta: 9:58:11, time: 0.089, data_time: 0.026, memory: 1125, loss: 2.5030 2023-01-03 17:43:54,961 - mmcls - INFO - Epoch [19][3800/5005] lr: 1.000e-01, eta: 9:58:02, time: 0.089, data_time: 0.000, memory: 1125, loss: 2.5119 2023-01-03 17:44:03,538 - mmcls - INFO - Epoch [19][3900/5005] lr: 1.000e-01, eta: 9:57:52, time: 0.086, data_time: 0.018, memory: 1125, loss: 2.5330 2023-01-03 17:44:12,320 - mmcls - INFO - Epoch [19][4000/5005] lr: 1.000e-01, eta: 9:57:43, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.5176 2023-01-03 17:44:20,899 - mmcls - INFO - Epoch [19][4100/5005] lr: 1.000e-01, eta: 9:57:34, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.5204 2023-01-03 17:44:29,681 - mmcls - INFO - Epoch [19][4200/5005] lr: 1.000e-01, eta: 9:57:25, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.5464 2023-01-03 17:44:38,603 - mmcls - INFO - Epoch [19][4300/5005] lr: 1.000e-01, eta: 9:57:16, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.5357 2023-01-03 17:44:47,330 - mmcls - INFO - Epoch [19][4400/5005] lr: 1.000e-01, eta: 9:57:07, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.5255 2023-01-03 17:44:56,046 - mmcls - INFO - Epoch [19][4500/5005] lr: 1.000e-01, eta: 9:56:58, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5273 2023-01-03 17:45:04,726 - mmcls - INFO - Epoch [19][4600/5005] lr: 1.000e-01, eta: 9:56:48, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5359 2023-01-03 17:45:13,525 - mmcls - INFO - Epoch [19][4700/5005] lr: 1.000e-01, eta: 9:56:39, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.5111 2023-01-03 17:45:22,277 - mmcls - INFO - Epoch [19][4800/5005] lr: 1.000e-01, eta: 9:56:30, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.5130 2023-01-03 17:45:31,134 - mmcls - INFO - Epoch [19][4900/5005] lr: 1.000e-01, eta: 9:56:21, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.5230 2023-01-03 17:45:40,382 - mmcls - INFO - Epoch [19][5000/5005] lr: 1.000e-01, eta: 9:56:14, time: 0.093, data_time: 0.020, memory: 1125, loss: 2.5393 2023-01-03 17:46:03,183 - mmcls - INFO - Epoch(val) [19][196] accuracy_top-1: 49.7360, accuracy_top-5: 75.2660 2023-01-03 17:46:14,342 - mmcls - INFO - Epoch [20][100/5005] lr: 1.000e-01, eta: 9:56:13, time: 0.111, data_time: 0.040, memory: 1125, loss: 2.4676 2023-01-03 17:46:23,506 - mmcls - INFO - Epoch [20][200/5005] lr: 1.000e-01, eta: 9:56:06, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.4770 2023-01-03 17:46:32,152 - mmcls - INFO - Epoch [20][300/5005] lr: 1.000e-01, eta: 9:55:56, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4562 2023-01-03 17:46:40,815 - mmcls - INFO - Epoch [20][400/5005] lr: 1.000e-01, eta: 9:55:47, time: 0.087, data_time: 0.035, memory: 1125, loss: 2.4487 2023-01-03 17:46:49,440 - mmcls - INFO - Epoch [20][500/5005] lr: 1.000e-01, eta: 9:55:37, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.4869 2023-01-03 17:46:58,113 - mmcls - INFO - Epoch [20][600/5005] lr: 1.000e-01, eta: 9:55:27, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4805 2023-01-03 17:47:06,765 - mmcls - INFO - Epoch [20][700/5005] lr: 1.000e-01, eta: 9:55:18, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4788 2023-01-03 17:47:15,331 - mmcls - INFO - Epoch [20][800/5005] lr: 1.000e-01, eta: 9:55:08, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4781 2023-01-03 17:47:24,253 - mmcls - INFO - Epoch [20][900/5005] lr: 1.000e-01, eta: 9:54:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4711 2023-01-03 17:47:33,058 - mmcls - INFO - Epoch [20][1000/5005] lr: 1.000e-01, eta: 9:54:51, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4810 2023-01-03 17:47:41,689 - mmcls - INFO - Epoch [20][1100/5005] lr: 1.000e-01, eta: 9:54:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4973 2023-01-03 17:47:50,757 - mmcls - INFO - Epoch [20][1200/5005] lr: 1.000e-01, eta: 9:54:33, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.4989 2023-01-03 17:47:59,586 - mmcls - INFO - Epoch [20][1300/5005] lr: 1.000e-01, eta: 9:54:24, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.4867 2023-01-03 17:48:08,413 - mmcls - INFO - Epoch [20][1400/5005] lr: 1.000e-01, eta: 9:54:15, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4976 2023-01-03 17:48:16,929 - mmcls - INFO - Epoch [20][1500/5005] lr: 1.000e-01, eta: 9:54:05, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.5270 2023-01-03 17:48:25,959 - mmcls - INFO - Epoch [20][1600/5005] lr: 1.000e-01, eta: 9:53:57, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.4991 2023-01-03 17:48:34,952 - mmcls - INFO - Epoch [20][1700/5005] lr: 1.000e-01, eta: 9:53:49, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.5057 2023-01-03 17:48:43,570 - mmcls - INFO - Epoch [20][1800/5005] lr: 1.000e-01, eta: 9:53:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5065 2023-01-03 17:48:52,114 - mmcls - INFO - Epoch [20][1900/5005] lr: 1.000e-01, eta: 9:53:30, time: 0.085, data_time: 0.037, memory: 1125, loss: 2.4983 2023-01-03 17:49:00,893 - mmcls - INFO - Epoch [20][2000/5005] lr: 1.000e-01, eta: 9:53:21, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.5206 2023-01-03 17:49:09,697 - mmcls - INFO - Epoch [20][2100/5005] lr: 1.000e-01, eta: 9:53:12, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.5334 2023-01-03 17:49:18,235 - mmcls - INFO - Epoch [20][2200/5005] lr: 1.000e-01, eta: 9:53:02, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.5017 2023-01-03 17:49:27,028 - mmcls - INFO - Epoch [20][2300/5005] lr: 1.000e-01, eta: 9:52:53, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.4857 2023-01-03 17:49:35,518 - mmcls - INFO - Epoch [20][2400/5005] lr: 1.000e-01, eta: 9:52:42, time: 0.085, data_time: 0.014, memory: 1125, loss: 2.5075 2023-01-03 17:49:44,216 - mmcls - INFO - Epoch [20][2500/5005] lr: 1.000e-01, eta: 9:52:33, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.5254 2023-01-03 17:49:52,986 - mmcls - INFO - Epoch [20][2600/5005] lr: 1.000e-01, eta: 9:52:24, time: 0.088, data_time: 0.030, memory: 1125, loss: 2.4849 2023-01-03 17:50:01,811 - mmcls - INFO - Epoch [20][2700/5005] lr: 1.000e-01, eta: 9:52:15, time: 0.088, data_time: 0.021, memory: 1125, loss: 2.5166 2023-01-03 17:50:10,521 - mmcls - INFO - Epoch [20][2800/5005] lr: 1.000e-01, eta: 9:52:06, time: 0.087, data_time: 0.023, memory: 1125, loss: 2.4934 2023-01-03 17:50:19,126 - mmcls - INFO - Epoch [20][2900/5005] lr: 1.000e-01, eta: 9:51:56, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.5356 2023-01-03 17:50:27,863 - mmcls - INFO - Epoch [20][3000/5005] lr: 1.000e-01, eta: 9:51:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5087 2023-01-03 17:50:36,626 - mmcls - INFO - Epoch [20][3100/5005] lr: 1.000e-01, eta: 9:51:38, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5262 2023-01-03 17:50:45,257 - mmcls - INFO - Epoch [20][3200/5005] lr: 1.000e-01, eta: 9:51:28, time: 0.086, data_time: 0.040, memory: 1125, loss: 2.5332 2023-01-03 17:50:53,819 - mmcls - INFO - Epoch [20][3300/5005] lr: 1.000e-01, eta: 9:51:18, time: 0.086, data_time: 0.035, memory: 1125, loss: 2.5276 2023-01-03 17:51:02,504 - mmcls - INFO - Epoch [20][3400/5005] lr: 1.000e-01, eta: 9:51:09, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.5014 2023-01-03 17:51:11,260 - mmcls - INFO - Epoch [20][3500/5005] lr: 1.000e-01, eta: 9:51:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.5173 2023-01-03 17:51:20,470 - mmcls - INFO - Epoch [20][3600/5005] lr: 1.000e-01, eta: 9:50:53, time: 0.092, data_time: 0.003, memory: 1125, loss: 2.5208 2023-01-03 17:51:29,075 - mmcls - INFO - Epoch [20][3700/5005] lr: 1.000e-01, eta: 9:50:43, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.5268 2023-01-03 17:51:37,811 - mmcls - INFO - Epoch [20][3800/5005] lr: 1.000e-01, eta: 9:50:34, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.5313 2023-01-03 17:51:46,554 - mmcls - INFO - Epoch [20][3900/5005] lr: 1.000e-01, eta: 9:50:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4961 2023-01-03 17:51:55,291 - mmcls - INFO - Epoch [20][4000/5005] lr: 1.000e-01, eta: 9:50:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4862 2023-01-03 17:52:04,058 - mmcls - INFO - Epoch [20][4100/5005] lr: 1.000e-01, eta: 9:50:06, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.5202 2023-01-03 17:52:12,730 - mmcls - INFO - Epoch [20][4200/5005] lr: 1.000e-01, eta: 9:49:57, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5327 2023-01-03 17:52:21,275 - mmcls - INFO - Epoch [20][4300/5005] lr: 1.000e-01, eta: 9:49:47, time: 0.085, data_time: 0.000, memory: 1125, loss: 2.5290 2023-01-03 17:52:30,003 - mmcls - INFO - Epoch [20][4400/5005] lr: 1.000e-01, eta: 9:49:38, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5026 2023-01-03 17:52:38,657 - mmcls - INFO - Epoch [20][4500/5005] lr: 1.000e-01, eta: 9:49:28, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.5069 2023-01-03 17:52:47,543 - mmcls - INFO - Epoch [20][4600/5005] lr: 1.000e-01, eta: 9:49:20, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.5005 2023-01-03 17:52:56,450 - mmcls - INFO - Epoch [20][4700/5005] lr: 1.000e-01, eta: 9:49:11, time: 0.089, data_time: 0.029, memory: 1125, loss: 2.5160 2023-01-03 17:53:05,470 - mmcls - INFO - Epoch [20][4800/5005] lr: 1.000e-01, eta: 9:49:03, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5339 2023-01-03 17:53:14,461 - mmcls - INFO - Epoch [20][4900/5005] lr: 1.000e-01, eta: 9:48:55, time: 0.090, data_time: 0.015, memory: 1125, loss: 2.5054 2023-01-03 17:53:23,135 - mmcls - INFO - Epoch [20][5000/5005] lr: 1.000e-01, eta: 9:48:46, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.4840 2023-01-03 17:53:46,787 - mmcls - INFO - Epoch(val) [20][196] accuracy_top-1: 47.7040, accuracy_top-5: 74.1900 2023-01-03 17:53:57,801 - mmcls - INFO - Epoch [21][100/5005] lr: 1.000e-01, eta: 9:48:43, time: 0.110, data_time: 0.045, memory: 1125, loss: 2.4738 2023-01-03 17:54:06,634 - mmcls - INFO - Epoch [21][200/5005] lr: 1.000e-01, eta: 9:48:34, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4647 2023-01-03 17:54:15,360 - mmcls - INFO - Epoch [21][300/5005] lr: 1.000e-01, eta: 9:48:25, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4659 2023-01-03 17:54:24,155 - mmcls - INFO - Epoch [21][400/5005] lr: 1.000e-01, eta: 9:48:16, time: 0.088, data_time: 0.032, memory: 1125, loss: 2.4900 2023-01-03 17:54:32,873 - mmcls - INFO - Epoch [21][500/5005] lr: 1.000e-01, eta: 9:48:07, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.4758 2023-01-03 17:54:41,543 - mmcls - INFO - Epoch [21][600/5005] lr: 1.000e-01, eta: 9:47:58, time: 0.087, data_time: 0.028, memory: 1125, loss: 2.4613 2023-01-03 17:54:50,407 - mmcls - INFO - Epoch [21][700/5005] lr: 1.000e-01, eta: 9:47:49, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.4927 2023-01-03 17:54:59,027 - mmcls - INFO - Epoch [21][800/5005] lr: 1.000e-01, eta: 9:47:39, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4955 2023-01-03 17:55:07,753 - mmcls - INFO - Epoch [21][900/5005] lr: 1.000e-01, eta: 9:47:30, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4549 2023-01-03 17:55:17,476 - mmcls - INFO - Epoch [21][1000/5005] lr: 1.000e-01, eta: 9:47:25, time: 0.097, data_time: 0.003, memory: 1125, loss: 2.4975 2023-01-03 17:55:27,059 - mmcls - INFO - Epoch [21][1100/5005] lr: 1.000e-01, eta: 9:47:19, time: 0.096, data_time: 0.019, memory: 1125, loss: 2.5158 2023-01-03 17:55:35,938 - mmcls - INFO - Epoch [21][1200/5005] lr: 1.000e-01, eta: 9:47:10, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.4996 2023-01-03 17:55:44,788 - mmcls - INFO - Epoch [21][1300/5005] lr: 1.000e-01, eta: 9:47:02, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.5054 2023-01-03 17:55:53,397 - mmcls - INFO - Epoch [21][1400/5005] lr: 1.000e-01, eta: 9:46:52, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.5180 2023-01-03 17:56:02,288 - mmcls - INFO - Epoch [21][1500/5005] lr: 1.000e-01, eta: 9:46:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4792 2023-01-03 17:56:10,908 - mmcls - INFO - Epoch [21][1600/5005] lr: 1.000e-01, eta: 9:46:34, time: 0.086, data_time: 0.038, memory: 1125, loss: 2.4847 2023-01-03 17:56:19,786 - mmcls - INFO - Epoch [21][1700/5005] lr: 1.000e-01, eta: 9:46:25, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.4916 2023-01-03 17:56:28,699 - mmcls - INFO - Epoch [21][1800/5005] lr: 1.000e-01, eta: 9:46:17, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4921 2023-01-03 17:56:37,570 - mmcls - INFO - Epoch [21][1900/5005] lr: 1.000e-01, eta: 9:46:08, time: 0.089, data_time: 0.021, memory: 1125, loss: 2.5107 2023-01-03 17:56:46,260 - mmcls - INFO - Epoch [21][2000/5005] lr: 1.000e-01, eta: 9:45:59, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5145 2023-01-03 17:56:55,381 - mmcls - INFO - Epoch [21][2100/5005] lr: 1.000e-01, eta: 9:45:51, time: 0.091, data_time: 0.019, memory: 1125, loss: 2.5135 2023-01-03 17:57:04,205 - mmcls - INFO - Epoch [21][2200/5005] lr: 1.000e-01, eta: 9:45:42, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.5145 2023-01-03 17:57:13,096 - mmcls - INFO - Epoch [21][2300/5005] lr: 1.000e-01, eta: 9:45:34, time: 0.089, data_time: 0.035, memory: 1125, loss: 2.5043 2023-01-03 17:57:21,838 - mmcls - INFO - Epoch [21][2400/5005] lr: 1.000e-01, eta: 9:45:24, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.5251 2023-01-03 17:57:30,387 - mmcls - INFO - Epoch [21][2500/5005] lr: 1.000e-01, eta: 9:45:14, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.5300 2023-01-03 17:57:39,074 - mmcls - INFO - Epoch [21][2600/5005] lr: 1.000e-01, eta: 9:45:05, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.4991 2023-01-03 17:57:47,736 - mmcls - INFO - Epoch [21][2700/5005] lr: 1.000e-01, eta: 9:44:56, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.4946 2023-01-03 17:57:56,840 - mmcls - INFO - Epoch [21][2800/5005] lr: 1.000e-01, eta: 9:44:48, time: 0.091, data_time: 0.036, memory: 1125, loss: 2.5009 2023-01-03 17:58:05,361 - mmcls - INFO - Epoch [21][2900/5005] lr: 1.000e-01, eta: 9:44:38, time: 0.085, data_time: 0.026, memory: 1125, loss: 2.5092 2023-01-03 17:58:14,238 - mmcls - INFO - Epoch [21][3000/5005] lr: 1.000e-01, eta: 9:44:29, time: 0.089, data_time: 0.047, memory: 1125, loss: 2.4958 2023-01-03 17:58:22,717 - mmcls - INFO - Epoch [21][3100/5005] lr: 1.000e-01, eta: 9:44:19, time: 0.085, data_time: 0.025, memory: 1125, loss: 2.4936 2023-01-03 17:58:31,426 - mmcls - INFO - Epoch [21][3200/5005] lr: 1.000e-01, eta: 9:44:10, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.4906 2023-01-03 17:58:40,318 - mmcls - INFO - Epoch [21][3300/5005] lr: 1.000e-01, eta: 9:44:01, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.5112 2023-01-03 17:58:49,390 - mmcls - INFO - Epoch [21][3400/5005] lr: 1.000e-01, eta: 9:43:53, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.4957 2023-01-03 17:58:58,186 - mmcls - INFO - Epoch [21][3500/5005] lr: 1.000e-01, eta: 9:43:44, time: 0.088, data_time: 0.039, memory: 1125, loss: 2.5107 2023-01-03 17:59:06,829 - mmcls - INFO - Epoch [21][3600/5005] lr: 1.000e-01, eta: 9:43:35, time: 0.087, data_time: 0.037, memory: 1125, loss: 2.5142 2023-01-03 17:59:15,490 - mmcls - INFO - Epoch [21][3700/5005] lr: 1.000e-01, eta: 9:43:25, time: 0.087, data_time: 0.021, memory: 1125, loss: 2.5194 2023-01-03 17:59:24,557 - mmcls - INFO - Epoch [21][3800/5005] lr: 1.000e-01, eta: 9:43:18, time: 0.091, data_time: 0.028, memory: 1125, loss: 2.5152 2023-01-03 17:59:33,197 - mmcls - INFO - Epoch [21][3900/5005] lr: 1.000e-01, eta: 9:43:08, time: 0.086, data_time: 0.025, memory: 1125, loss: 2.5061 2023-01-03 17:59:41,871 - mmcls - INFO - Epoch [21][4000/5005] lr: 1.000e-01, eta: 9:42:59, time: 0.087, data_time: 0.025, memory: 1125, loss: 2.4794 2023-01-03 17:59:51,214 - mmcls - INFO - Epoch [21][4100/5005] lr: 1.000e-01, eta: 9:42:52, time: 0.093, data_time: 0.009, memory: 1125, loss: 2.4835 2023-01-03 17:59:59,727 - mmcls - INFO - Epoch [21][4200/5005] lr: 1.000e-01, eta: 9:42:42, time: 0.085, data_time: 0.019, memory: 1125, loss: 2.4850 2023-01-03 18:00:08,577 - mmcls - INFO - Epoch [21][4300/5005] lr: 1.000e-01, eta: 9:42:33, time: 0.089, data_time: 0.034, memory: 1125, loss: 2.5192 2023-01-03 18:00:17,271 - mmcls - INFO - Epoch [21][4400/5005] lr: 1.000e-01, eta: 9:42:24, time: 0.087, data_time: 0.044, memory: 1125, loss: 2.5108 2023-01-03 18:00:26,044 - mmcls - INFO - Epoch [21][4500/5005] lr: 1.000e-01, eta: 9:42:15, time: 0.088, data_time: 0.045, memory: 1125, loss: 2.5186 2023-01-03 18:00:35,022 - mmcls - INFO - Epoch [21][4600/5005] lr: 1.000e-01, eta: 9:42:06, time: 0.090, data_time: 0.027, memory: 1125, loss: 2.5093 2023-01-03 18:00:44,118 - mmcls - INFO - Epoch [21][4700/5005] lr: 1.000e-01, eta: 9:41:59, time: 0.091, data_time: 0.027, memory: 1125, loss: 2.5042 2023-01-03 18:00:53,210 - mmcls - INFO - Epoch [21][4800/5005] lr: 1.000e-01, eta: 9:41:51, time: 0.091, data_time: 0.006, memory: 1125, loss: 2.4921 2023-01-03 18:01:02,667 - mmcls - INFO - Epoch [21][4900/5005] lr: 1.000e-01, eta: 9:41:44, time: 0.095, data_time: 0.025, memory: 1125, loss: 2.5075 2023-01-03 18:01:11,469 - mmcls - INFO - Epoch [21][5000/5005] lr: 1.000e-01, eta: 9:41:35, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.5337 2023-01-03 18:01:34,729 - mmcls - INFO - Epoch(val) [21][196] accuracy_top-1: 46.5660, accuracy_top-5: 72.8020 2023-01-03 18:01:45,656 - mmcls - INFO - Epoch [22][100/5005] lr: 1.000e-01, eta: 9:41:32, time: 0.109, data_time: 0.047, memory: 1125, loss: 2.4889 2023-01-03 18:01:54,673 - mmcls - INFO - Epoch [22][200/5005] lr: 1.000e-01, eta: 9:41:24, time: 0.090, data_time: 0.040, memory: 1125, loss: 2.4466 2023-01-03 18:02:03,655 - mmcls - INFO - Epoch [22][300/5005] lr: 1.000e-01, eta: 9:41:16, time: 0.090, data_time: 0.013, memory: 1125, loss: 2.4409 2023-01-03 18:02:12,275 - mmcls - INFO - Epoch [22][400/5005] lr: 1.000e-01, eta: 9:41:06, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.4582 2023-01-03 18:02:21,218 - mmcls - INFO - Epoch [22][500/5005] lr: 1.000e-01, eta: 9:40:58, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.5021 2023-01-03 18:02:29,785 - mmcls - INFO - Epoch [22][600/5005] lr: 1.000e-01, eta: 9:40:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4575 2023-01-03 18:02:38,645 - mmcls - INFO - Epoch [22][700/5005] lr: 1.000e-01, eta: 9:40:39, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5022 2023-01-03 18:02:47,268 - mmcls - INFO - Epoch [22][800/5005] lr: 1.000e-01, eta: 9:40:30, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4639 2023-01-03 18:02:56,057 - mmcls - INFO - Epoch [22][900/5005] lr: 1.000e-01, eta: 9:40:21, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.4777 2023-01-03 18:03:04,671 - mmcls - INFO - Epoch [22][1000/5005] lr: 1.000e-01, eta: 9:40:11, time: 0.086, data_time: 0.028, memory: 1125, loss: 2.4578 2023-01-03 18:03:13,508 - mmcls - INFO - Epoch [22][1100/5005] lr: 1.000e-01, eta: 9:40:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4717 2023-01-03 18:03:22,076 - mmcls - INFO - Epoch [22][1200/5005] lr: 1.000e-01, eta: 9:39:53, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4639 2023-01-03 18:03:30,724 - mmcls - INFO - Epoch [22][1300/5005] lr: 1.000e-01, eta: 9:39:43, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4984 2023-01-03 18:03:39,552 - mmcls - INFO - Epoch [22][1400/5005] lr: 1.000e-01, eta: 9:39:34, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4698 2023-01-03 18:03:48,513 - mmcls - INFO - Epoch [22][1500/5005] lr: 1.000e-01, eta: 9:39:26, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.4773 2023-01-03 18:03:57,525 - mmcls - INFO - Epoch [22][1600/5005] lr: 1.000e-01, eta: 9:39:18, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.4726 2023-01-03 18:04:06,352 - mmcls - INFO - Epoch [22][1700/5005] lr: 1.000e-01, eta: 9:39:09, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5041 2023-01-03 18:04:15,132 - mmcls - INFO - Epoch [22][1800/5005] lr: 1.000e-01, eta: 9:39:00, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4931 2023-01-03 18:04:23,772 - mmcls - INFO - Epoch [22][1900/5005] lr: 1.000e-01, eta: 9:38:51, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.5117 2023-01-03 18:04:32,359 - mmcls - INFO - Epoch [22][2000/5005] lr: 1.000e-01, eta: 9:38:41, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.5255 2023-01-03 18:04:40,923 - mmcls - INFO - Epoch [22][2100/5005] lr: 1.000e-01, eta: 9:38:31, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4949 2023-01-03 18:04:49,514 - mmcls - INFO - Epoch [22][2200/5005] lr: 1.000e-01, eta: 9:38:21, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4868 2023-01-03 18:04:58,187 - mmcls - INFO - Epoch [22][2300/5005] lr: 1.000e-01, eta: 9:38:12, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5026 2023-01-03 18:05:07,018 - mmcls - INFO - Epoch [22][2400/5005] lr: 1.000e-01, eta: 9:38:03, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4806 2023-01-03 18:05:15,695 - mmcls - INFO - Epoch [22][2500/5005] lr: 1.000e-01, eta: 9:37:54, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.5253 2023-01-03 18:05:24,414 - mmcls - INFO - Epoch [22][2600/5005] lr: 1.000e-01, eta: 9:37:45, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.5046 2023-01-03 18:05:33,383 - mmcls - INFO - Epoch [22][2700/5005] lr: 1.000e-01, eta: 9:37:36, time: 0.090, data_time: 0.017, memory: 1125, loss: 2.5198 2023-01-03 18:05:41,913 - mmcls - INFO - Epoch [22][2800/5005] lr: 1.000e-01, eta: 9:37:26, time: 0.085, data_time: 0.006, memory: 1125, loss: 2.4883 2023-01-03 18:05:50,856 - mmcls - INFO - Epoch [22][2900/5005] lr: 1.000e-01, eta: 9:37:18, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5121 2023-01-03 18:05:59,741 - mmcls - INFO - Epoch [22][3000/5005] lr: 1.000e-01, eta: 9:37:09, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.5062 2023-01-03 18:06:08,319 - mmcls - INFO - Epoch [22][3100/5005] lr: 1.000e-01, eta: 9:37:00, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.4698 2023-01-03 18:06:16,927 - mmcls - INFO - Epoch [22][3200/5005] lr: 1.000e-01, eta: 9:36:50, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5288 2023-01-03 18:06:25,544 - mmcls - INFO - Epoch [22][3300/5005] lr: 1.000e-01, eta: 9:36:40, time: 0.086, data_time: 0.008, memory: 1125, loss: 2.5068 2023-01-03 18:06:34,198 - mmcls - INFO - Epoch [22][3400/5005] lr: 1.000e-01, eta: 9:36:31, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4945 2023-01-03 18:06:42,766 - mmcls - INFO - Epoch [22][3500/5005] lr: 1.000e-01, eta: 9:36:21, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.4979 2023-01-03 18:06:51,389 - mmcls - INFO - Epoch [22][3600/5005] lr: 1.000e-01, eta: 9:36:12, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.4692 2023-01-03 18:07:00,024 - mmcls - INFO - Epoch [22][3700/5005] lr: 1.000e-01, eta: 9:36:02, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.5201 2023-01-03 18:07:08,934 - mmcls - INFO - Epoch [22][3800/5005] lr: 1.000e-01, eta: 9:35:54, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.5015 2023-01-03 18:07:17,690 - mmcls - INFO - Epoch [22][3900/5005] lr: 1.000e-01, eta: 9:35:45, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.5203 2023-01-03 18:07:26,237 - mmcls - INFO - Epoch [22][4000/5005] lr: 1.000e-01, eta: 9:35:35, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.5030 2023-01-03 18:07:34,873 - mmcls - INFO - Epoch [22][4100/5005] lr: 1.000e-01, eta: 9:35:25, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.5259 2023-01-03 18:07:43,756 - mmcls - INFO - Epoch [22][4200/5005] lr: 1.000e-01, eta: 9:35:17, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.4722 2023-01-03 18:07:52,348 - mmcls - INFO - Epoch [22][4300/5005] lr: 1.000e-01, eta: 9:35:07, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5055 2023-01-03 18:08:00,996 - mmcls - INFO - Epoch [22][4400/5005] lr: 1.000e-01, eta: 9:34:58, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4837 2023-01-03 18:08:09,832 - mmcls - INFO - Epoch [22][4500/5005] lr: 1.000e-01, eta: 9:34:49, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.5145 2023-01-03 18:08:18,652 - mmcls - INFO - Epoch [22][4600/5005] lr: 1.000e-01, eta: 9:34:40, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.4822 2023-01-03 18:08:27,354 - mmcls - INFO - Epoch [22][4700/5005] lr: 1.000e-01, eta: 9:34:31, time: 0.087, data_time: 0.018, memory: 1125, loss: 2.5097 2023-01-03 18:08:36,462 - mmcls - INFO - Epoch [22][4800/5005] lr: 1.000e-01, eta: 9:34:23, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.5113 2023-01-03 18:08:45,597 - mmcls - INFO - Epoch [22][4900/5005] lr: 1.000e-01, eta: 9:34:15, time: 0.091, data_time: 0.009, memory: 1125, loss: 2.4939 2023-01-03 18:08:54,860 - mmcls - INFO - Epoch [22][5000/5005] lr: 1.000e-01, eta: 9:34:08, time: 0.093, data_time: 0.010, memory: 1125, loss: 2.4850 2023-01-03 18:09:18,130 - mmcls - INFO - Epoch(val) [22][196] accuracy_top-1: 49.0780, accuracy_top-5: 75.0660 2023-01-03 18:09:29,322 - mmcls - INFO - Epoch [23][100/5005] lr: 1.000e-01, eta: 9:34:05, time: 0.112, data_time: 0.065, memory: 1125, loss: 2.4451 2023-01-03 18:09:38,183 - mmcls - INFO - Epoch [23][200/5005] lr: 1.000e-01, eta: 9:33:57, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.4714 2023-01-03 18:09:47,022 - mmcls - INFO - Epoch [23][300/5005] lr: 1.000e-01, eta: 9:33:48, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4865 2023-01-03 18:09:55,673 - mmcls - INFO - Epoch [23][400/5005] lr: 1.000e-01, eta: 9:33:38, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4538 2023-01-03 18:10:04,295 - mmcls - INFO - Epoch [23][500/5005] lr: 1.000e-01, eta: 9:33:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.4925 2023-01-03 18:10:13,006 - mmcls - INFO - Epoch [23][600/5005] lr: 1.000e-01, eta: 9:33:20, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4754 2023-01-03 18:10:21,720 - mmcls - INFO - Epoch [23][700/5005] lr: 1.000e-01, eta: 9:33:10, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.4378 2023-01-03 18:10:30,377 - mmcls - INFO - Epoch [23][800/5005] lr: 1.000e-01, eta: 9:33:01, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.4526 2023-01-03 18:10:39,225 - mmcls - INFO - Epoch [23][900/5005] lr: 1.000e-01, eta: 9:32:52, time: 0.088, data_time: 0.018, memory: 1125, loss: 2.4916 2023-01-03 18:10:47,921 - mmcls - INFO - Epoch [23][1000/5005] lr: 1.000e-01, eta: 9:32:43, time: 0.087, data_time: 0.042, memory: 1125, loss: 2.4933 2023-01-03 18:10:56,858 - mmcls - INFO - Epoch [23][1100/5005] lr: 1.000e-01, eta: 9:32:35, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.4840 2023-01-03 18:11:05,802 - mmcls - INFO - Epoch [23][1200/5005] lr: 1.000e-01, eta: 9:32:26, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4670 2023-01-03 18:11:14,347 - mmcls - INFO - Epoch [23][1300/5005] lr: 1.000e-01, eta: 9:32:16, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.4863 2023-01-03 18:11:23,009 - mmcls - INFO - Epoch [23][1400/5005] lr: 1.000e-01, eta: 9:32:07, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4743 2023-01-03 18:11:31,798 - mmcls - INFO - Epoch [23][1500/5005] lr: 1.000e-01, eta: 9:31:58, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4854 2023-01-03 18:11:40,346 - mmcls - INFO - Epoch [23][1600/5005] lr: 1.000e-01, eta: 9:31:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4616 2023-01-03 18:11:48,849 - mmcls - INFO - Epoch [23][1700/5005] lr: 1.000e-01, eta: 9:31:38, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.5021 2023-01-03 18:11:57,719 - mmcls - INFO - Epoch [23][1800/5005] lr: 1.000e-01, eta: 9:31:30, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.4935 2023-01-03 18:12:06,269 - mmcls - INFO - Epoch [23][1900/5005] lr: 1.000e-01, eta: 9:31:20, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.4816 2023-01-03 18:12:15,011 - mmcls - INFO - Epoch [23][2000/5005] lr: 1.000e-01, eta: 9:31:11, time: 0.087, data_time: 0.039, memory: 1125, loss: 2.4590 2023-01-03 18:12:23,528 - mmcls - INFO - Epoch [23][2100/5005] lr: 1.000e-01, eta: 9:31:01, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.4875 2023-01-03 18:12:32,266 - mmcls - INFO - Epoch [23][2200/5005] lr: 1.000e-01, eta: 9:30:52, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4705 2023-01-03 18:12:40,931 - mmcls - INFO - Epoch [23][2300/5005] lr: 1.000e-01, eta: 9:30:42, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.4937 2023-01-03 18:12:49,514 - mmcls - INFO - Epoch [23][2400/5005] lr: 1.000e-01, eta: 9:30:33, time: 0.086, data_time: 0.027, memory: 1125, loss: 2.4882 2023-01-03 18:12:58,227 - mmcls - INFO - Epoch [23][2500/5005] lr: 1.000e-01, eta: 9:30:23, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.4973 2023-01-03 18:13:07,098 - mmcls - INFO - Epoch [23][2600/5005] lr: 1.000e-01, eta: 9:30:15, time: 0.089, data_time: 0.025, memory: 1125, loss: 2.4896 2023-01-03 18:13:16,126 - mmcls - INFO - Epoch [23][2700/5005] lr: 1.000e-01, eta: 9:30:07, time: 0.090, data_time: 0.005, memory: 1125, loss: 2.4777 2023-01-03 18:13:24,744 - mmcls - INFO - Epoch [23][2800/5005] lr: 1.000e-01, eta: 9:29:57, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.4789 2023-01-03 18:13:33,595 - mmcls - INFO - Epoch [23][2900/5005] lr: 1.000e-01, eta: 9:29:48, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.5164 2023-01-03 18:13:42,400 - mmcls - INFO - Epoch [23][3000/5005] lr: 1.000e-01, eta: 9:29:39, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4900 2023-01-03 18:13:51,137 - mmcls - INFO - Epoch [23][3100/5005] lr: 1.000e-01, eta: 9:29:30, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.4822 2023-01-03 18:13:59,637 - mmcls - INFO - Epoch [23][3200/5005] lr: 1.000e-01, eta: 9:29:20, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.5035 2023-01-03 18:14:08,808 - mmcls - INFO - Epoch [23][3300/5005] lr: 1.000e-01, eta: 9:29:13, time: 0.092, data_time: 0.010, memory: 1125, loss: 2.5184 2023-01-03 18:14:17,944 - mmcls - INFO - Epoch [23][3400/5005] lr: 1.000e-01, eta: 9:29:05, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.5107 2023-01-03 18:14:26,517 - mmcls - INFO - Epoch [23][3500/5005] lr: 1.000e-01, eta: 9:28:55, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.5056 2023-01-03 18:14:35,516 - mmcls - INFO - Epoch [23][3600/5005] lr: 1.000e-01, eta: 9:28:47, time: 0.090, data_time: 0.033, memory: 1125, loss: 2.4482 2023-01-03 18:14:44,523 - mmcls - INFO - Epoch [23][3700/5005] lr: 1.000e-01, eta: 9:28:39, time: 0.090, data_time: 0.012, memory: 1125, loss: 2.5066 2023-01-03 18:14:53,140 - mmcls - INFO - Epoch [23][3800/5005] lr: 1.000e-01, eta: 9:28:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.5036 2023-01-03 18:15:01,602 - mmcls - INFO - Epoch [23][3900/5005] lr: 1.000e-01, eta: 9:28:19, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.4802 2023-01-03 18:15:10,501 - mmcls - INFO - Epoch [23][4000/5005] lr: 1.000e-01, eta: 9:28:11, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.4868 2023-01-03 18:15:19,064 - mmcls - INFO - Epoch [23][4100/5005] lr: 1.000e-01, eta: 9:28:01, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.5075 2023-01-03 18:15:28,059 - mmcls - INFO - Epoch [23][4200/5005] lr: 1.000e-01, eta: 9:27:53, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.5320 2023-01-03 18:15:36,647 - mmcls - INFO - Epoch [23][4300/5005] lr: 1.000e-01, eta: 9:27:43, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.4966 2023-01-03 18:15:45,410 - mmcls - INFO - Epoch [23][4400/5005] lr: 1.000e-01, eta: 9:27:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4830 2023-01-03 18:15:54,593 - mmcls - INFO - Epoch [23][4500/5005] lr: 1.000e-01, eta: 9:27:27, time: 0.092, data_time: 0.006, memory: 1125, loss: 2.4827 2023-01-03 18:16:03,386 - mmcls - INFO - Epoch [23][4600/5005] lr: 1.000e-01, eta: 9:27:18, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4820 2023-01-03 18:16:12,589 - mmcls - INFO - Epoch [23][4700/5005] lr: 1.000e-01, eta: 9:27:10, time: 0.092, data_time: 0.004, memory: 1125, loss: 2.5341 2023-01-03 18:16:21,061 - mmcls - INFO - Epoch [23][4800/5005] lr: 1.000e-01, eta: 9:27:00, time: 0.085, data_time: 0.024, memory: 1125, loss: 2.5038 2023-01-03 18:16:29,657 - mmcls - INFO - Epoch [23][4900/5005] lr: 1.000e-01, eta: 9:26:50, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.5028 2023-01-03 18:16:38,611 - mmcls - INFO - Epoch [23][5000/5005] lr: 1.000e-01, eta: 9:26:42, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.4934 2023-01-03 18:17:01,470 - mmcls - INFO - Epoch(val) [23][196] accuracy_top-1: 49.7680, accuracy_top-5: 75.2340 2023-01-03 18:17:12,633 - mmcls - INFO - Epoch [24][100/5005] lr: 1.000e-01, eta: 9:26:39, time: 0.111, data_time: 0.045, memory: 1125, loss: 2.4585 2023-01-03 18:17:21,542 - mmcls - INFO - Epoch [24][200/5005] lr: 1.000e-01, eta: 9:26:31, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4244 2023-01-03 18:17:30,336 - mmcls - INFO - Epoch [24][300/5005] lr: 1.000e-01, eta: 9:26:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4720 2023-01-03 18:17:39,271 - mmcls - INFO - Epoch [24][400/5005] lr: 1.000e-01, eta: 9:26:13, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4548 2023-01-03 18:17:47,925 - mmcls - INFO - Epoch [24][500/5005] lr: 1.000e-01, eta: 9:26:04, time: 0.087, data_time: 0.034, memory: 1125, loss: 2.4624 2023-01-03 18:17:56,606 - mmcls - INFO - Epoch [24][600/5005] lr: 1.000e-01, eta: 9:25:54, time: 0.087, data_time: 0.022, memory: 1125, loss: 2.4110 2023-01-03 18:18:05,426 - mmcls - INFO - Epoch [24][700/5005] lr: 1.000e-01, eta: 9:25:46, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4599 2023-01-03 18:18:13,915 - mmcls - INFO - Epoch [24][800/5005] lr: 1.000e-01, eta: 9:25:36, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.4388 2023-01-03 18:18:22,620 - mmcls - INFO - Epoch [24][900/5005] lr: 1.000e-01, eta: 9:25:26, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.4467 2023-01-03 18:18:31,437 - mmcls - INFO - Epoch [24][1000/5005] lr: 1.000e-01, eta: 9:25:18, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.4785 2023-01-03 18:18:40,342 - mmcls - INFO - Epoch [24][1100/5005] lr: 1.000e-01, eta: 9:25:09, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.4775 2023-01-03 18:18:49,503 - mmcls - INFO - Epoch [24][1200/5005] lr: 1.000e-01, eta: 9:25:01, time: 0.092, data_time: 0.029, memory: 1125, loss: 2.4680 2023-01-03 18:18:58,350 - mmcls - INFO - Epoch [24][1300/5005] lr: 1.000e-01, eta: 9:24:53, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4861 2023-01-03 18:19:06,968 - mmcls - INFO - Epoch [24][1400/5005] lr: 1.000e-01, eta: 9:24:43, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4482 2023-01-03 18:19:16,325 - mmcls - INFO - Epoch [24][1500/5005] lr: 1.000e-01, eta: 9:24:36, time: 0.094, data_time: 0.001, memory: 1125, loss: 2.4902 2023-01-03 18:19:25,360 - mmcls - INFO - Epoch [24][1600/5005] lr: 1.000e-01, eta: 9:24:28, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.4945 2023-01-03 18:19:33,878 - mmcls - INFO - Epoch [24][1700/5005] lr: 1.000e-01, eta: 9:24:18, time: 0.085, data_time: 0.041, memory: 1125, loss: 2.4742 2023-01-03 18:19:42,670 - mmcls - INFO - Epoch [24][1800/5005] lr: 1.000e-01, eta: 9:24:09, time: 0.088, data_time: 0.017, memory: 1125, loss: 2.4624 2023-01-03 18:19:51,703 - mmcls - INFO - Epoch [24][1900/5005] lr: 1.000e-01, eta: 9:24:01, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.5059 2023-01-03 18:20:00,186 - mmcls - INFO - Epoch [24][2000/5005] lr: 1.000e-01, eta: 9:23:51, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.4887 2023-01-03 18:20:09,026 - mmcls - INFO - Epoch [24][2100/5005] lr: 1.000e-01, eta: 9:23:42, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4851 2023-01-03 18:20:17,446 - mmcls - INFO - Epoch [24][2200/5005] lr: 1.000e-01, eta: 9:23:32, time: 0.084, data_time: 0.015, memory: 1125, loss: 2.4963 2023-01-03 18:20:26,122 - mmcls - INFO - Epoch [24][2300/5005] lr: 1.000e-01, eta: 9:23:23, time: 0.087, data_time: 0.034, memory: 1125, loss: 2.4598 2023-01-03 18:20:34,848 - mmcls - INFO - Epoch [24][2400/5005] lr: 1.000e-01, eta: 9:23:14, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.4823 2023-01-03 18:20:43,394 - mmcls - INFO - Epoch [24][2500/5005] lr: 1.000e-01, eta: 9:23:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4856 2023-01-03 18:20:52,121 - mmcls - INFO - Epoch [24][2600/5005] lr: 1.000e-01, eta: 9:22:55, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4674 2023-01-03 18:21:00,730 - mmcls - INFO - Epoch [24][2700/5005] lr: 1.000e-01, eta: 9:22:45, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.5126 2023-01-03 18:21:09,716 - mmcls - INFO - Epoch [24][2800/5005] lr: 1.000e-01, eta: 9:22:37, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.5032 2023-01-03 18:21:18,670 - mmcls - INFO - Epoch [24][2900/5005] lr: 1.000e-01, eta: 9:22:28, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.4873 2023-01-03 18:21:27,332 - mmcls - INFO - Epoch [24][3000/5005] lr: 1.000e-01, eta: 9:22:19, time: 0.087, data_time: 0.014, memory: 1125, loss: 2.4590 2023-01-03 18:21:35,850 - mmcls - INFO - Epoch [24][3100/5005] lr: 1.000e-01, eta: 9:22:09, time: 0.085, data_time: 0.005, memory: 1125, loss: 2.5078 2023-01-03 18:21:44,319 - mmcls - INFO - Epoch [24][3200/5005] lr: 1.000e-01, eta: 9:21:59, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.5089 2023-01-03 18:21:52,801 - mmcls - INFO - Epoch [24][3300/5005] lr: 1.000e-01, eta: 9:21:49, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.4903 2023-01-03 18:22:01,435 - mmcls - INFO - Epoch [24][3400/5005] lr: 1.000e-01, eta: 9:21:40, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4787 2023-01-03 18:22:10,104 - mmcls - INFO - Epoch [24][3500/5005] lr: 1.000e-01, eta: 9:21:31, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4839 2023-01-03 18:22:18,831 - mmcls - INFO - Epoch [24][3600/5005] lr: 1.000e-01, eta: 9:21:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4906 2023-01-03 18:22:27,905 - mmcls - INFO - Epoch [24][3700/5005] lr: 1.000e-01, eta: 9:21:14, time: 0.091, data_time: 0.022, memory: 1125, loss: 2.5108 2023-01-03 18:22:36,390 - mmcls - INFO - Epoch [24][3800/5005] lr: 1.000e-01, eta: 9:21:04, time: 0.085, data_time: 0.010, memory: 1125, loss: 2.4799 2023-01-03 18:22:45,190 - mmcls - INFO - Epoch [24][3900/5005] lr: 1.000e-01, eta: 9:20:55, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.4888 2023-01-03 18:22:54,026 - mmcls - INFO - Epoch [24][4000/5005] lr: 1.000e-01, eta: 9:20:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5014 2023-01-03 18:23:02,596 - mmcls - INFO - Epoch [24][4100/5005] lr: 1.000e-01, eta: 9:20:36, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4844 2023-01-03 18:23:11,154 - mmcls - INFO - Epoch [24][4200/5005] lr: 1.000e-01, eta: 9:20:27, time: 0.086, data_time: 0.040, memory: 1125, loss: 2.4890 2023-01-03 18:23:19,868 - mmcls - INFO - Epoch [24][4300/5005] lr: 1.000e-01, eta: 9:20:17, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.5042 2023-01-03 18:23:28,696 - mmcls - INFO - Epoch [24][4400/5005] lr: 1.000e-01, eta: 9:20:09, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.4625 2023-01-03 18:23:37,401 - mmcls - INFO - Epoch [24][4500/5005] lr: 1.000e-01, eta: 9:19:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.5186 2023-01-03 18:23:45,991 - mmcls - INFO - Epoch [24][4600/5005] lr: 1.000e-01, eta: 9:19:50, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4956 2023-01-03 18:23:54,718 - mmcls - INFO - Epoch [24][4700/5005] lr: 1.000e-01, eta: 9:19:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.4955 2023-01-03 18:24:03,449 - mmcls - INFO - Epoch [24][4800/5005] lr: 1.000e-01, eta: 9:19:32, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.5144 2023-01-03 18:24:12,276 - mmcls - INFO - Epoch [24][4900/5005] lr: 1.000e-01, eta: 9:19:23, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4792 2023-01-03 18:24:21,149 - mmcls - INFO - Epoch [24][5000/5005] lr: 1.000e-01, eta: 9:19:14, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.4988 2023-01-03 18:24:43,953 - mmcls - INFO - Epoch(val) [24][196] accuracy_top-1: 49.7260, accuracy_top-5: 75.3100 2023-01-03 18:24:54,898 - mmcls - INFO - Epoch [25][100/5005] lr: 1.000e-01, eta: 9:19:10, time: 0.109, data_time: 0.044, memory: 1125, loss: 2.4409 2023-01-03 18:25:03,508 - mmcls - INFO - Epoch [25][200/5005] lr: 1.000e-01, eta: 9:19:01, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.4468 2023-01-03 18:25:12,078 - mmcls - INFO - Epoch [25][300/5005] lr: 1.000e-01, eta: 9:18:51, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4567 2023-01-03 18:25:20,819 - mmcls - INFO - Epoch [25][400/5005] lr: 1.000e-01, eta: 9:18:42, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.4392 2023-01-03 18:25:29,601 - mmcls - INFO - Epoch [25][500/5005] lr: 1.000e-01, eta: 9:18:33, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.4403 2023-01-03 18:25:38,173 - mmcls - INFO - Epoch [25][600/5005] lr: 1.000e-01, eta: 9:18:23, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4658 2023-01-03 18:25:46,821 - mmcls - INFO - Epoch [25][700/5005] lr: 1.000e-01, eta: 9:18:14, time: 0.086, data_time: 0.026, memory: 1125, loss: 2.4490 2023-01-03 18:25:55,495 - mmcls - INFO - Epoch [25][800/5005] lr: 1.000e-01, eta: 9:18:05, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.4378 2023-01-03 18:26:04,312 - mmcls - INFO - Epoch [25][900/5005] lr: 1.000e-01, eta: 9:17:56, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4507 2023-01-03 18:26:13,035 - mmcls - INFO - Epoch [25][1000/5005] lr: 1.000e-01, eta: 9:17:47, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4547 2023-01-03 18:26:21,725 - mmcls - INFO - Epoch [25][1100/5005] lr: 1.000e-01, eta: 9:17:38, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4724 2023-01-03 18:26:30,653 - mmcls - INFO - Epoch [25][1200/5005] lr: 1.000e-01, eta: 9:17:29, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.5059 2023-01-03 18:26:39,310 - mmcls - INFO - Epoch [25][1300/5005] lr: 1.000e-01, eta: 9:17:20, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4559 2023-01-03 18:26:47,722 - mmcls - INFO - Epoch [25][1400/5005] lr: 1.000e-01, eta: 9:17:10, time: 0.084, data_time: 0.021, memory: 1125, loss: 2.4552 2023-01-03 18:26:56,614 - mmcls - INFO - Epoch [25][1500/5005] lr: 1.000e-01, eta: 9:17:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4475 2023-01-03 18:27:05,450 - mmcls - INFO - Epoch [25][1600/5005] lr: 1.000e-01, eta: 9:16:52, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.4475 2023-01-03 18:27:14,138 - mmcls - INFO - Epoch [25][1700/5005] lr: 1.000e-01, eta: 9:16:43, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.4761 2023-01-03 18:27:22,686 - mmcls - INFO - Epoch [25][1800/5005] lr: 1.000e-01, eta: 9:16:33, time: 0.085, data_time: 0.004, memory: 1125, loss: 2.4815 2023-01-03 18:27:31,563 - mmcls - INFO - Epoch [25][1900/5005] lr: 1.000e-01, eta: 9:16:25, time: 0.089, data_time: 0.022, memory: 1125, loss: 2.4796 2023-01-03 18:27:40,210 - mmcls - INFO - Epoch [25][2000/5005] lr: 1.000e-01, eta: 9:16:15, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4854 2023-01-03 18:27:48,873 - mmcls - INFO - Epoch [25][2100/5005] lr: 1.000e-01, eta: 9:16:06, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.4829 2023-01-03 18:27:57,509 - mmcls - INFO - Epoch [25][2200/5005] lr: 1.000e-01, eta: 9:15:57, time: 0.086, data_time: 0.021, memory: 1125, loss: 2.4813 2023-01-03 18:28:06,056 - mmcls - INFO - Epoch [25][2300/5005] lr: 1.000e-01, eta: 9:15:47, time: 0.085, data_time: 0.017, memory: 1125, loss: 2.4901 2023-01-03 18:28:14,873 - mmcls - INFO - Epoch [25][2400/5005] lr: 1.000e-01, eta: 9:15:38, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.4609 2023-01-03 18:28:23,933 - mmcls - INFO - Epoch [25][2500/5005] lr: 1.000e-01, eta: 9:15:30, time: 0.091, data_time: 0.020, memory: 1125, loss: 2.4790 2023-01-03 18:28:32,698 - mmcls - INFO - Epoch [25][2600/5005] lr: 1.000e-01, eta: 9:15:21, time: 0.088, data_time: 0.035, memory: 1125, loss: 2.4719 2023-01-03 18:28:41,333 - mmcls - INFO - Epoch [25][2700/5005] lr: 1.000e-01, eta: 9:15:12, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.4874 2023-01-03 18:28:50,232 - mmcls - INFO - Epoch [25][2800/5005] lr: 1.000e-01, eta: 9:15:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4687 2023-01-03 18:28:59,040 - mmcls - INFO - Epoch [25][2900/5005] lr: 1.000e-01, eta: 9:14:54, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.4741 2023-01-03 18:29:07,567 - mmcls - INFO - Epoch [25][3000/5005] lr: 1.000e-01, eta: 9:14:44, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.4804 2023-01-03 18:29:16,225 - mmcls - INFO - Epoch [25][3100/5005] lr: 1.000e-01, eta: 9:14:35, time: 0.087, data_time: 0.019, memory: 1125, loss: 2.4741 2023-01-03 18:29:24,798 - mmcls - INFO - Epoch [25][3200/5005] lr: 1.000e-01, eta: 9:14:26, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4782 2023-01-03 18:29:33,757 - mmcls - INFO - Epoch [25][3300/5005] lr: 1.000e-01, eta: 9:14:17, time: 0.090, data_time: 0.011, memory: 1125, loss: 2.4937 2023-01-03 18:29:42,590 - mmcls - INFO - Epoch [25][3400/5005] lr: 1.000e-01, eta: 9:14:08, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4843 2023-01-03 18:29:51,386 - mmcls - INFO - Epoch [25][3500/5005] lr: 1.000e-01, eta: 9:13:59, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.4755 2023-01-03 18:30:00,086 - mmcls - INFO - Epoch [25][3600/5005] lr: 1.000e-01, eta: 9:13:50, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4868 2023-01-03 18:30:08,665 - mmcls - INFO - Epoch [25][3700/5005] lr: 1.000e-01, eta: 9:13:41, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.4860 2023-01-03 18:30:17,401 - mmcls - INFO - Epoch [25][3800/5005] lr: 1.000e-01, eta: 9:13:32, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.4749 2023-01-03 18:30:26,188 - mmcls - INFO - Epoch [25][3900/5005] lr: 1.000e-01, eta: 9:13:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4644 2023-01-03 18:30:35,095 - mmcls - INFO - Epoch [25][4000/5005] lr: 1.000e-01, eta: 9:13:14, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.5005 2023-01-03 18:30:43,662 - mmcls - INFO - Epoch [25][4100/5005] lr: 1.000e-01, eta: 9:13:05, time: 0.086, data_time: 0.013, memory: 1125, loss: 2.4765 2023-01-03 18:30:52,310 - mmcls - INFO - Epoch [25][4200/5005] lr: 1.000e-01, eta: 9:12:55, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.4845 2023-01-03 18:31:00,969 - mmcls - INFO - Epoch [25][4300/5005] lr: 1.000e-01, eta: 9:12:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4960 2023-01-03 18:31:09,803 - mmcls - INFO - Epoch [25][4400/5005] lr: 1.000e-01, eta: 9:12:37, time: 0.088, data_time: 0.025, memory: 1125, loss: 2.4648 2023-01-03 18:31:18,881 - mmcls - INFO - Epoch [25][4500/5005] lr: 1.000e-01, eta: 9:12:29, time: 0.091, data_time: 0.040, memory: 1125, loss: 2.4740 2023-01-03 18:31:27,594 - mmcls - INFO - Epoch [25][4600/5005] lr: 1.000e-01, eta: 9:12:20, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4863 2023-01-03 18:31:36,477 - mmcls - INFO - Epoch [25][4700/5005] lr: 1.000e-01, eta: 9:12:11, time: 0.089, data_time: 0.018, memory: 1125, loss: 2.5008 2023-01-03 18:31:45,234 - mmcls - INFO - Epoch [25][4800/5005] lr: 1.000e-01, eta: 9:12:02, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.5047 2023-01-03 18:31:53,699 - mmcls - INFO - Epoch [25][4900/5005] lr: 1.000e-01, eta: 9:11:52, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.4934 2023-01-03 18:32:02,451 - mmcls - INFO - Epoch [25][5000/5005] lr: 1.000e-01, eta: 9:11:44, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4675 2023-01-03 18:32:25,037 - mmcls - INFO - Epoch(val) [25][196] accuracy_top-1: 49.6840, accuracy_top-5: 75.4460 2023-01-03 18:32:36,096 - mmcls - INFO - Epoch [26][100/5005] lr: 1.000e-01, eta: 9:11:40, time: 0.110, data_time: 0.037, memory: 1125, loss: 2.4666 2023-01-03 18:32:44,750 - mmcls - INFO - Epoch [26][200/5005] lr: 1.000e-01, eta: 9:11:30, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4385 2023-01-03 18:32:53,436 - mmcls - INFO - Epoch [26][300/5005] lr: 1.000e-01, eta: 9:11:21, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4471 2023-01-03 18:33:02,052 - mmcls - INFO - Epoch [26][400/5005] lr: 1.000e-01, eta: 9:11:12, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.4207 2023-01-03 18:33:10,850 - mmcls - INFO - Epoch [26][500/5005] lr: 1.000e-01, eta: 9:11:03, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.4489 2023-01-03 18:33:19,697 - mmcls - INFO - Epoch [26][600/5005] lr: 1.000e-01, eta: 9:10:54, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4251 2023-01-03 18:33:28,494 - mmcls - INFO - Epoch [26][700/5005] lr: 1.000e-01, eta: 9:10:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 2.4675 2023-01-03 18:33:37,476 - mmcls - INFO - Epoch [26][800/5005] lr: 1.000e-01, eta: 9:10:37, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.4434 2023-01-03 18:33:46,426 - mmcls - INFO - Epoch [26][900/5005] lr: 1.000e-01, eta: 9:10:28, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.4636 2023-01-03 18:33:55,492 - mmcls - INFO - Epoch [26][1000/5005] lr: 1.000e-01, eta: 9:10:20, time: 0.091, data_time: 0.007, memory: 1125, loss: 2.4489 2023-01-03 18:34:04,350 - mmcls - INFO - Epoch [26][1100/5005] lr: 1.000e-01, eta: 9:10:11, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4594 2023-01-03 18:34:13,019 - mmcls - INFO - Epoch [26][1200/5005] lr: 1.000e-01, eta: 9:10:02, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4572 2023-01-03 18:34:21,787 - mmcls - INFO - Epoch [26][1300/5005] lr: 1.000e-01, eta: 9:09:53, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.4605 2023-01-03 18:34:30,522 - mmcls - INFO - Epoch [26][1400/5005] lr: 1.000e-01, eta: 9:09:44, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4730 2023-01-03 18:34:39,612 - mmcls - INFO - Epoch [26][1500/5005] lr: 1.000e-01, eta: 9:09:36, time: 0.091, data_time: 0.023, memory: 1125, loss: 2.4736 2023-01-03 18:34:48,455 - mmcls - INFO - Epoch [26][1600/5005] lr: 1.000e-01, eta: 9:09:27, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.4542 2023-01-03 18:34:57,159 - mmcls - INFO - Epoch [26][1700/5005] lr: 1.000e-01, eta: 9:09:18, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4880 2023-01-03 18:35:06,213 - mmcls - INFO - Epoch [26][1800/5005] lr: 1.000e-01, eta: 9:09:10, time: 0.091, data_time: 0.021, memory: 1125, loss: 2.4768 2023-01-03 18:35:15,126 - mmcls - INFO - Epoch [26][1900/5005] lr: 1.000e-01, eta: 9:09:02, time: 0.089, data_time: 0.016, memory: 1125, loss: 2.4428 2023-01-03 18:35:24,299 - mmcls - INFO - Epoch [26][2000/5005] lr: 1.000e-01, eta: 9:08:54, time: 0.092, data_time: 0.008, memory: 1125, loss: 2.5018 2023-01-03 18:35:32,963 - mmcls - INFO - Epoch [26][2100/5005] lr: 1.000e-01, eta: 9:08:44, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.4821 2023-01-03 18:35:41,667 - mmcls - INFO - Epoch [26][2200/5005] lr: 1.000e-01, eta: 9:08:35, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4727 2023-01-03 18:35:50,485 - mmcls - INFO - Epoch [26][2300/5005] lr: 1.000e-01, eta: 9:08:26, time: 0.088, data_time: 0.007, memory: 1125, loss: 2.4845 2023-01-03 18:35:59,087 - mmcls - INFO - Epoch [26][2400/5005] lr: 1.000e-01, eta: 9:08:17, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4747 2023-01-03 18:36:07,724 - mmcls - INFO - Epoch [26][2500/5005] lr: 1.000e-01, eta: 9:08:08, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4614 2023-01-03 18:36:16,424 - mmcls - INFO - Epoch [26][2600/5005] lr: 1.000e-01, eta: 9:07:58, time: 0.087, data_time: 0.005, memory: 1125, loss: 2.4568 2023-01-03 18:36:25,197 - mmcls - INFO - Epoch [26][2700/5005] lr: 1.000e-01, eta: 9:07:49, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.4834 2023-01-03 18:36:33,714 - mmcls - INFO - Epoch [26][2800/5005] lr: 1.000e-01, eta: 9:07:40, time: 0.085, data_time: 0.031, memory: 1125, loss: 2.4690 2023-01-03 18:36:42,631 - mmcls - INFO - Epoch [26][2900/5005] lr: 1.000e-01, eta: 9:07:31, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4640 2023-01-03 18:36:51,518 - mmcls - INFO - Epoch [26][3000/5005] lr: 1.000e-01, eta: 9:07:23, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4611 2023-01-03 18:37:00,147 - mmcls - INFO - Epoch [26][3100/5005] lr: 1.000e-01, eta: 9:07:13, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.5033 2023-01-03 18:37:08,733 - mmcls - INFO - Epoch [26][3200/5005] lr: 1.000e-01, eta: 9:07:04, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4669 2023-01-03 18:37:17,336 - mmcls - INFO - Epoch [26][3300/5005] lr: 1.000e-01, eta: 9:06:54, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4741 2023-01-03 18:37:25,780 - mmcls - INFO - Epoch [26][3400/5005] lr: 1.000e-01, eta: 9:06:44, time: 0.084, data_time: 0.005, memory: 1125, loss: 2.5001 2023-01-03 18:37:34,641 - mmcls - INFO - Epoch [26][3500/5005] lr: 1.000e-01, eta: 9:06:36, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.4983 2023-01-03 18:37:43,247 - mmcls - INFO - Epoch [26][3600/5005] lr: 1.000e-01, eta: 9:06:26, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4783 2023-01-03 18:37:51,834 - mmcls - INFO - Epoch [26][3700/5005] lr: 1.000e-01, eta: 9:06:17, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4742 2023-01-03 18:38:00,671 - mmcls - INFO - Epoch [26][3800/5005] lr: 1.000e-01, eta: 9:06:08, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.4713 2023-01-03 18:38:09,232 - mmcls - INFO - Epoch [26][3900/5005] lr: 1.000e-01, eta: 9:05:58, time: 0.086, data_time: 0.009, memory: 1125, loss: 2.4846 2023-01-03 18:38:18,106 - mmcls - INFO - Epoch [26][4000/5005] lr: 1.000e-01, eta: 9:05:50, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4938 2023-01-03 18:38:27,024 - mmcls - INFO - Epoch [26][4100/5005] lr: 1.000e-01, eta: 9:05:41, time: 0.089, data_time: 0.027, memory: 1125, loss: 2.4846 2023-01-03 18:38:35,617 - mmcls - INFO - Epoch [26][4200/5005] lr: 1.000e-01, eta: 9:05:32, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.4601 2023-01-03 18:38:44,181 - mmcls - INFO - Epoch [26][4300/5005] lr: 1.000e-01, eta: 9:05:22, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.4395 2023-01-03 18:38:52,747 - mmcls - INFO - Epoch [26][4400/5005] lr: 1.000e-01, eta: 9:05:13, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.4701 2023-01-03 18:39:01,589 - mmcls - INFO - Epoch [26][4500/5005] lr: 1.000e-01, eta: 9:05:04, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4958 2023-01-03 18:39:10,281 - mmcls - INFO - Epoch [26][4600/5005] lr: 1.000e-01, eta: 9:04:55, time: 0.087, data_time: 0.028, memory: 1125, loss: 2.5130 2023-01-03 18:39:18,950 - mmcls - INFO - Epoch [26][4700/5005] lr: 1.000e-01, eta: 9:04:45, time: 0.087, data_time: 0.024, memory: 1125, loss: 2.5025 2023-01-03 18:39:27,589 - mmcls - INFO - Epoch [26][4800/5005] lr: 1.000e-01, eta: 9:04:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4855 2023-01-03 18:39:36,186 - mmcls - INFO - Epoch [26][4900/5005] lr: 1.000e-01, eta: 9:04:27, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.4833 2023-01-03 18:39:44,650 - mmcls - INFO - Epoch [26][5000/5005] lr: 1.000e-01, eta: 9:04:17, time: 0.085, data_time: 0.008, memory: 1125, loss: 2.4745 2023-01-03 18:40:07,128 - mmcls - INFO - Epoch(val) [26][196] accuracy_top-1: 49.6120, accuracy_top-5: 75.5400 2023-01-03 18:40:17,924 - mmcls - INFO - Epoch [27][100/5005] lr: 1.000e-01, eta: 9:04:12, time: 0.108, data_time: 0.041, memory: 1125, loss: 2.4060 2023-01-03 18:40:26,529 - mmcls - INFO - Epoch [27][200/5005] lr: 1.000e-01, eta: 9:04:03, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.4177 2023-01-03 18:40:35,281 - mmcls - INFO - Epoch [27][300/5005] lr: 1.000e-01, eta: 9:03:53, time: 0.087, data_time: 0.048, memory: 1125, loss: 2.4497 2023-01-03 18:40:43,910 - mmcls - INFO - Epoch [27][400/5005] lr: 1.000e-01, eta: 9:03:44, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.4486 2023-01-03 18:40:52,685 - mmcls - INFO - Epoch [27][500/5005] lr: 1.000e-01, eta: 9:03:35, time: 0.088, data_time: 0.011, memory: 1125, loss: 2.4565 2023-01-03 18:41:01,599 - mmcls - INFO - Epoch [27][600/5005] lr: 1.000e-01, eta: 9:03:27, time: 0.089, data_time: 0.003, memory: 1125, loss: 2.4610 2023-01-03 18:41:10,265 - mmcls - INFO - Epoch [27][700/5005] lr: 1.000e-01, eta: 9:03:17, time: 0.087, data_time: 0.017, memory: 1125, loss: 2.4651 2023-01-03 18:41:19,022 - mmcls - INFO - Epoch [27][800/5005] lr: 1.000e-01, eta: 9:03:08, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.4214 2023-01-03 18:41:27,603 - mmcls - INFO - Epoch [27][900/5005] lr: 1.000e-01, eta: 9:02:59, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4570 2023-01-03 18:41:36,229 - mmcls - INFO - Epoch [27][1000/5005] lr: 1.000e-01, eta: 9:02:50, time: 0.086, data_time: 0.002, memory: 1125, loss: 2.4565 2023-01-03 18:41:45,076 - mmcls - INFO - Epoch [27][1100/5005] lr: 1.000e-01, eta: 9:02:41, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.4708 2023-01-03 18:41:54,195 - mmcls - INFO - Epoch [27][1200/5005] lr: 1.000e-01, eta: 9:02:33, time: 0.091, data_time: 0.003, memory: 1125, loss: 2.4845 2023-01-03 18:42:02,899 - mmcls - INFO - Epoch [27][1300/5005] lr: 1.000e-01, eta: 9:02:24, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4743 2023-01-03 18:42:11,583 - mmcls - INFO - Epoch [27][1400/5005] lr: 1.000e-01, eta: 9:02:15, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4677 2023-01-03 18:42:20,314 - mmcls - INFO - Epoch [27][1500/5005] lr: 1.000e-01, eta: 9:02:05, time: 0.087, data_time: 0.036, memory: 1125, loss: 2.4613 2023-01-03 18:42:28,974 - mmcls - INFO - Epoch [27][1600/5005] lr: 1.000e-01, eta: 9:01:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.4686 2023-01-03 18:42:37,745 - mmcls - INFO - Epoch [27][1700/5005] lr: 1.000e-01, eta: 9:01:47, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.4536 2023-01-03 18:42:46,397 - mmcls - INFO - Epoch [27][1800/5005] lr: 1.000e-01, eta: 9:01:38, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.4606 2023-01-03 18:42:55,292 - mmcls - INFO - Epoch [27][1900/5005] lr: 1.000e-01, eta: 9:01:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4851 2023-01-03 18:43:03,971 - mmcls - INFO - Epoch [27][2000/5005] lr: 1.000e-01, eta: 9:01:20, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4842 2023-01-03 18:43:12,952 - mmcls - INFO - Epoch [27][2100/5005] lr: 1.000e-01, eta: 9:01:12, time: 0.090, data_time: 0.016, memory: 1125, loss: 2.4575 2023-01-03 18:43:21,740 - mmcls - INFO - Epoch [27][2200/5005] lr: 1.000e-01, eta: 9:01:03, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.4703 2023-01-03 18:43:30,506 - mmcls - INFO - Epoch [27][2300/5005] lr: 1.000e-01, eta: 9:00:54, time: 0.088, data_time: 0.029, memory: 1125, loss: 2.5015 2023-01-03 18:43:39,481 - mmcls - INFO - Epoch [27][2400/5005] lr: 1.000e-01, eta: 9:00:46, time: 0.090, data_time: 0.028, memory: 1125, loss: 2.4773 2023-01-03 18:43:48,066 - mmcls - INFO - Epoch [27][2500/5005] lr: 1.000e-01, eta: 9:00:36, time: 0.086, data_time: 0.014, memory: 1125, loss: 2.4747 2023-01-03 18:43:57,225 - mmcls - INFO - Epoch [27][2600/5005] lr: 1.000e-01, eta: 9:00:28, time: 0.092, data_time: 0.001, memory: 1125, loss: 2.4499 2023-01-03 18:44:05,908 - mmcls - INFO - Epoch [27][2700/5005] lr: 1.000e-01, eta: 9:00:19, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4696 2023-01-03 18:44:14,773 - mmcls - INFO - Epoch [27][2800/5005] lr: 1.000e-01, eta: 9:00:10, time: 0.089, data_time: 0.012, memory: 1125, loss: 2.4934 2023-01-03 18:44:23,790 - mmcls - INFO - Epoch [27][2900/5005] lr: 1.000e-01, eta: 9:00:02, time: 0.090, data_time: 0.039, memory: 1125, loss: 2.4545 2023-01-03 18:44:32,534 - mmcls - INFO - Epoch [27][3000/5005] lr: 1.000e-01, eta: 8:59:53, time: 0.087, data_time: 0.031, memory: 1125, loss: 2.4620 2023-01-03 18:44:41,056 - mmcls - INFO - Epoch [27][3100/5005] lr: 1.000e-01, eta: 8:59:43, time: 0.085, data_time: 0.007, memory: 1125, loss: 2.4614 2023-01-03 18:44:49,614 - mmcls - INFO - Epoch [27][3200/5005] lr: 1.000e-01, eta: 8:59:34, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.4778 2023-01-03 18:44:58,243 - mmcls - INFO - Epoch [27][3300/5005] lr: 1.000e-01, eta: 8:59:25, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4830 2023-01-03 18:45:06,894 - mmcls - INFO - Epoch [27][3400/5005] lr: 1.000e-01, eta: 8:59:15, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.4949 2023-01-03 18:45:15,788 - mmcls - INFO - Epoch [27][3500/5005] lr: 1.000e-01, eta: 8:59:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4766 2023-01-03 18:45:24,694 - mmcls - INFO - Epoch [27][3600/5005] lr: 1.000e-01, eta: 8:58:58, time: 0.089, data_time: 0.001, memory: 1125, loss: 2.4711 2023-01-03 18:45:33,473 - mmcls - INFO - Epoch [27][3700/5005] lr: 1.000e-01, eta: 8:58:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.4850 2023-01-03 18:45:41,962 - mmcls - INFO - Epoch [27][3800/5005] lr: 1.000e-01, eta: 8:58:39, time: 0.085, data_time: 0.015, memory: 1125, loss: 2.4964 2023-01-03 18:45:50,649 - mmcls - INFO - Epoch [27][3900/5005] lr: 1.000e-01, eta: 8:58:30, time: 0.087, data_time: 0.041, memory: 1125, loss: 2.4689 2023-01-03 18:45:59,220 - mmcls - INFO - Epoch [27][4000/5005] lr: 1.000e-01, eta: 8:58:21, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4734 2023-01-03 18:46:08,258 - mmcls - INFO - Epoch [27][4100/5005] lr: 1.000e-01, eta: 8:58:13, time: 0.090, data_time: 0.025, memory: 1125, loss: 2.4834 2023-01-03 18:46:17,130 - mmcls - INFO - Epoch [27][4200/5005] lr: 1.000e-01, eta: 8:58:04, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.4616 2023-01-03 18:46:25,592 - mmcls - INFO - Epoch [27][4300/5005] lr: 1.000e-01, eta: 8:57:54, time: 0.085, data_time: 0.026, memory: 1125, loss: 2.4690 2023-01-03 18:46:34,340 - mmcls - INFO - Epoch [27][4400/5005] lr: 1.000e-01, eta: 8:57:45, time: 0.087, data_time: 0.010, memory: 1125, loss: 2.4783 2023-01-03 18:46:42,924 - mmcls - INFO - Epoch [27][4500/5005] lr: 1.000e-01, eta: 8:57:36, time: 0.086, data_time: 0.010, memory: 1125, loss: 2.4934 2023-01-03 18:46:51,742 - mmcls - INFO - Epoch [27][4600/5005] lr: 1.000e-01, eta: 8:57:27, time: 0.088, data_time: 0.026, memory: 1125, loss: 2.4607 2023-01-03 18:47:00,445 - mmcls - INFO - Epoch [27][4700/5005] lr: 1.000e-01, eta: 8:57:18, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.4509 2023-01-03 18:47:09,181 - mmcls - INFO - Epoch [27][4800/5005] lr: 1.000e-01, eta: 8:57:09, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4825 2023-01-03 18:47:17,987 - mmcls - INFO - Epoch [27][4900/5005] lr: 1.000e-01, eta: 8:57:00, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4597 2023-01-03 18:47:26,675 - mmcls - INFO - Epoch [27][5000/5005] lr: 1.000e-01, eta: 8:56:51, time: 0.087, data_time: 0.040, memory: 1125, loss: 2.4762 2023-01-03 18:47:49,380 - mmcls - INFO - Epoch(val) [27][196] accuracy_top-1: 49.4140, accuracy_top-5: 75.2940 2023-01-03 18:48:00,634 - mmcls - INFO - Epoch [28][100/5005] lr: 1.000e-01, eta: 8:56:47, time: 0.112, data_time: 0.043, memory: 1125, loss: 2.4336 2023-01-03 18:48:09,582 - mmcls - INFO - Epoch [28][200/5005] lr: 1.000e-01, eta: 8:56:38, time: 0.090, data_time: 0.001, memory: 1125, loss: 2.3921 2023-01-03 18:48:18,137 - mmcls - INFO - Epoch [28][300/5005] lr: 1.000e-01, eta: 8:56:29, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4400 2023-01-03 18:48:26,808 - mmcls - INFO - Epoch [28][400/5005] lr: 1.000e-01, eta: 8:56:20, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4329 2023-01-03 18:48:35,402 - mmcls - INFO - Epoch [28][500/5005] lr: 1.000e-01, eta: 8:56:10, time: 0.086, data_time: 0.000, memory: 1125, loss: 2.4214 2023-01-03 18:48:44,147 - mmcls - INFO - Epoch [28][600/5005] lr: 1.000e-01, eta: 8:56:01, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.4253 2023-01-03 18:48:52,758 - mmcls - INFO - Epoch [28][700/5005] lr: 1.000e-01, eta: 8:55:52, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4590 2023-01-03 18:49:01,473 - mmcls - INFO - Epoch [28][800/5005] lr: 1.000e-01, eta: 8:55:43, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.4542 2023-01-03 18:49:10,610 - mmcls - INFO - Epoch [28][900/5005] lr: 1.000e-01, eta: 8:55:35, time: 0.091, data_time: 0.021, memory: 1125, loss: 2.4320 2023-01-03 18:49:19,365 - mmcls - INFO - Epoch [28][1000/5005] lr: 1.000e-01, eta: 8:55:26, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4603 2023-01-03 18:49:28,087 - mmcls - INFO - Epoch [28][1100/5005] lr: 1.000e-01, eta: 8:55:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4716 2023-01-03 18:49:36,899 - mmcls - INFO - Epoch [28][1200/5005] lr: 1.000e-01, eta: 8:55:08, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4667 2023-01-03 18:49:45,715 - mmcls - INFO - Epoch [28][1300/5005] lr: 1.000e-01, eta: 8:54:59, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.4747 2023-01-03 18:49:54,425 - mmcls - INFO - Epoch [28][1400/5005] lr: 1.000e-01, eta: 8:54:50, time: 0.087, data_time: 0.020, memory: 1125, loss: 2.4747 2023-01-03 18:50:03,020 - mmcls - INFO - Epoch [28][1500/5005] lr: 1.000e-01, eta: 8:54:40, time: 0.086, data_time: 0.019, memory: 1125, loss: 2.4670 2023-01-03 18:50:11,722 - mmcls - INFO - Epoch [28][1600/5005] lr: 1.000e-01, eta: 8:54:31, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.4698 2023-01-03 18:50:20,646 - mmcls - INFO - Epoch [28][1700/5005] lr: 1.000e-01, eta: 8:54:23, time: 0.089, data_time: 0.004, memory: 1125, loss: 2.4606 2023-01-03 18:50:29,566 - mmcls - INFO - Epoch [28][1800/5005] lr: 1.000e-01, eta: 8:54:14, time: 0.089, data_time: 0.017, memory: 1125, loss: 2.4540 2023-01-03 18:50:38,562 - mmcls - INFO - Epoch [28][1900/5005] lr: 1.000e-01, eta: 8:54:06, time: 0.090, data_time: 0.010, memory: 1125, loss: 2.4409 2023-01-03 18:50:47,429 - mmcls - INFO - Epoch [28][2000/5005] lr: 1.000e-01, eta: 8:53:57, time: 0.089, data_time: 0.014, memory: 1125, loss: 2.4751 2023-01-03 18:50:56,165 - mmcls - INFO - Epoch [28][2100/5005] lr: 1.000e-01, eta: 8:53:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4394 2023-01-03 18:51:04,736 - mmcls - INFO - Epoch [28][2200/5005] lr: 1.000e-01, eta: 8:53:39, time: 0.086, data_time: 0.017, memory: 1125, loss: 2.4641 2023-01-03 18:51:13,332 - mmcls - INFO - Epoch [28][2300/5005] lr: 1.000e-01, eta: 8:53:29, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.4544 2023-01-03 18:51:22,060 - mmcls - INFO - Epoch [28][2400/5005] lr: 1.000e-01, eta: 8:53:20, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4638 2023-01-03 18:51:30,770 - mmcls - INFO - Epoch [28][2500/5005] lr: 1.000e-01, eta: 8:53:11, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4461 2023-01-03 18:51:39,406 - mmcls - INFO - Epoch [28][2600/5005] lr: 1.000e-01, eta: 8:53:02, time: 0.086, data_time: 0.007, memory: 1125, loss: 2.4757 2023-01-03 18:51:48,046 - mmcls - INFO - Epoch [28][2700/5005] lr: 1.000e-01, eta: 8:52:53, time: 0.086, data_time: 0.005, memory: 1125, loss: 2.4634 2023-01-03 18:51:56,659 - mmcls - INFO - Epoch [28][2800/5005] lr: 1.000e-01, eta: 8:52:43, time: 0.086, data_time: 0.039, memory: 1125, loss: 2.4615 2023-01-03 18:52:05,239 - mmcls - INFO - Epoch [28][2900/5005] lr: 1.000e-01, eta: 8:52:34, time: 0.086, data_time: 0.029, memory: 1125, loss: 2.4883 2023-01-03 18:52:14,685 - mmcls - INFO - Epoch [28][3000/5005] lr: 1.000e-01, eta: 8:52:27, time: 0.095, data_time: 0.002, memory: 1125, loss: 2.4788 2023-01-03 18:52:23,226 - mmcls - INFO - Epoch [28][3100/5005] lr: 1.000e-01, eta: 8:52:17, time: 0.085, data_time: 0.021, memory: 1125, loss: 2.4797 2023-01-03 18:52:31,935 - mmcls - INFO - Epoch [28][3200/5005] lr: 1.000e-01, eta: 8:52:08, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4515 2023-01-03 18:52:40,683 - mmcls - INFO - Epoch [28][3300/5005] lr: 1.000e-01, eta: 8:51:59, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.4924 2023-01-03 18:52:49,334 - mmcls - INFO - Epoch [28][3400/5005] lr: 1.000e-01, eta: 8:51:50, time: 0.086, data_time: 0.003, memory: 1125, loss: 2.4346 2023-01-03 18:52:58,143 - mmcls - INFO - Epoch [28][3500/5005] lr: 1.000e-01, eta: 8:51:41, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.4703 2023-01-03 18:53:06,926 - mmcls - INFO - Epoch [28][3600/5005] lr: 1.000e-01, eta: 8:51:32, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.4504 2023-01-03 18:53:15,780 - mmcls - INFO - Epoch [28][3700/5005] lr: 1.000e-01, eta: 8:51:23, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4727 2023-01-03 18:53:24,508 - mmcls - INFO - Epoch [28][3800/5005] lr: 1.000e-01, eta: 8:51:14, time: 0.087, data_time: 0.006, memory: 1125, loss: 2.4996 2023-01-03 18:53:33,196 - mmcls - INFO - Epoch [28][3900/5005] lr: 1.000e-01, eta: 8:51:05, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4984 2023-01-03 18:53:42,242 - mmcls - INFO - Epoch [28][4000/5005] lr: 1.000e-01, eta: 8:50:57, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.5004 2023-01-03 18:53:50,990 - mmcls - INFO - Epoch [28][4100/5005] lr: 1.000e-01, eta: 8:50:48, time: 0.087, data_time: 0.015, memory: 1125, loss: 2.4883 2023-01-03 18:53:59,741 - mmcls - INFO - Epoch [28][4200/5005] lr: 1.000e-01, eta: 8:50:39, time: 0.087, data_time: 0.008, memory: 1125, loss: 2.4933 2023-01-03 18:54:08,503 - mmcls - INFO - Epoch [28][4300/5005] lr: 1.000e-01, eta: 8:50:30, time: 0.088, data_time: 0.028, memory: 1125, loss: 2.4947 2023-01-03 18:54:17,307 - mmcls - INFO - Epoch [28][4400/5005] lr: 1.000e-01, eta: 8:50:21, time: 0.088, data_time: 0.023, memory: 1125, loss: 2.4963 2023-01-03 18:54:26,312 - mmcls - INFO - Epoch [28][4500/5005] lr: 1.000e-01, eta: 8:50:13, time: 0.090, data_time: 0.002, memory: 1125, loss: 2.4481 2023-01-03 18:54:35,086 - mmcls - INFO - Epoch [28][4600/5005] lr: 1.000e-01, eta: 8:50:04, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.4534 2023-01-03 18:54:43,845 - mmcls - INFO - Epoch [28][4700/5005] lr: 1.000e-01, eta: 8:49:55, time: 0.088, data_time: 0.009, memory: 1125, loss: 2.4965 2023-01-03 18:54:52,399 - mmcls - INFO - Epoch [28][4800/5005] lr: 1.000e-01, eta: 8:49:45, time: 0.086, data_time: 0.020, memory: 1125, loss: 2.4827 2023-01-03 18:55:01,238 - mmcls - INFO - Epoch [28][4900/5005] lr: 1.000e-01, eta: 8:49:37, time: 0.088, data_time: 0.031, memory: 1125, loss: 2.4775 2023-01-03 18:55:10,067 - mmcls - INFO - Epoch [28][5000/5005] lr: 1.000e-01, eta: 8:49:28, time: 0.088, data_time: 0.004, memory: 1125, loss: 2.4828 2023-01-03 18:55:32,652 - mmcls - INFO - Epoch(val) [28][196] accuracy_top-1: 50.1920, accuracy_top-5: 75.6660 2023-01-03 18:55:43,602 - mmcls - INFO - Epoch [29][100/5005] lr: 1.000e-01, eta: 8:49:23, time: 0.109, data_time: 0.035, memory: 1125, loss: 2.4173 2023-01-03 18:55:52,059 - mmcls - INFO - Epoch [29][200/5005] lr: 1.000e-01, eta: 8:49:13, time: 0.085, data_time: 0.001, memory: 1125, loss: 2.4073 2023-01-03 18:56:00,888 - mmcls - INFO - Epoch [29][300/5005] lr: 1.000e-01, eta: 8:49:04, time: 0.088, data_time: 0.014, memory: 1125, loss: 2.4149 2023-01-03 18:56:09,389 - mmcls - INFO - Epoch [29][400/5005] lr: 1.000e-01, eta: 8:48:55, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.4338 2023-01-03 18:56:18,388 - mmcls - INFO - Epoch [29][500/5005] lr: 1.000e-01, eta: 8:48:46, time: 0.090, data_time: 0.004, memory: 1125, loss: 2.4161 2023-01-03 18:56:27,098 - mmcls - INFO - Epoch [29][600/5005] lr: 1.000e-01, eta: 8:48:37, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4258 2023-01-03 18:56:35,955 - mmcls - INFO - Epoch [29][700/5005] lr: 1.000e-01, eta: 8:48:29, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.4835 2023-01-03 18:56:44,528 - mmcls - INFO - Epoch [29][800/5005] lr: 1.000e-01, eta: 8:48:19, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4405 2023-01-03 18:56:53,449 - mmcls - INFO - Epoch [29][900/5005] lr: 1.000e-01, eta: 8:48:11, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.4460 2023-01-03 18:57:02,001 - mmcls - INFO - Epoch [29][1000/5005] lr: 1.000e-01, eta: 8:48:01, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4438 2023-01-03 18:57:10,798 - mmcls - INFO - Epoch [29][1100/5005] lr: 1.000e-01, eta: 8:47:52, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.4494 2023-01-03 18:57:19,737 - mmcls - INFO - Epoch [29][1200/5005] lr: 1.000e-01, eta: 8:47:44, time: 0.089, data_time: 0.005, memory: 1125, loss: 2.4476 2023-01-03 18:57:28,434 - mmcls - INFO - Epoch [29][1300/5005] lr: 1.000e-01, eta: 8:47:35, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.4589 2023-01-03 18:57:37,249 - mmcls - INFO - Epoch [29][1400/5005] lr: 1.000e-01, eta: 8:47:26, time: 0.088, data_time: 0.022, memory: 1125, loss: 2.4397 2023-01-03 18:57:45,704 - mmcls - INFO - Epoch [29][1500/5005] lr: 1.000e-01, eta: 8:47:16, time: 0.085, data_time: 0.016, memory: 1125, loss: 2.4678 2023-01-03 18:57:54,758 - mmcls - INFO - Epoch [29][1600/5005] lr: 1.000e-01, eta: 8:47:08, time: 0.091, data_time: 0.016, memory: 1125, loss: 2.4243 2023-01-03 18:58:03,373 - mmcls - INFO - Epoch [29][1700/5005] lr: 1.000e-01, eta: 8:46:58, time: 0.086, data_time: 0.016, memory: 1125, loss: 2.4635 2023-01-03 18:58:12,213 - mmcls - INFO - Epoch [29][1800/5005] lr: 1.000e-01, eta: 8:46:50, time: 0.088, data_time: 0.024, memory: 1125, loss: 2.4426 2023-01-03 18:58:20,864 - mmcls - INFO - Epoch [29][1900/5005] lr: 1.000e-01, eta: 8:46:40, time: 0.086, data_time: 0.049, memory: 1125, loss: 2.4565 2023-01-03 18:58:29,468 - mmcls - INFO - Epoch [29][2000/5005] lr: 1.000e-01, eta: 8:46:31, time: 0.086, data_time: 0.015, memory: 1125, loss: 2.4434 2023-01-03 18:58:38,340 - mmcls - INFO - Epoch [29][2100/5005] lr: 1.000e-01, eta: 8:46:22, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.4814 2023-01-03 18:58:47,090 - mmcls - INFO - Epoch [29][2200/5005] lr: 1.000e-01, eta: 8:46:13, time: 0.087, data_time: 0.011, memory: 1125, loss: 2.4747 2023-01-03 18:58:55,875 - mmcls - INFO - Epoch [29][2300/5005] lr: 1.000e-01, eta: 8:46:05, time: 0.088, data_time: 0.013, memory: 1125, loss: 2.4850 2023-01-03 18:59:04,658 - mmcls - INFO - Epoch [29][2400/5005] lr: 1.000e-01, eta: 8:45:56, time: 0.088, data_time: 0.015, memory: 1125, loss: 2.4653 2023-01-03 18:59:13,593 - mmcls - INFO - Epoch [29][2500/5005] lr: 1.000e-01, eta: 8:45:47, time: 0.089, data_time: 0.015, memory: 1125, loss: 2.4179 2023-01-03 18:59:22,263 - mmcls - INFO - Epoch [29][2600/5005] lr: 1.000e-01, eta: 8:45:38, time: 0.087, data_time: 0.007, memory: 1125, loss: 2.4753 2023-01-03 18:59:30,899 - mmcls - INFO - Epoch [29][2700/5005] lr: 1.000e-01, eta: 8:45:29, time: 0.086, data_time: 0.030, memory: 1125, loss: 2.4609 2023-01-03 18:59:39,813 - mmcls - INFO - Epoch [29][2800/5005] lr: 1.000e-01, eta: 8:45:20, time: 0.089, data_time: 0.032, memory: 1125, loss: 2.4555 2023-01-03 18:59:48,774 - mmcls - INFO - Epoch [29][2900/5005] lr: 1.000e-01, eta: 8:45:12, time: 0.090, data_time: 0.009, memory: 1125, loss: 2.4798 2023-01-03 18:59:57,475 - mmcls - INFO - Epoch [29][3000/5005] lr: 1.000e-01, eta: 8:45:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4395 2023-01-03 19:00:06,153 - mmcls - INFO - Epoch [29][3100/5005] lr: 1.000e-01, eta: 8:44:53, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.4757 2023-01-03 19:00:14,770 - mmcls - INFO - Epoch [29][3200/5005] lr: 1.000e-01, eta: 8:44:44, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4551 2023-01-03 19:00:23,643 - mmcls - INFO - Epoch [29][3300/5005] lr: 1.000e-01, eta: 8:44:35, time: 0.089, data_time: 0.006, memory: 1125, loss: 2.4830 2023-01-03 19:00:32,397 - mmcls - INFO - Epoch [29][3400/5005] lr: 1.000e-01, eta: 8:44:26, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.4536 2023-01-03 19:00:41,274 - mmcls - INFO - Epoch [29][3500/5005] lr: 1.000e-01, eta: 8:44:18, time: 0.089, data_time: 0.023, memory: 1125, loss: 2.4734 2023-01-03 19:00:50,353 - mmcls - INFO - Epoch [29][3600/5005] lr: 1.000e-01, eta: 8:44:10, time: 0.091, data_time: 0.001, memory: 1125, loss: 2.4828 2023-01-03 19:00:59,058 - mmcls - INFO - Epoch [29][3700/5005] lr: 1.000e-01, eta: 8:44:00, time: 0.087, data_time: 0.026, memory: 1125, loss: 2.4831 2023-01-03 19:01:07,783 - mmcls - INFO - Epoch [29][3800/5005] lr: 1.000e-01, eta: 8:43:51, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.4479 2023-01-03 19:01:16,545 - mmcls - INFO - Epoch [29][3900/5005] lr: 1.000e-01, eta: 8:43:42, time: 0.088, data_time: 0.016, memory: 1125, loss: 2.4647 2023-01-03 19:01:25,328 - mmcls - INFO - Epoch [29][4000/5005] lr: 1.000e-01, eta: 8:43:34, time: 0.088, data_time: 0.006, memory: 1125, loss: 2.4584 2023-01-03 19:01:34,131 - mmcls - INFO - Epoch [29][4100/5005] lr: 1.000e-01, eta: 8:43:25, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4763 2023-01-03 19:01:42,670 - mmcls - INFO - Epoch [29][4200/5005] lr: 1.000e-01, eta: 8:43:15, time: 0.085, data_time: 0.002, memory: 1125, loss: 2.4795 2023-01-03 19:01:51,128 - mmcls - INFO - Epoch [29][4300/5005] lr: 1.000e-01, eta: 8:43:06, time: 0.085, data_time: 0.011, memory: 1125, loss: 2.4671 2023-01-03 19:02:00,080 - mmcls - INFO - Epoch [29][4400/5005] lr: 1.000e-01, eta: 8:42:57, time: 0.090, data_time: 0.008, memory: 1125, loss: 2.4679 2023-01-03 19:02:08,864 - mmcls - INFO - Epoch [29][4500/5005] lr: 1.000e-01, eta: 8:42:48, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.4749 2023-01-03 19:02:17,871 - mmcls - INFO - Epoch [29][4600/5005] lr: 1.000e-01, eta: 8:42:40, time: 0.090, data_time: 0.007, memory: 1125, loss: 2.4828 2023-01-03 19:02:26,440 - mmcls - INFO - Epoch [29][4700/5005] lr: 1.000e-01, eta: 8:42:30, time: 0.086, data_time: 0.011, memory: 1125, loss: 2.4683 2023-01-03 19:02:35,193 - mmcls - INFO - Epoch [29][4800/5005] lr: 1.000e-01, eta: 8:42:21, time: 0.087, data_time: 0.033, memory: 1125, loss: 2.4820 2023-01-03 19:02:43,741 - mmcls - INFO - Epoch [29][4900/5005] lr: 1.000e-01, eta: 8:42:12, time: 0.085, data_time: 0.018, memory: 1125, loss: 2.4665 2023-01-03 19:02:52,419 - mmcls - INFO - Epoch [29][5000/5005] lr: 1.000e-01, eta: 8:42:03, time: 0.087, data_time: 0.012, memory: 1125, loss: 2.4986 2023-01-03 19:03:14,956 - mmcls - INFO - Epoch(val) [29][196] accuracy_top-1: 50.3220, accuracy_top-5: 75.8180 2023-01-03 19:03:25,993 - mmcls - INFO - Epoch [30][100/5005] lr: 1.000e-01, eta: 8:41:58, time: 0.110, data_time: 0.059, memory: 1125, loss: 2.4273 2023-01-03 19:03:34,789 - mmcls - INFO - Epoch [30][200/5005] lr: 1.000e-01, eta: 8:41:49, time: 0.088, data_time: 0.042, memory: 1125, loss: 2.3969 2023-01-03 19:03:43,633 - mmcls - INFO - Epoch [30][300/5005] lr: 1.000e-01, eta: 8:41:40, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.4378 2023-01-03 19:03:52,426 - mmcls - INFO - Epoch [30][400/5005] lr: 1.000e-01, eta: 8:41:31, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.4223 2023-01-03 19:04:01,045 - mmcls - INFO - Epoch [30][500/5005] lr: 1.000e-01, eta: 8:41:22, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4359 2023-01-03 19:04:09,870 - mmcls - INFO - Epoch [30][600/5005] lr: 1.000e-01, eta: 8:41:13, time: 0.088, data_time: 0.020, memory: 1125, loss: 2.4445 2023-01-03 19:04:18,797 - mmcls - INFO - Epoch [30][700/5005] lr: 1.000e-01, eta: 8:41:05, time: 0.089, data_time: 0.020, memory: 1125, loss: 2.4217 2023-01-03 19:04:27,609 - mmcls - INFO - Epoch [30][800/5005] lr: 1.000e-01, eta: 8:40:56, time: 0.088, data_time: 0.019, memory: 1125, loss: 2.3872 2023-01-03 19:04:36,273 - mmcls - INFO - Epoch [30][900/5005] lr: 1.000e-01, eta: 8:40:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 2.4273 2023-01-03 19:04:44,855 - mmcls - INFO - Epoch [30][1000/5005] lr: 1.000e-01, eta: 8:40:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 2.4686 2023-01-03 19:04:53,504 - mmcls - INFO - Epoch [30][1100/5005] lr: 1.000e-01, eta: 8:40:28, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4481 2023-01-03 19:05:02,259 - mmcls - INFO - Epoch [30][1200/5005] lr: 1.000e-01, eta: 8:40:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4502 2023-01-03 19:05:10,853 - mmcls - INFO - Epoch [30][1300/5005] lr: 1.000e-01, eta: 8:40:10, time: 0.086, data_time: 0.035, memory: 1125, loss: 2.4398 2023-01-03 19:05:19,499 - mmcls - INFO - Epoch [30][1400/5005] lr: 1.000e-01, eta: 8:40:01, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4497 2023-01-03 19:05:28,361 - mmcls - INFO - Epoch [30][1500/5005] lr: 1.000e-01, eta: 8:39:52, time: 0.089, data_time: 0.028, memory: 1125, loss: 2.4404 2023-01-03 19:05:36,964 - mmcls - INFO - Epoch [30][1600/5005] lr: 1.000e-01, eta: 8:39:43, time: 0.086, data_time: 0.024, memory: 1125, loss: 2.4744 2023-01-03 19:05:46,068 - mmcls - INFO - Epoch [30][1700/5005] lr: 1.000e-01, eta: 8:39:34, time: 0.091, data_time: 0.010, memory: 1125, loss: 2.4670 2023-01-03 19:05:54,718 - mmcls - INFO - Epoch [30][1800/5005] lr: 1.000e-01, eta: 8:39:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 2.4501 2023-01-03 19:06:03,343 - mmcls - INFO - Epoch [30][1900/5005] lr: 1.000e-01, eta: 8:39:16, time: 0.086, data_time: 0.012, memory: 1125, loss: 2.4158 2023-01-03 19:06:12,079 - mmcls - INFO - Epoch [30][2000/5005] lr: 1.000e-01, eta: 8:39:07, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.4745 2023-01-03 19:06:20,502 - mmcls - INFO - Epoch [30][2100/5005] lr: 1.000e-01, eta: 8:38:57, time: 0.084, data_time: 0.010, memory: 1125, loss: 2.4541 2023-01-03 19:06:29,413 - mmcls - INFO - Epoch [30][2200/5005] lr: 1.000e-01, eta: 8:38:49, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.4729 2023-01-03 19:06:38,239 - mmcls - INFO - Epoch [30][2300/5005] lr: 1.000e-01, eta: 8:38:40, time: 0.088, data_time: 0.033, memory: 1125, loss: 2.4445 2023-01-03 19:06:47,337 - mmcls - INFO - Epoch [30][2400/5005] lr: 1.000e-01, eta: 8:38:32, time: 0.091, data_time: 0.004, memory: 1125, loss: 2.4574 2023-01-03 19:06:56,267 - mmcls - INFO - Epoch [30][2500/5005] lr: 1.000e-01, eta: 8:38:23, time: 0.089, data_time: 0.002, memory: 1125, loss: 2.4522 2023-01-03 19:07:04,790 - mmcls - INFO - Epoch [30][2600/5005] lr: 1.000e-01, eta: 8:38:14, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.4699 2023-01-03 19:07:13,504 - mmcls - INFO - Epoch [30][2700/5005] lr: 1.000e-01, eta: 8:38:05, time: 0.087, data_time: 0.030, memory: 1125, loss: 2.4572 2023-01-03 19:07:22,118 - mmcls - INFO - Epoch [30][2800/5005] lr: 1.000e-01, eta: 8:37:55, time: 0.086, data_time: 0.035, memory: 1125, loss: 2.4553 2023-01-03 19:07:30,901 - mmcls - INFO - Epoch [30][2900/5005] lr: 1.000e-01, eta: 8:37:46, time: 0.088, data_time: 0.024, memory: 1125, loss: 2.4565 2023-01-03 19:07:39,699 - mmcls - INFO - Epoch [30][3000/5005] lr: 1.000e-01, eta: 8:37:38, time: 0.088, data_time: 0.008, memory: 1125, loss: 2.4464 2023-01-03 19:07:48,430 - mmcls - INFO - Epoch [30][3100/5005] lr: 1.000e-01, eta: 8:37:28, time: 0.087, data_time: 0.013, memory: 1125, loss: 2.4704 2023-01-03 19:07:57,386 - mmcls - INFO - Epoch [30][3200/5005] lr: 1.000e-01, eta: 8:37:20, time: 0.089, data_time: 0.007, memory: 1125, loss: 2.4867 2023-01-03 19:08:06,217 - mmcls - INFO - Epoch [30][3300/5005] lr: 1.000e-01, eta: 8:37:11, time: 0.088, data_time: 0.012, memory: 1125, loss: 2.4865 2023-01-03 19:08:14,854 - mmcls - INFO - Epoch [30][3400/5005] lr: 1.000e-01, eta: 8:37:02, time: 0.086, data_time: 0.035, memory: 1125, loss: 2.4581 2023-01-03 19:08:23,570 - mmcls - INFO - Epoch [30][3500/5005] lr: 1.000e-01, eta: 8:36:53, time: 0.087, data_time: 0.000, memory: 1125, loss: 2.4734 2023-01-03 19:08:32,329 - mmcls - INFO - Epoch [30][3600/5005] lr: 1.000e-01, eta: 8:36:44, time: 0.088, data_time: 0.000, memory: 1125, loss: 2.4667 2023-01-03 19:08:41,008 - mmcls - INFO - Epoch [30][3700/5005] lr: 1.000e-01, eta: 8:36:35, time: 0.087, data_time: 0.032, memory: 1125, loss: 2.4819 2023-01-03 19:08:49,783 - mmcls - INFO - Epoch [30][3800/5005] lr: 1.000e-01, eta: 8:36:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 2.4833 2023-01-03 19:08:59,107 - mmcls - INFO - Epoch [30][3900/5005] lr: 1.000e-01, eta: 8:36:18, time: 0.093, data_time: 0.001, memory: 1125, loss: 2.4787 2023-01-03 19:09:07,742 - mmcls - INFO - Epoch [30][4000/5005] lr: 1.000e-01, eta: 8:36:09, time: 0.086, data_time: 0.004, memory: 1125, loss: 2.4935 2023-01-03 19:09:16,285 - mmcls - INFO - Epoch [30][4100/5005] lr: 1.000e-01, eta: 8:36:00, time: 0.085, data_time: 0.012, memory: 1125, loss: 2.4465 2023-01-03 19:09:25,113 - mmcls - INFO - Epoch [30][4200/5005] lr: 1.000e-01, eta: 8:35:51, time: 0.088, data_time: 0.034, memory: 1125, loss: 2.4759 2023-01-03 19:09:33,773 - mmcls - INFO - Epoch [30][4300/5005] lr: 1.000e-01, eta: 8:35:42, time: 0.087, data_time: 0.042, memory: 1125, loss: 2.4669 2023-01-03 19:09:42,992 - mmcls - INFO - Epoch [30][4400/5005] lr: 1.000e-01, eta: 8:35:34, time: 0.092, data_time: 0.039, memory: 1125, loss: 2.4685 2023-01-03 19:09:52,150 - mmcls - INFO - Epoch [30][4500/5005] lr: 1.000e-01, eta: 8:35:26, time: 0.092, data_time: 0.004, memory: 1125, loss: 2.4483 2023-01-03 19:10:00,981 - mmcls - INFO - Epoch [30][4600/5005] lr: 1.000e-01, eta: 8:35:17, time: 0.088, data_time: 0.005, memory: 1125, loss: 2.4932 2023-01-03 19:10:09,747 - mmcls - INFO - Epoch [30][4700/5005] lr: 1.000e-01, eta: 8:35:08, time: 0.088, data_time: 0.003, memory: 1125, loss: 2.4515 2023-01-03 19:10:18,458 - mmcls - INFO - Epoch [30][4800/5005] lr: 1.000e-01, eta: 8:34:59, time: 0.087, data_time: 0.001, memory: 1125, loss: 2.5007 2023-01-03 19:10:27,094 - mmcls - INFO - Epoch [30][4900/5005] lr: 1.000e-01, eta: 8:34:50, time: 0.086, data_time: 0.022, memory: 1125, loss: 2.4805 2023-01-03 19:10:35,821 - mmcls - INFO - Epoch [30][5000/5005] lr: 1.000e-01, eta: 8:34:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 2.4751 2023-01-03 19:10:58,494 - mmcls - INFO - Epoch(val) [30][196] accuracy_top-1: 46.9700, accuracy_top-5: 72.9040 2023-01-03 19:11:09,659 - mmcls - INFO - Epoch [31][100/5005] lr: 1.000e-02, eta: 8:34:36, time: 0.111, data_time: 0.040, memory: 1125, loss: 2.2557 2023-01-03 19:11:18,561 - mmcls - INFO - Epoch [31][200/5005] lr: 1.000e-02, eta: 8:34:27, time: 0.089, data_time: 0.009, memory: 1125, loss: 2.1786 2023-01-03 19:11:27,349 - mmcls - INFO - Epoch [31][300/5005] lr: 1.000e-02, eta: 8:34:18, time: 0.088, data_time: 0.010, memory: 1125, loss: 2.1189 2023-01-03 19:11:36,025 - mmcls - INFO - Epoch [31][400/5005] lr: 1.000e-02, eta: 8:34:09, time: 0.087, data_time: 0.009, memory: 1125, loss: 2.0838 2023-01-03 19:11:44,704 - mmcls - INFO - Epoch [31][500/5005] lr: 1.000e-02, eta: 8:34:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0906 2023-01-03 19:11:53,400 - mmcls - INFO - Epoch [31][600/5005] lr: 1.000e-02, eta: 8:33:51, time: 0.087, data_time: 0.003, memory: 1125, loss: 2.0444 2023-01-03 19:12:02,362 - mmcls - INFO - Epoch [31][700/5005] lr: 1.000e-02, eta: 8:33:43, time: 0.090, data_time: 0.030, memory: 1125, loss: 2.0668 2023-01-03 19:12:10,952 - mmcls - INFO - Epoch [31][800/5005] lr: 1.000e-02, eta: 8:33:33, time: 0.086, data_time: 0.023, memory: 1125, loss: 2.0317 2023-01-03 19:12:19,373 - mmcls - INFO - Epoch [31][900/5005] lr: 1.000e-02, eta: 8:33:24, time: 0.084, data_time: 0.026, memory: 1125, loss: 2.0448 2023-01-03 19:12:28,260 - mmcls - INFO - Epoch [31][1000/5005] lr: 1.000e-02, eta: 8:33:15, time: 0.089, data_time: 0.033, memory: 1125, loss: 2.0175 2023-01-03 19:12:37,164 - mmcls - INFO - Epoch [31][1100/5005] lr: 1.000e-02, eta: 8:33:06, time: 0.089, data_time: 0.008, memory: 1125, loss: 2.0101 2023-01-03 19:12:45,881 - mmcls - INFO - Epoch [31][1200/5005] lr: 1.000e-02, eta: 8:32:57, time: 0.087, data_time: 0.016, memory: 1125, loss: 2.0004 2023-01-03 19:12:54,716 - mmcls - INFO - Epoch [31][1300/5005] lr: 1.000e-02, eta: 8:32:48, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.9840 2023-01-03 19:13:03,115 - mmcls - INFO - Epoch [31][1400/5005] lr: 1.000e-02, eta: 8:32:39, time: 0.084, data_time: 0.011, memory: 1125, loss: 2.0180 2023-01-03 19:13:11,838 - mmcls - INFO - Epoch [31][1500/5005] lr: 1.000e-02, eta: 8:32:30, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.9879 2023-01-03 19:13:20,835 - mmcls - INFO - Epoch [31][1600/5005] lr: 1.000e-02, eta: 8:32:21, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.9743 2023-01-03 19:13:29,642 - mmcls - INFO - Epoch [31][1700/5005] lr: 1.000e-02, eta: 8:32:12, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.9919 2023-01-03 19:13:38,551 - mmcls - INFO - Epoch [31][1800/5005] lr: 1.000e-02, eta: 8:32:04, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.9727 2023-01-03 19:13:47,284 - mmcls - INFO - Epoch [31][1900/5005] lr: 1.000e-02, eta: 8:31:55, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.9597 2023-01-03 19:13:55,741 - mmcls - INFO - Epoch [31][2000/5005] lr: 1.000e-02, eta: 8:31:45, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.9560 2023-01-03 19:14:04,286 - mmcls - INFO - Epoch [31][2100/5005] lr: 1.000e-02, eta: 8:31:36, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.9648 2023-01-03 19:14:13,041 - mmcls - INFO - Epoch [31][2200/5005] lr: 1.000e-02, eta: 8:31:27, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.9568 2023-01-03 19:14:21,870 - mmcls - INFO - Epoch [31][2300/5005] lr: 1.000e-02, eta: 8:31:18, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.9658 2023-01-03 19:14:30,442 - mmcls - INFO - Epoch [31][2400/5005] lr: 1.000e-02, eta: 8:31:09, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.9481 2023-01-03 19:14:39,178 - mmcls - INFO - Epoch [31][2500/5005] lr: 1.000e-02, eta: 8:31:00, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.9395 2023-01-03 19:14:47,847 - mmcls - INFO - Epoch [31][2600/5005] lr: 1.000e-02, eta: 8:30:50, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.9490 2023-01-03 19:14:56,549 - mmcls - INFO - Epoch [31][2700/5005] lr: 1.000e-02, eta: 8:30:41, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.9550 2023-01-03 19:15:05,191 - mmcls - INFO - Epoch [31][2800/5005] lr: 1.000e-02, eta: 8:30:32, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.9406 2023-01-03 19:15:13,946 - mmcls - INFO - Epoch [31][2900/5005] lr: 1.000e-02, eta: 8:30:23, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.9724 2023-01-03 19:15:22,695 - mmcls - INFO - Epoch [31][3000/5005] lr: 1.000e-02, eta: 8:30:14, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.9386 2023-01-03 19:15:31,293 - mmcls - INFO - Epoch [31][3100/5005] lr: 1.000e-02, eta: 8:30:05, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.9372 2023-01-03 19:15:40,424 - mmcls - INFO - Epoch [31][3200/5005] lr: 1.000e-02, eta: 8:29:57, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.9343 2023-01-03 19:15:49,042 - mmcls - INFO - Epoch [31][3300/5005] lr: 1.000e-02, eta: 8:29:48, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.9299 2023-01-03 19:15:57,560 - mmcls - INFO - Epoch [31][3400/5005] lr: 1.000e-02, eta: 8:29:38, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.9193 2023-01-03 19:16:06,149 - mmcls - INFO - Epoch [31][3500/5005] lr: 1.000e-02, eta: 8:29:29, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.9427 2023-01-03 19:16:14,773 - mmcls - INFO - Epoch [31][3600/5005] lr: 1.000e-02, eta: 8:29:20, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.9535 2023-01-03 19:16:23,421 - mmcls - INFO - Epoch [31][3700/5005] lr: 1.000e-02, eta: 8:29:10, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.9280 2023-01-03 19:16:32,134 - mmcls - INFO - Epoch [31][3800/5005] lr: 1.000e-02, eta: 8:29:01, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.9223 2023-01-03 19:16:41,070 - mmcls - INFO - Epoch [31][3900/5005] lr: 1.000e-02, eta: 8:28:53, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.9143 2023-01-03 19:16:49,945 - mmcls - INFO - Epoch [31][4000/5005] lr: 1.000e-02, eta: 8:28:44, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.9153 2023-01-03 19:16:58,726 - mmcls - INFO - Epoch [31][4100/5005] lr: 1.000e-02, eta: 8:28:35, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.9092 2023-01-03 19:17:07,304 - mmcls - INFO - Epoch [31][4200/5005] lr: 1.000e-02, eta: 8:28:26, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.9158 2023-01-03 19:17:16,148 - mmcls - INFO - Epoch [31][4300/5005] lr: 1.000e-02, eta: 8:28:17, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.9052 2023-01-03 19:17:24,690 - mmcls - INFO - Epoch [31][4400/5005] lr: 1.000e-02, eta: 8:28:08, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.9074 2023-01-03 19:17:33,341 - mmcls - INFO - Epoch [31][4500/5005] lr: 1.000e-02, eta: 8:27:59, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.9164 2023-01-03 19:17:42,173 - mmcls - INFO - Epoch [31][4600/5005] lr: 1.000e-02, eta: 8:27:50, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.8893 2023-01-03 19:17:50,865 - mmcls - INFO - Epoch [31][4700/5005] lr: 1.000e-02, eta: 8:27:41, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.9108 2023-01-03 19:17:59,604 - mmcls - INFO - Epoch [31][4800/5005] lr: 1.000e-02, eta: 8:27:32, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.9105 2023-01-03 19:18:08,018 - mmcls - INFO - Epoch [31][4900/5005] lr: 1.000e-02, eta: 8:27:22, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.9110 2023-01-03 19:18:16,783 - mmcls - INFO - Epoch [31][5000/5005] lr: 1.000e-02, eta: 8:27:13, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.9080 2023-01-03 19:18:39,450 - mmcls - INFO - Epoch(val) [31][196] accuracy_top-1: 62.3760, accuracy_top-5: 84.6060 2023-01-03 19:18:50,607 - mmcls - INFO - Epoch [32][100/5005] lr: 1.000e-02, eta: 8:27:08, time: 0.111, data_time: 0.038, memory: 1125, loss: 1.8718 2023-01-03 19:18:59,469 - mmcls - INFO - Epoch [32][200/5005] lr: 1.000e-02, eta: 8:26:59, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.8586 2023-01-03 19:19:08,193 - mmcls - INFO - Epoch [32][300/5005] lr: 1.000e-02, eta: 8:26:50, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.8798 2023-01-03 19:19:17,082 - mmcls - INFO - Epoch [32][400/5005] lr: 1.000e-02, eta: 8:26:42, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.8583 2023-01-03 19:19:25,761 - mmcls - INFO - Epoch [32][500/5005] lr: 1.000e-02, eta: 8:26:33, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.8783 2023-01-03 19:19:34,229 - mmcls - INFO - Epoch [32][600/5005] lr: 1.000e-02, eta: 8:26:23, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8832 2023-01-03 19:19:42,987 - mmcls - INFO - Epoch [32][700/5005] lr: 1.000e-02, eta: 8:26:14, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.8495 2023-01-03 19:19:52,222 - mmcls - INFO - Epoch [32][800/5005] lr: 1.000e-02, eta: 8:26:06, time: 0.092, data_time: 0.006, memory: 1125, loss: 1.8735 2023-01-03 19:20:00,852 - mmcls - INFO - Epoch [32][900/5005] lr: 1.000e-02, eta: 8:25:57, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.8772 2023-01-03 19:20:09,527 - mmcls - INFO - Epoch [32][1000/5005] lr: 1.000e-02, eta: 8:25:48, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.8563 2023-01-03 19:20:18,397 - mmcls - INFO - Epoch [32][1100/5005] lr: 1.000e-02, eta: 8:25:39, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.8574 2023-01-03 19:20:27,205 - mmcls - INFO - Epoch [32][1200/5005] lr: 1.000e-02, eta: 8:25:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8640 2023-01-03 19:20:36,254 - mmcls - INFO - Epoch [32][1300/5005] lr: 1.000e-02, eta: 8:25:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8644 2023-01-03 19:20:45,287 - mmcls - INFO - Epoch [32][1400/5005] lr: 1.000e-02, eta: 8:25:14, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.8634 2023-01-03 19:20:54,053 - mmcls - INFO - Epoch [32][1500/5005] lr: 1.000e-02, eta: 8:25:05, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.8477 2023-01-03 19:21:02,778 - mmcls - INFO - Epoch [32][1600/5005] lr: 1.000e-02, eta: 8:24:56, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8458 2023-01-03 19:21:11,374 - mmcls - INFO - Epoch [32][1700/5005] lr: 1.000e-02, eta: 8:24:46, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.8645 2023-01-03 19:21:19,868 - mmcls - INFO - Epoch [32][1800/5005] lr: 1.000e-02, eta: 8:24:37, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8569 2023-01-03 19:21:28,833 - mmcls - INFO - Epoch [32][1900/5005] lr: 1.000e-02, eta: 8:24:28, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.8505 2023-01-03 19:21:37,493 - mmcls - INFO - Epoch [32][2000/5005] lr: 1.000e-02, eta: 8:24:19, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8520 2023-01-03 19:21:46,423 - mmcls - INFO - Epoch [32][2100/5005] lr: 1.000e-02, eta: 8:24:11, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.8789 2023-01-03 19:21:55,395 - mmcls - INFO - Epoch [32][2200/5005] lr: 1.000e-02, eta: 8:24:02, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.8466 2023-01-03 19:22:03,968 - mmcls - INFO - Epoch [32][2300/5005] lr: 1.000e-02, eta: 8:23:53, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.8420 2023-01-03 19:22:13,000 - mmcls - INFO - Epoch [32][2400/5005] lr: 1.000e-02, eta: 8:23:45, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.8273 2023-01-03 19:22:21,704 - mmcls - INFO - Epoch [32][2500/5005] lr: 1.000e-02, eta: 8:23:35, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.8549 2023-01-03 19:22:30,549 - mmcls - INFO - Epoch [32][2600/5005] lr: 1.000e-02, eta: 8:23:27, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.8507 2023-01-03 19:22:39,082 - mmcls - INFO - Epoch [32][2700/5005] lr: 1.000e-02, eta: 8:23:17, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8431 2023-01-03 19:22:47,760 - mmcls - INFO - Epoch [32][2800/5005] lr: 1.000e-02, eta: 8:23:08, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8313 2023-01-03 19:22:56,509 - mmcls - INFO - Epoch [32][2900/5005] lr: 1.000e-02, eta: 8:22:59, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.8497 2023-01-03 19:23:05,487 - mmcls - INFO - Epoch [32][3000/5005] lr: 1.000e-02, eta: 8:22:51, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.8420 2023-01-03 19:23:14,183 - mmcls - INFO - Epoch [32][3100/5005] lr: 1.000e-02, eta: 8:22:42, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.8451 2023-01-03 19:23:22,953 - mmcls - INFO - Epoch [32][3200/5005] lr: 1.000e-02, eta: 8:22:33, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.8400 2023-01-03 19:23:31,558 - mmcls - INFO - Epoch [32][3300/5005] lr: 1.000e-02, eta: 8:22:24, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.8627 2023-01-03 19:23:40,163 - mmcls - INFO - Epoch [32][3400/5005] lr: 1.000e-02, eta: 8:22:14, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8520 2023-01-03 19:23:48,995 - mmcls - INFO - Epoch [32][3500/5005] lr: 1.000e-02, eta: 8:22:06, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.8491 2023-01-03 19:23:57,615 - mmcls - INFO - Epoch [32][3600/5005] lr: 1.000e-02, eta: 8:21:56, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.8534 2023-01-03 19:24:06,285 - mmcls - INFO - Epoch [32][3700/5005] lr: 1.000e-02, eta: 8:21:47, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.8401 2023-01-03 19:24:15,157 - mmcls - INFO - Epoch [32][3800/5005] lr: 1.000e-02, eta: 8:21:38, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.8516 2023-01-03 19:24:23,914 - mmcls - INFO - Epoch [32][3900/5005] lr: 1.000e-02, eta: 8:21:30, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.8589 2023-01-03 19:24:32,318 - mmcls - INFO - Epoch [32][4000/5005] lr: 1.000e-02, eta: 8:21:20, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.8520 2023-01-03 19:24:40,840 - mmcls - INFO - Epoch [32][4100/5005] lr: 1.000e-02, eta: 8:21:10, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.8409 2023-01-03 19:24:49,442 - mmcls - INFO - Epoch [32][4200/5005] lr: 1.000e-02, eta: 8:21:01, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.8470 2023-01-03 19:24:58,090 - mmcls - INFO - Epoch [32][4300/5005] lr: 1.000e-02, eta: 8:20:52, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.8543 2023-01-03 19:25:06,750 - mmcls - INFO - Epoch [32][4400/5005] lr: 1.000e-02, eta: 8:20:43, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8567 2023-01-03 19:25:15,598 - mmcls - INFO - Epoch [32][4500/5005] lr: 1.000e-02, eta: 8:20:34, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.8385 2023-01-03 19:25:24,351 - mmcls - INFO - Epoch [32][4600/5005] lr: 1.000e-02, eta: 8:20:25, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.8474 2023-01-03 19:25:32,989 - mmcls - INFO - Epoch [32][4700/5005] lr: 1.000e-02, eta: 8:20:16, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.8342 2023-01-03 19:25:41,865 - mmcls - INFO - Epoch [32][4800/5005] lr: 1.000e-02, eta: 8:20:07, time: 0.089, data_time: 0.035, memory: 1125, loss: 1.8492 2023-01-03 19:25:50,252 - mmcls - INFO - Epoch [32][4900/5005] lr: 1.000e-02, eta: 8:19:58, time: 0.084, data_time: 0.035, memory: 1125, loss: 1.8506 2023-01-03 19:25:58,883 - mmcls - INFO - Epoch [32][5000/5005] lr: 1.000e-02, eta: 8:19:48, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.8370 2023-01-03 19:26:21,518 - mmcls - INFO - Epoch(val) [32][196] accuracy_top-1: 63.2260, accuracy_top-5: 85.3400 2023-01-03 19:26:32,625 - mmcls - INFO - Epoch [33][100/5005] lr: 1.000e-02, eta: 8:19:43, time: 0.111, data_time: 0.058, memory: 1125, loss: 1.8012 2023-01-03 19:26:41,318 - mmcls - INFO - Epoch [33][200/5005] lr: 1.000e-02, eta: 8:19:34, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.8229 2023-01-03 19:26:49,946 - mmcls - INFO - Epoch [33][300/5005] lr: 1.000e-02, eta: 8:19:25, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.8209 2023-01-03 19:26:58,932 - mmcls - INFO - Epoch [33][400/5005] lr: 1.000e-02, eta: 8:19:16, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.8154 2023-01-03 19:27:07,550 - mmcls - INFO - Epoch [33][500/5005] lr: 1.000e-02, eta: 8:19:07, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8085 2023-01-03 19:27:16,624 - mmcls - INFO - Epoch [33][600/5005] lr: 1.000e-02, eta: 8:18:59, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.8110 2023-01-03 19:27:25,292 - mmcls - INFO - Epoch [33][700/5005] lr: 1.000e-02, eta: 8:18:50, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.8085 2023-01-03 19:27:34,262 - mmcls - INFO - Epoch [33][800/5005] lr: 1.000e-02, eta: 8:18:41, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8108 2023-01-03 19:27:42,900 - mmcls - INFO - Epoch [33][900/5005] lr: 1.000e-02, eta: 8:18:32, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.8256 2023-01-03 19:27:51,535 - mmcls - INFO - Epoch [33][1000/5005] lr: 1.000e-02, eta: 8:18:23, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7979 2023-01-03 19:28:00,234 - mmcls - INFO - Epoch [33][1100/5005] lr: 1.000e-02, eta: 8:18:14, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.8059 2023-01-03 19:28:08,950 - mmcls - INFO - Epoch [33][1200/5005] lr: 1.000e-02, eta: 8:18:05, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.7758 2023-01-03 19:28:17,781 - mmcls - INFO - Epoch [33][1300/5005] lr: 1.000e-02, eta: 8:17:56, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.8378 2023-01-03 19:28:26,327 - mmcls - INFO - Epoch [33][1400/5005] lr: 1.000e-02, eta: 8:17:47, time: 0.085, data_time: 0.038, memory: 1125, loss: 1.7962 2023-01-03 19:28:35,263 - mmcls - INFO - Epoch [33][1500/5005] lr: 1.000e-02, eta: 8:17:38, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.8103 2023-01-03 19:28:44,099 - mmcls - INFO - Epoch [33][1600/5005] lr: 1.000e-02, eta: 8:17:29, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.8185 2023-01-03 19:28:52,769 - mmcls - INFO - Epoch [33][1700/5005] lr: 1.000e-02, eta: 8:17:20, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.8091 2023-01-03 19:29:01,422 - mmcls - INFO - Epoch [33][1800/5005] lr: 1.000e-02, eta: 8:17:11, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.8391 2023-01-03 19:29:10,471 - mmcls - INFO - Epoch [33][1900/5005] lr: 1.000e-02, eta: 8:17:03, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7966 2023-01-03 19:29:19,778 - mmcls - INFO - Epoch [33][2000/5005] lr: 1.000e-02, eta: 8:16:55, time: 0.093, data_time: 0.012, memory: 1125, loss: 1.8244 2023-01-03 19:29:28,276 - mmcls - INFO - Epoch [33][2100/5005] lr: 1.000e-02, eta: 8:16:45, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.8073 2023-01-03 19:29:36,924 - mmcls - INFO - Epoch [33][2200/5005] lr: 1.000e-02, eta: 8:16:36, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.8112 2023-01-03 19:29:45,433 - mmcls - INFO - Epoch [33][2300/5005] lr: 1.000e-02, eta: 8:16:27, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.8126 2023-01-03 19:29:54,418 - mmcls - INFO - Epoch [33][2400/5005] lr: 1.000e-02, eta: 8:16:18, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8043 2023-01-03 19:30:03,154 - mmcls - INFO - Epoch [33][2500/5005] lr: 1.000e-02, eta: 8:16:09, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.7801 2023-01-03 19:30:11,813 - mmcls - INFO - Epoch [33][2600/5005] lr: 1.000e-02, eta: 8:16:00, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.8038 2023-01-03 19:30:20,638 - mmcls - INFO - Epoch [33][2700/5005] lr: 1.000e-02, eta: 8:15:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8174 2023-01-03 19:30:29,506 - mmcls - INFO - Epoch [33][2800/5005] lr: 1.000e-02, eta: 8:15:43, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7970 2023-01-03 19:30:38,522 - mmcls - INFO - Epoch [33][2900/5005] lr: 1.000e-02, eta: 8:15:34, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.8239 2023-01-03 19:30:47,229 - mmcls - INFO - Epoch [33][3000/5005] lr: 1.000e-02, eta: 8:15:25, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8221 2023-01-03 19:30:55,942 - mmcls - INFO - Epoch [33][3100/5005] lr: 1.000e-02, eta: 8:15:16, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7986 2023-01-03 19:31:04,943 - mmcls - INFO - Epoch [33][3200/5005] lr: 1.000e-02, eta: 8:15:08, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.8320 2023-01-03 19:31:13,723 - mmcls - INFO - Epoch [33][3300/5005] lr: 1.000e-02, eta: 8:14:59, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.8210 2023-01-03 19:31:22,414 - mmcls - INFO - Epoch [33][3400/5005] lr: 1.000e-02, eta: 8:14:50, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.8149 2023-01-03 19:31:31,364 - mmcls - INFO - Epoch [33][3500/5005] lr: 1.000e-02, eta: 8:14:42, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.8344 2023-01-03 19:31:39,920 - mmcls - INFO - Epoch [33][3600/5005] lr: 1.000e-02, eta: 8:14:32, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.8074 2023-01-03 19:31:48,548 - mmcls - INFO - Epoch [33][3700/5005] lr: 1.000e-02, eta: 8:14:23, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.8154 2023-01-03 19:31:57,529 - mmcls - INFO - Epoch [33][3800/5005] lr: 1.000e-02, eta: 8:14:15, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.8274 2023-01-03 19:32:06,263 - mmcls - INFO - Epoch [33][3900/5005] lr: 1.000e-02, eta: 8:14:06, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.8077 2023-01-03 19:32:15,234 - mmcls - INFO - Epoch [33][4000/5005] lr: 1.000e-02, eta: 8:13:57, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.8193 2023-01-03 19:32:23,905 - mmcls - INFO - Epoch [33][4100/5005] lr: 1.000e-02, eta: 8:13:48, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.8122 2023-01-03 19:32:32,689 - mmcls - INFO - Epoch [33][4200/5005] lr: 1.000e-02, eta: 8:13:39, time: 0.088, data_time: 0.034, memory: 1125, loss: 1.7952 2023-01-03 19:32:41,768 - mmcls - INFO - Epoch [33][4300/5005] lr: 1.000e-02, eta: 8:13:31, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.8143 2023-01-03 19:32:50,551 - mmcls - INFO - Epoch [33][4400/5005] lr: 1.000e-02, eta: 8:13:22, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7978 2023-01-03 19:32:59,173 - mmcls - INFO - Epoch [33][4500/5005] lr: 1.000e-02, eta: 8:13:13, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7861 2023-01-03 19:33:07,917 - mmcls - INFO - Epoch [33][4600/5005] lr: 1.000e-02, eta: 8:13:04, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.8240 2023-01-03 19:33:16,673 - mmcls - INFO - Epoch [33][4700/5005] lr: 1.000e-02, eta: 8:12:55, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.8143 2023-01-03 19:33:25,437 - mmcls - INFO - Epoch [33][4800/5005] lr: 1.000e-02, eta: 8:12:46, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.8051 2023-01-03 19:33:34,271 - mmcls - INFO - Epoch [33][4900/5005] lr: 1.000e-02, eta: 8:12:37, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7947 2023-01-03 19:33:43,283 - mmcls - INFO - Epoch [33][5000/5005] lr: 1.000e-02, eta: 8:12:29, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.8114 2023-01-03 19:34:05,803 - mmcls - INFO - Epoch(val) [33][196] accuracy_top-1: 63.7280, accuracy_top-5: 85.5700 2023-01-03 19:34:16,909 - mmcls - INFO - Epoch [34][100/5005] lr: 1.000e-02, eta: 8:12:23, time: 0.111, data_time: 0.057, memory: 1125, loss: 1.7634 2023-01-03 19:34:25,970 - mmcls - INFO - Epoch [34][200/5005] lr: 1.000e-02, eta: 8:12:15, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.7934 2023-01-03 19:34:34,731 - mmcls - INFO - Epoch [34][300/5005] lr: 1.000e-02, eta: 8:12:06, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7722 2023-01-03 19:34:43,264 - mmcls - INFO - Epoch [34][400/5005] lr: 1.000e-02, eta: 8:11:57, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7744 2023-01-03 19:34:52,117 - mmcls - INFO - Epoch [34][500/5005] lr: 1.000e-02, eta: 8:11:48, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7721 2023-01-03 19:35:00,670 - mmcls - INFO - Epoch [34][600/5005] lr: 1.000e-02, eta: 8:11:39, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.7615 2023-01-03 19:35:09,608 - mmcls - INFO - Epoch [34][700/5005] lr: 1.000e-02, eta: 8:11:30, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.7742 2023-01-03 19:35:18,348 - mmcls - INFO - Epoch [34][800/5005] lr: 1.000e-02, eta: 8:11:21, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7853 2023-01-03 19:35:27,497 - mmcls - INFO - Epoch [34][900/5005] lr: 1.000e-02, eta: 8:11:13, time: 0.091, data_time: 0.029, memory: 1125, loss: 1.7705 2023-01-03 19:35:35,999 - mmcls - INFO - Epoch [34][1000/5005] lr: 1.000e-02, eta: 8:11:03, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7951 2023-01-03 19:35:44,557 - mmcls - INFO - Epoch [34][1100/5005] lr: 1.000e-02, eta: 8:10:54, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7821 2023-01-03 19:35:53,117 - mmcls - INFO - Epoch [34][1200/5005] lr: 1.000e-02, eta: 8:10:45, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7529 2023-01-03 19:36:01,729 - mmcls - INFO - Epoch [34][1300/5005] lr: 1.000e-02, eta: 8:10:36, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7708 2023-01-03 19:36:10,189 - mmcls - INFO - Epoch [34][1400/5005] lr: 1.000e-02, eta: 8:10:26, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.7824 2023-01-03 19:36:18,726 - mmcls - INFO - Epoch [34][1500/5005] lr: 1.000e-02, eta: 8:10:17, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7848 2023-01-03 19:36:27,234 - mmcls - INFO - Epoch [34][1600/5005] lr: 1.000e-02, eta: 8:10:07, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7826 2023-01-03 19:36:36,318 - mmcls - INFO - Epoch [34][1700/5005] lr: 1.000e-02, eta: 8:09:59, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.7969 2023-01-03 19:36:44,881 - mmcls - INFO - Epoch [34][1800/5005] lr: 1.000e-02, eta: 8:09:50, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7789 2023-01-03 19:36:53,644 - mmcls - INFO - Epoch [34][1900/5005] lr: 1.000e-02, eta: 8:09:41, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7977 2023-01-03 19:37:02,369 - mmcls - INFO - Epoch [34][2000/5005] lr: 1.000e-02, eta: 8:09:32, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7776 2023-01-03 19:37:10,966 - mmcls - INFO - Epoch [34][2100/5005] lr: 1.000e-02, eta: 8:09:23, time: 0.086, data_time: 0.051, memory: 1125, loss: 1.7694 2023-01-03 19:37:19,759 - mmcls - INFO - Epoch [34][2200/5005] lr: 1.000e-02, eta: 8:09:14, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7812 2023-01-03 19:37:28,613 - mmcls - INFO - Epoch [34][2300/5005] lr: 1.000e-02, eta: 8:09:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7672 2023-01-03 19:37:37,280 - mmcls - INFO - Epoch [34][2400/5005] lr: 1.000e-02, eta: 8:08:56, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7848 2023-01-03 19:37:46,026 - mmcls - INFO - Epoch [34][2500/5005] lr: 1.000e-02, eta: 8:08:47, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7945 2023-01-03 19:37:54,761 - mmcls - INFO - Epoch [34][2600/5005] lr: 1.000e-02, eta: 8:08:38, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7779 2023-01-03 19:38:03,122 - mmcls - INFO - Epoch [34][2700/5005] lr: 1.000e-02, eta: 8:08:28, time: 0.084, data_time: 0.015, memory: 1125, loss: 1.7794 2023-01-03 19:38:12,036 - mmcls - INFO - Epoch [34][2800/5005] lr: 1.000e-02, eta: 8:08:20, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7776 2023-01-03 19:38:20,855 - mmcls - INFO - Epoch [34][2900/5005] lr: 1.000e-02, eta: 8:08:11, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7796 2023-01-03 19:38:29,351 - mmcls - INFO - Epoch [34][3000/5005] lr: 1.000e-02, eta: 8:08:01, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7611 2023-01-03 19:38:37,984 - mmcls - INFO - Epoch [34][3100/5005] lr: 1.000e-02, eta: 8:07:52, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7895 2023-01-03 19:38:46,466 - mmcls - INFO - Epoch [34][3200/5005] lr: 1.000e-02, eta: 8:07:43, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7862 2023-01-03 19:38:55,348 - mmcls - INFO - Epoch [34][3300/5005] lr: 1.000e-02, eta: 8:07:34, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7737 2023-01-03 19:39:03,896 - mmcls - INFO - Epoch [34][3400/5005] lr: 1.000e-02, eta: 8:07:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7867 2023-01-03 19:39:12,394 - mmcls - INFO - Epoch [34][3500/5005] lr: 1.000e-02, eta: 8:07:15, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7728 2023-01-03 19:39:21,367 - mmcls - INFO - Epoch [34][3600/5005] lr: 1.000e-02, eta: 8:07:07, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.7750 2023-01-03 19:39:30,250 - mmcls - INFO - Epoch [34][3700/5005] lr: 1.000e-02, eta: 8:06:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7638 2023-01-03 19:39:39,104 - mmcls - INFO - Epoch [34][3800/5005] lr: 1.000e-02, eta: 8:06:49, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7708 2023-01-03 19:39:48,041 - mmcls - INFO - Epoch [34][3900/5005] lr: 1.000e-02, eta: 8:06:41, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7711 2023-01-03 19:39:56,869 - mmcls - INFO - Epoch [34][4000/5005] lr: 1.000e-02, eta: 8:06:32, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7752 2023-01-03 19:40:05,597 - mmcls - INFO - Epoch [34][4100/5005] lr: 1.000e-02, eta: 8:06:23, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7807 2023-01-03 19:40:14,417 - mmcls - INFO - Epoch [34][4200/5005] lr: 1.000e-02, eta: 8:06:14, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7668 2023-01-03 19:40:23,064 - mmcls - INFO - Epoch [34][4300/5005] lr: 1.000e-02, eta: 8:06:05, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7986 2023-01-03 19:40:31,935 - mmcls - INFO - Epoch [34][4400/5005] lr: 1.000e-02, eta: 8:05:57, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7574 2023-01-03 19:40:40,763 - mmcls - INFO - Epoch [34][4500/5005] lr: 1.000e-02, eta: 8:05:48, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7783 2023-01-03 19:40:49,814 - mmcls - INFO - Epoch [34][4600/5005] lr: 1.000e-02, eta: 8:05:39, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7792 2023-01-03 19:40:58,619 - mmcls - INFO - Epoch [34][4700/5005] lr: 1.000e-02, eta: 8:05:31, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7902 2023-01-03 19:41:07,194 - mmcls - INFO - Epoch [34][4800/5005] lr: 1.000e-02, eta: 8:05:21, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7546 2023-01-03 19:41:15,993 - mmcls - INFO - Epoch [34][4900/5005] lr: 1.000e-02, eta: 8:05:13, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7959 2023-01-03 19:41:24,709 - mmcls - INFO - Epoch [34][5000/5005] lr: 1.000e-02, eta: 8:05:04, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7859 2023-01-03 19:41:47,420 - mmcls - INFO - Epoch(val) [34][196] accuracy_top-1: 64.5200, accuracy_top-5: 86.0300 2023-01-03 19:41:58,225 - mmcls - INFO - Epoch [35][100/5005] lr: 1.000e-02, eta: 8:04:57, time: 0.108, data_time: 0.038, memory: 1125, loss: 1.7345 2023-01-03 19:42:07,341 - mmcls - INFO - Epoch [35][200/5005] lr: 1.000e-02, eta: 8:04:49, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.7472 2023-01-03 19:42:16,183 - mmcls - INFO - Epoch [35][300/5005] lr: 1.000e-02, eta: 8:04:40, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7373 2023-01-03 19:42:25,026 - mmcls - INFO - Epoch [35][400/5005] lr: 1.000e-02, eta: 8:04:31, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7534 2023-01-03 19:42:33,812 - mmcls - INFO - Epoch [35][500/5005] lr: 1.000e-02, eta: 8:04:23, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7741 2023-01-03 19:42:42,411 - mmcls - INFO - Epoch [35][600/5005] lr: 1.000e-02, eta: 8:04:13, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.7265 2023-01-03 19:42:51,099 - mmcls - INFO - Epoch [35][700/5005] lr: 1.000e-02, eta: 8:04:04, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7323 2023-01-03 19:42:59,710 - mmcls - INFO - Epoch [35][800/5005] lr: 1.000e-02, eta: 8:03:55, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7747 2023-01-03 19:43:08,329 - mmcls - INFO - Epoch [35][900/5005] lr: 1.000e-02, eta: 8:03:46, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.7518 2023-01-03 19:43:17,085 - mmcls - INFO - Epoch [35][1000/5005] lr: 1.000e-02, eta: 8:03:37, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.7550 2023-01-03 19:43:25,931 - mmcls - INFO - Epoch [35][1100/5005] lr: 1.000e-02, eta: 8:03:28, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7555 2023-01-03 19:43:34,880 - mmcls - INFO - Epoch [35][1200/5005] lr: 1.000e-02, eta: 8:03:20, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7648 2023-01-03 19:43:43,632 - mmcls - INFO - Epoch [35][1300/5005] lr: 1.000e-02, eta: 8:03:11, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7739 2023-01-03 19:43:52,255 - mmcls - INFO - Epoch [35][1400/5005] lr: 1.000e-02, eta: 8:03:02, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7436 2023-01-03 19:44:00,898 - mmcls - INFO - Epoch [35][1500/5005] lr: 1.000e-02, eta: 8:02:53, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7925 2023-01-03 19:44:09,470 - mmcls - INFO - Epoch [35][1600/5005] lr: 1.000e-02, eta: 8:02:43, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7518 2023-01-03 19:44:18,272 - mmcls - INFO - Epoch [35][1700/5005] lr: 1.000e-02, eta: 8:02:34, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7373 2023-01-03 19:44:27,348 - mmcls - INFO - Epoch [35][1800/5005] lr: 1.000e-02, eta: 8:02:26, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.7597 2023-01-03 19:44:35,780 - mmcls - INFO - Epoch [35][1900/5005] lr: 1.000e-02, eta: 8:02:17, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7482 2023-01-03 19:44:44,298 - mmcls - INFO - Epoch [35][2000/5005] lr: 1.000e-02, eta: 8:02:07, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7246 2023-01-03 19:44:52,819 - mmcls - INFO - Epoch [35][2100/5005] lr: 1.000e-02, eta: 8:01:58, time: 0.085, data_time: 0.035, memory: 1125, loss: 1.7591 2023-01-03 19:45:01,525 - mmcls - INFO - Epoch [35][2200/5005] lr: 1.000e-02, eta: 8:01:49, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7607 2023-01-03 19:45:10,284 - mmcls - INFO - Epoch [35][2300/5005] lr: 1.000e-02, eta: 8:01:40, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7872 2023-01-03 19:45:18,996 - mmcls - INFO - Epoch [35][2400/5005] lr: 1.000e-02, eta: 8:01:31, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7522 2023-01-03 19:45:28,013 - mmcls - INFO - Epoch [35][2500/5005] lr: 1.000e-02, eta: 8:01:23, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7764 2023-01-03 19:45:36,648 - mmcls - INFO - Epoch [35][2600/5005] lr: 1.000e-02, eta: 8:01:13, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7714 2023-01-03 19:45:45,565 - mmcls - INFO - Epoch [35][2700/5005] lr: 1.000e-02, eta: 8:01:05, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7670 2023-01-03 19:45:53,986 - mmcls - INFO - Epoch [35][2800/5005] lr: 1.000e-02, eta: 8:00:55, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7599 2023-01-03 19:46:02,587 - mmcls - INFO - Epoch [35][2900/5005] lr: 1.000e-02, eta: 8:00:46, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7389 2023-01-03 19:46:11,471 - mmcls - INFO - Epoch [35][3000/5005] lr: 1.000e-02, eta: 8:00:37, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7713 2023-01-03 19:46:20,041 - mmcls - INFO - Epoch [35][3100/5005] lr: 1.000e-02, eta: 8:00:28, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7595 2023-01-03 19:46:29,334 - mmcls - INFO - Epoch [35][3200/5005] lr: 1.000e-02, eta: 8:00:20, time: 0.093, data_time: 0.026, memory: 1125, loss: 1.7871 2023-01-03 19:46:37,874 - mmcls - INFO - Epoch [35][3300/5005] lr: 1.000e-02, eta: 8:00:11, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7762 2023-01-03 19:46:46,410 - mmcls - INFO - Epoch [35][3400/5005] lr: 1.000e-02, eta: 8:00:02, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7620 2023-01-03 19:46:55,182 - mmcls - INFO - Epoch [35][3500/5005] lr: 1.000e-02, eta: 7:59:53, time: 0.088, data_time: 0.040, memory: 1125, loss: 1.7819 2023-01-03 19:47:03,848 - mmcls - INFO - Epoch [35][3600/5005] lr: 1.000e-02, eta: 7:59:44, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7861 2023-01-03 19:47:12,629 - mmcls - INFO - Epoch [35][3700/5005] lr: 1.000e-02, eta: 7:59:35, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7819 2023-01-03 19:47:21,205 - mmcls - INFO - Epoch [35][3800/5005] lr: 1.000e-02, eta: 7:59:25, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7716 2023-01-03 19:47:29,906 - mmcls - INFO - Epoch [35][3900/5005] lr: 1.000e-02, eta: 7:59:16, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7684 2023-01-03 19:47:38,716 - mmcls - INFO - Epoch [35][4000/5005] lr: 1.000e-02, eta: 7:59:08, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.7631 2023-01-03 19:47:47,394 - mmcls - INFO - Epoch [35][4100/5005] lr: 1.000e-02, eta: 7:58:59, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7569 2023-01-03 19:47:56,175 - mmcls - INFO - Epoch [35][4200/5005] lr: 1.000e-02, eta: 7:58:50, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7626 2023-01-03 19:48:04,525 - mmcls - INFO - Epoch [35][4300/5005] lr: 1.000e-02, eta: 7:58:40, time: 0.083, data_time: 0.016, memory: 1125, loss: 1.7680 2023-01-03 19:48:13,335 - mmcls - INFO - Epoch [35][4400/5005] lr: 1.000e-02, eta: 7:58:31, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7598 2023-01-03 19:48:22,042 - mmcls - INFO - Epoch [35][4500/5005] lr: 1.000e-02, eta: 7:58:22, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7583 2023-01-03 19:48:30,612 - mmcls - INFO - Epoch [35][4600/5005] lr: 1.000e-02, eta: 7:58:13, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7434 2023-01-03 19:48:39,334 - mmcls - INFO - Epoch [35][4700/5005] lr: 1.000e-02, eta: 7:58:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7724 2023-01-03 19:48:48,350 - mmcls - INFO - Epoch [35][4800/5005] lr: 1.000e-02, eta: 7:57:56, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7572 2023-01-03 19:48:57,131 - mmcls - INFO - Epoch [35][4900/5005] lr: 1.000e-02, eta: 7:57:47, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7600 2023-01-03 19:49:05,807 - mmcls - INFO - Epoch [35][5000/5005] lr: 1.000e-02, eta: 7:57:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7742 2023-01-03 19:49:28,417 - mmcls - INFO - Epoch(val) [35][196] accuracy_top-1: 64.2960, accuracy_top-5: 86.1160 2023-01-03 19:49:39,427 - mmcls - INFO - Epoch [36][100/5005] lr: 1.000e-02, eta: 7:57:32, time: 0.110, data_time: 0.061, memory: 1125, loss: 1.7245 2023-01-03 19:49:47,963 - mmcls - INFO - Epoch [36][200/5005] lr: 1.000e-02, eta: 7:57:22, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7420 2023-01-03 19:49:56,793 - mmcls - INFO - Epoch [36][300/5005] lr: 1.000e-02, eta: 7:57:14, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7289 2023-01-03 19:50:05,233 - mmcls - INFO - Epoch [36][400/5005] lr: 1.000e-02, eta: 7:57:04, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.7254 2023-01-03 19:50:13,917 - mmcls - INFO - Epoch [36][500/5005] lr: 1.000e-02, eta: 7:56:55, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7337 2023-01-03 19:50:22,490 - mmcls - INFO - Epoch [36][600/5005] lr: 1.000e-02, eta: 7:56:46, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7290 2023-01-03 19:50:31,157 - mmcls - INFO - Epoch [36][700/5005] lr: 1.000e-02, eta: 7:56:37, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7361 2023-01-03 19:50:39,717 - mmcls - INFO - Epoch [36][800/5005] lr: 1.000e-02, eta: 7:56:27, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7475 2023-01-03 19:50:48,455 - mmcls - INFO - Epoch [36][900/5005] lr: 1.000e-02, eta: 7:56:18, time: 0.087, data_time: 0.048, memory: 1125, loss: 1.7400 2023-01-03 19:50:57,010 - mmcls - INFO - Epoch [36][1000/5005] lr: 1.000e-02, eta: 7:56:09, time: 0.086, data_time: 0.037, memory: 1125, loss: 1.7555 2023-01-03 19:51:05,998 - mmcls - INFO - Epoch [36][1100/5005] lr: 1.000e-02, eta: 7:56:01, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7535 2023-01-03 19:51:14,681 - mmcls - INFO - Epoch [36][1200/5005] lr: 1.000e-02, eta: 7:55:52, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7284 2023-01-03 19:51:23,435 - mmcls - INFO - Epoch [36][1300/5005] lr: 1.000e-02, eta: 7:55:43, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.7511 2023-01-03 19:51:32,231 - mmcls - INFO - Epoch [36][1400/5005] lr: 1.000e-02, eta: 7:55:34, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7555 2023-01-03 19:51:41,347 - mmcls - INFO - Epoch [36][1500/5005] lr: 1.000e-02, eta: 7:55:26, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7306 2023-01-03 19:51:50,040 - mmcls - INFO - Epoch [36][1600/5005] lr: 1.000e-02, eta: 7:55:17, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.7379 2023-01-03 19:51:58,896 - mmcls - INFO - Epoch [36][1700/5005] lr: 1.000e-02, eta: 7:55:08, time: 0.089, data_time: 0.030, memory: 1125, loss: 1.7369 2023-01-03 19:52:07,986 - mmcls - INFO - Epoch [36][1800/5005] lr: 1.000e-02, eta: 7:55:00, time: 0.091, data_time: 0.032, memory: 1125, loss: 1.7402 2023-01-03 19:52:16,640 - mmcls - INFO - Epoch [36][1900/5005] lr: 1.000e-02, eta: 7:54:51, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7309 2023-01-03 19:52:25,286 - mmcls - INFO - Epoch [36][2000/5005] lr: 1.000e-02, eta: 7:54:41, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7557 2023-01-03 19:52:34,281 - mmcls - INFO - Epoch [36][2100/5005] lr: 1.000e-02, eta: 7:54:33, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7489 2023-01-03 19:52:42,970 - mmcls - INFO - Epoch [36][2200/5005] lr: 1.000e-02, eta: 7:54:24, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7519 2023-01-03 19:52:51,922 - mmcls - INFO - Epoch [36][2300/5005] lr: 1.000e-02, eta: 7:54:15, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7228 2023-01-03 19:53:00,574 - mmcls - INFO - Epoch [36][2400/5005] lr: 1.000e-02, eta: 7:54:06, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7364 2023-01-03 19:53:09,287 - mmcls - INFO - Epoch [36][2500/5005] lr: 1.000e-02, eta: 7:53:57, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7528 2023-01-03 19:53:17,791 - mmcls - INFO - Epoch [36][2600/5005] lr: 1.000e-02, eta: 7:53:48, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7534 2023-01-03 19:53:26,541 - mmcls - INFO - Epoch [36][2700/5005] lr: 1.000e-02, eta: 7:53:39, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7411 2023-01-03 19:53:35,277 - mmcls - INFO - Epoch [36][2800/5005] lr: 1.000e-02, eta: 7:53:30, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7502 2023-01-03 19:53:43,946 - mmcls - INFO - Epoch [36][2900/5005] lr: 1.000e-02, eta: 7:53:21, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7479 2023-01-03 19:53:52,629 - mmcls - INFO - Epoch [36][3000/5005] lr: 1.000e-02, eta: 7:53:12, time: 0.087, data_time: 0.035, memory: 1125, loss: 1.7377 2023-01-03 19:54:01,184 - mmcls - INFO - Epoch [36][3100/5005] lr: 1.000e-02, eta: 7:53:03, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.7454 2023-01-03 19:54:10,165 - mmcls - INFO - Epoch [36][3200/5005] lr: 1.000e-02, eta: 7:52:54, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7445 2023-01-03 19:54:18,946 - mmcls - INFO - Epoch [36][3300/5005] lr: 1.000e-02, eta: 7:52:45, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7665 2023-01-03 19:54:27,537 - mmcls - INFO - Epoch [36][3400/5005] lr: 1.000e-02, eta: 7:52:36, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7433 2023-01-03 19:54:36,225 - mmcls - INFO - Epoch [36][3500/5005] lr: 1.000e-02, eta: 7:52:27, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7442 2023-01-03 19:54:44,952 - mmcls - INFO - Epoch [36][3600/5005] lr: 1.000e-02, eta: 7:52:18, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7670 2023-01-03 19:54:53,754 - mmcls - INFO - Epoch [36][3700/5005] lr: 1.000e-02, eta: 7:52:09, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7510 2023-01-03 19:55:02,278 - mmcls - INFO - Epoch [36][3800/5005] lr: 1.000e-02, eta: 7:52:00, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7393 2023-01-03 19:55:11,296 - mmcls - INFO - Epoch [36][3900/5005] lr: 1.000e-02, eta: 7:51:52, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.7751 2023-01-03 19:55:19,842 - mmcls - INFO - Epoch [36][4000/5005] lr: 1.000e-02, eta: 7:51:42, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7665 2023-01-03 19:55:28,418 - mmcls - INFO - Epoch [36][4100/5005] lr: 1.000e-02, eta: 7:51:33, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7552 2023-01-03 19:55:36,986 - mmcls - INFO - Epoch [36][4200/5005] lr: 1.000e-02, eta: 7:51:24, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7634 2023-01-03 19:55:46,317 - mmcls - INFO - Epoch [36][4300/5005] lr: 1.000e-02, eta: 7:51:16, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.7477 2023-01-03 19:55:55,110 - mmcls - INFO - Epoch [36][4400/5005] lr: 1.000e-02, eta: 7:51:07, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7908 2023-01-03 19:56:03,760 - mmcls - INFO - Epoch [36][4500/5005] lr: 1.000e-02, eta: 7:50:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7517 2023-01-03 19:56:12,679 - mmcls - INFO - Epoch [36][4600/5005] lr: 1.000e-02, eta: 7:50:49, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7685 2023-01-03 19:56:21,890 - mmcls - INFO - Epoch [36][4700/5005] lr: 1.000e-02, eta: 7:50:41, time: 0.092, data_time: 0.013, memory: 1125, loss: 1.7532 2023-01-03 19:56:30,548 - mmcls - INFO - Epoch [36][4800/5005] lr: 1.000e-02, eta: 7:50:32, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7479 2023-01-03 19:56:39,205 - mmcls - INFO - Epoch [36][4900/5005] lr: 1.000e-02, eta: 7:50:23, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7493 2023-01-03 19:56:47,872 - mmcls - INFO - Epoch [36][5000/5005] lr: 1.000e-02, eta: 7:50:14, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7515 2023-01-03 19:57:10,623 - mmcls - INFO - Epoch(val) [36][196] accuracy_top-1: 64.5340, accuracy_top-5: 86.2000 2023-01-03 19:57:21,422 - mmcls - INFO - Epoch [37][100/5005] lr: 1.000e-02, eta: 7:50:08, time: 0.108, data_time: 0.045, memory: 1125, loss: 1.7054 2023-01-03 19:57:30,102 - mmcls - INFO - Epoch [37][200/5005] lr: 1.000e-02, eta: 7:49:58, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7250 2023-01-03 19:57:39,021 - mmcls - INFO - Epoch [37][300/5005] lr: 1.000e-02, eta: 7:49:50, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6999 2023-01-03 19:57:47,691 - mmcls - INFO - Epoch [37][400/5005] lr: 1.000e-02, eta: 7:49:41, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7194 2023-01-03 19:57:56,176 - mmcls - INFO - Epoch [37][500/5005] lr: 1.000e-02, eta: 7:49:31, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7364 2023-01-03 19:58:04,973 - mmcls - INFO - Epoch [37][600/5005] lr: 1.000e-02, eta: 7:49:23, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7205 2023-01-03 19:58:13,837 - mmcls - INFO - Epoch [37][700/5005] lr: 1.000e-02, eta: 7:49:14, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.7482 2023-01-03 19:58:22,752 - mmcls - INFO - Epoch [37][800/5005] lr: 1.000e-02, eta: 7:49:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7326 2023-01-03 19:58:31,732 - mmcls - INFO - Epoch [37][900/5005] lr: 1.000e-02, eta: 7:48:57, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7598 2023-01-03 19:58:40,346 - mmcls - INFO - Epoch [37][1000/5005] lr: 1.000e-02, eta: 7:48:48, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7329 2023-01-03 19:58:49,008 - mmcls - INFO - Epoch [37][1100/5005] lr: 1.000e-02, eta: 7:48:39, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.7448 2023-01-03 19:58:57,821 - mmcls - INFO - Epoch [37][1200/5005] lr: 1.000e-02, eta: 7:48:30, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7475 2023-01-03 19:59:06,294 - mmcls - INFO - Epoch [37][1300/5005] lr: 1.000e-02, eta: 7:48:20, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7391 2023-01-03 19:59:15,110 - mmcls - INFO - Epoch [37][1400/5005] lr: 1.000e-02, eta: 7:48:12, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7120 2023-01-03 19:59:23,977 - mmcls - INFO - Epoch [37][1500/5005] lr: 1.000e-02, eta: 7:48:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7431 2023-01-03 19:59:32,713 - mmcls - INFO - Epoch [37][1600/5005] lr: 1.000e-02, eta: 7:47:54, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7522 2023-01-03 19:59:41,355 - mmcls - INFO - Epoch [37][1700/5005] lr: 1.000e-02, eta: 7:47:45, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7336 2023-01-03 19:59:50,283 - mmcls - INFO - Epoch [37][1800/5005] lr: 1.000e-02, eta: 7:47:36, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.7106 2023-01-03 19:59:58,890 - mmcls - INFO - Epoch [37][1900/5005] lr: 1.000e-02, eta: 7:47:27, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7263 2023-01-03 20:00:07,569 - mmcls - INFO - Epoch [37][2000/5005] lr: 1.000e-02, eta: 7:47:18, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7182 2023-01-03 20:00:16,349 - mmcls - INFO - Epoch [37][2100/5005] lr: 1.000e-02, eta: 7:47:09, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7329 2023-01-03 20:00:25,080 - mmcls - INFO - Epoch [37][2200/5005] lr: 1.000e-02, eta: 7:47:00, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7462 2023-01-03 20:00:33,717 - mmcls - INFO - Epoch [37][2300/5005] lr: 1.000e-02, eta: 7:46:51, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7517 2023-01-03 20:00:42,486 - mmcls - INFO - Epoch [37][2400/5005] lr: 1.000e-02, eta: 7:46:42, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.7785 2023-01-03 20:00:51,288 - mmcls - INFO - Epoch [37][2500/5005] lr: 1.000e-02, eta: 7:46:33, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.7466 2023-01-03 20:01:00,096 - mmcls - INFO - Epoch [37][2600/5005] lr: 1.000e-02, eta: 7:46:25, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7278 2023-01-03 20:01:09,050 - mmcls - INFO - Epoch [37][2700/5005] lr: 1.000e-02, eta: 7:46:16, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.7274 2023-01-03 20:01:17,941 - mmcls - INFO - Epoch [37][2800/5005] lr: 1.000e-02, eta: 7:46:07, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7652 2023-01-03 20:01:26,550 - mmcls - INFO - Epoch [37][2900/5005] lr: 1.000e-02, eta: 7:45:58, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7417 2023-01-03 20:01:35,701 - mmcls - INFO - Epoch [37][3000/5005] lr: 1.000e-02, eta: 7:45:50, time: 0.092, data_time: 0.023, memory: 1125, loss: 1.7328 2023-01-03 20:01:44,306 - mmcls - INFO - Epoch [37][3100/5005] lr: 1.000e-02, eta: 7:45:41, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7357 2023-01-03 20:01:53,209 - mmcls - INFO - Epoch [37][3200/5005] lr: 1.000e-02, eta: 7:45:32, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7368 2023-01-03 20:02:02,045 - mmcls - INFO - Epoch [37][3300/5005] lr: 1.000e-02, eta: 7:45:23, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7352 2023-01-03 20:02:10,774 - mmcls - INFO - Epoch [37][3400/5005] lr: 1.000e-02, eta: 7:45:14, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7380 2023-01-03 20:02:19,433 - mmcls - INFO - Epoch [37][3500/5005] lr: 1.000e-02, eta: 7:45:05, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7409 2023-01-03 20:02:28,357 - mmcls - INFO - Epoch [37][3600/5005] lr: 1.000e-02, eta: 7:44:57, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7408 2023-01-03 20:02:37,106 - mmcls - INFO - Epoch [37][3700/5005] lr: 1.000e-02, eta: 7:44:48, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7698 2023-01-03 20:02:45,635 - mmcls - INFO - Epoch [37][3800/5005] lr: 1.000e-02, eta: 7:44:39, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7296 2023-01-03 20:02:54,474 - mmcls - INFO - Epoch [37][3900/5005] lr: 1.000e-02, eta: 7:44:30, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7543 2023-01-03 20:03:03,325 - mmcls - INFO - Epoch [37][4000/5005] lr: 1.000e-02, eta: 7:44:21, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7624 2023-01-03 20:03:12,085 - mmcls - INFO - Epoch [37][4100/5005] lr: 1.000e-02, eta: 7:44:12, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7437 2023-01-03 20:03:20,987 - mmcls - INFO - Epoch [37][4200/5005] lr: 1.000e-02, eta: 7:44:04, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7299 2023-01-03 20:03:29,473 - mmcls - INFO - Epoch [37][4300/5005] lr: 1.000e-02, eta: 7:43:54, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7375 2023-01-03 20:03:38,305 - mmcls - INFO - Epoch [37][4400/5005] lr: 1.000e-02, eta: 7:43:45, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7319 2023-01-03 20:03:47,247 - mmcls - INFO - Epoch [37][4500/5005] lr: 1.000e-02, eta: 7:43:37, time: 0.089, data_time: 0.040, memory: 1125, loss: 1.7526 2023-01-03 20:03:55,953 - mmcls - INFO - Epoch [37][4600/5005] lr: 1.000e-02, eta: 7:43:28, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7539 2023-01-03 20:04:04,589 - mmcls - INFO - Epoch [37][4700/5005] lr: 1.000e-02, eta: 7:43:19, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7513 2023-01-03 20:04:13,230 - mmcls - INFO - Epoch [37][4800/5005] lr: 1.000e-02, eta: 7:43:10, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7662 2023-01-03 20:04:22,102 - mmcls - INFO - Epoch [37][4900/5005] lr: 1.000e-02, eta: 7:43:01, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.7517 2023-01-03 20:04:30,704 - mmcls - INFO - Epoch [37][5000/5005] lr: 1.000e-02, eta: 7:42:52, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7303 2023-01-03 20:04:53,215 - mmcls - INFO - Epoch(val) [37][196] accuracy_top-1: 64.9800, accuracy_top-5: 86.3740 2023-01-03 20:05:04,186 - mmcls - INFO - Epoch [38][100/5005] lr: 1.000e-02, eta: 7:42:45, time: 0.110, data_time: 0.071, memory: 1125, loss: 1.7130 2023-01-03 20:05:12,953 - mmcls - INFO - Epoch [38][200/5005] lr: 1.000e-02, eta: 7:42:37, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7127 2023-01-03 20:05:21,618 - mmcls - INFO - Epoch [38][300/5005] lr: 1.000e-02, eta: 7:42:28, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7261 2023-01-03 20:05:30,384 - mmcls - INFO - Epoch [38][400/5005] lr: 1.000e-02, eta: 7:42:19, time: 0.088, data_time: 0.047, memory: 1125, loss: 1.7131 2023-01-03 20:05:39,487 - mmcls - INFO - Epoch [38][500/5005] lr: 1.000e-02, eta: 7:42:10, time: 0.091, data_time: 0.037, memory: 1125, loss: 1.7394 2023-01-03 20:05:48,385 - mmcls - INFO - Epoch [38][600/5005] lr: 1.000e-02, eta: 7:42:02, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7134 2023-01-03 20:05:56,921 - mmcls - INFO - Epoch [38][700/5005] lr: 1.000e-02, eta: 7:41:52, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.7221 2023-01-03 20:06:05,485 - mmcls - INFO - Epoch [38][800/5005] lr: 1.000e-02, eta: 7:41:43, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7016 2023-01-03 20:06:14,136 - mmcls - INFO - Epoch [38][900/5005] lr: 1.000e-02, eta: 7:41:34, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7117 2023-01-03 20:06:22,652 - mmcls - INFO - Epoch [38][1000/5005] lr: 1.000e-02, eta: 7:41:25, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7094 2023-01-03 20:06:31,251 - mmcls - INFO - Epoch [38][1100/5005] lr: 1.000e-02, eta: 7:41:16, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7096 2023-01-03 20:06:40,220 - mmcls - INFO - Epoch [38][1200/5005] lr: 1.000e-02, eta: 7:41:07, time: 0.090, data_time: 0.036, memory: 1125, loss: 1.7414 2023-01-03 20:06:49,108 - mmcls - INFO - Epoch [38][1300/5005] lr: 1.000e-02, eta: 7:40:58, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7305 2023-01-03 20:06:57,725 - mmcls - INFO - Epoch [38][1400/5005] lr: 1.000e-02, eta: 7:40:49, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7154 2023-01-03 20:07:06,365 - mmcls - INFO - Epoch [38][1500/5005] lr: 1.000e-02, eta: 7:40:40, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7347 2023-01-03 20:07:15,045 - mmcls - INFO - Epoch [38][1600/5005] lr: 1.000e-02, eta: 7:40:31, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7314 2023-01-03 20:07:23,912 - mmcls - INFO - Epoch [38][1700/5005] lr: 1.000e-02, eta: 7:40:22, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7449 2023-01-03 20:07:32,667 - mmcls - INFO - Epoch [38][1800/5005] lr: 1.000e-02, eta: 7:40:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7350 2023-01-03 20:07:41,421 - mmcls - INFO - Epoch [38][1900/5005] lr: 1.000e-02, eta: 7:40:05, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7345 2023-01-03 20:07:50,222 - mmcls - INFO - Epoch [38][2000/5005] lr: 1.000e-02, eta: 7:39:56, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7039 2023-01-03 20:07:59,038 - mmcls - INFO - Epoch [38][2100/5005] lr: 1.000e-02, eta: 7:39:47, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7233 2023-01-03 20:08:07,852 - mmcls - INFO - Epoch [38][2200/5005] lr: 1.000e-02, eta: 7:39:38, time: 0.088, data_time: 0.045, memory: 1125, loss: 1.7443 2023-01-03 20:08:16,647 - mmcls - INFO - Epoch [38][2300/5005] lr: 1.000e-02, eta: 7:39:29, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7112 2023-01-03 20:08:25,780 - mmcls - INFO - Epoch [38][2400/5005] lr: 1.000e-02, eta: 7:39:21, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7327 2023-01-03 20:08:34,368 - mmcls - INFO - Epoch [38][2500/5005] lr: 1.000e-02, eta: 7:39:12, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7399 2023-01-03 20:08:43,125 - mmcls - INFO - Epoch [38][2600/5005] lr: 1.000e-02, eta: 7:39:03, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.7173 2023-01-03 20:08:51,599 - mmcls - INFO - Epoch [38][2700/5005] lr: 1.000e-02, eta: 7:38:54, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7632 2023-01-03 20:09:00,300 - mmcls - INFO - Epoch [38][2800/5005] lr: 1.000e-02, eta: 7:38:45, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7493 2023-01-03 20:09:08,997 - mmcls - INFO - Epoch [38][2900/5005] lr: 1.000e-02, eta: 7:38:36, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.7275 2023-01-03 20:09:17,540 - mmcls - INFO - Epoch [38][3000/5005] lr: 1.000e-02, eta: 7:38:26, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7362 2023-01-03 20:09:26,172 - mmcls - INFO - Epoch [38][3100/5005] lr: 1.000e-02, eta: 7:38:17, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7095 2023-01-03 20:09:35,108 - mmcls - INFO - Epoch [38][3200/5005] lr: 1.000e-02, eta: 7:38:09, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.7526 2023-01-03 20:09:44,022 - mmcls - INFO - Epoch [38][3300/5005] lr: 1.000e-02, eta: 7:38:00, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7615 2023-01-03 20:09:52,853 - mmcls - INFO - Epoch [38][3400/5005] lr: 1.000e-02, eta: 7:37:51, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7407 2023-01-03 20:10:01,405 - mmcls - INFO - Epoch [38][3500/5005] lr: 1.000e-02, eta: 7:37:42, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7353 2023-01-03 20:10:10,180 - mmcls - INFO - Epoch [38][3600/5005] lr: 1.000e-02, eta: 7:37:33, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7579 2023-01-03 20:10:18,771 - mmcls - INFO - Epoch [38][3700/5005] lr: 1.000e-02, eta: 7:37:24, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7395 2023-01-03 20:10:27,651 - mmcls - INFO - Epoch [38][3800/5005] lr: 1.000e-02, eta: 7:37:15, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7404 2023-01-03 20:10:36,290 - mmcls - INFO - Epoch [38][3900/5005] lr: 1.000e-02, eta: 7:37:06, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7138 2023-01-03 20:10:44,994 - mmcls - INFO - Epoch [38][4000/5005] lr: 1.000e-02, eta: 7:36:57, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7337 2023-01-03 20:10:53,470 - mmcls - INFO - Epoch [38][4100/5005] lr: 1.000e-02, eta: 7:36:48, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7374 2023-01-03 20:11:02,253 - mmcls - INFO - Epoch [38][4200/5005] lr: 1.000e-02, eta: 7:36:39, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7562 2023-01-03 20:11:10,803 - mmcls - INFO - Epoch [38][4300/5005] lr: 1.000e-02, eta: 7:36:30, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7431 2023-01-03 20:11:19,739 - mmcls - INFO - Epoch [38][4400/5005] lr: 1.000e-02, eta: 7:36:21, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7370 2023-01-03 20:11:28,391 - mmcls - INFO - Epoch [38][4500/5005] lr: 1.000e-02, eta: 7:36:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7451 2023-01-03 20:11:37,104 - mmcls - INFO - Epoch [38][4600/5005] lr: 1.000e-02, eta: 7:36:03, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7303 2023-01-03 20:11:45,870 - mmcls - INFO - Epoch [38][4700/5005] lr: 1.000e-02, eta: 7:35:54, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7670 2023-01-03 20:11:54,653 - mmcls - INFO - Epoch [38][4800/5005] lr: 1.000e-02, eta: 7:35:46, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7397 2023-01-03 20:12:03,299 - mmcls - INFO - Epoch [38][4900/5005] lr: 1.000e-02, eta: 7:35:36, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7476 2023-01-03 20:12:11,962 - mmcls - INFO - Epoch [38][5000/5005] lr: 1.000e-02, eta: 7:35:27, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.7382 2023-01-03 20:12:34,505 - mmcls - INFO - Epoch(val) [38][196] accuracy_top-1: 64.9120, accuracy_top-5: 86.2980 2023-01-03 20:12:45,544 - mmcls - INFO - Epoch [39][100/5005] lr: 1.000e-02, eta: 7:35:21, time: 0.110, data_time: 0.070, memory: 1125, loss: 1.7145 2023-01-03 20:12:54,068 - mmcls - INFO - Epoch [39][200/5005] lr: 1.000e-02, eta: 7:35:12, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7068 2023-01-03 20:13:02,867 - mmcls - INFO - Epoch [39][300/5005] lr: 1.000e-02, eta: 7:35:03, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6880 2023-01-03 20:13:11,727 - mmcls - INFO - Epoch [39][400/5005] lr: 1.000e-02, eta: 7:34:54, time: 0.089, data_time: 0.032, memory: 1125, loss: 1.7112 2023-01-03 20:13:20,439 - mmcls - INFO - Epoch [39][500/5005] lr: 1.000e-02, eta: 7:34:45, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7033 2023-01-03 20:13:29,385 - mmcls - INFO - Epoch [39][600/5005] lr: 1.000e-02, eta: 7:34:37, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7126 2023-01-03 20:13:38,103 - mmcls - INFO - Epoch [39][700/5005] lr: 1.000e-02, eta: 7:34:28, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7237 2023-01-03 20:13:46,860 - mmcls - INFO - Epoch [39][800/5005] lr: 1.000e-02, eta: 7:34:19, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7228 2023-01-03 20:13:55,748 - mmcls - INFO - Epoch [39][900/5005] lr: 1.000e-02, eta: 7:34:10, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7294 2023-01-03 20:14:04,269 - mmcls - INFO - Epoch [39][1000/5005] lr: 1.000e-02, eta: 7:34:01, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.7205 2023-01-03 20:14:13,266 - mmcls - INFO - Epoch [39][1100/5005] lr: 1.000e-02, eta: 7:33:52, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7034 2023-01-03 20:14:21,933 - mmcls - INFO - Epoch [39][1200/5005] lr: 1.000e-02, eta: 7:33:43, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.7436 2023-01-03 20:14:30,686 - mmcls - INFO - Epoch [39][1300/5005] lr: 1.000e-02, eta: 7:33:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7120 2023-01-03 20:14:39,397 - mmcls - INFO - Epoch [39][1400/5005] lr: 1.000e-02, eta: 7:33:26, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7371 2023-01-03 20:14:48,229 - mmcls - INFO - Epoch [39][1500/5005] lr: 1.000e-02, eta: 7:33:17, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7315 2023-01-03 20:14:57,011 - mmcls - INFO - Epoch [39][1600/5005] lr: 1.000e-02, eta: 7:33:08, time: 0.088, data_time: 0.047, memory: 1125, loss: 1.7161 2023-01-03 20:15:05,773 - mmcls - INFO - Epoch [39][1700/5005] lr: 1.000e-02, eta: 7:32:59, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7433 2023-01-03 20:15:14,443 - mmcls - INFO - Epoch [39][1800/5005] lr: 1.000e-02, eta: 7:32:50, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7392 2023-01-03 20:15:23,391 - mmcls - INFO - Epoch [39][1900/5005] lr: 1.000e-02, eta: 7:32:41, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7192 2023-01-03 20:15:32,060 - mmcls - INFO - Epoch [39][2000/5005] lr: 1.000e-02, eta: 7:32:32, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7239 2023-01-03 20:15:40,774 - mmcls - INFO - Epoch [39][2100/5005] lr: 1.000e-02, eta: 7:32:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7116 2023-01-03 20:15:49,672 - mmcls - INFO - Epoch [39][2200/5005] lr: 1.000e-02, eta: 7:32:15, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7289 2023-01-03 20:15:58,237 - mmcls - INFO - Epoch [39][2300/5005] lr: 1.000e-02, eta: 7:32:06, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7189 2023-01-03 20:16:07,107 - mmcls - INFO - Epoch [39][2400/5005] lr: 1.000e-02, eta: 7:31:57, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7380 2023-01-03 20:16:15,818 - mmcls - INFO - Epoch [39][2500/5005] lr: 1.000e-02, eta: 7:31:48, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7046 2023-01-03 20:16:24,349 - mmcls - INFO - Epoch [39][2600/5005] lr: 1.000e-02, eta: 7:31:39, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7485 2023-01-03 20:16:33,267 - mmcls - INFO - Epoch [39][2700/5005] lr: 1.000e-02, eta: 7:31:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7299 2023-01-03 20:16:42,206 - mmcls - INFO - Epoch [39][2800/5005] lr: 1.000e-02, eta: 7:31:21, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7464 2023-01-03 20:16:50,917 - mmcls - INFO - Epoch [39][2900/5005] lr: 1.000e-02, eta: 7:31:12, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7654 2023-01-03 20:16:59,832 - mmcls - INFO - Epoch [39][3000/5005] lr: 1.000e-02, eta: 7:31:04, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7321 2023-01-03 20:17:08,513 - mmcls - INFO - Epoch [39][3100/5005] lr: 1.000e-02, eta: 7:30:55, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.7208 2023-01-03 20:17:17,007 - mmcls - INFO - Epoch [39][3200/5005] lr: 1.000e-02, eta: 7:30:46, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7202 2023-01-03 20:17:25,522 - mmcls - INFO - Epoch [39][3300/5005] lr: 1.000e-02, eta: 7:30:36, time: 0.085, data_time: 0.035, memory: 1125, loss: 1.7439 2023-01-03 20:17:34,215 - mmcls - INFO - Epoch [39][3400/5005] lr: 1.000e-02, eta: 7:30:27, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7180 2023-01-03 20:17:43,094 - mmcls - INFO - Epoch [39][3500/5005] lr: 1.000e-02, eta: 7:30:19, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7478 2023-01-03 20:17:51,849 - mmcls - INFO - Epoch [39][3600/5005] lr: 1.000e-02, eta: 7:30:10, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7645 2023-01-03 20:18:00,464 - mmcls - INFO - Epoch [39][3700/5005] lr: 1.000e-02, eta: 7:30:01, time: 0.086, data_time: 0.038, memory: 1125, loss: 1.7353 2023-01-03 20:18:09,350 - mmcls - INFO - Epoch [39][3800/5005] lr: 1.000e-02, eta: 7:29:52, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7532 2023-01-03 20:18:18,142 - mmcls - INFO - Epoch [39][3900/5005] lr: 1.000e-02, eta: 7:29:43, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7345 2023-01-03 20:18:27,055 - mmcls - INFO - Epoch [39][4000/5005] lr: 1.000e-02, eta: 7:29:34, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.7435 2023-01-03 20:18:36,130 - mmcls - INFO - Epoch [39][4100/5005] lr: 1.000e-02, eta: 7:29:26, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7380 2023-01-03 20:18:44,709 - mmcls - INFO - Epoch [39][4200/5005] lr: 1.000e-02, eta: 7:29:17, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7449 2023-01-03 20:18:53,226 - mmcls - INFO - Epoch [39][4300/5005] lr: 1.000e-02, eta: 7:29:08, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7268 2023-01-03 20:19:01,843 - mmcls - INFO - Epoch [39][4400/5005] lr: 1.000e-02, eta: 7:28:58, time: 0.086, data_time: 0.041, memory: 1125, loss: 1.7411 2023-01-03 20:19:10,395 - mmcls - INFO - Epoch [39][4500/5005] lr: 1.000e-02, eta: 7:28:49, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7574 2023-01-03 20:19:18,779 - mmcls - INFO - Epoch [39][4600/5005] lr: 1.000e-02, eta: 7:28:40, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7451 2023-01-03 20:19:27,779 - mmcls - INFO - Epoch [39][4700/5005] lr: 1.000e-02, eta: 7:28:31, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7316 2023-01-03 20:19:36,416 - mmcls - INFO - Epoch [39][4800/5005] lr: 1.000e-02, eta: 7:28:22, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7284 2023-01-03 20:19:45,007 - mmcls - INFO - Epoch [39][4900/5005] lr: 1.000e-02, eta: 7:28:13, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7360 2023-01-03 20:19:54,160 - mmcls - INFO - Epoch [39][5000/5005] lr: 1.000e-02, eta: 7:28:05, time: 0.092, data_time: 0.016, memory: 1125, loss: 1.7353 2023-01-03 20:20:16,715 - mmcls - INFO - Epoch(val) [39][196] accuracy_top-1: 64.2780, accuracy_top-5: 85.8920 2023-01-03 20:20:27,703 - mmcls - INFO - Epoch [40][100/5005] lr: 1.000e-02, eta: 7:27:58, time: 0.110, data_time: 0.033, memory: 1125, loss: 1.6982 2023-01-03 20:20:36,639 - mmcls - INFO - Epoch [40][200/5005] lr: 1.000e-02, eta: 7:27:50, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7123 2023-01-03 20:20:45,212 - mmcls - INFO - Epoch [40][300/5005] lr: 1.000e-02, eta: 7:27:40, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7267 2023-01-03 20:20:54,143 - mmcls - INFO - Epoch [40][400/5005] lr: 1.000e-02, eta: 7:27:32, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7003 2023-01-03 20:21:02,745 - mmcls - INFO - Epoch [40][500/5005] lr: 1.000e-02, eta: 7:27:23, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7144 2023-01-03 20:21:11,378 - mmcls - INFO - Epoch [40][600/5005] lr: 1.000e-02, eta: 7:27:14, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7069 2023-01-03 20:21:20,358 - mmcls - INFO - Epoch [40][700/5005] lr: 1.000e-02, eta: 7:27:05, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6935 2023-01-03 20:21:28,904 - mmcls - INFO - Epoch [40][800/5005] lr: 1.000e-02, eta: 7:26:56, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7113 2023-01-03 20:21:37,717 - mmcls - INFO - Epoch [40][900/5005] lr: 1.000e-02, eta: 7:26:47, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7076 2023-01-03 20:21:46,528 - mmcls - INFO - Epoch [40][1000/5005] lr: 1.000e-02, eta: 7:26:38, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7131 2023-01-03 20:21:55,291 - mmcls - INFO - Epoch [40][1100/5005] lr: 1.000e-02, eta: 7:26:29, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7173 2023-01-03 20:22:05,116 - mmcls - INFO - Epoch [40][1200/5005] lr: 1.000e-02, eta: 7:26:22, time: 0.098, data_time: 0.004, memory: 1125, loss: 1.7301 2023-01-03 20:22:13,829 - mmcls - INFO - Epoch [40][1300/5005] lr: 1.000e-02, eta: 7:26:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7226 2023-01-03 20:22:22,709 - mmcls - INFO - Epoch [40][1400/5005] lr: 1.000e-02, eta: 7:26:05, time: 0.089, data_time: 0.038, memory: 1125, loss: 1.7054 2023-01-03 20:22:31,310 - mmcls - INFO - Epoch [40][1500/5005] lr: 1.000e-02, eta: 7:25:55, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7402 2023-01-03 20:22:40,268 - mmcls - INFO - Epoch [40][1600/5005] lr: 1.000e-02, eta: 7:25:47, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7034 2023-01-03 20:22:48,670 - mmcls - INFO - Epoch [40][1700/5005] lr: 1.000e-02, eta: 7:25:37, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.7382 2023-01-03 20:22:57,657 - mmcls - INFO - Epoch [40][1800/5005] lr: 1.000e-02, eta: 7:25:29, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.7028 2023-01-03 20:23:06,645 - mmcls - INFO - Epoch [40][1900/5005] lr: 1.000e-02, eta: 7:25:20, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7216 2023-01-03 20:23:15,075 - mmcls - INFO - Epoch [40][2000/5005] lr: 1.000e-02, eta: 7:25:11, time: 0.084, data_time: 0.017, memory: 1125, loss: 1.7137 2023-01-03 20:23:24,016 - mmcls - INFO - Epoch [40][2100/5005] lr: 1.000e-02, eta: 7:25:02, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7117 2023-01-03 20:23:32,548 - mmcls - INFO - Epoch [40][2200/5005] lr: 1.000e-02, eta: 7:24:53, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.7321 2023-01-03 20:23:41,027 - mmcls - INFO - Epoch [40][2300/5005] lr: 1.000e-02, eta: 7:24:44, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7078 2023-01-03 20:23:49,849 - mmcls - INFO - Epoch [40][2400/5005] lr: 1.000e-02, eta: 7:24:35, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7315 2023-01-03 20:23:58,619 - mmcls - INFO - Epoch [40][2500/5005] lr: 1.000e-02, eta: 7:24:26, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7433 2023-01-03 20:24:07,449 - mmcls - INFO - Epoch [40][2600/5005] lr: 1.000e-02, eta: 7:24:17, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7253 2023-01-03 20:24:16,357 - mmcls - INFO - Epoch [40][2700/5005] lr: 1.000e-02, eta: 7:24:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7388 2023-01-03 20:24:25,429 - mmcls - INFO - Epoch [40][2800/5005] lr: 1.000e-02, eta: 7:24:00, time: 0.091, data_time: 0.037, memory: 1125, loss: 1.7216 2023-01-03 20:24:34,230 - mmcls - INFO - Epoch [40][2900/5005] lr: 1.000e-02, eta: 7:23:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7246 2023-01-03 20:24:42,845 - mmcls - INFO - Epoch [40][3000/5005] lr: 1.000e-02, eta: 7:23:42, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7335 2023-01-03 20:24:51,564 - mmcls - INFO - Epoch [40][3100/5005] lr: 1.000e-02, eta: 7:23:34, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7459 2023-01-03 20:25:00,283 - mmcls - INFO - Epoch [40][3200/5005] lr: 1.000e-02, eta: 7:23:25, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.7461 2023-01-03 20:25:09,060 - mmcls - INFO - Epoch [40][3300/5005] lr: 1.000e-02, eta: 7:23:16, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7285 2023-01-03 20:25:17,823 - mmcls - INFO - Epoch [40][3400/5005] lr: 1.000e-02, eta: 7:23:07, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7383 2023-01-03 20:25:26,574 - mmcls - INFO - Epoch [40][3500/5005] lr: 1.000e-02, eta: 7:22:58, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7475 2023-01-03 20:25:35,377 - mmcls - INFO - Epoch [40][3600/5005] lr: 1.000e-02, eta: 7:22:49, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7431 2023-01-03 20:25:43,912 - mmcls - INFO - Epoch [40][3700/5005] lr: 1.000e-02, eta: 7:22:40, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.7339 2023-01-03 20:25:52,449 - mmcls - INFO - Epoch [40][3800/5005] lr: 1.000e-02, eta: 7:22:31, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7231 2023-01-03 20:26:01,409 - mmcls - INFO - Epoch [40][3900/5005] lr: 1.000e-02, eta: 7:22:22, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7321 2023-01-03 20:26:10,105 - mmcls - INFO - Epoch [40][4000/5005] lr: 1.000e-02, eta: 7:22:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7078 2023-01-03 20:26:18,921 - mmcls - INFO - Epoch [40][4100/5005] lr: 1.000e-02, eta: 7:22:04, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7243 2023-01-03 20:26:27,414 - mmcls - INFO - Epoch [40][4200/5005] lr: 1.000e-02, eta: 7:21:55, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7209 2023-01-03 20:26:35,989 - mmcls - INFO - Epoch [40][4300/5005] lr: 1.000e-02, eta: 7:21:46, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7288 2023-01-03 20:26:44,829 - mmcls - INFO - Epoch [40][4400/5005] lr: 1.000e-02, eta: 7:21:37, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7425 2023-01-03 20:26:53,546 - mmcls - INFO - Epoch [40][4500/5005] lr: 1.000e-02, eta: 7:21:28, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7613 2023-01-03 20:27:02,401 - mmcls - INFO - Epoch [40][4600/5005] lr: 1.000e-02, eta: 7:21:19, time: 0.089, data_time: 0.032, memory: 1125, loss: 1.7430 2023-01-03 20:27:10,982 - mmcls - INFO - Epoch [40][4700/5005] lr: 1.000e-02, eta: 7:21:10, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7199 2023-01-03 20:27:19,820 - mmcls - INFO - Epoch [40][4800/5005] lr: 1.000e-02, eta: 7:21:02, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.7478 2023-01-03 20:27:28,398 - mmcls - INFO - Epoch [40][4900/5005] lr: 1.000e-02, eta: 7:20:52, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7105 2023-01-03 20:27:37,330 - mmcls - INFO - Epoch [40][5000/5005] lr: 1.000e-02, eta: 7:20:44, time: 0.089, data_time: 0.030, memory: 1125, loss: 1.7391 2023-01-03 20:27:59,912 - mmcls - INFO - Epoch(val) [40][196] accuracy_top-1: 64.6040, accuracy_top-5: 86.2000 2023-01-03 20:28:10,858 - mmcls - INFO - Epoch [41][100/5005] lr: 1.000e-02, eta: 7:20:37, time: 0.109, data_time: 0.047, memory: 1125, loss: 1.7075 2023-01-03 20:28:19,749 - mmcls - INFO - Epoch [41][200/5005] lr: 1.000e-02, eta: 7:20:28, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6807 2023-01-03 20:28:28,500 - mmcls - INFO - Epoch [41][300/5005] lr: 1.000e-02, eta: 7:20:20, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7203 2023-01-03 20:28:37,517 - mmcls - INFO - Epoch [41][400/5005] lr: 1.000e-02, eta: 7:20:11, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7004 2023-01-03 20:28:46,103 - mmcls - INFO - Epoch [41][500/5005] lr: 1.000e-02, eta: 7:20:02, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7198 2023-01-03 20:28:54,788 - mmcls - INFO - Epoch [41][600/5005] lr: 1.000e-02, eta: 7:19:53, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7165 2023-01-03 20:29:03,779 - mmcls - INFO - Epoch [41][700/5005] lr: 1.000e-02, eta: 7:19:44, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7144 2023-01-03 20:29:12,851 - mmcls - INFO - Epoch [41][800/5005] lr: 1.000e-02, eta: 7:19:36, time: 0.091, data_time: 0.034, memory: 1125, loss: 1.7315 2023-01-03 20:29:21,623 - mmcls - INFO - Epoch [41][900/5005] lr: 1.000e-02, eta: 7:19:27, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7099 2023-01-03 20:29:30,325 - mmcls - INFO - Epoch [41][1000/5005] lr: 1.000e-02, eta: 7:19:18, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.7453 2023-01-03 20:29:38,834 - mmcls - INFO - Epoch [41][1100/5005] lr: 1.000e-02, eta: 7:19:09, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.7066 2023-01-03 20:29:47,958 - mmcls - INFO - Epoch [41][1200/5005] lr: 1.000e-02, eta: 7:19:01, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7143 2023-01-03 20:29:56,582 - mmcls - INFO - Epoch [41][1300/5005] lr: 1.000e-02, eta: 7:18:51, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7325 2023-01-03 20:30:05,390 - mmcls - INFO - Epoch [41][1400/5005] lr: 1.000e-02, eta: 7:18:43, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7210 2023-01-03 20:30:14,062 - mmcls - INFO - Epoch [41][1500/5005] lr: 1.000e-02, eta: 7:18:34, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7192 2023-01-03 20:30:22,967 - mmcls - INFO - Epoch [41][1600/5005] lr: 1.000e-02, eta: 7:18:25, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7419 2023-01-03 20:30:31,721 - mmcls - INFO - Epoch [41][1700/5005] lr: 1.000e-02, eta: 7:18:16, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.7391 2023-01-03 20:30:40,690 - mmcls - INFO - Epoch [41][1800/5005] lr: 1.000e-02, eta: 7:18:08, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6987 2023-01-03 20:30:49,419 - mmcls - INFO - Epoch [41][1900/5005] lr: 1.000e-02, eta: 7:17:59, time: 0.087, data_time: 0.035, memory: 1125, loss: 1.7103 2023-01-03 20:30:57,864 - mmcls - INFO - Epoch [41][2000/5005] lr: 1.000e-02, eta: 7:17:49, time: 0.084, data_time: 0.021, memory: 1125, loss: 1.7321 2023-01-03 20:31:06,830 - mmcls - INFO - Epoch [41][2100/5005] lr: 1.000e-02, eta: 7:17:41, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.7463 2023-01-03 20:31:15,958 - mmcls - INFO - Epoch [41][2200/5005] lr: 1.000e-02, eta: 7:17:32, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7363 2023-01-03 20:31:24,521 - mmcls - INFO - Epoch [41][2300/5005] lr: 1.000e-02, eta: 7:17:23, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7351 2023-01-03 20:31:33,243 - mmcls - INFO - Epoch [41][2400/5005] lr: 1.000e-02, eta: 7:17:14, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7306 2023-01-03 20:31:41,908 - mmcls - INFO - Epoch [41][2500/5005] lr: 1.000e-02, eta: 7:17:05, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7208 2023-01-03 20:31:50,617 - mmcls - INFO - Epoch [41][2600/5005] lr: 1.000e-02, eta: 7:16:56, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7175 2023-01-03 20:31:59,355 - mmcls - INFO - Epoch [41][2700/5005] lr: 1.000e-02, eta: 7:16:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7420 2023-01-03 20:32:08,288 - mmcls - INFO - Epoch [41][2800/5005] lr: 1.000e-02, eta: 7:16:39, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7032 2023-01-03 20:32:16,973 - mmcls - INFO - Epoch [41][2900/5005] lr: 1.000e-02, eta: 7:16:30, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7285 2023-01-03 20:32:25,557 - mmcls - INFO - Epoch [41][3000/5005] lr: 1.000e-02, eta: 7:16:21, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7289 2023-01-03 20:32:34,325 - mmcls - INFO - Epoch [41][3100/5005] lr: 1.000e-02, eta: 7:16:12, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7446 2023-01-03 20:32:43,078 - mmcls - INFO - Epoch [41][3200/5005] lr: 1.000e-02, eta: 7:16:03, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7481 2023-01-03 20:32:51,747 - mmcls - INFO - Epoch [41][3300/5005] lr: 1.000e-02, eta: 7:15:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7416 2023-01-03 20:33:00,487 - mmcls - INFO - Epoch [41][3400/5005] lr: 1.000e-02, eta: 7:15:45, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7347 2023-01-03 20:33:09,168 - mmcls - INFO - Epoch [41][3500/5005] lr: 1.000e-02, eta: 7:15:36, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7579 2023-01-03 20:33:17,879 - mmcls - INFO - Epoch [41][3600/5005] lr: 1.000e-02, eta: 7:15:27, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7435 2023-01-03 20:33:26,572 - mmcls - INFO - Epoch [41][3700/5005] lr: 1.000e-02, eta: 7:15:18, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7700 2023-01-03 20:33:35,750 - mmcls - INFO - Epoch [41][3800/5005] lr: 1.000e-02, eta: 7:15:10, time: 0.092, data_time: 0.024, memory: 1125, loss: 1.7363 2023-01-03 20:33:44,311 - mmcls - INFO - Epoch [41][3900/5005] lr: 1.000e-02, eta: 7:15:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7160 2023-01-03 20:33:53,026 - mmcls - INFO - Epoch [41][4000/5005] lr: 1.000e-02, eta: 7:14:52, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7329 2023-01-03 20:34:01,747 - mmcls - INFO - Epoch [41][4100/5005] lr: 1.000e-02, eta: 7:14:43, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7060 2023-01-03 20:34:10,369 - mmcls - INFO - Epoch [41][4200/5005] lr: 1.000e-02, eta: 7:14:34, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7406 2023-01-03 20:34:19,185 - mmcls - INFO - Epoch [41][4300/5005] lr: 1.000e-02, eta: 7:14:25, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7447 2023-01-03 20:34:28,061 - mmcls - INFO - Epoch [41][4400/5005] lr: 1.000e-02, eta: 7:14:16, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7548 2023-01-03 20:34:36,915 - mmcls - INFO - Epoch [41][4500/5005] lr: 1.000e-02, eta: 7:14:07, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7391 2023-01-03 20:34:45,701 - mmcls - INFO - Epoch [41][4600/5005] lr: 1.000e-02, eta: 7:13:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7442 2023-01-03 20:34:54,589 - mmcls - INFO - Epoch [41][4700/5005] lr: 1.000e-02, eta: 7:13:50, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7283 2023-01-03 20:35:03,370 - mmcls - INFO - Epoch [41][4800/5005] lr: 1.000e-02, eta: 7:13:41, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7581 2023-01-03 20:35:11,983 - mmcls - INFO - Epoch [41][4900/5005] lr: 1.000e-02, eta: 7:13:32, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7542 2023-01-03 20:35:20,589 - mmcls - INFO - Epoch [41][5000/5005] lr: 1.000e-02, eta: 7:13:23, time: 0.086, data_time: 0.052, memory: 1125, loss: 1.7251 2023-01-03 20:35:43,188 - mmcls - INFO - Epoch(val) [41][196] accuracy_top-1: 64.7300, accuracy_top-5: 86.3380 2023-01-03 20:35:54,334 - mmcls - INFO - Epoch [42][100/5005] lr: 1.000e-02, eta: 7:13:16, time: 0.111, data_time: 0.044, memory: 1125, loss: 1.7042 2023-01-03 20:36:03,286 - mmcls - INFO - Epoch [42][200/5005] lr: 1.000e-02, eta: 7:13:08, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.6857 2023-01-03 20:36:12,343 - mmcls - INFO - Epoch [42][300/5005] lr: 1.000e-02, eta: 7:12:59, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.6795 2023-01-03 20:36:21,295 - mmcls - INFO - Epoch [42][400/5005] lr: 1.000e-02, eta: 7:12:51, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6846 2023-01-03 20:36:29,966 - mmcls - INFO - Epoch [42][500/5005] lr: 1.000e-02, eta: 7:12:42, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7066 2023-01-03 20:36:38,379 - mmcls - INFO - Epoch [42][600/5005] lr: 1.000e-02, eta: 7:12:32, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.6939 2023-01-03 20:36:47,069 - mmcls - INFO - Epoch [42][700/5005] lr: 1.000e-02, eta: 7:12:23, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7012 2023-01-03 20:36:55,726 - mmcls - INFO - Epoch [42][800/5005] lr: 1.000e-02, eta: 7:12:14, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7099 2023-01-03 20:37:04,333 - mmcls - INFO - Epoch [42][900/5005] lr: 1.000e-02, eta: 7:12:05, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7142 2023-01-03 20:37:13,267 - mmcls - INFO - Epoch [42][1000/5005] lr: 1.000e-02, eta: 7:11:57, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7026 2023-01-03 20:37:21,898 - mmcls - INFO - Epoch [42][1100/5005] lr: 1.000e-02, eta: 7:11:48, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7163 2023-01-03 20:37:30,600 - mmcls - INFO - Epoch [42][1200/5005] lr: 1.000e-02, eta: 7:11:39, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.6988 2023-01-03 20:37:39,556 - mmcls - INFO - Epoch [42][1300/5005] lr: 1.000e-02, eta: 7:11:30, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7055 2023-01-03 20:37:48,557 - mmcls - INFO - Epoch [42][1400/5005] lr: 1.000e-02, eta: 7:11:21, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7091 2023-01-03 20:37:57,480 - mmcls - INFO - Epoch [42][1500/5005] lr: 1.000e-02, eta: 7:11:13, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6926 2023-01-03 20:38:06,113 - mmcls - INFO - Epoch [42][1600/5005] lr: 1.000e-02, eta: 7:11:04, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7217 2023-01-03 20:38:14,964 - mmcls - INFO - Epoch [42][1700/5005] lr: 1.000e-02, eta: 7:10:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7293 2023-01-03 20:38:23,933 - mmcls - INFO - Epoch [42][1800/5005] lr: 1.000e-02, eta: 7:10:46, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7335 2023-01-03 20:38:32,890 - mmcls - INFO - Epoch [42][1900/5005] lr: 1.000e-02, eta: 7:10:38, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7005 2023-01-03 20:38:41,641 - mmcls - INFO - Epoch [42][2000/5005] lr: 1.000e-02, eta: 7:10:29, time: 0.087, data_time: 0.040, memory: 1125, loss: 1.7035 2023-01-03 20:38:50,519 - mmcls - INFO - Epoch [42][2100/5005] lr: 1.000e-02, eta: 7:10:20, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7303 2023-01-03 20:38:59,176 - mmcls - INFO - Epoch [42][2200/5005] lr: 1.000e-02, eta: 7:10:11, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7196 2023-01-03 20:39:07,976 - mmcls - INFO - Epoch [42][2300/5005] lr: 1.000e-02, eta: 7:10:02, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7250 2023-01-03 20:39:17,083 - mmcls - INFO - Epoch [42][2400/5005] lr: 1.000e-02, eta: 7:09:54, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.7319 2023-01-03 20:39:25,803 - mmcls - INFO - Epoch [42][2500/5005] lr: 1.000e-02, eta: 7:09:45, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.7093 2023-01-03 20:39:34,604 - mmcls - INFO - Epoch [42][2600/5005] lr: 1.000e-02, eta: 7:09:36, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7122 2023-01-03 20:39:43,262 - mmcls - INFO - Epoch [42][2700/5005] lr: 1.000e-02, eta: 7:09:27, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7312 2023-01-03 20:39:52,277 - mmcls - INFO - Epoch [42][2800/5005] lr: 1.000e-02, eta: 7:09:19, time: 0.090, data_time: 0.026, memory: 1125, loss: 1.7281 2023-01-03 20:40:00,822 - mmcls - INFO - Epoch [42][2900/5005] lr: 1.000e-02, eta: 7:09:10, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.7403 2023-01-03 20:40:10,204 - mmcls - INFO - Epoch [42][3000/5005] lr: 1.000e-02, eta: 7:09:02, time: 0.094, data_time: 0.009, memory: 1125, loss: 1.7328 2023-01-03 20:40:19,081 - mmcls - INFO - Epoch [42][3100/5005] lr: 1.000e-02, eta: 7:08:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7228 2023-01-03 20:40:28,214 - mmcls - INFO - Epoch [42][3200/5005] lr: 1.000e-02, eta: 7:08:45, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7289 2023-01-03 20:40:36,947 - mmcls - INFO - Epoch [42][3300/5005] lr: 1.000e-02, eta: 7:08:36, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7444 2023-01-03 20:40:45,963 - mmcls - INFO - Epoch [42][3400/5005] lr: 1.000e-02, eta: 7:08:27, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7497 2023-01-03 20:40:54,759 - mmcls - INFO - Epoch [42][3500/5005] lr: 1.000e-02, eta: 7:08:18, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7356 2023-01-03 20:41:03,554 - mmcls - INFO - Epoch [42][3600/5005] lr: 1.000e-02, eta: 7:08:09, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7365 2023-01-03 20:41:12,264 - mmcls - INFO - Epoch [42][3700/5005] lr: 1.000e-02, eta: 7:08:01, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7451 2023-01-03 20:41:20,910 - mmcls - INFO - Epoch [42][3800/5005] lr: 1.000e-02, eta: 7:07:51, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7430 2023-01-03 20:41:29,707 - mmcls - INFO - Epoch [42][3900/5005] lr: 1.000e-02, eta: 7:07:43, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.7556 2023-01-03 20:41:38,250 - mmcls - INFO - Epoch [42][4000/5005] lr: 1.000e-02, eta: 7:07:33, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7130 2023-01-03 20:41:47,180 - mmcls - INFO - Epoch [42][4100/5005] lr: 1.000e-02, eta: 7:07:25, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7435 2023-01-03 20:41:56,207 - mmcls - INFO - Epoch [42][4200/5005] lr: 1.000e-02, eta: 7:07:16, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7336 2023-01-03 20:42:04,801 - mmcls - INFO - Epoch [42][4300/5005] lr: 1.000e-02, eta: 7:07:07, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7324 2023-01-03 20:42:13,415 - mmcls - INFO - Epoch [42][4400/5005] lr: 1.000e-02, eta: 7:06:58, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7531 2023-01-03 20:42:22,098 - mmcls - INFO - Epoch [42][4500/5005] lr: 1.000e-02, eta: 7:06:49, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7485 2023-01-03 20:42:30,912 - mmcls - INFO - Epoch [42][4600/5005] lr: 1.000e-02, eta: 7:06:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7421 2023-01-03 20:42:39,660 - mmcls - INFO - Epoch [42][4700/5005] lr: 1.000e-02, eta: 7:06:31, time: 0.087, data_time: 0.045, memory: 1125, loss: 1.7395 2023-01-03 20:42:48,294 - mmcls - INFO - Epoch [42][4800/5005] lr: 1.000e-02, eta: 7:06:22, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7459 2023-01-03 20:42:56,798 - mmcls - INFO - Epoch [42][4900/5005] lr: 1.000e-02, eta: 7:06:13, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7511 2023-01-03 20:43:05,448 - mmcls - INFO - Epoch [42][5000/5005] lr: 1.000e-02, eta: 7:06:04, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7402 2023-01-03 20:43:28,169 - mmcls - INFO - Epoch(val) [42][196] accuracy_top-1: 64.6720, accuracy_top-5: 86.0440 2023-01-03 20:43:38,936 - mmcls - INFO - Epoch [43][100/5005] lr: 1.000e-02, eta: 7:05:57, time: 0.107, data_time: 0.050, memory: 1125, loss: 1.7031 2023-01-03 20:43:47,696 - mmcls - INFO - Epoch [43][200/5005] lr: 1.000e-02, eta: 7:05:48, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6738 2023-01-03 20:43:56,239 - mmcls - INFO - Epoch [43][300/5005] lr: 1.000e-02, eta: 7:05:39, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7166 2023-01-03 20:44:04,870 - mmcls - INFO - Epoch [43][400/5005] lr: 1.000e-02, eta: 7:05:30, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6941 2023-01-03 20:44:14,021 - mmcls - INFO - Epoch [43][500/5005] lr: 1.000e-02, eta: 7:05:22, time: 0.092, data_time: 0.028, memory: 1125, loss: 1.7095 2023-01-03 20:44:22,849 - mmcls - INFO - Epoch [43][600/5005] lr: 1.000e-02, eta: 7:05:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6816 2023-01-03 20:44:31,485 - mmcls - INFO - Epoch [43][700/5005] lr: 1.000e-02, eta: 7:05:04, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7203 2023-01-03 20:44:40,140 - mmcls - INFO - Epoch [43][800/5005] lr: 1.000e-02, eta: 7:04:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7191 2023-01-03 20:44:49,034 - mmcls - INFO - Epoch [43][900/5005] lr: 1.000e-02, eta: 7:04:46, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.7088 2023-01-03 20:44:57,675 - mmcls - INFO - Epoch [43][1000/5005] lr: 1.000e-02, eta: 7:04:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7323 2023-01-03 20:45:06,205 - mmcls - INFO - Epoch [43][1100/5005] lr: 1.000e-02, eta: 7:04:28, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7310 2023-01-03 20:45:15,117 - mmcls - INFO - Epoch [43][1200/5005] lr: 1.000e-02, eta: 7:04:19, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7127 2023-01-03 20:45:24,088 - mmcls - INFO - Epoch [43][1300/5005] lr: 1.000e-02, eta: 7:04:11, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7207 2023-01-03 20:45:32,835 - mmcls - INFO - Epoch [43][1400/5005] lr: 1.000e-02, eta: 7:04:02, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.7169 2023-01-03 20:45:41,749 - mmcls - INFO - Epoch [43][1500/5005] lr: 1.000e-02, eta: 7:03:53, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.7163 2023-01-03 20:45:50,717 - mmcls - INFO - Epoch [43][1600/5005] lr: 1.000e-02, eta: 7:03:44, time: 0.090, data_time: 0.034, memory: 1125, loss: 1.7166 2023-01-03 20:45:59,278 - mmcls - INFO - Epoch [43][1700/5005] lr: 1.000e-02, eta: 7:03:35, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.7387 2023-01-03 20:46:08,030 - mmcls - INFO - Epoch [43][1800/5005] lr: 1.000e-02, eta: 7:03:26, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7264 2023-01-03 20:46:16,715 - mmcls - INFO - Epoch [43][1900/5005] lr: 1.000e-02, eta: 7:03:17, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7187 2023-01-03 20:46:25,426 - mmcls - INFO - Epoch [43][2000/5005] lr: 1.000e-02, eta: 7:03:09, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7186 2023-01-03 20:46:34,160 - mmcls - INFO - Epoch [43][2100/5005] lr: 1.000e-02, eta: 7:03:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7117 2023-01-03 20:46:42,972 - mmcls - INFO - Epoch [43][2200/5005] lr: 1.000e-02, eta: 7:02:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7239 2023-01-03 20:46:51,894 - mmcls - INFO - Epoch [43][2300/5005] lr: 1.000e-02, eta: 7:02:42, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7134 2023-01-03 20:47:00,334 - mmcls - INFO - Epoch [43][2400/5005] lr: 1.000e-02, eta: 7:02:33, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7356 2023-01-03 20:47:09,088 - mmcls - INFO - Epoch [43][2500/5005] lr: 1.000e-02, eta: 7:02:24, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7474 2023-01-03 20:47:17,645 - mmcls - INFO - Epoch [43][2600/5005] lr: 1.000e-02, eta: 7:02:15, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7188 2023-01-03 20:47:26,620 - mmcls - INFO - Epoch [43][2700/5005] lr: 1.000e-02, eta: 7:02:06, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.7379 2023-01-03 20:47:35,219 - mmcls - INFO - Epoch [43][2800/5005] lr: 1.000e-02, eta: 7:01:57, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7368 2023-01-03 20:47:43,935 - mmcls - INFO - Epoch [43][2900/5005] lr: 1.000e-02, eta: 7:01:48, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7331 2023-01-03 20:47:52,972 - mmcls - INFO - Epoch [43][3000/5005] lr: 1.000e-02, eta: 7:01:40, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7262 2023-01-03 20:48:01,961 - mmcls - INFO - Epoch [43][3100/5005] lr: 1.000e-02, eta: 7:01:31, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.7299 2023-01-03 20:48:10,766 - mmcls - INFO - Epoch [43][3200/5005] lr: 1.000e-02, eta: 7:01:22, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7301 2023-01-03 20:48:19,772 - mmcls - INFO - Epoch [43][3300/5005] lr: 1.000e-02, eta: 7:01:14, time: 0.090, data_time: 0.021, memory: 1125, loss: 1.7306 2023-01-03 20:48:28,421 - mmcls - INFO - Epoch [43][3400/5005] lr: 1.000e-02, eta: 7:01:05, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7339 2023-01-03 20:48:37,396 - mmcls - INFO - Epoch [43][3500/5005] lr: 1.000e-02, eta: 7:00:56, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7378 2023-01-03 20:48:46,245 - mmcls - INFO - Epoch [43][3600/5005] lr: 1.000e-02, eta: 7:00:47, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7451 2023-01-03 20:48:54,928 - mmcls - INFO - Epoch [43][3700/5005] lr: 1.000e-02, eta: 7:00:38, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.7414 2023-01-03 20:49:03,798 - mmcls - INFO - Epoch [43][3800/5005] lr: 1.000e-02, eta: 7:00:30, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.7340 2023-01-03 20:49:12,394 - mmcls - INFO - Epoch [43][3900/5005] lr: 1.000e-02, eta: 7:00:21, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7233 2023-01-03 20:49:21,823 - mmcls - INFO - Epoch [43][4000/5005] lr: 1.000e-02, eta: 7:00:13, time: 0.094, data_time: 0.004, memory: 1125, loss: 1.7236 2023-01-03 20:49:30,332 - mmcls - INFO - Epoch [43][4100/5005] lr: 1.000e-02, eta: 7:00:03, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7171 2023-01-03 20:49:38,988 - mmcls - INFO - Epoch [43][4200/5005] lr: 1.000e-02, eta: 6:59:54, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7512 2023-01-03 20:49:47,628 - mmcls - INFO - Epoch [43][4300/5005] lr: 1.000e-02, eta: 6:59:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7254 2023-01-03 20:49:56,225 - mmcls - INFO - Epoch [43][4400/5005] lr: 1.000e-02, eta: 6:59:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7500 2023-01-03 20:50:05,020 - mmcls - INFO - Epoch [43][4500/5005] lr: 1.000e-02, eta: 6:59:28, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7132 2023-01-03 20:50:13,848 - mmcls - INFO - Epoch [43][4600/5005] lr: 1.000e-02, eta: 6:59:19, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7354 2023-01-03 20:50:22,560 - mmcls - INFO - Epoch [43][4700/5005] lr: 1.000e-02, eta: 6:59:10, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7374 2023-01-03 20:50:31,397 - mmcls - INFO - Epoch [43][4800/5005] lr: 1.000e-02, eta: 6:59:01, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7352 2023-01-03 20:50:40,240 - mmcls - INFO - Epoch [43][4900/5005] lr: 1.000e-02, eta: 6:58:52, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7201 2023-01-03 20:50:49,000 - mmcls - INFO - Epoch [43][5000/5005] lr: 1.000e-02, eta: 6:58:43, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7250 2023-01-03 20:51:11,659 - mmcls - INFO - Epoch(val) [43][196] accuracy_top-1: 64.4740, accuracy_top-5: 86.1340 2023-01-03 20:51:23,205 - mmcls - INFO - Epoch [44][100/5005] lr: 1.000e-02, eta: 6:58:37, time: 0.115, data_time: 0.036, memory: 1125, loss: 1.6715 2023-01-03 20:51:31,646 - mmcls - INFO - Epoch [44][200/5005] lr: 1.000e-02, eta: 6:58:28, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.6755 2023-01-03 20:51:40,344 - mmcls - INFO - Epoch [44][300/5005] lr: 1.000e-02, eta: 6:58:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6888 2023-01-03 20:51:49,091 - mmcls - INFO - Epoch [44][400/5005] lr: 1.000e-02, eta: 6:58:10, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6966 2023-01-03 20:51:57,977 - mmcls - INFO - Epoch [44][500/5005] lr: 1.000e-02, eta: 6:58:01, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.7141 2023-01-03 20:52:06,908 - mmcls - INFO - Epoch [44][600/5005] lr: 1.000e-02, eta: 6:57:53, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7130 2023-01-03 20:52:15,574 - mmcls - INFO - Epoch [44][700/5005] lr: 1.000e-02, eta: 6:57:44, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7115 2023-01-03 20:52:24,307 - mmcls - INFO - Epoch [44][800/5005] lr: 1.000e-02, eta: 6:57:35, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7189 2023-01-03 20:52:32,882 - mmcls - INFO - Epoch [44][900/5005] lr: 1.000e-02, eta: 6:57:26, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7456 2023-01-03 20:52:41,420 - mmcls - INFO - Epoch [44][1000/5005] lr: 1.000e-02, eta: 6:57:17, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7136 2023-01-03 20:52:50,711 - mmcls - INFO - Epoch [44][1100/5005] lr: 1.000e-02, eta: 6:57:08, time: 0.093, data_time: 0.015, memory: 1125, loss: 1.7100 2023-01-03 20:52:59,449 - mmcls - INFO - Epoch [44][1200/5005] lr: 1.000e-02, eta: 6:56:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7206 2023-01-03 20:53:08,330 - mmcls - INFO - Epoch [44][1300/5005] lr: 1.000e-02, eta: 6:56:51, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.7248 2023-01-03 20:53:16,980 - mmcls - INFO - Epoch [44][1400/5005] lr: 1.000e-02, eta: 6:56:42, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7227 2023-01-03 20:53:25,904 - mmcls - INFO - Epoch [44][1500/5005] lr: 1.000e-02, eta: 6:56:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7225 2023-01-03 20:53:34,411 - mmcls - INFO - Epoch [44][1600/5005] lr: 1.000e-02, eta: 6:56:24, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7125 2023-01-03 20:53:42,984 - mmcls - INFO - Epoch [44][1700/5005] lr: 1.000e-02, eta: 6:56:15, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7244 2023-01-03 20:53:51,767 - mmcls - INFO - Epoch [44][1800/5005] lr: 1.000e-02, eta: 6:56:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7465 2023-01-03 20:54:00,230 - mmcls - INFO - Epoch [44][1900/5005] lr: 1.000e-02, eta: 6:55:57, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7223 2023-01-03 20:54:08,975 - mmcls - INFO - Epoch [44][2000/5005] lr: 1.000e-02, eta: 6:55:48, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7353 2023-01-03 20:54:17,447 - mmcls - INFO - Epoch [44][2100/5005] lr: 1.000e-02, eta: 6:55:39, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7469 2023-01-03 20:54:26,257 - mmcls - INFO - Epoch [44][2200/5005] lr: 1.000e-02, eta: 6:55:30, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7492 2023-01-03 20:54:35,050 - mmcls - INFO - Epoch [44][2300/5005] lr: 1.000e-02, eta: 6:55:21, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7133 2023-01-03 20:54:43,661 - mmcls - INFO - Epoch [44][2400/5005] lr: 1.000e-02, eta: 6:55:12, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.7227 2023-01-03 20:54:52,394 - mmcls - INFO - Epoch [44][2500/5005] lr: 1.000e-02, eta: 6:55:03, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7175 2023-01-03 20:55:00,944 - mmcls - INFO - Epoch [44][2600/5005] lr: 1.000e-02, eta: 6:54:54, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.7123 2023-01-03 20:55:09,722 - mmcls - INFO - Epoch [44][2700/5005] lr: 1.000e-02, eta: 6:54:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7136 2023-01-03 20:55:18,609 - mmcls - INFO - Epoch [44][2800/5005] lr: 1.000e-02, eta: 6:54:36, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7345 2023-01-03 20:55:27,295 - mmcls - INFO - Epoch [44][2900/5005] lr: 1.000e-02, eta: 6:54:27, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7403 2023-01-03 20:55:35,744 - mmcls - INFO - Epoch [44][3000/5005] lr: 1.000e-02, eta: 6:54:18, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.7423 2023-01-03 20:55:44,441 - mmcls - INFO - Epoch [44][3100/5005] lr: 1.000e-02, eta: 6:54:09, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7420 2023-01-03 20:55:53,252 - mmcls - INFO - Epoch [44][3200/5005] lr: 1.000e-02, eta: 6:54:00, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7623 2023-01-03 20:56:02,209 - mmcls - INFO - Epoch [44][3300/5005] lr: 1.000e-02, eta: 6:53:52, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7232 2023-01-03 20:56:11,275 - mmcls - INFO - Epoch [44][3400/5005] lr: 1.000e-02, eta: 6:53:43, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7301 2023-01-03 20:56:19,926 - mmcls - INFO - Epoch [44][3500/5005] lr: 1.000e-02, eta: 6:53:34, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7068 2023-01-03 20:56:28,731 - mmcls - INFO - Epoch [44][3600/5005] lr: 1.000e-02, eta: 6:53:25, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7417 2023-01-03 20:56:37,264 - mmcls - INFO - Epoch [44][3700/5005] lr: 1.000e-02, eta: 6:53:16, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7296 2023-01-03 20:56:46,086 - mmcls - INFO - Epoch [44][3800/5005] lr: 1.000e-02, eta: 6:53:07, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7319 2023-01-03 20:56:54,630 - mmcls - INFO - Epoch [44][3900/5005] lr: 1.000e-02, eta: 6:52:58, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7434 2023-01-03 20:57:03,418 - mmcls - INFO - Epoch [44][4000/5005] lr: 1.000e-02, eta: 6:52:49, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7221 2023-01-03 20:57:11,991 - mmcls - INFO - Epoch [44][4100/5005] lr: 1.000e-02, eta: 6:52:40, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7315 2023-01-03 20:57:20,737 - mmcls - INFO - Epoch [44][4200/5005] lr: 1.000e-02, eta: 6:52:31, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7236 2023-01-03 20:57:29,872 - mmcls - INFO - Epoch [44][4300/5005] lr: 1.000e-02, eta: 6:52:23, time: 0.091, data_time: 0.032, memory: 1125, loss: 1.7383 2023-01-03 20:57:38,291 - mmcls - INFO - Epoch [44][4400/5005] lr: 1.000e-02, eta: 6:52:14, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7390 2023-01-03 20:57:47,126 - mmcls - INFO - Epoch [44][4500/5005] lr: 1.000e-02, eta: 6:52:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7149 2023-01-03 20:57:55,782 - mmcls - INFO - Epoch [44][4600/5005] lr: 1.000e-02, eta: 6:51:56, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7247 2023-01-03 20:58:04,721 - mmcls - INFO - Epoch [44][4700/5005] lr: 1.000e-02, eta: 6:51:47, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7331 2023-01-03 20:58:13,749 - mmcls - INFO - Epoch [44][4800/5005] lr: 1.000e-02, eta: 6:51:39, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.7249 2023-01-03 20:58:22,419 - mmcls - INFO - Epoch [44][4900/5005] lr: 1.000e-02, eta: 6:51:30, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7102 2023-01-03 20:58:31,325 - mmcls - INFO - Epoch [44][5000/5005] lr: 1.000e-02, eta: 6:51:21, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7456 2023-01-03 20:58:53,970 - mmcls - INFO - Epoch(val) [44][196] accuracy_top-1: 64.3180, accuracy_top-5: 86.0940 2023-01-03 20:59:05,030 - mmcls - INFO - Epoch [45][100/5005] lr: 1.000e-02, eta: 6:51:14, time: 0.110, data_time: 0.049, memory: 1125, loss: 1.7200 2023-01-03 20:59:13,876 - mmcls - INFO - Epoch [45][200/5005] lr: 1.000e-02, eta: 6:51:05, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.6925 2023-01-03 20:59:22,290 - mmcls - INFO - Epoch [45][300/5005] lr: 1.000e-02, eta: 6:50:56, time: 0.084, data_time: 0.009, memory: 1125, loss: 1.7039 2023-01-03 20:59:31,143 - mmcls - INFO - Epoch [45][400/5005] lr: 1.000e-02, eta: 6:50:47, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.6994 2023-01-03 20:59:39,850 - mmcls - INFO - Epoch [45][500/5005] lr: 1.000e-02, eta: 6:50:38, time: 0.087, data_time: 0.040, memory: 1125, loss: 1.7104 2023-01-03 20:59:48,923 - mmcls - INFO - Epoch [45][600/5005] lr: 1.000e-02, eta: 6:50:30, time: 0.091, data_time: 0.036, memory: 1125, loss: 1.6932 2023-01-03 20:59:57,567 - mmcls - INFO - Epoch [45][700/5005] lr: 1.000e-02, eta: 6:50:21, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.7276 2023-01-03 21:00:06,730 - mmcls - INFO - Epoch [45][800/5005] lr: 1.000e-02, eta: 6:50:13, time: 0.092, data_time: 0.007, memory: 1125, loss: 1.7069 2023-01-03 21:00:15,305 - mmcls - INFO - Epoch [45][900/5005] lr: 1.000e-02, eta: 6:50:04, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7220 2023-01-03 21:00:24,176 - mmcls - INFO - Epoch [45][1000/5005] lr: 1.000e-02, eta: 6:49:55, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7166 2023-01-03 21:00:33,073 - mmcls - INFO - Epoch [45][1100/5005] lr: 1.000e-02, eta: 6:49:46, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7210 2023-01-03 21:00:41,779 - mmcls - INFO - Epoch [45][1200/5005] lr: 1.000e-02, eta: 6:49:37, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6991 2023-01-03 21:00:50,725 - mmcls - INFO - Epoch [45][1300/5005] lr: 1.000e-02, eta: 6:49:29, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7408 2023-01-03 21:00:59,358 - mmcls - INFO - Epoch [45][1400/5005] lr: 1.000e-02, eta: 6:49:19, time: 0.086, data_time: 0.039, memory: 1125, loss: 1.7213 2023-01-03 21:01:07,968 - mmcls - INFO - Epoch [45][1500/5005] lr: 1.000e-02, eta: 6:49:10, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7252 2023-01-03 21:01:17,092 - mmcls - INFO - Epoch [45][1600/5005] lr: 1.000e-02, eta: 6:49:02, time: 0.091, data_time: 0.035, memory: 1125, loss: 1.7287 2023-01-03 21:01:25,627 - mmcls - INFO - Epoch [45][1700/5005] lr: 1.000e-02, eta: 6:48:53, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7240 2023-01-03 21:01:34,202 - mmcls - INFO - Epoch [45][1800/5005] lr: 1.000e-02, eta: 6:48:44, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.7195 2023-01-03 21:01:43,330 - mmcls - INFO - Epoch [45][1900/5005] lr: 1.000e-02, eta: 6:48:35, time: 0.091, data_time: 0.033, memory: 1125, loss: 1.7134 2023-01-03 21:01:51,879 - mmcls - INFO - Epoch [45][2000/5005] lr: 1.000e-02, eta: 6:48:26, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7398 2023-01-03 21:02:00,956 - mmcls - INFO - Epoch [45][2100/5005] lr: 1.000e-02, eta: 6:48:18, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7131 2023-01-03 21:02:09,902 - mmcls - INFO - Epoch [45][2200/5005] lr: 1.000e-02, eta: 6:48:09, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7241 2023-01-03 21:02:18,291 - mmcls - INFO - Epoch [45][2300/5005] lr: 1.000e-02, eta: 6:48:00, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7447 2023-01-03 21:02:26,987 - mmcls - INFO - Epoch [45][2400/5005] lr: 1.000e-02, eta: 6:47:51, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7265 2023-01-03 21:02:36,209 - mmcls - INFO - Epoch [45][2500/5005] lr: 1.000e-02, eta: 6:47:43, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.6994 2023-01-03 21:02:45,119 - mmcls - INFO - Epoch [45][2600/5005] lr: 1.000e-02, eta: 6:47:34, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7297 2023-01-03 21:02:53,756 - mmcls - INFO - Epoch [45][2700/5005] lr: 1.000e-02, eta: 6:47:25, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.7348 2023-01-03 21:03:02,470 - mmcls - INFO - Epoch [45][2800/5005] lr: 1.000e-02, eta: 6:47:16, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7393 2023-01-03 21:03:11,213 - mmcls - INFO - Epoch [45][2900/5005] lr: 1.000e-02, eta: 6:47:07, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7623 2023-01-03 21:03:19,824 - mmcls - INFO - Epoch [45][3000/5005] lr: 1.000e-02, eta: 6:46:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7277 2023-01-03 21:03:28,543 - mmcls - INFO - Epoch [45][3100/5005] lr: 1.000e-02, eta: 6:46:49, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7418 2023-01-03 21:03:37,211 - mmcls - INFO - Epoch [45][3200/5005] lr: 1.000e-02, eta: 6:46:40, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7300 2023-01-03 21:03:45,807 - mmcls - INFO - Epoch [45][3300/5005] lr: 1.000e-02, eta: 6:46:31, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7194 2023-01-03 21:03:54,660 - mmcls - INFO - Epoch [45][3400/5005] lr: 1.000e-02, eta: 6:46:22, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7235 2023-01-03 21:04:03,529 - mmcls - INFO - Epoch [45][3500/5005] lr: 1.000e-02, eta: 6:46:14, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7212 2023-01-03 21:04:12,450 - mmcls - INFO - Epoch [45][3600/5005] lr: 1.000e-02, eta: 6:46:05, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.7167 2023-01-03 21:04:21,115 - mmcls - INFO - Epoch [45][3700/5005] lr: 1.000e-02, eta: 6:45:56, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7323 2023-01-03 21:04:30,025 - mmcls - INFO - Epoch [45][3800/5005] lr: 1.000e-02, eta: 6:45:47, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7232 2023-01-03 21:04:38,693 - mmcls - INFO - Epoch [45][3900/5005] lr: 1.000e-02, eta: 6:45:38, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7487 2023-01-03 21:04:47,216 - mmcls - INFO - Epoch [45][4000/5005] lr: 1.000e-02, eta: 6:45:29, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7196 2023-01-03 21:04:55,930 - mmcls - INFO - Epoch [45][4100/5005] lr: 1.000e-02, eta: 6:45:20, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7478 2023-01-03 21:05:04,729 - mmcls - INFO - Epoch [45][4200/5005] lr: 1.000e-02, eta: 6:45:11, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7304 2023-01-03 21:05:13,355 - mmcls - INFO - Epoch [45][4300/5005] lr: 1.000e-02, eta: 6:45:02, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7296 2023-01-03 21:05:22,333 - mmcls - INFO - Epoch [45][4400/5005] lr: 1.000e-02, eta: 6:44:54, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7409 2023-01-03 21:05:30,962 - mmcls - INFO - Epoch [45][4500/5005] lr: 1.000e-02, eta: 6:44:45, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7392 2023-01-03 21:05:39,501 - mmcls - INFO - Epoch [45][4600/5005] lr: 1.000e-02, eta: 6:44:36, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7414 2023-01-03 21:05:48,311 - mmcls - INFO - Epoch [45][4700/5005] lr: 1.000e-02, eta: 6:44:27, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7561 2023-01-03 21:05:57,107 - mmcls - INFO - Epoch [45][4800/5005] lr: 1.000e-02, eta: 6:44:18, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7320 2023-01-03 21:06:05,632 - mmcls - INFO - Epoch [45][4900/5005] lr: 1.000e-02, eta: 6:44:09, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7365 2023-01-03 21:06:14,545 - mmcls - INFO - Epoch [45][5000/5005] lr: 1.000e-02, eta: 6:44:00, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7334 2023-01-03 21:06:37,443 - mmcls - INFO - Epoch(val) [45][196] accuracy_top-1: 64.6480, accuracy_top-5: 86.1700 2023-01-03 21:06:48,540 - mmcls - INFO - Epoch [46][100/5005] lr: 1.000e-02, eta: 6:43:53, time: 0.111, data_time: 0.055, memory: 1125, loss: 1.6955 2023-01-03 21:06:57,221 - mmcls - INFO - Epoch [46][200/5005] lr: 1.000e-02, eta: 6:43:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.6923 2023-01-03 21:07:05,861 - mmcls - INFO - Epoch [46][300/5005] lr: 1.000e-02, eta: 6:43:35, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7075 2023-01-03 21:07:14,604 - mmcls - INFO - Epoch [46][400/5005] lr: 1.000e-02, eta: 6:43:26, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7148 2023-01-03 21:07:23,341 - mmcls - INFO - Epoch [46][500/5005] lr: 1.000e-02, eta: 6:43:17, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.6816 2023-01-03 21:07:32,074 - mmcls - INFO - Epoch [46][600/5005] lr: 1.000e-02, eta: 6:43:09, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6901 2023-01-03 21:07:41,095 - mmcls - INFO - Epoch [46][700/5005] lr: 1.000e-02, eta: 6:43:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.6996 2023-01-03 21:07:50,119 - mmcls - INFO - Epoch [46][800/5005] lr: 1.000e-02, eta: 6:42:51, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7075 2023-01-03 21:07:58,956 - mmcls - INFO - Epoch [46][900/5005] lr: 1.000e-02, eta: 6:42:43, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7195 2023-01-03 21:08:07,807 - mmcls - INFO - Epoch [46][1000/5005] lr: 1.000e-02, eta: 6:42:34, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.6967 2023-01-03 21:08:16,351 - mmcls - INFO - Epoch [46][1100/5005] lr: 1.000e-02, eta: 6:42:25, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.7184 2023-01-03 21:08:25,186 - mmcls - INFO - Epoch [46][1200/5005] lr: 1.000e-02, eta: 6:42:16, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7105 2023-01-03 21:08:34,020 - mmcls - INFO - Epoch [46][1300/5005] lr: 1.000e-02, eta: 6:42:07, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7223 2023-01-03 21:08:42,599 - mmcls - INFO - Epoch [46][1400/5005] lr: 1.000e-02, eta: 6:41:58, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.7307 2023-01-03 21:08:51,662 - mmcls - INFO - Epoch [46][1500/5005] lr: 1.000e-02, eta: 6:41:50, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.7157 2023-01-03 21:09:00,337 - mmcls - INFO - Epoch [46][1600/5005] lr: 1.000e-02, eta: 6:41:41, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7043 2023-01-03 21:09:09,171 - mmcls - INFO - Epoch [46][1700/5005] lr: 1.000e-02, eta: 6:41:32, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.7114 2023-01-03 21:09:17,873 - mmcls - INFO - Epoch [46][1800/5005] lr: 1.000e-02, eta: 6:41:23, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.7307 2023-01-03 21:09:26,550 - mmcls - INFO - Epoch [46][1900/5005] lr: 1.000e-02, eta: 6:41:14, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7117 2023-01-03 21:09:35,255 - mmcls - INFO - Epoch [46][2000/5005] lr: 1.000e-02, eta: 6:41:05, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7247 2023-01-03 21:09:43,758 - mmcls - INFO - Epoch [46][2100/5005] lr: 1.000e-02, eta: 6:40:56, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7253 2023-01-03 21:09:52,530 - mmcls - INFO - Epoch [46][2200/5005] lr: 1.000e-02, eta: 6:40:47, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.7042 2023-01-03 21:10:01,156 - mmcls - INFO - Epoch [46][2300/5005] lr: 1.000e-02, eta: 6:40:38, time: 0.086, data_time: 0.034, memory: 1125, loss: 1.7224 2023-01-03 21:10:09,732 - mmcls - INFO - Epoch [46][2400/5005] lr: 1.000e-02, eta: 6:40:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7071 2023-01-03 21:10:18,522 - mmcls - INFO - Epoch [46][2500/5005] lr: 1.000e-02, eta: 6:40:20, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7203 2023-01-03 21:10:27,121 - mmcls - INFO - Epoch [46][2600/5005] lr: 1.000e-02, eta: 6:40:11, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.6972 2023-01-03 21:10:36,107 - mmcls - INFO - Epoch [46][2700/5005] lr: 1.000e-02, eta: 6:40:02, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7085 2023-01-03 21:10:44,815 - mmcls - INFO - Epoch [46][2800/5005] lr: 1.000e-02, eta: 6:39:54, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7305 2023-01-03 21:10:53,598 - mmcls - INFO - Epoch [46][2900/5005] lr: 1.000e-02, eta: 6:39:45, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7343 2023-01-03 21:11:02,733 - mmcls - INFO - Epoch [46][3000/5005] lr: 1.000e-02, eta: 6:39:36, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7472 2023-01-03 21:11:11,323 - mmcls - INFO - Epoch [46][3100/5005] lr: 1.000e-02, eta: 6:39:27, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7423 2023-01-03 21:11:19,942 - mmcls - INFO - Epoch [46][3200/5005] lr: 1.000e-02, eta: 6:39:18, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7358 2023-01-03 21:11:28,686 - mmcls - INFO - Epoch [46][3300/5005] lr: 1.000e-02, eta: 6:39:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7475 2023-01-03 21:11:37,625 - mmcls - INFO - Epoch [46][3400/5005] lr: 1.000e-02, eta: 6:39:01, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7341 2023-01-03 21:11:46,423 - mmcls - INFO - Epoch [46][3500/5005] lr: 1.000e-02, eta: 6:38:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7309 2023-01-03 21:11:54,994 - mmcls - INFO - Epoch [46][3600/5005] lr: 1.000e-02, eta: 6:38:43, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7360 2023-01-03 21:12:04,006 - mmcls - INFO - Epoch [46][3700/5005] lr: 1.000e-02, eta: 6:38:34, time: 0.090, data_time: 0.034, memory: 1125, loss: 1.7541 2023-01-03 21:12:13,001 - mmcls - INFO - Epoch [46][3800/5005] lr: 1.000e-02, eta: 6:38:26, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7227 2023-01-03 21:12:21,903 - mmcls - INFO - Epoch [46][3900/5005] lr: 1.000e-02, eta: 6:38:17, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7297 2023-01-03 21:12:30,628 - mmcls - INFO - Epoch [46][4000/5005] lr: 1.000e-02, eta: 6:38:08, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7267 2023-01-03 21:12:39,531 - mmcls - INFO - Epoch [46][4100/5005] lr: 1.000e-02, eta: 6:37:59, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7332 2023-01-03 21:12:48,142 - mmcls - INFO - Epoch [46][4200/5005] lr: 1.000e-02, eta: 6:37:50, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7465 2023-01-03 21:12:57,234 - mmcls - INFO - Epoch [46][4300/5005] lr: 1.000e-02, eta: 6:37:42, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7527 2023-01-03 21:13:06,154 - mmcls - INFO - Epoch [46][4400/5005] lr: 1.000e-02, eta: 6:37:33, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7196 2023-01-03 21:13:14,839 - mmcls - INFO - Epoch [46][4500/5005] lr: 1.000e-02, eta: 6:37:24, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7316 2023-01-03 21:13:23,443 - mmcls - INFO - Epoch [46][4600/5005] lr: 1.000e-02, eta: 6:37:15, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7514 2023-01-03 21:13:32,260 - mmcls - INFO - Epoch [46][4700/5005] lr: 1.000e-02, eta: 6:37:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7323 2023-01-03 21:13:40,855 - mmcls - INFO - Epoch [46][4800/5005] lr: 1.000e-02, eta: 6:36:57, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7312 2023-01-03 21:13:49,490 - mmcls - INFO - Epoch [46][4900/5005] lr: 1.000e-02, eta: 6:36:48, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7721 2023-01-03 21:13:58,233 - mmcls - INFO - Epoch [46][5000/5005] lr: 1.000e-02, eta: 6:36:39, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7184 2023-01-03 21:14:20,799 - mmcls - INFO - Epoch(val) [46][196] accuracy_top-1: 64.3080, accuracy_top-5: 85.9420 2023-01-03 21:14:31,850 - mmcls - INFO - Epoch [47][100/5005] lr: 1.000e-02, eta: 6:36:32, time: 0.110, data_time: 0.068, memory: 1125, loss: 1.7024 2023-01-03 21:14:40,646 - mmcls - INFO - Epoch [47][200/5005] lr: 1.000e-02, eta: 6:36:23, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.6739 2023-01-03 21:14:49,141 - mmcls - INFO - Epoch [47][300/5005] lr: 1.000e-02, eta: 6:36:14, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.7146 2023-01-03 21:14:57,735 - mmcls - INFO - Epoch [47][400/5005] lr: 1.000e-02, eta: 6:36:05, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7310 2023-01-03 21:15:06,371 - mmcls - INFO - Epoch [47][500/5005] lr: 1.000e-02, eta: 6:35:56, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7096 2023-01-03 21:15:15,165 - mmcls - INFO - Epoch [47][600/5005] lr: 1.000e-02, eta: 6:35:47, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7260 2023-01-03 21:15:23,856 - mmcls - INFO - Epoch [47][700/5005] lr: 1.000e-02, eta: 6:35:38, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7128 2023-01-03 21:15:32,547 - mmcls - INFO - Epoch [47][800/5005] lr: 1.000e-02, eta: 6:35:29, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7227 2023-01-03 21:15:41,410 - mmcls - INFO - Epoch [47][900/5005] lr: 1.000e-02, eta: 6:35:21, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7230 2023-01-03 21:15:49,857 - mmcls - INFO - Epoch [47][1000/5005] lr: 1.000e-02, eta: 6:35:12, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.7357 2023-01-03 21:15:58,677 - mmcls - INFO - Epoch [47][1100/5005] lr: 1.000e-02, eta: 6:35:03, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7140 2023-01-03 21:16:07,674 - mmcls - INFO - Epoch [47][1200/5005] lr: 1.000e-02, eta: 6:34:54, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.6946 2023-01-03 21:16:16,288 - mmcls - INFO - Epoch [47][1300/5005] lr: 1.000e-02, eta: 6:34:45, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7194 2023-01-03 21:16:25,319 - mmcls - INFO - Epoch [47][1400/5005] lr: 1.000e-02, eta: 6:34:37, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7030 2023-01-03 21:16:33,943 - mmcls - INFO - Epoch [47][1500/5005] lr: 1.000e-02, eta: 6:34:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7093 2023-01-03 21:16:42,680 - mmcls - INFO - Epoch [47][1600/5005] lr: 1.000e-02, eta: 6:34:19, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7157 2023-01-03 21:16:51,549 - mmcls - INFO - Epoch [47][1700/5005] lr: 1.000e-02, eta: 6:34:10, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.7218 2023-01-03 21:16:59,910 - mmcls - INFO - Epoch [47][1800/5005] lr: 1.000e-02, eta: 6:34:01, time: 0.084, data_time: 0.030, memory: 1125, loss: 1.7259 2023-01-03 21:17:08,705 - mmcls - INFO - Epoch [47][1900/5005] lr: 1.000e-02, eta: 6:33:52, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7245 2023-01-03 21:17:17,518 - mmcls - INFO - Epoch [47][2000/5005] lr: 1.000e-02, eta: 6:33:43, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7194 2023-01-03 21:17:26,495 - mmcls - INFO - Epoch [47][2100/5005] lr: 1.000e-02, eta: 6:33:34, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7371 2023-01-03 21:17:35,453 - mmcls - INFO - Epoch [47][2200/5005] lr: 1.000e-02, eta: 6:33:26, time: 0.090, data_time: 0.020, memory: 1125, loss: 1.7460 2023-01-03 21:17:44,192 - mmcls - INFO - Epoch [47][2300/5005] lr: 1.000e-02, eta: 6:33:17, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7111 2023-01-03 21:17:52,811 - mmcls - INFO - Epoch [47][2400/5005] lr: 1.000e-02, eta: 6:33:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7072 2023-01-03 21:18:01,538 - mmcls - INFO - Epoch [47][2500/5005] lr: 1.000e-02, eta: 6:32:59, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7401 2023-01-03 21:18:10,769 - mmcls - INFO - Epoch [47][2600/5005] lr: 1.000e-02, eta: 6:32:51, time: 0.092, data_time: 0.010, memory: 1125, loss: 1.7364 2023-01-03 21:18:19,234 - mmcls - INFO - Epoch [47][2700/5005] lr: 1.000e-02, eta: 6:32:41, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7140 2023-01-03 21:18:28,095 - mmcls - INFO - Epoch [47][2800/5005] lr: 1.000e-02, eta: 6:32:33, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7373 2023-01-03 21:18:36,873 - mmcls - INFO - Epoch [47][2900/5005] lr: 1.000e-02, eta: 6:32:24, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7180 2023-01-03 21:18:45,407 - mmcls - INFO - Epoch [47][3000/5005] lr: 1.000e-02, eta: 6:32:15, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7217 2023-01-03 21:18:54,074 - mmcls - INFO - Epoch [47][3100/5005] lr: 1.000e-02, eta: 6:32:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7250 2023-01-03 21:19:02,776 - mmcls - INFO - Epoch [47][3200/5005] lr: 1.000e-02, eta: 6:31:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7514 2023-01-03 21:19:11,501 - mmcls - INFO - Epoch [47][3300/5005] lr: 1.000e-02, eta: 6:31:48, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7312 2023-01-03 21:19:20,499 - mmcls - INFO - Epoch [47][3400/5005] lr: 1.000e-02, eta: 6:31:39, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7233 2023-01-03 21:19:29,345 - mmcls - INFO - Epoch [47][3500/5005] lr: 1.000e-02, eta: 6:31:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7079 2023-01-03 21:19:37,807 - mmcls - INFO - Epoch [47][3600/5005] lr: 1.000e-02, eta: 6:31:21, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7261 2023-01-03 21:19:46,742 - mmcls - INFO - Epoch [47][3700/5005] lr: 1.000e-02, eta: 6:31:13, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7317 2023-01-03 21:19:55,984 - mmcls - INFO - Epoch [47][3800/5005] lr: 1.000e-02, eta: 6:31:04, time: 0.092, data_time: 0.029, memory: 1125, loss: 1.7227 2023-01-03 21:20:04,898 - mmcls - INFO - Epoch [47][3900/5005] lr: 1.000e-02, eta: 6:30:56, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7280 2023-01-03 21:20:13,425 - mmcls - INFO - Epoch [47][4000/5005] lr: 1.000e-02, eta: 6:30:47, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7417 2023-01-03 21:20:22,449 - mmcls - INFO - Epoch [47][4100/5005] lr: 1.000e-02, eta: 6:30:38, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7539 2023-01-03 21:20:31,368 - mmcls - INFO - Epoch [47][4200/5005] lr: 1.000e-02, eta: 6:30:29, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7508 2023-01-03 21:20:40,478 - mmcls - INFO - Epoch [47][4300/5005] lr: 1.000e-02, eta: 6:30:21, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7254 2023-01-03 21:20:49,108 - mmcls - INFO - Epoch [47][4400/5005] lr: 1.000e-02, eta: 6:30:12, time: 0.086, data_time: 0.033, memory: 1125, loss: 1.7450 2023-01-03 21:20:57,892 - mmcls - INFO - Epoch [47][4500/5005] lr: 1.000e-02, eta: 6:30:03, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.7413 2023-01-03 21:21:06,486 - mmcls - INFO - Epoch [47][4600/5005] lr: 1.000e-02, eta: 6:29:54, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7505 2023-01-03 21:21:15,029 - mmcls - INFO - Epoch [47][4700/5005] lr: 1.000e-02, eta: 6:29:45, time: 0.085, data_time: 0.028, memory: 1125, loss: 1.7256 2023-01-03 21:21:23,909 - mmcls - INFO - Epoch [47][4800/5005] lr: 1.000e-02, eta: 6:29:36, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.7354 2023-01-03 21:21:32,562 - mmcls - INFO - Epoch [47][4900/5005] lr: 1.000e-02, eta: 6:29:27, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7415 2023-01-03 21:21:41,493 - mmcls - INFO - Epoch [47][5000/5005] lr: 1.000e-02, eta: 6:29:19, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7445 2023-01-03 21:22:04,056 - mmcls - INFO - Epoch(val) [47][196] accuracy_top-1: 64.6660, accuracy_top-5: 86.1820 2023-01-03 21:22:14,965 - mmcls - INFO - Epoch [48][100/5005] lr: 1.000e-02, eta: 6:29:11, time: 0.109, data_time: 0.040, memory: 1125, loss: 1.7123 2023-01-03 21:22:23,435 - mmcls - INFO - Epoch [48][200/5005] lr: 1.000e-02, eta: 6:29:02, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6883 2023-01-03 21:22:32,077 - mmcls - INFO - Epoch [48][300/5005] lr: 1.000e-02, eta: 6:28:53, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6966 2023-01-03 21:22:40,785 - mmcls - INFO - Epoch [48][400/5005] lr: 1.000e-02, eta: 6:28:44, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7019 2023-01-03 21:22:49,417 - mmcls - INFO - Epoch [48][500/5005] lr: 1.000e-02, eta: 6:28:35, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.7034 2023-01-03 21:22:58,299 - mmcls - INFO - Epoch [48][600/5005] lr: 1.000e-02, eta: 6:28:26, time: 0.089, data_time: 0.042, memory: 1125, loss: 1.7205 2023-01-03 21:23:07,025 - mmcls - INFO - Epoch [48][700/5005] lr: 1.000e-02, eta: 6:28:17, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7125 2023-01-03 21:23:15,602 - mmcls - INFO - Epoch [48][800/5005] lr: 1.000e-02, eta: 6:28:08, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7172 2023-01-03 21:23:24,649 - mmcls - INFO - Epoch [48][900/5005] lr: 1.000e-02, eta: 6:28:00, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7005 2023-01-03 21:23:33,504 - mmcls - INFO - Epoch [48][1000/5005] lr: 1.000e-02, eta: 6:27:51, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7334 2023-01-03 21:23:42,335 - mmcls - INFO - Epoch [48][1100/5005] lr: 1.000e-02, eta: 6:27:42, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7373 2023-01-03 21:23:50,949 - mmcls - INFO - Epoch [48][1200/5005] lr: 1.000e-02, eta: 6:27:33, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.7387 2023-01-03 21:23:59,907 - mmcls - INFO - Epoch [48][1300/5005] lr: 1.000e-02, eta: 6:27:25, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7258 2023-01-03 21:24:08,714 - mmcls - INFO - Epoch [48][1400/5005] lr: 1.000e-02, eta: 6:27:16, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7102 2023-01-03 21:24:17,300 - mmcls - INFO - Epoch [48][1500/5005] lr: 1.000e-02, eta: 6:27:07, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7248 2023-01-03 21:24:25,929 - mmcls - INFO - Epoch [48][1600/5005] lr: 1.000e-02, eta: 6:26:58, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7188 2023-01-03 21:24:34,602 - mmcls - INFO - Epoch [48][1700/5005] lr: 1.000e-02, eta: 6:26:49, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7028 2023-01-03 21:24:43,754 - mmcls - INFO - Epoch [48][1800/5005] lr: 1.000e-02, eta: 6:26:40, time: 0.092, data_time: 0.021, memory: 1125, loss: 1.6933 2023-01-03 21:24:52,581 - mmcls - INFO - Epoch [48][1900/5005] lr: 1.000e-02, eta: 6:26:32, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7458 2023-01-03 21:25:01,220 - mmcls - INFO - Epoch [48][2000/5005] lr: 1.000e-02, eta: 6:26:23, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7197 2023-01-03 21:25:10,260 - mmcls - INFO - Epoch [48][2100/5005] lr: 1.000e-02, eta: 6:26:14, time: 0.090, data_time: 0.032, memory: 1125, loss: 1.7242 2023-01-03 21:25:18,983 - mmcls - INFO - Epoch [48][2200/5005] lr: 1.000e-02, eta: 6:26:05, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7247 2023-01-03 21:25:27,629 - mmcls - INFO - Epoch [48][2300/5005] lr: 1.000e-02, eta: 6:25:56, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7288 2023-01-03 21:25:36,517 - mmcls - INFO - Epoch [48][2400/5005] lr: 1.000e-02, eta: 6:25:47, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7284 2023-01-03 21:25:45,449 - mmcls - INFO - Epoch [48][2500/5005] lr: 1.000e-02, eta: 6:25:39, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7405 2023-01-03 21:25:54,161 - mmcls - INFO - Epoch [48][2600/5005] lr: 1.000e-02, eta: 6:25:30, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7399 2023-01-03 21:26:02,780 - mmcls - INFO - Epoch [48][2700/5005] lr: 1.000e-02, eta: 6:25:21, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7196 2023-01-03 21:26:11,563 - mmcls - INFO - Epoch [48][2800/5005] lr: 1.000e-02, eta: 6:25:12, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7219 2023-01-03 21:26:20,023 - mmcls - INFO - Epoch [48][2900/5005] lr: 1.000e-02, eta: 6:25:03, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7273 2023-01-03 21:26:28,677 - mmcls - INFO - Epoch [48][3000/5005] lr: 1.000e-02, eta: 6:24:54, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.7411 2023-01-03 21:26:37,381 - mmcls - INFO - Epoch [48][3100/5005] lr: 1.000e-02, eta: 6:24:45, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7289 2023-01-03 21:26:46,190 - mmcls - INFO - Epoch [48][3200/5005] lr: 1.000e-02, eta: 6:24:36, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7194 2023-01-03 21:26:55,228 - mmcls - INFO - Epoch [48][3300/5005] lr: 1.000e-02, eta: 6:24:28, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.7382 2023-01-03 21:27:04,283 - mmcls - INFO - Epoch [48][3400/5005] lr: 1.000e-02, eta: 6:24:19, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7447 2023-01-03 21:27:13,200 - mmcls - INFO - Epoch [48][3500/5005] lr: 1.000e-02, eta: 6:24:10, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7136 2023-01-03 21:27:21,754 - mmcls - INFO - Epoch [48][3600/5005] lr: 1.000e-02, eta: 6:24:01, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7494 2023-01-03 21:27:30,304 - mmcls - INFO - Epoch [48][3700/5005] lr: 1.000e-02, eta: 6:23:52, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7385 2023-01-03 21:27:39,362 - mmcls - INFO - Epoch [48][3800/5005] lr: 1.000e-02, eta: 6:23:44, time: 0.091, data_time: 0.008, memory: 1125, loss: 1.7243 2023-01-03 21:27:48,437 - mmcls - INFO - Epoch [48][3900/5005] lr: 1.000e-02, eta: 6:23:35, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.7223 2023-01-03 21:27:57,302 - mmcls - INFO - Epoch [48][4000/5005] lr: 1.000e-02, eta: 6:23:26, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7170 2023-01-03 21:28:05,973 - mmcls - INFO - Epoch [48][4100/5005] lr: 1.000e-02, eta: 6:23:17, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7205 2023-01-03 21:28:15,152 - mmcls - INFO - Epoch [48][4200/5005] lr: 1.000e-02, eta: 6:23:09, time: 0.092, data_time: 0.022, memory: 1125, loss: 1.7375 2023-01-03 21:28:24,099 - mmcls - INFO - Epoch [48][4300/5005] lr: 1.000e-02, eta: 6:23:00, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7423 2023-01-03 21:28:32,777 - mmcls - INFO - Epoch [48][4400/5005] lr: 1.000e-02, eta: 6:22:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7358 2023-01-03 21:28:41,462 - mmcls - INFO - Epoch [48][4500/5005] lr: 1.000e-02, eta: 6:22:43, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7399 2023-01-03 21:28:50,389 - mmcls - INFO - Epoch [48][4600/5005] lr: 1.000e-02, eta: 6:22:34, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.7329 2023-01-03 21:28:59,315 - mmcls - INFO - Epoch [48][4700/5005] lr: 1.000e-02, eta: 6:22:25, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7114 2023-01-03 21:29:07,732 - mmcls - INFO - Epoch [48][4800/5005] lr: 1.000e-02, eta: 6:22:16, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.7435 2023-01-03 21:29:16,427 - mmcls - INFO - Epoch [48][4900/5005] lr: 1.000e-02, eta: 6:22:07, time: 0.087, data_time: 0.046, memory: 1125, loss: 1.7069 2023-01-03 21:29:25,239 - mmcls - INFO - Epoch [48][5000/5005] lr: 1.000e-02, eta: 6:21:58, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7612 2023-01-03 21:29:47,823 - mmcls - INFO - Epoch(val) [48][196] accuracy_top-1: 64.6300, accuracy_top-5: 86.1240 2023-01-03 21:29:58,702 - mmcls - INFO - Epoch [49][100/5005] lr: 1.000e-02, eta: 6:21:51, time: 0.109, data_time: 0.054, memory: 1125, loss: 1.7008 2023-01-03 21:30:07,518 - mmcls - INFO - Epoch [49][200/5005] lr: 1.000e-02, eta: 6:21:42, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6749 2023-01-03 21:30:16,222 - mmcls - INFO - Epoch [49][300/5005] lr: 1.000e-02, eta: 6:21:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7261 2023-01-03 21:30:25,218 - mmcls - INFO - Epoch [49][400/5005] lr: 1.000e-02, eta: 6:21:24, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.6991 2023-01-03 21:30:34,182 - mmcls - INFO - Epoch [49][500/5005] lr: 1.000e-02, eta: 6:21:16, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.6971 2023-01-03 21:30:42,837 - mmcls - INFO - Epoch [49][600/5005] lr: 1.000e-02, eta: 6:21:07, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7031 2023-01-03 21:30:51,664 - mmcls - INFO - Epoch [49][700/5005] lr: 1.000e-02, eta: 6:20:58, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7088 2023-01-03 21:31:00,659 - mmcls - INFO - Epoch [49][800/5005] lr: 1.000e-02, eta: 6:20:49, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7008 2023-01-03 21:31:09,442 - mmcls - INFO - Epoch [49][900/5005] lr: 1.000e-02, eta: 6:20:41, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.6974 2023-01-03 21:31:18,090 - mmcls - INFO - Epoch [49][1000/5005] lr: 1.000e-02, eta: 6:20:32, time: 0.086, data_time: 0.036, memory: 1125, loss: 1.7140 2023-01-03 21:31:26,561 - mmcls - INFO - Epoch [49][1100/5005] lr: 1.000e-02, eta: 6:20:22, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6746 2023-01-03 21:31:35,349 - mmcls - INFO - Epoch [49][1200/5005] lr: 1.000e-02, eta: 6:20:14, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.6920 2023-01-03 21:31:43,968 - mmcls - INFO - Epoch [49][1300/5005] lr: 1.000e-02, eta: 6:20:05, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7237 2023-01-03 21:31:52,422 - mmcls - INFO - Epoch [49][1400/5005] lr: 1.000e-02, eta: 6:19:55, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7479 2023-01-03 21:32:01,336 - mmcls - INFO - Epoch [49][1500/5005] lr: 1.000e-02, eta: 6:19:47, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.7069 2023-01-03 21:32:10,137 - mmcls - INFO - Epoch [49][1600/5005] lr: 1.000e-02, eta: 6:19:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7030 2023-01-03 21:32:18,957 - mmcls - INFO - Epoch [49][1700/5005] lr: 1.000e-02, eta: 6:19:29, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7034 2023-01-03 21:32:28,386 - mmcls - INFO - Epoch [49][1800/5005] lr: 1.000e-02, eta: 6:19:21, time: 0.094, data_time: 0.018, memory: 1125, loss: 1.7099 2023-01-03 21:32:37,358 - mmcls - INFO - Epoch [49][1900/5005] lr: 1.000e-02, eta: 6:19:12, time: 0.090, data_time: 0.044, memory: 1125, loss: 1.7264 2023-01-03 21:32:46,057 - mmcls - INFO - Epoch [49][2000/5005] lr: 1.000e-02, eta: 6:19:03, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7275 2023-01-03 21:32:54,872 - mmcls - INFO - Epoch [49][2100/5005] lr: 1.000e-02, eta: 6:18:55, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7341 2023-01-03 21:33:03,457 - mmcls - INFO - Epoch [49][2200/5005] lr: 1.000e-02, eta: 6:18:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7365 2023-01-03 21:33:12,616 - mmcls - INFO - Epoch [49][2300/5005] lr: 1.000e-02, eta: 6:18:37, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7278 2023-01-03 21:33:21,429 - mmcls - INFO - Epoch [49][2400/5005] lr: 1.000e-02, eta: 6:18:28, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.6848 2023-01-03 21:33:30,418 - mmcls - INFO - Epoch [49][2500/5005] lr: 1.000e-02, eta: 6:18:20, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.7045 2023-01-03 21:33:39,107 - mmcls - INFO - Epoch [49][2600/5005] lr: 1.000e-02, eta: 6:18:11, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7061 2023-01-03 21:33:47,792 - mmcls - INFO - Epoch [49][2700/5005] lr: 1.000e-02, eta: 6:18:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7266 2023-01-03 21:33:56,364 - mmcls - INFO - Epoch [49][2800/5005] lr: 1.000e-02, eta: 6:17:53, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7284 2023-01-03 21:34:05,212 - mmcls - INFO - Epoch [49][2900/5005] lr: 1.000e-02, eta: 6:17:44, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7353 2023-01-03 21:34:14,290 - mmcls - INFO - Epoch [49][3000/5005] lr: 1.000e-02, eta: 6:17:36, time: 0.091, data_time: 0.043, memory: 1125, loss: 1.7229 2023-01-03 21:34:22,895 - mmcls - INFO - Epoch [49][3100/5005] lr: 1.000e-02, eta: 6:17:26, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.7212 2023-01-03 21:34:31,356 - mmcls - INFO - Epoch [49][3200/5005] lr: 1.000e-02, eta: 6:17:17, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7137 2023-01-03 21:34:40,023 - mmcls - INFO - Epoch [49][3300/5005] lr: 1.000e-02, eta: 6:17:08, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7199 2023-01-03 21:34:48,722 - mmcls - INFO - Epoch [49][3400/5005] lr: 1.000e-02, eta: 6:16:59, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.7347 2023-01-03 21:34:57,242 - mmcls - INFO - Epoch [49][3500/5005] lr: 1.000e-02, eta: 6:16:50, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7501 2023-01-03 21:35:06,205 - mmcls - INFO - Epoch [49][3600/5005] lr: 1.000e-02, eta: 6:16:42, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7378 2023-01-03 21:35:14,977 - mmcls - INFO - Epoch [49][3700/5005] lr: 1.000e-02, eta: 6:16:33, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7512 2023-01-03 21:35:23,493 - mmcls - INFO - Epoch [49][3800/5005] lr: 1.000e-02, eta: 6:16:24, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7247 2023-01-03 21:35:32,711 - mmcls - INFO - Epoch [49][3900/5005] lr: 1.000e-02, eta: 6:16:15, time: 0.092, data_time: 0.005, memory: 1125, loss: 1.7332 2023-01-03 21:35:41,632 - mmcls - INFO - Epoch [49][4000/5005] lr: 1.000e-02, eta: 6:16:07, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7434 2023-01-03 21:35:50,229 - mmcls - INFO - Epoch [49][4100/5005] lr: 1.000e-02, eta: 6:15:58, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7569 2023-01-03 21:35:59,476 - mmcls - INFO - Epoch [49][4200/5005] lr: 1.000e-02, eta: 6:15:49, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7499 2023-01-03 21:36:08,003 - mmcls - INFO - Epoch [49][4300/5005] lr: 1.000e-02, eta: 6:15:40, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7537 2023-01-03 21:36:16,718 - mmcls - INFO - Epoch [49][4400/5005] lr: 1.000e-02, eta: 6:15:31, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7592 2023-01-03 21:36:25,376 - mmcls - INFO - Epoch [49][4500/5005] lr: 1.000e-02, eta: 6:15:22, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7331 2023-01-03 21:36:34,079 - mmcls - INFO - Epoch [49][4600/5005] lr: 1.000e-02, eta: 6:15:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7367 2023-01-03 21:36:42,654 - mmcls - INFO - Epoch [49][4700/5005] lr: 1.000e-02, eta: 6:15:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7375 2023-01-03 21:36:51,409 - mmcls - INFO - Epoch [49][4800/5005] lr: 1.000e-02, eta: 6:14:55, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.7453 2023-01-03 21:37:00,595 - mmcls - INFO - Epoch [49][4900/5005] lr: 1.000e-02, eta: 6:14:47, time: 0.092, data_time: 0.038, memory: 1125, loss: 1.7353 2023-01-03 21:37:09,256 - mmcls - INFO - Epoch [49][5000/5005] lr: 1.000e-02, eta: 6:14:38, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7321 2023-01-03 21:37:31,741 - mmcls - INFO - Epoch(val) [49][196] accuracy_top-1: 64.5960, accuracy_top-5: 85.9360 2023-01-03 21:37:42,889 - mmcls - INFO - Epoch [50][100/5005] lr: 1.000e-02, eta: 6:14:31, time: 0.111, data_time: 0.040, memory: 1125, loss: 1.7059 2023-01-03 21:37:51,263 - mmcls - INFO - Epoch [50][200/5005] lr: 1.000e-02, eta: 6:14:22, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.6966 2023-01-03 21:38:00,090 - mmcls - INFO - Epoch [50][300/5005] lr: 1.000e-02, eta: 6:14:13, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6930 2023-01-03 21:38:08,777 - mmcls - INFO - Epoch [50][400/5005] lr: 1.000e-02, eta: 6:14:04, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7074 2023-01-03 21:38:17,370 - mmcls - INFO - Epoch [50][500/5005] lr: 1.000e-02, eta: 6:13:55, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.7077 2023-01-03 21:38:26,214 - mmcls - INFO - Epoch [50][600/5005] lr: 1.000e-02, eta: 6:13:46, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7089 2023-01-03 21:38:34,603 - mmcls - INFO - Epoch [50][700/5005] lr: 1.000e-02, eta: 6:13:37, time: 0.084, data_time: 0.034, memory: 1125, loss: 1.7079 2023-01-03 21:38:43,530 - mmcls - INFO - Epoch [50][800/5005] lr: 1.000e-02, eta: 6:13:28, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7097 2023-01-03 21:38:52,368 - mmcls - INFO - Epoch [50][900/5005] lr: 1.000e-02, eta: 6:13:19, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7172 2023-01-03 21:39:00,994 - mmcls - INFO - Epoch [50][1000/5005] lr: 1.000e-02, eta: 6:13:10, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7158 2023-01-03 21:39:09,745 - mmcls - INFO - Epoch [50][1100/5005] lr: 1.000e-02, eta: 6:13:02, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7060 2023-01-03 21:39:18,745 - mmcls - INFO - Epoch [50][1200/5005] lr: 1.000e-02, eta: 6:12:53, time: 0.090, data_time: 0.037, memory: 1125, loss: 1.7127 2023-01-03 21:39:27,578 - mmcls - INFO - Epoch [50][1300/5005] lr: 1.000e-02, eta: 6:12:44, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7026 2023-01-03 21:39:36,645 - mmcls - INFO - Epoch [50][1400/5005] lr: 1.000e-02, eta: 6:12:36, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.6875 2023-01-03 21:39:45,438 - mmcls - INFO - Epoch [50][1500/5005] lr: 1.000e-02, eta: 6:12:27, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7471 2023-01-03 21:39:54,070 - mmcls - INFO - Epoch [50][1600/5005] lr: 1.000e-02, eta: 6:12:18, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7038 2023-01-03 21:40:02,935 - mmcls - INFO - Epoch [50][1700/5005] lr: 1.000e-02, eta: 6:12:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7236 2023-01-03 21:40:11,772 - mmcls - INFO - Epoch [50][1800/5005] lr: 1.000e-02, eta: 6:12:00, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7230 2023-01-03 21:40:20,456 - mmcls - INFO - Epoch [50][1900/5005] lr: 1.000e-02, eta: 6:11:51, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7095 2023-01-03 21:40:29,164 - mmcls - INFO - Epoch [50][2000/5005] lr: 1.000e-02, eta: 6:11:42, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7313 2023-01-03 21:40:38,009 - mmcls - INFO - Epoch [50][2100/5005] lr: 1.000e-02, eta: 6:11:34, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.7299 2023-01-03 21:40:47,186 - mmcls - INFO - Epoch [50][2200/5005] lr: 1.000e-02, eta: 6:11:25, time: 0.092, data_time: 0.012, memory: 1125, loss: 1.7227 2023-01-03 21:40:55,829 - mmcls - INFO - Epoch [50][2300/5005] lr: 1.000e-02, eta: 6:11:16, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7168 2023-01-03 21:41:04,706 - mmcls - INFO - Epoch [50][2400/5005] lr: 1.000e-02, eta: 6:11:07, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7252 2023-01-03 21:41:13,208 - mmcls - INFO - Epoch [50][2500/5005] lr: 1.000e-02, eta: 6:10:58, time: 0.085, data_time: 0.028, memory: 1125, loss: 1.7116 2023-01-03 21:41:21,939 - mmcls - INFO - Epoch [50][2600/5005] lr: 1.000e-02, eta: 6:10:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7103 2023-01-03 21:41:30,983 - mmcls - INFO - Epoch [50][2700/5005] lr: 1.000e-02, eta: 6:10:41, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.7246 2023-01-03 21:41:39,962 - mmcls - INFO - Epoch [50][2800/5005] lr: 1.000e-02, eta: 6:10:32, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.7118 2023-01-03 21:41:48,526 - mmcls - INFO - Epoch [50][2900/5005] lr: 1.000e-02, eta: 6:10:23, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7344 2023-01-03 21:41:57,294 - mmcls - INFO - Epoch [50][3000/5005] lr: 1.000e-02, eta: 6:10:14, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7070 2023-01-03 21:42:06,184 - mmcls - INFO - Epoch [50][3100/5005] lr: 1.000e-02, eta: 6:10:06, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7436 2023-01-03 21:42:15,189 - mmcls - INFO - Epoch [50][3200/5005] lr: 1.000e-02, eta: 6:09:57, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7480 2023-01-03 21:42:23,850 - mmcls - INFO - Epoch [50][3300/5005] lr: 1.000e-02, eta: 6:09:48, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.7366 2023-01-03 21:42:32,582 - mmcls - INFO - Epoch [50][3400/5005] lr: 1.000e-02, eta: 6:09:39, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7202 2023-01-03 21:42:41,328 - mmcls - INFO - Epoch [50][3500/5005] lr: 1.000e-02, eta: 6:09:30, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7288 2023-01-03 21:42:50,136 - mmcls - INFO - Epoch [50][3600/5005] lr: 1.000e-02, eta: 6:09:22, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7333 2023-01-03 21:42:59,065 - mmcls - INFO - Epoch [50][3700/5005] lr: 1.000e-02, eta: 6:09:13, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7493 2023-01-03 21:43:07,912 - mmcls - INFO - Epoch [50][3800/5005] lr: 1.000e-02, eta: 6:09:04, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7153 2023-01-03 21:43:16,925 - mmcls - INFO - Epoch [50][3900/5005] lr: 1.000e-02, eta: 6:08:56, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.7276 2023-01-03 21:43:25,667 - mmcls - INFO - Epoch [50][4000/5005] lr: 1.000e-02, eta: 6:08:47, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7414 2023-01-03 21:43:34,498 - mmcls - INFO - Epoch [50][4100/5005] lr: 1.000e-02, eta: 6:08:38, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7266 2023-01-03 21:43:43,071 - mmcls - INFO - Epoch [50][4200/5005] lr: 1.000e-02, eta: 6:08:29, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7198 2023-01-03 21:43:51,734 - mmcls - INFO - Epoch [50][4300/5005] lr: 1.000e-02, eta: 6:08:20, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7313 2023-01-03 21:44:00,459 - mmcls - INFO - Epoch [50][4400/5005] lr: 1.000e-02, eta: 6:08:11, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7363 2023-01-03 21:44:09,301 - mmcls - INFO - Epoch [50][4500/5005] lr: 1.000e-02, eta: 6:08:02, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.7334 2023-01-03 21:44:17,830 - mmcls - INFO - Epoch [50][4600/5005] lr: 1.000e-02, eta: 6:07:53, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.7284 2023-01-03 21:44:26,833 - mmcls - INFO - Epoch [50][4700/5005] lr: 1.000e-02, eta: 6:07:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7419 2023-01-03 21:44:35,597 - mmcls - INFO - Epoch [50][4800/5005] lr: 1.000e-02, eta: 6:07:36, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.7283 2023-01-03 21:44:44,476 - mmcls - INFO - Epoch [50][4900/5005] lr: 1.000e-02, eta: 6:07:27, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7337 2023-01-03 21:44:53,320 - mmcls - INFO - Epoch [50][5000/5005] lr: 1.000e-02, eta: 6:07:18, time: 0.088, data_time: 0.036, memory: 1125, loss: 1.7425 2023-01-03 21:44:53,667 - mmcls - INFO - Saving checkpoint at 50 epochs 2023-01-03 21:45:16,150 - mmcls - INFO - Epoch(val) [50][196] accuracy_top-1: 63.8300, accuracy_top-5: 85.6040 2023-01-03 21:45:27,094 - mmcls - INFO - Epoch [51][100/5005] lr: 1.000e-02, eta: 6:07:11, time: 0.109, data_time: 0.051, memory: 1125, loss: 1.7148 2023-01-03 21:45:35,898 - mmcls - INFO - Epoch [51][200/5005] lr: 1.000e-02, eta: 6:07:02, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7035 2023-01-03 21:45:44,974 - mmcls - INFO - Epoch [51][300/5005] lr: 1.000e-02, eta: 6:06:53, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.7075 2023-01-03 21:45:53,533 - mmcls - INFO - Epoch [51][400/5005] lr: 1.000e-02, eta: 6:06:44, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.6907 2023-01-03 21:46:02,014 - mmcls - INFO - Epoch [51][500/5005] lr: 1.000e-02, eta: 6:06:35, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6981 2023-01-03 21:46:10,919 - mmcls - INFO - Epoch [51][600/5005] lr: 1.000e-02, eta: 6:06:26, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.6828 2023-01-03 21:46:19,681 - mmcls - INFO - Epoch [51][700/5005] lr: 1.000e-02, eta: 6:06:17, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6754 2023-01-03 21:46:28,746 - mmcls - INFO - Epoch [51][800/5005] lr: 1.000e-02, eta: 6:06:09, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.7112 2023-01-03 21:46:37,386 - mmcls - INFO - Epoch [51][900/5005] lr: 1.000e-02, eta: 6:06:00, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.7080 2023-01-03 21:46:46,303 - mmcls - INFO - Epoch [51][1000/5005] lr: 1.000e-02, eta: 6:05:51, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.7069 2023-01-03 21:46:55,229 - mmcls - INFO - Epoch [51][1100/5005] lr: 1.000e-02, eta: 6:05:43, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.7014 2023-01-03 21:47:04,562 - mmcls - INFO - Epoch [51][1200/5005] lr: 1.000e-02, eta: 6:05:34, time: 0.093, data_time: 0.012, memory: 1125, loss: 1.6961 2023-01-03 21:47:12,971 - mmcls - INFO - Epoch [51][1300/5005] lr: 1.000e-02, eta: 6:05:25, time: 0.084, data_time: 0.012, memory: 1125, loss: 1.7039 2023-01-03 21:47:21,807 - mmcls - INFO - Epoch [51][1400/5005] lr: 1.000e-02, eta: 6:05:16, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7174 2023-01-03 21:47:30,954 - mmcls - INFO - Epoch [51][1500/5005] lr: 1.000e-02, eta: 6:05:08, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7152 2023-01-03 21:47:39,863 - mmcls - INFO - Epoch [51][1600/5005] lr: 1.000e-02, eta: 6:04:59, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6933 2023-01-03 21:47:48,535 - mmcls - INFO - Epoch [51][1700/5005] lr: 1.000e-02, eta: 6:04:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7169 2023-01-03 21:47:57,187 - mmcls - INFO - Epoch [51][1800/5005] lr: 1.000e-02, eta: 6:04:41, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7142 2023-01-03 21:48:06,115 - mmcls - INFO - Epoch [51][1900/5005] lr: 1.000e-02, eta: 6:04:33, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7180 2023-01-03 21:48:14,909 - mmcls - INFO - Epoch [51][2000/5005] lr: 1.000e-02, eta: 6:04:24, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7144 2023-01-03 21:48:23,427 - mmcls - INFO - Epoch [51][2100/5005] lr: 1.000e-02, eta: 6:04:15, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7178 2023-01-03 21:48:32,521 - mmcls - INFO - Epoch [51][2200/5005] lr: 1.000e-02, eta: 6:04:06, time: 0.091, data_time: 0.021, memory: 1125, loss: 1.7273 2023-01-03 21:48:40,972 - mmcls - INFO - Epoch [51][2300/5005] lr: 1.000e-02, eta: 6:03:57, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.7127 2023-01-03 21:48:49,591 - mmcls - INFO - Epoch [51][2400/5005] lr: 1.000e-02, eta: 6:03:48, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.7318 2023-01-03 21:48:58,251 - mmcls - INFO - Epoch [51][2500/5005] lr: 1.000e-02, eta: 6:03:39, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6834 2023-01-03 21:49:07,097 - mmcls - INFO - Epoch [51][2600/5005] lr: 1.000e-02, eta: 6:03:30, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7306 2023-01-03 21:49:15,844 - mmcls - INFO - Epoch [51][2700/5005] lr: 1.000e-02, eta: 6:03:21, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7175 2023-01-03 21:49:24,512 - mmcls - INFO - Epoch [51][2800/5005] lr: 1.000e-02, eta: 6:03:12, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.6985 2023-01-03 21:49:33,139 - mmcls - INFO - Epoch [51][2900/5005] lr: 1.000e-02, eta: 6:03:03, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.7207 2023-01-03 21:49:41,984 - mmcls - INFO - Epoch [51][3000/5005] lr: 1.000e-02, eta: 6:02:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7063 2023-01-03 21:49:50,776 - mmcls - INFO - Epoch [51][3100/5005] lr: 1.000e-02, eta: 6:02:46, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7350 2023-01-03 21:49:59,554 - mmcls - INFO - Epoch [51][3200/5005] lr: 1.000e-02, eta: 6:02:37, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7285 2023-01-03 21:50:08,359 - mmcls - INFO - Epoch [51][3300/5005] lr: 1.000e-02, eta: 6:02:28, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.7429 2023-01-03 21:50:16,890 - mmcls - INFO - Epoch [51][3400/5005] lr: 1.000e-02, eta: 6:02:19, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7255 2023-01-03 21:50:25,765 - mmcls - INFO - Epoch [51][3500/5005] lr: 1.000e-02, eta: 6:02:10, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7383 2023-01-03 21:50:34,876 - mmcls - INFO - Epoch [51][3600/5005] lr: 1.000e-02, eta: 6:02:02, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7373 2023-01-03 21:50:44,012 - mmcls - INFO - Epoch [51][3700/5005] lr: 1.000e-02, eta: 6:01:53, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7419 2023-01-03 21:50:52,786 - mmcls - INFO - Epoch [51][3800/5005] lr: 1.000e-02, eta: 6:01:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7498 2023-01-03 21:51:01,650 - mmcls - INFO - Epoch [51][3900/5005] lr: 1.000e-02, eta: 6:01:36, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7328 2023-01-03 21:51:10,308 - mmcls - INFO - Epoch [51][4000/5005] lr: 1.000e-02, eta: 6:01:27, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7247 2023-01-03 21:51:19,020 - mmcls - INFO - Epoch [51][4100/5005] lr: 1.000e-02, eta: 6:01:18, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7461 2023-01-03 21:51:27,637 - mmcls - INFO - Epoch [51][4200/5005] lr: 1.000e-02, eta: 6:01:09, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7041 2023-01-03 21:51:36,393 - mmcls - INFO - Epoch [51][4300/5005] lr: 1.000e-02, eta: 6:01:00, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.6927 2023-01-03 21:51:45,495 - mmcls - INFO - Epoch [51][4400/5005] lr: 1.000e-02, eta: 6:00:52, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.7281 2023-01-03 21:51:54,214 - mmcls - INFO - Epoch [51][4500/5005] lr: 1.000e-02, eta: 6:00:43, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7229 2023-01-03 21:52:02,963 - mmcls - INFO - Epoch [51][4600/5005] lr: 1.000e-02, eta: 6:00:34, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7395 2023-01-03 21:52:11,590 - mmcls - INFO - Epoch [51][4700/5005] lr: 1.000e-02, eta: 6:00:25, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7577 2023-01-03 21:52:20,287 - mmcls - INFO - Epoch [51][4800/5005] lr: 1.000e-02, eta: 6:00:16, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.7219 2023-01-03 21:52:29,021 - mmcls - INFO - Epoch [51][4900/5005] lr: 1.000e-02, eta: 6:00:07, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7326 2023-01-03 21:52:37,911 - mmcls - INFO - Epoch [51][5000/5005] lr: 1.000e-02, eta: 5:59:58, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7263 2023-01-03 21:53:00,456 - mmcls - INFO - Epoch(val) [51][196] accuracy_top-1: 64.0660, accuracy_top-5: 85.8940 2023-01-03 21:53:11,311 - mmcls - INFO - Epoch [52][100/5005] lr: 1.000e-02, eta: 5:59:51, time: 0.108, data_time: 0.050, memory: 1125, loss: 1.7022 2023-01-03 21:53:20,014 - mmcls - INFO - Epoch [52][200/5005] lr: 1.000e-02, eta: 5:59:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7064 2023-01-03 21:53:28,676 - mmcls - INFO - Epoch [52][300/5005] lr: 1.000e-02, eta: 5:59:33, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.6920 2023-01-03 21:53:37,441 - mmcls - INFO - Epoch [52][400/5005] lr: 1.000e-02, eta: 5:59:24, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6810 2023-01-03 21:53:47,114 - mmcls - INFO - Epoch [52][500/5005] lr: 1.000e-02, eta: 5:59:16, time: 0.097, data_time: 0.001, memory: 1125, loss: 1.7284 2023-01-03 21:53:55,694 - mmcls - INFO - Epoch [52][600/5005] lr: 1.000e-02, eta: 5:59:07, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.6988 2023-01-03 21:54:04,792 - mmcls - INFO - Epoch [52][700/5005] lr: 1.000e-02, eta: 5:58:58, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.7017 2023-01-03 21:54:13,529 - mmcls - INFO - Epoch [52][800/5005] lr: 1.000e-02, eta: 5:58:49, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.6986 2023-01-03 21:54:22,057 - mmcls - INFO - Epoch [52][900/5005] lr: 1.000e-02, eta: 5:58:40, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.7121 2023-01-03 21:54:30,741 - mmcls - INFO - Epoch [52][1000/5005] lr: 1.000e-02, eta: 5:58:31, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.7000 2023-01-03 21:54:39,835 - mmcls - INFO - Epoch [52][1100/5005] lr: 1.000e-02, eta: 5:58:23, time: 0.091, data_time: 0.028, memory: 1125, loss: 1.7130 2023-01-03 21:54:48,695 - mmcls - INFO - Epoch [52][1200/5005] lr: 1.000e-02, eta: 5:58:14, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7172 2023-01-03 21:54:57,557 - mmcls - INFO - Epoch [52][1300/5005] lr: 1.000e-02, eta: 5:58:05, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7187 2023-01-03 21:55:06,449 - mmcls - INFO - Epoch [52][1400/5005] lr: 1.000e-02, eta: 5:57:57, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7008 2023-01-03 21:55:15,308 - mmcls - INFO - Epoch [52][1500/5005] lr: 1.000e-02, eta: 5:57:48, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7051 2023-01-03 21:55:24,216 - mmcls - INFO - Epoch [52][1600/5005] lr: 1.000e-02, eta: 5:57:39, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6977 2023-01-03 21:55:33,074 - mmcls - INFO - Epoch [52][1700/5005] lr: 1.000e-02, eta: 5:57:30, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.7201 2023-01-03 21:55:41,856 - mmcls - INFO - Epoch [52][1800/5005] lr: 1.000e-02, eta: 5:57:22, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7022 2023-01-03 21:55:50,780 - mmcls - INFO - Epoch [52][1900/5005] lr: 1.000e-02, eta: 5:57:13, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7088 2023-01-03 21:55:59,703 - mmcls - INFO - Epoch [52][2000/5005] lr: 1.000e-02, eta: 5:57:04, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7225 2023-01-03 21:56:08,823 - mmcls - INFO - Epoch [52][2100/5005] lr: 1.000e-02, eta: 5:56:56, time: 0.091, data_time: 0.045, memory: 1125, loss: 1.7056 2023-01-03 21:56:17,606 - mmcls - INFO - Epoch [52][2200/5005] lr: 1.000e-02, eta: 5:56:47, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.7145 2023-01-03 21:56:26,424 - mmcls - INFO - Epoch [52][2300/5005] lr: 1.000e-02, eta: 5:56:38, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7134 2023-01-03 21:56:35,238 - mmcls - INFO - Epoch [52][2400/5005] lr: 1.000e-02, eta: 5:56:29, time: 0.088, data_time: 0.039, memory: 1125, loss: 1.7173 2023-01-03 21:56:43,872 - mmcls - INFO - Epoch [52][2500/5005] lr: 1.000e-02, eta: 5:56:20, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.7245 2023-01-03 21:56:52,454 - mmcls - INFO - Epoch [52][2600/5005] lr: 1.000e-02, eta: 5:56:11, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7021 2023-01-03 21:57:01,491 - mmcls - INFO - Epoch [52][2700/5005] lr: 1.000e-02, eta: 5:56:03, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7173 2023-01-03 21:57:10,263 - mmcls - INFO - Epoch [52][2800/5005] lr: 1.000e-02, eta: 5:55:54, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7471 2023-01-03 21:57:19,205 - mmcls - INFO - Epoch [52][2900/5005] lr: 1.000e-02, eta: 5:55:45, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7222 2023-01-03 21:57:27,925 - mmcls - INFO - Epoch [52][3000/5005] lr: 1.000e-02, eta: 5:55:36, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7003 2023-01-03 21:57:36,414 - mmcls - INFO - Epoch [52][3100/5005] lr: 1.000e-02, eta: 5:55:27, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.6760 2023-01-03 21:57:45,342 - mmcls - INFO - Epoch [52][3200/5005] lr: 1.000e-02, eta: 5:55:18, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7141 2023-01-03 21:57:53,990 - mmcls - INFO - Epoch [52][3300/5005] lr: 1.000e-02, eta: 5:55:09, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.7260 2023-01-03 21:58:02,644 - mmcls - INFO - Epoch [52][3400/5005] lr: 1.000e-02, eta: 5:55:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7405 2023-01-03 21:58:11,441 - mmcls - INFO - Epoch [52][3500/5005] lr: 1.000e-02, eta: 5:54:52, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7242 2023-01-03 21:58:20,372 - mmcls - INFO - Epoch [52][3600/5005] lr: 1.000e-02, eta: 5:54:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7419 2023-01-03 21:58:29,170 - mmcls - INFO - Epoch [52][3700/5005] lr: 1.000e-02, eta: 5:54:34, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.6962 2023-01-03 21:58:37,629 - mmcls - INFO - Epoch [52][3800/5005] lr: 1.000e-02, eta: 5:54:25, time: 0.085, data_time: 0.032, memory: 1125, loss: 1.7537 2023-01-03 21:58:46,161 - mmcls - INFO - Epoch [52][3900/5005] lr: 1.000e-02, eta: 5:54:16, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7388 2023-01-03 21:58:54,974 - mmcls - INFO - Epoch [52][4000/5005] lr: 1.000e-02, eta: 5:54:07, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7319 2023-01-03 21:59:03,754 - mmcls - INFO - Epoch [52][4100/5005] lr: 1.000e-02, eta: 5:53:58, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7345 2023-01-03 21:59:12,424 - mmcls - INFO - Epoch [52][4200/5005] lr: 1.000e-02, eta: 5:53:49, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.7168 2023-01-03 21:59:21,206 - mmcls - INFO - Epoch [52][4300/5005] lr: 1.000e-02, eta: 5:53:41, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7335 2023-01-03 21:59:29,941 - mmcls - INFO - Epoch [52][4400/5005] lr: 1.000e-02, eta: 5:53:32, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.7061 2023-01-03 21:59:38,605 - mmcls - INFO - Epoch [52][4500/5005] lr: 1.000e-02, eta: 5:53:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7245 2023-01-03 21:59:47,119 - mmcls - INFO - Epoch [52][4600/5005] lr: 1.000e-02, eta: 5:53:14, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.7543 2023-01-03 21:59:55,559 - mmcls - INFO - Epoch [52][4700/5005] lr: 1.000e-02, eta: 5:53:05, time: 0.084, data_time: 0.028, memory: 1125, loss: 1.7177 2023-01-03 22:00:04,431 - mmcls - INFO - Epoch [52][4800/5005] lr: 1.000e-02, eta: 5:52:56, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7294 2023-01-03 22:00:12,938 - mmcls - INFO - Epoch [52][4900/5005] lr: 1.000e-02, eta: 5:52:47, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.7697 2023-01-03 22:00:21,713 - mmcls - INFO - Epoch [52][5000/5005] lr: 1.000e-02, eta: 5:52:38, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7303 2023-01-03 22:00:44,332 - mmcls - INFO - Epoch(val) [52][196] accuracy_top-1: 64.2520, accuracy_top-5: 85.8620 2023-01-03 22:00:55,273 - mmcls - INFO - Epoch [53][100/5005] lr: 1.000e-02, eta: 5:52:30, time: 0.109, data_time: 0.035, memory: 1125, loss: 1.7015 2023-01-03 22:01:04,049 - mmcls - INFO - Epoch [53][200/5005] lr: 1.000e-02, eta: 5:52:21, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.6840 2023-01-03 22:01:12,788 - mmcls - INFO - Epoch [53][300/5005] lr: 1.000e-02, eta: 5:52:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6948 2023-01-03 22:01:21,463 - mmcls - INFO - Epoch [53][400/5005] lr: 1.000e-02, eta: 5:52:04, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6987 2023-01-03 22:01:30,089 - mmcls - INFO - Epoch [53][500/5005] lr: 1.000e-02, eta: 5:51:55, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.6875 2023-01-03 22:01:38,751 - mmcls - INFO - Epoch [53][600/5005] lr: 1.000e-02, eta: 5:51:46, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7027 2023-01-03 22:01:47,567 - mmcls - INFO - Epoch [53][700/5005] lr: 1.000e-02, eta: 5:51:37, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7098 2023-01-03 22:01:56,839 - mmcls - INFO - Epoch [53][800/5005] lr: 1.000e-02, eta: 5:51:28, time: 0.093, data_time: 0.007, memory: 1125, loss: 1.7254 2023-01-03 22:02:05,996 - mmcls - INFO - Epoch [53][900/5005] lr: 1.000e-02, eta: 5:51:20, time: 0.091, data_time: 0.020, memory: 1125, loss: 1.6707 2023-01-03 22:02:14,668 - mmcls - INFO - Epoch [53][1000/5005] lr: 1.000e-02, eta: 5:51:11, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7221 2023-01-03 22:02:23,537 - mmcls - INFO - Epoch [53][1100/5005] lr: 1.000e-02, eta: 5:51:02, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6990 2023-01-03 22:02:32,321 - mmcls - INFO - Epoch [53][1200/5005] lr: 1.000e-02, eta: 5:50:53, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6916 2023-01-03 22:02:41,103 - mmcls - INFO - Epoch [53][1300/5005] lr: 1.000e-02, eta: 5:50:45, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7199 2023-01-03 22:02:50,193 - mmcls - INFO - Epoch [53][1400/5005] lr: 1.000e-02, eta: 5:50:36, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.6973 2023-01-03 22:02:58,722 - mmcls - INFO - Epoch [53][1500/5005] lr: 1.000e-02, eta: 5:50:27, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6917 2023-01-03 22:03:07,451 - mmcls - INFO - Epoch [53][1600/5005] lr: 1.000e-02, eta: 5:50:18, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7040 2023-01-03 22:03:16,302 - mmcls - INFO - Epoch [53][1700/5005] lr: 1.000e-02, eta: 5:50:09, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7033 2023-01-03 22:03:24,773 - mmcls - INFO - Epoch [53][1800/5005] lr: 1.000e-02, eta: 5:50:00, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.6923 2023-01-03 22:03:33,873 - mmcls - INFO - Epoch [53][1900/5005] lr: 1.000e-02, eta: 5:49:52, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.7159 2023-01-03 22:03:42,727 - mmcls - INFO - Epoch [53][2000/5005] lr: 1.000e-02, eta: 5:49:43, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7457 2023-01-03 22:03:51,542 - mmcls - INFO - Epoch [53][2100/5005] lr: 1.000e-02, eta: 5:49:34, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7169 2023-01-03 22:04:00,167 - mmcls - INFO - Epoch [53][2200/5005] lr: 1.000e-02, eta: 5:49:25, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7211 2023-01-03 22:04:08,701 - mmcls - INFO - Epoch [53][2300/5005] lr: 1.000e-02, eta: 5:49:16, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7406 2023-01-03 22:04:17,713 - mmcls - INFO - Epoch [53][2400/5005] lr: 1.000e-02, eta: 5:49:07, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.7419 2023-01-03 22:04:26,592 - mmcls - INFO - Epoch [53][2500/5005] lr: 1.000e-02, eta: 5:48:59, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.7138 2023-01-03 22:04:35,268 - mmcls - INFO - Epoch [53][2600/5005] lr: 1.000e-02, eta: 5:48:50, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.7195 2023-01-03 22:04:44,168 - mmcls - INFO - Epoch [53][2700/5005] lr: 1.000e-02, eta: 5:48:41, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7058 2023-01-03 22:04:53,261 - mmcls - INFO - Epoch [53][2800/5005] lr: 1.000e-02, eta: 5:48:32, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.7175 2023-01-03 22:05:02,052 - mmcls - INFO - Epoch [53][2900/5005] lr: 1.000e-02, eta: 5:48:24, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7139 2023-01-03 22:05:11,180 - mmcls - INFO - Epoch [53][3000/5005] lr: 1.000e-02, eta: 5:48:15, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.7237 2023-01-03 22:05:19,657 - mmcls - INFO - Epoch [53][3100/5005] lr: 1.000e-02, eta: 5:48:06, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.7516 2023-01-03 22:05:28,245 - mmcls - INFO - Epoch [53][3200/5005] lr: 1.000e-02, eta: 5:47:57, time: 0.086, data_time: 0.035, memory: 1125, loss: 1.7213 2023-01-03 22:05:37,235 - mmcls - INFO - Epoch [53][3300/5005] lr: 1.000e-02, eta: 5:47:48, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.7129 2023-01-03 22:05:45,697 - mmcls - INFO - Epoch [53][3400/5005] lr: 1.000e-02, eta: 5:47:39, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7288 2023-01-03 22:05:54,574 - mmcls - INFO - Epoch [53][3500/5005] lr: 1.000e-02, eta: 5:47:31, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.7450 2023-01-03 22:06:03,224 - mmcls - INFO - Epoch [53][3600/5005] lr: 1.000e-02, eta: 5:47:22, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7305 2023-01-03 22:06:11,865 - mmcls - INFO - Epoch [53][3700/5005] lr: 1.000e-02, eta: 5:47:13, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7380 2023-01-03 22:06:21,162 - mmcls - INFO - Epoch [53][3800/5005] lr: 1.000e-02, eta: 5:47:04, time: 0.093, data_time: 0.008, memory: 1125, loss: 1.7151 2023-01-03 22:06:29,915 - mmcls - INFO - Epoch [53][3900/5005] lr: 1.000e-02, eta: 5:46:55, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7248 2023-01-03 22:06:38,538 - mmcls - INFO - Epoch [53][4000/5005] lr: 1.000e-02, eta: 5:46:46, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.7247 2023-01-03 22:06:47,358 - mmcls - INFO - Epoch [53][4100/5005] lr: 1.000e-02, eta: 5:46:38, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.7373 2023-01-03 22:06:56,040 - mmcls - INFO - Epoch [53][4200/5005] lr: 1.000e-02, eta: 5:46:29, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7332 2023-01-03 22:07:04,656 - mmcls - INFO - Epoch [53][4300/5005] lr: 1.000e-02, eta: 5:46:20, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7264 2023-01-03 22:07:13,488 - mmcls - INFO - Epoch [53][4400/5005] lr: 1.000e-02, eta: 5:46:11, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7348 2023-01-03 22:07:22,098 - mmcls - INFO - Epoch [53][4500/5005] lr: 1.000e-02, eta: 5:46:02, time: 0.086, data_time: 0.034, memory: 1125, loss: 1.7267 2023-01-03 22:07:30,815 - mmcls - INFO - Epoch [53][4600/5005] lr: 1.000e-02, eta: 5:45:53, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7350 2023-01-03 22:07:39,617 - mmcls - INFO - Epoch [53][4700/5005] lr: 1.000e-02, eta: 5:45:44, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7325 2023-01-03 22:07:48,143 - mmcls - INFO - Epoch [53][4800/5005] lr: 1.000e-02, eta: 5:45:35, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7168 2023-01-03 22:07:56,820 - mmcls - INFO - Epoch [53][4900/5005] lr: 1.000e-02, eta: 5:45:26, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7385 2023-01-03 22:08:05,640 - mmcls - INFO - Epoch [53][5000/5005] lr: 1.000e-02, eta: 5:45:17, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7078 2023-01-03 22:08:28,185 - mmcls - INFO - Epoch(val) [53][196] accuracy_top-1: 64.2380, accuracy_top-5: 86.0000 2023-01-03 22:08:39,143 - mmcls - INFO - Epoch [54][100/5005] lr: 1.000e-02, eta: 5:45:10, time: 0.109, data_time: 0.052, memory: 1125, loss: 1.6891 2023-01-03 22:08:47,547 - mmcls - INFO - Epoch [54][200/5005] lr: 1.000e-02, eta: 5:45:01, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.7105 2023-01-03 22:08:56,618 - mmcls - INFO - Epoch [54][300/5005] lr: 1.000e-02, eta: 5:44:52, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.6945 2023-01-03 22:09:05,186 - mmcls - INFO - Epoch [54][400/5005] lr: 1.000e-02, eta: 5:44:43, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7007 2023-01-03 22:09:14,115 - mmcls - INFO - Epoch [54][500/5005] lr: 1.000e-02, eta: 5:44:34, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.6955 2023-01-03 22:09:22,827 - mmcls - INFO - Epoch [54][600/5005] lr: 1.000e-02, eta: 5:44:25, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7024 2023-01-03 22:09:31,555 - mmcls - INFO - Epoch [54][700/5005] lr: 1.000e-02, eta: 5:44:16, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6756 2023-01-03 22:09:40,550 - mmcls - INFO - Epoch [54][800/5005] lr: 1.000e-02, eta: 5:44:08, time: 0.090, data_time: 0.035, memory: 1125, loss: 1.6853 2023-01-03 22:09:49,191 - mmcls - INFO - Epoch [54][900/5005] lr: 1.000e-02, eta: 5:43:59, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.6701 2023-01-03 22:09:57,989 - mmcls - INFO - Epoch [54][1000/5005] lr: 1.000e-02, eta: 5:43:50, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7188 2023-01-03 22:10:07,000 - mmcls - INFO - Epoch [54][1100/5005] lr: 1.000e-02, eta: 5:43:41, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.7043 2023-01-03 22:10:15,822 - mmcls - INFO - Epoch [54][1200/5005] lr: 1.000e-02, eta: 5:43:33, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6801 2023-01-03 22:10:24,362 - mmcls - INFO - Epoch [54][1300/5005] lr: 1.000e-02, eta: 5:43:24, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7210 2023-01-03 22:10:33,401 - mmcls - INFO - Epoch [54][1400/5005] lr: 1.000e-02, eta: 5:43:15, time: 0.090, data_time: 0.021, memory: 1125, loss: 1.7240 2023-01-03 22:10:42,379 - mmcls - INFO - Epoch [54][1500/5005] lr: 1.000e-02, eta: 5:43:06, time: 0.090, data_time: 0.024, memory: 1125, loss: 1.7057 2023-01-03 22:10:51,163 - mmcls - INFO - Epoch [54][1600/5005] lr: 1.000e-02, eta: 5:42:57, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.7033 2023-01-03 22:10:59,973 - mmcls - INFO - Epoch [54][1700/5005] lr: 1.000e-02, eta: 5:42:49, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7050 2023-01-03 22:11:08,537 - mmcls - INFO - Epoch [54][1800/5005] lr: 1.000e-02, eta: 5:42:40, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.7000 2023-01-03 22:11:17,516 - mmcls - INFO - Epoch [54][1900/5005] lr: 1.000e-02, eta: 5:42:31, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.7041 2023-01-03 22:11:26,258 - mmcls - INFO - Epoch [54][2000/5005] lr: 1.000e-02, eta: 5:42:22, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7306 2023-01-03 22:11:34,795 - mmcls - INFO - Epoch [54][2100/5005] lr: 1.000e-02, eta: 5:42:13, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.7246 2023-01-03 22:11:43,399 - mmcls - INFO - Epoch [54][2200/5005] lr: 1.000e-02, eta: 5:42:04, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7176 2023-01-03 22:11:52,405 - mmcls - INFO - Epoch [54][2300/5005] lr: 1.000e-02, eta: 5:41:55, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7101 2023-01-03 22:12:01,387 - mmcls - INFO - Epoch [54][2400/5005] lr: 1.000e-02, eta: 5:41:47, time: 0.090, data_time: 0.027, memory: 1125, loss: 1.7326 2023-01-03 22:12:10,040 - mmcls - INFO - Epoch [54][2500/5005] lr: 1.000e-02, eta: 5:41:38, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.7269 2023-01-03 22:12:18,698 - mmcls - INFO - Epoch [54][2600/5005] lr: 1.000e-02, eta: 5:41:29, time: 0.087, data_time: 0.043, memory: 1125, loss: 1.7454 2023-01-03 22:12:27,289 - mmcls - INFO - Epoch [54][2700/5005] lr: 1.000e-02, eta: 5:41:20, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7208 2023-01-03 22:12:35,757 - mmcls - INFO - Epoch [54][2800/5005] lr: 1.000e-02, eta: 5:41:11, time: 0.085, data_time: 0.036, memory: 1125, loss: 1.7151 2023-01-03 22:12:44,481 - mmcls - INFO - Epoch [54][2900/5005] lr: 1.000e-02, eta: 5:41:02, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7021 2023-01-03 22:12:53,253 - mmcls - INFO - Epoch [54][3000/5005] lr: 1.000e-02, eta: 5:40:53, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.6895 2023-01-03 22:13:01,928 - mmcls - INFO - Epoch [54][3100/5005] lr: 1.000e-02, eta: 5:40:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7154 2023-01-03 22:13:10,611 - mmcls - INFO - Epoch [54][3200/5005] lr: 1.000e-02, eta: 5:40:35, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.7014 2023-01-03 22:13:19,172 - mmcls - INFO - Epoch [54][3300/5005] lr: 1.000e-02, eta: 5:40:26, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7329 2023-01-03 22:13:28,050 - mmcls - INFO - Epoch [54][3400/5005] lr: 1.000e-02, eta: 5:40:18, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7147 2023-01-03 22:13:36,952 - mmcls - INFO - Epoch [54][3500/5005] lr: 1.000e-02, eta: 5:40:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7169 2023-01-03 22:13:45,553 - mmcls - INFO - Epoch [54][3600/5005] lr: 1.000e-02, eta: 5:40:00, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7254 2023-01-03 22:13:54,222 - mmcls - INFO - Epoch [54][3700/5005] lr: 1.000e-02, eta: 5:39:51, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7071 2023-01-03 22:14:02,891 - mmcls - INFO - Epoch [54][3800/5005] lr: 1.000e-02, eta: 5:39:42, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7377 2023-01-03 22:14:11,492 - mmcls - INFO - Epoch [54][3900/5005] lr: 1.000e-02, eta: 5:39:33, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.7188 2023-01-03 22:14:20,267 - mmcls - INFO - Epoch [54][4000/5005] lr: 1.000e-02, eta: 5:39:24, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.7321 2023-01-03 22:14:28,963 - mmcls - INFO - Epoch [54][4100/5005] lr: 1.000e-02, eta: 5:39:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7520 2023-01-03 22:14:37,757 - mmcls - INFO - Epoch [54][4200/5005] lr: 1.000e-02, eta: 5:39:06, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7339 2023-01-03 22:14:46,731 - mmcls - INFO - Epoch [54][4300/5005] lr: 1.000e-02, eta: 5:38:58, time: 0.090, data_time: 0.033, memory: 1125, loss: 1.7309 2023-01-03 22:14:55,415 - mmcls - INFO - Epoch [54][4400/5005] lr: 1.000e-02, eta: 5:38:49, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7315 2023-01-03 22:15:04,263 - mmcls - INFO - Epoch [54][4500/5005] lr: 1.000e-02, eta: 5:38:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.7314 2023-01-03 22:15:13,292 - mmcls - INFO - Epoch [54][4600/5005] lr: 1.000e-02, eta: 5:38:31, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7301 2023-01-03 22:15:22,001 - mmcls - INFO - Epoch [54][4700/5005] lr: 1.000e-02, eta: 5:38:23, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7465 2023-01-03 22:15:30,554 - mmcls - INFO - Epoch [54][4800/5005] lr: 1.000e-02, eta: 5:38:14, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7335 2023-01-03 22:15:39,603 - mmcls - INFO - Epoch [54][4900/5005] lr: 1.000e-02, eta: 5:38:05, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.7279 2023-01-03 22:15:48,335 - mmcls - INFO - Epoch [54][5000/5005] lr: 1.000e-02, eta: 5:37:56, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.7396 2023-01-03 22:16:11,044 - mmcls - INFO - Epoch(val) [54][196] accuracy_top-1: 64.8520, accuracy_top-5: 86.1600 2023-01-03 22:16:22,028 - mmcls - INFO - Epoch [55][100/5005] lr: 1.000e-02, eta: 5:37:48, time: 0.110, data_time: 0.042, memory: 1125, loss: 1.6594 2023-01-03 22:16:30,850 - mmcls - INFO - Epoch [55][200/5005] lr: 1.000e-02, eta: 5:37:39, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7022 2023-01-03 22:16:39,367 - mmcls - INFO - Epoch [55][300/5005] lr: 1.000e-02, eta: 5:37:30, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6875 2023-01-03 22:16:47,755 - mmcls - INFO - Epoch [55][400/5005] lr: 1.000e-02, eta: 5:37:21, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.6792 2023-01-03 22:16:56,416 - mmcls - INFO - Epoch [55][500/5005] lr: 1.000e-02, eta: 5:37:12, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.6678 2023-01-03 22:17:04,949 - mmcls - INFO - Epoch [55][600/5005] lr: 1.000e-02, eta: 5:37:03, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.6858 2023-01-03 22:17:13,678 - mmcls - INFO - Epoch [55][700/5005] lr: 1.000e-02, eta: 5:36:54, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6798 2023-01-03 22:17:22,354 - mmcls - INFO - Epoch [55][800/5005] lr: 1.000e-02, eta: 5:36:45, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.6927 2023-01-03 22:17:30,911 - mmcls - INFO - Epoch [55][900/5005] lr: 1.000e-02, eta: 5:36:36, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.7005 2023-01-03 22:17:39,938 - mmcls - INFO - Epoch [55][1000/5005] lr: 1.000e-02, eta: 5:36:28, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.6980 2023-01-03 22:17:48,669 - mmcls - INFO - Epoch [55][1100/5005] lr: 1.000e-02, eta: 5:36:19, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.6816 2023-01-03 22:17:57,624 - mmcls - INFO - Epoch [55][1200/5005] lr: 1.000e-02, eta: 5:36:10, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.7014 2023-01-03 22:18:06,698 - mmcls - INFO - Epoch [55][1300/5005] lr: 1.000e-02, eta: 5:36:02, time: 0.091, data_time: 0.033, memory: 1125, loss: 1.6998 2023-01-03 22:18:15,479 - mmcls - INFO - Epoch [55][1400/5005] lr: 1.000e-02, eta: 5:35:53, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6800 2023-01-03 22:18:24,439 - mmcls - INFO - Epoch [55][1500/5005] lr: 1.000e-02, eta: 5:35:44, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7136 2023-01-03 22:18:33,135 - mmcls - INFO - Epoch [55][1600/5005] lr: 1.000e-02, eta: 5:35:35, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7123 2023-01-03 22:18:41,942 - mmcls - INFO - Epoch [55][1700/5005] lr: 1.000e-02, eta: 5:35:26, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7064 2023-01-03 22:18:50,618 - mmcls - INFO - Epoch [55][1800/5005] lr: 1.000e-02, eta: 5:35:18, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7063 2023-01-03 22:18:59,252 - mmcls - INFO - Epoch [55][1900/5005] lr: 1.000e-02, eta: 5:35:09, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.7126 2023-01-03 22:19:08,061 - mmcls - INFO - Epoch [55][2000/5005] lr: 1.000e-02, eta: 5:35:00, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.7163 2023-01-03 22:19:17,037 - mmcls - INFO - Epoch [55][2100/5005] lr: 1.000e-02, eta: 5:34:51, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.7093 2023-01-03 22:19:25,930 - mmcls - INFO - Epoch [55][2200/5005] lr: 1.000e-02, eta: 5:34:42, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.7042 2023-01-03 22:19:34,522 - mmcls - INFO - Epoch [55][2300/5005] lr: 1.000e-02, eta: 5:34:33, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7476 2023-01-03 22:19:43,388 - mmcls - INFO - Epoch [55][2400/5005] lr: 1.000e-02, eta: 5:34:25, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7193 2023-01-03 22:19:52,031 - mmcls - INFO - Epoch [55][2500/5005] lr: 1.000e-02, eta: 5:34:16, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.6960 2023-01-03 22:20:00,685 - mmcls - INFO - Epoch [55][2600/5005] lr: 1.000e-02, eta: 5:34:07, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7007 2023-01-03 22:20:09,674 - mmcls - INFO - Epoch [55][2700/5005] lr: 1.000e-02, eta: 5:33:58, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.7281 2023-01-03 22:20:18,805 - mmcls - INFO - Epoch [55][2800/5005] lr: 1.000e-02, eta: 5:33:50, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7297 2023-01-03 22:20:27,782 - mmcls - INFO - Epoch [55][2900/5005] lr: 1.000e-02, eta: 5:33:41, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.7115 2023-01-03 22:20:36,296 - mmcls - INFO - Epoch [55][3000/5005] lr: 1.000e-02, eta: 5:33:32, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.7285 2023-01-03 22:20:45,394 - mmcls - INFO - Epoch [55][3100/5005] lr: 1.000e-02, eta: 5:33:23, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.7075 2023-01-03 22:20:54,400 - mmcls - INFO - Epoch [55][3200/5005] lr: 1.000e-02, eta: 5:33:15, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.7054 2023-01-03 22:21:03,278 - mmcls - INFO - Epoch [55][3300/5005] lr: 1.000e-02, eta: 5:33:06, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6944 2023-01-03 22:21:12,229 - mmcls - INFO - Epoch [55][3400/5005] lr: 1.000e-02, eta: 5:32:57, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.7448 2023-01-03 22:21:21,155 - mmcls - INFO - Epoch [55][3500/5005] lr: 1.000e-02, eta: 5:32:48, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.7489 2023-01-03 22:21:29,857 - mmcls - INFO - Epoch [55][3600/5005] lr: 1.000e-02, eta: 5:32:40, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.7094 2023-01-03 22:21:38,709 - mmcls - INFO - Epoch [55][3700/5005] lr: 1.000e-02, eta: 5:32:31, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.6976 2023-01-03 22:21:47,305 - mmcls - INFO - Epoch [55][3800/5005] lr: 1.000e-02, eta: 5:32:22, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7244 2023-01-03 22:21:56,088 - mmcls - INFO - Epoch [55][3900/5005] lr: 1.000e-02, eta: 5:32:13, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.7363 2023-01-03 22:22:04,773 - mmcls - INFO - Epoch [55][4000/5005] lr: 1.000e-02, eta: 5:32:04, time: 0.087, data_time: 0.040, memory: 1125, loss: 1.7236 2023-01-03 22:22:13,405 - mmcls - INFO - Epoch [55][4100/5005] lr: 1.000e-02, eta: 5:31:55, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7361 2023-01-03 22:22:21,954 - mmcls - INFO - Epoch [55][4200/5005] lr: 1.000e-02, eta: 5:31:46, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7390 2023-01-03 22:22:30,468 - mmcls - INFO - Epoch [55][4300/5005] lr: 1.000e-02, eta: 5:31:37, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.7292 2023-01-03 22:22:39,124 - mmcls - INFO - Epoch [55][4400/5005] lr: 1.000e-02, eta: 5:31:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.7350 2023-01-03 22:22:47,732 - mmcls - INFO - Epoch [55][4500/5005] lr: 1.000e-02, eta: 5:31:19, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.7437 2023-01-03 22:22:56,486 - mmcls - INFO - Epoch [55][4600/5005] lr: 1.000e-02, eta: 5:31:10, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.7514 2023-01-03 22:23:05,000 - mmcls - INFO - Epoch [55][4700/5005] lr: 1.000e-02, eta: 5:31:01, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.7292 2023-01-03 22:23:13,728 - mmcls - INFO - Epoch [55][4800/5005] lr: 1.000e-02, eta: 5:30:52, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.6986 2023-01-03 22:23:22,556 - mmcls - INFO - Epoch [55][4900/5005] lr: 1.000e-02, eta: 5:30:44, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7291 2023-01-03 22:23:31,117 - mmcls - INFO - Epoch [55][5000/5005] lr: 1.000e-02, eta: 5:30:35, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7377 2023-01-03 22:23:53,809 - mmcls - INFO - Epoch(val) [55][196] accuracy_top-1: 64.3660, accuracy_top-5: 85.9840 2023-01-03 22:24:04,832 - mmcls - INFO - Epoch [56][100/5005] lr: 1.000e-02, eta: 5:30:27, time: 0.110, data_time: 0.068, memory: 1125, loss: 1.6785 2023-01-03 22:24:13,397 - mmcls - INFO - Epoch [56][200/5005] lr: 1.000e-02, eta: 5:30:18, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.6721 2023-01-03 22:24:22,271 - mmcls - INFO - Epoch [56][300/5005] lr: 1.000e-02, eta: 5:30:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6759 2023-01-03 22:24:31,389 - mmcls - INFO - Epoch [56][400/5005] lr: 1.000e-02, eta: 5:30:00, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.6730 2023-01-03 22:24:40,000 - mmcls - INFO - Epoch [56][500/5005] lr: 1.000e-02, eta: 5:29:52, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.6921 2023-01-03 22:24:48,561 - mmcls - INFO - Epoch [56][600/5005] lr: 1.000e-02, eta: 5:29:43, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6836 2023-01-03 22:24:57,104 - mmcls - INFO - Epoch [56][700/5005] lr: 1.000e-02, eta: 5:29:33, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.6855 2023-01-03 22:25:06,414 - mmcls - INFO - Epoch [56][800/5005] lr: 1.000e-02, eta: 5:29:25, time: 0.093, data_time: 0.003, memory: 1125, loss: 1.6991 2023-01-03 22:25:15,312 - mmcls - INFO - Epoch [56][900/5005] lr: 1.000e-02, eta: 5:29:16, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.6858 2023-01-03 22:25:24,061 - mmcls - INFO - Epoch [56][1000/5005] lr: 1.000e-02, eta: 5:29:07, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.7041 2023-01-03 22:25:32,879 - mmcls - INFO - Epoch [56][1100/5005] lr: 1.000e-02, eta: 5:28:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6985 2023-01-03 22:25:41,754 - mmcls - INFO - Epoch [56][1200/5005] lr: 1.000e-02, eta: 5:28:50, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.7047 2023-01-03 22:25:50,559 - mmcls - INFO - Epoch [56][1300/5005] lr: 1.000e-02, eta: 5:28:41, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.6972 2023-01-03 22:25:59,430 - mmcls - INFO - Epoch [56][1400/5005] lr: 1.000e-02, eta: 5:28:32, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7286 2023-01-03 22:26:08,061 - mmcls - INFO - Epoch [56][1500/5005] lr: 1.000e-02, eta: 5:28:23, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.6891 2023-01-03 22:26:16,835 - mmcls - INFO - Epoch [56][1600/5005] lr: 1.000e-02, eta: 5:28:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6979 2023-01-03 22:26:25,299 - mmcls - INFO - Epoch [56][1700/5005] lr: 1.000e-02, eta: 5:28:06, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.6906 2023-01-03 22:26:34,021 - mmcls - INFO - Epoch [56][1800/5005] lr: 1.000e-02, eta: 5:27:57, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7258 2023-01-03 22:26:42,650 - mmcls - INFO - Epoch [56][1900/5005] lr: 1.000e-02, eta: 5:27:48, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7056 2023-01-03 22:26:51,135 - mmcls - INFO - Epoch [56][2000/5005] lr: 1.000e-02, eta: 5:27:39, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.7178 2023-01-03 22:26:59,743 - mmcls - INFO - Epoch [56][2100/5005] lr: 1.000e-02, eta: 5:27:30, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7026 2023-01-03 22:27:08,516 - mmcls - INFO - Epoch [56][2200/5005] lr: 1.000e-02, eta: 5:27:21, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7172 2023-01-03 22:27:17,322 - mmcls - INFO - Epoch [56][2300/5005] lr: 1.000e-02, eta: 5:27:12, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.7190 2023-01-03 22:27:25,783 - mmcls - INFO - Epoch [56][2400/5005] lr: 1.000e-02, eta: 5:27:03, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7246 2023-01-03 22:27:34,365 - mmcls - INFO - Epoch [56][2500/5005] lr: 1.000e-02, eta: 5:26:54, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.7044 2023-01-03 22:27:43,061 - mmcls - INFO - Epoch [56][2600/5005] lr: 1.000e-02, eta: 5:26:45, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7099 2023-01-03 22:27:51,527 - mmcls - INFO - Epoch [56][2700/5005] lr: 1.000e-02, eta: 5:26:36, time: 0.085, data_time: 0.024, memory: 1125, loss: 1.7443 2023-01-03 22:28:00,628 - mmcls - INFO - Epoch [56][2800/5005] lr: 1.000e-02, eta: 5:26:27, time: 0.091, data_time: 0.022, memory: 1125, loss: 1.7345 2023-01-03 22:28:09,362 - mmcls - INFO - Epoch [56][2900/5005] lr: 1.000e-02, eta: 5:26:19, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7083 2023-01-03 22:28:18,224 - mmcls - INFO - Epoch [56][3000/5005] lr: 1.000e-02, eta: 5:26:10, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.7162 2023-01-03 22:28:26,863 - mmcls - INFO - Epoch [56][3100/5005] lr: 1.000e-02, eta: 5:26:01, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.7262 2023-01-03 22:28:35,362 - mmcls - INFO - Epoch [56][3200/5005] lr: 1.000e-02, eta: 5:25:52, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7081 2023-01-03 22:28:44,241 - mmcls - INFO - Epoch [56][3300/5005] lr: 1.000e-02, eta: 5:25:43, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7130 2023-01-03 22:28:52,844 - mmcls - INFO - Epoch [56][3400/5005] lr: 1.000e-02, eta: 5:25:34, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.6964 2023-01-03 22:29:01,628 - mmcls - INFO - Epoch [56][3500/5005] lr: 1.000e-02, eta: 5:25:25, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7123 2023-01-03 22:29:10,485 - mmcls - INFO - Epoch [56][3600/5005] lr: 1.000e-02, eta: 5:25:16, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.7168 2023-01-03 22:29:19,264 - mmcls - INFO - Epoch [56][3700/5005] lr: 1.000e-02, eta: 5:25:08, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.7282 2023-01-03 22:29:27,921 - mmcls - INFO - Epoch [56][3800/5005] lr: 1.000e-02, eta: 5:24:59, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.7237 2023-01-03 22:29:36,750 - mmcls - INFO - Epoch [56][3900/5005] lr: 1.000e-02, eta: 5:24:50, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7072 2023-01-03 22:29:45,606 - mmcls - INFO - Epoch [56][4000/5005] lr: 1.000e-02, eta: 5:24:41, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7144 2023-01-03 22:29:54,161 - mmcls - INFO - Epoch [56][4100/5005] lr: 1.000e-02, eta: 5:24:32, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.7612 2023-01-03 22:30:03,290 - mmcls - INFO - Epoch [56][4200/5005] lr: 1.000e-02, eta: 5:24:24, time: 0.091, data_time: 0.035, memory: 1125, loss: 1.7361 2023-01-03 22:30:12,018 - mmcls - INFO - Epoch [56][4300/5005] lr: 1.000e-02, eta: 5:24:15, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7038 2023-01-03 22:30:20,793 - mmcls - INFO - Epoch [56][4400/5005] lr: 1.000e-02, eta: 5:24:06, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7344 2023-01-03 22:30:29,525 - mmcls - INFO - Epoch [56][4500/5005] lr: 1.000e-02, eta: 5:23:57, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.7268 2023-01-03 22:30:38,434 - mmcls - INFO - Epoch [56][4600/5005] lr: 1.000e-02, eta: 5:23:48, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.7292 2023-01-03 22:30:47,054 - mmcls - INFO - Epoch [56][4700/5005] lr: 1.000e-02, eta: 5:23:39, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7083 2023-01-03 22:30:55,821 - mmcls - INFO - Epoch [56][4800/5005] lr: 1.000e-02, eta: 5:23:31, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7325 2023-01-03 22:31:04,704 - mmcls - INFO - Epoch [56][4900/5005] lr: 1.000e-02, eta: 5:23:22, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7329 2023-01-03 22:31:13,421 - mmcls - INFO - Epoch [56][5000/5005] lr: 1.000e-02, eta: 5:23:13, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.7182 2023-01-03 22:31:36,179 - mmcls - INFO - Epoch(val) [56][196] accuracy_top-1: 64.3240, accuracy_top-5: 86.0060 2023-01-03 22:31:47,037 - mmcls - INFO - Epoch [57][100/5005] lr: 1.000e-02, eta: 5:23:05, time: 0.108, data_time: 0.037, memory: 1125, loss: 1.6682 2023-01-03 22:31:55,634 - mmcls - INFO - Epoch [57][200/5005] lr: 1.000e-02, eta: 5:22:56, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.6713 2023-01-03 22:32:04,576 - mmcls - INFO - Epoch [57][300/5005] lr: 1.000e-02, eta: 5:22:47, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.6915 2023-01-03 22:32:13,481 - mmcls - INFO - Epoch [57][400/5005] lr: 1.000e-02, eta: 5:22:38, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6847 2023-01-03 22:32:22,145 - mmcls - INFO - Epoch [57][500/5005] lr: 1.000e-02, eta: 5:22:30, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.6921 2023-01-03 22:32:31,037 - mmcls - INFO - Epoch [57][600/5005] lr: 1.000e-02, eta: 5:22:21, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.6810 2023-01-03 22:32:39,684 - mmcls - INFO - Epoch [57][700/5005] lr: 1.000e-02, eta: 5:22:12, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7080 2023-01-03 22:32:48,902 - mmcls - INFO - Epoch [57][800/5005] lr: 1.000e-02, eta: 5:22:03, time: 0.092, data_time: 0.033, memory: 1125, loss: 1.6926 2023-01-03 22:32:57,634 - mmcls - INFO - Epoch [57][900/5005] lr: 1.000e-02, eta: 5:21:55, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.6717 2023-01-03 22:33:06,428 - mmcls - INFO - Epoch [57][1000/5005] lr: 1.000e-02, eta: 5:21:46, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7044 2023-01-03 22:33:15,254 - mmcls - INFO - Epoch [57][1100/5005] lr: 1.000e-02, eta: 5:21:37, time: 0.088, data_time: 0.046, memory: 1125, loss: 1.6918 2023-01-03 22:33:23,876 - mmcls - INFO - Epoch [57][1200/5005] lr: 1.000e-02, eta: 5:21:28, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6773 2023-01-03 22:33:32,810 - mmcls - INFO - Epoch [57][1300/5005] lr: 1.000e-02, eta: 5:21:19, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7259 2023-01-03 22:33:41,754 - mmcls - INFO - Epoch [57][1400/5005] lr: 1.000e-02, eta: 5:21:11, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.7184 2023-01-03 22:33:50,613 - mmcls - INFO - Epoch [57][1500/5005] lr: 1.000e-02, eta: 5:21:02, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7104 2023-01-03 22:33:59,332 - mmcls - INFO - Epoch [57][1600/5005] lr: 1.000e-02, eta: 5:20:53, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.6809 2023-01-03 22:34:08,770 - mmcls - INFO - Epoch [57][1700/5005] lr: 1.000e-02, eta: 5:20:45, time: 0.094, data_time: 0.008, memory: 1125, loss: 1.7151 2023-01-03 22:34:17,534 - mmcls - INFO - Epoch [57][1800/5005] lr: 1.000e-02, eta: 5:20:36, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7029 2023-01-03 22:34:26,148 - mmcls - INFO - Epoch [57][1900/5005] lr: 1.000e-02, eta: 5:20:27, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.6806 2023-01-03 22:34:34,809 - mmcls - INFO - Epoch [57][2000/5005] lr: 1.000e-02, eta: 5:20:18, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.7210 2023-01-03 22:34:43,457 - mmcls - INFO - Epoch [57][2100/5005] lr: 1.000e-02, eta: 5:20:09, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7005 2023-01-03 22:34:52,075 - mmcls - INFO - Epoch [57][2200/5005] lr: 1.000e-02, eta: 5:20:00, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7238 2023-01-03 22:35:00,953 - mmcls - INFO - Epoch [57][2300/5005] lr: 1.000e-02, eta: 5:19:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6902 2023-01-03 22:35:09,984 - mmcls - INFO - Epoch [57][2400/5005] lr: 1.000e-02, eta: 5:19:43, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.6950 2023-01-03 22:35:18,930 - mmcls - INFO - Epoch [57][2500/5005] lr: 1.000e-02, eta: 5:19:34, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.7030 2023-01-03 22:35:27,692 - mmcls - INFO - Epoch [57][2600/5005] lr: 1.000e-02, eta: 5:19:25, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.7328 2023-01-03 22:35:36,256 - mmcls - INFO - Epoch [57][2700/5005] lr: 1.000e-02, eta: 5:19:16, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7376 2023-01-03 22:35:44,868 - mmcls - INFO - Epoch [57][2800/5005] lr: 1.000e-02, eta: 5:19:07, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7178 2023-01-03 22:35:53,692 - mmcls - INFO - Epoch [57][2900/5005] lr: 1.000e-02, eta: 5:18:58, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7292 2023-01-03 22:36:02,378 - mmcls - INFO - Epoch [57][3000/5005] lr: 1.000e-02, eta: 5:18:49, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.7217 2023-01-03 22:36:11,154 - mmcls - INFO - Epoch [57][3100/5005] lr: 1.000e-02, eta: 5:18:41, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.6983 2023-01-03 22:36:19,847 - mmcls - INFO - Epoch [57][3200/5005] lr: 1.000e-02, eta: 5:18:32, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7136 2023-01-03 22:36:28,498 - mmcls - INFO - Epoch [57][3300/5005] lr: 1.000e-02, eta: 5:18:23, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.7069 2023-01-03 22:36:37,349 - mmcls - INFO - Epoch [57][3400/5005] lr: 1.000e-02, eta: 5:18:14, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7043 2023-01-03 22:36:46,436 - mmcls - INFO - Epoch [57][3500/5005] lr: 1.000e-02, eta: 5:18:05, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.7233 2023-01-03 22:36:55,324 - mmcls - INFO - Epoch [57][3600/5005] lr: 1.000e-02, eta: 5:17:57, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.7242 2023-01-03 22:37:04,505 - mmcls - INFO - Epoch [57][3700/5005] lr: 1.000e-02, eta: 5:17:48, time: 0.092, data_time: 0.015, memory: 1125, loss: 1.7095 2023-01-03 22:37:13,332 - mmcls - INFO - Epoch [57][3800/5005] lr: 1.000e-02, eta: 5:17:39, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.7143 2023-01-03 22:37:21,913 - mmcls - INFO - Epoch [57][3900/5005] lr: 1.000e-02, eta: 5:17:30, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.7275 2023-01-03 22:37:30,649 - mmcls - INFO - Epoch [57][4000/5005] lr: 1.000e-02, eta: 5:17:22, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.7198 2023-01-03 22:37:39,416 - mmcls - INFO - Epoch [57][4100/5005] lr: 1.000e-02, eta: 5:17:13, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7171 2023-01-03 22:37:47,856 - mmcls - INFO - Epoch [57][4200/5005] lr: 1.000e-02, eta: 5:17:04, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.7192 2023-01-03 22:37:56,550 - mmcls - INFO - Epoch [57][4300/5005] lr: 1.000e-02, eta: 5:16:55, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7234 2023-01-03 22:38:05,890 - mmcls - INFO - Epoch [57][4400/5005] lr: 1.000e-02, eta: 5:16:46, time: 0.093, data_time: 0.031, memory: 1125, loss: 1.7280 2023-01-03 22:38:14,464 - mmcls - INFO - Epoch [57][4500/5005] lr: 1.000e-02, eta: 5:16:37, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.7180 2023-01-03 22:38:23,171 - mmcls - INFO - Epoch [57][4600/5005] lr: 1.000e-02, eta: 5:16:28, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.7045 2023-01-03 22:38:32,197 - mmcls - INFO - Epoch [57][4700/5005] lr: 1.000e-02, eta: 5:16:20, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7189 2023-01-03 22:38:41,458 - mmcls - INFO - Epoch [57][4800/5005] lr: 1.000e-02, eta: 5:16:11, time: 0.093, data_time: 0.020, memory: 1125, loss: 1.7300 2023-01-03 22:38:50,120 - mmcls - INFO - Epoch [57][4900/5005] lr: 1.000e-02, eta: 5:16:02, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7159 2023-01-03 22:38:58,701 - mmcls - INFO - Epoch [57][5000/5005] lr: 1.000e-02, eta: 5:15:53, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.7464 2023-01-03 22:39:21,392 - mmcls - INFO - Epoch(val) [57][196] accuracy_top-1: 64.9520, accuracy_top-5: 86.3100 2023-01-03 22:39:32,348 - mmcls - INFO - Epoch [58][100/5005] lr: 1.000e-02, eta: 5:15:45, time: 0.109, data_time: 0.053, memory: 1125, loss: 1.6588 2023-01-03 22:39:41,026 - mmcls - INFO - Epoch [58][200/5005] lr: 1.000e-02, eta: 5:15:37, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6869 2023-01-03 22:39:49,877 - mmcls - INFO - Epoch [58][300/5005] lr: 1.000e-02, eta: 5:15:28, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.6649 2023-01-03 22:39:58,610 - mmcls - INFO - Epoch [58][400/5005] lr: 1.000e-02, eta: 5:15:19, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.6670 2023-01-03 22:40:07,153 - mmcls - INFO - Epoch [58][500/5005] lr: 1.000e-02, eta: 5:15:10, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.6908 2023-01-03 22:40:15,629 - mmcls - INFO - Epoch [58][600/5005] lr: 1.000e-02, eta: 5:15:01, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.6897 2023-01-03 22:40:24,395 - mmcls - INFO - Epoch [58][700/5005] lr: 1.000e-02, eta: 5:14:52, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.6908 2023-01-03 22:40:33,588 - mmcls - INFO - Epoch [58][800/5005] lr: 1.000e-02, eta: 5:14:43, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.7041 2023-01-03 22:40:42,574 - mmcls - INFO - Epoch [58][900/5005] lr: 1.000e-02, eta: 5:14:35, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.6956 2023-01-03 22:40:51,454 - mmcls - INFO - Epoch [58][1000/5005] lr: 1.000e-02, eta: 5:14:26, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.6851 2023-01-03 22:41:00,362 - mmcls - INFO - Epoch [58][1100/5005] lr: 1.000e-02, eta: 5:14:17, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.6978 2023-01-03 22:41:09,148 - mmcls - INFO - Epoch [58][1200/5005] lr: 1.000e-02, eta: 5:14:08, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.6887 2023-01-03 22:41:18,274 - mmcls - INFO - Epoch [58][1300/5005] lr: 1.000e-02, eta: 5:14:00, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.6937 2023-01-03 22:41:27,152 - mmcls - INFO - Epoch [58][1400/5005] lr: 1.000e-02, eta: 5:13:51, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.6991 2023-01-03 22:41:35,720 - mmcls - INFO - Epoch [58][1500/5005] lr: 1.000e-02, eta: 5:13:42, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.7134 2023-01-03 22:41:44,173 - mmcls - INFO - Epoch [58][1600/5005] lr: 1.000e-02, eta: 5:13:33, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.7051 2023-01-03 22:41:52,930 - mmcls - INFO - Epoch [58][1700/5005] lr: 1.000e-02, eta: 5:13:24, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7142 2023-01-03 22:42:01,455 - mmcls - INFO - Epoch [58][1800/5005] lr: 1.000e-02, eta: 5:13:15, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7051 2023-01-03 22:42:10,282 - mmcls - INFO - Epoch [58][1900/5005] lr: 1.000e-02, eta: 5:13:06, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.6800 2023-01-03 22:42:19,050 - mmcls - INFO - Epoch [58][2000/5005] lr: 1.000e-02, eta: 5:12:58, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6991 2023-01-03 22:42:28,262 - mmcls - INFO - Epoch [58][2100/5005] lr: 1.000e-02, eta: 5:12:49, time: 0.092, data_time: 0.019, memory: 1125, loss: 1.7033 2023-01-03 22:42:37,069 - mmcls - INFO - Epoch [58][2200/5005] lr: 1.000e-02, eta: 5:12:40, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.7260 2023-01-03 22:42:45,614 - mmcls - INFO - Epoch [58][2300/5005] lr: 1.000e-02, eta: 5:12:31, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.7104 2023-01-03 22:42:54,373 - mmcls - INFO - Epoch [58][2400/5005] lr: 1.000e-02, eta: 5:12:22, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.6960 2023-01-03 22:43:03,253 - mmcls - INFO - Epoch [58][2500/5005] lr: 1.000e-02, eta: 5:12:14, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.7235 2023-01-03 22:43:12,329 - mmcls - INFO - Epoch [58][2600/5005] lr: 1.000e-02, eta: 5:12:05, time: 0.091, data_time: 0.041, memory: 1125, loss: 1.7177 2023-01-03 22:43:20,872 - mmcls - INFO - Epoch [58][2700/5005] lr: 1.000e-02, eta: 5:11:56, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.7056 2023-01-03 22:43:29,418 - mmcls - INFO - Epoch [58][2800/5005] lr: 1.000e-02, eta: 5:11:47, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.6832 2023-01-03 22:43:38,111 - mmcls - INFO - Epoch [58][2900/5005] lr: 1.000e-02, eta: 5:11:38, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.7258 2023-01-03 22:43:46,621 - mmcls - INFO - Epoch [58][3000/5005] lr: 1.000e-02, eta: 5:11:29, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.7164 2023-01-03 22:43:55,241 - mmcls - INFO - Epoch [58][3100/5005] lr: 1.000e-02, eta: 5:11:20, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.7269 2023-01-03 22:44:04,136 - mmcls - INFO - Epoch [58][3200/5005] lr: 1.000e-02, eta: 5:11:11, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.7213 2023-01-03 22:44:13,089 - mmcls - INFO - Epoch [58][3300/5005] lr: 1.000e-02, eta: 5:11:03, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.7379 2023-01-03 22:44:21,653 - mmcls - INFO - Epoch [58][3400/5005] lr: 1.000e-02, eta: 5:10:54, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.7115 2023-01-03 22:44:30,633 - mmcls - INFO - Epoch [58][3500/5005] lr: 1.000e-02, eta: 5:10:45, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6927 2023-01-03 22:44:39,060 - mmcls - INFO - Epoch [58][3600/5005] lr: 1.000e-02, eta: 5:10:36, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.7267 2023-01-03 22:44:47,803 - mmcls - INFO - Epoch [58][3700/5005] lr: 1.000e-02, eta: 5:10:27, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7159 2023-01-03 22:44:57,069 - mmcls - INFO - Epoch [58][3800/5005] lr: 1.000e-02, eta: 5:10:19, time: 0.093, data_time: 0.023, memory: 1125, loss: 1.7268 2023-01-03 22:45:05,720 - mmcls - INFO - Epoch [58][3900/5005] lr: 1.000e-02, eta: 5:10:10, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7130 2023-01-03 22:45:14,238 - mmcls - INFO - Epoch [58][4000/5005] lr: 1.000e-02, eta: 5:10:01, time: 0.085, data_time: 0.042, memory: 1125, loss: 1.7202 2023-01-03 22:45:23,450 - mmcls - INFO - Epoch [58][4100/5005] lr: 1.000e-02, eta: 5:09:52, time: 0.092, data_time: 0.028, memory: 1125, loss: 1.7310 2023-01-03 22:45:31,962 - mmcls - INFO - Epoch [58][4200/5005] lr: 1.000e-02, eta: 5:09:43, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.7444 2023-01-03 22:45:40,655 - mmcls - INFO - Epoch [58][4300/5005] lr: 1.000e-02, eta: 5:09:34, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.6941 2023-01-03 22:45:49,518 - mmcls - INFO - Epoch [58][4400/5005] lr: 1.000e-02, eta: 5:09:26, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7168 2023-01-03 22:45:58,234 - mmcls - INFO - Epoch [58][4500/5005] lr: 1.000e-02, eta: 5:09:17, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.7431 2023-01-03 22:46:07,124 - mmcls - INFO - Epoch [58][4600/5005] lr: 1.000e-02, eta: 5:09:08, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.7375 2023-01-03 22:46:16,004 - mmcls - INFO - Epoch [58][4700/5005] lr: 1.000e-02, eta: 5:08:59, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.7182 2023-01-03 22:46:24,777 - mmcls - INFO - Epoch [58][4800/5005] lr: 1.000e-02, eta: 5:08:50, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7133 2023-01-03 22:46:33,598 - mmcls - INFO - Epoch [58][4900/5005] lr: 1.000e-02, eta: 5:08:42, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.7401 2023-01-03 22:46:41,878 - mmcls - INFO - Epoch [58][5000/5005] lr: 1.000e-02, eta: 5:08:32, time: 0.083, data_time: 0.011, memory: 1125, loss: 1.7174 2023-01-03 22:47:04,887 - mmcls - INFO - Epoch(val) [58][196] accuracy_top-1: 64.5600, accuracy_top-5: 85.8420 2023-01-03 22:47:15,746 - mmcls - INFO - Epoch [59][100/5005] lr: 1.000e-02, eta: 5:08:24, time: 0.108, data_time: 0.056, memory: 1125, loss: 1.6690 2023-01-03 22:47:24,598 - mmcls - INFO - Epoch [59][200/5005] lr: 1.000e-02, eta: 5:08:15, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.6632 2023-01-03 22:47:33,327 - mmcls - INFO - Epoch [59][300/5005] lr: 1.000e-02, eta: 5:08:07, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.6868 2023-01-03 22:47:42,433 - mmcls - INFO - Epoch [59][400/5005] lr: 1.000e-02, eta: 5:07:58, time: 0.091, data_time: 0.028, memory: 1125, loss: 1.6897 2023-01-03 22:47:51,207 - mmcls - INFO - Epoch [59][500/5005] lr: 1.000e-02, eta: 5:07:49, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.7004 2023-01-03 22:47:59,734 - mmcls - INFO - Epoch [59][600/5005] lr: 1.000e-02, eta: 5:07:40, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.6868 2023-01-03 22:48:08,530 - mmcls - INFO - Epoch [59][700/5005] lr: 1.000e-02, eta: 5:07:31, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.6984 2023-01-03 22:48:17,327 - mmcls - INFO - Epoch [59][800/5005] lr: 1.000e-02, eta: 5:07:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.6671 2023-01-03 22:48:26,122 - mmcls - INFO - Epoch [59][900/5005] lr: 1.000e-02, eta: 5:07:14, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.7039 2023-01-03 22:48:34,838 - mmcls - INFO - Epoch [59][1000/5005] lr: 1.000e-02, eta: 5:07:05, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.7005 2023-01-03 22:48:43,554 - mmcls - INFO - Epoch [59][1100/5005] lr: 1.000e-02, eta: 5:06:56, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.6879 2023-01-03 22:48:52,118 - mmcls - INFO - Epoch [59][1200/5005] lr: 1.000e-02, eta: 5:06:47, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.6851 2023-01-03 22:49:01,096 - mmcls - INFO - Epoch [59][1300/5005] lr: 1.000e-02, eta: 5:06:38, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.6915 2023-01-03 22:49:09,694 - mmcls - INFO - Epoch [59][1400/5005] lr: 1.000e-02, eta: 5:06:29, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7276 2023-01-03 22:49:18,463 - mmcls - INFO - Epoch [59][1500/5005] lr: 1.000e-02, eta: 5:06:21, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7272 2023-01-03 22:49:27,389 - mmcls - INFO - Epoch [59][1600/5005] lr: 1.000e-02, eta: 5:06:12, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7049 2023-01-03 22:49:36,141 - mmcls - INFO - Epoch [59][1700/5005] lr: 1.000e-02, eta: 5:06:03, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7007 2023-01-03 22:49:45,316 - mmcls - INFO - Epoch [59][1800/5005] lr: 1.000e-02, eta: 5:05:54, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.7300 2023-01-03 22:49:54,154 - mmcls - INFO - Epoch [59][1900/5005] lr: 1.000e-02, eta: 5:05:46, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7244 2023-01-03 22:50:03,027 - mmcls - INFO - Epoch [59][2000/5005] lr: 1.000e-02, eta: 5:05:37, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.7206 2023-01-03 22:50:12,087 - mmcls - INFO - Epoch [59][2100/5005] lr: 1.000e-02, eta: 5:05:28, time: 0.091, data_time: 0.022, memory: 1125, loss: 1.7107 2023-01-03 22:50:20,703 - mmcls - INFO - Epoch [59][2200/5005] lr: 1.000e-02, eta: 5:05:19, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.6863 2023-01-03 22:50:29,757 - mmcls - INFO - Epoch [59][2300/5005] lr: 1.000e-02, eta: 5:05:11, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.7243 2023-01-03 22:50:38,268 - mmcls - INFO - Epoch [59][2400/5005] lr: 1.000e-02, eta: 5:05:02, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.7032 2023-01-03 22:50:47,138 - mmcls - INFO - Epoch [59][2500/5005] lr: 1.000e-02, eta: 5:04:53, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.6895 2023-01-03 22:50:55,787 - mmcls - INFO - Epoch [59][2600/5005] lr: 1.000e-02, eta: 5:04:44, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.6998 2023-01-03 22:51:04,496 - mmcls - INFO - Epoch [59][2700/5005] lr: 1.000e-02, eta: 5:04:35, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7026 2023-01-03 22:51:13,066 - mmcls - INFO - Epoch [59][2800/5005] lr: 1.000e-02, eta: 5:04:26, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.7093 2023-01-03 22:51:21,940 - mmcls - INFO - Epoch [59][2900/5005] lr: 1.000e-02, eta: 5:04:17, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.6967 2023-01-03 22:51:30,751 - mmcls - INFO - Epoch [59][3000/5005] lr: 1.000e-02, eta: 5:04:09, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.7075 2023-01-03 22:51:39,744 - mmcls - INFO - Epoch [59][3100/5005] lr: 1.000e-02, eta: 5:04:00, time: 0.090, data_time: 0.044, memory: 1125, loss: 1.7190 2023-01-03 22:51:48,325 - mmcls - INFO - Epoch [59][3200/5005] lr: 1.000e-02, eta: 5:03:51, time: 0.086, data_time: 0.044, memory: 1125, loss: 1.6823 2023-01-03 22:51:57,007 - mmcls - INFO - Epoch [59][3300/5005] lr: 1.000e-02, eta: 5:03:42, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.7047 2023-01-03 22:52:05,839 - mmcls - INFO - Epoch [59][3400/5005] lr: 1.000e-02, eta: 5:03:33, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.7052 2023-01-03 22:52:14,444 - mmcls - INFO - Epoch [59][3500/5005] lr: 1.000e-02, eta: 5:03:24, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.6989 2023-01-03 22:52:22,983 - mmcls - INFO - Epoch [59][3600/5005] lr: 1.000e-02, eta: 5:03:15, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.6866 2023-01-03 22:52:31,769 - mmcls - INFO - Epoch [59][3700/5005] lr: 1.000e-02, eta: 5:03:06, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.6921 2023-01-03 22:52:40,503 - mmcls - INFO - Epoch [59][3800/5005] lr: 1.000e-02, eta: 5:02:58, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6987 2023-01-03 22:52:49,384 - mmcls - INFO - Epoch [59][3900/5005] lr: 1.000e-02, eta: 5:02:49, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7369 2023-01-03 22:52:58,254 - mmcls - INFO - Epoch [59][4000/5005] lr: 1.000e-02, eta: 5:02:40, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7192 2023-01-03 22:53:06,663 - mmcls - INFO - Epoch [59][4100/5005] lr: 1.000e-02, eta: 5:02:31, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.7136 2023-01-03 22:53:15,371 - mmcls - INFO - Epoch [59][4200/5005] lr: 1.000e-02, eta: 5:02:22, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7202 2023-01-03 22:53:24,149 - mmcls - INFO - Epoch [59][4300/5005] lr: 1.000e-02, eta: 5:02:13, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.6987 2023-01-03 22:53:33,008 - mmcls - INFO - Epoch [59][4400/5005] lr: 1.000e-02, eta: 5:02:05, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.7312 2023-01-03 22:53:41,846 - mmcls - INFO - Epoch [59][4500/5005] lr: 1.000e-02, eta: 5:01:56, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.7305 2023-01-03 22:53:51,239 - mmcls - INFO - Epoch [59][4600/5005] lr: 1.000e-02, eta: 5:01:47, time: 0.094, data_time: 0.049, memory: 1125, loss: 1.7369 2023-01-03 22:53:59,894 - mmcls - INFO - Epoch [59][4700/5005] lr: 1.000e-02, eta: 5:01:38, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.6947 2023-01-03 22:54:08,991 - mmcls - INFO - Epoch [59][4800/5005] lr: 1.000e-02, eta: 5:01:30, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.7252 2023-01-03 22:54:17,653 - mmcls - INFO - Epoch [59][4900/5005] lr: 1.000e-02, eta: 5:01:21, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7104 2023-01-03 22:54:26,488 - mmcls - INFO - Epoch [59][5000/5005] lr: 1.000e-02, eta: 5:01:12, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.7263 2023-01-03 22:54:49,113 - mmcls - INFO - Epoch(val) [59][196] accuracy_top-1: 64.8380, accuracy_top-5: 86.2560 2023-01-03 22:55:00,327 - mmcls - INFO - Epoch [60][100/5005] lr: 1.000e-02, eta: 5:01:04, time: 0.112, data_time: 0.059, memory: 1125, loss: 1.6720 2023-01-03 22:55:09,223 - mmcls - INFO - Epoch [60][200/5005] lr: 1.000e-02, eta: 5:00:55, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.6973 2023-01-03 22:55:18,289 - mmcls - INFO - Epoch [60][300/5005] lr: 1.000e-02, eta: 5:00:47, time: 0.091, data_time: 0.037, memory: 1125, loss: 1.6854 2023-01-03 22:55:26,750 - mmcls - INFO - Epoch [60][400/5005] lr: 1.000e-02, eta: 5:00:38, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.7162 2023-01-03 22:55:35,584 - mmcls - INFO - Epoch [60][500/5005] lr: 1.000e-02, eta: 5:00:29, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.6852 2023-01-03 22:55:44,251 - mmcls - INFO - Epoch [60][600/5005] lr: 1.000e-02, eta: 5:00:20, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6818 2023-01-03 22:55:52,943 - mmcls - INFO - Epoch [60][700/5005] lr: 1.000e-02, eta: 5:00:11, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.6736 2023-01-03 22:56:01,749 - mmcls - INFO - Epoch [60][800/5005] lr: 1.000e-02, eta: 5:00:02, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.6885 2023-01-03 22:56:10,340 - mmcls - INFO - Epoch [60][900/5005] lr: 1.000e-02, eta: 4:59:53, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.6882 2023-01-03 22:56:18,801 - mmcls - INFO - Epoch [60][1000/5005] lr: 1.000e-02, eta: 4:59:44, time: 0.085, data_time: 0.034, memory: 1125, loss: 1.7145 2023-01-03 22:56:27,869 - mmcls - INFO - Epoch [60][1100/5005] lr: 1.000e-02, eta: 4:59:36, time: 0.091, data_time: 0.036, memory: 1125, loss: 1.6719 2023-01-03 22:56:37,267 - mmcls - INFO - Epoch [60][1200/5005] lr: 1.000e-02, eta: 4:59:27, time: 0.094, data_time: 0.018, memory: 1125, loss: 1.7053 2023-01-03 22:56:45,845 - mmcls - INFO - Epoch [60][1300/5005] lr: 1.000e-02, eta: 4:59:18, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.6853 2023-01-03 22:56:54,525 - mmcls - INFO - Epoch [60][1400/5005] lr: 1.000e-02, eta: 4:59:10, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6966 2023-01-03 22:57:03,547 - mmcls - INFO - Epoch [60][1500/5005] lr: 1.000e-02, eta: 4:59:01, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.7284 2023-01-03 22:57:12,686 - mmcls - INFO - Epoch [60][1600/5005] lr: 1.000e-02, eta: 4:58:52, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.6891 2023-01-03 22:57:21,982 - mmcls - INFO - Epoch [60][1700/5005] lr: 1.000e-02, eta: 4:58:44, time: 0.093, data_time: 0.008, memory: 1125, loss: 1.6939 2023-01-03 22:57:30,724 - mmcls - INFO - Epoch [60][1800/5005] lr: 1.000e-02, eta: 4:58:35, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7088 2023-01-03 22:57:39,391 - mmcls - INFO - Epoch [60][1900/5005] lr: 1.000e-02, eta: 4:58:26, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.6877 2023-01-03 22:57:48,494 - mmcls - INFO - Epoch [60][2000/5005] lr: 1.000e-02, eta: 4:58:17, time: 0.091, data_time: 0.006, memory: 1125, loss: 1.7129 2023-01-03 22:57:57,209 - mmcls - INFO - Epoch [60][2100/5005] lr: 1.000e-02, eta: 4:58:09, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.6929 2023-01-03 22:58:05,698 - mmcls - INFO - Epoch [60][2200/5005] lr: 1.000e-02, eta: 4:58:00, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.6952 2023-01-03 22:58:14,641 - mmcls - INFO - Epoch [60][2300/5005] lr: 1.000e-02, eta: 4:57:51, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.7084 2023-01-03 22:58:23,439 - mmcls - INFO - Epoch [60][2400/5005] lr: 1.000e-02, eta: 4:57:42, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.7009 2023-01-03 22:58:31,860 - mmcls - INFO - Epoch [60][2500/5005] lr: 1.000e-02, eta: 4:57:33, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.6993 2023-01-03 22:58:40,784 - mmcls - INFO - Epoch [60][2600/5005] lr: 1.000e-02, eta: 4:57:24, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.7020 2023-01-03 22:58:49,604 - mmcls - INFO - Epoch [60][2700/5005] lr: 1.000e-02, eta: 4:57:15, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.7092 2023-01-03 22:58:58,056 - mmcls - INFO - Epoch [60][2800/5005] lr: 1.000e-02, eta: 4:57:06, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.7260 2023-01-03 22:59:07,308 - mmcls - INFO - Epoch [60][2900/5005] lr: 1.000e-02, eta: 4:56:58, time: 0.092, data_time: 0.009, memory: 1125, loss: 1.7302 2023-01-03 22:59:16,077 - mmcls - INFO - Epoch [60][3000/5005] lr: 1.000e-02, eta: 4:56:49, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.6914 2023-01-03 22:59:24,779 - mmcls - INFO - Epoch [60][3100/5005] lr: 1.000e-02, eta: 4:56:40, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.7303 2023-01-03 22:59:33,824 - mmcls - INFO - Epoch [60][3200/5005] lr: 1.000e-02, eta: 4:56:31, time: 0.091, data_time: 0.019, memory: 1125, loss: 1.7060 2023-01-03 22:59:42,929 - mmcls - INFO - Epoch [60][3300/5005] lr: 1.000e-02, eta: 4:56:23, time: 0.091, data_time: 0.028, memory: 1125, loss: 1.7232 2023-01-03 22:59:51,517 - mmcls - INFO - Epoch [60][3400/5005] lr: 1.000e-02, eta: 4:56:14, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.7225 2023-01-03 23:00:00,191 - mmcls - INFO - Epoch [60][3500/5005] lr: 1.000e-02, eta: 4:56:05, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.7130 2023-01-03 23:00:09,062 - mmcls - INFO - Epoch [60][3600/5005] lr: 1.000e-02, eta: 4:55:56, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.7257 2023-01-03 23:00:17,668 - mmcls - INFO - Epoch [60][3700/5005] lr: 1.000e-02, eta: 4:55:47, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.7262 2023-01-03 23:00:26,272 - mmcls - INFO - Epoch [60][3800/5005] lr: 1.000e-02, eta: 4:55:38, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.7019 2023-01-03 23:00:34,926 - mmcls - INFO - Epoch [60][3900/5005] lr: 1.000e-02, eta: 4:55:29, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.7153 2023-01-03 23:00:43,581 - mmcls - INFO - Epoch [60][4000/5005] lr: 1.000e-02, eta: 4:55:21, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.7129 2023-01-03 23:00:52,466 - mmcls - INFO - Epoch [60][4100/5005] lr: 1.000e-02, eta: 4:55:12, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.7137 2023-01-03 23:01:01,562 - mmcls - INFO - Epoch [60][4200/5005] lr: 1.000e-02, eta: 4:55:03, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.6994 2023-01-03 23:01:10,105 - mmcls - INFO - Epoch [60][4300/5005] lr: 1.000e-02, eta: 4:54:54, time: 0.085, data_time: 0.031, memory: 1125, loss: 1.7449 2023-01-03 23:01:19,003 - mmcls - INFO - Epoch [60][4400/5005] lr: 1.000e-02, eta: 4:54:45, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.7293 2023-01-03 23:01:27,789 - mmcls - INFO - Epoch [60][4500/5005] lr: 1.000e-02, eta: 4:54:37, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.7170 2023-01-03 23:01:36,462 - mmcls - INFO - Epoch [60][4600/5005] lr: 1.000e-02, eta: 4:54:28, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.7053 2023-01-03 23:01:44,994 - mmcls - INFO - Epoch [60][4700/5005] lr: 1.000e-02, eta: 4:54:19, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.7260 2023-01-03 23:01:53,962 - mmcls - INFO - Epoch [60][4800/5005] lr: 1.000e-02, eta: 4:54:10, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.7207 2023-01-03 23:02:02,731 - mmcls - INFO - Epoch [60][4900/5005] lr: 1.000e-02, eta: 4:54:01, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.7183 2023-01-03 23:02:11,408 - mmcls - INFO - Epoch [60][5000/5005] lr: 1.000e-02, eta: 4:53:52, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.7110 2023-01-03 23:02:34,061 - mmcls - INFO - Epoch(val) [60][196] accuracy_top-1: 64.5160, accuracy_top-5: 86.0500 2023-01-03 23:02:44,912 - mmcls - INFO - Epoch [61][100/5005] lr: 1.000e-03, eta: 4:53:44, time: 0.108, data_time: 0.053, memory: 1125, loss: 1.6283 2023-01-03 23:02:53,634 - mmcls - INFO - Epoch [61][200/5005] lr: 1.000e-03, eta: 4:53:35, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.6135 2023-01-03 23:03:02,558 - mmcls - INFO - Epoch [61][300/5005] lr: 1.000e-03, eta: 4:53:27, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.6041 2023-01-03 23:03:11,309 - mmcls - INFO - Epoch [61][400/5005] lr: 1.000e-03, eta: 4:53:18, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.5656 2023-01-03 23:03:20,062 - mmcls - INFO - Epoch [61][500/5005] lr: 1.000e-03, eta: 4:53:09, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.5597 2023-01-03 23:03:28,887 - mmcls - INFO - Epoch [61][600/5005] lr: 1.000e-03, eta: 4:53:00, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.5589 2023-01-03 23:03:37,544 - mmcls - INFO - Epoch [61][700/5005] lr: 1.000e-03, eta: 4:52:51, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.5348 2023-01-03 23:03:46,696 - mmcls - INFO - Epoch [61][800/5005] lr: 1.000e-03, eta: 4:52:43, time: 0.092, data_time: 0.004, memory: 1125, loss: 1.5601 2023-01-03 23:03:55,267 - mmcls - INFO - Epoch [61][900/5005] lr: 1.000e-03, eta: 4:52:34, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.5592 2023-01-03 23:04:03,736 - mmcls - INFO - Epoch [61][1000/5005] lr: 1.000e-03, eta: 4:52:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.5277 2023-01-03 23:04:12,433 - mmcls - INFO - Epoch [61][1100/5005] lr: 1.000e-03, eta: 4:52:16, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.5190 2023-01-03 23:04:21,194 - mmcls - INFO - Epoch [61][1200/5005] lr: 1.000e-03, eta: 4:52:07, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.5361 2023-01-03 23:04:30,023 - mmcls - INFO - Epoch [61][1300/5005] lr: 1.000e-03, eta: 4:51:58, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.5436 2023-01-03 23:04:38,526 - mmcls - INFO - Epoch [61][1400/5005] lr: 1.000e-03, eta: 4:51:49, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.5230 2023-01-03 23:04:47,395 - mmcls - INFO - Epoch [61][1500/5005] lr: 1.000e-03, eta: 4:51:40, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.5286 2023-01-03 23:04:56,088 - mmcls - INFO - Epoch [61][1600/5005] lr: 1.000e-03, eta: 4:51:31, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.5275 2023-01-03 23:05:04,750 - mmcls - INFO - Epoch [61][1700/5005] lr: 1.000e-03, eta: 4:51:22, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.5388 2023-01-03 23:05:13,402 - mmcls - INFO - Epoch [61][1800/5005] lr: 1.000e-03, eta: 4:51:14, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.5344 2023-01-03 23:05:22,358 - mmcls - INFO - Epoch [61][1900/5005] lr: 1.000e-03, eta: 4:51:05, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.5154 2023-01-03 23:05:30,761 - mmcls - INFO - Epoch [61][2000/5005] lr: 1.000e-03, eta: 4:50:56, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.5379 2023-01-03 23:05:39,468 - mmcls - INFO - Epoch [61][2100/5005] lr: 1.000e-03, eta: 4:50:47, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.5126 2023-01-03 23:05:48,333 - mmcls - INFO - Epoch [61][2200/5005] lr: 1.000e-03, eta: 4:50:38, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.5258 2023-01-03 23:05:56,938 - mmcls - INFO - Epoch [61][2300/5005] lr: 1.000e-03, eta: 4:50:29, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.5283 2023-01-03 23:06:05,649 - mmcls - INFO - Epoch [61][2400/5005] lr: 1.000e-03, eta: 4:50:20, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.5089 2023-01-03 23:06:14,506 - mmcls - INFO - Epoch [61][2500/5005] lr: 1.000e-03, eta: 4:50:12, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.5145 2023-01-03 23:06:23,367 - mmcls - INFO - Epoch [61][2600/5005] lr: 1.000e-03, eta: 4:50:03, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.5124 2023-01-03 23:06:32,212 - mmcls - INFO - Epoch [61][2700/5005] lr: 1.000e-03, eta: 4:49:54, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4983 2023-01-03 23:06:41,029 - mmcls - INFO - Epoch [61][2800/5005] lr: 1.000e-03, eta: 4:49:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.5002 2023-01-03 23:06:49,746 - mmcls - INFO - Epoch [61][2900/5005] lr: 1.000e-03, eta: 4:49:36, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.5000 2023-01-03 23:06:58,543 - mmcls - INFO - Epoch [61][3000/5005] lr: 1.000e-03, eta: 4:49:27, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.5094 2023-01-03 23:07:07,186 - mmcls - INFO - Epoch [61][3100/5005] lr: 1.000e-03, eta: 4:49:19, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.5018 2023-01-03 23:07:16,023 - mmcls - INFO - Epoch [61][3200/5005] lr: 1.000e-03, eta: 4:49:10, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4998 2023-01-03 23:07:24,678 - mmcls - INFO - Epoch [61][3300/5005] lr: 1.000e-03, eta: 4:49:01, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.5159 2023-01-03 23:07:33,493 - mmcls - INFO - Epoch [61][3400/5005] lr: 1.000e-03, eta: 4:48:52, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.5100 2023-01-03 23:07:42,398 - mmcls - INFO - Epoch [61][3500/5005] lr: 1.000e-03, eta: 4:48:43, time: 0.089, data_time: 0.036, memory: 1125, loss: 1.5207 2023-01-03 23:07:51,054 - mmcls - INFO - Epoch [61][3600/5005] lr: 1.000e-03, eta: 4:48:34, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.5188 2023-01-03 23:08:00,195 - mmcls - INFO - Epoch [61][3700/5005] lr: 1.000e-03, eta: 4:48:26, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.5077 2023-01-03 23:08:09,189 - mmcls - INFO - Epoch [61][3800/5005] lr: 1.000e-03, eta: 4:48:17, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.4894 2023-01-03 23:08:18,039 - mmcls - INFO - Epoch [61][3900/5005] lr: 1.000e-03, eta: 4:48:08, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.5112 2023-01-03 23:08:26,845 - mmcls - INFO - Epoch [61][4000/5005] lr: 1.000e-03, eta: 4:48:00, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.5190 2023-01-03 23:08:35,496 - mmcls - INFO - Epoch [61][4100/5005] lr: 1.000e-03, eta: 4:47:51, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.5382 2023-01-03 23:08:44,081 - mmcls - INFO - Epoch [61][4200/5005] lr: 1.000e-03, eta: 4:47:42, time: 0.086, data_time: 0.037, memory: 1125, loss: 1.4943 2023-01-03 23:08:52,779 - mmcls - INFO - Epoch [61][4300/5005] lr: 1.000e-03, eta: 4:47:33, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.4910 2023-01-03 23:09:01,514 - mmcls - INFO - Epoch [61][4400/5005] lr: 1.000e-03, eta: 4:47:24, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.4794 2023-01-03 23:09:10,319 - mmcls - INFO - Epoch [61][4500/5005] lr: 1.000e-03, eta: 4:47:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4930 2023-01-03 23:09:18,953 - mmcls - INFO - Epoch [61][4600/5005] lr: 1.000e-03, eta: 4:47:06, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.5025 2023-01-03 23:09:27,942 - mmcls - INFO - Epoch [61][4700/5005] lr: 1.000e-03, eta: 4:46:58, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4746 2023-01-03 23:09:36,785 - mmcls - INFO - Epoch [61][4800/5005] lr: 1.000e-03, eta: 4:46:49, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.5194 2023-01-03 23:09:45,620 - mmcls - INFO - Epoch [61][4900/5005] lr: 1.000e-03, eta: 4:46:40, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4992 2023-01-03 23:09:54,190 - mmcls - INFO - Epoch [61][5000/5005] lr: 1.000e-03, eta: 4:46:31, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.5144 2023-01-03 23:10:16,850 - mmcls - INFO - Epoch(val) [61][196] accuracy_top-1: 68.3360, accuracy_top-5: 88.3880 2023-01-03 23:10:28,154 - mmcls - INFO - Epoch [62][100/5005] lr: 1.000e-03, eta: 4:46:23, time: 0.113, data_time: 0.073, memory: 1125, loss: 1.4986 2023-01-03 23:10:36,892 - mmcls - INFO - Epoch [62][200/5005] lr: 1.000e-03, eta: 4:46:14, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.5084 2023-01-03 23:10:45,960 - mmcls - INFO - Epoch [62][300/5005] lr: 1.000e-03, eta: 4:46:06, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.4721 2023-01-03 23:10:54,683 - mmcls - INFO - Epoch [62][400/5005] lr: 1.000e-03, eta: 4:45:57, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.4802 2023-01-03 23:11:03,497 - mmcls - INFO - Epoch [62][500/5005] lr: 1.000e-03, eta: 4:45:48, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4736 2023-01-03 23:11:12,002 - mmcls - INFO - Epoch [62][600/5005] lr: 1.000e-03, eta: 4:45:39, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.4710 2023-01-03 23:11:20,744 - mmcls - INFO - Epoch [62][700/5005] lr: 1.000e-03, eta: 4:45:30, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4802 2023-01-03 23:11:29,460 - mmcls - INFO - Epoch [62][800/5005] lr: 1.000e-03, eta: 4:45:21, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4640 2023-01-03 23:11:38,052 - mmcls - INFO - Epoch [62][900/5005] lr: 1.000e-03, eta: 4:45:12, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.4786 2023-01-03 23:11:46,869 - mmcls - INFO - Epoch [62][1000/5005] lr: 1.000e-03, eta: 4:45:03, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.4941 2023-01-03 23:11:55,899 - mmcls - INFO - Epoch [62][1100/5005] lr: 1.000e-03, eta: 4:44:55, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.4792 2023-01-03 23:12:04,935 - mmcls - INFO - Epoch [62][1200/5005] lr: 1.000e-03, eta: 4:44:46, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.4749 2023-01-03 23:12:13,522 - mmcls - INFO - Epoch [62][1300/5005] lr: 1.000e-03, eta: 4:44:37, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.4854 2023-01-03 23:12:22,126 - mmcls - INFO - Epoch [62][1400/5005] lr: 1.000e-03, eta: 4:44:28, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.4839 2023-01-03 23:12:30,899 - mmcls - INFO - Epoch [62][1500/5005] lr: 1.000e-03, eta: 4:44:19, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4791 2023-01-03 23:12:39,553 - mmcls - INFO - Epoch [62][1600/5005] lr: 1.000e-03, eta: 4:44:10, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.4800 2023-01-03 23:12:48,260 - mmcls - INFO - Epoch [62][1700/5005] lr: 1.000e-03, eta: 4:44:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4981 2023-01-03 23:12:57,017 - mmcls - INFO - Epoch [62][1800/5005] lr: 1.000e-03, eta: 4:43:53, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.4967 2023-01-03 23:13:05,793 - mmcls - INFO - Epoch [62][1900/5005] lr: 1.000e-03, eta: 4:43:44, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.5065 2023-01-03 23:13:14,337 - mmcls - INFO - Epoch [62][2000/5005] lr: 1.000e-03, eta: 4:43:35, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.4792 2023-01-03 23:13:22,881 - mmcls - INFO - Epoch [62][2100/5005] lr: 1.000e-03, eta: 4:43:26, time: 0.085, data_time: 0.038, memory: 1125, loss: 1.4731 2023-01-03 23:13:31,645 - mmcls - INFO - Epoch [62][2200/5005] lr: 1.000e-03, eta: 4:43:17, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4993 2023-01-03 23:13:40,658 - mmcls - INFO - Epoch [62][2300/5005] lr: 1.000e-03, eta: 4:43:08, time: 0.090, data_time: 0.010, memory: 1125, loss: 1.4810 2023-01-03 23:13:49,410 - mmcls - INFO - Epoch [62][2400/5005] lr: 1.000e-03, eta: 4:43:00, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4652 2023-01-03 23:13:57,918 - mmcls - INFO - Epoch [62][2500/5005] lr: 1.000e-03, eta: 4:42:51, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.4905 2023-01-03 23:14:06,359 - mmcls - INFO - Epoch [62][2600/5005] lr: 1.000e-03, eta: 4:42:42, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.4600 2023-01-03 23:14:15,051 - mmcls - INFO - Epoch [62][2700/5005] lr: 1.000e-03, eta: 4:42:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4658 2023-01-03 23:14:23,954 - mmcls - INFO - Epoch [62][2800/5005] lr: 1.000e-03, eta: 4:42:24, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.4620 2023-01-03 23:14:32,859 - mmcls - INFO - Epoch [62][2900/5005] lr: 1.000e-03, eta: 4:42:15, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4917 2023-01-03 23:14:41,668 - mmcls - INFO - Epoch [62][3000/5005] lr: 1.000e-03, eta: 4:42:06, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4850 2023-01-03 23:14:50,488 - mmcls - INFO - Epoch [62][3100/5005] lr: 1.000e-03, eta: 4:41:58, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.4776 2023-01-03 23:14:59,126 - mmcls - INFO - Epoch [62][3200/5005] lr: 1.000e-03, eta: 4:41:49, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.4743 2023-01-03 23:15:07,717 - mmcls - INFO - Epoch [62][3300/5005] lr: 1.000e-03, eta: 4:41:40, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4988 2023-01-03 23:15:16,378 - mmcls - INFO - Epoch [62][3400/5005] lr: 1.000e-03, eta: 4:41:31, time: 0.087, data_time: 0.045, memory: 1125, loss: 1.4860 2023-01-03 23:15:25,064 - mmcls - INFO - Epoch [62][3500/5005] lr: 1.000e-03, eta: 4:41:22, time: 0.087, data_time: 0.045, memory: 1125, loss: 1.4669 2023-01-03 23:15:33,863 - mmcls - INFO - Epoch [62][3600/5005] lr: 1.000e-03, eta: 4:41:13, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4848 2023-01-03 23:15:42,493 - mmcls - INFO - Epoch [62][3700/5005] lr: 1.000e-03, eta: 4:41:04, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4887 2023-01-03 23:15:51,805 - mmcls - INFO - Epoch [62][3800/5005] lr: 1.000e-03, eta: 4:40:56, time: 0.093, data_time: 0.001, memory: 1125, loss: 1.4792 2023-01-03 23:16:00,602 - mmcls - INFO - Epoch [62][3900/5005] lr: 1.000e-03, eta: 4:40:47, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.4845 2023-01-03 23:16:09,303 - mmcls - INFO - Epoch [62][4000/5005] lr: 1.000e-03, eta: 4:40:38, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.4770 2023-01-03 23:16:18,069 - mmcls - INFO - Epoch [62][4100/5005] lr: 1.000e-03, eta: 4:40:29, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.4832 2023-01-03 23:16:26,916 - mmcls - INFO - Epoch [62][4200/5005] lr: 1.000e-03, eta: 4:40:20, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4889 2023-01-03 23:16:35,769 - mmcls - INFO - Epoch [62][4300/5005] lr: 1.000e-03, eta: 4:40:12, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.4559 2023-01-03 23:16:44,374 - mmcls - INFO - Epoch [62][4400/5005] lr: 1.000e-03, eta: 4:40:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4939 2023-01-03 23:16:52,929 - mmcls - INFO - Epoch [62][4500/5005] lr: 1.000e-03, eta: 4:39:54, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.4576 2023-01-03 23:17:01,750 - mmcls - INFO - Epoch [62][4600/5005] lr: 1.000e-03, eta: 4:39:45, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4716 2023-01-03 23:17:10,626 - mmcls - INFO - Epoch [62][4700/5005] lr: 1.000e-03, eta: 4:39:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4879 2023-01-03 23:17:19,580 - mmcls - INFO - Epoch [62][4800/5005] lr: 1.000e-03, eta: 4:39:28, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.4642 2023-01-03 23:17:27,999 - mmcls - INFO - Epoch [62][4900/5005] lr: 1.000e-03, eta: 4:39:18, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.4651 2023-01-03 23:17:36,945 - mmcls - INFO - Epoch [62][5000/5005] lr: 1.000e-03, eta: 4:39:10, time: 0.089, data_time: 0.045, memory: 1125, loss: 1.4673 2023-01-03 23:17:59,575 - mmcls - INFO - Epoch(val) [62][196] accuracy_top-1: 68.7500, accuracy_top-5: 88.5480 2023-01-03 23:18:10,440 - mmcls - INFO - Epoch [63][100/5005] lr: 1.000e-03, eta: 4:39:01, time: 0.108, data_time: 0.049, memory: 1125, loss: 1.4619 2023-01-03 23:18:19,199 - mmcls - INFO - Epoch [63][200/5005] lr: 1.000e-03, eta: 4:38:53, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4429 2023-01-03 23:18:27,801 - mmcls - INFO - Epoch [63][300/5005] lr: 1.000e-03, eta: 4:38:44, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4700 2023-01-03 23:18:36,668 - mmcls - INFO - Epoch [63][400/5005] lr: 1.000e-03, eta: 4:38:35, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4708 2023-01-03 23:18:45,377 - mmcls - INFO - Epoch [63][500/5005] lr: 1.000e-03, eta: 4:38:26, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.4471 2023-01-03 23:18:54,232 - mmcls - INFO - Epoch [63][600/5005] lr: 1.000e-03, eta: 4:38:17, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.4494 2023-01-03 23:19:03,156 - mmcls - INFO - Epoch [63][700/5005] lr: 1.000e-03, eta: 4:38:09, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.4483 2023-01-03 23:19:12,290 - mmcls - INFO - Epoch [63][800/5005] lr: 1.000e-03, eta: 4:38:00, time: 0.091, data_time: 0.024, memory: 1125, loss: 1.4691 2023-01-03 23:19:20,876 - mmcls - INFO - Epoch [63][900/5005] lr: 1.000e-03, eta: 4:37:51, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.4788 2023-01-03 23:19:29,481 - mmcls - INFO - Epoch [63][1000/5005] lr: 1.000e-03, eta: 4:37:42, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.4729 2023-01-03 23:19:38,173 - mmcls - INFO - Epoch [63][1100/5005] lr: 1.000e-03, eta: 4:37:33, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4687 2023-01-03 23:19:46,803 - mmcls - INFO - Epoch [63][1200/5005] lr: 1.000e-03, eta: 4:37:24, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.4619 2023-01-03 23:19:55,560 - mmcls - INFO - Epoch [63][1300/5005] lr: 1.000e-03, eta: 4:37:15, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.4661 2023-01-03 23:20:04,161 - mmcls - INFO - Epoch [63][1400/5005] lr: 1.000e-03, eta: 4:37:06, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.4866 2023-01-03 23:20:13,025 - mmcls - INFO - Epoch [63][1500/5005] lr: 1.000e-03, eta: 4:36:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4728 2023-01-03 23:20:21,611 - mmcls - INFO - Epoch [63][1600/5005] lr: 1.000e-03, eta: 4:36:49, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.4841 2023-01-03 23:20:30,570 - mmcls - INFO - Epoch [63][1700/5005] lr: 1.000e-03, eta: 4:36:40, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.4570 2023-01-03 23:20:39,358 - mmcls - INFO - Epoch [63][1800/5005] lr: 1.000e-03, eta: 4:36:31, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4892 2023-01-03 23:20:48,205 - mmcls - INFO - Epoch [63][1900/5005] lr: 1.000e-03, eta: 4:36:22, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4488 2023-01-03 23:20:56,966 - mmcls - INFO - Epoch [63][2000/5005] lr: 1.000e-03, eta: 4:36:14, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4649 2023-01-03 23:21:05,589 - mmcls - INFO - Epoch [63][2100/5005] lr: 1.000e-03, eta: 4:36:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4584 2023-01-03 23:21:14,312 - mmcls - INFO - Epoch [63][2200/5005] lr: 1.000e-03, eta: 4:35:56, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4751 2023-01-03 23:21:23,128 - mmcls - INFO - Epoch [63][2300/5005] lr: 1.000e-03, eta: 4:35:47, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4610 2023-01-03 23:21:31,866 - mmcls - INFO - Epoch [63][2400/5005] lr: 1.000e-03, eta: 4:35:38, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4725 2023-01-03 23:21:40,639 - mmcls - INFO - Epoch [63][2500/5005] lr: 1.000e-03, eta: 4:35:29, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4823 2023-01-03 23:21:49,411 - mmcls - INFO - Epoch [63][2600/5005] lr: 1.000e-03, eta: 4:35:21, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.4793 2023-01-03 23:21:58,092 - mmcls - INFO - Epoch [63][2700/5005] lr: 1.000e-03, eta: 4:35:12, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4817 2023-01-03 23:22:06,877 - mmcls - INFO - Epoch [63][2800/5005] lr: 1.000e-03, eta: 4:35:03, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.4635 2023-01-03 23:22:15,415 - mmcls - INFO - Epoch [63][2900/5005] lr: 1.000e-03, eta: 4:34:54, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4556 2023-01-03 23:22:24,262 - mmcls - INFO - Epoch [63][3000/5005] lr: 1.000e-03, eta: 4:34:45, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.4557 2023-01-03 23:22:33,251 - mmcls - INFO - Epoch [63][3100/5005] lr: 1.000e-03, eta: 4:34:36, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.4729 2023-01-03 23:22:41,795 - mmcls - INFO - Epoch [63][3200/5005] lr: 1.000e-03, eta: 4:34:27, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.4793 2023-01-03 23:22:50,560 - mmcls - INFO - Epoch [63][3300/5005] lr: 1.000e-03, eta: 4:34:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4470 2023-01-03 23:22:59,302 - mmcls - INFO - Epoch [63][3400/5005] lr: 1.000e-03, eta: 4:34:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4843 2023-01-03 23:23:08,159 - mmcls - INFO - Epoch [63][3500/5005] lr: 1.000e-03, eta: 4:34:01, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.4615 2023-01-03 23:23:16,707 - mmcls - INFO - Epoch [63][3600/5005] lr: 1.000e-03, eta: 4:33:52, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.4507 2023-01-03 23:23:25,489 - mmcls - INFO - Epoch [63][3700/5005] lr: 1.000e-03, eta: 4:33:43, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4596 2023-01-03 23:23:34,185 - mmcls - INFO - Epoch [63][3800/5005] lr: 1.000e-03, eta: 4:33:34, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4546 2023-01-03 23:23:42,884 - mmcls - INFO - Epoch [63][3900/5005] lr: 1.000e-03, eta: 4:33:25, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.4694 2023-01-03 23:23:51,944 - mmcls - INFO - Epoch [63][4000/5005] lr: 1.000e-03, eta: 4:33:17, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.4525 2023-01-03 23:24:00,519 - mmcls - INFO - Epoch [63][4100/5005] lr: 1.000e-03, eta: 4:33:08, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.4648 2023-01-03 23:24:09,060 - mmcls - INFO - Epoch [63][4200/5005] lr: 1.000e-03, eta: 4:32:59, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.4452 2023-01-03 23:24:17,672 - mmcls - INFO - Epoch [63][4300/5005] lr: 1.000e-03, eta: 4:32:50, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.4483 2023-01-03 23:24:26,111 - mmcls - INFO - Epoch [63][4400/5005] lr: 1.000e-03, eta: 4:32:41, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.4475 2023-01-03 23:24:34,929 - mmcls - INFO - Epoch [63][4500/5005] lr: 1.000e-03, eta: 4:32:32, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.4418 2023-01-03 23:24:44,141 - mmcls - INFO - Epoch [63][4600/5005] lr: 1.000e-03, eta: 4:32:24, time: 0.092, data_time: 0.032, memory: 1125, loss: 1.4586 2023-01-03 23:24:52,909 - mmcls - INFO - Epoch [63][4700/5005] lr: 1.000e-03, eta: 4:32:15, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4796 2023-01-03 23:25:01,653 - mmcls - INFO - Epoch [63][4800/5005] lr: 1.000e-03, eta: 4:32:06, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.4465 2023-01-03 23:25:10,301 - mmcls - INFO - Epoch [63][4900/5005] lr: 1.000e-03, eta: 4:31:57, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.4712 2023-01-03 23:25:19,149 - mmcls - INFO - Epoch [63][5000/5005] lr: 1.000e-03, eta: 4:31:48, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.4494 2023-01-03 23:25:41,757 - mmcls - INFO - Epoch(val) [63][196] accuracy_top-1: 68.8780, accuracy_top-5: 88.6620 2023-01-03 23:25:53,136 - mmcls - INFO - Epoch [64][100/5005] lr: 1.000e-03, eta: 4:31:40, time: 0.113, data_time: 0.046, memory: 1125, loss: 1.4389 2023-01-03 23:26:02,168 - mmcls - INFO - Epoch [64][200/5005] lr: 1.000e-03, eta: 4:31:32, time: 0.090, data_time: 0.047, memory: 1125, loss: 1.4589 2023-01-03 23:26:10,952 - mmcls - INFO - Epoch [64][300/5005] lr: 1.000e-03, eta: 4:31:23, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4566 2023-01-03 23:26:19,664 - mmcls - INFO - Epoch [64][400/5005] lr: 1.000e-03, eta: 4:31:14, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.4428 2023-01-03 23:26:28,428 - mmcls - INFO - Epoch [64][500/5005] lr: 1.000e-03, eta: 4:31:05, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.4601 2023-01-03 23:26:37,266 - mmcls - INFO - Epoch [64][600/5005] lr: 1.000e-03, eta: 4:30:56, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.4593 2023-01-03 23:26:46,456 - mmcls - INFO - Epoch [64][700/5005] lr: 1.000e-03, eta: 4:30:48, time: 0.092, data_time: 0.043, memory: 1125, loss: 1.4541 2023-01-03 23:26:55,625 - mmcls - INFO - Epoch [64][800/5005] lr: 1.000e-03, eta: 4:30:39, time: 0.092, data_time: 0.031, memory: 1125, loss: 1.4450 2023-01-03 23:27:04,362 - mmcls - INFO - Epoch [64][900/5005] lr: 1.000e-03, eta: 4:30:30, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.4629 2023-01-03 23:27:12,922 - mmcls - INFO - Epoch [64][1000/5005] lr: 1.000e-03, eta: 4:30:21, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.4560 2023-01-03 23:27:21,679 - mmcls - INFO - Epoch [64][1100/5005] lr: 1.000e-03, eta: 4:30:12, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4704 2023-01-03 23:27:30,244 - mmcls - INFO - Epoch [64][1200/5005] lr: 1.000e-03, eta: 4:30:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4691 2023-01-03 23:27:38,998 - mmcls - INFO - Epoch [64][1300/5005] lr: 1.000e-03, eta: 4:29:55, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4450 2023-01-03 23:27:47,569 - mmcls - INFO - Epoch [64][1400/5005] lr: 1.000e-03, eta: 4:29:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4727 2023-01-03 23:27:56,263 - mmcls - INFO - Epoch [64][1500/5005] lr: 1.000e-03, eta: 4:29:37, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4681 2023-01-03 23:28:04,989 - mmcls - INFO - Epoch [64][1600/5005] lr: 1.000e-03, eta: 4:29:28, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4439 2023-01-03 23:28:13,689 - mmcls - INFO - Epoch [64][1700/5005] lr: 1.000e-03, eta: 4:29:19, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.4622 2023-01-03 23:28:22,233 - mmcls - INFO - Epoch [64][1800/5005] lr: 1.000e-03, eta: 4:29:10, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.4460 2023-01-03 23:28:31,055 - mmcls - INFO - Epoch [64][1900/5005] lr: 1.000e-03, eta: 4:29:01, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.4583 2023-01-03 23:28:39,926 - mmcls - INFO - Epoch [64][2000/5005] lr: 1.000e-03, eta: 4:28:53, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.4679 2023-01-03 23:28:48,483 - mmcls - INFO - Epoch [64][2100/5005] lr: 1.000e-03, eta: 4:28:44, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4334 2023-01-03 23:28:57,091 - mmcls - INFO - Epoch [64][2200/5005] lr: 1.000e-03, eta: 4:28:35, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.4512 2023-01-03 23:29:06,213 - mmcls - INFO - Epoch [64][2300/5005] lr: 1.000e-03, eta: 4:28:26, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.4527 2023-01-03 23:29:14,918 - mmcls - INFO - Epoch [64][2400/5005] lr: 1.000e-03, eta: 4:28:17, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4403 2023-01-03 23:29:23,917 - mmcls - INFO - Epoch [64][2500/5005] lr: 1.000e-03, eta: 4:28:08, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4340 2023-01-03 23:29:32,826 - mmcls - INFO - Epoch [64][2600/5005] lr: 1.000e-03, eta: 4:28:00, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.4771 2023-01-03 23:29:41,449 - mmcls - INFO - Epoch [64][2700/5005] lr: 1.000e-03, eta: 4:27:51, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.4599 2023-01-03 23:29:50,076 - mmcls - INFO - Epoch [64][2800/5005] lr: 1.000e-03, eta: 4:27:42, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4509 2023-01-03 23:29:58,504 - mmcls - INFO - Epoch [64][2900/5005] lr: 1.000e-03, eta: 4:27:33, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.4660 2023-01-03 23:30:07,569 - mmcls - INFO - Epoch [64][3000/5005] lr: 1.000e-03, eta: 4:27:24, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.4419 2023-01-03 23:30:16,166 - mmcls - INFO - Epoch [64][3100/5005] lr: 1.000e-03, eta: 4:27:15, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.4461 2023-01-03 23:30:24,695 - mmcls - INFO - Epoch [64][3200/5005] lr: 1.000e-03, eta: 4:27:06, time: 0.085, data_time: 0.034, memory: 1125, loss: 1.4484 2023-01-03 23:30:33,709 - mmcls - INFO - Epoch [64][3300/5005] lr: 1.000e-03, eta: 4:26:58, time: 0.090, data_time: 0.052, memory: 1125, loss: 1.4363 2023-01-03 23:30:42,171 - mmcls - INFO - Epoch [64][3400/5005] lr: 1.000e-03, eta: 4:26:49, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.4517 2023-01-03 23:30:50,751 - mmcls - INFO - Epoch [64][3500/5005] lr: 1.000e-03, eta: 4:26:40, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4717 2023-01-03 23:30:59,623 - mmcls - INFO - Epoch [64][3600/5005] lr: 1.000e-03, eta: 4:26:31, time: 0.089, data_time: 0.000, memory: 1125, loss: 1.4499 2023-01-03 23:31:08,649 - mmcls - INFO - Epoch [64][3700/5005] lr: 1.000e-03, eta: 4:26:22, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.4453 2023-01-03 23:31:17,319 - mmcls - INFO - Epoch [64][3800/5005] lr: 1.000e-03, eta: 4:26:13, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.4348 2023-01-03 23:31:25,976 - mmcls - INFO - Epoch [64][3900/5005] lr: 1.000e-03, eta: 4:26:04, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.4453 2023-01-03 23:31:34,639 - mmcls - INFO - Epoch [64][4000/5005] lr: 1.000e-03, eta: 4:25:56, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4558 2023-01-03 23:31:43,566 - mmcls - INFO - Epoch [64][4100/5005] lr: 1.000e-03, eta: 4:25:47, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.4280 2023-01-03 23:31:52,183 - mmcls - INFO - Epoch [64][4200/5005] lr: 1.000e-03, eta: 4:25:38, time: 0.086, data_time: 0.033, memory: 1125, loss: 1.4327 2023-01-03 23:32:00,914 - mmcls - INFO - Epoch [64][4300/5005] lr: 1.000e-03, eta: 4:25:29, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.4536 2023-01-03 23:32:09,575 - mmcls - INFO - Epoch [64][4400/5005] lr: 1.000e-03, eta: 4:25:20, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.4705 2023-01-03 23:32:18,370 - mmcls - INFO - Epoch [64][4500/5005] lr: 1.000e-03, eta: 4:25:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.4321 2023-01-03 23:32:27,343 - mmcls - INFO - Epoch [64][4600/5005] lr: 1.000e-03, eta: 4:25:03, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4392 2023-01-03 23:32:36,281 - mmcls - INFO - Epoch [64][4700/5005] lr: 1.000e-03, eta: 4:24:54, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4664 2023-01-03 23:32:45,131 - mmcls - INFO - Epoch [64][4800/5005] lr: 1.000e-03, eta: 4:24:45, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4359 2023-01-03 23:32:53,730 - mmcls - INFO - Epoch [64][4900/5005] lr: 1.000e-03, eta: 4:24:36, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4369 2023-01-03 23:33:02,534 - mmcls - INFO - Epoch [64][5000/5005] lr: 1.000e-03, eta: 4:24:27, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4600 2023-01-03 23:33:24,988 - mmcls - INFO - Epoch(val) [64][196] accuracy_top-1: 68.9920, accuracy_top-5: 88.7240 2023-01-03 23:33:35,960 - mmcls - INFO - Epoch [65][100/5005] lr: 1.000e-03, eta: 4:24:19, time: 0.109, data_time: 0.061, memory: 1125, loss: 1.4263 2023-01-03 23:33:44,831 - mmcls - INFO - Epoch [65][200/5005] lr: 1.000e-03, eta: 4:24:10, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4266 2023-01-03 23:33:53,850 - mmcls - INFO - Epoch [65][300/5005] lr: 1.000e-03, eta: 4:24:02, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.4500 2023-01-03 23:34:02,733 - mmcls - INFO - Epoch [65][400/5005] lr: 1.000e-03, eta: 4:23:53, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4210 2023-01-03 23:34:11,536 - mmcls - INFO - Epoch [65][500/5005] lr: 1.000e-03, eta: 4:23:44, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4414 2023-01-03 23:34:20,398 - mmcls - INFO - Epoch [65][600/5005] lr: 1.000e-03, eta: 4:23:35, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.4423 2023-01-03 23:34:28,906 - mmcls - INFO - Epoch [65][700/5005] lr: 1.000e-03, eta: 4:23:26, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.4636 2023-01-03 23:34:37,468 - mmcls - INFO - Epoch [65][800/5005] lr: 1.000e-03, eta: 4:23:17, time: 0.086, data_time: 0.046, memory: 1125, loss: 1.4319 2023-01-03 23:34:46,512 - mmcls - INFO - Epoch [65][900/5005] lr: 1.000e-03, eta: 4:23:09, time: 0.090, data_time: 0.030, memory: 1125, loss: 1.4500 2023-01-03 23:34:55,306 - mmcls - INFO - Epoch [65][1000/5005] lr: 1.000e-03, eta: 4:23:00, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4358 2023-01-03 23:35:04,313 - mmcls - INFO - Epoch [65][1100/5005] lr: 1.000e-03, eta: 4:22:51, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.4608 2023-01-03 23:35:12,788 - mmcls - INFO - Epoch [65][1200/5005] lr: 1.000e-03, eta: 4:22:42, time: 0.085, data_time: 0.029, memory: 1125, loss: 1.4507 2023-01-03 23:35:21,478 - mmcls - INFO - Epoch [65][1300/5005] lr: 1.000e-03, eta: 4:22:33, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4600 2023-01-03 23:35:30,127 - mmcls - INFO - Epoch [65][1400/5005] lr: 1.000e-03, eta: 4:22:24, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.4208 2023-01-03 23:35:38,954 - mmcls - INFO - Epoch [65][1500/5005] lr: 1.000e-03, eta: 4:22:16, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.4649 2023-01-03 23:35:47,630 - mmcls - INFO - Epoch [65][1600/5005] lr: 1.000e-03, eta: 4:22:07, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.4376 2023-01-03 23:35:56,271 - mmcls - INFO - Epoch [65][1700/5005] lr: 1.000e-03, eta: 4:21:58, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4662 2023-01-03 23:36:05,119 - mmcls - INFO - Epoch [65][1800/5005] lr: 1.000e-03, eta: 4:21:49, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4399 2023-01-03 23:36:13,819 - mmcls - INFO - Epoch [65][1900/5005] lr: 1.000e-03, eta: 4:21:40, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.4388 2023-01-03 23:36:22,644 - mmcls - INFO - Epoch [65][2000/5005] lr: 1.000e-03, eta: 4:21:31, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.4286 2023-01-03 23:36:31,316 - mmcls - INFO - Epoch [65][2100/5005] lr: 1.000e-03, eta: 4:21:23, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.4642 2023-01-03 23:36:40,620 - mmcls - INFO - Epoch [65][2200/5005] lr: 1.000e-03, eta: 4:21:14, time: 0.093, data_time: 0.003, memory: 1125, loss: 1.4427 2023-01-03 23:36:49,365 - mmcls - INFO - Epoch [65][2300/5005] lr: 1.000e-03, eta: 4:21:05, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.4274 2023-01-03 23:36:58,064 - mmcls - INFO - Epoch [65][2400/5005] lr: 1.000e-03, eta: 4:20:56, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.4442 2023-01-03 23:37:06,510 - mmcls - INFO - Epoch [65][2500/5005] lr: 1.000e-03, eta: 4:20:47, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.4463 2023-01-03 23:37:15,045 - mmcls - INFO - Epoch [65][2600/5005] lr: 1.000e-03, eta: 4:20:38, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.4543 2023-01-03 23:37:23,496 - mmcls - INFO - Epoch [65][2700/5005] lr: 1.000e-03, eta: 4:20:29, time: 0.084, data_time: 0.018, memory: 1125, loss: 1.4446 2023-01-03 23:37:32,238 - mmcls - INFO - Epoch [65][2800/5005] lr: 1.000e-03, eta: 4:20:20, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.4266 2023-01-03 23:37:41,181 - mmcls - INFO - Epoch [65][2900/5005] lr: 1.000e-03, eta: 4:20:12, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.4548 2023-01-03 23:37:49,921 - mmcls - INFO - Epoch [65][3000/5005] lr: 1.000e-03, eta: 4:20:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4177 2023-01-03 23:37:58,603 - mmcls - INFO - Epoch [65][3100/5005] lr: 1.000e-03, eta: 4:19:54, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4428 2023-01-03 23:38:07,514 - mmcls - INFO - Epoch [65][3200/5005] lr: 1.000e-03, eta: 4:19:45, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.4424 2023-01-03 23:38:16,266 - mmcls - INFO - Epoch [65][3300/5005] lr: 1.000e-03, eta: 4:19:36, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4507 2023-01-03 23:38:24,865 - mmcls - INFO - Epoch [65][3400/5005] lr: 1.000e-03, eta: 4:19:28, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4520 2023-01-03 23:38:33,762 - mmcls - INFO - Epoch [65][3500/5005] lr: 1.000e-03, eta: 4:19:19, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4292 2023-01-03 23:38:42,319 - mmcls - INFO - Epoch [65][3600/5005] lr: 1.000e-03, eta: 4:19:10, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.4105 2023-01-03 23:38:51,053 - mmcls - INFO - Epoch [65][3700/5005] lr: 1.000e-03, eta: 4:19:01, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.4363 2023-01-03 23:38:59,720 - mmcls - INFO - Epoch [65][3800/5005] lr: 1.000e-03, eta: 4:18:52, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4311 2023-01-03 23:39:08,776 - mmcls - INFO - Epoch [65][3900/5005] lr: 1.000e-03, eta: 4:18:43, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.4172 2023-01-03 23:39:17,355 - mmcls - INFO - Epoch [65][4000/5005] lr: 1.000e-03, eta: 4:18:34, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.4441 2023-01-03 23:39:25,774 - mmcls - INFO - Epoch [65][4100/5005] lr: 1.000e-03, eta: 4:18:25, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.4217 2023-01-03 23:39:34,731 - mmcls - INFO - Epoch [65][4200/5005] lr: 1.000e-03, eta: 4:18:17, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4184 2023-01-03 23:39:43,640 - mmcls - INFO - Epoch [65][4300/5005] lr: 1.000e-03, eta: 4:18:08, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4228 2023-01-03 23:39:52,126 - mmcls - INFO - Epoch [65][4400/5005] lr: 1.000e-03, eta: 4:17:59, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.4301 2023-01-03 23:40:00,909 - mmcls - INFO - Epoch [65][4500/5005] lr: 1.000e-03, eta: 4:17:50, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4379 2023-01-03 23:40:09,774 - mmcls - INFO - Epoch [65][4600/5005] lr: 1.000e-03, eta: 4:17:41, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4501 2023-01-03 23:40:18,627 - mmcls - INFO - Epoch [65][4700/5005] lr: 1.000e-03, eta: 4:17:33, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4402 2023-01-03 23:40:27,354 - mmcls - INFO - Epoch [65][4800/5005] lr: 1.000e-03, eta: 4:17:24, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4499 2023-01-03 23:40:36,079 - mmcls - INFO - Epoch [65][4900/5005] lr: 1.000e-03, eta: 4:17:15, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.4500 2023-01-03 23:40:44,881 - mmcls - INFO - Epoch [65][5000/5005] lr: 1.000e-03, eta: 4:17:06, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.4313 2023-01-03 23:41:07,643 - mmcls - INFO - Epoch(val) [65][196] accuracy_top-1: 69.0700, accuracy_top-5: 88.7920 2023-01-03 23:41:19,063 - mmcls - INFO - Epoch [66][100/5005] lr: 1.000e-03, eta: 4:16:58, time: 0.114, data_time: 0.049, memory: 1125, loss: 1.4354 2023-01-03 23:41:28,122 - mmcls - INFO - Epoch [66][200/5005] lr: 1.000e-03, eta: 4:16:49, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.4568 2023-01-03 23:41:36,689 - mmcls - INFO - Epoch [66][300/5005] lr: 1.000e-03, eta: 4:16:40, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4310 2023-01-03 23:41:45,558 - mmcls - INFO - Epoch [66][400/5005] lr: 1.000e-03, eta: 4:16:32, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4344 2023-01-03 23:41:54,167 - mmcls - INFO - Epoch [66][500/5005] lr: 1.000e-03, eta: 4:16:23, time: 0.086, data_time: 0.040, memory: 1125, loss: 1.4281 2023-01-03 23:42:02,704 - mmcls - INFO - Epoch [66][600/5005] lr: 1.000e-03, eta: 4:16:14, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.4235 2023-01-03 23:42:11,509 - mmcls - INFO - Epoch [66][700/5005] lr: 1.000e-03, eta: 4:16:05, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4222 2023-01-03 23:42:20,061 - mmcls - INFO - Epoch [66][800/5005] lr: 1.000e-03, eta: 4:15:56, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.4353 2023-01-03 23:42:28,759 - mmcls - INFO - Epoch [66][900/5005] lr: 1.000e-03, eta: 4:15:47, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.4420 2023-01-03 23:42:37,540 - mmcls - INFO - Epoch [66][1000/5005] lr: 1.000e-03, eta: 4:15:38, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4245 2023-01-03 23:42:46,502 - mmcls - INFO - Epoch [66][1100/5005] lr: 1.000e-03, eta: 4:15:30, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4432 2023-01-03 23:42:55,223 - mmcls - INFO - Epoch [66][1200/5005] lr: 1.000e-03, eta: 4:15:21, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.4472 2023-01-03 23:43:03,873 - mmcls - INFO - Epoch [66][1300/5005] lr: 1.000e-03, eta: 4:15:12, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.4264 2023-01-03 23:43:12,730 - mmcls - INFO - Epoch [66][1400/5005] lr: 1.000e-03, eta: 4:15:03, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4446 2023-01-03 23:43:21,519 - mmcls - INFO - Epoch [66][1500/5005] lr: 1.000e-03, eta: 4:14:54, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.4362 2023-01-03 23:43:30,149 - mmcls - INFO - Epoch [66][1600/5005] lr: 1.000e-03, eta: 4:14:45, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4340 2023-01-03 23:43:38,842 - mmcls - INFO - Epoch [66][1700/5005] lr: 1.000e-03, eta: 4:14:37, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4386 2023-01-03 23:43:47,580 - mmcls - INFO - Epoch [66][1800/5005] lr: 1.000e-03, eta: 4:14:28, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.4263 2023-01-03 23:43:56,514 - mmcls - INFO - Epoch [66][1900/5005] lr: 1.000e-03, eta: 4:14:19, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.4281 2023-01-03 23:44:05,358 - mmcls - INFO - Epoch [66][2000/5005] lr: 1.000e-03, eta: 4:14:10, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.4237 2023-01-03 23:44:13,974 - mmcls - INFO - Epoch [66][2100/5005] lr: 1.000e-03, eta: 4:14:01, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.4385 2023-01-03 23:44:22,626 - mmcls - INFO - Epoch [66][2200/5005] lr: 1.000e-03, eta: 4:13:52, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.4253 2023-01-03 23:44:31,320 - mmcls - INFO - Epoch [66][2300/5005] lr: 1.000e-03, eta: 4:13:43, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4462 2023-01-03 23:44:40,149 - mmcls - INFO - Epoch [66][2400/5005] lr: 1.000e-03, eta: 4:13:35, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.4282 2023-01-03 23:44:49,084 - mmcls - INFO - Epoch [66][2500/5005] lr: 1.000e-03, eta: 4:13:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4085 2023-01-03 23:44:57,527 - mmcls - INFO - Epoch [66][2600/5005] lr: 1.000e-03, eta: 4:13:17, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.4559 2023-01-03 23:45:06,367 - mmcls - INFO - Epoch [66][2700/5005] lr: 1.000e-03, eta: 4:13:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4109 2023-01-03 23:45:15,025 - mmcls - INFO - Epoch [66][2800/5005] lr: 1.000e-03, eta: 4:12:59, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4282 2023-01-03 23:45:23,741 - mmcls - INFO - Epoch [66][2900/5005] lr: 1.000e-03, eta: 4:12:50, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4347 2023-01-03 23:45:32,601 - mmcls - INFO - Epoch [66][3000/5005] lr: 1.000e-03, eta: 4:12:42, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.4026 2023-01-03 23:45:41,063 - mmcls - INFO - Epoch [66][3100/5005] lr: 1.000e-03, eta: 4:12:33, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.4372 2023-01-03 23:45:50,078 - mmcls - INFO - Epoch [66][3200/5005] lr: 1.000e-03, eta: 4:12:24, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.4525 2023-01-03 23:45:58,974 - mmcls - INFO - Epoch [66][3300/5005] lr: 1.000e-03, eta: 4:12:15, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.4380 2023-01-03 23:46:07,815 - mmcls - INFO - Epoch [66][3400/5005] lr: 1.000e-03, eta: 4:12:06, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.4070 2023-01-03 23:46:16,502 - mmcls - INFO - Epoch [66][3500/5005] lr: 1.000e-03, eta: 4:11:58, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4153 2023-01-03 23:46:25,438 - mmcls - INFO - Epoch [66][3600/5005] lr: 1.000e-03, eta: 4:11:49, time: 0.089, data_time: 0.040, memory: 1125, loss: 1.4349 2023-01-03 23:46:34,313 - mmcls - INFO - Epoch [66][3700/5005] lr: 1.000e-03, eta: 4:11:40, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.4253 2023-01-03 23:46:43,031 - mmcls - INFO - Epoch [66][3800/5005] lr: 1.000e-03, eta: 4:11:31, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4346 2023-01-03 23:46:51,617 - mmcls - INFO - Epoch [66][3900/5005] lr: 1.000e-03, eta: 4:11:22, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4329 2023-01-03 23:47:00,389 - mmcls - INFO - Epoch [66][4000/5005] lr: 1.000e-03, eta: 4:11:13, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4554 2023-01-03 23:47:09,023 - mmcls - INFO - Epoch [66][4100/5005] lr: 1.000e-03, eta: 4:11:05, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4368 2023-01-03 23:47:17,551 - mmcls - INFO - Epoch [66][4200/5005] lr: 1.000e-03, eta: 4:10:56, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.4250 2023-01-03 23:47:26,542 - mmcls - INFO - Epoch [66][4300/5005] lr: 1.000e-03, eta: 4:10:47, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.4387 2023-01-03 23:47:35,085 - mmcls - INFO - Epoch [66][4400/5005] lr: 1.000e-03, eta: 4:10:38, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.4378 2023-01-03 23:47:43,923 - mmcls - INFO - Epoch [66][4500/5005] lr: 1.000e-03, eta: 4:10:29, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.4590 2023-01-03 23:47:52,918 - mmcls - INFO - Epoch [66][4600/5005] lr: 1.000e-03, eta: 4:10:20, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.4352 2023-01-03 23:48:01,702 - mmcls - INFO - Epoch [66][4700/5005] lr: 1.000e-03, eta: 4:10:12, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4331 2023-01-03 23:48:10,314 - mmcls - INFO - Epoch [66][4800/5005] lr: 1.000e-03, eta: 4:10:03, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4303 2023-01-03 23:48:19,102 - mmcls - INFO - Epoch [66][4900/5005] lr: 1.000e-03, eta: 4:09:54, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4367 2023-01-03 23:48:27,945 - mmcls - INFO - Epoch [66][5000/5005] lr: 1.000e-03, eta: 4:09:45, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.4495 2023-01-03 23:48:50,698 - mmcls - INFO - Epoch(val) [66][196] accuracy_top-1: 69.0620, accuracy_top-5: 88.8260 2023-01-03 23:49:01,962 - mmcls - INFO - Epoch [67][100/5005] lr: 1.000e-03, eta: 4:09:37, time: 0.112, data_time: 0.063, memory: 1125, loss: 1.4379 2023-01-03 23:49:10,582 - mmcls - INFO - Epoch [67][200/5005] lr: 1.000e-03, eta: 4:09:28, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3932 2023-01-03 23:49:19,333 - mmcls - INFO - Epoch [67][300/5005] lr: 1.000e-03, eta: 4:09:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4217 2023-01-03 23:49:27,971 - mmcls - INFO - Epoch [67][400/5005] lr: 1.000e-03, eta: 4:09:10, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.4378 2023-01-03 23:49:36,654 - mmcls - INFO - Epoch [67][500/5005] lr: 1.000e-03, eta: 4:09:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4187 2023-01-03 23:49:45,441 - mmcls - INFO - Epoch [67][600/5005] lr: 1.000e-03, eta: 4:08:53, time: 0.088, data_time: 0.041, memory: 1125, loss: 1.4383 2023-01-03 23:49:54,168 - mmcls - INFO - Epoch [67][700/5005] lr: 1.000e-03, eta: 4:08:44, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.4201 2023-01-03 23:50:03,047 - mmcls - INFO - Epoch [67][800/5005] lr: 1.000e-03, eta: 4:08:35, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4134 2023-01-03 23:50:11,692 - mmcls - INFO - Epoch [67][900/5005] lr: 1.000e-03, eta: 4:08:26, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4331 2023-01-03 23:50:20,345 - mmcls - INFO - Epoch [67][1000/5005] lr: 1.000e-03, eta: 4:08:17, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4169 2023-01-03 23:50:29,573 - mmcls - INFO - Epoch [67][1100/5005] lr: 1.000e-03, eta: 4:08:09, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.4231 2023-01-03 23:50:38,215 - mmcls - INFO - Epoch [67][1200/5005] lr: 1.000e-03, eta: 4:08:00, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4385 2023-01-03 23:50:46,733 - mmcls - INFO - Epoch [67][1300/5005] lr: 1.000e-03, eta: 4:07:51, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4295 2023-01-03 23:50:55,304 - mmcls - INFO - Epoch [67][1400/5005] lr: 1.000e-03, eta: 4:07:42, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.4241 2023-01-03 23:51:04,184 - mmcls - INFO - Epoch [67][1500/5005] lr: 1.000e-03, eta: 4:07:33, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.4371 2023-01-03 23:51:13,110 - mmcls - INFO - Epoch [67][1600/5005] lr: 1.000e-03, eta: 4:07:24, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4379 2023-01-03 23:51:21,730 - mmcls - INFO - Epoch [67][1700/5005] lr: 1.000e-03, eta: 4:07:15, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.4300 2023-01-03 23:51:30,402 - mmcls - INFO - Epoch [67][1800/5005] lr: 1.000e-03, eta: 4:07:07, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4319 2023-01-03 23:51:39,834 - mmcls - INFO - Epoch [67][1900/5005] lr: 1.000e-03, eta: 4:06:58, time: 0.094, data_time: 0.001, memory: 1125, loss: 1.4100 2023-01-03 23:51:48,551 - mmcls - INFO - Epoch [67][2000/5005] lr: 1.000e-03, eta: 4:06:49, time: 0.087, data_time: 0.039, memory: 1125, loss: 1.4168 2023-01-03 23:51:57,737 - mmcls - INFO - Epoch [67][2100/5005] lr: 1.000e-03, eta: 4:06:41, time: 0.092, data_time: 0.044, memory: 1125, loss: 1.4124 2023-01-03 23:52:06,468 - mmcls - INFO - Epoch [67][2200/5005] lr: 1.000e-03, eta: 4:06:32, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.4264 2023-01-03 23:52:15,030 - mmcls - INFO - Epoch [67][2300/5005] lr: 1.000e-03, eta: 4:06:23, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.4288 2023-01-03 23:52:23,438 - mmcls - INFO - Epoch [67][2400/5005] lr: 1.000e-03, eta: 4:06:14, time: 0.084, data_time: 0.009, memory: 1125, loss: 1.4401 2023-01-03 23:52:32,294 - mmcls - INFO - Epoch [67][2500/5005] lr: 1.000e-03, eta: 4:06:05, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4250 2023-01-03 23:52:40,874 - mmcls - INFO - Epoch [67][2600/5005] lr: 1.000e-03, eta: 4:05:56, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.4040 2023-01-03 23:52:49,828 - mmcls - INFO - Epoch [67][2700/5005] lr: 1.000e-03, eta: 4:05:47, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4242 2023-01-03 23:52:58,322 - mmcls - INFO - Epoch [67][2800/5005] lr: 1.000e-03, eta: 4:05:38, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4404 2023-01-03 23:53:06,881 - mmcls - INFO - Epoch [67][2900/5005] lr: 1.000e-03, eta: 4:05:29, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.4214 2023-01-03 23:53:15,657 - mmcls - INFO - Epoch [67][3000/5005] lr: 1.000e-03, eta: 4:05:21, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.4173 2023-01-03 23:53:25,017 - mmcls - INFO - Epoch [67][3100/5005] lr: 1.000e-03, eta: 4:05:12, time: 0.094, data_time: 0.012, memory: 1125, loss: 1.4345 2023-01-03 23:53:33,681 - mmcls - INFO - Epoch [67][3200/5005] lr: 1.000e-03, eta: 4:05:03, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4368 2023-01-03 23:53:42,731 - mmcls - INFO - Epoch [67][3300/5005] lr: 1.000e-03, eta: 4:04:55, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.4307 2023-01-03 23:53:51,446 - mmcls - INFO - Epoch [67][3400/5005] lr: 1.000e-03, eta: 4:04:46, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4367 2023-01-03 23:54:00,169 - mmcls - INFO - Epoch [67][3500/5005] lr: 1.000e-03, eta: 4:04:37, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.4065 2023-01-03 23:54:08,716 - mmcls - INFO - Epoch [67][3600/5005] lr: 1.000e-03, eta: 4:04:28, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.4313 2023-01-03 23:54:17,490 - mmcls - INFO - Epoch [67][3700/5005] lr: 1.000e-03, eta: 4:04:19, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4400 2023-01-03 23:54:26,290 - mmcls - INFO - Epoch [67][3800/5005] lr: 1.000e-03, eta: 4:04:10, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4201 2023-01-03 23:54:34,824 - mmcls - INFO - Epoch [67][3900/5005] lr: 1.000e-03, eta: 4:04:01, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.4126 2023-01-03 23:54:43,584 - mmcls - INFO - Epoch [67][4000/5005] lr: 1.000e-03, eta: 4:03:53, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.4387 2023-01-03 23:54:52,277 - mmcls - INFO - Epoch [67][4100/5005] lr: 1.000e-03, eta: 4:03:44, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4352 2023-01-03 23:55:00,851 - mmcls - INFO - Epoch [67][4200/5005] lr: 1.000e-03, eta: 4:03:35, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.4348 2023-01-03 23:55:09,737 - mmcls - INFO - Epoch [67][4300/5005] lr: 1.000e-03, eta: 4:03:26, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4288 2023-01-03 23:55:18,157 - mmcls - INFO - Epoch [67][4400/5005] lr: 1.000e-03, eta: 4:03:17, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.4384 2023-01-03 23:55:26,633 - mmcls - INFO - Epoch [67][4500/5005] lr: 1.000e-03, eta: 4:03:08, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.4200 2023-01-03 23:55:35,362 - mmcls - INFO - Epoch [67][4600/5005] lr: 1.000e-03, eta: 4:02:59, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.4328 2023-01-03 23:55:44,146 - mmcls - INFO - Epoch [67][4700/5005] lr: 1.000e-03, eta: 4:02:50, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4343 2023-01-03 23:55:53,459 - mmcls - INFO - Epoch [67][4800/5005] lr: 1.000e-03, eta: 4:02:42, time: 0.093, data_time: 0.029, memory: 1125, loss: 1.4412 2023-01-03 23:56:02,289 - mmcls - INFO - Epoch [67][4900/5005] lr: 1.000e-03, eta: 4:02:33, time: 0.088, data_time: 0.000, memory: 1125, loss: 1.4252 2023-01-03 23:56:11,075 - mmcls - INFO - Epoch [67][5000/5005] lr: 1.000e-03, eta: 4:02:24, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4171 2023-01-03 23:56:33,743 - mmcls - INFO - Epoch(val) [67][196] accuracy_top-1: 69.0900, accuracy_top-5: 88.8860 2023-01-03 23:56:44,693 - mmcls - INFO - Epoch [68][100/5005] lr: 1.000e-03, eta: 4:02:16, time: 0.109, data_time: 0.059, memory: 1125, loss: 1.4405 2023-01-03 23:56:53,527 - mmcls - INFO - Epoch [68][200/5005] lr: 1.000e-03, eta: 4:02:07, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.4083 2023-01-03 23:57:02,180 - mmcls - INFO - Epoch [68][300/5005] lr: 1.000e-03, eta: 4:01:58, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4224 2023-01-03 23:57:10,857 - mmcls - INFO - Epoch [68][400/5005] lr: 1.000e-03, eta: 4:01:49, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4263 2023-01-03 23:57:19,492 - mmcls - INFO - Epoch [68][500/5005] lr: 1.000e-03, eta: 4:01:40, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4198 2023-01-03 23:57:28,256 - mmcls - INFO - Epoch [68][600/5005] lr: 1.000e-03, eta: 4:01:31, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4080 2023-01-03 23:57:36,865 - mmcls - INFO - Epoch [68][700/5005] lr: 1.000e-03, eta: 4:01:23, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.4353 2023-01-03 23:57:45,472 - mmcls - INFO - Epoch [68][800/5005] lr: 1.000e-03, eta: 4:01:14, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.4277 2023-01-03 23:57:54,133 - mmcls - INFO - Epoch [68][900/5005] lr: 1.000e-03, eta: 4:01:05, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.4256 2023-01-03 23:58:02,941 - mmcls - INFO - Epoch [68][1000/5005] lr: 1.000e-03, eta: 4:00:56, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.4082 2023-01-03 23:58:11,355 - mmcls - INFO - Epoch [68][1100/5005] lr: 1.000e-03, eta: 4:00:47, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.4131 2023-01-03 23:58:20,443 - mmcls - INFO - Epoch [68][1200/5005] lr: 1.000e-03, eta: 4:00:38, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.4130 2023-01-03 23:58:28,981 - mmcls - INFO - Epoch [68][1300/5005] lr: 1.000e-03, eta: 4:00:29, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.4000 2023-01-03 23:58:37,618 - mmcls - INFO - Epoch [68][1400/5005] lr: 1.000e-03, eta: 4:00:20, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.4062 2023-01-03 23:58:46,164 - mmcls - INFO - Epoch [68][1500/5005] lr: 1.000e-03, eta: 4:00:12, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.4437 2023-01-03 23:58:55,095 - mmcls - INFO - Epoch [68][1600/5005] lr: 1.000e-03, eta: 4:00:03, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4139 2023-01-03 23:59:03,790 - mmcls - INFO - Epoch [68][1700/5005] lr: 1.000e-03, eta: 3:59:54, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.4224 2023-01-03 23:59:12,550 - mmcls - INFO - Epoch [68][1800/5005] lr: 1.000e-03, eta: 3:59:45, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.4113 2023-01-03 23:59:21,244 - mmcls - INFO - Epoch [68][1900/5005] lr: 1.000e-03, eta: 3:59:36, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.4153 2023-01-03 23:59:29,769 - mmcls - INFO - Epoch [68][2000/5005] lr: 1.000e-03, eta: 3:59:27, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.4481 2023-01-03 23:59:38,891 - mmcls - INFO - Epoch [68][2100/5005] lr: 1.000e-03, eta: 3:59:19, time: 0.091, data_time: 0.019, memory: 1125, loss: 1.4094 2023-01-03 23:59:47,532 - mmcls - INFO - Epoch [68][2200/5005] lr: 1.000e-03, eta: 3:59:10, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4163 2023-01-03 23:59:56,444 - mmcls - INFO - Epoch [68][2300/5005] lr: 1.000e-03, eta: 3:59:01, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.4101 2023-01-04 00:00:05,407 - mmcls - INFO - Epoch [68][2400/5005] lr: 1.000e-03, eta: 3:58:52, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4314 2023-01-04 00:00:14,007 - mmcls - INFO - Epoch [68][2500/5005] lr: 1.000e-03, eta: 3:58:43, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.4217 2023-01-04 00:00:22,841 - mmcls - INFO - Epoch [68][2600/5005] lr: 1.000e-03, eta: 3:58:35, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4155 2023-01-04 00:00:31,685 - mmcls - INFO - Epoch [68][2700/5005] lr: 1.000e-03, eta: 3:58:26, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.4129 2023-01-04 00:00:40,473 - mmcls - INFO - Epoch [68][2800/5005] lr: 1.000e-03, eta: 3:58:17, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4291 2023-01-04 00:00:49,263 - mmcls - INFO - Epoch [68][2900/5005] lr: 1.000e-03, eta: 3:58:08, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4075 2023-01-04 00:00:57,843 - mmcls - INFO - Epoch [68][3000/5005] lr: 1.000e-03, eta: 3:57:59, time: 0.086, data_time: 0.033, memory: 1125, loss: 1.4181 2023-01-04 00:01:06,662 - mmcls - INFO - Epoch [68][3100/5005] lr: 1.000e-03, eta: 3:57:50, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4138 2023-01-04 00:01:15,340 - mmcls - INFO - Epoch [68][3200/5005] lr: 1.000e-03, eta: 3:57:42, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4199 2023-01-04 00:01:24,313 - mmcls - INFO - Epoch [68][3300/5005] lr: 1.000e-03, eta: 3:57:33, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4200 2023-01-04 00:01:33,110 - mmcls - INFO - Epoch [68][3400/5005] lr: 1.000e-03, eta: 3:57:24, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.4176 2023-01-04 00:01:41,940 - mmcls - INFO - Epoch [68][3500/5005] lr: 1.000e-03, eta: 3:57:15, time: 0.088, data_time: 0.041, memory: 1125, loss: 1.4258 2023-01-04 00:01:50,694 - mmcls - INFO - Epoch [68][3600/5005] lr: 1.000e-03, eta: 3:57:06, time: 0.088, data_time: 0.038, memory: 1125, loss: 1.4284 2023-01-04 00:02:00,040 - mmcls - INFO - Epoch [68][3700/5005] lr: 1.000e-03, eta: 3:56:58, time: 0.093, data_time: 0.033, memory: 1125, loss: 1.4259 2023-01-04 00:02:08,782 - mmcls - INFO - Epoch [68][3800/5005] lr: 1.000e-03, eta: 3:56:49, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.4507 2023-01-04 00:02:17,560 - mmcls - INFO - Epoch [68][3900/5005] lr: 1.000e-03, eta: 3:56:40, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4036 2023-01-04 00:02:26,301 - mmcls - INFO - Epoch [68][4000/5005] lr: 1.000e-03, eta: 3:56:31, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4207 2023-01-04 00:02:35,381 - mmcls - INFO - Epoch [68][4100/5005] lr: 1.000e-03, eta: 3:56:23, time: 0.091, data_time: 0.018, memory: 1125, loss: 1.4213 2023-01-04 00:02:44,176 - mmcls - INFO - Epoch [68][4200/5005] lr: 1.000e-03, eta: 3:56:14, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.4167 2023-01-04 00:02:53,100 - mmcls - INFO - Epoch [68][4300/5005] lr: 1.000e-03, eta: 3:56:05, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.4228 2023-01-04 00:03:01,678 - mmcls - INFO - Epoch [68][4400/5005] lr: 1.000e-03, eta: 3:55:56, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4143 2023-01-04 00:03:10,581 - mmcls - INFO - Epoch [68][4500/5005] lr: 1.000e-03, eta: 3:55:47, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4415 2023-01-04 00:03:19,365 - mmcls - INFO - Epoch [68][4600/5005] lr: 1.000e-03, eta: 3:55:39, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.4210 2023-01-04 00:03:28,504 - mmcls - INFO - Epoch [68][4700/5005] lr: 1.000e-03, eta: 3:55:30, time: 0.091, data_time: 0.025, memory: 1125, loss: 1.4229 2023-01-04 00:03:37,279 - mmcls - INFO - Epoch [68][4800/5005] lr: 1.000e-03, eta: 3:55:21, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.4253 2023-01-04 00:03:46,021 - mmcls - INFO - Epoch [68][4900/5005] lr: 1.000e-03, eta: 3:55:12, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4212 2023-01-04 00:03:55,072 - mmcls - INFO - Epoch [68][5000/5005] lr: 1.000e-03, eta: 3:55:04, time: 0.091, data_time: 0.020, memory: 1125, loss: 1.4328 2023-01-04 00:04:17,896 - mmcls - INFO - Epoch(val) [68][196] accuracy_top-1: 69.2260, accuracy_top-5: 88.8140 2023-01-04 00:04:28,673 - mmcls - INFO - Epoch [69][100/5005] lr: 1.000e-03, eta: 3:54:55, time: 0.108, data_time: 0.042, memory: 1125, loss: 1.4226 2023-01-04 00:04:37,279 - mmcls - INFO - Epoch [69][200/5005] lr: 1.000e-03, eta: 3:54:46, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.4170 2023-01-04 00:04:46,044 - mmcls - INFO - Epoch [69][300/5005] lr: 1.000e-03, eta: 3:54:37, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.4101 2023-01-04 00:04:54,641 - mmcls - INFO - Epoch [69][400/5005] lr: 1.000e-03, eta: 3:54:28, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.4260 2023-01-04 00:05:03,348 - mmcls - INFO - Epoch [69][500/5005] lr: 1.000e-03, eta: 3:54:20, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.4113 2023-01-04 00:05:12,033 - mmcls - INFO - Epoch [69][600/5005] lr: 1.000e-03, eta: 3:54:11, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.4073 2023-01-04 00:05:21,134 - mmcls - INFO - Epoch [69][700/5005] lr: 1.000e-03, eta: 3:54:02, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.4033 2023-01-04 00:05:29,809 - mmcls - INFO - Epoch [69][800/5005] lr: 1.000e-03, eta: 3:53:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4279 2023-01-04 00:05:38,517 - mmcls - INFO - Epoch [69][900/5005] lr: 1.000e-03, eta: 3:53:44, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4259 2023-01-04 00:05:47,416 - mmcls - INFO - Epoch [69][1000/5005] lr: 1.000e-03, eta: 3:53:36, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.4265 2023-01-04 00:05:56,107 - mmcls - INFO - Epoch [69][1100/5005] lr: 1.000e-03, eta: 3:53:27, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.4235 2023-01-04 00:06:04,623 - mmcls - INFO - Epoch [69][1200/5005] lr: 1.000e-03, eta: 3:53:18, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.4272 2023-01-04 00:06:13,367 - mmcls - INFO - Epoch [69][1300/5005] lr: 1.000e-03, eta: 3:53:09, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.4168 2023-01-04 00:06:21,999 - mmcls - INFO - Epoch [69][1400/5005] lr: 1.000e-03, eta: 3:53:00, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.4169 2023-01-04 00:06:30,798 - mmcls - INFO - Epoch [69][1500/5005] lr: 1.000e-03, eta: 3:52:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4309 2023-01-04 00:06:39,575 - mmcls - INFO - Epoch [69][1600/5005] lr: 1.000e-03, eta: 3:52:42, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4195 2023-01-04 00:06:48,110 - mmcls - INFO - Epoch [69][1700/5005] lr: 1.000e-03, eta: 3:52:34, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.4098 2023-01-04 00:06:56,788 - mmcls - INFO - Epoch [69][1800/5005] lr: 1.000e-03, eta: 3:52:25, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4131 2023-01-04 00:07:05,722 - mmcls - INFO - Epoch [69][1900/5005] lr: 1.000e-03, eta: 3:52:16, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.4134 2023-01-04 00:07:14,661 - mmcls - INFO - Epoch [69][2000/5005] lr: 1.000e-03, eta: 3:52:07, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4094 2023-01-04 00:07:23,225 - mmcls - INFO - Epoch [69][2100/5005] lr: 1.000e-03, eta: 3:51:58, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4257 2023-01-04 00:07:31,922 - mmcls - INFO - Epoch [69][2200/5005] lr: 1.000e-03, eta: 3:51:49, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3967 2023-01-04 00:07:40,610 - mmcls - INFO - Epoch [69][2300/5005] lr: 1.000e-03, eta: 3:51:41, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4378 2023-01-04 00:07:49,609 - mmcls - INFO - Epoch [69][2400/5005] lr: 1.000e-03, eta: 3:51:32, time: 0.090, data_time: 0.050, memory: 1125, loss: 1.3966 2023-01-04 00:07:58,078 - mmcls - INFO - Epoch [69][2500/5005] lr: 1.000e-03, eta: 3:51:23, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.4183 2023-01-04 00:08:06,936 - mmcls - INFO - Epoch [69][2600/5005] lr: 1.000e-03, eta: 3:51:14, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.4019 2023-01-04 00:08:15,848 - mmcls - INFO - Epoch [69][2700/5005] lr: 1.000e-03, eta: 3:51:05, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.4072 2023-01-04 00:08:24,682 - mmcls - INFO - Epoch [69][2800/5005] lr: 1.000e-03, eta: 3:50:57, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4007 2023-01-04 00:08:33,441 - mmcls - INFO - Epoch [69][2900/5005] lr: 1.000e-03, eta: 3:50:48, time: 0.088, data_time: 0.034, memory: 1125, loss: 1.4218 2023-01-04 00:08:41,936 - mmcls - INFO - Epoch [69][3000/5005] lr: 1.000e-03, eta: 3:50:39, time: 0.085, data_time: 0.025, memory: 1125, loss: 1.4055 2023-01-04 00:08:50,530 - mmcls - INFO - Epoch [69][3100/5005] lr: 1.000e-03, eta: 3:50:30, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.4109 2023-01-04 00:08:59,446 - mmcls - INFO - Epoch [69][3200/5005] lr: 1.000e-03, eta: 3:50:21, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4304 2023-01-04 00:09:08,215 - mmcls - INFO - Epoch [69][3300/5005] lr: 1.000e-03, eta: 3:50:12, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.4091 2023-01-04 00:09:16,804 - mmcls - INFO - Epoch [69][3400/5005] lr: 1.000e-03, eta: 3:50:03, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4279 2023-01-04 00:09:25,291 - mmcls - INFO - Epoch [69][3500/5005] lr: 1.000e-03, eta: 3:49:54, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.4168 2023-01-04 00:09:34,180 - mmcls - INFO - Epoch [69][3600/5005] lr: 1.000e-03, eta: 3:49:46, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4238 2023-01-04 00:09:42,630 - mmcls - INFO - Epoch [69][3700/5005] lr: 1.000e-03, eta: 3:49:37, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.4274 2023-01-04 00:09:51,365 - mmcls - INFO - Epoch [69][3800/5005] lr: 1.000e-03, eta: 3:49:28, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.4342 2023-01-04 00:10:00,139 - mmcls - INFO - Epoch [69][3900/5005] lr: 1.000e-03, eta: 3:49:19, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4298 2023-01-04 00:10:08,921 - mmcls - INFO - Epoch [69][4000/5005] lr: 1.000e-03, eta: 3:49:10, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.4114 2023-01-04 00:10:17,757 - mmcls - INFO - Epoch [69][4100/5005] lr: 1.000e-03, eta: 3:49:01, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.4229 2023-01-04 00:10:26,656 - mmcls - INFO - Epoch [69][4200/5005] lr: 1.000e-03, eta: 3:48:53, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4154 2023-01-04 00:10:35,515 - mmcls - INFO - Epoch [69][4300/5005] lr: 1.000e-03, eta: 3:48:44, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4045 2023-01-04 00:10:44,147 - mmcls - INFO - Epoch [69][4400/5005] lr: 1.000e-03, eta: 3:48:35, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.4159 2023-01-04 00:10:53,004 - mmcls - INFO - Epoch [69][4500/5005] lr: 1.000e-03, eta: 3:48:26, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.4331 2023-01-04 00:11:01,691 - mmcls - INFO - Epoch [69][4600/5005] lr: 1.000e-03, eta: 3:48:17, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.4211 2023-01-04 00:11:10,294 - mmcls - INFO - Epoch [69][4700/5005] lr: 1.000e-03, eta: 3:48:08, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.4293 2023-01-04 00:11:18,848 - mmcls - INFO - Epoch [69][4800/5005] lr: 1.000e-03, eta: 3:48:00, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.4114 2023-01-04 00:11:27,432 - mmcls - INFO - Epoch [69][4900/5005] lr: 1.000e-03, eta: 3:47:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4105 2023-01-04 00:11:36,393 - mmcls - INFO - Epoch [69][5000/5005] lr: 1.000e-03, eta: 3:47:42, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4157 2023-01-04 00:11:59,013 - mmcls - INFO - Epoch(val) [69][196] accuracy_top-1: 69.1820, accuracy_top-5: 88.9480 2023-01-04 00:12:09,859 - mmcls - INFO - Epoch [70][100/5005] lr: 1.000e-03, eta: 3:47:33, time: 0.108, data_time: 0.031, memory: 1125, loss: 1.4192 2023-01-04 00:12:18,515 - mmcls - INFO - Epoch [70][200/5005] lr: 1.000e-03, eta: 3:47:24, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3991 2023-01-04 00:12:27,175 - mmcls - INFO - Epoch [70][300/5005] lr: 1.000e-03, eta: 3:47:16, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.4116 2023-01-04 00:12:35,886 - mmcls - INFO - Epoch [70][400/5005] lr: 1.000e-03, eta: 3:47:07, time: 0.087, data_time: 0.025, memory: 1125, loss: 1.3977 2023-01-04 00:12:44,591 - mmcls - INFO - Epoch [70][500/5005] lr: 1.000e-03, eta: 3:46:58, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4026 2023-01-04 00:12:53,314 - mmcls - INFO - Epoch [70][600/5005] lr: 1.000e-03, eta: 3:46:49, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.4196 2023-01-04 00:13:02,018 - mmcls - INFO - Epoch [70][700/5005] lr: 1.000e-03, eta: 3:46:40, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.4027 2023-01-04 00:13:10,621 - mmcls - INFO - Epoch [70][800/5005] lr: 1.000e-03, eta: 3:46:31, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.4158 2023-01-04 00:13:19,221 - mmcls - INFO - Epoch [70][900/5005] lr: 1.000e-03, eta: 3:46:22, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4041 2023-01-04 00:13:28,079 - mmcls - INFO - Epoch [70][1000/5005] lr: 1.000e-03, eta: 3:46:14, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3915 2023-01-04 00:13:36,887 - mmcls - INFO - Epoch [70][1100/5005] lr: 1.000e-03, eta: 3:46:05, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.4174 2023-01-04 00:13:45,682 - mmcls - INFO - Epoch [70][1200/5005] lr: 1.000e-03, eta: 3:45:56, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4116 2023-01-04 00:13:54,551 - mmcls - INFO - Epoch [70][1300/5005] lr: 1.000e-03, eta: 3:45:47, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3962 2023-01-04 00:14:03,062 - mmcls - INFO - Epoch [70][1400/5005] lr: 1.000e-03, eta: 3:45:38, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.4066 2023-01-04 00:14:11,539 - mmcls - INFO - Epoch [70][1500/5005] lr: 1.000e-03, eta: 3:45:29, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.4251 2023-01-04 00:14:20,301 - mmcls - INFO - Epoch [70][1600/5005] lr: 1.000e-03, eta: 3:45:21, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4198 2023-01-04 00:14:29,131 - mmcls - INFO - Epoch [70][1700/5005] lr: 1.000e-03, eta: 3:45:12, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.4193 2023-01-04 00:14:37,821 - mmcls - INFO - Epoch [70][1800/5005] lr: 1.000e-03, eta: 3:45:03, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4201 2023-01-04 00:14:46,533 - mmcls - INFO - Epoch [70][1900/5005] lr: 1.000e-03, eta: 3:44:54, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3916 2023-01-04 00:14:55,347 - mmcls - INFO - Epoch [70][2000/5005] lr: 1.000e-03, eta: 3:44:45, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4104 2023-01-04 00:15:04,332 - mmcls - INFO - Epoch [70][2100/5005] lr: 1.000e-03, eta: 3:44:36, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.4086 2023-01-04 00:15:13,025 - mmcls - INFO - Epoch [70][2200/5005] lr: 1.000e-03, eta: 3:44:28, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3921 2023-01-04 00:15:21,521 - mmcls - INFO - Epoch [70][2300/5005] lr: 1.000e-03, eta: 3:44:19, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.4084 2023-01-04 00:15:30,656 - mmcls - INFO - Epoch [70][2400/5005] lr: 1.000e-03, eta: 3:44:10, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.4072 2023-01-04 00:15:39,619 - mmcls - INFO - Epoch [70][2500/5005] lr: 1.000e-03, eta: 3:44:01, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.4152 2023-01-04 00:15:48,051 - mmcls - INFO - Epoch [70][2600/5005] lr: 1.000e-03, eta: 3:43:52, time: 0.084, data_time: 0.019, memory: 1125, loss: 1.4124 2023-01-04 00:15:56,761 - mmcls - INFO - Epoch [70][2700/5005] lr: 1.000e-03, eta: 3:43:43, time: 0.087, data_time: 0.039, memory: 1125, loss: 1.4172 2023-01-04 00:16:05,340 - mmcls - INFO - Epoch [70][2800/5005] lr: 1.000e-03, eta: 3:43:35, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3955 2023-01-04 00:16:14,756 - mmcls - INFO - Epoch [70][2900/5005] lr: 1.000e-03, eta: 3:43:26, time: 0.094, data_time: 0.028, memory: 1125, loss: 1.4015 2023-01-04 00:16:23,498 - mmcls - INFO - Epoch [70][3000/5005] lr: 1.000e-03, eta: 3:43:17, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4198 2023-01-04 00:16:32,123 - mmcls - INFO - Epoch [70][3100/5005] lr: 1.000e-03, eta: 3:43:08, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4355 2023-01-04 00:16:40,775 - mmcls - INFO - Epoch [70][3200/5005] lr: 1.000e-03, eta: 3:42:59, time: 0.086, data_time: 0.043, memory: 1125, loss: 1.3939 2023-01-04 00:16:49,671 - mmcls - INFO - Epoch [70][3300/5005] lr: 1.000e-03, eta: 3:42:51, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3957 2023-01-04 00:16:58,642 - mmcls - INFO - Epoch [70][3400/5005] lr: 1.000e-03, eta: 3:42:42, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.4170 2023-01-04 00:17:07,572 - mmcls - INFO - Epoch [70][3500/5005] lr: 1.000e-03, eta: 3:42:33, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4066 2023-01-04 00:17:16,289 - mmcls - INFO - Epoch [70][3600/5005] lr: 1.000e-03, eta: 3:42:24, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.4020 2023-01-04 00:17:25,231 - mmcls - INFO - Epoch [70][3700/5005] lr: 1.000e-03, eta: 3:42:16, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4058 2023-01-04 00:17:34,170 - mmcls - INFO - Epoch [70][3800/5005] lr: 1.000e-03, eta: 3:42:07, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4445 2023-01-04 00:17:42,915 - mmcls - INFO - Epoch [70][3900/5005] lr: 1.000e-03, eta: 3:41:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4267 2023-01-04 00:17:51,574 - mmcls - INFO - Epoch [70][4000/5005] lr: 1.000e-03, eta: 3:41:49, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4113 2023-01-04 00:18:00,237 - mmcls - INFO - Epoch [70][4100/5005] lr: 1.000e-03, eta: 3:41:40, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4263 2023-01-04 00:18:09,016 - mmcls - INFO - Epoch [70][4200/5005] lr: 1.000e-03, eta: 3:41:31, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.4090 2023-01-04 00:18:17,670 - mmcls - INFO - Epoch [70][4300/5005] lr: 1.000e-03, eta: 3:41:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4200 2023-01-04 00:18:26,781 - mmcls - INFO - Epoch [70][4400/5005] lr: 1.000e-03, eta: 3:41:14, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.4274 2023-01-04 00:18:35,281 - mmcls - INFO - Epoch [70][4500/5005] lr: 1.000e-03, eta: 3:41:05, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.4201 2023-01-04 00:18:44,079 - mmcls - INFO - Epoch [70][4600/5005] lr: 1.000e-03, eta: 3:40:56, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.4132 2023-01-04 00:18:52,891 - mmcls - INFO - Epoch [70][4700/5005] lr: 1.000e-03, eta: 3:40:47, time: 0.088, data_time: 0.047, memory: 1125, loss: 1.4157 2023-01-04 00:19:02,424 - mmcls - INFO - Epoch [70][4800/5005] lr: 1.000e-03, eta: 3:40:39, time: 0.095, data_time: 0.010, memory: 1125, loss: 1.4310 2023-01-04 00:19:11,054 - mmcls - INFO - Epoch [70][4900/5005] lr: 1.000e-03, eta: 3:40:30, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3992 2023-01-04 00:19:19,809 - mmcls - INFO - Epoch [70][5000/5005] lr: 1.000e-03, eta: 3:40:21, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4129 2023-01-04 00:19:42,430 - mmcls - INFO - Epoch(val) [70][196] accuracy_top-1: 69.2200, accuracy_top-5: 88.9600 2023-01-04 00:19:53,740 - mmcls - INFO - Epoch [71][100/5005] lr: 1.000e-03, eta: 3:40:13, time: 0.113, data_time: 0.060, memory: 1125, loss: 1.4010 2023-01-04 00:20:02,540 - mmcls - INFO - Epoch [71][200/5005] lr: 1.000e-03, eta: 3:40:04, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3886 2023-01-04 00:20:11,380 - mmcls - INFO - Epoch [71][300/5005] lr: 1.000e-03, eta: 3:39:55, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.4070 2023-01-04 00:20:19,963 - mmcls - INFO - Epoch [71][400/5005] lr: 1.000e-03, eta: 3:39:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4049 2023-01-04 00:20:28,793 - mmcls - INFO - Epoch [71][500/5005] lr: 1.000e-03, eta: 3:39:38, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4139 2023-01-04 00:20:37,481 - mmcls - INFO - Epoch [71][600/5005] lr: 1.000e-03, eta: 3:39:29, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4149 2023-01-04 00:20:46,275 - mmcls - INFO - Epoch [71][700/5005] lr: 1.000e-03, eta: 3:39:20, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3838 2023-01-04 00:20:54,901 - mmcls - INFO - Epoch [71][800/5005] lr: 1.000e-03, eta: 3:39:11, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.4279 2023-01-04 00:21:03,771 - mmcls - INFO - Epoch [71][900/5005] lr: 1.000e-03, eta: 3:39:02, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4223 2023-01-04 00:21:12,301 - mmcls - INFO - Epoch [71][1000/5005] lr: 1.000e-03, eta: 3:38:53, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.4181 2023-01-04 00:21:21,282 - mmcls - INFO - Epoch [71][1100/5005] lr: 1.000e-03, eta: 3:38:45, time: 0.090, data_time: 0.047, memory: 1125, loss: 1.4139 2023-01-04 00:21:30,153 - mmcls - INFO - Epoch [71][1200/5005] lr: 1.000e-03, eta: 3:38:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3987 2023-01-04 00:21:38,714 - mmcls - INFO - Epoch [71][1300/5005] lr: 1.000e-03, eta: 3:38:27, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4183 2023-01-04 00:21:47,461 - mmcls - INFO - Epoch [71][1400/5005] lr: 1.000e-03, eta: 3:38:18, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.4000 2023-01-04 00:21:56,208 - mmcls - INFO - Epoch [71][1500/5005] lr: 1.000e-03, eta: 3:38:09, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.4129 2023-01-04 00:22:04,996 - mmcls - INFO - Epoch [71][1600/5005] lr: 1.000e-03, eta: 3:38:00, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4201 2023-01-04 00:22:13,793 - mmcls - INFO - Epoch [71][1700/5005] lr: 1.000e-03, eta: 3:37:52, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.4208 2023-01-04 00:22:22,750 - mmcls - INFO - Epoch [71][1800/5005] lr: 1.000e-03, eta: 3:37:43, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.4007 2023-01-04 00:22:31,533 - mmcls - INFO - Epoch [71][1900/5005] lr: 1.000e-03, eta: 3:37:34, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4241 2023-01-04 00:22:40,231 - mmcls - INFO - Epoch [71][2000/5005] lr: 1.000e-03, eta: 3:37:25, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3888 2023-01-04 00:22:49,329 - mmcls - INFO - Epoch [71][2100/5005] lr: 1.000e-03, eta: 3:37:16, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.4156 2023-01-04 00:22:57,884 - mmcls - INFO - Epoch [71][2200/5005] lr: 1.000e-03, eta: 3:37:08, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.4017 2023-01-04 00:23:06,538 - mmcls - INFO - Epoch [71][2300/5005] lr: 1.000e-03, eta: 3:36:59, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3824 2023-01-04 00:23:15,156 - mmcls - INFO - Epoch [71][2400/5005] lr: 1.000e-03, eta: 3:36:50, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.4150 2023-01-04 00:23:24,385 - mmcls - INFO - Epoch [71][2500/5005] lr: 1.000e-03, eta: 3:36:41, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.4084 2023-01-04 00:23:33,355 - mmcls - INFO - Epoch [71][2600/5005] lr: 1.000e-03, eta: 3:36:32, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.4013 2023-01-04 00:23:42,050 - mmcls - INFO - Epoch [71][2700/5005] lr: 1.000e-03, eta: 3:36:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4096 2023-01-04 00:23:50,595 - mmcls - INFO - Epoch [71][2800/5005] lr: 1.000e-03, eta: 3:36:15, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.4079 2023-01-04 00:23:59,199 - mmcls - INFO - Epoch [71][2900/5005] lr: 1.000e-03, eta: 3:36:06, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.4078 2023-01-04 00:24:07,801 - mmcls - INFO - Epoch [71][3000/5005] lr: 1.000e-03, eta: 3:35:57, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4094 2023-01-04 00:24:16,517 - mmcls - INFO - Epoch [71][3100/5005] lr: 1.000e-03, eta: 3:35:48, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.4094 2023-01-04 00:24:25,302 - mmcls - INFO - Epoch [71][3200/5005] lr: 1.000e-03, eta: 3:35:39, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4178 2023-01-04 00:24:34,086 - mmcls - INFO - Epoch [71][3300/5005] lr: 1.000e-03, eta: 3:35:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4048 2023-01-04 00:24:42,857 - mmcls - INFO - Epoch [71][3400/5005] lr: 1.000e-03, eta: 3:35:22, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4163 2023-01-04 00:24:51,278 - mmcls - INFO - Epoch [71][3500/5005] lr: 1.000e-03, eta: 3:35:13, time: 0.084, data_time: 0.033, memory: 1125, loss: 1.4133 2023-01-04 00:24:59,915 - mmcls - INFO - Epoch [71][3600/5005] lr: 1.000e-03, eta: 3:35:04, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.4096 2023-01-04 00:25:08,823 - mmcls - INFO - Epoch [71][3700/5005] lr: 1.000e-03, eta: 3:34:55, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.4095 2023-01-04 00:25:17,652 - mmcls - INFO - Epoch [71][3800/5005] lr: 1.000e-03, eta: 3:34:46, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3941 2023-01-04 00:25:26,407 - mmcls - INFO - Epoch [71][3900/5005] lr: 1.000e-03, eta: 3:34:37, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.4025 2023-01-04 00:25:35,245 - mmcls - INFO - Epoch [71][4000/5005] lr: 1.000e-03, eta: 3:34:29, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3983 2023-01-04 00:25:43,989 - mmcls - INFO - Epoch [71][4100/5005] lr: 1.000e-03, eta: 3:34:20, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3853 2023-01-04 00:25:52,593 - mmcls - INFO - Epoch [71][4200/5005] lr: 1.000e-03, eta: 3:34:11, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.4176 2023-01-04 00:26:01,306 - mmcls - INFO - Epoch [71][4300/5005] lr: 1.000e-03, eta: 3:34:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4093 2023-01-04 00:26:09,812 - mmcls - INFO - Epoch [71][4400/5005] lr: 1.000e-03, eta: 3:33:53, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.4106 2023-01-04 00:26:18,604 - mmcls - INFO - Epoch [71][4500/5005] lr: 1.000e-03, eta: 3:33:44, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4034 2023-01-04 00:26:27,246 - mmcls - INFO - Epoch [71][4600/5005] lr: 1.000e-03, eta: 3:33:35, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.4013 2023-01-04 00:26:36,053 - mmcls - INFO - Epoch [71][4700/5005] lr: 1.000e-03, eta: 3:33:27, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4260 2023-01-04 00:26:44,906 - mmcls - INFO - Epoch [71][4800/5005] lr: 1.000e-03, eta: 3:33:18, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.4033 2023-01-04 00:26:53,834 - mmcls - INFO - Epoch [71][4900/5005] lr: 1.000e-03, eta: 3:33:09, time: 0.089, data_time: 0.025, memory: 1125, loss: 1.3958 2023-01-04 00:27:02,593 - mmcls - INFO - Epoch [71][5000/5005] lr: 1.000e-03, eta: 3:33:00, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3908 2023-01-04 00:27:25,251 - mmcls - INFO - Epoch(val) [71][196] accuracy_top-1: 69.3400, accuracy_top-5: 88.9820 2023-01-04 00:27:36,528 - mmcls - INFO - Epoch [72][100/5005] lr: 1.000e-03, eta: 3:32:52, time: 0.113, data_time: 0.050, memory: 1125, loss: 1.4024 2023-01-04 00:27:45,436 - mmcls - INFO - Epoch [72][200/5005] lr: 1.000e-03, eta: 3:32:43, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4078 2023-01-04 00:27:54,308 - mmcls - INFO - Epoch [72][300/5005] lr: 1.000e-03, eta: 3:32:34, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.3750 2023-01-04 00:28:03,138 - mmcls - INFO - Epoch [72][400/5005] lr: 1.000e-03, eta: 3:32:25, time: 0.088, data_time: 0.038, memory: 1125, loss: 1.4112 2023-01-04 00:28:11,746 - mmcls - INFO - Epoch [72][500/5005] lr: 1.000e-03, eta: 3:32:17, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3915 2023-01-04 00:28:20,714 - mmcls - INFO - Epoch [72][600/5005] lr: 1.000e-03, eta: 3:32:08, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.4030 2023-01-04 00:28:29,753 - mmcls - INFO - Epoch [72][700/5005] lr: 1.000e-03, eta: 3:31:59, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.3987 2023-01-04 00:28:38,544 - mmcls - INFO - Epoch [72][800/5005] lr: 1.000e-03, eta: 3:31:50, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.4003 2023-01-04 00:28:47,111 - mmcls - INFO - Epoch [72][900/5005] lr: 1.000e-03, eta: 3:31:41, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4124 2023-01-04 00:28:55,713 - mmcls - INFO - Epoch [72][1000/5005] lr: 1.000e-03, eta: 3:31:33, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3983 2023-01-04 00:29:04,392 - mmcls - INFO - Epoch [72][1100/5005] lr: 1.000e-03, eta: 3:31:24, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4065 2023-01-04 00:29:13,232 - mmcls - INFO - Epoch [72][1200/5005] lr: 1.000e-03, eta: 3:31:15, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4137 2023-01-04 00:29:21,781 - mmcls - INFO - Epoch [72][1300/5005] lr: 1.000e-03, eta: 3:31:06, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.4109 2023-01-04 00:29:30,372 - mmcls - INFO - Epoch [72][1400/5005] lr: 1.000e-03, eta: 3:30:57, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3715 2023-01-04 00:29:39,072 - mmcls - INFO - Epoch [72][1500/5005] lr: 1.000e-03, eta: 3:30:48, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3957 2023-01-04 00:29:47,658 - mmcls - INFO - Epoch [72][1600/5005] lr: 1.000e-03, eta: 3:30:39, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3950 2023-01-04 00:29:56,514 - mmcls - INFO - Epoch [72][1700/5005] lr: 1.000e-03, eta: 3:30:31, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4104 2023-01-04 00:30:05,286 - mmcls - INFO - Epoch [72][1800/5005] lr: 1.000e-03, eta: 3:30:22, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3973 2023-01-04 00:30:13,954 - mmcls - INFO - Epoch [72][1900/5005] lr: 1.000e-03, eta: 3:30:13, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.3902 2023-01-04 00:30:22,544 - mmcls - INFO - Epoch [72][2000/5005] lr: 1.000e-03, eta: 3:30:04, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.4124 2023-01-04 00:30:30,992 - mmcls - INFO - Epoch [72][2100/5005] lr: 1.000e-03, eta: 3:29:55, time: 0.084, data_time: 0.034, memory: 1125, loss: 1.3812 2023-01-04 00:30:39,611 - mmcls - INFO - Epoch [72][2200/5005] lr: 1.000e-03, eta: 3:29:46, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.4027 2023-01-04 00:30:48,192 - mmcls - INFO - Epoch [72][2300/5005] lr: 1.000e-03, eta: 3:29:37, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3990 2023-01-04 00:30:56,813 - mmcls - INFO - Epoch [72][2400/5005] lr: 1.000e-03, eta: 3:29:28, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4039 2023-01-04 00:31:05,720 - mmcls - INFO - Epoch [72][2500/5005] lr: 1.000e-03, eta: 3:29:20, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.3929 2023-01-04 00:31:14,349 - mmcls - INFO - Epoch [72][2600/5005] lr: 1.000e-03, eta: 3:29:11, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.4067 2023-01-04 00:31:23,297 - mmcls - INFO - Epoch [72][2700/5005] lr: 1.000e-03, eta: 3:29:02, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4295 2023-01-04 00:31:32,122 - mmcls - INFO - Epoch [72][2800/5005] lr: 1.000e-03, eta: 3:28:53, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4057 2023-01-04 00:31:41,091 - mmcls - INFO - Epoch [72][2900/5005] lr: 1.000e-03, eta: 3:28:44, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.3886 2023-01-04 00:31:49,822 - mmcls - INFO - Epoch [72][3000/5005] lr: 1.000e-03, eta: 3:28:36, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.4209 2023-01-04 00:31:58,783 - mmcls - INFO - Epoch [72][3100/5005] lr: 1.000e-03, eta: 3:28:27, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.3952 2023-01-04 00:32:07,653 - mmcls - INFO - Epoch [72][3200/5005] lr: 1.000e-03, eta: 3:28:18, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.4046 2023-01-04 00:32:16,475 - mmcls - INFO - Epoch [72][3300/5005] lr: 1.000e-03, eta: 3:28:09, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.4121 2023-01-04 00:32:25,303 - mmcls - INFO - Epoch [72][3400/5005] lr: 1.000e-03, eta: 3:28:00, time: 0.088, data_time: 0.040, memory: 1125, loss: 1.4208 2023-01-04 00:32:34,070 - mmcls - INFO - Epoch [72][3500/5005] lr: 1.000e-03, eta: 3:27:52, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.3901 2023-01-04 00:32:42,857 - mmcls - INFO - Epoch [72][3600/5005] lr: 1.000e-03, eta: 3:27:43, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.4057 2023-01-04 00:32:51,921 - mmcls - INFO - Epoch [72][3700/5005] lr: 1.000e-03, eta: 3:27:34, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3942 2023-01-04 00:33:00,683 - mmcls - INFO - Epoch [72][3800/5005] lr: 1.000e-03, eta: 3:27:25, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4171 2023-01-04 00:33:09,592 - mmcls - INFO - Epoch [72][3900/5005] lr: 1.000e-03, eta: 3:27:17, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3993 2023-01-04 00:33:18,484 - mmcls - INFO - Epoch [72][4000/5005] lr: 1.000e-03, eta: 3:27:08, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3972 2023-01-04 00:33:27,290 - mmcls - INFO - Epoch [72][4100/5005] lr: 1.000e-03, eta: 3:26:59, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4079 2023-01-04 00:33:35,736 - mmcls - INFO - Epoch [72][4200/5005] lr: 1.000e-03, eta: 3:26:50, time: 0.084, data_time: 0.006, memory: 1125, loss: 1.3942 2023-01-04 00:33:44,649 - mmcls - INFO - Epoch [72][4300/5005] lr: 1.000e-03, eta: 3:26:41, time: 0.089, data_time: 0.035, memory: 1125, loss: 1.4066 2023-01-04 00:33:53,824 - mmcls - INFO - Epoch [72][4400/5005] lr: 1.000e-03, eta: 3:26:33, time: 0.092, data_time: 0.049, memory: 1125, loss: 1.3790 2023-01-04 00:34:02,371 - mmcls - INFO - Epoch [72][4500/5005] lr: 1.000e-03, eta: 3:26:24, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3920 2023-01-04 00:34:11,097 - mmcls - INFO - Epoch [72][4600/5005] lr: 1.000e-03, eta: 3:26:15, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4075 2023-01-04 00:34:19,583 - mmcls - INFO - Epoch [72][4700/5005] lr: 1.000e-03, eta: 3:26:06, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.4036 2023-01-04 00:34:28,699 - mmcls - INFO - Epoch [72][4800/5005] lr: 1.000e-03, eta: 3:25:57, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.3979 2023-01-04 00:34:37,284 - mmcls - INFO - Epoch [72][4900/5005] lr: 1.000e-03, eta: 3:25:48, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3980 2023-01-04 00:34:46,313 - mmcls - INFO - Epoch [72][5000/5005] lr: 1.000e-03, eta: 3:25:40, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4079 2023-01-04 00:35:08,940 - mmcls - INFO - Epoch(val) [72][196] accuracy_top-1: 69.4520, accuracy_top-5: 88.9200 2023-01-04 00:35:20,229 - mmcls - INFO - Epoch [73][100/5005] lr: 1.000e-03, eta: 3:25:31, time: 0.113, data_time: 0.027, memory: 1125, loss: 1.3947 2023-01-04 00:35:28,962 - mmcls - INFO - Epoch [73][200/5005] lr: 1.000e-03, eta: 3:25:22, time: 0.087, data_time: 0.044, memory: 1125, loss: 1.4068 2023-01-04 00:35:37,756 - mmcls - INFO - Epoch [73][300/5005] lr: 1.000e-03, eta: 3:25:14, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3850 2023-01-04 00:35:46,197 - mmcls - INFO - Epoch [73][400/5005] lr: 1.000e-03, eta: 3:25:05, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.3794 2023-01-04 00:35:54,983 - mmcls - INFO - Epoch [73][500/5005] lr: 1.000e-03, eta: 3:24:56, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3831 2023-01-04 00:36:03,643 - mmcls - INFO - Epoch [73][600/5005] lr: 1.000e-03, eta: 3:24:47, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3862 2023-01-04 00:36:12,400 - mmcls - INFO - Epoch [73][700/5005] lr: 1.000e-03, eta: 3:24:38, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3975 2023-01-04 00:36:21,301 - mmcls - INFO - Epoch [73][800/5005] lr: 1.000e-03, eta: 3:24:29, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3841 2023-01-04 00:36:30,205 - mmcls - INFO - Epoch [73][900/5005] lr: 1.000e-03, eta: 3:24:21, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4023 2023-01-04 00:36:38,895 - mmcls - INFO - Epoch [73][1000/5005] lr: 1.000e-03, eta: 3:24:12, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4112 2023-01-04 00:36:47,361 - mmcls - INFO - Epoch [73][1100/5005] lr: 1.000e-03, eta: 3:24:03, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3955 2023-01-04 00:36:56,000 - mmcls - INFO - Epoch [73][1200/5005] lr: 1.000e-03, eta: 3:23:54, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.4273 2023-01-04 00:37:04,956 - mmcls - INFO - Epoch [73][1300/5005] lr: 1.000e-03, eta: 3:23:45, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.4148 2023-01-04 00:37:13,561 - mmcls - INFO - Epoch [73][1400/5005] lr: 1.000e-03, eta: 3:23:36, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3999 2023-01-04 00:37:22,418 - mmcls - INFO - Epoch [73][1500/5005] lr: 1.000e-03, eta: 3:23:27, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4161 2023-01-04 00:37:30,992 - mmcls - INFO - Epoch [73][1600/5005] lr: 1.000e-03, eta: 3:23:19, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3783 2023-01-04 00:37:39,717 - mmcls - INFO - Epoch [73][1700/5005] lr: 1.000e-03, eta: 3:23:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4056 2023-01-04 00:37:48,374 - mmcls - INFO - Epoch [73][1800/5005] lr: 1.000e-03, eta: 3:23:01, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.4047 2023-01-04 00:37:56,809 - mmcls - INFO - Epoch [73][1900/5005] lr: 1.000e-03, eta: 3:22:52, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.4025 2023-01-04 00:38:05,762 - mmcls - INFO - Epoch [73][2000/5005] lr: 1.000e-03, eta: 3:22:43, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3847 2023-01-04 00:38:14,166 - mmcls - INFO - Epoch [73][2100/5005] lr: 1.000e-03, eta: 3:22:34, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.4125 2023-01-04 00:38:22,776 - mmcls - INFO - Epoch [73][2200/5005] lr: 1.000e-03, eta: 3:22:25, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.4050 2023-01-04 00:38:31,834 - mmcls - INFO - Epoch [73][2300/5005] lr: 1.000e-03, eta: 3:22:17, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.4258 2023-01-04 00:38:40,875 - mmcls - INFO - Epoch [73][2400/5005] lr: 1.000e-03, eta: 3:22:08, time: 0.090, data_time: 0.047, memory: 1125, loss: 1.4109 2023-01-04 00:38:49,823 - mmcls - INFO - Epoch [73][2500/5005] lr: 1.000e-03, eta: 3:21:59, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.3674 2023-01-04 00:38:58,740 - mmcls - INFO - Epoch [73][2600/5005] lr: 1.000e-03, eta: 3:21:50, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4015 2023-01-04 00:39:07,603 - mmcls - INFO - Epoch [73][2700/5005] lr: 1.000e-03, eta: 3:21:42, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.4116 2023-01-04 00:39:16,496 - mmcls - INFO - Epoch [73][2800/5005] lr: 1.000e-03, eta: 3:21:33, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3854 2023-01-04 00:39:25,321 - mmcls - INFO - Epoch [73][2900/5005] lr: 1.000e-03, eta: 3:21:24, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3820 2023-01-04 00:39:33,997 - mmcls - INFO - Epoch [73][3000/5005] lr: 1.000e-03, eta: 3:21:15, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.3914 2023-01-04 00:39:42,738 - mmcls - INFO - Epoch [73][3100/5005] lr: 1.000e-03, eta: 3:21:06, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3940 2023-01-04 00:39:51,298 - mmcls - INFO - Epoch [73][3200/5005] lr: 1.000e-03, eta: 3:20:57, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4128 2023-01-04 00:39:59,844 - mmcls - INFO - Epoch [73][3300/5005] lr: 1.000e-03, eta: 3:20:49, time: 0.085, data_time: 0.000, memory: 1125, loss: 1.4014 2023-01-04 00:40:08,583 - mmcls - INFO - Epoch [73][3400/5005] lr: 1.000e-03, eta: 3:20:40, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3974 2023-01-04 00:40:17,317 - mmcls - INFO - Epoch [73][3500/5005] lr: 1.000e-03, eta: 3:20:31, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.4066 2023-01-04 00:40:26,059 - mmcls - INFO - Epoch [73][3600/5005] lr: 1.000e-03, eta: 3:20:22, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.4057 2023-01-04 00:40:34,911 - mmcls - INFO - Epoch [73][3700/5005] lr: 1.000e-03, eta: 3:20:13, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.4154 2023-01-04 00:40:43,875 - mmcls - INFO - Epoch [73][3800/5005] lr: 1.000e-03, eta: 3:20:05, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4068 2023-01-04 00:40:52,643 - mmcls - INFO - Epoch [73][3900/5005] lr: 1.000e-03, eta: 3:19:56, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3852 2023-01-04 00:41:01,231 - mmcls - INFO - Epoch [73][4000/5005] lr: 1.000e-03, eta: 3:19:47, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4176 2023-01-04 00:41:09,930 - mmcls - INFO - Epoch [73][4100/5005] lr: 1.000e-03, eta: 3:19:38, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3988 2023-01-04 00:41:18,685 - mmcls - INFO - Epoch [73][4200/5005] lr: 1.000e-03, eta: 3:19:29, time: 0.087, data_time: 0.033, memory: 1125, loss: 1.3719 2023-01-04 00:41:27,878 - mmcls - INFO - Epoch [73][4300/5005] lr: 1.000e-03, eta: 3:19:21, time: 0.092, data_time: 0.052, memory: 1125, loss: 1.3945 2023-01-04 00:41:36,822 - mmcls - INFO - Epoch [73][4400/5005] lr: 1.000e-03, eta: 3:19:12, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.4001 2023-01-04 00:41:45,648 - mmcls - INFO - Epoch [73][4500/5005] lr: 1.000e-03, eta: 3:19:03, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3974 2023-01-04 00:41:54,723 - mmcls - INFO - Epoch [73][4600/5005] lr: 1.000e-03, eta: 3:18:54, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.4292 2023-01-04 00:42:03,413 - mmcls - INFO - Epoch [73][4700/5005] lr: 1.000e-03, eta: 3:18:45, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.4011 2023-01-04 00:42:11,979 - mmcls - INFO - Epoch [73][4800/5005] lr: 1.000e-03, eta: 3:18:36, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3826 2023-01-04 00:42:20,994 - mmcls - INFO - Epoch [73][4900/5005] lr: 1.000e-03, eta: 3:18:28, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.4084 2023-01-04 00:42:29,890 - mmcls - INFO - Epoch [73][5000/5005] lr: 1.000e-03, eta: 3:18:19, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.4025 2023-01-04 00:42:52,529 - mmcls - INFO - Epoch(val) [73][196] accuracy_top-1: 69.5740, accuracy_top-5: 89.0040 2023-01-04 00:43:03,654 - mmcls - INFO - Epoch [74][100/5005] lr: 1.000e-03, eta: 3:18:10, time: 0.111, data_time: 0.058, memory: 1125, loss: 1.4043 2023-01-04 00:43:12,505 - mmcls - INFO - Epoch [74][200/5005] lr: 1.000e-03, eta: 3:18:02, time: 0.089, data_time: 0.049, memory: 1125, loss: 1.3879 2023-01-04 00:43:21,560 - mmcls - INFO - Epoch [74][300/5005] lr: 1.000e-03, eta: 3:17:53, time: 0.091, data_time: 0.031, memory: 1125, loss: 1.3886 2023-01-04 00:43:30,502 - mmcls - INFO - Epoch [74][400/5005] lr: 1.000e-03, eta: 3:17:44, time: 0.089, data_time: 0.048, memory: 1125, loss: 1.3952 2023-01-04 00:43:39,056 - mmcls - INFO - Epoch [74][500/5005] lr: 1.000e-03, eta: 3:17:35, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3846 2023-01-04 00:43:47,996 - mmcls - INFO - Epoch [74][600/5005] lr: 1.000e-03, eta: 3:17:27, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3889 2023-01-04 00:43:56,833 - mmcls - INFO - Epoch [74][700/5005] lr: 1.000e-03, eta: 3:17:18, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.3912 2023-01-04 00:44:05,467 - mmcls - INFO - Epoch [74][800/5005] lr: 1.000e-03, eta: 3:17:09, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4111 2023-01-04 00:44:14,145 - mmcls - INFO - Epoch [74][900/5005] lr: 1.000e-03, eta: 3:17:00, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4092 2023-01-04 00:44:22,765 - mmcls - INFO - Epoch [74][1000/5005] lr: 1.000e-03, eta: 3:16:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4032 2023-01-04 00:44:31,814 - mmcls - INFO - Epoch [74][1100/5005] lr: 1.000e-03, eta: 3:16:42, time: 0.090, data_time: 0.027, memory: 1125, loss: 1.3746 2023-01-04 00:44:40,439 - mmcls - INFO - Epoch [74][1200/5005] lr: 1.000e-03, eta: 3:16:34, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3749 2023-01-04 00:44:49,532 - mmcls - INFO - Epoch [74][1300/5005] lr: 1.000e-03, eta: 3:16:25, time: 0.091, data_time: 0.033, memory: 1125, loss: 1.4032 2023-01-04 00:44:58,121 - mmcls - INFO - Epoch [74][1400/5005] lr: 1.000e-03, eta: 3:16:16, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3722 2023-01-04 00:45:07,215 - mmcls - INFO - Epoch [74][1500/5005] lr: 1.000e-03, eta: 3:16:07, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.3889 2023-01-04 00:45:15,666 - mmcls - INFO - Epoch [74][1600/5005] lr: 1.000e-03, eta: 3:15:58, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.4006 2023-01-04 00:45:24,222 - mmcls - INFO - Epoch [74][1700/5005] lr: 1.000e-03, eta: 3:15:49, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3943 2023-01-04 00:45:33,240 - mmcls - INFO - Epoch [74][1800/5005] lr: 1.000e-03, eta: 3:15:41, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3676 2023-01-04 00:45:42,579 - mmcls - INFO - Epoch [74][1900/5005] lr: 1.000e-03, eta: 3:15:32, time: 0.093, data_time: 0.003, memory: 1125, loss: 1.4084 2023-01-04 00:45:51,282 - mmcls - INFO - Epoch [74][2000/5005] lr: 1.000e-03, eta: 3:15:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3934 2023-01-04 00:45:59,920 - mmcls - INFO - Epoch [74][2100/5005] lr: 1.000e-03, eta: 3:15:14, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.4030 2023-01-04 00:46:08,677 - mmcls - INFO - Epoch [74][2200/5005] lr: 1.000e-03, eta: 3:15:06, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.4113 2023-01-04 00:46:17,465 - mmcls - INFO - Epoch [74][2300/5005] lr: 1.000e-03, eta: 3:14:57, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4018 2023-01-04 00:46:26,448 - mmcls - INFO - Epoch [74][2400/5005] lr: 1.000e-03, eta: 3:14:48, time: 0.090, data_time: 0.027, memory: 1125, loss: 1.3933 2023-01-04 00:46:35,302 - mmcls - INFO - Epoch [74][2500/5005] lr: 1.000e-03, eta: 3:14:39, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.4128 2023-01-04 00:46:43,896 - mmcls - INFO - Epoch [74][2600/5005] lr: 1.000e-03, eta: 3:14:30, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3947 2023-01-04 00:46:52,830 - mmcls - INFO - Epoch [74][2700/5005] lr: 1.000e-03, eta: 3:14:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3683 2023-01-04 00:47:01,628 - mmcls - INFO - Epoch [74][2800/5005] lr: 1.000e-03, eta: 3:14:13, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.4068 2023-01-04 00:47:10,220 - mmcls - INFO - Epoch [74][2900/5005] lr: 1.000e-03, eta: 3:14:04, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3811 2023-01-04 00:47:18,770 - mmcls - INFO - Epoch [74][3000/5005] lr: 1.000e-03, eta: 3:13:55, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.4055 2023-01-04 00:47:27,345 - mmcls - INFO - Epoch [74][3100/5005] lr: 1.000e-03, eta: 3:13:46, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3970 2023-01-04 00:47:35,878 - mmcls - INFO - Epoch [74][3200/5005] lr: 1.000e-03, eta: 3:13:37, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3836 2023-01-04 00:47:44,460 - mmcls - INFO - Epoch [74][3300/5005] lr: 1.000e-03, eta: 3:13:28, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3935 2023-01-04 00:47:53,285 - mmcls - INFO - Epoch [74][3400/5005] lr: 1.000e-03, eta: 3:13:19, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.4117 2023-01-04 00:48:01,609 - mmcls - INFO - Epoch [74][3500/5005] lr: 1.000e-03, eta: 3:13:11, time: 0.083, data_time: 0.005, memory: 1125, loss: 1.3724 2023-01-04 00:48:10,455 - mmcls - INFO - Epoch [74][3600/5005] lr: 1.000e-03, eta: 3:13:02, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.4029 2023-01-04 00:48:19,318 - mmcls - INFO - Epoch [74][3700/5005] lr: 1.000e-03, eta: 3:12:53, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.4238 2023-01-04 00:48:27,943 - mmcls - INFO - Epoch [74][3800/5005] lr: 1.000e-03, eta: 3:12:44, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3870 2023-01-04 00:48:36,489 - mmcls - INFO - Epoch [74][3900/5005] lr: 1.000e-03, eta: 3:12:35, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.4024 2023-01-04 00:48:45,157 - mmcls - INFO - Epoch [74][4000/5005] lr: 1.000e-03, eta: 3:12:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3748 2023-01-04 00:48:53,966 - mmcls - INFO - Epoch [74][4100/5005] lr: 1.000e-03, eta: 3:12:18, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.4155 2023-01-04 00:49:02,447 - mmcls - INFO - Epoch [74][4200/5005] lr: 1.000e-03, eta: 3:12:09, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.3929 2023-01-04 00:49:11,283 - mmcls - INFO - Epoch [74][4300/5005] lr: 1.000e-03, eta: 3:12:00, time: 0.088, data_time: 0.037, memory: 1125, loss: 1.4015 2023-01-04 00:49:20,038 - mmcls - INFO - Epoch [74][4400/5005] lr: 1.000e-03, eta: 3:11:51, time: 0.088, data_time: 0.036, memory: 1125, loss: 1.3841 2023-01-04 00:49:28,777 - mmcls - INFO - Epoch [74][4500/5005] lr: 1.000e-03, eta: 3:11:42, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.4074 2023-01-04 00:49:37,479 - mmcls - INFO - Epoch [74][4600/5005] lr: 1.000e-03, eta: 3:11:33, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4044 2023-01-04 00:49:46,176 - mmcls - INFO - Epoch [74][4700/5005] lr: 1.000e-03, eta: 3:11:24, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4131 2023-01-04 00:49:54,748 - mmcls - INFO - Epoch [74][4800/5005] lr: 1.000e-03, eta: 3:11:16, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4035 2023-01-04 00:50:03,618 - mmcls - INFO - Epoch [74][4900/5005] lr: 1.000e-03, eta: 3:11:07, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3928 2023-01-04 00:50:12,349 - mmcls - INFO - Epoch [74][5000/5005] lr: 1.000e-03, eta: 3:10:58, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3907 2023-01-04 00:50:34,898 - mmcls - INFO - Epoch(val) [74][196] accuracy_top-1: 69.5300, accuracy_top-5: 89.0200 2023-01-04 00:50:46,217 - mmcls - INFO - Epoch [75][100/5005] lr: 1.000e-03, eta: 3:10:49, time: 0.113, data_time: 0.043, memory: 1125, loss: 1.3996 2023-01-04 00:50:55,021 - mmcls - INFO - Epoch [75][200/5005] lr: 1.000e-03, eta: 3:10:41, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3744 2023-01-04 00:51:03,890 - mmcls - INFO - Epoch [75][300/5005] lr: 1.000e-03, eta: 3:10:32, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.3827 2023-01-04 00:51:12,692 - mmcls - INFO - Epoch [75][400/5005] lr: 1.000e-03, eta: 3:10:23, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3952 2023-01-04 00:51:21,626 - mmcls - INFO - Epoch [75][500/5005] lr: 1.000e-03, eta: 3:10:14, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.3981 2023-01-04 00:51:30,294 - mmcls - INFO - Epoch [75][600/5005] lr: 1.000e-03, eta: 3:10:05, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.4064 2023-01-04 00:51:38,970 - mmcls - INFO - Epoch [75][700/5005] lr: 1.000e-03, eta: 3:09:57, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3584 2023-01-04 00:51:47,614 - mmcls - INFO - Epoch [75][800/5005] lr: 1.000e-03, eta: 3:09:48, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.4032 2023-01-04 00:51:56,271 - mmcls - INFO - Epoch [75][900/5005] lr: 1.000e-03, eta: 3:09:39, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3904 2023-01-04 00:52:04,834 - mmcls - INFO - Epoch [75][1000/5005] lr: 1.000e-03, eta: 3:09:30, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3913 2023-01-04 00:52:13,611 - mmcls - INFO - Epoch [75][1100/5005] lr: 1.000e-03, eta: 3:09:21, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3983 2023-01-04 00:52:22,202 - mmcls - INFO - Epoch [75][1200/5005] lr: 1.000e-03, eta: 3:09:12, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3900 2023-01-04 00:52:31,215 - mmcls - INFO - Epoch [75][1300/5005] lr: 1.000e-03, eta: 3:09:04, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.3981 2023-01-04 00:52:39,953 - mmcls - INFO - Epoch [75][1400/5005] lr: 1.000e-03, eta: 3:08:55, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3891 2023-01-04 00:52:48,783 - mmcls - INFO - Epoch [75][1500/5005] lr: 1.000e-03, eta: 3:08:46, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3876 2023-01-04 00:52:57,310 - mmcls - INFO - Epoch [75][1600/5005] lr: 1.000e-03, eta: 3:08:37, time: 0.085, data_time: 0.034, memory: 1125, loss: 1.3790 2023-01-04 00:53:06,097 - mmcls - INFO - Epoch [75][1700/5005] lr: 1.000e-03, eta: 3:08:28, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3905 2023-01-04 00:53:14,618 - mmcls - INFO - Epoch [75][1800/5005] lr: 1.000e-03, eta: 3:08:19, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3835 2023-01-04 00:53:23,387 - mmcls - INFO - Epoch [75][1900/5005] lr: 1.000e-03, eta: 3:08:10, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4019 2023-01-04 00:53:32,077 - mmcls - INFO - Epoch [75][2000/5005] lr: 1.000e-03, eta: 3:08:02, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.4176 2023-01-04 00:53:40,784 - mmcls - INFO - Epoch [75][2100/5005] lr: 1.000e-03, eta: 3:07:53, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3767 2023-01-04 00:53:49,573 - mmcls - INFO - Epoch [75][2200/5005] lr: 1.000e-03, eta: 3:07:44, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3901 2023-01-04 00:53:58,240 - mmcls - INFO - Epoch [75][2300/5005] lr: 1.000e-03, eta: 3:07:35, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.3839 2023-01-04 00:54:06,931 - mmcls - INFO - Epoch [75][2400/5005] lr: 1.000e-03, eta: 3:07:26, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3720 2023-01-04 00:54:15,328 - mmcls - INFO - Epoch [75][2500/5005] lr: 1.000e-03, eta: 3:07:17, time: 0.084, data_time: 0.026, memory: 1125, loss: 1.4072 2023-01-04 00:54:24,238 - mmcls - INFO - Epoch [75][2600/5005] lr: 1.000e-03, eta: 3:07:09, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.4011 2023-01-04 00:54:33,206 - mmcls - INFO - Epoch [75][2700/5005] lr: 1.000e-03, eta: 3:07:00, time: 0.090, data_time: 0.041, memory: 1125, loss: 1.4160 2023-01-04 00:54:41,625 - mmcls - INFO - Epoch [75][2800/5005] lr: 1.000e-03, eta: 3:06:51, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.4059 2023-01-04 00:54:50,290 - mmcls - INFO - Epoch [75][2900/5005] lr: 1.000e-03, eta: 3:06:42, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.4014 2023-01-04 00:54:59,241 - mmcls - INFO - Epoch [75][3000/5005] lr: 1.000e-03, eta: 3:06:33, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4011 2023-01-04 00:55:08,245 - mmcls - INFO - Epoch [75][3100/5005] lr: 1.000e-03, eta: 3:06:25, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.4028 2023-01-04 00:55:16,967 - mmcls - INFO - Epoch [75][3200/5005] lr: 1.000e-03, eta: 3:06:16, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3858 2023-01-04 00:55:25,512 - mmcls - INFO - Epoch [75][3300/5005] lr: 1.000e-03, eta: 3:06:07, time: 0.085, data_time: 0.017, memory: 1125, loss: 1.3953 2023-01-04 00:55:34,598 - mmcls - INFO - Epoch [75][3400/5005] lr: 1.000e-03, eta: 3:05:58, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.3817 2023-01-04 00:55:43,220 - mmcls - INFO - Epoch [75][3500/5005] lr: 1.000e-03, eta: 3:05:49, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3886 2023-01-04 00:55:52,111 - mmcls - INFO - Epoch [75][3600/5005] lr: 1.000e-03, eta: 3:05:40, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.3985 2023-01-04 00:56:00,945 - mmcls - INFO - Epoch [75][3700/5005] lr: 1.000e-03, eta: 3:05:32, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.4066 2023-01-04 00:56:09,462 - mmcls - INFO - Epoch [75][3800/5005] lr: 1.000e-03, eta: 3:05:23, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3988 2023-01-04 00:56:18,162 - mmcls - INFO - Epoch [75][3900/5005] lr: 1.000e-03, eta: 3:05:14, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3904 2023-01-04 00:56:26,987 - mmcls - INFO - Epoch [75][4000/5005] lr: 1.000e-03, eta: 3:05:05, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.3905 2023-01-04 00:56:35,871 - mmcls - INFO - Epoch [75][4100/5005] lr: 1.000e-03, eta: 3:04:56, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.4010 2023-01-04 00:56:44,590 - mmcls - INFO - Epoch [75][4200/5005] lr: 1.000e-03, eta: 3:04:47, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.4099 2023-01-04 00:56:53,546 - mmcls - INFO - Epoch [75][4300/5005] lr: 1.000e-03, eta: 3:04:39, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.4000 2023-01-04 00:57:01,846 - mmcls - INFO - Epoch [75][4400/5005] lr: 1.000e-03, eta: 3:04:30, time: 0.083, data_time: 0.033, memory: 1125, loss: 1.3866 2023-01-04 00:57:10,526 - mmcls - INFO - Epoch [75][4500/5005] lr: 1.000e-03, eta: 3:04:21, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3975 2023-01-04 00:57:19,426 - mmcls - INFO - Epoch [75][4600/5005] lr: 1.000e-03, eta: 3:04:12, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.3797 2023-01-04 00:57:28,226 - mmcls - INFO - Epoch [75][4700/5005] lr: 1.000e-03, eta: 3:04:03, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.4004 2023-01-04 00:57:37,185 - mmcls - INFO - Epoch [75][4800/5005] lr: 1.000e-03, eta: 3:03:55, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3881 2023-01-04 00:57:45,586 - mmcls - INFO - Epoch [75][4900/5005] lr: 1.000e-03, eta: 3:03:46, time: 0.084, data_time: 0.032, memory: 1125, loss: 1.4026 2023-01-04 00:57:54,264 - mmcls - INFO - Epoch [75][5000/5005] lr: 1.000e-03, eta: 3:03:37, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3744 2023-01-04 00:58:16,782 - mmcls - INFO - Epoch(val) [75][196] accuracy_top-1: 69.5940, accuracy_top-5: 89.1280 2023-01-04 00:58:27,778 - mmcls - INFO - Epoch [76][100/5005] lr: 1.000e-03, eta: 3:03:28, time: 0.110, data_time: 0.044, memory: 1125, loss: 1.3781 2023-01-04 00:58:36,626 - mmcls - INFO - Epoch [76][200/5005] lr: 1.000e-03, eta: 3:03:19, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3736 2023-01-04 00:58:45,125 - mmcls - INFO - Epoch [76][300/5005] lr: 1.000e-03, eta: 3:03:10, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.3996 2023-01-04 00:58:53,773 - mmcls - INFO - Epoch [76][400/5005] lr: 1.000e-03, eta: 3:03:02, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3830 2023-01-04 00:59:02,213 - mmcls - INFO - Epoch [76][500/5005] lr: 1.000e-03, eta: 3:02:53, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.3811 2023-01-04 00:59:10,820 - mmcls - INFO - Epoch [76][600/5005] lr: 1.000e-03, eta: 3:02:44, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.4011 2023-01-04 00:59:19,749 - mmcls - INFO - Epoch [76][700/5005] lr: 1.000e-03, eta: 3:02:35, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3906 2023-01-04 00:59:28,184 - mmcls - INFO - Epoch [76][800/5005] lr: 1.000e-03, eta: 3:02:26, time: 0.084, data_time: 0.003, memory: 1125, loss: 1.3668 2023-01-04 00:59:37,035 - mmcls - INFO - Epoch [76][900/5005] lr: 1.000e-03, eta: 3:02:17, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.3748 2023-01-04 00:59:45,650 - mmcls - INFO - Epoch [76][1000/5005] lr: 1.000e-03, eta: 3:02:08, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3819 2023-01-04 00:59:54,447 - mmcls - INFO - Epoch [76][1100/5005] lr: 1.000e-03, eta: 3:02:00, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.4008 2023-01-04 01:00:03,334 - mmcls - INFO - Epoch [76][1200/5005] lr: 1.000e-03, eta: 3:01:51, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.4007 2023-01-04 01:00:11,943 - mmcls - INFO - Epoch [76][1300/5005] lr: 1.000e-03, eta: 3:01:42, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.4064 2023-01-04 01:00:20,795 - mmcls - INFO - Epoch [76][1400/5005] lr: 1.000e-03, eta: 3:01:33, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3757 2023-01-04 01:00:29,650 - mmcls - INFO - Epoch [76][1500/5005] lr: 1.000e-03, eta: 3:01:24, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3907 2023-01-04 01:00:38,139 - mmcls - INFO - Epoch [76][1600/5005] lr: 1.000e-03, eta: 3:01:15, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3829 2023-01-04 01:00:47,228 - mmcls - INFO - Epoch [76][1700/5005] lr: 1.000e-03, eta: 3:01:07, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3860 2023-01-04 01:00:55,904 - mmcls - INFO - Epoch [76][1800/5005] lr: 1.000e-03, eta: 3:00:58, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.3923 2023-01-04 01:01:04,904 - mmcls - INFO - Epoch [76][1900/5005] lr: 1.000e-03, eta: 3:00:49, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.4100 2023-01-04 01:01:13,467 - mmcls - INFO - Epoch [76][2000/5005] lr: 1.000e-03, eta: 3:00:40, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3979 2023-01-04 01:01:22,225 - mmcls - INFO - Epoch [76][2100/5005] lr: 1.000e-03, eta: 3:00:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.4034 2023-01-04 01:01:31,014 - mmcls - INFO - Epoch [76][2200/5005] lr: 1.000e-03, eta: 3:00:23, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3805 2023-01-04 01:01:39,964 - mmcls - INFO - Epoch [76][2300/5005] lr: 1.000e-03, eta: 3:00:14, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3670 2023-01-04 01:01:48,835 - mmcls - INFO - Epoch [76][2400/5005] lr: 1.000e-03, eta: 3:00:05, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3737 2023-01-04 01:01:57,540 - mmcls - INFO - Epoch [76][2500/5005] lr: 1.000e-03, eta: 2:59:56, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3735 2023-01-04 01:02:06,363 - mmcls - INFO - Epoch [76][2600/5005] lr: 1.000e-03, eta: 2:59:47, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3725 2023-01-04 01:02:15,091 - mmcls - INFO - Epoch [76][2700/5005] lr: 1.000e-03, eta: 2:59:39, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3998 2023-01-04 01:02:23,957 - mmcls - INFO - Epoch [76][2800/5005] lr: 1.000e-03, eta: 2:59:30, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3879 2023-01-04 01:02:32,474 - mmcls - INFO - Epoch [76][2900/5005] lr: 1.000e-03, eta: 2:59:21, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4074 2023-01-04 01:02:41,156 - mmcls - INFO - Epoch [76][3000/5005] lr: 1.000e-03, eta: 2:59:12, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.4001 2023-01-04 01:02:50,104 - mmcls - INFO - Epoch [76][3100/5005] lr: 1.000e-03, eta: 2:59:03, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.3917 2023-01-04 01:02:58,855 - mmcls - INFO - Epoch [76][3200/5005] lr: 1.000e-03, eta: 2:58:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3923 2023-01-04 01:03:07,517 - mmcls - INFO - Epoch [76][3300/5005] lr: 1.000e-03, eta: 2:58:46, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3945 2023-01-04 01:03:16,209 - mmcls - INFO - Epoch [76][3400/5005] lr: 1.000e-03, eta: 2:58:37, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3895 2023-01-04 01:03:25,000 - mmcls - INFO - Epoch [76][3500/5005] lr: 1.000e-03, eta: 2:58:28, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.4054 2023-01-04 01:03:33,795 - mmcls - INFO - Epoch [76][3600/5005] lr: 1.000e-03, eta: 2:58:19, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3600 2023-01-04 01:03:42,499 - mmcls - INFO - Epoch [76][3700/5005] lr: 1.000e-03, eta: 2:58:10, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3750 2023-01-04 01:03:51,370 - mmcls - INFO - Epoch [76][3800/5005] lr: 1.000e-03, eta: 2:58:02, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3819 2023-01-04 01:04:00,023 - mmcls - INFO - Epoch [76][3900/5005] lr: 1.000e-03, eta: 2:57:53, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3955 2023-01-04 01:04:08,660 - mmcls - INFO - Epoch [76][4000/5005] lr: 1.000e-03, eta: 2:57:44, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.4033 2023-01-04 01:04:17,333 - mmcls - INFO - Epoch [76][4100/5005] lr: 1.000e-03, eta: 2:57:35, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3842 2023-01-04 01:04:26,266 - mmcls - INFO - Epoch [76][4200/5005] lr: 1.000e-03, eta: 2:57:26, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.4029 2023-01-04 01:04:35,060 - mmcls - INFO - Epoch [76][4300/5005] lr: 1.000e-03, eta: 2:57:17, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3926 2023-01-04 01:04:43,607 - mmcls - INFO - Epoch [76][4400/5005] lr: 1.000e-03, eta: 2:57:09, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3674 2023-01-04 01:04:52,341 - mmcls - INFO - Epoch [76][4500/5005] lr: 1.000e-03, eta: 2:57:00, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.4075 2023-01-04 01:05:01,006 - mmcls - INFO - Epoch [76][4600/5005] lr: 1.000e-03, eta: 2:56:51, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3971 2023-01-04 01:05:09,577 - mmcls - INFO - Epoch [76][4700/5005] lr: 1.000e-03, eta: 2:56:42, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.3836 2023-01-04 01:05:18,584 - mmcls - INFO - Epoch [76][4800/5005] lr: 1.000e-03, eta: 2:56:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3908 2023-01-04 01:05:27,205 - mmcls - INFO - Epoch [76][4900/5005] lr: 1.000e-03, eta: 2:56:24, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3994 2023-01-04 01:05:35,851 - mmcls - INFO - Epoch [76][5000/5005] lr: 1.000e-03, eta: 2:56:16, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3761 2023-01-04 01:05:58,658 - mmcls - INFO - Epoch(val) [76][196] accuracy_top-1: 69.3840, accuracy_top-5: 89.1060 2023-01-04 01:06:10,138 - mmcls - INFO - Epoch [77][100/5005] lr: 1.000e-03, eta: 2:56:07, time: 0.115, data_time: 0.062, memory: 1125, loss: 1.3614 2023-01-04 01:06:18,867 - mmcls - INFO - Epoch [77][200/5005] lr: 1.000e-03, eta: 2:55:58, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.3716 2023-01-04 01:06:27,742 - mmcls - INFO - Epoch [77][300/5005] lr: 1.000e-03, eta: 2:55:49, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.3931 2023-01-04 01:06:36,408 - mmcls - INFO - Epoch [77][400/5005] lr: 1.000e-03, eta: 2:55:41, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3771 2023-01-04 01:06:45,101 - mmcls - INFO - Epoch [77][500/5005] lr: 1.000e-03, eta: 2:55:32, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.3983 2023-01-04 01:06:53,890 - mmcls - INFO - Epoch [77][600/5005] lr: 1.000e-03, eta: 2:55:23, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.3559 2023-01-04 01:07:02,480 - mmcls - INFO - Epoch [77][700/5005] lr: 1.000e-03, eta: 2:55:14, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3961 2023-01-04 01:07:11,196 - mmcls - INFO - Epoch [77][800/5005] lr: 1.000e-03, eta: 2:55:05, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.4010 2023-01-04 01:07:19,976 - mmcls - INFO - Epoch [77][900/5005] lr: 1.000e-03, eta: 2:54:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3880 2023-01-04 01:07:28,678 - mmcls - INFO - Epoch [77][1000/5005] lr: 1.000e-03, eta: 2:54:48, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3854 2023-01-04 01:07:37,204 - mmcls - INFO - Epoch [77][1100/5005] lr: 1.000e-03, eta: 2:54:39, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.3866 2023-01-04 01:07:46,017 - mmcls - INFO - Epoch [77][1200/5005] lr: 1.000e-03, eta: 2:54:30, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3745 2023-01-04 01:07:54,684 - mmcls - INFO - Epoch [77][1300/5005] lr: 1.000e-03, eta: 2:54:21, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3961 2023-01-04 01:08:03,393 - mmcls - INFO - Epoch [77][1400/5005] lr: 1.000e-03, eta: 2:54:12, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.4041 2023-01-04 01:08:11,995 - mmcls - INFO - Epoch [77][1500/5005] lr: 1.000e-03, eta: 2:54:03, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3936 2023-01-04 01:08:20,717 - mmcls - INFO - Epoch [77][1600/5005] lr: 1.000e-03, eta: 2:53:55, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3773 2023-01-04 01:08:29,517 - mmcls - INFO - Epoch [77][1700/5005] lr: 1.000e-03, eta: 2:53:46, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3851 2023-01-04 01:08:38,407 - mmcls - INFO - Epoch [77][1800/5005] lr: 1.000e-03, eta: 2:53:37, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.4049 2023-01-04 01:08:46,969 - mmcls - INFO - Epoch [77][1900/5005] lr: 1.000e-03, eta: 2:53:28, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3732 2023-01-04 01:08:55,605 - mmcls - INFO - Epoch [77][2000/5005] lr: 1.000e-03, eta: 2:53:19, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3848 2023-01-04 01:09:04,377 - mmcls - INFO - Epoch [77][2100/5005] lr: 1.000e-03, eta: 2:53:10, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.3839 2023-01-04 01:09:13,080 - mmcls - INFO - Epoch [77][2200/5005] lr: 1.000e-03, eta: 2:53:02, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3979 2023-01-04 01:09:22,005 - mmcls - INFO - Epoch [77][2300/5005] lr: 1.000e-03, eta: 2:52:53, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.3879 2023-01-04 01:09:30,609 - mmcls - INFO - Epoch [77][2400/5005] lr: 1.000e-03, eta: 2:52:44, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3756 2023-01-04 01:09:39,082 - mmcls - INFO - Epoch [77][2500/5005] lr: 1.000e-03, eta: 2:52:35, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.4166 2023-01-04 01:09:47,821 - mmcls - INFO - Epoch [77][2600/5005] lr: 1.000e-03, eta: 2:52:26, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.3751 2023-01-04 01:09:56,632 - mmcls - INFO - Epoch [77][2700/5005] lr: 1.000e-03, eta: 2:52:17, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3853 2023-01-04 01:10:05,793 - mmcls - INFO - Epoch [77][2800/5005] lr: 1.000e-03, eta: 2:52:09, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.3829 2023-01-04 01:10:14,385 - mmcls - INFO - Epoch [77][2900/5005] lr: 1.000e-03, eta: 2:52:00, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.4069 2023-01-04 01:10:23,519 - mmcls - INFO - Epoch [77][3000/5005] lr: 1.000e-03, eta: 2:51:51, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.3952 2023-01-04 01:10:32,287 - mmcls - INFO - Epoch [77][3100/5005] lr: 1.000e-03, eta: 2:51:42, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3762 2023-01-04 01:10:41,174 - mmcls - INFO - Epoch [77][3200/5005] lr: 1.000e-03, eta: 2:51:34, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.4081 2023-01-04 01:10:49,943 - mmcls - INFO - Epoch [77][3300/5005] lr: 1.000e-03, eta: 2:51:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3882 2023-01-04 01:10:58,689 - mmcls - INFO - Epoch [77][3400/5005] lr: 1.000e-03, eta: 2:51:16, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.3716 2023-01-04 01:11:07,226 - mmcls - INFO - Epoch [77][3500/5005] lr: 1.000e-03, eta: 2:51:07, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.3999 2023-01-04 01:11:15,787 - mmcls - INFO - Epoch [77][3600/5005] lr: 1.000e-03, eta: 2:50:58, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.4059 2023-01-04 01:11:24,406 - mmcls - INFO - Epoch [77][3700/5005] lr: 1.000e-03, eta: 2:50:49, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3952 2023-01-04 01:11:33,364 - mmcls - INFO - Epoch [77][3800/5005] lr: 1.000e-03, eta: 2:50:40, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.3865 2023-01-04 01:11:41,983 - mmcls - INFO - Epoch [77][3900/5005] lr: 1.000e-03, eta: 2:50:32, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.3702 2023-01-04 01:11:50,923 - mmcls - INFO - Epoch [77][4000/5005] lr: 1.000e-03, eta: 2:50:23, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3785 2023-01-04 01:11:59,812 - mmcls - INFO - Epoch [77][4100/5005] lr: 1.000e-03, eta: 2:50:14, time: 0.089, data_time: 0.052, memory: 1125, loss: 1.3922 2023-01-04 01:12:08,703 - mmcls - INFO - Epoch [77][4200/5005] lr: 1.000e-03, eta: 2:50:05, time: 0.089, data_time: 0.035, memory: 1125, loss: 1.4032 2023-01-04 01:12:17,341 - mmcls - INFO - Epoch [77][4300/5005] lr: 1.000e-03, eta: 2:49:56, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3965 2023-01-04 01:12:26,242 - mmcls - INFO - Epoch [77][4400/5005] lr: 1.000e-03, eta: 2:49:48, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.3798 2023-01-04 01:12:34,757 - mmcls - INFO - Epoch [77][4500/5005] lr: 1.000e-03, eta: 2:49:39, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.4060 2023-01-04 01:12:43,360 - mmcls - INFO - Epoch [77][4600/5005] lr: 1.000e-03, eta: 2:49:30, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.3994 2023-01-04 01:12:52,194 - mmcls - INFO - Epoch [77][4700/5005] lr: 1.000e-03, eta: 2:49:21, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.3903 2023-01-04 01:13:00,864 - mmcls - INFO - Epoch [77][4800/5005] lr: 1.000e-03, eta: 2:49:12, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3891 2023-01-04 01:13:09,729 - mmcls - INFO - Epoch [77][4900/5005] lr: 1.000e-03, eta: 2:49:03, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.3890 2023-01-04 01:13:18,327 - mmcls - INFO - Epoch [77][5000/5005] lr: 1.000e-03, eta: 2:48:55, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.3987 2023-01-04 01:13:41,108 - mmcls - INFO - Epoch(val) [77][196] accuracy_top-1: 69.5880, accuracy_top-5: 89.1660 2023-01-04 01:13:52,578 - mmcls - INFO - Epoch [78][100/5005] lr: 1.000e-03, eta: 2:48:46, time: 0.114, data_time: 0.056, memory: 1125, loss: 1.3618 2023-01-04 01:14:01,005 - mmcls - INFO - Epoch [78][200/5005] lr: 1.000e-03, eta: 2:48:37, time: 0.084, data_time: 0.008, memory: 1125, loss: 1.3848 2023-01-04 01:14:09,733 - mmcls - INFO - Epoch [78][300/5005] lr: 1.000e-03, eta: 2:48:28, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3849 2023-01-04 01:14:18,824 - mmcls - INFO - Epoch [78][400/5005] lr: 1.000e-03, eta: 2:48:20, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.3801 2023-01-04 01:14:27,384 - mmcls - INFO - Epoch [78][500/5005] lr: 1.000e-03, eta: 2:48:11, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3837 2023-01-04 01:14:35,847 - mmcls - INFO - Epoch [78][600/5005] lr: 1.000e-03, eta: 2:48:02, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3799 2023-01-04 01:14:44,603 - mmcls - INFO - Epoch [78][700/5005] lr: 1.000e-03, eta: 2:47:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3717 2023-01-04 01:14:53,326 - mmcls - INFO - Epoch [78][800/5005] lr: 1.000e-03, eta: 2:47:44, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3887 2023-01-04 01:15:01,829 - mmcls - INFO - Epoch [78][900/5005] lr: 1.000e-03, eta: 2:47:35, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3949 2023-01-04 01:15:10,588 - mmcls - INFO - Epoch [78][1000/5005] lr: 1.000e-03, eta: 2:47:26, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3986 2023-01-04 01:15:19,369 - mmcls - INFO - Epoch [78][1100/5005] lr: 1.000e-03, eta: 2:47:18, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.3770 2023-01-04 01:15:27,796 - mmcls - INFO - Epoch [78][1200/5005] lr: 1.000e-03, eta: 2:47:09, time: 0.084, data_time: 0.019, memory: 1125, loss: 1.3627 2023-01-04 01:15:36,631 - mmcls - INFO - Epoch [78][1300/5005] lr: 1.000e-03, eta: 2:47:00, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3965 2023-01-04 01:15:45,457 - mmcls - INFO - Epoch [78][1400/5005] lr: 1.000e-03, eta: 2:46:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3790 2023-01-04 01:15:54,312 - mmcls - INFO - Epoch [78][1500/5005] lr: 1.000e-03, eta: 2:46:42, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3850 2023-01-04 01:16:02,994 - mmcls - INFO - Epoch [78][1600/5005] lr: 1.000e-03, eta: 2:46:33, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3760 2023-01-04 01:16:11,571 - mmcls - INFO - Epoch [78][1700/5005] lr: 1.000e-03, eta: 2:46:25, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3763 2023-01-04 01:16:20,236 - mmcls - INFO - Epoch [78][1800/5005] lr: 1.000e-03, eta: 2:46:16, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3735 2023-01-04 01:16:28,960 - mmcls - INFO - Epoch [78][1900/5005] lr: 1.000e-03, eta: 2:46:07, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3867 2023-01-04 01:16:38,081 - mmcls - INFO - Epoch [78][2000/5005] lr: 1.000e-03, eta: 2:45:58, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3948 2023-01-04 01:16:46,749 - mmcls - INFO - Epoch [78][2100/5005] lr: 1.000e-03, eta: 2:45:49, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3626 2023-01-04 01:16:55,473 - mmcls - INFO - Epoch [78][2200/5005] lr: 1.000e-03, eta: 2:45:41, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3920 2023-01-04 01:17:04,340 - mmcls - INFO - Epoch [78][2300/5005] lr: 1.000e-03, eta: 2:45:32, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3889 2023-01-04 01:17:13,058 - mmcls - INFO - Epoch [78][2400/5005] lr: 1.000e-03, eta: 2:45:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3797 2023-01-04 01:17:21,893 - mmcls - INFO - Epoch [78][2500/5005] lr: 1.000e-03, eta: 2:45:14, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.4030 2023-01-04 01:17:31,198 - mmcls - INFO - Epoch [78][2600/5005] lr: 1.000e-03, eta: 2:45:05, time: 0.093, data_time: 0.018, memory: 1125, loss: 1.3728 2023-01-04 01:17:40,249 - mmcls - INFO - Epoch [78][2700/5005] lr: 1.000e-03, eta: 2:44:57, time: 0.091, data_time: 0.045, memory: 1125, loss: 1.4155 2023-01-04 01:17:49,056 - mmcls - INFO - Epoch [78][2800/5005] lr: 1.000e-03, eta: 2:44:48, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3952 2023-01-04 01:17:57,851 - mmcls - INFO - Epoch [78][2900/5005] lr: 1.000e-03, eta: 2:44:39, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3913 2023-01-04 01:18:06,457 - mmcls - INFO - Epoch [78][3000/5005] lr: 1.000e-03, eta: 2:44:30, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.4000 2023-01-04 01:18:15,284 - mmcls - INFO - Epoch [78][3100/5005] lr: 1.000e-03, eta: 2:44:21, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3830 2023-01-04 01:18:24,351 - mmcls - INFO - Epoch [78][3200/5005] lr: 1.000e-03, eta: 2:44:13, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.3979 2023-01-04 01:18:33,229 - mmcls - INFO - Epoch [78][3300/5005] lr: 1.000e-03, eta: 2:44:04, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3975 2023-01-04 01:18:42,372 - mmcls - INFO - Epoch [78][3400/5005] lr: 1.000e-03, eta: 2:43:55, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3784 2023-01-04 01:18:51,065 - mmcls - INFO - Epoch [78][3500/5005] lr: 1.000e-03, eta: 2:43:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3985 2023-01-04 01:18:59,640 - mmcls - INFO - Epoch [78][3600/5005] lr: 1.000e-03, eta: 2:43:38, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3876 2023-01-04 01:19:08,342 - mmcls - INFO - Epoch [78][3700/5005] lr: 1.000e-03, eta: 2:43:29, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.4042 2023-01-04 01:19:16,923 - mmcls - INFO - Epoch [78][3800/5005] lr: 1.000e-03, eta: 2:43:20, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3721 2023-01-04 01:19:25,786 - mmcls - INFO - Epoch [78][3900/5005] lr: 1.000e-03, eta: 2:43:11, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.3876 2023-01-04 01:19:34,416 - mmcls - INFO - Epoch [78][4000/5005] lr: 1.000e-03, eta: 2:43:02, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3951 2023-01-04 01:19:42,845 - mmcls - INFO - Epoch [78][4100/5005] lr: 1.000e-03, eta: 2:42:53, time: 0.084, data_time: 0.007, memory: 1125, loss: 1.4003 2023-01-04 01:19:51,467 - mmcls - INFO - Epoch [78][4200/5005] lr: 1.000e-03, eta: 2:42:44, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3781 2023-01-04 01:20:00,208 - mmcls - INFO - Epoch [78][4300/5005] lr: 1.000e-03, eta: 2:42:36, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3701 2023-01-04 01:20:08,923 - mmcls - INFO - Epoch [78][4400/5005] lr: 1.000e-03, eta: 2:42:27, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.4029 2023-01-04 01:20:17,488 - mmcls - INFO - Epoch [78][4500/5005] lr: 1.000e-03, eta: 2:42:18, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3878 2023-01-04 01:20:26,241 - mmcls - INFO - Epoch [78][4600/5005] lr: 1.000e-03, eta: 2:42:09, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3825 2023-01-04 01:20:34,845 - mmcls - INFO - Epoch [78][4700/5005] lr: 1.000e-03, eta: 2:42:00, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.3735 2023-01-04 01:20:43,466 - mmcls - INFO - Epoch [78][4800/5005] lr: 1.000e-03, eta: 2:41:51, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3898 2023-01-04 01:20:52,525 - mmcls - INFO - Epoch [78][4900/5005] lr: 1.000e-03, eta: 2:41:43, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.3773 2023-01-04 01:21:01,294 - mmcls - INFO - Epoch [78][5000/5005] lr: 1.000e-03, eta: 2:41:34, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.4091 2023-01-04 01:21:23,848 - mmcls - INFO - Epoch(val) [78][196] accuracy_top-1: 69.6160, accuracy_top-5: 89.1220 2023-01-04 01:21:35,117 - mmcls - INFO - Epoch [79][100/5005] lr: 1.000e-03, eta: 2:41:25, time: 0.112, data_time: 0.035, memory: 1125, loss: 1.3906 2023-01-04 01:21:43,980 - mmcls - INFO - Epoch [79][200/5005] lr: 1.000e-03, eta: 2:41:16, time: 0.089, data_time: 0.030, memory: 1125, loss: 1.3753 2023-01-04 01:21:52,759 - mmcls - INFO - Epoch [79][300/5005] lr: 1.000e-03, eta: 2:41:08, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3841 2023-01-04 01:22:01,709 - mmcls - INFO - Epoch [79][400/5005] lr: 1.000e-03, eta: 2:40:59, time: 0.090, data_time: 0.027, memory: 1125, loss: 1.3674 2023-01-04 01:22:10,524 - mmcls - INFO - Epoch [79][500/5005] lr: 1.000e-03, eta: 2:40:50, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3897 2023-01-04 01:22:19,526 - mmcls - INFO - Epoch [79][600/5005] lr: 1.000e-03, eta: 2:40:41, time: 0.090, data_time: 0.030, memory: 1125, loss: 1.3786 2023-01-04 01:22:28,292 - mmcls - INFO - Epoch [79][700/5005] lr: 1.000e-03, eta: 2:40:32, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3747 2023-01-04 01:22:36,743 - mmcls - INFO - Epoch [79][800/5005] lr: 1.000e-03, eta: 2:40:24, time: 0.084, data_time: 0.026, memory: 1125, loss: 1.3784 2023-01-04 01:22:45,565 - mmcls - INFO - Epoch [79][900/5005] lr: 1.000e-03, eta: 2:40:15, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.3611 2023-01-04 01:22:54,341 - mmcls - INFO - Epoch [79][1000/5005] lr: 1.000e-03, eta: 2:40:06, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3919 2023-01-04 01:23:03,080 - mmcls - INFO - Epoch [79][1100/5005] lr: 1.000e-03, eta: 2:39:57, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3815 2023-01-04 01:23:11,871 - mmcls - INFO - Epoch [79][1200/5005] lr: 1.000e-03, eta: 2:39:48, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3792 2023-01-04 01:23:20,672 - mmcls - INFO - Epoch [79][1300/5005] lr: 1.000e-03, eta: 2:39:39, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3780 2023-01-04 01:23:29,299 - mmcls - INFO - Epoch [79][1400/5005] lr: 1.000e-03, eta: 2:39:31, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3809 2023-01-04 01:23:38,259 - mmcls - INFO - Epoch [79][1500/5005] lr: 1.000e-03, eta: 2:39:22, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3849 2023-01-04 01:23:47,186 - mmcls - INFO - Epoch [79][1600/5005] lr: 1.000e-03, eta: 2:39:13, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3790 2023-01-04 01:23:55,888 - mmcls - INFO - Epoch [79][1700/5005] lr: 1.000e-03, eta: 2:39:04, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3989 2023-01-04 01:24:04,491 - mmcls - INFO - Epoch [79][1800/5005] lr: 1.000e-03, eta: 2:38:55, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3875 2023-01-04 01:24:13,197 - mmcls - INFO - Epoch [79][1900/5005] lr: 1.000e-03, eta: 2:38:47, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3637 2023-01-04 01:24:21,768 - mmcls - INFO - Epoch [79][2000/5005] lr: 1.000e-03, eta: 2:38:38, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3903 2023-01-04 01:24:30,276 - mmcls - INFO - Epoch [79][2100/5005] lr: 1.000e-03, eta: 2:38:29, time: 0.085, data_time: 0.027, memory: 1125, loss: 1.4100 2023-01-04 01:24:39,483 - mmcls - INFO - Epoch [79][2200/5005] lr: 1.000e-03, eta: 2:38:20, time: 0.092, data_time: 0.017, memory: 1125, loss: 1.3833 2023-01-04 01:24:47,978 - mmcls - INFO - Epoch [79][2300/5005] lr: 1.000e-03, eta: 2:38:11, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3915 2023-01-04 01:24:56,888 - mmcls - INFO - Epoch [79][2400/5005] lr: 1.000e-03, eta: 2:38:02, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.3733 2023-01-04 01:25:05,460 - mmcls - INFO - Epoch [79][2500/5005] lr: 1.000e-03, eta: 2:37:54, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3822 2023-01-04 01:25:14,456 - mmcls - INFO - Epoch [79][2600/5005] lr: 1.000e-03, eta: 2:37:45, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.3896 2023-01-04 01:25:23,207 - mmcls - INFO - Epoch [79][2700/5005] lr: 1.000e-03, eta: 2:37:36, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.4042 2023-01-04 01:25:31,841 - mmcls - INFO - Epoch [79][2800/5005] lr: 1.000e-03, eta: 2:37:27, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.3697 2023-01-04 01:25:40,630 - mmcls - INFO - Epoch [79][2900/5005] lr: 1.000e-03, eta: 2:37:18, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.3949 2023-01-04 01:25:49,263 - mmcls - INFO - Epoch [79][3000/5005] lr: 1.000e-03, eta: 2:37:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3729 2023-01-04 01:25:57,912 - mmcls - INFO - Epoch [79][3100/5005] lr: 1.000e-03, eta: 2:37:01, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3744 2023-01-04 01:26:06,337 - mmcls - INFO - Epoch [79][3200/5005] lr: 1.000e-03, eta: 2:36:52, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.3861 2023-01-04 01:26:15,344 - mmcls - INFO - Epoch [79][3300/5005] lr: 1.000e-03, eta: 2:36:43, time: 0.090, data_time: 0.035, memory: 1125, loss: 1.3835 2023-01-04 01:26:23,987 - mmcls - INFO - Epoch [79][3400/5005] lr: 1.000e-03, eta: 2:36:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3698 2023-01-04 01:26:32,790 - mmcls - INFO - Epoch [79][3500/5005] lr: 1.000e-03, eta: 2:36:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3922 2023-01-04 01:26:41,306 - mmcls - INFO - Epoch [79][3600/5005] lr: 1.000e-03, eta: 2:36:16, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.4098 2023-01-04 01:26:50,236 - mmcls - INFO - Epoch [79][3700/5005] lr: 1.000e-03, eta: 2:36:08, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.4026 2023-01-04 01:26:58,888 - mmcls - INFO - Epoch [79][3800/5005] lr: 1.000e-03, eta: 2:35:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3893 2023-01-04 01:27:07,762 - mmcls - INFO - Epoch [79][3900/5005] lr: 1.000e-03, eta: 2:35:50, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.3976 2023-01-04 01:27:16,466 - mmcls - INFO - Epoch [79][4000/5005] lr: 1.000e-03, eta: 2:35:41, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3968 2023-01-04 01:27:25,188 - mmcls - INFO - Epoch [79][4100/5005] lr: 1.000e-03, eta: 2:35:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3924 2023-01-04 01:27:33,606 - mmcls - INFO - Epoch [79][4200/5005] lr: 1.000e-03, eta: 2:35:23, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.3955 2023-01-04 01:27:42,732 - mmcls - INFO - Epoch [79][4300/5005] lr: 1.000e-03, eta: 2:35:15, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.3952 2023-01-04 01:27:51,334 - mmcls - INFO - Epoch [79][4400/5005] lr: 1.000e-03, eta: 2:35:06, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3742 2023-01-04 01:28:00,334 - mmcls - INFO - Epoch [79][4500/5005] lr: 1.000e-03, eta: 2:34:57, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.3901 2023-01-04 01:28:09,197 - mmcls - INFO - Epoch [79][4600/5005] lr: 1.000e-03, eta: 2:34:48, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3808 2023-01-04 01:28:17,833 - mmcls - INFO - Epoch [79][4700/5005] lr: 1.000e-03, eta: 2:34:40, time: 0.086, data_time: 0.036, memory: 1125, loss: 1.3788 2023-01-04 01:28:26,450 - mmcls - INFO - Epoch [79][4800/5005] lr: 1.000e-03, eta: 2:34:31, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3681 2023-01-04 01:28:35,598 - mmcls - INFO - Epoch [79][4900/5005] lr: 1.000e-03, eta: 2:34:22, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.3964 2023-01-04 01:28:44,515 - mmcls - INFO - Epoch [79][5000/5005] lr: 1.000e-03, eta: 2:34:13, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3873 2023-01-04 01:29:07,125 - mmcls - INFO - Epoch(val) [79][196] accuracy_top-1: 69.5740, accuracy_top-5: 89.0760 2023-01-04 01:29:18,192 - mmcls - INFO - Epoch [80][100/5005] lr: 1.000e-03, eta: 2:34:04, time: 0.110, data_time: 0.036, memory: 1125, loss: 1.3396 2023-01-04 01:29:26,714 - mmcls - INFO - Epoch [80][200/5005] lr: 1.000e-03, eta: 2:33:56, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3831 2023-01-04 01:29:35,243 - mmcls - INFO - Epoch [80][300/5005] lr: 1.000e-03, eta: 2:33:47, time: 0.085, data_time: 0.015, memory: 1125, loss: 1.3728 2023-01-04 01:29:43,818 - mmcls - INFO - Epoch [80][400/5005] lr: 1.000e-03, eta: 2:33:38, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3697 2023-01-04 01:29:52,888 - mmcls - INFO - Epoch [80][500/5005] lr: 1.000e-03, eta: 2:33:29, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.3596 2023-01-04 01:30:02,313 - mmcls - INFO - Epoch [80][600/5005] lr: 1.000e-03, eta: 2:33:20, time: 0.094, data_time: 0.049, memory: 1125, loss: 1.3694 2023-01-04 01:30:10,927 - mmcls - INFO - Epoch [80][700/5005] lr: 1.000e-03, eta: 2:33:12, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3678 2023-01-04 01:30:19,587 - mmcls - INFO - Epoch [80][800/5005] lr: 1.000e-03, eta: 2:33:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3750 2023-01-04 01:30:28,300 - mmcls - INFO - Epoch [80][900/5005] lr: 1.000e-03, eta: 2:32:54, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3680 2023-01-04 01:30:37,094 - mmcls - INFO - Epoch [80][1000/5005] lr: 1.000e-03, eta: 2:32:45, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3862 2023-01-04 01:30:46,092 - mmcls - INFO - Epoch [80][1100/5005] lr: 1.000e-03, eta: 2:32:36, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.3797 2023-01-04 01:30:55,039 - mmcls - INFO - Epoch [80][1200/5005] lr: 1.000e-03, eta: 2:32:28, time: 0.089, data_time: 0.043, memory: 1125, loss: 1.3615 2023-01-04 01:31:03,709 - mmcls - INFO - Epoch [80][1300/5005] lr: 1.000e-03, eta: 2:32:19, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.3931 2023-01-04 01:31:12,667 - mmcls - INFO - Epoch [80][1400/5005] lr: 1.000e-03, eta: 2:32:10, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.3638 2023-01-04 01:31:21,553 - mmcls - INFO - Epoch [80][1500/5005] lr: 1.000e-03, eta: 2:32:01, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3739 2023-01-04 01:31:30,477 - mmcls - INFO - Epoch [80][1600/5005] lr: 1.000e-03, eta: 2:31:52, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3902 2023-01-04 01:31:39,067 - mmcls - INFO - Epoch [80][1700/5005] lr: 1.000e-03, eta: 2:31:44, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3830 2023-01-04 01:31:47,785 - mmcls - INFO - Epoch [80][1800/5005] lr: 1.000e-03, eta: 2:31:35, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3799 2023-01-04 01:31:56,693 - mmcls - INFO - Epoch [80][1900/5005] lr: 1.000e-03, eta: 2:31:26, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.4019 2023-01-04 01:32:05,538 - mmcls - INFO - Epoch [80][2000/5005] lr: 1.000e-03, eta: 2:31:17, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3786 2023-01-04 01:32:14,251 - mmcls - INFO - Epoch [80][2100/5005] lr: 1.000e-03, eta: 2:31:08, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3702 2023-01-04 01:32:22,936 - mmcls - INFO - Epoch [80][2200/5005] lr: 1.000e-03, eta: 2:30:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3769 2023-01-04 01:32:31,796 - mmcls - INFO - Epoch [80][2300/5005] lr: 1.000e-03, eta: 2:30:51, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3570 2023-01-04 01:32:40,759 - mmcls - INFO - Epoch [80][2400/5005] lr: 1.000e-03, eta: 2:30:42, time: 0.090, data_time: 0.038, memory: 1125, loss: 1.3918 2023-01-04 01:32:50,151 - mmcls - INFO - Epoch [80][2500/5005] lr: 1.000e-03, eta: 2:30:33, time: 0.094, data_time: 0.027, memory: 1125, loss: 1.3794 2023-01-04 01:32:58,752 - mmcls - INFO - Epoch [80][2600/5005] lr: 1.000e-03, eta: 2:30:24, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3827 2023-01-04 01:33:07,356 - mmcls - INFO - Epoch [80][2700/5005] lr: 1.000e-03, eta: 2:30:16, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3727 2023-01-04 01:33:16,042 - mmcls - INFO - Epoch [80][2800/5005] lr: 1.000e-03, eta: 2:30:07, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3789 2023-01-04 01:33:25,551 - mmcls - INFO - Epoch [80][2900/5005] lr: 1.000e-03, eta: 2:29:58, time: 0.095, data_time: 0.037, memory: 1125, loss: 1.3896 2023-01-04 01:33:34,181 - mmcls - INFO - Epoch [80][3000/5005] lr: 1.000e-03, eta: 2:29:49, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3803 2023-01-04 01:33:42,841 - mmcls - INFO - Epoch [80][3100/5005] lr: 1.000e-03, eta: 2:29:40, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3665 2023-01-04 01:33:51,605 - mmcls - INFO - Epoch [80][3200/5005] lr: 1.000e-03, eta: 2:29:32, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.3693 2023-01-04 01:34:00,105 - mmcls - INFO - Epoch [80][3300/5005] lr: 1.000e-03, eta: 2:29:23, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.3835 2023-01-04 01:34:08,952 - mmcls - INFO - Epoch [80][3400/5005] lr: 1.000e-03, eta: 2:29:14, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3901 2023-01-04 01:34:17,579 - mmcls - INFO - Epoch [80][3500/5005] lr: 1.000e-03, eta: 2:29:05, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3763 2023-01-04 01:34:26,337 - mmcls - INFO - Epoch [80][3600/5005] lr: 1.000e-03, eta: 2:28:56, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3808 2023-01-04 01:34:34,801 - mmcls - INFO - Epoch [80][3700/5005] lr: 1.000e-03, eta: 2:28:47, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3844 2023-01-04 01:34:43,590 - mmcls - INFO - Epoch [80][3800/5005] lr: 1.000e-03, eta: 2:28:39, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3933 2023-01-04 01:34:52,361 - mmcls - INFO - Epoch [80][3900/5005] lr: 1.000e-03, eta: 2:28:30, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3822 2023-01-04 01:35:00,937 - mmcls - INFO - Epoch [80][4000/5005] lr: 1.000e-03, eta: 2:28:21, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.4133 2023-01-04 01:35:09,742 - mmcls - INFO - Epoch [80][4100/5005] lr: 1.000e-03, eta: 2:28:12, time: 0.088, data_time: 0.039, memory: 1125, loss: 1.3725 2023-01-04 01:35:18,450 - mmcls - INFO - Epoch [80][4200/5005] lr: 1.000e-03, eta: 2:28:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3751 2023-01-04 01:35:27,501 - mmcls - INFO - Epoch [80][4300/5005] lr: 1.000e-03, eta: 2:27:55, time: 0.091, data_time: 0.011, memory: 1125, loss: 1.3760 2023-01-04 01:35:36,198 - mmcls - INFO - Epoch [80][4400/5005] lr: 1.000e-03, eta: 2:27:46, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3821 2023-01-04 01:35:45,035 - mmcls - INFO - Epoch [80][4500/5005] lr: 1.000e-03, eta: 2:27:37, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4074 2023-01-04 01:35:53,589 - mmcls - INFO - Epoch [80][4600/5005] lr: 1.000e-03, eta: 2:27:28, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3836 2023-01-04 01:36:02,385 - mmcls - INFO - Epoch [80][4700/5005] lr: 1.000e-03, eta: 2:27:19, time: 0.088, data_time: 0.038, memory: 1125, loss: 1.3909 2023-01-04 01:36:11,211 - mmcls - INFO - Epoch [80][4800/5005] lr: 1.000e-03, eta: 2:27:10, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.3972 2023-01-04 01:36:19,850 - mmcls - INFO - Epoch [80][4900/5005] lr: 1.000e-03, eta: 2:27:02, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3867 2023-01-04 01:36:28,551 - mmcls - INFO - Epoch [80][5000/5005] lr: 1.000e-03, eta: 2:26:53, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3867 2023-01-04 01:36:51,125 - mmcls - INFO - Epoch(val) [80][196] accuracy_top-1: 69.6340, accuracy_top-5: 89.1060 2023-01-04 01:37:02,095 - mmcls - INFO - Epoch [81][100/5005] lr: 1.000e-03, eta: 2:26:44, time: 0.109, data_time: 0.057, memory: 1125, loss: 1.3826 2023-01-04 01:37:10,766 - mmcls - INFO - Epoch [81][200/5005] lr: 1.000e-03, eta: 2:26:35, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3789 2023-01-04 01:37:19,462 - mmcls - INFO - Epoch [81][300/5005] lr: 1.000e-03, eta: 2:26:26, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3555 2023-01-04 01:37:28,160 - mmcls - INFO - Epoch [81][400/5005] lr: 1.000e-03, eta: 2:26:17, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3728 2023-01-04 01:37:36,757 - mmcls - INFO - Epoch [81][500/5005] lr: 1.000e-03, eta: 2:26:09, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3833 2023-01-04 01:37:45,691 - mmcls - INFO - Epoch [81][600/5005] lr: 1.000e-03, eta: 2:26:00, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3493 2023-01-04 01:37:54,509 - mmcls - INFO - Epoch [81][700/5005] lr: 1.000e-03, eta: 2:25:51, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3658 2023-01-04 01:38:02,995 - mmcls - INFO - Epoch [81][800/5005] lr: 1.000e-03, eta: 2:25:42, time: 0.085, data_time: 0.029, memory: 1125, loss: 1.3569 2023-01-04 01:38:12,126 - mmcls - INFO - Epoch [81][900/5005] lr: 1.000e-03, eta: 2:25:33, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.3799 2023-01-04 01:38:21,055 - mmcls - INFO - Epoch [81][1000/5005] lr: 1.000e-03, eta: 2:25:25, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3777 2023-01-04 01:38:29,567 - mmcls - INFO - Epoch [81][1100/5005] lr: 1.000e-03, eta: 2:25:16, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3518 2023-01-04 01:38:38,128 - mmcls - INFO - Epoch [81][1200/5005] lr: 1.000e-03, eta: 2:25:07, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3733 2023-01-04 01:38:46,746 - mmcls - INFO - Epoch [81][1300/5005] lr: 1.000e-03, eta: 2:24:58, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3726 2023-01-04 01:38:55,624 - mmcls - INFO - Epoch [81][1400/5005] lr: 1.000e-03, eta: 2:24:49, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3834 2023-01-04 01:39:04,390 - mmcls - INFO - Epoch [81][1500/5005] lr: 1.000e-03, eta: 2:24:40, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.3852 2023-01-04 01:39:13,092 - mmcls - INFO - Epoch [81][1600/5005] lr: 1.000e-03, eta: 2:24:32, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.4001 2023-01-04 01:39:21,780 - mmcls - INFO - Epoch [81][1700/5005] lr: 1.000e-03, eta: 2:24:23, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3757 2023-01-04 01:39:30,561 - mmcls - INFO - Epoch [81][1800/5005] lr: 1.000e-03, eta: 2:24:14, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3585 2023-01-04 01:39:39,030 - mmcls - INFO - Epoch [81][1900/5005] lr: 1.000e-03, eta: 2:24:05, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3799 2023-01-04 01:39:47,785 - mmcls - INFO - Epoch [81][2000/5005] lr: 1.000e-03, eta: 2:23:56, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3881 2023-01-04 01:39:56,450 - mmcls - INFO - Epoch [81][2100/5005] lr: 1.000e-03, eta: 2:23:47, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3632 2023-01-04 01:40:05,446 - mmcls - INFO - Epoch [81][2200/5005] lr: 1.000e-03, eta: 2:23:39, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.3603 2023-01-04 01:40:14,211 - mmcls - INFO - Epoch [81][2300/5005] lr: 1.000e-03, eta: 2:23:30, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3807 2023-01-04 01:40:23,384 - mmcls - INFO - Epoch [81][2400/5005] lr: 1.000e-03, eta: 2:23:21, time: 0.092, data_time: 0.025, memory: 1125, loss: 1.3558 2023-01-04 01:40:32,026 - mmcls - INFO - Epoch [81][2500/5005] lr: 1.000e-03, eta: 2:23:12, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3860 2023-01-04 01:40:40,803 - mmcls - INFO - Epoch [81][2600/5005] lr: 1.000e-03, eta: 2:23:03, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3759 2023-01-04 01:40:49,634 - mmcls - INFO - Epoch [81][2700/5005] lr: 1.000e-03, eta: 2:22:55, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3914 2023-01-04 01:40:58,150 - mmcls - INFO - Epoch [81][2800/5005] lr: 1.000e-03, eta: 2:22:46, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.3830 2023-01-04 01:41:07,081 - mmcls - INFO - Epoch [81][2900/5005] lr: 1.000e-03, eta: 2:22:37, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3773 2023-01-04 01:41:15,847 - mmcls - INFO - Epoch [81][3000/5005] lr: 1.000e-03, eta: 2:22:28, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3727 2023-01-04 01:41:24,643 - mmcls - INFO - Epoch [81][3100/5005] lr: 1.000e-03, eta: 2:22:19, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.4089 2023-01-04 01:41:33,225 - mmcls - INFO - Epoch [81][3200/5005] lr: 1.000e-03, eta: 2:22:11, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.3739 2023-01-04 01:41:41,878 - mmcls - INFO - Epoch [81][3300/5005] lr: 1.000e-03, eta: 2:22:02, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3940 2023-01-04 01:41:50,768 - mmcls - INFO - Epoch [81][3400/5005] lr: 1.000e-03, eta: 2:21:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3874 2023-01-04 01:41:59,746 - mmcls - INFO - Epoch [81][3500/5005] lr: 1.000e-03, eta: 2:21:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3669 2023-01-04 01:42:08,365 - mmcls - INFO - Epoch [81][3600/5005] lr: 1.000e-03, eta: 2:21:35, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3751 2023-01-04 01:42:17,217 - mmcls - INFO - Epoch [81][3700/5005] lr: 1.000e-03, eta: 2:21:27, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.3960 2023-01-04 01:42:25,886 - mmcls - INFO - Epoch [81][3800/5005] lr: 1.000e-03, eta: 2:21:18, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3931 2023-01-04 01:42:34,443 - mmcls - INFO - Epoch [81][3900/5005] lr: 1.000e-03, eta: 2:21:09, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.3774 2023-01-04 01:42:43,357 - mmcls - INFO - Epoch [81][4000/5005] lr: 1.000e-03, eta: 2:21:00, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3590 2023-01-04 01:42:51,894 - mmcls - INFO - Epoch [81][4100/5005] lr: 1.000e-03, eta: 2:20:51, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3669 2023-01-04 01:43:00,647 - mmcls - INFO - Epoch [81][4200/5005] lr: 1.000e-03, eta: 2:20:42, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3934 2023-01-04 01:43:09,384 - mmcls - INFO - Epoch [81][4300/5005] lr: 1.000e-03, eta: 2:20:34, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3904 2023-01-04 01:43:18,502 - mmcls - INFO - Epoch [81][4400/5005] lr: 1.000e-03, eta: 2:20:25, time: 0.091, data_time: 0.015, memory: 1125, loss: 1.3877 2023-01-04 01:43:27,018 - mmcls - INFO - Epoch [81][4500/5005] lr: 1.000e-03, eta: 2:20:16, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.3802 2023-01-04 01:43:35,750 - mmcls - INFO - Epoch [81][4600/5005] lr: 1.000e-03, eta: 2:20:07, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3665 2023-01-04 01:43:44,945 - mmcls - INFO - Epoch [81][4700/5005] lr: 1.000e-03, eta: 2:19:58, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.3805 2023-01-04 01:43:53,528 - mmcls - INFO - Epoch [81][4800/5005] lr: 1.000e-03, eta: 2:19:50, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.4001 2023-01-04 01:44:02,557 - mmcls - INFO - Epoch [81][4900/5005] lr: 1.000e-03, eta: 2:19:41, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.3781 2023-01-04 01:44:11,398 - mmcls - INFO - Epoch [81][5000/5005] lr: 1.000e-03, eta: 2:19:32, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3738 2023-01-04 01:44:34,199 - mmcls - INFO - Epoch(val) [81][196] accuracy_top-1: 69.8320, accuracy_top-5: 89.2200 2023-01-04 01:44:45,465 - mmcls - INFO - Epoch [82][100/5005] lr: 1.000e-03, eta: 2:19:23, time: 0.112, data_time: 0.044, memory: 1125, loss: 1.3682 2023-01-04 01:44:53,993 - mmcls - INFO - Epoch [82][200/5005] lr: 1.000e-03, eta: 2:19:14, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3890 2023-01-04 01:45:02,966 - mmcls - INFO - Epoch [82][300/5005] lr: 1.000e-03, eta: 2:19:06, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.3592 2023-01-04 01:45:11,694 - mmcls - INFO - Epoch [82][400/5005] lr: 1.000e-03, eta: 2:18:57, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3742 2023-01-04 01:45:20,387 - mmcls - INFO - Epoch [82][500/5005] lr: 1.000e-03, eta: 2:18:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3682 2023-01-04 01:45:29,204 - mmcls - INFO - Epoch [82][600/5005] lr: 1.000e-03, eta: 2:18:39, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3528 2023-01-04 01:45:37,762 - mmcls - INFO - Epoch [82][700/5005] lr: 1.000e-03, eta: 2:18:30, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3794 2023-01-04 01:45:46,367 - mmcls - INFO - Epoch [82][800/5005] lr: 1.000e-03, eta: 2:18:21, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3662 2023-01-04 01:45:55,333 - mmcls - INFO - Epoch [82][900/5005] lr: 1.000e-03, eta: 2:18:13, time: 0.090, data_time: 0.015, memory: 1125, loss: 1.3831 2023-01-04 01:46:04,396 - mmcls - INFO - Epoch [82][1000/5005] lr: 1.000e-03, eta: 2:18:04, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.3865 2023-01-04 01:46:13,061 - mmcls - INFO - Epoch [82][1100/5005] lr: 1.000e-03, eta: 2:17:55, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.3686 2023-01-04 01:46:21,536 - mmcls - INFO - Epoch [82][1200/5005] lr: 1.000e-03, eta: 2:17:46, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3552 2023-01-04 01:46:30,394 - mmcls - INFO - Epoch [82][1300/5005] lr: 1.000e-03, eta: 2:17:37, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3748 2023-01-04 01:46:38,964 - mmcls - INFO - Epoch [82][1400/5005] lr: 1.000e-03, eta: 2:17:29, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3670 2023-01-04 01:46:48,087 - mmcls - INFO - Epoch [82][1500/5005] lr: 1.000e-03, eta: 2:17:20, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.3758 2023-01-04 01:46:56,862 - mmcls - INFO - Epoch [82][1600/5005] lr: 1.000e-03, eta: 2:17:11, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3767 2023-01-04 01:47:05,532 - mmcls - INFO - Epoch [82][1700/5005] lr: 1.000e-03, eta: 2:17:02, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3736 2023-01-04 01:47:14,414 - mmcls - INFO - Epoch [82][1800/5005] lr: 1.000e-03, eta: 2:16:53, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3880 2023-01-04 01:47:23,167 - mmcls - INFO - Epoch [82][1900/5005] lr: 1.000e-03, eta: 2:16:45, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3834 2023-01-04 01:47:31,813 - mmcls - INFO - Epoch [82][2000/5005] lr: 1.000e-03, eta: 2:16:36, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3895 2023-01-04 01:47:40,531 - mmcls - INFO - Epoch [82][2100/5005] lr: 1.000e-03, eta: 2:16:27, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3726 2023-01-04 01:47:49,292 - mmcls - INFO - Epoch [82][2200/5005] lr: 1.000e-03, eta: 2:16:18, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3667 2023-01-04 01:47:57,898 - mmcls - INFO - Epoch [82][2300/5005] lr: 1.000e-03, eta: 2:16:09, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3754 2023-01-04 01:48:06,694 - mmcls - INFO - Epoch [82][2400/5005] lr: 1.000e-03, eta: 2:16:00, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3937 2023-01-04 01:48:15,261 - mmcls - INFO - Epoch [82][2500/5005] lr: 1.000e-03, eta: 2:15:52, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3949 2023-01-04 01:48:23,795 - mmcls - INFO - Epoch [82][2600/5005] lr: 1.000e-03, eta: 2:15:43, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.3839 2023-01-04 01:48:32,518 - mmcls - INFO - Epoch [82][2700/5005] lr: 1.000e-03, eta: 2:15:34, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3718 2023-01-04 01:48:41,349 - mmcls - INFO - Epoch [82][2800/5005] lr: 1.000e-03, eta: 2:15:25, time: 0.088, data_time: 0.032, memory: 1125, loss: 1.3766 2023-01-04 01:48:50,152 - mmcls - INFO - Epoch [82][2900/5005] lr: 1.000e-03, eta: 2:15:16, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3735 2023-01-04 01:48:59,153 - mmcls - INFO - Epoch [82][3000/5005] lr: 1.000e-03, eta: 2:15:08, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.3784 2023-01-04 01:49:07,812 - mmcls - INFO - Epoch [82][3100/5005] lr: 1.000e-03, eta: 2:14:59, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3676 2023-01-04 01:49:16,841 - mmcls - INFO - Epoch [82][3200/5005] lr: 1.000e-03, eta: 2:14:50, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.3703 2023-01-04 01:49:25,976 - mmcls - INFO - Epoch [82][3300/5005] lr: 1.000e-03, eta: 2:14:41, time: 0.091, data_time: 0.023, memory: 1125, loss: 1.3774 2023-01-04 01:49:34,741 - mmcls - INFO - Epoch [82][3400/5005] lr: 1.000e-03, eta: 2:14:32, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3960 2023-01-04 01:49:43,527 - mmcls - INFO - Epoch [82][3500/5005] lr: 1.000e-03, eta: 2:14:24, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3781 2023-01-04 01:49:52,048 - mmcls - INFO - Epoch [82][3600/5005] lr: 1.000e-03, eta: 2:14:15, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.4049 2023-01-04 01:50:01,084 - mmcls - INFO - Epoch [82][3700/5005] lr: 1.000e-03, eta: 2:14:06, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3866 2023-01-04 01:50:09,512 - mmcls - INFO - Epoch [82][3800/5005] lr: 1.000e-03, eta: 2:13:57, time: 0.084, data_time: 0.001, memory: 1125, loss: 1.3671 2023-01-04 01:50:18,157 - mmcls - INFO - Epoch [82][3900/5005] lr: 1.000e-03, eta: 2:13:48, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3683 2023-01-04 01:50:27,284 - mmcls - INFO - Epoch [82][4000/5005] lr: 1.000e-03, eta: 2:13:40, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3739 2023-01-04 01:50:35,983 - mmcls - INFO - Epoch [82][4100/5005] lr: 1.000e-03, eta: 2:13:31, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3902 2023-01-04 01:50:44,770 - mmcls - INFO - Epoch [82][4200/5005] lr: 1.000e-03, eta: 2:13:22, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.3825 2023-01-04 01:50:53,593 - mmcls - INFO - Epoch [82][4300/5005] lr: 1.000e-03, eta: 2:13:13, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.3858 2023-01-04 01:51:02,396 - mmcls - INFO - Epoch [82][4400/5005] lr: 1.000e-03, eta: 2:13:04, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3743 2023-01-04 01:51:10,934 - mmcls - INFO - Epoch [82][4500/5005] lr: 1.000e-03, eta: 2:12:55, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.4040 2023-01-04 01:51:19,831 - mmcls - INFO - Epoch [82][4600/5005] lr: 1.000e-03, eta: 2:12:47, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3724 2023-01-04 01:51:28,666 - mmcls - INFO - Epoch [82][4700/5005] lr: 1.000e-03, eta: 2:12:38, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3669 2023-01-04 01:51:37,582 - mmcls - INFO - Epoch [82][4800/5005] lr: 1.000e-03, eta: 2:12:29, time: 0.089, data_time: 0.038, memory: 1125, loss: 1.3727 2023-01-04 01:51:46,385 - mmcls - INFO - Epoch [82][4900/5005] lr: 1.000e-03, eta: 2:12:20, time: 0.088, data_time: 0.032, memory: 1125, loss: 1.3619 2023-01-04 01:51:55,128 - mmcls - INFO - Epoch [82][5000/5005] lr: 1.000e-03, eta: 2:12:11, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3937 2023-01-04 01:52:17,876 - mmcls - INFO - Epoch(val) [82][196] accuracy_top-1: 69.7920, accuracy_top-5: 89.2960 2023-01-04 01:52:28,937 - mmcls - INFO - Epoch [83][100/5005] lr: 1.000e-03, eta: 2:12:03, time: 0.110, data_time: 0.047, memory: 1125, loss: 1.3740 2023-01-04 01:52:38,026 - mmcls - INFO - Epoch [83][200/5005] lr: 1.000e-03, eta: 2:11:54, time: 0.091, data_time: 0.042, memory: 1125, loss: 1.3467 2023-01-04 01:52:46,461 - mmcls - INFO - Epoch [83][300/5005] lr: 1.000e-03, eta: 2:11:45, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.3642 2023-01-04 01:52:55,517 - mmcls - INFO - Epoch [83][400/5005] lr: 1.000e-03, eta: 2:11:36, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.3667 2023-01-04 01:53:04,096 - mmcls - INFO - Epoch [83][500/5005] lr: 1.000e-03, eta: 2:11:27, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3390 2023-01-04 01:53:13,055 - mmcls - INFO - Epoch [83][600/5005] lr: 1.000e-03, eta: 2:11:19, time: 0.090, data_time: 0.042, memory: 1125, loss: 1.3509 2023-01-04 01:53:22,117 - mmcls - INFO - Epoch [83][700/5005] lr: 1.000e-03, eta: 2:11:10, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3771 2023-01-04 01:53:31,009 - mmcls - INFO - Epoch [83][800/5005] lr: 1.000e-03, eta: 2:11:01, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3732 2023-01-04 01:53:39,895 - mmcls - INFO - Epoch [83][900/5005] lr: 1.000e-03, eta: 2:10:52, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3715 2023-01-04 01:53:48,474 - mmcls - INFO - Epoch [83][1000/5005] lr: 1.000e-03, eta: 2:10:43, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3700 2023-01-04 01:53:57,086 - mmcls - INFO - Epoch [83][1100/5005] lr: 1.000e-03, eta: 2:10:35, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3547 2023-01-04 01:54:05,839 - mmcls - INFO - Epoch [83][1200/5005] lr: 1.000e-03, eta: 2:10:26, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3727 2023-01-04 01:54:14,337 - mmcls - INFO - Epoch [83][1300/5005] lr: 1.000e-03, eta: 2:10:17, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3796 2023-01-04 01:54:23,335 - mmcls - INFO - Epoch [83][1400/5005] lr: 1.000e-03, eta: 2:10:08, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.3821 2023-01-04 01:54:31,881 - mmcls - INFO - Epoch [83][1500/5005] lr: 1.000e-03, eta: 2:09:59, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3912 2023-01-04 01:54:41,054 - mmcls - INFO - Epoch [83][1600/5005] lr: 1.000e-03, eta: 2:09:51, time: 0.092, data_time: 0.004, memory: 1125, loss: 1.3843 2023-01-04 01:54:49,803 - mmcls - INFO - Epoch [83][1700/5005] lr: 1.000e-03, eta: 2:09:42, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3596 2023-01-04 01:54:58,342 - mmcls - INFO - Epoch [83][1800/5005] lr: 1.000e-03, eta: 2:09:33, time: 0.085, data_time: 0.026, memory: 1125, loss: 1.3803 2023-01-04 01:55:06,866 - mmcls - INFO - Epoch [83][1900/5005] lr: 1.000e-03, eta: 2:09:24, time: 0.085, data_time: 0.033, memory: 1125, loss: 1.3710 2023-01-04 01:55:15,638 - mmcls - INFO - Epoch [83][2000/5005] lr: 1.000e-03, eta: 2:09:15, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3728 2023-01-04 01:55:24,603 - mmcls - INFO - Epoch [83][2100/5005] lr: 1.000e-03, eta: 2:09:06, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3517 2023-01-04 01:55:33,293 - mmcls - INFO - Epoch [83][2200/5005] lr: 1.000e-03, eta: 2:08:58, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3705 2023-01-04 01:55:42,121 - mmcls - INFO - Epoch [83][2300/5005] lr: 1.000e-03, eta: 2:08:49, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3662 2023-01-04 01:55:51,157 - mmcls - INFO - Epoch [83][2400/5005] lr: 1.000e-03, eta: 2:08:40, time: 0.090, data_time: 0.033, memory: 1125, loss: 1.3736 2023-01-04 01:55:59,916 - mmcls - INFO - Epoch [83][2500/5005] lr: 1.000e-03, eta: 2:08:31, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3772 2023-01-04 01:56:08,959 - mmcls - INFO - Epoch [83][2600/5005] lr: 1.000e-03, eta: 2:08:22, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.3926 2023-01-04 01:56:17,782 - mmcls - INFO - Epoch [83][2700/5005] lr: 1.000e-03, eta: 2:08:14, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3669 2023-01-04 01:56:26,877 - mmcls - INFO - Epoch [83][2800/5005] lr: 1.000e-03, eta: 2:08:05, time: 0.091, data_time: 0.027, memory: 1125, loss: 1.3604 2023-01-04 01:56:35,461 - mmcls - INFO - Epoch [83][2900/5005] lr: 1.000e-03, eta: 2:07:56, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3554 2023-01-04 01:56:44,138 - mmcls - INFO - Epoch [83][3000/5005] lr: 1.000e-03, eta: 2:07:47, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3793 2023-01-04 01:56:53,018 - mmcls - INFO - Epoch [83][3100/5005] lr: 1.000e-03, eta: 2:07:38, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.3736 2023-01-04 01:57:01,791 - mmcls - INFO - Epoch [83][3200/5005] lr: 1.000e-03, eta: 2:07:30, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3718 2023-01-04 01:57:10,510 - mmcls - INFO - Epoch [83][3300/5005] lr: 1.000e-03, eta: 2:07:21, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3813 2023-01-04 01:57:19,293 - mmcls - INFO - Epoch [83][3400/5005] lr: 1.000e-03, eta: 2:07:12, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3672 2023-01-04 01:57:28,106 - mmcls - INFO - Epoch [83][3500/5005] lr: 1.000e-03, eta: 2:07:03, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.3679 2023-01-04 01:57:37,007 - mmcls - INFO - Epoch [83][3600/5005] lr: 1.000e-03, eta: 2:06:54, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3723 2023-01-04 01:57:46,233 - mmcls - INFO - Epoch [83][3700/5005] lr: 1.000e-03, eta: 2:06:46, time: 0.092, data_time: 0.018, memory: 1125, loss: 1.3749 2023-01-04 01:57:54,960 - mmcls - INFO - Epoch [83][3800/5005] lr: 1.000e-03, eta: 2:06:37, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3646 2023-01-04 01:58:03,428 - mmcls - INFO - Epoch [83][3900/5005] lr: 1.000e-03, eta: 2:06:28, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3778 2023-01-04 01:58:12,559 - mmcls - INFO - Epoch [83][4000/5005] lr: 1.000e-03, eta: 2:06:19, time: 0.091, data_time: 0.027, memory: 1125, loss: 1.3920 2023-01-04 01:58:21,061 - mmcls - INFO - Epoch [83][4100/5005] lr: 1.000e-03, eta: 2:06:10, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3817 2023-01-04 01:58:29,640 - mmcls - INFO - Epoch [83][4200/5005] lr: 1.000e-03, eta: 2:06:02, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.3779 2023-01-04 01:58:38,283 - mmcls - INFO - Epoch [83][4300/5005] lr: 1.000e-03, eta: 2:05:53, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3597 2023-01-04 01:58:47,009 - mmcls - INFO - Epoch [83][4400/5005] lr: 1.000e-03, eta: 2:05:44, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3721 2023-01-04 01:58:55,976 - mmcls - INFO - Epoch [83][4500/5005] lr: 1.000e-03, eta: 2:05:35, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3970 2023-01-04 01:59:04,524 - mmcls - INFO - Epoch [83][4600/5005] lr: 1.000e-03, eta: 2:05:26, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3808 2023-01-04 01:59:13,110 - mmcls - INFO - Epoch [83][4700/5005] lr: 1.000e-03, eta: 2:05:17, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3937 2023-01-04 01:59:22,058 - mmcls - INFO - Epoch [83][4800/5005] lr: 1.000e-03, eta: 2:05:09, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.3744 2023-01-04 01:59:31,323 - mmcls - INFO - Epoch [83][4900/5005] lr: 1.000e-03, eta: 2:05:00, time: 0.093, data_time: 0.010, memory: 1125, loss: 1.3624 2023-01-04 01:59:39,874 - mmcls - INFO - Epoch [83][5000/5005] lr: 1.000e-03, eta: 2:04:51, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3674 2023-01-04 02:00:02,433 - mmcls - INFO - Epoch(val) [83][196] accuracy_top-1: 69.5120, accuracy_top-5: 89.2240 2023-01-04 02:00:13,515 - mmcls - INFO - Epoch [84][100/5005] lr: 1.000e-03, eta: 2:04:42, time: 0.111, data_time: 0.063, memory: 1125, loss: 1.3772 2023-01-04 02:00:22,163 - mmcls - INFO - Epoch [84][200/5005] lr: 1.000e-03, eta: 2:04:33, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3572 2023-01-04 02:00:30,889 - mmcls - INFO - Epoch [84][300/5005] lr: 1.000e-03, eta: 2:04:25, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3786 2023-01-04 02:00:39,965 - mmcls - INFO - Epoch [84][400/5005] lr: 1.000e-03, eta: 2:04:16, time: 0.091, data_time: 0.031, memory: 1125, loss: 1.3372 2023-01-04 02:00:48,585 - mmcls - INFO - Epoch [84][500/5005] lr: 1.000e-03, eta: 2:04:07, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3498 2023-01-04 02:00:57,637 - mmcls - INFO - Epoch [84][600/5005] lr: 1.000e-03, eta: 2:03:58, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3803 2023-01-04 02:01:06,656 - mmcls - INFO - Epoch [84][700/5005] lr: 1.000e-03, eta: 2:03:49, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3715 2023-01-04 02:01:15,088 - mmcls - INFO - Epoch [84][800/5005] lr: 1.000e-03, eta: 2:03:41, time: 0.084, data_time: 0.018, memory: 1125, loss: 1.3513 2023-01-04 02:01:24,089 - mmcls - INFO - Epoch [84][900/5005] lr: 1.000e-03, eta: 2:03:32, time: 0.090, data_time: 0.038, memory: 1125, loss: 1.3522 2023-01-04 02:01:32,978 - mmcls - INFO - Epoch [84][1000/5005] lr: 1.000e-03, eta: 2:03:23, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3734 2023-01-04 02:01:41,805 - mmcls - INFO - Epoch [84][1100/5005] lr: 1.000e-03, eta: 2:03:14, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.3899 2023-01-04 02:01:51,064 - mmcls - INFO - Epoch [84][1200/5005] lr: 1.000e-03, eta: 2:03:05, time: 0.093, data_time: 0.024, memory: 1125, loss: 1.3676 2023-01-04 02:01:59,525 - mmcls - INFO - Epoch [84][1300/5005] lr: 1.000e-03, eta: 2:02:57, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.3909 2023-01-04 02:02:08,313 - mmcls - INFO - Epoch [84][1400/5005] lr: 1.000e-03, eta: 2:02:48, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.3723 2023-01-04 02:02:17,089 - mmcls - INFO - Epoch [84][1500/5005] lr: 1.000e-03, eta: 2:02:39, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3681 2023-01-04 02:02:25,656 - mmcls - INFO - Epoch [84][1600/5005] lr: 1.000e-03, eta: 2:02:30, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3780 2023-01-04 02:02:34,477 - mmcls - INFO - Epoch [84][1700/5005] lr: 1.000e-03, eta: 2:02:21, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3557 2023-01-04 02:02:42,949 - mmcls - INFO - Epoch [84][1800/5005] lr: 1.000e-03, eta: 2:02:12, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3606 2023-01-04 02:02:51,670 - mmcls - INFO - Epoch [84][1900/5005] lr: 1.000e-03, eta: 2:02:04, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3728 2023-01-04 02:03:00,323 - mmcls - INFO - Epoch [84][2000/5005] lr: 1.000e-03, eta: 2:01:55, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3594 2023-01-04 02:03:09,120 - mmcls - INFO - Epoch [84][2100/5005] lr: 1.000e-03, eta: 2:01:46, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3765 2023-01-04 02:03:17,692 - mmcls - INFO - Epoch [84][2200/5005] lr: 1.000e-03, eta: 2:01:37, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3645 2023-01-04 02:03:26,157 - mmcls - INFO - Epoch [84][2300/5005] lr: 1.000e-03, eta: 2:01:28, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3812 2023-01-04 02:03:34,687 - mmcls - INFO - Epoch [84][2400/5005] lr: 1.000e-03, eta: 2:01:19, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3614 2023-01-04 02:03:43,461 - mmcls - INFO - Epoch [84][2500/5005] lr: 1.000e-03, eta: 2:01:11, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3798 2023-01-04 02:03:52,337 - mmcls - INFO - Epoch [84][2600/5005] lr: 1.000e-03, eta: 2:01:02, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3773 2023-01-04 02:04:01,505 - mmcls - INFO - Epoch [84][2700/5005] lr: 1.000e-03, eta: 2:00:53, time: 0.092, data_time: 0.021, memory: 1125, loss: 1.3749 2023-01-04 02:04:10,212 - mmcls - INFO - Epoch [84][2800/5005] lr: 1.000e-03, eta: 2:00:44, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3899 2023-01-04 02:04:18,794 - mmcls - INFO - Epoch [84][2900/5005] lr: 1.000e-03, eta: 2:00:35, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3615 2023-01-04 02:04:27,463 - mmcls - INFO - Epoch [84][3000/5005] lr: 1.000e-03, eta: 2:00:27, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3628 2023-01-04 02:04:36,054 - mmcls - INFO - Epoch [84][3100/5005] lr: 1.000e-03, eta: 2:00:18, time: 0.086, data_time: 0.029, memory: 1125, loss: 1.3745 2023-01-04 02:04:44,751 - mmcls - INFO - Epoch [84][3200/5005] lr: 1.000e-03, eta: 2:00:09, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3706 2023-01-04 02:04:53,650 - mmcls - INFO - Epoch [84][3300/5005] lr: 1.000e-03, eta: 2:00:00, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.3806 2023-01-04 02:05:02,681 - mmcls - INFO - Epoch [84][3400/5005] lr: 1.000e-03, eta: 1:59:51, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.3551 2023-01-04 02:05:11,467 - mmcls - INFO - Epoch [84][3500/5005] lr: 1.000e-03, eta: 1:59:43, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3678 2023-01-04 02:05:20,372 - mmcls - INFO - Epoch [84][3600/5005] lr: 1.000e-03, eta: 1:59:34, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.3896 2023-01-04 02:05:29,199 - mmcls - INFO - Epoch [84][3700/5005] lr: 1.000e-03, eta: 1:59:25, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3657 2023-01-04 02:05:37,841 - mmcls - INFO - Epoch [84][3800/5005] lr: 1.000e-03, eta: 1:59:16, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3602 2023-01-04 02:05:46,745 - mmcls - INFO - Epoch [84][3900/5005] lr: 1.000e-03, eta: 1:59:07, time: 0.089, data_time: 0.022, memory: 1125, loss: 1.3691 2023-01-04 02:05:55,613 - mmcls - INFO - Epoch [84][4000/5005] lr: 1.000e-03, eta: 1:58:59, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3942 2023-01-04 02:06:04,637 - mmcls - INFO - Epoch [84][4100/5005] lr: 1.000e-03, eta: 1:58:50, time: 0.090, data_time: 0.002, memory: 1125, loss: 1.4027 2023-01-04 02:06:13,561 - mmcls - INFO - Epoch [84][4200/5005] lr: 1.000e-03, eta: 1:58:41, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3826 2023-01-04 02:06:22,355 - mmcls - INFO - Epoch [84][4300/5005] lr: 1.000e-03, eta: 1:58:32, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3785 2023-01-04 02:06:31,220 - mmcls - INFO - Epoch [84][4400/5005] lr: 1.000e-03, eta: 1:58:23, time: 0.089, data_time: 0.036, memory: 1125, loss: 1.3536 2023-01-04 02:06:40,021 - mmcls - INFO - Epoch [84][4500/5005] lr: 1.000e-03, eta: 1:58:15, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3823 2023-01-04 02:06:48,799 - mmcls - INFO - Epoch [84][4600/5005] lr: 1.000e-03, eta: 1:58:06, time: 0.088, data_time: 0.031, memory: 1125, loss: 1.3792 2023-01-04 02:06:57,849 - mmcls - INFO - Epoch [84][4700/5005] lr: 1.000e-03, eta: 1:57:57, time: 0.091, data_time: 0.034, memory: 1125, loss: 1.3873 2023-01-04 02:07:06,527 - mmcls - INFO - Epoch [84][4800/5005] lr: 1.000e-03, eta: 1:57:48, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3756 2023-01-04 02:07:15,265 - mmcls - INFO - Epoch [84][4900/5005] lr: 1.000e-03, eta: 1:57:39, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3567 2023-01-04 02:07:23,838 - mmcls - INFO - Epoch [84][5000/5005] lr: 1.000e-03, eta: 1:57:31, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3818 2023-01-04 02:07:46,445 - mmcls - INFO - Epoch(val) [84][196] accuracy_top-1: 69.6820, accuracy_top-5: 89.2040 2023-01-04 02:07:57,544 - mmcls - INFO - Epoch [85][100/5005] lr: 1.000e-03, eta: 1:57:22, time: 0.111, data_time: 0.036, memory: 1125, loss: 1.3756 2023-01-04 02:08:06,266 - mmcls - INFO - Epoch [85][200/5005] lr: 1.000e-03, eta: 1:57:13, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3849 2023-01-04 02:08:14,933 - mmcls - INFO - Epoch [85][300/5005] lr: 1.000e-03, eta: 1:57:04, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3854 2023-01-04 02:08:23,527 - mmcls - INFO - Epoch [85][400/5005] lr: 1.000e-03, eta: 1:56:55, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3485 2023-01-04 02:08:32,174 - mmcls - INFO - Epoch [85][500/5005] lr: 1.000e-03, eta: 1:56:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3816 2023-01-04 02:08:40,902 - mmcls - INFO - Epoch [85][600/5005] lr: 1.000e-03, eta: 1:56:37, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3664 2023-01-04 02:08:49,786 - mmcls - INFO - Epoch [85][700/5005] lr: 1.000e-03, eta: 1:56:29, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3704 2023-01-04 02:08:58,494 - mmcls - INFO - Epoch [85][800/5005] lr: 1.000e-03, eta: 1:56:20, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3658 2023-01-04 02:09:07,795 - mmcls - INFO - Epoch [85][900/5005] lr: 1.000e-03, eta: 1:56:11, time: 0.093, data_time: 0.006, memory: 1125, loss: 1.3642 2023-01-04 02:09:16,359 - mmcls - INFO - Epoch [85][1000/5005] lr: 1.000e-03, eta: 1:56:02, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3600 2023-01-04 02:09:25,110 - mmcls - INFO - Epoch [85][1100/5005] lr: 1.000e-03, eta: 1:55:53, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.3490 2023-01-04 02:09:33,740 - mmcls - INFO - Epoch [85][1200/5005] lr: 1.000e-03, eta: 1:55:45, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3532 2023-01-04 02:09:42,333 - mmcls - INFO - Epoch [85][1300/5005] lr: 1.000e-03, eta: 1:55:36, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3719 2023-01-04 02:09:51,107 - mmcls - INFO - Epoch [85][1400/5005] lr: 1.000e-03, eta: 1:55:27, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3842 2023-01-04 02:09:59,516 - mmcls - INFO - Epoch [85][1500/5005] lr: 1.000e-03, eta: 1:55:18, time: 0.084, data_time: 0.018, memory: 1125, loss: 1.3555 2023-01-04 02:10:08,091 - mmcls - INFO - Epoch [85][1600/5005] lr: 1.000e-03, eta: 1:55:09, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3746 2023-01-04 02:10:16,931 - mmcls - INFO - Epoch [85][1700/5005] lr: 1.000e-03, eta: 1:55:00, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.3622 2023-01-04 02:10:25,776 - mmcls - INFO - Epoch [85][1800/5005] lr: 1.000e-03, eta: 1:54:52, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3513 2023-01-04 02:10:34,772 - mmcls - INFO - Epoch [85][1900/5005] lr: 1.000e-03, eta: 1:54:43, time: 0.090, data_time: 0.034, memory: 1125, loss: 1.3735 2023-01-04 02:10:43,934 - mmcls - INFO - Epoch [85][2000/5005] lr: 1.000e-03, eta: 1:54:34, time: 0.092, data_time: 0.015, memory: 1125, loss: 1.3706 2023-01-04 02:10:52,468 - mmcls - INFO - Epoch [85][2100/5005] lr: 1.000e-03, eta: 1:54:25, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3755 2023-01-04 02:11:00,920 - mmcls - INFO - Epoch [85][2200/5005] lr: 1.000e-03, eta: 1:54:16, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3714 2023-01-04 02:11:09,755 - mmcls - INFO - Epoch [85][2300/5005] lr: 1.000e-03, eta: 1:54:08, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3715 2023-01-04 02:11:18,394 - mmcls - INFO - Epoch [85][2400/5005] lr: 1.000e-03, eta: 1:53:59, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3876 2023-01-04 02:11:27,418 - mmcls - INFO - Epoch [85][2500/5005] lr: 1.000e-03, eta: 1:53:50, time: 0.090, data_time: 0.042, memory: 1125, loss: 1.3441 2023-01-04 02:11:36,221 - mmcls - INFO - Epoch [85][2600/5005] lr: 1.000e-03, eta: 1:53:41, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3649 2023-01-04 02:11:45,231 - mmcls - INFO - Epoch [85][2700/5005] lr: 1.000e-03, eta: 1:53:32, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3689 2023-01-04 02:11:54,041 - mmcls - INFO - Epoch [85][2800/5005] lr: 1.000e-03, eta: 1:53:24, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3686 2023-01-04 02:12:02,940 - mmcls - INFO - Epoch [85][2900/5005] lr: 1.000e-03, eta: 1:53:15, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3589 2023-01-04 02:12:11,635 - mmcls - INFO - Epoch [85][3000/5005] lr: 1.000e-03, eta: 1:53:06, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3606 2023-01-04 02:12:20,214 - mmcls - INFO - Epoch [85][3100/5005] lr: 1.000e-03, eta: 1:52:57, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3735 2023-01-04 02:12:28,933 - mmcls - INFO - Epoch [85][3200/5005] lr: 1.000e-03, eta: 1:52:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3499 2023-01-04 02:12:37,820 - mmcls - INFO - Epoch [85][3300/5005] lr: 1.000e-03, eta: 1:52:40, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3804 2023-01-04 02:12:46,814 - mmcls - INFO - Epoch [85][3400/5005] lr: 1.000e-03, eta: 1:52:31, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3893 2023-01-04 02:12:55,422 - mmcls - INFO - Epoch [85][3500/5005] lr: 1.000e-03, eta: 1:52:22, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3749 2023-01-04 02:13:04,104 - mmcls - INFO - Epoch [85][3600/5005] lr: 1.000e-03, eta: 1:52:13, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3659 2023-01-04 02:13:12,868 - mmcls - INFO - Epoch [85][3700/5005] lr: 1.000e-03, eta: 1:52:04, time: 0.088, data_time: 0.013, memory: 1125, loss: 1.3759 2023-01-04 02:13:21,779 - mmcls - INFO - Epoch [85][3800/5005] lr: 1.000e-03, eta: 1:51:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3778 2023-01-04 02:13:30,514 - mmcls - INFO - Epoch [85][3900/5005] lr: 1.000e-03, eta: 1:51:47, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3625 2023-01-04 02:13:39,079 - mmcls - INFO - Epoch [85][4000/5005] lr: 1.000e-03, eta: 1:51:38, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3823 2023-01-04 02:13:47,844 - mmcls - INFO - Epoch [85][4100/5005] lr: 1.000e-03, eta: 1:51:29, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.3761 2023-01-04 02:13:56,375 - mmcls - INFO - Epoch [85][4200/5005] lr: 1.000e-03, eta: 1:51:20, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.3667 2023-01-04 02:14:05,060 - mmcls - INFO - Epoch [85][4300/5005] lr: 1.000e-03, eta: 1:51:11, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3847 2023-01-04 02:14:13,996 - mmcls - INFO - Epoch [85][4400/5005] lr: 1.000e-03, eta: 1:51:03, time: 0.089, data_time: 0.034, memory: 1125, loss: 1.3698 2023-01-04 02:14:22,797 - mmcls - INFO - Epoch [85][4500/5005] lr: 1.000e-03, eta: 1:50:54, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3395 2023-01-04 02:14:31,577 - mmcls - INFO - Epoch [85][4600/5005] lr: 1.000e-03, eta: 1:50:45, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3671 2023-01-04 02:14:40,368 - mmcls - INFO - Epoch [85][4700/5005] lr: 1.000e-03, eta: 1:50:36, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3658 2023-01-04 02:14:49,023 - mmcls - INFO - Epoch [85][4800/5005] lr: 1.000e-03, eta: 1:50:27, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3790 2023-01-04 02:14:57,546 - mmcls - INFO - Epoch [85][4900/5005] lr: 1.000e-03, eta: 1:50:19, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3783 2023-01-04 02:15:06,334 - mmcls - INFO - Epoch [85][5000/5005] lr: 1.000e-03, eta: 1:50:10, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3637 2023-01-04 02:15:28,963 - mmcls - INFO - Epoch(val) [85][196] accuracy_top-1: 69.7980, accuracy_top-5: 89.2500 2023-01-04 02:15:40,120 - mmcls - INFO - Epoch [86][100/5005] lr: 1.000e-03, eta: 1:50:01, time: 0.111, data_time: 0.043, memory: 1125, loss: 1.3423 2023-01-04 02:15:49,178 - mmcls - INFO - Epoch [86][200/5005] lr: 1.000e-03, eta: 1:49:52, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3424 2023-01-04 02:15:57,971 - mmcls - INFO - Epoch [86][300/5005] lr: 1.000e-03, eta: 1:49:43, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3585 2023-01-04 02:16:06,626 - mmcls - INFO - Epoch [86][400/5005] lr: 1.000e-03, eta: 1:49:34, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3684 2023-01-04 02:16:15,495 - mmcls - INFO - Epoch [86][500/5005] lr: 1.000e-03, eta: 1:49:26, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3693 2023-01-04 02:16:24,119 - mmcls - INFO - Epoch [86][600/5005] lr: 1.000e-03, eta: 1:49:17, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3675 2023-01-04 02:16:33,195 - mmcls - INFO - Epoch [86][700/5005] lr: 1.000e-03, eta: 1:49:08, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3644 2023-01-04 02:16:41,881 - mmcls - INFO - Epoch [86][800/5005] lr: 1.000e-03, eta: 1:48:59, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3865 2023-01-04 02:16:50,319 - mmcls - INFO - Epoch [86][900/5005] lr: 1.000e-03, eta: 1:48:50, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.3658 2023-01-04 02:16:58,869 - mmcls - INFO - Epoch [86][1000/5005] lr: 1.000e-03, eta: 1:48:41, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3556 2023-01-04 02:17:07,410 - mmcls - INFO - Epoch [86][1100/5005] lr: 1.000e-03, eta: 1:48:33, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3627 2023-01-04 02:17:15,991 - mmcls - INFO - Epoch [86][1200/5005] lr: 1.000e-03, eta: 1:48:24, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3688 2023-01-04 02:17:25,087 - mmcls - INFO - Epoch [86][1300/5005] lr: 1.000e-03, eta: 1:48:15, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3770 2023-01-04 02:17:33,831 - mmcls - INFO - Epoch [86][1400/5005] lr: 1.000e-03, eta: 1:48:06, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3756 2023-01-04 02:17:42,597 - mmcls - INFO - Epoch [86][1500/5005] lr: 1.000e-03, eta: 1:47:57, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3636 2023-01-04 02:17:51,692 - mmcls - INFO - Epoch [86][1600/5005] lr: 1.000e-03, eta: 1:47:49, time: 0.091, data_time: 0.026, memory: 1125, loss: 1.3601 2023-01-04 02:18:00,553 - mmcls - INFO - Epoch [86][1700/5005] lr: 1.000e-03, eta: 1:47:40, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3636 2023-01-04 02:18:09,137 - mmcls - INFO - Epoch [86][1800/5005] lr: 1.000e-03, eta: 1:47:31, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3692 2023-01-04 02:18:17,745 - mmcls - INFO - Epoch [86][1900/5005] lr: 1.000e-03, eta: 1:47:22, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3607 2023-01-04 02:18:26,709 - mmcls - INFO - Epoch [86][2000/5005] lr: 1.000e-03, eta: 1:47:13, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.3637 2023-01-04 02:18:35,476 - mmcls - INFO - Epoch [86][2100/5005] lr: 1.000e-03, eta: 1:47:05, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3638 2023-01-04 02:18:44,046 - mmcls - INFO - Epoch [86][2200/5005] lr: 1.000e-03, eta: 1:46:56, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3835 2023-01-04 02:18:52,836 - mmcls - INFO - Epoch [86][2300/5005] lr: 1.000e-03, eta: 1:46:47, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.3647 2023-01-04 02:19:01,423 - mmcls - INFO - Epoch [86][2400/5005] lr: 1.000e-03, eta: 1:46:38, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3691 2023-01-04 02:19:10,142 - mmcls - INFO - Epoch [86][2500/5005] lr: 1.000e-03, eta: 1:46:29, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.3750 2023-01-04 02:19:19,097 - mmcls - INFO - Epoch [86][2600/5005] lr: 1.000e-03, eta: 1:46:20, time: 0.089, data_time: 0.033, memory: 1125, loss: 1.3780 2023-01-04 02:19:27,666 - mmcls - INFO - Epoch [86][2700/5005] lr: 1.000e-03, eta: 1:46:12, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3690 2023-01-04 02:19:36,439 - mmcls - INFO - Epoch [86][2800/5005] lr: 1.000e-03, eta: 1:46:03, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3705 2023-01-04 02:19:45,089 - mmcls - INFO - Epoch [86][2900/5005] lr: 1.000e-03, eta: 1:45:54, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3846 2023-01-04 02:19:54,134 - mmcls - INFO - Epoch [86][3000/5005] lr: 1.000e-03, eta: 1:45:45, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3804 2023-01-04 02:20:02,805 - mmcls - INFO - Epoch [86][3100/5005] lr: 1.000e-03, eta: 1:45:36, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3843 2023-01-04 02:20:11,439 - mmcls - INFO - Epoch [86][3200/5005] lr: 1.000e-03, eta: 1:45:28, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3511 2023-01-04 02:20:20,545 - mmcls - INFO - Epoch [86][3300/5005] lr: 1.000e-03, eta: 1:45:19, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.3734 2023-01-04 02:20:29,432 - mmcls - INFO - Epoch [86][3400/5005] lr: 1.000e-03, eta: 1:45:10, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3595 2023-01-04 02:20:37,983 - mmcls - INFO - Epoch [86][3500/5005] lr: 1.000e-03, eta: 1:45:01, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3674 2023-01-04 02:20:46,605 - mmcls - INFO - Epoch [86][3600/5005] lr: 1.000e-03, eta: 1:44:52, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3650 2023-01-04 02:20:55,416 - mmcls - INFO - Epoch [86][3700/5005] lr: 1.000e-03, eta: 1:44:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3715 2023-01-04 02:21:04,040 - mmcls - INFO - Epoch [86][3800/5005] lr: 1.000e-03, eta: 1:44:35, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3698 2023-01-04 02:21:13,053 - mmcls - INFO - Epoch [86][3900/5005] lr: 1.000e-03, eta: 1:44:26, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.3630 2023-01-04 02:21:21,897 - mmcls - INFO - Epoch [86][4000/5005] lr: 1.000e-03, eta: 1:44:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3623 2023-01-04 02:21:30,812 - mmcls - INFO - Epoch [86][4100/5005] lr: 1.000e-03, eta: 1:44:08, time: 0.089, data_time: 0.016, memory: 1125, loss: 1.3827 2023-01-04 02:21:39,726 - mmcls - INFO - Epoch [86][4200/5005] lr: 1.000e-03, eta: 1:44:00, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3594 2023-01-04 02:21:48,591 - mmcls - INFO - Epoch [86][4300/5005] lr: 1.000e-03, eta: 1:43:51, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3986 2023-01-04 02:21:57,418 - mmcls - INFO - Epoch [86][4400/5005] lr: 1.000e-03, eta: 1:43:42, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3721 2023-01-04 02:22:06,376 - mmcls - INFO - Epoch [86][4500/5005] lr: 1.000e-03, eta: 1:43:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3322 2023-01-04 02:22:14,934 - mmcls - INFO - Epoch [86][4600/5005] lr: 1.000e-03, eta: 1:43:24, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3681 2023-01-04 02:22:24,030 - mmcls - INFO - Epoch [86][4700/5005] lr: 1.000e-03, eta: 1:43:16, time: 0.091, data_time: 0.021, memory: 1125, loss: 1.3820 2023-01-04 02:22:32,828 - mmcls - INFO - Epoch [86][4800/5005] lr: 1.000e-03, eta: 1:43:07, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.3880 2023-01-04 02:22:41,401 - mmcls - INFO - Epoch [86][4900/5005] lr: 1.000e-03, eta: 1:42:58, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3689 2023-01-04 02:22:50,353 - mmcls - INFO - Epoch [86][5000/5005] lr: 1.000e-03, eta: 1:42:49, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3775 2023-01-04 02:23:12,962 - mmcls - INFO - Epoch(val) [86][196] accuracy_top-1: 69.8360, accuracy_top-5: 89.2000 2023-01-04 02:23:23,981 - mmcls - INFO - Epoch [87][100/5005] lr: 1.000e-03, eta: 1:42:40, time: 0.110, data_time: 0.032, memory: 1125, loss: 1.3689 2023-01-04 02:23:32,706 - mmcls - INFO - Epoch [87][200/5005] lr: 1.000e-03, eta: 1:42:31, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3607 2023-01-04 02:23:41,573 - mmcls - INFO - Epoch [87][300/5005] lr: 1.000e-03, eta: 1:42:23, time: 0.089, data_time: 0.006, memory: 1125, loss: 1.3574 2023-01-04 02:23:50,345 - mmcls - INFO - Epoch [87][400/5005] lr: 1.000e-03, eta: 1:42:14, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3552 2023-01-04 02:23:59,097 - mmcls - INFO - Epoch [87][500/5005] lr: 1.000e-03, eta: 1:42:05, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3297 2023-01-04 02:24:07,715 - mmcls - INFO - Epoch [87][600/5005] lr: 1.000e-03, eta: 1:41:56, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3695 2023-01-04 02:24:17,115 - mmcls - INFO - Epoch [87][700/5005] lr: 1.000e-03, eta: 1:41:47, time: 0.094, data_time: 0.008, memory: 1125, loss: 1.3477 2023-01-04 02:24:25,758 - mmcls - INFO - Epoch [87][800/5005] lr: 1.000e-03, eta: 1:41:39, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3616 2023-01-04 02:24:34,609 - mmcls - INFO - Epoch [87][900/5005] lr: 1.000e-03, eta: 1:41:30, time: 0.089, data_time: 0.042, memory: 1125, loss: 1.3479 2023-01-04 02:24:43,330 - mmcls - INFO - Epoch [87][1000/5005] lr: 1.000e-03, eta: 1:41:21, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3695 2023-01-04 02:24:52,325 - mmcls - INFO - Epoch [87][1100/5005] lr: 1.000e-03, eta: 1:41:12, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.3887 2023-01-04 02:25:00,936 - mmcls - INFO - Epoch [87][1200/5005] lr: 1.000e-03, eta: 1:41:03, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3655 2023-01-04 02:25:09,721 - mmcls - INFO - Epoch [87][1300/5005] lr: 1.000e-03, eta: 1:40:55, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3786 2023-01-04 02:25:18,275 - mmcls - INFO - Epoch [87][1400/5005] lr: 1.000e-03, eta: 1:40:46, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3490 2023-01-04 02:25:26,914 - mmcls - INFO - Epoch [87][1500/5005] lr: 1.000e-03, eta: 1:40:37, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3487 2023-01-04 02:25:35,530 - mmcls - INFO - Epoch [87][1600/5005] lr: 1.000e-03, eta: 1:40:28, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3437 2023-01-04 02:25:44,133 - mmcls - INFO - Epoch [87][1700/5005] lr: 1.000e-03, eta: 1:40:19, time: 0.086, data_time: 0.020, memory: 1125, loss: 1.3574 2023-01-04 02:25:52,913 - mmcls - INFO - Epoch [87][1800/5005] lr: 1.000e-03, eta: 1:40:10, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.3582 2023-01-04 02:26:02,144 - mmcls - INFO - Epoch [87][1900/5005] lr: 1.000e-03, eta: 1:40:02, time: 0.092, data_time: 0.022, memory: 1125, loss: 1.3695 2023-01-04 02:26:10,846 - mmcls - INFO - Epoch [87][2000/5005] lr: 1.000e-03, eta: 1:39:53, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3480 2023-01-04 02:26:19,465 - mmcls - INFO - Epoch [87][2100/5005] lr: 1.000e-03, eta: 1:39:44, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3570 2023-01-04 02:26:28,304 - mmcls - INFO - Epoch [87][2200/5005] lr: 1.000e-03, eta: 1:39:35, time: 0.088, data_time: 0.016, memory: 1125, loss: 1.3714 2023-01-04 02:26:37,135 - mmcls - INFO - Epoch [87][2300/5005] lr: 1.000e-03, eta: 1:39:26, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3707 2023-01-04 02:26:45,936 - mmcls - INFO - Epoch [87][2400/5005] lr: 1.000e-03, eta: 1:39:18, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3774 2023-01-04 02:26:54,655 - mmcls - INFO - Epoch [87][2500/5005] lr: 1.000e-03, eta: 1:39:09, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3812 2023-01-04 02:27:03,560 - mmcls - INFO - Epoch [87][2600/5005] lr: 1.000e-03, eta: 1:39:00, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3655 2023-01-04 02:27:12,311 - mmcls - INFO - Epoch [87][2700/5005] lr: 1.000e-03, eta: 1:38:51, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3617 2023-01-04 02:27:20,963 - mmcls - INFO - Epoch [87][2800/5005] lr: 1.000e-03, eta: 1:38:42, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3769 2023-01-04 02:27:29,486 - mmcls - INFO - Epoch [87][2900/5005] lr: 1.000e-03, eta: 1:38:34, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3579 2023-01-04 02:27:38,100 - mmcls - INFO - Epoch [87][3000/5005] lr: 1.000e-03, eta: 1:38:25, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3637 2023-01-04 02:27:46,731 - mmcls - INFO - Epoch [87][3100/5005] lr: 1.000e-03, eta: 1:38:16, time: 0.086, data_time: 0.014, memory: 1125, loss: 1.3800 2023-01-04 02:27:55,349 - mmcls - INFO - Epoch [87][3200/5005] lr: 1.000e-03, eta: 1:38:07, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3629 2023-01-04 02:28:03,829 - mmcls - INFO - Epoch [87][3300/5005] lr: 1.000e-03, eta: 1:37:58, time: 0.085, data_time: 0.025, memory: 1125, loss: 1.3810 2023-01-04 02:28:12,576 - mmcls - INFO - Epoch [87][3400/5005] lr: 1.000e-03, eta: 1:37:49, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3633 2023-01-04 02:28:21,371 - mmcls - INFO - Epoch [87][3500/5005] lr: 1.000e-03, eta: 1:37:41, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3813 2023-01-04 02:28:30,151 - mmcls - INFO - Epoch [87][3600/5005] lr: 1.000e-03, eta: 1:37:32, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3615 2023-01-04 02:28:38,952 - mmcls - INFO - Epoch [87][3700/5005] lr: 1.000e-03, eta: 1:37:23, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3767 2023-01-04 02:28:47,851 - mmcls - INFO - Epoch [87][3800/5005] lr: 1.000e-03, eta: 1:37:14, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3563 2023-01-04 02:28:56,540 - mmcls - INFO - Epoch [87][3900/5005] lr: 1.000e-03, eta: 1:37:05, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3660 2023-01-04 02:29:05,544 - mmcls - INFO - Epoch [87][4000/5005] lr: 1.000e-03, eta: 1:36:57, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3726 2023-01-04 02:29:14,550 - mmcls - INFO - Epoch [87][4100/5005] lr: 1.000e-03, eta: 1:36:48, time: 0.090, data_time: 0.019, memory: 1125, loss: 1.3553 2023-01-04 02:29:23,456 - mmcls - INFO - Epoch [87][4200/5005] lr: 1.000e-03, eta: 1:36:39, time: 0.089, data_time: 0.040, memory: 1125, loss: 1.3707 2023-01-04 02:29:32,069 - mmcls - INFO - Epoch [87][4300/5005] lr: 1.000e-03, eta: 1:36:30, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3525 2023-01-04 02:29:40,799 - mmcls - INFO - Epoch [87][4400/5005] lr: 1.000e-03, eta: 1:36:21, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.3764 2023-01-04 02:29:49,893 - mmcls - INFO - Epoch [87][4500/5005] lr: 1.000e-03, eta: 1:36:13, time: 0.091, data_time: 0.015, memory: 1125, loss: 1.3711 2023-01-04 02:29:59,081 - mmcls - INFO - Epoch [87][4600/5005] lr: 1.000e-03, eta: 1:36:04, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.3471 2023-01-04 02:30:08,040 - mmcls - INFO - Epoch [87][4700/5005] lr: 1.000e-03, eta: 1:35:55, time: 0.090, data_time: 0.026, memory: 1125, loss: 1.3734 2023-01-04 02:30:16,947 - mmcls - INFO - Epoch [87][4800/5005] lr: 1.000e-03, eta: 1:35:46, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3744 2023-01-04 02:30:25,770 - mmcls - INFO - Epoch [87][4900/5005] lr: 1.000e-03, eta: 1:35:37, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3825 2023-01-04 02:30:34,272 - mmcls - INFO - Epoch [87][5000/5005] lr: 1.000e-03, eta: 1:35:29, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.3725 2023-01-04 02:30:56,879 - mmcls - INFO - Epoch(val) [87][196] accuracy_top-1: 69.7200, accuracy_top-5: 89.1920 2023-01-04 02:31:07,713 - mmcls - INFO - Epoch [88][100/5005] lr: 1.000e-03, eta: 1:35:20, time: 0.108, data_time: 0.056, memory: 1125, loss: 1.3610 2023-01-04 02:31:16,169 - mmcls - INFO - Epoch [88][200/5005] lr: 1.000e-03, eta: 1:35:11, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3805 2023-01-04 02:31:25,241 - mmcls - INFO - Epoch [88][300/5005] lr: 1.000e-03, eta: 1:35:02, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3512 2023-01-04 02:31:33,729 - mmcls - INFO - Epoch [88][400/5005] lr: 1.000e-03, eta: 1:34:53, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3382 2023-01-04 02:31:42,268 - mmcls - INFO - Epoch [88][500/5005] lr: 1.000e-03, eta: 1:34:44, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3568 2023-01-04 02:31:50,973 - mmcls - INFO - Epoch [88][600/5005] lr: 1.000e-03, eta: 1:34:35, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3558 2023-01-04 02:31:59,610 - mmcls - INFO - Epoch [88][700/5005] lr: 1.000e-03, eta: 1:34:27, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3631 2023-01-04 02:32:08,258 - mmcls - INFO - Epoch [88][800/5005] lr: 1.000e-03, eta: 1:34:18, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3448 2023-01-04 02:32:16,977 - mmcls - INFO - Epoch [88][900/5005] lr: 1.000e-03, eta: 1:34:09, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3453 2023-01-04 02:32:25,635 - mmcls - INFO - Epoch [88][1000/5005] lr: 1.000e-03, eta: 1:34:00, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.3513 2023-01-04 02:32:34,854 - mmcls - INFO - Epoch [88][1100/5005] lr: 1.000e-03, eta: 1:33:51, time: 0.092, data_time: 0.007, memory: 1125, loss: 1.3654 2023-01-04 02:32:43,434 - mmcls - INFO - Epoch [88][1200/5005] lr: 1.000e-03, eta: 1:33:43, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3632 2023-01-04 02:32:52,279 - mmcls - INFO - Epoch [88][1300/5005] lr: 1.000e-03, eta: 1:33:34, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3785 2023-01-04 02:33:00,749 - mmcls - INFO - Epoch [88][1400/5005] lr: 1.000e-03, eta: 1:33:25, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3584 2023-01-04 02:33:09,477 - mmcls - INFO - Epoch [88][1500/5005] lr: 1.000e-03, eta: 1:33:16, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3614 2023-01-04 02:33:18,515 - mmcls - INFO - Epoch [88][1600/5005] lr: 1.000e-03, eta: 1:33:07, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.3747 2023-01-04 02:33:26,995 - mmcls - INFO - Epoch [88][1700/5005] lr: 1.000e-03, eta: 1:32:58, time: 0.085, data_time: 0.027, memory: 1125, loss: 1.3473 2023-01-04 02:33:36,088 - mmcls - INFO - Epoch [88][1800/5005] lr: 1.000e-03, eta: 1:32:50, time: 0.091, data_time: 0.039, memory: 1125, loss: 1.3528 2023-01-04 02:33:44,989 - mmcls - INFO - Epoch [88][1900/5005] lr: 1.000e-03, eta: 1:32:41, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.3710 2023-01-04 02:33:53,592 - mmcls - INFO - Epoch [88][2000/5005] lr: 1.000e-03, eta: 1:32:32, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3544 2023-01-04 02:34:02,266 - mmcls - INFO - Epoch [88][2100/5005] lr: 1.000e-03, eta: 1:32:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3602 2023-01-04 02:34:11,013 - mmcls - INFO - Epoch [88][2200/5005] lr: 1.000e-03, eta: 1:32:14, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3541 2023-01-04 02:34:19,768 - mmcls - INFO - Epoch [88][2300/5005] lr: 1.000e-03, eta: 1:32:06, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3760 2023-01-04 02:34:28,549 - mmcls - INFO - Epoch [88][2400/5005] lr: 1.000e-03, eta: 1:31:57, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3407 2023-01-04 02:34:37,791 - mmcls - INFO - Epoch [88][2500/5005] lr: 1.000e-03, eta: 1:31:48, time: 0.092, data_time: 0.018, memory: 1125, loss: 1.3942 2023-01-04 02:34:46,187 - mmcls - INFO - Epoch [88][2600/5005] lr: 1.000e-03, eta: 1:31:39, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.3501 2023-01-04 02:34:54,923 - mmcls - INFO - Epoch [88][2700/5005] lr: 1.000e-03, eta: 1:31:30, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3578 2023-01-04 02:35:03,666 - mmcls - INFO - Epoch [88][2800/5005] lr: 1.000e-03, eta: 1:31:22, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3617 2023-01-04 02:35:12,799 - mmcls - INFO - Epoch [88][2900/5005] lr: 1.000e-03, eta: 1:31:13, time: 0.091, data_time: 0.026, memory: 1125, loss: 1.3721 2023-01-04 02:35:21,476 - mmcls - INFO - Epoch [88][3000/5005] lr: 1.000e-03, eta: 1:31:04, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3557 2023-01-04 02:35:30,525 - mmcls - INFO - Epoch [88][3100/5005] lr: 1.000e-03, eta: 1:30:55, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3649 2023-01-04 02:35:38,981 - mmcls - INFO - Epoch [88][3200/5005] lr: 1.000e-03, eta: 1:30:46, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3617 2023-01-04 02:35:47,977 - mmcls - INFO - Epoch [88][3300/5005] lr: 1.000e-03, eta: 1:30:38, time: 0.090, data_time: 0.009, memory: 1125, loss: 1.3771 2023-01-04 02:35:56,619 - mmcls - INFO - Epoch [88][3400/5005] lr: 1.000e-03, eta: 1:30:29, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3696 2023-01-04 02:36:05,171 - mmcls - INFO - Epoch [88][3500/5005] lr: 1.000e-03, eta: 1:30:20, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3704 2023-01-04 02:36:13,905 - mmcls - INFO - Epoch [88][3600/5005] lr: 1.000e-03, eta: 1:30:11, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3641 2023-01-04 02:36:22,682 - mmcls - INFO - Epoch [88][3700/5005] lr: 1.000e-03, eta: 1:30:02, time: 0.088, data_time: 0.028, memory: 1125, loss: 1.3815 2023-01-04 02:36:31,580 - mmcls - INFO - Epoch [88][3800/5005] lr: 1.000e-03, eta: 1:29:54, time: 0.089, data_time: 0.050, memory: 1125, loss: 1.3651 2023-01-04 02:36:40,700 - mmcls - INFO - Epoch [88][3900/5005] lr: 1.000e-03, eta: 1:29:45, time: 0.091, data_time: 0.025, memory: 1125, loss: 1.3450 2023-01-04 02:36:49,673 - mmcls - INFO - Epoch [88][4000/5005] lr: 1.000e-03, eta: 1:29:36, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3540 2023-01-04 02:36:58,363 - mmcls - INFO - Epoch [88][4100/5005] lr: 1.000e-03, eta: 1:29:27, time: 0.087, data_time: 0.034, memory: 1125, loss: 1.3710 2023-01-04 02:37:07,099 - mmcls - INFO - Epoch [88][4200/5005] lr: 1.000e-03, eta: 1:29:18, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3688 2023-01-04 02:37:15,923 - mmcls - INFO - Epoch [88][4300/5005] lr: 1.000e-03, eta: 1:29:10, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3702 2023-01-04 02:37:24,773 - mmcls - INFO - Epoch [88][4400/5005] lr: 1.000e-03, eta: 1:29:01, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3610 2023-01-04 02:37:33,528 - mmcls - INFO - Epoch [88][4500/5005] lr: 1.000e-03, eta: 1:28:52, time: 0.088, data_time: 0.024, memory: 1125, loss: 1.3775 2023-01-04 02:37:42,117 - mmcls - INFO - Epoch [88][4600/5005] lr: 1.000e-03, eta: 1:28:43, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3702 2023-01-04 02:37:51,194 - mmcls - INFO - Epoch [88][4700/5005] lr: 1.000e-03, eta: 1:28:34, time: 0.091, data_time: 0.009, memory: 1125, loss: 1.3588 2023-01-04 02:37:59,785 - mmcls - INFO - Epoch [88][4800/5005] lr: 1.000e-03, eta: 1:28:26, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3849 2023-01-04 02:38:08,567 - mmcls - INFO - Epoch [88][4900/5005] lr: 1.000e-03, eta: 1:28:17, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3521 2023-01-04 02:38:17,286 - mmcls - INFO - Epoch [88][5000/5005] lr: 1.000e-03, eta: 1:28:08, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.3688 2023-01-04 02:38:39,770 - mmcls - INFO - Epoch(val) [88][196] accuracy_top-1: 69.6920, accuracy_top-5: 89.1660 2023-01-04 02:38:50,565 - mmcls - INFO - Epoch [89][100/5005] lr: 1.000e-03, eta: 1:27:59, time: 0.108, data_time: 0.031, memory: 1125, loss: 1.3466 2023-01-04 02:38:59,286 - mmcls - INFO - Epoch [89][200/5005] lr: 1.000e-03, eta: 1:27:50, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3503 2023-01-04 02:39:08,085 - mmcls - INFO - Epoch [89][300/5005] lr: 1.000e-03, eta: 1:27:41, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3440 2023-01-04 02:39:16,885 - mmcls - INFO - Epoch [89][400/5005] lr: 1.000e-03, eta: 1:27:32, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3479 2023-01-04 02:39:25,601 - mmcls - INFO - Epoch [89][500/5005] lr: 1.000e-03, eta: 1:27:24, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3553 2023-01-04 02:39:34,467 - mmcls - INFO - Epoch [89][600/5005] lr: 1.000e-03, eta: 1:27:15, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3516 2023-01-04 02:39:43,348 - mmcls - INFO - Epoch [89][700/5005] lr: 1.000e-03, eta: 1:27:06, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.3621 2023-01-04 02:39:52,109 - mmcls - INFO - Epoch [89][800/5005] lr: 1.000e-03, eta: 1:26:57, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3453 2023-01-04 02:40:00,865 - mmcls - INFO - Epoch [89][900/5005] lr: 1.000e-03, eta: 1:26:48, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3580 2023-01-04 02:40:09,278 - mmcls - INFO - Epoch [89][1000/5005] lr: 1.000e-03, eta: 1:26:40, time: 0.084, data_time: 0.004, memory: 1125, loss: 1.3514 2023-01-04 02:40:17,778 - mmcls - INFO - Epoch [89][1100/5005] lr: 1.000e-03, eta: 1:26:31, time: 0.085, data_time: 0.004, memory: 1125, loss: 1.3913 2023-01-04 02:40:26,509 - mmcls - INFO - Epoch [89][1200/5005] lr: 1.000e-03, eta: 1:26:22, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3511 2023-01-04 02:40:35,181 - mmcls - INFO - Epoch [89][1300/5005] lr: 1.000e-03, eta: 1:26:13, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3562 2023-01-04 02:40:43,852 - mmcls - INFO - Epoch [89][1400/5005] lr: 1.000e-03, eta: 1:26:04, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3814 2023-01-04 02:40:52,441 - mmcls - INFO - Epoch [89][1500/5005] lr: 1.000e-03, eta: 1:25:55, time: 0.086, data_time: 0.035, memory: 1125, loss: 1.3513 2023-01-04 02:41:01,182 - mmcls - INFO - Epoch [89][1600/5005] lr: 1.000e-03, eta: 1:25:47, time: 0.087, data_time: 0.043, memory: 1125, loss: 1.3676 2023-01-04 02:41:09,923 - mmcls - INFO - Epoch [89][1700/5005] lr: 1.000e-03, eta: 1:25:38, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3557 2023-01-04 02:41:18,526 - mmcls - INFO - Epoch [89][1800/5005] lr: 1.000e-03, eta: 1:25:29, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3671 2023-01-04 02:41:27,304 - mmcls - INFO - Epoch [89][1900/5005] lr: 1.000e-03, eta: 1:25:20, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3487 2023-01-04 02:41:36,089 - mmcls - INFO - Epoch [89][2000/5005] lr: 1.000e-03, eta: 1:25:11, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3659 2023-01-04 02:41:44,829 - mmcls - INFO - Epoch [89][2100/5005] lr: 1.000e-03, eta: 1:25:03, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3763 2023-01-04 02:41:53,457 - mmcls - INFO - Epoch [89][2200/5005] lr: 1.000e-03, eta: 1:24:54, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3500 2023-01-04 02:42:02,447 - mmcls - INFO - Epoch [89][2300/5005] lr: 1.000e-03, eta: 1:24:45, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.3691 2023-01-04 02:42:11,129 - mmcls - INFO - Epoch [89][2400/5005] lr: 1.000e-03, eta: 1:24:36, time: 0.087, data_time: 0.017, memory: 1125, loss: 1.3663 2023-01-04 02:42:20,049 - mmcls - INFO - Epoch [89][2500/5005] lr: 1.000e-03, eta: 1:24:27, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3783 2023-01-04 02:42:28,898 - mmcls - INFO - Epoch [89][2600/5005] lr: 1.000e-03, eta: 1:24:18, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3686 2023-01-04 02:42:37,704 - mmcls - INFO - Epoch [89][2700/5005] lr: 1.000e-03, eta: 1:24:10, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.3645 2023-01-04 02:42:46,381 - mmcls - INFO - Epoch [89][2800/5005] lr: 1.000e-03, eta: 1:24:01, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3747 2023-01-04 02:42:55,113 - mmcls - INFO - Epoch [89][2900/5005] lr: 1.000e-03, eta: 1:23:52, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3635 2023-01-04 02:43:04,122 - mmcls - INFO - Epoch [89][3000/5005] lr: 1.000e-03, eta: 1:23:43, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.3712 2023-01-04 02:43:13,009 - mmcls - INFO - Epoch [89][3100/5005] lr: 1.000e-03, eta: 1:23:34, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3485 2023-01-04 02:43:21,849 - mmcls - INFO - Epoch [89][3200/5005] lr: 1.000e-03, eta: 1:23:26, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3721 2023-01-04 02:43:30,299 - mmcls - INFO - Epoch [89][3300/5005] lr: 1.000e-03, eta: 1:23:17, time: 0.084, data_time: 0.013, memory: 1125, loss: 1.3702 2023-01-04 02:43:39,085 - mmcls - INFO - Epoch [89][3400/5005] lr: 1.000e-03, eta: 1:23:08, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3494 2023-01-04 02:43:47,661 - mmcls - INFO - Epoch [89][3500/5005] lr: 1.000e-03, eta: 1:22:59, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3544 2023-01-04 02:43:56,202 - mmcls - INFO - Epoch [89][3600/5005] lr: 1.000e-03, eta: 1:22:50, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3827 2023-01-04 02:44:04,891 - mmcls - INFO - Epoch [89][3700/5005] lr: 1.000e-03, eta: 1:22:42, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.3702 2023-01-04 02:44:13,583 - mmcls - INFO - Epoch [89][3800/5005] lr: 1.000e-03, eta: 1:22:33, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3632 2023-01-04 02:44:22,629 - mmcls - INFO - Epoch [89][3900/5005] lr: 1.000e-03, eta: 1:22:24, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.3844 2023-01-04 02:44:31,353 - mmcls - INFO - Epoch [89][4000/5005] lr: 1.000e-03, eta: 1:22:15, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3626 2023-01-04 02:44:40,266 - mmcls - INFO - Epoch [89][4100/5005] lr: 1.000e-03, eta: 1:22:06, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.3869 2023-01-04 02:44:49,006 - mmcls - INFO - Epoch [89][4200/5005] lr: 1.000e-03, eta: 1:21:58, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3476 2023-01-04 02:44:57,803 - mmcls - INFO - Epoch [89][4300/5005] lr: 1.000e-03, eta: 1:21:49, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3664 2023-01-04 02:45:06,341 - mmcls - INFO - Epoch [89][4400/5005] lr: 1.000e-03, eta: 1:21:40, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3587 2023-01-04 02:45:15,202 - mmcls - INFO - Epoch [89][4500/5005] lr: 1.000e-03, eta: 1:21:31, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.3681 2023-01-04 02:45:23,989 - mmcls - INFO - Epoch [89][4600/5005] lr: 1.000e-03, eta: 1:21:22, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3709 2023-01-04 02:45:32,460 - mmcls - INFO - Epoch [89][4700/5005] lr: 1.000e-03, eta: 1:21:13, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.3434 2023-01-04 02:45:41,045 - mmcls - INFO - Epoch [89][4800/5005] lr: 1.000e-03, eta: 1:21:05, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3637 2023-01-04 02:45:50,068 - mmcls - INFO - Epoch [89][4900/5005] lr: 1.000e-03, eta: 1:20:56, time: 0.090, data_time: 0.036, memory: 1125, loss: 1.3710 2023-01-04 02:45:58,715 - mmcls - INFO - Epoch [89][5000/5005] lr: 1.000e-03, eta: 1:20:47, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3616 2023-01-04 02:46:21,311 - mmcls - INFO - Epoch(val) [89][196] accuracy_top-1: 69.7160, accuracy_top-5: 89.2380 2023-01-04 02:46:32,485 - mmcls - INFO - Epoch [90][100/5005] lr: 1.000e-03, eta: 1:20:38, time: 0.111, data_time: 0.074, memory: 1125, loss: 1.3506 2023-01-04 02:46:41,011 - mmcls - INFO - Epoch [90][200/5005] lr: 1.000e-03, eta: 1:20:29, time: 0.085, data_time: 0.009, memory: 1125, loss: 1.3594 2023-01-04 02:46:50,130 - mmcls - INFO - Epoch [90][300/5005] lr: 1.000e-03, eta: 1:20:20, time: 0.091, data_time: 0.031, memory: 1125, loss: 1.3448 2023-01-04 02:46:58,566 - mmcls - INFO - Epoch [90][400/5005] lr: 1.000e-03, eta: 1:20:12, time: 0.084, data_time: 0.023, memory: 1125, loss: 1.3390 2023-01-04 02:47:07,275 - mmcls - INFO - Epoch [90][500/5005] lr: 1.000e-03, eta: 1:20:03, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3497 2023-01-04 02:47:15,821 - mmcls - INFO - Epoch [90][600/5005] lr: 1.000e-03, eta: 1:19:54, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3466 2023-01-04 02:47:24,533 - mmcls - INFO - Epoch [90][700/5005] lr: 1.000e-03, eta: 1:19:45, time: 0.087, data_time: 0.024, memory: 1125, loss: 1.3620 2023-01-04 02:47:33,599 - mmcls - INFO - Epoch [90][800/5005] lr: 1.000e-03, eta: 1:19:36, time: 0.091, data_time: 0.043, memory: 1125, loss: 1.3328 2023-01-04 02:47:42,553 - mmcls - INFO - Epoch [90][900/5005] lr: 1.000e-03, eta: 1:19:28, time: 0.090, data_time: 0.041, memory: 1125, loss: 1.3483 2023-01-04 02:47:51,169 - mmcls - INFO - Epoch [90][1000/5005] lr: 1.000e-03, eta: 1:19:19, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.3641 2023-01-04 02:47:59,807 - mmcls - INFO - Epoch [90][1100/5005] lr: 1.000e-03, eta: 1:19:10, time: 0.086, data_time: 0.007, memory: 1125, loss: 1.3598 2023-01-04 02:48:08,653 - mmcls - INFO - Epoch [90][1200/5005] lr: 1.000e-03, eta: 1:19:01, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3683 2023-01-04 02:48:17,259 - mmcls - INFO - Epoch [90][1300/5005] lr: 1.000e-03, eta: 1:18:52, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3414 2023-01-04 02:48:26,392 - mmcls - INFO - Epoch [90][1400/5005] lr: 1.000e-03, eta: 1:18:43, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.3635 2023-01-04 02:48:35,018 - mmcls - INFO - Epoch [90][1500/5005] lr: 1.000e-03, eta: 1:18:35, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3554 2023-01-04 02:48:43,699 - mmcls - INFO - Epoch [90][1600/5005] lr: 1.000e-03, eta: 1:18:26, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3684 2023-01-04 02:48:52,185 - mmcls - INFO - Epoch [90][1700/5005] lr: 1.000e-03, eta: 1:18:17, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3756 2023-01-04 02:49:01,238 - mmcls - INFO - Epoch [90][1800/5005] lr: 1.000e-03, eta: 1:18:08, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.3421 2023-01-04 02:49:09,753 - mmcls - INFO - Epoch [90][1900/5005] lr: 1.000e-03, eta: 1:17:59, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.3606 2023-01-04 02:49:18,364 - mmcls - INFO - Epoch [90][2000/5005] lr: 1.000e-03, eta: 1:17:51, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3551 2023-01-04 02:49:26,946 - mmcls - INFO - Epoch [90][2100/5005] lr: 1.000e-03, eta: 1:17:42, time: 0.086, data_time: 0.023, memory: 1125, loss: 1.3862 2023-01-04 02:49:35,379 - mmcls - INFO - Epoch [90][2200/5005] lr: 1.000e-03, eta: 1:17:33, time: 0.084, data_time: 0.011, memory: 1125, loss: 1.3724 2023-01-04 02:49:44,135 - mmcls - INFO - Epoch [90][2300/5005] lr: 1.000e-03, eta: 1:17:24, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3622 2023-01-04 02:49:52,841 - mmcls - INFO - Epoch [90][2400/5005] lr: 1.000e-03, eta: 1:17:15, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3312 2023-01-04 02:50:01,528 - mmcls - INFO - Epoch [90][2500/5005] lr: 1.000e-03, eta: 1:17:06, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3387 2023-01-04 02:50:10,450 - mmcls - INFO - Epoch [90][2600/5005] lr: 1.000e-03, eta: 1:16:58, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3426 2023-01-04 02:50:19,265 - mmcls - INFO - Epoch [90][2700/5005] lr: 1.000e-03, eta: 1:16:49, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3745 2023-01-04 02:50:27,941 - mmcls - INFO - Epoch [90][2800/5005] lr: 1.000e-03, eta: 1:16:40, time: 0.087, data_time: 0.023, memory: 1125, loss: 1.3321 2023-01-04 02:50:36,747 - mmcls - INFO - Epoch [90][2900/5005] lr: 1.000e-03, eta: 1:16:31, time: 0.088, data_time: 0.034, memory: 1125, loss: 1.3615 2023-01-04 02:50:45,535 - mmcls - INFO - Epoch [90][3000/5005] lr: 1.000e-03, eta: 1:16:22, time: 0.088, data_time: 0.041, memory: 1125, loss: 1.3410 2023-01-04 02:50:54,241 - mmcls - INFO - Epoch [90][3100/5005] lr: 1.000e-03, eta: 1:16:14, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3512 2023-01-04 02:51:03,088 - mmcls - INFO - Epoch [90][3200/5005] lr: 1.000e-03, eta: 1:16:05, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3544 2023-01-04 02:51:11,994 - mmcls - INFO - Epoch [90][3300/5005] lr: 1.000e-03, eta: 1:15:56, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3777 2023-01-04 02:51:20,918 - mmcls - INFO - Epoch [90][3400/5005] lr: 1.000e-03, eta: 1:15:47, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3654 2023-01-04 02:51:29,579 - mmcls - INFO - Epoch [90][3500/5005] lr: 1.000e-03, eta: 1:15:38, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3617 2023-01-04 02:51:38,278 - mmcls - INFO - Epoch [90][3600/5005] lr: 1.000e-03, eta: 1:15:30, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.3781 2023-01-04 02:51:47,306 - mmcls - INFO - Epoch [90][3700/5005] lr: 1.000e-03, eta: 1:15:21, time: 0.090, data_time: 0.005, memory: 1125, loss: 1.3574 2023-01-04 02:51:55,948 - mmcls - INFO - Epoch [90][3800/5005] lr: 1.000e-03, eta: 1:15:12, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3542 2023-01-04 02:52:04,828 - mmcls - INFO - Epoch [90][3900/5005] lr: 1.000e-03, eta: 1:15:03, time: 0.089, data_time: 0.021, memory: 1125, loss: 1.3650 2023-01-04 02:52:13,397 - mmcls - INFO - Epoch [90][4000/5005] lr: 1.000e-03, eta: 1:14:54, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3532 2023-01-04 02:52:21,880 - mmcls - INFO - Epoch [90][4100/5005] lr: 1.000e-03, eta: 1:14:45, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3476 2023-01-04 02:52:30,592 - mmcls - INFO - Epoch [90][4200/5005] lr: 1.000e-03, eta: 1:14:37, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3771 2023-01-04 02:52:39,276 - mmcls - INFO - Epoch [90][4300/5005] lr: 1.000e-03, eta: 1:14:28, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3811 2023-01-04 02:52:47,870 - mmcls - INFO - Epoch [90][4400/5005] lr: 1.000e-03, eta: 1:14:19, time: 0.086, data_time: 0.039, memory: 1125, loss: 1.3683 2023-01-04 02:52:56,922 - mmcls - INFO - Epoch [90][4500/5005] lr: 1.000e-03, eta: 1:14:10, time: 0.091, data_time: 0.015, memory: 1125, loss: 1.3696 2023-01-04 02:53:05,502 - mmcls - INFO - Epoch [90][4600/5005] lr: 1.000e-03, eta: 1:14:01, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3616 2023-01-04 02:53:13,987 - mmcls - INFO - Epoch [90][4700/5005] lr: 1.000e-03, eta: 1:13:53, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3836 2023-01-04 02:53:22,455 - mmcls - INFO - Epoch [90][4800/5005] lr: 1.000e-03, eta: 1:13:44, time: 0.085, data_time: 0.034, memory: 1125, loss: 1.3749 2023-01-04 02:53:31,245 - mmcls - INFO - Epoch [90][4900/5005] lr: 1.000e-03, eta: 1:13:35, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3797 2023-01-04 02:53:40,217 - mmcls - INFO - Epoch [90][5000/5005] lr: 1.000e-03, eta: 1:13:26, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3772 2023-01-04 02:54:02,900 - mmcls - INFO - Epoch(val) [90][196] accuracy_top-1: 69.7560, accuracy_top-5: 89.0840 2023-01-04 02:54:13,938 - mmcls - INFO - Epoch [91][100/5005] lr: 1.000e-04, eta: 1:13:17, time: 0.110, data_time: 0.051, memory: 1125, loss: 1.3510 2023-01-04 02:54:22,537 - mmcls - INFO - Epoch [91][200/5005] lr: 1.000e-04, eta: 1:13:08, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3424 2023-01-04 02:54:31,536 - mmcls - INFO - Epoch [91][300/5005] lr: 1.000e-04, eta: 1:13:00, time: 0.090, data_time: 0.029, memory: 1125, loss: 1.3293 2023-01-04 02:54:40,720 - mmcls - INFO - Epoch [91][400/5005] lr: 1.000e-04, eta: 1:12:51, time: 0.092, data_time: 0.020, memory: 1125, loss: 1.3266 2023-01-04 02:54:49,599 - mmcls - INFO - Epoch [91][500/5005] lr: 1.000e-04, eta: 1:12:42, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3406 2023-01-04 02:54:58,241 - mmcls - INFO - Epoch [91][600/5005] lr: 1.000e-04, eta: 1:12:33, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3309 2023-01-04 02:55:07,040 - mmcls - INFO - Epoch [91][700/5005] lr: 1.000e-04, eta: 1:12:24, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3285 2023-01-04 02:55:15,682 - mmcls - INFO - Epoch [91][800/5005] lr: 1.000e-04, eta: 1:12:15, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3358 2023-01-04 02:55:24,507 - mmcls - INFO - Epoch [91][900/5005] lr: 1.000e-04, eta: 1:12:07, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.3338 2023-01-04 02:55:33,050 - mmcls - INFO - Epoch [91][1000/5005] lr: 1.000e-04, eta: 1:11:58, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3547 2023-01-04 02:55:42,159 - mmcls - INFO - Epoch [91][1100/5005] lr: 1.000e-04, eta: 1:11:49, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.3172 2023-01-04 02:55:50,605 - mmcls - INFO - Epoch [91][1200/5005] lr: 1.000e-04, eta: 1:11:40, time: 0.084, data_time: 0.020, memory: 1125, loss: 1.3313 2023-01-04 02:55:59,432 - mmcls - INFO - Epoch [91][1300/5005] lr: 1.000e-04, eta: 1:11:31, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3241 2023-01-04 02:56:08,937 - mmcls - INFO - Epoch [91][1400/5005] lr: 1.000e-04, eta: 1:11:23, time: 0.095, data_time: 0.016, memory: 1125, loss: 1.3192 2023-01-04 02:56:17,926 - mmcls - INFO - Epoch [91][1500/5005] lr: 1.000e-04, eta: 1:11:14, time: 0.090, data_time: 0.018, memory: 1125, loss: 1.3029 2023-01-04 02:56:26,738 - mmcls - INFO - Epoch [91][1600/5005] lr: 1.000e-04, eta: 1:11:05, time: 0.088, data_time: 0.036, memory: 1125, loss: 1.3281 2023-01-04 02:56:35,554 - mmcls - INFO - Epoch [91][1700/5005] lr: 1.000e-04, eta: 1:10:56, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3113 2023-01-04 02:56:44,201 - mmcls - INFO - Epoch [91][1800/5005] lr: 1.000e-04, eta: 1:10:48, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3450 2023-01-04 02:56:52,674 - mmcls - INFO - Epoch [91][1900/5005] lr: 1.000e-04, eta: 1:10:39, time: 0.085, data_time: 0.005, memory: 1125, loss: 1.3267 2023-01-04 02:57:01,560 - mmcls - INFO - Epoch [91][2000/5005] lr: 1.000e-04, eta: 1:10:30, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.3214 2023-01-04 02:57:09,974 - mmcls - INFO - Epoch [91][2100/5005] lr: 1.000e-04, eta: 1:10:21, time: 0.084, data_time: 0.032, memory: 1125, loss: 1.3180 2023-01-04 02:57:18,591 - mmcls - INFO - Epoch [91][2200/5005] lr: 1.000e-04, eta: 1:10:12, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3232 2023-01-04 02:57:27,474 - mmcls - INFO - Epoch [91][2300/5005] lr: 1.000e-04, eta: 1:10:03, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3390 2023-01-04 02:57:36,719 - mmcls - INFO - Epoch [91][2400/5005] lr: 1.000e-04, eta: 1:09:55, time: 0.093, data_time: 0.009, memory: 1125, loss: 1.3170 2023-01-04 02:57:45,452 - mmcls - INFO - Epoch [91][2500/5005] lr: 1.000e-04, eta: 1:09:46, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3409 2023-01-04 02:57:54,249 - mmcls - INFO - Epoch [91][2600/5005] lr: 1.000e-04, eta: 1:09:37, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.3247 2023-01-04 02:58:03,149 - mmcls - INFO - Epoch [91][2700/5005] lr: 1.000e-04, eta: 1:09:28, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.3314 2023-01-04 02:58:12,027 - mmcls - INFO - Epoch [91][2800/5005] lr: 1.000e-04, eta: 1:09:19, time: 0.089, data_time: 0.017, memory: 1125, loss: 1.3233 2023-01-04 02:58:20,850 - mmcls - INFO - Epoch [91][2900/5005] lr: 1.000e-04, eta: 1:09:11, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.3323 2023-01-04 02:58:29,539 - mmcls - INFO - Epoch [91][3000/5005] lr: 1.000e-04, eta: 1:09:02, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3169 2023-01-04 02:58:38,268 - mmcls - INFO - Epoch [91][3100/5005] lr: 1.000e-04, eta: 1:08:53, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3006 2023-01-04 02:58:47,095 - mmcls - INFO - Epoch [91][3200/5005] lr: 1.000e-04, eta: 1:08:44, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3239 2023-01-04 02:58:55,720 - mmcls - INFO - Epoch [91][3300/5005] lr: 1.000e-04, eta: 1:08:35, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3284 2023-01-04 02:59:04,433 - mmcls - INFO - Epoch [91][3400/5005] lr: 1.000e-04, eta: 1:08:27, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3380 2023-01-04 02:59:13,250 - mmcls - INFO - Epoch [91][3500/5005] lr: 1.000e-04, eta: 1:08:18, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3272 2023-01-04 02:59:22,078 - mmcls - INFO - Epoch [91][3600/5005] lr: 1.000e-04, eta: 1:08:09, time: 0.088, data_time: 0.033, memory: 1125, loss: 1.3140 2023-01-04 02:59:30,825 - mmcls - INFO - Epoch [91][3700/5005] lr: 1.000e-04, eta: 1:08:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3304 2023-01-04 02:59:39,606 - mmcls - INFO - Epoch [91][3800/5005] lr: 1.000e-04, eta: 1:07:51, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3227 2023-01-04 02:59:48,191 - mmcls - INFO - Epoch [91][3900/5005] lr: 1.000e-04, eta: 1:07:43, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3203 2023-01-04 02:59:56,938 - mmcls - INFO - Epoch [91][4000/5005] lr: 1.000e-04, eta: 1:07:34, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3438 2023-01-04 03:00:05,470 - mmcls - INFO - Epoch [91][4100/5005] lr: 1.000e-04, eta: 1:07:25, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3362 2023-01-04 03:00:14,283 - mmcls - INFO - Epoch [91][4200/5005] lr: 1.000e-04, eta: 1:07:16, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3201 2023-01-04 03:00:22,841 - mmcls - INFO - Epoch [91][4300/5005] lr: 1.000e-04, eta: 1:07:07, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.3154 2023-01-04 03:00:31,509 - mmcls - INFO - Epoch [91][4400/5005] lr: 1.000e-04, eta: 1:06:58, time: 0.087, data_time: 0.041, memory: 1125, loss: 1.3509 2023-01-04 03:00:40,049 - mmcls - INFO - Epoch [91][4500/5005] lr: 1.000e-04, eta: 1:06:50, time: 0.085, data_time: 0.008, memory: 1125, loss: 1.3148 2023-01-04 03:00:48,843 - mmcls - INFO - Epoch [91][4600/5005] lr: 1.000e-04, eta: 1:06:41, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3299 2023-01-04 03:00:57,679 - mmcls - INFO - Epoch [91][4700/5005] lr: 1.000e-04, eta: 1:06:32, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.3433 2023-01-04 03:01:06,531 - mmcls - INFO - Epoch [91][4800/5005] lr: 1.000e-04, eta: 1:06:23, time: 0.089, data_time: 0.028, memory: 1125, loss: 1.3444 2023-01-04 03:01:15,075 - mmcls - INFO - Epoch [91][4900/5005] lr: 1.000e-04, eta: 1:06:14, time: 0.085, data_time: 0.021, memory: 1125, loss: 1.3432 2023-01-04 03:01:23,854 - mmcls - INFO - Epoch [91][5000/5005] lr: 1.000e-04, eta: 1:06:06, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3234 2023-01-04 03:01:46,438 - mmcls - INFO - Epoch(val) [91][196] accuracy_top-1: 70.1380, accuracy_top-5: 89.3860 2023-01-04 03:01:57,597 - mmcls - INFO - Epoch [92][100/5005] lr: 1.000e-04, eta: 1:05:57, time: 0.111, data_time: 0.048, memory: 1125, loss: 1.2971 2023-01-04 03:02:06,576 - mmcls - INFO - Epoch [92][200/5005] lr: 1.000e-04, eta: 1:05:48, time: 0.090, data_time: 0.008, memory: 1125, loss: 1.3255 2023-01-04 03:02:15,450 - mmcls - INFO - Epoch [92][300/5005] lr: 1.000e-04, eta: 1:05:39, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.2997 2023-01-04 03:02:24,217 - mmcls - INFO - Epoch [92][400/5005] lr: 1.000e-04, eta: 1:05:30, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3173 2023-01-04 03:02:33,103 - mmcls - INFO - Epoch [92][500/5005] lr: 1.000e-04, eta: 1:05:21, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3423 2023-01-04 03:02:41,845 - mmcls - INFO - Epoch [92][600/5005] lr: 1.000e-04, eta: 1:05:13, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3260 2023-01-04 03:02:50,740 - mmcls - INFO - Epoch [92][700/5005] lr: 1.000e-04, eta: 1:05:04, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.3182 2023-01-04 03:02:59,840 - mmcls - INFO - Epoch [92][800/5005] lr: 1.000e-04, eta: 1:04:55, time: 0.091, data_time: 0.012, memory: 1125, loss: 1.3407 2023-01-04 03:03:08,669 - mmcls - INFO - Epoch [92][900/5005] lr: 1.000e-04, eta: 1:04:46, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3268 2023-01-04 03:03:17,349 - mmcls - INFO - Epoch [92][1000/5005] lr: 1.000e-04, eta: 1:04:37, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3153 2023-01-04 03:03:26,138 - mmcls - INFO - Epoch [92][1100/5005] lr: 1.000e-04, eta: 1:04:29, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3216 2023-01-04 03:03:34,819 - mmcls - INFO - Epoch [92][1200/5005] lr: 1.000e-04, eta: 1:04:20, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3233 2023-01-04 03:03:44,028 - mmcls - INFO - Epoch [92][1300/5005] lr: 1.000e-04, eta: 1:04:11, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.3332 2023-01-04 03:03:52,692 - mmcls - INFO - Epoch [92][1400/5005] lr: 1.000e-04, eta: 1:04:02, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3392 2023-01-04 03:04:01,266 - mmcls - INFO - Epoch [92][1500/5005] lr: 1.000e-04, eta: 1:03:53, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3152 2023-01-04 03:04:10,062 - mmcls - INFO - Epoch [92][1600/5005] lr: 1.000e-04, eta: 1:03:44, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3113 2023-01-04 03:04:18,716 - mmcls - INFO - Epoch [92][1700/5005] lr: 1.000e-04, eta: 1:03:36, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3237 2023-01-04 03:04:27,609 - mmcls - INFO - Epoch [92][1800/5005] lr: 1.000e-04, eta: 1:03:27, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3379 2023-01-04 03:04:36,177 - mmcls - INFO - Epoch [92][1900/5005] lr: 1.000e-04, eta: 1:03:18, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3245 2023-01-04 03:04:44,874 - mmcls - INFO - Epoch [92][2000/5005] lr: 1.000e-04, eta: 1:03:09, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3343 2023-01-04 03:04:53,647 - mmcls - INFO - Epoch [92][2100/5005] lr: 1.000e-04, eta: 1:03:00, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3253 2023-01-04 03:05:02,353 - mmcls - INFO - Epoch [92][2200/5005] lr: 1.000e-04, eta: 1:02:52, time: 0.087, data_time: 0.020, memory: 1125, loss: 1.3252 2023-01-04 03:05:11,259 - mmcls - INFO - Epoch [92][2300/5005] lr: 1.000e-04, eta: 1:02:43, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3316 2023-01-04 03:05:20,087 - mmcls - INFO - Epoch [92][2400/5005] lr: 1.000e-04, eta: 1:02:34, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3330 2023-01-04 03:05:28,912 - mmcls - INFO - Epoch [92][2500/5005] lr: 1.000e-04, eta: 1:02:25, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3195 2023-01-04 03:05:37,655 - mmcls - INFO - Epoch [92][2600/5005] lr: 1.000e-04, eta: 1:02:16, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3299 2023-01-04 03:05:46,681 - mmcls - INFO - Epoch [92][2700/5005] lr: 1.000e-04, eta: 1:02:08, time: 0.090, data_time: 0.013, memory: 1125, loss: 1.3209 2023-01-04 03:05:55,124 - mmcls - INFO - Epoch [92][2800/5005] lr: 1.000e-04, eta: 1:01:59, time: 0.084, data_time: 0.014, memory: 1125, loss: 1.3187 2023-01-04 03:06:04,155 - mmcls - INFO - Epoch [92][2900/5005] lr: 1.000e-04, eta: 1:01:50, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3245 2023-01-04 03:06:12,825 - mmcls - INFO - Epoch [92][3000/5005] lr: 1.000e-04, eta: 1:01:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3269 2023-01-04 03:06:21,653 - mmcls - INFO - Epoch [92][3100/5005] lr: 1.000e-04, eta: 1:01:32, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3561 2023-01-04 03:06:30,301 - mmcls - INFO - Epoch [92][3200/5005] lr: 1.000e-04, eta: 1:01:24, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3170 2023-01-04 03:06:39,140 - mmcls - INFO - Epoch [92][3300/5005] lr: 1.000e-04, eta: 1:01:15, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3362 2023-01-04 03:06:47,780 - mmcls - INFO - Epoch [92][3400/5005] lr: 1.000e-04, eta: 1:01:06, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3261 2023-01-04 03:06:56,328 - mmcls - INFO - Epoch [92][3500/5005] lr: 1.000e-04, eta: 1:00:57, time: 0.085, data_time: 0.025, memory: 1125, loss: 1.3147 2023-01-04 03:07:04,993 - mmcls - INFO - Epoch [92][3600/5005] lr: 1.000e-04, eta: 1:00:48, time: 0.087, data_time: 0.038, memory: 1125, loss: 1.3198 2023-01-04 03:07:13,804 - mmcls - INFO - Epoch [92][3700/5005] lr: 1.000e-04, eta: 1:00:39, time: 0.088, data_time: 0.025, memory: 1125, loss: 1.3242 2023-01-04 03:07:22,435 - mmcls - INFO - Epoch [92][3800/5005] lr: 1.000e-04, eta: 1:00:31, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3243 2023-01-04 03:07:31,153 - mmcls - INFO - Epoch [92][3900/5005] lr: 1.000e-04, eta: 1:00:22, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.3364 2023-01-04 03:07:40,063 - mmcls - INFO - Epoch [92][4000/5005] lr: 1.000e-04, eta: 1:00:13, time: 0.089, data_time: 0.026, memory: 1125, loss: 1.3266 2023-01-04 03:07:48,900 - mmcls - INFO - Epoch [92][4100/5005] lr: 1.000e-04, eta: 1:00:04, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3095 2023-01-04 03:07:57,462 - mmcls - INFO - Epoch [92][4200/5005] lr: 1.000e-04, eta: 0:59:55, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3052 2023-01-04 03:08:06,599 - mmcls - INFO - Epoch [92][4300/5005] lr: 1.000e-04, eta: 0:59:47, time: 0.091, data_time: 0.002, memory: 1125, loss: 1.3056 2023-01-04 03:08:15,166 - mmcls - INFO - Epoch [92][4400/5005] lr: 1.000e-04, eta: 0:59:38, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3033 2023-01-04 03:08:23,750 - mmcls - INFO - Epoch [92][4500/5005] lr: 1.000e-04, eta: 0:59:29, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3161 2023-01-04 03:08:32,500 - mmcls - INFO - Epoch [92][4600/5005] lr: 1.000e-04, eta: 0:59:20, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3317 2023-01-04 03:08:41,218 - mmcls - INFO - Epoch [92][4700/5005] lr: 1.000e-04, eta: 0:59:11, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.3352 2023-01-04 03:08:49,808 - mmcls - INFO - Epoch [92][4800/5005] lr: 1.000e-04, eta: 0:59:03, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3214 2023-01-04 03:08:58,468 - mmcls - INFO - Epoch [92][4900/5005] lr: 1.000e-04, eta: 0:58:54, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3271 2023-01-04 03:09:07,632 - mmcls - INFO - Epoch [92][5000/5005] lr: 1.000e-04, eta: 0:58:45, time: 0.092, data_time: 0.027, memory: 1125, loss: 1.3280 2023-01-04 03:09:30,223 - mmcls - INFO - Epoch(val) [92][196] accuracy_top-1: 70.0680, accuracy_top-5: 89.4160 2023-01-04 03:09:41,318 - mmcls - INFO - Epoch [93][100/5005] lr: 1.000e-04, eta: 0:58:36, time: 0.111, data_time: 0.041, memory: 1125, loss: 1.3132 2023-01-04 03:09:50,336 - mmcls - INFO - Epoch [93][200/5005] lr: 1.000e-04, eta: 0:58:27, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.3005 2023-01-04 03:09:59,003 - mmcls - INFO - Epoch [93][300/5005] lr: 1.000e-04, eta: 0:58:18, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3149 2023-01-04 03:10:08,086 - mmcls - INFO - Epoch [93][400/5005] lr: 1.000e-04, eta: 0:58:10, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.3235 2023-01-04 03:10:16,672 - mmcls - INFO - Epoch [93][500/5005] lr: 1.000e-04, eta: 0:58:01, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3096 2023-01-04 03:10:25,453 - mmcls - INFO - Epoch [93][600/5005] lr: 1.000e-04, eta: 0:57:52, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.3417 2023-01-04 03:10:34,304 - mmcls - INFO - Epoch [93][700/5005] lr: 1.000e-04, eta: 0:57:43, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.3020 2023-01-04 03:10:42,967 - mmcls - INFO - Epoch [93][800/5005] lr: 1.000e-04, eta: 0:57:34, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3156 2023-01-04 03:10:52,034 - mmcls - INFO - Epoch [93][900/5005] lr: 1.000e-04, eta: 0:57:25, time: 0.091, data_time: 0.014, memory: 1125, loss: 1.3295 2023-01-04 03:11:01,088 - mmcls - INFO - Epoch [93][1000/5005] lr: 1.000e-04, eta: 0:57:17, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3388 2023-01-04 03:11:09,564 - mmcls - INFO - Epoch [93][1100/5005] lr: 1.000e-04, eta: 0:57:08, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3096 2023-01-04 03:11:18,605 - mmcls - INFO - Epoch [93][1200/5005] lr: 1.000e-04, eta: 0:56:59, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3107 2023-01-04 03:11:27,166 - mmcls - INFO - Epoch [93][1300/5005] lr: 1.000e-04, eta: 0:56:50, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3178 2023-01-04 03:11:35,667 - mmcls - INFO - Epoch [93][1400/5005] lr: 1.000e-04, eta: 0:56:41, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.3091 2023-01-04 03:11:44,639 - mmcls - INFO - Epoch [93][1500/5005] lr: 1.000e-04, eta: 0:56:33, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3383 2023-01-04 03:11:53,476 - mmcls - INFO - Epoch [93][1600/5005] lr: 1.000e-04, eta: 0:56:24, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.3191 2023-01-04 03:12:02,271 - mmcls - INFO - Epoch [93][1700/5005] lr: 1.000e-04, eta: 0:56:15, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3215 2023-01-04 03:12:10,869 - mmcls - INFO - Epoch [93][1800/5005] lr: 1.000e-04, eta: 0:56:06, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3321 2023-01-04 03:12:20,002 - mmcls - INFO - Epoch [93][1900/5005] lr: 1.000e-04, eta: 0:55:57, time: 0.091, data_time: 0.003, memory: 1125, loss: 1.3220 2023-01-04 03:12:28,874 - mmcls - INFO - Epoch [93][2000/5005] lr: 1.000e-04, eta: 0:55:49, time: 0.089, data_time: 0.023, memory: 1125, loss: 1.3042 2023-01-04 03:12:37,732 - mmcls - INFO - Epoch [93][2100/5005] lr: 1.000e-04, eta: 0:55:40, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3193 2023-01-04 03:12:46,700 - mmcls - INFO - Epoch [93][2200/5005] lr: 1.000e-04, eta: 0:55:31, time: 0.090, data_time: 0.034, memory: 1125, loss: 1.3280 2023-01-04 03:12:55,692 - mmcls - INFO - Epoch [93][2300/5005] lr: 1.000e-04, eta: 0:55:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3081 2023-01-04 03:13:04,288 - mmcls - INFO - Epoch [93][2400/5005] lr: 1.000e-04, eta: 0:55:13, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3219 2023-01-04 03:13:13,001 - mmcls - INFO - Epoch [93][2500/5005] lr: 1.000e-04, eta: 0:55:05, time: 0.087, data_time: 0.015, memory: 1125, loss: 1.3314 2023-01-04 03:13:21,972 - mmcls - INFO - Epoch [93][2600/5005] lr: 1.000e-04, eta: 0:54:56, time: 0.090, data_time: 0.025, memory: 1125, loss: 1.3324 2023-01-04 03:13:30,687 - mmcls - INFO - Epoch [93][2700/5005] lr: 1.000e-04, eta: 0:54:47, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3209 2023-01-04 03:13:39,482 - mmcls - INFO - Epoch [93][2800/5005] lr: 1.000e-04, eta: 0:54:38, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3066 2023-01-04 03:13:48,210 - mmcls - INFO - Epoch [93][2900/5005] lr: 1.000e-04, eta: 0:54:29, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3119 2023-01-04 03:13:57,172 - mmcls - INFO - Epoch [93][3000/5005] lr: 1.000e-04, eta: 0:54:21, time: 0.090, data_time: 0.033, memory: 1125, loss: 1.3107 2023-01-04 03:14:05,693 - mmcls - INFO - Epoch [93][3100/5005] lr: 1.000e-04, eta: 0:54:12, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3139 2023-01-04 03:14:14,314 - mmcls - INFO - Epoch [93][3200/5005] lr: 1.000e-04, eta: 0:54:03, time: 0.086, data_time: 0.017, memory: 1125, loss: 1.3345 2023-01-04 03:14:23,332 - mmcls - INFO - Epoch [93][3300/5005] lr: 1.000e-04, eta: 0:53:54, time: 0.090, data_time: 0.004, memory: 1125, loss: 1.2898 2023-01-04 03:14:31,937 - mmcls - INFO - Epoch [93][3400/5005] lr: 1.000e-04, eta: 0:53:45, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3101 2023-01-04 03:14:40,654 - mmcls - INFO - Epoch [93][3500/5005] lr: 1.000e-04, eta: 0:53:37, time: 0.087, data_time: 0.039, memory: 1125, loss: 1.3291 2023-01-04 03:14:49,462 - mmcls - INFO - Epoch [93][3600/5005] lr: 1.000e-04, eta: 0:53:28, time: 0.088, data_time: 0.035, memory: 1125, loss: 1.3123 2023-01-04 03:14:58,199 - mmcls - INFO - Epoch [93][3700/5005] lr: 1.000e-04, eta: 0:53:19, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.2991 2023-01-04 03:15:06,960 - mmcls - INFO - Epoch [93][3800/5005] lr: 1.000e-04, eta: 0:53:10, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3182 2023-01-04 03:15:16,175 - mmcls - INFO - Epoch [93][3900/5005] lr: 1.000e-04, eta: 0:53:01, time: 0.092, data_time: 0.001, memory: 1125, loss: 1.3127 2023-01-04 03:15:25,102 - mmcls - INFO - Epoch [93][4000/5005] lr: 1.000e-04, eta: 0:52:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3141 2023-01-04 03:15:33,736 - mmcls - INFO - Epoch [93][4100/5005] lr: 1.000e-04, eta: 0:52:44, time: 0.086, data_time: 0.019, memory: 1125, loss: 1.3274 2023-01-04 03:15:42,461 - mmcls - INFO - Epoch [93][4200/5005] lr: 1.000e-04, eta: 0:52:35, time: 0.087, data_time: 0.021, memory: 1125, loss: 1.3055 2023-01-04 03:15:51,418 - mmcls - INFO - Epoch [93][4300/5005] lr: 1.000e-04, eta: 0:52:26, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3347 2023-01-04 03:16:00,181 - mmcls - INFO - Epoch [93][4400/5005] lr: 1.000e-04, eta: 0:52:17, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3279 2023-01-04 03:16:08,624 - mmcls - INFO - Epoch [93][4500/5005] lr: 1.000e-04, eta: 0:52:09, time: 0.084, data_time: 0.010, memory: 1125, loss: 1.3232 2023-01-04 03:16:17,257 - mmcls - INFO - Epoch [93][4600/5005] lr: 1.000e-04, eta: 0:52:00, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.3190 2023-01-04 03:16:26,345 - mmcls - INFO - Epoch [93][4700/5005] lr: 1.000e-04, eta: 0:51:51, time: 0.091, data_time: 0.017, memory: 1125, loss: 1.3331 2023-01-04 03:16:34,924 - mmcls - INFO - Epoch [93][4800/5005] lr: 1.000e-04, eta: 0:51:42, time: 0.086, data_time: 0.009, memory: 1125, loss: 1.2987 2023-01-04 03:16:43,412 - mmcls - INFO - Epoch [93][4900/5005] lr: 1.000e-04, eta: 0:51:33, time: 0.085, data_time: 0.018, memory: 1125, loss: 1.3301 2023-01-04 03:16:52,241 - mmcls - INFO - Epoch [93][5000/5005] lr: 1.000e-04, eta: 0:51:24, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3155 2023-01-04 03:17:14,869 - mmcls - INFO - Epoch(val) [93][196] accuracy_top-1: 70.3200, accuracy_top-5: 89.3660 2023-01-04 03:17:26,103 - mmcls - INFO - Epoch [94][100/5005] lr: 1.000e-04, eta: 0:51:15, time: 0.112, data_time: 0.041, memory: 1125, loss: 1.3307 2023-01-04 03:17:34,787 - mmcls - INFO - Epoch [94][200/5005] lr: 1.000e-04, eta: 0:51:07, time: 0.087, data_time: 0.000, memory: 1125, loss: 1.3304 2023-01-04 03:17:43,781 - mmcls - INFO - Epoch [94][300/5005] lr: 1.000e-04, eta: 0:50:58, time: 0.090, data_time: 0.017, memory: 1125, loss: 1.3269 2023-01-04 03:17:52,859 - mmcls - INFO - Epoch [94][400/5005] lr: 1.000e-04, eta: 0:50:49, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.3200 2023-01-04 03:18:01,712 - mmcls - INFO - Epoch [94][500/5005] lr: 1.000e-04, eta: 0:50:40, time: 0.089, data_time: 0.024, memory: 1125, loss: 1.3013 2023-01-04 03:18:10,274 - mmcls - INFO - Epoch [94][600/5005] lr: 1.000e-04, eta: 0:50:31, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3277 2023-01-04 03:18:18,974 - mmcls - INFO - Epoch [94][700/5005] lr: 1.000e-04, eta: 0:50:23, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3327 2023-01-04 03:18:27,751 - mmcls - INFO - Epoch [94][800/5005] lr: 1.000e-04, eta: 0:50:14, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3131 2023-01-04 03:18:36,244 - mmcls - INFO - Epoch [94][900/5005] lr: 1.000e-04, eta: 0:50:05, time: 0.085, data_time: 0.023, memory: 1125, loss: 1.2925 2023-01-04 03:18:44,970 - mmcls - INFO - Epoch [94][1000/5005] lr: 1.000e-04, eta: 0:49:56, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3299 2023-01-04 03:18:53,658 - mmcls - INFO - Epoch [94][1100/5005] lr: 1.000e-04, eta: 0:49:47, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3124 2023-01-04 03:19:02,681 - mmcls - INFO - Epoch [94][1200/5005] lr: 1.000e-04, eta: 0:49:38, time: 0.090, data_time: 0.012, memory: 1125, loss: 1.3173 2023-01-04 03:19:11,280 - mmcls - INFO - Epoch [94][1300/5005] lr: 1.000e-04, eta: 0:49:30, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3418 2023-01-04 03:19:19,993 - mmcls - INFO - Epoch [94][1400/5005] lr: 1.000e-04, eta: 0:49:21, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3191 2023-01-04 03:19:29,157 - mmcls - INFO - Epoch [94][1500/5005] lr: 1.000e-04, eta: 0:49:12, time: 0.092, data_time: 0.028, memory: 1125, loss: 1.3227 2023-01-04 03:19:37,670 - mmcls - INFO - Epoch [94][1600/5005] lr: 1.000e-04, eta: 0:49:03, time: 0.085, data_time: 0.022, memory: 1125, loss: 1.3172 2023-01-04 03:19:46,522 - mmcls - INFO - Epoch [94][1700/5005] lr: 1.000e-04, eta: 0:48:54, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3326 2023-01-04 03:19:55,318 - mmcls - INFO - Epoch [94][1800/5005] lr: 1.000e-04, eta: 0:48:46, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.3126 2023-01-04 03:20:04,321 - mmcls - INFO - Epoch [94][1900/5005] lr: 1.000e-04, eta: 0:48:37, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3258 2023-01-04 03:20:13,422 - mmcls - INFO - Epoch [94][2000/5005] lr: 1.000e-04, eta: 0:48:28, time: 0.091, data_time: 0.001, memory: 1125, loss: 1.3232 2023-01-04 03:20:22,253 - mmcls - INFO - Epoch [94][2100/5005] lr: 1.000e-04, eta: 0:48:19, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3343 2023-01-04 03:20:31,365 - mmcls - INFO - Epoch [94][2200/5005] lr: 1.000e-04, eta: 0:48:10, time: 0.091, data_time: 0.018, memory: 1125, loss: 1.3187 2023-01-04 03:20:39,985 - mmcls - INFO - Epoch [94][2300/5005] lr: 1.000e-04, eta: 0:48:02, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3116 2023-01-04 03:20:48,659 - mmcls - INFO - Epoch [94][2400/5005] lr: 1.000e-04, eta: 0:47:53, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3109 2023-01-04 03:20:57,313 - mmcls - INFO - Epoch [94][2500/5005] lr: 1.000e-04, eta: 0:47:44, time: 0.086, data_time: 0.008, memory: 1125, loss: 1.3154 2023-01-04 03:21:05,919 - mmcls - INFO - Epoch [94][2600/5005] lr: 1.000e-04, eta: 0:47:35, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3086 2023-01-04 03:21:14,525 - mmcls - INFO - Epoch [94][2700/5005] lr: 1.000e-04, eta: 0:47:26, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3059 2023-01-04 03:21:23,246 - mmcls - INFO - Epoch [94][2800/5005] lr: 1.000e-04, eta: 0:47:18, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3268 2023-01-04 03:21:32,167 - mmcls - INFO - Epoch [94][2900/5005] lr: 1.000e-04, eta: 0:47:09, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3115 2023-01-04 03:21:40,696 - mmcls - INFO - Epoch [94][3000/5005] lr: 1.000e-04, eta: 0:47:00, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3130 2023-01-04 03:21:49,572 - mmcls - INFO - Epoch [94][3100/5005] lr: 1.000e-04, eta: 0:46:51, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3196 2023-01-04 03:21:58,318 - mmcls - INFO - Epoch [94][3200/5005] lr: 1.000e-04, eta: 0:46:42, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.3128 2023-01-04 03:22:07,252 - mmcls - INFO - Epoch [94][3300/5005] lr: 1.000e-04, eta: 0:46:34, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3040 2023-01-04 03:22:16,383 - mmcls - INFO - Epoch [94][3400/5005] lr: 1.000e-04, eta: 0:46:25, time: 0.091, data_time: 0.023, memory: 1125, loss: 1.3170 2023-01-04 03:22:24,935 - mmcls - INFO - Epoch [94][3500/5005] lr: 1.000e-04, eta: 0:46:16, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3051 2023-01-04 03:22:33,849 - mmcls - INFO - Epoch [94][3600/5005] lr: 1.000e-04, eta: 0:46:07, time: 0.089, data_time: 0.039, memory: 1125, loss: 1.3029 2023-01-04 03:22:42,804 - mmcls - INFO - Epoch [94][3700/5005] lr: 1.000e-04, eta: 0:45:58, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3327 2023-01-04 03:22:51,695 - mmcls - INFO - Epoch [94][3800/5005] lr: 1.000e-04, eta: 0:45:50, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.3233 2023-01-04 03:23:00,214 - mmcls - INFO - Epoch [94][3900/5005] lr: 1.000e-04, eta: 0:45:41, time: 0.085, data_time: 0.020, memory: 1125, loss: 1.3275 2023-01-04 03:23:08,953 - mmcls - INFO - Epoch [94][4000/5005] lr: 1.000e-04, eta: 0:45:32, time: 0.087, data_time: 0.026, memory: 1125, loss: 1.3181 2023-01-04 03:23:17,445 - mmcls - INFO - Epoch [94][4100/5005] lr: 1.000e-04, eta: 0:45:23, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.3105 2023-01-04 03:23:26,274 - mmcls - INFO - Epoch [94][4200/5005] lr: 1.000e-04, eta: 0:45:14, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3103 2023-01-04 03:23:35,018 - mmcls - INFO - Epoch [94][4300/5005] lr: 1.000e-04, eta: 0:45:06, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3147 2023-01-04 03:23:43,629 - mmcls - INFO - Epoch [94][4400/5005] lr: 1.000e-04, eta: 0:44:57, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3229 2023-01-04 03:23:52,410 - mmcls - INFO - Epoch [94][4500/5005] lr: 1.000e-04, eta: 0:44:48, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3232 2023-01-04 03:24:00,837 - mmcls - INFO - Epoch [94][4600/5005] lr: 1.000e-04, eta: 0:44:39, time: 0.084, data_time: 0.022, memory: 1125, loss: 1.3219 2023-01-04 03:24:09,872 - mmcls - INFO - Epoch [94][4700/5005] lr: 1.000e-04, eta: 0:44:30, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3191 2023-01-04 03:24:18,509 - mmcls - INFO - Epoch [94][4800/5005] lr: 1.000e-04, eta: 0:44:21, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3067 2023-01-04 03:24:27,045 - mmcls - INFO - Epoch [94][4900/5005] lr: 1.000e-04, eta: 0:44:13, time: 0.085, data_time: 0.041, memory: 1125, loss: 1.2878 2023-01-04 03:24:35,909 - mmcls - INFO - Epoch [94][5000/5005] lr: 1.000e-04, eta: 0:44:04, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3370 2023-01-04 03:24:58,678 - mmcls - INFO - Epoch(val) [94][196] accuracy_top-1: 70.0600, accuracy_top-5: 89.4140 2023-01-04 03:25:09,618 - mmcls - INFO - Epoch [95][100/5005] lr: 1.000e-04, eta: 0:43:55, time: 0.109, data_time: 0.039, memory: 1125, loss: 1.2971 2023-01-04 03:25:18,270 - mmcls - INFO - Epoch [95][200/5005] lr: 1.000e-04, eta: 0:43:46, time: 0.086, data_time: 0.000, memory: 1125, loss: 1.3198 2023-01-04 03:25:26,880 - mmcls - INFO - Epoch [95][300/5005] lr: 1.000e-04, eta: 0:43:37, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3186 2023-01-04 03:25:35,728 - mmcls - INFO - Epoch [95][400/5005] lr: 1.000e-04, eta: 0:43:28, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3227 2023-01-04 03:25:44,777 - mmcls - INFO - Epoch [95][500/5005] lr: 1.000e-04, eta: 0:43:19, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3291 2023-01-04 03:25:53,990 - mmcls - INFO - Epoch [95][600/5005] lr: 1.000e-04, eta: 0:43:11, time: 0.092, data_time: 0.018, memory: 1125, loss: 1.3022 2023-01-04 03:26:02,773 - mmcls - INFO - Epoch [95][700/5005] lr: 1.000e-04, eta: 0:43:02, time: 0.088, data_time: 0.006, memory: 1125, loss: 1.3215 2023-01-04 03:26:11,232 - mmcls - INFO - Epoch [95][800/5005] lr: 1.000e-04, eta: 0:42:53, time: 0.085, data_time: 0.010, memory: 1125, loss: 1.3277 2023-01-04 03:26:20,249 - mmcls - INFO - Epoch [95][900/5005] lr: 1.000e-04, eta: 0:42:44, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3216 2023-01-04 03:26:29,058 - mmcls - INFO - Epoch [95][1000/5005] lr: 1.000e-04, eta: 0:42:35, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.2941 2023-01-04 03:26:37,866 - mmcls - INFO - Epoch [95][1100/5005] lr: 1.000e-04, eta: 0:42:27, time: 0.088, data_time: 0.022, memory: 1125, loss: 1.3140 2023-01-04 03:26:46,616 - mmcls - INFO - Epoch [95][1200/5005] lr: 1.000e-04, eta: 0:42:18, time: 0.088, data_time: 0.014, memory: 1125, loss: 1.3142 2023-01-04 03:26:55,564 - mmcls - INFO - Epoch [95][1300/5005] lr: 1.000e-04, eta: 0:42:09, time: 0.089, data_time: 0.013, memory: 1125, loss: 1.3274 2023-01-04 03:27:04,266 - mmcls - INFO - Epoch [95][1400/5005] lr: 1.000e-04, eta: 0:42:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3113 2023-01-04 03:27:13,030 - mmcls - INFO - Epoch [95][1500/5005] lr: 1.000e-04, eta: 0:41:51, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3055 2023-01-04 03:27:21,916 - mmcls - INFO - Epoch [95][1600/5005] lr: 1.000e-04, eta: 0:41:43, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3085 2023-01-04 03:27:30,780 - mmcls - INFO - Epoch [95][1700/5005] lr: 1.000e-04, eta: 0:41:34, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3147 2023-01-04 03:27:39,438 - mmcls - INFO - Epoch [95][1800/5005] lr: 1.000e-04, eta: 0:41:25, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3128 2023-01-04 03:27:48,470 - mmcls - INFO - Epoch [95][1900/5005] lr: 1.000e-04, eta: 0:41:16, time: 0.090, data_time: 0.030, memory: 1125, loss: 1.3348 2023-01-04 03:27:57,118 - mmcls - INFO - Epoch [95][2000/5005] lr: 1.000e-04, eta: 0:41:07, time: 0.087, data_time: 0.027, memory: 1125, loss: 1.3016 2023-01-04 03:28:06,163 - mmcls - INFO - Epoch [95][2100/5005] lr: 1.000e-04, eta: 0:40:59, time: 0.090, data_time: 0.022, memory: 1125, loss: 1.3270 2023-01-04 03:28:15,161 - mmcls - INFO - Epoch [95][2200/5005] lr: 1.000e-04, eta: 0:40:50, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3252 2023-01-04 03:28:24,218 - mmcls - INFO - Epoch [95][2300/5005] lr: 1.000e-04, eta: 0:40:41, time: 0.091, data_time: 0.005, memory: 1125, loss: 1.3251 2023-01-04 03:28:32,732 - mmcls - INFO - Epoch [95][2400/5005] lr: 1.000e-04, eta: 0:40:32, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3059 2023-01-04 03:28:41,319 - mmcls - INFO - Epoch [95][2500/5005] lr: 1.000e-04, eta: 0:40:23, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3206 2023-01-04 03:28:50,135 - mmcls - INFO - Epoch [95][2600/5005] lr: 1.000e-04, eta: 0:40:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3067 2023-01-04 03:28:58,899 - mmcls - INFO - Epoch [95][2700/5005] lr: 1.000e-04, eta: 0:40:06, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3265 2023-01-04 03:29:07,865 - mmcls - INFO - Epoch [95][2800/5005] lr: 1.000e-04, eta: 0:39:57, time: 0.090, data_time: 0.014, memory: 1125, loss: 1.3115 2023-01-04 03:29:16,632 - mmcls - INFO - Epoch [95][2900/5005] lr: 1.000e-04, eta: 0:39:48, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.2951 2023-01-04 03:29:25,309 - mmcls - INFO - Epoch [95][3000/5005] lr: 1.000e-04, eta: 0:39:39, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3029 2023-01-04 03:29:33,938 - mmcls - INFO - Epoch [95][3100/5005] lr: 1.000e-04, eta: 0:39:31, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.3174 2023-01-04 03:29:42,916 - mmcls - INFO - Epoch [95][3200/5005] lr: 1.000e-04, eta: 0:39:22, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3494 2023-01-04 03:29:51,704 - mmcls - INFO - Epoch [95][3300/5005] lr: 1.000e-04, eta: 0:39:13, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.3069 2023-01-04 03:30:00,433 - mmcls - INFO - Epoch [95][3400/5005] lr: 1.000e-04, eta: 0:39:04, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3113 2023-01-04 03:30:09,165 - mmcls - INFO - Epoch [95][3500/5005] lr: 1.000e-04, eta: 0:38:55, time: 0.087, data_time: 0.029, memory: 1125, loss: 1.3417 2023-01-04 03:30:17,878 - mmcls - INFO - Epoch [95][3600/5005] lr: 1.000e-04, eta: 0:38:47, time: 0.087, data_time: 0.018, memory: 1125, loss: 1.3436 2023-01-04 03:30:26,879 - mmcls - INFO - Epoch [95][3700/5005] lr: 1.000e-04, eta: 0:38:38, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.3099 2023-01-04 03:30:35,657 - mmcls - INFO - Epoch [95][3800/5005] lr: 1.000e-04, eta: 0:38:29, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3080 2023-01-04 03:30:44,523 - mmcls - INFO - Epoch [95][3900/5005] lr: 1.000e-04, eta: 0:38:20, time: 0.089, data_time: 0.032, memory: 1125, loss: 1.3202 2023-01-04 03:30:53,173 - mmcls - INFO - Epoch [95][4000/5005] lr: 1.000e-04, eta: 0:38:11, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3103 2023-01-04 03:31:01,857 - mmcls - INFO - Epoch [95][4100/5005] lr: 1.000e-04, eta: 0:38:03, time: 0.087, data_time: 0.032, memory: 1125, loss: 1.3166 2023-01-04 03:31:10,488 - mmcls - INFO - Epoch [95][4200/5005] lr: 1.000e-04, eta: 0:37:54, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3104 2023-01-04 03:31:19,411 - mmcls - INFO - Epoch [95][4300/5005] lr: 1.000e-04, eta: 0:37:45, time: 0.089, data_time: 0.004, memory: 1125, loss: 1.3022 2023-01-04 03:31:28,404 - mmcls - INFO - Epoch [95][4400/5005] lr: 1.000e-04, eta: 0:37:36, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.3243 2023-01-04 03:31:37,245 - mmcls - INFO - Epoch [95][4500/5005] lr: 1.000e-04, eta: 0:37:27, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3174 2023-01-04 03:31:45,845 - mmcls - INFO - Epoch [95][4600/5005] lr: 1.000e-04, eta: 0:37:18, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3158 2023-01-04 03:31:54,486 - mmcls - INFO - Epoch [95][4700/5005] lr: 1.000e-04, eta: 0:37:10, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.3238 2023-01-04 03:32:03,192 - mmcls - INFO - Epoch [95][4800/5005] lr: 1.000e-04, eta: 0:37:01, time: 0.087, data_time: 0.035, memory: 1125, loss: 1.3085 2023-01-04 03:32:12,082 - mmcls - INFO - Epoch [95][4900/5005] lr: 1.000e-04, eta: 0:36:52, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3127 2023-01-04 03:32:20,912 - mmcls - INFO - Epoch [95][5000/5005] lr: 1.000e-04, eta: 0:36:43, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.2926 2023-01-04 03:32:43,596 - mmcls - INFO - Epoch(val) [95][196] accuracy_top-1: 70.1480, accuracy_top-5: 89.4080 2023-01-04 03:32:54,669 - mmcls - INFO - Epoch [96][100/5005] lr: 1.000e-04, eta: 0:36:34, time: 0.111, data_time: 0.062, memory: 1125, loss: 1.3236 2023-01-04 03:33:03,493 - mmcls - INFO - Epoch [96][200/5005] lr: 1.000e-04, eta: 0:36:25, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3024 2023-01-04 03:33:12,423 - mmcls - INFO - Epoch [96][300/5005] lr: 1.000e-04, eta: 0:36:17, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3098 2023-01-04 03:33:21,289 - mmcls - INFO - Epoch [96][400/5005] lr: 1.000e-04, eta: 0:36:08, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3145 2023-01-04 03:33:30,059 - mmcls - INFO - Epoch [96][500/5005] lr: 1.000e-04, eta: 0:35:59, time: 0.088, data_time: 0.029, memory: 1125, loss: 1.3057 2023-01-04 03:33:38,709 - mmcls - INFO - Epoch [96][600/5005] lr: 1.000e-04, eta: 0:35:50, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3189 2023-01-04 03:33:47,623 - mmcls - INFO - Epoch [96][700/5005] lr: 1.000e-04, eta: 0:35:41, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3328 2023-01-04 03:33:55,993 - mmcls - INFO - Epoch [96][800/5005] lr: 1.000e-04, eta: 0:35:32, time: 0.084, data_time: 0.005, memory: 1125, loss: 1.3275 2023-01-04 03:34:05,062 - mmcls - INFO - Epoch [96][900/5005] lr: 1.000e-04, eta: 0:35:24, time: 0.091, data_time: 0.031, memory: 1125, loss: 1.3017 2023-01-04 03:34:13,818 - mmcls - INFO - Epoch [96][1000/5005] lr: 1.000e-04, eta: 0:35:15, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3121 2023-01-04 03:34:22,399 - mmcls - INFO - Epoch [96][1100/5005] lr: 1.000e-04, eta: 0:35:06, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3216 2023-01-04 03:34:31,022 - mmcls - INFO - Epoch [96][1200/5005] lr: 1.000e-04, eta: 0:34:57, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3031 2023-01-04 03:34:39,869 - mmcls - INFO - Epoch [96][1300/5005] lr: 1.000e-04, eta: 0:34:48, time: 0.088, data_time: 0.041, memory: 1125, loss: 1.3314 2023-01-04 03:34:48,749 - mmcls - INFO - Epoch [96][1400/5005] lr: 1.000e-04, eta: 0:34:40, time: 0.089, data_time: 0.011, memory: 1125, loss: 1.2931 2023-01-04 03:34:57,907 - mmcls - INFO - Epoch [96][1500/5005] lr: 1.000e-04, eta: 0:34:31, time: 0.092, data_time: 0.018, memory: 1125, loss: 1.3016 2023-01-04 03:35:06,449 - mmcls - INFO - Epoch [96][1600/5005] lr: 1.000e-04, eta: 0:34:22, time: 0.085, data_time: 0.006, memory: 1125, loss: 1.3149 2023-01-04 03:35:15,182 - mmcls - INFO - Epoch [96][1700/5005] lr: 1.000e-04, eta: 0:34:13, time: 0.087, data_time: 0.028, memory: 1125, loss: 1.3292 2023-01-04 03:35:23,977 - mmcls - INFO - Epoch [96][1800/5005] lr: 1.000e-04, eta: 0:34:04, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.3114 2023-01-04 03:35:32,820 - mmcls - INFO - Epoch [96][1900/5005] lr: 1.000e-04, eta: 0:33:56, time: 0.088, data_time: 0.044, memory: 1125, loss: 1.3042 2023-01-04 03:35:41,601 - mmcls - INFO - Epoch [96][2000/5005] lr: 1.000e-04, eta: 0:33:47, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3275 2023-01-04 03:35:50,427 - mmcls - INFO - Epoch [96][2100/5005] lr: 1.000e-04, eta: 0:33:38, time: 0.088, data_time: 0.026, memory: 1125, loss: 1.3071 2023-01-04 03:35:59,257 - mmcls - INFO - Epoch [96][2200/5005] lr: 1.000e-04, eta: 0:33:29, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.3152 2023-01-04 03:36:07,822 - mmcls - INFO - Epoch [96][2300/5005] lr: 1.000e-04, eta: 0:33:20, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3099 2023-01-04 03:36:16,768 - mmcls - INFO - Epoch [96][2400/5005] lr: 1.000e-04, eta: 0:33:12, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3240 2023-01-04 03:36:25,470 - mmcls - INFO - Epoch [96][2500/5005] lr: 1.000e-04, eta: 0:33:03, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3236 2023-01-04 03:36:34,061 - mmcls - INFO - Epoch [96][2600/5005] lr: 1.000e-04, eta: 0:32:54, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3188 2023-01-04 03:36:42,819 - mmcls - INFO - Epoch [96][2700/5005] lr: 1.000e-04, eta: 0:32:45, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3125 2023-01-04 03:36:51,548 - mmcls - INFO - Epoch [96][2800/5005] lr: 1.000e-04, eta: 0:32:36, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3398 2023-01-04 03:37:00,280 - mmcls - INFO - Epoch [96][2900/5005] lr: 1.000e-04, eta: 0:32:28, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3142 2023-01-04 03:37:08,843 - mmcls - INFO - Epoch [96][3000/5005] lr: 1.000e-04, eta: 0:32:19, time: 0.086, data_time: 0.022, memory: 1125, loss: 1.3246 2023-01-04 03:37:17,529 - mmcls - INFO - Epoch [96][3100/5005] lr: 1.000e-04, eta: 0:32:10, time: 0.087, data_time: 0.043, memory: 1125, loss: 1.3190 2023-01-04 03:37:26,478 - mmcls - INFO - Epoch [96][3200/5005] lr: 1.000e-04, eta: 0:32:01, time: 0.090, data_time: 0.016, memory: 1125, loss: 1.3162 2023-01-04 03:37:35,273 - mmcls - INFO - Epoch [96][3300/5005] lr: 1.000e-04, eta: 0:31:52, time: 0.088, data_time: 0.008, memory: 1125, loss: 1.2933 2023-01-04 03:37:43,921 - mmcls - INFO - Epoch [96][3400/5005] lr: 1.000e-04, eta: 0:31:43, time: 0.086, data_time: 0.010, memory: 1125, loss: 1.2943 2023-01-04 03:37:52,450 - mmcls - INFO - Epoch [96][3500/5005] lr: 1.000e-04, eta: 0:31:35, time: 0.085, data_time: 0.019, memory: 1125, loss: 1.2970 2023-01-04 03:38:01,177 - mmcls - INFO - Epoch [96][3600/5005] lr: 1.000e-04, eta: 0:31:26, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3352 2023-01-04 03:38:09,981 - mmcls - INFO - Epoch [96][3700/5005] lr: 1.000e-04, eta: 0:31:17, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3126 2023-01-04 03:38:18,903 - mmcls - INFO - Epoch [96][3800/5005] lr: 1.000e-04, eta: 0:31:08, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.3294 2023-01-04 03:38:27,962 - mmcls - INFO - Epoch [96][3900/5005] lr: 1.000e-04, eta: 0:30:59, time: 0.091, data_time: 0.013, memory: 1125, loss: 1.3093 2023-01-04 03:38:36,537 - mmcls - INFO - Epoch [96][4000/5005] lr: 1.000e-04, eta: 0:30:51, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3001 2023-01-04 03:38:45,447 - mmcls - INFO - Epoch [96][4100/5005] lr: 1.000e-04, eta: 0:30:42, time: 0.089, data_time: 0.019, memory: 1125, loss: 1.3141 2023-01-04 03:38:54,118 - mmcls - INFO - Epoch [96][4200/5005] lr: 1.000e-04, eta: 0:30:33, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3061 2023-01-04 03:39:02,830 - mmcls - INFO - Epoch [96][4300/5005] lr: 1.000e-04, eta: 0:30:24, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3098 2023-01-04 03:39:11,508 - mmcls - INFO - Epoch [96][4400/5005] lr: 1.000e-04, eta: 0:30:15, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3219 2023-01-04 03:39:20,243 - mmcls - INFO - Epoch [96][4500/5005] lr: 1.000e-04, eta: 0:30:07, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3247 2023-01-04 03:39:29,189 - mmcls - INFO - Epoch [96][4600/5005] lr: 1.000e-04, eta: 0:29:58, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.3319 2023-01-04 03:39:37,978 - mmcls - INFO - Epoch [96][4700/5005] lr: 1.000e-04, eta: 0:29:49, time: 0.088, data_time: 0.002, memory: 1125, loss: 1.3008 2023-01-04 03:39:46,816 - mmcls - INFO - Epoch [96][4800/5005] lr: 1.000e-04, eta: 0:29:40, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3218 2023-01-04 03:39:55,353 - mmcls - INFO - Epoch [96][4900/5005] lr: 1.000e-04, eta: 0:29:31, time: 0.085, data_time: 0.028, memory: 1125, loss: 1.3172 2023-01-04 03:40:03,822 - mmcls - INFO - Epoch [96][5000/5005] lr: 1.000e-04, eta: 0:29:23, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.3270 2023-01-04 03:40:26,593 - mmcls - INFO - Epoch(val) [96][196] accuracy_top-1: 70.3640, accuracy_top-5: 89.4200 2023-01-04 03:40:37,682 - mmcls - INFO - Epoch [97][100/5005] lr: 1.000e-04, eta: 0:29:13, time: 0.111, data_time: 0.040, memory: 1125, loss: 1.3079 2023-01-04 03:40:46,207 - mmcls - INFO - Epoch [97][200/5005] lr: 1.000e-04, eta: 0:29:05, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3038 2023-01-04 03:40:55,093 - mmcls - INFO - Epoch [97][300/5005] lr: 1.000e-04, eta: 0:28:56, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3043 2023-01-04 03:41:03,734 - mmcls - INFO - Epoch [97][400/5005] lr: 1.000e-04, eta: 0:28:47, time: 0.086, data_time: 0.036, memory: 1125, loss: 1.3121 2023-01-04 03:41:12,579 - mmcls - INFO - Epoch [97][500/5005] lr: 1.000e-04, eta: 0:28:38, time: 0.088, data_time: 0.004, memory: 1125, loss: 1.3199 2023-01-04 03:41:21,580 - mmcls - INFO - Epoch [97][600/5005] lr: 1.000e-04, eta: 0:28:29, time: 0.090, data_time: 0.023, memory: 1125, loss: 1.3453 2023-01-04 03:41:30,320 - mmcls - INFO - Epoch [97][700/5005] lr: 1.000e-04, eta: 0:28:21, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3147 2023-01-04 03:41:39,368 - mmcls - INFO - Epoch [97][800/5005] lr: 1.000e-04, eta: 0:28:12, time: 0.090, data_time: 0.024, memory: 1125, loss: 1.3293 2023-01-04 03:41:48,479 - mmcls - INFO - Epoch [97][900/5005] lr: 1.000e-04, eta: 0:28:03, time: 0.091, data_time: 0.043, memory: 1125, loss: 1.3184 2023-01-04 03:41:57,262 - mmcls - INFO - Epoch [97][1000/5005] lr: 1.000e-04, eta: 0:27:54, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3088 2023-01-04 03:42:05,798 - mmcls - INFO - Epoch [97][1100/5005] lr: 1.000e-04, eta: 0:27:45, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.2995 2023-01-04 03:42:14,515 - mmcls - INFO - Epoch [97][1200/5005] lr: 1.000e-04, eta: 0:27:37, time: 0.087, data_time: 0.004, memory: 1125, loss: 1.3102 2023-01-04 03:42:23,516 - mmcls - INFO - Epoch [97][1300/5005] lr: 1.000e-04, eta: 0:27:28, time: 0.090, data_time: 0.038, memory: 1125, loss: 1.2833 2023-01-04 03:42:32,315 - mmcls - INFO - Epoch [97][1400/5005] lr: 1.000e-04, eta: 0:27:19, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3083 2023-01-04 03:42:41,279 - mmcls - INFO - Epoch [97][1500/5005] lr: 1.000e-04, eta: 0:27:10, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.3353 2023-01-04 03:42:50,151 - mmcls - INFO - Epoch [97][1600/5005] lr: 1.000e-04, eta: 0:27:01, time: 0.089, data_time: 0.010, memory: 1125, loss: 1.3135 2023-01-04 03:42:58,696 - mmcls - INFO - Epoch [97][1700/5005] lr: 1.000e-04, eta: 0:26:53, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3156 2023-01-04 03:43:07,588 - mmcls - INFO - Epoch [97][1800/5005] lr: 1.000e-04, eta: 0:26:44, time: 0.089, data_time: 0.015, memory: 1125, loss: 1.3174 2023-01-04 03:43:16,283 - mmcls - INFO - Epoch [97][1900/5005] lr: 1.000e-04, eta: 0:26:35, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3071 2023-01-04 03:43:25,078 - mmcls - INFO - Epoch [97][2000/5005] lr: 1.000e-04, eta: 0:26:26, time: 0.088, data_time: 0.023, memory: 1125, loss: 1.3066 2023-01-04 03:43:33,818 - mmcls - INFO - Epoch [97][2100/5005] lr: 1.000e-04, eta: 0:26:17, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3034 2023-01-04 03:43:42,714 - mmcls - INFO - Epoch [97][2200/5005] lr: 1.000e-04, eta: 0:26:09, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3228 2023-01-04 03:43:51,404 - mmcls - INFO - Epoch [97][2300/5005] lr: 1.000e-04, eta: 0:26:00, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3241 2023-01-04 03:43:59,961 - mmcls - INFO - Epoch [97][2400/5005] lr: 1.000e-04, eta: 0:25:51, time: 0.086, data_time: 0.015, memory: 1125, loss: 1.3254 2023-01-04 03:44:08,523 - mmcls - INFO - Epoch [97][2500/5005] lr: 1.000e-04, eta: 0:25:42, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3031 2023-01-04 03:44:17,043 - mmcls - INFO - Epoch [97][2600/5005] lr: 1.000e-04, eta: 0:25:33, time: 0.085, data_time: 0.012, memory: 1125, loss: 1.3109 2023-01-04 03:44:25,975 - mmcls - INFO - Epoch [97][2700/5005] lr: 1.000e-04, eta: 0:25:24, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3161 2023-01-04 03:44:34,988 - mmcls - INFO - Epoch [97][2800/5005] lr: 1.000e-04, eta: 0:25:16, time: 0.090, data_time: 0.031, memory: 1125, loss: 1.3278 2023-01-04 03:44:43,743 - mmcls - INFO - Epoch [97][2900/5005] lr: 1.000e-04, eta: 0:25:07, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3170 2023-01-04 03:44:52,349 - mmcls - INFO - Epoch [97][3000/5005] lr: 1.000e-04, eta: 0:24:58, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3025 2023-01-04 03:45:01,202 - mmcls - INFO - Epoch [97][3100/5005] lr: 1.000e-04, eta: 0:24:49, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3131 2023-01-04 03:45:09,853 - mmcls - INFO - Epoch [97][3200/5005] lr: 1.000e-04, eta: 0:24:40, time: 0.087, data_time: 0.022, memory: 1125, loss: 1.3149 2023-01-04 03:45:18,630 - mmcls - INFO - Epoch [97][3300/5005] lr: 1.000e-04, eta: 0:24:32, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3024 2023-01-04 03:45:27,190 - mmcls - INFO - Epoch [97][3400/5005] lr: 1.000e-04, eta: 0:24:23, time: 0.085, data_time: 0.016, memory: 1125, loss: 1.3076 2023-01-04 03:45:35,969 - mmcls - INFO - Epoch [97][3500/5005] lr: 1.000e-04, eta: 0:24:14, time: 0.088, data_time: 0.027, memory: 1125, loss: 1.3105 2023-01-04 03:45:44,709 - mmcls - INFO - Epoch [97][3600/5005] lr: 1.000e-04, eta: 0:24:05, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3174 2023-01-04 03:45:53,357 - mmcls - INFO - Epoch [97][3700/5005] lr: 1.000e-04, eta: 0:23:56, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3116 2023-01-04 03:46:02,041 - mmcls - INFO - Epoch [97][3800/5005] lr: 1.000e-04, eta: 0:23:48, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.3090 2023-01-04 03:46:11,120 - mmcls - INFO - Epoch [97][3900/5005] lr: 1.000e-04, eta: 0:23:39, time: 0.091, data_time: 0.008, memory: 1125, loss: 1.3099 2023-01-04 03:46:19,848 - mmcls - INFO - Epoch [97][4000/5005] lr: 1.000e-04, eta: 0:23:30, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3060 2023-01-04 03:46:28,544 - mmcls - INFO - Epoch [97][4100/5005] lr: 1.000e-04, eta: 0:23:21, time: 0.087, data_time: 0.031, memory: 1125, loss: 1.3108 2023-01-04 03:46:37,255 - mmcls - INFO - Epoch [97][4200/5005] lr: 1.000e-04, eta: 0:23:12, time: 0.087, data_time: 0.010, memory: 1125, loss: 1.3119 2023-01-04 03:46:46,131 - mmcls - INFO - Epoch [97][4300/5005] lr: 1.000e-04, eta: 0:23:04, time: 0.089, data_time: 0.027, memory: 1125, loss: 1.3062 2023-01-04 03:46:54,878 - mmcls - INFO - Epoch [97][4400/5005] lr: 1.000e-04, eta: 0:22:55, time: 0.088, data_time: 0.048, memory: 1125, loss: 1.3349 2023-01-04 03:47:03,598 - mmcls - INFO - Epoch [97][4500/5005] lr: 1.000e-04, eta: 0:22:46, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3182 2023-01-04 03:47:12,744 - mmcls - INFO - Epoch [97][4600/5005] lr: 1.000e-04, eta: 0:22:37, time: 0.092, data_time: 0.003, memory: 1125, loss: 1.3235 2023-01-04 03:47:21,907 - mmcls - INFO - Epoch [97][4700/5005] lr: 1.000e-04, eta: 0:22:28, time: 0.092, data_time: 0.009, memory: 1125, loss: 1.3127 2023-01-04 03:47:30,614 - mmcls - INFO - Epoch [97][4800/5005] lr: 1.000e-04, eta: 0:22:20, time: 0.087, data_time: 0.011, memory: 1125, loss: 1.3220 2023-01-04 03:47:39,387 - mmcls - INFO - Epoch [97][4900/5005] lr: 1.000e-04, eta: 0:22:11, time: 0.088, data_time: 0.010, memory: 1125, loss: 1.3129 2023-01-04 03:47:47,942 - mmcls - INFO - Epoch [97][5000/5005] lr: 1.000e-04, eta: 0:22:02, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3114 2023-01-04 03:48:10,721 - mmcls - INFO - Epoch(val) [97][196] accuracy_top-1: 70.3740, accuracy_top-5: 89.4220 2023-01-04 03:48:21,714 - mmcls - INFO - Epoch [98][100/5005] lr: 1.000e-04, eta: 0:21:53, time: 0.110, data_time: 0.034, memory: 1125, loss: 1.3191 2023-01-04 03:48:30,193 - mmcls - INFO - Epoch [98][200/5005] lr: 1.000e-04, eta: 0:21:44, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3256 2023-01-04 03:48:39,059 - mmcls - INFO - Epoch [98][300/5005] lr: 1.000e-04, eta: 0:21:35, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.2860 2023-01-04 03:48:47,943 - mmcls - INFO - Epoch [98][400/5005] lr: 1.000e-04, eta: 0:21:26, time: 0.089, data_time: 0.030, memory: 1125, loss: 1.2990 2023-01-04 03:48:56,552 - mmcls - INFO - Epoch [98][500/5005] lr: 1.000e-04, eta: 0:21:18, time: 0.086, data_time: 0.032, memory: 1125, loss: 1.3311 2023-01-04 03:49:05,442 - mmcls - INFO - Epoch [98][600/5005] lr: 1.000e-04, eta: 0:21:09, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3124 2023-01-04 03:49:14,335 - mmcls - INFO - Epoch [98][700/5005] lr: 1.000e-04, eta: 0:21:00, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.3199 2023-01-04 03:49:22,922 - mmcls - INFO - Epoch [98][800/5005] lr: 1.000e-04, eta: 0:20:51, time: 0.086, data_time: 0.016, memory: 1125, loss: 1.3251 2023-01-04 03:49:31,541 - mmcls - INFO - Epoch [98][900/5005] lr: 1.000e-04, eta: 0:20:42, time: 0.086, data_time: 0.013, memory: 1125, loss: 1.3448 2023-01-04 03:49:40,278 - mmcls - INFO - Epoch [98][1000/5005] lr: 1.000e-04, eta: 0:20:33, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3154 2023-01-04 03:49:49,213 - mmcls - INFO - Epoch [98][1100/5005] lr: 1.000e-04, eta: 0:20:25, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3177 2023-01-04 03:49:57,963 - mmcls - INFO - Epoch [98][1200/5005] lr: 1.000e-04, eta: 0:20:16, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3185 2023-01-04 03:50:06,617 - mmcls - INFO - Epoch [98][1300/5005] lr: 1.000e-04, eta: 0:20:07, time: 0.086, data_time: 0.012, memory: 1125, loss: 1.3223 2023-01-04 03:50:15,343 - mmcls - INFO - Epoch [98][1400/5005] lr: 1.000e-04, eta: 0:19:58, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3192 2023-01-04 03:50:24,585 - mmcls - INFO - Epoch [98][1500/5005] lr: 1.000e-04, eta: 0:19:49, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.3167 2023-01-04 03:50:33,296 - mmcls - INFO - Epoch [98][1600/5005] lr: 1.000e-04, eta: 0:19:41, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3330 2023-01-04 03:50:42,101 - mmcls - INFO - Epoch [98][1700/5005] lr: 1.000e-04, eta: 0:19:32, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3220 2023-01-04 03:50:50,840 - mmcls - INFO - Epoch [98][1800/5005] lr: 1.000e-04, eta: 0:19:23, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3069 2023-01-04 03:50:59,702 - mmcls - INFO - Epoch [98][1900/5005] lr: 1.000e-04, eta: 0:19:14, time: 0.089, data_time: 0.007, memory: 1125, loss: 1.3213 2023-01-04 03:51:08,374 - mmcls - INFO - Epoch [98][2000/5005] lr: 1.000e-04, eta: 0:19:05, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3298 2023-01-04 03:51:17,474 - mmcls - INFO - Epoch [98][2100/5005] lr: 1.000e-04, eta: 0:18:57, time: 0.091, data_time: 0.016, memory: 1125, loss: 1.3285 2023-01-04 03:51:26,169 - mmcls - INFO - Epoch [98][2200/5005] lr: 1.000e-04, eta: 0:18:48, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3069 2023-01-04 03:51:34,719 - mmcls - INFO - Epoch [98][2300/5005] lr: 1.000e-04, eta: 0:18:39, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3298 2023-01-04 03:51:43,688 - mmcls - INFO - Epoch [98][2400/5005] lr: 1.000e-04, eta: 0:18:30, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3000 2023-01-04 03:51:52,684 - mmcls - INFO - Epoch [98][2500/5005] lr: 1.000e-04, eta: 0:18:21, time: 0.090, data_time: 0.029, memory: 1125, loss: 1.3144 2023-01-04 03:52:01,147 - mmcls - INFO - Epoch [98][2600/5005] lr: 1.000e-04, eta: 0:18:13, time: 0.085, data_time: 0.003, memory: 1125, loss: 1.3214 2023-01-04 03:52:09,883 - mmcls - INFO - Epoch [98][2700/5005] lr: 1.000e-04, eta: 0:18:04, time: 0.087, data_time: 0.008, memory: 1125, loss: 1.3286 2023-01-04 03:52:18,534 - mmcls - INFO - Epoch [98][2800/5005] lr: 1.000e-04, eta: 0:17:55, time: 0.086, data_time: 0.001, memory: 1125, loss: 1.3061 2023-01-04 03:52:27,239 - mmcls - INFO - Epoch [98][2900/5005] lr: 1.000e-04, eta: 0:17:46, time: 0.087, data_time: 0.013, memory: 1125, loss: 1.2946 2023-01-04 03:52:36,026 - mmcls - INFO - Epoch [98][3000/5005] lr: 1.000e-04, eta: 0:17:37, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.2995 2023-01-04 03:52:44,934 - mmcls - INFO - Epoch [98][3100/5005] lr: 1.000e-04, eta: 0:17:29, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3112 2023-01-04 03:52:54,089 - mmcls - INFO - Epoch [98][3200/5005] lr: 1.000e-04, eta: 0:17:20, time: 0.091, data_time: 0.004, memory: 1125, loss: 1.3088 2023-01-04 03:53:02,901 - mmcls - INFO - Epoch [98][3300/5005] lr: 1.000e-04, eta: 0:17:11, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3210 2023-01-04 03:53:11,852 - mmcls - INFO - Epoch [98][3400/5005] lr: 1.000e-04, eta: 0:17:02, time: 0.089, data_time: 0.031, memory: 1125, loss: 1.3128 2023-01-04 03:53:20,714 - mmcls - INFO - Epoch [98][3500/5005] lr: 1.000e-04, eta: 0:16:53, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3221 2023-01-04 03:53:29,264 - mmcls - INFO - Epoch [98][3600/5005] lr: 1.000e-04, eta: 0:16:45, time: 0.086, data_time: 0.018, memory: 1125, loss: 1.3337 2023-01-04 03:53:38,079 - mmcls - INFO - Epoch [98][3700/5005] lr: 1.000e-04, eta: 0:16:36, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3198 2023-01-04 03:53:46,922 - mmcls - INFO - Epoch [98][3800/5005] lr: 1.000e-04, eta: 0:16:27, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3184 2023-01-04 03:53:55,746 - mmcls - INFO - Epoch [98][3900/5005] lr: 1.000e-04, eta: 0:16:18, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.2959 2023-01-04 03:54:04,396 - mmcls - INFO - Epoch [98][4000/5005] lr: 1.000e-04, eta: 0:16:09, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3212 2023-01-04 03:54:13,181 - mmcls - INFO - Epoch [98][4100/5005] lr: 1.000e-04, eta: 0:16:01, time: 0.088, data_time: 0.020, memory: 1125, loss: 1.3148 2023-01-04 03:54:21,833 - mmcls - INFO - Epoch [98][4200/5005] lr: 1.000e-04, eta: 0:15:52, time: 0.086, data_time: 0.035, memory: 1125, loss: 1.3248 2023-01-04 03:54:30,834 - mmcls - INFO - Epoch [98][4300/5005] lr: 1.000e-04, eta: 0:15:43, time: 0.090, data_time: 0.006, memory: 1125, loss: 1.3036 2023-01-04 03:54:39,508 - mmcls - INFO - Epoch [98][4400/5005] lr: 1.000e-04, eta: 0:15:34, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.2864 2023-01-04 03:54:48,068 - mmcls - INFO - Epoch [98][4500/5005] lr: 1.000e-04, eta: 0:15:25, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3143 2023-01-04 03:54:56,702 - mmcls - INFO - Epoch [98][4600/5005] lr: 1.000e-04, eta: 0:15:16, time: 0.086, data_time: 0.028, memory: 1125, loss: 1.3098 2023-01-04 03:55:05,342 - mmcls - INFO - Epoch [98][4700/5005] lr: 1.000e-04, eta: 0:15:08, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3256 2023-01-04 03:55:14,290 - mmcls - INFO - Epoch [98][4800/5005] lr: 1.000e-04, eta: 0:14:59, time: 0.089, data_time: 0.029, memory: 1125, loss: 1.3095 2023-01-04 03:55:23,137 - mmcls - INFO - Epoch [98][4900/5005] lr: 1.000e-04, eta: 0:14:50, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3024 2023-01-04 03:55:32,249 - mmcls - INFO - Epoch [98][5000/5005] lr: 1.000e-04, eta: 0:14:41, time: 0.091, data_time: 0.019, memory: 1125, loss: 1.3063 2023-01-04 03:55:54,750 - mmcls - INFO - Epoch(val) [98][196] accuracy_top-1: 70.2920, accuracy_top-5: 89.5560 2023-01-04 03:56:05,695 - mmcls - INFO - Epoch [99][100/5005] lr: 1.000e-04, eta: 0:14:32, time: 0.109, data_time: 0.057, memory: 1125, loss: 1.3110 2023-01-04 03:56:14,703 - mmcls - INFO - Epoch [99][200/5005] lr: 1.000e-04, eta: 0:14:23, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3082 2023-01-04 03:56:23,558 - mmcls - INFO - Epoch [99][300/5005] lr: 1.000e-04, eta: 0:14:14, time: 0.089, data_time: 0.049, memory: 1125, loss: 1.2983 2023-01-04 03:56:32,038 - mmcls - INFO - Epoch [99][400/5005] lr: 1.000e-04, eta: 0:14:06, time: 0.085, data_time: 0.014, memory: 1125, loss: 1.3155 2023-01-04 03:56:41,152 - mmcls - INFO - Epoch [99][500/5005] lr: 1.000e-04, eta: 0:13:57, time: 0.091, data_time: 0.010, memory: 1125, loss: 1.2977 2023-01-04 03:56:49,795 - mmcls - INFO - Epoch [99][600/5005] lr: 1.000e-04, eta: 0:13:48, time: 0.086, data_time: 0.021, memory: 1125, loss: 1.3222 2023-01-04 03:56:58,534 - mmcls - INFO - Epoch [99][700/5005] lr: 1.000e-04, eta: 0:13:39, time: 0.087, data_time: 0.012, memory: 1125, loss: 1.3157 2023-01-04 03:57:07,147 - mmcls - INFO - Epoch [99][800/5005] lr: 1.000e-04, eta: 0:13:30, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.3071 2023-01-04 03:57:16,131 - mmcls - INFO - Epoch [99][900/5005] lr: 1.000e-04, eta: 0:13:22, time: 0.090, data_time: 0.011, memory: 1125, loss: 1.3254 2023-01-04 03:57:24,958 - mmcls - INFO - Epoch [99][1000/5005] lr: 1.000e-04, eta: 0:13:13, time: 0.088, data_time: 0.009, memory: 1125, loss: 1.3324 2023-01-04 03:57:33,573 - mmcls - INFO - Epoch [99][1100/5005] lr: 1.000e-04, eta: 0:13:04, time: 0.086, data_time: 0.004, memory: 1125, loss: 1.3013 2023-01-04 03:57:42,248 - mmcls - INFO - Epoch [99][1200/5005] lr: 1.000e-04, eta: 0:12:55, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3075 2023-01-04 03:57:51,036 - mmcls - INFO - Epoch [99][1300/5005] lr: 1.000e-04, eta: 0:12:46, time: 0.088, data_time: 0.021, memory: 1125, loss: 1.3109 2023-01-04 03:57:59,649 - mmcls - INFO - Epoch [99][1400/5005] lr: 1.000e-04, eta: 0:12:38, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3282 2023-01-04 03:58:08,517 - mmcls - INFO - Epoch [99][1500/5005] lr: 1.000e-04, eta: 0:12:29, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3000 2023-01-04 03:58:17,105 - mmcls - INFO - Epoch [99][1600/5005] lr: 1.000e-04, eta: 0:12:20, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.2938 2023-01-04 03:58:25,610 - mmcls - INFO - Epoch [99][1700/5005] lr: 1.000e-04, eta: 0:12:11, time: 0.085, data_time: 0.013, memory: 1125, loss: 1.3271 2023-01-04 03:58:34,408 - mmcls - INFO - Epoch [99][1800/5005] lr: 1.000e-04, eta: 0:12:02, time: 0.088, data_time: 0.007, memory: 1125, loss: 1.3290 2023-01-04 03:58:43,224 - mmcls - INFO - Epoch [99][1900/5005] lr: 1.000e-04, eta: 0:11:54, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3112 2023-01-04 03:58:51,961 - mmcls - INFO - Epoch [99][2000/5005] lr: 1.000e-04, eta: 0:11:45, time: 0.087, data_time: 0.007, memory: 1125, loss: 1.3024 2023-01-04 03:59:00,849 - mmcls - INFO - Epoch [99][2100/5005] lr: 1.000e-04, eta: 0:11:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3154 2023-01-04 03:59:09,486 - mmcls - INFO - Epoch [99][2200/5005] lr: 1.000e-04, eta: 0:11:27, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.3343 2023-01-04 03:59:18,680 - mmcls - INFO - Epoch [99][2300/5005] lr: 1.000e-04, eta: 0:11:18, time: 0.092, data_time: 0.009, memory: 1125, loss: 1.2991 2023-01-04 03:59:27,636 - mmcls - INFO - Epoch [99][2400/5005] lr: 1.000e-04, eta: 0:11:10, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3011 2023-01-04 03:59:36,361 - mmcls - INFO - Epoch [99][2500/5005] lr: 1.000e-04, eta: 0:11:01, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.2918 2023-01-04 03:59:44,930 - mmcls - INFO - Epoch [99][2600/5005] lr: 1.000e-04, eta: 0:10:52, time: 0.086, data_time: 0.030, memory: 1125, loss: 1.3157 2023-01-04 03:59:53,525 - mmcls - INFO - Epoch [99][2700/5005] lr: 1.000e-04, eta: 0:10:43, time: 0.086, data_time: 0.024, memory: 1125, loss: 1.2980 2023-01-04 04:00:02,018 - mmcls - INFO - Epoch [99][2800/5005] lr: 1.000e-04, eta: 0:10:34, time: 0.085, data_time: 0.002, memory: 1125, loss: 1.3135 2023-01-04 04:00:10,677 - mmcls - INFO - Epoch [99][2900/5005] lr: 1.000e-04, eta: 0:10:26, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.3206 2023-01-04 04:00:19,666 - mmcls - INFO - Epoch [99][3000/5005] lr: 1.000e-04, eta: 0:10:17, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3065 2023-01-04 04:00:28,240 - mmcls - INFO - Epoch [99][3100/5005] lr: 1.000e-04, eta: 0:10:08, time: 0.086, data_time: 0.027, memory: 1125, loss: 1.2967 2023-01-04 04:00:36,866 - mmcls - INFO - Epoch [99][3200/5005] lr: 1.000e-04, eta: 0:09:59, time: 0.086, data_time: 0.005, memory: 1125, loss: 1.3097 2023-01-04 04:00:45,855 - mmcls - INFO - Epoch [99][3300/5005] lr: 1.000e-04, eta: 0:09:50, time: 0.090, data_time: 0.003, memory: 1125, loss: 1.3111 2023-01-04 04:00:54,617 - mmcls - INFO - Epoch [99][3400/5005] lr: 1.000e-04, eta: 0:09:41, time: 0.088, data_time: 0.001, memory: 1125, loss: 1.3027 2023-01-04 04:01:03,631 - mmcls - INFO - Epoch [99][3500/5005] lr: 1.000e-04, eta: 0:09:33, time: 0.090, data_time: 0.007, memory: 1125, loss: 1.2976 2023-01-04 04:01:12,356 - mmcls - INFO - Epoch [99][3600/5005] lr: 1.000e-04, eta: 0:09:24, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.2958 2023-01-04 04:01:21,245 - mmcls - INFO - Epoch [99][3700/5005] lr: 1.000e-04, eta: 0:09:15, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3008 2023-01-04 04:01:29,887 - mmcls - INFO - Epoch [99][3800/5005] lr: 1.000e-04, eta: 0:09:06, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3126 2023-01-04 04:01:38,340 - mmcls - INFO - Epoch [99][3900/5005] lr: 1.000e-04, eta: 0:08:57, time: 0.085, data_time: 0.007, memory: 1125, loss: 1.3214 2023-01-04 04:01:46,972 - mmcls - INFO - Epoch [99][4000/5005] lr: 1.000e-04, eta: 0:08:49, time: 0.086, data_time: 0.025, memory: 1125, loss: 1.3170 2023-01-04 04:01:56,075 - mmcls - INFO - Epoch [99][4100/5005] lr: 1.000e-04, eta: 0:08:40, time: 0.091, data_time: 0.007, memory: 1125, loss: 1.2985 2023-01-04 04:02:05,266 - mmcls - INFO - Epoch [99][4200/5005] lr: 1.000e-04, eta: 0:08:31, time: 0.092, data_time: 0.002, memory: 1125, loss: 1.3022 2023-01-04 04:02:13,673 - mmcls - INFO - Epoch [99][4300/5005] lr: 1.000e-04, eta: 0:08:22, time: 0.084, data_time: 0.002, memory: 1125, loss: 1.3136 2023-01-04 04:02:22,290 - mmcls - INFO - Epoch [99][4400/5005] lr: 1.000e-04, eta: 0:08:13, time: 0.086, data_time: 0.031, memory: 1125, loss: 1.3182 2023-01-04 04:02:30,957 - mmcls - INFO - Epoch [99][4500/5005] lr: 1.000e-04, eta: 0:08:05, time: 0.087, data_time: 0.030, memory: 1125, loss: 1.3030 2023-01-04 04:02:40,179 - mmcls - INFO - Epoch [99][4600/5005] lr: 1.000e-04, eta: 0:07:56, time: 0.092, data_time: 0.024, memory: 1125, loss: 1.3071 2023-01-04 04:02:48,826 - mmcls - INFO - Epoch [99][4700/5005] lr: 1.000e-04, eta: 0:07:47, time: 0.087, data_time: 0.036, memory: 1125, loss: 1.3198 2023-01-04 04:02:57,669 - mmcls - INFO - Epoch [99][4800/5005] lr: 1.000e-04, eta: 0:07:38, time: 0.088, data_time: 0.015, memory: 1125, loss: 1.2971 2023-01-04 04:03:06,619 - mmcls - INFO - Epoch [99][4900/5005] lr: 1.000e-04, eta: 0:07:29, time: 0.090, data_time: 0.028, memory: 1125, loss: 1.3291 2023-01-04 04:03:15,403 - mmcls - INFO - Epoch [99][5000/5005] lr: 1.000e-04, eta: 0:07:21, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3440 2023-01-04 04:03:38,077 - mmcls - INFO - Epoch(val) [99][196] accuracy_top-1: 70.2240, accuracy_top-5: 89.5020 2023-01-04 04:03:48,922 - mmcls - INFO - Epoch [100][100/5005] lr: 1.000e-04, eta: 0:07:11, time: 0.108, data_time: 0.038, memory: 1125, loss: 1.3140 2023-01-04 04:03:57,726 - mmcls - INFO - Epoch [100][200/5005] lr: 1.000e-04, eta: 0:07:03, time: 0.088, data_time: 0.005, memory: 1125, loss: 1.2898 2023-01-04 04:04:06,536 - mmcls - INFO - Epoch [100][300/5005] lr: 1.000e-04, eta: 0:06:54, time: 0.088, data_time: 0.030, memory: 1125, loss: 1.2998 2023-01-04 04:04:15,457 - mmcls - INFO - Epoch [100][400/5005] lr: 1.000e-04, eta: 0:06:45, time: 0.089, data_time: 0.012, memory: 1125, loss: 1.3280 2023-01-04 04:04:24,328 - mmcls - INFO - Epoch [100][500/5005] lr: 1.000e-04, eta: 0:06:36, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3081 2023-01-04 04:04:33,270 - mmcls - INFO - Epoch [100][600/5005] lr: 1.000e-04, eta: 0:06:27, time: 0.089, data_time: 0.009, memory: 1125, loss: 1.3038 2023-01-04 04:04:41,994 - mmcls - INFO - Epoch [100][700/5005] lr: 1.000e-04, eta: 0:06:19, time: 0.087, data_time: 0.001, memory: 1125, loss: 1.2925 2023-01-04 04:04:50,631 - mmcls - INFO - Epoch [100][800/5005] lr: 1.000e-04, eta: 0:06:10, time: 0.086, data_time: 0.011, memory: 1125, loss: 1.3319 2023-01-04 04:04:59,581 - mmcls - INFO - Epoch [100][900/5005] lr: 1.000e-04, eta: 0:06:01, time: 0.090, data_time: 0.029, memory: 1125, loss: 1.3279 2023-01-04 04:05:08,610 - mmcls - INFO - Epoch [100][1000/5005] lr: 1.000e-04, eta: 0:05:52, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3137 2023-01-04 04:05:17,396 - mmcls - INFO - Epoch [100][1100/5005] lr: 1.000e-04, eta: 0:05:43, time: 0.088, data_time: 0.019, memory: 1125, loss: 1.3020 2023-01-04 04:05:26,058 - mmcls - INFO - Epoch [100][1200/5005] lr: 1.000e-04, eta: 0:05:35, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3006 2023-01-04 04:05:34,794 - mmcls - INFO - Epoch [100][1300/5005] lr: 1.000e-04, eta: 0:05:26, time: 0.087, data_time: 0.006, memory: 1125, loss: 1.3252 2023-01-04 04:05:43,710 - mmcls - INFO - Epoch [100][1400/5005] lr: 1.000e-04, eta: 0:05:17, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3150 2023-01-04 04:05:52,432 - mmcls - INFO - Epoch [100][1500/5005] lr: 1.000e-04, eta: 0:05:08, time: 0.087, data_time: 0.003, memory: 1125, loss: 1.3282 2023-01-04 04:06:01,141 - mmcls - INFO - Epoch [100][1600/5005] lr: 1.000e-04, eta: 0:04:59, time: 0.087, data_time: 0.009, memory: 1125, loss: 1.3120 2023-01-04 04:06:09,867 - mmcls - INFO - Epoch [100][1700/5005] lr: 1.000e-04, eta: 0:04:51, time: 0.087, data_time: 0.005, memory: 1125, loss: 1.3113 2023-01-04 04:06:18,518 - mmcls - INFO - Epoch [100][1800/5005] lr: 1.000e-04, eta: 0:04:42, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.2839 2023-01-04 04:06:27,305 - mmcls - INFO - Epoch [100][1900/5005] lr: 1.000e-04, eta: 0:04:33, time: 0.088, data_time: 0.017, memory: 1125, loss: 1.3131 2023-01-04 04:06:36,029 - mmcls - INFO - Epoch [100][2000/5005] lr: 1.000e-04, eta: 0:04:24, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.3003 2023-01-04 04:06:44,635 - mmcls - INFO - Epoch [100][2100/5005] lr: 1.000e-04, eta: 0:04:15, time: 0.086, data_time: 0.026, memory: 1125, loss: 1.3139 2023-01-04 04:06:53,376 - mmcls - INFO - Epoch [100][2200/5005] lr: 1.000e-04, eta: 0:04:06, time: 0.087, data_time: 0.042, memory: 1125, loss: 1.3018 2023-01-04 04:07:02,500 - mmcls - INFO - Epoch [100][2300/5005] lr: 1.000e-04, eta: 0:03:58, time: 0.091, data_time: 0.018, memory: 1125, loss: 1.3160 2023-01-04 04:07:11,055 - mmcls - INFO - Epoch [100][2400/5005] lr: 1.000e-04, eta: 0:03:49, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.2964 2023-01-04 04:07:19,759 - mmcls - INFO - Epoch [100][2500/5005] lr: 1.000e-04, eta: 0:03:40, time: 0.087, data_time: 0.002, memory: 1125, loss: 1.3090 2023-01-04 04:07:28,380 - mmcls - INFO - Epoch [100][2600/5005] lr: 1.000e-04, eta: 0:03:31, time: 0.086, data_time: 0.006, memory: 1125, loss: 1.3246 2023-01-04 04:07:37,014 - mmcls - INFO - Epoch [100][2700/5005] lr: 1.000e-04, eta: 0:03:22, time: 0.086, data_time: 0.033, memory: 1125, loss: 1.3149 2023-01-04 04:07:45,992 - mmcls - INFO - Epoch [100][2800/5005] lr: 1.000e-04, eta: 0:03:14, time: 0.090, data_time: 0.001, memory: 1125, loss: 1.3204 2023-01-04 04:07:54,938 - mmcls - INFO - Epoch [100][2900/5005] lr: 1.000e-04, eta: 0:03:05, time: 0.089, data_time: 0.003, memory: 1125, loss: 1.3128 2023-01-04 04:08:03,708 - mmcls - INFO - Epoch [100][3000/5005] lr: 1.000e-04, eta: 0:02:56, time: 0.088, data_time: 0.011, memory: 1125, loss: 1.3033 2023-01-04 04:08:12,213 - mmcls - INFO - Epoch [100][3100/5005] lr: 1.000e-04, eta: 0:02:47, time: 0.085, data_time: 0.011, memory: 1125, loss: 1.3194 2023-01-04 04:08:20,934 - mmcls - INFO - Epoch [100][3200/5005] lr: 1.000e-04, eta: 0:02:38, time: 0.087, data_time: 0.014, memory: 1125, loss: 1.3229 2023-01-04 04:08:29,801 - mmcls - INFO - Epoch [100][3300/5005] lr: 1.000e-04, eta: 0:02:30, time: 0.089, data_time: 0.014, memory: 1125, loss: 1.3191 2023-01-04 04:08:38,542 - mmcls - INFO - Epoch [100][3400/5005] lr: 1.000e-04, eta: 0:02:21, time: 0.088, data_time: 0.018, memory: 1125, loss: 1.2966 2023-01-04 04:08:47,417 - mmcls - INFO - Epoch [100][3500/5005] lr: 1.000e-04, eta: 0:02:12, time: 0.089, data_time: 0.020, memory: 1125, loss: 1.3205 2023-01-04 04:08:56,430 - mmcls - INFO - Epoch [100][3600/5005] lr: 1.000e-04, eta: 0:02:03, time: 0.090, data_time: 0.036, memory: 1125, loss: 1.3105 2023-01-04 04:09:05,169 - mmcls - INFO - Epoch [100][3700/5005] lr: 1.000e-04, eta: 0:01:54, time: 0.087, data_time: 0.019, memory: 1125, loss: 1.2810 2023-01-04 04:09:13,994 - mmcls - INFO - Epoch [100][3800/5005] lr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.012, memory: 1125, loss: 1.3143 2023-01-04 04:09:22,890 - mmcls - INFO - Epoch [100][3900/5005] lr: 1.000e-04, eta: 0:01:37, time: 0.089, data_time: 0.008, memory: 1125, loss: 1.2995 2023-01-04 04:09:31,669 - mmcls - INFO - Epoch [100][4000/5005] lr: 1.000e-04, eta: 0:01:28, time: 0.088, data_time: 0.003, memory: 1125, loss: 1.3128 2023-01-04 04:09:40,309 - mmcls - INFO - Epoch [100][4100/5005] lr: 1.000e-04, eta: 0:01:19, time: 0.086, data_time: 0.003, memory: 1125, loss: 1.3171 2023-01-04 04:09:48,928 - mmcls - INFO - Epoch [100][4200/5005] lr: 1.000e-04, eta: 0:01:10, time: 0.086, data_time: 0.002, memory: 1125, loss: 1.3298 2023-01-04 04:09:57,798 - mmcls - INFO - Epoch [100][4300/5005] lr: 1.000e-04, eta: 0:01:02, time: 0.089, data_time: 0.001, memory: 1125, loss: 1.3102 2023-01-04 04:10:06,335 - mmcls - INFO - Epoch [100][4400/5005] lr: 1.000e-04, eta: 0:00:53, time: 0.085, data_time: 0.001, memory: 1125, loss: 1.3299 2023-01-04 04:10:15,182 - mmcls - INFO - Epoch [100][4500/5005] lr: 1.000e-04, eta: 0:00:44, time: 0.089, data_time: 0.002, memory: 1125, loss: 1.3240 2023-01-04 04:10:24,122 - mmcls - INFO - Epoch [100][4600/5005] lr: 1.000e-04, eta: 0:00:35, time: 0.089, data_time: 0.018, memory: 1125, loss: 1.3216 2023-01-04 04:10:32,844 - mmcls - INFO - Epoch [100][4700/5005] lr: 1.000e-04, eta: 0:00:26, time: 0.087, data_time: 0.016, memory: 1125, loss: 1.3141 2023-01-04 04:10:41,786 - mmcls - INFO - Epoch [100][4800/5005] lr: 1.000e-04, eta: 0:00:18, time: 0.089, data_time: 0.035, memory: 1125, loss: 1.3268 2023-01-04 04:10:50,222 - mmcls - INFO - Epoch [100][4900/5005] lr: 1.000e-04, eta: 0:00:09, time: 0.084, data_time: 0.015, memory: 1125, loss: 1.3135 2023-01-04 04:10:59,074 - mmcls - INFO - Epoch [100][5000/5005] lr: 1.000e-04, eta: 0:00:00, time: 0.089, data_time: 0.005, memory: 1125, loss: 1.3223 2023-01-04 04:10:59,463 - mmcls - INFO - Saving checkpoint at 100 epochs 2023-01-04 04:11:21,750 - mmcls - INFO - Epoch(val) [100][196] accuracy_top-1: 70.3500, accuracy_top-5: 89.4340 ================================================ FILE: classification-NO-activation-function/logs/resnet_norelu.log ================================================ 2023-01-04 15:10:41,671 - mmcls - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.6.12 |Anaconda, Inc.| (default, Sep 8 2020, 23:10:56) [GCC 7.3.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: Tesla V100-PCIE-32GB CUDA_HOME: /usr/local/cuda NVCC: Cuda compilation tools, release 10.2, V10.2.8 GCC: gcc (Ubuntu 7.5.0-6ubuntu2) 7.5.0 PyTorch: 1.9.0+cu102 PyTorch compiling details: PyTorch built with: - GCC 7.3 - C++ Version: 201402 - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb) - OpenMP 201511 (a.k.a. OpenMP 4.5) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 10.2 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70 - CuDNN 7.6.5 - Magma 2.5.2 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=10.2, CUDNN_VERSION=7.6.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0+cu102 OpenCV: 4.5.5 MMCV: 1.5.1 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler: 10.2 MMClassification: 0.24.0+91b85bb ------------------------------------------------------------ 2023-01-04 15:10:41,672 - mmcls - INFO - Distributed training: True 2023-01-04 15:10:41,805 - mmcls - INFO - Config: model = dict( type='ImageClassifier', backbone=dict( type='ResNet', depth=18, num_stages=4, out_indices=(3, ), style='pytorch'), neck=dict(type='GlobalAveragePooling'), head=dict( type='LinearClsHead', num_classes=1000, in_channels=512, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5))) dataset_type = 'ImageNet' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ] data = dict( samples_per_gpu=32, workers_per_gpu=2, train=dict( type='ImageNet', data_prefix='data/imagenet/train', pipeline=[ dict(type='LoadImageFromFile'), dict(type='RandomResizedCrop', size=224), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ]), val=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ]), test=dict( type='ImageNet', data_prefix='data/imagenet/val', ann_file='data/imagenet/meta/val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', size=(256, -1)), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ])) evaluation = dict(interval=1, metric='accuracy') optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) optimizer_config = dict(grad_clip=None) lr_config = dict(policy='step', step=[30, 60, 90]) runner = dict(type='EpochBasedRunner', max_epochs=100) checkpoint_config = dict(interval=50) log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = None workflow = [('train', 1)] work_dir = './work_dirs/resnet18_8xb32_in1k_pi' gpu_ids = range(0, 8) 2023-01-04 15:10:47,234 - mmcls - INFO - Set random seed to 57587029, deterministic: False 2023-01-04 15:10:47,418 - mmcls - INFO - initialize ResNet with init_cfg [{'type': 'Kaiming', 'layer': ['Conv2d']}, {'type': 'Constant', 'val': 1, 'layer': ['_BatchNorm', 'GroupNorm']}] 2023-01-04 15:10:47,501 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01} Name of parameter - Initialization information backbone.conv1.weight - torch.Size([64, 3, 7, 7]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.0.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.0.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv1.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn1.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.bn1.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer1.1.bn2.weight - torch.Size([64]): Initialized by user-defined `init_weights` in ResNet backbone.layer1.1.bn2.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv1.weight - torch.Size([128, 64, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.0.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.0.weight - torch.Size([128, 64, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.0.downsample.1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.0.downsample.1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv1.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn1.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.bn1.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer2.1.bn2.weight - torch.Size([128]): Initialized by user-defined `init_weights` in ResNet backbone.layer2.1.bn2.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv1.weight - torch.Size([256, 128, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.0.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.0.weight - torch.Size([256, 128, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.0.downsample.1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.0.downsample.1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv1.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn1.weight - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.bn1.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer3.1.bn2.weight - torch.Size([256]): Initialized by user-defined `init_weights` in ResNet backbone.layer3.1.bn2.bias - torch.Size([256]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv1.weight - torch.Size([512, 256, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.0.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.0.downsample.1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.0.downsample.1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv1.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn1.weight - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.bn1.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): KaimingInit: a=0, mode=fan_out, nonlinearity=relu, distribution =normal, bias=0 backbone.layer4.1.bn2.weight - torch.Size([512]): Initialized by user-defined `init_weights` in ResNet backbone.layer4.1.bn2.bias - torch.Size([512]): The value is the same before and after calling `init_weights` of ImageClassifier head.fc.weight - torch.Size([1000, 512]): NormalInit: mean=0, std=0.01, bias=0 head.fc.bias - torch.Size([1000]): NormalInit: mean=0, std=0.01, bias=0 2023-01-04 15:11:16,442 - mmcls - INFO - Start running, host: jiankangdeng@lhrai102-lx, work_dir: /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi 2023-01-04 15:11:16,442 - mmcls - INFO - Hooks will be executed in the following order: before_run: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_epoch: (VERY_HIGH ) StepLrUpdaterHook (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_train_iter: (VERY_HIGH ) StepLrUpdaterHook (LOW ) IterTimerHook (LOW ) DistEvalHook -------------------- after_train_iter: (ABOVE_NORMAL) DistOptimizerHook (NORMAL ) CheckpointHook (LOW ) IterTimerHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- after_train_epoch: (NORMAL ) CheckpointHook (LOW ) DistEvalHook (VERY_LOW ) TextLoggerHook -------------------- before_val_epoch: (NORMAL ) DistSamplerSeedHook (LOW ) IterTimerHook (VERY_LOW ) TextLoggerHook -------------------- before_val_iter: (LOW ) IterTimerHook -------------------- after_val_iter: (LOW ) IterTimerHook -------------------- after_val_epoch: (VERY_LOW ) TextLoggerHook -------------------- after_run: (VERY_LOW ) TextLoggerHook -------------------- 2023-01-04 15:11:16,443 - mmcls - INFO - workflow: [('train', 1)], max: 100 epochs 2023-01-04 15:11:16,443 - mmcls - INFO - Checkpoints will be saved to /home/jiankangdeng/projects/classification/mmclassification_V0.24.0/work_dirs/resnet18_8xb32_in1k_pi by HardDiskBackend. 2023-01-04 15:11:28,577 - mmcls - INFO - Epoch [1][100/5005] lr: 1.000e-01, eta: 16:50:32, time: 0.121, data_time: 0.029, memory: 928, loss: 6.7395 2023-01-04 15:11:37,438 - mmcls - INFO - Epoch [1][200/5005] lr: 1.000e-01, eta: 14:34:46, time: 0.089, data_time: 0.008, memory: 928, loss: 6.5939 2023-01-04 15:11:46,354 - mmcls - INFO - Epoch [1][300/5005] lr: 1.000e-01, eta: 13:50:17, time: 0.089, data_time: 0.001, memory: 928, loss: 6.4922 2023-01-04 15:11:55,291 - mmcls - INFO - Epoch [1][400/5005] lr: 1.000e-01, eta: 13:28:56, time: 0.089, data_time: 0.001, memory: 928, loss: 6.4055 2023-01-04 15:12:03,819 - mmcls - INFO - Epoch [1][500/5005] lr: 1.000e-01, eta: 13:09:03, time: 0.085, data_time: 0.004, memory: 928, loss: 6.3595 2023-01-04 15:12:12,514 - mmcls - INFO - Epoch [1][600/5005] lr: 1.000e-01, eta: 12:58:19, time: 0.087, data_time: 0.007, memory: 928, loss: 6.3061 2023-01-04 15:12:21,388 - mmcls - INFO - Epoch [1][700/5005] lr: 1.000e-01, eta: 12:52:24, time: 0.089, data_time: 0.011, memory: 928, loss: 6.2641 2023-01-04 15:12:30,359 - mmcls - INFO - Epoch [1][800/5005] lr: 1.000e-01, eta: 12:49:11, time: 0.090, data_time: 0.001, memory: 928, loss: 6.2340 2023-01-04 15:12:39,119 - mmcls - INFO - Epoch [1][900/5005] lr: 1.000e-01, eta: 12:44:38, time: 0.088, data_time: 0.002, memory: 928, loss: 6.2221 2023-01-04 15:12:47,860 - mmcls - INFO - Epoch [1][1000/5005] lr: 1.000e-01, eta: 12:40:47, time: 0.087, data_time: 0.013, memory: 928, loss: 6.1993 2023-01-04 15:12:56,925 - mmcls - INFO - Epoch [1][1100/5005] lr: 1.000e-01, eta: 12:40:05, time: 0.091, data_time: 0.016, memory: 928, loss: 6.1824 2023-01-04 15:13:06,178 - mmcls - INFO - Epoch [1][1200/5005] lr: 1.000e-01, eta: 12:40:45, time: 0.092, data_time: 0.001, memory: 928, loss: 6.1719 2023-01-04 15:13:15,013 - mmcls - INFO - Epoch [1][1300/5005] lr: 1.000e-01, eta: 12:38:40, time: 0.088, data_time: 0.001, memory: 928, loss: 6.1480 2023-01-04 15:13:23,774 - mmcls - INFO - Epoch [1][1400/5005] lr: 1.000e-01, eta: 12:36:25, time: 0.088, data_time: 0.012, memory: 928, loss: 6.1345 2023-01-04 15:13:32,475 - mmcls - INFO - Epoch [1][1500/5005] lr: 1.000e-01, eta: 12:34:02, time: 0.087, data_time: 0.002, memory: 928, loss: 6.1187 2023-01-04 15:13:41,135 - mmcls - INFO - Epoch [1][1600/5005] lr: 1.000e-01, eta: 12:31:47, time: 0.087, data_time: 0.003, memory: 928, loss: 6.1191 2023-01-04 15:13:50,066 - mmcls - INFO - Epoch [1][1700/5005] lr: 1.000e-01, eta: 12:31:03, time: 0.089, data_time: 0.004, memory: 928, loss: 6.0862 2023-01-04 15:13:59,101 - mmcls - INFO - Epoch [1][1800/5005] lr: 1.000e-01, eta: 12:30:57, time: 0.090, data_time: 0.001, memory: 928, loss: 6.0892 2023-01-04 15:14:08,038 - mmcls - INFO - Epoch [1][1900/5005] lr: 1.000e-01, eta: 12:30:20, time: 0.089, data_time: 0.015, memory: 928, loss: 6.0618 2023-01-04 15:14:16,852 - mmcls - INFO - Epoch [1][2000/5005] lr: 1.000e-01, eta: 12:29:17, time: 0.088, data_time: 0.013, memory: 928, loss: 6.0565 2023-01-04 15:14:25,510 - mmcls - INFO - Epoch [1][2100/5005] lr: 1.000e-01, eta: 12:27:44, time: 0.087, data_time: 0.010, memory: 928, loss: 6.0436 2023-01-04 15:14:34,195 - mmcls - INFO - Epoch [1][2200/5005] lr: 1.000e-01, eta: 12:26:22, time: 0.087, data_time: 0.007, memory: 928, loss: 6.0503 2023-01-04 15:14:42,714 - mmcls - INFO - Epoch [1][2300/5005] lr: 1.000e-01, eta: 12:24:28, time: 0.085, data_time: 0.005, memory: 928, loss: 6.0324 2023-01-04 15:14:51,311 - mmcls - INFO - Epoch [1][2400/5005] lr: 1.000e-01, eta: 12:23:05, time: 0.086, data_time: 0.008, memory: 928, loss: 6.0265 2023-01-04 15:15:00,046 - mmcls - INFO - Epoch [1][2500/5005] lr: 1.000e-01, eta: 12:22:12, time: 0.087, data_time: 0.010, memory: 928, loss: 6.0058 2023-01-04 15:15:08,837 - mmcls - INFO - Epoch [1][2600/5005] lr: 1.000e-01, eta: 12:21:34, time: 0.088, data_time: 0.006, memory: 928, loss: 5.9983 2023-01-04 15:15:17,811 - mmcls - INFO - Epoch [1][2700/5005] lr: 1.000e-01, eta: 12:21:33, time: 0.090, data_time: 0.030, memory: 928, loss: 5.9877 2023-01-04 15:15:26,828 - mmcls - INFO - Epoch [1][2800/5005] lr: 1.000e-01, eta: 12:21:37, time: 0.090, data_time: 0.001, memory: 928, loss: 5.9932 2023-01-04 15:15:35,665 - mmcls - INFO - Epoch [1][2900/5005] lr: 1.000e-01, eta: 12:21:10, time: 0.088, data_time: 0.011, memory: 928, loss: 5.9754 2023-01-04 15:15:44,374 - mmcls - INFO - Epoch [1][3000/5005] lr: 1.000e-01, eta: 12:20:23, time: 0.087, data_time: 0.013, memory: 928, loss: 5.9695 2023-01-04 15:15:53,221 - mmcls - INFO - Epoch [1][3100/5005] lr: 1.000e-01, eta: 12:20:01, time: 0.088, data_time: 0.020, memory: 928, loss: 5.9515 2023-01-04 15:16:02,227 - mmcls - INFO - Epoch [1][3200/5005] lr: 1.000e-01, eta: 12:20:06, time: 0.090, data_time: 0.006, memory: 928, loss: 5.9519 2023-01-04 15:16:11,200 - mmcls - INFO - Epoch [1][3300/5005] lr: 1.000e-01, eta: 12:20:03, time: 0.090, data_time: 0.011, memory: 928, loss: 5.9541 2023-01-04 15:16:19,876 - mmcls - INFO - Epoch [1][3400/5005] lr: 1.000e-01, eta: 12:19:15, time: 0.087, data_time: 0.005, memory: 928, loss: 5.9269 2023-01-04 15:16:28,875 - mmcls - INFO - Epoch [1][3500/5005] lr: 1.000e-01, eta: 12:19:16, time: 0.090, data_time: 0.001, memory: 928, loss: 5.9199 2023-01-04 15:16:37,873 - mmcls - INFO - Epoch [1][3600/5005] lr: 1.000e-01, eta: 12:19:18, time: 0.090, data_time: 0.027, memory: 928, loss: 5.9149 2023-01-04 15:16:46,610 - mmcls - INFO - Epoch [1][3700/5005] lr: 1.000e-01, eta: 12:18:44, time: 0.087, data_time: 0.004, memory: 928, loss: 5.9133 2023-01-04 15:16:55,436 - mmcls - INFO - Epoch [1][3800/5005] lr: 1.000e-01, eta: 12:18:22, time: 0.088, data_time: 0.006, memory: 928, loss: 5.9081 2023-01-04 15:17:04,191 - mmcls - INFO - Epoch [1][3900/5005] lr: 1.000e-01, eta: 12:17:53, time: 0.088, data_time: 0.005, memory: 928, loss: 5.9085 2023-01-04 15:17:13,064 - mmcls - INFO - Epoch [1][4000/5005] lr: 1.000e-01, eta: 12:17:38, time: 0.089, data_time: 0.005, memory: 928, loss: 5.8858 2023-01-04 15:17:22,085 - mmcls - INFO - Epoch [1][4100/5005] lr: 1.000e-01, eta: 12:17:43, time: 0.090, data_time: 0.011, memory: 928, loss: 5.8997 2023-01-04 15:17:31,336 - mmcls - INFO - Epoch [1][4200/5005] lr: 1.000e-01, eta: 12:18:13, time: 0.092, data_time: 0.007, memory: 928, loss: 5.8978 2023-01-04 15:17:40,322 - mmcls - INFO - Epoch [1][4300/5005] lr: 1.000e-01, eta: 12:18:12, time: 0.090, data_time: 0.005, memory: 928, loss: 5.8896 2023-01-04 15:17:49,104 - mmcls - INFO - Epoch [1][4400/5005] lr: 1.000e-01, eta: 12:17:46, time: 0.088, data_time: 0.015, memory: 928, loss: 5.8744 2023-01-04 15:17:57,894 - mmcls - INFO - Epoch [1][4500/5005] lr: 1.000e-01, eta: 12:17:21, time: 0.088, data_time: 0.012, memory: 928, loss: 5.8636 2023-01-04 15:18:07,056 - mmcls - INFO - Epoch [1][4600/5005] lr: 1.000e-01, eta: 12:17:38, time: 0.092, data_time: 0.022, memory: 928, loss: 5.8582 2023-01-04 15:18:15,891 - mmcls - INFO - Epoch [1][4700/5005] lr: 1.000e-01, eta: 12:17:20, time: 0.088, data_time: 0.021, memory: 928, loss: 5.8447 2023-01-04 15:18:24,651 - mmcls - INFO - Epoch [1][4800/5005] lr: 1.000e-01, eta: 12:16:54, time: 0.088, data_time: 0.032, memory: 928, loss: 5.8417 2023-01-04 15:18:33,359 - mmcls - INFO - Epoch [1][4900/5005] lr: 1.000e-01, eta: 12:16:23, time: 0.087, data_time: 0.004, memory: 928, loss: 5.8609 2023-01-04 15:18:42,617 - mmcls - INFO - Epoch [1][5000/5005] lr: 1.000e-01, eta: 12:16:49, time: 0.093, data_time: 0.001, memory: 928, loss: 5.8492 2023-01-04 15:19:06,421 - mmcls - INFO - Epoch(val) [1][196] accuracy_top-1: 4.3440, accuracy_top-5: 12.6540 2023-01-04 15:19:17,483 - mmcls - INFO - Epoch [2][100/5005] lr: 1.000e-01, eta: 12:19:23, time: 0.110, data_time: 0.036, memory: 928, loss: 5.8502 2023-01-04 15:19:26,285 - mmcls - INFO - Epoch [2][200/5005] lr: 1.000e-01, eta: 12:18:58, time: 0.088, data_time: 0.023, memory: 928, loss: 5.8321 2023-01-04 15:19:35,413 - mmcls - INFO - Epoch [2][300/5005] lr: 1.000e-01, eta: 12:19:06, time: 0.091, data_time: 0.003, memory: 928, loss: 5.8442 2023-01-04 15:19:44,401 - mmcls - INFO - Epoch [2][400/5005] lr: 1.000e-01, eta: 12:19:00, time: 0.090, data_time: 0.009, memory: 928, loss: 5.8364 2023-01-04 15:19:52,840 - mmcls - INFO - Epoch [2][500/5005] lr: 1.000e-01, eta: 12:18:05, time: 0.084, data_time: 0.005, memory: 928, loss: 5.8206 2023-01-04 15:20:01,758 - mmcls - INFO - Epoch [2][600/5005] lr: 1.000e-01, eta: 12:17:52, time: 0.089, data_time: 0.002, memory: 928, loss: 5.8263 2023-01-04 15:20:10,374 - mmcls - INFO - Epoch [2][700/5005] lr: 1.000e-01, eta: 12:17:15, time: 0.086, data_time: 0.021, memory: 928, loss: 5.8219 2023-01-04 15:20:19,328 - mmcls - INFO - Epoch [2][800/5005] lr: 1.000e-01, eta: 12:17:08, time: 0.090, data_time: 0.013, memory: 928, loss: 5.8029 2023-01-04 15:20:28,060 - mmcls - INFO - Epoch [2][900/5005] lr: 1.000e-01, eta: 12:16:42, time: 0.087, data_time: 0.002, memory: 928, loss: 5.8130 2023-01-04 15:20:36,800 - mmcls - INFO - Epoch [2][1000/5005] lr: 1.000e-01, eta: 12:16:15, time: 0.087, data_time: 0.001, memory: 928, loss: 5.7985 2023-01-04 15:20:45,819 - mmcls - INFO - Epoch [2][1100/5005] lr: 1.000e-01, eta: 12:16:14, time: 0.090, data_time: 0.001, memory: 928, loss: 5.8009 2023-01-04 15:20:54,440 - mmcls - INFO - Epoch [2][1200/5005] lr: 1.000e-01, eta: 12:15:39, time: 0.086, data_time: 0.002, memory: 928, loss: 5.7843 2023-01-04 15:21:03,070 - mmcls - INFO - Epoch [2][1300/5005] lr: 1.000e-01, eta: 12:15:07, time: 0.086, data_time: 0.003, memory: 928, loss: 5.8084 2023-01-04 15:21:11,589 - mmcls - INFO - Epoch [2][1400/5005] lr: 1.000e-01, eta: 12:14:27, time: 0.085, data_time: 0.002, memory: 928, loss: 5.7997 2023-01-04 15:21:20,588 - mmcls - INFO - Epoch [2][1500/5005] lr: 1.000e-01, eta: 12:14:24, time: 0.090, data_time: 0.002, memory: 928, loss: 5.7827 2023-01-04 15:21:29,393 - mmcls - INFO - Epoch [2][1600/5005] lr: 1.000e-01, eta: 12:14:06, time: 0.088, data_time: 0.004, memory: 928, loss: 5.7832 2023-01-04 15:21:38,145 - mmcls - INFO - Epoch [2][1700/5005] lr: 1.000e-01, eta: 12:13:46, time: 0.088, data_time: 0.009, memory: 928, loss: 5.7894 2023-01-04 15:21:47,026 - mmcls - INFO - Epoch [2][1800/5005] lr: 1.000e-01, eta: 12:13:34, time: 0.089, data_time: 0.014, memory: 928, loss: 5.7914 2023-01-04 15:21:55,907 - mmcls - INFO - Epoch [2][1900/5005] lr: 1.000e-01, eta: 12:13:22, time: 0.089, data_time: 0.004, memory: 928, loss: 5.7744 2023-01-04 15:22:04,477 - mmcls - INFO - Epoch [2][2000/5005] lr: 1.000e-01, eta: 12:12:49, time: 0.086, data_time: 0.004, memory: 928, loss: 5.8033 2023-01-04 15:22:13,571 - mmcls - INFO - Epoch [2][2100/5005] lr: 1.000e-01, eta: 12:12:53, time: 0.091, data_time: 0.001, memory: 928, loss: 5.7764 2023-01-04 15:22:22,093 - mmcls - INFO - Epoch [2][2200/5005] lr: 1.000e-01, eta: 12:12:17, time: 0.085, data_time: 0.002, memory: 928, loss: 5.7867 2023-01-04 15:22:31,080 - mmcls - INFO - Epoch [2][2300/5005] lr: 1.000e-01, eta: 12:12:14, time: 0.090, data_time: 0.018, memory: 928, loss: 5.7649 2023-01-04 15:22:39,731 - mmcls - INFO - Epoch [2][2400/5005] lr: 1.000e-01, eta: 12:11:48, time: 0.087, data_time: 0.002, memory: 928, loss: 5.7729 2023-01-04 15:22:48,451 - mmcls - INFO - Epoch [2][2500/5005] lr: 1.000e-01, eta: 12:11:26, time: 0.087, data_time: 0.005, memory: 928, loss: 5.7640 2023-01-04 15:22:57,425 - mmcls - INFO - Epoch [2][2600/5005] lr: 1.000e-01, eta: 12:11:22, time: 0.090, data_time: 0.006, memory: 928, loss: 5.7520 2023-01-04 15:23:06,332 - mmcls - INFO - Epoch [2][2700/5005] lr: 1.000e-01, eta: 12:11:13, time: 0.089, data_time: 0.003, memory: 928, loss: 5.7593 2023-01-04 15:23:15,641 - mmcls - INFO - Epoch [2][2800/5005] lr: 1.000e-01, eta: 12:11:29, time: 0.093, data_time: 0.010, memory: 928, loss: 5.7615 2023-01-04 15:23:24,443 - mmcls - INFO - Epoch [2][2900/5005] lr: 1.000e-01, eta: 12:11:14, time: 0.088, data_time: 0.011, memory: 928, loss: 5.7453 2023-01-04 15:23:33,255 - mmcls - INFO - Epoch [2][3000/5005] lr: 1.000e-01, eta: 12:11:00, time: 0.088, data_time: 0.004, memory: 928, loss: 5.7471 2023-01-04 15:23:42,247 - mmcls - INFO - Epoch [2][3100/5005] lr: 1.000e-01, eta: 12:10:55, time: 0.090, data_time: 0.002, memory: 928, loss: 5.7398 2023-01-04 15:23:51,136 - mmcls - INFO - Epoch [2][3200/5005] lr: 1.000e-01, eta: 12:10:46, time: 0.089, data_time: 0.006, memory: 928, loss: 5.7542 2023-01-04 15:24:00,006 - mmcls - INFO - Epoch [2][3300/5005] lr: 1.000e-01, eta: 12:10:35, time: 0.089, data_time: 0.002, memory: 928, loss: 5.7390 2023-01-04 15:24:08,841 - mmcls - INFO - Epoch [2][3400/5005] lr: 1.000e-01, eta: 12:10:21, time: 0.088, data_time: 0.011, memory: 928, loss: 5.7325 2023-01-04 15:24:17,701 - mmcls - INFO - Epoch [2][3500/5005] lr: 1.000e-01, eta: 12:10:10, time: 0.089, data_time: 0.001, memory: 928, loss: 5.7742 2023-01-04 15:24:26,607 - mmcls - INFO - Epoch [2][3600/5005] lr: 1.000e-01, eta: 12:10:01, time: 0.089, data_time: 0.008, memory: 928, loss: 5.7344 2023-01-04 15:24:35,492 - mmcls - INFO - Epoch [2][3700/5005] lr: 1.000e-01, eta: 12:09:51, time: 0.089, data_time: 0.001, memory: 928, loss: 5.7395 2023-01-04 15:24:44,405 - mmcls - INFO - Epoch [2][3800/5005] lr: 1.000e-01, eta: 12:09:43, time: 0.089, data_time: 0.007, memory: 928, loss: 5.7364 2023-01-04 15:24:53,174 - mmcls - INFO - Epoch [2][3900/5005] lr: 1.000e-01, eta: 12:09:26, time: 0.088, data_time: 0.003, memory: 928, loss: 5.7253 2023-01-04 15:25:02,119 - mmcls - INFO - Epoch [2][4000/5005] lr: 1.000e-01, eta: 12:09:19, time: 0.089, data_time: 0.002, memory: 928, loss: 5.7247 2023-01-04 15:25:11,233 - mmcls - INFO - Epoch [2][4100/5005] lr: 1.000e-01, eta: 12:09:22, time: 0.091, data_time: 0.002, memory: 928, loss: 5.7267 2023-01-04 15:25:20,017 - mmcls - INFO - Epoch [2][4200/5005] lr: 1.000e-01, eta: 12:09:07, time: 0.088, data_time: 0.010, memory: 928, loss: 5.7235 2023-01-04 15:25:28,624 - mmcls - INFO - Epoch [2][4300/5005] lr: 1.000e-01, eta: 12:08:42, time: 0.086, data_time: 0.004, memory: 928, loss: 5.7428 2023-01-04 15:25:37,240 - mmcls - INFO - Epoch [2][4400/5005] lr: 1.000e-01, eta: 12:08:18, time: 0.086, data_time: 0.007, memory: 928, loss: 5.7095 2023-01-04 15:25:45,789 - mmcls - INFO - Epoch [2][4500/5005] lr: 1.000e-01, eta: 12:07:51, time: 0.085, data_time: 0.003, memory: 928, loss: 5.7190 2023-01-04 15:25:54,692 - mmcls - INFO - Epoch [2][4600/5005] lr: 1.000e-01, eta: 12:07:42, time: 0.089, data_time: 0.008, memory: 928, loss: 5.7414 2023-01-04 15:26:03,872 - mmcls - INFO - Epoch [2][4700/5005] lr: 1.000e-01, eta: 12:07:48, time: 0.092, data_time: 0.029, memory: 928, loss: 5.7176 2023-01-04 15:26:12,597 - mmcls - INFO - Epoch [2][4800/5005] lr: 1.000e-01, eta: 12:07:30, time: 0.087, data_time: 0.009, memory: 928, loss: 5.6865 2023-01-04 15:26:21,352 - mmcls - INFO - Epoch [2][4900/5005] lr: 1.000e-01, eta: 12:07:15, time: 0.088, data_time: 0.001, memory: 928, loss: 5.7138 2023-01-04 15:26:30,093 - mmcls - INFO - Epoch [2][5000/5005] lr: 1.000e-01, eta: 12:06:58, time: 0.087, data_time: 0.005, memory: 928, loss: 5.7016 2023-01-04 15:26:53,136 - mmcls - INFO - Epoch(val) [2][196] accuracy_top-1: 3.8800, accuracy_top-5: 10.9480 2023-01-04 15:27:04,260 - mmcls - INFO - Epoch [3][100/5005] lr: 1.000e-01, eta: 12:08:14, time: 0.111, data_time: 0.036, memory: 928, loss: 5.7093 2023-01-04 15:27:13,004 - mmcls - INFO - Epoch [3][200/5005] lr: 1.000e-01, eta: 12:07:57, time: 0.087, data_time: 0.016, memory: 928, loss: 5.7154 2023-01-04 15:27:21,823 - mmcls - INFO - Epoch [3][300/5005] lr: 1.000e-01, eta: 12:07:44, time: 0.088, data_time: 0.003, memory: 928, loss: 5.7044 2023-01-04 15:27:30,613 - mmcls - INFO - Epoch [3][400/5005] lr: 1.000e-01, eta: 12:07:30, time: 0.088, data_time: 0.001, memory: 928, loss: 5.6961 2023-01-04 15:27:39,513 - mmcls - INFO - Epoch [3][500/5005] lr: 1.000e-01, eta: 12:07:20, time: 0.089, data_time: 0.020, memory: 928, loss: 5.6850 2023-01-04 15:27:48,728 - mmcls - INFO - Epoch [3][600/5005] lr: 1.000e-01, eta: 12:07:26, time: 0.092, data_time: 0.011, memory: 928, loss: 5.7082 2023-01-04 15:27:57,172 - mmcls - INFO - Epoch [3][700/5005] lr: 1.000e-01, eta: 12:06:55, time: 0.084, data_time: 0.002, memory: 928, loss: 5.6740 2023-01-04 15:28:05,944 - mmcls - INFO - Epoch [3][800/5005] lr: 1.000e-01, eta: 12:06:41, time: 0.088, data_time: 0.003, memory: 928, loss: 5.6898 2023-01-04 15:28:14,598 - mmcls - INFO - Epoch [3][900/5005] lr: 1.000e-01, eta: 12:06:21, time: 0.087, data_time: 0.008, memory: 928, loss: 5.6928 2023-01-04 15:28:23,612 - mmcls - INFO - Epoch [3][1000/5005] lr: 1.000e-01, eta: 12:06:17, time: 0.090, data_time: 0.001, memory: 928, loss: 5.7001 2023-01-04 15:28:32,850 - mmcls - INFO - Epoch [3][1100/5005] lr: 1.000e-01, eta: 12:06:22, time: 0.092, data_time: 0.002, memory: 928, loss: 5.6799 2023-01-04 15:28:41,443 - mmcls - INFO - Epoch [3][1200/5005] lr: 1.000e-01, eta: 12:06:00, time: 0.086, data_time: 0.004, memory: 928, loss: 5.6823 2023-01-04 15:28:50,071 - mmcls - INFO - Epoch [3][1300/5005] lr: 1.000e-01, eta: 12:05:39, time: 0.086, data_time: 0.029, memory: 928, loss: 5.6929 2023-01-04 15:28:58,786 - mmcls - INFO - Epoch [3][1400/5005] lr: 1.000e-01, eta: 12:05:22, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6876 2023-01-04 15:29:07,821 - mmcls - INFO - Epoch [3][1500/5005] lr: 1.000e-01, eta: 12:05:19, time: 0.090, data_time: 0.002, memory: 928, loss: 5.6964 2023-01-04 15:29:16,406 - mmcls - INFO - Epoch [3][1600/5005] lr: 1.000e-01, eta: 12:04:56, time: 0.086, data_time: 0.003, memory: 928, loss: 5.6826 2023-01-04 15:29:25,558 - mmcls - INFO - Epoch [3][1700/5005] lr: 1.000e-01, eta: 12:04:58, time: 0.091, data_time: 0.001, memory: 928, loss: 5.6936 2023-01-04 15:29:34,478 - mmcls - INFO - Epoch [3][1800/5005] lr: 1.000e-01, eta: 12:04:50, time: 0.089, data_time: 0.008, memory: 928, loss: 5.6836 2023-01-04 15:29:43,151 - mmcls - INFO - Epoch [3][1900/5005] lr: 1.000e-01, eta: 12:04:32, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6790 2023-01-04 15:29:51,886 - mmcls - INFO - Epoch [3][2000/5005] lr: 1.000e-01, eta: 12:04:16, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6979 2023-01-04 15:30:01,175 - mmcls - INFO - Epoch [3][2100/5005] lr: 1.000e-01, eta: 12:04:23, time: 0.093, data_time: 0.001, memory: 928, loss: 5.6845 2023-01-04 15:30:10,119 - mmcls - INFO - Epoch [3][2200/5005] lr: 1.000e-01, eta: 12:04:16, time: 0.089, data_time: 0.004, memory: 928, loss: 5.6751 2023-01-04 15:30:18,997 - mmcls - INFO - Epoch [3][2300/5005] lr: 1.000e-01, eta: 12:04:06, time: 0.089, data_time: 0.001, memory: 928, loss: 5.6690 2023-01-04 15:30:27,741 - mmcls - INFO - Epoch [3][2400/5005] lr: 1.000e-01, eta: 12:03:51, time: 0.087, data_time: 0.008, memory: 928, loss: 5.6571 2023-01-04 15:30:36,733 - mmcls - INFO - Epoch [3][2500/5005] lr: 1.000e-01, eta: 12:03:46, time: 0.090, data_time: 0.002, memory: 928, loss: 5.6731 2023-01-04 15:30:45,477 - mmcls - INFO - Epoch [3][2600/5005] lr: 1.000e-01, eta: 12:03:31, time: 0.087, data_time: 0.005, memory: 928, loss: 5.6569 2023-01-04 15:30:54,312 - mmcls - INFO - Epoch [3][2700/5005] lr: 1.000e-01, eta: 12:03:20, time: 0.088, data_time: 0.016, memory: 928, loss: 5.6613 2023-01-04 15:31:03,154 - mmcls - INFO - Epoch [3][2800/5005] lr: 1.000e-01, eta: 12:03:09, time: 0.088, data_time: 0.002, memory: 928, loss: 5.6653 2023-01-04 15:31:12,151 - mmcls - INFO - Epoch [3][2900/5005] lr: 1.000e-01, eta: 12:03:03, time: 0.090, data_time: 0.001, memory: 928, loss: 5.6701 2023-01-04 15:31:20,895 - mmcls - INFO - Epoch [3][3000/5005] lr: 1.000e-01, eta: 12:02:49, time: 0.088, data_time: 0.015, memory: 928, loss: 5.6756 2023-01-04 15:31:29,623 - mmcls - INFO - Epoch [3][3100/5005] lr: 1.000e-01, eta: 12:02:34, time: 0.087, data_time: 0.013, memory: 928, loss: 5.6387 2023-01-04 15:31:38,582 - mmcls - INFO - Epoch [3][3200/5005] lr: 1.000e-01, eta: 12:02:27, time: 0.090, data_time: 0.020, memory: 928, loss: 5.6532 2023-01-04 15:31:47,442 - mmcls - INFO - Epoch [3][3300/5005] lr: 1.000e-01, eta: 12:02:17, time: 0.089, data_time: 0.001, memory: 928, loss: 5.6573 2023-01-04 15:31:55,890 - mmcls - INFO - Epoch [3][3400/5005] lr: 1.000e-01, eta: 12:01:52, time: 0.084, data_time: 0.003, memory: 928, loss: 5.6567 2023-01-04 15:32:04,379 - mmcls - INFO - Epoch [3][3500/5005] lr: 1.000e-01, eta: 12:01:28, time: 0.085, data_time: 0.004, memory: 928, loss: 5.6452 2023-01-04 15:32:12,933 - mmcls - INFO - Epoch [3][3600/5005] lr: 1.000e-01, eta: 12:01:07, time: 0.086, data_time: 0.008, memory: 928, loss: 5.6496 2023-01-04 15:32:21,597 - mmcls - INFO - Epoch [3][3700/5005] lr: 1.000e-01, eta: 12:00:50, time: 0.087, data_time: 0.009, memory: 928, loss: 5.6638 2023-01-04 15:32:30,232 - mmcls - INFO - Epoch [3][3800/5005] lr: 1.000e-01, eta: 12:00:33, time: 0.086, data_time: 0.015, memory: 928, loss: 5.6494 2023-01-04 15:32:39,081 - mmcls - INFO - Epoch [3][3900/5005] lr: 1.000e-01, eta: 12:00:23, time: 0.089, data_time: 0.012, memory: 928, loss: 5.6207 2023-01-04 15:32:47,755 - mmcls - INFO - Epoch [3][4000/5005] lr: 1.000e-01, eta: 12:00:07, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6475 2023-01-04 15:32:56,417 - mmcls - INFO - Epoch [3][4100/5005] lr: 1.000e-01, eta: 11:59:50, time: 0.087, data_time: 0.021, memory: 928, loss: 5.6279 2023-01-04 15:33:05,386 - mmcls - INFO - Epoch [3][4200/5005] lr: 1.000e-01, eta: 11:59:44, time: 0.090, data_time: 0.036, memory: 928, loss: 5.6414 2023-01-04 15:33:14,256 - mmcls - INFO - Epoch [3][4300/5005] lr: 1.000e-01, eta: 11:59:35, time: 0.089, data_time: 0.002, memory: 928, loss: 5.6510 2023-01-04 15:33:23,232 - mmcls - INFO - Epoch [3][4400/5005] lr: 1.000e-01, eta: 11:59:29, time: 0.090, data_time: 0.002, memory: 928, loss: 5.6503 2023-01-04 15:33:32,370 - mmcls - INFO - Epoch [3][4500/5005] lr: 1.000e-01, eta: 11:59:29, time: 0.091, data_time: 0.018, memory: 928, loss: 5.6469 2023-01-04 15:33:41,321 - mmcls - INFO - Epoch [3][4600/5005] lr: 1.000e-01, eta: 11:59:22, time: 0.090, data_time: 0.011, memory: 928, loss: 5.6476 2023-01-04 15:33:50,030 - mmcls - INFO - Epoch [3][4700/5005] lr: 1.000e-01, eta: 11:59:07, time: 0.087, data_time: 0.008, memory: 928, loss: 5.6375 2023-01-04 15:33:58,848 - mmcls - INFO - Epoch [3][4800/5005] lr: 1.000e-01, eta: 11:58:57, time: 0.088, data_time: 0.001, memory: 928, loss: 5.6186 2023-01-04 15:34:07,464 - mmcls - INFO - Epoch [3][4900/5005] lr: 1.000e-01, eta: 11:58:39, time: 0.086, data_time: 0.001, memory: 928, loss: 5.6271 2023-01-04 15:34:16,578 - mmcls - INFO - Epoch [3][5000/5005] lr: 1.000e-01, eta: 11:58:38, time: 0.091, data_time: 0.001, memory: 928, loss: 5.6248 2023-01-04 15:34:39,382 - mmcls - INFO - Epoch(val) [3][196] accuracy_top-1: 4.4440, accuracy_top-5: 12.2240 2023-01-04 15:34:50,460 - mmcls - INFO - Epoch [4][100/5005] lr: 1.000e-01, eta: 11:59:24, time: 0.111, data_time: 0.037, memory: 928, loss: 5.6246 2023-01-04 15:34:59,211 - mmcls - INFO - Epoch [4][200/5005] lr: 1.000e-01, eta: 11:59:11, time: 0.088, data_time: 0.007, memory: 928, loss: 5.6257 2023-01-04 15:35:07,959 - mmcls - INFO - Epoch [4][300/5005] lr: 1.000e-01, eta: 11:58:57, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6128 2023-01-04 15:35:16,585 - mmcls - INFO - Epoch [4][400/5005] lr: 1.000e-01, eta: 11:58:40, time: 0.086, data_time: 0.003, memory: 928, loss: 5.6279 2023-01-04 15:35:25,687 - mmcls - INFO - Epoch [4][500/5005] lr: 1.000e-01, eta: 11:58:37, time: 0.091, data_time: 0.015, memory: 928, loss: 5.6190 2023-01-04 15:35:34,642 - mmcls - INFO - Epoch [4][600/5005] lr: 1.000e-01, eta: 11:58:30, time: 0.089, data_time: 0.014, memory: 928, loss: 5.6253 2023-01-04 15:35:43,424 - mmcls - INFO - Epoch [4][700/5005] lr: 1.000e-01, eta: 11:58:18, time: 0.088, data_time: 0.008, memory: 928, loss: 5.6263 2023-01-04 15:35:52,197 - mmcls - INFO - Epoch [4][800/5005] lr: 1.000e-01, eta: 11:58:06, time: 0.088, data_time: 0.001, memory: 928, loss: 5.6077 2023-01-04 15:36:00,885 - mmcls - INFO - Epoch [4][900/5005] lr: 1.000e-01, eta: 11:57:51, time: 0.087, data_time: 0.001, memory: 928, loss: 5.6368 2023-01-04 15:36:09,333 - mmcls - INFO - Epoch [4][1000/5005] lr: 1.000e-01, eta: 11:57:28, time: 0.084, data_time: 0.003, memory: 928, loss: 5.6131 2023-01-04 15:36:18,093 - mmcls - INFO - Epoch [4][1100/5005] lr: 1.000e-01, eta: 11:57:16, time: 0.088, data_time: 0.023, memory: 928, loss: 5.6194 2023-01-04 15:36:26,952 - mmcls - INFO - Epoch [4][1200/5005] lr: 1.000e-01, eta: 11:57:06, time: 0.089, data_time: 0.036, memory: 928, loss: 5.6230 2023-01-04 15:36:35,580 - mmcls - INFO - Epoch [4][1300/5005] lr: 1.000e-01, eta: 11:56:50, time: 0.086, data_time: 0.007, memory: 928, loss: 5.6289 2023-01-04 15:36:44,384 - mmcls - INFO - Epoch [4][1400/5005] lr: 1.000e-01, eta: 11:56:38, time: 0.088, data_time: 0.005, memory: 928, loss: 5.6068 2023-01-04 15:36:52,908 - mmcls - INFO - Epoch [4][1500/5005] lr: 1.000e-01, eta: 11:56:19, time: 0.085, data_time: 0.011, memory: 928, loss: 5.6164 2023-01-04 15:37:01,710 - mmcls - INFO - Epoch [4][1600/5005] lr: 1.000e-01, eta: 11:56:08, time: 0.088, data_time: 0.031, memory: 928, loss: 5.6069 2023-01-04 15:37:10,320 - mmcls - INFO - Epoch [4][1700/5005] lr: 1.000e-01, eta: 11:55:51, time: 0.086, data_time: 0.020, memory: 928, loss: 5.6210 2023-01-04 15:37:18,878 - mmcls - INFO - Epoch [4][1800/5005] lr: 1.000e-01, eta: 11:55:33, time: 0.086, data_time: 0.007, memory: 928, loss: 5.6131 2023-01-04 15:37:27,860 - mmcls - INFO - Epoch [4][1900/5005] lr: 1.000e-01, eta: 11:55:27, time: 0.090, data_time: 0.001, memory: 928, loss: 5.5916 2023-01-04 15:37:36,525 - mmcls - INFO - Epoch [4][2000/5005] lr: 1.000e-01, eta: 11:55:12, time: 0.087, data_time: 0.012, memory: 928, loss: 5.6198 2023-01-04 15:37:45,092 - mmcls - INFO - Epoch [4][2100/5005] lr: 1.000e-01, eta: 11:54:55, time: 0.086, data_time: 0.001, memory: 928, loss: 5.6211 2023-01-04 15:37:54,098 - mmcls - INFO - Epoch [4][2200/5005] lr: 1.000e-01, eta: 11:54:49, time: 0.090, data_time: 0.001, memory: 928, loss: 5.6006 2023-01-04 15:38:03,053 - mmcls - INFO - Epoch [4][2300/5005] lr: 1.000e-01, eta: 11:54:42, time: 0.089, data_time: 0.002, memory: 928, loss: 5.6115 2023-01-04 15:38:11,795 - mmcls - INFO - Epoch [4][2400/5005] lr: 1.000e-01, eta: 11:54:30, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5960 2023-01-04 15:38:20,819 - mmcls - INFO - Epoch [4][2500/5005] lr: 1.000e-01, eta: 11:54:25, time: 0.090, data_time: 0.003, memory: 928, loss: 5.5862 2023-01-04 15:38:29,590 - mmcls - INFO - Epoch [4][2600/5005] lr: 1.000e-01, eta: 11:54:14, time: 0.088, data_time: 0.022, memory: 928, loss: 5.6165 2023-01-04 15:38:38,240 - mmcls - INFO - Epoch [4][2700/5005] lr: 1.000e-01, eta: 11:53:59, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5764 2023-01-04 15:38:47,242 - mmcls - INFO - Epoch [4][2800/5005] lr: 1.000e-01, eta: 11:53:53, time: 0.090, data_time: 0.002, memory: 928, loss: 5.6036 2023-01-04 15:38:55,951 - mmcls - INFO - Epoch [4][2900/5005] lr: 1.000e-01, eta: 11:53:40, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5876 2023-01-04 15:39:04,829 - mmcls - INFO - Epoch [4][3000/5005] lr: 1.000e-01, eta: 11:53:31, time: 0.089, data_time: 0.011, memory: 928, loss: 5.6112 2023-01-04 15:39:13,574 - mmcls - INFO - Epoch [4][3100/5005] lr: 1.000e-01, eta: 11:53:19, time: 0.087, data_time: 0.021, memory: 928, loss: 5.6140 2023-01-04 15:39:22,593 - mmcls - INFO - Epoch [4][3200/5005] lr: 1.000e-01, eta: 11:53:14, time: 0.090, data_time: 0.036, memory: 928, loss: 5.5781 2023-01-04 15:39:31,499 - mmcls - INFO - Epoch [4][3300/5005] lr: 1.000e-01, eta: 11:53:06, time: 0.089, data_time: 0.005, memory: 928, loss: 5.5856 2023-01-04 15:39:40,054 - mmcls - INFO - Epoch [4][3400/5005] lr: 1.000e-01, eta: 11:52:48, time: 0.085, data_time: 0.009, memory: 928, loss: 5.5865 2023-01-04 15:39:48,970 - mmcls - INFO - Epoch [4][3500/5005] lr: 1.000e-01, eta: 11:52:41, time: 0.089, data_time: 0.012, memory: 928, loss: 5.5918 2023-01-04 15:39:57,854 - mmcls - INFO - Epoch [4][3600/5005] lr: 1.000e-01, eta: 11:52:32, time: 0.089, data_time: 0.003, memory: 928, loss: 5.6019 2023-01-04 15:40:06,702 - mmcls - INFO - Epoch [4][3700/5005] lr: 1.000e-01, eta: 11:52:22, time: 0.088, data_time: 0.012, memory: 928, loss: 5.5907 2023-01-04 15:40:15,437 - mmcls - INFO - Epoch [4][3800/5005] lr: 1.000e-01, eta: 11:52:10, time: 0.087, data_time: 0.004, memory: 928, loss: 5.6012 2023-01-04 15:40:24,121 - mmcls - INFO - Epoch [4][3900/5005] lr: 1.000e-01, eta: 11:51:57, time: 0.087, data_time: 0.026, memory: 928, loss: 5.6020 2023-01-04 15:40:32,941 - mmcls - INFO - Epoch [4][4000/5005] lr: 1.000e-01, eta: 11:51:46, time: 0.088, data_time: 0.014, memory: 928, loss: 5.5683 2023-01-04 15:40:41,773 - mmcls - INFO - Epoch [4][4100/5005] lr: 1.000e-01, eta: 11:51:37, time: 0.088, data_time: 0.008, memory: 928, loss: 5.5944 2023-01-04 15:40:50,202 - mmcls - INFO - Epoch [4][4200/5005] lr: 1.000e-01, eta: 11:51:17, time: 0.084, data_time: 0.027, memory: 928, loss: 5.5743 2023-01-04 15:40:59,202 - mmcls - INFO - Epoch [4][4300/5005] lr: 1.000e-01, eta: 11:51:11, time: 0.090, data_time: 0.010, memory: 928, loss: 5.5973 2023-01-04 15:41:07,882 - mmcls - INFO - Epoch [4][4400/5005] lr: 1.000e-01, eta: 11:50:58, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5979 2023-01-04 15:41:16,727 - mmcls - INFO - Epoch [4][4500/5005] lr: 1.000e-01, eta: 11:50:48, time: 0.088, data_time: 0.002, memory: 928, loss: 5.6079 2023-01-04 15:41:25,267 - mmcls - INFO - Epoch [4][4600/5005] lr: 1.000e-01, eta: 11:50:31, time: 0.085, data_time: 0.010, memory: 928, loss: 5.5701 2023-01-04 15:41:34,160 - mmcls - INFO - Epoch [4][4700/5005] lr: 1.000e-01, eta: 11:50:23, time: 0.089, data_time: 0.017, memory: 928, loss: 5.5825 2023-01-04 15:41:43,284 - mmcls - INFO - Epoch [4][4800/5005] lr: 1.000e-01, eta: 11:50:20, time: 0.091, data_time: 0.016, memory: 928, loss: 5.5981 2023-01-04 15:41:52,145 - mmcls - INFO - Epoch [4][4900/5005] lr: 1.000e-01, eta: 11:50:11, time: 0.089, data_time: 0.015, memory: 928, loss: 5.5770 2023-01-04 15:42:00,771 - mmcls - INFO - Epoch [4][5000/5005] lr: 1.000e-01, eta: 11:49:56, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5755 2023-01-04 15:42:23,648 - mmcls - INFO - Epoch(val) [4][196] accuracy_top-1: 4.4920, accuracy_top-5: 12.2320 2023-01-04 15:42:34,620 - mmcls - INFO - Epoch [5][100/5005] lr: 1.000e-01, eta: 11:50:26, time: 0.110, data_time: 0.034, memory: 928, loss: 5.5445 2023-01-04 15:42:43,617 - mmcls - INFO - Epoch [5][200/5005] lr: 1.000e-01, eta: 11:50:21, time: 0.090, data_time: 0.004, memory: 928, loss: 5.5816 2023-01-04 15:42:52,108 - mmcls - INFO - Epoch [5][300/5005] lr: 1.000e-01, eta: 11:50:02, time: 0.085, data_time: 0.021, memory: 928, loss: 5.5756 2023-01-04 15:43:00,842 - mmcls - INFO - Epoch [5][400/5005] lr: 1.000e-01, eta: 11:49:50, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5566 2023-01-04 15:43:09,441 - mmcls - INFO - Epoch [5][500/5005] lr: 1.000e-01, eta: 11:49:35, time: 0.086, data_time: 0.020, memory: 928, loss: 5.5906 2023-01-04 15:43:18,113 - mmcls - INFO - Epoch [5][600/5005] lr: 1.000e-01, eta: 11:49:21, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5652 2023-01-04 15:43:27,139 - mmcls - INFO - Epoch [5][700/5005] lr: 1.000e-01, eta: 11:49:16, time: 0.090, data_time: 0.001, memory: 928, loss: 5.5767 2023-01-04 15:43:35,879 - mmcls - INFO - Epoch [5][800/5005] lr: 1.000e-01, eta: 11:49:04, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5873 2023-01-04 15:43:44,875 - mmcls - INFO - Epoch [5][900/5005] lr: 1.000e-01, eta: 11:48:58, time: 0.090, data_time: 0.005, memory: 928, loss: 5.6030 2023-01-04 15:43:53,642 - mmcls - INFO - Epoch [5][1000/5005] lr: 1.000e-01, eta: 11:48:47, time: 0.088, data_time: 0.006, memory: 928, loss: 5.5736 2023-01-04 15:44:02,513 - mmcls - INFO - Epoch [5][1100/5005] lr: 1.000e-01, eta: 11:48:38, time: 0.089, data_time: 0.005, memory: 928, loss: 5.5753 2023-01-04 15:44:11,318 - mmcls - INFO - Epoch [5][1200/5005] lr: 1.000e-01, eta: 11:48:28, time: 0.088, data_time: 0.020, memory: 928, loss: 5.5835 2023-01-04 15:44:20,233 - mmcls - INFO - Epoch [5][1300/5005] lr: 1.000e-01, eta: 11:48:20, time: 0.089, data_time: 0.007, memory: 928, loss: 5.5906 2023-01-04 15:44:28,914 - mmcls - INFO - Epoch [5][1400/5005] lr: 1.000e-01, eta: 11:48:07, time: 0.087, data_time: 0.005, memory: 928, loss: 5.5450 2023-01-04 15:44:37,475 - mmcls - INFO - Epoch [5][1500/5005] lr: 1.000e-01, eta: 11:47:51, time: 0.086, data_time: 0.012, memory: 928, loss: 5.5944 2023-01-04 15:44:46,328 - mmcls - INFO - Epoch [5][1600/5005] lr: 1.000e-01, eta: 11:47:42, time: 0.089, data_time: 0.004, memory: 928, loss: 5.5683 2023-01-04 15:44:55,442 - mmcls - INFO - Epoch [5][1700/5005] lr: 1.000e-01, eta: 11:47:39, time: 0.091, data_time: 0.002, memory: 928, loss: 5.5950 2023-01-04 15:45:04,139 - mmcls - INFO - Epoch [5][1800/5005] lr: 1.000e-01, eta: 11:47:26, time: 0.087, data_time: 0.028, memory: 928, loss: 5.5673 2023-01-04 15:45:12,857 - mmcls - INFO - Epoch [5][1900/5005] lr: 1.000e-01, eta: 11:47:14, time: 0.087, data_time: 0.023, memory: 928, loss: 5.5755 2023-01-04 15:45:21,634 - mmcls - INFO - Epoch [5][2000/5005] lr: 1.000e-01, eta: 11:47:03, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5715 2023-01-04 15:45:30,525 - mmcls - INFO - Epoch [5][2100/5005] lr: 1.000e-01, eta: 11:46:54, time: 0.089, data_time: 0.001, memory: 928, loss: 5.5445 2023-01-04 15:45:39,204 - mmcls - INFO - Epoch [5][2200/5005] lr: 1.000e-01, eta: 11:46:42, time: 0.087, data_time: 0.008, memory: 928, loss: 5.5634 2023-01-04 15:45:48,282 - mmcls - INFO - Epoch [5][2300/5005] lr: 1.000e-01, eta: 11:46:37, time: 0.091, data_time: 0.001, memory: 928, loss: 5.5590 2023-01-04 15:45:56,893 - mmcls - INFO - Epoch [5][2400/5005] lr: 1.000e-01, eta: 11:46:23, time: 0.086, data_time: 0.015, memory: 928, loss: 5.5490 2023-01-04 15:46:05,588 - mmcls - INFO - Epoch [5][2500/5005] lr: 1.000e-01, eta: 11:46:10, time: 0.087, data_time: 0.007, memory: 928, loss: 5.5632 2023-01-04 15:46:14,115 - mmcls - INFO - Epoch [5][2600/5005] lr: 1.000e-01, eta: 11:45:55, time: 0.085, data_time: 0.004, memory: 928, loss: 5.5678 2023-01-04 15:46:22,718 - mmcls - INFO - Epoch [5][2700/5005] lr: 1.000e-01, eta: 11:45:40, time: 0.086, data_time: 0.010, memory: 928, loss: 5.5709 2023-01-04 15:46:31,503 - mmcls - INFO - Epoch [5][2800/5005] lr: 1.000e-01, eta: 11:45:30, time: 0.088, data_time: 0.009, memory: 928, loss: 5.5702 2023-01-04 15:46:40,223 - mmcls - INFO - Epoch [5][2900/5005] lr: 1.000e-01, eta: 11:45:18, time: 0.087, data_time: 0.016, memory: 928, loss: 5.5759 2023-01-04 15:46:49,257 - mmcls - INFO - Epoch [5][3000/5005] lr: 1.000e-01, eta: 11:45:12, time: 0.090, data_time: 0.012, memory: 928, loss: 5.5600 2023-01-04 15:46:58,108 - mmcls - INFO - Epoch [5][3100/5005] lr: 1.000e-01, eta: 11:45:03, time: 0.089, data_time: 0.014, memory: 928, loss: 5.5545 2023-01-04 15:47:06,988 - mmcls - INFO - Epoch [5][3200/5005] lr: 1.000e-01, eta: 11:44:55, time: 0.089, data_time: 0.006, memory: 928, loss: 5.5517 2023-01-04 15:47:15,671 - mmcls - INFO - Epoch [5][3300/5005] lr: 1.000e-01, eta: 11:44:42, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5694 2023-01-04 15:47:24,338 - mmcls - INFO - Epoch [5][3400/5005] lr: 1.000e-01, eta: 11:44:29, time: 0.087, data_time: 0.010, memory: 928, loss: 5.5649 2023-01-04 15:47:33,138 - mmcls - INFO - Epoch [5][3500/5005] lr: 1.000e-01, eta: 11:44:19, time: 0.088, data_time: 0.035, memory: 928, loss: 5.5629 2023-01-04 15:47:41,897 - mmcls - INFO - Epoch [5][3600/5005] lr: 1.000e-01, eta: 11:44:08, time: 0.088, data_time: 0.011, memory: 928, loss: 5.5596 2023-01-04 15:47:50,683 - mmcls - INFO - Epoch [5][3700/5005] lr: 1.000e-01, eta: 11:43:58, time: 0.088, data_time: 0.006, memory: 928, loss: 5.5712 2023-01-04 15:47:59,509 - mmcls - INFO - Epoch [5][3800/5005] lr: 1.000e-01, eta: 11:43:49, time: 0.088, data_time: 0.023, memory: 928, loss: 5.5817 2023-01-04 15:48:08,262 - mmcls - INFO - Epoch [5][3900/5005] lr: 1.000e-01, eta: 11:43:38, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5616 2023-01-04 15:48:16,948 - mmcls - INFO - Epoch [5][4000/5005] lr: 1.000e-01, eta: 11:43:25, time: 0.087, data_time: 0.004, memory: 928, loss: 5.5775 2023-01-04 15:48:25,815 - mmcls - INFO - Epoch [5][4100/5005] lr: 1.000e-01, eta: 11:43:17, time: 0.089, data_time: 0.001, memory: 928, loss: 5.5547 2023-01-04 15:48:34,502 - mmcls - INFO - Epoch [5][4200/5005] lr: 1.000e-01, eta: 11:43:04, time: 0.087, data_time: 0.007, memory: 928, loss: 5.5539 2023-01-04 15:48:43,191 - mmcls - INFO - Epoch [5][4300/5005] lr: 1.000e-01, eta: 11:42:52, time: 0.087, data_time: 0.005, memory: 928, loss: 5.5635 2023-01-04 15:48:52,731 - mmcls - INFO - Epoch [5][4400/5005] lr: 1.000e-01, eta: 11:42:57, time: 0.095, data_time: 0.001, memory: 928, loss: 5.5640 2023-01-04 15:49:01,459 - mmcls - INFO - Epoch [5][4500/5005] lr: 1.000e-01, eta: 11:42:45, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5749 2023-01-04 15:49:10,288 - mmcls - INFO - Epoch [5][4600/5005] lr: 1.000e-01, eta: 11:42:36, time: 0.088, data_time: 0.004, memory: 928, loss: 5.5575 2023-01-04 15:49:19,137 - mmcls - INFO - Epoch [5][4700/5005] lr: 1.000e-01, eta: 11:42:27, time: 0.088, data_time: 0.009, memory: 928, loss: 5.5526 2023-01-04 15:49:27,736 - mmcls - INFO - Epoch [5][4800/5005] lr: 1.000e-01, eta: 11:42:13, time: 0.086, data_time: 0.038, memory: 928, loss: 5.5670 2023-01-04 15:49:36,448 - mmcls - INFO - Epoch [5][4900/5005] lr: 1.000e-01, eta: 11:42:01, time: 0.087, data_time: 0.012, memory: 928, loss: 5.5617 2023-01-04 15:49:45,056 - mmcls - INFO - Epoch [5][5000/5005] lr: 1.000e-01, eta: 11:41:48, time: 0.086, data_time: 0.011, memory: 928, loss: 5.5594 2023-01-04 15:50:07,998 - mmcls - INFO - Epoch(val) [5][196] accuracy_top-1: 4.8740, accuracy_top-5: 13.0580 2023-01-04 15:50:18,983 - mmcls - INFO - Epoch [6][100/5005] lr: 1.000e-01, eta: 11:42:10, time: 0.110, data_time: 0.039, memory: 928, loss: 5.5321 2023-01-04 15:50:27,762 - mmcls - INFO - Epoch [6][200/5005] lr: 1.000e-01, eta: 11:41:59, time: 0.088, data_time: 0.029, memory: 928, loss: 5.5453 2023-01-04 15:50:36,669 - mmcls - INFO - Epoch [6][300/5005] lr: 1.000e-01, eta: 11:41:51, time: 0.089, data_time: 0.018, memory: 928, loss: 5.5407 2023-01-04 15:50:45,615 - mmcls - INFO - Epoch [6][400/5005] lr: 1.000e-01, eta: 11:41:44, time: 0.089, data_time: 0.017, memory: 928, loss: 5.5559 2023-01-04 15:50:54,711 - mmcls - INFO - Epoch [6][500/5005] lr: 1.000e-01, eta: 11:41:40, time: 0.091, data_time: 0.005, memory: 928, loss: 5.5408 2023-01-04 15:51:03,570 - mmcls - INFO - Epoch [6][600/5005] lr: 1.000e-01, eta: 11:41:31, time: 0.089, data_time: 0.025, memory: 928, loss: 5.5412 2023-01-04 15:51:12,363 - mmcls - INFO - Epoch [6][700/5005] lr: 1.000e-01, eta: 11:41:20, time: 0.088, data_time: 0.029, memory: 928, loss: 5.5317 2023-01-04 15:51:20,934 - mmcls - INFO - Epoch [6][800/5005] lr: 1.000e-01, eta: 11:41:06, time: 0.086, data_time: 0.013, memory: 928, loss: 5.5731 2023-01-04 15:51:29,540 - mmcls - INFO - Epoch [6][900/5005] lr: 1.000e-01, eta: 11:40:52, time: 0.086, data_time: 0.010, memory: 928, loss: 5.5349 2023-01-04 15:51:38,362 - mmcls - INFO - Epoch [6][1000/5005] lr: 1.000e-01, eta: 11:40:43, time: 0.088, data_time: 0.015, memory: 928, loss: 5.5558 2023-01-04 15:51:47,096 - mmcls - INFO - Epoch [6][1100/5005] lr: 1.000e-01, eta: 11:40:32, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5396 2023-01-04 15:51:56,206 - mmcls - INFO - Epoch [6][1200/5005] lr: 1.000e-01, eta: 11:40:28, time: 0.091, data_time: 0.001, memory: 928, loss: 5.5514 2023-01-04 15:52:05,149 - mmcls - INFO - Epoch [6][1300/5005] lr: 1.000e-01, eta: 11:40:20, time: 0.089, data_time: 0.003, memory: 928, loss: 5.5691 2023-01-04 15:52:13,806 - mmcls - INFO - Epoch [6][1400/5005] lr: 1.000e-01, eta: 11:40:07, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5618 2023-01-04 15:52:22,453 - mmcls - INFO - Epoch [6][1500/5005] lr: 1.000e-01, eta: 11:39:55, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5474 2023-01-04 15:52:31,561 - mmcls - INFO - Epoch [6][1600/5005] lr: 1.000e-01, eta: 11:39:50, time: 0.091, data_time: 0.003, memory: 928, loss: 5.5566 2023-01-04 15:52:40,610 - mmcls - INFO - Epoch [6][1700/5005] lr: 1.000e-01, eta: 11:39:45, time: 0.090, data_time: 0.011, memory: 928, loss: 5.5647 2023-01-04 15:52:49,263 - mmcls - INFO - Epoch [6][1800/5005] lr: 1.000e-01, eta: 11:39:32, time: 0.086, data_time: 0.019, memory: 928, loss: 5.5452 2023-01-04 15:52:57,996 - mmcls - INFO - Epoch [6][1900/5005] lr: 1.000e-01, eta: 11:39:21, time: 0.087, data_time: 0.005, memory: 928, loss: 5.5615 2023-01-04 15:53:06,815 - mmcls - INFO - Epoch [6][2000/5005] lr: 1.000e-01, eta: 11:39:11, time: 0.088, data_time: 0.005, memory: 928, loss: 5.5510 2023-01-04 15:53:15,461 - mmcls - INFO - Epoch [6][2100/5005] lr: 1.000e-01, eta: 11:38:59, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5328 2023-01-04 15:53:24,339 - mmcls - INFO - Epoch [6][2200/5005] lr: 1.000e-01, eta: 11:38:50, time: 0.089, data_time: 0.004, memory: 928, loss: 5.5698 2023-01-04 15:53:32,936 - mmcls - INFO - Epoch [6][2300/5005] lr: 1.000e-01, eta: 11:38:37, time: 0.086, data_time: 0.003, memory: 928, loss: 5.5476 2023-01-04 15:53:41,717 - mmcls - INFO - Epoch [6][2400/5005] lr: 1.000e-01, eta: 11:38:27, time: 0.088, data_time: 0.009, memory: 928, loss: 5.5640 2023-01-04 15:53:50,388 - mmcls - INFO - Epoch [6][2500/5005] lr: 1.000e-01, eta: 11:38:15, time: 0.087, data_time: 0.016, memory: 928, loss: 5.5383 2023-01-04 15:53:59,187 - mmcls - INFO - Epoch [6][2600/5005] lr: 1.000e-01, eta: 11:38:05, time: 0.088, data_time: 0.013, memory: 928, loss: 5.5434 2023-01-04 15:54:07,784 - mmcls - INFO - Epoch [6][2700/5005] lr: 1.000e-01, eta: 11:37:51, time: 0.086, data_time: 0.009, memory: 928, loss: 5.5610 2023-01-04 15:54:16,510 - mmcls - INFO - Epoch [6][2800/5005] lr: 1.000e-01, eta: 11:37:40, time: 0.087, data_time: 0.017, memory: 928, loss: 5.5687 2023-01-04 15:54:25,254 - mmcls - INFO - Epoch [6][2900/5005] lr: 1.000e-01, eta: 11:37:29, time: 0.087, data_time: 0.006, memory: 928, loss: 5.5351 2023-01-04 15:54:34,216 - mmcls - INFO - Epoch [6][3000/5005] lr: 1.000e-01, eta: 11:37:22, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5410 2023-01-04 15:54:43,010 - mmcls - INFO - Epoch [6][3100/5005] lr: 1.000e-01, eta: 11:37:13, time: 0.088, data_time: 0.001, memory: 928, loss: 5.5448 2023-01-04 15:54:52,016 - mmcls - INFO - Epoch [6][3200/5005] lr: 1.000e-01, eta: 11:37:06, time: 0.090, data_time: 0.006, memory: 928, loss: 5.5557 2023-01-04 15:55:00,625 - mmcls - INFO - Epoch [6][3300/5005] lr: 1.000e-01, eta: 11:36:53, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5586 2023-01-04 15:55:09,194 - mmcls - INFO - Epoch [6][3400/5005] lr: 1.000e-01, eta: 11:36:40, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5429 2023-01-04 15:55:18,108 - mmcls - INFO - Epoch [6][3500/5005] lr: 1.000e-01, eta: 11:36:32, time: 0.089, data_time: 0.002, memory: 928, loss: 5.5361 2023-01-04 15:55:26,707 - mmcls - INFO - Epoch [6][3600/5005] lr: 1.000e-01, eta: 11:36:19, time: 0.086, data_time: 0.013, memory: 928, loss: 5.5446 2023-01-04 15:55:35,415 - mmcls - INFO - Epoch [6][3700/5005] lr: 1.000e-01, eta: 11:36:07, time: 0.087, data_time: 0.021, memory: 928, loss: 5.5298 2023-01-04 15:55:44,107 - mmcls - INFO - Epoch [6][3800/5005] lr: 1.000e-01, eta: 11:35:56, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5455 2023-01-04 15:55:53,077 - mmcls - INFO - Epoch [6][3900/5005] lr: 1.000e-01, eta: 11:35:49, time: 0.090, data_time: 0.028, memory: 928, loss: 5.5389 2023-01-04 15:56:01,904 - mmcls - INFO - Epoch [6][4000/5005] lr: 1.000e-01, eta: 11:35:39, time: 0.088, data_time: 0.007, memory: 928, loss: 5.5561 2023-01-04 15:56:10,556 - mmcls - INFO - Epoch [6][4100/5005] lr: 1.000e-01, eta: 11:35:27, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5222 2023-01-04 15:56:19,254 - mmcls - INFO - Epoch [6][4200/5005] lr: 1.000e-01, eta: 11:35:16, time: 0.087, data_time: 0.006, memory: 928, loss: 5.5518 2023-01-04 15:56:28,191 - mmcls - INFO - Epoch [6][4300/5005] lr: 1.000e-01, eta: 11:35:09, time: 0.089, data_time: 0.006, memory: 928, loss: 5.5510 2023-01-04 15:56:37,106 - mmcls - INFO - Epoch [6][4400/5005] lr: 1.000e-01, eta: 11:35:01, time: 0.089, data_time: 0.009, memory: 928, loss: 5.5429 2023-01-04 15:56:45,873 - mmcls - INFO - Epoch [6][4500/5005] lr: 1.000e-01, eta: 11:34:50, time: 0.088, data_time: 0.003, memory: 928, loss: 5.5268 2023-01-04 15:56:54,673 - mmcls - INFO - Epoch [6][4600/5005] lr: 1.000e-01, eta: 11:34:41, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5579 2023-01-04 15:57:03,176 - mmcls - INFO - Epoch [6][4700/5005] lr: 1.000e-01, eta: 11:34:26, time: 0.085, data_time: 0.011, memory: 928, loss: 5.5650 2023-01-04 15:57:12,049 - mmcls - INFO - Epoch [6][4800/5005] lr: 1.000e-01, eta: 11:34:18, time: 0.089, data_time: 0.001, memory: 928, loss: 5.5482 2023-01-04 15:57:20,662 - mmcls - INFO - Epoch [6][4900/5005] lr: 1.000e-01, eta: 11:34:05, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5277 2023-01-04 15:57:29,258 - mmcls - INFO - Epoch [6][5000/5005] lr: 1.000e-01, eta: 11:33:52, time: 0.086, data_time: 0.003, memory: 928, loss: 5.5478 2023-01-04 15:57:51,991 - mmcls - INFO - Epoch(val) [6][196] accuracy_top-1: 5.0180, accuracy_top-5: 13.8500 2023-01-04 15:58:02,968 - mmcls - INFO - Epoch [7][100/5005] lr: 1.000e-01, eta: 11:34:09, time: 0.110, data_time: 0.053, memory: 928, loss: 5.5271 2023-01-04 15:58:11,505 - mmcls - INFO - Epoch [7][200/5005] lr: 1.000e-01, eta: 11:33:55, time: 0.085, data_time: 0.027, memory: 928, loss: 5.5204 2023-01-04 15:58:20,096 - mmcls - INFO - Epoch [7][300/5005] lr: 1.000e-01, eta: 11:33:42, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5369 2023-01-04 15:58:28,703 - mmcls - INFO - Epoch [7][400/5005] lr: 1.000e-01, eta: 11:33:29, time: 0.086, data_time: 0.007, memory: 928, loss: 5.5453 2023-01-04 15:58:37,707 - mmcls - INFO - Epoch [7][500/5005] lr: 1.000e-01, eta: 11:33:23, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5333 2023-01-04 15:58:46,288 - mmcls - INFO - Epoch [7][600/5005] lr: 1.000e-01, eta: 11:33:10, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5436 2023-01-04 15:58:55,462 - mmcls - INFO - Epoch [7][700/5005] lr: 1.000e-01, eta: 11:33:06, time: 0.092, data_time: 0.009, memory: 928, loss: 5.5479 2023-01-04 15:59:04,242 - mmcls - INFO - Epoch [7][800/5005] lr: 1.000e-01, eta: 11:32:56, time: 0.088, data_time: 0.027, memory: 928, loss: 5.5190 2023-01-04 15:59:13,060 - mmcls - INFO - Epoch [7][900/5005] lr: 1.000e-01, eta: 11:32:47, time: 0.088, data_time: 0.003, memory: 928, loss: 5.5464 2023-01-04 15:59:21,847 - mmcls - INFO - Epoch [7][1000/5005] lr: 1.000e-01, eta: 11:32:37, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5345 2023-01-04 15:59:30,829 - mmcls - INFO - Epoch [7][1100/5005] lr: 1.000e-01, eta: 11:32:30, time: 0.090, data_time: 0.005, memory: 928, loss: 5.5489 2023-01-04 15:59:40,129 - mmcls - INFO - Epoch [7][1200/5005] lr: 1.000e-01, eta: 11:32:28, time: 0.093, data_time: 0.005, memory: 928, loss: 5.5482 2023-01-04 15:59:48,785 - mmcls - INFO - Epoch [7][1300/5005] lr: 1.000e-01, eta: 11:32:16, time: 0.087, data_time: 0.010, memory: 928, loss: 5.5235 2023-01-04 15:59:57,282 - mmcls - INFO - Epoch [7][1400/5005] lr: 1.000e-01, eta: 11:32:02, time: 0.085, data_time: 0.029, memory: 928, loss: 5.5083 2023-01-04 16:00:06,095 - mmcls - INFO - Epoch [7][1500/5005] lr: 1.000e-01, eta: 11:31:53, time: 0.088, data_time: 0.005, memory: 928, loss: 5.5283 2023-01-04 16:00:14,791 - mmcls - INFO - Epoch [7][1600/5005] lr: 1.000e-01, eta: 11:31:41, time: 0.087, data_time: 0.003, memory: 928, loss: 5.5413 2023-01-04 16:00:23,905 - mmcls - INFO - Epoch [7][1700/5005] lr: 1.000e-01, eta: 11:31:36, time: 0.091, data_time: 0.002, memory: 928, loss: 5.5216 2023-01-04 16:00:32,730 - mmcls - INFO - Epoch [7][1800/5005] lr: 1.000e-01, eta: 11:31:27, time: 0.088, data_time: 0.015, memory: 928, loss: 5.5251 2023-01-04 16:00:41,463 - mmcls - INFO - Epoch [7][1900/5005] lr: 1.000e-01, eta: 11:31:16, time: 0.087, data_time: 0.032, memory: 928, loss: 5.5480 2023-01-04 16:00:50,206 - mmcls - INFO - Epoch [7][2000/5005] lr: 1.000e-01, eta: 11:31:06, time: 0.087, data_time: 0.024, memory: 928, loss: 5.5417 2023-01-04 16:00:59,039 - mmcls - INFO - Epoch [7][2100/5005] lr: 1.000e-01, eta: 11:30:57, time: 0.088, data_time: 0.033, memory: 928, loss: 5.5372 2023-01-04 16:01:07,691 - mmcls - INFO - Epoch [7][2200/5005] lr: 1.000e-01, eta: 11:30:45, time: 0.087, data_time: 0.038, memory: 928, loss: 5.5441 2023-01-04 16:01:16,618 - mmcls - INFO - Epoch [7][2300/5005] lr: 1.000e-01, eta: 11:30:37, time: 0.089, data_time: 0.015, memory: 928, loss: 5.5248 2023-01-04 16:01:25,247 - mmcls - INFO - Epoch [7][2400/5005] lr: 1.000e-01, eta: 11:30:25, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5319 2023-01-04 16:01:33,783 - mmcls - INFO - Epoch [7][2500/5005] lr: 1.000e-01, eta: 11:30:12, time: 0.085, data_time: 0.002, memory: 928, loss: 5.5299 2023-01-04 16:01:42,394 - mmcls - INFO - Epoch [7][2600/5005] lr: 1.000e-01, eta: 11:30:00, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5376 2023-01-04 16:01:51,024 - mmcls - INFO - Epoch [7][2700/5005] lr: 1.000e-01, eta: 11:29:47, time: 0.086, data_time: 0.008, memory: 928, loss: 5.5230 2023-01-04 16:02:00,000 - mmcls - INFO - Epoch [7][2800/5005] lr: 1.000e-01, eta: 11:29:41, time: 0.090, data_time: 0.012, memory: 928, loss: 5.5456 2023-01-04 16:02:08,582 - mmcls - INFO - Epoch [7][2900/5005] lr: 1.000e-01, eta: 11:29:28, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5389 2023-01-04 16:02:17,287 - mmcls - INFO - Epoch [7][3000/5005] lr: 1.000e-01, eta: 11:29:17, time: 0.087, data_time: 0.003, memory: 928, loss: 5.5447 2023-01-04 16:02:26,192 - mmcls - INFO - Epoch [7][3100/5005] lr: 1.000e-01, eta: 11:29:09, time: 0.089, data_time: 0.001, memory: 928, loss: 5.5380 2023-01-04 16:02:35,307 - mmcls - INFO - Epoch [7][3200/5005] lr: 1.000e-01, eta: 11:29:04, time: 0.091, data_time: 0.018, memory: 928, loss: 5.5394 2023-01-04 16:02:43,946 - mmcls - INFO - Epoch [7][3300/5005] lr: 1.000e-01, eta: 11:28:52, time: 0.086, data_time: 0.024, memory: 928, loss: 5.5278 2023-01-04 16:02:52,478 - mmcls - INFO - Epoch [7][3400/5005] lr: 1.000e-01, eta: 11:28:39, time: 0.085, data_time: 0.019, memory: 928, loss: 5.5293 2023-01-04 16:03:01,266 - mmcls - INFO - Epoch [7][3500/5005] lr: 1.000e-01, eta: 11:28:29, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5108 2023-01-04 16:03:10,299 - mmcls - INFO - Epoch [7][3600/5005] lr: 1.000e-01, eta: 11:28:23, time: 0.090, data_time: 0.018, memory: 928, loss: 5.5438 2023-01-04 16:03:19,073 - mmcls - INFO - Epoch [7][3700/5005] lr: 1.000e-01, eta: 11:28:13, time: 0.088, data_time: 0.005, memory: 928, loss: 5.5491 2023-01-04 16:03:27,809 - mmcls - INFO - Epoch [7][3800/5005] lr: 1.000e-01, eta: 11:28:03, time: 0.087, data_time: 0.004, memory: 928, loss: 5.5268 2023-01-04 16:03:36,434 - mmcls - INFO - Epoch [7][3900/5005] lr: 1.000e-01, eta: 11:27:51, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5301 2023-01-04 16:03:45,267 - mmcls - INFO - Epoch [7][4000/5005] lr: 1.000e-01, eta: 11:27:42, time: 0.088, data_time: 0.007, memory: 928, loss: 5.5283 2023-01-04 16:03:53,899 - mmcls - INFO - Epoch [7][4100/5005] lr: 1.000e-01, eta: 11:27:30, time: 0.086, data_time: 0.015, memory: 928, loss: 5.5607 2023-01-04 16:04:02,592 - mmcls - INFO - Epoch [7][4200/5005] lr: 1.000e-01, eta: 11:27:19, time: 0.087, data_time: 0.041, memory: 928, loss: 5.5143 2023-01-04 16:04:11,604 - mmcls - INFO - Epoch [7][4300/5005] lr: 1.000e-01, eta: 11:27:12, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5344 2023-01-04 16:04:20,256 - mmcls - INFO - Epoch [7][4400/5005] lr: 1.000e-01, eta: 11:27:01, time: 0.087, data_time: 0.004, memory: 928, loss: 5.5216 2023-01-04 16:04:28,994 - mmcls - INFO - Epoch [7][4500/5005] lr: 1.000e-01, eta: 11:26:51, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5245 2023-01-04 16:04:37,956 - mmcls - INFO - Epoch [7][4600/5005] lr: 1.000e-01, eta: 11:26:44, time: 0.090, data_time: 0.007, memory: 928, loss: 5.5241 2023-01-04 16:04:46,549 - mmcls - INFO - Epoch [7][4700/5005] lr: 1.000e-01, eta: 11:26:31, time: 0.086, data_time: 0.009, memory: 928, loss: 5.5235 2023-01-04 16:04:55,226 - mmcls - INFO - Epoch [7][4800/5005] lr: 1.000e-01, eta: 11:26:20, time: 0.087, data_time: 0.007, memory: 928, loss: 5.5325 2023-01-04 16:05:03,758 - mmcls - INFO - Epoch [7][4900/5005] lr: 1.000e-01, eta: 11:26:07, time: 0.085, data_time: 0.020, memory: 928, loss: 5.5105 2023-01-04 16:05:12,448 - mmcls - INFO - Epoch [7][5000/5005] lr: 1.000e-01, eta: 11:25:56, time: 0.087, data_time: 0.018, memory: 928, loss: 5.5349 2023-01-04 16:05:35,287 - mmcls - INFO - Epoch(val) [7][196] accuracy_top-1: 5.2260, accuracy_top-5: 13.9840 2023-01-04 16:05:46,090 - mmcls - INFO - Epoch [8][100/5005] lr: 1.000e-01, eta: 11:26:07, time: 0.108, data_time: 0.036, memory: 928, loss: 5.5068 2023-01-04 16:05:54,774 - mmcls - INFO - Epoch [8][200/5005] lr: 1.000e-01, eta: 11:25:56, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4920 2023-01-04 16:06:03,692 - mmcls - INFO - Epoch [8][300/5005] lr: 1.000e-01, eta: 11:25:48, time: 0.089, data_time: 0.019, memory: 928, loss: 5.5275 2023-01-04 16:06:12,519 - mmcls - INFO - Epoch [8][400/5005] lr: 1.000e-01, eta: 11:25:39, time: 0.088, data_time: 0.025, memory: 928, loss: 5.5130 2023-01-04 16:06:21,326 - mmcls - INFO - Epoch [8][500/5005] lr: 1.000e-01, eta: 11:25:29, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5202 2023-01-04 16:06:30,085 - mmcls - INFO - Epoch [8][600/5005] lr: 1.000e-01, eta: 11:25:19, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5053 2023-01-04 16:06:38,740 - mmcls - INFO - Epoch [8][700/5005] lr: 1.000e-01, eta: 11:25:08, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5150 2023-01-04 16:06:47,708 - mmcls - INFO - Epoch [8][800/5005] lr: 1.000e-01, eta: 11:25:01, time: 0.090, data_time: 0.028, memory: 928, loss: 5.5082 2023-01-04 16:06:56,467 - mmcls - INFO - Epoch [8][900/5005] lr: 1.000e-01, eta: 11:24:51, time: 0.088, data_time: 0.010, memory: 928, loss: 5.5100 2023-01-04 16:07:05,303 - mmcls - INFO - Epoch [8][1000/5005] lr: 1.000e-01, eta: 11:24:42, time: 0.088, data_time: 0.009, memory: 928, loss: 5.5074 2023-01-04 16:07:13,927 - mmcls - INFO - Epoch [8][1100/5005] lr: 1.000e-01, eta: 11:24:30, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5147 2023-01-04 16:07:23,013 - mmcls - INFO - Epoch [8][1200/5005] lr: 1.000e-01, eta: 11:24:24, time: 0.091, data_time: 0.004, memory: 928, loss: 5.5154 2023-01-04 16:07:31,548 - mmcls - INFO - Epoch [8][1300/5005] lr: 1.000e-01, eta: 11:24:11, time: 0.085, data_time: 0.023, memory: 928, loss: 5.5310 2023-01-04 16:07:40,333 - mmcls - INFO - Epoch [8][1400/5005] lr: 1.000e-01, eta: 11:24:02, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5155 2023-01-04 16:07:49,495 - mmcls - INFO - Epoch [8][1500/5005] lr: 1.000e-01, eta: 11:23:57, time: 0.092, data_time: 0.001, memory: 928, loss: 5.5304 2023-01-04 16:07:58,696 - mmcls - INFO - Epoch [8][1600/5005] lr: 1.000e-01, eta: 11:23:53, time: 0.092, data_time: 0.001, memory: 928, loss: 5.4967 2023-01-04 16:08:07,491 - mmcls - INFO - Epoch [8][1700/5005] lr: 1.000e-01, eta: 11:23:43, time: 0.088, data_time: 0.008, memory: 928, loss: 5.5164 2023-01-04 16:08:16,037 - mmcls - INFO - Epoch [8][1800/5005] lr: 1.000e-01, eta: 11:23:31, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4979 2023-01-04 16:08:24,484 - mmcls - INFO - Epoch [8][1900/5005] lr: 1.000e-01, eta: 11:23:17, time: 0.084, data_time: 0.011, memory: 928, loss: 5.5027 2023-01-04 16:08:33,196 - mmcls - INFO - Epoch [8][2000/5005] lr: 1.000e-01, eta: 11:23:06, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5108 2023-01-04 16:08:42,201 - mmcls - INFO - Epoch [8][2100/5005] lr: 1.000e-01, eta: 11:22:59, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5092 2023-01-04 16:08:50,786 - mmcls - INFO - Epoch [8][2200/5005] lr: 1.000e-01, eta: 11:22:47, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5332 2023-01-04 16:08:59,624 - mmcls - INFO - Epoch [8][2300/5005] lr: 1.000e-01, eta: 11:22:38, time: 0.088, data_time: 0.001, memory: 928, loss: 5.5130 2023-01-04 16:09:08,176 - mmcls - INFO - Epoch [8][2400/5005] lr: 1.000e-01, eta: 11:22:26, time: 0.085, data_time: 0.007, memory: 928, loss: 5.5465 2023-01-04 16:09:16,689 - mmcls - INFO - Epoch [8][2500/5005] lr: 1.000e-01, eta: 11:22:13, time: 0.085, data_time: 0.011, memory: 928, loss: 5.5102 2023-01-04 16:09:25,317 - mmcls - INFO - Epoch [8][2600/5005] lr: 1.000e-01, eta: 11:22:02, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5155 2023-01-04 16:09:34,046 - mmcls - INFO - Epoch [8][2700/5005] lr: 1.000e-01, eta: 11:21:51, time: 0.087, data_time: 0.006, memory: 928, loss: 5.5358 2023-01-04 16:09:43,164 - mmcls - INFO - Epoch [8][2800/5005] lr: 1.000e-01, eta: 11:21:46, time: 0.091, data_time: 0.002, memory: 928, loss: 5.5255 2023-01-04 16:09:52,148 - mmcls - INFO - Epoch [8][2900/5005] lr: 1.000e-01, eta: 11:21:39, time: 0.090, data_time: 0.007, memory: 928, loss: 5.5057 2023-01-04 16:10:01,213 - mmcls - INFO - Epoch [8][3000/5005] lr: 1.000e-01, eta: 11:21:33, time: 0.091, data_time: 0.001, memory: 928, loss: 5.5104 2023-01-04 16:10:10,378 - mmcls - INFO - Epoch [8][3100/5005] lr: 1.000e-01, eta: 11:21:28, time: 0.092, data_time: 0.001, memory: 928, loss: 5.5097 2023-01-04 16:10:19,189 - mmcls - INFO - Epoch [8][3200/5005] lr: 1.000e-01, eta: 11:21:18, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4925 2023-01-04 16:10:28,034 - mmcls - INFO - Epoch [8][3300/5005] lr: 1.000e-01, eta: 11:21:10, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5049 2023-01-04 16:10:37,569 - mmcls - INFO - Epoch [8][3400/5005] lr: 1.000e-01, eta: 11:21:09, time: 0.095, data_time: 0.015, memory: 928, loss: 5.5025 2023-01-04 16:10:46,101 - mmcls - INFO - Epoch [8][3500/5005] lr: 1.000e-01, eta: 11:20:56, time: 0.085, data_time: 0.023, memory: 928, loss: 5.5290 2023-01-04 16:10:54,946 - mmcls - INFO - Epoch [8][3600/5005] lr: 1.000e-01, eta: 11:20:48, time: 0.088, data_time: 0.004, memory: 928, loss: 5.5046 2023-01-04 16:11:03,636 - mmcls - INFO - Epoch [8][3700/5005] lr: 1.000e-01, eta: 11:20:37, time: 0.087, data_time: 0.006, memory: 928, loss: 5.5008 2023-01-04 16:11:12,479 - mmcls - INFO - Epoch [8][3800/5005] lr: 1.000e-01, eta: 11:20:28, time: 0.088, data_time: 0.002, memory: 928, loss: 5.5178 2023-01-04 16:11:21,455 - mmcls - INFO - Epoch [8][3900/5005] lr: 1.000e-01, eta: 11:20:21, time: 0.090, data_time: 0.002, memory: 928, loss: 5.5243 2023-01-04 16:11:30,132 - mmcls - INFO - Epoch [8][4000/5005] lr: 1.000e-01, eta: 11:20:10, time: 0.087, data_time: 0.022, memory: 928, loss: 5.5198 2023-01-04 16:11:38,829 - mmcls - INFO - Epoch [8][4100/5005] lr: 1.000e-01, eta: 11:19:59, time: 0.087, data_time: 0.027, memory: 928, loss: 5.5073 2023-01-04 16:11:47,493 - mmcls - INFO - Epoch [8][4200/5005] lr: 1.000e-01, eta: 11:19:48, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4995 2023-01-04 16:11:56,201 - mmcls - INFO - Epoch [8][4300/5005] lr: 1.000e-01, eta: 11:19:38, time: 0.087, data_time: 0.000, memory: 928, loss: 5.5109 2023-01-04 16:12:04,932 - mmcls - INFO - Epoch [8][4400/5005] lr: 1.000e-01, eta: 11:19:28, time: 0.087, data_time: 0.017, memory: 928, loss: 5.5215 2023-01-04 16:12:14,003 - mmcls - INFO - Epoch [8][4500/5005] lr: 1.000e-01, eta: 11:19:22, time: 0.091, data_time: 0.004, memory: 928, loss: 5.4796 2023-01-04 16:12:22,843 - mmcls - INFO - Epoch [8][4600/5005] lr: 1.000e-01, eta: 11:19:13, time: 0.088, data_time: 0.012, memory: 928, loss: 5.5089 2023-01-04 16:12:31,732 - mmcls - INFO - Epoch [8][4700/5005] lr: 1.000e-01, eta: 11:19:04, time: 0.089, data_time: 0.022, memory: 928, loss: 5.5003 2023-01-04 16:12:40,448 - mmcls - INFO - Epoch [8][4800/5005] lr: 1.000e-01, eta: 11:18:54, time: 0.087, data_time: 0.033, memory: 928, loss: 5.4959 2023-01-04 16:12:49,096 - mmcls - INFO - Epoch [8][4900/5005] lr: 1.000e-01, eta: 11:18:43, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5141 2023-01-04 16:12:57,971 - mmcls - INFO - Epoch [8][5000/5005] lr: 1.000e-01, eta: 11:18:35, time: 0.089, data_time: 0.004, memory: 928, loss: 5.5141 2023-01-04 16:13:20,733 - mmcls - INFO - Epoch(val) [8][196] accuracy_top-1: 4.3560, accuracy_top-5: 11.5340 2023-01-04 16:13:31,533 - mmcls - INFO - Epoch [9][100/5005] lr: 1.000e-01, eta: 11:18:42, time: 0.108, data_time: 0.038, memory: 928, loss: 5.4933 2023-01-04 16:13:40,268 - mmcls - INFO - Epoch [9][200/5005] lr: 1.000e-01, eta: 11:18:32, time: 0.087, data_time: 0.014, memory: 928, loss: 5.4906 2023-01-04 16:13:49,124 - mmcls - INFO - Epoch [9][300/5005] lr: 1.000e-01, eta: 11:18:23, time: 0.088, data_time: 0.010, memory: 928, loss: 5.5069 2023-01-04 16:13:58,089 - mmcls - INFO - Epoch [9][400/5005] lr: 1.000e-01, eta: 11:18:16, time: 0.090, data_time: 0.001, memory: 928, loss: 5.5201 2023-01-04 16:14:06,896 - mmcls - INFO - Epoch [9][500/5005] lr: 1.000e-01, eta: 11:18:07, time: 0.088, data_time: 0.001, memory: 928, loss: 5.5101 2023-01-04 16:14:15,922 - mmcls - INFO - Epoch [9][600/5005] lr: 1.000e-01, eta: 11:18:00, time: 0.090, data_time: 0.006, memory: 928, loss: 5.5189 2023-01-04 16:14:24,796 - mmcls - INFO - Epoch [9][700/5005] lr: 1.000e-01, eta: 11:17:51, time: 0.089, data_time: 0.002, memory: 928, loss: 5.5088 2023-01-04 16:14:33,735 - mmcls - INFO - Epoch [9][800/5005] lr: 1.000e-01, eta: 11:17:43, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4956 2023-01-04 16:14:42,289 - mmcls - INFO - Epoch [9][900/5005] lr: 1.000e-01, eta: 11:17:31, time: 0.086, data_time: 0.007, memory: 928, loss: 5.5224 2023-01-04 16:14:50,865 - mmcls - INFO - Epoch [9][1000/5005] lr: 1.000e-01, eta: 11:17:20, time: 0.086, data_time: 0.017, memory: 928, loss: 5.4918 2023-01-04 16:14:59,491 - mmcls - INFO - Epoch [9][1100/5005] lr: 1.000e-01, eta: 11:17:08, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5012 2023-01-04 16:15:08,308 - mmcls - INFO - Epoch [9][1200/5005] lr: 1.000e-01, eta: 11:16:59, time: 0.088, data_time: 0.001, memory: 928, loss: 5.5171 2023-01-04 16:15:16,968 - mmcls - INFO - Epoch [9][1300/5005] lr: 1.000e-01, eta: 11:16:48, time: 0.087, data_time: 0.004, memory: 928, loss: 5.5212 2023-01-04 16:15:25,637 - mmcls - INFO - Epoch [9][1400/5005] lr: 1.000e-01, eta: 11:16:37, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4957 2023-01-04 16:15:34,216 - mmcls - INFO - Epoch [9][1500/5005] lr: 1.000e-01, eta: 11:16:26, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4965 2023-01-04 16:15:43,059 - mmcls - INFO - Epoch [9][1600/5005] lr: 1.000e-01, eta: 11:16:17, time: 0.089, data_time: 0.013, memory: 928, loss: 5.4945 2023-01-04 16:15:51,638 - mmcls - INFO - Epoch [9][1700/5005] lr: 1.000e-01, eta: 11:16:05, time: 0.086, data_time: 0.026, memory: 928, loss: 5.5062 2023-01-04 16:16:00,513 - mmcls - INFO - Epoch [9][1800/5005] lr: 1.000e-01, eta: 11:15:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.5131 2023-01-04 16:16:09,253 - mmcls - INFO - Epoch [9][1900/5005] lr: 1.000e-01, eta: 11:15:46, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5066 2023-01-04 16:16:18,178 - mmcls - INFO - Epoch [9][2000/5005] lr: 1.000e-01, eta: 11:15:39, time: 0.089, data_time: 0.006, memory: 928, loss: 5.5045 2023-01-04 16:16:26,986 - mmcls - INFO - Epoch [9][2100/5005] lr: 1.000e-01, eta: 11:15:29, time: 0.088, data_time: 0.012, memory: 928, loss: 5.5028 2023-01-04 16:16:35,632 - mmcls - INFO - Epoch [9][2200/5005] lr: 1.000e-01, eta: 11:15:18, time: 0.086, data_time: 0.020, memory: 928, loss: 5.4819 2023-01-04 16:16:44,779 - mmcls - INFO - Epoch [9][2300/5005] lr: 1.000e-01, eta: 11:15:13, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4933 2023-01-04 16:16:53,830 - mmcls - INFO - Epoch [9][2400/5005] lr: 1.000e-01, eta: 11:15:06, time: 0.090, data_time: 0.014, memory: 928, loss: 5.5109 2023-01-04 16:17:02,640 - mmcls - INFO - Epoch [9][2500/5005] lr: 1.000e-01, eta: 11:14:57, time: 0.088, data_time: 0.008, memory: 928, loss: 5.5040 2023-01-04 16:17:11,155 - mmcls - INFO - Epoch [9][2600/5005] lr: 1.000e-01, eta: 11:14:44, time: 0.085, data_time: 0.007, memory: 928, loss: 5.4885 2023-01-04 16:17:19,953 - mmcls - INFO - Epoch [9][2700/5005] lr: 1.000e-01, eta: 11:14:35, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4959 2023-01-04 16:17:28,595 - mmcls - INFO - Epoch [9][2800/5005] lr: 1.000e-01, eta: 11:14:24, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4996 2023-01-04 16:17:37,074 - mmcls - INFO - Epoch [9][2900/5005] lr: 1.000e-01, eta: 11:14:12, time: 0.085, data_time: 0.001, memory: 928, loss: 5.5103 2023-01-04 16:17:45,842 - mmcls - INFO - Epoch [9][3000/5005] lr: 1.000e-01, eta: 11:14:02, time: 0.088, data_time: 0.016, memory: 928, loss: 5.4868 2023-01-04 16:17:54,910 - mmcls - INFO - Epoch [9][3100/5005] lr: 1.000e-01, eta: 11:13:55, time: 0.091, data_time: 0.003, memory: 928, loss: 5.5028 2023-01-04 16:18:03,608 - mmcls - INFO - Epoch [9][3200/5005] lr: 1.000e-01, eta: 11:13:45, time: 0.087, data_time: 0.005, memory: 928, loss: 5.5043 2023-01-04 16:18:12,468 - mmcls - INFO - Epoch [9][3300/5005] lr: 1.000e-01, eta: 11:13:36, time: 0.089, data_time: 0.010, memory: 928, loss: 5.5033 2023-01-04 16:18:21,169 - mmcls - INFO - Epoch [9][3400/5005] lr: 1.000e-01, eta: 11:13:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4996 2023-01-04 16:18:29,777 - mmcls - INFO - Epoch [9][3500/5005] lr: 1.000e-01, eta: 11:13:15, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4917 2023-01-04 16:18:38,352 - mmcls - INFO - Epoch [9][3600/5005] lr: 1.000e-01, eta: 11:13:03, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5011 2023-01-04 16:18:46,931 - mmcls - INFO - Epoch [9][3700/5005] lr: 1.000e-01, eta: 11:12:52, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4821 2023-01-04 16:18:55,480 - mmcls - INFO - Epoch [9][3800/5005] lr: 1.000e-01, eta: 11:12:40, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4936 2023-01-04 16:19:04,071 - mmcls - INFO - Epoch [9][3900/5005] lr: 1.000e-01, eta: 11:12:28, time: 0.086, data_time: 0.026, memory: 928, loss: 5.5162 2023-01-04 16:19:12,742 - mmcls - INFO - Epoch [9][4000/5005] lr: 1.000e-01, eta: 11:12:18, time: 0.087, data_time: 0.025, memory: 928, loss: 5.4909 2023-01-04 16:19:21,539 - mmcls - INFO - Epoch [9][4100/5005] lr: 1.000e-01, eta: 11:12:09, time: 0.088, data_time: 0.013, memory: 928, loss: 5.5033 2023-01-04 16:19:30,257 - mmcls - INFO - Epoch [9][4200/5005] lr: 1.000e-01, eta: 11:11:58, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4897 2023-01-04 16:19:39,085 - mmcls - INFO - Epoch [9][4300/5005] lr: 1.000e-01, eta: 11:11:50, time: 0.088, data_time: 0.003, memory: 928, loss: 5.5055 2023-01-04 16:19:47,852 - mmcls - INFO - Epoch [9][4400/5005] lr: 1.000e-01, eta: 11:11:40, time: 0.088, data_time: 0.018, memory: 928, loss: 5.4916 2023-01-04 16:19:56,421 - mmcls - INFO - Epoch [9][4500/5005] lr: 1.000e-01, eta: 11:11:28, time: 0.086, data_time: 0.001, memory: 928, loss: 5.5089 2023-01-04 16:20:05,257 - mmcls - INFO - Epoch [9][4600/5005] lr: 1.000e-01, eta: 11:11:20, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4860 2023-01-04 16:20:14,243 - mmcls - INFO - Epoch [9][4700/5005] lr: 1.000e-01, eta: 11:11:12, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4789 2023-01-04 16:20:22,972 - mmcls - INFO - Epoch [9][4800/5005] lr: 1.000e-01, eta: 11:11:02, time: 0.087, data_time: 0.014, memory: 928, loss: 5.5026 2023-01-04 16:20:31,510 - mmcls - INFO - Epoch [9][4900/5005] lr: 1.000e-01, eta: 11:10:50, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4983 2023-01-04 16:20:40,118 - mmcls - INFO - Epoch [9][5000/5005] lr: 1.000e-01, eta: 11:10:39, time: 0.086, data_time: 0.023, memory: 928, loss: 5.4873 2023-01-04 16:21:03,154 - mmcls - INFO - Epoch(val) [9][196] accuracy_top-1: 5.4760, accuracy_top-5: 14.2380 2023-01-04 16:21:13,805 - mmcls - INFO - Epoch [10][100/5005] lr: 1.000e-01, eta: 11:10:44, time: 0.106, data_time: 0.047, memory: 928, loss: 5.4892 2023-01-04 16:21:22,661 - mmcls - INFO - Epoch [10][200/5005] lr: 1.000e-01, eta: 11:10:35, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4816 2023-01-04 16:21:31,695 - mmcls - INFO - Epoch [10][300/5005] lr: 1.000e-01, eta: 11:10:28, time: 0.090, data_time: 0.010, memory: 928, loss: 5.5092 2023-01-04 16:21:40,519 - mmcls - INFO - Epoch [10][400/5005] lr: 1.000e-01, eta: 11:10:19, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4933 2023-01-04 16:21:49,085 - mmcls - INFO - Epoch [10][500/5005] lr: 1.000e-01, eta: 11:10:07, time: 0.086, data_time: 0.014, memory: 928, loss: 5.4933 2023-01-04 16:21:58,306 - mmcls - INFO - Epoch [10][600/5005] lr: 1.000e-01, eta: 11:10:02, time: 0.092, data_time: 0.008, memory: 928, loss: 5.4956 2023-01-04 16:22:07,171 - mmcls - INFO - Epoch [10][700/5005] lr: 1.000e-01, eta: 11:09:54, time: 0.089, data_time: 0.015, memory: 928, loss: 5.4848 2023-01-04 16:22:16,519 - mmcls - INFO - Epoch [10][800/5005] lr: 1.000e-01, eta: 11:09:50, time: 0.093, data_time: 0.051, memory: 928, loss: 5.4896 2023-01-04 16:22:25,315 - mmcls - INFO - Epoch [10][900/5005] lr: 1.000e-01, eta: 11:09:41, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4912 2023-01-04 16:22:34,583 - mmcls - INFO - Epoch [10][1000/5005] lr: 1.000e-01, eta: 11:09:36, time: 0.093, data_time: 0.009, memory: 928, loss: 5.4867 2023-01-04 16:22:43,348 - mmcls - INFO - Epoch [10][1100/5005] lr: 1.000e-01, eta: 11:09:27, time: 0.088, data_time: 0.011, memory: 928, loss: 5.5022 2023-01-04 16:22:52,176 - mmcls - INFO - Epoch [10][1200/5005] lr: 1.000e-01, eta: 11:09:18, time: 0.088, data_time: 0.007, memory: 928, loss: 5.5066 2023-01-04 16:23:01,001 - mmcls - INFO - Epoch [10][1300/5005] lr: 1.000e-01, eta: 11:09:09, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4945 2023-01-04 16:23:09,727 - mmcls - INFO - Epoch [10][1400/5005] lr: 1.000e-01, eta: 11:08:59, time: 0.087, data_time: 0.001, memory: 928, loss: 5.5075 2023-01-04 16:23:18,438 - mmcls - INFO - Epoch [10][1500/5005] lr: 1.000e-01, eta: 11:08:48, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4796 2023-01-04 16:23:27,002 - mmcls - INFO - Epoch [10][1600/5005] lr: 1.000e-01, eta: 11:08:37, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4862 2023-01-04 16:23:35,846 - mmcls - INFO - Epoch [10][1700/5005] lr: 1.000e-01, eta: 11:08:28, time: 0.088, data_time: 0.012, memory: 928, loss: 5.5046 2023-01-04 16:23:44,633 - mmcls - INFO - Epoch [10][1800/5005] lr: 1.000e-01, eta: 11:08:19, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4880 2023-01-04 16:23:53,852 - mmcls - INFO - Epoch [10][1900/5005] lr: 1.000e-01, eta: 11:08:14, time: 0.092, data_time: 0.017, memory: 928, loss: 5.4943 2023-01-04 16:24:02,440 - mmcls - INFO - Epoch [10][2000/5005] lr: 1.000e-01, eta: 11:08:02, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4789 2023-01-04 16:24:11,101 - mmcls - INFO - Epoch [10][2100/5005] lr: 1.000e-01, eta: 11:07:52, time: 0.086, data_time: 0.000, memory: 928, loss: 5.4966 2023-01-04 16:24:19,761 - mmcls - INFO - Epoch [10][2200/5005] lr: 1.000e-01, eta: 11:07:41, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4897 2023-01-04 16:24:28,629 - mmcls - INFO - Epoch [10][2300/5005] lr: 1.000e-01, eta: 11:07:33, time: 0.089, data_time: 0.013, memory: 928, loss: 5.4843 2023-01-04 16:24:37,090 - mmcls - INFO - Epoch [10][2400/5005] lr: 1.000e-01, eta: 11:07:20, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4994 2023-01-04 16:24:46,012 - mmcls - INFO - Epoch [10][2500/5005] lr: 1.000e-01, eta: 11:07:12, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4778 2023-01-04 16:24:55,008 - mmcls - INFO - Epoch [10][2600/5005] lr: 1.000e-01, eta: 11:07:05, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4847 2023-01-04 16:25:03,683 - mmcls - INFO - Epoch [10][2700/5005] lr: 1.000e-01, eta: 11:06:54, time: 0.087, data_time: 0.002, memory: 928, loss: 5.5137 2023-01-04 16:25:12,717 - mmcls - INFO - Epoch [10][2800/5005] lr: 1.000e-01, eta: 11:06:47, time: 0.090, data_time: 0.007, memory: 928, loss: 5.4974 2023-01-04 16:25:21,869 - mmcls - INFO - Epoch [10][2900/5005] lr: 1.000e-01, eta: 11:06:41, time: 0.091, data_time: 0.005, memory: 928, loss: 5.5060 2023-01-04 16:25:30,769 - mmcls - INFO - Epoch [10][3000/5005] lr: 1.000e-01, eta: 11:06:33, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4960 2023-01-04 16:25:39,381 - mmcls - INFO - Epoch [10][3100/5005] lr: 1.000e-01, eta: 11:06:22, time: 0.086, data_time: 0.004, memory: 928, loss: 5.5101 2023-01-04 16:25:47,783 - mmcls - INFO - Epoch [10][3200/5005] lr: 1.000e-01, eta: 11:06:09, time: 0.084, data_time: 0.008, memory: 928, loss: 5.4898 2023-01-04 16:25:56,419 - mmcls - INFO - Epoch [10][3300/5005] lr: 1.000e-01, eta: 11:05:59, time: 0.086, data_time: 0.012, memory: 928, loss: 5.4956 2023-01-04 16:26:05,125 - mmcls - INFO - Epoch [10][3400/5005] lr: 1.000e-01, eta: 11:05:49, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4856 2023-01-04 16:26:13,920 - mmcls - INFO - Epoch [10][3500/5005] lr: 1.000e-01, eta: 11:05:39, time: 0.088, data_time: 0.017, memory: 928, loss: 5.4836 2023-01-04 16:26:22,487 - mmcls - INFO - Epoch [10][3600/5005] lr: 1.000e-01, eta: 11:05:28, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4960 2023-01-04 16:26:31,216 - mmcls - INFO - Epoch [10][3700/5005] lr: 1.000e-01, eta: 11:05:18, time: 0.087, data_time: 0.014, memory: 928, loss: 5.4866 2023-01-04 16:26:39,883 - mmcls - INFO - Epoch [10][3800/5005] lr: 1.000e-01, eta: 11:05:08, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4965 2023-01-04 16:26:49,207 - mmcls - INFO - Epoch [10][3900/5005] lr: 1.000e-01, eta: 11:05:03, time: 0.093, data_time: 0.010, memory: 928, loss: 5.4838 2023-01-04 16:26:57,690 - mmcls - INFO - Epoch [10][4000/5005] lr: 1.000e-01, eta: 11:04:51, time: 0.085, data_time: 0.001, memory: 928, loss: 5.5013 2023-01-04 16:27:06,542 - mmcls - INFO - Epoch [10][4100/5005] lr: 1.000e-01, eta: 11:04:42, time: 0.088, data_time: 0.026, memory: 928, loss: 5.4950 2023-01-04 16:27:15,586 - mmcls - INFO - Epoch [10][4200/5005] lr: 1.000e-01, eta: 11:04:36, time: 0.091, data_time: 0.015, memory: 928, loss: 5.4903 2023-01-04 16:27:24,401 - mmcls - INFO - Epoch [10][4300/5005] lr: 1.000e-01, eta: 11:04:27, time: 0.088, data_time: 0.015, memory: 928, loss: 5.4941 2023-01-04 16:27:33,334 - mmcls - INFO - Epoch [10][4400/5005] lr: 1.000e-01, eta: 11:04:19, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4755 2023-01-04 16:27:41,956 - mmcls - INFO - Epoch [10][4500/5005] lr: 1.000e-01, eta: 11:04:08, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4892 2023-01-04 16:27:50,764 - mmcls - INFO - Epoch [10][4600/5005] lr: 1.000e-01, eta: 11:03:59, time: 0.088, data_time: 0.013, memory: 928, loss: 5.4877 2023-01-04 16:27:59,547 - mmcls - INFO - Epoch [10][4700/5005] lr: 1.000e-01, eta: 11:03:49, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4911 2023-01-04 16:28:08,180 - mmcls - INFO - Epoch [10][4800/5005] lr: 1.000e-01, eta: 11:03:39, time: 0.086, data_time: 0.015, memory: 928, loss: 5.5009 2023-01-04 16:28:17,159 - mmcls - INFO - Epoch [10][4900/5005] lr: 1.000e-01, eta: 11:03:31, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4969 2023-01-04 16:28:25,846 - mmcls - INFO - Epoch [10][5000/5005] lr: 1.000e-01, eta: 11:03:21, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4930 2023-01-04 16:28:48,666 - mmcls - INFO - Epoch(val) [10][196] accuracy_top-1: 4.9860, accuracy_top-5: 13.8700 2023-01-04 16:28:59,782 - mmcls - INFO - Epoch [11][100/5005] lr: 1.000e-01, eta: 11:03:28, time: 0.111, data_time: 0.047, memory: 928, loss: 5.4736 2023-01-04 16:29:08,522 - mmcls - INFO - Epoch [11][200/5005] lr: 1.000e-01, eta: 11:03:18, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4741 2023-01-04 16:29:17,482 - mmcls - INFO - Epoch [11][300/5005] lr: 1.000e-01, eta: 11:03:10, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4817 2023-01-04 16:29:26,148 - mmcls - INFO - Epoch [11][400/5005] lr: 1.000e-01, eta: 11:03:00, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4735 2023-01-04 16:29:34,887 - mmcls - INFO - Epoch [11][500/5005] lr: 1.000e-01, eta: 11:02:50, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4796 2023-01-04 16:29:43,742 - mmcls - INFO - Epoch [11][600/5005] lr: 1.000e-01, eta: 11:02:42, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4937 2023-01-04 16:29:52,509 - mmcls - INFO - Epoch [11][700/5005] lr: 1.000e-01, eta: 11:02:32, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4874 2023-01-04 16:30:00,971 - mmcls - INFO - Epoch [11][800/5005] lr: 1.000e-01, eta: 11:02:20, time: 0.085, data_time: 0.004, memory: 928, loss: 5.4927 2023-01-04 16:30:09,524 - mmcls - INFO - Epoch [11][900/5005] lr: 1.000e-01, eta: 11:02:09, time: 0.085, data_time: 0.006, memory: 928, loss: 5.4756 2023-01-04 16:30:18,447 - mmcls - INFO - Epoch [11][1000/5005] lr: 1.000e-01, eta: 11:02:00, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4831 2023-01-04 16:30:27,100 - mmcls - INFO - Epoch [11][1100/5005] lr: 1.000e-01, eta: 11:01:50, time: 0.087, data_time: 0.018, memory: 928, loss: 5.4853 2023-01-04 16:30:35,847 - mmcls - INFO - Epoch [11][1200/5005] lr: 1.000e-01, eta: 11:01:40, time: 0.087, data_time: 0.006, memory: 928, loss: 5.4915 2023-01-04 16:30:44,914 - mmcls - INFO - Epoch [11][1300/5005] lr: 1.000e-01, eta: 11:01:34, time: 0.091, data_time: 0.022, memory: 928, loss: 5.4899 2023-01-04 16:30:53,455 - mmcls - INFO - Epoch [11][1400/5005] lr: 1.000e-01, eta: 11:01:22, time: 0.085, data_time: 0.006, memory: 928, loss: 5.5138 2023-01-04 16:31:01,993 - mmcls - INFO - Epoch [11][1500/5005] lr: 1.000e-01, eta: 11:01:11, time: 0.085, data_time: 0.002, memory: 928, loss: 5.5029 2023-01-04 16:31:10,853 - mmcls - INFO - Epoch [11][1600/5005] lr: 1.000e-01, eta: 11:01:02, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4653 2023-01-04 16:31:19,842 - mmcls - INFO - Epoch [11][1700/5005] lr: 1.000e-01, eta: 11:00:55, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4802 2023-01-04 16:31:28,314 - mmcls - INFO - Epoch [11][1800/5005] lr: 1.000e-01, eta: 11:00:42, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4786 2023-01-04 16:31:37,087 - mmcls - INFO - Epoch [11][1900/5005] lr: 1.000e-01, eta: 11:00:33, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4748 2023-01-04 16:31:45,974 - mmcls - INFO - Epoch [11][2000/5005] lr: 1.000e-01, eta: 11:00:25, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4678 2023-01-04 16:31:54,931 - mmcls - INFO - Epoch [11][2100/5005] lr: 1.000e-01, eta: 11:00:17, time: 0.090, data_time: 0.012, memory: 928, loss: 5.4970 2023-01-04 16:32:03,707 - mmcls - INFO - Epoch [11][2200/5005] lr: 1.000e-01, eta: 11:00:08, time: 0.088, data_time: 0.011, memory: 928, loss: 5.4881 2023-01-04 16:32:12,371 - mmcls - INFO - Epoch [11][2300/5005] lr: 1.000e-01, eta: 10:59:57, time: 0.087, data_time: 0.022, memory: 928, loss: 5.4745 2023-01-04 16:32:21,016 - mmcls - INFO - Epoch [11][2400/5005] lr: 1.000e-01, eta: 10:59:47, time: 0.086, data_time: 0.005, memory: 928, loss: 5.4892 2023-01-04 16:32:29,493 - mmcls - INFO - Epoch [11][2500/5005] lr: 1.000e-01, eta: 10:59:35, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4770 2023-01-04 16:32:38,606 - mmcls - INFO - Epoch [11][2600/5005] lr: 1.000e-01, eta: 10:59:28, time: 0.091, data_time: 0.015, memory: 928, loss: 5.4920 2023-01-04 16:32:47,298 - mmcls - INFO - Epoch [11][2700/5005] lr: 1.000e-01, eta: 10:59:18, time: 0.087, data_time: 0.017, memory: 928, loss: 5.4810 2023-01-04 16:32:56,138 - mmcls - INFO - Epoch [11][2800/5005] lr: 1.000e-01, eta: 10:59:10, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4988 2023-01-04 16:33:04,780 - mmcls - INFO - Epoch [11][2900/5005] lr: 1.000e-01, eta: 10:58:59, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4898 2023-01-04 16:33:13,505 - mmcls - INFO - Epoch [11][3000/5005] lr: 1.000e-01, eta: 10:58:49, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4764 2023-01-04 16:33:22,210 - mmcls - INFO - Epoch [11][3100/5005] lr: 1.000e-01, eta: 10:58:39, time: 0.087, data_time: 0.003, memory: 928, loss: 5.5079 2023-01-04 16:33:31,754 - mmcls - INFO - Epoch [11][3200/5005] lr: 1.000e-01, eta: 10:58:36, time: 0.095, data_time: 0.025, memory: 928, loss: 5.4646 2023-01-04 16:33:40,624 - mmcls - INFO - Epoch [11][3300/5005] lr: 1.000e-01, eta: 10:58:28, time: 0.089, data_time: 0.014, memory: 928, loss: 5.4742 2023-01-04 16:33:49,485 - mmcls - INFO - Epoch [11][3400/5005] lr: 1.000e-01, eta: 10:58:19, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4943 2023-01-04 16:33:58,357 - mmcls - INFO - Epoch [11][3500/5005] lr: 1.000e-01, eta: 10:58:11, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4714 2023-01-04 16:34:07,307 - mmcls - INFO - Epoch [11][3600/5005] lr: 1.000e-01, eta: 10:58:03, time: 0.089, data_time: 0.003, memory: 928, loss: 5.5019 2023-01-04 16:34:15,843 - mmcls - INFO - Epoch [11][3700/5005] lr: 1.000e-01, eta: 10:57:52, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4737 2023-01-04 16:34:24,588 - mmcls - INFO - Epoch [11][3800/5005] lr: 1.000e-01, eta: 10:57:42, time: 0.087, data_time: 0.008, memory: 928, loss: 5.4740 2023-01-04 16:34:33,459 - mmcls - INFO - Epoch [11][3900/5005] lr: 1.000e-01, eta: 10:57:33, time: 0.089, data_time: 0.006, memory: 928, loss: 5.4782 2023-01-04 16:34:42,234 - mmcls - INFO - Epoch [11][4000/5005] lr: 1.000e-01, eta: 10:57:24, time: 0.088, data_time: 0.011, memory: 928, loss: 5.4629 2023-01-04 16:34:51,084 - mmcls - INFO - Epoch [11][4100/5005] lr: 1.000e-01, eta: 10:57:15, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4813 2023-01-04 16:35:00,084 - mmcls - INFO - Epoch [11][4200/5005] lr: 1.000e-01, eta: 10:57:08, time: 0.090, data_time: 0.005, memory: 928, loss: 5.4766 2023-01-04 16:35:09,011 - mmcls - INFO - Epoch [11][4300/5005] lr: 1.000e-01, eta: 10:57:00, time: 0.089, data_time: 0.021, memory: 928, loss: 5.5054 2023-01-04 16:35:17,782 - mmcls - INFO - Epoch [11][4400/5005] lr: 1.000e-01, eta: 10:56:50, time: 0.088, data_time: 0.024, memory: 928, loss: 5.4984 2023-01-04 16:35:26,683 - mmcls - INFO - Epoch [11][4500/5005] lr: 1.000e-01, eta: 10:56:42, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4888 2023-01-04 16:35:35,462 - mmcls - INFO - Epoch [11][4600/5005] lr: 1.000e-01, eta: 10:56:33, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4758 2023-01-04 16:35:44,008 - mmcls - INFO - Epoch [11][4700/5005] lr: 1.000e-01, eta: 10:56:22, time: 0.085, data_time: 0.022, memory: 928, loss: 5.4875 2023-01-04 16:35:52,490 - mmcls - INFO - Epoch [11][4800/5005] lr: 1.000e-01, eta: 10:56:10, time: 0.085, data_time: 0.034, memory: 928, loss: 5.4937 2023-01-04 16:36:01,284 - mmcls - INFO - Epoch [11][4900/5005] lr: 1.000e-01, eta: 10:56:01, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4789 2023-01-04 16:36:09,944 - mmcls - INFO - Epoch [11][5000/5005] lr: 1.000e-01, eta: 10:55:50, time: 0.087, data_time: 0.023, memory: 928, loss: 5.4789 2023-01-04 16:36:32,777 - mmcls - INFO - Epoch(val) [11][196] accuracy_top-1: 4.9460, accuracy_top-5: 13.6020 2023-01-04 16:36:43,744 - mmcls - INFO - Epoch [12][100/5005] lr: 1.000e-01, eta: 10:55:55, time: 0.109, data_time: 0.033, memory: 928, loss: 5.4754 2023-01-04 16:36:52,900 - mmcls - INFO - Epoch [12][200/5005] lr: 1.000e-01, eta: 10:55:48, time: 0.092, data_time: 0.008, memory: 928, loss: 5.4709 2023-01-04 16:37:01,536 - mmcls - INFO - Epoch [12][300/5005] lr: 1.000e-01, eta: 10:55:38, time: 0.086, data_time: 0.018, memory: 928, loss: 5.5063 2023-01-04 16:37:10,693 - mmcls - INFO - Epoch [12][400/5005] lr: 1.000e-01, eta: 10:55:32, time: 0.092, data_time: 0.016, memory: 928, loss: 5.4586 2023-01-04 16:37:19,469 - mmcls - INFO - Epoch [12][500/5005] lr: 1.000e-01, eta: 10:55:22, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4919 2023-01-04 16:37:28,112 - mmcls - INFO - Epoch [12][600/5005] lr: 1.000e-01, eta: 10:55:12, time: 0.086, data_time: 0.003, memory: 928, loss: 5.5011 2023-01-04 16:37:37,354 - mmcls - INFO - Epoch [12][700/5005] lr: 1.000e-01, eta: 10:55:06, time: 0.092, data_time: 0.025, memory: 928, loss: 5.4811 2023-01-04 16:37:46,238 - mmcls - INFO - Epoch [12][800/5005] lr: 1.000e-01, eta: 10:54:58, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4709 2023-01-04 16:37:55,055 - mmcls - INFO - Epoch [12][900/5005] lr: 1.000e-01, eta: 10:54:49, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4863 2023-01-04 16:38:03,878 - mmcls - INFO - Epoch [12][1000/5005] lr: 1.000e-01, eta: 10:54:40, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4703 2023-01-04 16:38:12,705 - mmcls - INFO - Epoch [12][1100/5005] lr: 1.000e-01, eta: 10:54:31, time: 0.088, data_time: 0.004, memory: 928, loss: 5.5052 2023-01-04 16:38:21,509 - mmcls - INFO - Epoch [12][1200/5005] lr: 1.000e-01, eta: 10:54:22, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4770 2023-01-04 16:38:30,010 - mmcls - INFO - Epoch [12][1300/5005] lr: 1.000e-01, eta: 10:54:10, time: 0.085, data_time: 0.009, memory: 928, loss: 5.4807 2023-01-04 16:38:38,634 - mmcls - INFO - Epoch [12][1400/5005] lr: 1.000e-01, eta: 10:54:00, time: 0.086, data_time: 0.013, memory: 928, loss: 5.4720 2023-01-04 16:38:47,519 - mmcls - INFO - Epoch [12][1500/5005] lr: 1.000e-01, eta: 10:53:51, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4904 2023-01-04 16:38:56,632 - mmcls - INFO - Epoch [12][1600/5005] lr: 1.000e-01, eta: 10:53:45, time: 0.091, data_time: 0.003, memory: 928, loss: 5.4735 2023-01-04 16:39:05,339 - mmcls - INFO - Epoch [12][1700/5005] lr: 1.000e-01, eta: 10:53:35, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4839 2023-01-04 16:39:14,020 - mmcls - INFO - Epoch [12][1800/5005] lr: 1.000e-01, eta: 10:53:25, time: 0.087, data_time: 0.013, memory: 928, loss: 5.4666 2023-01-04 16:39:22,641 - mmcls - INFO - Epoch [12][1900/5005] lr: 1.000e-01, eta: 10:53:14, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4880 2023-01-04 16:39:31,588 - mmcls - INFO - Epoch [12][2000/5005] lr: 1.000e-01, eta: 10:53:06, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4730 2023-01-04 16:39:40,200 - mmcls - INFO - Epoch [12][2100/5005] lr: 1.000e-01, eta: 10:52:56, time: 0.086, data_time: 0.002, memory: 928, loss: 5.5025 2023-01-04 16:39:48,684 - mmcls - INFO - Epoch [12][2200/5005] lr: 1.000e-01, eta: 10:52:44, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4758 2023-01-04 16:39:57,935 - mmcls - INFO - Epoch [12][2300/5005] lr: 1.000e-01, eta: 10:52:38, time: 0.093, data_time: 0.002, memory: 928, loss: 5.4495 2023-01-04 16:40:06,580 - mmcls - INFO - Epoch [12][2400/5005] lr: 1.000e-01, eta: 10:52:28, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4871 2023-01-04 16:40:15,352 - mmcls - INFO - Epoch [12][2500/5005] lr: 1.000e-01, eta: 10:52:19, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4866 2023-01-04 16:40:24,300 - mmcls - INFO - Epoch [12][2600/5005] lr: 1.000e-01, eta: 10:52:11, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4572 2023-01-04 16:40:33,072 - mmcls - INFO - Epoch [12][2700/5005] lr: 1.000e-01, eta: 10:52:01, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4807 2023-01-04 16:40:41,849 - mmcls - INFO - Epoch [12][2800/5005] lr: 1.000e-01, eta: 10:51:52, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4807 2023-01-04 16:40:50,418 - mmcls - INFO - Epoch [12][2900/5005] lr: 1.000e-01, eta: 10:51:41, time: 0.086, data_time: 0.026, memory: 928, loss: 5.4630 2023-01-04 16:40:59,282 - mmcls - INFO - Epoch [12][3000/5005] lr: 1.000e-01, eta: 10:51:33, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4950 2023-01-04 16:41:08,262 - mmcls - INFO - Epoch [12][3100/5005] lr: 1.000e-01, eta: 10:51:25, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4545 2023-01-04 16:41:17,147 - mmcls - INFO - Epoch [12][3200/5005] lr: 1.000e-01, eta: 10:51:16, time: 0.089, data_time: 0.006, memory: 928, loss: 5.4953 2023-01-04 16:41:25,749 - mmcls - INFO - Epoch [12][3300/5005] lr: 1.000e-01, eta: 10:51:06, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4720 2023-01-04 16:41:34,600 - mmcls - INFO - Epoch [12][3400/5005] lr: 1.000e-01, eta: 10:50:57, time: 0.089, data_time: 0.007, memory: 928, loss: 5.4839 2023-01-04 16:41:43,276 - mmcls - INFO - Epoch [12][3500/5005] lr: 1.000e-01, eta: 10:50:47, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4878 2023-01-04 16:41:52,264 - mmcls - INFO - Epoch [12][3600/5005] lr: 1.000e-01, eta: 10:50:39, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4810 2023-01-04 16:42:01,269 - mmcls - INFO - Epoch [12][3700/5005] lr: 1.000e-01, eta: 10:50:32, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4556 2023-01-04 16:42:10,255 - mmcls - INFO - Epoch [12][3800/5005] lr: 1.000e-01, eta: 10:50:24, time: 0.090, data_time: 0.015, memory: 928, loss: 5.4547 2023-01-04 16:42:18,907 - mmcls - INFO - Epoch [12][3900/5005] lr: 1.000e-01, eta: 10:50:14, time: 0.086, data_time: 0.009, memory: 928, loss: 5.4968 2023-01-04 16:42:27,706 - mmcls - INFO - Epoch [12][4000/5005] lr: 1.000e-01, eta: 10:50:05, time: 0.088, data_time: 0.020, memory: 928, loss: 5.4822 2023-01-04 16:42:36,557 - mmcls - INFO - Epoch [12][4100/5005] lr: 1.000e-01, eta: 10:49:56, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4798 2023-01-04 16:42:45,184 - mmcls - INFO - Epoch [12][4200/5005] lr: 1.000e-01, eta: 10:49:45, time: 0.086, data_time: 0.028, memory: 928, loss: 5.4709 2023-01-04 16:42:53,717 - mmcls - INFO - Epoch [12][4300/5005] lr: 1.000e-01, eta: 10:49:34, time: 0.085, data_time: 0.011, memory: 928, loss: 5.4970 2023-01-04 16:43:02,666 - mmcls - INFO - Epoch [12][4400/5005] lr: 1.000e-01, eta: 10:49:26, time: 0.090, data_time: 0.014, memory: 928, loss: 5.4656 2023-01-04 16:43:11,300 - mmcls - INFO - Epoch [12][4500/5005] lr: 1.000e-01, eta: 10:49:16, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4746 2023-01-04 16:43:20,527 - mmcls - INFO - Epoch [12][4600/5005] lr: 1.000e-01, eta: 10:49:10, time: 0.092, data_time: 0.000, memory: 928, loss: 5.4741 2023-01-04 16:43:29,148 - mmcls - INFO - Epoch [12][4700/5005] lr: 1.000e-01, eta: 10:49:00, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4910 2023-01-04 16:43:38,090 - mmcls - INFO - Epoch [12][4800/5005] lr: 1.000e-01, eta: 10:48:52, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4838 2023-01-04 16:43:46,985 - mmcls - INFO - Epoch [12][4900/5005] lr: 1.000e-01, eta: 10:48:43, time: 0.089, data_time: 0.020, memory: 928, loss: 5.4702 2023-01-04 16:43:55,747 - mmcls - INFO - Epoch [12][5000/5005] lr: 1.000e-01, eta: 10:48:34, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4712 2023-01-04 16:44:18,512 - mmcls - INFO - Epoch(val) [12][196] accuracy_top-1: 5.0720, accuracy_top-5: 13.6000 2023-01-04 16:44:29,349 - mmcls - INFO - Epoch [13][100/5005] lr: 1.000e-01, eta: 10:48:36, time: 0.108, data_time: 0.033, memory: 928, loss: 5.4794 2023-01-04 16:44:38,170 - mmcls - INFO - Epoch [13][200/5005] lr: 1.000e-01, eta: 10:48:27, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4801 2023-01-04 16:44:47,086 - mmcls - INFO - Epoch [13][300/5005] lr: 1.000e-01, eta: 10:48:19, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4712 2023-01-04 16:44:55,988 - mmcls - INFO - Epoch [13][400/5005] lr: 1.000e-01, eta: 10:48:10, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4651 2023-01-04 16:45:04,717 - mmcls - INFO - Epoch [13][500/5005] lr: 1.000e-01, eta: 10:48:01, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4880 2023-01-04 16:45:13,410 - mmcls - INFO - Epoch [13][600/5005] lr: 1.000e-01, eta: 10:47:51, time: 0.087, data_time: 0.010, memory: 928, loss: 5.4742 2023-01-04 16:45:22,122 - mmcls - INFO - Epoch [13][700/5005] lr: 1.000e-01, eta: 10:47:41, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4556 2023-01-04 16:45:30,865 - mmcls - INFO - Epoch [13][800/5005] lr: 1.000e-01, eta: 10:47:31, time: 0.087, data_time: 0.020, memory: 928, loss: 5.4719 2023-01-04 16:45:39,819 - mmcls - INFO - Epoch [13][900/5005] lr: 1.000e-01, eta: 10:47:23, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4744 2023-01-04 16:45:48,338 - mmcls - INFO - Epoch [13][1000/5005] lr: 1.000e-01, eta: 10:47:12, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4761 2023-01-04 16:45:57,650 - mmcls - INFO - Epoch [13][1100/5005] lr: 1.000e-01, eta: 10:47:07, time: 0.093, data_time: 0.001, memory: 928, loss: 5.4679 2023-01-04 16:46:06,370 - mmcls - INFO - Epoch [13][1200/5005] lr: 1.000e-01, eta: 10:46:57, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4570 2023-01-04 16:46:15,199 - mmcls - INFO - Epoch [13][1300/5005] lr: 1.000e-01, eta: 10:46:48, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4730 2023-01-04 16:46:24,085 - mmcls - INFO - Epoch [13][1400/5005] lr: 1.000e-01, eta: 10:46:40, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4686 2023-01-04 16:46:32,737 - mmcls - INFO - Epoch [13][1500/5005] lr: 1.000e-01, eta: 10:46:30, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4755 2023-01-04 16:46:41,814 - mmcls - INFO - Epoch [13][1600/5005] lr: 1.000e-01, eta: 10:46:22, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4679 2023-01-04 16:46:50,640 - mmcls - INFO - Epoch [13][1700/5005] lr: 1.000e-01, eta: 10:46:14, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4741 2023-01-04 16:46:59,598 - mmcls - INFO - Epoch [13][1800/5005] lr: 1.000e-01, eta: 10:46:06, time: 0.090, data_time: 0.016, memory: 928, loss: 5.4692 2023-01-04 16:47:08,372 - mmcls - INFO - Epoch [13][1900/5005] lr: 1.000e-01, eta: 10:45:56, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4574 2023-01-04 16:47:17,064 - mmcls - INFO - Epoch [13][2000/5005] lr: 1.000e-01, eta: 10:45:46, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4816 2023-01-04 16:47:25,577 - mmcls - INFO - Epoch [13][2100/5005] lr: 1.000e-01, eta: 10:45:35, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4834 2023-01-04 16:47:34,546 - mmcls - INFO - Epoch [13][2200/5005] lr: 1.000e-01, eta: 10:45:27, time: 0.090, data_time: 0.012, memory: 928, loss: 5.4662 2023-01-04 16:47:43,456 - mmcls - INFO - Epoch [13][2300/5005] lr: 1.000e-01, eta: 10:45:19, time: 0.089, data_time: 0.027, memory: 928, loss: 5.4783 2023-01-04 16:47:52,392 - mmcls - INFO - Epoch [13][2400/5005] lr: 1.000e-01, eta: 10:45:11, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4697 2023-01-04 16:48:01,658 - mmcls - INFO - Epoch [13][2500/5005] lr: 1.000e-01, eta: 10:45:05, time: 0.093, data_time: 0.000, memory: 928, loss: 5.4781 2023-01-04 16:48:10,328 - mmcls - INFO - Epoch [13][2600/5005] lr: 1.000e-01, eta: 10:44:55, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4612 2023-01-04 16:48:19,124 - mmcls - INFO - Epoch [13][2700/5005] lr: 1.000e-01, eta: 10:44:46, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4707 2023-01-04 16:48:27,765 - mmcls - INFO - Epoch [13][2800/5005] lr: 1.000e-01, eta: 10:44:36, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4793 2023-01-04 16:48:36,514 - mmcls - INFO - Epoch [13][2900/5005] lr: 1.000e-01, eta: 10:44:26, time: 0.087, data_time: 0.036, memory: 928, loss: 5.4702 2023-01-04 16:48:45,120 - mmcls - INFO - Epoch [13][3000/5005] lr: 1.000e-01, eta: 10:44:16, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4767 2023-01-04 16:48:54,310 - mmcls - INFO - Epoch [13][3100/5005] lr: 1.000e-01, eta: 10:44:09, time: 0.092, data_time: 0.002, memory: 928, loss: 5.4640 2023-01-04 16:49:03,374 - mmcls - INFO - Epoch [13][3200/5005] lr: 1.000e-01, eta: 10:44:02, time: 0.091, data_time: 0.003, memory: 928, loss: 5.4561 2023-01-04 16:49:11,952 - mmcls - INFO - Epoch [13][3300/5005] lr: 1.000e-01, eta: 10:43:51, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4796 2023-01-04 16:49:20,626 - mmcls - INFO - Epoch [13][3400/5005] lr: 1.000e-01, eta: 10:43:41, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4783 2023-01-04 16:49:29,532 - mmcls - INFO - Epoch [13][3500/5005] lr: 1.000e-01, eta: 10:43:33, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4809 2023-01-04 16:49:38,394 - mmcls - INFO - Epoch [13][3600/5005] lr: 1.000e-01, eta: 10:43:24, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4687 2023-01-04 16:49:47,179 - mmcls - INFO - Epoch [13][3700/5005] lr: 1.000e-01, eta: 10:43:15, time: 0.088, data_time: 0.025, memory: 928, loss: 5.4720 2023-01-04 16:49:55,841 - mmcls - INFO - Epoch [13][3800/5005] lr: 1.000e-01, eta: 10:43:05, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4595 2023-01-04 16:50:04,540 - mmcls - INFO - Epoch [13][3900/5005] lr: 1.000e-01, eta: 10:42:55, time: 0.087, data_time: 0.026, memory: 928, loss: 5.4654 2023-01-04 16:50:13,425 - mmcls - INFO - Epoch [13][4000/5005] lr: 1.000e-01, eta: 10:42:47, time: 0.089, data_time: 0.024, memory: 928, loss: 5.4599 2023-01-04 16:50:22,220 - mmcls - INFO - Epoch [13][4100/5005] lr: 1.000e-01, eta: 10:42:38, time: 0.088, data_time: 0.015, memory: 928, loss: 5.4730 2023-01-04 16:50:31,182 - mmcls - INFO - Epoch [13][4200/5005] lr: 1.000e-01, eta: 10:42:30, time: 0.090, data_time: 0.012, memory: 928, loss: 5.4592 2023-01-04 16:50:39,992 - mmcls - INFO - Epoch [13][4300/5005] lr: 1.000e-01, eta: 10:42:21, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4565 2023-01-04 16:50:48,590 - mmcls - INFO - Epoch [13][4400/5005] lr: 1.000e-01, eta: 10:42:10, time: 0.086, data_time: 0.020, memory: 928, loss: 5.4668 2023-01-04 16:50:57,448 - mmcls - INFO - Epoch [13][4500/5005] lr: 1.000e-01, eta: 10:42:02, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4609 2023-01-04 16:51:06,342 - mmcls - INFO - Epoch [13][4600/5005] lr: 1.000e-01, eta: 10:41:53, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4629 2023-01-04 16:51:15,198 - mmcls - INFO - Epoch [13][4700/5005] lr: 1.000e-01, eta: 10:41:44, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4402 2023-01-04 16:51:24,114 - mmcls - INFO - Epoch [13][4800/5005] lr: 1.000e-01, eta: 10:41:36, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4613 2023-01-04 16:51:33,313 - mmcls - INFO - Epoch [13][4900/5005] lr: 1.000e-01, eta: 10:41:30, time: 0.092, data_time: 0.014, memory: 928, loss: 5.4737 2023-01-04 16:51:42,144 - mmcls - INFO - Epoch [13][5000/5005] lr: 1.000e-01, eta: 10:41:21, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4471 2023-01-04 16:52:05,233 - mmcls - INFO - Epoch(val) [13][196] accuracy_top-1: 5.6820, accuracy_top-5: 15.3040 2023-01-04 16:52:16,084 - mmcls - INFO - Epoch [14][100/5005] lr: 1.000e-01, eta: 10:41:22, time: 0.108, data_time: 0.036, memory: 928, loss: 5.4477 2023-01-04 16:52:24,946 - mmcls - INFO - Epoch [14][200/5005] lr: 1.000e-01, eta: 10:41:13, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4706 2023-01-04 16:52:33,769 - mmcls - INFO - Epoch [14][300/5005] lr: 1.000e-01, eta: 10:41:04, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4454 2023-01-04 16:52:42,424 - mmcls - INFO - Epoch [14][400/5005] lr: 1.000e-01, eta: 10:40:54, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4755 2023-01-04 16:52:51,190 - mmcls - INFO - Epoch [14][500/5005] lr: 1.000e-01, eta: 10:40:45, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4380 2023-01-04 16:52:59,809 - mmcls - INFO - Epoch [14][600/5005] lr: 1.000e-01, eta: 10:40:35, time: 0.086, data_time: 0.005, memory: 928, loss: 5.4793 2023-01-04 16:53:08,524 - mmcls - INFO - Epoch [14][700/5005] lr: 1.000e-01, eta: 10:40:25, time: 0.087, data_time: 0.012, memory: 928, loss: 5.4609 2023-01-04 16:53:17,186 - mmcls - INFO - Epoch [14][800/5005] lr: 1.000e-01, eta: 10:40:15, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4732 2023-01-04 16:53:26,257 - mmcls - INFO - Epoch [14][900/5005] lr: 1.000e-01, eta: 10:40:07, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4596 2023-01-04 16:53:35,085 - mmcls - INFO - Epoch [14][1000/5005] lr: 1.000e-01, eta: 10:39:59, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4603 2023-01-04 16:53:43,926 - mmcls - INFO - Epoch [14][1100/5005] lr: 1.000e-01, eta: 10:39:50, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4581 2023-01-04 16:53:52,676 - mmcls - INFO - Epoch [14][1200/5005] lr: 1.000e-01, eta: 10:39:40, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4553 2023-01-04 16:54:01,447 - mmcls - INFO - Epoch [14][1300/5005] lr: 1.000e-01, eta: 10:39:31, time: 0.088, data_time: 0.031, memory: 928, loss: 5.4583 2023-01-04 16:54:10,283 - mmcls - INFO - Epoch [14][1400/5005] lr: 1.000e-01, eta: 10:39:22, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4431 2023-01-04 16:54:19,026 - mmcls - INFO - Epoch [14][1500/5005] lr: 1.000e-01, eta: 10:39:13, time: 0.087, data_time: 0.015, memory: 928, loss: 5.4582 2023-01-04 16:54:27,452 - mmcls - INFO - Epoch [14][1600/5005] lr: 1.000e-01, eta: 10:39:01, time: 0.084, data_time: 0.004, memory: 928, loss: 5.4601 2023-01-04 16:54:36,260 - mmcls - INFO - Epoch [14][1700/5005] lr: 1.000e-01, eta: 10:38:52, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4519 2023-01-04 16:54:45,122 - mmcls - INFO - Epoch [14][1800/5005] lr: 1.000e-01, eta: 10:38:43, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4606 2023-01-04 16:54:53,850 - mmcls - INFO - Epoch [14][1900/5005] lr: 1.000e-01, eta: 10:38:34, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4505 2023-01-04 16:55:02,802 - mmcls - INFO - Epoch [14][2000/5005] lr: 1.000e-01, eta: 10:38:26, time: 0.090, data_time: 0.009, memory: 928, loss: 5.4462 2023-01-04 16:55:11,737 - mmcls - INFO - Epoch [14][2100/5005] lr: 1.000e-01, eta: 10:38:18, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4559 2023-01-04 16:55:20,715 - mmcls - INFO - Epoch [14][2200/5005] lr: 1.000e-01, eta: 10:38:10, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4750 2023-01-04 16:55:29,356 - mmcls - INFO - Epoch [14][2300/5005] lr: 1.000e-01, eta: 10:38:00, time: 0.086, data_time: 0.015, memory: 928, loss: 5.4562 2023-01-04 16:55:38,052 - mmcls - INFO - Epoch [14][2400/5005] lr: 1.000e-01, eta: 10:37:50, time: 0.087, data_time: 0.008, memory: 928, loss: 5.4808 2023-01-04 16:55:46,809 - mmcls - INFO - Epoch [14][2500/5005] lr: 1.000e-01, eta: 10:37:40, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4634 2023-01-04 16:55:55,474 - mmcls - INFO - Epoch [14][2600/5005] lr: 1.000e-01, eta: 10:37:30, time: 0.086, data_time: 0.015, memory: 928, loss: 5.4609 2023-01-04 16:56:04,219 - mmcls - INFO - Epoch [14][2700/5005] lr: 1.000e-01, eta: 10:37:21, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4471 2023-01-04 16:56:13,096 - mmcls - INFO - Epoch [14][2800/5005] lr: 1.000e-01, eta: 10:37:12, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4598 2023-01-04 16:56:21,913 - mmcls - INFO - Epoch [14][2900/5005] lr: 1.000e-01, eta: 10:37:03, time: 0.088, data_time: 0.035, memory: 928, loss: 5.4432 2023-01-04 16:56:30,711 - mmcls - INFO - Epoch [14][3000/5005] lr: 1.000e-01, eta: 10:36:54, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4434 2023-01-04 16:56:39,463 - mmcls - INFO - Epoch [14][3100/5005] lr: 1.000e-01, eta: 10:36:45, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4886 2023-01-04 16:56:48,355 - mmcls - INFO - Epoch [14][3200/5005] lr: 1.000e-01, eta: 10:36:36, time: 0.089, data_time: 0.018, memory: 928, loss: 5.4571 2023-01-04 16:56:57,095 - mmcls - INFO - Epoch [14][3300/5005] lr: 1.000e-01, eta: 10:36:27, time: 0.087, data_time: 0.012, memory: 928, loss: 5.4347 2023-01-04 16:57:05,741 - mmcls - INFO - Epoch [14][3400/5005] lr: 1.000e-01, eta: 10:36:17, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4381 2023-01-04 16:57:14,346 - mmcls - INFO - Epoch [14][3500/5005] lr: 1.000e-01, eta: 10:36:07, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4510 2023-01-04 16:57:23,108 - mmcls - INFO - Epoch [14][3600/5005] lr: 1.000e-01, eta: 10:35:57, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4418 2023-01-04 16:57:31,862 - mmcls - INFO - Epoch [14][3700/5005] lr: 1.000e-01, eta: 10:35:48, time: 0.087, data_time: 0.000, memory: 928, loss: 5.4714 2023-01-04 16:57:40,340 - mmcls - INFO - Epoch [14][3800/5005] lr: 1.000e-01, eta: 10:35:37, time: 0.085, data_time: 0.015, memory: 928, loss: 5.4627 2023-01-04 16:57:48,966 - mmcls - INFO - Epoch [14][3900/5005] lr: 1.000e-01, eta: 10:35:27, time: 0.086, data_time: 0.014, memory: 928, loss: 5.4438 2023-01-04 16:57:57,883 - mmcls - INFO - Epoch [14][4000/5005] lr: 1.000e-01, eta: 10:35:19, time: 0.089, data_time: 0.029, memory: 928, loss: 5.4413 2023-01-04 16:58:06,296 - mmcls - INFO - Epoch [14][4100/5005] lr: 1.000e-01, eta: 10:35:07, time: 0.084, data_time: 0.005, memory: 928, loss: 5.4352 2023-01-04 16:58:15,363 - mmcls - INFO - Epoch [14][4200/5005] lr: 1.000e-01, eta: 10:35:00, time: 0.091, data_time: 0.022, memory: 928, loss: 5.4648 2023-01-04 16:58:24,227 - mmcls - INFO - Epoch [14][4300/5005] lr: 1.000e-01, eta: 10:34:51, time: 0.089, data_time: 0.012, memory: 928, loss: 5.4499 2023-01-04 16:58:33,181 - mmcls - INFO - Epoch [14][4400/5005] lr: 1.000e-01, eta: 10:34:43, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4432 2023-01-04 16:58:42,021 - mmcls - INFO - Epoch [14][4500/5005] lr: 1.000e-01, eta: 10:34:34, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4679 2023-01-04 16:58:50,888 - mmcls - INFO - Epoch [14][4600/5005] lr: 1.000e-01, eta: 10:34:25, time: 0.089, data_time: 0.022, memory: 928, loss: 5.4521 2023-01-04 16:59:00,141 - mmcls - INFO - Epoch [14][4700/5005] lr: 1.000e-01, eta: 10:34:19, time: 0.092, data_time: 0.002, memory: 928, loss: 5.4391 2023-01-04 16:59:08,860 - mmcls - INFO - Epoch [14][4800/5005] lr: 1.000e-01, eta: 10:34:10, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4667 2023-01-04 16:59:17,402 - mmcls - INFO - Epoch [14][4900/5005] lr: 1.000e-01, eta: 10:33:59, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4459 2023-01-04 16:59:26,207 - mmcls - INFO - Epoch [14][5000/5005] lr: 1.000e-01, eta: 10:33:50, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4661 2023-01-04 16:59:49,143 - mmcls - INFO - Epoch(val) [14][196] accuracy_top-1: 4.6260, accuracy_top-5: 12.9140 2023-01-04 17:00:00,035 - mmcls - INFO - Epoch [15][100/5005] lr: 1.000e-01, eta: 10:33:50, time: 0.109, data_time: 0.046, memory: 928, loss: 5.4610 2023-01-04 17:00:08,784 - mmcls - INFO - Epoch [15][200/5005] lr: 1.000e-01, eta: 10:33:41, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4600 2023-01-04 17:00:17,818 - mmcls - INFO - Epoch [15][300/5005] lr: 1.000e-01, eta: 10:33:33, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4366 2023-01-04 17:00:26,781 - mmcls - INFO - Epoch [15][400/5005] lr: 1.000e-01, eta: 10:33:25, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4419 2023-01-04 17:00:35,700 - mmcls - INFO - Epoch [15][500/5005] lr: 1.000e-01, eta: 10:33:17, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4331 2023-01-04 17:00:44,553 - mmcls - INFO - Epoch [15][600/5005] lr: 1.000e-01, eta: 10:33:08, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4626 2023-01-04 17:00:53,048 - mmcls - INFO - Epoch [15][700/5005] lr: 1.000e-01, eta: 10:32:57, time: 0.085, data_time: 0.027, memory: 928, loss: 5.4116 2023-01-04 17:01:01,732 - mmcls - INFO - Epoch [15][800/5005] lr: 1.000e-01, eta: 10:32:47, time: 0.087, data_time: 0.011, memory: 928, loss: 5.4353 2023-01-04 17:01:10,294 - mmcls - INFO - Epoch [15][900/5005] lr: 1.000e-01, eta: 10:32:37, time: 0.085, data_time: 0.017, memory: 928, loss: 5.4550 2023-01-04 17:01:18,878 - mmcls - INFO - Epoch [15][1000/5005] lr: 1.000e-01, eta: 10:32:27, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4353 2023-01-04 17:01:27,666 - mmcls - INFO - Epoch [15][1100/5005] lr: 1.000e-01, eta: 10:32:17, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4433 2023-01-04 17:01:36,547 - mmcls - INFO - Epoch [15][1200/5005] lr: 1.000e-01, eta: 10:32:09, time: 0.089, data_time: 0.016, memory: 928, loss: 5.4246 2023-01-04 17:01:45,143 - mmcls - INFO - Epoch [15][1300/5005] lr: 1.000e-01, eta: 10:31:59, time: 0.086, data_time: 0.010, memory: 928, loss: 5.4296 2023-01-04 17:01:53,700 - mmcls - INFO - Epoch [15][1400/5005] lr: 1.000e-01, eta: 10:31:48, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4547 2023-01-04 17:02:02,454 - mmcls - INFO - Epoch [15][1500/5005] lr: 1.000e-01, eta: 10:31:39, time: 0.087, data_time: 0.029, memory: 928, loss: 5.4646 2023-01-04 17:02:11,040 - mmcls - INFO - Epoch [15][1600/5005] lr: 1.000e-01, eta: 10:31:28, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4530 2023-01-04 17:02:19,793 - mmcls - INFO - Epoch [15][1700/5005] lr: 1.000e-01, eta: 10:31:19, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4559 2023-01-04 17:02:28,621 - mmcls - INFO - Epoch [15][1800/5005] lr: 1.000e-01, eta: 10:31:10, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4558 2023-01-04 17:02:37,338 - mmcls - INFO - Epoch [15][1900/5005] lr: 1.000e-01, eta: 10:31:01, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4509 2023-01-04 17:02:45,954 - mmcls - INFO - Epoch [15][2000/5005] lr: 1.000e-01, eta: 10:30:50, time: 0.086, data_time: 0.023, memory: 928, loss: 5.4459 2023-01-04 17:02:54,686 - mmcls - INFO - Epoch [15][2100/5005] lr: 1.000e-01, eta: 10:30:41, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4436 2023-01-04 17:03:03,471 - mmcls - INFO - Epoch [15][2200/5005] lr: 1.000e-01, eta: 10:30:32, time: 0.088, data_time: 0.013, memory: 928, loss: 5.4521 2023-01-04 17:03:12,516 - mmcls - INFO - Epoch [15][2300/5005] lr: 1.000e-01, eta: 10:30:24, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4407 2023-01-04 17:03:21,322 - mmcls - INFO - Epoch [15][2400/5005] lr: 1.000e-01, eta: 10:30:15, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4491 2023-01-04 17:03:30,101 - mmcls - INFO - Epoch [15][2500/5005] lr: 1.000e-01, eta: 10:30:06, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4427 2023-01-04 17:03:38,709 - mmcls - INFO - Epoch [15][2600/5005] lr: 1.000e-01, eta: 10:29:56, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4457 2023-01-04 17:03:47,429 - mmcls - INFO - Epoch [15][2700/5005] lr: 1.000e-01, eta: 10:29:46, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4402 2023-01-04 17:03:55,944 - mmcls - INFO - Epoch [15][2800/5005] lr: 1.000e-01, eta: 10:29:36, time: 0.085, data_time: 0.012, memory: 928, loss: 5.4402 2023-01-04 17:04:04,767 - mmcls - INFO - Epoch [15][2900/5005] lr: 1.000e-01, eta: 10:29:27, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4499 2023-01-04 17:04:13,585 - mmcls - INFO - Epoch [15][3000/5005] lr: 1.000e-01, eta: 10:29:18, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4475 2023-01-04 17:04:22,444 - mmcls - INFO - Epoch [15][3100/5005] lr: 1.000e-01, eta: 10:29:09, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4572 2023-01-04 17:04:31,026 - mmcls - INFO - Epoch [15][3200/5005] lr: 1.000e-01, eta: 10:28:59, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4549 2023-01-04 17:04:39,791 - mmcls - INFO - Epoch [15][3300/5005] lr: 1.000e-01, eta: 10:28:50, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4420 2023-01-04 17:04:48,471 - mmcls - INFO - Epoch [15][3400/5005] lr: 1.000e-01, eta: 10:28:40, time: 0.087, data_time: 0.025, memory: 928, loss: 5.4633 2023-01-04 17:04:57,681 - mmcls - INFO - Epoch [15][3500/5005] lr: 1.000e-01, eta: 10:28:33, time: 0.092, data_time: 0.001, memory: 928, loss: 5.4244 2023-01-04 17:05:06,578 - mmcls - INFO - Epoch [15][3600/5005] lr: 1.000e-01, eta: 10:28:25, time: 0.089, data_time: 0.010, memory: 928, loss: 5.4626 2023-01-04 17:05:15,459 - mmcls - INFO - Epoch [15][3700/5005] lr: 1.000e-01, eta: 10:28:16, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4681 2023-01-04 17:05:24,530 - mmcls - INFO - Epoch [15][3800/5005] lr: 1.000e-01, eta: 10:28:09, time: 0.091, data_time: 0.011, memory: 928, loss: 5.4483 2023-01-04 17:05:33,889 - mmcls - INFO - Epoch [15][3900/5005] lr: 1.000e-01, eta: 10:28:03, time: 0.094, data_time: 0.018, memory: 928, loss: 5.4285 2023-01-04 17:05:42,627 - mmcls - INFO - Epoch [15][4000/5005] lr: 1.000e-01, eta: 10:27:54, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4577 2023-01-04 17:05:51,375 - mmcls - INFO - Epoch [15][4100/5005] lr: 1.000e-01, eta: 10:27:44, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4411 2023-01-04 17:06:00,125 - mmcls - INFO - Epoch [15][4200/5005] lr: 1.000e-01, eta: 10:27:35, time: 0.088, data_time: 0.033, memory: 928, loss: 5.4635 2023-01-04 17:06:08,921 - mmcls - INFO - Epoch [15][4300/5005] lr: 1.000e-01, eta: 10:27:26, time: 0.088, data_time: 0.017, memory: 928, loss: 5.4247 2023-01-04 17:06:17,554 - mmcls - INFO - Epoch [15][4400/5005] lr: 1.000e-01, eta: 10:27:16, time: 0.086, data_time: 0.017, memory: 928, loss: 5.4541 2023-01-04 17:06:26,536 - mmcls - INFO - Epoch [15][4500/5005] lr: 1.000e-01, eta: 10:27:08, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4298 2023-01-04 17:06:34,915 - mmcls - INFO - Epoch [15][4600/5005] lr: 1.000e-01, eta: 10:26:56, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4456 2023-01-04 17:06:43,692 - mmcls - INFO - Epoch [15][4700/5005] lr: 1.000e-01, eta: 10:26:47, time: 0.088, data_time: 0.012, memory: 928, loss: 5.4531 2023-01-04 17:06:52,412 - mmcls - INFO - Epoch [15][4800/5005] lr: 1.000e-01, eta: 10:26:38, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4220 2023-01-04 17:07:01,443 - mmcls - INFO - Epoch [15][4900/5005] lr: 1.000e-01, eta: 10:26:30, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4558 2023-01-04 17:07:10,151 - mmcls - INFO - Epoch [15][5000/5005] lr: 1.000e-01, eta: 10:26:20, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4622 2023-01-04 17:07:32,960 - mmcls - INFO - Epoch(val) [15][196] accuracy_top-1: 4.0540, accuracy_top-5: 11.2260 2023-01-04 17:07:44,065 - mmcls - INFO - Epoch [16][100/5005] lr: 1.000e-01, eta: 10:26:21, time: 0.111, data_time: 0.030, memory: 928, loss: 5.4478 2023-01-04 17:07:52,593 - mmcls - INFO - Epoch [16][200/5005] lr: 1.000e-01, eta: 10:26:11, time: 0.085, data_time: 0.020, memory: 928, loss: 5.4344 2023-01-04 17:08:01,576 - mmcls - INFO - Epoch [16][300/5005] lr: 1.000e-01, eta: 10:26:03, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4233 2023-01-04 17:08:10,421 - mmcls - INFO - Epoch [16][400/5005] lr: 1.000e-01, eta: 10:25:54, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4376 2023-01-04 17:08:19,136 - mmcls - INFO - Epoch [16][500/5005] lr: 1.000e-01, eta: 10:25:45, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4317 2023-01-04 17:08:27,735 - mmcls - INFO - Epoch [16][600/5005] lr: 1.000e-01, eta: 10:25:34, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4172 2023-01-04 17:08:36,615 - mmcls - INFO - Epoch [16][700/5005] lr: 1.000e-01, eta: 10:25:26, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4202 2023-01-04 17:08:45,423 - mmcls - INFO - Epoch [16][800/5005] lr: 1.000e-01, eta: 10:25:17, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4442 2023-01-04 17:08:54,410 - mmcls - INFO - Epoch [16][900/5005] lr: 1.000e-01, eta: 10:25:09, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4391 2023-01-04 17:09:02,989 - mmcls - INFO - Epoch [16][1000/5005] lr: 1.000e-01, eta: 10:24:59, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4480 2023-01-04 17:09:11,947 - mmcls - INFO - Epoch [16][1100/5005] lr: 1.000e-01, eta: 10:24:50, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4493 2023-01-04 17:09:20,605 - mmcls - INFO - Epoch [16][1200/5005] lr: 1.000e-01, eta: 10:24:40, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4283 2023-01-04 17:09:29,287 - mmcls - INFO - Epoch [16][1300/5005] lr: 1.000e-01, eta: 10:24:31, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4589 2023-01-04 17:09:37,876 - mmcls - INFO - Epoch [16][1400/5005] lr: 1.000e-01, eta: 10:24:21, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4472 2023-01-04 17:09:46,620 - mmcls - INFO - Epoch [16][1500/5005] lr: 1.000e-01, eta: 10:24:11, time: 0.087, data_time: 0.015, memory: 928, loss: 5.4393 2023-01-04 17:09:55,479 - mmcls - INFO - Epoch [16][1600/5005] lr: 1.000e-01, eta: 10:24:03, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4464 2023-01-04 17:10:04,096 - mmcls - INFO - Epoch [16][1700/5005] lr: 1.000e-01, eta: 10:23:53, time: 0.086, data_time: 0.019, memory: 928, loss: 5.4361 2023-01-04 17:10:12,781 - mmcls - INFO - Epoch [16][1800/5005] lr: 1.000e-01, eta: 10:23:43, time: 0.087, data_time: 0.015, memory: 928, loss: 5.4403 2023-01-04 17:10:21,357 - mmcls - INFO - Epoch [16][1900/5005] lr: 1.000e-01, eta: 10:23:33, time: 0.086, data_time: 0.023, memory: 928, loss: 5.4352 2023-01-04 17:10:29,965 - mmcls - INFO - Epoch [16][2000/5005] lr: 1.000e-01, eta: 10:23:23, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4461 2023-01-04 17:10:38,683 - mmcls - INFO - Epoch [16][2100/5005] lr: 1.000e-01, eta: 10:23:13, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4491 2023-01-04 17:10:47,412 - mmcls - INFO - Epoch [16][2200/5005] lr: 1.000e-01, eta: 10:23:04, time: 0.087, data_time: 0.010, memory: 928, loss: 5.4294 2023-01-04 17:10:56,144 - mmcls - INFO - Epoch [16][2300/5005] lr: 1.000e-01, eta: 10:22:54, time: 0.087, data_time: 0.028, memory: 928, loss: 5.4525 2023-01-04 17:11:05,010 - mmcls - INFO - Epoch [16][2400/5005] lr: 1.000e-01, eta: 10:22:46, time: 0.089, data_time: 0.035, memory: 928, loss: 5.4270 2023-01-04 17:11:13,910 - mmcls - INFO - Epoch [16][2500/5005] lr: 1.000e-01, eta: 10:22:37, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4436 2023-01-04 17:11:22,547 - mmcls - INFO - Epoch [16][2600/5005] lr: 1.000e-01, eta: 10:22:27, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4355 2023-01-04 17:11:31,137 - mmcls - INFO - Epoch [16][2700/5005] lr: 1.000e-01, eta: 10:22:17, time: 0.086, data_time: 0.028, memory: 928, loss: 5.4536 2023-01-04 17:11:40,392 - mmcls - INFO - Epoch [16][2800/5005] lr: 1.000e-01, eta: 10:22:11, time: 0.093, data_time: 0.007, memory: 928, loss: 5.4440 2023-01-04 17:11:48,953 - mmcls - INFO - Epoch [16][2900/5005] lr: 1.000e-01, eta: 10:22:00, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4350 2023-01-04 17:11:57,949 - mmcls - INFO - Epoch [16][3000/5005] lr: 1.000e-01, eta: 10:21:52, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4176 2023-01-04 17:12:06,969 - mmcls - INFO - Epoch [16][3100/5005] lr: 1.000e-01, eta: 10:21:44, time: 0.090, data_time: 0.010, memory: 928, loss: 5.4527 2023-01-04 17:12:15,622 - mmcls - INFO - Epoch [16][3200/5005] lr: 1.000e-01, eta: 10:21:35, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4329 2023-01-04 17:12:24,188 - mmcls - INFO - Epoch [16][3300/5005] lr: 1.000e-01, eta: 10:21:24, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4363 2023-01-04 17:12:33,019 - mmcls - INFO - Epoch [16][3400/5005] lr: 1.000e-01, eta: 10:21:15, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4428 2023-01-04 17:12:41,646 - mmcls - INFO - Epoch [16][3500/5005] lr: 1.000e-01, eta: 10:21:06, time: 0.086, data_time: 0.019, memory: 928, loss: 5.4397 2023-01-04 17:12:50,567 - mmcls - INFO - Epoch [16][3600/5005] lr: 1.000e-01, eta: 10:20:57, time: 0.089, data_time: 0.023, memory: 928, loss: 5.4439 2023-01-04 17:12:59,557 - mmcls - INFO - Epoch [16][3700/5005] lr: 1.000e-01, eta: 10:20:49, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4443 2023-01-04 17:13:08,115 - mmcls - INFO - Epoch [16][3800/5005] lr: 1.000e-01, eta: 10:20:39, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4334 2023-01-04 17:13:16,709 - mmcls - INFO - Epoch [16][3900/5005] lr: 1.000e-01, eta: 10:20:29, time: 0.086, data_time: 0.017, memory: 928, loss: 5.4448 2023-01-04 17:13:25,462 - mmcls - INFO - Epoch [16][4000/5005] lr: 1.000e-01, eta: 10:20:20, time: 0.087, data_time: 0.039, memory: 928, loss: 5.4382 2023-01-04 17:13:34,458 - mmcls - INFO - Epoch [16][4100/5005] lr: 1.000e-01, eta: 10:20:12, time: 0.090, data_time: 0.005, memory: 928, loss: 5.4535 2023-01-04 17:13:43,050 - mmcls - INFO - Epoch [16][4200/5005] lr: 1.000e-01, eta: 10:20:01, time: 0.086, data_time: 0.015, memory: 928, loss: 5.4632 2023-01-04 17:13:51,743 - mmcls - INFO - Epoch [16][4300/5005] lr: 1.000e-01, eta: 10:19:52, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4453 2023-01-04 17:14:00,921 - mmcls - INFO - Epoch [16][4400/5005] lr: 1.000e-01, eta: 10:19:45, time: 0.092, data_time: 0.024, memory: 928, loss: 5.4406 2023-01-04 17:14:09,795 - mmcls - INFO - Epoch [16][4500/5005] lr: 1.000e-01, eta: 10:19:36, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4429 2023-01-04 17:14:18,638 - mmcls - INFO - Epoch [16][4600/5005] lr: 1.000e-01, eta: 10:19:28, time: 0.088, data_time: 0.013, memory: 928, loss: 5.4349 2023-01-04 17:14:27,111 - mmcls - INFO - Epoch [16][4700/5005] lr: 1.000e-01, eta: 10:19:17, time: 0.085, data_time: 0.009, memory: 928, loss: 5.4577 2023-01-04 17:14:36,113 - mmcls - INFO - Epoch [16][4800/5005] lr: 1.000e-01, eta: 10:19:09, time: 0.090, data_time: 0.020, memory: 928, loss: 5.4570 2023-01-04 17:14:44,867 - mmcls - INFO - Epoch [16][4900/5005] lr: 1.000e-01, eta: 10:19:00, time: 0.088, data_time: 0.022, memory: 928, loss: 5.4394 2023-01-04 17:14:53,434 - mmcls - INFO - Epoch [16][5000/5005] lr: 1.000e-01, eta: 10:18:49, time: 0.086, data_time: 0.019, memory: 928, loss: 5.4362 2023-01-04 17:15:16,371 - mmcls - INFO - Epoch(val) [16][196] accuracy_top-1: 5.0260, accuracy_top-5: 13.1060 2023-01-04 17:15:27,468 - mmcls - INFO - Epoch [17][100/5005] lr: 1.000e-01, eta: 10:18:49, time: 0.111, data_time: 0.037, memory: 928, loss: 5.4307 2023-01-04 17:15:36,197 - mmcls - INFO - Epoch [17][200/5005] lr: 1.000e-01, eta: 10:18:40, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4305 2023-01-04 17:15:45,024 - mmcls - INFO - Epoch [17][300/5005] lr: 1.000e-01, eta: 10:18:31, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4424 2023-01-04 17:15:53,704 - mmcls - INFO - Epoch [17][400/5005] lr: 1.000e-01, eta: 10:18:22, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4353 2023-01-04 17:16:02,345 - mmcls - INFO - Epoch [17][500/5005] lr: 1.000e-01, eta: 10:18:12, time: 0.086, data_time: 0.021, memory: 928, loss: 5.4092 2023-01-04 17:16:11,140 - mmcls - INFO - Epoch [17][600/5005] lr: 1.000e-01, eta: 10:18:03, time: 0.088, data_time: 0.012, memory: 928, loss: 5.4438 2023-01-04 17:16:19,841 - mmcls - INFO - Epoch [17][700/5005] lr: 1.000e-01, eta: 10:17:53, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4325 2023-01-04 17:16:28,322 - mmcls - INFO - Epoch [17][800/5005] lr: 1.000e-01, eta: 10:17:43, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4338 2023-01-04 17:16:36,930 - mmcls - INFO - Epoch [17][900/5005] lr: 1.000e-01, eta: 10:17:33, time: 0.086, data_time: 0.027, memory: 928, loss: 5.4309 2023-01-04 17:16:45,341 - mmcls - INFO - Epoch [17][1000/5005] lr: 1.000e-01, eta: 10:17:22, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4490 2023-01-04 17:16:53,898 - mmcls - INFO - Epoch [17][1100/5005] lr: 1.000e-01, eta: 10:17:11, time: 0.086, data_time: 0.005, memory: 928, loss: 5.4271 2023-01-04 17:17:02,535 - mmcls - INFO - Epoch [17][1200/5005] lr: 1.000e-01, eta: 10:17:01, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4294 2023-01-04 17:17:11,331 - mmcls - INFO - Epoch [17][1300/5005] lr: 1.000e-01, eta: 10:16:52, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4226 2023-01-04 17:17:20,039 - mmcls - INFO - Epoch [17][1400/5005] lr: 1.000e-01, eta: 10:16:43, time: 0.087, data_time: 0.011, memory: 928, loss: 5.4521 2023-01-04 17:17:28,802 - mmcls - INFO - Epoch [17][1500/5005] lr: 1.000e-01, eta: 10:16:34, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4264 2023-01-04 17:17:37,789 - mmcls - INFO - Epoch [17][1600/5005] lr: 1.000e-01, eta: 10:16:26, time: 0.090, data_time: 0.011, memory: 928, loss: 5.4250 2023-01-04 17:17:46,714 - mmcls - INFO - Epoch [17][1700/5005] lr: 1.000e-01, eta: 10:16:17, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4281 2023-01-04 17:17:55,379 - mmcls - INFO - Epoch [17][1800/5005] lr: 1.000e-01, eta: 10:16:08, time: 0.087, data_time: 0.006, memory: 928, loss: 5.4456 2023-01-04 17:18:04,310 - mmcls - INFO - Epoch [17][1900/5005] lr: 1.000e-01, eta: 10:15:59, time: 0.089, data_time: 0.015, memory: 928, loss: 5.4278 2023-01-04 17:18:13,312 - mmcls - INFO - Epoch [17][2000/5005] lr: 1.000e-01, eta: 10:15:51, time: 0.090, data_time: 0.009, memory: 928, loss: 5.4256 2023-01-04 17:18:21,985 - mmcls - INFO - Epoch [17][2100/5005] lr: 1.000e-01, eta: 10:15:42, time: 0.087, data_time: 0.006, memory: 928, loss: 5.4241 2023-01-04 17:18:30,876 - mmcls - INFO - Epoch [17][2200/5005] lr: 1.000e-01, eta: 10:15:33, time: 0.089, data_time: 0.006, memory: 928, loss: 5.4287 2023-01-04 17:18:39,951 - mmcls - INFO - Epoch [17][2300/5005] lr: 1.000e-01, eta: 10:15:26, time: 0.091, data_time: 0.025, memory: 928, loss: 5.4458 2023-01-04 17:18:48,627 - mmcls - INFO - Epoch [17][2400/5005] lr: 1.000e-01, eta: 10:15:16, time: 0.087, data_time: 0.029, memory: 928, loss: 5.4227 2023-01-04 17:18:57,360 - mmcls - INFO - Epoch [17][2500/5005] lr: 1.000e-01, eta: 10:15:07, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4416 2023-01-04 17:19:06,366 - mmcls - INFO - Epoch [17][2600/5005] lr: 1.000e-01, eta: 10:14:59, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4434 2023-01-04 17:19:15,162 - mmcls - INFO - Epoch [17][2700/5005] lr: 1.000e-01, eta: 10:14:50, time: 0.088, data_time: 0.013, memory: 928, loss: 5.4162 2023-01-04 17:19:23,708 - mmcls - INFO - Epoch [17][2800/5005] lr: 1.000e-01, eta: 10:14:39, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4270 2023-01-04 17:19:32,606 - mmcls - INFO - Epoch [17][2900/5005] lr: 1.000e-01, eta: 10:14:31, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4502 2023-01-04 17:19:41,453 - mmcls - INFO - Epoch [17][3000/5005] lr: 1.000e-01, eta: 10:14:22, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4284 2023-01-04 17:19:50,249 - mmcls - INFO - Epoch [17][3100/5005] lr: 1.000e-01, eta: 10:14:13, time: 0.088, data_time: 0.005, memory: 928, loss: 5.4241 2023-01-04 17:19:58,928 - mmcls - INFO - Epoch [17][3200/5005] lr: 1.000e-01, eta: 10:14:04, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4367 2023-01-04 17:20:07,868 - mmcls - INFO - Epoch [17][3300/5005] lr: 1.000e-01, eta: 10:13:55, time: 0.089, data_time: 0.008, memory: 928, loss: 5.4307 2023-01-04 17:20:16,624 - mmcls - INFO - Epoch [17][3400/5005] lr: 1.000e-01, eta: 10:13:46, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4315 2023-01-04 17:20:25,338 - mmcls - INFO - Epoch [17][3500/5005] lr: 1.000e-01, eta: 10:13:37, time: 0.087, data_time: 0.008, memory: 928, loss: 5.4385 2023-01-04 17:20:34,052 - mmcls - INFO - Epoch [17][3600/5005] lr: 1.000e-01, eta: 10:13:27, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4197 2023-01-04 17:20:42,787 - mmcls - INFO - Epoch [17][3700/5005] lr: 1.000e-01, eta: 10:13:18, time: 0.087, data_time: 0.014, memory: 928, loss: 5.4414 2023-01-04 17:20:51,763 - mmcls - INFO - Epoch [17][3800/5005] lr: 1.000e-01, eta: 10:13:10, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4039 2023-01-04 17:21:00,570 - mmcls - INFO - Epoch [17][3900/5005] lr: 1.000e-01, eta: 10:13:01, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4335 2023-01-04 17:21:09,355 - mmcls - INFO - Epoch [17][4000/5005] lr: 1.000e-01, eta: 10:12:52, time: 0.088, data_time: 0.016, memory: 928, loss: 5.4029 2023-01-04 17:21:17,735 - mmcls - INFO - Epoch [17][4100/5005] lr: 1.000e-01, eta: 10:12:41, time: 0.084, data_time: 0.015, memory: 928, loss: 5.4271 2023-01-04 17:21:26,547 - mmcls - INFO - Epoch [17][4200/5005] lr: 1.000e-01, eta: 10:12:32, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4180 2023-01-04 17:21:35,393 - mmcls - INFO - Epoch [17][4300/5005] lr: 1.000e-01, eta: 10:12:23, time: 0.089, data_time: 0.013, memory: 928, loss: 5.4312 2023-01-04 17:21:44,204 - mmcls - INFO - Epoch [17][4400/5005] lr: 1.000e-01, eta: 10:12:14, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4408 2023-01-04 17:21:53,105 - mmcls - INFO - Epoch [17][4500/5005] lr: 1.000e-01, eta: 10:12:06, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4263 2023-01-04 17:22:01,782 - mmcls - INFO - Epoch [17][4600/5005] lr: 1.000e-01, eta: 10:11:56, time: 0.087, data_time: 0.020, memory: 928, loss: 5.4462 2023-01-04 17:22:10,546 - mmcls - INFO - Epoch [17][4700/5005] lr: 1.000e-01, eta: 10:11:47, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4375 2023-01-04 17:22:19,411 - mmcls - INFO - Epoch [17][4800/5005] lr: 1.000e-01, eta: 10:11:38, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4414 2023-01-04 17:22:28,190 - mmcls - INFO - Epoch [17][4900/5005] lr: 1.000e-01, eta: 10:11:29, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4482 2023-01-04 17:22:36,728 - mmcls - INFO - Epoch [17][5000/5005] lr: 1.000e-01, eta: 10:11:19, time: 0.085, data_time: 0.008, memory: 928, loss: 5.4294 2023-01-04 17:22:59,574 - mmcls - INFO - Epoch(val) [17][196] accuracy_top-1: 3.4120, accuracy_top-5: 9.6580 2023-01-04 17:23:10,519 - mmcls - INFO - Epoch [18][100/5005] lr: 1.000e-01, eta: 10:11:18, time: 0.109, data_time: 0.038, memory: 928, loss: 5.4124 2023-01-04 17:23:19,667 - mmcls - INFO - Epoch [18][200/5005] lr: 1.000e-01, eta: 10:11:10, time: 0.091, data_time: 0.022, memory: 928, loss: 5.3969 2023-01-04 17:23:28,242 - mmcls - INFO - Epoch [18][300/5005] lr: 1.000e-01, eta: 10:11:00, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4158 2023-01-04 17:23:36,841 - mmcls - INFO - Epoch [18][400/5005] lr: 1.000e-01, eta: 10:10:50, time: 0.086, data_time: 0.021, memory: 928, loss: 5.4150 2023-01-04 17:23:45,941 - mmcls - INFO - Epoch [18][500/5005] lr: 1.000e-01, eta: 10:10:43, time: 0.091, data_time: 0.008, memory: 928, loss: 5.4051 2023-01-04 17:23:54,419 - mmcls - INFO - Epoch [18][600/5005] lr: 1.000e-01, eta: 10:10:32, time: 0.085, data_time: 0.012, memory: 928, loss: 5.4142 2023-01-04 17:24:03,244 - mmcls - INFO - Epoch [18][700/5005] lr: 1.000e-01, eta: 10:10:23, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4255 2023-01-04 17:24:12,262 - mmcls - INFO - Epoch [18][800/5005] lr: 1.000e-01, eta: 10:10:15, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4414 2023-01-04 17:24:20,864 - mmcls - INFO - Epoch [18][900/5005] lr: 1.000e-01, eta: 10:10:06, time: 0.086, data_time: 0.036, memory: 928, loss: 5.4127 2023-01-04 17:24:29,512 - mmcls - INFO - Epoch [18][1000/5005] lr: 1.000e-01, eta: 10:09:56, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4210 2023-01-04 17:24:38,336 - mmcls - INFO - Epoch [18][1100/5005] lr: 1.000e-01, eta: 10:09:47, time: 0.088, data_time: 0.023, memory: 928, loss: 5.4287 2023-01-04 17:24:46,906 - mmcls - INFO - Epoch [18][1200/5005] lr: 1.000e-01, eta: 10:09:37, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4071 2023-01-04 17:24:55,634 - mmcls - INFO - Epoch [18][1300/5005] lr: 1.000e-01, eta: 10:09:28, time: 0.087, data_time: 0.036, memory: 928, loss: 5.4378 2023-01-04 17:25:04,347 - mmcls - INFO - Epoch [18][1400/5005] lr: 1.000e-01, eta: 10:09:18, time: 0.087, data_time: 0.011, memory: 928, loss: 5.4100 2023-01-04 17:25:12,861 - mmcls - INFO - Epoch [18][1500/5005] lr: 1.000e-01, eta: 10:09:08, time: 0.085, data_time: 0.033, memory: 928, loss: 5.4105 2023-01-04 17:25:21,846 - mmcls - INFO - Epoch [18][1600/5005] lr: 1.000e-01, eta: 10:09:00, time: 0.090, data_time: 0.011, memory: 928, loss: 5.4160 2023-01-04 17:25:30,364 - mmcls - INFO - Epoch [18][1700/5005] lr: 1.000e-01, eta: 10:08:49, time: 0.085, data_time: 0.022, memory: 928, loss: 5.4088 2023-01-04 17:25:39,259 - mmcls - INFO - Epoch [18][1800/5005] lr: 1.000e-01, eta: 10:08:41, time: 0.089, data_time: 0.010, memory: 928, loss: 5.4308 2023-01-04 17:25:47,920 - mmcls - INFO - Epoch [18][1900/5005] lr: 1.000e-01, eta: 10:08:31, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4054 2023-01-04 17:25:57,075 - mmcls - INFO - Epoch [18][2000/5005] lr: 1.000e-01, eta: 10:08:24, time: 0.092, data_time: 0.001, memory: 928, loss: 5.4284 2023-01-04 17:26:05,558 - mmcls - INFO - Epoch [18][2100/5005] lr: 1.000e-01, eta: 10:08:13, time: 0.085, data_time: 0.001, memory: 928, loss: 5.3968 2023-01-04 17:26:14,512 - mmcls - INFO - Epoch [18][2200/5005] lr: 1.000e-01, eta: 10:08:05, time: 0.090, data_time: 0.006, memory: 928, loss: 5.4295 2023-01-04 17:26:23,257 - mmcls - INFO - Epoch [18][2300/5005] lr: 1.000e-01, eta: 10:07:56, time: 0.087, data_time: 0.018, memory: 928, loss: 5.4210 2023-01-04 17:26:32,135 - mmcls - INFO - Epoch [18][2400/5005] lr: 1.000e-01, eta: 10:07:47, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4334 2023-01-04 17:26:40,785 - mmcls - INFO - Epoch [18][2500/5005] lr: 1.000e-01, eta: 10:07:38, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4201 2023-01-04 17:26:49,646 - mmcls - INFO - Epoch [18][2600/5005] lr: 1.000e-01, eta: 10:07:29, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4212 2023-01-04 17:26:58,427 - mmcls - INFO - Epoch [18][2700/5005] lr: 1.000e-01, eta: 10:07:20, time: 0.088, data_time: 0.022, memory: 928, loss: 5.4318 2023-01-04 17:27:06,935 - mmcls - INFO - Epoch [18][2800/5005] lr: 1.000e-01, eta: 10:07:10, time: 0.085, data_time: 0.006, memory: 928, loss: 5.4353 2023-01-04 17:27:15,637 - mmcls - INFO - Epoch [18][2900/5005] lr: 1.000e-01, eta: 10:07:00, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4306 2023-01-04 17:27:24,262 - mmcls - INFO - Epoch [18][3000/5005] lr: 1.000e-01, eta: 10:06:50, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4131 2023-01-04 17:27:32,889 - mmcls - INFO - Epoch [18][3100/5005] lr: 1.000e-01, eta: 10:06:41, time: 0.086, data_time: 0.010, memory: 928, loss: 5.4190 2023-01-04 17:27:41,519 - mmcls - INFO - Epoch [18][3200/5005] lr: 1.000e-01, eta: 10:06:31, time: 0.086, data_time: 0.015, memory: 928, loss: 5.4493 2023-01-04 17:27:50,389 - mmcls - INFO - Epoch [18][3300/5005] lr: 1.000e-01, eta: 10:06:22, time: 0.089, data_time: 0.018, memory: 928, loss: 5.4342 2023-01-04 17:27:59,000 - mmcls - INFO - Epoch [18][3400/5005] lr: 1.000e-01, eta: 10:06:12, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4256 2023-01-04 17:28:07,392 - mmcls - INFO - Epoch [18][3500/5005] lr: 1.000e-01, eta: 10:06:01, time: 0.084, data_time: 0.005, memory: 928, loss: 5.4051 2023-01-04 17:28:16,045 - mmcls - INFO - Epoch [18][3600/5005] lr: 1.000e-01, eta: 10:05:52, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4255 2023-01-04 17:28:25,111 - mmcls - INFO - Epoch [18][3700/5005] lr: 1.000e-01, eta: 10:05:44, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4245 2023-01-04 17:28:34,046 - mmcls - INFO - Epoch [18][3800/5005] lr: 1.000e-01, eta: 10:05:36, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4247 2023-01-04 17:28:43,009 - mmcls - INFO - Epoch [18][3900/5005] lr: 1.000e-01, eta: 10:05:28, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4333 2023-01-04 17:28:51,991 - mmcls - INFO - Epoch [18][4000/5005] lr: 1.000e-01, eta: 10:05:19, time: 0.090, data_time: 0.018, memory: 928, loss: 5.4313 2023-01-04 17:29:00,632 - mmcls - INFO - Epoch [18][4100/5005] lr: 1.000e-01, eta: 10:05:10, time: 0.086, data_time: 0.010, memory: 928, loss: 5.4383 2023-01-04 17:29:09,262 - mmcls - INFO - Epoch [18][4200/5005] lr: 1.000e-01, eta: 10:05:00, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4485 2023-01-04 17:29:18,276 - mmcls - INFO - Epoch [18][4300/5005] lr: 1.000e-01, eta: 10:04:52, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4252 2023-01-04 17:29:27,208 - mmcls - INFO - Epoch [18][4400/5005] lr: 1.000e-01, eta: 10:04:44, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4192 2023-01-04 17:29:35,759 - mmcls - INFO - Epoch [18][4500/5005] lr: 1.000e-01, eta: 10:04:34, time: 0.086, data_time: 0.009, memory: 928, loss: 5.4252 2023-01-04 17:29:44,635 - mmcls - INFO - Epoch [18][4600/5005] lr: 1.000e-01, eta: 10:04:25, time: 0.089, data_time: 0.005, memory: 928, loss: 5.4164 2023-01-04 17:29:53,188 - mmcls - INFO - Epoch [18][4700/5005] lr: 1.000e-01, eta: 10:04:15, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4199 2023-01-04 17:30:01,812 - mmcls - INFO - Epoch [18][4800/5005] lr: 1.000e-01, eta: 10:04:05, time: 0.086, data_time: 0.009, memory: 928, loss: 5.4470 2023-01-04 17:30:10,590 - mmcls - INFO - Epoch [18][4900/5005] lr: 1.000e-01, eta: 10:03:56, time: 0.088, data_time: 0.021, memory: 928, loss: 5.4553 2023-01-04 17:30:19,330 - mmcls - INFO - Epoch [18][5000/5005] lr: 1.000e-01, eta: 10:03:47, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4135 2023-01-04 17:30:42,329 - mmcls - INFO - Epoch(val) [18][196] accuracy_top-1: 4.3120, accuracy_top-5: 11.6280 2023-01-04 17:30:53,313 - mmcls - INFO - Epoch [19][100/5005] lr: 1.000e-01, eta: 10:03:45, time: 0.110, data_time: 0.031, memory: 928, loss: 5.3949 2023-01-04 17:31:01,944 - mmcls - INFO - Epoch [19][200/5005] lr: 1.000e-01, eta: 10:03:36, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4001 2023-01-04 17:31:10,793 - mmcls - INFO - Epoch [19][300/5005] lr: 1.000e-01, eta: 10:03:27, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4193 2023-01-04 17:31:19,667 - mmcls - INFO - Epoch [19][400/5005] lr: 1.000e-01, eta: 10:03:18, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4204 2023-01-04 17:31:28,225 - mmcls - INFO - Epoch [19][500/5005] lr: 1.000e-01, eta: 10:03:08, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4272 2023-01-04 17:31:37,170 - mmcls - INFO - Epoch [19][600/5005] lr: 1.000e-01, eta: 10:03:00, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4313 2023-01-04 17:31:45,789 - mmcls - INFO - Epoch [19][700/5005] lr: 1.000e-01, eta: 10:02:50, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4097 2023-01-04 17:31:54,757 - mmcls - INFO - Epoch [19][800/5005] lr: 1.000e-01, eta: 10:02:42, time: 0.090, data_time: 0.009, memory: 928, loss: 5.4305 2023-01-04 17:32:03,614 - mmcls - INFO - Epoch [19][900/5005] lr: 1.000e-01, eta: 10:02:33, time: 0.089, data_time: 0.010, memory: 928, loss: 5.4199 2023-01-04 17:32:12,776 - mmcls - INFO - Epoch [19][1000/5005] lr: 1.000e-01, eta: 10:02:26, time: 0.092, data_time: 0.003, memory: 928, loss: 5.4160 2023-01-04 17:32:21,424 - mmcls - INFO - Epoch [19][1100/5005] lr: 1.000e-01, eta: 10:02:16, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4304 2023-01-04 17:32:30,193 - mmcls - INFO - Epoch [19][1200/5005] lr: 1.000e-01, eta: 10:02:07, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4251 2023-01-04 17:32:38,857 - mmcls - INFO - Epoch [19][1300/5005] lr: 1.000e-01, eta: 10:01:58, time: 0.087, data_time: 0.012, memory: 928, loss: 5.4202 2023-01-04 17:32:47,822 - mmcls - INFO - Epoch [19][1400/5005] lr: 1.000e-01, eta: 10:01:49, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4049 2023-01-04 17:32:56,587 - mmcls - INFO - Epoch [19][1500/5005] lr: 1.000e-01, eta: 10:01:40, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4344 2023-01-04 17:33:05,238 - mmcls - INFO - Epoch [19][1600/5005] lr: 1.000e-01, eta: 10:01:31, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4315 2023-01-04 17:33:14,521 - mmcls - INFO - Epoch [19][1700/5005] lr: 1.000e-01, eta: 10:01:24, time: 0.093, data_time: 0.002, memory: 928, loss: 5.4169 2023-01-04 17:33:23,214 - mmcls - INFO - Epoch [19][1800/5005] lr: 1.000e-01, eta: 10:01:14, time: 0.087, data_time: 0.023, memory: 928, loss: 5.3944 2023-01-04 17:33:31,807 - mmcls - INFO - Epoch [19][1900/5005] lr: 1.000e-01, eta: 10:01:04, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4096 2023-01-04 17:33:40,499 - mmcls - INFO - Epoch [19][2000/5005] lr: 1.000e-01, eta: 10:00:55, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4061 2023-01-04 17:33:49,185 - mmcls - INFO - Epoch [19][2100/5005] lr: 1.000e-01, eta: 10:00:46, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4263 2023-01-04 17:33:57,827 - mmcls - INFO - Epoch [19][2200/5005] lr: 1.000e-01, eta: 10:00:36, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4136 2023-01-04 17:34:06,758 - mmcls - INFO - Epoch [19][2300/5005] lr: 1.000e-01, eta: 10:00:27, time: 0.089, data_time: 0.012, memory: 928, loss: 5.4207 2023-01-04 17:34:15,362 - mmcls - INFO - Epoch [19][2400/5005] lr: 1.000e-01, eta: 10:00:18, time: 0.086, data_time: 0.013, memory: 928, loss: 5.4384 2023-01-04 17:34:23,960 - mmcls - INFO - Epoch [19][2500/5005] lr: 1.000e-01, eta: 10:00:08, time: 0.086, data_time: 0.026, memory: 928, loss: 5.4159 2023-01-04 17:34:32,622 - mmcls - INFO - Epoch [19][2600/5005] lr: 1.000e-01, eta: 9:59:58, time: 0.087, data_time: 0.017, memory: 928, loss: 5.4043 2023-01-04 17:34:41,552 - mmcls - INFO - Epoch [19][2700/5005] lr: 1.000e-01, eta: 9:59:50, time: 0.089, data_time: 0.008, memory: 928, loss: 5.4216 2023-01-04 17:34:50,605 - mmcls - INFO - Epoch [19][2800/5005] lr: 1.000e-01, eta: 9:59:42, time: 0.091, data_time: 0.006, memory: 928, loss: 5.4033 2023-01-04 17:34:59,742 - mmcls - INFO - Epoch [19][2900/5005] lr: 1.000e-01, eta: 9:59:35, time: 0.091, data_time: 0.005, memory: 928, loss: 5.4064 2023-01-04 17:35:08,355 - mmcls - INFO - Epoch [19][3000/5005] lr: 1.000e-01, eta: 9:59:25, time: 0.086, data_time: 0.013, memory: 928, loss: 5.4340 2023-01-04 17:35:17,051 - mmcls - INFO - Epoch [19][3100/5005] lr: 1.000e-01, eta: 9:59:15, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4124 2023-01-04 17:35:25,873 - mmcls - INFO - Epoch [19][3200/5005] lr: 1.000e-01, eta: 9:59:07, time: 0.088, data_time: 0.021, memory: 928, loss: 5.4230 2023-01-04 17:35:34,506 - mmcls - INFO - Epoch [19][3300/5005] lr: 1.000e-01, eta: 9:58:57, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4108 2023-01-04 17:35:43,360 - mmcls - INFO - Epoch [19][3400/5005] lr: 1.000e-01, eta: 9:58:48, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4201 2023-01-04 17:35:52,201 - mmcls - INFO - Epoch [19][3500/5005] lr: 1.000e-01, eta: 9:58:39, time: 0.089, data_time: 0.040, memory: 928, loss: 5.4349 2023-01-04 17:36:00,896 - mmcls - INFO - Epoch [19][3600/5005] lr: 1.000e-01, eta: 9:58:30, time: 0.087, data_time: 0.021, memory: 928, loss: 5.4094 2023-01-04 17:36:10,074 - mmcls - INFO - Epoch [19][3700/5005] lr: 1.000e-01, eta: 9:58:23, time: 0.092, data_time: 0.001, memory: 928, loss: 5.4115 2023-01-04 17:36:18,793 - mmcls - INFO - Epoch [19][3800/5005] lr: 1.000e-01, eta: 9:58:13, time: 0.087, data_time: 0.027, memory: 928, loss: 5.4314 2023-01-04 17:36:27,383 - mmcls - INFO - Epoch [19][3900/5005] lr: 1.000e-01, eta: 9:58:03, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3906 2023-01-04 17:36:36,213 - mmcls - INFO - Epoch [19][4000/5005] lr: 1.000e-01, eta: 9:57:55, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4318 2023-01-04 17:36:44,791 - mmcls - INFO - Epoch [19][4100/5005] lr: 1.000e-01, eta: 9:57:45, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4027 2023-01-04 17:36:53,395 - mmcls - INFO - Epoch [19][4200/5005] lr: 1.000e-01, eta: 9:57:35, time: 0.086, data_time: 0.041, memory: 928, loss: 5.4209 2023-01-04 17:37:02,393 - mmcls - INFO - Epoch [19][4300/5005] lr: 1.000e-01, eta: 9:57:27, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4200 2023-01-04 17:37:11,381 - mmcls - INFO - Epoch [19][4400/5005] lr: 1.000e-01, eta: 9:57:19, time: 0.090, data_time: 0.014, memory: 928, loss: 5.4052 2023-01-04 17:37:19,920 - mmcls - INFO - Epoch [19][4500/5005] lr: 1.000e-01, eta: 9:57:09, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4307 2023-01-04 17:37:28,597 - mmcls - INFO - Epoch [19][4600/5005] lr: 1.000e-01, eta: 9:56:59, time: 0.087, data_time: 0.014, memory: 928, loss: 5.4190 2023-01-04 17:37:37,314 - mmcls - INFO - Epoch [19][4700/5005] lr: 1.000e-01, eta: 9:56:50, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4248 2023-01-04 17:37:46,173 - mmcls - INFO - Epoch [19][4800/5005] lr: 1.000e-01, eta: 9:56:41, time: 0.088, data_time: 0.003, memory: 928, loss: 5.3972 2023-01-04 17:37:55,151 - mmcls - INFO - Epoch [19][4900/5005] lr: 1.000e-01, eta: 9:56:33, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3882 2023-01-04 17:38:03,975 - mmcls - INFO - Epoch [19][5000/5005] lr: 1.000e-01, eta: 9:56:24, time: 0.088, data_time: 0.034, memory: 928, loss: 5.4117 2023-01-04 17:38:26,697 - mmcls - INFO - Epoch(val) [19][196] accuracy_top-1: 4.0220, accuracy_top-5: 11.6040 2023-01-04 17:38:38,004 - mmcls - INFO - Epoch [20][100/5005] lr: 1.000e-01, eta: 9:56:24, time: 0.113, data_time: 0.054, memory: 928, loss: 5.3870 2023-01-04 17:38:47,035 - mmcls - INFO - Epoch [20][200/5005] lr: 1.000e-01, eta: 9:56:16, time: 0.090, data_time: 0.010, memory: 928, loss: 5.4100 2023-01-04 17:38:55,780 - mmcls - INFO - Epoch [20][300/5005] lr: 1.000e-01, eta: 9:56:06, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4041 2023-01-04 17:39:04,999 - mmcls - INFO - Epoch [20][400/5005] lr: 1.000e-01, eta: 9:55:59, time: 0.092, data_time: 0.002, memory: 928, loss: 5.4055 2023-01-04 17:39:13,935 - mmcls - INFO - Epoch [20][500/5005] lr: 1.000e-01, eta: 9:55:51, time: 0.089, data_time: 0.024, memory: 928, loss: 5.4023 2023-01-04 17:39:22,731 - mmcls - INFO - Epoch [20][600/5005] lr: 1.000e-01, eta: 9:55:42, time: 0.088, data_time: 0.016, memory: 928, loss: 5.4158 2023-01-04 17:39:31,511 - mmcls - INFO - Epoch [20][700/5005] lr: 1.000e-01, eta: 9:55:33, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4027 2023-01-04 17:39:40,158 - mmcls - INFO - Epoch [20][800/5005] lr: 1.000e-01, eta: 9:55:23, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4122 2023-01-04 17:39:48,880 - mmcls - INFO - Epoch [20][900/5005] lr: 1.000e-01, eta: 9:55:14, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4438 2023-01-04 17:39:57,800 - mmcls - INFO - Epoch [20][1000/5005] lr: 1.000e-01, eta: 9:55:05, time: 0.089, data_time: 0.004, memory: 928, loss: 5.4002 2023-01-04 17:40:06,498 - mmcls - INFO - Epoch [20][1100/5005] lr: 1.000e-01, eta: 9:54:56, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4198 2023-01-04 17:40:15,206 - mmcls - INFO - Epoch [20][1200/5005] lr: 1.000e-01, eta: 9:54:47, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4035 2023-01-04 17:40:23,817 - mmcls - INFO - Epoch [20][1300/5005] lr: 1.000e-01, eta: 9:54:37, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4313 2023-01-04 17:40:32,612 - mmcls - INFO - Epoch [20][1400/5005] lr: 1.000e-01, eta: 9:54:28, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3972 2023-01-04 17:40:41,684 - mmcls - INFO - Epoch [20][1500/5005] lr: 1.000e-01, eta: 9:54:20, time: 0.091, data_time: 0.002, memory: 928, loss: 5.3954 2023-01-04 17:40:50,469 - mmcls - INFO - Epoch [20][1600/5005] lr: 1.000e-01, eta: 9:54:11, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4200 2023-01-04 17:40:59,003 - mmcls - INFO - Epoch [20][1700/5005] lr: 1.000e-01, eta: 9:54:01, time: 0.085, data_time: 0.007, memory: 928, loss: 5.4062 2023-01-04 17:41:07,751 - mmcls - INFO - Epoch [20][1800/5005] lr: 1.000e-01, eta: 9:53:52, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4109 2023-01-04 17:41:16,293 - mmcls - INFO - Epoch [20][1900/5005] lr: 1.000e-01, eta: 9:53:42, time: 0.085, data_time: 0.002, memory: 928, loss: 5.3977 2023-01-04 17:41:25,075 - mmcls - INFO - Epoch [20][2000/5005] lr: 1.000e-01, eta: 9:53:33, time: 0.088, data_time: 0.020, memory: 928, loss: 5.4140 2023-01-04 17:41:33,690 - mmcls - INFO - Epoch [20][2100/5005] lr: 1.000e-01, eta: 9:53:23, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4007 2023-01-04 17:41:42,326 - mmcls - INFO - Epoch [20][2200/5005] lr: 1.000e-01, eta: 9:53:14, time: 0.086, data_time: 0.012, memory: 928, loss: 5.4019 2023-01-04 17:41:50,888 - mmcls - INFO - Epoch [20][2300/5005] lr: 1.000e-01, eta: 9:53:04, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4123 2023-01-04 17:41:59,723 - mmcls - INFO - Epoch [20][2400/5005] lr: 1.000e-01, eta: 9:52:55, time: 0.088, data_time: 0.008, memory: 928, loss: 5.4128 2023-01-04 17:42:08,342 - mmcls - INFO - Epoch [20][2500/5005] lr: 1.000e-01, eta: 9:52:45, time: 0.086, data_time: 0.006, memory: 928, loss: 5.4208 2023-01-04 17:42:17,130 - mmcls - INFO - Epoch [20][2600/5005] lr: 1.000e-01, eta: 9:52:36, time: 0.088, data_time: 0.010, memory: 928, loss: 5.3847 2023-01-04 17:42:25,829 - mmcls - INFO - Epoch [20][2700/5005] lr: 1.000e-01, eta: 9:52:27, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4087 2023-01-04 17:42:34,470 - mmcls - INFO - Epoch [20][2800/5005] lr: 1.000e-01, eta: 9:52:17, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3991 2023-01-04 17:42:43,374 - mmcls - INFO - Epoch [20][2900/5005] lr: 1.000e-01, eta: 9:52:09, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4102 2023-01-04 17:42:52,007 - mmcls - INFO - Epoch [20][3000/5005] lr: 1.000e-01, eta: 9:51:59, time: 0.086, data_time: 0.003, memory: 928, loss: 5.4080 2023-01-04 17:43:00,598 - mmcls - INFO - Epoch [20][3100/5005] lr: 1.000e-01, eta: 9:51:49, time: 0.086, data_time: 0.008, memory: 928, loss: 5.3994 2023-01-04 17:43:09,006 - mmcls - INFO - Epoch [20][3200/5005] lr: 1.000e-01, eta: 9:51:39, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4201 2023-01-04 17:43:17,815 - mmcls - INFO - Epoch [20][3300/5005] lr: 1.000e-01, eta: 9:51:30, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4153 2023-01-04 17:43:26,257 - mmcls - INFO - Epoch [20][3400/5005] lr: 1.000e-01, eta: 9:51:19, time: 0.084, data_time: 0.009, memory: 928, loss: 5.4277 2023-01-04 17:43:35,285 - mmcls - INFO - Epoch [20][3500/5005] lr: 1.000e-01, eta: 9:51:11, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4269 2023-01-04 17:43:43,963 - mmcls - INFO - Epoch [20][3600/5005] lr: 1.000e-01, eta: 9:51:02, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4038 2023-01-04 17:43:52,927 - mmcls - INFO - Epoch [20][3700/5005] lr: 1.000e-01, eta: 9:50:54, time: 0.090, data_time: 0.044, memory: 928, loss: 5.4139 2023-01-04 17:44:01,591 - mmcls - INFO - Epoch [20][3800/5005] lr: 1.000e-01, eta: 9:50:44, time: 0.087, data_time: 0.050, memory: 928, loss: 5.4279 2023-01-04 17:44:10,254 - mmcls - INFO - Epoch [20][3900/5005] lr: 1.000e-01, eta: 9:50:35, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4065 2023-01-04 17:44:19,263 - mmcls - INFO - Epoch [20][4000/5005] lr: 1.000e-01, eta: 9:50:27, time: 0.090, data_time: 0.002, memory: 928, loss: 5.3880 2023-01-04 17:44:27,926 - mmcls - INFO - Epoch [20][4100/5005] lr: 1.000e-01, eta: 9:50:17, time: 0.087, data_time: 0.005, memory: 928, loss: 5.4228 2023-01-04 17:44:36,760 - mmcls - INFO - Epoch [20][4200/5005] lr: 1.000e-01, eta: 9:50:08, time: 0.088, data_time: 0.029, memory: 928, loss: 5.3988 2023-01-04 17:44:45,573 - mmcls - INFO - Epoch [20][4300/5005] lr: 1.000e-01, eta: 9:49:59, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4199 2023-01-04 17:44:54,344 - mmcls - INFO - Epoch [20][4400/5005] lr: 1.000e-01, eta: 9:49:50, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4062 2023-01-04 17:45:03,390 - mmcls - INFO - Epoch [20][4500/5005] lr: 1.000e-01, eta: 9:49:42, time: 0.090, data_time: 0.006, memory: 928, loss: 5.3964 2023-01-04 17:45:11,882 - mmcls - INFO - Epoch [20][4600/5005] lr: 1.000e-01, eta: 9:49:32, time: 0.085, data_time: 0.008, memory: 928, loss: 5.4383 2023-01-04 17:45:20,507 - mmcls - INFO - Epoch [20][4700/5005] lr: 1.000e-01, eta: 9:49:23, time: 0.086, data_time: 0.024, memory: 928, loss: 5.3867 2023-01-04 17:45:29,754 - mmcls - INFO - Epoch [20][4800/5005] lr: 1.000e-01, eta: 9:49:16, time: 0.092, data_time: 0.041, memory: 928, loss: 5.3925 2023-01-04 17:45:38,389 - mmcls - INFO - Epoch [20][4900/5005] lr: 1.000e-01, eta: 9:49:06, time: 0.086, data_time: 0.002, memory: 928, loss: 5.4118 2023-01-04 17:45:46,936 - mmcls - INFO - Epoch [20][5000/5005] lr: 1.000e-01, eta: 9:48:56, time: 0.085, data_time: 0.014, memory: 928, loss: 5.4140 2023-01-04 17:46:09,752 - mmcls - INFO - Epoch(val) [20][196] accuracy_top-1: 4.1680, accuracy_top-5: 11.5700 2023-01-04 17:46:20,951 - mmcls - INFO - Epoch [21][100/5005] lr: 1.000e-01, eta: 9:48:54, time: 0.112, data_time: 0.050, memory: 928, loss: 5.4228 2023-01-04 17:46:29,981 - mmcls - INFO - Epoch [21][200/5005] lr: 1.000e-01, eta: 9:48:46, time: 0.090, data_time: 0.004, memory: 928, loss: 5.4097 2023-01-04 17:46:38,702 - mmcls - INFO - Epoch [21][300/5005] lr: 1.000e-01, eta: 9:48:37, time: 0.087, data_time: 0.013, memory: 928, loss: 5.4139 2023-01-04 17:46:47,326 - mmcls - INFO - Epoch [21][400/5005] lr: 1.000e-01, eta: 9:48:27, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3856 2023-01-04 17:46:55,978 - mmcls - INFO - Epoch [21][500/5005] lr: 1.000e-01, eta: 9:48:18, time: 0.086, data_time: 0.017, memory: 928, loss: 5.4133 2023-01-04 17:47:04,454 - mmcls - INFO - Epoch [21][600/5005] lr: 1.000e-01, eta: 9:48:08, time: 0.085, data_time: 0.011, memory: 928, loss: 5.3976 2023-01-04 17:47:13,354 - mmcls - INFO - Epoch [21][700/5005] lr: 1.000e-01, eta: 9:47:59, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3748 2023-01-04 17:47:22,260 - mmcls - INFO - Epoch [21][800/5005] lr: 1.000e-01, eta: 9:47:51, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4011 2023-01-04 17:47:30,872 - mmcls - INFO - Epoch [21][900/5005] lr: 1.000e-01, eta: 9:47:41, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4020 2023-01-04 17:47:39,649 - mmcls - INFO - Epoch [21][1000/5005] lr: 1.000e-01, eta: 9:47:32, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3863 2023-01-04 17:47:48,329 - mmcls - INFO - Epoch [21][1100/5005] lr: 1.000e-01, eta: 9:47:23, time: 0.087, data_time: 0.015, memory: 928, loss: 5.4044 2023-01-04 17:47:56,851 - mmcls - INFO - Epoch [21][1200/5005] lr: 1.000e-01, eta: 9:47:12, time: 0.085, data_time: 0.026, memory: 928, loss: 5.4016 2023-01-04 17:48:05,755 - mmcls - INFO - Epoch [21][1300/5005] lr: 1.000e-01, eta: 9:47:04, time: 0.089, data_time: 0.011, memory: 928, loss: 5.3953 2023-01-04 17:48:14,482 - mmcls - INFO - Epoch [21][1400/5005] lr: 1.000e-01, eta: 9:46:55, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3955 2023-01-04 17:48:23,185 - mmcls - INFO - Epoch [21][1500/5005] lr: 1.000e-01, eta: 9:46:45, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4069 2023-01-04 17:48:32,530 - mmcls - INFO - Epoch [21][1600/5005] lr: 1.000e-01, eta: 9:46:39, time: 0.093, data_time: 0.019, memory: 928, loss: 5.3945 2023-01-04 17:48:41,349 - mmcls - INFO - Epoch [21][1700/5005] lr: 1.000e-01, eta: 9:46:30, time: 0.088, data_time: 0.015, memory: 928, loss: 5.4015 2023-01-04 17:48:50,173 - mmcls - INFO - Epoch [21][1800/5005] lr: 1.000e-01, eta: 9:46:21, time: 0.088, data_time: 0.011, memory: 928, loss: 5.4094 2023-01-04 17:48:59,237 - mmcls - INFO - Epoch [21][1900/5005] lr: 1.000e-01, eta: 9:46:13, time: 0.091, data_time: 0.020, memory: 928, loss: 5.3913 2023-01-04 17:49:08,001 - mmcls - INFO - Epoch [21][2000/5005] lr: 1.000e-01, eta: 9:46:04, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4035 2023-01-04 17:49:17,335 - mmcls - INFO - Epoch [21][2100/5005] lr: 1.000e-01, eta: 9:45:57, time: 0.093, data_time: 0.001, memory: 928, loss: 5.4191 2023-01-04 17:49:26,340 - mmcls - INFO - Epoch [21][2200/5005] lr: 1.000e-01, eta: 9:45:49, time: 0.090, data_time: 0.007, memory: 928, loss: 5.4136 2023-01-04 17:49:35,377 - mmcls - INFO - Epoch [21][2300/5005] lr: 1.000e-01, eta: 9:45:41, time: 0.090, data_time: 0.005, memory: 928, loss: 5.4130 2023-01-04 17:49:44,131 - mmcls - INFO - Epoch [21][2400/5005] lr: 1.000e-01, eta: 9:45:32, time: 0.088, data_time: 0.012, memory: 928, loss: 5.4162 2023-01-04 17:49:53,204 - mmcls - INFO - Epoch [21][2500/5005] lr: 1.000e-01, eta: 9:45:24, time: 0.091, data_time: 0.001, memory: 928, loss: 5.4239 2023-01-04 17:50:02,067 - mmcls - INFO - Epoch [21][2600/5005] lr: 1.000e-01, eta: 9:45:15, time: 0.089, data_time: 0.027, memory: 928, loss: 5.4158 2023-01-04 17:50:10,939 - mmcls - INFO - Epoch [21][2700/5005] lr: 1.000e-01, eta: 9:45:07, time: 0.089, data_time: 0.003, memory: 928, loss: 5.4082 2023-01-04 17:50:19,818 - mmcls - INFO - Epoch [21][2800/5005] lr: 1.000e-01, eta: 9:44:58, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3946 2023-01-04 17:50:28,606 - mmcls - INFO - Epoch [21][2900/5005] lr: 1.000e-01, eta: 9:44:49, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4215 2023-01-04 17:50:37,279 - mmcls - INFO - Epoch [21][3000/5005] lr: 1.000e-01, eta: 9:44:40, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4123 2023-01-04 17:50:45,971 - mmcls - INFO - Epoch [21][3100/5005] lr: 1.000e-01, eta: 9:44:30, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4164 2023-01-04 17:50:54,734 - mmcls - INFO - Epoch [21][3200/5005] lr: 1.000e-01, eta: 9:44:21, time: 0.088, data_time: 0.001, memory: 928, loss: 5.4097 2023-01-04 17:51:03,498 - mmcls - INFO - Epoch [21][3300/5005] lr: 1.000e-01, eta: 9:44:12, time: 0.088, data_time: 0.037, memory: 928, loss: 5.3949 2023-01-04 17:51:12,542 - mmcls - INFO - Epoch [21][3400/5005] lr: 1.000e-01, eta: 9:44:04, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4138 2023-01-04 17:51:21,298 - mmcls - INFO - Epoch [21][3500/5005] lr: 1.000e-01, eta: 9:43:55, time: 0.088, data_time: 0.006, memory: 928, loss: 5.4118 2023-01-04 17:51:30,121 - mmcls - INFO - Epoch [21][3600/5005] lr: 1.000e-01, eta: 9:43:46, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4264 2023-01-04 17:51:39,094 - mmcls - INFO - Epoch [21][3700/5005] lr: 1.000e-01, eta: 9:43:38, time: 0.090, data_time: 0.002, memory: 928, loss: 5.3909 2023-01-04 17:51:47,501 - mmcls - INFO - Epoch [21][3800/5005] lr: 1.000e-01, eta: 9:43:27, time: 0.084, data_time: 0.012, memory: 928, loss: 5.4037 2023-01-04 17:51:56,163 - mmcls - INFO - Epoch [21][3900/5005] lr: 1.000e-01, eta: 9:43:18, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4069 2023-01-04 17:52:04,924 - mmcls - INFO - Epoch [21][4000/5005] lr: 1.000e-01, eta: 9:43:09, time: 0.088, data_time: 0.002, memory: 928, loss: 5.4004 2023-01-04 17:52:13,865 - mmcls - INFO - Epoch [21][4100/5005] lr: 1.000e-01, eta: 9:43:01, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4005 2023-01-04 17:52:22,497 - mmcls - INFO - Epoch [21][4200/5005] lr: 1.000e-01, eta: 9:42:51, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3988 2023-01-04 17:52:31,912 - mmcls - INFO - Epoch [21][4300/5005] lr: 1.000e-01, eta: 9:42:44, time: 0.094, data_time: 0.011, memory: 928, loss: 5.3853 2023-01-04 17:52:40,603 - mmcls - INFO - Epoch [21][4400/5005] lr: 1.000e-01, eta: 9:42:35, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4272 2023-01-04 17:52:49,518 - mmcls - INFO - Epoch [21][4500/5005] lr: 1.000e-01, eta: 9:42:26, time: 0.089, data_time: 0.016, memory: 928, loss: 5.4047 2023-01-04 17:52:58,082 - mmcls - INFO - Epoch [21][4600/5005] lr: 1.000e-01, eta: 9:42:17, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4148 2023-01-04 17:53:06,654 - mmcls - INFO - Epoch [21][4700/5005] lr: 1.000e-01, eta: 9:42:07, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4008 2023-01-04 17:53:15,344 - mmcls - INFO - Epoch [21][4800/5005] lr: 1.000e-01, eta: 9:41:57, time: 0.087, data_time: 0.003, memory: 928, loss: 5.4011 2023-01-04 17:53:24,079 - mmcls - INFO - Epoch [21][4900/5005] lr: 1.000e-01, eta: 9:41:48, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4003 2023-01-04 17:53:32,987 - mmcls - INFO - Epoch [21][5000/5005] lr: 1.000e-01, eta: 9:41:40, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3946 2023-01-04 17:53:56,002 - mmcls - INFO - Epoch(val) [21][196] accuracy_top-1: 4.7680, accuracy_top-5: 13.3460 2023-01-04 17:54:06,819 - mmcls - INFO - Epoch [22][100/5005] lr: 1.000e-01, eta: 9:41:36, time: 0.108, data_time: 0.060, memory: 928, loss: 5.3842 2023-01-04 17:54:15,761 - mmcls - INFO - Epoch [22][200/5005] lr: 1.000e-01, eta: 9:41:28, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3735 2023-01-04 17:54:24,507 - mmcls - INFO - Epoch [22][300/5005] lr: 1.000e-01, eta: 9:41:19, time: 0.087, data_time: 0.020, memory: 928, loss: 5.4167 2023-01-04 17:54:33,101 - mmcls - INFO - Epoch [22][400/5005] lr: 1.000e-01, eta: 9:41:09, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4007 2023-01-04 17:54:41,952 - mmcls - INFO - Epoch [22][500/5005] lr: 1.000e-01, eta: 9:41:00, time: 0.088, data_time: 0.003, memory: 928, loss: 5.3897 2023-01-04 17:54:50,621 - mmcls - INFO - Epoch [22][600/5005] lr: 1.000e-01, eta: 9:40:51, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3992 2023-01-04 17:54:59,520 - mmcls - INFO - Epoch [22][700/5005] lr: 1.000e-01, eta: 9:40:42, time: 0.089, data_time: 0.016, memory: 928, loss: 5.4174 2023-01-04 17:55:08,271 - mmcls - INFO - Epoch [22][800/5005] lr: 1.000e-01, eta: 9:40:33, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4196 2023-01-04 17:55:17,078 - mmcls - INFO - Epoch [22][900/5005] lr: 1.000e-01, eta: 9:40:24, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3956 2023-01-04 17:55:25,488 - mmcls - INFO - Epoch [22][1000/5005] lr: 1.000e-01, eta: 9:40:14, time: 0.084, data_time: 0.008, memory: 928, loss: 5.4267 2023-01-04 17:55:34,277 - mmcls - INFO - Epoch [22][1100/5005] lr: 1.000e-01, eta: 9:40:05, time: 0.088, data_time: 0.003, memory: 928, loss: 5.3962 2023-01-04 17:55:43,110 - mmcls - INFO - Epoch [22][1200/5005] lr: 1.000e-01, eta: 9:39:56, time: 0.088, data_time: 0.007, memory: 928, loss: 5.4050 2023-01-04 17:55:51,757 - mmcls - INFO - Epoch [22][1300/5005] lr: 1.000e-01, eta: 9:39:47, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3773 2023-01-04 17:56:00,901 - mmcls - INFO - Epoch [22][1400/5005] lr: 1.000e-01, eta: 9:39:39, time: 0.091, data_time: 0.021, memory: 928, loss: 5.3979 2023-01-04 17:56:09,641 - mmcls - INFO - Epoch [22][1500/5005] lr: 1.000e-01, eta: 9:39:30, time: 0.087, data_time: 0.016, memory: 928, loss: 5.4109 2023-01-04 17:56:18,062 - mmcls - INFO - Epoch [22][1600/5005] lr: 1.000e-01, eta: 9:39:19, time: 0.084, data_time: 0.001, memory: 928, loss: 5.4021 2023-01-04 17:56:26,766 - mmcls - INFO - Epoch [22][1700/5005] lr: 1.000e-01, eta: 9:39:10, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4006 2023-01-04 17:56:35,675 - mmcls - INFO - Epoch [22][1800/5005] lr: 1.000e-01, eta: 9:39:02, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4288 2023-01-04 17:56:44,598 - mmcls - INFO - Epoch [22][1900/5005] lr: 1.000e-01, eta: 9:38:53, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3963 2023-01-04 17:56:53,209 - mmcls - INFO - Epoch [22][2000/5005] lr: 1.000e-01, eta: 9:38:44, time: 0.086, data_time: 0.013, memory: 928, loss: 5.4060 2023-01-04 17:57:01,650 - mmcls - INFO - Epoch [22][2100/5005] lr: 1.000e-01, eta: 9:38:33, time: 0.084, data_time: 0.004, memory: 928, loss: 5.3951 2023-01-04 17:57:10,993 - mmcls - INFO - Epoch [22][2200/5005] lr: 1.000e-01, eta: 9:38:26, time: 0.093, data_time: 0.003, memory: 928, loss: 5.3856 2023-01-04 17:57:19,709 - mmcls - INFO - Epoch [22][2300/5005] lr: 1.000e-01, eta: 9:38:17, time: 0.087, data_time: 0.004, memory: 928, loss: 5.4025 2023-01-04 17:57:28,377 - mmcls - INFO - Epoch [22][2400/5005] lr: 1.000e-01, eta: 9:38:08, time: 0.087, data_time: 0.020, memory: 928, loss: 5.3818 2023-01-04 17:57:36,993 - mmcls - INFO - Epoch [22][2500/5005] lr: 1.000e-01, eta: 9:37:58, time: 0.086, data_time: 0.019, memory: 928, loss: 5.3739 2023-01-04 17:57:45,618 - mmcls - INFO - Epoch [22][2600/5005] lr: 1.000e-01, eta: 9:37:49, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3967 2023-01-04 17:57:54,614 - mmcls - INFO - Epoch [22][2700/5005] lr: 1.000e-01, eta: 9:37:40, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4046 2023-01-04 17:58:03,614 - mmcls - INFO - Epoch [22][2800/5005] lr: 1.000e-01, eta: 9:37:32, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4117 2023-01-04 17:58:12,190 - mmcls - INFO - Epoch [22][2900/5005] lr: 1.000e-01, eta: 9:37:22, time: 0.086, data_time: 0.029, memory: 928, loss: 5.4045 2023-01-04 17:58:20,854 - mmcls - INFO - Epoch [22][3000/5005] lr: 1.000e-01, eta: 9:37:13, time: 0.086, data_time: 0.008, memory: 928, loss: 5.4048 2023-01-04 17:58:30,070 - mmcls - INFO - Epoch [22][3100/5005] lr: 1.000e-01, eta: 9:37:06, time: 0.092, data_time: 0.020, memory: 928, loss: 5.3888 2023-01-04 17:58:38,993 - mmcls - INFO - Epoch [22][3200/5005] lr: 1.000e-01, eta: 9:36:57, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3896 2023-01-04 17:58:47,736 - mmcls - INFO - Epoch [22][3300/5005] lr: 1.000e-01, eta: 9:36:48, time: 0.088, data_time: 0.017, memory: 928, loss: 5.4106 2023-01-04 17:58:56,471 - mmcls - INFO - Epoch [22][3400/5005] lr: 1.000e-01, eta: 9:36:39, time: 0.087, data_time: 0.028, memory: 928, loss: 5.3933 2023-01-04 17:59:05,504 - mmcls - INFO - Epoch [22][3500/5005] lr: 1.000e-01, eta: 9:36:31, time: 0.090, data_time: 0.008, memory: 928, loss: 5.4048 2023-01-04 17:59:14,197 - mmcls - INFO - Epoch [22][3600/5005] lr: 1.000e-01, eta: 9:36:21, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3923 2023-01-04 17:59:23,647 - mmcls - INFO - Epoch [22][3700/5005] lr: 1.000e-01, eta: 9:36:15, time: 0.094, data_time: 0.005, memory: 928, loss: 5.3748 2023-01-04 17:59:32,191 - mmcls - INFO - Epoch [22][3800/5005] lr: 1.000e-01, eta: 9:36:05, time: 0.085, data_time: 0.002, memory: 928, loss: 5.4070 2023-01-04 17:59:41,094 - mmcls - INFO - Epoch [22][3900/5005] lr: 1.000e-01, eta: 9:35:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.4077 2023-01-04 17:59:50,228 - mmcls - INFO - Epoch [22][4000/5005] lr: 1.000e-01, eta: 9:35:49, time: 0.091, data_time: 0.008, memory: 928, loss: 5.4149 2023-01-04 17:59:58,819 - mmcls - INFO - Epoch [22][4100/5005] lr: 1.000e-01, eta: 9:35:39, time: 0.086, data_time: 0.031, memory: 928, loss: 5.3985 2023-01-04 18:00:07,607 - mmcls - INFO - Epoch [22][4200/5005] lr: 1.000e-01, eta: 9:35:30, time: 0.088, data_time: 0.010, memory: 928, loss: 5.4052 2023-01-04 18:00:16,463 - mmcls - INFO - Epoch [22][4300/5005] lr: 1.000e-01, eta: 9:35:21, time: 0.089, data_time: 0.013, memory: 928, loss: 5.4042 2023-01-04 18:00:25,396 - mmcls - INFO - Epoch [22][4400/5005] lr: 1.000e-01, eta: 9:35:13, time: 0.089, data_time: 0.027, memory: 928, loss: 5.4124 2023-01-04 18:00:34,111 - mmcls - INFO - Epoch [22][4500/5005] lr: 1.000e-01, eta: 9:35:04, time: 0.087, data_time: 0.007, memory: 928, loss: 5.4177 2023-01-04 18:00:42,872 - mmcls - INFO - Epoch [22][4600/5005] lr: 1.000e-01, eta: 9:34:55, time: 0.088, data_time: 0.009, memory: 928, loss: 5.4039 2023-01-04 18:00:51,735 - mmcls - INFO - Epoch [22][4700/5005] lr: 1.000e-01, eta: 9:34:46, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3821 2023-01-04 18:01:00,556 - mmcls - INFO - Epoch [22][4800/5005] lr: 1.000e-01, eta: 9:34:37, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3980 2023-01-04 18:01:09,382 - mmcls - INFO - Epoch [22][4900/5005] lr: 1.000e-01, eta: 9:34:28, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3826 2023-01-04 18:01:18,213 - mmcls - INFO - Epoch [22][5000/5005] lr: 1.000e-01, eta: 9:34:19, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3851 2023-01-04 18:01:40,948 - mmcls - INFO - Epoch(val) [22][196] accuracy_top-1: 5.0060, accuracy_top-5: 13.5180 2023-01-04 18:01:51,674 - mmcls - INFO - Epoch [23][100/5005] lr: 1.000e-01, eta: 9:34:15, time: 0.107, data_time: 0.031, memory: 928, loss: 5.3829 2023-01-04 18:02:00,277 - mmcls - INFO - Epoch [23][200/5005] lr: 1.000e-01, eta: 9:34:06, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4027 2023-01-04 18:02:08,944 - mmcls - INFO - Epoch [23][300/5005] lr: 1.000e-01, eta: 9:33:56, time: 0.087, data_time: 0.016, memory: 928, loss: 5.3938 2023-01-04 18:02:17,569 - mmcls - INFO - Epoch [23][400/5005] lr: 1.000e-01, eta: 9:33:47, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3861 2023-01-04 18:02:26,615 - mmcls - INFO - Epoch [23][500/5005] lr: 1.000e-01, eta: 9:33:39, time: 0.090, data_time: 0.011, memory: 928, loss: 5.3915 2023-01-04 18:02:35,438 - mmcls - INFO - Epoch [23][600/5005] lr: 1.000e-01, eta: 9:33:30, time: 0.088, data_time: 0.030, memory: 928, loss: 5.4085 2023-01-04 18:02:44,233 - mmcls - INFO - Epoch [23][700/5005] lr: 1.000e-01, eta: 9:33:21, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3835 2023-01-04 18:02:52,816 - mmcls - INFO - Epoch [23][800/5005] lr: 1.000e-01, eta: 9:33:11, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3802 2023-01-04 18:03:01,457 - mmcls - INFO - Epoch [23][900/5005] lr: 1.000e-01, eta: 9:33:02, time: 0.086, data_time: 0.007, memory: 928, loss: 5.3821 2023-01-04 18:03:10,207 - mmcls - INFO - Epoch [23][1000/5005] lr: 1.000e-01, eta: 9:32:52, time: 0.088, data_time: 0.008, memory: 928, loss: 5.3773 2023-01-04 18:03:18,978 - mmcls - INFO - Epoch [23][1100/5005] lr: 1.000e-01, eta: 9:32:43, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3844 2023-01-04 18:03:27,818 - mmcls - INFO - Epoch [23][1200/5005] lr: 1.000e-01, eta: 9:32:35, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4021 2023-01-04 18:03:36,591 - mmcls - INFO - Epoch [23][1300/5005] lr: 1.000e-01, eta: 9:32:26, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3911 2023-01-04 18:03:45,264 - mmcls - INFO - Epoch [23][1400/5005] lr: 1.000e-01, eta: 9:32:16, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3993 2023-01-04 18:03:54,537 - mmcls - INFO - Epoch [23][1500/5005] lr: 1.000e-01, eta: 9:32:09, time: 0.093, data_time: 0.001, memory: 928, loss: 5.3988 2023-01-04 18:04:03,107 - mmcls - INFO - Epoch [23][1600/5005] lr: 1.000e-01, eta: 9:31:59, time: 0.086, data_time: 0.015, memory: 928, loss: 5.3985 2023-01-04 18:04:11,909 - mmcls - INFO - Epoch [23][1700/5005] lr: 1.000e-01, eta: 9:31:50, time: 0.088, data_time: 0.021, memory: 928, loss: 5.3910 2023-01-04 18:04:20,955 - mmcls - INFO - Epoch [23][1800/5005] lr: 1.000e-01, eta: 9:31:42, time: 0.090, data_time: 0.013, memory: 928, loss: 5.4131 2023-01-04 18:04:30,153 - mmcls - INFO - Epoch [23][1900/5005] lr: 1.000e-01, eta: 9:31:35, time: 0.092, data_time: 0.033, memory: 928, loss: 5.3974 2023-01-04 18:04:39,228 - mmcls - INFO - Epoch [23][2000/5005] lr: 1.000e-01, eta: 9:31:27, time: 0.091, data_time: 0.009, memory: 928, loss: 5.3972 2023-01-04 18:04:48,215 - mmcls - INFO - Epoch [23][2100/5005] lr: 1.000e-01, eta: 9:31:18, time: 0.090, data_time: 0.003, memory: 928, loss: 5.4060 2023-01-04 18:04:57,217 - mmcls - INFO - Epoch [23][2200/5005] lr: 1.000e-01, eta: 9:31:10, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4008 2023-01-04 18:05:06,455 - mmcls - INFO - Epoch [23][2300/5005] lr: 1.000e-01, eta: 9:31:03, time: 0.092, data_time: 0.002, memory: 928, loss: 5.4051 2023-01-04 18:05:15,278 - mmcls - INFO - Epoch [23][2400/5005] lr: 1.000e-01, eta: 9:30:54, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3964 2023-01-04 18:05:24,236 - mmcls - INFO - Epoch [23][2500/5005] lr: 1.000e-01, eta: 9:30:46, time: 0.090, data_time: 0.016, memory: 928, loss: 5.4027 2023-01-04 18:05:33,250 - mmcls - INFO - Epoch [23][2600/5005] lr: 1.000e-01, eta: 9:30:37, time: 0.090, data_time: 0.001, memory: 928, loss: 5.3985 2023-01-04 18:05:41,894 - mmcls - INFO - Epoch [23][2700/5005] lr: 1.000e-01, eta: 9:30:28, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4065 2023-01-04 18:05:50,828 - mmcls - INFO - Epoch [23][2800/5005] lr: 1.000e-01, eta: 9:30:19, time: 0.089, data_time: 0.017, memory: 928, loss: 5.3860 2023-01-04 18:05:59,415 - mmcls - INFO - Epoch [23][2900/5005] lr: 1.000e-01, eta: 9:30:10, time: 0.086, data_time: 0.011, memory: 928, loss: 5.4068 2023-01-04 18:06:08,127 - mmcls - INFO - Epoch [23][3000/5005] lr: 1.000e-01, eta: 9:30:01, time: 0.087, data_time: 0.008, memory: 928, loss: 5.4197 2023-01-04 18:06:16,986 - mmcls - INFO - Epoch [23][3100/5005] lr: 1.000e-01, eta: 9:29:52, time: 0.089, data_time: 0.001, memory: 928, loss: 5.4141 2023-01-04 18:06:25,954 - mmcls - INFO - Epoch [23][3200/5005] lr: 1.000e-01, eta: 9:29:43, time: 0.090, data_time: 0.002, memory: 928, loss: 5.4140 2023-01-04 18:06:34,655 - mmcls - INFO - Epoch [23][3300/5005] lr: 1.000e-01, eta: 9:29:34, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4035 2023-01-04 18:06:43,311 - mmcls - INFO - Epoch [23][3400/5005] lr: 1.000e-01, eta: 9:29:25, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4059 2023-01-04 18:06:51,768 - mmcls - INFO - Epoch [23][3500/5005] lr: 1.000e-01, eta: 9:29:15, time: 0.084, data_time: 0.010, memory: 928, loss: 5.4042 2023-01-04 18:07:00,501 - mmcls - INFO - Epoch [23][3600/5005] lr: 1.000e-01, eta: 9:29:06, time: 0.087, data_time: 0.021, memory: 928, loss: 5.3993 2023-01-04 18:07:08,995 - mmcls - INFO - Epoch [23][3700/5005] lr: 1.000e-01, eta: 9:28:56, time: 0.085, data_time: 0.005, memory: 928, loss: 5.3901 2023-01-04 18:07:17,656 - mmcls - INFO - Epoch [23][3800/5005] lr: 1.000e-01, eta: 9:28:46, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3985 2023-01-04 18:07:26,310 - mmcls - INFO - Epoch [23][3900/5005] lr: 1.000e-01, eta: 9:28:37, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3923 2023-01-04 18:07:35,172 - mmcls - INFO - Epoch [23][4000/5005] lr: 1.000e-01, eta: 9:28:28, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3730 2023-01-04 18:07:43,839 - mmcls - INFO - Epoch [23][4100/5005] lr: 1.000e-01, eta: 9:28:19, time: 0.087, data_time: 0.023, memory: 928, loss: 5.3854 2023-01-04 18:07:52,689 - mmcls - INFO - Epoch [23][4200/5005] lr: 1.000e-01, eta: 9:28:10, time: 0.088, data_time: 0.011, memory: 928, loss: 5.3674 2023-01-04 18:08:01,438 - mmcls - INFO - Epoch [23][4300/5005] lr: 1.000e-01, eta: 9:28:01, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3765 2023-01-04 18:08:10,006 - mmcls - INFO - Epoch [23][4400/5005] lr: 1.000e-01, eta: 9:27:51, time: 0.086, data_time: 0.015, memory: 928, loss: 5.3885 2023-01-04 18:08:18,450 - mmcls - INFO - Epoch [23][4500/5005] lr: 1.000e-01, eta: 9:27:41, time: 0.084, data_time: 0.001, memory: 928, loss: 5.3972 2023-01-04 18:08:27,079 - mmcls - INFO - Epoch [23][4600/5005] lr: 1.000e-01, eta: 9:27:31, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3883 2023-01-04 18:08:35,819 - mmcls - INFO - Epoch [23][4700/5005] lr: 1.000e-01, eta: 9:27:22, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3835 2023-01-04 18:08:44,374 - mmcls - INFO - Epoch [23][4800/5005] lr: 1.000e-01, eta: 9:27:13, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4085 2023-01-04 18:08:52,915 - mmcls - INFO - Epoch [23][4900/5005] lr: 1.000e-01, eta: 9:27:03, time: 0.085, data_time: 0.009, memory: 928, loss: 5.3763 2023-01-04 18:09:01,763 - mmcls - INFO - Epoch [23][5000/5005] lr: 1.000e-01, eta: 9:26:54, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3871 2023-01-04 18:09:25,613 - mmcls - INFO - Epoch(val) [23][196] accuracy_top-1: 5.1260, accuracy_top-5: 13.4180 2023-01-04 18:09:36,819 - mmcls - INFO - Epoch [24][100/5005] lr: 1.000e-01, eta: 9:26:51, time: 0.112, data_time: 0.040, memory: 928, loss: 5.3840 2023-01-04 18:09:45,353 - mmcls - INFO - Epoch [24][200/5005] lr: 1.000e-01, eta: 9:26:41, time: 0.085, data_time: 0.042, memory: 928, loss: 5.3884 2023-01-04 18:09:54,040 - mmcls - INFO - Epoch [24][300/5005] lr: 1.000e-01, eta: 9:26:32, time: 0.087, data_time: 0.029, memory: 928, loss: 5.3907 2023-01-04 18:10:02,755 - mmcls - INFO - Epoch [24][400/5005] lr: 1.000e-01, eta: 9:26:23, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3966 2023-01-04 18:10:11,769 - mmcls - INFO - Epoch [24][500/5005] lr: 1.000e-01, eta: 9:26:15, time: 0.090, data_time: 0.005, memory: 928, loss: 5.3844 2023-01-04 18:10:20,506 - mmcls - INFO - Epoch [24][600/5005] lr: 1.000e-01, eta: 9:26:06, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3913 2023-01-04 18:10:29,279 - mmcls - INFO - Epoch [24][700/5005] lr: 1.000e-01, eta: 9:25:57, time: 0.088, data_time: 0.010, memory: 928, loss: 5.3846 2023-01-04 18:10:38,184 - mmcls - INFO - Epoch [24][800/5005] lr: 1.000e-01, eta: 9:25:48, time: 0.089, data_time: 0.019, memory: 928, loss: 5.3835 2023-01-04 18:10:46,832 - mmcls - INFO - Epoch [24][900/5005] lr: 1.000e-01, eta: 9:25:39, time: 0.086, data_time: 0.029, memory: 928, loss: 5.3813 2023-01-04 18:10:55,595 - mmcls - INFO - Epoch [24][1000/5005] lr: 1.000e-01, eta: 9:25:30, time: 0.088, data_time: 0.011, memory: 928, loss: 5.4071 2023-01-04 18:11:04,404 - mmcls - INFO - Epoch [24][1100/5005] lr: 1.000e-01, eta: 9:25:21, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4058 2023-01-04 18:11:12,839 - mmcls - INFO - Epoch [24][1200/5005] lr: 1.000e-01, eta: 9:25:10, time: 0.084, data_time: 0.004, memory: 928, loss: 5.3898 2023-01-04 18:11:21,705 - mmcls - INFO - Epoch [24][1300/5005] lr: 1.000e-01, eta: 9:25:02, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3774 2023-01-04 18:11:30,514 - mmcls - INFO - Epoch [24][1400/5005] lr: 1.000e-01, eta: 9:24:53, time: 0.088, data_time: 0.008, memory: 928, loss: 5.3857 2023-01-04 18:11:39,189 - mmcls - INFO - Epoch [24][1500/5005] lr: 1.000e-01, eta: 9:24:44, time: 0.087, data_time: 0.002, memory: 928, loss: 5.4035 2023-01-04 18:11:48,070 - mmcls - INFO - Epoch [24][1600/5005] lr: 1.000e-01, eta: 9:24:35, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3864 2023-01-04 18:11:56,870 - mmcls - INFO - Epoch [24][1700/5005] lr: 1.000e-01, eta: 9:24:26, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4014 2023-01-04 18:12:05,678 - mmcls - INFO - Epoch [24][1800/5005] lr: 1.000e-01, eta: 9:24:17, time: 0.088, data_time: 0.022, memory: 928, loss: 5.3961 2023-01-04 18:12:14,314 - mmcls - INFO - Epoch [24][1900/5005] lr: 1.000e-01, eta: 9:24:08, time: 0.086, data_time: 0.009, memory: 928, loss: 5.3977 2023-01-04 18:12:23,207 - mmcls - INFO - Epoch [24][2000/5005] lr: 1.000e-01, eta: 9:23:59, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3971 2023-01-04 18:12:32,327 - mmcls - INFO - Epoch [24][2100/5005] lr: 1.000e-01, eta: 9:23:51, time: 0.091, data_time: 0.004, memory: 928, loss: 5.3698 2023-01-04 18:12:41,070 - mmcls - INFO - Epoch [24][2200/5005] lr: 1.000e-01, eta: 9:23:42, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3925 2023-01-04 18:12:49,713 - mmcls - INFO - Epoch [24][2300/5005] lr: 1.000e-01, eta: 9:23:33, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3984 2023-01-04 18:12:58,533 - mmcls - INFO - Epoch [24][2400/5005] lr: 1.000e-01, eta: 9:23:24, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3947 2023-01-04 18:13:07,033 - mmcls - INFO - Epoch [24][2500/5005] lr: 1.000e-01, eta: 9:23:14, time: 0.085, data_time: 0.008, memory: 928, loss: 5.3935 2023-01-04 18:13:15,796 - mmcls - INFO - Epoch [24][2600/5005] lr: 1.000e-01, eta: 9:23:05, time: 0.088, data_time: 0.006, memory: 928, loss: 5.3984 2023-01-04 18:13:24,423 - mmcls - INFO - Epoch [24][2700/5005] lr: 1.000e-01, eta: 9:22:55, time: 0.086, data_time: 0.008, memory: 928, loss: 5.3907 2023-01-04 18:13:33,213 - mmcls - INFO - Epoch [24][2800/5005] lr: 1.000e-01, eta: 9:22:46, time: 0.088, data_time: 0.027, memory: 928, loss: 5.3955 2023-01-04 18:13:42,294 - mmcls - INFO - Epoch [24][2900/5005] lr: 1.000e-01, eta: 9:22:38, time: 0.091, data_time: 0.005, memory: 928, loss: 5.3672 2023-01-04 18:13:51,115 - mmcls - INFO - Epoch [24][3000/5005] lr: 1.000e-01, eta: 9:22:30, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3905 2023-01-04 18:14:00,040 - mmcls - INFO - Epoch [24][3100/5005] lr: 1.000e-01, eta: 9:22:21, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3855 2023-01-04 18:14:08,641 - mmcls - INFO - Epoch [24][3200/5005] lr: 1.000e-01, eta: 9:22:12, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3970 2023-01-04 18:14:17,254 - mmcls - INFO - Epoch [24][3300/5005] lr: 1.000e-01, eta: 9:22:02, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3869 2023-01-04 18:14:25,984 - mmcls - INFO - Epoch [24][3400/5005] lr: 1.000e-01, eta: 9:21:53, time: 0.087, data_time: 0.016, memory: 928, loss: 5.3992 2023-01-04 18:14:34,652 - mmcls - INFO - Epoch [24][3500/5005] lr: 1.000e-01, eta: 9:21:44, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3880 2023-01-04 18:14:43,196 - mmcls - INFO - Epoch [24][3600/5005] lr: 1.000e-01, eta: 9:21:34, time: 0.085, data_time: 0.019, memory: 928, loss: 5.3818 2023-01-04 18:14:52,183 - mmcls - INFO - Epoch [24][3700/5005] lr: 1.000e-01, eta: 9:21:26, time: 0.090, data_time: 0.001, memory: 928, loss: 5.4001 2023-01-04 18:15:01,392 - mmcls - INFO - Epoch [24][3800/5005] lr: 1.000e-01, eta: 9:21:18, time: 0.092, data_time: 0.007, memory: 928, loss: 5.3779 2023-01-04 18:15:10,293 - mmcls - INFO - Epoch [24][3900/5005] lr: 1.000e-01, eta: 9:21:09, time: 0.089, data_time: 0.006, memory: 928, loss: 5.4144 2023-01-04 18:15:19,059 - mmcls - INFO - Epoch [24][4000/5005] lr: 1.000e-01, eta: 9:21:00, time: 0.088, data_time: 0.000, memory: 928, loss: 5.3858 2023-01-04 18:15:28,429 - mmcls - INFO - Epoch [24][4100/5005] lr: 1.000e-01, eta: 9:20:53, time: 0.094, data_time: 0.032, memory: 928, loss: 5.3903 2023-01-04 18:15:37,549 - mmcls - INFO - Epoch [24][4200/5005] lr: 1.000e-01, eta: 9:20:45, time: 0.091, data_time: 0.025, memory: 928, loss: 5.3683 2023-01-04 18:15:46,306 - mmcls - INFO - Epoch [24][4300/5005] lr: 1.000e-01, eta: 9:20:36, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3871 2023-01-04 18:15:55,188 - mmcls - INFO - Epoch [24][4400/5005] lr: 1.000e-01, eta: 9:20:28, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3826 2023-01-04 18:16:04,405 - mmcls - INFO - Epoch [24][4500/5005] lr: 1.000e-01, eta: 9:20:20, time: 0.092, data_time: 0.001, memory: 928, loss: 5.3702 2023-01-04 18:16:13,293 - mmcls - INFO - Epoch [24][4600/5005] lr: 1.000e-01, eta: 9:20:11, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3964 2023-01-04 18:16:22,090 - mmcls - INFO - Epoch [24][4700/5005] lr: 1.000e-01, eta: 9:20:02, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3935 2023-01-04 18:16:30,802 - mmcls - INFO - Epoch [24][4800/5005] lr: 1.000e-01, eta: 9:19:53, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3951 2023-01-04 18:16:39,661 - mmcls - INFO - Epoch [24][4900/5005] lr: 1.000e-01, eta: 9:19:45, time: 0.088, data_time: 0.004, memory: 928, loss: 5.4091 2023-01-04 18:16:48,484 - mmcls - INFO - Epoch [24][5000/5005] lr: 1.000e-01, eta: 9:19:36, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3854 2023-01-04 18:17:12,103 - mmcls - INFO - Epoch(val) [24][196] accuracy_top-1: 4.0560, accuracy_top-5: 11.3160 2023-01-04 18:17:22,964 - mmcls - INFO - Epoch [25][100/5005] lr: 1.000e-01, eta: 9:19:31, time: 0.108, data_time: 0.043, memory: 928, loss: 5.3704 2023-01-04 18:17:31,848 - mmcls - INFO - Epoch [25][200/5005] lr: 1.000e-01, eta: 9:19:23, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3864 2023-01-04 18:17:40,790 - mmcls - INFO - Epoch [25][300/5005] lr: 1.000e-01, eta: 9:19:14, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3735 2023-01-04 18:17:49,558 - mmcls - INFO - Epoch [25][400/5005] lr: 1.000e-01, eta: 9:19:05, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3943 2023-01-04 18:17:58,185 - mmcls - INFO - Epoch [25][500/5005] lr: 1.000e-01, eta: 9:18:56, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3699 2023-01-04 18:18:07,015 - mmcls - INFO - Epoch [25][600/5005] lr: 1.000e-01, eta: 9:18:47, time: 0.088, data_time: 0.024, memory: 928, loss: 5.4007 2023-01-04 18:18:15,558 - mmcls - INFO - Epoch [25][700/5005] lr: 1.000e-01, eta: 9:18:37, time: 0.086, data_time: 0.029, memory: 928, loss: 5.3768 2023-01-04 18:18:24,473 - mmcls - INFO - Epoch [25][800/5005] lr: 1.000e-01, eta: 9:18:29, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3905 2023-01-04 18:18:33,124 - mmcls - INFO - Epoch [25][900/5005] lr: 1.000e-01, eta: 9:18:19, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3918 2023-01-04 18:18:41,999 - mmcls - INFO - Epoch [25][1000/5005] lr: 1.000e-01, eta: 9:18:11, time: 0.089, data_time: 0.032, memory: 928, loss: 5.3763 2023-01-04 18:18:51,116 - mmcls - INFO - Epoch [25][1100/5005] lr: 1.000e-01, eta: 9:18:03, time: 0.091, data_time: 0.010, memory: 928, loss: 5.3902 2023-01-04 18:18:59,928 - mmcls - INFO - Epoch [25][1200/5005] lr: 1.000e-01, eta: 9:17:54, time: 0.088, data_time: 0.003, memory: 928, loss: 5.3740 2023-01-04 18:19:08,545 - mmcls - INFO - Epoch [25][1300/5005] lr: 1.000e-01, eta: 9:17:44, time: 0.086, data_time: 0.001, memory: 928, loss: 5.4090 2023-01-04 18:19:17,257 - mmcls - INFO - Epoch [25][1400/5005] lr: 1.000e-01, eta: 9:17:35, time: 0.087, data_time: 0.026, memory: 928, loss: 5.3799 2023-01-04 18:19:25,763 - mmcls - INFO - Epoch [25][1500/5005] lr: 1.000e-01, eta: 9:17:25, time: 0.085, data_time: 0.010, memory: 928, loss: 5.3783 2023-01-04 18:19:34,411 - mmcls - INFO - Epoch [25][1600/5005] lr: 1.000e-01, eta: 9:17:16, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3953 2023-01-04 18:19:43,204 - mmcls - INFO - Epoch [25][1700/5005] lr: 1.000e-01, eta: 9:17:07, time: 0.088, data_time: 0.008, memory: 928, loss: 5.3949 2023-01-04 18:19:51,860 - mmcls - INFO - Epoch [25][1800/5005] lr: 1.000e-01, eta: 9:16:58, time: 0.087, data_time: 0.012, memory: 928, loss: 5.3810 2023-01-04 18:20:00,778 - mmcls - INFO - Epoch [25][1900/5005] lr: 1.000e-01, eta: 9:16:49, time: 0.089, data_time: 0.008, memory: 928, loss: 5.3822 2023-01-04 18:20:09,243 - mmcls - INFO - Epoch [25][2000/5005] lr: 1.000e-01, eta: 9:16:39, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4042 2023-01-04 18:20:17,813 - mmcls - INFO - Epoch [25][2100/5005] lr: 1.000e-01, eta: 9:16:30, time: 0.086, data_time: 0.006, memory: 928, loss: 5.3792 2023-01-04 18:20:26,534 - mmcls - INFO - Epoch [25][2200/5005] lr: 1.000e-01, eta: 9:16:20, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3936 2023-01-04 18:20:35,195 - mmcls - INFO - Epoch [25][2300/5005] lr: 1.000e-01, eta: 9:16:11, time: 0.087, data_time: 0.021, memory: 928, loss: 5.4070 2023-01-04 18:20:43,834 - mmcls - INFO - Epoch [25][2400/5005] lr: 1.000e-01, eta: 9:16:02, time: 0.086, data_time: 0.039, memory: 928, loss: 5.4030 2023-01-04 18:20:52,529 - mmcls - INFO - Epoch [25][2500/5005] lr: 1.000e-01, eta: 9:15:52, time: 0.087, data_time: 0.008, memory: 928, loss: 5.3714 2023-01-04 18:21:01,206 - mmcls - INFO - Epoch [25][2600/5005] lr: 1.000e-01, eta: 9:15:43, time: 0.087, data_time: 0.001, memory: 928, loss: 5.4014 2023-01-04 18:21:09,962 - mmcls - INFO - Epoch [25][2700/5005] lr: 1.000e-01, eta: 9:15:34, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3968 2023-01-04 18:21:18,682 - mmcls - INFO - Epoch [25][2800/5005] lr: 1.000e-01, eta: 9:15:25, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3898 2023-01-04 18:21:27,465 - mmcls - INFO - Epoch [25][2900/5005] lr: 1.000e-01, eta: 9:15:16, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3944 2023-01-04 18:21:36,103 - mmcls - INFO - Epoch [25][3000/5005] lr: 1.000e-01, eta: 9:15:06, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3903 2023-01-04 18:21:44,657 - mmcls - INFO - Epoch [25][3100/5005] lr: 1.000e-01, eta: 9:14:57, time: 0.086, data_time: 0.005, memory: 928, loss: 5.3584 2023-01-04 18:21:53,537 - mmcls - INFO - Epoch [25][3200/5005] lr: 1.000e-01, eta: 9:14:48, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3846 2023-01-04 18:22:02,385 - mmcls - INFO - Epoch [25][3300/5005] lr: 1.000e-01, eta: 9:14:39, time: 0.088, data_time: 0.003, memory: 928, loss: 5.4222 2023-01-04 18:22:11,230 - mmcls - INFO - Epoch [25][3400/5005] lr: 1.000e-01, eta: 9:14:31, time: 0.088, data_time: 0.011, memory: 928, loss: 5.3983 2023-01-04 18:22:19,947 - mmcls - INFO - Epoch [25][3500/5005] lr: 1.000e-01, eta: 9:14:21, time: 0.087, data_time: 0.003, memory: 928, loss: 5.3671 2023-01-04 18:22:28,639 - mmcls - INFO - Epoch [25][3600/5005] lr: 1.000e-01, eta: 9:14:12, time: 0.087, data_time: 0.014, memory: 928, loss: 5.3806 2023-01-04 18:22:37,241 - mmcls - INFO - Epoch [25][3700/5005] lr: 1.000e-01, eta: 9:14:03, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4058 2023-01-04 18:22:45,930 - mmcls - INFO - Epoch [25][3800/5005] lr: 1.000e-01, eta: 9:13:53, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3612 2023-01-04 18:22:54,454 - mmcls - INFO - Epoch [25][3900/5005] lr: 1.000e-01, eta: 9:13:44, time: 0.085, data_time: 0.002, memory: 928, loss: 5.3730 2023-01-04 18:23:03,414 - mmcls - INFO - Epoch [25][4000/5005] lr: 1.000e-01, eta: 9:13:35, time: 0.090, data_time: 0.012, memory: 928, loss: 5.3969 2023-01-04 18:23:12,144 - mmcls - INFO - Epoch [25][4100/5005] lr: 1.000e-01, eta: 9:13:26, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3877 2023-01-04 18:23:21,012 - mmcls - INFO - Epoch [25][4200/5005] lr: 1.000e-01, eta: 9:13:18, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3754 2023-01-04 18:23:29,914 - mmcls - INFO - Epoch [25][4300/5005] lr: 1.000e-01, eta: 9:13:09, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3717 2023-01-04 18:23:38,847 - mmcls - INFO - Epoch [25][4400/5005] lr: 1.000e-01, eta: 9:13:00, time: 0.089, data_time: 0.018, memory: 928, loss: 5.3945 2023-01-04 18:23:47,644 - mmcls - INFO - Epoch [25][4500/5005] lr: 1.000e-01, eta: 9:12:52, time: 0.088, data_time: 0.024, memory: 928, loss: 5.3747 2023-01-04 18:23:56,512 - mmcls - INFO - Epoch [25][4600/5005] lr: 1.000e-01, eta: 9:12:43, time: 0.089, data_time: 0.042, memory: 928, loss: 5.3712 2023-01-04 18:24:05,161 - mmcls - INFO - Epoch [25][4700/5005] lr: 1.000e-01, eta: 9:12:33, time: 0.086, data_time: 0.024, memory: 928, loss: 5.3906 2023-01-04 18:24:13,679 - mmcls - INFO - Epoch [25][4800/5005] lr: 1.000e-01, eta: 9:12:24, time: 0.085, data_time: 0.001, memory: 928, loss: 5.3820 2023-01-04 18:24:22,430 - mmcls - INFO - Epoch [25][4900/5005] lr: 1.000e-01, eta: 9:12:15, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3939 2023-01-04 18:24:31,393 - mmcls - INFO - Epoch [25][5000/5005] lr: 1.000e-01, eta: 9:12:06, time: 0.090, data_time: 0.007, memory: 928, loss: 5.3851 2023-01-04 18:24:54,191 - mmcls - INFO - Epoch(val) [25][196] accuracy_top-1: 4.2000, accuracy_top-5: 11.1960 2023-01-04 18:25:05,207 - mmcls - INFO - Epoch [26][100/5005] lr: 1.000e-01, eta: 9:12:02, time: 0.110, data_time: 0.071, memory: 928, loss: 5.3468 2023-01-04 18:25:14,289 - mmcls - INFO - Epoch [26][200/5005] lr: 1.000e-01, eta: 9:11:54, time: 0.091, data_time: 0.042, memory: 928, loss: 5.3834 2023-01-04 18:25:22,883 - mmcls - INFO - Epoch [26][300/5005] lr: 1.000e-01, eta: 9:11:45, time: 0.086, data_time: 0.010, memory: 928, loss: 5.3680 2023-01-04 18:25:31,592 - mmcls - INFO - Epoch [26][400/5005] lr: 1.000e-01, eta: 9:11:35, time: 0.087, data_time: 0.009, memory: 928, loss: 5.4018 2023-01-04 18:25:40,442 - mmcls - INFO - Epoch [26][500/5005] lr: 1.000e-01, eta: 9:11:27, time: 0.088, data_time: 0.019, memory: 928, loss: 5.4029 2023-01-04 18:25:49,238 - mmcls - INFO - Epoch [26][600/5005] lr: 1.000e-01, eta: 9:11:18, time: 0.088, data_time: 0.024, memory: 928, loss: 5.3931 2023-01-04 18:25:57,695 - mmcls - INFO - Epoch [26][700/5005] lr: 1.000e-01, eta: 9:11:08, time: 0.084, data_time: 0.003, memory: 928, loss: 5.3938 2023-01-04 18:26:06,934 - mmcls - INFO - Epoch [26][800/5005] lr: 1.000e-01, eta: 9:11:00, time: 0.093, data_time: 0.007, memory: 928, loss: 5.3909 2023-01-04 18:26:15,762 - mmcls - INFO - Epoch [26][900/5005] lr: 1.000e-01, eta: 9:10:51, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3885 2023-01-04 18:26:24,599 - mmcls - INFO - Epoch [26][1000/5005] lr: 1.000e-01, eta: 9:10:43, time: 0.088, data_time: 0.013, memory: 928, loss: 5.3812 2023-01-04 18:26:33,362 - mmcls - INFO - Epoch [26][1100/5005] lr: 1.000e-01, eta: 9:10:34, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3779 2023-01-04 18:26:42,494 - mmcls - INFO - Epoch [26][1200/5005] lr: 1.000e-01, eta: 9:10:26, time: 0.091, data_time: 0.029, memory: 928, loss: 5.3847 2023-01-04 18:26:51,459 - mmcls - INFO - Epoch [26][1300/5005] lr: 1.000e-01, eta: 9:10:17, time: 0.090, data_time: 0.002, memory: 928, loss: 5.3889 2023-01-04 18:27:00,104 - mmcls - INFO - Epoch [26][1400/5005] lr: 1.000e-01, eta: 9:10:08, time: 0.087, data_time: 0.037, memory: 928, loss: 5.3544 2023-01-04 18:27:08,760 - mmcls - INFO - Epoch [26][1500/5005] lr: 1.000e-01, eta: 9:09:59, time: 0.087, data_time: 0.037, memory: 928, loss: 5.3843 2023-01-04 18:27:17,401 - mmcls - INFO - Epoch [26][1600/5005] lr: 1.000e-01, eta: 9:09:49, time: 0.086, data_time: 0.014, memory: 928, loss: 5.3992 2023-01-04 18:27:26,107 - mmcls - INFO - Epoch [26][1700/5005] lr: 1.000e-01, eta: 9:09:40, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3806 2023-01-04 18:27:34,722 - mmcls - INFO - Epoch [26][1800/5005] lr: 1.000e-01, eta: 9:09:30, time: 0.086, data_time: 0.011, memory: 928, loss: 5.3790 2023-01-04 18:27:43,718 - mmcls - INFO - Epoch [26][1900/5005] lr: 1.000e-01, eta: 9:09:22, time: 0.090, data_time: 0.004, memory: 928, loss: 5.3843 2023-01-04 18:27:52,331 - mmcls - INFO - Epoch [26][2000/5005] lr: 1.000e-01, eta: 9:09:13, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3756 2023-01-04 18:28:01,169 - mmcls - INFO - Epoch [26][2100/5005] lr: 1.000e-01, eta: 9:09:04, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3963 2023-01-04 18:28:09,849 - mmcls - INFO - Epoch [26][2200/5005] lr: 1.000e-01, eta: 9:08:55, time: 0.087, data_time: 0.003, memory: 928, loss: 5.3784 2023-01-04 18:28:18,788 - mmcls - INFO - Epoch [26][2300/5005] lr: 1.000e-01, eta: 9:08:46, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3738 2023-01-04 18:28:27,455 - mmcls - INFO - Epoch [26][2400/5005] lr: 1.000e-01, eta: 9:08:37, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3864 2023-01-04 18:28:36,353 - mmcls - INFO - Epoch [26][2500/5005] lr: 1.000e-01, eta: 9:08:28, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3845 2023-01-04 18:28:44,916 - mmcls - INFO - Epoch [26][2600/5005] lr: 1.000e-01, eta: 9:08:19, time: 0.086, data_time: 0.008, memory: 928, loss: 5.3807 2023-01-04 18:28:53,604 - mmcls - INFO - Epoch [26][2700/5005] lr: 1.000e-01, eta: 9:08:09, time: 0.087, data_time: 0.009, memory: 928, loss: 5.3929 2023-01-04 18:29:02,442 - mmcls - INFO - Epoch [26][2800/5005] lr: 1.000e-01, eta: 9:08:01, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3996 2023-01-04 18:29:11,397 - mmcls - INFO - Epoch [26][2900/5005] lr: 1.000e-01, eta: 9:07:52, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3863 2023-01-04 18:29:20,064 - mmcls - INFO - Epoch [26][3000/5005] lr: 1.000e-01, eta: 9:07:43, time: 0.087, data_time: 0.014, memory: 928, loss: 5.3790 2023-01-04 18:29:29,042 - mmcls - INFO - Epoch [26][3100/5005] lr: 1.000e-01, eta: 9:07:35, time: 0.090, data_time: 0.019, memory: 928, loss: 5.3648 2023-01-04 18:29:37,877 - mmcls - INFO - Epoch [26][3200/5005] lr: 1.000e-01, eta: 9:07:26, time: 0.088, data_time: 0.021, memory: 928, loss: 5.3981 2023-01-04 18:29:46,926 - mmcls - INFO - Epoch [26][3300/5005] lr: 1.000e-01, eta: 9:07:18, time: 0.091, data_time: 0.003, memory: 928, loss: 5.3792 2023-01-04 18:29:55,984 - mmcls - INFO - Epoch [26][3400/5005] lr: 1.000e-01, eta: 9:07:09, time: 0.091, data_time: 0.027, memory: 928, loss: 5.3846 2023-01-04 18:30:04,702 - mmcls - INFO - Epoch [26][3500/5005] lr: 1.000e-01, eta: 9:07:00, time: 0.087, data_time: 0.027, memory: 928, loss: 5.4096 2023-01-04 18:30:13,158 - mmcls - INFO - Epoch [26][3600/5005] lr: 1.000e-01, eta: 9:06:50, time: 0.085, data_time: 0.004, memory: 928, loss: 5.4165 2023-01-04 18:30:21,808 - mmcls - INFO - Epoch [26][3700/5005] lr: 1.000e-01, eta: 9:06:41, time: 0.086, data_time: 0.013, memory: 928, loss: 5.3901 2023-01-04 18:30:30,393 - mmcls - INFO - Epoch [26][3800/5005] lr: 1.000e-01, eta: 9:06:31, time: 0.086, data_time: 0.004, memory: 928, loss: 5.4127 2023-01-04 18:30:38,863 - mmcls - INFO - Epoch [26][3900/5005] lr: 1.000e-01, eta: 9:06:22, time: 0.085, data_time: 0.028, memory: 928, loss: 5.3873 2023-01-04 18:30:47,481 - mmcls - INFO - Epoch [26][4000/5005] lr: 1.000e-01, eta: 9:06:12, time: 0.086, data_time: 0.017, memory: 928, loss: 5.3677 2023-01-04 18:30:56,101 - mmcls - INFO - Epoch [26][4100/5005] lr: 1.000e-01, eta: 9:06:03, time: 0.086, data_time: 0.015, memory: 928, loss: 5.3885 2023-01-04 18:31:05,147 - mmcls - INFO - Epoch [26][4200/5005] lr: 1.000e-01, eta: 9:05:55, time: 0.090, data_time: 0.012, memory: 928, loss: 5.3868 2023-01-04 18:31:13,841 - mmcls - INFO - Epoch [26][4300/5005] lr: 1.000e-01, eta: 9:05:45, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3804 2023-01-04 18:31:22,577 - mmcls - INFO - Epoch [26][4400/5005] lr: 1.000e-01, eta: 9:05:36, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3834 2023-01-04 18:31:31,410 - mmcls - INFO - Epoch [26][4500/5005] lr: 1.000e-01, eta: 9:05:28, time: 0.088, data_time: 0.009, memory: 928, loss: 5.3638 2023-01-04 18:31:40,324 - mmcls - INFO - Epoch [26][4600/5005] lr: 1.000e-01, eta: 9:05:19, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3844 2023-01-04 18:31:49,368 - mmcls - INFO - Epoch [26][4700/5005] lr: 1.000e-01, eta: 9:05:11, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3777 2023-01-04 18:31:58,161 - mmcls - INFO - Epoch [26][4800/5005] lr: 1.000e-01, eta: 9:05:02, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3926 2023-01-04 18:32:06,939 - mmcls - INFO - Epoch [26][4900/5005] lr: 1.000e-01, eta: 9:04:53, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3962 2023-01-04 18:32:15,565 - mmcls - INFO - Epoch [26][5000/5005] lr: 1.000e-01, eta: 9:04:43, time: 0.086, data_time: 0.007, memory: 928, loss: 5.4013 2023-01-04 18:32:38,348 - mmcls - INFO - Epoch(val) [26][196] accuracy_top-1: 5.4520, accuracy_top-5: 14.4220 2023-01-04 18:32:49,150 - mmcls - INFO - Epoch [27][100/5005] lr: 1.000e-01, eta: 9:04:39, time: 0.108, data_time: 0.052, memory: 928, loss: 5.3623 2023-01-04 18:32:57,877 - mmcls - INFO - Epoch [27][200/5005] lr: 1.000e-01, eta: 9:04:29, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3792 2023-01-04 18:33:06,552 - mmcls - INFO - Epoch [27][300/5005] lr: 1.000e-01, eta: 9:04:20, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3869 2023-01-04 18:33:15,484 - mmcls - INFO - Epoch [27][400/5005] lr: 1.000e-01, eta: 9:04:12, time: 0.089, data_time: 0.009, memory: 928, loss: 5.3852 2023-01-04 18:33:24,365 - mmcls - INFO - Epoch [27][500/5005] lr: 1.000e-01, eta: 9:04:03, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3943 2023-01-04 18:33:33,373 - mmcls - INFO - Epoch [27][600/5005] lr: 1.000e-01, eta: 9:03:55, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3768 2023-01-04 18:33:42,202 - mmcls - INFO - Epoch [27][700/5005] lr: 1.000e-01, eta: 9:03:46, time: 0.088, data_time: 0.023, memory: 928, loss: 5.3653 2023-01-04 18:33:50,721 - mmcls - INFO - Epoch [27][800/5005] lr: 1.000e-01, eta: 9:03:36, time: 0.085, data_time: 0.002, memory: 928, loss: 5.3819 2023-01-04 18:33:59,506 - mmcls - INFO - Epoch [27][900/5005] lr: 1.000e-01, eta: 9:03:27, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3906 2023-01-04 18:34:08,463 - mmcls - INFO - Epoch [27][1000/5005] lr: 1.000e-01, eta: 9:03:19, time: 0.090, data_time: 0.015, memory: 928, loss: 5.3798 2023-01-04 18:34:17,313 - mmcls - INFO - Epoch [27][1100/5005] lr: 1.000e-01, eta: 9:03:10, time: 0.089, data_time: 0.013, memory: 928, loss: 5.3909 2023-01-04 18:34:26,160 - mmcls - INFO - Epoch [27][1200/5005] lr: 1.000e-01, eta: 9:03:01, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3894 2023-01-04 18:34:35,106 - mmcls - INFO - Epoch [27][1300/5005] lr: 1.000e-01, eta: 9:02:53, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3748 2023-01-04 18:34:43,946 - mmcls - INFO - Epoch [27][1400/5005] lr: 1.000e-01, eta: 9:02:44, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3788 2023-01-04 18:34:52,876 - mmcls - INFO - Epoch [27][1500/5005] lr: 1.000e-01, eta: 9:02:35, time: 0.089, data_time: 0.027, memory: 928, loss: 5.3891 2023-01-04 18:35:01,567 - mmcls - INFO - Epoch [27][1600/5005] lr: 1.000e-01, eta: 9:02:26, time: 0.087, data_time: 0.011, memory: 928, loss: 5.3459 2023-01-04 18:35:10,400 - mmcls - INFO - Epoch [27][1700/5005] lr: 1.000e-01, eta: 9:02:17, time: 0.088, data_time: 0.005, memory: 928, loss: 5.3700 2023-01-04 18:35:19,285 - mmcls - INFO - Epoch [27][1800/5005] lr: 1.000e-01, eta: 9:02:09, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3858 2023-01-04 18:35:28,231 - mmcls - INFO - Epoch [27][1900/5005] lr: 1.000e-01, eta: 9:02:00, time: 0.090, data_time: 0.036, memory: 928, loss: 5.3874 2023-01-04 18:35:37,093 - mmcls - INFO - Epoch [27][2000/5005] lr: 1.000e-01, eta: 9:01:52, time: 0.089, data_time: 0.005, memory: 928, loss: 5.3710 2023-01-04 18:35:45,917 - mmcls - INFO - Epoch [27][2100/5005] lr: 1.000e-01, eta: 9:01:43, time: 0.088, data_time: 0.027, memory: 928, loss: 5.3794 2023-01-04 18:35:54,512 - mmcls - INFO - Epoch [27][2200/5005] lr: 1.000e-01, eta: 9:01:33, time: 0.086, data_time: 0.034, memory: 928, loss: 5.3929 2023-01-04 18:36:03,347 - mmcls - INFO - Epoch [27][2300/5005] lr: 1.000e-01, eta: 9:01:24, time: 0.088, data_time: 0.025, memory: 928, loss: 5.3839 2023-01-04 18:36:11,826 - mmcls - INFO - Epoch [27][2400/5005] lr: 1.000e-01, eta: 9:01:15, time: 0.085, data_time: 0.012, memory: 928, loss: 5.3631 2023-01-04 18:36:20,525 - mmcls - INFO - Epoch [27][2500/5005] lr: 1.000e-01, eta: 9:01:05, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3841 2023-01-04 18:36:29,059 - mmcls - INFO - Epoch [27][2600/5005] lr: 1.000e-01, eta: 9:00:56, time: 0.085, data_time: 0.004, memory: 928, loss: 5.3959 2023-01-04 18:36:38,038 - mmcls - INFO - Epoch [27][2700/5005] lr: 1.000e-01, eta: 9:00:47, time: 0.090, data_time: 0.001, memory: 928, loss: 5.3870 2023-01-04 18:36:46,751 - mmcls - INFO - Epoch [27][2800/5005] lr: 1.000e-01, eta: 9:00:38, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3873 2023-01-04 18:36:55,589 - mmcls - INFO - Epoch [27][2900/5005] lr: 1.000e-01, eta: 9:00:29, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3926 2023-01-04 18:37:04,390 - mmcls - INFO - Epoch [27][3000/5005] lr: 1.000e-01, eta: 9:00:21, time: 0.088, data_time: 0.016, memory: 928, loss: 5.4006 2023-01-04 18:37:12,901 - mmcls - INFO - Epoch [27][3100/5005] lr: 1.000e-01, eta: 9:00:11, time: 0.085, data_time: 0.016, memory: 928, loss: 5.4055 2023-01-04 18:37:21,702 - mmcls - INFO - Epoch [27][3200/5005] lr: 1.000e-01, eta: 9:00:02, time: 0.088, data_time: 0.014, memory: 928, loss: 5.3666 2023-01-04 18:37:30,349 - mmcls - INFO - Epoch [27][3300/5005] lr: 1.000e-01, eta: 8:59:53, time: 0.086, data_time: 0.006, memory: 928, loss: 5.3924 2023-01-04 18:37:39,153 - mmcls - INFO - Epoch [27][3400/5005] lr: 1.000e-01, eta: 8:59:44, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3895 2023-01-04 18:37:47,968 - mmcls - INFO - Epoch [27][3500/5005] lr: 1.000e-01, eta: 8:59:35, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3736 2023-01-04 18:37:56,499 - mmcls - INFO - Epoch [27][3600/5005] lr: 1.000e-01, eta: 8:59:25, time: 0.085, data_time: 0.007, memory: 928, loss: 5.3913 2023-01-04 18:38:05,055 - mmcls - INFO - Epoch [27][3700/5005] lr: 1.000e-01, eta: 8:59:16, time: 0.086, data_time: 0.015, memory: 928, loss: 5.3944 2023-01-04 18:38:13,930 - mmcls - INFO - Epoch [27][3800/5005] lr: 1.000e-01, eta: 8:59:07, time: 0.089, data_time: 0.017, memory: 928, loss: 5.3846 2023-01-04 18:38:22,612 - mmcls - INFO - Epoch [27][3900/5005] lr: 1.000e-01, eta: 8:58:58, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3655 2023-01-04 18:38:31,321 - mmcls - INFO - Epoch [27][4000/5005] lr: 1.000e-01, eta: 8:58:49, time: 0.087, data_time: 0.017, memory: 928, loss: 5.3776 2023-01-04 18:38:39,976 - mmcls - INFO - Epoch [27][4100/5005] lr: 1.000e-01, eta: 8:58:39, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3855 2023-01-04 18:38:48,585 - mmcls - INFO - Epoch [27][4200/5005] lr: 1.000e-01, eta: 8:58:30, time: 0.086, data_time: 0.027, memory: 928, loss: 5.3915 2023-01-04 18:38:57,280 - mmcls - INFO - Epoch [27][4300/5005] lr: 1.000e-01, eta: 8:58:21, time: 0.087, data_time: 0.009, memory: 928, loss: 5.3656 2023-01-04 18:39:06,249 - mmcls - INFO - Epoch [27][4400/5005] lr: 1.000e-01, eta: 8:58:12, time: 0.090, data_time: 0.004, memory: 928, loss: 5.3900 2023-01-04 18:39:15,322 - mmcls - INFO - Epoch [27][4500/5005] lr: 1.000e-01, eta: 8:58:04, time: 0.091, data_time: 0.005, memory: 928, loss: 5.3900 2023-01-04 18:39:24,038 - mmcls - INFO - Epoch [27][4600/5005] lr: 1.000e-01, eta: 8:57:55, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3702 2023-01-04 18:39:32,907 - mmcls - INFO - Epoch [27][4700/5005] lr: 1.000e-01, eta: 8:57:46, time: 0.089, data_time: 0.016, memory: 928, loss: 5.3825 2023-01-04 18:39:41,554 - mmcls - INFO - Epoch [27][4800/5005] lr: 1.000e-01, eta: 8:57:37, time: 0.087, data_time: 0.011, memory: 928, loss: 5.3995 2023-01-04 18:39:50,614 - mmcls - INFO - Epoch [27][4900/5005] lr: 1.000e-01, eta: 8:57:29, time: 0.091, data_time: 0.008, memory: 928, loss: 5.3707 2023-01-04 18:39:59,484 - mmcls - INFO - Epoch [27][5000/5005] lr: 1.000e-01, eta: 8:57:20, time: 0.089, data_time: 0.005, memory: 928, loss: 5.3693 2023-01-04 18:40:22,262 - mmcls - INFO - Epoch(val) [27][196] accuracy_top-1: 5.0180, accuracy_top-5: 13.1980 2023-01-04 18:40:33,567 - mmcls - INFO - Epoch [28][100/5005] lr: 1.000e-01, eta: 8:57:16, time: 0.113, data_time: 0.044, memory: 928, loss: 5.3876 2023-01-04 18:40:42,642 - mmcls - INFO - Epoch [28][200/5005] lr: 1.000e-01, eta: 8:57:08, time: 0.091, data_time: 0.001, memory: 928, loss: 5.3861 2023-01-04 18:40:51,402 - mmcls - INFO - Epoch [28][300/5005] lr: 1.000e-01, eta: 8:56:59, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3839 2023-01-04 18:41:00,108 - mmcls - INFO - Epoch [28][400/5005] lr: 1.000e-01, eta: 8:56:50, time: 0.087, data_time: 0.020, memory: 928, loss: 5.3566 2023-01-04 18:41:08,887 - mmcls - INFO - Epoch [28][500/5005] lr: 1.000e-01, eta: 8:56:41, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3774 2023-01-04 18:41:17,696 - mmcls - INFO - Epoch [28][600/5005] lr: 1.000e-01, eta: 8:56:32, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3835 2023-01-04 18:41:26,315 - mmcls - INFO - Epoch [28][700/5005] lr: 1.000e-01, eta: 8:56:23, time: 0.086, data_time: 0.018, memory: 928, loss: 5.3811 2023-01-04 18:41:34,967 - mmcls - INFO - Epoch [28][800/5005] lr: 1.000e-01, eta: 8:56:14, time: 0.086, data_time: 0.036, memory: 928, loss: 5.3869 2023-01-04 18:41:43,796 - mmcls - INFO - Epoch [28][900/5005] lr: 1.000e-01, eta: 8:56:05, time: 0.088, data_time: 0.017, memory: 928, loss: 5.3960 2023-01-04 18:41:52,538 - mmcls - INFO - Epoch [28][1000/5005] lr: 1.000e-01, eta: 8:55:56, time: 0.087, data_time: 0.017, memory: 928, loss: 5.3590 2023-01-04 18:42:01,645 - mmcls - INFO - Epoch [28][1100/5005] lr: 1.000e-01, eta: 8:55:48, time: 0.091, data_time: 0.001, memory: 928, loss: 5.3799 2023-01-04 18:42:10,314 - mmcls - INFO - Epoch [28][1200/5005] lr: 1.000e-01, eta: 8:55:38, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3991 2023-01-04 18:42:19,500 - mmcls - INFO - Epoch [28][1300/5005] lr: 1.000e-01, eta: 8:55:30, time: 0.092, data_time: 0.038, memory: 928, loss: 5.3832 2023-01-04 18:42:28,293 - mmcls - INFO - Epoch [28][1400/5005] lr: 1.000e-01, eta: 8:55:22, time: 0.088, data_time: 0.034, memory: 928, loss: 5.4026 2023-01-04 18:42:36,952 - mmcls - INFO - Epoch [28][1500/5005] lr: 1.000e-01, eta: 8:55:12, time: 0.087, data_time: 0.025, memory: 928, loss: 5.3727 2023-01-04 18:42:45,870 - mmcls - INFO - Epoch [28][1600/5005] lr: 1.000e-01, eta: 8:55:04, time: 0.089, data_time: 0.003, memory: 928, loss: 5.3905 2023-01-04 18:42:54,805 - mmcls - INFO - Epoch [28][1700/5005] lr: 1.000e-01, eta: 8:54:55, time: 0.089, data_time: 0.010, memory: 928, loss: 5.3900 2023-01-04 18:43:03,518 - mmcls - INFO - Epoch [28][1800/5005] lr: 1.000e-01, eta: 8:54:46, time: 0.087, data_time: 0.028, memory: 928, loss: 5.3834 2023-01-04 18:43:12,540 - mmcls - INFO - Epoch [28][1900/5005] lr: 1.000e-01, eta: 8:54:38, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3716 2023-01-04 18:43:21,353 - mmcls - INFO - Epoch [28][2000/5005] lr: 1.000e-01, eta: 8:54:29, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3705 2023-01-04 18:43:29,818 - mmcls - INFO - Epoch [28][2100/5005] lr: 1.000e-01, eta: 8:54:19, time: 0.085, data_time: 0.018, memory: 928, loss: 5.3626 2023-01-04 18:43:38,441 - mmcls - INFO - Epoch [28][2200/5005] lr: 1.000e-01, eta: 8:54:10, time: 0.086, data_time: 0.017, memory: 928, loss: 5.3844 2023-01-04 18:43:47,509 - mmcls - INFO - Epoch [28][2300/5005] lr: 1.000e-01, eta: 8:54:02, time: 0.091, data_time: 0.014, memory: 928, loss: 5.3867 2023-01-04 18:43:56,528 - mmcls - INFO - Epoch [28][2400/5005] lr: 1.000e-01, eta: 8:53:53, time: 0.090, data_time: 0.015, memory: 928, loss: 5.3629 2023-01-04 18:44:05,284 - mmcls - INFO - Epoch [28][2500/5005] lr: 1.000e-01, eta: 8:53:44, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3895 2023-01-04 18:44:14,154 - mmcls - INFO - Epoch [28][2600/5005] lr: 1.000e-01, eta: 8:53:36, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3837 2023-01-04 18:44:22,882 - mmcls - INFO - Epoch [28][2700/5005] lr: 1.000e-01, eta: 8:53:26, time: 0.087, data_time: 0.002, memory: 928, loss: 5.3712 2023-01-04 18:44:31,871 - mmcls - INFO - Epoch [28][2800/5005] lr: 1.000e-01, eta: 8:53:18, time: 0.090, data_time: 0.011, memory: 928, loss: 5.3811 2023-01-04 18:44:40,660 - mmcls - INFO - Epoch [28][2900/5005] lr: 1.000e-01, eta: 8:53:09, time: 0.088, data_time: 0.009, memory: 928, loss: 5.3865 2023-01-04 18:44:49,462 - mmcls - INFO - Epoch [28][3000/5005] lr: 1.000e-01, eta: 8:53:00, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3670 2023-01-04 18:44:58,131 - mmcls - INFO - Epoch [28][3100/5005] lr: 1.000e-01, eta: 8:52:51, time: 0.087, data_time: 0.008, memory: 928, loss: 5.3644 2023-01-04 18:45:07,098 - mmcls - INFO - Epoch [28][3200/5005] lr: 1.000e-01, eta: 8:52:43, time: 0.090, data_time: 0.010, memory: 928, loss: 5.3666 2023-01-04 18:45:15,651 - mmcls - INFO - Epoch [28][3300/5005] lr: 1.000e-01, eta: 8:52:33, time: 0.085, data_time: 0.004, memory: 928, loss: 5.3733 2023-01-04 18:45:24,288 - mmcls - INFO - Epoch [28][3400/5005] lr: 1.000e-01, eta: 8:52:24, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3820 2023-01-04 18:45:33,010 - mmcls - INFO - Epoch [28][3500/5005] lr: 1.000e-01, eta: 8:52:15, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3760 2023-01-04 18:45:41,904 - mmcls - INFO - Epoch [28][3600/5005] lr: 1.000e-01, eta: 8:52:06, time: 0.089, data_time: 0.021, memory: 928, loss: 5.3810 2023-01-04 18:45:50,522 - mmcls - INFO - Epoch [28][3700/5005] lr: 1.000e-01, eta: 8:51:57, time: 0.086, data_time: 0.009, memory: 928, loss: 5.3713 2023-01-04 18:45:59,484 - mmcls - INFO - Epoch [28][3800/5005] lr: 1.000e-01, eta: 8:51:48, time: 0.090, data_time: 0.016, memory: 928, loss: 5.3935 2023-01-04 18:46:08,064 - mmcls - INFO - Epoch [28][3900/5005] lr: 1.000e-01, eta: 8:51:39, time: 0.086, data_time: 0.012, memory: 928, loss: 5.3830 2023-01-04 18:46:16,990 - mmcls - INFO - Epoch [28][4000/5005] lr: 1.000e-01, eta: 8:51:30, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3978 2023-01-04 18:46:25,661 - mmcls - INFO - Epoch [28][4100/5005] lr: 1.000e-01, eta: 8:51:21, time: 0.087, data_time: 0.005, memory: 928, loss: 5.3934 2023-01-04 18:46:34,340 - mmcls - INFO - Epoch [28][4200/5005] lr: 1.000e-01, eta: 8:51:12, time: 0.087, data_time: 0.010, memory: 928, loss: 5.3761 2023-01-04 18:46:43,104 - mmcls - INFO - Epoch [28][4300/5005] lr: 1.000e-01, eta: 8:51:03, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3809 2023-01-04 18:46:51,972 - mmcls - INFO - Epoch [28][4400/5005] lr: 1.000e-01, eta: 8:50:54, time: 0.089, data_time: 0.009, memory: 928, loss: 5.4008 2023-01-04 18:47:00,603 - mmcls - INFO - Epoch [28][4500/5005] lr: 1.000e-01, eta: 8:50:45, time: 0.086, data_time: 0.010, memory: 928, loss: 5.3742 2023-01-04 18:47:09,379 - mmcls - INFO - Epoch [28][4600/5005] lr: 1.000e-01, eta: 8:50:36, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3759 2023-01-04 18:47:18,320 - mmcls - INFO - Epoch [28][4700/5005] lr: 1.000e-01, eta: 8:50:27, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3887 2023-01-04 18:47:27,060 - mmcls - INFO - Epoch [28][4800/5005] lr: 1.000e-01, eta: 8:50:18, time: 0.087, data_time: 0.037, memory: 928, loss: 5.3664 2023-01-04 18:47:35,488 - mmcls - INFO - Epoch [28][4900/5005] lr: 1.000e-01, eta: 8:50:08, time: 0.084, data_time: 0.002, memory: 928, loss: 5.3672 2023-01-04 18:47:44,414 - mmcls - INFO - Epoch [28][5000/5005] lr: 1.000e-01, eta: 8:50:00, time: 0.089, data_time: 0.002, memory: 928, loss: 5.3782 2023-01-04 18:48:07,111 - mmcls - INFO - Epoch(val) [28][196] accuracy_top-1: 3.6180, accuracy_top-5: 10.1440 2023-01-04 18:48:18,393 - mmcls - INFO - Epoch [29][100/5005] lr: 1.000e-01, eta: 8:49:55, time: 0.113, data_time: 0.055, memory: 928, loss: 5.3712 2023-01-04 18:48:27,362 - mmcls - INFO - Epoch [29][200/5005] lr: 1.000e-01, eta: 8:49:47, time: 0.090, data_time: 0.007, memory: 928, loss: 5.3822 2023-01-04 18:48:36,174 - mmcls - INFO - Epoch [29][300/5005] lr: 1.000e-01, eta: 8:49:38, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3867 2023-01-04 18:48:45,010 - mmcls - INFO - Epoch [29][400/5005] lr: 1.000e-01, eta: 8:49:29, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3829 2023-01-04 18:48:53,872 - mmcls - INFO - Epoch [29][500/5005] lr: 1.000e-01, eta: 8:49:21, time: 0.089, data_time: 0.007, memory: 928, loss: 5.3860 2023-01-04 18:49:02,820 - mmcls - INFO - Epoch [29][600/5005] lr: 1.000e-01, eta: 8:49:12, time: 0.090, data_time: 0.017, memory: 928, loss: 5.3634 2023-01-04 18:49:11,559 - mmcls - INFO - Epoch [29][700/5005] lr: 1.000e-01, eta: 8:49:03, time: 0.087, data_time: 0.012, memory: 928, loss: 5.4059 2023-01-04 18:49:20,369 - mmcls - INFO - Epoch [29][800/5005] lr: 1.000e-01, eta: 8:48:54, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3751 2023-01-04 18:49:29,077 - mmcls - INFO - Epoch [29][900/5005] lr: 1.000e-01, eta: 8:48:45, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3905 2023-01-04 18:49:37,696 - mmcls - INFO - Epoch [29][1000/5005] lr: 1.000e-01, eta: 8:48:36, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3790 2023-01-04 18:49:46,323 - mmcls - INFO - Epoch [29][1100/5005] lr: 1.000e-01, eta: 8:48:26, time: 0.086, data_time: 0.013, memory: 928, loss: 5.3844 2023-01-04 18:49:55,240 - mmcls - INFO - Epoch [29][1200/5005] lr: 1.000e-01, eta: 8:48:18, time: 0.089, data_time: 0.042, memory: 928, loss: 5.3741 2023-01-04 18:50:04,134 - mmcls - INFO - Epoch [29][1300/5005] lr: 1.000e-01, eta: 8:48:09, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3551 2023-01-04 18:50:12,811 - mmcls - INFO - Epoch [29][1400/5005] lr: 1.000e-01, eta: 8:48:00, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3845 2023-01-04 18:50:21,861 - mmcls - INFO - Epoch [29][1500/5005] lr: 1.000e-01, eta: 8:47:52, time: 0.090, data_time: 0.001, memory: 928, loss: 5.3763 2023-01-04 18:50:30,636 - mmcls - INFO - Epoch [29][1600/5005] lr: 1.000e-01, eta: 8:47:43, time: 0.088, data_time: 0.019, memory: 928, loss: 5.3704 2023-01-04 18:50:39,448 - mmcls - INFO - Epoch [29][1700/5005] lr: 1.000e-01, eta: 8:47:34, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3663 2023-01-04 18:50:48,193 - mmcls - INFO - Epoch [29][1800/5005] lr: 1.000e-01, eta: 8:47:25, time: 0.088, data_time: 0.006, memory: 928, loss: 5.3689 2023-01-04 18:50:57,333 - mmcls - INFO - Epoch [29][1900/5005] lr: 1.000e-01, eta: 8:47:17, time: 0.091, data_time: 0.004, memory: 928, loss: 5.3776 2023-01-04 18:51:06,055 - mmcls - INFO - Epoch [29][2000/5005] lr: 1.000e-01, eta: 8:47:08, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3850 2023-01-04 18:51:15,024 - mmcls - INFO - Epoch [29][2100/5005] lr: 1.000e-01, eta: 8:46:59, time: 0.090, data_time: 0.013, memory: 928, loss: 5.3645 2023-01-04 18:51:23,720 - mmcls - INFO - Epoch [29][2200/5005] lr: 1.000e-01, eta: 8:46:50, time: 0.087, data_time: 0.010, memory: 928, loss: 5.4116 2023-01-04 18:51:32,747 - mmcls - INFO - Epoch [29][2300/5005] lr: 1.000e-01, eta: 8:46:42, time: 0.090, data_time: 0.007, memory: 928, loss: 5.3629 2023-01-04 18:51:41,468 - mmcls - INFO - Epoch [29][2400/5005] lr: 1.000e-01, eta: 8:46:33, time: 0.087, data_time: 0.004, memory: 928, loss: 5.3831 2023-01-04 18:51:50,085 - mmcls - INFO - Epoch [29][2500/5005] lr: 1.000e-01, eta: 8:46:23, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3721 2023-01-04 18:51:58,819 - mmcls - INFO - Epoch [29][2600/5005] lr: 1.000e-01, eta: 8:46:14, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3785 2023-01-04 18:52:07,370 - mmcls - INFO - Epoch [29][2700/5005] lr: 1.000e-01, eta: 8:46:05, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3832 2023-01-04 18:52:16,387 - mmcls - INFO - Epoch [29][2800/5005] lr: 1.000e-01, eta: 8:45:56, time: 0.090, data_time: 0.022, memory: 928, loss: 5.3727 2023-01-04 18:52:25,224 - mmcls - INFO - Epoch [29][2900/5005] lr: 1.000e-01, eta: 8:45:48, time: 0.088, data_time: 0.006, memory: 928, loss: 5.3754 2023-01-04 18:52:34,293 - mmcls - INFO - Epoch [29][3000/5005] lr: 1.000e-01, eta: 8:45:39, time: 0.091, data_time: 0.003, memory: 928, loss: 5.3656 2023-01-04 18:52:43,052 - mmcls - INFO - Epoch [29][3100/5005] lr: 1.000e-01, eta: 8:45:30, time: 0.088, data_time: 0.032, memory: 928, loss: 5.3800 2023-01-04 18:52:51,817 - mmcls - INFO - Epoch [29][3200/5005] lr: 1.000e-01, eta: 8:45:21, time: 0.088, data_time: 0.011, memory: 928, loss: 5.3843 2023-01-04 18:53:00,160 - mmcls - INFO - Epoch [29][3300/5005] lr: 1.000e-01, eta: 8:45:11, time: 0.083, data_time: 0.033, memory: 928, loss: 5.3787 2023-01-04 18:53:08,925 - mmcls - INFO - Epoch [29][3400/5005] lr: 1.000e-01, eta: 8:45:02, time: 0.088, data_time: 0.002, memory: 928, loss: 5.3722 2023-01-04 18:53:17,529 - mmcls - INFO - Epoch [29][3500/5005] lr: 1.000e-01, eta: 8:44:53, time: 0.086, data_time: 0.004, memory: 928, loss: 5.3754 2023-01-04 18:53:26,274 - mmcls - INFO - Epoch [29][3600/5005] lr: 1.000e-01, eta: 8:44:44, time: 0.087, data_time: 0.006, memory: 928, loss: 5.3644 2023-01-04 18:53:35,165 - mmcls - INFO - Epoch [29][3700/5005] lr: 1.000e-01, eta: 8:44:35, time: 0.089, data_time: 0.005, memory: 928, loss: 5.3805 2023-01-04 18:53:43,845 - mmcls - INFO - Epoch [29][3800/5005] lr: 1.000e-01, eta: 8:44:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3653 2023-01-04 18:53:52,646 - mmcls - INFO - Epoch [29][3900/5005] lr: 1.000e-01, eta: 8:44:17, time: 0.088, data_time: 0.014, memory: 928, loss: 5.3733 2023-01-04 18:54:01,201 - mmcls - INFO - Epoch [29][4000/5005] lr: 1.000e-01, eta: 8:44:08, time: 0.085, data_time: 0.002, memory: 928, loss: 5.3850 2023-01-04 18:54:09,943 - mmcls - INFO - Epoch [29][4100/5005] lr: 1.000e-01, eta: 8:43:59, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3830 2023-01-04 18:54:18,655 - mmcls - INFO - Epoch [29][4200/5005] lr: 1.000e-01, eta: 8:43:50, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3681 2023-01-04 18:54:27,199 - mmcls - INFO - Epoch [29][4300/5005] lr: 1.000e-01, eta: 8:43:40, time: 0.086, data_time: 0.013, memory: 928, loss: 5.3681 2023-01-04 18:54:36,270 - mmcls - INFO - Epoch [29][4400/5005] lr: 1.000e-01, eta: 8:43:32, time: 0.091, data_time: 0.019, memory: 928, loss: 5.3808 2023-01-04 18:54:44,918 - mmcls - INFO - Epoch [29][4500/5005] lr: 1.000e-01, eta: 8:43:23, time: 0.086, data_time: 0.043, memory: 928, loss: 5.3800 2023-01-04 18:54:53,504 - mmcls - INFO - Epoch [29][4600/5005] lr: 1.000e-01, eta: 8:43:13, time: 0.086, data_time: 0.011, memory: 928, loss: 5.3848 2023-01-04 18:55:01,955 - mmcls - INFO - Epoch [29][4700/5005] lr: 1.000e-01, eta: 8:43:03, time: 0.085, data_time: 0.004, memory: 928, loss: 5.3839 2023-01-04 18:55:10,861 - mmcls - INFO - Epoch [29][4800/5005] lr: 1.000e-01, eta: 8:42:55, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3876 2023-01-04 18:55:19,740 - mmcls - INFO - Epoch [29][4900/5005] lr: 1.000e-01, eta: 8:42:46, time: 0.089, data_time: 0.044, memory: 928, loss: 5.3787 2023-01-04 18:55:28,507 - mmcls - INFO - Epoch [29][5000/5005] lr: 1.000e-01, eta: 8:42:37, time: 0.088, data_time: 0.022, memory: 928, loss: 5.3781 2023-01-04 18:55:51,269 - mmcls - INFO - Epoch(val) [29][196] accuracy_top-1: 3.3940, accuracy_top-5: 9.8000 2023-01-04 18:56:02,418 - mmcls - INFO - Epoch [30][100/5005] lr: 1.000e-01, eta: 8:42:32, time: 0.111, data_time: 0.050, memory: 928, loss: 5.3753 2023-01-04 18:56:11,003 - mmcls - INFO - Epoch [30][200/5005] lr: 1.000e-01, eta: 8:42:23, time: 0.086, data_time: 0.002, memory: 928, loss: 5.3760 2023-01-04 18:56:19,786 - mmcls - INFO - Epoch [30][300/5005] lr: 1.000e-01, eta: 8:42:14, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3743 2023-01-04 18:56:28,315 - mmcls - INFO - Epoch [30][400/5005] lr: 1.000e-01, eta: 8:42:05, time: 0.085, data_time: 0.001, memory: 928, loss: 5.3788 2023-01-04 18:56:37,004 - mmcls - INFO - Epoch [30][500/5005] lr: 1.000e-01, eta: 8:41:55, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3492 2023-01-04 18:56:45,484 - mmcls - INFO - Epoch [30][600/5005] lr: 1.000e-01, eta: 8:41:46, time: 0.085, data_time: 0.001, memory: 928, loss: 5.4001 2023-01-04 18:56:54,107 - mmcls - INFO - Epoch [30][700/5005] lr: 1.000e-01, eta: 8:41:36, time: 0.086, data_time: 0.023, memory: 928, loss: 5.3629 2023-01-04 18:57:02,841 - mmcls - INFO - Epoch [30][800/5005] lr: 1.000e-01, eta: 8:41:27, time: 0.087, data_time: 0.013, memory: 928, loss: 5.3863 2023-01-04 18:57:11,621 - mmcls - INFO - Epoch [30][900/5005] lr: 1.000e-01, eta: 8:41:18, time: 0.088, data_time: 0.029, memory: 928, loss: 5.3612 2023-01-04 18:57:20,216 - mmcls - INFO - Epoch [30][1000/5005] lr: 1.000e-01, eta: 8:41:09, time: 0.086, data_time: 0.017, memory: 928, loss: 5.3651 2023-01-04 18:57:28,978 - mmcls - INFO - Epoch [30][1100/5005] lr: 1.000e-01, eta: 8:41:00, time: 0.088, data_time: 0.001, memory: 928, loss: 5.3719 2023-01-04 18:57:37,420 - mmcls - INFO - Epoch [30][1200/5005] lr: 1.000e-01, eta: 8:40:50, time: 0.084, data_time: 0.017, memory: 928, loss: 5.3740 2023-01-04 18:57:46,083 - mmcls - INFO - Epoch [30][1300/5005] lr: 1.000e-01, eta: 8:40:41, time: 0.087, data_time: 0.021, memory: 928, loss: 5.3894 2023-01-04 18:57:54,722 - mmcls - INFO - Epoch [30][1400/5005] lr: 1.000e-01, eta: 8:40:32, time: 0.086, data_time: 0.007, memory: 928, loss: 5.3926 2023-01-04 18:58:03,622 - mmcls - INFO - Epoch [30][1500/5005] lr: 1.000e-01, eta: 8:40:23, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3891 2023-01-04 18:58:12,500 - mmcls - INFO - Epoch [30][1600/5005] lr: 1.000e-01, eta: 8:40:15, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3674 2023-01-04 18:58:21,399 - mmcls - INFO - Epoch [30][1700/5005] lr: 1.000e-01, eta: 8:40:06, time: 0.089, data_time: 0.021, memory: 928, loss: 5.3836 2023-01-04 18:58:30,113 - mmcls - INFO - Epoch [30][1800/5005] lr: 1.000e-01, eta: 8:39:57, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3756 2023-01-04 18:58:38,815 - mmcls - INFO - Epoch [30][1900/5005] lr: 1.000e-01, eta: 8:39:48, time: 0.087, data_time: 0.011, memory: 928, loss: 5.3882 2023-01-04 18:58:47,586 - mmcls - INFO - Epoch [30][2000/5005] lr: 1.000e-01, eta: 8:39:39, time: 0.088, data_time: 0.009, memory: 928, loss: 5.3545 2023-01-04 18:58:56,072 - mmcls - INFO - Epoch [30][2100/5005] lr: 1.000e-01, eta: 8:39:29, time: 0.085, data_time: 0.014, memory: 928, loss: 5.3557 2023-01-04 18:59:05,171 - mmcls - INFO - Epoch [30][2200/5005] lr: 1.000e-01, eta: 8:39:21, time: 0.091, data_time: 0.011, memory: 928, loss: 5.3871 2023-01-04 18:59:14,254 - mmcls - INFO - Epoch [30][2300/5005] lr: 1.000e-01, eta: 8:39:13, time: 0.091, data_time: 0.001, memory: 928, loss: 5.3735 2023-01-04 18:59:23,101 - mmcls - INFO - Epoch [30][2400/5005] lr: 1.000e-01, eta: 8:39:04, time: 0.089, data_time: 0.016, memory: 928, loss: 5.3616 2023-01-04 18:59:31,718 - mmcls - INFO - Epoch [30][2500/5005] lr: 1.000e-01, eta: 8:38:55, time: 0.086, data_time: 0.010, memory: 928, loss: 5.3804 2023-01-04 18:59:40,612 - mmcls - INFO - Epoch [30][2600/5005] lr: 1.000e-01, eta: 8:38:46, time: 0.089, data_time: 0.001, memory: 928, loss: 5.3642 2023-01-04 18:59:49,229 - mmcls - INFO - Epoch [30][2700/5005] lr: 1.000e-01, eta: 8:38:37, time: 0.086, data_time: 0.001, memory: 928, loss: 5.3861 2023-01-04 18:59:57,785 - mmcls - INFO - Epoch [30][2800/5005] lr: 1.000e-01, eta: 8:38:27, time: 0.085, data_time: 0.003, memory: 928, loss: 5.4058 2023-01-04 19:00:06,576 - mmcls - INFO - Epoch [30][2900/5005] lr: 1.000e-01, eta: 8:38:18, time: 0.088, data_time: 0.004, memory: 928, loss: 5.3697 2023-01-04 19:00:15,779 - mmcls - INFO - Epoch [30][3000/5005] lr: 1.000e-01, eta: 8:38:10, time: 0.092, data_time: 0.001, memory: 928, loss: 5.3957 2023-01-04 19:00:24,264 - mmcls - INFO - Epoch [30][3100/5005] lr: 1.000e-01, eta: 8:38:01, time: 0.085, data_time: 0.011, memory: 928, loss: 5.3839 2023-01-04 19:00:33,733 - mmcls - INFO - Epoch [30][3200/5005] lr: 1.000e-01, eta: 8:37:53, time: 0.095, data_time: 0.001, memory: 928, loss: 5.3828 2023-01-04 19:00:42,440 - mmcls - INFO - Epoch [30][3300/5005] lr: 1.000e-01, eta: 8:37:44, time: 0.087, data_time: 0.001, memory: 928, loss: 5.3857 2023-01-04 19:00:51,359 - mmcls - INFO - Epoch [30][3400/5005] lr: 1.000e-01, eta: 8:37:36, time: 0.089, data_time: 0.004, memory: 928, loss: 5.3767 2023-01-04 19:01:00,107 - mmcls - INFO - Epoch [30][3500/5005] lr: 1.000e-01, eta: 8:37:27, time: 0.088, data_time: 0.009, memory: 928, loss: 5.3694 2023-01-04 19:01:09,088 - mmcls - INFO - Epoch [30][3600/5005] lr: 1.000e-01, eta: 8:37:18, time: 0.090, data_time: 0.032, memory: 928, loss: 5.3783 2023-01-04 19:01:17,757 - mmcls - INFO - Epoch [30][3700/5005] lr: 1.000e-01, eta: 8:37:09, time: 0.087, data_time: 0.021, memory: 928, loss: 5.3819 2023-01-04 19:01:26,831 - mmcls - INFO - Epoch [30][3800/5005] lr: 1.000e-01, eta: 8:37:01, time: 0.091, data_time: 0.002, memory: 928, loss: 5.3958 2023-01-04 19:01:36,048 - mmcls - INFO - Epoch [30][3900/5005] lr: 1.000e-01, eta: 8:36:53, time: 0.092, data_time: 0.039, memory: 928, loss: 5.3827 2023-01-04 19:01:45,112 - mmcls - INFO - Epoch [30][4000/5005] lr: 1.000e-01, eta: 8:36:45, time: 0.091, data_time: 0.020, memory: 928, loss: 5.3674 2023-01-04 19:01:53,685 - mmcls - INFO - Epoch [30][4100/5005] lr: 1.000e-01, eta: 8:36:35, time: 0.086, data_time: 0.000, memory: 928, loss: 5.3784 2023-01-04 19:02:02,377 - mmcls - INFO - Epoch [30][4200/5005] lr: 1.000e-01, eta: 8:36:26, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3800 2023-01-04 19:02:11,039 - mmcls - INFO - Epoch [30][4300/5005] lr: 1.000e-01, eta: 8:36:17, time: 0.087, data_time: 0.007, memory: 928, loss: 5.3812 2023-01-04 19:02:19,718 - mmcls - INFO - Epoch [30][4400/5005] lr: 1.000e-01, eta: 8:36:08, time: 0.087, data_time: 0.016, memory: 928, loss: 5.3753 2023-01-04 19:02:28,675 - mmcls - INFO - Epoch [30][4500/5005] lr: 1.000e-01, eta: 8:35:59, time: 0.090, data_time: 0.003, memory: 928, loss: 5.3735 2023-01-04 19:02:37,423 - mmcls - INFO - Epoch [30][4600/5005] lr: 1.000e-01, eta: 8:35:50, time: 0.087, data_time: 0.015, memory: 928, loss: 5.3756 2023-01-04 19:02:46,355 - mmcls - INFO - Epoch [30][4700/5005] lr: 1.000e-01, eta: 8:35:42, time: 0.089, data_time: 0.021, memory: 928, loss: 5.3642 2023-01-04 19:02:54,795 - mmcls - INFO - Epoch [30][4800/5005] lr: 1.000e-01, eta: 8:35:32, time: 0.084, data_time: 0.011, memory: 928, loss: 5.3786 2023-01-04 19:03:03,731 - mmcls - INFO - Epoch [30][4900/5005] lr: 1.000e-01, eta: 8:35:23, time: 0.089, data_time: 0.006, memory: 928, loss: 5.3708 2023-01-04 19:03:12,494 - mmcls - INFO - Epoch [30][5000/5005] lr: 1.000e-01, eta: 8:35:14, time: 0.088, data_time: 0.007, memory: 928, loss: 5.3762 2023-01-04 19:03:35,286 - mmcls - INFO - Epoch(val) [30][196] accuracy_top-1: 4.9000, accuracy_top-5: 13.6880 2023-01-04 19:03:46,239 - mmcls - INFO - Epoch [31][100/5005] lr: 1.000e-02, eta: 8:35:09, time: 0.109, data_time: 0.043, memory: 928, loss: 5.2582 2023-01-04 19:03:54,975 - mmcls - INFO - Epoch [31][200/5005] lr: 1.000e-02, eta: 8:35:00, time: 0.087, data_time: 0.022, memory: 928, loss: 5.2071 2023-01-04 19:04:04,085 - mmcls - INFO - Epoch [31][300/5005] lr: 1.000e-02, eta: 8:34:52, time: 0.091, data_time: 0.003, memory: 928, loss: 5.1841 2023-01-04 19:04:12,878 - mmcls - INFO - Epoch [31][400/5005] lr: 1.000e-02, eta: 8:34:43, time: 0.088, data_time: 0.003, memory: 928, loss: 5.1974 2023-01-04 19:04:21,818 - mmcls - INFO - Epoch [31][500/5005] lr: 1.000e-02, eta: 8:34:34, time: 0.089, data_time: 0.005, memory: 928, loss: 5.1785 2023-01-04 19:04:30,654 - mmcls - INFO - Epoch [31][600/5005] lr: 1.000e-02, eta: 8:34:26, time: 0.088, data_time: 0.001, memory: 928, loss: 5.1665 2023-01-04 19:04:39,537 - mmcls - INFO - Epoch [31][700/5005] lr: 1.000e-02, eta: 8:34:17, time: 0.089, data_time: 0.019, memory: 928, loss: 5.1773 2023-01-04 19:04:48,454 - mmcls - INFO - Epoch [31][800/5005] lr: 1.000e-02, eta: 8:34:08, time: 0.089, data_time: 0.002, memory: 928, loss: 5.1612 2023-01-04 19:04:57,360 - mmcls - INFO - Epoch [31][900/5005] lr: 1.000e-02, eta: 8:34:00, time: 0.089, data_time: 0.003, memory: 928, loss: 5.1607 2023-01-04 19:05:06,636 - mmcls - INFO - Epoch [31][1000/5005] lr: 1.000e-02, eta: 8:33:52, time: 0.093, data_time: 0.012, memory: 928, loss: 5.1604 2023-01-04 19:05:15,339 - mmcls - INFO - Epoch [31][1100/5005] lr: 1.000e-02, eta: 8:33:43, time: 0.087, data_time: 0.024, memory: 928, loss: 5.1634 2023-01-04 19:05:24,203 - mmcls - INFO - Epoch [31][1200/5005] lr: 1.000e-02, eta: 8:33:34, time: 0.089, data_time: 0.013, memory: 928, loss: 5.1740 2023-01-04 19:05:32,858 - mmcls - INFO - Epoch [31][1300/5005] lr: 1.000e-02, eta: 8:33:25, time: 0.087, data_time: 0.001, memory: 928, loss: 5.1532 2023-01-04 19:05:41,838 - mmcls - INFO - Epoch [31][1400/5005] lr: 1.000e-02, eta: 8:33:16, time: 0.090, data_time: 0.004, memory: 928, loss: 5.1453 2023-01-04 19:05:50,378 - mmcls - INFO - Epoch [31][1500/5005] lr: 1.000e-02, eta: 8:33:07, time: 0.085, data_time: 0.037, memory: 928, loss: 5.1528 2023-01-04 19:05:59,137 - mmcls - INFO - Epoch [31][1600/5005] lr: 1.000e-02, eta: 8:32:58, time: 0.088, data_time: 0.006, memory: 928, loss: 5.1518 2023-01-04 19:06:08,029 - mmcls - INFO - Epoch [31][1700/5005] lr: 1.000e-02, eta: 8:32:49, time: 0.089, data_time: 0.005, memory: 928, loss: 5.1394 2023-01-04 19:06:16,922 - mmcls - INFO - Epoch [31][1800/5005] lr: 1.000e-02, eta: 8:32:40, time: 0.089, data_time: 0.011, memory: 928, loss: 5.1451 2023-01-04 19:06:25,739 - mmcls - INFO - Epoch [31][1900/5005] lr: 1.000e-02, eta: 8:32:32, time: 0.088, data_time: 0.003, memory: 928, loss: 5.1636 2023-01-04 19:06:34,418 - mmcls - INFO - Epoch [31][2000/5005] lr: 1.000e-02, eta: 8:32:22, time: 0.087, data_time: 0.002, memory: 928, loss: 5.1387 2023-01-04 19:06:43,111 - mmcls - INFO - Epoch [31][2100/5005] lr: 1.000e-02, eta: 8:32:13, time: 0.087, data_time: 0.018, memory: 928, loss: 5.1626 2023-01-04 19:06:51,868 - mmcls - INFO - Epoch [31][2200/5005] lr: 1.000e-02, eta: 8:32:04, time: 0.087, data_time: 0.003, memory: 928, loss: 5.1355 2023-01-04 19:07:00,498 - mmcls - INFO - Epoch [31][2300/5005] lr: 1.000e-02, eta: 8:31:55, time: 0.086, data_time: 0.011, memory: 928, loss: 5.1361 2023-01-04 19:07:09,408 - mmcls - INFO - Epoch [31][2400/5005] lr: 1.000e-02, eta: 8:31:46, time: 0.089, data_time: 0.020, memory: 928, loss: 5.1508 2023-01-04 19:07:18,069 - mmcls - INFO - Epoch [31][2500/5005] lr: 1.000e-02, eta: 8:31:37, time: 0.087, data_time: 0.006, memory: 928, loss: 5.1319 2023-01-04 19:07:27,009 - mmcls - INFO - Epoch [31][2600/5005] lr: 1.000e-02, eta: 8:31:29, time: 0.089, data_time: 0.002, memory: 928, loss: 5.1236 2023-01-04 19:07:36,116 - mmcls - INFO - Epoch [31][2700/5005] lr: 1.000e-02, eta: 8:31:20, time: 0.091, data_time: 0.001, memory: 928, loss: 5.1384 2023-01-04 19:07:44,714 - mmcls - INFO - Epoch [31][2800/5005] lr: 1.000e-02, eta: 8:31:11, time: 0.086, data_time: 0.009, memory: 928, loss: 5.1343 2023-01-04 19:07:53,373 - mmcls - INFO - Epoch [31][2900/5005] lr: 1.000e-02, eta: 8:31:02, time: 0.087, data_time: 0.036, memory: 928, loss: 5.1274 2023-01-04 19:08:01,951 - mmcls - INFO - Epoch [31][3000/5005] lr: 1.000e-02, eta: 8:30:53, time: 0.086, data_time: 0.001, memory: 928, loss: 5.1357 2023-01-04 19:08:10,823 - mmcls - INFO - Epoch [31][3100/5005] lr: 1.000e-02, eta: 8:30:44, time: 0.089, data_time: 0.011, memory: 928, loss: 5.1180 2023-01-04 19:08:19,588 - mmcls - INFO - Epoch [31][3200/5005] lr: 1.000e-02, eta: 8:30:35, time: 0.088, data_time: 0.034, memory: 928, loss: 5.1350 2023-01-04 19:08:28,253 - mmcls - INFO - Epoch [31][3300/5005] lr: 1.000e-02, eta: 8:30:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.1346 2023-01-04 19:08:36,821 - mmcls - INFO - Epoch [31][3400/5005] lr: 1.000e-02, eta: 8:30:16, time: 0.086, data_time: 0.003, memory: 928, loss: 5.1200 2023-01-04 19:08:45,903 - mmcls - INFO - Epoch [31][3500/5005] lr: 1.000e-02, eta: 8:30:08, time: 0.091, data_time: 0.001, memory: 928, loss: 5.1295 2023-01-04 19:08:54,798 - mmcls - INFO - Epoch [31][3600/5005] lr: 1.000e-02, eta: 8:29:59, time: 0.089, data_time: 0.021, memory: 928, loss: 5.1441 2023-01-04 19:09:03,464 - mmcls - INFO - Epoch [31][3700/5005] lr: 1.000e-02, eta: 8:29:50, time: 0.087, data_time: 0.014, memory: 928, loss: 5.1324 2023-01-04 19:09:12,431 - mmcls - INFO - Epoch [31][3800/5005] lr: 1.000e-02, eta: 8:29:42, time: 0.090, data_time: 0.012, memory: 928, loss: 5.1372 2023-01-04 19:09:21,228 - mmcls - INFO - Epoch [31][3900/5005] lr: 1.000e-02, eta: 8:29:33, time: 0.088, data_time: 0.031, memory: 928, loss: 5.1017 2023-01-04 19:09:29,769 - mmcls - INFO - Epoch [31][4000/5005] lr: 1.000e-02, eta: 8:29:23, time: 0.085, data_time: 0.012, memory: 928, loss: 5.1249 2023-01-04 19:09:38,413 - mmcls - INFO - Epoch [31][4100/5005] lr: 1.000e-02, eta: 8:29:14, time: 0.086, data_time: 0.003, memory: 928, loss: 5.1439 2023-01-04 19:09:46,963 - mmcls - INFO - Epoch [31][4200/5005] lr: 1.000e-02, eta: 8:29:05, time: 0.086, data_time: 0.006, memory: 928, loss: 5.1332 2023-01-04 19:09:55,667 - mmcls - INFO - Epoch [31][4300/5005] lr: 1.000e-02, eta: 8:28:56, time: 0.087, data_time: 0.020, memory: 928, loss: 5.1506 2023-01-04 19:10:04,414 - mmcls - INFO - Epoch [31][4400/5005] lr: 1.000e-02, eta: 8:28:47, time: 0.087, data_time: 0.027, memory: 928, loss: 5.1356 2023-01-04 19:10:13,077 - mmcls - INFO - Epoch [31][4500/5005] lr: 1.000e-02, eta: 8:28:37, time: 0.087, data_time: 0.007, memory: 928, loss: 5.1190 2023-01-04 19:10:21,619 - mmcls - INFO - Epoch [31][4600/5005] lr: 1.000e-02, eta: 8:28:28, time: 0.086, data_time: 0.003, memory: 928, loss: 5.1200 2023-01-04 19:10:30,403 - mmcls - INFO - Epoch [31][4700/5005] lr: 1.000e-02, eta: 8:28:19, time: 0.088, data_time: 0.030, memory: 928, loss: 5.1254 2023-01-04 19:10:38,887 - mmcls - INFO - Epoch [31][4800/5005] lr: 1.000e-02, eta: 8:28:09, time: 0.085, data_time: 0.001, memory: 928, loss: 5.1057 2023-01-04 19:10:47,615 - mmcls - INFO - Epoch [31][4900/5005] lr: 1.000e-02, eta: 8:28:00, time: 0.087, data_time: 0.014, memory: 928, loss: 5.1071 2023-01-04 19:10:56,367 - mmcls - INFO - Epoch [31][5000/5005] lr: 1.000e-02, eta: 8:27:51, time: 0.087, data_time: 0.001, memory: 928, loss: 5.1113 2023-01-04 19:11:19,271 - mmcls - INFO - Epoch(val) [31][196] accuracy_top-1: 12.8400, accuracy_top-5: 28.0800 2023-01-04 19:11:30,364 - mmcls - INFO - Epoch [32][100/5005] lr: 1.000e-02, eta: 8:27:46, time: 0.111, data_time: 0.036, memory: 928, loss: 5.0991 2023-01-04 19:11:38,890 - mmcls - INFO - Epoch [32][200/5005] lr: 1.000e-02, eta: 8:27:37, time: 0.085, data_time: 0.001, memory: 928, loss: 5.1179 2023-01-04 19:11:47,602 - mmcls - INFO - Epoch [32][300/5005] lr: 1.000e-02, eta: 8:27:28, time: 0.087, data_time: 0.004, memory: 928, loss: 5.1076 2023-01-04 19:11:56,217 - mmcls - INFO - Epoch [32][400/5005] lr: 1.000e-02, eta: 8:27:18, time: 0.086, data_time: 0.002, memory: 928, loss: 5.1160 2023-01-04 19:12:05,123 - mmcls - INFO - Epoch [32][500/5005] lr: 1.000e-02, eta: 8:27:10, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0991 2023-01-04 19:12:13,919 - mmcls - INFO - Epoch [32][600/5005] lr: 1.000e-02, eta: 8:27:01, time: 0.088, data_time: 0.009, memory: 928, loss: 5.1196 2023-01-04 19:12:22,376 - mmcls - INFO - Epoch [32][700/5005] lr: 1.000e-02, eta: 8:26:51, time: 0.085, data_time: 0.017, memory: 928, loss: 5.1101 2023-01-04 19:12:31,366 - mmcls - INFO - Epoch [32][800/5005] lr: 1.000e-02, eta: 8:26:43, time: 0.090, data_time: 0.004, memory: 928, loss: 5.1287 2023-01-04 19:12:40,164 - mmcls - INFO - Epoch [32][900/5005] lr: 1.000e-02, eta: 8:26:34, time: 0.088, data_time: 0.001, memory: 928, loss: 5.1067 2023-01-04 19:12:48,723 - mmcls - INFO - Epoch [32][1000/5005] lr: 1.000e-02, eta: 8:26:24, time: 0.086, data_time: 0.012, memory: 928, loss: 5.1040 2023-01-04 19:12:57,619 - mmcls - INFO - Epoch [32][1100/5005] lr: 1.000e-02, eta: 8:26:16, time: 0.089, data_time: 0.001, memory: 928, loss: 5.1081 2023-01-04 19:13:06,461 - mmcls - INFO - Epoch [32][1200/5005] lr: 1.000e-02, eta: 8:26:07, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0977 2023-01-04 19:13:15,289 - mmcls - INFO - Epoch [32][1300/5005] lr: 1.000e-02, eta: 8:25:58, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0836 2023-01-04 19:13:23,879 - mmcls - INFO - Epoch [32][1400/5005] lr: 1.000e-02, eta: 8:25:49, time: 0.086, data_time: 0.001, memory: 928, loss: 5.1137 2023-01-04 19:13:32,582 - mmcls - INFO - Epoch [32][1500/5005] lr: 1.000e-02, eta: 8:25:40, time: 0.087, data_time: 0.000, memory: 928, loss: 5.1134 2023-01-04 19:13:41,086 - mmcls - INFO - Epoch [32][1600/5005] lr: 1.000e-02, eta: 8:25:30, time: 0.085, data_time: 0.013, memory: 928, loss: 5.1298 2023-01-04 19:13:49,586 - mmcls - INFO - Epoch [32][1700/5005] lr: 1.000e-02, eta: 8:25:21, time: 0.085, data_time: 0.013, memory: 928, loss: 5.1113 2023-01-04 19:13:58,825 - mmcls - INFO - Epoch [32][1800/5005] lr: 1.000e-02, eta: 8:25:13, time: 0.092, data_time: 0.001, memory: 928, loss: 5.0963 2023-01-04 19:14:07,597 - mmcls - INFO - Epoch [32][1900/5005] lr: 1.000e-02, eta: 8:25:04, time: 0.088, data_time: 0.001, memory: 928, loss: 5.1089 2023-01-04 19:14:16,388 - mmcls - INFO - Epoch [32][2000/5005] lr: 1.000e-02, eta: 8:24:55, time: 0.088, data_time: 0.006, memory: 928, loss: 5.1068 2023-01-04 19:14:25,363 - mmcls - INFO - Epoch [32][2100/5005] lr: 1.000e-02, eta: 8:24:46, time: 0.090, data_time: 0.006, memory: 928, loss: 5.1086 2023-01-04 19:14:34,174 - mmcls - INFO - Epoch [32][2200/5005] lr: 1.000e-02, eta: 8:24:37, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0929 2023-01-04 19:14:42,821 - mmcls - INFO - Epoch [32][2300/5005] lr: 1.000e-02, eta: 8:24:28, time: 0.086, data_time: 0.001, memory: 928, loss: 5.1096 2023-01-04 19:14:51,426 - mmcls - INFO - Epoch [32][2400/5005] lr: 1.000e-02, eta: 8:24:19, time: 0.086, data_time: 0.005, memory: 928, loss: 5.1200 2023-01-04 19:15:00,077 - mmcls - INFO - Epoch [32][2500/5005] lr: 1.000e-02, eta: 8:24:10, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0992 2023-01-04 19:15:09,077 - mmcls - INFO - Epoch [32][2600/5005] lr: 1.000e-02, eta: 8:24:01, time: 0.090, data_time: 0.033, memory: 928, loss: 5.0966 2023-01-04 19:15:18,006 - mmcls - INFO - Epoch [32][2700/5005] lr: 1.000e-02, eta: 8:23:53, time: 0.089, data_time: 0.004, memory: 928, loss: 5.1179 2023-01-04 19:15:26,745 - mmcls - INFO - Epoch [32][2800/5005] lr: 1.000e-02, eta: 8:23:44, time: 0.087, data_time: 0.004, memory: 928, loss: 5.1210 2023-01-04 19:15:36,022 - mmcls - INFO - Epoch [32][2900/5005] lr: 1.000e-02, eta: 8:23:36, time: 0.093, data_time: 0.005, memory: 928, loss: 5.1287 2023-01-04 19:15:44,913 - mmcls - INFO - Epoch [32][3000/5005] lr: 1.000e-02, eta: 8:23:27, time: 0.089, data_time: 0.026, memory: 928, loss: 5.1118 2023-01-04 19:15:53,508 - mmcls - INFO - Epoch [32][3100/5005] lr: 1.000e-02, eta: 8:23:18, time: 0.086, data_time: 0.025, memory: 928, loss: 5.1031 2023-01-04 19:16:02,605 - mmcls - INFO - Epoch [32][3200/5005] lr: 1.000e-02, eta: 8:23:10, time: 0.091, data_time: 0.002, memory: 928, loss: 5.1011 2023-01-04 19:16:11,422 - mmcls - INFO - Epoch [32][3300/5005] lr: 1.000e-02, eta: 8:23:01, time: 0.088, data_time: 0.027, memory: 928, loss: 5.1058 2023-01-04 19:16:20,775 - mmcls - INFO - Epoch [32][3400/5005] lr: 1.000e-02, eta: 8:22:53, time: 0.093, data_time: 0.016, memory: 928, loss: 5.1037 2023-01-04 19:16:29,439 - mmcls - INFO - Epoch [32][3500/5005] lr: 1.000e-02, eta: 8:22:44, time: 0.087, data_time: 0.036, memory: 928, loss: 5.0919 2023-01-04 19:16:38,375 - mmcls - INFO - Epoch [32][3600/5005] lr: 1.000e-02, eta: 8:22:35, time: 0.089, data_time: 0.014, memory: 928, loss: 5.1296 2023-01-04 19:16:47,387 - mmcls - INFO - Epoch [32][3700/5005] lr: 1.000e-02, eta: 8:22:27, time: 0.090, data_time: 0.024, memory: 928, loss: 5.1107 2023-01-04 19:16:56,329 - mmcls - INFO - Epoch [32][3800/5005] lr: 1.000e-02, eta: 8:22:18, time: 0.089, data_time: 0.001, memory: 928, loss: 5.1071 2023-01-04 19:17:04,999 - mmcls - INFO - Epoch [32][3900/5005] lr: 1.000e-02, eta: 8:22:09, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0884 2023-01-04 19:17:13,901 - mmcls - INFO - Epoch [32][4000/5005] lr: 1.000e-02, eta: 8:22:01, time: 0.089, data_time: 0.003, memory: 928, loss: 5.1212 2023-01-04 19:17:22,400 - mmcls - INFO - Epoch [32][4100/5005] lr: 1.000e-02, eta: 8:21:51, time: 0.085, data_time: 0.019, memory: 928, loss: 5.1131 2023-01-04 19:17:31,089 - mmcls - INFO - Epoch [32][4200/5005] lr: 1.000e-02, eta: 8:21:42, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0993 2023-01-04 19:17:39,659 - mmcls - INFO - Epoch [32][4300/5005] lr: 1.000e-02, eta: 8:21:33, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0998 2023-01-04 19:17:48,264 - mmcls - INFO - Epoch [32][4400/5005] lr: 1.000e-02, eta: 8:21:23, time: 0.086, data_time: 0.007, memory: 928, loss: 5.1026 2023-01-04 19:17:56,915 - mmcls - INFO - Epoch [32][4500/5005] lr: 1.000e-02, eta: 8:21:14, time: 0.087, data_time: 0.002, memory: 928, loss: 5.1075 2023-01-04 19:18:05,927 - mmcls - INFO - Epoch [32][4600/5005] lr: 1.000e-02, eta: 8:21:06, time: 0.090, data_time: 0.001, memory: 928, loss: 5.1001 2023-01-04 19:18:14,693 - mmcls - INFO - Epoch [32][4700/5005] lr: 1.000e-02, eta: 8:20:57, time: 0.088, data_time: 0.015, memory: 928, loss: 5.1030 2023-01-04 19:18:23,171 - mmcls - INFO - Epoch [32][4800/5005] lr: 1.000e-02, eta: 8:20:47, time: 0.085, data_time: 0.006, memory: 928, loss: 5.0967 2023-01-04 19:18:31,834 - mmcls - INFO - Epoch [32][4900/5005] lr: 1.000e-02, eta: 8:20:38, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0965 2023-01-04 19:18:40,491 - mmcls - INFO - Epoch [32][5000/5005] lr: 1.000e-02, eta: 8:20:29, time: 0.087, data_time: 0.037, memory: 928, loss: 5.1050 2023-01-04 19:19:03,303 - mmcls - INFO - Epoch(val) [32][196] accuracy_top-1: 11.4880, accuracy_top-5: 25.7780 2023-01-04 19:19:14,181 - mmcls - INFO - Epoch [33][100/5005] lr: 1.000e-02, eta: 8:20:23, time: 0.109, data_time: 0.030, memory: 928, loss: 5.1092 2023-01-04 19:19:23,019 - mmcls - INFO - Epoch [33][200/5005] lr: 1.000e-02, eta: 8:20:14, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0894 2023-01-04 19:19:32,017 - mmcls - INFO - Epoch [33][300/5005] lr: 1.000e-02, eta: 8:20:06, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0937 2023-01-04 19:19:40,867 - mmcls - INFO - Epoch [33][400/5005] lr: 1.000e-02, eta: 8:19:57, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0949 2023-01-04 19:19:49,420 - mmcls - INFO - Epoch [33][500/5005] lr: 1.000e-02, eta: 8:19:47, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0952 2023-01-04 19:19:58,686 - mmcls - INFO - Epoch [33][600/5005] lr: 1.000e-02, eta: 8:19:40, time: 0.093, data_time: 0.004, memory: 928, loss: 5.1079 2023-01-04 19:20:07,489 - mmcls - INFO - Epoch [33][700/5005] lr: 1.000e-02, eta: 8:19:31, time: 0.088, data_time: 0.000, memory: 928, loss: 5.0928 2023-01-04 19:20:16,178 - mmcls - INFO - Epoch [33][800/5005] lr: 1.000e-02, eta: 8:19:22, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0647 2023-01-04 19:20:25,345 - mmcls - INFO - Epoch [33][900/5005] lr: 1.000e-02, eta: 8:19:13, time: 0.092, data_time: 0.002, memory: 928, loss: 5.0941 2023-01-04 19:20:33,851 - mmcls - INFO - Epoch [33][1000/5005] lr: 1.000e-02, eta: 8:19:04, time: 0.085, data_time: 0.021, memory: 928, loss: 5.1108 2023-01-04 19:20:42,548 - mmcls - INFO - Epoch [33][1100/5005] lr: 1.000e-02, eta: 8:18:55, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0911 2023-01-04 19:20:51,773 - mmcls - INFO - Epoch [33][1200/5005] lr: 1.000e-02, eta: 8:18:47, time: 0.092, data_time: 0.013, memory: 928, loss: 5.0994 2023-01-04 19:21:00,625 - mmcls - INFO - Epoch [33][1300/5005] lr: 1.000e-02, eta: 8:18:38, time: 0.089, data_time: 0.014, memory: 928, loss: 5.1076 2023-01-04 19:21:09,410 - mmcls - INFO - Epoch [33][1400/5005] lr: 1.000e-02, eta: 8:18:29, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0675 2023-01-04 19:21:17,955 - mmcls - INFO - Epoch [33][1500/5005] lr: 1.000e-02, eta: 8:18:20, time: 0.086, data_time: 0.018, memory: 928, loss: 5.1012 2023-01-04 19:21:26,940 - mmcls - INFO - Epoch [33][1600/5005] lr: 1.000e-02, eta: 8:18:11, time: 0.090, data_time: 0.011, memory: 928, loss: 5.1222 2023-01-04 19:21:35,874 - mmcls - INFO - Epoch [33][1700/5005] lr: 1.000e-02, eta: 8:18:03, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0928 2023-01-04 19:21:44,855 - mmcls - INFO - Epoch [33][1800/5005] lr: 1.000e-02, eta: 8:17:54, time: 0.090, data_time: 0.021, memory: 928, loss: 5.0992 2023-01-04 19:21:53,605 - mmcls - INFO - Epoch [33][1900/5005] lr: 1.000e-02, eta: 8:17:45, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0826 2023-01-04 19:22:02,117 - mmcls - INFO - Epoch [33][2000/5005] lr: 1.000e-02, eta: 8:17:36, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0854 2023-01-04 19:22:10,948 - mmcls - INFO - Epoch [33][2100/5005] lr: 1.000e-02, eta: 8:17:27, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0987 2023-01-04 19:22:19,983 - mmcls - INFO - Epoch [33][2200/5005] lr: 1.000e-02, eta: 8:17:19, time: 0.090, data_time: 0.009, memory: 928, loss: 5.0819 2023-01-04 19:22:28,974 - mmcls - INFO - Epoch [33][2300/5005] lr: 1.000e-02, eta: 8:17:10, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0972 2023-01-04 19:22:37,741 - mmcls - INFO - Epoch [33][2400/5005] lr: 1.000e-02, eta: 8:17:01, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0937 2023-01-04 19:22:46,492 - mmcls - INFO - Epoch [33][2500/5005] lr: 1.000e-02, eta: 8:16:52, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0865 2023-01-04 19:22:55,126 - mmcls - INFO - Epoch [33][2600/5005] lr: 1.000e-02, eta: 8:16:43, time: 0.086, data_time: 0.042, memory: 928, loss: 5.0941 2023-01-04 19:23:03,745 - mmcls - INFO - Epoch [33][2700/5005] lr: 1.000e-02, eta: 8:16:34, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0979 2023-01-04 19:23:12,412 - mmcls - INFO - Epoch [33][2800/5005] lr: 1.000e-02, eta: 8:16:25, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0922 2023-01-04 19:23:21,296 - mmcls - INFO - Epoch [33][2900/5005] lr: 1.000e-02, eta: 8:16:16, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0786 2023-01-04 19:23:29,977 - mmcls - INFO - Epoch [33][3000/5005] lr: 1.000e-02, eta: 8:16:07, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0543 2023-01-04 19:23:38,487 - mmcls - INFO - Epoch [33][3100/5005] lr: 1.000e-02, eta: 8:15:57, time: 0.085, data_time: 0.014, memory: 928, loss: 5.1033 2023-01-04 19:23:47,237 - mmcls - INFO - Epoch [33][3200/5005] lr: 1.000e-02, eta: 8:15:48, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0949 2023-01-04 19:23:56,107 - mmcls - INFO - Epoch [33][3300/5005] lr: 1.000e-02, eta: 8:15:40, time: 0.089, data_time: 0.015, memory: 928, loss: 5.1019 2023-01-04 19:24:04,819 - mmcls - INFO - Epoch [33][3400/5005] lr: 1.000e-02, eta: 8:15:30, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0794 2023-01-04 19:24:13,655 - mmcls - INFO - Epoch [33][3500/5005] lr: 1.000e-02, eta: 8:15:22, time: 0.088, data_time: 0.002, memory: 928, loss: 5.1083 2023-01-04 19:24:22,775 - mmcls - INFO - Epoch [33][3600/5005] lr: 1.000e-02, eta: 8:15:13, time: 0.091, data_time: 0.009, memory: 928, loss: 5.0831 2023-01-04 19:24:31,604 - mmcls - INFO - Epoch [33][3700/5005] lr: 1.000e-02, eta: 8:15:05, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0862 2023-01-04 19:24:40,161 - mmcls - INFO - Epoch [33][3800/5005] lr: 1.000e-02, eta: 8:14:55, time: 0.085, data_time: 0.001, memory: 928, loss: 5.1065 2023-01-04 19:24:48,951 - mmcls - INFO - Epoch [33][3900/5005] lr: 1.000e-02, eta: 8:14:46, time: 0.088, data_time: 0.015, memory: 928, loss: 5.1013 2023-01-04 19:24:57,540 - mmcls - INFO - Epoch [33][4000/5005] lr: 1.000e-02, eta: 8:14:37, time: 0.086, data_time: 0.027, memory: 928, loss: 5.0995 2023-01-04 19:25:06,549 - mmcls - INFO - Epoch [33][4100/5005] lr: 1.000e-02, eta: 8:14:29, time: 0.090, data_time: 0.011, memory: 928, loss: 5.1103 2023-01-04 19:25:15,415 - mmcls - INFO - Epoch [33][4200/5005] lr: 1.000e-02, eta: 8:14:20, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0903 2023-01-04 19:25:24,323 - mmcls - INFO - Epoch [33][4300/5005] lr: 1.000e-02, eta: 8:14:11, time: 0.089, data_time: 0.011, memory: 928, loss: 5.1081 2023-01-04 19:25:33,081 - mmcls - INFO - Epoch [33][4400/5005] lr: 1.000e-02, eta: 8:14:02, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0961 2023-01-04 19:25:41,592 - mmcls - INFO - Epoch [33][4500/5005] lr: 1.000e-02, eta: 8:13:53, time: 0.085, data_time: 0.020, memory: 928, loss: 5.0964 2023-01-04 19:25:50,128 - mmcls - INFO - Epoch [33][4600/5005] lr: 1.000e-02, eta: 8:13:43, time: 0.085, data_time: 0.009, memory: 928, loss: 5.1194 2023-01-04 19:25:58,895 - mmcls - INFO - Epoch [33][4700/5005] lr: 1.000e-02, eta: 8:13:34, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0888 2023-01-04 19:26:07,629 - mmcls - INFO - Epoch [33][4800/5005] lr: 1.000e-02, eta: 8:13:25, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0929 2023-01-04 19:26:16,454 - mmcls - INFO - Epoch [33][4900/5005] lr: 1.000e-02, eta: 8:13:17, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0943 2023-01-04 19:26:25,176 - mmcls - INFO - Epoch [33][5000/5005] lr: 1.000e-02, eta: 8:13:08, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0902 2023-01-04 19:26:47,980 - mmcls - INFO - Epoch(val) [33][196] accuracy_top-1: 12.5000, accuracy_top-5: 27.3460 2023-01-04 19:26:58,858 - mmcls - INFO - Epoch [34][100/5005] lr: 1.000e-02, eta: 8:13:02, time: 0.109, data_time: 0.056, memory: 928, loss: 5.0965 2023-01-04 19:27:07,695 - mmcls - INFO - Epoch [34][200/5005] lr: 1.000e-02, eta: 8:12:53, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0873 2023-01-04 19:27:16,881 - mmcls - INFO - Epoch [34][300/5005] lr: 1.000e-02, eta: 8:12:45, time: 0.092, data_time: 0.016, memory: 928, loss: 5.0932 2023-01-04 19:27:25,347 - mmcls - INFO - Epoch [34][400/5005] lr: 1.000e-02, eta: 8:12:35, time: 0.085, data_time: 0.015, memory: 928, loss: 5.0757 2023-01-04 19:27:33,935 - mmcls - INFO - Epoch [34][500/5005] lr: 1.000e-02, eta: 8:12:26, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0710 2023-01-04 19:27:42,669 - mmcls - INFO - Epoch [34][600/5005] lr: 1.000e-02, eta: 8:12:17, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0900 2023-01-04 19:27:51,473 - mmcls - INFO - Epoch [34][700/5005] lr: 1.000e-02, eta: 8:12:08, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0997 2023-01-04 19:28:00,395 - mmcls - INFO - Epoch [34][800/5005] lr: 1.000e-02, eta: 8:11:59, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0791 2023-01-04 19:28:08,975 - mmcls - INFO - Epoch [34][900/5005] lr: 1.000e-02, eta: 8:11:50, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0994 2023-01-04 19:28:17,989 - mmcls - INFO - Epoch [34][1000/5005] lr: 1.000e-02, eta: 8:11:42, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0944 2023-01-04 19:28:26,838 - mmcls - INFO - Epoch [34][1100/5005] lr: 1.000e-02, eta: 8:11:33, time: 0.089, data_time: 0.006, memory: 928, loss: 5.1117 2023-01-04 19:28:35,556 - mmcls - INFO - Epoch [34][1200/5005] lr: 1.000e-02, eta: 8:11:24, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0912 2023-01-04 19:28:44,460 - mmcls - INFO - Epoch [34][1300/5005] lr: 1.000e-02, eta: 8:11:15, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0915 2023-01-04 19:28:53,299 - mmcls - INFO - Epoch [34][1400/5005] lr: 1.000e-02, eta: 8:11:06, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0901 2023-01-04 19:29:01,891 - mmcls - INFO - Epoch [34][1500/5005] lr: 1.000e-02, eta: 8:10:57, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0852 2023-01-04 19:29:10,529 - mmcls - INFO - Epoch [34][1600/5005] lr: 1.000e-02, eta: 8:10:48, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0889 2023-01-04 19:29:19,061 - mmcls - INFO - Epoch [34][1700/5005] lr: 1.000e-02, eta: 8:10:38, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0745 2023-01-04 19:29:27,948 - mmcls - INFO - Epoch [34][1800/5005] lr: 1.000e-02, eta: 8:10:30, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0924 2023-01-04 19:29:36,541 - mmcls - INFO - Epoch [34][1900/5005] lr: 1.000e-02, eta: 8:10:20, time: 0.086, data_time: 0.015, memory: 928, loss: 5.0734 2023-01-04 19:29:45,252 - mmcls - INFO - Epoch [34][2000/5005] lr: 1.000e-02, eta: 8:10:11, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0779 2023-01-04 19:29:54,126 - mmcls - INFO - Epoch [34][2100/5005] lr: 1.000e-02, eta: 8:10:03, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0825 2023-01-04 19:30:02,587 - mmcls - INFO - Epoch [34][2200/5005] lr: 1.000e-02, eta: 8:09:53, time: 0.085, data_time: 0.007, memory: 928, loss: 5.1003 2023-01-04 19:30:11,205 - mmcls - INFO - Epoch [34][2300/5005] lr: 1.000e-02, eta: 8:09:44, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0885 2023-01-04 19:30:19,964 - mmcls - INFO - Epoch [34][2400/5005] lr: 1.000e-02, eta: 8:09:35, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0767 2023-01-04 19:30:28,865 - mmcls - INFO - Epoch [34][2500/5005] lr: 1.000e-02, eta: 8:09:26, time: 0.089, data_time: 0.020, memory: 928, loss: 5.0859 2023-01-04 19:30:37,426 - mmcls - INFO - Epoch [34][2600/5005] lr: 1.000e-02, eta: 8:09:17, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0889 2023-01-04 19:30:46,296 - mmcls - INFO - Epoch [34][2700/5005] lr: 1.000e-02, eta: 8:09:08, time: 0.089, data_time: 0.001, memory: 928, loss: 5.1045 2023-01-04 19:30:55,148 - mmcls - INFO - Epoch [34][2800/5005] lr: 1.000e-02, eta: 8:08:59, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0949 2023-01-04 19:31:03,758 - mmcls - INFO - Epoch [34][2900/5005] lr: 1.000e-02, eta: 8:08:50, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0835 2023-01-04 19:31:12,833 - mmcls - INFO - Epoch [34][3000/5005] lr: 1.000e-02, eta: 8:08:42, time: 0.091, data_time: 0.010, memory: 928, loss: 5.0799 2023-01-04 19:31:21,392 - mmcls - INFO - Epoch [34][3100/5005] lr: 1.000e-02, eta: 8:08:32, time: 0.086, data_time: 0.027, memory: 928, loss: 5.1063 2023-01-04 19:31:29,961 - mmcls - INFO - Epoch [34][3200/5005] lr: 1.000e-02, eta: 8:08:23, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0783 2023-01-04 19:31:38,548 - mmcls - INFO - Epoch [34][3300/5005] lr: 1.000e-02, eta: 8:08:14, time: 0.086, data_time: 0.027, memory: 928, loss: 5.0975 2023-01-04 19:31:47,080 - mmcls - INFO - Epoch [34][3400/5005] lr: 1.000e-02, eta: 8:08:04, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0876 2023-01-04 19:31:55,840 - mmcls - INFO - Epoch [34][3500/5005] lr: 1.000e-02, eta: 8:07:56, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0857 2023-01-04 19:32:04,810 - mmcls - INFO - Epoch [34][3600/5005] lr: 1.000e-02, eta: 8:07:47, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0905 2023-01-04 19:32:13,667 - mmcls - INFO - Epoch [34][3700/5005] lr: 1.000e-02, eta: 8:07:38, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0816 2023-01-04 19:32:22,388 - mmcls - INFO - Epoch [34][3800/5005] lr: 1.000e-02, eta: 8:07:29, time: 0.087, data_time: 0.008, memory: 928, loss: 5.1118 2023-01-04 19:32:31,295 - mmcls - INFO - Epoch [34][3900/5005] lr: 1.000e-02, eta: 8:07:21, time: 0.089, data_time: 0.001, memory: 928, loss: 5.1047 2023-01-04 19:32:40,082 - mmcls - INFO - Epoch [34][4000/5005] lr: 1.000e-02, eta: 8:07:12, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0788 2023-01-04 19:32:48,567 - mmcls - INFO - Epoch [34][4100/5005] lr: 1.000e-02, eta: 8:07:02, time: 0.085, data_time: 0.013, memory: 928, loss: 5.1008 2023-01-04 19:32:57,681 - mmcls - INFO - Epoch [34][4200/5005] lr: 1.000e-02, eta: 8:06:54, time: 0.091, data_time: 0.016, memory: 928, loss: 5.0874 2023-01-04 19:33:06,198 - mmcls - INFO - Epoch [34][4300/5005] lr: 1.000e-02, eta: 8:06:44, time: 0.085, data_time: 0.014, memory: 928, loss: 5.0843 2023-01-04 19:33:15,318 - mmcls - INFO - Epoch [34][4400/5005] lr: 1.000e-02, eta: 8:06:36, time: 0.091, data_time: 0.001, memory: 928, loss: 5.1033 2023-01-04 19:33:23,856 - mmcls - INFO - Epoch [34][4500/5005] lr: 1.000e-02, eta: 8:06:27, time: 0.085, data_time: 0.026, memory: 928, loss: 5.0869 2023-01-04 19:33:32,659 - mmcls - INFO - Epoch [34][4600/5005] lr: 1.000e-02, eta: 8:06:18, time: 0.088, data_time: 0.026, memory: 928, loss: 5.0929 2023-01-04 19:33:41,574 - mmcls - INFO - Epoch [34][4700/5005] lr: 1.000e-02, eta: 8:06:09, time: 0.089, data_time: 0.027, memory: 928, loss: 5.0878 2023-01-04 19:33:50,462 - mmcls - INFO - Epoch [34][4800/5005] lr: 1.000e-02, eta: 8:06:01, time: 0.089, data_time: 0.006, memory: 928, loss: 5.1109 2023-01-04 19:33:59,213 - mmcls - INFO - Epoch [34][4900/5005] lr: 1.000e-02, eta: 8:05:52, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0910 2023-01-04 19:34:08,100 - mmcls - INFO - Epoch [34][5000/5005] lr: 1.000e-02, eta: 8:05:43, time: 0.089, data_time: 0.033, memory: 928, loss: 5.0699 2023-01-04 19:34:30,792 - mmcls - INFO - Epoch(val) [34][196] accuracy_top-1: 12.5160, accuracy_top-5: 28.0500 2023-01-04 19:34:41,782 - mmcls - INFO - Epoch [35][100/5005] lr: 1.000e-02, eta: 8:05:37, time: 0.110, data_time: 0.050, memory: 928, loss: 5.0453 2023-01-04 19:34:50,512 - mmcls - INFO - Epoch [35][200/5005] lr: 1.000e-02, eta: 8:05:28, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0713 2023-01-04 19:34:59,076 - mmcls - INFO - Epoch [35][300/5005] lr: 1.000e-02, eta: 8:05:19, time: 0.086, data_time: 0.017, memory: 928, loss: 5.0691 2023-01-04 19:35:07,930 - mmcls - INFO - Epoch [35][400/5005] lr: 1.000e-02, eta: 8:05:10, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0962 2023-01-04 19:35:16,836 - mmcls - INFO - Epoch [35][500/5005] lr: 1.000e-02, eta: 8:05:01, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0798 2023-01-04 19:35:25,417 - mmcls - INFO - Epoch [35][600/5005] lr: 1.000e-02, eta: 8:04:52, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0945 2023-01-04 19:35:34,480 - mmcls - INFO - Epoch [35][700/5005] lr: 1.000e-02, eta: 8:04:44, time: 0.091, data_time: 0.008, memory: 928, loss: 5.0795 2023-01-04 19:35:43,364 - mmcls - INFO - Epoch [35][800/5005] lr: 1.000e-02, eta: 8:04:35, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0906 2023-01-04 19:35:52,043 - mmcls - INFO - Epoch [35][900/5005] lr: 1.000e-02, eta: 8:04:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0928 2023-01-04 19:36:00,823 - mmcls - INFO - Epoch [35][1000/5005] lr: 1.000e-02, eta: 8:04:17, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0498 2023-01-04 19:36:09,265 - mmcls - INFO - Epoch [35][1100/5005] lr: 1.000e-02, eta: 8:04:07, time: 0.084, data_time: 0.003, memory: 928, loss: 5.0853 2023-01-04 19:36:18,053 - mmcls - INFO - Epoch [35][1200/5005] lr: 1.000e-02, eta: 8:03:58, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0772 2023-01-04 19:36:26,751 - mmcls - INFO - Epoch [35][1300/5005] lr: 1.000e-02, eta: 8:03:49, time: 0.087, data_time: 0.011, memory: 928, loss: 5.1008 2023-01-04 19:36:35,526 - mmcls - INFO - Epoch [35][1400/5005] lr: 1.000e-02, eta: 8:03:40, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0896 2023-01-04 19:36:44,205 - mmcls - INFO - Epoch [35][1500/5005] lr: 1.000e-02, eta: 8:03:31, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0915 2023-01-04 19:36:52,898 - mmcls - INFO - Epoch [35][1600/5005] lr: 1.000e-02, eta: 8:03:22, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0775 2023-01-04 19:37:01,647 - mmcls - INFO - Epoch [35][1700/5005] lr: 1.000e-02, eta: 8:03:13, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0773 2023-01-04 19:37:10,297 - mmcls - INFO - Epoch [35][1800/5005] lr: 1.000e-02, eta: 8:03:04, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0896 2023-01-04 19:37:19,194 - mmcls - INFO - Epoch [35][1900/5005] lr: 1.000e-02, eta: 8:02:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0815 2023-01-04 19:37:28,127 - mmcls - INFO - Epoch [35][2000/5005] lr: 1.000e-02, eta: 8:02:47, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0881 2023-01-04 19:37:36,775 - mmcls - INFO - Epoch [35][2100/5005] lr: 1.000e-02, eta: 8:02:38, time: 0.087, data_time: 0.001, memory: 928, loss: 5.1014 2023-01-04 19:37:45,682 - mmcls - INFO - Epoch [35][2200/5005] lr: 1.000e-02, eta: 8:02:29, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0925 2023-01-04 19:37:54,447 - mmcls - INFO - Epoch [35][2300/5005] lr: 1.000e-02, eta: 8:02:20, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0879 2023-01-04 19:38:03,136 - mmcls - INFO - Epoch [35][2400/5005] lr: 1.000e-02, eta: 8:02:11, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0847 2023-01-04 19:38:11,953 - mmcls - INFO - Epoch [35][2500/5005] lr: 1.000e-02, eta: 8:02:02, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0840 2023-01-04 19:38:20,438 - mmcls - INFO - Epoch [35][2600/5005] lr: 1.000e-02, eta: 8:01:53, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0703 2023-01-04 19:38:28,975 - mmcls - INFO - Epoch [35][2700/5005] lr: 1.000e-02, eta: 8:01:43, time: 0.085, data_time: 0.001, memory: 928, loss: 5.1040 2023-01-04 19:38:37,594 - mmcls - INFO - Epoch [35][2800/5005] lr: 1.000e-02, eta: 8:01:34, time: 0.086, data_time: 0.002, memory: 928, loss: 5.1003 2023-01-04 19:38:46,318 - mmcls - INFO - Epoch [35][2900/5005] lr: 1.000e-02, eta: 8:01:25, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0932 2023-01-04 19:38:54,862 - mmcls - INFO - Epoch [35][3000/5005] lr: 1.000e-02, eta: 8:01:16, time: 0.085, data_time: 0.008, memory: 928, loss: 5.0853 2023-01-04 19:39:03,662 - mmcls - INFO - Epoch [35][3100/5005] lr: 1.000e-02, eta: 8:01:07, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0661 2023-01-04 19:39:12,306 - mmcls - INFO - Epoch [35][3200/5005] lr: 1.000e-02, eta: 8:00:58, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0857 2023-01-04 19:39:20,955 - mmcls - INFO - Epoch [35][3300/5005] lr: 1.000e-02, eta: 8:00:49, time: 0.086, data_time: 0.007, memory: 928, loss: 5.1079 2023-01-04 19:39:29,653 - mmcls - INFO - Epoch [35][3400/5005] lr: 1.000e-02, eta: 8:00:40, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0969 2023-01-04 19:39:38,279 - mmcls - INFO - Epoch [35][3500/5005] lr: 1.000e-02, eta: 8:00:30, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0894 2023-01-04 19:39:47,215 - mmcls - INFO - Epoch [35][3600/5005] lr: 1.000e-02, eta: 8:00:22, time: 0.089, data_time: 0.006, memory: 928, loss: 5.1026 2023-01-04 19:39:56,137 - mmcls - INFO - Epoch [35][3700/5005] lr: 1.000e-02, eta: 8:00:13, time: 0.089, data_time: 0.025, memory: 928, loss: 5.0736 2023-01-04 19:40:05,047 - mmcls - INFO - Epoch [35][3800/5005] lr: 1.000e-02, eta: 8:00:04, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0821 2023-01-04 19:40:13,905 - mmcls - INFO - Epoch [35][3900/5005] lr: 1.000e-02, eta: 7:59:56, time: 0.089, data_time: 0.031, memory: 928, loss: 5.0937 2023-01-04 19:40:22,547 - mmcls - INFO - Epoch [35][4000/5005] lr: 1.000e-02, eta: 7:59:47, time: 0.086, data_time: 0.024, memory: 928, loss: 5.0821 2023-01-04 19:40:31,617 - mmcls - INFO - Epoch [35][4100/5005] lr: 1.000e-02, eta: 7:59:38, time: 0.091, data_time: 0.011, memory: 928, loss: 5.0784 2023-01-04 19:40:40,326 - mmcls - INFO - Epoch [35][4200/5005] lr: 1.000e-02, eta: 7:59:29, time: 0.087, data_time: 0.027, memory: 928, loss: 5.1167 2023-01-04 19:40:48,902 - mmcls - INFO - Epoch [35][4300/5005] lr: 1.000e-02, eta: 7:59:20, time: 0.086, data_time: 0.008, memory: 928, loss: 5.1089 2023-01-04 19:40:57,744 - mmcls - INFO - Epoch [35][4400/5005] lr: 1.000e-02, eta: 7:59:11, time: 0.089, data_time: 0.004, memory: 928, loss: 5.1060 2023-01-04 19:41:06,463 - mmcls - INFO - Epoch [35][4500/5005] lr: 1.000e-02, eta: 7:59:02, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0787 2023-01-04 19:41:15,113 - mmcls - INFO - Epoch [35][4600/5005] lr: 1.000e-02, eta: 7:58:53, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0824 2023-01-04 19:41:23,889 - mmcls - INFO - Epoch [35][4700/5005] lr: 1.000e-02, eta: 7:58:44, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0816 2023-01-04 19:41:32,655 - mmcls - INFO - Epoch [35][4800/5005] lr: 1.000e-02, eta: 7:58:35, time: 0.088, data_time: 0.001, memory: 928, loss: 5.1016 2023-01-04 19:41:41,637 - mmcls - INFO - Epoch [35][4900/5005] lr: 1.000e-02, eta: 7:58:27, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0769 2023-01-04 19:41:50,836 - mmcls - INFO - Epoch [35][5000/5005] lr: 1.000e-02, eta: 7:58:18, time: 0.092, data_time: 0.007, memory: 928, loss: 5.0622 2023-01-04 19:42:13,522 - mmcls - INFO - Epoch(val) [35][196] accuracy_top-1: 12.1480, accuracy_top-5: 27.3840 2023-01-04 19:42:24,497 - mmcls - INFO - Epoch [36][100/5005] lr: 1.000e-02, eta: 7:58:12, time: 0.110, data_time: 0.075, memory: 928, loss: 5.0736 2023-01-04 19:42:33,231 - mmcls - INFO - Epoch [36][200/5005] lr: 1.000e-02, eta: 7:58:03, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0705 2023-01-04 19:42:41,814 - mmcls - INFO - Epoch [36][300/5005] lr: 1.000e-02, eta: 7:57:54, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0774 2023-01-04 19:42:51,452 - mmcls - INFO - Epoch [36][400/5005] lr: 1.000e-02, eta: 7:57:47, time: 0.096, data_time: 0.010, memory: 928, loss: 5.0762 2023-01-04 19:43:00,072 - mmcls - INFO - Epoch [36][500/5005] lr: 1.000e-02, eta: 7:57:38, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0889 2023-01-04 19:43:08,921 - mmcls - INFO - Epoch [36][600/5005] lr: 1.000e-02, eta: 7:57:29, time: 0.089, data_time: 0.032, memory: 928, loss: 5.0620 2023-01-04 19:43:17,705 - mmcls - INFO - Epoch [36][700/5005] lr: 1.000e-02, eta: 7:57:20, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0771 2023-01-04 19:43:26,487 - mmcls - INFO - Epoch [36][800/5005] lr: 1.000e-02, eta: 7:57:11, time: 0.088, data_time: 0.025, memory: 928, loss: 5.0666 2023-01-04 19:43:35,141 - mmcls - INFO - Epoch [36][900/5005] lr: 1.000e-02, eta: 7:57:02, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0675 2023-01-04 19:43:43,974 - mmcls - INFO - Epoch [36][1000/5005] lr: 1.000e-02, eta: 7:56:53, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0984 2023-01-04 19:43:52,624 - mmcls - INFO - Epoch [36][1100/5005] lr: 1.000e-02, eta: 7:56:44, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0815 2023-01-04 19:44:01,311 - mmcls - INFO - Epoch [36][1200/5005] lr: 1.000e-02, eta: 7:56:35, time: 0.087, data_time: 0.015, memory: 928, loss: 5.1041 2023-01-04 19:44:10,042 - mmcls - INFO - Epoch [36][1300/5005] lr: 1.000e-02, eta: 7:56:26, time: 0.087, data_time: 0.023, memory: 928, loss: 5.1002 2023-01-04 19:44:18,720 - mmcls - INFO - Epoch [36][1400/5005] lr: 1.000e-02, eta: 7:56:17, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0779 2023-01-04 19:44:27,381 - mmcls - INFO - Epoch [36][1500/5005] lr: 1.000e-02, eta: 7:56:08, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0871 2023-01-04 19:44:36,209 - mmcls - INFO - Epoch [36][1600/5005] lr: 1.000e-02, eta: 7:55:59, time: 0.088, data_time: 0.018, memory: 928, loss: 5.0830 2023-01-04 19:44:44,871 - mmcls - INFO - Epoch [36][1700/5005] lr: 1.000e-02, eta: 7:55:50, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0908 2023-01-04 19:44:53,674 - mmcls - INFO - Epoch [36][1800/5005] lr: 1.000e-02, eta: 7:55:41, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0831 2023-01-04 19:45:02,196 - mmcls - INFO - Epoch [36][1900/5005] lr: 1.000e-02, eta: 7:55:32, time: 0.085, data_time: 0.019, memory: 928, loss: 5.0863 2023-01-04 19:45:10,971 - mmcls - INFO - Epoch [36][2000/5005] lr: 1.000e-02, eta: 7:55:23, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0649 2023-01-04 19:45:19,981 - mmcls - INFO - Epoch [36][2100/5005] lr: 1.000e-02, eta: 7:55:14, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0742 2023-01-04 19:45:28,563 - mmcls - INFO - Epoch [36][2200/5005] lr: 1.000e-02, eta: 7:55:05, time: 0.086, data_time: 0.017, memory: 928, loss: 5.0737 2023-01-04 19:45:37,172 - mmcls - INFO - Epoch [36][2300/5005] lr: 1.000e-02, eta: 7:54:56, time: 0.086, data_time: 0.026, memory: 928, loss: 5.0883 2023-01-04 19:45:45,664 - mmcls - INFO - Epoch [36][2400/5005] lr: 1.000e-02, eta: 7:54:46, time: 0.085, data_time: 0.010, memory: 928, loss: 5.0773 2023-01-04 19:45:54,351 - mmcls - INFO - Epoch [36][2500/5005] lr: 1.000e-02, eta: 7:54:37, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0614 2023-01-04 19:46:02,887 - mmcls - INFO - Epoch [36][2600/5005] lr: 1.000e-02, eta: 7:54:28, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0787 2023-01-04 19:46:11,730 - mmcls - INFO - Epoch [36][2700/5005] lr: 1.000e-02, eta: 7:54:19, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0856 2023-01-04 19:46:20,688 - mmcls - INFO - Epoch [36][2800/5005] lr: 1.000e-02, eta: 7:54:10, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0809 2023-01-04 19:46:29,648 - mmcls - INFO - Epoch [36][2900/5005] lr: 1.000e-02, eta: 7:54:02, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0923 2023-01-04 19:46:39,141 - mmcls - INFO - Epoch [36][3000/5005] lr: 1.000e-02, eta: 7:53:54, time: 0.095, data_time: 0.048, memory: 928, loss: 5.0789 2023-01-04 19:46:48,075 - mmcls - INFO - Epoch [36][3100/5005] lr: 1.000e-02, eta: 7:53:46, time: 0.089, data_time: 0.014, memory: 928, loss: 5.0629 2023-01-04 19:46:56,793 - mmcls - INFO - Epoch [36][3200/5005] lr: 1.000e-02, eta: 7:53:37, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0736 2023-01-04 19:47:05,585 - mmcls - INFO - Epoch [36][3300/5005] lr: 1.000e-02, eta: 7:53:28, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0486 2023-01-04 19:47:14,584 - mmcls - INFO - Epoch [36][3400/5005] lr: 1.000e-02, eta: 7:53:19, time: 0.090, data_time: 0.022, memory: 928, loss: 5.0770 2023-01-04 19:47:23,375 - mmcls - INFO - Epoch [36][3500/5005] lr: 1.000e-02, eta: 7:53:10, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0839 2023-01-04 19:47:32,243 - mmcls - INFO - Epoch [36][3600/5005] lr: 1.000e-02, eta: 7:53:02, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0668 2023-01-04 19:47:41,068 - mmcls - INFO - Epoch [36][3700/5005] lr: 1.000e-02, eta: 7:52:53, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0616 2023-01-04 19:47:49,782 - mmcls - INFO - Epoch [36][3800/5005] lr: 1.000e-02, eta: 7:52:44, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0884 2023-01-04 19:47:58,389 - mmcls - INFO - Epoch [36][3900/5005] lr: 1.000e-02, eta: 7:52:35, time: 0.086, data_time: 0.027, memory: 928, loss: 5.0678 2023-01-04 19:48:07,083 - mmcls - INFO - Epoch [36][4000/5005] lr: 1.000e-02, eta: 7:52:26, time: 0.087, data_time: 0.013, memory: 928, loss: 5.1209 2023-01-04 19:48:15,692 - mmcls - INFO - Epoch [36][4100/5005] lr: 1.000e-02, eta: 7:52:16, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0900 2023-01-04 19:48:24,199 - mmcls - INFO - Epoch [36][4200/5005] lr: 1.000e-02, eta: 7:52:07, time: 0.085, data_time: 0.010, memory: 928, loss: 5.1027 2023-01-04 19:48:32,928 - mmcls - INFO - Epoch [36][4300/5005] lr: 1.000e-02, eta: 7:51:58, time: 0.087, data_time: 0.039, memory: 928, loss: 5.0792 2023-01-04 19:48:41,646 - mmcls - INFO - Epoch [36][4400/5005] lr: 1.000e-02, eta: 7:51:49, time: 0.087, data_time: 0.033, memory: 928, loss: 5.0913 2023-01-04 19:48:50,440 - mmcls - INFO - Epoch [36][4500/5005] lr: 1.000e-02, eta: 7:51:40, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0918 2023-01-04 19:48:59,155 - mmcls - INFO - Epoch [36][4600/5005] lr: 1.000e-02, eta: 7:51:31, time: 0.087, data_time: 0.015, memory: 928, loss: 5.0898 2023-01-04 19:49:07,971 - mmcls - INFO - Epoch [36][4700/5005] lr: 1.000e-02, eta: 7:51:22, time: 0.088, data_time: 0.032, memory: 928, loss: 5.0804 2023-01-04 19:49:16,823 - mmcls - INFO - Epoch [36][4800/5005] lr: 1.000e-02, eta: 7:51:13, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0910 2023-01-04 19:49:26,001 - mmcls - INFO - Epoch [36][4900/5005] lr: 1.000e-02, eta: 7:51:05, time: 0.092, data_time: 0.004, memory: 928, loss: 5.0724 2023-01-04 19:49:34,688 - mmcls - INFO - Epoch [36][5000/5005] lr: 1.000e-02, eta: 7:50:56, time: 0.087, data_time: 0.007, memory: 928, loss: 5.1018 2023-01-04 19:49:57,774 - mmcls - INFO - Epoch(val) [36][196] accuracy_top-1: 13.1380, accuracy_top-5: 28.7100 2023-01-04 19:50:09,102 - mmcls - INFO - Epoch [37][100/5005] lr: 1.000e-02, eta: 7:50:51, time: 0.113, data_time: 0.038, memory: 928, loss: 5.0739 2023-01-04 19:50:17,734 - mmcls - INFO - Epoch [37][200/5005] lr: 1.000e-02, eta: 7:50:41, time: 0.086, data_time: 0.015, memory: 928, loss: 5.0647 2023-01-04 19:50:26,604 - mmcls - INFO - Epoch [37][300/5005] lr: 1.000e-02, eta: 7:50:33, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0634 2023-01-04 19:50:35,262 - mmcls - INFO - Epoch [37][400/5005] lr: 1.000e-02, eta: 7:50:24, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0790 2023-01-04 19:50:43,822 - mmcls - INFO - Epoch [37][500/5005] lr: 1.000e-02, eta: 7:50:14, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0797 2023-01-04 19:50:52,666 - mmcls - INFO - Epoch [37][600/5005] lr: 1.000e-02, eta: 7:50:06, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0885 2023-01-04 19:51:01,436 - mmcls - INFO - Epoch [37][700/5005] lr: 1.000e-02, eta: 7:49:57, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0834 2023-01-04 19:51:09,876 - mmcls - INFO - Epoch [37][800/5005] lr: 1.000e-02, eta: 7:49:47, time: 0.084, data_time: 0.017, memory: 928, loss: 5.0690 2023-01-04 19:51:18,793 - mmcls - INFO - Epoch [37][900/5005] lr: 1.000e-02, eta: 7:49:38, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0906 2023-01-04 19:51:27,435 - mmcls - INFO - Epoch [37][1000/5005] lr: 1.000e-02, eta: 7:49:29, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0711 2023-01-04 19:51:36,294 - mmcls - INFO - Epoch [37][1100/5005] lr: 1.000e-02, eta: 7:49:21, time: 0.089, data_time: 0.013, memory: 928, loss: 5.0889 2023-01-04 19:51:45,096 - mmcls - INFO - Epoch [37][1200/5005] lr: 1.000e-02, eta: 7:49:12, time: 0.088, data_time: 0.036, memory: 928, loss: 5.0846 2023-01-04 19:51:53,717 - mmcls - INFO - Epoch [37][1300/5005] lr: 1.000e-02, eta: 7:49:02, time: 0.086, data_time: 0.016, memory: 928, loss: 5.0779 2023-01-04 19:52:02,481 - mmcls - INFO - Epoch [37][1400/5005] lr: 1.000e-02, eta: 7:48:54, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0591 2023-01-04 19:52:11,296 - mmcls - INFO - Epoch [37][1500/5005] lr: 1.000e-02, eta: 7:48:45, time: 0.088, data_time: 0.012, memory: 928, loss: 5.0707 2023-01-04 19:52:19,910 - mmcls - INFO - Epoch [37][1600/5005] lr: 1.000e-02, eta: 7:48:36, time: 0.086, data_time: 0.029, memory: 928, loss: 5.0610 2023-01-04 19:52:28,698 - mmcls - INFO - Epoch [37][1700/5005] lr: 1.000e-02, eta: 7:48:27, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0863 2023-01-04 19:52:37,110 - mmcls - INFO - Epoch [37][1800/5005] lr: 1.000e-02, eta: 7:48:17, time: 0.084, data_time: 0.005, memory: 928, loss: 5.0761 2023-01-04 19:52:45,878 - mmcls - INFO - Epoch [37][1900/5005] lr: 1.000e-02, eta: 7:48:08, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0768 2023-01-04 19:52:54,684 - mmcls - INFO - Epoch [37][2000/5005] lr: 1.000e-02, eta: 7:47:59, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0929 2023-01-04 19:53:03,398 - mmcls - INFO - Epoch [37][2100/5005] lr: 1.000e-02, eta: 7:47:50, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0888 2023-01-04 19:53:12,143 - mmcls - INFO - Epoch [37][2200/5005] lr: 1.000e-02, eta: 7:47:41, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0786 2023-01-04 19:53:21,372 - mmcls - INFO - Epoch [37][2300/5005] lr: 1.000e-02, eta: 7:47:33, time: 0.092, data_time: 0.004, memory: 928, loss: 5.0534 2023-01-04 19:53:30,294 - mmcls - INFO - Epoch [37][2400/5005] lr: 1.000e-02, eta: 7:47:25, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0949 2023-01-04 19:53:39,188 - mmcls - INFO - Epoch [37][2500/5005] lr: 1.000e-02, eta: 7:47:16, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0749 2023-01-04 19:53:47,814 - mmcls - INFO - Epoch [37][2600/5005] lr: 1.000e-02, eta: 7:47:07, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0593 2023-01-04 19:53:56,537 - mmcls - INFO - Epoch [37][2700/5005] lr: 1.000e-02, eta: 7:46:58, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0759 2023-01-04 19:54:05,470 - mmcls - INFO - Epoch [37][2800/5005] lr: 1.000e-02, eta: 7:46:49, time: 0.089, data_time: 0.021, memory: 928, loss: 5.0877 2023-01-04 19:54:14,242 - mmcls - INFO - Epoch [37][2900/5005] lr: 1.000e-02, eta: 7:46:40, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0886 2023-01-04 19:54:22,999 - mmcls - INFO - Epoch [37][3000/5005] lr: 1.000e-02, eta: 7:46:31, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0612 2023-01-04 19:54:31,715 - mmcls - INFO - Epoch [37][3100/5005] lr: 1.000e-02, eta: 7:46:22, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0807 2023-01-04 19:54:40,276 - mmcls - INFO - Epoch [37][3200/5005] lr: 1.000e-02, eta: 7:46:13, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0873 2023-01-04 19:54:48,988 - mmcls - INFO - Epoch [37][3300/5005] lr: 1.000e-02, eta: 7:46:04, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0815 2023-01-04 19:54:57,654 - mmcls - INFO - Epoch [37][3400/5005] lr: 1.000e-02, eta: 7:45:55, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0461 2023-01-04 19:55:06,444 - mmcls - INFO - Epoch [37][3500/5005] lr: 1.000e-02, eta: 7:45:46, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0471 2023-01-04 19:55:15,179 - mmcls - INFO - Epoch [37][3600/5005] lr: 1.000e-02, eta: 7:45:37, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0881 2023-01-04 19:55:23,763 - mmcls - INFO - Epoch [37][3700/5005] lr: 1.000e-02, eta: 7:45:28, time: 0.086, data_time: 0.022, memory: 928, loss: 5.0844 2023-01-04 19:55:32,535 - mmcls - INFO - Epoch [37][3800/5005] lr: 1.000e-02, eta: 7:45:19, time: 0.088, data_time: 0.030, memory: 928, loss: 5.0692 2023-01-04 19:55:41,696 - mmcls - INFO - Epoch [37][3900/5005] lr: 1.000e-02, eta: 7:45:11, time: 0.092, data_time: 0.001, memory: 928, loss: 5.0502 2023-01-04 19:55:50,323 - mmcls - INFO - Epoch [37][4000/5005] lr: 1.000e-02, eta: 7:45:02, time: 0.086, data_time: 0.030, memory: 928, loss: 5.0903 2023-01-04 19:55:59,319 - mmcls - INFO - Epoch [37][4100/5005] lr: 1.000e-02, eta: 7:44:53, time: 0.090, data_time: 0.037, memory: 928, loss: 5.0817 2023-01-04 19:56:08,223 - mmcls - INFO - Epoch [37][4200/5005] lr: 1.000e-02, eta: 7:44:44, time: 0.089, data_time: 0.031, memory: 928, loss: 5.0923 2023-01-04 19:56:17,124 - mmcls - INFO - Epoch [37][4300/5005] lr: 1.000e-02, eta: 7:44:36, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0784 2023-01-04 19:56:26,030 - mmcls - INFO - Epoch [37][4400/5005] lr: 1.000e-02, eta: 7:44:27, time: 0.089, data_time: 0.019, memory: 928, loss: 5.0632 2023-01-04 19:56:34,730 - mmcls - INFO - Epoch [37][4500/5005] lr: 1.000e-02, eta: 7:44:18, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0755 2023-01-04 19:56:43,691 - mmcls - INFO - Epoch [37][4600/5005] lr: 1.000e-02, eta: 7:44:09, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0755 2023-01-04 19:56:52,206 - mmcls - INFO - Epoch [37][4700/5005] lr: 1.000e-02, eta: 7:44:00, time: 0.085, data_time: 0.010, memory: 928, loss: 5.0686 2023-01-04 19:57:01,158 - mmcls - INFO - Epoch [37][4800/5005] lr: 1.000e-02, eta: 7:43:51, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0968 2023-01-04 19:57:09,646 - mmcls - INFO - Epoch [37][4900/5005] lr: 1.000e-02, eta: 7:43:42, time: 0.085, data_time: 0.025, memory: 928, loss: 5.0868 2023-01-04 19:57:18,681 - mmcls - INFO - Epoch [37][5000/5005] lr: 1.000e-02, eta: 7:43:34, time: 0.090, data_time: 0.019, memory: 928, loss: 5.0847 2023-01-04 19:57:41,509 - mmcls - INFO - Epoch(val) [37][196] accuracy_top-1: 11.9320, accuracy_top-5: 26.4800 2023-01-04 19:57:52,464 - mmcls - INFO - Epoch [38][100/5005] lr: 1.000e-02, eta: 7:43:27, time: 0.109, data_time: 0.047, memory: 928, loss: 5.0837 2023-01-04 19:58:01,382 - mmcls - INFO - Epoch [38][200/5005] lr: 1.000e-02, eta: 7:43:19, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0533 2023-01-04 19:58:10,176 - mmcls - INFO - Epoch [38][300/5005] lr: 1.000e-02, eta: 7:43:10, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0433 2023-01-04 19:58:18,649 - mmcls - INFO - Epoch [38][400/5005] lr: 1.000e-02, eta: 7:43:00, time: 0.085, data_time: 0.009, memory: 928, loss: 5.0716 2023-01-04 19:58:27,208 - mmcls - INFO - Epoch [38][500/5005] lr: 1.000e-02, eta: 7:42:51, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0591 2023-01-04 19:58:36,382 - mmcls - INFO - Epoch [38][600/5005] lr: 1.000e-02, eta: 7:42:43, time: 0.092, data_time: 0.007, memory: 928, loss: 5.0693 2023-01-04 19:58:45,038 - mmcls - INFO - Epoch [38][700/5005] lr: 1.000e-02, eta: 7:42:34, time: 0.087, data_time: 0.019, memory: 928, loss: 5.0732 2023-01-04 19:58:54,159 - mmcls - INFO - Epoch [38][800/5005] lr: 1.000e-02, eta: 7:42:25, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0577 2023-01-04 19:59:03,180 - mmcls - INFO - Epoch [38][900/5005] lr: 1.000e-02, eta: 7:42:17, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0663 2023-01-04 19:59:12,100 - mmcls - INFO - Epoch [38][1000/5005] lr: 1.000e-02, eta: 7:42:08, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0743 2023-01-04 19:59:20,920 - mmcls - INFO - Epoch [38][1100/5005] lr: 1.000e-02, eta: 7:41:59, time: 0.088, data_time: 0.012, memory: 928, loss: 5.0776 2023-01-04 19:59:29,709 - mmcls - INFO - Epoch [38][1200/5005] lr: 1.000e-02, eta: 7:41:50, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0632 2023-01-04 19:59:38,582 - mmcls - INFO - Epoch [38][1300/5005] lr: 1.000e-02, eta: 7:41:42, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0799 2023-01-04 19:59:47,139 - mmcls - INFO - Epoch [38][1400/5005] lr: 1.000e-02, eta: 7:41:32, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0876 2023-01-04 19:59:55,965 - mmcls - INFO - Epoch [38][1500/5005] lr: 1.000e-02, eta: 7:41:24, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0824 2023-01-04 20:00:04,821 - mmcls - INFO - Epoch [38][1600/5005] lr: 1.000e-02, eta: 7:41:15, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0748 2023-01-04 20:00:13,495 - mmcls - INFO - Epoch [38][1700/5005] lr: 1.000e-02, eta: 7:41:06, time: 0.087, data_time: 0.006, memory: 928, loss: 5.1020 2023-01-04 20:00:22,312 - mmcls - INFO - Epoch [38][1800/5005] lr: 1.000e-02, eta: 7:40:57, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0813 2023-01-04 20:00:31,448 - mmcls - INFO - Epoch [38][1900/5005] lr: 1.000e-02, eta: 7:40:49, time: 0.091, data_time: 0.003, memory: 928, loss: 5.0741 2023-01-04 20:00:40,083 - mmcls - INFO - Epoch [38][2000/5005] lr: 1.000e-02, eta: 7:40:40, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0698 2023-01-04 20:00:48,865 - mmcls - INFO - Epoch [38][2100/5005] lr: 1.000e-02, eta: 7:40:31, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0978 2023-01-04 20:00:58,115 - mmcls - INFO - Epoch [38][2200/5005] lr: 1.000e-02, eta: 7:40:23, time: 0.093, data_time: 0.001, memory: 928, loss: 5.0757 2023-01-04 20:01:06,705 - mmcls - INFO - Epoch [38][2300/5005] lr: 1.000e-02, eta: 7:40:13, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0706 2023-01-04 20:01:15,436 - mmcls - INFO - Epoch [38][2400/5005] lr: 1.000e-02, eta: 7:40:04, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0861 2023-01-04 20:01:24,171 - mmcls - INFO - Epoch [38][2500/5005] lr: 1.000e-02, eta: 7:39:55, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0627 2023-01-04 20:01:33,058 - mmcls - INFO - Epoch [38][2600/5005] lr: 1.000e-02, eta: 7:39:47, time: 0.089, data_time: 0.020, memory: 928, loss: 5.0653 2023-01-04 20:01:41,989 - mmcls - INFO - Epoch [38][2700/5005] lr: 1.000e-02, eta: 7:39:38, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0708 2023-01-04 20:01:50,403 - mmcls - INFO - Epoch [38][2800/5005] lr: 1.000e-02, eta: 7:39:29, time: 0.084, data_time: 0.013, memory: 928, loss: 5.0810 2023-01-04 20:01:59,397 - mmcls - INFO - Epoch [38][2900/5005] lr: 1.000e-02, eta: 7:39:20, time: 0.090, data_time: 0.010, memory: 928, loss: 5.0818 2023-01-04 20:02:08,519 - mmcls - INFO - Epoch [38][3000/5005] lr: 1.000e-02, eta: 7:39:12, time: 0.091, data_time: 0.022, memory: 928, loss: 5.0691 2023-01-04 20:02:17,417 - mmcls - INFO - Epoch [38][3100/5005] lr: 1.000e-02, eta: 7:39:03, time: 0.089, data_time: 0.033, memory: 928, loss: 5.0727 2023-01-04 20:02:25,899 - mmcls - INFO - Epoch [38][3200/5005] lr: 1.000e-02, eta: 7:38:54, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0802 2023-01-04 20:02:34,484 - mmcls - INFO - Epoch [38][3300/5005] lr: 1.000e-02, eta: 7:38:44, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0965 2023-01-04 20:02:43,642 - mmcls - INFO - Epoch [38][3400/5005] lr: 1.000e-02, eta: 7:38:36, time: 0.092, data_time: 0.004, memory: 928, loss: 5.0645 2023-01-04 20:02:52,396 - mmcls - INFO - Epoch [38][3500/5005] lr: 1.000e-02, eta: 7:38:27, time: 0.088, data_time: 0.043, memory: 928, loss: 5.0835 2023-01-04 20:03:00,838 - mmcls - INFO - Epoch [38][3600/5005] lr: 1.000e-02, eta: 7:38:18, time: 0.084, data_time: 0.019, memory: 928, loss: 5.1039 2023-01-04 20:03:10,115 - mmcls - INFO - Epoch [38][3700/5005] lr: 1.000e-02, eta: 7:38:10, time: 0.093, data_time: 0.003, memory: 928, loss: 5.0805 2023-01-04 20:03:18,782 - mmcls - INFO - Epoch [38][3800/5005] lr: 1.000e-02, eta: 7:38:01, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0800 2023-01-04 20:03:27,481 - mmcls - INFO - Epoch [38][3900/5005] lr: 1.000e-02, eta: 7:37:52, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0706 2023-01-04 20:03:36,173 - mmcls - INFO - Epoch [38][4000/5005] lr: 1.000e-02, eta: 7:37:42, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0786 2023-01-04 20:03:45,072 - mmcls - INFO - Epoch [38][4100/5005] lr: 1.000e-02, eta: 7:37:34, time: 0.089, data_time: 0.045, memory: 928, loss: 5.0937 2023-01-04 20:03:54,035 - mmcls - INFO - Epoch [38][4200/5005] lr: 1.000e-02, eta: 7:37:25, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0639 2023-01-04 20:04:02,972 - mmcls - INFO - Epoch [38][4300/5005] lr: 1.000e-02, eta: 7:37:17, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0945 2023-01-04 20:04:11,670 - mmcls - INFO - Epoch [38][4400/5005] lr: 1.000e-02, eta: 7:37:08, time: 0.087, data_time: 0.020, memory: 928, loss: 5.0697 2023-01-04 20:04:20,720 - mmcls - INFO - Epoch [38][4500/5005] lr: 1.000e-02, eta: 7:36:59, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0867 2023-01-04 20:04:29,797 - mmcls - INFO - Epoch [38][4600/5005] lr: 1.000e-02, eta: 7:36:51, time: 0.091, data_time: 0.006, memory: 928, loss: 5.0668 2023-01-04 20:04:38,622 - mmcls - INFO - Epoch [38][4700/5005] lr: 1.000e-02, eta: 7:36:42, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0826 2023-01-04 20:04:47,129 - mmcls - INFO - Epoch [38][4800/5005] lr: 1.000e-02, eta: 7:36:33, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0747 2023-01-04 20:04:56,053 - mmcls - INFO - Epoch [38][4900/5005] lr: 1.000e-02, eta: 7:36:24, time: 0.089, data_time: 0.023, memory: 928, loss: 5.0993 2023-01-04 20:05:04,562 - mmcls - INFO - Epoch [38][5000/5005] lr: 1.000e-02, eta: 7:36:15, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0788 2023-01-04 20:05:27,486 - mmcls - INFO - Epoch(val) [38][196] accuracy_top-1: 10.7980, accuracy_top-5: 24.9140 2023-01-04 20:05:38,900 - mmcls - INFO - Epoch [39][100/5005] lr: 1.000e-02, eta: 7:36:09, time: 0.114, data_time: 0.041, memory: 928, loss: 5.0572 2023-01-04 20:05:47,881 - mmcls - INFO - Epoch [39][200/5005] lr: 1.000e-02, eta: 7:36:00, time: 0.090, data_time: 0.011, memory: 928, loss: 5.0685 2023-01-04 20:05:56,835 - mmcls - INFO - Epoch [39][300/5005] lr: 1.000e-02, eta: 7:35:52, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0641 2023-01-04 20:06:05,526 - mmcls - INFO - Epoch [39][400/5005] lr: 1.000e-02, eta: 7:35:43, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0868 2023-01-04 20:06:14,116 - mmcls - INFO - Epoch [39][500/5005] lr: 1.000e-02, eta: 7:35:33, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0443 2023-01-04 20:06:22,774 - mmcls - INFO - Epoch [39][600/5005] lr: 1.000e-02, eta: 7:35:24, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0799 2023-01-04 20:06:31,341 - mmcls - INFO - Epoch [39][700/5005] lr: 1.000e-02, eta: 7:35:15, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0924 2023-01-04 20:06:40,027 - mmcls - INFO - Epoch [39][800/5005] lr: 1.000e-02, eta: 7:35:06, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0464 2023-01-04 20:06:48,772 - mmcls - INFO - Epoch [39][900/5005] lr: 1.000e-02, eta: 7:34:57, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0660 2023-01-04 20:06:57,200 - mmcls - INFO - Epoch [39][1000/5005] lr: 1.000e-02, eta: 7:34:48, time: 0.084, data_time: 0.009, memory: 928, loss: 5.0772 2023-01-04 20:07:06,238 - mmcls - INFO - Epoch [39][1100/5005] lr: 1.000e-02, eta: 7:34:39, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0597 2023-01-04 20:07:15,081 - mmcls - INFO - Epoch [39][1200/5005] lr: 1.000e-02, eta: 7:34:30, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0744 2023-01-04 20:07:23,885 - mmcls - INFO - Epoch [39][1300/5005] lr: 1.000e-02, eta: 7:34:21, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0596 2023-01-04 20:07:32,748 - mmcls - INFO - Epoch [39][1400/5005] lr: 1.000e-02, eta: 7:34:13, time: 0.089, data_time: 0.013, memory: 928, loss: 5.0744 2023-01-04 20:07:41,300 - mmcls - INFO - Epoch [39][1500/5005] lr: 1.000e-02, eta: 7:34:03, time: 0.086, data_time: 0.022, memory: 928, loss: 5.0751 2023-01-04 20:07:50,049 - mmcls - INFO - Epoch [39][1600/5005] lr: 1.000e-02, eta: 7:33:54, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0727 2023-01-04 20:07:58,662 - mmcls - INFO - Epoch [39][1700/5005] lr: 1.000e-02, eta: 7:33:45, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0845 2023-01-04 20:08:07,393 - mmcls - INFO - Epoch [39][1800/5005] lr: 1.000e-02, eta: 7:33:36, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0712 2023-01-04 20:08:15,917 - mmcls - INFO - Epoch [39][1900/5005] lr: 1.000e-02, eta: 7:33:27, time: 0.085, data_time: 0.006, memory: 928, loss: 5.0607 2023-01-04 20:08:24,443 - mmcls - INFO - Epoch [39][2000/5005] lr: 1.000e-02, eta: 7:33:18, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0823 2023-01-04 20:08:33,763 - mmcls - INFO - Epoch [39][2100/5005] lr: 1.000e-02, eta: 7:33:10, time: 0.093, data_time: 0.001, memory: 928, loss: 5.0394 2023-01-04 20:08:42,345 - mmcls - INFO - Epoch [39][2200/5005] lr: 1.000e-02, eta: 7:33:01, time: 0.086, data_time: 0.007, memory: 928, loss: 5.1032 2023-01-04 20:08:51,214 - mmcls - INFO - Epoch [39][2300/5005] lr: 1.000e-02, eta: 7:32:52, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0779 2023-01-04 20:08:59,838 - mmcls - INFO - Epoch [39][2400/5005] lr: 1.000e-02, eta: 7:32:43, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0428 2023-01-04 20:09:08,605 - mmcls - INFO - Epoch [39][2500/5005] lr: 1.000e-02, eta: 7:32:34, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0745 2023-01-04 20:09:17,317 - mmcls - INFO - Epoch [39][2600/5005] lr: 1.000e-02, eta: 7:32:25, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0682 2023-01-04 20:09:25,874 - mmcls - INFO - Epoch [39][2700/5005] lr: 1.000e-02, eta: 7:32:15, time: 0.085, data_time: 0.017, memory: 928, loss: 5.0605 2023-01-04 20:09:34,892 - mmcls - INFO - Epoch [39][2800/5005] lr: 1.000e-02, eta: 7:32:07, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0655 2023-01-04 20:09:43,595 - mmcls - INFO - Epoch [39][2900/5005] lr: 1.000e-02, eta: 7:31:58, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0941 2023-01-04 20:09:52,460 - mmcls - INFO - Epoch [39][3000/5005] lr: 1.000e-02, eta: 7:31:49, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0639 2023-01-04 20:10:01,129 - mmcls - INFO - Epoch [39][3100/5005] lr: 1.000e-02, eta: 7:31:40, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0817 2023-01-04 20:10:09,912 - mmcls - INFO - Epoch [39][3200/5005] lr: 1.000e-02, eta: 7:31:31, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0483 2023-01-04 20:10:18,609 - mmcls - INFO - Epoch [39][3300/5005] lr: 1.000e-02, eta: 7:31:22, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0601 2023-01-04 20:10:27,355 - mmcls - INFO - Epoch [39][3400/5005] lr: 1.000e-02, eta: 7:31:13, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0559 2023-01-04 20:10:36,015 - mmcls - INFO - Epoch [39][3500/5005] lr: 1.000e-02, eta: 7:31:04, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0919 2023-01-04 20:10:44,713 - mmcls - INFO - Epoch [39][3600/5005] lr: 1.000e-02, eta: 7:30:55, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0842 2023-01-04 20:10:53,547 - mmcls - INFO - Epoch [39][3700/5005] lr: 1.000e-02, eta: 7:30:46, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0973 2023-01-04 20:11:02,330 - mmcls - INFO - Epoch [39][3800/5005] lr: 1.000e-02, eta: 7:30:38, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0781 2023-01-04 20:11:11,001 - mmcls - INFO - Epoch [39][3900/5005] lr: 1.000e-02, eta: 7:30:28, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0807 2023-01-04 20:11:19,861 - mmcls - INFO - Epoch [39][4000/5005] lr: 1.000e-02, eta: 7:30:20, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0654 2023-01-04 20:11:28,520 - mmcls - INFO - Epoch [39][4100/5005] lr: 1.000e-02, eta: 7:30:11, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0681 2023-01-04 20:11:37,209 - mmcls - INFO - Epoch [39][4200/5005] lr: 1.000e-02, eta: 7:30:02, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0817 2023-01-04 20:11:45,978 - mmcls - INFO - Epoch [39][4300/5005] lr: 1.000e-02, eta: 7:29:53, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0799 2023-01-04 20:11:54,547 - mmcls - INFO - Epoch [39][4400/5005] lr: 1.000e-02, eta: 7:29:43, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0914 2023-01-04 20:12:03,916 - mmcls - INFO - Epoch [39][4500/5005] lr: 1.000e-02, eta: 7:29:35, time: 0.094, data_time: 0.034, memory: 928, loss: 5.0735 2023-01-04 20:12:12,546 - mmcls - INFO - Epoch [39][4600/5005] lr: 1.000e-02, eta: 7:29:26, time: 0.086, data_time: 0.032, memory: 928, loss: 5.0739 2023-01-04 20:12:21,242 - mmcls - INFO - Epoch [39][4700/5005] lr: 1.000e-02, eta: 7:29:17, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0868 2023-01-04 20:12:30,023 - mmcls - INFO - Epoch [39][4800/5005] lr: 1.000e-02, eta: 7:29:08, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0604 2023-01-04 20:12:38,754 - mmcls - INFO - Epoch [39][4900/5005] lr: 1.000e-02, eta: 7:28:59, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0916 2023-01-04 20:12:47,646 - mmcls - INFO - Epoch [39][5000/5005] lr: 1.000e-02, eta: 7:28:51, time: 0.089, data_time: 0.021, memory: 928, loss: 5.0765 2023-01-04 20:13:10,467 - mmcls - INFO - Epoch(val) [39][196] accuracy_top-1: 9.6180, accuracy_top-5: 22.2460 2023-01-04 20:13:21,371 - mmcls - INFO - Epoch [40][100/5005] lr: 1.000e-02, eta: 7:28:44, time: 0.109, data_time: 0.044, memory: 928, loss: 5.0832 2023-01-04 20:13:29,782 - mmcls - INFO - Epoch [40][200/5005] lr: 1.000e-02, eta: 7:28:35, time: 0.084, data_time: 0.026, memory: 928, loss: 5.0913 2023-01-04 20:13:38,253 - mmcls - INFO - Epoch [40][300/5005] lr: 1.000e-02, eta: 7:28:25, time: 0.085, data_time: 0.019, memory: 928, loss: 5.0663 2023-01-04 20:13:47,140 - mmcls - INFO - Epoch [40][400/5005] lr: 1.000e-02, eta: 7:28:16, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0617 2023-01-04 20:13:56,043 - mmcls - INFO - Epoch [40][500/5005] lr: 1.000e-02, eta: 7:28:08, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0710 2023-01-04 20:14:04,687 - mmcls - INFO - Epoch [40][600/5005] lr: 1.000e-02, eta: 7:27:59, time: 0.086, data_time: 0.020, memory: 928, loss: 5.0636 2023-01-04 20:14:13,474 - mmcls - INFO - Epoch [40][700/5005] lr: 1.000e-02, eta: 7:27:50, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0731 2023-01-04 20:14:23,037 - mmcls - INFO - Epoch [40][800/5005] lr: 1.000e-02, eta: 7:27:42, time: 0.095, data_time: 0.019, memory: 928, loss: 5.0807 2023-01-04 20:14:31,908 - mmcls - INFO - Epoch [40][900/5005] lr: 1.000e-02, eta: 7:27:33, time: 0.089, data_time: 0.037, memory: 928, loss: 5.0755 2023-01-04 20:14:40,660 - mmcls - INFO - Epoch [40][1000/5005] lr: 1.000e-02, eta: 7:27:24, time: 0.087, data_time: 0.036, memory: 928, loss: 5.0867 2023-01-04 20:14:49,346 - mmcls - INFO - Epoch [40][1100/5005] lr: 1.000e-02, eta: 7:27:15, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0755 2023-01-04 20:14:58,042 - mmcls - INFO - Epoch [40][1200/5005] lr: 1.000e-02, eta: 7:27:06, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0600 2023-01-04 20:15:06,904 - mmcls - INFO - Epoch [40][1300/5005] lr: 1.000e-02, eta: 7:26:58, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0820 2023-01-04 20:15:15,329 - mmcls - INFO - Epoch [40][1400/5005] lr: 1.000e-02, eta: 7:26:48, time: 0.084, data_time: 0.022, memory: 928, loss: 5.0713 2023-01-04 20:15:24,047 - mmcls - INFO - Epoch [40][1500/5005] lr: 1.000e-02, eta: 7:26:39, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0675 2023-01-04 20:15:32,985 - mmcls - INFO - Epoch [40][1600/5005] lr: 1.000e-02, eta: 7:26:31, time: 0.089, data_time: 0.017, memory: 928, loss: 5.0878 2023-01-04 20:15:41,496 - mmcls - INFO - Epoch [40][1700/5005] lr: 1.000e-02, eta: 7:26:21, time: 0.085, data_time: 0.028, memory: 928, loss: 5.0949 2023-01-04 20:15:50,255 - mmcls - INFO - Epoch [40][1800/5005] lr: 1.000e-02, eta: 7:26:12, time: 0.088, data_time: 0.012, memory: 928, loss: 5.0501 2023-01-04 20:15:58,838 - mmcls - INFO - Epoch [40][1900/5005] lr: 1.000e-02, eta: 7:26:03, time: 0.086, data_time: 0.021, memory: 928, loss: 5.0694 2023-01-04 20:16:07,347 - mmcls - INFO - Epoch [40][2000/5005] lr: 1.000e-02, eta: 7:25:54, time: 0.085, data_time: 0.029, memory: 928, loss: 5.0665 2023-01-04 20:16:16,533 - mmcls - INFO - Epoch [40][2100/5005] lr: 1.000e-02, eta: 7:25:46, time: 0.092, data_time: 0.008, memory: 928, loss: 5.0758 2023-01-04 20:16:25,522 - mmcls - INFO - Epoch [40][2200/5005] lr: 1.000e-02, eta: 7:25:37, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0932 2023-01-04 20:16:33,934 - mmcls - INFO - Epoch [40][2300/5005] lr: 1.000e-02, eta: 7:25:28, time: 0.084, data_time: 0.014, memory: 928, loss: 5.0637 2023-01-04 20:16:42,392 - mmcls - INFO - Epoch [40][2400/5005] lr: 1.000e-02, eta: 7:25:18, time: 0.085, data_time: 0.019, memory: 928, loss: 5.0606 2023-01-04 20:16:51,283 - mmcls - INFO - Epoch [40][2500/5005] lr: 1.000e-02, eta: 7:25:10, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0511 2023-01-04 20:17:00,539 - mmcls - INFO - Epoch [40][2600/5005] lr: 1.000e-02, eta: 7:25:01, time: 0.092, data_time: 0.015, memory: 928, loss: 5.0982 2023-01-04 20:17:08,960 - mmcls - INFO - Epoch [40][2700/5005] lr: 1.000e-02, eta: 7:24:52, time: 0.084, data_time: 0.014, memory: 928, loss: 5.0752 2023-01-04 20:17:17,919 - mmcls - INFO - Epoch [40][2800/5005] lr: 1.000e-02, eta: 7:24:43, time: 0.090, data_time: 0.009, memory: 928, loss: 5.0626 2023-01-04 20:17:26,453 - mmcls - INFO - Epoch [40][2900/5005] lr: 1.000e-02, eta: 7:24:34, time: 0.085, data_time: 0.021, memory: 928, loss: 5.0929 2023-01-04 20:17:35,049 - mmcls - INFO - Epoch [40][3000/5005] lr: 1.000e-02, eta: 7:24:25, time: 0.086, data_time: 0.020, memory: 928, loss: 5.0599 2023-01-04 20:17:44,076 - mmcls - INFO - Epoch [40][3100/5005] lr: 1.000e-02, eta: 7:24:16, time: 0.090, data_time: 0.010, memory: 928, loss: 5.0677 2023-01-04 20:17:52,882 - mmcls - INFO - Epoch [40][3200/5005] lr: 1.000e-02, eta: 7:24:08, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0986 2023-01-04 20:18:01,449 - mmcls - INFO - Epoch [40][3300/5005] lr: 1.000e-02, eta: 7:23:58, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0488 2023-01-04 20:18:10,148 - mmcls - INFO - Epoch [40][3400/5005] lr: 1.000e-02, eta: 7:23:49, time: 0.087, data_time: 0.019, memory: 928, loss: 5.0796 2023-01-04 20:18:18,881 - mmcls - INFO - Epoch [40][3500/5005] lr: 1.000e-02, eta: 7:23:40, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0564 2023-01-04 20:18:27,574 - mmcls - INFO - Epoch [40][3600/5005] lr: 1.000e-02, eta: 7:23:31, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0511 2023-01-04 20:18:36,375 - mmcls - INFO - Epoch [40][3700/5005] lr: 1.000e-02, eta: 7:23:23, time: 0.088, data_time: 0.021, memory: 928, loss: 5.0610 2023-01-04 20:18:45,057 - mmcls - INFO - Epoch [40][3800/5005] lr: 1.000e-02, eta: 7:23:13, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0717 2023-01-04 20:18:53,871 - mmcls - INFO - Epoch [40][3900/5005] lr: 1.000e-02, eta: 7:23:05, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0716 2023-01-04 20:19:02,783 - mmcls - INFO - Epoch [40][4000/5005] lr: 1.000e-02, eta: 7:22:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0768 2023-01-04 20:19:11,638 - mmcls - INFO - Epoch [40][4100/5005] lr: 1.000e-02, eta: 7:22:47, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0800 2023-01-04 20:19:20,278 - mmcls - INFO - Epoch [40][4200/5005] lr: 1.000e-02, eta: 7:22:38, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0650 2023-01-04 20:19:28,776 - mmcls - INFO - Epoch [40][4300/5005] lr: 1.000e-02, eta: 7:22:29, time: 0.085, data_time: 0.014, memory: 928, loss: 5.0761 2023-01-04 20:19:37,461 - mmcls - INFO - Epoch [40][4400/5005] lr: 1.000e-02, eta: 7:22:20, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0671 2023-01-04 20:19:46,062 - mmcls - INFO - Epoch [40][4500/5005] lr: 1.000e-02, eta: 7:22:11, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0691 2023-01-04 20:19:54,958 - mmcls - INFO - Epoch [40][4600/5005] lr: 1.000e-02, eta: 7:22:02, time: 0.089, data_time: 0.029, memory: 928, loss: 5.0673 2023-01-04 20:20:03,618 - mmcls - INFO - Epoch [40][4700/5005] lr: 1.000e-02, eta: 7:21:53, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0730 2023-01-04 20:20:12,948 - mmcls - INFO - Epoch [40][4800/5005] lr: 1.000e-02, eta: 7:21:45, time: 0.093, data_time: 0.002, memory: 928, loss: 5.0769 2023-01-04 20:20:21,395 - mmcls - INFO - Epoch [40][4900/5005] lr: 1.000e-02, eta: 7:21:35, time: 0.084, data_time: 0.007, memory: 928, loss: 5.1038 2023-01-04 20:20:30,181 - mmcls - INFO - Epoch [40][5000/5005] lr: 1.000e-02, eta: 7:21:27, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0749 2023-01-04 20:20:52,941 - mmcls - INFO - Epoch(val) [40][196] accuracy_top-1: 10.6960, accuracy_top-5: 24.3360 2023-01-04 20:21:03,707 - mmcls - INFO - Epoch [41][100/5005] lr: 1.000e-02, eta: 7:21:19, time: 0.107, data_time: 0.046, memory: 928, loss: 5.0581 2023-01-04 20:21:12,160 - mmcls - INFO - Epoch [41][200/5005] lr: 1.000e-02, eta: 7:21:10, time: 0.085, data_time: 0.024, memory: 928, loss: 5.0463 2023-01-04 20:21:21,093 - mmcls - INFO - Epoch [41][300/5005] lr: 1.000e-02, eta: 7:21:01, time: 0.089, data_time: 0.018, memory: 928, loss: 5.0550 2023-01-04 20:21:29,880 - mmcls - INFO - Epoch [41][400/5005] lr: 1.000e-02, eta: 7:20:53, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0780 2023-01-04 20:21:39,148 - mmcls - INFO - Epoch [41][500/5005] lr: 1.000e-02, eta: 7:20:44, time: 0.093, data_time: 0.003, memory: 928, loss: 5.0652 2023-01-04 20:21:48,144 - mmcls - INFO - Epoch [41][600/5005] lr: 1.000e-02, eta: 7:20:36, time: 0.090, data_time: 0.020, memory: 928, loss: 5.0621 2023-01-04 20:21:56,690 - mmcls - INFO - Epoch [41][700/5005] lr: 1.000e-02, eta: 7:20:27, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0770 2023-01-04 20:22:05,666 - mmcls - INFO - Epoch [41][800/5005] lr: 1.000e-02, eta: 7:20:18, time: 0.090, data_time: 0.014, memory: 928, loss: 5.0578 2023-01-04 20:22:14,574 - mmcls - INFO - Epoch [41][900/5005] lr: 1.000e-02, eta: 7:20:09, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0549 2023-01-04 20:22:23,143 - mmcls - INFO - Epoch [41][1000/5005] lr: 1.000e-02, eta: 7:20:00, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0679 2023-01-04 20:22:31,858 - mmcls - INFO - Epoch [41][1100/5005] lr: 1.000e-02, eta: 7:19:51, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0711 2023-01-04 20:22:40,833 - mmcls - INFO - Epoch [41][1200/5005] lr: 1.000e-02, eta: 7:19:43, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0729 2023-01-04 20:22:49,928 - mmcls - INFO - Epoch [41][1300/5005] lr: 1.000e-02, eta: 7:19:34, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0601 2023-01-04 20:22:58,810 - mmcls - INFO - Epoch [41][1400/5005] lr: 1.000e-02, eta: 7:19:25, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0664 2023-01-04 20:23:07,641 - mmcls - INFO - Epoch [41][1500/5005] lr: 1.000e-02, eta: 7:19:17, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0570 2023-01-04 20:23:16,205 - mmcls - INFO - Epoch [41][1600/5005] lr: 1.000e-02, eta: 7:19:07, time: 0.086, data_time: 0.028, memory: 928, loss: 5.0685 2023-01-04 20:23:25,000 - mmcls - INFO - Epoch [41][1700/5005] lr: 1.000e-02, eta: 7:18:59, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0730 2023-01-04 20:23:33,478 - mmcls - INFO - Epoch [41][1800/5005] lr: 1.000e-02, eta: 7:18:49, time: 0.085, data_time: 0.016, memory: 928, loss: 5.0538 2023-01-04 20:23:42,257 - mmcls - INFO - Epoch [41][1900/5005] lr: 1.000e-02, eta: 7:18:40, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0515 2023-01-04 20:23:51,099 - mmcls - INFO - Epoch [41][2000/5005] lr: 1.000e-02, eta: 7:18:32, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0501 2023-01-04 20:23:59,952 - mmcls - INFO - Epoch [41][2100/5005] lr: 1.000e-02, eta: 7:18:23, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0713 2023-01-04 20:24:08,690 - mmcls - INFO - Epoch [41][2200/5005] lr: 1.000e-02, eta: 7:18:14, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0591 2023-01-04 20:24:17,275 - mmcls - INFO - Epoch [41][2300/5005] lr: 1.000e-02, eta: 7:18:05, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0548 2023-01-04 20:24:26,322 - mmcls - INFO - Epoch [41][2400/5005] lr: 1.000e-02, eta: 7:17:56, time: 0.090, data_time: 0.014, memory: 928, loss: 5.0837 2023-01-04 20:24:35,291 - mmcls - INFO - Epoch [41][2500/5005] lr: 1.000e-02, eta: 7:17:48, time: 0.090, data_time: 0.015, memory: 928, loss: 5.0832 2023-01-04 20:24:43,831 - mmcls - INFO - Epoch [41][2600/5005] lr: 1.000e-02, eta: 7:17:38, time: 0.085, data_time: 0.009, memory: 928, loss: 5.0860 2023-01-04 20:24:52,754 - mmcls - INFO - Epoch [41][2700/5005] lr: 1.000e-02, eta: 7:17:30, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0655 2023-01-04 20:25:01,419 - mmcls - INFO - Epoch [41][2800/5005] lr: 1.000e-02, eta: 7:17:21, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0627 2023-01-04 20:25:10,207 - mmcls - INFO - Epoch [41][2900/5005] lr: 1.000e-02, eta: 7:17:12, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0758 2023-01-04 20:25:18,818 - mmcls - INFO - Epoch [41][3000/5005] lr: 1.000e-02, eta: 7:17:03, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0668 2023-01-04 20:25:27,250 - mmcls - INFO - Epoch [41][3100/5005] lr: 1.000e-02, eta: 7:16:53, time: 0.084, data_time: 0.005, memory: 928, loss: 5.0921 2023-01-04 20:25:35,946 - mmcls - INFO - Epoch [41][3200/5005] lr: 1.000e-02, eta: 7:16:44, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0668 2023-01-04 20:25:44,632 - mmcls - INFO - Epoch [41][3300/5005] lr: 1.000e-02, eta: 7:16:35, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0614 2023-01-04 20:25:53,387 - mmcls - INFO - Epoch [41][3400/5005] lr: 1.000e-02, eta: 7:16:26, time: 0.088, data_time: 0.000, memory: 928, loss: 5.0837 2023-01-04 20:26:02,100 - mmcls - INFO - Epoch [41][3500/5005] lr: 1.000e-02, eta: 7:16:17, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0936 2023-01-04 20:26:11,248 - mmcls - INFO - Epoch [41][3600/5005] lr: 1.000e-02, eta: 7:16:09, time: 0.091, data_time: 0.011, memory: 928, loss: 5.0686 2023-01-04 20:26:20,028 - mmcls - INFO - Epoch [41][3700/5005] lr: 1.000e-02, eta: 7:16:00, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0943 2023-01-04 20:26:28,657 - mmcls - INFO - Epoch [41][3800/5005] lr: 1.000e-02, eta: 7:15:51, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0612 2023-01-04 20:26:37,451 - mmcls - INFO - Epoch [41][3900/5005] lr: 1.000e-02, eta: 7:15:42, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0660 2023-01-04 20:26:46,314 - mmcls - INFO - Epoch [41][4000/5005] lr: 1.000e-02, eta: 7:15:33, time: 0.089, data_time: 0.033, memory: 928, loss: 5.0639 2023-01-04 20:26:54,893 - mmcls - INFO - Epoch [41][4100/5005] lr: 1.000e-02, eta: 7:15:24, time: 0.086, data_time: 0.023, memory: 928, loss: 5.0848 2023-01-04 20:27:03,712 - mmcls - INFO - Epoch [41][4200/5005] lr: 1.000e-02, eta: 7:15:15, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0654 2023-01-04 20:27:12,438 - mmcls - INFO - Epoch [41][4300/5005] lr: 1.000e-02, eta: 7:15:06, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0804 2023-01-04 20:27:21,067 - mmcls - INFO - Epoch [41][4400/5005] lr: 1.000e-02, eta: 7:14:57, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0777 2023-01-04 20:27:29,723 - mmcls - INFO - Epoch [41][4500/5005] lr: 1.000e-02, eta: 7:14:48, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0925 2023-01-04 20:27:38,389 - mmcls - INFO - Epoch [41][4600/5005] lr: 1.000e-02, eta: 7:14:39, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0800 2023-01-04 20:27:47,247 - mmcls - INFO - Epoch [41][4700/5005] lr: 1.000e-02, eta: 7:14:30, time: 0.089, data_time: 0.023, memory: 928, loss: 5.0685 2023-01-04 20:27:55,943 - mmcls - INFO - Epoch [41][4800/5005] lr: 1.000e-02, eta: 7:14:21, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0753 2023-01-04 20:28:04,652 - mmcls - INFO - Epoch [41][4900/5005] lr: 1.000e-02, eta: 7:14:12, time: 0.087, data_time: 0.015, memory: 928, loss: 5.0678 2023-01-04 20:28:13,605 - mmcls - INFO - Epoch [41][5000/5005] lr: 1.000e-02, eta: 7:14:04, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0942 2023-01-04 20:28:36,304 - mmcls - INFO - Epoch(val) [41][196] accuracy_top-1: 11.8540, accuracy_top-5: 26.4460 2023-01-04 20:28:47,579 - mmcls - INFO - Epoch [42][100/5005] lr: 1.000e-02, eta: 7:13:57, time: 0.112, data_time: 0.066, memory: 928, loss: 5.0622 2023-01-04 20:28:56,202 - mmcls - INFO - Epoch [42][200/5005] lr: 1.000e-02, eta: 7:13:48, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0623 2023-01-04 20:29:04,940 - mmcls - INFO - Epoch [42][300/5005] lr: 1.000e-02, eta: 7:13:39, time: 0.087, data_time: 0.022, memory: 928, loss: 5.0377 2023-01-04 20:29:13,712 - mmcls - INFO - Epoch [42][400/5005] lr: 1.000e-02, eta: 7:13:31, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0647 2023-01-04 20:29:22,358 - mmcls - INFO - Epoch [42][500/5005] lr: 1.000e-02, eta: 7:13:21, time: 0.086, data_time: 0.009, memory: 928, loss: 5.1003 2023-01-04 20:29:31,252 - mmcls - INFO - Epoch [42][600/5005] lr: 1.000e-02, eta: 7:13:13, time: 0.089, data_time: 0.021, memory: 928, loss: 5.0679 2023-01-04 20:29:39,950 - mmcls - INFO - Epoch [42][700/5005] lr: 1.000e-02, eta: 7:13:04, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0634 2023-01-04 20:29:48,814 - mmcls - INFO - Epoch [42][800/5005] lr: 1.000e-02, eta: 7:12:55, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0524 2023-01-04 20:29:57,529 - mmcls - INFO - Epoch [42][900/5005] lr: 1.000e-02, eta: 7:12:46, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0546 2023-01-04 20:30:06,295 - mmcls - INFO - Epoch [42][1000/5005] lr: 1.000e-02, eta: 7:12:37, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0677 2023-01-04 20:30:14,851 - mmcls - INFO - Epoch [42][1100/5005] lr: 1.000e-02, eta: 7:12:28, time: 0.085, data_time: 0.013, memory: 928, loss: 5.0959 2023-01-04 20:30:23,916 - mmcls - INFO - Epoch [42][1200/5005] lr: 1.000e-02, eta: 7:12:19, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0860 2023-01-04 20:30:32,621 - mmcls - INFO - Epoch [42][1300/5005] lr: 1.000e-02, eta: 7:12:10, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0767 2023-01-04 20:30:41,497 - mmcls - INFO - Epoch [42][1400/5005] lr: 1.000e-02, eta: 7:12:02, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0653 2023-01-04 20:30:50,242 - mmcls - INFO - Epoch [42][1500/5005] lr: 1.000e-02, eta: 7:11:53, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0637 2023-01-04 20:30:58,999 - mmcls - INFO - Epoch [42][1600/5005] lr: 1.000e-02, eta: 7:11:44, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0402 2023-01-04 20:31:07,258 - mmcls - INFO - Epoch [42][1700/5005] lr: 1.000e-02, eta: 7:11:34, time: 0.083, data_time: 0.003, memory: 928, loss: 5.0681 2023-01-04 20:31:16,087 - mmcls - INFO - Epoch [42][1800/5005] lr: 1.000e-02, eta: 7:11:25, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0633 2023-01-04 20:31:25,070 - mmcls - INFO - Epoch [42][1900/5005] lr: 1.000e-02, eta: 7:11:17, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0538 2023-01-04 20:31:33,608 - mmcls - INFO - Epoch [42][2000/5005] lr: 1.000e-02, eta: 7:11:08, time: 0.085, data_time: 0.025, memory: 928, loss: 5.0625 2023-01-04 20:31:42,495 - mmcls - INFO - Epoch [42][2100/5005] lr: 1.000e-02, eta: 7:10:59, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0678 2023-01-04 20:31:51,219 - mmcls - INFO - Epoch [42][2200/5005] lr: 1.000e-02, eta: 7:10:50, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0670 2023-01-04 20:31:59,835 - mmcls - INFO - Epoch [42][2300/5005] lr: 1.000e-02, eta: 7:10:41, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0652 2023-01-04 20:32:08,413 - mmcls - INFO - Epoch [42][2400/5005] lr: 1.000e-02, eta: 7:10:32, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0609 2023-01-04 20:32:17,237 - mmcls - INFO - Epoch [42][2500/5005] lr: 1.000e-02, eta: 7:10:23, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0552 2023-01-04 20:32:25,901 - mmcls - INFO - Epoch [42][2600/5005] lr: 1.000e-02, eta: 7:10:14, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0741 2023-01-04 20:32:34,570 - mmcls - INFO - Epoch [42][2700/5005] lr: 1.000e-02, eta: 7:10:05, time: 0.087, data_time: 0.033, memory: 928, loss: 5.0505 2023-01-04 20:32:43,299 - mmcls - INFO - Epoch [42][2800/5005] lr: 1.000e-02, eta: 7:09:56, time: 0.087, data_time: 0.031, memory: 928, loss: 5.0690 2023-01-04 20:32:52,205 - mmcls - INFO - Epoch [42][2900/5005] lr: 1.000e-02, eta: 7:09:47, time: 0.089, data_time: 0.036, memory: 928, loss: 5.0739 2023-01-04 20:33:01,126 - mmcls - INFO - Epoch [42][3000/5005] lr: 1.000e-02, eta: 7:09:38, time: 0.089, data_time: 0.019, memory: 928, loss: 5.0932 2023-01-04 20:33:10,153 - mmcls - INFO - Epoch [42][3100/5005] lr: 1.000e-02, eta: 7:09:30, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0783 2023-01-04 20:33:18,724 - mmcls - INFO - Epoch [42][3200/5005] lr: 1.000e-02, eta: 7:09:21, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0727 2023-01-04 20:33:27,589 - mmcls - INFO - Epoch [42][3300/5005] lr: 1.000e-02, eta: 7:09:12, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0895 2023-01-04 20:33:36,258 - mmcls - INFO - Epoch [42][3400/5005] lr: 1.000e-02, eta: 7:09:03, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0470 2023-01-04 20:33:44,928 - mmcls - INFO - Epoch [42][3500/5005] lr: 1.000e-02, eta: 7:08:54, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0705 2023-01-04 20:33:53,608 - mmcls - INFO - Epoch [42][3600/5005] lr: 1.000e-02, eta: 7:08:45, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0982 2023-01-04 20:34:02,343 - mmcls - INFO - Epoch [42][3700/5005] lr: 1.000e-02, eta: 7:08:36, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0601 2023-01-04 20:34:11,470 - mmcls - INFO - Epoch [42][3800/5005] lr: 1.000e-02, eta: 7:08:28, time: 0.091, data_time: 0.023, memory: 928, loss: 5.0728 2023-01-04 20:34:20,379 - mmcls - INFO - Epoch [42][3900/5005] lr: 1.000e-02, eta: 7:08:19, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0789 2023-01-04 20:34:29,887 - mmcls - INFO - Epoch [42][4000/5005] lr: 1.000e-02, eta: 7:08:11, time: 0.095, data_time: 0.014, memory: 928, loss: 5.0755 2023-01-04 20:34:38,694 - mmcls - INFO - Epoch [42][4100/5005] lr: 1.000e-02, eta: 7:08:02, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0717 2023-01-04 20:34:47,385 - mmcls - INFO - Epoch [42][4200/5005] lr: 1.000e-02, eta: 7:07:53, time: 0.087, data_time: 0.026, memory: 928, loss: 5.0625 2023-01-04 20:34:55,957 - mmcls - INFO - Epoch [42][4300/5005] lr: 1.000e-02, eta: 7:07:44, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0696 2023-01-04 20:35:04,792 - mmcls - INFO - Epoch [42][4400/5005] lr: 1.000e-02, eta: 7:07:35, time: 0.088, data_time: 0.046, memory: 928, loss: 5.0851 2023-01-04 20:35:13,693 - mmcls - INFO - Epoch [42][4500/5005] lr: 1.000e-02, eta: 7:07:27, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0616 2023-01-04 20:35:22,389 - mmcls - INFO - Epoch [42][4600/5005] lr: 1.000e-02, eta: 7:07:18, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0524 2023-01-04 20:35:31,170 - mmcls - INFO - Epoch [42][4700/5005] lr: 1.000e-02, eta: 7:07:09, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0600 2023-01-04 20:35:40,065 - mmcls - INFO - Epoch [42][4800/5005] lr: 1.000e-02, eta: 7:07:00, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0832 2023-01-04 20:35:49,164 - mmcls - INFO - Epoch [42][4900/5005] lr: 1.000e-02, eta: 7:06:52, time: 0.091, data_time: 0.013, memory: 928, loss: 5.0905 2023-01-04 20:35:57,788 - mmcls - INFO - Epoch [42][5000/5005] lr: 1.000e-02, eta: 7:06:42, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0702 2023-01-04 20:36:20,560 - mmcls - INFO - Epoch(val) [42][196] accuracy_top-1: 11.7260, accuracy_top-5: 26.0880 2023-01-04 20:36:31,464 - mmcls - INFO - Epoch [43][100/5005] lr: 1.000e-02, eta: 7:06:35, time: 0.109, data_time: 0.048, memory: 928, loss: 5.0776 2023-01-04 20:36:40,146 - mmcls - INFO - Epoch [43][200/5005] lr: 1.000e-02, eta: 7:06:26, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0646 2023-01-04 20:36:48,815 - mmcls - INFO - Epoch [43][300/5005] lr: 1.000e-02, eta: 7:06:17, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0672 2023-01-04 20:36:57,311 - mmcls - INFO - Epoch [43][400/5005] lr: 1.000e-02, eta: 7:06:08, time: 0.085, data_time: 0.012, memory: 928, loss: 5.0798 2023-01-04 20:37:05,974 - mmcls - INFO - Epoch [43][500/5005] lr: 1.000e-02, eta: 7:05:59, time: 0.087, data_time: 0.032, memory: 928, loss: 5.0827 2023-01-04 20:37:14,473 - mmcls - INFO - Epoch [43][600/5005] lr: 1.000e-02, eta: 7:05:50, time: 0.085, data_time: 0.033, memory: 928, loss: 5.0526 2023-01-04 20:37:23,190 - mmcls - INFO - Epoch [43][700/5005] lr: 1.000e-02, eta: 7:05:41, time: 0.087, data_time: 0.022, memory: 928, loss: 5.0758 2023-01-04 20:37:32,197 - mmcls - INFO - Epoch [43][800/5005] lr: 1.000e-02, eta: 7:05:32, time: 0.090, data_time: 0.007, memory: 928, loss: 5.0598 2023-01-04 20:37:40,851 - mmcls - INFO - Epoch [43][900/5005] lr: 1.000e-02, eta: 7:05:23, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0657 2023-01-04 20:37:49,346 - mmcls - INFO - Epoch [43][1000/5005] lr: 1.000e-02, eta: 7:05:14, time: 0.085, data_time: 0.005, memory: 928, loss: 5.0607 2023-01-04 20:37:58,226 - mmcls - INFO - Epoch [43][1100/5005] lr: 1.000e-02, eta: 7:05:05, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0643 2023-01-04 20:38:06,722 - mmcls - INFO - Epoch [43][1200/5005] lr: 1.000e-02, eta: 7:04:56, time: 0.085, data_time: 0.010, memory: 928, loss: 5.0682 2023-01-04 20:38:15,399 - mmcls - INFO - Epoch [43][1300/5005] lr: 1.000e-02, eta: 7:04:47, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0674 2023-01-04 20:38:23,961 - mmcls - INFO - Epoch [43][1400/5005] lr: 1.000e-02, eta: 7:04:38, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0976 2023-01-04 20:38:32,701 - mmcls - INFO - Epoch [43][1500/5005] lr: 1.000e-02, eta: 7:04:29, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0836 2023-01-04 20:38:41,459 - mmcls - INFO - Epoch [43][1600/5005] lr: 1.000e-02, eta: 7:04:20, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0601 2023-01-04 20:38:50,526 - mmcls - INFO - Epoch [43][1700/5005] lr: 1.000e-02, eta: 7:04:11, time: 0.091, data_time: 0.019, memory: 928, loss: 5.0840 2023-01-04 20:38:59,161 - mmcls - INFO - Epoch [43][1800/5005] lr: 1.000e-02, eta: 7:04:02, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0357 2023-01-04 20:39:07,857 - mmcls - INFO - Epoch [43][1900/5005] lr: 1.000e-02, eta: 7:03:53, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0591 2023-01-04 20:39:16,812 - mmcls - INFO - Epoch [43][2000/5005] lr: 1.000e-02, eta: 7:03:45, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0605 2023-01-04 20:39:25,624 - mmcls - INFO - Epoch [43][2100/5005] lr: 1.000e-02, eta: 7:03:36, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0613 2023-01-04 20:39:34,217 - mmcls - INFO - Epoch [43][2200/5005] lr: 1.000e-02, eta: 7:03:27, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0700 2023-01-04 20:39:43,072 - mmcls - INFO - Epoch [43][2300/5005] lr: 1.000e-02, eta: 7:03:18, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0540 2023-01-04 20:39:51,868 - mmcls - INFO - Epoch [43][2400/5005] lr: 1.000e-02, eta: 7:03:09, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0817 2023-01-04 20:40:00,893 - mmcls - INFO - Epoch [43][2500/5005] lr: 1.000e-02, eta: 7:03:01, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0755 2023-01-04 20:40:09,424 - mmcls - INFO - Epoch [43][2600/5005] lr: 1.000e-02, eta: 7:02:51, time: 0.085, data_time: 0.010, memory: 928, loss: 5.0816 2023-01-04 20:40:18,137 - mmcls - INFO - Epoch [43][2700/5005] lr: 1.000e-02, eta: 7:02:42, time: 0.087, data_time: 0.025, memory: 928, loss: 5.0606 2023-01-04 20:40:26,801 - mmcls - INFO - Epoch [43][2800/5005] lr: 1.000e-02, eta: 7:02:33, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0553 2023-01-04 20:40:35,303 - mmcls - INFO - Epoch [43][2900/5005] lr: 1.000e-02, eta: 7:02:24, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0894 2023-01-04 20:40:44,079 - mmcls - INFO - Epoch [43][3000/5005] lr: 1.000e-02, eta: 7:02:15, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0617 2023-01-04 20:40:53,252 - mmcls - INFO - Epoch [43][3100/5005] lr: 1.000e-02, eta: 7:02:07, time: 0.092, data_time: 0.012, memory: 928, loss: 5.0645 2023-01-04 20:41:01,973 - mmcls - INFO - Epoch [43][3200/5005] lr: 1.000e-02, eta: 7:01:58, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0663 2023-01-04 20:41:10,877 - mmcls - INFO - Epoch [43][3300/5005] lr: 1.000e-02, eta: 7:01:49, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0758 2023-01-04 20:41:19,724 - mmcls - INFO - Epoch [43][3400/5005] lr: 1.000e-02, eta: 7:01:41, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0815 2023-01-04 20:41:28,493 - mmcls - INFO - Epoch [43][3500/5005] lr: 1.000e-02, eta: 7:01:32, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0709 2023-01-04 20:41:37,221 - mmcls - INFO - Epoch [43][3600/5005] lr: 1.000e-02, eta: 7:01:23, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0695 2023-01-04 20:41:45,977 - mmcls - INFO - Epoch [43][3700/5005] lr: 1.000e-02, eta: 7:01:14, time: 0.088, data_time: 0.005, memory: 928, loss: 5.1089 2023-01-04 20:41:54,822 - mmcls - INFO - Epoch [43][3800/5005] lr: 1.000e-02, eta: 7:01:05, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0685 2023-01-04 20:42:03,401 - mmcls - INFO - Epoch [43][3900/5005] lr: 1.000e-02, eta: 7:00:56, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0760 2023-01-04 20:42:12,453 - mmcls - INFO - Epoch [43][4000/5005] lr: 1.000e-02, eta: 7:00:47, time: 0.091, data_time: 0.045, memory: 928, loss: 5.0559 2023-01-04 20:42:21,236 - mmcls - INFO - Epoch [43][4100/5005] lr: 1.000e-02, eta: 7:00:38, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0710 2023-01-04 20:42:30,385 - mmcls - INFO - Epoch [43][4200/5005] lr: 1.000e-02, eta: 7:00:30, time: 0.091, data_time: 0.004, memory: 928, loss: 5.0596 2023-01-04 20:42:39,595 - mmcls - INFO - Epoch [43][4300/5005] lr: 1.000e-02, eta: 7:00:22, time: 0.092, data_time: 0.002, memory: 928, loss: 5.0651 2023-01-04 20:42:48,196 - mmcls - INFO - Epoch [43][4400/5005] lr: 1.000e-02, eta: 7:00:13, time: 0.086, data_time: 0.024, memory: 928, loss: 5.0622 2023-01-04 20:42:57,036 - mmcls - INFO - Epoch [43][4500/5005] lr: 1.000e-02, eta: 7:00:04, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0757 2023-01-04 20:43:05,779 - mmcls - INFO - Epoch [43][4600/5005] lr: 1.000e-02, eta: 6:59:55, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0513 2023-01-04 20:43:14,817 - mmcls - INFO - Epoch [43][4700/5005] lr: 1.000e-02, eta: 6:59:46, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0490 2023-01-04 20:43:23,649 - mmcls - INFO - Epoch [43][4800/5005] lr: 1.000e-02, eta: 6:59:38, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0422 2023-01-04 20:43:32,741 - mmcls - INFO - Epoch [43][4900/5005] lr: 1.000e-02, eta: 6:59:29, time: 0.091, data_time: 0.005, memory: 928, loss: 5.0708 2023-01-04 20:43:41,242 - mmcls - INFO - Epoch [43][5000/5005] lr: 1.000e-02, eta: 6:59:20, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0563 2023-01-04 20:44:04,049 - mmcls - INFO - Epoch(val) [43][196] accuracy_top-1: 11.6040, accuracy_top-5: 26.3460 2023-01-04 20:44:14,836 - mmcls - INFO - Epoch [44][100/5005] lr: 1.000e-02, eta: 6:59:13, time: 0.108, data_time: 0.049, memory: 928, loss: 5.0772 2023-01-04 20:44:23,598 - mmcls - INFO - Epoch [44][200/5005] lr: 1.000e-02, eta: 6:59:04, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0825 2023-01-04 20:44:32,551 - mmcls - INFO - Epoch [44][300/5005] lr: 1.000e-02, eta: 6:58:55, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0832 2023-01-04 20:44:41,835 - mmcls - INFO - Epoch [44][400/5005] lr: 1.000e-02, eta: 6:58:47, time: 0.093, data_time: 0.002, memory: 928, loss: 5.0910 2023-01-04 20:44:50,627 - mmcls - INFO - Epoch [44][500/5005] lr: 1.000e-02, eta: 6:58:38, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0409 2023-01-04 20:44:59,303 - mmcls - INFO - Epoch [44][600/5005] lr: 1.000e-02, eta: 6:58:29, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0550 2023-01-04 20:45:07,942 - mmcls - INFO - Epoch [44][700/5005] lr: 1.000e-02, eta: 6:58:20, time: 0.086, data_time: 0.039, memory: 928, loss: 5.0453 2023-01-04 20:45:16,560 - mmcls - INFO - Epoch [44][800/5005] lr: 1.000e-02, eta: 6:58:11, time: 0.086, data_time: 0.024, memory: 928, loss: 5.0976 2023-01-04 20:45:25,017 - mmcls - INFO - Epoch [44][900/5005] lr: 1.000e-02, eta: 6:58:02, time: 0.084, data_time: 0.011, memory: 928, loss: 5.0557 2023-01-04 20:45:33,898 - mmcls - INFO - Epoch [44][1000/5005] lr: 1.000e-02, eta: 6:57:53, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0639 2023-01-04 20:45:42,702 - mmcls - INFO - Epoch [44][1100/5005] lr: 1.000e-02, eta: 6:57:44, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0460 2023-01-04 20:45:51,471 - mmcls - INFO - Epoch [44][1200/5005] lr: 1.000e-02, eta: 6:57:35, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0704 2023-01-04 20:46:00,090 - mmcls - INFO - Epoch [44][1300/5005] lr: 1.000e-02, eta: 6:57:26, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0740 2023-01-04 20:46:08,693 - mmcls - INFO - Epoch [44][1400/5005] lr: 1.000e-02, eta: 6:57:17, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0669 2023-01-04 20:46:17,273 - mmcls - INFO - Epoch [44][1500/5005] lr: 1.000e-02, eta: 6:57:08, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0660 2023-01-04 20:46:26,085 - mmcls - INFO - Epoch [44][1600/5005] lr: 1.000e-02, eta: 6:56:59, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0485 2023-01-04 20:46:34,811 - mmcls - INFO - Epoch [44][1700/5005] lr: 1.000e-02, eta: 6:56:50, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0563 2023-01-04 20:46:43,388 - mmcls - INFO - Epoch [44][1800/5005] lr: 1.000e-02, eta: 6:56:41, time: 0.086, data_time: 0.032, memory: 928, loss: 5.0676 2023-01-04 20:46:52,130 - mmcls - INFO - Epoch [44][1900/5005] lr: 1.000e-02, eta: 6:56:32, time: 0.087, data_time: 0.020, memory: 928, loss: 5.0914 2023-01-04 20:47:00,886 - mmcls - INFO - Epoch [44][2000/5005] lr: 1.000e-02, eta: 6:56:23, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0438 2023-01-04 20:47:09,473 - mmcls - INFO - Epoch [44][2100/5005] lr: 1.000e-02, eta: 6:56:14, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0603 2023-01-04 20:47:18,152 - mmcls - INFO - Epoch [44][2200/5005] lr: 1.000e-02, eta: 6:56:05, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0636 2023-01-04 20:47:26,879 - mmcls - INFO - Epoch [44][2300/5005] lr: 1.000e-02, eta: 6:55:56, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0646 2023-01-04 20:47:35,696 - mmcls - INFO - Epoch [44][2400/5005] lr: 1.000e-02, eta: 6:55:47, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0420 2023-01-04 20:47:44,347 - mmcls - INFO - Epoch [44][2500/5005] lr: 1.000e-02, eta: 6:55:38, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0511 2023-01-04 20:47:53,059 - mmcls - INFO - Epoch [44][2600/5005] lr: 1.000e-02, eta: 6:55:29, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0703 2023-01-04 20:48:01,881 - mmcls - INFO - Epoch [44][2700/5005] lr: 1.000e-02, eta: 6:55:20, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0602 2023-01-04 20:48:10,666 - mmcls - INFO - Epoch [44][2800/5005] lr: 1.000e-02, eta: 6:55:12, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0776 2023-01-04 20:48:19,565 - mmcls - INFO - Epoch [44][2900/5005] lr: 1.000e-02, eta: 6:55:03, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0690 2023-01-04 20:48:28,144 - mmcls - INFO - Epoch [44][3000/5005] lr: 1.000e-02, eta: 6:54:54, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0875 2023-01-04 20:48:36,652 - mmcls - INFO - Epoch [44][3100/5005] lr: 1.000e-02, eta: 6:54:44, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0443 2023-01-04 20:48:45,300 - mmcls - INFO - Epoch [44][3200/5005] lr: 1.000e-02, eta: 6:54:35, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0689 2023-01-04 20:48:53,970 - mmcls - INFO - Epoch [44][3300/5005] lr: 1.000e-02, eta: 6:54:26, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0731 2023-01-04 20:49:02,433 - mmcls - INFO - Epoch [44][3400/5005] lr: 1.000e-02, eta: 6:54:17, time: 0.084, data_time: 0.011, memory: 928, loss: 5.0634 2023-01-04 20:49:11,496 - mmcls - INFO - Epoch [44][3500/5005] lr: 1.000e-02, eta: 6:54:09, time: 0.091, data_time: 0.007, memory: 928, loss: 5.0401 2023-01-04 20:49:20,066 - mmcls - INFO - Epoch [44][3600/5005] lr: 1.000e-02, eta: 6:53:59, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0672 2023-01-04 20:49:28,728 - mmcls - INFO - Epoch [44][3700/5005] lr: 1.000e-02, eta: 6:53:50, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0645 2023-01-04 20:49:37,489 - mmcls - INFO - Epoch [44][3800/5005] lr: 1.000e-02, eta: 6:53:42, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0675 2023-01-04 20:49:46,297 - mmcls - INFO - Epoch [44][3900/5005] lr: 1.000e-02, eta: 6:53:33, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0495 2023-01-04 20:49:54,993 - mmcls - INFO - Epoch [44][4000/5005] lr: 1.000e-02, eta: 6:53:24, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0874 2023-01-04 20:50:04,009 - mmcls - INFO - Epoch [44][4100/5005] lr: 1.000e-02, eta: 6:53:15, time: 0.090, data_time: 0.016, memory: 928, loss: 5.0736 2023-01-04 20:50:12,663 - mmcls - INFO - Epoch [44][4200/5005] lr: 1.000e-02, eta: 6:53:06, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0664 2023-01-04 20:50:21,289 - mmcls - INFO - Epoch [44][4300/5005] lr: 1.000e-02, eta: 6:52:57, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0918 2023-01-04 20:50:30,196 - mmcls - INFO - Epoch [44][4400/5005] lr: 1.000e-02, eta: 6:52:48, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0499 2023-01-04 20:50:39,108 - mmcls - INFO - Epoch [44][4500/5005] lr: 1.000e-02, eta: 6:52:40, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0767 2023-01-04 20:50:47,869 - mmcls - INFO - Epoch [44][4600/5005] lr: 1.000e-02, eta: 6:52:31, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0725 2023-01-04 20:50:56,518 - mmcls - INFO - Epoch [44][4700/5005] lr: 1.000e-02, eta: 6:52:22, time: 0.087, data_time: 0.020, memory: 928, loss: 5.0575 2023-01-04 20:51:05,370 - mmcls - INFO - Epoch [44][4800/5005] lr: 1.000e-02, eta: 6:52:13, time: 0.089, data_time: 0.030, memory: 928, loss: 5.0667 2023-01-04 20:51:14,112 - mmcls - INFO - Epoch [44][4900/5005] lr: 1.000e-02, eta: 6:52:04, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0553 2023-01-04 20:51:22,881 - mmcls - INFO - Epoch [44][5000/5005] lr: 1.000e-02, eta: 6:51:55, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0691 2023-01-04 20:51:45,711 - mmcls - INFO - Epoch(val) [44][196] accuracy_top-1: 8.6820, accuracy_top-5: 20.0420 2023-01-04 20:51:56,844 - mmcls - INFO - Epoch [45][100/5005] lr: 1.000e-02, eta: 6:51:48, time: 0.111, data_time: 0.052, memory: 928, loss: 5.0665 2023-01-04 20:52:05,462 - mmcls - INFO - Epoch [45][200/5005] lr: 1.000e-02, eta: 6:51:39, time: 0.086, data_time: 0.036, memory: 928, loss: 5.0672 2023-01-04 20:52:14,187 - mmcls - INFO - Epoch [45][300/5005] lr: 1.000e-02, eta: 6:51:30, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0270 2023-01-04 20:52:23,006 - mmcls - INFO - Epoch [45][400/5005] lr: 1.000e-02, eta: 6:51:21, time: 0.088, data_time: 0.029, memory: 928, loss: 5.0669 2023-01-04 20:52:31,710 - mmcls - INFO - Epoch [45][500/5005] lr: 1.000e-02, eta: 6:51:12, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0567 2023-01-04 20:52:40,708 - mmcls - INFO - Epoch [45][600/5005] lr: 1.000e-02, eta: 6:51:04, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0459 2023-01-04 20:52:49,476 - mmcls - INFO - Epoch [45][700/5005] lr: 1.000e-02, eta: 6:50:55, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0628 2023-01-04 20:52:58,055 - mmcls - INFO - Epoch [45][800/5005] lr: 1.000e-02, eta: 6:50:46, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0613 2023-01-04 20:53:06,744 - mmcls - INFO - Epoch [45][900/5005] lr: 1.000e-02, eta: 6:50:37, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0691 2023-01-04 20:53:15,518 - mmcls - INFO - Epoch [45][1000/5005] lr: 1.000e-02, eta: 6:50:28, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0711 2023-01-04 20:53:24,296 - mmcls - INFO - Epoch [45][1100/5005] lr: 1.000e-02, eta: 6:50:19, time: 0.088, data_time: 0.025, memory: 928, loss: 5.0812 2023-01-04 20:53:33,078 - mmcls - INFO - Epoch [45][1200/5005] lr: 1.000e-02, eta: 6:50:10, time: 0.088, data_time: 0.029, memory: 928, loss: 5.0744 2023-01-04 20:53:41,816 - mmcls - INFO - Epoch [45][1300/5005] lr: 1.000e-02, eta: 6:50:01, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0693 2023-01-04 20:53:50,593 - mmcls - INFO - Epoch [45][1400/5005] lr: 1.000e-02, eta: 6:49:52, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0649 2023-01-04 20:53:59,322 - mmcls - INFO - Epoch [45][1500/5005] lr: 1.000e-02, eta: 6:49:44, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0391 2023-01-04 20:54:07,956 - mmcls - INFO - Epoch [45][1600/5005] lr: 1.000e-02, eta: 6:49:35, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0716 2023-01-04 20:54:16,576 - mmcls - INFO - Epoch [45][1700/5005] lr: 1.000e-02, eta: 6:49:25, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0524 2023-01-04 20:54:25,254 - mmcls - INFO - Epoch [45][1800/5005] lr: 1.000e-02, eta: 6:49:16, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0714 2023-01-04 20:54:33,846 - mmcls - INFO - Epoch [45][1900/5005] lr: 1.000e-02, eta: 6:49:07, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0667 2023-01-04 20:54:42,712 - mmcls - INFO - Epoch [45][2000/5005] lr: 1.000e-02, eta: 6:48:59, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0625 2023-01-04 20:54:51,671 - mmcls - INFO - Epoch [45][2100/5005] lr: 1.000e-02, eta: 6:48:50, time: 0.090, data_time: 0.010, memory: 928, loss: 5.0676 2023-01-04 20:55:00,431 - mmcls - INFO - Epoch [45][2200/5005] lr: 1.000e-02, eta: 6:48:41, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0621 2023-01-04 20:55:08,983 - mmcls - INFO - Epoch [45][2300/5005] lr: 1.000e-02, eta: 6:48:32, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0774 2023-01-04 20:55:17,726 - mmcls - INFO - Epoch [45][2400/5005] lr: 1.000e-02, eta: 6:48:23, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0657 2023-01-04 20:55:26,956 - mmcls - INFO - Epoch [45][2500/5005] lr: 1.000e-02, eta: 6:48:15, time: 0.092, data_time: 0.003, memory: 928, loss: 5.0688 2023-01-04 20:55:35,523 - mmcls - INFO - Epoch [45][2600/5005] lr: 1.000e-02, eta: 6:48:06, time: 0.086, data_time: 0.038, memory: 928, loss: 5.0569 2023-01-04 20:55:44,440 - mmcls - INFO - Epoch [45][2700/5005] lr: 1.000e-02, eta: 6:47:57, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0686 2023-01-04 20:55:53,120 - mmcls - INFO - Epoch [45][2800/5005] lr: 1.000e-02, eta: 6:47:48, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0802 2023-01-04 20:56:02,157 - mmcls - INFO - Epoch [45][2900/5005] lr: 1.000e-02, eta: 6:47:39, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0395 2023-01-04 20:56:10,732 - mmcls - INFO - Epoch [45][3000/5005] lr: 1.000e-02, eta: 6:47:30, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0658 2023-01-04 20:56:19,451 - mmcls - INFO - Epoch [45][3100/5005] lr: 1.000e-02, eta: 6:47:21, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0641 2023-01-04 20:56:28,308 - mmcls - INFO - Epoch [45][3200/5005] lr: 1.000e-02, eta: 6:47:12, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0629 2023-01-04 20:56:36,952 - mmcls - INFO - Epoch [45][3300/5005] lr: 1.000e-02, eta: 6:47:03, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0651 2023-01-04 20:56:45,810 - mmcls - INFO - Epoch [45][3400/5005] lr: 1.000e-02, eta: 6:46:55, time: 0.089, data_time: 0.011, memory: 928, loss: 5.0596 2023-01-04 20:56:54,327 - mmcls - INFO - Epoch [45][3500/5005] lr: 1.000e-02, eta: 6:46:45, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0855 2023-01-04 20:57:03,138 - mmcls - INFO - Epoch [45][3600/5005] lr: 1.000e-02, eta: 6:46:37, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0609 2023-01-04 20:57:12,325 - mmcls - INFO - Epoch [45][3700/5005] lr: 1.000e-02, eta: 6:46:28, time: 0.092, data_time: 0.005, memory: 928, loss: 5.0613 2023-01-04 20:57:21,140 - mmcls - INFO - Epoch [45][3800/5005] lr: 1.000e-02, eta: 6:46:19, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0748 2023-01-04 20:57:29,950 - mmcls - INFO - Epoch [45][3900/5005] lr: 1.000e-02, eta: 6:46:11, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0786 2023-01-04 20:57:38,893 - mmcls - INFO - Epoch [45][4000/5005] lr: 1.000e-02, eta: 6:46:02, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0465 2023-01-04 20:57:47,730 - mmcls - INFO - Epoch [45][4100/5005] lr: 1.000e-02, eta: 6:45:53, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0639 2023-01-04 20:57:56,698 - mmcls - INFO - Epoch [45][4200/5005] lr: 1.000e-02, eta: 6:45:45, time: 0.090, data_time: 0.006, memory: 928, loss: 5.0486 2023-01-04 20:58:05,931 - mmcls - INFO - Epoch [45][4300/5005] lr: 1.000e-02, eta: 6:45:36, time: 0.092, data_time: 0.001, memory: 928, loss: 5.0808 2023-01-04 20:58:14,725 - mmcls - INFO - Epoch [45][4400/5005] lr: 1.000e-02, eta: 6:45:27, time: 0.088, data_time: 0.026, memory: 928, loss: 5.0549 2023-01-04 20:58:23,659 - mmcls - INFO - Epoch [45][4500/5005] lr: 1.000e-02, eta: 6:45:19, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0560 2023-01-04 20:58:32,372 - mmcls - INFO - Epoch [45][4600/5005] lr: 1.000e-02, eta: 6:45:10, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0666 2023-01-04 20:58:40,877 - mmcls - INFO - Epoch [45][4700/5005] lr: 1.000e-02, eta: 6:45:01, time: 0.085, data_time: 0.015, memory: 928, loss: 5.0685 2023-01-04 20:58:49,683 - mmcls - INFO - Epoch [45][4800/5005] lr: 1.000e-02, eta: 6:44:52, time: 0.088, data_time: 0.012, memory: 928, loss: 5.0597 2023-01-04 20:58:58,430 - mmcls - INFO - Epoch [45][4900/5005] lr: 1.000e-02, eta: 6:44:43, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0577 2023-01-04 20:59:07,187 - mmcls - INFO - Epoch [45][5000/5005] lr: 1.000e-02, eta: 6:44:34, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0605 2023-01-04 20:59:30,041 - mmcls - INFO - Epoch(val) [45][196] accuracy_top-1: 10.7300, accuracy_top-5: 24.4520 2023-01-04 20:59:40,757 - mmcls - INFO - Epoch [46][100/5005] lr: 1.000e-02, eta: 6:44:26, time: 0.107, data_time: 0.051, memory: 928, loss: 5.0394 2023-01-04 20:59:49,573 - mmcls - INFO - Epoch [46][200/5005] lr: 1.000e-02, eta: 6:44:18, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0374 2023-01-04 20:59:58,069 - mmcls - INFO - Epoch [46][300/5005] lr: 1.000e-02, eta: 6:44:08, time: 0.085, data_time: 0.016, memory: 928, loss: 5.0646 2023-01-04 21:00:07,282 - mmcls - INFO - Epoch [46][400/5005] lr: 1.000e-02, eta: 6:44:00, time: 0.092, data_time: 0.003, memory: 928, loss: 5.0305 2023-01-04 21:00:15,959 - mmcls - INFO - Epoch [46][500/5005] lr: 1.000e-02, eta: 6:43:51, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0543 2023-01-04 21:00:24,706 - mmcls - INFO - Epoch [46][600/5005] lr: 1.000e-02, eta: 6:43:42, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0567 2023-01-04 21:00:33,278 - mmcls - INFO - Epoch [46][700/5005] lr: 1.000e-02, eta: 6:43:33, time: 0.086, data_time: 0.020, memory: 928, loss: 5.0625 2023-01-04 21:00:42,227 - mmcls - INFO - Epoch [46][800/5005] lr: 1.000e-02, eta: 6:43:24, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0779 2023-01-04 21:00:51,377 - mmcls - INFO - Epoch [46][900/5005] lr: 1.000e-02, eta: 6:43:16, time: 0.091, data_time: 0.002, memory: 928, loss: 5.0522 2023-01-04 21:01:00,204 - mmcls - INFO - Epoch [46][1000/5005] lr: 1.000e-02, eta: 6:43:07, time: 0.088, data_time: 0.031, memory: 928, loss: 5.0681 2023-01-04 21:01:09,515 - mmcls - INFO - Epoch [46][1100/5005] lr: 1.000e-02, eta: 6:42:59, time: 0.093, data_time: 0.007, memory: 928, loss: 5.0773 2023-01-04 21:01:18,309 - mmcls - INFO - Epoch [46][1200/5005] lr: 1.000e-02, eta: 6:42:50, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0576 2023-01-04 21:01:27,465 - mmcls - INFO - Epoch [46][1300/5005] lr: 1.000e-02, eta: 6:42:42, time: 0.092, data_time: 0.001, memory: 928, loss: 5.0543 2023-01-04 21:01:36,071 - mmcls - INFO - Epoch [46][1400/5005] lr: 1.000e-02, eta: 6:42:33, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0670 2023-01-04 21:01:45,033 - mmcls - INFO - Epoch [46][1500/5005] lr: 1.000e-02, eta: 6:42:24, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0700 2023-01-04 21:01:53,612 - mmcls - INFO - Epoch [46][1600/5005] lr: 1.000e-02, eta: 6:42:15, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0835 2023-01-04 21:02:02,181 - mmcls - INFO - Epoch [46][1700/5005] lr: 1.000e-02, eta: 6:42:06, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0349 2023-01-04 21:02:10,913 - mmcls - INFO - Epoch [46][1800/5005] lr: 1.000e-02, eta: 6:41:57, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0882 2023-01-04 21:02:19,806 - mmcls - INFO - Epoch [46][1900/5005] lr: 1.000e-02, eta: 6:41:48, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0787 2023-01-04 21:02:28,624 - mmcls - INFO - Epoch [46][2000/5005] lr: 1.000e-02, eta: 6:41:39, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0868 2023-01-04 21:02:37,343 - mmcls - INFO - Epoch [46][2100/5005] lr: 1.000e-02, eta: 6:41:30, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0722 2023-01-04 21:02:45,791 - mmcls - INFO - Epoch [46][2200/5005] lr: 1.000e-02, eta: 6:41:21, time: 0.084, data_time: 0.013, memory: 928, loss: 5.0519 2023-01-04 21:02:54,506 - mmcls - INFO - Epoch [46][2300/5005] lr: 1.000e-02, eta: 6:41:12, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0523 2023-01-04 21:03:03,247 - mmcls - INFO - Epoch [46][2400/5005] lr: 1.000e-02, eta: 6:41:03, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0465 2023-01-04 21:03:11,917 - mmcls - INFO - Epoch [46][2500/5005] lr: 1.000e-02, eta: 6:40:54, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0657 2023-01-04 21:03:20,550 - mmcls - INFO - Epoch [46][2600/5005] lr: 1.000e-02, eta: 6:40:45, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0513 2023-01-04 21:03:29,559 - mmcls - INFO - Epoch [46][2700/5005] lr: 1.000e-02, eta: 6:40:37, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0635 2023-01-04 21:03:38,951 - mmcls - INFO - Epoch [46][2800/5005] lr: 1.000e-02, eta: 6:40:28, time: 0.094, data_time: 0.008, memory: 928, loss: 5.0481 2023-01-04 21:03:47,939 - mmcls - INFO - Epoch [46][2900/5005] lr: 1.000e-02, eta: 6:40:20, time: 0.090, data_time: 0.013, memory: 928, loss: 5.0745 2023-01-04 21:03:56,518 - mmcls - INFO - Epoch [46][3000/5005] lr: 1.000e-02, eta: 6:40:11, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0675 2023-01-04 21:04:05,408 - mmcls - INFO - Epoch [46][3100/5005] lr: 1.000e-02, eta: 6:40:02, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0691 2023-01-04 21:04:13,883 - mmcls - INFO - Epoch [46][3200/5005] lr: 1.000e-02, eta: 6:39:53, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0253 2023-01-04 21:04:22,621 - mmcls - INFO - Epoch [46][3300/5005] lr: 1.000e-02, eta: 6:39:44, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0716 2023-01-04 21:04:32,059 - mmcls - INFO - Epoch [46][3400/5005] lr: 1.000e-02, eta: 6:39:36, time: 0.094, data_time: 0.001, memory: 928, loss: 5.0418 2023-01-04 21:04:41,102 - mmcls - INFO - Epoch [46][3500/5005] lr: 1.000e-02, eta: 6:39:27, time: 0.090, data_time: 0.009, memory: 928, loss: 5.0828 2023-01-04 21:04:49,819 - mmcls - INFO - Epoch [46][3600/5005] lr: 1.000e-02, eta: 6:39:18, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0732 2023-01-04 21:04:58,803 - mmcls - INFO - Epoch [46][3700/5005] lr: 1.000e-02, eta: 6:39:10, time: 0.090, data_time: 0.011, memory: 928, loss: 5.0567 2023-01-04 21:05:07,704 - mmcls - INFO - Epoch [46][3800/5005] lr: 1.000e-02, eta: 6:39:01, time: 0.089, data_time: 0.011, memory: 928, loss: 5.0701 2023-01-04 21:05:16,657 - mmcls - INFO - Epoch [46][3900/5005] lr: 1.000e-02, eta: 6:38:52, time: 0.089, data_time: 0.019, memory: 928, loss: 5.0775 2023-01-04 21:05:25,202 - mmcls - INFO - Epoch [46][4000/5005] lr: 1.000e-02, eta: 6:38:43, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0758 2023-01-04 21:05:34,063 - mmcls - INFO - Epoch [46][4100/5005] lr: 1.000e-02, eta: 6:38:34, time: 0.089, data_time: 0.031, memory: 928, loss: 5.0554 2023-01-04 21:05:42,897 - mmcls - INFO - Epoch [46][4200/5005] lr: 1.000e-02, eta: 6:38:25, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0776 2023-01-04 21:05:51,891 - mmcls - INFO - Epoch [46][4300/5005] lr: 1.000e-02, eta: 6:38:17, time: 0.090, data_time: 0.020, memory: 928, loss: 5.0772 2023-01-04 21:06:00,445 - mmcls - INFO - Epoch [46][4400/5005] lr: 1.000e-02, eta: 6:38:08, time: 0.085, data_time: 0.024, memory: 928, loss: 5.0612 2023-01-04 21:06:09,532 - mmcls - INFO - Epoch [46][4500/5005] lr: 1.000e-02, eta: 6:37:59, time: 0.091, data_time: 0.004, memory: 928, loss: 5.0567 2023-01-04 21:06:18,473 - mmcls - INFO - Epoch [46][4600/5005] lr: 1.000e-02, eta: 6:37:51, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0580 2023-01-04 21:06:27,323 - mmcls - INFO - Epoch [46][4700/5005] lr: 1.000e-02, eta: 6:37:42, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0627 2023-01-04 21:06:35,887 - mmcls - INFO - Epoch [46][4800/5005] lr: 1.000e-02, eta: 6:37:33, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0591 2023-01-04 21:06:44,603 - mmcls - INFO - Epoch [46][4900/5005] lr: 1.000e-02, eta: 6:37:24, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0611 2023-01-04 21:06:53,238 - mmcls - INFO - Epoch [46][5000/5005] lr: 1.000e-02, eta: 6:37:15, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0336 2023-01-04 21:07:15,983 - mmcls - INFO - Epoch(val) [46][196] accuracy_top-1: 10.3480, accuracy_top-5: 23.5820 2023-01-04 21:07:26,977 - mmcls - INFO - Epoch [47][100/5005] lr: 1.000e-02, eta: 6:37:07, time: 0.110, data_time: 0.059, memory: 928, loss: 5.0469 2023-01-04 21:07:35,736 - mmcls - INFO - Epoch [47][200/5005] lr: 1.000e-02, eta: 6:36:59, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0277 2023-01-04 21:07:44,358 - mmcls - INFO - Epoch [47][300/5005] lr: 1.000e-02, eta: 6:36:49, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0629 2023-01-04 21:07:53,104 - mmcls - INFO - Epoch [47][400/5005] lr: 1.000e-02, eta: 6:36:41, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0548 2023-01-04 21:08:01,948 - mmcls - INFO - Epoch [47][500/5005] lr: 1.000e-02, eta: 6:36:32, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0416 2023-01-04 21:08:10,667 - mmcls - INFO - Epoch [47][600/5005] lr: 1.000e-02, eta: 6:36:23, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0544 2023-01-04 21:08:19,585 - mmcls - INFO - Epoch [47][700/5005] lr: 1.000e-02, eta: 6:36:14, time: 0.089, data_time: 0.015, memory: 928, loss: 5.0688 2023-01-04 21:08:28,204 - mmcls - INFO - Epoch [47][800/5005] lr: 1.000e-02, eta: 6:36:05, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0584 2023-01-04 21:08:37,103 - mmcls - INFO - Epoch [47][900/5005] lr: 1.000e-02, eta: 6:35:56, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0517 2023-01-04 21:08:45,875 - mmcls - INFO - Epoch [47][1000/5005] lr: 1.000e-02, eta: 6:35:48, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0379 2023-01-04 21:08:54,238 - mmcls - INFO - Epoch [47][1100/5005] lr: 1.000e-02, eta: 6:35:38, time: 0.084, data_time: 0.015, memory: 928, loss: 5.0711 2023-01-04 21:09:03,341 - mmcls - INFO - Epoch [47][1200/5005] lr: 1.000e-02, eta: 6:35:30, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0641 2023-01-04 21:09:12,311 - mmcls - INFO - Epoch [47][1300/5005] lr: 1.000e-02, eta: 6:35:21, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0660 2023-01-04 21:09:21,138 - mmcls - INFO - Epoch [47][1400/5005] lr: 1.000e-02, eta: 6:35:12, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0671 2023-01-04 21:09:30,052 - mmcls - INFO - Epoch [47][1500/5005] lr: 1.000e-02, eta: 6:35:03, time: 0.089, data_time: 0.023, memory: 928, loss: 5.0608 2023-01-04 21:09:38,572 - mmcls - INFO - Epoch [47][1600/5005] lr: 1.000e-02, eta: 6:34:54, time: 0.085, data_time: 0.021, memory: 928, loss: 5.0642 2023-01-04 21:09:47,220 - mmcls - INFO - Epoch [47][1700/5005] lr: 1.000e-02, eta: 6:34:45, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0459 2023-01-04 21:09:56,193 - mmcls - INFO - Epoch [47][1800/5005] lr: 1.000e-02, eta: 6:34:37, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0462 2023-01-04 21:10:04,812 - mmcls - INFO - Epoch [47][1900/5005] lr: 1.000e-02, eta: 6:34:28, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0631 2023-01-04 21:10:13,887 - mmcls - INFO - Epoch [47][2000/5005] lr: 1.000e-02, eta: 6:34:19, time: 0.091, data_time: 0.002, memory: 928, loss: 5.0432 2023-01-04 21:10:22,862 - mmcls - INFO - Epoch [47][2100/5005] lr: 1.000e-02, eta: 6:34:10, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0916 2023-01-04 21:10:31,368 - mmcls - INFO - Epoch [47][2200/5005] lr: 1.000e-02, eta: 6:34:01, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0645 2023-01-04 21:10:40,263 - mmcls - INFO - Epoch [47][2300/5005] lr: 1.000e-02, eta: 6:33:53, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0520 2023-01-04 21:10:48,840 - mmcls - INFO - Epoch [47][2400/5005] lr: 1.000e-02, eta: 6:33:43, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0609 2023-01-04 21:10:57,419 - mmcls - INFO - Epoch [47][2500/5005] lr: 1.000e-02, eta: 6:33:34, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0468 2023-01-04 21:11:06,247 - mmcls - INFO - Epoch [47][2600/5005] lr: 1.000e-02, eta: 6:33:26, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0378 2023-01-04 21:11:14,841 - mmcls - INFO - Epoch [47][2700/5005] lr: 1.000e-02, eta: 6:33:16, time: 0.086, data_time: 0.031, memory: 928, loss: 5.0768 2023-01-04 21:11:23,706 - mmcls - INFO - Epoch [47][2800/5005] lr: 1.000e-02, eta: 6:33:08, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0655 2023-01-04 21:11:32,492 - mmcls - INFO - Epoch [47][2900/5005] lr: 1.000e-02, eta: 6:32:59, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0640 2023-01-04 21:11:40,963 - mmcls - INFO - Epoch [47][3000/5005] lr: 1.000e-02, eta: 6:32:50, time: 0.085, data_time: 0.009, memory: 928, loss: 5.0594 2023-01-04 21:11:49,773 - mmcls - INFO - Epoch [47][3100/5005] lr: 1.000e-02, eta: 6:32:41, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0616 2023-01-04 21:11:58,694 - mmcls - INFO - Epoch [47][3200/5005] lr: 1.000e-02, eta: 6:32:32, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0732 2023-01-04 21:12:07,423 - mmcls - INFO - Epoch [47][3300/5005] lr: 1.000e-02, eta: 6:32:23, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0715 2023-01-04 21:12:16,269 - mmcls - INFO - Epoch [47][3400/5005] lr: 1.000e-02, eta: 6:32:14, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0458 2023-01-04 21:12:24,916 - mmcls - INFO - Epoch [47][3500/5005] lr: 1.000e-02, eta: 6:32:05, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0646 2023-01-04 21:12:33,504 - mmcls - INFO - Epoch [47][3600/5005] lr: 1.000e-02, eta: 6:31:56, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0711 2023-01-04 21:12:42,236 - mmcls - INFO - Epoch [47][3700/5005] lr: 1.000e-02, eta: 6:31:47, time: 0.087, data_time: 0.024, memory: 928, loss: 5.0714 2023-01-04 21:12:51,070 - mmcls - INFO - Epoch [47][3800/5005] lr: 1.000e-02, eta: 6:31:39, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0720 2023-01-04 21:12:59,697 - mmcls - INFO - Epoch [47][3900/5005] lr: 1.000e-02, eta: 6:31:29, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0582 2023-01-04 21:13:08,485 - mmcls - INFO - Epoch [47][4000/5005] lr: 1.000e-02, eta: 6:31:21, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0481 2023-01-04 21:13:17,270 - mmcls - INFO - Epoch [47][4100/5005] lr: 1.000e-02, eta: 6:31:12, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0672 2023-01-04 21:13:26,197 - mmcls - INFO - Epoch [47][4200/5005] lr: 1.000e-02, eta: 6:31:03, time: 0.089, data_time: 0.023, memory: 928, loss: 5.0671 2023-01-04 21:13:34,982 - mmcls - INFO - Epoch [47][4300/5005] lr: 1.000e-02, eta: 6:30:54, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0487 2023-01-04 21:13:43,602 - mmcls - INFO - Epoch [47][4400/5005] lr: 1.000e-02, eta: 6:30:45, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0712 2023-01-04 21:13:52,310 - mmcls - INFO - Epoch [47][4500/5005] lr: 1.000e-02, eta: 6:30:36, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0471 2023-01-04 21:14:00,874 - mmcls - INFO - Epoch [47][4600/5005] lr: 1.000e-02, eta: 6:30:27, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0650 2023-01-04 21:14:09,460 - mmcls - INFO - Epoch [47][4700/5005] lr: 1.000e-02, eta: 6:30:18, time: 0.086, data_time: 0.029, memory: 928, loss: 5.0523 2023-01-04 21:14:18,404 - mmcls - INFO - Epoch [47][4800/5005] lr: 1.000e-02, eta: 6:30:09, time: 0.089, data_time: 0.031, memory: 928, loss: 5.0624 2023-01-04 21:14:27,162 - mmcls - INFO - Epoch [47][4900/5005] lr: 1.000e-02, eta: 6:30:00, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0517 2023-01-04 21:14:35,893 - mmcls - INFO - Epoch [47][5000/5005] lr: 1.000e-02, eta: 6:29:52, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0517 2023-01-04 21:14:58,538 - mmcls - INFO - Epoch(val) [47][196] accuracy_top-1: 10.0200, accuracy_top-5: 23.0860 2023-01-04 21:15:09,482 - mmcls - INFO - Epoch [48][100/5005] lr: 1.000e-02, eta: 6:29:44, time: 0.109, data_time: 0.035, memory: 928, loss: 5.0407 2023-01-04 21:15:18,449 - mmcls - INFO - Epoch [48][200/5005] lr: 1.000e-02, eta: 6:29:36, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0497 2023-01-04 21:15:27,135 - mmcls - INFO - Epoch [48][300/5005] lr: 1.000e-02, eta: 6:29:27, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0289 2023-01-04 21:15:35,859 - mmcls - INFO - Epoch [48][400/5005] lr: 1.000e-02, eta: 6:29:18, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0551 2023-01-04 21:15:44,829 - mmcls - INFO - Epoch [48][500/5005] lr: 1.000e-02, eta: 6:29:09, time: 0.090, data_time: 0.010, memory: 928, loss: 5.0559 2023-01-04 21:15:53,668 - mmcls - INFO - Epoch [48][600/5005] lr: 1.000e-02, eta: 6:29:00, time: 0.088, data_time: 0.027, memory: 928, loss: 5.0563 2023-01-04 21:16:02,324 - mmcls - INFO - Epoch [48][700/5005] lr: 1.000e-02, eta: 6:28:51, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0733 2023-01-04 21:16:11,210 - mmcls - INFO - Epoch [48][800/5005] lr: 1.000e-02, eta: 6:28:42, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0095 2023-01-04 21:16:19,869 - mmcls - INFO - Epoch [48][900/5005] lr: 1.000e-02, eta: 6:28:33, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0185 2023-01-04 21:16:29,063 - mmcls - INFO - Epoch [48][1000/5005] lr: 1.000e-02, eta: 6:28:25, time: 0.092, data_time: 0.017, memory: 928, loss: 5.0451 2023-01-04 21:16:38,200 - mmcls - INFO - Epoch [48][1100/5005] lr: 1.000e-02, eta: 6:28:17, time: 0.091, data_time: 0.011, memory: 928, loss: 5.0713 2023-01-04 21:16:46,865 - mmcls - INFO - Epoch [48][1200/5005] lr: 1.000e-02, eta: 6:28:08, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0441 2023-01-04 21:16:55,668 - mmcls - INFO - Epoch [48][1300/5005] lr: 1.000e-02, eta: 6:27:59, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0543 2023-01-04 21:17:04,387 - mmcls - INFO - Epoch [48][1400/5005] lr: 1.000e-02, eta: 6:27:50, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0554 2023-01-04 21:17:13,258 - mmcls - INFO - Epoch [48][1500/5005] lr: 1.000e-02, eta: 6:27:41, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0607 2023-01-04 21:17:21,853 - mmcls - INFO - Epoch [48][1600/5005] lr: 1.000e-02, eta: 6:27:32, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0541 2023-01-04 21:17:30,637 - mmcls - INFO - Epoch [48][1700/5005] lr: 1.000e-02, eta: 6:27:23, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0655 2023-01-04 21:17:39,242 - mmcls - INFO - Epoch [48][1800/5005] lr: 1.000e-02, eta: 6:27:14, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0688 2023-01-04 21:17:47,783 - mmcls - INFO - Epoch [48][1900/5005] lr: 1.000e-02, eta: 6:27:05, time: 0.085, data_time: 0.005, memory: 928, loss: 5.0456 2023-01-04 21:17:56,368 - mmcls - INFO - Epoch [48][2000/5005] lr: 1.000e-02, eta: 6:26:56, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0542 2023-01-04 21:18:04,991 - mmcls - INFO - Epoch [48][2100/5005] lr: 1.000e-02, eta: 6:26:47, time: 0.086, data_time: 0.021, memory: 928, loss: 5.0902 2023-01-04 21:18:13,868 - mmcls - INFO - Epoch [48][2200/5005] lr: 1.000e-02, eta: 6:26:38, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0430 2023-01-04 21:18:22,581 - mmcls - INFO - Epoch [48][2300/5005] lr: 1.000e-02, eta: 6:26:29, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0592 2023-01-04 21:18:31,239 - mmcls - INFO - Epoch [48][2400/5005] lr: 1.000e-02, eta: 6:26:20, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0594 2023-01-04 21:18:40,083 - mmcls - INFO - Epoch [48][2500/5005] lr: 1.000e-02, eta: 6:26:11, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0594 2023-01-04 21:18:48,797 - mmcls - INFO - Epoch [48][2600/5005] lr: 1.000e-02, eta: 6:26:02, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0600 2023-01-04 21:18:57,550 - mmcls - INFO - Epoch [48][2700/5005] lr: 1.000e-02, eta: 6:25:54, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0434 2023-01-04 21:19:06,168 - mmcls - INFO - Epoch [48][2800/5005] lr: 1.000e-02, eta: 6:25:44, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0645 2023-01-04 21:19:15,103 - mmcls - INFO - Epoch [48][2900/5005] lr: 1.000e-02, eta: 6:25:36, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0660 2023-01-04 21:19:23,870 - mmcls - INFO - Epoch [48][3000/5005] lr: 1.000e-02, eta: 6:25:27, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0774 2023-01-04 21:19:32,477 - mmcls - INFO - Epoch [48][3100/5005] lr: 1.000e-02, eta: 6:25:18, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0717 2023-01-04 21:19:41,197 - mmcls - INFO - Epoch [48][3200/5005] lr: 1.000e-02, eta: 6:25:09, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0459 2023-01-04 21:19:49,896 - mmcls - INFO - Epoch [48][3300/5005] lr: 1.000e-02, eta: 6:25:00, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0722 2023-01-04 21:19:58,574 - mmcls - INFO - Epoch [48][3400/5005] lr: 1.000e-02, eta: 6:24:51, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0473 2023-01-04 21:20:07,649 - mmcls - INFO - Epoch [48][3500/5005] lr: 1.000e-02, eta: 6:24:42, time: 0.091, data_time: 0.008, memory: 928, loss: 5.0412 2023-01-04 21:20:16,432 - mmcls - INFO - Epoch [48][3600/5005] lr: 1.000e-02, eta: 6:24:34, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0617 2023-01-04 21:20:24,935 - mmcls - INFO - Epoch [48][3700/5005] lr: 1.000e-02, eta: 6:24:24, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0713 2023-01-04 21:20:33,618 - mmcls - INFO - Epoch [48][3800/5005] lr: 1.000e-02, eta: 6:24:15, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0725 2023-01-04 21:20:42,253 - mmcls - INFO - Epoch [48][3900/5005] lr: 1.000e-02, eta: 6:24:06, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0412 2023-01-04 21:20:51,127 - mmcls - INFO - Epoch [48][4000/5005] lr: 1.000e-02, eta: 6:23:58, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0498 2023-01-04 21:20:59,883 - mmcls - INFO - Epoch [48][4100/5005] lr: 1.000e-02, eta: 6:23:49, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0655 2023-01-04 21:21:08,635 - mmcls - INFO - Epoch [48][4200/5005] lr: 1.000e-02, eta: 6:23:40, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0387 2023-01-04 21:21:17,386 - mmcls - INFO - Epoch [48][4300/5005] lr: 1.000e-02, eta: 6:23:31, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0589 2023-01-04 21:21:25,966 - mmcls - INFO - Epoch [48][4400/5005] lr: 1.000e-02, eta: 6:23:22, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0620 2023-01-04 21:21:35,128 - mmcls - INFO - Epoch [48][4500/5005] lr: 1.000e-02, eta: 6:23:14, time: 0.092, data_time: 0.003, memory: 928, loss: 5.0437 2023-01-04 21:21:44,031 - mmcls - INFO - Epoch [48][4600/5005] lr: 1.000e-02, eta: 6:23:05, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0335 2023-01-04 21:21:52,973 - mmcls - INFO - Epoch [48][4700/5005] lr: 1.000e-02, eta: 6:22:56, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0562 2023-01-04 21:22:01,643 - mmcls - INFO - Epoch [48][4800/5005] lr: 1.000e-02, eta: 6:22:47, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0595 2023-01-04 21:22:09,973 - mmcls - INFO - Epoch [48][4900/5005] lr: 1.000e-02, eta: 6:22:38, time: 0.083, data_time: 0.006, memory: 928, loss: 5.0888 2023-01-04 21:22:18,418 - mmcls - INFO - Epoch [48][5000/5005] lr: 1.000e-02, eta: 6:22:29, time: 0.084, data_time: 0.015, memory: 928, loss: 5.0605 2023-01-04 21:22:41,087 - mmcls - INFO - Epoch(val) [48][196] accuracy_top-1: 9.2200, accuracy_top-5: 21.4240 2023-01-04 21:22:52,266 - mmcls - INFO - Epoch [49][100/5005] lr: 1.000e-02, eta: 6:22:21, time: 0.112, data_time: 0.053, memory: 928, loss: 5.0634 2023-01-04 21:23:01,126 - mmcls - INFO - Epoch [49][200/5005] lr: 1.000e-02, eta: 6:22:13, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0418 2023-01-04 21:23:10,113 - mmcls - INFO - Epoch [49][300/5005] lr: 1.000e-02, eta: 6:22:04, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0595 2023-01-04 21:23:18,944 - mmcls - INFO - Epoch [49][400/5005] lr: 1.000e-02, eta: 6:21:55, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0505 2023-01-04 21:23:27,476 - mmcls - INFO - Epoch [49][500/5005] lr: 1.000e-02, eta: 6:21:46, time: 0.085, data_time: 0.012, memory: 928, loss: 5.0409 2023-01-04 21:23:36,065 - mmcls - INFO - Epoch [49][600/5005] lr: 1.000e-02, eta: 6:21:37, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0625 2023-01-04 21:23:44,853 - mmcls - INFO - Epoch [49][700/5005] lr: 1.000e-02, eta: 6:21:28, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0475 2023-01-04 21:23:53,357 - mmcls - INFO - Epoch [49][800/5005] lr: 1.000e-02, eta: 6:21:19, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0469 2023-01-04 21:24:02,418 - mmcls - INFO - Epoch [49][900/5005] lr: 1.000e-02, eta: 6:21:10, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0506 2023-01-04 21:24:11,160 - mmcls - INFO - Epoch [49][1000/5005] lr: 1.000e-02, eta: 6:21:01, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0646 2023-01-04 21:24:20,163 - mmcls - INFO - Epoch [49][1100/5005] lr: 1.000e-02, eta: 6:20:53, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0528 2023-01-04 21:24:28,707 - mmcls - INFO - Epoch [49][1200/5005] lr: 1.000e-02, eta: 6:20:44, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0488 2023-01-04 21:24:37,346 - mmcls - INFO - Epoch [49][1300/5005] lr: 1.000e-02, eta: 6:20:35, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0736 2023-01-04 21:24:46,371 - mmcls - INFO - Epoch [49][1400/5005] lr: 1.000e-02, eta: 6:20:26, time: 0.090, data_time: 0.016, memory: 928, loss: 5.0490 2023-01-04 21:24:55,073 - mmcls - INFO - Epoch [49][1500/5005] lr: 1.000e-02, eta: 6:20:17, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0417 2023-01-04 21:25:03,698 - mmcls - INFO - Epoch [49][1600/5005] lr: 1.000e-02, eta: 6:20:08, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0343 2023-01-04 21:25:12,542 - mmcls - INFO - Epoch [49][1700/5005] lr: 1.000e-02, eta: 6:19:59, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0646 2023-01-04 21:25:21,116 - mmcls - INFO - Epoch [49][1800/5005] lr: 1.000e-02, eta: 6:19:50, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0557 2023-01-04 21:25:30,019 - mmcls - INFO - Epoch [49][1900/5005] lr: 1.000e-02, eta: 6:19:42, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0369 2023-01-04 21:25:38,981 - mmcls - INFO - Epoch [49][2000/5005] lr: 1.000e-02, eta: 6:19:33, time: 0.090, data_time: 0.039, memory: 928, loss: 5.0374 2023-01-04 21:25:47,716 - mmcls - INFO - Epoch [49][2100/5005] lr: 1.000e-02, eta: 6:19:24, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0607 2023-01-04 21:25:56,671 - mmcls - INFO - Epoch [49][2200/5005] lr: 1.000e-02, eta: 6:19:15, time: 0.090, data_time: 0.004, memory: 928, loss: 5.0630 2023-01-04 21:26:05,558 - mmcls - INFO - Epoch [49][2300/5005] lr: 1.000e-02, eta: 6:19:07, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0617 2023-01-04 21:26:14,312 - mmcls - INFO - Epoch [49][2400/5005] lr: 1.000e-02, eta: 6:18:58, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0700 2023-01-04 21:26:23,101 - mmcls - INFO - Epoch [49][2500/5005] lr: 1.000e-02, eta: 6:18:49, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0485 2023-01-04 21:26:31,676 - mmcls - INFO - Epoch [49][2600/5005] lr: 1.000e-02, eta: 6:18:40, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0676 2023-01-04 21:26:40,416 - mmcls - INFO - Epoch [49][2700/5005] lr: 1.000e-02, eta: 6:18:31, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0563 2023-01-04 21:26:49,489 - mmcls - INFO - Epoch [49][2800/5005] lr: 1.000e-02, eta: 6:18:22, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0201 2023-01-04 21:26:58,288 - mmcls - INFO - Epoch [49][2900/5005] lr: 1.000e-02, eta: 6:18:13, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0734 2023-01-04 21:27:07,197 - mmcls - INFO - Epoch [49][3000/5005] lr: 1.000e-02, eta: 6:18:05, time: 0.089, data_time: 0.008, memory: 928, loss: 5.0574 2023-01-04 21:27:16,020 - mmcls - INFO - Epoch [49][3100/5005] lr: 1.000e-02, eta: 6:17:56, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0459 2023-01-04 21:27:24,794 - mmcls - INFO - Epoch [49][3200/5005] lr: 1.000e-02, eta: 6:17:47, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0341 2023-01-04 21:27:33,426 - mmcls - INFO - Epoch [49][3300/5005] lr: 1.000e-02, eta: 6:17:38, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0700 2023-01-04 21:27:42,299 - mmcls - INFO - Epoch [49][3400/5005] lr: 1.000e-02, eta: 6:17:29, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0486 2023-01-04 21:27:50,900 - mmcls - INFO - Epoch [49][3500/5005] lr: 1.000e-02, eta: 6:17:20, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0537 2023-01-04 21:27:59,757 - mmcls - INFO - Epoch [49][3600/5005] lr: 1.000e-02, eta: 6:17:11, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0641 2023-01-04 21:28:08,312 - mmcls - INFO - Epoch [49][3700/5005] lr: 1.000e-02, eta: 6:17:02, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0542 2023-01-04 21:28:17,007 - mmcls - INFO - Epoch [49][3800/5005] lr: 1.000e-02, eta: 6:16:53, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0710 2023-01-04 21:28:25,746 - mmcls - INFO - Epoch [49][3900/5005] lr: 1.000e-02, eta: 6:16:45, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0458 2023-01-04 21:28:34,500 - mmcls - INFO - Epoch [49][4000/5005] lr: 1.000e-02, eta: 6:16:36, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0665 2023-01-04 21:28:43,099 - mmcls - INFO - Epoch [49][4100/5005] lr: 1.000e-02, eta: 6:16:27, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0674 2023-01-04 21:28:51,738 - mmcls - INFO - Epoch [49][4200/5005] lr: 1.000e-02, eta: 6:16:18, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0631 2023-01-04 21:29:00,653 - mmcls - INFO - Epoch [49][4300/5005] lr: 1.000e-02, eta: 6:16:09, time: 0.089, data_time: 0.024, memory: 928, loss: 5.0624 2023-01-04 21:29:09,350 - mmcls - INFO - Epoch [49][4400/5005] lr: 1.000e-02, eta: 6:16:00, time: 0.087, data_time: 0.037, memory: 928, loss: 5.0556 2023-01-04 21:29:18,025 - mmcls - INFO - Epoch [49][4500/5005] lr: 1.000e-02, eta: 6:15:51, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0414 2023-01-04 21:29:26,799 - mmcls - INFO - Epoch [49][4600/5005] lr: 1.000e-02, eta: 6:15:42, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0864 2023-01-04 21:29:35,802 - mmcls - INFO - Epoch [49][4700/5005] lr: 1.000e-02, eta: 6:15:33, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0650 2023-01-04 21:29:44,687 - mmcls - INFO - Epoch [49][4800/5005] lr: 1.000e-02, eta: 6:15:25, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0347 2023-01-04 21:29:53,591 - mmcls - INFO - Epoch [49][4900/5005] lr: 1.000e-02, eta: 6:15:16, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0323 2023-01-04 21:30:02,210 - mmcls - INFO - Epoch [49][5000/5005] lr: 1.000e-02, eta: 6:15:07, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0635 2023-01-04 21:30:24,856 - mmcls - INFO - Epoch(val) [49][196] accuracy_top-1: 10.5920, accuracy_top-5: 23.9340 2023-01-04 21:30:35,939 - mmcls - INFO - Epoch [50][100/5005] lr: 1.000e-02, eta: 6:15:00, time: 0.111, data_time: 0.037, memory: 928, loss: 5.0506 2023-01-04 21:30:44,774 - mmcls - INFO - Epoch [50][200/5005] lr: 1.000e-02, eta: 6:14:51, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0419 2023-01-04 21:30:53,679 - mmcls - INFO - Epoch [50][300/5005] lr: 1.000e-02, eta: 6:14:42, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0411 2023-01-04 21:31:02,645 - mmcls - INFO - Epoch [50][400/5005] lr: 1.000e-02, eta: 6:14:33, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0454 2023-01-04 21:31:11,550 - mmcls - INFO - Epoch [50][500/5005] lr: 1.000e-02, eta: 6:14:25, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0566 2023-01-04 21:31:20,888 - mmcls - INFO - Epoch [50][600/5005] lr: 1.000e-02, eta: 6:14:16, time: 0.093, data_time: 0.005, memory: 928, loss: 5.0480 2023-01-04 21:31:29,467 - mmcls - INFO - Epoch [50][700/5005] lr: 1.000e-02, eta: 6:14:07, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0526 2023-01-04 21:31:38,417 - mmcls - INFO - Epoch [50][800/5005] lr: 1.000e-02, eta: 6:13:59, time: 0.090, data_time: 0.016, memory: 928, loss: 5.0239 2023-01-04 21:31:47,044 - mmcls - INFO - Epoch [50][900/5005] lr: 1.000e-02, eta: 6:13:50, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0494 2023-01-04 21:31:55,738 - mmcls - INFO - Epoch [50][1000/5005] lr: 1.000e-02, eta: 6:13:41, time: 0.087, data_time: 0.033, memory: 928, loss: 5.0561 2023-01-04 21:32:04,540 - mmcls - INFO - Epoch [50][1100/5005] lr: 1.000e-02, eta: 6:13:32, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0383 2023-01-04 21:32:13,393 - mmcls - INFO - Epoch [50][1200/5005] lr: 1.000e-02, eta: 6:13:23, time: 0.089, data_time: 0.025, memory: 928, loss: 5.0404 2023-01-04 21:32:22,176 - mmcls - INFO - Epoch [50][1300/5005] lr: 1.000e-02, eta: 6:13:14, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0450 2023-01-04 21:32:30,898 - mmcls - INFO - Epoch [50][1400/5005] lr: 1.000e-02, eta: 6:13:05, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0509 2023-01-04 21:32:39,426 - mmcls - INFO - Epoch [50][1500/5005] lr: 1.000e-02, eta: 6:12:56, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0524 2023-01-04 21:32:47,990 - mmcls - INFO - Epoch [50][1600/5005] lr: 1.000e-02, eta: 6:12:47, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0427 2023-01-04 21:32:56,865 - mmcls - INFO - Epoch [50][1700/5005] lr: 1.000e-02, eta: 6:12:38, time: 0.089, data_time: 0.020, memory: 928, loss: 5.0362 2023-01-04 21:33:05,681 - mmcls - INFO - Epoch [50][1800/5005] lr: 1.000e-02, eta: 6:12:29, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0327 2023-01-04 21:33:14,349 - mmcls - INFO - Epoch [50][1900/5005] lr: 1.000e-02, eta: 6:12:21, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0570 2023-01-04 21:33:23,038 - mmcls - INFO - Epoch [50][2000/5005] lr: 1.000e-02, eta: 6:12:12, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0502 2023-01-04 21:33:31,541 - mmcls - INFO - Epoch [50][2100/5005] lr: 1.000e-02, eta: 6:12:02, time: 0.085, data_time: 0.008, memory: 928, loss: 5.0692 2023-01-04 21:33:40,662 - mmcls - INFO - Epoch [50][2200/5005] lr: 1.000e-02, eta: 6:11:54, time: 0.091, data_time: 0.010, memory: 928, loss: 5.0438 2023-01-04 21:33:49,411 - mmcls - INFO - Epoch [50][2300/5005] lr: 1.000e-02, eta: 6:11:45, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0482 2023-01-04 21:33:58,409 - mmcls - INFO - Epoch [50][2400/5005] lr: 1.000e-02, eta: 6:11:36, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0582 2023-01-04 21:34:07,264 - mmcls - INFO - Epoch [50][2500/5005] lr: 1.000e-02, eta: 6:11:28, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0398 2023-01-04 21:34:15,973 - mmcls - INFO - Epoch [50][2600/5005] lr: 1.000e-02, eta: 6:11:19, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0466 2023-01-04 21:34:24,632 - mmcls - INFO - Epoch [50][2700/5005] lr: 1.000e-02, eta: 6:11:10, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0516 2023-01-04 21:34:33,152 - mmcls - INFO - Epoch [50][2800/5005] lr: 1.000e-02, eta: 6:11:01, time: 0.085, data_time: 0.015, memory: 928, loss: 5.0474 2023-01-04 21:34:41,777 - mmcls - INFO - Epoch [50][2900/5005] lr: 1.000e-02, eta: 6:10:52, time: 0.086, data_time: 0.030, memory: 928, loss: 5.0793 2023-01-04 21:34:50,476 - mmcls - INFO - Epoch [50][3000/5005] lr: 1.000e-02, eta: 6:10:43, time: 0.087, data_time: 0.019, memory: 928, loss: 5.0720 2023-01-04 21:34:59,172 - mmcls - INFO - Epoch [50][3100/5005] lr: 1.000e-02, eta: 6:10:34, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0266 2023-01-04 21:35:07,872 - mmcls - INFO - Epoch [50][3200/5005] lr: 1.000e-02, eta: 6:10:25, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0627 2023-01-04 21:35:16,629 - mmcls - INFO - Epoch [50][3300/5005] lr: 1.000e-02, eta: 6:10:16, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0473 2023-01-04 21:35:25,548 - mmcls - INFO - Epoch [50][3400/5005] lr: 1.000e-02, eta: 6:10:07, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0554 2023-01-04 21:35:34,299 - mmcls - INFO - Epoch [50][3500/5005] lr: 1.000e-02, eta: 6:09:58, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0568 2023-01-04 21:35:43,157 - mmcls - INFO - Epoch [50][3600/5005] lr: 1.000e-02, eta: 6:09:49, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0457 2023-01-04 21:35:51,849 - mmcls - INFO - Epoch [50][3700/5005] lr: 1.000e-02, eta: 6:09:41, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0619 2023-01-04 21:36:00,772 - mmcls - INFO - Epoch [50][3800/5005] lr: 1.000e-02, eta: 6:09:32, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0625 2023-01-04 21:36:09,311 - mmcls - INFO - Epoch [50][3900/5005] lr: 1.000e-02, eta: 6:09:23, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0469 2023-01-04 21:36:18,175 - mmcls - INFO - Epoch [50][4000/5005] lr: 1.000e-02, eta: 6:09:14, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0407 2023-01-04 21:36:27,017 - mmcls - INFO - Epoch [50][4100/5005] lr: 1.000e-02, eta: 6:09:05, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0469 2023-01-04 21:36:35,707 - mmcls - INFO - Epoch [50][4200/5005] lr: 1.000e-02, eta: 6:08:56, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0578 2023-01-04 21:36:44,312 - mmcls - INFO - Epoch [50][4300/5005] lr: 1.000e-02, eta: 6:08:47, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0509 2023-01-04 21:36:53,082 - mmcls - INFO - Epoch [50][4400/5005] lr: 1.000e-02, eta: 6:08:38, time: 0.088, data_time: 0.036, memory: 928, loss: 5.0945 2023-01-04 21:37:01,972 - mmcls - INFO - Epoch [50][4500/5005] lr: 1.000e-02, eta: 6:08:30, time: 0.089, data_time: 0.004, memory: 928, loss: 5.0465 2023-01-04 21:37:10,560 - mmcls - INFO - Epoch [50][4600/5005] lr: 1.000e-02, eta: 6:08:21, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0292 2023-01-04 21:37:19,430 - mmcls - INFO - Epoch [50][4700/5005] lr: 1.000e-02, eta: 6:08:12, time: 0.089, data_time: 0.014, memory: 928, loss: 5.0413 2023-01-04 21:37:28,213 - mmcls - INFO - Epoch [50][4800/5005] lr: 1.000e-02, eta: 6:08:03, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0517 2023-01-04 21:37:36,914 - mmcls - INFO - Epoch [50][4900/5005] lr: 1.000e-02, eta: 6:07:54, time: 0.087, data_time: 0.031, memory: 928, loss: 5.0678 2023-01-04 21:37:45,455 - mmcls - INFO - Epoch [50][5000/5005] lr: 1.000e-02, eta: 6:07:45, time: 0.085, data_time: 0.011, memory: 928, loss: 5.0772 2023-01-04 21:37:45,786 - mmcls - INFO - Saving checkpoint at 50 epochs 2023-01-04 21:38:08,275 - mmcls - INFO - Epoch(val) [50][196] accuracy_top-1: 8.1060, accuracy_top-5: 19.3340 2023-01-04 21:38:19,342 - mmcls - INFO - Epoch [51][100/5005] lr: 1.000e-02, eta: 6:07:37, time: 0.110, data_time: 0.036, memory: 928, loss: 5.0349 2023-01-04 21:38:28,034 - mmcls - INFO - Epoch [51][200/5005] lr: 1.000e-02, eta: 6:07:28, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0299 2023-01-04 21:38:37,128 - mmcls - INFO - Epoch [51][300/5005] lr: 1.000e-02, eta: 6:07:20, time: 0.091, data_time: 0.029, memory: 928, loss: 5.0601 2023-01-04 21:38:45,993 - mmcls - INFO - Epoch [51][400/5005] lr: 1.000e-02, eta: 6:07:11, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0586 2023-01-04 21:38:54,667 - mmcls - INFO - Epoch [51][500/5005] lr: 1.000e-02, eta: 6:07:02, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0438 2023-01-04 21:39:03,465 - mmcls - INFO - Epoch [51][600/5005] lr: 1.000e-02, eta: 6:06:53, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0593 2023-01-04 21:39:12,005 - mmcls - INFO - Epoch [51][700/5005] lr: 1.000e-02, eta: 6:06:44, time: 0.085, data_time: 0.008, memory: 928, loss: 5.0472 2023-01-04 21:39:20,780 - mmcls - INFO - Epoch [51][800/5005] lr: 1.000e-02, eta: 6:06:35, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0529 2023-01-04 21:39:29,395 - mmcls - INFO - Epoch [51][900/5005] lr: 1.000e-02, eta: 6:06:26, time: 0.086, data_time: 0.017, memory: 928, loss: 5.0313 2023-01-04 21:39:37,908 - mmcls - INFO - Epoch [51][1000/5005] lr: 1.000e-02, eta: 6:06:17, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0529 2023-01-04 21:39:46,654 - mmcls - INFO - Epoch [51][1100/5005] lr: 1.000e-02, eta: 6:06:08, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0560 2023-01-04 21:39:55,553 - mmcls - INFO - Epoch [51][1200/5005] lr: 1.000e-02, eta: 6:06:00, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0344 2023-01-04 21:40:04,294 - mmcls - INFO - Epoch [51][1300/5005] lr: 1.000e-02, eta: 6:05:51, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0500 2023-01-04 21:40:13,079 - mmcls - INFO - Epoch [51][1400/5005] lr: 1.000e-02, eta: 6:05:42, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0335 2023-01-04 21:40:21,980 - mmcls - INFO - Epoch [51][1500/5005] lr: 1.000e-02, eta: 6:05:33, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0562 2023-01-04 21:40:30,186 - mmcls - INFO - Epoch [51][1600/5005] lr: 1.000e-02, eta: 6:05:24, time: 0.082, data_time: 0.016, memory: 928, loss: 5.0581 2023-01-04 21:40:38,973 - mmcls - INFO - Epoch [51][1700/5005] lr: 1.000e-02, eta: 6:05:15, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0424 2023-01-04 21:40:47,424 - mmcls - INFO - Epoch [51][1800/5005] lr: 1.000e-02, eta: 6:05:06, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0304 2023-01-04 21:40:55,997 - mmcls - INFO - Epoch [51][1900/5005] lr: 1.000e-02, eta: 6:04:57, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0476 2023-01-04 21:41:05,220 - mmcls - INFO - Epoch [51][2000/5005] lr: 1.000e-02, eta: 6:04:48, time: 0.092, data_time: 0.007, memory: 928, loss: 5.0431 2023-01-04 21:41:13,837 - mmcls - INFO - Epoch [51][2100/5005] lr: 1.000e-02, eta: 6:04:39, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0462 2023-01-04 21:41:22,581 - mmcls - INFO - Epoch [51][2200/5005] lr: 1.000e-02, eta: 6:04:30, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0577 2023-01-04 21:41:31,328 - mmcls - INFO - Epoch [51][2300/5005] lr: 1.000e-02, eta: 6:04:21, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0193 2023-01-04 21:41:40,097 - mmcls - INFO - Epoch [51][2400/5005] lr: 1.000e-02, eta: 6:04:13, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0572 2023-01-04 21:41:48,832 - mmcls - INFO - Epoch [51][2500/5005] lr: 1.000e-02, eta: 6:04:04, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0377 2023-01-04 21:41:57,918 - mmcls - INFO - Epoch [51][2600/5005] lr: 1.000e-02, eta: 6:03:55, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0368 2023-01-04 21:42:06,630 - mmcls - INFO - Epoch [51][2700/5005] lr: 1.000e-02, eta: 6:03:46, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0502 2023-01-04 21:42:15,201 - mmcls - INFO - Epoch [51][2800/5005] lr: 1.000e-02, eta: 6:03:37, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0640 2023-01-04 21:42:24,300 - mmcls - INFO - Epoch [51][2900/5005] lr: 1.000e-02, eta: 6:03:29, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0747 2023-01-04 21:42:32,804 - mmcls - INFO - Epoch [51][3000/5005] lr: 1.000e-02, eta: 6:03:19, time: 0.085, data_time: 0.035, memory: 928, loss: 5.0510 2023-01-04 21:42:41,841 - mmcls - INFO - Epoch [51][3100/5005] lr: 1.000e-02, eta: 6:03:11, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0493 2023-01-04 21:42:50,314 - mmcls - INFO - Epoch [51][3200/5005] lr: 1.000e-02, eta: 6:03:02, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0731 2023-01-04 21:42:59,329 - mmcls - INFO - Epoch [51][3300/5005] lr: 1.000e-02, eta: 6:02:53, time: 0.090, data_time: 0.013, memory: 928, loss: 5.0360 2023-01-04 21:43:08,125 - mmcls - INFO - Epoch [51][3400/5005] lr: 1.000e-02, eta: 6:02:44, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0515 2023-01-04 21:43:16,867 - mmcls - INFO - Epoch [51][3500/5005] lr: 1.000e-02, eta: 6:02:35, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0532 2023-01-04 21:43:25,858 - mmcls - INFO - Epoch [51][3600/5005] lr: 1.000e-02, eta: 6:02:27, time: 0.090, data_time: 0.049, memory: 928, loss: 5.0267 2023-01-04 21:43:34,668 - mmcls - INFO - Epoch [51][3700/5005] lr: 1.000e-02, eta: 6:02:18, time: 0.088, data_time: 0.021, memory: 928, loss: 5.0502 2023-01-04 21:43:43,340 - mmcls - INFO - Epoch [51][3800/5005] lr: 1.000e-02, eta: 6:02:09, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0566 2023-01-04 21:43:52,066 - mmcls - INFO - Epoch [51][3900/5005] lr: 1.000e-02, eta: 6:02:00, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0628 2023-01-04 21:44:00,702 - mmcls - INFO - Epoch [51][4000/5005] lr: 1.000e-02, eta: 6:01:51, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0615 2023-01-04 21:44:09,242 - mmcls - INFO - Epoch [51][4100/5005] lr: 1.000e-02, eta: 6:01:42, time: 0.085, data_time: 0.006, memory: 928, loss: 5.0600 2023-01-04 21:44:17,898 - mmcls - INFO - Epoch [51][4200/5005] lr: 1.000e-02, eta: 6:01:33, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0364 2023-01-04 21:44:26,493 - mmcls - INFO - Epoch [51][4300/5005] lr: 1.000e-02, eta: 6:01:24, time: 0.086, data_time: 0.029, memory: 928, loss: 5.0581 2023-01-04 21:44:35,068 - mmcls - INFO - Epoch [51][4400/5005] lr: 1.000e-02, eta: 6:01:15, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0355 2023-01-04 21:44:43,688 - mmcls - INFO - Epoch [51][4500/5005] lr: 1.000e-02, eta: 6:01:06, time: 0.086, data_time: 0.027, memory: 928, loss: 5.0665 2023-01-04 21:44:52,469 - mmcls - INFO - Epoch [51][4600/5005] lr: 1.000e-02, eta: 6:00:57, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0613 2023-01-04 21:45:01,229 - mmcls - INFO - Epoch [51][4700/5005] lr: 1.000e-02, eta: 6:00:48, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0510 2023-01-04 21:45:09,783 - mmcls - INFO - Epoch [51][4800/5005] lr: 1.000e-02, eta: 6:00:39, time: 0.085, data_time: 0.013, memory: 928, loss: 5.0222 2023-01-04 21:45:18,354 - mmcls - INFO - Epoch [51][4900/5005] lr: 1.000e-02, eta: 6:00:30, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0611 2023-01-04 21:45:27,292 - mmcls - INFO - Epoch [51][5000/5005] lr: 1.000e-02, eta: 6:00:21, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0498 2023-01-04 21:45:50,007 - mmcls - INFO - Epoch(val) [51][196] accuracy_top-1: 10.1380, accuracy_top-5: 22.9220 2023-01-04 21:46:01,110 - mmcls - INFO - Epoch [52][100/5005] lr: 1.000e-02, eta: 6:00:14, time: 0.111, data_time: 0.065, memory: 928, loss: 5.0261 2023-01-04 21:46:09,771 - mmcls - INFO - Epoch [52][200/5005] lr: 1.000e-02, eta: 6:00:05, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0306 2023-01-04 21:46:18,742 - mmcls - INFO - Epoch [52][300/5005] lr: 1.000e-02, eta: 5:59:56, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0397 2023-01-04 21:46:27,275 - mmcls - INFO - Epoch [52][400/5005] lr: 1.000e-02, eta: 5:59:47, time: 0.085, data_time: 0.005, memory: 928, loss: 5.0465 2023-01-04 21:46:35,861 - mmcls - INFO - Epoch [52][500/5005] lr: 1.000e-02, eta: 5:59:38, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0514 2023-01-04 21:46:44,493 - mmcls - INFO - Epoch [52][600/5005] lr: 1.000e-02, eta: 5:59:29, time: 0.086, data_time: 0.015, memory: 928, loss: 5.0372 2023-01-04 21:46:53,190 - mmcls - INFO - Epoch [52][700/5005] lr: 1.000e-02, eta: 5:59:20, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0539 2023-01-04 21:47:02,165 - mmcls - INFO - Epoch [52][800/5005] lr: 1.000e-02, eta: 5:59:11, time: 0.090, data_time: 0.035, memory: 928, loss: 5.0278 2023-01-04 21:47:10,902 - mmcls - INFO - Epoch [52][900/5005] lr: 1.000e-02, eta: 5:59:03, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0282 2023-01-04 21:47:19,555 - mmcls - INFO - Epoch [52][1000/5005] lr: 1.000e-02, eta: 5:58:54, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0380 2023-01-04 21:47:28,110 - mmcls - INFO - Epoch [52][1100/5005] lr: 1.000e-02, eta: 5:58:45, time: 0.085, data_time: 0.009, memory: 928, loss: 5.0520 2023-01-04 21:47:36,940 - mmcls - INFO - Epoch [52][1200/5005] lr: 1.000e-02, eta: 5:58:36, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0657 2023-01-04 21:47:45,762 - mmcls - INFO - Epoch [52][1300/5005] lr: 1.000e-02, eta: 5:58:27, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0600 2023-01-04 21:47:54,820 - mmcls - INFO - Epoch [52][1400/5005] lr: 1.000e-02, eta: 5:58:18, time: 0.091, data_time: 0.013, memory: 928, loss: 5.0391 2023-01-04 21:48:03,811 - mmcls - INFO - Epoch [52][1500/5005] lr: 1.000e-02, eta: 5:58:10, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0305 2023-01-04 21:48:12,981 - mmcls - INFO - Epoch [52][1600/5005] lr: 1.000e-02, eta: 5:58:01, time: 0.092, data_time: 0.000, memory: 928, loss: 5.0324 2023-01-04 21:48:21,632 - mmcls - INFO - Epoch [52][1700/5005] lr: 1.000e-02, eta: 5:57:52, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0540 2023-01-04 21:48:30,582 - mmcls - INFO - Epoch [52][1800/5005] lr: 1.000e-02, eta: 5:57:44, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0400 2023-01-04 21:48:39,204 - mmcls - INFO - Epoch [52][1900/5005] lr: 1.000e-02, eta: 5:57:35, time: 0.086, data_time: 0.035, memory: 928, loss: 5.0620 2023-01-04 21:48:47,551 - mmcls - INFO - Epoch [52][2000/5005] lr: 1.000e-02, eta: 5:57:25, time: 0.084, data_time: 0.007, memory: 928, loss: 5.0402 2023-01-04 21:48:56,670 - mmcls - INFO - Epoch [52][2100/5005] lr: 1.000e-02, eta: 5:57:17, time: 0.091, data_time: 0.034, memory: 928, loss: 5.0460 2023-01-04 21:49:05,662 - mmcls - INFO - Epoch [52][2200/5005] lr: 1.000e-02, eta: 5:57:08, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0656 2023-01-04 21:49:14,312 - mmcls - INFO - Epoch [52][2300/5005] lr: 1.000e-02, eta: 5:56:59, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0290 2023-01-04 21:49:23,024 - mmcls - INFO - Epoch [52][2400/5005] lr: 1.000e-02, eta: 5:56:50, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0308 2023-01-04 21:49:31,598 - mmcls - INFO - Epoch [52][2500/5005] lr: 1.000e-02, eta: 5:56:41, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0240 2023-01-04 21:49:40,713 - mmcls - INFO - Epoch [52][2600/5005] lr: 1.000e-02, eta: 5:56:33, time: 0.091, data_time: 0.003, memory: 928, loss: 5.0370 2023-01-04 21:49:49,244 - mmcls - INFO - Epoch [52][2700/5005] lr: 1.000e-02, eta: 5:56:24, time: 0.085, data_time: 0.014, memory: 928, loss: 5.0709 2023-01-04 21:49:58,013 - mmcls - INFO - Epoch [52][2800/5005] lr: 1.000e-02, eta: 5:56:15, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0605 2023-01-04 21:50:06,772 - mmcls - INFO - Epoch [52][2900/5005] lr: 1.000e-02, eta: 5:56:06, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0499 2023-01-04 21:50:15,715 - mmcls - INFO - Epoch [52][3000/5005] lr: 1.000e-02, eta: 5:55:57, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0480 2023-01-04 21:50:24,533 - mmcls - INFO - Epoch [52][3100/5005] lr: 1.000e-02, eta: 5:55:48, time: 0.088, data_time: 0.028, memory: 928, loss: 5.0488 2023-01-04 21:50:33,422 - mmcls - INFO - Epoch [52][3200/5005] lr: 1.000e-02, eta: 5:55:40, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0567 2023-01-04 21:50:42,006 - mmcls - INFO - Epoch [52][3300/5005] lr: 1.000e-02, eta: 5:55:30, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0536 2023-01-04 21:50:50,858 - mmcls - INFO - Epoch [52][3400/5005] lr: 1.000e-02, eta: 5:55:22, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0368 2023-01-04 21:50:59,388 - mmcls - INFO - Epoch [52][3500/5005] lr: 1.000e-02, eta: 5:55:13, time: 0.085, data_time: 0.006, memory: 928, loss: 5.0638 2023-01-04 21:51:08,172 - mmcls - INFO - Epoch [52][3600/5005] lr: 1.000e-02, eta: 5:55:04, time: 0.088, data_time: 0.016, memory: 928, loss: 5.0441 2023-01-04 21:51:16,892 - mmcls - INFO - Epoch [52][3700/5005] lr: 1.000e-02, eta: 5:54:55, time: 0.087, data_time: 0.018, memory: 928, loss: 5.0259 2023-01-04 21:51:25,594 - mmcls - INFO - Epoch [52][3800/5005] lr: 1.000e-02, eta: 5:54:46, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0558 2023-01-04 21:51:34,224 - mmcls - INFO - Epoch [52][3900/5005] lr: 1.000e-02, eta: 5:54:37, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0418 2023-01-04 21:51:43,060 - mmcls - INFO - Epoch [52][4000/5005] lr: 1.000e-02, eta: 5:54:28, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0449 2023-01-04 21:51:51,994 - mmcls - INFO - Epoch [52][4100/5005] lr: 1.000e-02, eta: 5:54:19, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0560 2023-01-04 21:52:00,654 - mmcls - INFO - Epoch [52][4200/5005] lr: 1.000e-02, eta: 5:54:10, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0411 2023-01-04 21:52:09,406 - mmcls - INFO - Epoch [52][4300/5005] lr: 1.000e-02, eta: 5:54:02, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0447 2023-01-04 21:52:18,476 - mmcls - INFO - Epoch [52][4400/5005] lr: 1.000e-02, eta: 5:53:53, time: 0.091, data_time: 0.011, memory: 928, loss: 5.0551 2023-01-04 21:52:27,011 - mmcls - INFO - Epoch [52][4500/5005] lr: 1.000e-02, eta: 5:53:44, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0699 2023-01-04 21:52:35,670 - mmcls - INFO - Epoch [52][4600/5005] lr: 1.000e-02, eta: 5:53:35, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0591 2023-01-04 21:52:44,319 - mmcls - INFO - Epoch [52][4700/5005] lr: 1.000e-02, eta: 5:53:26, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0430 2023-01-04 21:52:53,107 - mmcls - INFO - Epoch [52][4800/5005] lr: 1.000e-02, eta: 5:53:17, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0415 2023-01-04 21:53:01,716 - mmcls - INFO - Epoch [52][4900/5005] lr: 1.000e-02, eta: 5:53:08, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0335 2023-01-04 21:53:10,702 - mmcls - INFO - Epoch [52][5000/5005] lr: 1.000e-02, eta: 5:52:59, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0484 2023-01-04 21:53:33,522 - mmcls - INFO - Epoch(val) [52][196] accuracy_top-1: 11.9200, accuracy_top-5: 26.5460 2023-01-04 21:53:44,655 - mmcls - INFO - Epoch [53][100/5005] lr: 1.000e-02, eta: 5:52:52, time: 0.111, data_time: 0.044, memory: 928, loss: 5.0426 2023-01-04 21:53:53,554 - mmcls - INFO - Epoch [53][200/5005] lr: 1.000e-02, eta: 5:52:43, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0182 2023-01-04 21:54:02,253 - mmcls - INFO - Epoch [53][300/5005] lr: 1.000e-02, eta: 5:52:34, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0340 2023-01-04 21:54:10,770 - mmcls - INFO - Epoch [53][400/5005] lr: 1.000e-02, eta: 5:52:25, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0263 2023-01-04 21:54:20,173 - mmcls - INFO - Epoch [53][500/5005] lr: 1.000e-02, eta: 5:52:17, time: 0.094, data_time: 0.018, memory: 928, loss: 5.0645 2023-01-04 21:54:28,772 - mmcls - INFO - Epoch [53][600/5005] lr: 1.000e-02, eta: 5:52:08, time: 0.086, data_time: 0.026, memory: 928, loss: 5.0145 2023-01-04 21:54:37,453 - mmcls - INFO - Epoch [53][700/5005] lr: 1.000e-02, eta: 5:51:59, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0361 2023-01-04 21:54:46,061 - mmcls - INFO - Epoch [53][800/5005] lr: 1.000e-02, eta: 5:51:50, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0569 2023-01-04 21:54:54,708 - mmcls - INFO - Epoch [53][900/5005] lr: 1.000e-02, eta: 5:51:41, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0436 2023-01-04 21:55:03,514 - mmcls - INFO - Epoch [53][1000/5005] lr: 1.000e-02, eta: 5:51:32, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0465 2023-01-04 21:55:12,184 - mmcls - INFO - Epoch [53][1100/5005] lr: 1.000e-02, eta: 5:51:23, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0611 2023-01-04 21:55:20,751 - mmcls - INFO - Epoch [53][1200/5005] lr: 1.000e-02, eta: 5:51:14, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0346 2023-01-04 21:55:29,527 - mmcls - INFO - Epoch [53][1300/5005] lr: 1.000e-02, eta: 5:51:05, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0288 2023-01-04 21:55:38,345 - mmcls - INFO - Epoch [53][1400/5005] lr: 1.000e-02, eta: 5:50:56, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0532 2023-01-04 21:55:47,695 - mmcls - INFO - Epoch [53][1500/5005] lr: 1.000e-02, eta: 5:50:48, time: 0.093, data_time: 0.025, memory: 928, loss: 5.0422 2023-01-04 21:55:56,487 - mmcls - INFO - Epoch [53][1600/5005] lr: 1.000e-02, eta: 5:50:39, time: 0.088, data_time: 0.025, memory: 928, loss: 5.0433 2023-01-04 21:56:05,452 - mmcls - INFO - Epoch [53][1700/5005] lr: 1.000e-02, eta: 5:50:31, time: 0.090, data_time: 0.013, memory: 928, loss: 5.0524 2023-01-04 21:56:14,310 - mmcls - INFO - Epoch [53][1800/5005] lr: 1.000e-02, eta: 5:50:22, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0582 2023-01-04 21:56:23,095 - mmcls - INFO - Epoch [53][1900/5005] lr: 1.000e-02, eta: 5:50:13, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0445 2023-01-04 21:56:31,875 - mmcls - INFO - Epoch [53][2000/5005] lr: 1.000e-02, eta: 5:50:04, time: 0.088, data_time: 0.024, memory: 928, loss: 5.0421 2023-01-04 21:56:40,786 - mmcls - INFO - Epoch [53][2100/5005] lr: 1.000e-02, eta: 5:49:55, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0428 2023-01-04 21:56:49,582 - mmcls - INFO - Epoch [53][2200/5005] lr: 1.000e-02, eta: 5:49:47, time: 0.088, data_time: 0.017, memory: 928, loss: 5.0444 2023-01-04 21:56:58,407 - mmcls - INFO - Epoch [53][2300/5005] lr: 1.000e-02, eta: 5:49:38, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0576 2023-01-04 21:57:07,002 - mmcls - INFO - Epoch [53][2400/5005] lr: 1.000e-02, eta: 5:49:29, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0354 2023-01-04 21:57:15,708 - mmcls - INFO - Epoch [53][2500/5005] lr: 1.000e-02, eta: 5:49:20, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0390 2023-01-04 21:57:24,331 - mmcls - INFO - Epoch [53][2600/5005] lr: 1.000e-02, eta: 5:49:11, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0303 2023-01-04 21:57:33,207 - mmcls - INFO - Epoch [53][2700/5005] lr: 1.000e-02, eta: 5:49:02, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0385 2023-01-04 21:57:41,814 - mmcls - INFO - Epoch [53][2800/5005] lr: 1.000e-02, eta: 5:48:53, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0424 2023-01-04 21:57:50,515 - mmcls - INFO - Epoch [53][2900/5005] lr: 1.000e-02, eta: 5:48:44, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0237 2023-01-04 21:57:59,256 - mmcls - INFO - Epoch [53][3000/5005] lr: 1.000e-02, eta: 5:48:35, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0371 2023-01-04 21:58:08,228 - mmcls - INFO - Epoch [53][3100/5005] lr: 1.000e-02, eta: 5:48:27, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0524 2023-01-04 21:58:17,261 - mmcls - INFO - Epoch [53][3200/5005] lr: 1.000e-02, eta: 5:48:18, time: 0.090, data_time: 0.007, memory: 928, loss: 5.0505 2023-01-04 21:58:25,732 - mmcls - INFO - Epoch [53][3300/5005] lr: 1.000e-02, eta: 5:48:09, time: 0.085, data_time: 0.024, memory: 928, loss: 5.0250 2023-01-04 21:58:34,256 - mmcls - INFO - Epoch [53][3400/5005] lr: 1.000e-02, eta: 5:48:00, time: 0.085, data_time: 0.025, memory: 928, loss: 5.0409 2023-01-04 21:58:43,116 - mmcls - INFO - Epoch [53][3500/5005] lr: 1.000e-02, eta: 5:47:51, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0281 2023-01-04 21:58:52,115 - mmcls - INFO - Epoch [53][3600/5005] lr: 1.000e-02, eta: 5:47:42, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0480 2023-01-04 21:59:01,049 - mmcls - INFO - Epoch [53][3700/5005] lr: 1.000e-02, eta: 5:47:34, time: 0.089, data_time: 0.005, memory: 928, loss: 5.0545 2023-01-04 21:59:09,538 - mmcls - INFO - Epoch [53][3800/5005] lr: 1.000e-02, eta: 5:47:25, time: 0.085, data_time: 0.013, memory: 928, loss: 5.0584 2023-01-04 21:59:18,192 - mmcls - INFO - Epoch [53][3900/5005] lr: 1.000e-02, eta: 5:47:16, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0424 2023-01-04 21:59:27,000 - mmcls - INFO - Epoch [53][4000/5005] lr: 1.000e-02, eta: 5:47:07, time: 0.088, data_time: 0.021, memory: 928, loss: 5.0406 2023-01-04 21:59:35,874 - mmcls - INFO - Epoch [53][4100/5005] lr: 1.000e-02, eta: 5:46:58, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0378 2023-01-04 21:59:44,436 - mmcls - INFO - Epoch [53][4200/5005] lr: 1.000e-02, eta: 5:46:49, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0389 2023-01-04 21:59:53,361 - mmcls - INFO - Epoch [53][4300/5005] lr: 1.000e-02, eta: 5:46:40, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0418 2023-01-04 22:00:02,092 - mmcls - INFO - Epoch [53][4400/5005] lr: 1.000e-02, eta: 5:46:31, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0311 2023-01-04 22:00:10,823 - mmcls - INFO - Epoch [53][4500/5005] lr: 1.000e-02, eta: 5:46:22, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0558 2023-01-04 22:00:19,553 - mmcls - INFO - Epoch [53][4600/5005] lr: 1.000e-02, eta: 5:46:14, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0347 2023-01-04 22:00:28,153 - mmcls - INFO - Epoch [53][4700/5005] lr: 1.000e-02, eta: 5:46:05, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0571 2023-01-04 22:00:36,704 - mmcls - INFO - Epoch [53][4800/5005] lr: 1.000e-02, eta: 5:45:55, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0479 2023-01-04 22:00:45,346 - mmcls - INFO - Epoch [53][4900/5005] lr: 1.000e-02, eta: 5:45:46, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0640 2023-01-04 22:00:54,023 - mmcls - INFO - Epoch [53][5000/5005] lr: 1.000e-02, eta: 5:45:38, time: 0.087, data_time: 0.011, memory: 928, loss: 5.0522 2023-01-04 22:01:16,896 - mmcls - INFO - Epoch(val) [53][196] accuracy_top-1: 10.4160, accuracy_top-5: 23.8220 2023-01-04 22:01:28,070 - mmcls - INFO - Epoch [54][100/5005] lr: 1.000e-02, eta: 5:45:30, time: 0.112, data_time: 0.049, memory: 928, loss: 5.0270 2023-01-04 22:01:37,041 - mmcls - INFO - Epoch [54][200/5005] lr: 1.000e-02, eta: 5:45:21, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0383 2023-01-04 22:01:45,526 - mmcls - INFO - Epoch [54][300/5005] lr: 1.000e-02, eta: 5:45:12, time: 0.085, data_time: 0.013, memory: 928, loss: 5.0534 2023-01-04 22:01:54,463 - mmcls - INFO - Epoch [54][400/5005] lr: 1.000e-02, eta: 5:45:03, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0099 2023-01-04 22:02:03,553 - mmcls - INFO - Epoch [54][500/5005] lr: 1.000e-02, eta: 5:44:55, time: 0.091, data_time: 0.012, memory: 928, loss: 5.0130 2023-01-04 22:02:12,578 - mmcls - INFO - Epoch [54][600/5005] lr: 1.000e-02, eta: 5:44:46, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0427 2023-01-04 22:02:21,173 - mmcls - INFO - Epoch [54][700/5005] lr: 1.000e-02, eta: 5:44:37, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0187 2023-01-04 22:02:30,196 - mmcls - INFO - Epoch [54][800/5005] lr: 1.000e-02, eta: 5:44:29, time: 0.090, data_time: 0.007, memory: 928, loss: 5.0430 2023-01-04 22:02:38,588 - mmcls - INFO - Epoch [54][900/5005] lr: 1.000e-02, eta: 5:44:19, time: 0.084, data_time: 0.005, memory: 928, loss: 5.0536 2023-01-04 22:02:47,231 - mmcls - INFO - Epoch [54][1000/5005] lr: 1.000e-02, eta: 5:44:10, time: 0.086, data_time: 0.010, memory: 928, loss: 5.0354 2023-01-04 22:02:56,043 - mmcls - INFO - Epoch [54][1100/5005] lr: 1.000e-02, eta: 5:44:02, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0387 2023-01-04 22:03:04,900 - mmcls - INFO - Epoch [54][1200/5005] lr: 1.000e-02, eta: 5:43:53, time: 0.089, data_time: 0.016, memory: 928, loss: 5.0419 2023-01-04 22:03:13,708 - mmcls - INFO - Epoch [54][1300/5005] lr: 1.000e-02, eta: 5:43:44, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0179 2023-01-04 22:03:22,320 - mmcls - INFO - Epoch [54][1400/5005] lr: 1.000e-02, eta: 5:43:35, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0199 2023-01-04 22:03:30,914 - mmcls - INFO - Epoch [54][1500/5005] lr: 1.000e-02, eta: 5:43:26, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0286 2023-01-04 22:03:39,782 - mmcls - INFO - Epoch [54][1600/5005] lr: 1.000e-02, eta: 5:43:17, time: 0.089, data_time: 0.011, memory: 928, loss: 5.0517 2023-01-04 22:03:48,338 - mmcls - INFO - Epoch [54][1700/5005] lr: 1.000e-02, eta: 5:43:08, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0766 2023-01-04 22:03:57,321 - mmcls - INFO - Epoch [54][1800/5005] lr: 1.000e-02, eta: 5:43:00, time: 0.090, data_time: 0.011, memory: 928, loss: 5.0246 2023-01-04 22:04:05,918 - mmcls - INFO - Epoch [54][1900/5005] lr: 1.000e-02, eta: 5:42:51, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0269 2023-01-04 22:04:14,570 - mmcls - INFO - Epoch [54][2000/5005] lr: 1.000e-02, eta: 5:42:42, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0513 2023-01-04 22:04:23,454 - mmcls - INFO - Epoch [54][2100/5005] lr: 1.000e-02, eta: 5:42:33, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0354 2023-01-04 22:04:32,172 - mmcls - INFO - Epoch [54][2200/5005] lr: 1.000e-02, eta: 5:42:24, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0248 2023-01-04 22:04:41,278 - mmcls - INFO - Epoch [54][2300/5005] lr: 1.000e-02, eta: 5:42:15, time: 0.091, data_time: 0.007, memory: 928, loss: 5.0695 2023-01-04 22:04:50,008 - mmcls - INFO - Epoch [54][2400/5005] lr: 1.000e-02, eta: 5:42:06, time: 0.087, data_time: 0.021, memory: 928, loss: 5.0499 2023-01-04 22:04:58,756 - mmcls - INFO - Epoch [54][2500/5005] lr: 1.000e-02, eta: 5:41:58, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0390 2023-01-04 22:05:07,488 - mmcls - INFO - Epoch [54][2600/5005] lr: 1.000e-02, eta: 5:41:49, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0410 2023-01-04 22:05:16,122 - mmcls - INFO - Epoch [54][2700/5005] lr: 1.000e-02, eta: 5:41:40, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0701 2023-01-04 22:05:25,126 - mmcls - INFO - Epoch [54][2800/5005] lr: 1.000e-02, eta: 5:41:31, time: 0.090, data_time: 0.003, memory: 928, loss: 5.0325 2023-01-04 22:05:33,759 - mmcls - INFO - Epoch [54][2900/5005] lr: 1.000e-02, eta: 5:41:22, time: 0.086, data_time: 0.015, memory: 928, loss: 5.0531 2023-01-04 22:05:42,677 - mmcls - INFO - Epoch [54][3000/5005] lr: 1.000e-02, eta: 5:41:13, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0387 2023-01-04 22:05:51,406 - mmcls - INFO - Epoch [54][3100/5005] lr: 1.000e-02, eta: 5:41:04, time: 0.087, data_time: 0.025, memory: 928, loss: 5.0338 2023-01-04 22:05:59,978 - mmcls - INFO - Epoch [54][3200/5005] lr: 1.000e-02, eta: 5:40:55, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0233 2023-01-04 22:06:09,075 - mmcls - INFO - Epoch [54][3300/5005] lr: 1.000e-02, eta: 5:40:47, time: 0.091, data_time: 0.010, memory: 928, loss: 5.0384 2023-01-04 22:06:17,938 - mmcls - INFO - Epoch [54][3400/5005] lr: 1.000e-02, eta: 5:40:38, time: 0.089, data_time: 0.025, memory: 928, loss: 5.0525 2023-01-04 22:06:26,712 - mmcls - INFO - Epoch [54][3500/5005] lr: 1.000e-02, eta: 5:40:29, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0373 2023-01-04 22:06:35,256 - mmcls - INFO - Epoch [54][3600/5005] lr: 1.000e-02, eta: 5:40:20, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0479 2023-01-04 22:06:44,032 - mmcls - INFO - Epoch [54][3700/5005] lr: 1.000e-02, eta: 5:40:11, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0345 2023-01-04 22:06:52,502 - mmcls - INFO - Epoch [54][3800/5005] lr: 1.000e-02, eta: 5:40:02, time: 0.085, data_time: 0.017, memory: 928, loss: 5.0497 2023-01-04 22:07:01,534 - mmcls - INFO - Epoch [54][3900/5005] lr: 1.000e-02, eta: 5:39:54, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0477 2023-01-04 22:07:10,308 - mmcls - INFO - Epoch [54][4000/5005] lr: 1.000e-02, eta: 5:39:45, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0608 2023-01-04 22:07:18,840 - mmcls - INFO - Epoch [54][4100/5005] lr: 1.000e-02, eta: 5:39:36, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0633 2023-01-04 22:07:27,492 - mmcls - INFO - Epoch [54][4200/5005] lr: 1.000e-02, eta: 5:39:27, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0164 2023-01-04 22:07:36,051 - mmcls - INFO - Epoch [54][4300/5005] lr: 1.000e-02, eta: 5:39:18, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0621 2023-01-04 22:07:44,929 - mmcls - INFO - Epoch [54][4400/5005] lr: 1.000e-02, eta: 5:39:09, time: 0.089, data_time: 0.025, memory: 928, loss: 5.0342 2023-01-04 22:07:53,692 - mmcls - INFO - Epoch [54][4500/5005] lr: 1.000e-02, eta: 5:39:00, time: 0.088, data_time: 0.018, memory: 928, loss: 5.0498 2023-01-04 22:08:02,252 - mmcls - INFO - Epoch [54][4600/5005] lr: 1.000e-02, eta: 5:38:51, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0362 2023-01-04 22:08:11,193 - mmcls - INFO - Epoch [54][4700/5005] lr: 1.000e-02, eta: 5:38:42, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0215 2023-01-04 22:08:19,886 - mmcls - INFO - Epoch [54][4800/5005] lr: 1.000e-02, eta: 5:38:33, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0447 2023-01-04 22:08:28,645 - mmcls - INFO - Epoch [54][4900/5005] lr: 1.000e-02, eta: 5:38:25, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0232 2023-01-04 22:08:37,431 - mmcls - INFO - Epoch [54][5000/5005] lr: 1.000e-02, eta: 5:38:16, time: 0.088, data_time: 0.040, memory: 928, loss: 5.0578 2023-01-04 22:09:00,261 - mmcls - INFO - Epoch(val) [54][196] accuracy_top-1: 10.4900, accuracy_top-5: 23.8420 2023-01-04 22:09:11,114 - mmcls - INFO - Epoch [55][100/5005] lr: 1.000e-02, eta: 5:38:08, time: 0.108, data_time: 0.047, memory: 928, loss: 5.0331 2023-01-04 22:09:19,951 - mmcls - INFO - Epoch [55][200/5005] lr: 1.000e-02, eta: 5:37:59, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0149 2023-01-04 22:09:28,979 - mmcls - INFO - Epoch [55][300/5005] lr: 1.000e-02, eta: 5:37:50, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0235 2023-01-04 22:09:37,663 - mmcls - INFO - Epoch [55][400/5005] lr: 1.000e-02, eta: 5:37:41, time: 0.087, data_time: 0.031, memory: 928, loss: 5.0489 2023-01-04 22:09:46,568 - mmcls - INFO - Epoch [55][500/5005] lr: 1.000e-02, eta: 5:37:33, time: 0.089, data_time: 0.033, memory: 928, loss: 5.0233 2023-01-04 22:09:54,876 - mmcls - INFO - Epoch [55][600/5005] lr: 1.000e-02, eta: 5:37:23, time: 0.083, data_time: 0.022, memory: 928, loss: 5.0348 2023-01-04 22:10:03,429 - mmcls - INFO - Epoch [55][700/5005] lr: 1.000e-02, eta: 5:37:14, time: 0.085, data_time: 0.021, memory: 928, loss: 5.0198 2023-01-04 22:10:12,282 - mmcls - INFO - Epoch [55][800/5005] lr: 1.000e-02, eta: 5:37:06, time: 0.089, data_time: 0.013, memory: 928, loss: 5.0442 2023-01-04 22:10:21,113 - mmcls - INFO - Epoch [55][900/5005] lr: 1.000e-02, eta: 5:36:57, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0097 2023-01-04 22:10:29,896 - mmcls - INFO - Epoch [55][1000/5005] lr: 1.000e-02, eta: 5:36:48, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0170 2023-01-04 22:10:38,859 - mmcls - INFO - Epoch [55][1100/5005] lr: 1.000e-02, eta: 5:36:39, time: 0.090, data_time: 0.013, memory: 928, loss: 5.0518 2023-01-04 22:10:47,847 - mmcls - INFO - Epoch [55][1200/5005] lr: 1.000e-02, eta: 5:36:31, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0244 2023-01-04 22:10:57,045 - mmcls - INFO - Epoch [55][1300/5005] lr: 1.000e-02, eta: 5:36:22, time: 0.092, data_time: 0.005, memory: 928, loss: 5.0245 2023-01-04 22:11:05,681 - mmcls - INFO - Epoch [55][1400/5005] lr: 1.000e-02, eta: 5:36:13, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0515 2023-01-04 22:11:14,115 - mmcls - INFO - Epoch [55][1500/5005] lr: 1.000e-02, eta: 5:36:04, time: 0.084, data_time: 0.001, memory: 928, loss: 5.0630 2023-01-04 22:11:23,199 - mmcls - INFO - Epoch [55][1600/5005] lr: 1.000e-02, eta: 5:35:55, time: 0.091, data_time: 0.008, memory: 928, loss: 5.0609 2023-01-04 22:11:31,847 - mmcls - INFO - Epoch [55][1700/5005] lr: 1.000e-02, eta: 5:35:46, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0552 2023-01-04 22:11:40,896 - mmcls - INFO - Epoch [55][1800/5005] lr: 1.000e-02, eta: 5:35:38, time: 0.090, data_time: 0.004, memory: 928, loss: 5.0373 2023-01-04 22:11:49,556 - mmcls - INFO - Epoch [55][1900/5005] lr: 1.000e-02, eta: 5:35:29, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0145 2023-01-04 22:11:58,329 - mmcls - INFO - Epoch [55][2000/5005] lr: 1.000e-02, eta: 5:35:20, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0368 2023-01-04 22:12:07,070 - mmcls - INFO - Epoch [55][2100/5005] lr: 1.000e-02, eta: 5:35:11, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0593 2023-01-04 22:12:15,655 - mmcls - INFO - Epoch [55][2200/5005] lr: 1.000e-02, eta: 5:35:02, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0338 2023-01-04 22:12:24,163 - mmcls - INFO - Epoch [55][2300/5005] lr: 1.000e-02, eta: 5:34:53, time: 0.085, data_time: 0.033, memory: 928, loss: 5.0097 2023-01-04 22:12:32,859 - mmcls - INFO - Epoch [55][2400/5005] lr: 1.000e-02, eta: 5:34:44, time: 0.087, data_time: 0.040, memory: 928, loss: 5.0493 2023-01-04 22:12:41,551 - mmcls - INFO - Epoch [55][2500/5005] lr: 1.000e-02, eta: 5:34:35, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0607 2023-01-04 22:12:50,302 - mmcls - INFO - Epoch [55][2600/5005] lr: 1.000e-02, eta: 5:34:26, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0337 2023-01-04 22:12:58,967 - mmcls - INFO - Epoch [55][2700/5005] lr: 1.000e-02, eta: 5:34:17, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0451 2023-01-04 22:13:07,710 - mmcls - INFO - Epoch [55][2800/5005] lr: 1.000e-02, eta: 5:34:09, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0272 2023-01-04 22:13:16,544 - mmcls - INFO - Epoch [55][2900/5005] lr: 1.000e-02, eta: 5:34:00, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0197 2023-01-04 22:13:25,487 - mmcls - INFO - Epoch [55][3000/5005] lr: 1.000e-02, eta: 5:33:51, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0627 2023-01-04 22:13:34,521 - mmcls - INFO - Epoch [55][3100/5005] lr: 1.000e-02, eta: 5:33:42, time: 0.090, data_time: 0.020, memory: 928, loss: 5.0402 2023-01-04 22:13:43,088 - mmcls - INFO - Epoch [55][3200/5005] lr: 1.000e-02, eta: 5:33:33, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0455 2023-01-04 22:13:51,926 - mmcls - INFO - Epoch [55][3300/5005] lr: 1.000e-02, eta: 5:33:25, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0234 2023-01-04 22:14:00,953 - mmcls - INFO - Epoch [55][3400/5005] lr: 1.000e-02, eta: 5:33:16, time: 0.090, data_time: 0.008, memory: 928, loss: 5.0480 2023-01-04 22:14:09,639 - mmcls - INFO - Epoch [55][3500/5005] lr: 1.000e-02, eta: 5:33:07, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0678 2023-01-04 22:14:18,476 - mmcls - INFO - Epoch [55][3600/5005] lr: 1.000e-02, eta: 5:32:58, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0324 2023-01-04 22:14:27,277 - mmcls - INFO - Epoch [55][3700/5005] lr: 1.000e-02, eta: 5:32:49, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0139 2023-01-04 22:14:36,134 - mmcls - INFO - Epoch [55][3800/5005] lr: 1.000e-02, eta: 5:32:41, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0526 2023-01-04 22:14:45,221 - mmcls - INFO - Epoch [55][3900/5005] lr: 1.000e-02, eta: 5:32:32, time: 0.091, data_time: 0.010, memory: 928, loss: 5.0287 2023-01-04 22:14:54,078 - mmcls - INFO - Epoch [55][4000/5005] lr: 1.000e-02, eta: 5:32:23, time: 0.089, data_time: 0.014, memory: 928, loss: 5.0061 2023-01-04 22:15:02,599 - mmcls - INFO - Epoch [55][4100/5005] lr: 1.000e-02, eta: 5:32:14, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0367 2023-01-04 22:15:11,338 - mmcls - INFO - Epoch [55][4200/5005] lr: 1.000e-02, eta: 5:32:05, time: 0.087, data_time: 0.028, memory: 928, loss: 5.0332 2023-01-04 22:15:20,600 - mmcls - INFO - Epoch [55][4300/5005] lr: 1.000e-02, eta: 5:31:57, time: 0.093, data_time: 0.010, memory: 928, loss: 5.0503 2023-01-04 22:15:29,284 - mmcls - INFO - Epoch [55][4400/5005] lr: 1.000e-02, eta: 5:31:48, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0723 2023-01-04 22:15:37,925 - mmcls - INFO - Epoch [55][4500/5005] lr: 1.000e-02, eta: 5:31:39, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0433 2023-01-04 22:15:46,765 - mmcls - INFO - Epoch [55][4600/5005] lr: 1.000e-02, eta: 5:31:30, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0463 2023-01-04 22:15:55,787 - mmcls - INFO - Epoch [55][4700/5005] lr: 1.000e-02, eta: 5:31:21, time: 0.090, data_time: 0.006, memory: 928, loss: 5.0248 2023-01-04 22:16:04,737 - mmcls - INFO - Epoch [55][4800/5005] lr: 1.000e-02, eta: 5:31:13, time: 0.090, data_time: 0.006, memory: 928, loss: 5.0463 2023-01-04 22:16:13,216 - mmcls - INFO - Epoch [55][4900/5005] lr: 1.000e-02, eta: 5:31:04, time: 0.085, data_time: 0.007, memory: 928, loss: 5.0404 2023-01-04 22:16:22,211 - mmcls - INFO - Epoch [55][5000/5005] lr: 1.000e-02, eta: 5:30:55, time: 0.090, data_time: 0.035, memory: 928, loss: 5.0492 2023-01-04 22:16:45,034 - mmcls - INFO - Epoch(val) [55][196] accuracy_top-1: 9.7320, accuracy_top-5: 22.4360 2023-01-04 22:16:55,979 - mmcls - INFO - Epoch [56][100/5005] lr: 1.000e-02, eta: 5:30:47, time: 0.109, data_time: 0.039, memory: 928, loss: 5.0028 2023-01-04 22:17:04,779 - mmcls - INFO - Epoch [56][200/5005] lr: 1.000e-02, eta: 5:30:38, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0352 2023-01-04 22:17:13,500 - mmcls - INFO - Epoch [56][300/5005] lr: 1.000e-02, eta: 5:30:29, time: 0.087, data_time: 0.004, memory: 928, loss: 4.9977 2023-01-04 22:17:22,305 - mmcls - INFO - Epoch [56][400/5005] lr: 1.000e-02, eta: 5:30:21, time: 0.088, data_time: 0.009, memory: 928, loss: 5.0454 2023-01-04 22:17:31,042 - mmcls - INFO - Epoch [56][500/5005] lr: 1.000e-02, eta: 5:30:12, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0512 2023-01-04 22:17:39,982 - mmcls - INFO - Epoch [56][600/5005] lr: 1.000e-02, eta: 5:30:03, time: 0.089, data_time: 0.018, memory: 928, loss: 5.0445 2023-01-04 22:17:48,673 - mmcls - INFO - Epoch [56][700/5005] lr: 1.000e-02, eta: 5:29:54, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0073 2023-01-04 22:17:57,358 - mmcls - INFO - Epoch [56][800/5005] lr: 1.000e-02, eta: 5:29:45, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0374 2023-01-04 22:18:05,970 - mmcls - INFO - Epoch [56][900/5005] lr: 1.000e-02, eta: 5:29:36, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0359 2023-01-04 22:18:14,932 - mmcls - INFO - Epoch [56][1000/5005] lr: 1.000e-02, eta: 5:29:27, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0236 2023-01-04 22:18:24,090 - mmcls - INFO - Epoch [56][1100/5005] lr: 1.000e-02, eta: 5:29:19, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0329 2023-01-04 22:18:32,567 - mmcls - INFO - Epoch [56][1200/5005] lr: 1.000e-02, eta: 5:29:10, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0333 2023-01-04 22:18:41,414 - mmcls - INFO - Epoch [56][1300/5005] lr: 1.000e-02, eta: 5:29:01, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0441 2023-01-04 22:18:50,006 - mmcls - INFO - Epoch [56][1400/5005] lr: 1.000e-02, eta: 5:28:52, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0300 2023-01-04 22:18:58,931 - mmcls - INFO - Epoch [56][1500/5005] lr: 1.000e-02, eta: 5:28:43, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0179 2023-01-04 22:19:07,766 - mmcls - INFO - Epoch [56][1600/5005] lr: 1.000e-02, eta: 5:28:34, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0191 2023-01-04 22:19:16,611 - mmcls - INFO - Epoch [56][1700/5005] lr: 1.000e-02, eta: 5:28:26, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0244 2023-01-04 22:19:25,196 - mmcls - INFO - Epoch [56][1800/5005] lr: 1.000e-02, eta: 5:28:17, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0592 2023-01-04 22:19:33,969 - mmcls - INFO - Epoch [56][1900/5005] lr: 1.000e-02, eta: 5:28:08, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0259 2023-01-04 22:19:42,626 - mmcls - INFO - Epoch [56][2000/5005] lr: 1.000e-02, eta: 5:27:59, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0367 2023-01-04 22:19:51,271 - mmcls - INFO - Epoch [56][2100/5005] lr: 1.000e-02, eta: 5:27:50, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0610 2023-01-04 22:20:00,284 - mmcls - INFO - Epoch [56][2200/5005] lr: 1.000e-02, eta: 5:27:41, time: 0.090, data_time: 0.017, memory: 928, loss: 5.0355 2023-01-04 22:20:09,145 - mmcls - INFO - Epoch [56][2300/5005] lr: 1.000e-02, eta: 5:27:33, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0332 2023-01-04 22:20:17,821 - mmcls - INFO - Epoch [56][2400/5005] lr: 1.000e-02, eta: 5:27:24, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0306 2023-01-04 22:20:26,620 - mmcls - INFO - Epoch [56][2500/5005] lr: 1.000e-02, eta: 5:27:15, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0390 2023-01-04 22:20:35,453 - mmcls - INFO - Epoch [56][2600/5005] lr: 1.000e-02, eta: 5:27:06, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0319 2023-01-04 22:20:44,327 - mmcls - INFO - Epoch [56][2700/5005] lr: 1.000e-02, eta: 5:26:57, time: 0.089, data_time: 0.015, memory: 928, loss: 5.0473 2023-01-04 22:20:53,012 - mmcls - INFO - Epoch [56][2800/5005] lr: 1.000e-02, eta: 5:26:48, time: 0.087, data_time: 0.024, memory: 928, loss: 5.0347 2023-01-04 22:21:01,735 - mmcls - INFO - Epoch [56][2900/5005] lr: 1.000e-02, eta: 5:26:39, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0323 2023-01-04 22:21:10,742 - mmcls - INFO - Epoch [56][3000/5005] lr: 1.000e-02, eta: 5:26:31, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0156 2023-01-04 22:21:19,617 - mmcls - INFO - Epoch [56][3100/5005] lr: 1.000e-02, eta: 5:26:22, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0480 2023-01-04 22:21:28,892 - mmcls - INFO - Epoch [56][3200/5005] lr: 1.000e-02, eta: 5:26:13, time: 0.093, data_time: 0.001, memory: 928, loss: 5.0485 2023-01-04 22:21:37,540 - mmcls - INFO - Epoch [56][3300/5005] lr: 1.000e-02, eta: 5:26:05, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0400 2023-01-04 22:21:46,157 - mmcls - INFO - Epoch [56][3400/5005] lr: 1.000e-02, eta: 5:25:56, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0503 2023-01-04 22:21:54,963 - mmcls - INFO - Epoch [56][3500/5005] lr: 1.000e-02, eta: 5:25:47, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0367 2023-01-04 22:22:03,804 - mmcls - INFO - Epoch [56][3600/5005] lr: 1.000e-02, eta: 5:25:38, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0488 2023-01-04 22:22:12,675 - mmcls - INFO - Epoch [56][3700/5005] lr: 1.000e-02, eta: 5:25:29, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0481 2023-01-04 22:22:21,479 - mmcls - INFO - Epoch [56][3800/5005] lr: 1.000e-02, eta: 5:25:20, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0327 2023-01-04 22:22:30,054 - mmcls - INFO - Epoch [56][3900/5005] lr: 1.000e-02, eta: 5:25:11, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0492 2023-01-04 22:22:38,606 - mmcls - INFO - Epoch [56][4000/5005] lr: 1.000e-02, eta: 5:25:02, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0294 2023-01-04 22:22:47,241 - mmcls - INFO - Epoch [56][4100/5005] lr: 1.000e-02, eta: 5:24:53, time: 0.086, data_time: 0.013, memory: 928, loss: 5.0436 2023-01-04 22:22:56,158 - mmcls - INFO - Epoch [56][4200/5005] lr: 1.000e-02, eta: 5:24:45, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0341 2023-01-04 22:23:04,791 - mmcls - INFO - Epoch [56][4300/5005] lr: 1.000e-02, eta: 5:24:36, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0342 2023-01-04 22:23:13,662 - mmcls - INFO - Epoch [56][4400/5005] lr: 1.000e-02, eta: 5:24:27, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0322 2023-01-04 22:23:22,252 - mmcls - INFO - Epoch [56][4500/5005] lr: 1.000e-02, eta: 5:24:18, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0372 2023-01-04 22:23:31,408 - mmcls - INFO - Epoch [56][4600/5005] lr: 1.000e-02, eta: 5:24:09, time: 0.091, data_time: 0.024, memory: 928, loss: 5.0250 2023-01-04 22:23:40,203 - mmcls - INFO - Epoch [56][4700/5005] lr: 1.000e-02, eta: 5:24:01, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0538 2023-01-04 22:23:48,940 - mmcls - INFO - Epoch [56][4800/5005] lr: 1.000e-02, eta: 5:23:52, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0215 2023-01-04 22:23:57,625 - mmcls - INFO - Epoch [56][4900/5005] lr: 1.000e-02, eta: 5:23:43, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0549 2023-01-04 22:24:06,381 - mmcls - INFO - Epoch [56][5000/5005] lr: 1.000e-02, eta: 5:23:34, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0445 2023-01-04 22:24:29,346 - mmcls - INFO - Epoch(val) [56][196] accuracy_top-1: 10.0860, accuracy_top-5: 23.2660 2023-01-04 22:24:40,189 - mmcls - INFO - Epoch [57][100/5005] lr: 1.000e-02, eta: 5:23:26, time: 0.108, data_time: 0.059, memory: 928, loss: 5.0396 2023-01-04 22:24:49,037 - mmcls - INFO - Epoch [57][200/5005] lr: 1.000e-02, eta: 5:23:17, time: 0.089, data_time: 0.003, memory: 928, loss: 5.0213 2023-01-04 22:24:57,817 - mmcls - INFO - Epoch [57][300/5005] lr: 1.000e-02, eta: 5:23:08, time: 0.088, data_time: 0.008, memory: 928, loss: 5.0214 2023-01-04 22:25:06,506 - mmcls - INFO - Epoch [57][400/5005] lr: 1.000e-02, eta: 5:22:59, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0425 2023-01-04 22:25:15,063 - mmcls - INFO - Epoch [57][500/5005] lr: 1.000e-02, eta: 5:22:50, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0301 2023-01-04 22:25:23,578 - mmcls - INFO - Epoch [57][600/5005] lr: 1.000e-02, eta: 5:22:41, time: 0.085, data_time: 0.005, memory: 928, loss: 5.0258 2023-01-04 22:25:32,152 - mmcls - INFO - Epoch [57][700/5005] lr: 1.000e-02, eta: 5:22:32, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0203 2023-01-04 22:25:40,902 - mmcls - INFO - Epoch [57][800/5005] lr: 1.000e-02, eta: 5:22:23, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0304 2023-01-04 22:25:49,891 - mmcls - INFO - Epoch [57][900/5005] lr: 1.000e-02, eta: 5:22:15, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0414 2023-01-04 22:25:58,530 - mmcls - INFO - Epoch [57][1000/5005] lr: 1.000e-02, eta: 5:22:06, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0525 2023-01-04 22:26:07,327 - mmcls - INFO - Epoch [57][1100/5005] lr: 1.000e-02, eta: 5:21:57, time: 0.088, data_time: 0.019, memory: 928, loss: 5.0514 2023-01-04 22:26:15,902 - mmcls - INFO - Epoch [57][1200/5005] lr: 1.000e-02, eta: 5:21:48, time: 0.086, data_time: 0.004, memory: 928, loss: 5.0471 2023-01-04 22:26:24,512 - mmcls - INFO - Epoch [57][1300/5005] lr: 1.000e-02, eta: 5:21:39, time: 0.086, data_time: 0.009, memory: 928, loss: 5.0291 2023-01-04 22:26:33,252 - mmcls - INFO - Epoch [57][1400/5005] lr: 1.000e-02, eta: 5:21:30, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0418 2023-01-04 22:26:42,386 - mmcls - INFO - Epoch [57][1500/5005] lr: 1.000e-02, eta: 5:21:21, time: 0.091, data_time: 0.019, memory: 928, loss: 5.0264 2023-01-04 22:26:51,105 - mmcls - INFO - Epoch [57][1600/5005] lr: 1.000e-02, eta: 5:21:13, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0290 2023-01-04 22:26:59,919 - mmcls - INFO - Epoch [57][1700/5005] lr: 1.000e-02, eta: 5:21:04, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0360 2023-01-04 22:27:08,634 - mmcls - INFO - Epoch [57][1800/5005] lr: 1.000e-02, eta: 5:20:55, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0403 2023-01-04 22:27:17,526 - mmcls - INFO - Epoch [57][1900/5005] lr: 1.000e-02, eta: 5:20:46, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0068 2023-01-04 22:27:26,276 - mmcls - INFO - Epoch [57][2000/5005] lr: 1.000e-02, eta: 5:20:37, time: 0.088, data_time: 0.021, memory: 928, loss: 5.0311 2023-01-04 22:27:35,136 - mmcls - INFO - Epoch [57][2100/5005] lr: 1.000e-02, eta: 5:20:28, time: 0.089, data_time: 0.013, memory: 928, loss: 5.0084 2023-01-04 22:27:44,062 - mmcls - INFO - Epoch [57][2200/5005] lr: 1.000e-02, eta: 5:20:20, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0271 2023-01-04 22:27:52,871 - mmcls - INFO - Epoch [57][2300/5005] lr: 1.000e-02, eta: 5:20:11, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0418 2023-01-04 22:28:01,879 - mmcls - INFO - Epoch [57][2400/5005] lr: 1.000e-02, eta: 5:20:02, time: 0.090, data_time: 0.007, memory: 928, loss: 5.0358 2023-01-04 22:28:10,581 - mmcls - INFO - Epoch [57][2500/5005] lr: 1.000e-02, eta: 5:19:53, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0445 2023-01-04 22:28:19,245 - mmcls - INFO - Epoch [57][2600/5005] lr: 1.000e-02, eta: 5:19:44, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0194 2023-01-04 22:28:28,212 - mmcls - INFO - Epoch [57][2700/5005] lr: 1.000e-02, eta: 5:19:36, time: 0.090, data_time: 0.004, memory: 928, loss: 5.0293 2023-01-04 22:28:36,868 - mmcls - INFO - Epoch [57][2800/5005] lr: 1.000e-02, eta: 5:19:27, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0068 2023-01-04 22:28:45,585 - mmcls - INFO - Epoch [57][2900/5005] lr: 1.000e-02, eta: 5:19:18, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0376 2023-01-04 22:28:54,329 - mmcls - INFO - Epoch [57][3000/5005] lr: 1.000e-02, eta: 5:19:09, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0427 2023-01-04 22:29:03,070 - mmcls - INFO - Epoch [57][3100/5005] lr: 1.000e-02, eta: 5:19:00, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0263 2023-01-04 22:29:11,663 - mmcls - INFO - Epoch [57][3200/5005] lr: 1.000e-02, eta: 5:18:51, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0320 2023-01-04 22:29:20,172 - mmcls - INFO - Epoch [57][3300/5005] lr: 1.000e-02, eta: 5:18:42, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0512 2023-01-04 22:29:29,005 - mmcls - INFO - Epoch [57][3400/5005] lr: 1.000e-02, eta: 5:18:33, time: 0.088, data_time: 0.014, memory: 928, loss: 5.0230 2023-01-04 22:29:37,608 - mmcls - INFO - Epoch [57][3500/5005] lr: 1.000e-02, eta: 5:18:24, time: 0.086, data_time: 0.003, memory: 928, loss: 5.0317 2023-01-04 22:29:46,336 - mmcls - INFO - Epoch [57][3600/5005] lr: 1.000e-02, eta: 5:18:15, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0388 2023-01-04 22:29:55,120 - mmcls - INFO - Epoch [57][3700/5005] lr: 1.000e-02, eta: 5:18:07, time: 0.088, data_time: 0.030, memory: 928, loss: 5.0205 2023-01-04 22:30:03,759 - mmcls - INFO - Epoch [57][3800/5005] lr: 1.000e-02, eta: 5:17:58, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0398 2023-01-04 22:30:12,456 - mmcls - INFO - Epoch [57][3900/5005] lr: 1.000e-02, eta: 5:17:49, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0290 2023-01-04 22:30:21,180 - mmcls - INFO - Epoch [57][4000/5005] lr: 1.000e-02, eta: 5:17:40, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0323 2023-01-04 22:30:29,999 - mmcls - INFO - Epoch [57][4100/5005] lr: 1.000e-02, eta: 5:17:31, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0462 2023-01-04 22:30:38,781 - mmcls - INFO - Epoch [57][4200/5005] lr: 1.000e-02, eta: 5:17:22, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0442 2023-01-04 22:30:47,576 - mmcls - INFO - Epoch [57][4300/5005] lr: 1.000e-02, eta: 5:17:13, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0315 2023-01-04 22:30:56,459 - mmcls - INFO - Epoch [57][4400/5005] lr: 1.000e-02, eta: 5:17:05, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0397 2023-01-04 22:31:05,297 - mmcls - INFO - Epoch [57][4500/5005] lr: 1.000e-02, eta: 5:16:56, time: 0.088, data_time: 0.011, memory: 928, loss: 5.0469 2023-01-04 22:31:13,749 - mmcls - INFO - Epoch [57][4600/5005] lr: 1.000e-02, eta: 5:16:47, time: 0.084, data_time: 0.010, memory: 928, loss: 5.0174 2023-01-04 22:31:22,695 - mmcls - INFO - Epoch [57][4700/5005] lr: 1.000e-02, eta: 5:16:38, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0357 2023-01-04 22:31:31,143 - mmcls - INFO - Epoch [57][4800/5005] lr: 1.000e-02, eta: 5:16:29, time: 0.084, data_time: 0.013, memory: 928, loss: 5.0157 2023-01-04 22:31:39,948 - mmcls - INFO - Epoch [57][4900/5005] lr: 1.000e-02, eta: 5:16:20, time: 0.088, data_time: 0.026, memory: 928, loss: 5.0180 2023-01-04 22:31:48,719 - mmcls - INFO - Epoch [57][5000/5005] lr: 1.000e-02, eta: 5:16:11, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0599 2023-01-04 22:32:11,449 - mmcls - INFO - Epoch(val) [57][196] accuracy_top-1: 12.0060, accuracy_top-5: 26.7400 2023-01-04 22:32:22,532 - mmcls - INFO - Epoch [58][100/5005] lr: 1.000e-02, eta: 5:16:03, time: 0.111, data_time: 0.033, memory: 928, loss: 5.0181 2023-01-04 22:32:31,400 - mmcls - INFO - Epoch [58][200/5005] lr: 1.000e-02, eta: 5:15:54, time: 0.089, data_time: 0.009, memory: 928, loss: 5.0170 2023-01-04 22:32:40,434 - mmcls - INFO - Epoch [58][300/5005] lr: 1.000e-02, eta: 5:15:46, time: 0.090, data_time: 0.020, memory: 928, loss: 5.0132 2023-01-04 22:32:49,445 - mmcls - INFO - Epoch [58][400/5005] lr: 1.000e-02, eta: 5:15:37, time: 0.090, data_time: 0.012, memory: 928, loss: 5.0203 2023-01-04 22:32:58,288 - mmcls - INFO - Epoch [58][500/5005] lr: 1.000e-02, eta: 5:15:28, time: 0.088, data_time: 0.015, memory: 928, loss: 5.0240 2023-01-04 22:33:07,259 - mmcls - INFO - Epoch [58][600/5005] lr: 1.000e-02, eta: 5:15:20, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0271 2023-01-04 22:33:16,010 - mmcls - INFO - Epoch [58][700/5005] lr: 1.000e-02, eta: 5:15:11, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0142 2023-01-04 22:33:25,048 - mmcls - INFO - Epoch [58][800/5005] lr: 1.000e-02, eta: 5:15:02, time: 0.090, data_time: 0.037, memory: 928, loss: 5.0011 2023-01-04 22:33:33,919 - mmcls - INFO - Epoch [58][900/5005] lr: 1.000e-02, eta: 5:14:53, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0164 2023-01-04 22:33:42,552 - mmcls - INFO - Epoch [58][1000/5005] lr: 1.000e-02, eta: 5:14:44, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0210 2023-01-04 22:33:51,106 - mmcls - INFO - Epoch [58][1100/5005] lr: 1.000e-02, eta: 5:14:35, time: 0.085, data_time: 0.003, memory: 928, loss: 5.0428 2023-01-04 22:34:00,118 - mmcls - INFO - Epoch [58][1200/5005] lr: 1.000e-02, eta: 5:14:27, time: 0.090, data_time: 0.002, memory: 928, loss: 5.0227 2023-01-04 22:34:09,270 - mmcls - INFO - Epoch [58][1300/5005] lr: 1.000e-02, eta: 5:14:18, time: 0.092, data_time: 0.002, memory: 928, loss: 5.0448 2023-01-04 22:34:17,951 - mmcls - INFO - Epoch [58][1400/5005] lr: 1.000e-02, eta: 5:14:09, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0013 2023-01-04 22:34:26,604 - mmcls - INFO - Epoch [58][1500/5005] lr: 1.000e-02, eta: 5:14:00, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0305 2023-01-04 22:34:35,419 - mmcls - INFO - Epoch [58][1600/5005] lr: 1.000e-02, eta: 5:13:52, time: 0.088, data_time: 0.018, memory: 928, loss: 5.0230 2023-01-04 22:34:43,751 - mmcls - INFO - Epoch [58][1700/5005] lr: 1.000e-02, eta: 5:13:42, time: 0.083, data_time: 0.006, memory: 928, loss: 5.0549 2023-01-04 22:34:52,471 - mmcls - INFO - Epoch [58][1800/5005] lr: 1.000e-02, eta: 5:13:33, time: 0.087, data_time: 0.017, memory: 928, loss: 5.0277 2023-01-04 22:35:01,092 - mmcls - INFO - Epoch [58][1900/5005] lr: 1.000e-02, eta: 5:13:24, time: 0.086, data_time: 0.008, memory: 928, loss: 5.0128 2023-01-04 22:35:09,918 - mmcls - INFO - Epoch [58][2000/5005] lr: 1.000e-02, eta: 5:13:16, time: 0.088, data_time: 0.020, memory: 928, loss: 5.0327 2023-01-04 22:35:18,690 - mmcls - INFO - Epoch [58][2100/5005] lr: 1.000e-02, eta: 5:13:07, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0412 2023-01-04 22:35:27,677 - mmcls - INFO - Epoch [58][2200/5005] lr: 1.000e-02, eta: 5:12:58, time: 0.090, data_time: 0.019, memory: 928, loss: 5.0425 2023-01-04 22:35:36,370 - mmcls - INFO - Epoch [58][2300/5005] lr: 1.000e-02, eta: 5:12:49, time: 0.087, data_time: 0.024, memory: 928, loss: 5.0557 2023-01-04 22:35:45,170 - mmcls - INFO - Epoch [58][2400/5005] lr: 1.000e-02, eta: 5:12:40, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0326 2023-01-04 22:35:53,987 - mmcls - INFO - Epoch [58][2500/5005] lr: 1.000e-02, eta: 5:12:32, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0382 2023-01-04 22:36:02,680 - mmcls - INFO - Epoch [58][2600/5005] lr: 1.000e-02, eta: 5:12:23, time: 0.087, data_time: 0.022, memory: 928, loss: 5.0349 2023-01-04 22:36:11,428 - mmcls - INFO - Epoch [58][2700/5005] lr: 1.000e-02, eta: 5:12:14, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0435 2023-01-04 22:36:20,140 - mmcls - INFO - Epoch [58][2800/5005] lr: 1.000e-02, eta: 5:12:05, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0579 2023-01-04 22:36:29,063 - mmcls - INFO - Epoch [58][2900/5005] lr: 1.000e-02, eta: 5:11:56, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0189 2023-01-04 22:36:37,963 - mmcls - INFO - Epoch [58][3000/5005] lr: 1.000e-02, eta: 5:11:47, time: 0.089, data_time: 0.012, memory: 928, loss: 5.0427 2023-01-04 22:36:46,585 - mmcls - INFO - Epoch [58][3100/5005] lr: 1.000e-02, eta: 5:11:38, time: 0.086, data_time: 0.014, memory: 928, loss: 5.0439 2023-01-04 22:36:55,248 - mmcls - INFO - Epoch [58][3200/5005] lr: 1.000e-02, eta: 5:11:30, time: 0.087, data_time: 0.012, memory: 928, loss: 5.0536 2023-01-04 22:37:04,112 - mmcls - INFO - Epoch [58][3300/5005] lr: 1.000e-02, eta: 5:11:21, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0305 2023-01-04 22:37:12,791 - mmcls - INFO - Epoch [58][3400/5005] lr: 1.000e-02, eta: 5:11:12, time: 0.087, data_time: 0.041, memory: 928, loss: 5.0052 2023-01-04 22:37:21,907 - mmcls - INFO - Epoch [58][3500/5005] lr: 1.000e-02, eta: 5:11:03, time: 0.091, data_time: 0.009, memory: 928, loss: 5.0531 2023-01-04 22:37:30,361 - mmcls - INFO - Epoch [58][3600/5005] lr: 1.000e-02, eta: 5:10:54, time: 0.084, data_time: 0.009, memory: 928, loss: 5.0447 2023-01-04 22:37:38,870 - mmcls - INFO - Epoch [58][3700/5005] lr: 1.000e-02, eta: 5:10:45, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0200 2023-01-04 22:37:48,331 - mmcls - INFO - Epoch [58][3800/5005] lr: 1.000e-02, eta: 5:10:37, time: 0.095, data_time: 0.005, memory: 928, loss: 5.0130 2023-01-04 22:37:56,991 - mmcls - INFO - Epoch [58][3900/5005] lr: 1.000e-02, eta: 5:10:28, time: 0.087, data_time: 0.009, memory: 928, loss: 5.0392 2023-01-04 22:38:05,846 - mmcls - INFO - Epoch [58][4000/5005] lr: 1.000e-02, eta: 5:10:19, time: 0.089, data_time: 0.000, memory: 928, loss: 5.0468 2023-01-04 22:38:14,752 - mmcls - INFO - Epoch [58][4100/5005] lr: 1.000e-02, eta: 5:10:10, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0474 2023-01-04 22:38:23,363 - mmcls - INFO - Epoch [58][4200/5005] lr: 1.000e-02, eta: 5:10:01, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0178 2023-01-04 22:38:32,073 - mmcls - INFO - Epoch [58][4300/5005] lr: 1.000e-02, eta: 5:09:52, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0563 2023-01-04 22:38:40,849 - mmcls - INFO - Epoch [58][4400/5005] lr: 1.000e-02, eta: 5:09:44, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0359 2023-01-04 22:38:49,714 - mmcls - INFO - Epoch [58][4500/5005] lr: 1.000e-02, eta: 5:09:35, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0156 2023-01-04 22:38:58,789 - mmcls - INFO - Epoch [58][4600/5005] lr: 1.000e-02, eta: 5:09:26, time: 0.091, data_time: 0.001, memory: 928, loss: 5.0316 2023-01-04 22:39:07,488 - mmcls - INFO - Epoch [58][4700/5005] lr: 1.000e-02, eta: 5:09:17, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0522 2023-01-04 22:39:15,993 - mmcls - INFO - Epoch [58][4800/5005] lr: 1.000e-02, eta: 5:09:08, time: 0.085, data_time: 0.004, memory: 928, loss: 5.0448 2023-01-04 22:39:24,701 - mmcls - INFO - Epoch [58][4900/5005] lr: 1.000e-02, eta: 5:08:59, time: 0.087, data_time: 0.005, memory: 928, loss: 5.0266 2023-01-04 22:39:33,657 - mmcls - INFO - Epoch [58][5000/5005] lr: 1.000e-02, eta: 5:08:51, time: 0.089, data_time: 0.014, memory: 928, loss: 5.0255 2023-01-04 22:39:56,233 - mmcls - INFO - Epoch(val) [58][196] accuracy_top-1: 7.0600, accuracy_top-5: 17.0500 2023-01-04 22:40:07,056 - mmcls - INFO - Epoch [59][100/5005] lr: 1.000e-02, eta: 5:08:43, time: 0.108, data_time: 0.041, memory: 928, loss: 5.0297 2023-01-04 22:40:16,044 - mmcls - INFO - Epoch [59][200/5005] lr: 1.000e-02, eta: 5:08:34, time: 0.090, data_time: 0.005, memory: 928, loss: 5.0396 2023-01-04 22:40:24,667 - mmcls - INFO - Epoch [59][300/5005] lr: 1.000e-02, eta: 5:08:25, time: 0.086, data_time: 0.006, memory: 928, loss: 5.0455 2023-01-04 22:40:33,430 - mmcls - INFO - Epoch [59][400/5005] lr: 1.000e-02, eta: 5:08:16, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0306 2023-01-04 22:40:42,648 - mmcls - INFO - Epoch [59][500/5005] lr: 1.000e-02, eta: 5:08:07, time: 0.092, data_time: 0.005, memory: 928, loss: 5.0208 2023-01-04 22:40:51,453 - mmcls - INFO - Epoch [59][600/5005] lr: 1.000e-02, eta: 5:07:59, time: 0.088, data_time: 0.007, memory: 928, loss: 5.0391 2023-01-04 22:41:00,161 - mmcls - INFO - Epoch [59][700/5005] lr: 1.000e-02, eta: 5:07:50, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0288 2023-01-04 22:41:08,999 - mmcls - INFO - Epoch [59][800/5005] lr: 1.000e-02, eta: 5:07:41, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0346 2023-01-04 22:41:17,844 - mmcls - INFO - Epoch [59][900/5005] lr: 1.000e-02, eta: 5:07:32, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0175 2023-01-04 22:41:26,534 - mmcls - INFO - Epoch [59][1000/5005] lr: 1.000e-02, eta: 5:07:23, time: 0.087, data_time: 0.007, memory: 928, loss: 5.0128 2023-01-04 22:41:35,316 - mmcls - INFO - Epoch [59][1100/5005] lr: 1.000e-02, eta: 5:07:14, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0309 2023-01-04 22:41:43,976 - mmcls - INFO - Epoch [59][1200/5005] lr: 1.000e-02, eta: 5:07:06, time: 0.087, data_time: 0.030, memory: 928, loss: 5.0449 2023-01-04 22:41:52,662 - mmcls - INFO - Epoch [59][1300/5005] lr: 1.000e-02, eta: 5:06:57, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0232 2023-01-04 22:42:01,496 - mmcls - INFO - Epoch [59][1400/5005] lr: 1.000e-02, eta: 5:06:48, time: 0.088, data_time: 0.023, memory: 928, loss: 5.0227 2023-01-04 22:42:10,407 - mmcls - INFO - Epoch [59][1500/5005] lr: 1.000e-02, eta: 5:06:39, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0249 2023-01-04 22:42:19,141 - mmcls - INFO - Epoch [59][1600/5005] lr: 1.000e-02, eta: 5:06:30, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0269 2023-01-04 22:42:27,852 - mmcls - INFO - Epoch [59][1700/5005] lr: 1.000e-02, eta: 5:06:21, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0308 2023-01-04 22:42:36,454 - mmcls - INFO - Epoch [59][1800/5005] lr: 1.000e-02, eta: 5:06:12, time: 0.086, data_time: 0.024, memory: 928, loss: 5.0151 2023-01-04 22:42:45,286 - mmcls - INFO - Epoch [59][1900/5005] lr: 1.000e-02, eta: 5:06:04, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0122 2023-01-04 22:42:54,040 - mmcls - INFO - Epoch [59][2000/5005] lr: 1.000e-02, eta: 5:05:55, time: 0.088, data_time: 0.010, memory: 928, loss: 5.0540 2023-01-04 22:43:02,601 - mmcls - INFO - Epoch [59][2100/5005] lr: 1.000e-02, eta: 5:05:46, time: 0.086, data_time: 0.018, memory: 928, loss: 5.0477 2023-01-04 22:43:11,487 - mmcls - INFO - Epoch [59][2200/5005] lr: 1.000e-02, eta: 5:05:37, time: 0.089, data_time: 0.006, memory: 928, loss: 5.0307 2023-01-04 22:43:20,757 - mmcls - INFO - Epoch [59][2300/5005] lr: 1.000e-02, eta: 5:05:28, time: 0.093, data_time: 0.002, memory: 928, loss: 5.0407 2023-01-04 22:43:29,450 - mmcls - INFO - Epoch [59][2400/5005] lr: 1.000e-02, eta: 5:05:19, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0241 2023-01-04 22:43:38,135 - mmcls - INFO - Epoch [59][2500/5005] lr: 1.000e-02, eta: 5:05:11, time: 0.087, data_time: 0.022, memory: 928, loss: 5.0155 2023-01-04 22:43:46,782 - mmcls - INFO - Epoch [59][2600/5005] lr: 1.000e-02, eta: 5:05:02, time: 0.087, data_time: 0.023, memory: 928, loss: 5.0359 2023-01-04 22:43:55,812 - mmcls - INFO - Epoch [59][2700/5005] lr: 1.000e-02, eta: 5:04:53, time: 0.090, data_time: 0.018, memory: 928, loss: 5.0387 2023-01-04 22:44:04,541 - mmcls - INFO - Epoch [59][2800/5005] lr: 1.000e-02, eta: 5:04:44, time: 0.087, data_time: 0.003, memory: 928, loss: 5.0168 2023-01-04 22:44:13,329 - mmcls - INFO - Epoch [59][2900/5005] lr: 1.000e-02, eta: 5:04:35, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0316 2023-01-04 22:44:22,297 - mmcls - INFO - Epoch [59][3000/5005] lr: 1.000e-02, eta: 5:04:27, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0344 2023-01-04 22:44:31,250 - mmcls - INFO - Epoch [59][3100/5005] lr: 1.000e-02, eta: 5:04:18, time: 0.090, data_time: 0.001, memory: 928, loss: 5.0241 2023-01-04 22:44:39,922 - mmcls - INFO - Epoch [59][3200/5005] lr: 1.000e-02, eta: 5:04:09, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0242 2023-01-04 22:44:48,625 - mmcls - INFO - Epoch [59][3300/5005] lr: 1.000e-02, eta: 5:04:00, time: 0.087, data_time: 0.006, memory: 928, loss: 5.0087 2023-01-04 22:44:57,220 - mmcls - INFO - Epoch [59][3400/5005] lr: 1.000e-02, eta: 5:03:51, time: 0.086, data_time: 0.001, memory: 928, loss: 5.0331 2023-01-04 22:45:06,376 - mmcls - INFO - Epoch [59][3500/5005] lr: 1.000e-02, eta: 5:03:42, time: 0.092, data_time: 0.009, memory: 928, loss: 5.0634 2023-01-04 22:45:15,103 - mmcls - INFO - Epoch [59][3600/5005] lr: 1.000e-02, eta: 5:03:34, time: 0.087, data_time: 0.008, memory: 928, loss: 5.0173 2023-01-04 22:45:23,681 - mmcls - INFO - Epoch [59][3700/5005] lr: 1.000e-02, eta: 5:03:25, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0260 2023-01-04 22:45:32,580 - mmcls - INFO - Epoch [59][3800/5005] lr: 1.000e-02, eta: 5:03:16, time: 0.089, data_time: 0.030, memory: 928, loss: 5.0578 2023-01-04 22:45:41,158 - mmcls - INFO - Epoch [59][3900/5005] lr: 1.000e-02, eta: 5:03:07, time: 0.086, data_time: 0.011, memory: 928, loss: 5.0209 2023-01-04 22:45:49,901 - mmcls - INFO - Epoch [59][4000/5005] lr: 1.000e-02, eta: 5:02:58, time: 0.087, data_time: 0.014, memory: 928, loss: 5.0127 2023-01-04 22:45:58,830 - mmcls - INFO - Epoch [59][4100/5005] lr: 1.000e-02, eta: 5:02:49, time: 0.089, data_time: 0.010, memory: 928, loss: 5.0406 2023-01-04 22:46:07,380 - mmcls - INFO - Epoch [59][4200/5005] lr: 1.000e-02, eta: 5:02:40, time: 0.085, data_time: 0.002, memory: 928, loss: 5.0257 2023-01-04 22:46:16,223 - mmcls - INFO - Epoch [59][4300/5005] lr: 1.000e-02, eta: 5:02:31, time: 0.088, data_time: 0.022, memory: 928, loss: 5.0189 2023-01-04 22:46:24,872 - mmcls - INFO - Epoch [59][4400/5005] lr: 1.000e-02, eta: 5:02:23, time: 0.087, data_time: 0.013, memory: 928, loss: 5.0259 2023-01-04 22:46:33,597 - mmcls - INFO - Epoch [59][4500/5005] lr: 1.000e-02, eta: 5:02:14, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0222 2023-01-04 22:46:42,222 - mmcls - INFO - Epoch [59][4600/5005] lr: 1.000e-02, eta: 5:02:05, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0466 2023-01-04 22:46:50,985 - mmcls - INFO - Epoch [59][4700/5005] lr: 1.000e-02, eta: 5:01:56, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0246 2023-01-04 22:46:59,992 - mmcls - INFO - Epoch [59][4800/5005] lr: 1.000e-02, eta: 5:01:47, time: 0.090, data_time: 0.016, memory: 928, loss: 5.0041 2023-01-04 22:47:08,957 - mmcls - INFO - Epoch [59][4900/5005] lr: 1.000e-02, eta: 5:01:38, time: 0.090, data_time: 0.006, memory: 928, loss: 5.0396 2023-01-04 22:47:17,646 - mmcls - INFO - Epoch [59][5000/5005] lr: 1.000e-02, eta: 5:01:30, time: 0.087, data_time: 0.010, memory: 928, loss: 5.0270 2023-01-04 22:47:40,345 - mmcls - INFO - Epoch(val) [59][196] accuracy_top-1: 11.4520, accuracy_top-5: 25.7140 2023-01-04 22:47:51,280 - mmcls - INFO - Epoch [60][100/5005] lr: 1.000e-02, eta: 5:01:21, time: 0.109, data_time: 0.049, memory: 928, loss: 5.0115 2023-01-04 22:47:59,687 - mmcls - INFO - Epoch [60][200/5005] lr: 1.000e-02, eta: 5:01:12, time: 0.084, data_time: 0.003, memory: 928, loss: 5.0339 2023-01-04 22:48:08,106 - mmcls - INFO - Epoch [60][300/5005] lr: 1.000e-02, eta: 5:01:03, time: 0.084, data_time: 0.006, memory: 928, loss: 5.0216 2023-01-04 22:48:16,788 - mmcls - INFO - Epoch [60][400/5005] lr: 1.000e-02, eta: 5:00:54, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0111 2023-01-04 22:48:25,515 - mmcls - INFO - Epoch [60][500/5005] lr: 1.000e-02, eta: 5:00:45, time: 0.087, data_time: 0.015, memory: 928, loss: 5.0312 2023-01-04 22:48:34,097 - mmcls - INFO - Epoch [60][600/5005] lr: 1.000e-02, eta: 5:00:36, time: 0.086, data_time: 0.002, memory: 928, loss: 5.0208 2023-01-04 22:48:43,353 - mmcls - INFO - Epoch [60][700/5005] lr: 1.000e-02, eta: 5:00:28, time: 0.093, data_time: 0.006, memory: 928, loss: 5.0534 2023-01-04 22:48:52,065 - mmcls - INFO - Epoch [60][800/5005] lr: 1.000e-02, eta: 5:00:19, time: 0.087, data_time: 0.016, memory: 928, loss: 5.0097 2023-01-04 22:49:00,974 - mmcls - INFO - Epoch [60][900/5005] lr: 1.000e-02, eta: 5:00:10, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0172 2023-01-04 22:49:09,735 - mmcls - INFO - Epoch [60][1000/5005] lr: 1.000e-02, eta: 5:00:01, time: 0.088, data_time: 0.003, memory: 928, loss: 5.0144 2023-01-04 22:49:18,213 - mmcls - INFO - Epoch [60][1100/5005] lr: 1.000e-02, eta: 4:59:52, time: 0.085, data_time: 0.008, memory: 928, loss: 5.0425 2023-01-04 22:49:26,967 - mmcls - INFO - Epoch [60][1200/5005] lr: 1.000e-02, eta: 4:59:44, time: 0.087, data_time: 0.025, memory: 928, loss: 5.0159 2023-01-04 22:49:35,894 - mmcls - INFO - Epoch [60][1300/5005] lr: 1.000e-02, eta: 4:59:35, time: 0.089, data_time: 0.002, memory: 928, loss: 5.0371 2023-01-04 22:49:44,410 - mmcls - INFO - Epoch [60][1400/5005] lr: 1.000e-02, eta: 4:59:26, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0191 2023-01-04 22:49:53,205 - mmcls - INFO - Epoch [60][1500/5005] lr: 1.000e-02, eta: 4:59:17, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0107 2023-01-04 22:50:02,033 - mmcls - INFO - Epoch [60][1600/5005] lr: 1.000e-02, eta: 4:59:08, time: 0.088, data_time: 0.036, memory: 928, loss: 5.0067 2023-01-04 22:50:10,796 - mmcls - INFO - Epoch [60][1700/5005] lr: 1.000e-02, eta: 4:58:59, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0166 2023-01-04 22:50:19,635 - mmcls - INFO - Epoch [60][1800/5005] lr: 1.000e-02, eta: 4:58:51, time: 0.088, data_time: 0.005, memory: 928, loss: 5.0409 2023-01-04 22:50:28,534 - mmcls - INFO - Epoch [60][1900/5005] lr: 1.000e-02, eta: 4:58:42, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0427 2023-01-04 22:50:37,097 - mmcls - INFO - Epoch [60][2000/5005] lr: 1.000e-02, eta: 4:58:33, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0539 2023-01-04 22:50:45,578 - mmcls - INFO - Epoch [60][2100/5005] lr: 1.000e-02, eta: 4:58:24, time: 0.085, data_time: 0.001, memory: 928, loss: 5.0326 2023-01-04 22:50:54,521 - mmcls - INFO - Epoch [60][2200/5005] lr: 1.000e-02, eta: 4:58:15, time: 0.089, data_time: 0.007, memory: 928, loss: 5.0433 2023-01-04 22:51:03,541 - mmcls - INFO - Epoch [60][2300/5005] lr: 1.000e-02, eta: 4:58:06, time: 0.090, data_time: 0.021, memory: 928, loss: 5.0275 2023-01-04 22:51:12,380 - mmcls - INFO - Epoch [60][2400/5005] lr: 1.000e-02, eta: 4:57:58, time: 0.088, data_time: 0.034, memory: 928, loss: 5.0238 2023-01-04 22:51:20,914 - mmcls - INFO - Epoch [60][2500/5005] lr: 1.000e-02, eta: 4:57:49, time: 0.085, data_time: 0.031, memory: 928, loss: 5.0311 2023-01-04 22:51:29,691 - mmcls - INFO - Epoch [60][2600/5005] lr: 1.000e-02, eta: 4:57:40, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0194 2023-01-04 22:51:38,478 - mmcls - INFO - Epoch [60][2700/5005] lr: 1.000e-02, eta: 4:57:31, time: 0.088, data_time: 0.013, memory: 928, loss: 5.0292 2023-01-04 22:51:47,589 - mmcls - INFO - Epoch [60][2800/5005] lr: 1.000e-02, eta: 4:57:22, time: 0.091, data_time: 0.013, memory: 928, loss: 4.9874 2023-01-04 22:51:56,559 - mmcls - INFO - Epoch [60][2900/5005] lr: 1.000e-02, eta: 4:57:14, time: 0.090, data_time: 0.004, memory: 928, loss: 5.0309 2023-01-04 22:52:05,307 - mmcls - INFO - Epoch [60][3000/5005] lr: 1.000e-02, eta: 4:57:05, time: 0.087, data_time: 0.001, memory: 928, loss: 5.0166 2023-01-04 22:52:13,893 - mmcls - INFO - Epoch [60][3100/5005] lr: 1.000e-02, eta: 4:56:56, time: 0.086, data_time: 0.005, memory: 928, loss: 5.0460 2023-01-04 22:52:22,843 - mmcls - INFO - Epoch [60][3200/5005] lr: 1.000e-02, eta: 4:56:47, time: 0.089, data_time: 0.018, memory: 928, loss: 5.0205 2023-01-04 22:52:31,645 - mmcls - INFO - Epoch [60][3300/5005] lr: 1.000e-02, eta: 4:56:38, time: 0.088, data_time: 0.018, memory: 928, loss: 5.0356 2023-01-04 22:52:40,226 - mmcls - INFO - Epoch [60][3400/5005] lr: 1.000e-02, eta: 4:56:29, time: 0.086, data_time: 0.007, memory: 928, loss: 5.0323 2023-01-04 22:52:49,129 - mmcls - INFO - Epoch [60][3500/5005] lr: 1.000e-02, eta: 4:56:20, time: 0.089, data_time: 0.024, memory: 928, loss: 5.0215 2023-01-04 22:52:57,877 - mmcls - INFO - Epoch [60][3600/5005] lr: 1.000e-02, eta: 4:56:12, time: 0.087, data_time: 0.004, memory: 928, loss: 5.0400 2023-01-04 22:53:06,656 - mmcls - INFO - Epoch [60][3700/5005] lr: 1.000e-02, eta: 4:56:03, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0111 2023-01-04 22:53:15,316 - mmcls - INFO - Epoch [60][3800/5005] lr: 1.000e-02, eta: 4:55:54, time: 0.087, data_time: 0.002, memory: 928, loss: 5.0376 2023-01-04 22:53:24,101 - mmcls - INFO - Epoch [60][3900/5005] lr: 1.000e-02, eta: 4:55:45, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0372 2023-01-04 22:53:32,718 - mmcls - INFO - Epoch [60][4000/5005] lr: 1.000e-02, eta: 4:55:36, time: 0.086, data_time: 0.012, memory: 928, loss: 5.0334 2023-01-04 22:53:41,218 - mmcls - INFO - Epoch [60][4100/5005] lr: 1.000e-02, eta: 4:55:27, time: 0.085, data_time: 0.036, memory: 928, loss: 5.0351 2023-01-04 22:53:50,052 - mmcls - INFO - Epoch [60][4200/5005] lr: 1.000e-02, eta: 4:55:18, time: 0.088, data_time: 0.004, memory: 928, loss: 5.0185 2023-01-04 22:53:58,885 - mmcls - INFO - Epoch [60][4300/5005] lr: 1.000e-02, eta: 4:55:09, time: 0.088, data_time: 0.002, memory: 928, loss: 5.0097 2023-01-04 22:54:07,829 - mmcls - INFO - Epoch [60][4400/5005] lr: 1.000e-02, eta: 4:55:01, time: 0.089, data_time: 0.001, memory: 928, loss: 5.0191 2023-01-04 22:54:16,628 - mmcls - INFO - Epoch [60][4500/5005] lr: 1.000e-02, eta: 4:54:52, time: 0.088, data_time: 0.001, memory: 928, loss: 5.0269 2023-01-04 22:54:25,183 - mmcls - INFO - Epoch [60][4600/5005] lr: 1.000e-02, eta: 4:54:43, time: 0.086, data_time: 0.017, memory: 928, loss: 5.0271 2023-01-04 22:54:33,718 - mmcls - INFO - Epoch [60][4700/5005] lr: 1.000e-02, eta: 4:54:34, time: 0.085, data_time: 0.012, memory: 928, loss: 5.0408 2023-01-04 22:54:42,549 - mmcls - INFO - Epoch [60][4800/5005] lr: 1.000e-02, eta: 4:54:25, time: 0.088, data_time: 0.006, memory: 928, loss: 5.0258 2023-01-04 22:54:51,194 - mmcls - INFO - Epoch [60][4900/5005] lr: 1.000e-02, eta: 4:54:16, time: 0.086, data_time: 0.020, memory: 928, loss: 5.0400 2023-01-04 22:55:00,211 - mmcls - INFO - Epoch [60][5000/5005] lr: 1.000e-02, eta: 4:54:07, time: 0.090, data_time: 0.009, memory: 928, loss: 5.0143 2023-01-04 22:55:22,895 - mmcls - INFO - Epoch(val) [60][196] accuracy_top-1: 9.5760, accuracy_top-5: 22.2380 2023-01-04 22:55:33,818 - mmcls - INFO - Epoch [61][100/5005] lr: 1.000e-03, eta: 4:53:59, time: 0.109, data_time: 0.054, memory: 928, loss: 4.9478 2023-01-04 22:55:42,396 - mmcls - INFO - Epoch [61][200/5005] lr: 1.000e-03, eta: 4:53:50, time: 0.086, data_time: 0.005, memory: 928, loss: 4.9633 2023-01-04 22:55:51,260 - mmcls - INFO - Epoch [61][300/5005] lr: 1.000e-03, eta: 4:53:41, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9512 2023-01-04 22:55:59,712 - mmcls - INFO - Epoch [61][400/5005] lr: 1.000e-03, eta: 4:53:32, time: 0.084, data_time: 0.004, memory: 928, loss: 4.9229 2023-01-04 22:56:08,432 - mmcls - INFO - Epoch [61][500/5005] lr: 1.000e-03, eta: 4:53:24, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9093 2023-01-04 22:56:17,256 - mmcls - INFO - Epoch [61][600/5005] lr: 1.000e-03, eta: 4:53:15, time: 0.088, data_time: 0.006, memory: 928, loss: 4.9167 2023-01-04 22:56:26,081 - mmcls - INFO - Epoch [61][700/5005] lr: 1.000e-03, eta: 4:53:06, time: 0.088, data_time: 0.016, memory: 928, loss: 4.9485 2023-01-04 22:56:34,870 - mmcls - INFO - Epoch [61][800/5005] lr: 1.000e-03, eta: 4:52:57, time: 0.088, data_time: 0.004, memory: 928, loss: 4.9358 2023-01-04 22:56:43,655 - mmcls - INFO - Epoch [61][900/5005] lr: 1.000e-03, eta: 4:52:48, time: 0.088, data_time: 0.005, memory: 928, loss: 4.9306 2023-01-04 22:56:52,317 - mmcls - INFO - Epoch [61][1000/5005] lr: 1.000e-03, eta: 4:52:39, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8843 2023-01-04 22:57:01,463 - mmcls - INFO - Epoch [61][1100/5005] lr: 1.000e-03, eta: 4:52:31, time: 0.091, data_time: 0.017, memory: 928, loss: 4.9304 2023-01-04 22:57:10,234 - mmcls - INFO - Epoch [61][1200/5005] lr: 1.000e-03, eta: 4:52:22, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8936 2023-01-04 22:57:19,006 - mmcls - INFO - Epoch [61][1300/5005] lr: 1.000e-03, eta: 4:52:13, time: 0.088, data_time: 0.034, memory: 928, loss: 4.8937 2023-01-04 22:57:27,613 - mmcls - INFO - Epoch [61][1400/5005] lr: 1.000e-03, eta: 4:52:04, time: 0.086, data_time: 0.005, memory: 928, loss: 4.9300 2023-01-04 22:57:36,319 - mmcls - INFO - Epoch [61][1500/5005] lr: 1.000e-03, eta: 4:51:55, time: 0.087, data_time: 0.002, memory: 928, loss: 4.9242 2023-01-04 22:57:44,987 - mmcls - INFO - Epoch [61][1600/5005] lr: 1.000e-03, eta: 4:51:46, time: 0.087, data_time: 0.002, memory: 928, loss: 4.9254 2023-01-04 22:57:53,466 - mmcls - INFO - Epoch [61][1700/5005] lr: 1.000e-03, eta: 4:51:37, time: 0.085, data_time: 0.012, memory: 928, loss: 4.9286 2023-01-04 22:58:02,361 - mmcls - INFO - Epoch [61][1800/5005] lr: 1.000e-03, eta: 4:51:29, time: 0.089, data_time: 0.009, memory: 928, loss: 4.9071 2023-01-04 22:58:11,155 - mmcls - INFO - Epoch [61][1900/5005] lr: 1.000e-03, eta: 4:51:20, time: 0.088, data_time: 0.010, memory: 928, loss: 4.9103 2023-01-04 22:58:19,839 - mmcls - INFO - Epoch [61][2000/5005] lr: 1.000e-03, eta: 4:51:11, time: 0.087, data_time: 0.007, memory: 928, loss: 4.9095 2023-01-04 22:58:28,318 - mmcls - INFO - Epoch [61][2100/5005] lr: 1.000e-03, eta: 4:51:02, time: 0.085, data_time: 0.006, memory: 928, loss: 4.9175 2023-01-04 22:58:37,053 - mmcls - INFO - Epoch [61][2200/5005] lr: 1.000e-03, eta: 4:50:53, time: 0.087, data_time: 0.005, memory: 928, loss: 4.9165 2023-01-04 22:58:45,815 - mmcls - INFO - Epoch [61][2300/5005] lr: 1.000e-03, eta: 4:50:44, time: 0.088, data_time: 0.004, memory: 928, loss: 4.9035 2023-01-04 22:58:54,817 - mmcls - INFO - Epoch [61][2400/5005] lr: 1.000e-03, eta: 4:50:35, time: 0.090, data_time: 0.023, memory: 928, loss: 4.8883 2023-01-04 22:59:03,450 - mmcls - INFO - Epoch [61][2500/5005] lr: 1.000e-03, eta: 4:50:26, time: 0.086, data_time: 0.019, memory: 928, loss: 4.9186 2023-01-04 22:59:12,210 - mmcls - INFO - Epoch [61][2600/5005] lr: 1.000e-03, eta: 4:50:18, time: 0.088, data_time: 0.002, memory: 928, loss: 4.9117 2023-01-04 22:59:20,754 - mmcls - INFO - Epoch [61][2700/5005] lr: 1.000e-03, eta: 4:50:09, time: 0.085, data_time: 0.009, memory: 928, loss: 4.9238 2023-01-04 22:59:29,251 - mmcls - INFO - Epoch [61][2800/5005] lr: 1.000e-03, eta: 4:50:00, time: 0.085, data_time: 0.017, memory: 928, loss: 4.9069 2023-01-04 22:59:38,141 - mmcls - INFO - Epoch [61][2900/5005] lr: 1.000e-03, eta: 4:49:51, time: 0.089, data_time: 0.007, memory: 928, loss: 4.9026 2023-01-04 22:59:47,077 - mmcls - INFO - Epoch [61][3000/5005] lr: 1.000e-03, eta: 4:49:42, time: 0.089, data_time: 0.002, memory: 928, loss: 4.9148 2023-01-04 22:59:56,470 - mmcls - INFO - Epoch [61][3100/5005] lr: 1.000e-03, eta: 4:49:34, time: 0.094, data_time: 0.013, memory: 928, loss: 4.9164 2023-01-04 23:00:05,333 - mmcls - INFO - Epoch [61][3200/5005] lr: 1.000e-03, eta: 4:49:25, time: 0.089, data_time: 0.019, memory: 928, loss: 4.9071 2023-01-04 23:00:14,085 - mmcls - INFO - Epoch [61][3300/5005] lr: 1.000e-03, eta: 4:49:16, time: 0.088, data_time: 0.014, memory: 928, loss: 4.9017 2023-01-04 23:00:22,919 - mmcls - INFO - Epoch [61][3400/5005] lr: 1.000e-03, eta: 4:49:07, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8910 2023-01-04 23:00:31,538 - mmcls - INFO - Epoch [61][3500/5005] lr: 1.000e-03, eta: 4:48:58, time: 0.086, data_time: 0.002, memory: 928, loss: 4.9042 2023-01-04 23:00:40,248 - mmcls - INFO - Epoch [61][3600/5005] lr: 1.000e-03, eta: 4:48:49, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9112 2023-01-04 23:00:48,980 - mmcls - INFO - Epoch [61][3700/5005] lr: 1.000e-03, eta: 4:48:40, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9187 2023-01-04 23:00:57,834 - mmcls - INFO - Epoch [61][3800/5005] lr: 1.000e-03, eta: 4:48:32, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8989 2023-01-04 23:01:06,788 - mmcls - INFO - Epoch [61][3900/5005] lr: 1.000e-03, eta: 4:48:23, time: 0.090, data_time: 0.001, memory: 928, loss: 4.9011 2023-01-04 23:01:15,656 - mmcls - INFO - Epoch [61][4000/5005] lr: 1.000e-03, eta: 4:48:14, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9166 2023-01-04 23:01:24,533 - mmcls - INFO - Epoch [61][4100/5005] lr: 1.000e-03, eta: 4:48:05, time: 0.089, data_time: 0.001, memory: 928, loss: 4.9011 2023-01-04 23:01:33,202 - mmcls - INFO - Epoch [61][4200/5005] lr: 1.000e-03, eta: 4:47:56, time: 0.087, data_time: 0.023, memory: 928, loss: 4.9104 2023-01-04 23:01:41,775 - mmcls - INFO - Epoch [61][4300/5005] lr: 1.000e-03, eta: 4:47:47, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8821 2023-01-04 23:01:50,655 - mmcls - INFO - Epoch [61][4400/5005] lr: 1.000e-03, eta: 4:47:39, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8911 2023-01-04 23:01:59,825 - mmcls - INFO - Epoch [61][4500/5005] lr: 1.000e-03, eta: 4:47:30, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8932 2023-01-04 23:02:08,336 - mmcls - INFO - Epoch [61][4600/5005] lr: 1.000e-03, eta: 4:47:21, time: 0.085, data_time: 0.033, memory: 928, loss: 4.9043 2023-01-04 23:02:17,665 - mmcls - INFO - Epoch [61][4700/5005] lr: 1.000e-03, eta: 4:47:13, time: 0.093, data_time: 0.024, memory: 928, loss: 4.8873 2023-01-04 23:02:26,544 - mmcls - INFO - Epoch [61][4800/5005] lr: 1.000e-03, eta: 4:47:04, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9084 2023-01-04 23:02:35,331 - mmcls - INFO - Epoch [61][4900/5005] lr: 1.000e-03, eta: 4:46:55, time: 0.088, data_time: 0.006, memory: 928, loss: 4.9320 2023-01-04 23:02:44,019 - mmcls - INFO - Epoch [61][5000/5005] lr: 1.000e-03, eta: 4:46:46, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8859 2023-01-04 23:03:06,748 - mmcls - INFO - Epoch(val) [61][196] accuracy_top-1: 16.9300, accuracy_top-5: 34.8440 2023-01-04 23:03:17,855 - mmcls - INFO - Epoch [62][100/5005] lr: 1.000e-03, eta: 4:46:38, time: 0.111, data_time: 0.042, memory: 928, loss: 4.8940 2023-01-04 23:03:26,538 - mmcls - INFO - Epoch [62][200/5005] lr: 1.000e-03, eta: 4:46:29, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8911 2023-01-04 23:03:35,146 - mmcls - INFO - Epoch [62][300/5005] lr: 1.000e-03, eta: 4:46:20, time: 0.086, data_time: 0.041, memory: 928, loss: 4.9182 2023-01-04 23:03:43,781 - mmcls - INFO - Epoch [62][400/5005] lr: 1.000e-03, eta: 4:46:11, time: 0.086, data_time: 0.003, memory: 928, loss: 4.9064 2023-01-04 23:03:52,401 - mmcls - INFO - Epoch [62][500/5005] lr: 1.000e-03, eta: 4:46:02, time: 0.086, data_time: 0.030, memory: 928, loss: 4.8994 2023-01-04 23:04:01,189 - mmcls - INFO - Epoch [62][600/5005] lr: 1.000e-03, eta: 4:45:54, time: 0.088, data_time: 0.023, memory: 928, loss: 4.9105 2023-01-04 23:04:09,921 - mmcls - INFO - Epoch [62][700/5005] lr: 1.000e-03, eta: 4:45:45, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9112 2023-01-04 23:04:18,827 - mmcls - INFO - Epoch [62][800/5005] lr: 1.000e-03, eta: 4:45:36, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8993 2023-01-04 23:04:27,660 - mmcls - INFO - Epoch [62][900/5005] lr: 1.000e-03, eta: 4:45:27, time: 0.088, data_time: 0.012, memory: 928, loss: 4.9134 2023-01-04 23:04:36,218 - mmcls - INFO - Epoch [62][1000/5005] lr: 1.000e-03, eta: 4:45:18, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8857 2023-01-04 23:04:45,127 - mmcls - INFO - Epoch [62][1100/5005] lr: 1.000e-03, eta: 4:45:09, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8897 2023-01-04 23:04:54,193 - mmcls - INFO - Epoch [62][1200/5005] lr: 1.000e-03, eta: 4:45:01, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8954 2023-01-04 23:05:02,945 - mmcls - INFO - Epoch [62][1300/5005] lr: 1.000e-03, eta: 4:44:52, time: 0.088, data_time: 0.001, memory: 928, loss: 4.9047 2023-01-04 23:05:11,836 - mmcls - INFO - Epoch [62][1400/5005] lr: 1.000e-03, eta: 4:44:43, time: 0.089, data_time: 0.002, memory: 928, loss: 4.9060 2023-01-04 23:05:20,662 - mmcls - INFO - Epoch [62][1500/5005] lr: 1.000e-03, eta: 4:44:34, time: 0.088, data_time: 0.001, memory: 928, loss: 4.9029 2023-01-04 23:05:29,398 - mmcls - INFO - Epoch [62][1600/5005] lr: 1.000e-03, eta: 4:44:25, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8921 2023-01-04 23:05:38,108 - mmcls - INFO - Epoch [62][1700/5005] lr: 1.000e-03, eta: 4:44:16, time: 0.087, data_time: 0.015, memory: 928, loss: 4.9103 2023-01-04 23:05:46,870 - mmcls - INFO - Epoch [62][1800/5005] lr: 1.000e-03, eta: 4:44:08, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8937 2023-01-04 23:05:56,059 - mmcls - INFO - Epoch [62][1900/5005] lr: 1.000e-03, eta: 4:43:59, time: 0.092, data_time: 0.006, memory: 928, loss: 4.8919 2023-01-04 23:06:04,725 - mmcls - INFO - Epoch [62][2000/5005] lr: 1.000e-03, eta: 4:43:50, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8823 2023-01-04 23:06:13,792 - mmcls - INFO - Epoch [62][2100/5005] lr: 1.000e-03, eta: 4:43:41, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8990 2023-01-04 23:06:22,426 - mmcls - INFO - Epoch [62][2200/5005] lr: 1.000e-03, eta: 4:43:33, time: 0.086, data_time: 0.003, memory: 928, loss: 4.9090 2023-01-04 23:06:31,127 - mmcls - INFO - Epoch [62][2300/5005] lr: 1.000e-03, eta: 4:43:24, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8844 2023-01-04 23:06:39,856 - mmcls - INFO - Epoch [62][2400/5005] lr: 1.000e-03, eta: 4:43:15, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8840 2023-01-04 23:06:48,396 - mmcls - INFO - Epoch [62][2500/5005] lr: 1.000e-03, eta: 4:43:06, time: 0.085, data_time: 0.010, memory: 928, loss: 4.9058 2023-01-04 23:06:57,069 - mmcls - INFO - Epoch [62][2600/5005] lr: 1.000e-03, eta: 4:42:57, time: 0.087, data_time: 0.014, memory: 928, loss: 4.9209 2023-01-04 23:07:05,847 - mmcls - INFO - Epoch [62][2700/5005] lr: 1.000e-03, eta: 4:42:48, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8960 2023-01-04 23:07:14,710 - mmcls - INFO - Epoch [62][2800/5005] lr: 1.000e-03, eta: 4:42:39, time: 0.089, data_time: 0.001, memory: 928, loss: 4.9130 2023-01-04 23:07:23,625 - mmcls - INFO - Epoch [62][2900/5005] lr: 1.000e-03, eta: 4:42:31, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8979 2023-01-04 23:07:32,542 - mmcls - INFO - Epoch [62][3000/5005] lr: 1.000e-03, eta: 4:42:22, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8991 2023-01-04 23:07:41,240 - mmcls - INFO - Epoch [62][3100/5005] lr: 1.000e-03, eta: 4:42:13, time: 0.087, data_time: 0.003, memory: 928, loss: 4.9058 2023-01-04 23:07:50,391 - mmcls - INFO - Epoch [62][3200/5005] lr: 1.000e-03, eta: 4:42:04, time: 0.091, data_time: 0.018, memory: 928, loss: 4.8916 2023-01-04 23:07:59,101 - mmcls - INFO - Epoch [62][3300/5005] lr: 1.000e-03, eta: 4:41:55, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8846 2023-01-04 23:08:07,859 - mmcls - INFO - Epoch [62][3400/5005] lr: 1.000e-03, eta: 4:41:47, time: 0.088, data_time: 0.010, memory: 928, loss: 4.9022 2023-01-04 23:08:16,599 - mmcls - INFO - Epoch [62][3500/5005] lr: 1.000e-03, eta: 4:41:38, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8640 2023-01-04 23:08:25,267 - mmcls - INFO - Epoch [62][3600/5005] lr: 1.000e-03, eta: 4:41:29, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8914 2023-01-04 23:08:34,196 - mmcls - INFO - Epoch [62][3700/5005] lr: 1.000e-03, eta: 4:41:20, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8998 2023-01-04 23:08:42,766 - mmcls - INFO - Epoch [62][3800/5005] lr: 1.000e-03, eta: 4:41:11, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8729 2023-01-04 23:08:51,560 - mmcls - INFO - Epoch [62][3900/5005] lr: 1.000e-03, eta: 4:41:02, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8959 2023-01-04 23:09:00,296 - mmcls - INFO - Epoch [62][4000/5005] lr: 1.000e-03, eta: 4:40:53, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8976 2023-01-04 23:09:09,349 - mmcls - INFO - Epoch [62][4100/5005] lr: 1.000e-03, eta: 4:40:45, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8416 2023-01-04 23:09:17,912 - mmcls - INFO - Epoch [62][4200/5005] lr: 1.000e-03, eta: 4:40:36, time: 0.086, data_time: 0.002, memory: 928, loss: 4.9034 2023-01-04 23:09:27,140 - mmcls - INFO - Epoch [62][4300/5005] lr: 1.000e-03, eta: 4:40:27, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8869 2023-01-04 23:09:35,750 - mmcls - INFO - Epoch [62][4400/5005] lr: 1.000e-03, eta: 4:40:18, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8948 2023-01-04 23:09:44,752 - mmcls - INFO - Epoch [62][4500/5005] lr: 1.000e-03, eta: 4:40:10, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8902 2023-01-04 23:09:53,831 - mmcls - INFO - Epoch [62][4600/5005] lr: 1.000e-03, eta: 4:40:01, time: 0.091, data_time: 0.009, memory: 928, loss: 4.8786 2023-01-04 23:10:02,683 - mmcls - INFO - Epoch [62][4700/5005] lr: 1.000e-03, eta: 4:39:52, time: 0.088, data_time: 0.002, memory: 928, loss: 4.9073 2023-01-04 23:10:11,640 - mmcls - INFO - Epoch [62][4800/5005] lr: 1.000e-03, eta: 4:39:43, time: 0.090, data_time: 0.017, memory: 928, loss: 4.8808 2023-01-04 23:10:20,416 - mmcls - INFO - Epoch [62][4900/5005] lr: 1.000e-03, eta: 4:39:35, time: 0.088, data_time: 0.005, memory: 928, loss: 4.9123 2023-01-04 23:10:29,151 - mmcls - INFO - Epoch [62][5000/5005] lr: 1.000e-03, eta: 4:39:26, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8791 2023-01-04 23:10:51,913 - mmcls - INFO - Epoch(val) [62][196] accuracy_top-1: 16.6240, accuracy_top-5: 34.3200 2023-01-04 23:11:02,763 - mmcls - INFO - Epoch [63][100/5005] lr: 1.000e-03, eta: 4:39:17, time: 0.108, data_time: 0.046, memory: 928, loss: 4.9047 2023-01-04 23:11:11,397 - mmcls - INFO - Epoch [63][200/5005] lr: 1.000e-03, eta: 4:39:08, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8968 2023-01-04 23:11:20,375 - mmcls - INFO - Epoch [63][300/5005] lr: 1.000e-03, eta: 4:39:00, time: 0.090, data_time: 0.030, memory: 928, loss: 4.9014 2023-01-04 23:11:29,166 - mmcls - INFO - Epoch [63][400/5005] lr: 1.000e-03, eta: 4:38:51, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8832 2023-01-04 23:11:37,921 - mmcls - INFO - Epoch [63][500/5005] lr: 1.000e-03, eta: 4:38:42, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8886 2023-01-04 23:11:46,604 - mmcls - INFO - Epoch [63][600/5005] lr: 1.000e-03, eta: 4:38:33, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8711 2023-01-04 23:11:55,192 - mmcls - INFO - Epoch [63][700/5005] lr: 1.000e-03, eta: 4:38:24, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8828 2023-01-04 23:12:03,840 - mmcls - INFO - Epoch [63][800/5005] lr: 1.000e-03, eta: 4:38:15, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8884 2023-01-04 23:12:12,693 - mmcls - INFO - Epoch [63][900/5005] lr: 1.000e-03, eta: 4:38:06, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8770 2023-01-04 23:12:21,245 - mmcls - INFO - Epoch [63][1000/5005] lr: 1.000e-03, eta: 4:37:57, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8505 2023-01-04 23:12:30,216 - mmcls - INFO - Epoch [63][1100/5005] lr: 1.000e-03, eta: 4:37:49, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8761 2023-01-04 23:12:38,955 - mmcls - INFO - Epoch [63][1200/5005] lr: 1.000e-03, eta: 4:37:40, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8922 2023-01-04 23:12:47,616 - mmcls - INFO - Epoch [63][1300/5005] lr: 1.000e-03, eta: 4:37:31, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8874 2023-01-04 23:12:56,437 - mmcls - INFO - Epoch [63][1400/5005] lr: 1.000e-03, eta: 4:37:22, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8836 2023-01-04 23:13:04,965 - mmcls - INFO - Epoch [63][1500/5005] lr: 1.000e-03, eta: 4:37:13, time: 0.085, data_time: 0.015, memory: 928, loss: 4.9097 2023-01-04 23:13:13,454 - mmcls - INFO - Epoch [63][1600/5005] lr: 1.000e-03, eta: 4:37:04, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8910 2023-01-04 23:13:22,244 - mmcls - INFO - Epoch [63][1700/5005] lr: 1.000e-03, eta: 4:36:55, time: 0.088, data_time: 0.026, memory: 928, loss: 4.8722 2023-01-04 23:13:30,910 - mmcls - INFO - Epoch [63][1800/5005] lr: 1.000e-03, eta: 4:36:46, time: 0.087, data_time: 0.040, memory: 928, loss: 4.8679 2023-01-04 23:13:39,689 - mmcls - INFO - Epoch [63][1900/5005] lr: 1.000e-03, eta: 4:36:38, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8636 2023-01-04 23:13:48,581 - mmcls - INFO - Epoch [63][2000/5005] lr: 1.000e-03, eta: 4:36:29, time: 0.089, data_time: 0.001, memory: 928, loss: 4.9082 2023-01-04 23:13:57,303 - mmcls - INFO - Epoch [63][2100/5005] lr: 1.000e-03, eta: 4:36:20, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8908 2023-01-04 23:14:05,963 - mmcls - INFO - Epoch [63][2200/5005] lr: 1.000e-03, eta: 4:36:11, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8992 2023-01-04 23:14:15,029 - mmcls - INFO - Epoch [63][2300/5005] lr: 1.000e-03, eta: 4:36:02, time: 0.091, data_time: 0.021, memory: 928, loss: 4.9014 2023-01-04 23:14:23,876 - mmcls - INFO - Epoch [63][2400/5005] lr: 1.000e-03, eta: 4:35:54, time: 0.088, data_time: 0.004, memory: 928, loss: 4.9033 2023-01-04 23:14:32,523 - mmcls - INFO - Epoch [63][2500/5005] lr: 1.000e-03, eta: 4:35:45, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8963 2023-01-04 23:14:41,459 - mmcls - INFO - Epoch [63][2600/5005] lr: 1.000e-03, eta: 4:35:36, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8815 2023-01-04 23:14:50,186 - mmcls - INFO - Epoch [63][2700/5005] lr: 1.000e-03, eta: 4:35:27, time: 0.087, data_time: 0.004, memory: 928, loss: 4.9150 2023-01-04 23:14:58,977 - mmcls - INFO - Epoch [63][2800/5005] lr: 1.000e-03, eta: 4:35:18, time: 0.088, data_time: 0.017, memory: 928, loss: 4.9000 2023-01-04 23:15:08,172 - mmcls - INFO - Epoch [63][2900/5005] lr: 1.000e-03, eta: 4:35:10, time: 0.092, data_time: 0.010, memory: 928, loss: 4.8849 2023-01-04 23:15:17,120 - mmcls - INFO - Epoch [63][3000/5005] lr: 1.000e-03, eta: 4:35:01, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8806 2023-01-04 23:15:25,741 - mmcls - INFO - Epoch [63][3100/5005] lr: 1.000e-03, eta: 4:34:52, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8925 2023-01-04 23:15:34,820 - mmcls - INFO - Epoch [63][3200/5005] lr: 1.000e-03, eta: 4:34:43, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8817 2023-01-04 23:15:43,493 - mmcls - INFO - Epoch [63][3300/5005] lr: 1.000e-03, eta: 4:34:34, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8874 2023-01-04 23:15:52,098 - mmcls - INFO - Epoch [63][3400/5005] lr: 1.000e-03, eta: 4:34:25, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8913 2023-01-04 23:16:01,022 - mmcls - INFO - Epoch [63][3500/5005] lr: 1.000e-03, eta: 4:34:17, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8902 2023-01-04 23:16:09,925 - mmcls - INFO - Epoch [63][3600/5005] lr: 1.000e-03, eta: 4:34:08, time: 0.089, data_time: 0.006, memory: 928, loss: 4.9025 2023-01-04 23:16:18,487 - mmcls - INFO - Epoch [63][3700/5005] lr: 1.000e-03, eta: 4:33:59, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8984 2023-01-04 23:16:27,350 - mmcls - INFO - Epoch [63][3800/5005] lr: 1.000e-03, eta: 4:33:50, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8753 2023-01-04 23:16:36,141 - mmcls - INFO - Epoch [63][3900/5005] lr: 1.000e-03, eta: 4:33:41, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8874 2023-01-04 23:16:44,840 - mmcls - INFO - Epoch [63][4000/5005] lr: 1.000e-03, eta: 4:33:32, time: 0.087, data_time: 0.052, memory: 928, loss: 4.9155 2023-01-04 23:16:53,501 - mmcls - INFO - Epoch [63][4100/5005] lr: 1.000e-03, eta: 4:33:24, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8907 2023-01-04 23:17:02,393 - mmcls - INFO - Epoch [63][4200/5005] lr: 1.000e-03, eta: 4:33:15, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8960 2023-01-04 23:17:11,207 - mmcls - INFO - Epoch [63][4300/5005] lr: 1.000e-03, eta: 4:33:06, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8875 2023-01-04 23:17:20,045 - mmcls - INFO - Epoch [63][4400/5005] lr: 1.000e-03, eta: 4:32:57, time: 0.088, data_time: 0.036, memory: 928, loss: 4.8955 2023-01-04 23:17:28,892 - mmcls - INFO - Epoch [63][4500/5005] lr: 1.000e-03, eta: 4:32:48, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8979 2023-01-04 23:17:37,667 - mmcls - INFO - Epoch [63][4600/5005] lr: 1.000e-03, eta: 4:32:40, time: 0.088, data_time: 0.030, memory: 928, loss: 4.8862 2023-01-04 23:17:46,487 - mmcls - INFO - Epoch [63][4700/5005] lr: 1.000e-03, eta: 4:32:31, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8771 2023-01-04 23:17:55,597 - mmcls - INFO - Epoch [63][4800/5005] lr: 1.000e-03, eta: 4:32:22, time: 0.091, data_time: 0.012, memory: 928, loss: 4.8994 2023-01-04 23:18:04,055 - mmcls - INFO - Epoch [63][4900/5005] lr: 1.000e-03, eta: 4:32:13, time: 0.085, data_time: 0.005, memory: 928, loss: 4.9079 2023-01-04 23:18:12,729 - mmcls - INFO - Epoch [63][5000/5005] lr: 1.000e-03, eta: 4:32:04, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8890 2023-01-04 23:18:35,575 - mmcls - INFO - Epoch(val) [63][196] accuracy_top-1: 16.7900, accuracy_top-5: 34.4480 2023-01-04 23:18:46,870 - mmcls - INFO - Epoch [64][100/5005] lr: 1.000e-03, eta: 4:31:56, time: 0.113, data_time: 0.060, memory: 928, loss: 4.8820 2023-01-04 23:18:55,282 - mmcls - INFO - Epoch [64][200/5005] lr: 1.000e-03, eta: 4:31:47, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8840 2023-01-04 23:19:03,757 - mmcls - INFO - Epoch [64][300/5005] lr: 1.000e-03, eta: 4:31:38, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8764 2023-01-04 23:19:12,436 - mmcls - INFO - Epoch [64][400/5005] lr: 1.000e-03, eta: 4:31:29, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8899 2023-01-04 23:19:21,459 - mmcls - INFO - Epoch [64][500/5005] lr: 1.000e-03, eta: 4:31:20, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8375 2023-01-04 23:19:30,108 - mmcls - INFO - Epoch [64][600/5005] lr: 1.000e-03, eta: 4:31:12, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8981 2023-01-04 23:19:38,797 - mmcls - INFO - Epoch [64][700/5005] lr: 1.000e-03, eta: 4:31:03, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8611 2023-01-04 23:19:47,804 - mmcls - INFO - Epoch [64][800/5005] lr: 1.000e-03, eta: 4:30:54, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8695 2023-01-04 23:19:56,623 - mmcls - INFO - Epoch [64][900/5005] lr: 1.000e-03, eta: 4:30:45, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8802 2023-01-04 23:20:05,263 - mmcls - INFO - Epoch [64][1000/5005] lr: 1.000e-03, eta: 4:30:36, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8873 2023-01-04 23:20:14,093 - mmcls - INFO - Epoch [64][1100/5005] lr: 1.000e-03, eta: 4:30:27, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8779 2023-01-04 23:20:22,918 - mmcls - INFO - Epoch [64][1200/5005] lr: 1.000e-03, eta: 4:30:19, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8971 2023-01-04 23:20:31,922 - mmcls - INFO - Epoch [64][1300/5005] lr: 1.000e-03, eta: 4:30:10, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8835 2023-01-04 23:20:40,926 - mmcls - INFO - Epoch [64][1400/5005] lr: 1.000e-03, eta: 4:30:01, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8779 2023-01-04 23:20:49,554 - mmcls - INFO - Epoch [64][1500/5005] lr: 1.000e-03, eta: 4:29:52, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8925 2023-01-04 23:20:58,364 - mmcls - INFO - Epoch [64][1600/5005] lr: 1.000e-03, eta: 4:29:43, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8894 2023-01-04 23:21:06,982 - mmcls - INFO - Epoch [64][1700/5005] lr: 1.000e-03, eta: 4:29:35, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8843 2023-01-04 23:21:15,911 - mmcls - INFO - Epoch [64][1800/5005] lr: 1.000e-03, eta: 4:29:26, time: 0.089, data_time: 0.037, memory: 928, loss: 4.8998 2023-01-04 23:21:24,609 - mmcls - INFO - Epoch [64][1900/5005] lr: 1.000e-03, eta: 4:29:17, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8826 2023-01-04 23:21:33,456 - mmcls - INFO - Epoch [64][2000/5005] lr: 1.000e-03, eta: 4:29:08, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8938 2023-01-04 23:21:42,538 - mmcls - INFO - Epoch [64][2100/5005] lr: 1.000e-03, eta: 4:28:59, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8773 2023-01-04 23:21:51,210 - mmcls - INFO - Epoch [64][2200/5005] lr: 1.000e-03, eta: 4:28:51, time: 0.087, data_time: 0.034, memory: 928, loss: 4.8949 2023-01-04 23:21:59,969 - mmcls - INFO - Epoch [64][2300/5005] lr: 1.000e-03, eta: 4:28:42, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8914 2023-01-04 23:22:08,739 - mmcls - INFO - Epoch [64][2400/5005] lr: 1.000e-03, eta: 4:28:33, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8888 2023-01-04 23:22:17,717 - mmcls - INFO - Epoch [64][2500/5005] lr: 1.000e-03, eta: 4:28:24, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8706 2023-01-04 23:22:26,417 - mmcls - INFO - Epoch [64][2600/5005] lr: 1.000e-03, eta: 4:28:15, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8902 2023-01-04 23:22:35,098 - mmcls - INFO - Epoch [64][2700/5005] lr: 1.000e-03, eta: 4:28:06, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8686 2023-01-04 23:22:43,773 - mmcls - INFO - Epoch [64][2800/5005] lr: 1.000e-03, eta: 4:27:57, time: 0.087, data_time: 0.018, memory: 928, loss: 4.9081 2023-01-04 23:22:52,439 - mmcls - INFO - Epoch [64][2900/5005] lr: 1.000e-03, eta: 4:27:49, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8847 2023-01-04 23:23:01,239 - mmcls - INFO - Epoch [64][3000/5005] lr: 1.000e-03, eta: 4:27:40, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8771 2023-01-04 23:23:09,943 - mmcls - INFO - Epoch [64][3100/5005] lr: 1.000e-03, eta: 4:27:31, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8938 2023-01-04 23:23:18,716 - mmcls - INFO - Epoch [64][3200/5005] lr: 1.000e-03, eta: 4:27:22, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8824 2023-01-04 23:23:27,058 - mmcls - INFO - Epoch [64][3300/5005] lr: 1.000e-03, eta: 4:27:13, time: 0.084, data_time: 0.014, memory: 928, loss: 4.8806 2023-01-04 23:23:35,555 - mmcls - INFO - Epoch [64][3400/5005] lr: 1.000e-03, eta: 4:27:04, time: 0.085, data_time: 0.039, memory: 928, loss: 4.8810 2023-01-04 23:23:44,352 - mmcls - INFO - Epoch [64][3500/5005] lr: 1.000e-03, eta: 4:26:55, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8815 2023-01-04 23:23:52,977 - mmcls - INFO - Epoch [64][3600/5005] lr: 1.000e-03, eta: 4:26:46, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8703 2023-01-04 23:24:01,845 - mmcls - INFO - Epoch [64][3700/5005] lr: 1.000e-03, eta: 4:26:37, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8731 2023-01-04 23:24:10,647 - mmcls - INFO - Epoch [64][3800/5005] lr: 1.000e-03, eta: 4:26:29, time: 0.088, data_time: 0.000, memory: 928, loss: 4.8932 2023-01-04 23:24:19,427 - mmcls - INFO - Epoch [64][3900/5005] lr: 1.000e-03, eta: 4:26:20, time: 0.088, data_time: 0.002, memory: 928, loss: 4.9059 2023-01-04 23:24:28,066 - mmcls - INFO - Epoch [64][4000/5005] lr: 1.000e-03, eta: 4:26:11, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8792 2023-01-04 23:24:36,910 - mmcls - INFO - Epoch [64][4100/5005] lr: 1.000e-03, eta: 4:26:02, time: 0.088, data_time: 0.005, memory: 928, loss: 4.9011 2023-01-04 23:24:45,446 - mmcls - INFO - Epoch [64][4200/5005] lr: 1.000e-03, eta: 4:25:53, time: 0.085, data_time: 0.001, memory: 928, loss: 4.9015 2023-01-04 23:24:54,463 - mmcls - INFO - Epoch [64][4300/5005] lr: 1.000e-03, eta: 4:25:44, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8976 2023-01-04 23:25:03,021 - mmcls - INFO - Epoch [64][4400/5005] lr: 1.000e-03, eta: 4:25:35, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8582 2023-01-04 23:25:11,650 - mmcls - INFO - Epoch [64][4500/5005] lr: 1.000e-03, eta: 4:25:26, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8975 2023-01-04 23:25:20,498 - mmcls - INFO - Epoch [64][4600/5005] lr: 1.000e-03, eta: 4:25:18, time: 0.088, data_time: 0.001, memory: 928, loss: 4.9002 2023-01-04 23:25:29,406 - mmcls - INFO - Epoch [64][4700/5005] lr: 1.000e-03, eta: 4:25:09, time: 0.089, data_time: 0.000, memory: 928, loss: 4.8712 2023-01-04 23:25:38,489 - mmcls - INFO - Epoch [64][4800/5005] lr: 1.000e-03, eta: 4:25:00, time: 0.091, data_time: 0.013, memory: 928, loss: 4.8980 2023-01-04 23:25:47,042 - mmcls - INFO - Epoch [64][4900/5005] lr: 1.000e-03, eta: 4:24:51, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8995 2023-01-04 23:25:55,870 - mmcls - INFO - Epoch [64][5000/5005] lr: 1.000e-03, eta: 4:24:42, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8918 2023-01-04 23:26:18,886 - mmcls - INFO - Epoch(val) [64][196] accuracy_top-1: 16.8880, accuracy_top-5: 34.5660 2023-01-04 23:26:29,645 - mmcls - INFO - Epoch [65][100/5005] lr: 1.000e-03, eta: 4:24:34, time: 0.107, data_time: 0.038, memory: 928, loss: 4.8802 2023-01-04 23:26:38,314 - mmcls - INFO - Epoch [65][200/5005] lr: 1.000e-03, eta: 4:24:25, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8793 2023-01-04 23:26:47,326 - mmcls - INFO - Epoch [65][300/5005] lr: 1.000e-03, eta: 4:24:16, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8770 2023-01-04 23:26:56,077 - mmcls - INFO - Epoch [65][400/5005] lr: 1.000e-03, eta: 4:24:08, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8568 2023-01-04 23:27:05,076 - mmcls - INFO - Epoch [65][500/5005] lr: 1.000e-03, eta: 4:23:59, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8777 2023-01-04 23:27:13,794 - mmcls - INFO - Epoch [65][600/5005] lr: 1.000e-03, eta: 4:23:50, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8719 2023-01-04 23:27:22,491 - mmcls - INFO - Epoch [65][700/5005] lr: 1.000e-03, eta: 4:23:41, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8883 2023-01-04 23:27:31,262 - mmcls - INFO - Epoch [65][800/5005] lr: 1.000e-03, eta: 4:23:32, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8907 2023-01-04 23:27:39,952 - mmcls - INFO - Epoch [65][900/5005] lr: 1.000e-03, eta: 4:23:23, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8764 2023-01-04 23:27:48,541 - mmcls - INFO - Epoch [65][1000/5005] lr: 1.000e-03, eta: 4:23:14, time: 0.086, data_time: 0.001, memory: 928, loss: 4.9004 2023-01-04 23:27:57,398 - mmcls - INFO - Epoch [65][1100/5005] lr: 1.000e-03, eta: 4:23:06, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8753 2023-01-04 23:28:05,906 - mmcls - INFO - Epoch [65][1200/5005] lr: 1.000e-03, eta: 4:22:57, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8878 2023-01-04 23:28:14,551 - mmcls - INFO - Epoch [65][1300/5005] lr: 1.000e-03, eta: 4:22:48, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8942 2023-01-04 23:28:23,196 - mmcls - INFO - Epoch [65][1400/5005] lr: 1.000e-03, eta: 4:22:39, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8966 2023-01-04 23:28:31,713 - mmcls - INFO - Epoch [65][1500/5005] lr: 1.000e-03, eta: 4:22:30, time: 0.085, data_time: 0.016, memory: 928, loss: 4.8843 2023-01-04 23:28:40,654 - mmcls - INFO - Epoch [65][1600/5005] lr: 1.000e-03, eta: 4:22:21, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8917 2023-01-04 23:28:49,353 - mmcls - INFO - Epoch [65][1700/5005] lr: 1.000e-03, eta: 4:22:12, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8737 2023-01-04 23:28:57,960 - mmcls - INFO - Epoch [65][1800/5005] lr: 1.000e-03, eta: 4:22:03, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8759 2023-01-04 23:29:06,884 - mmcls - INFO - Epoch [65][1900/5005] lr: 1.000e-03, eta: 4:21:55, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9071 2023-01-04 23:29:15,705 - mmcls - INFO - Epoch [65][2000/5005] lr: 1.000e-03, eta: 4:21:46, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8794 2023-01-04 23:29:24,856 - mmcls - INFO - Epoch [65][2100/5005] lr: 1.000e-03, eta: 4:21:37, time: 0.092, data_time: 0.002, memory: 928, loss: 4.8597 2023-01-04 23:29:33,348 - mmcls - INFO - Epoch [65][2200/5005] lr: 1.000e-03, eta: 4:21:28, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8604 2023-01-04 23:29:41,963 - mmcls - INFO - Epoch [65][2300/5005] lr: 1.000e-03, eta: 4:21:19, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8828 2023-01-04 23:29:50,852 - mmcls - INFO - Epoch [65][2400/5005] lr: 1.000e-03, eta: 4:21:10, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8934 2023-01-04 23:29:59,569 - mmcls - INFO - Epoch [65][2500/5005] lr: 1.000e-03, eta: 4:21:02, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8687 2023-01-04 23:30:08,514 - mmcls - INFO - Epoch [65][2600/5005] lr: 1.000e-03, eta: 4:20:53, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8959 2023-01-04 23:30:16,994 - mmcls - INFO - Epoch [65][2700/5005] lr: 1.000e-03, eta: 4:20:44, time: 0.085, data_time: 0.018, memory: 928, loss: 4.8872 2023-01-04 23:30:25,773 - mmcls - INFO - Epoch [65][2800/5005] lr: 1.000e-03, eta: 4:20:35, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8804 2023-01-04 23:30:34,417 - mmcls - INFO - Epoch [65][2900/5005] lr: 1.000e-03, eta: 4:20:26, time: 0.086, data_time: 0.025, memory: 928, loss: 4.9052 2023-01-04 23:30:42,944 - mmcls - INFO - Epoch [65][3000/5005] lr: 1.000e-03, eta: 4:20:17, time: 0.085, data_time: 0.014, memory: 928, loss: 4.8638 2023-01-04 23:30:51,995 - mmcls - INFO - Epoch [65][3100/5005] lr: 1.000e-03, eta: 4:20:08, time: 0.091, data_time: 0.035, memory: 928, loss: 4.9013 2023-01-04 23:31:00,748 - mmcls - INFO - Epoch [65][3200/5005] lr: 1.000e-03, eta: 4:20:00, time: 0.087, data_time: 0.001, memory: 928, loss: 4.9178 2023-01-04 23:31:09,687 - mmcls - INFO - Epoch [65][3300/5005] lr: 1.000e-03, eta: 4:19:51, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8575 2023-01-04 23:31:18,257 - mmcls - INFO - Epoch [65][3400/5005] lr: 1.000e-03, eta: 4:19:42, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8720 2023-01-04 23:31:26,936 - mmcls - INFO - Epoch [65][3500/5005] lr: 1.000e-03, eta: 4:19:33, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8856 2023-01-04 23:31:35,682 - mmcls - INFO - Epoch [65][3600/5005] lr: 1.000e-03, eta: 4:19:24, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8886 2023-01-04 23:31:44,478 - mmcls - INFO - Epoch [65][3700/5005] lr: 1.000e-03, eta: 4:19:15, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8651 2023-01-04 23:31:53,266 - mmcls - INFO - Epoch [65][3800/5005] lr: 1.000e-03, eta: 4:19:06, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8934 2023-01-04 23:32:02,047 - mmcls - INFO - Epoch [65][3900/5005] lr: 1.000e-03, eta: 4:18:58, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8680 2023-01-04 23:32:10,707 - mmcls - INFO - Epoch [65][4000/5005] lr: 1.000e-03, eta: 4:18:49, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8643 2023-01-04 23:32:19,718 - mmcls - INFO - Epoch [65][4100/5005] lr: 1.000e-03, eta: 4:18:40, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8962 2023-01-04 23:32:28,406 - mmcls - INFO - Epoch [65][4200/5005] lr: 1.000e-03, eta: 4:18:31, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8790 2023-01-04 23:32:37,150 - mmcls - INFO - Epoch [65][4300/5005] lr: 1.000e-03, eta: 4:18:22, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8984 2023-01-04 23:32:45,694 - mmcls - INFO - Epoch [65][4400/5005] lr: 1.000e-03, eta: 4:18:13, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8740 2023-01-04 23:32:54,333 - mmcls - INFO - Epoch [65][4500/5005] lr: 1.000e-03, eta: 4:18:04, time: 0.086, data_time: 0.013, memory: 928, loss: 4.8624 2023-01-04 23:33:03,153 - mmcls - INFO - Epoch [65][4600/5005] lr: 1.000e-03, eta: 4:17:56, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8888 2023-01-04 23:33:12,209 - mmcls - INFO - Epoch [65][4700/5005] lr: 1.000e-03, eta: 4:17:47, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8910 2023-01-04 23:33:20,859 - mmcls - INFO - Epoch [65][4800/5005] lr: 1.000e-03, eta: 4:17:38, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8707 2023-01-04 23:33:29,749 - mmcls - INFO - Epoch [65][4900/5005] lr: 1.000e-03, eta: 4:17:29, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8810 2023-01-04 23:33:39,091 - mmcls - INFO - Epoch [65][5000/5005] lr: 1.000e-03, eta: 4:17:21, time: 0.093, data_time: 0.001, memory: 928, loss: 4.9083 2023-01-04 23:34:01,824 - mmcls - INFO - Epoch(val) [65][196] accuracy_top-1: 17.4340, accuracy_top-5: 35.4260 2023-01-04 23:34:12,738 - mmcls - INFO - Epoch [66][100/5005] lr: 1.000e-03, eta: 4:17:12, time: 0.109, data_time: 0.044, memory: 928, loss: 4.8719 2023-01-04 23:34:21,293 - mmcls - INFO - Epoch [66][200/5005] lr: 1.000e-03, eta: 4:17:03, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8626 2023-01-04 23:34:30,296 - mmcls - INFO - Epoch [66][300/5005] lr: 1.000e-03, eta: 4:16:55, time: 0.090, data_time: 0.010, memory: 928, loss: 4.8619 2023-01-04 23:34:38,681 - mmcls - INFO - Epoch [66][400/5005] lr: 1.000e-03, eta: 4:16:46, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8595 2023-01-04 23:34:47,249 - mmcls - INFO - Epoch [66][500/5005] lr: 1.000e-03, eta: 4:16:37, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8764 2023-01-04 23:34:56,521 - mmcls - INFO - Epoch [66][600/5005] lr: 1.000e-03, eta: 4:16:28, time: 0.093, data_time: 0.010, memory: 928, loss: 4.8638 2023-01-04 23:35:05,277 - mmcls - INFO - Epoch [66][700/5005] lr: 1.000e-03, eta: 4:16:19, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8969 2023-01-04 23:35:13,992 - mmcls - INFO - Epoch [66][800/5005] lr: 1.000e-03, eta: 4:16:10, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8657 2023-01-04 23:35:22,820 - mmcls - INFO - Epoch [66][900/5005] lr: 1.000e-03, eta: 4:16:02, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8925 2023-01-04 23:35:31,181 - mmcls - INFO - Epoch [66][1000/5005] lr: 1.000e-03, eta: 4:15:53, time: 0.083, data_time: 0.008, memory: 928, loss: 4.8843 2023-01-04 23:35:39,947 - mmcls - INFO - Epoch [66][1100/5005] lr: 1.000e-03, eta: 4:15:44, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8858 2023-01-04 23:35:48,760 - mmcls - INFO - Epoch [66][1200/5005] lr: 1.000e-03, eta: 4:15:35, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8557 2023-01-04 23:35:57,382 - mmcls - INFO - Epoch [66][1300/5005] lr: 1.000e-03, eta: 4:15:26, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8619 2023-01-04 23:36:06,102 - mmcls - INFO - Epoch [66][1400/5005] lr: 1.000e-03, eta: 4:15:17, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8702 2023-01-04 23:36:15,183 - mmcls - INFO - Epoch [66][1500/5005] lr: 1.000e-03, eta: 4:15:08, time: 0.091, data_time: 0.015, memory: 928, loss: 4.8633 2023-01-04 23:36:24,083 - mmcls - INFO - Epoch [66][1600/5005] lr: 1.000e-03, eta: 4:15:00, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8855 2023-01-04 23:36:32,757 - mmcls - INFO - Epoch [66][1700/5005] lr: 1.000e-03, eta: 4:14:51, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8785 2023-01-04 23:36:41,621 - mmcls - INFO - Epoch [66][1800/5005] lr: 1.000e-03, eta: 4:14:42, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8684 2023-01-04 23:36:50,329 - mmcls - INFO - Epoch [66][1900/5005] lr: 1.000e-03, eta: 4:14:33, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8824 2023-01-04 23:36:59,149 - mmcls - INFO - Epoch [66][2000/5005] lr: 1.000e-03, eta: 4:14:24, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8916 2023-01-04 23:37:07,857 - mmcls - INFO - Epoch [66][2100/5005] lr: 1.000e-03, eta: 4:14:15, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8749 2023-01-04 23:37:16,616 - mmcls - INFO - Epoch [66][2200/5005] lr: 1.000e-03, eta: 4:14:07, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8810 2023-01-04 23:37:25,198 - mmcls - INFO - Epoch [66][2300/5005] lr: 1.000e-03, eta: 4:13:58, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8822 2023-01-04 23:37:33,857 - mmcls - INFO - Epoch [66][2400/5005] lr: 1.000e-03, eta: 4:13:49, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8722 2023-01-04 23:37:42,514 - mmcls - INFO - Epoch [66][2500/5005] lr: 1.000e-03, eta: 4:13:40, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8779 2023-01-04 23:37:51,169 - mmcls - INFO - Epoch [66][2600/5005] lr: 1.000e-03, eta: 4:13:31, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8940 2023-01-04 23:38:00,054 - mmcls - INFO - Epoch [66][2700/5005] lr: 1.000e-03, eta: 4:13:22, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8833 2023-01-04 23:38:08,893 - mmcls - INFO - Epoch [66][2800/5005] lr: 1.000e-03, eta: 4:13:13, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8818 2023-01-04 23:38:17,578 - mmcls - INFO - Epoch [66][2900/5005] lr: 1.000e-03, eta: 4:13:04, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8931 2023-01-04 23:38:26,273 - mmcls - INFO - Epoch [66][3000/5005] lr: 1.000e-03, eta: 4:12:56, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8605 2023-01-04 23:38:35,145 - mmcls - INFO - Epoch [66][3100/5005] lr: 1.000e-03, eta: 4:12:47, time: 0.089, data_time: 0.004, memory: 928, loss: 4.9019 2023-01-04 23:38:43,777 - mmcls - INFO - Epoch [66][3200/5005] lr: 1.000e-03, eta: 4:12:38, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8786 2023-01-04 23:38:52,430 - mmcls - INFO - Epoch [66][3300/5005] lr: 1.000e-03, eta: 4:12:29, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8830 2023-01-04 23:39:01,089 - mmcls - INFO - Epoch [66][3400/5005] lr: 1.000e-03, eta: 4:12:20, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8768 2023-01-04 23:39:10,060 - mmcls - INFO - Epoch [66][3500/5005] lr: 1.000e-03, eta: 4:12:11, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8914 2023-01-04 23:39:18,922 - mmcls - INFO - Epoch [66][3600/5005] lr: 1.000e-03, eta: 4:12:03, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8860 2023-01-04 23:39:27,410 - mmcls - INFO - Epoch [66][3700/5005] lr: 1.000e-03, eta: 4:11:54, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8650 2023-01-04 23:39:36,420 - mmcls - INFO - Epoch [66][3800/5005] lr: 1.000e-03, eta: 4:11:45, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8998 2023-01-04 23:39:45,278 - mmcls - INFO - Epoch [66][3900/5005] lr: 1.000e-03, eta: 4:11:36, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8663 2023-01-04 23:39:54,178 - mmcls - INFO - Epoch [66][4000/5005] lr: 1.000e-03, eta: 4:11:27, time: 0.089, data_time: 0.004, memory: 928, loss: 4.9098 2023-01-04 23:40:02,751 - mmcls - INFO - Epoch [66][4100/5005] lr: 1.000e-03, eta: 4:11:18, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8860 2023-01-04 23:40:11,862 - mmcls - INFO - Epoch [66][4200/5005] lr: 1.000e-03, eta: 4:11:10, time: 0.091, data_time: 0.004, memory: 928, loss: 4.8639 2023-01-04 23:40:20,646 - mmcls - INFO - Epoch [66][4300/5005] lr: 1.000e-03, eta: 4:11:01, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8634 2023-01-04 23:40:29,490 - mmcls - INFO - Epoch [66][4400/5005] lr: 1.000e-03, eta: 4:10:52, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8696 2023-01-04 23:40:38,678 - mmcls - INFO - Epoch [66][4500/5005] lr: 1.000e-03, eta: 4:10:43, time: 0.092, data_time: 0.014, memory: 928, loss: 4.8733 2023-01-04 23:40:47,783 - mmcls - INFO - Epoch [66][4600/5005] lr: 1.000e-03, eta: 4:10:35, time: 0.091, data_time: 0.018, memory: 928, loss: 4.8837 2023-01-04 23:40:56,307 - mmcls - INFO - Epoch [66][4700/5005] lr: 1.000e-03, eta: 4:10:26, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8904 2023-01-04 23:41:05,031 - mmcls - INFO - Epoch [66][4800/5005] lr: 1.000e-03, eta: 4:10:17, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8559 2023-01-04 23:41:13,939 - mmcls - INFO - Epoch [66][4900/5005] lr: 1.000e-03, eta: 4:10:08, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8859 2023-01-04 23:41:22,699 - mmcls - INFO - Epoch [66][5000/5005] lr: 1.000e-03, eta: 4:09:59, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8721 2023-01-04 23:41:45,506 - mmcls - INFO - Epoch(val) [66][196] accuracy_top-1: 17.7020, accuracy_top-5: 35.9180 2023-01-04 23:41:56,346 - mmcls - INFO - Epoch [67][100/5005] lr: 1.000e-03, eta: 4:09:51, time: 0.108, data_time: 0.059, memory: 928, loss: 4.8816 2023-01-04 23:42:05,168 - mmcls - INFO - Epoch [67][200/5005] lr: 1.000e-03, eta: 4:09:42, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8948 2023-01-04 23:42:13,874 - mmcls - INFO - Epoch [67][300/5005] lr: 1.000e-03, eta: 4:09:33, time: 0.087, data_time: 0.030, memory: 928, loss: 4.8563 2023-01-04 23:42:22,765 - mmcls - INFO - Epoch [67][400/5005] lr: 1.000e-03, eta: 4:09:24, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8880 2023-01-04 23:42:31,323 - mmcls - INFO - Epoch [67][500/5005] lr: 1.000e-03, eta: 4:09:16, time: 0.085, data_time: 0.030, memory: 928, loss: 4.8686 2023-01-04 23:42:40,109 - mmcls - INFO - Epoch [67][600/5005] lr: 1.000e-03, eta: 4:09:07, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8639 2023-01-04 23:42:48,867 - mmcls - INFO - Epoch [67][700/5005] lr: 1.000e-03, eta: 4:08:58, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8596 2023-01-04 23:42:57,669 - mmcls - INFO - Epoch [67][800/5005] lr: 1.000e-03, eta: 4:08:49, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8882 2023-01-04 23:43:06,355 - mmcls - INFO - Epoch [67][900/5005] lr: 1.000e-03, eta: 4:08:40, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8748 2023-01-04 23:43:14,985 - mmcls - INFO - Epoch [67][1000/5005] lr: 1.000e-03, eta: 4:08:31, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8598 2023-01-04 23:43:24,135 - mmcls - INFO - Epoch [67][1100/5005] lr: 1.000e-03, eta: 4:08:23, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8664 2023-01-04 23:43:33,471 - mmcls - INFO - Epoch [67][1200/5005] lr: 1.000e-03, eta: 4:08:14, time: 0.093, data_time: 0.005, memory: 928, loss: 4.8786 2023-01-04 23:43:41,946 - mmcls - INFO - Epoch [67][1300/5005] lr: 1.000e-03, eta: 4:08:05, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8753 2023-01-04 23:43:50,973 - mmcls - INFO - Epoch [67][1400/5005] lr: 1.000e-03, eta: 4:07:56, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8917 2023-01-04 23:43:59,489 - mmcls - INFO - Epoch [67][1500/5005] lr: 1.000e-03, eta: 4:07:47, time: 0.085, data_time: 0.019, memory: 928, loss: 4.8910 2023-01-04 23:44:08,337 - mmcls - INFO - Epoch [67][1600/5005] lr: 1.000e-03, eta: 4:07:39, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8889 2023-01-04 23:44:16,907 - mmcls - INFO - Epoch [67][1700/5005] lr: 1.000e-03, eta: 4:07:30, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8941 2023-01-04 23:44:25,353 - mmcls - INFO - Epoch [67][1800/5005] lr: 1.000e-03, eta: 4:07:21, time: 0.084, data_time: 0.003, memory: 928, loss: 4.8872 2023-01-04 23:44:34,319 - mmcls - INFO - Epoch [67][1900/5005] lr: 1.000e-03, eta: 4:07:12, time: 0.090, data_time: 0.016, memory: 928, loss: 4.8920 2023-01-04 23:44:42,949 - mmcls - INFO - Epoch [67][2000/5005] lr: 1.000e-03, eta: 4:07:03, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8658 2023-01-04 23:44:51,755 - mmcls - INFO - Epoch [67][2100/5005] lr: 1.000e-03, eta: 4:06:54, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8720 2023-01-04 23:45:00,416 - mmcls - INFO - Epoch [67][2200/5005] lr: 1.000e-03, eta: 4:06:45, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8641 2023-01-04 23:45:09,122 - mmcls - INFO - Epoch [67][2300/5005] lr: 1.000e-03, eta: 4:06:36, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8848 2023-01-04 23:45:17,658 - mmcls - INFO - Epoch [67][2400/5005] lr: 1.000e-03, eta: 4:06:27, time: 0.085, data_time: 0.024, memory: 928, loss: 4.8987 2023-01-04 23:45:26,817 - mmcls - INFO - Epoch [67][2500/5005] lr: 1.000e-03, eta: 4:06:19, time: 0.092, data_time: 0.031, memory: 928, loss: 4.8885 2023-01-04 23:45:35,767 - mmcls - INFO - Epoch [67][2600/5005] lr: 1.000e-03, eta: 4:06:10, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8962 2023-01-04 23:45:44,384 - mmcls - INFO - Epoch [67][2700/5005] lr: 1.000e-03, eta: 4:06:01, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8777 2023-01-04 23:45:53,327 - mmcls - INFO - Epoch [67][2800/5005] lr: 1.000e-03, eta: 4:05:52, time: 0.090, data_time: 0.008, memory: 928, loss: 4.8684 2023-01-04 23:46:02,189 - mmcls - INFO - Epoch [67][2900/5005] lr: 1.000e-03, eta: 4:05:44, time: 0.089, data_time: 0.001, memory: 928, loss: 4.9032 2023-01-04 23:46:11,045 - mmcls - INFO - Epoch [67][3000/5005] lr: 1.000e-03, eta: 4:05:35, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8638 2023-01-04 23:46:19,861 - mmcls - INFO - Epoch [67][3100/5005] lr: 1.000e-03, eta: 4:05:26, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8620 2023-01-04 23:46:28,480 - mmcls - INFO - Epoch [67][3200/5005] lr: 1.000e-03, eta: 4:05:17, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8483 2023-01-04 23:46:37,307 - mmcls - INFO - Epoch [67][3300/5005] lr: 1.000e-03, eta: 4:05:08, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8887 2023-01-04 23:46:46,156 - mmcls - INFO - Epoch [67][3400/5005] lr: 1.000e-03, eta: 4:04:59, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8667 2023-01-04 23:46:55,042 - mmcls - INFO - Epoch [67][3500/5005] lr: 1.000e-03, eta: 4:04:51, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8884 2023-01-04 23:47:03,861 - mmcls - INFO - Epoch [67][3600/5005] lr: 1.000e-03, eta: 4:04:42, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8715 2023-01-04 23:47:12,706 - mmcls - INFO - Epoch [67][3700/5005] lr: 1.000e-03, eta: 4:04:33, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8789 2023-01-04 23:47:21,368 - mmcls - INFO - Epoch [67][3800/5005] lr: 1.000e-03, eta: 4:04:24, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8784 2023-01-04 23:47:30,105 - mmcls - INFO - Epoch [67][3900/5005] lr: 1.000e-03, eta: 4:04:15, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8617 2023-01-04 23:47:39,387 - mmcls - INFO - Epoch [67][4000/5005] lr: 1.000e-03, eta: 4:04:07, time: 0.093, data_time: 0.008, memory: 928, loss: 4.8880 2023-01-04 23:47:48,280 - mmcls - INFO - Epoch [67][4100/5005] lr: 1.000e-03, eta: 4:03:58, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8820 2023-01-04 23:47:57,419 - mmcls - INFO - Epoch [67][4200/5005] lr: 1.000e-03, eta: 4:03:49, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8721 2023-01-04 23:48:05,945 - mmcls - INFO - Epoch [67][4300/5005] lr: 1.000e-03, eta: 4:03:40, time: 0.085, data_time: 0.012, memory: 928, loss: 4.8834 2023-01-04 23:48:14,474 - mmcls - INFO - Epoch [67][4400/5005] lr: 1.000e-03, eta: 4:03:31, time: 0.085, data_time: 0.001, memory: 928, loss: 4.8897 2023-01-04 23:48:23,418 - mmcls - INFO - Epoch [67][4500/5005] lr: 1.000e-03, eta: 4:03:23, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8939 2023-01-04 23:48:32,531 - mmcls - INFO - Epoch [67][4600/5005] lr: 1.000e-03, eta: 4:03:14, time: 0.091, data_time: 0.022, memory: 928, loss: 4.8811 2023-01-04 23:48:41,242 - mmcls - INFO - Epoch [67][4700/5005] lr: 1.000e-03, eta: 4:03:05, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8667 2023-01-04 23:48:50,206 - mmcls - INFO - Epoch [67][4800/5005] lr: 1.000e-03, eta: 4:02:56, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8631 2023-01-04 23:48:58,857 - mmcls - INFO - Epoch [67][4900/5005] lr: 1.000e-03, eta: 4:02:47, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8960 2023-01-04 23:49:07,379 - mmcls - INFO - Epoch [67][5000/5005] lr: 1.000e-03, eta: 4:02:39, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8581 2023-01-04 23:49:30,058 - mmcls - INFO - Epoch(val) [67][196] accuracy_top-1: 17.0160, accuracy_top-5: 34.7920 2023-01-04 23:49:41,210 - mmcls - INFO - Epoch [68][100/5005] lr: 1.000e-03, eta: 4:02:30, time: 0.111, data_time: 0.032, memory: 928, loss: 4.8622 2023-01-04 23:49:49,936 - mmcls - INFO - Epoch [68][200/5005] lr: 1.000e-03, eta: 4:02:21, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8635 2023-01-04 23:49:58,705 - mmcls - INFO - Epoch [68][300/5005] lr: 1.000e-03, eta: 4:02:12, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8769 2023-01-04 23:50:07,290 - mmcls - INFO - Epoch [68][400/5005] lr: 1.000e-03, eta: 4:02:04, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8922 2023-01-04 23:50:16,427 - mmcls - INFO - Epoch [68][500/5005] lr: 1.000e-03, eta: 4:01:55, time: 0.091, data_time: 0.028, memory: 928, loss: 4.8619 2023-01-04 23:50:25,106 - mmcls - INFO - Epoch [68][600/5005] lr: 1.000e-03, eta: 4:01:46, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8714 2023-01-04 23:50:33,940 - mmcls - INFO - Epoch [68][700/5005] lr: 1.000e-03, eta: 4:01:37, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8811 2023-01-04 23:50:42,712 - mmcls - INFO - Epoch [68][800/5005] lr: 1.000e-03, eta: 4:01:28, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8763 2023-01-04 23:50:51,443 - mmcls - INFO - Epoch [68][900/5005] lr: 1.000e-03, eta: 4:01:20, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8823 2023-01-04 23:51:00,214 - mmcls - INFO - Epoch [68][1000/5005] lr: 1.000e-03, eta: 4:01:11, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8799 2023-01-04 23:51:09,010 - mmcls - INFO - Epoch [68][1100/5005] lr: 1.000e-03, eta: 4:01:02, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8813 2023-01-04 23:51:17,728 - mmcls - INFO - Epoch [68][1200/5005] lr: 1.000e-03, eta: 4:00:53, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8923 2023-01-04 23:51:26,446 - mmcls - INFO - Epoch [68][1300/5005] lr: 1.000e-03, eta: 4:00:44, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8537 2023-01-04 23:51:35,344 - mmcls - INFO - Epoch [68][1400/5005] lr: 1.000e-03, eta: 4:00:35, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8500 2023-01-04 23:51:44,174 - mmcls - INFO - Epoch [68][1500/5005] lr: 1.000e-03, eta: 4:00:27, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8816 2023-01-04 23:51:52,951 - mmcls - INFO - Epoch [68][1600/5005] lr: 1.000e-03, eta: 4:00:18, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8681 2023-01-04 23:52:02,093 - mmcls - INFO - Epoch [68][1700/5005] lr: 1.000e-03, eta: 4:00:09, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8773 2023-01-04 23:52:10,774 - mmcls - INFO - Epoch [68][1800/5005] lr: 1.000e-03, eta: 4:00:00, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8730 2023-01-04 23:52:19,276 - mmcls - INFO - Epoch [68][1900/5005] lr: 1.000e-03, eta: 3:59:51, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8962 2023-01-04 23:52:28,587 - mmcls - INFO - Epoch [68][2000/5005] lr: 1.000e-03, eta: 3:59:43, time: 0.093, data_time: 0.015, memory: 928, loss: 4.8722 2023-01-04 23:52:37,819 - mmcls - INFO - Epoch [68][2100/5005] lr: 1.000e-03, eta: 3:59:34, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8861 2023-01-04 23:52:46,468 - mmcls - INFO - Epoch [68][2200/5005] lr: 1.000e-03, eta: 3:59:25, time: 0.086, data_time: 0.029, memory: 928, loss: 4.8775 2023-01-04 23:52:55,131 - mmcls - INFO - Epoch [68][2300/5005] lr: 1.000e-03, eta: 3:59:16, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8888 2023-01-04 23:53:04,126 - mmcls - INFO - Epoch [68][2400/5005] lr: 1.000e-03, eta: 3:59:08, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8787 2023-01-04 23:53:12,679 - mmcls - INFO - Epoch [68][2500/5005] lr: 1.000e-03, eta: 3:58:59, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8574 2023-01-04 23:53:21,310 - mmcls - INFO - Epoch [68][2600/5005] lr: 1.000e-03, eta: 3:58:50, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8665 2023-01-04 23:53:30,154 - mmcls - INFO - Epoch [68][2700/5005] lr: 1.000e-03, eta: 3:58:41, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8714 2023-01-04 23:53:38,599 - mmcls - INFO - Epoch [68][2800/5005] lr: 1.000e-03, eta: 3:58:32, time: 0.084, data_time: 0.007, memory: 928, loss: 4.8776 2023-01-04 23:53:47,360 - mmcls - INFO - Epoch [68][2900/5005] lr: 1.000e-03, eta: 3:58:23, time: 0.088, data_time: 0.005, memory: 928, loss: 4.9054 2023-01-04 23:53:56,108 - mmcls - INFO - Epoch [68][3000/5005] lr: 1.000e-03, eta: 3:58:14, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8674 2023-01-04 23:54:05,001 - mmcls - INFO - Epoch [68][3100/5005] lr: 1.000e-03, eta: 3:58:05, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8663 2023-01-04 23:54:13,696 - mmcls - INFO - Epoch [68][3200/5005] lr: 1.000e-03, eta: 3:57:57, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8473 2023-01-04 23:54:22,486 - mmcls - INFO - Epoch [68][3300/5005] lr: 1.000e-03, eta: 3:57:48, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8792 2023-01-04 23:54:31,398 - mmcls - INFO - Epoch [68][3400/5005] lr: 1.000e-03, eta: 3:57:39, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8593 2023-01-04 23:54:40,000 - mmcls - INFO - Epoch [68][3500/5005] lr: 1.000e-03, eta: 3:57:30, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8625 2023-01-04 23:54:48,645 - mmcls - INFO - Epoch [68][3600/5005] lr: 1.000e-03, eta: 3:57:21, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8958 2023-01-04 23:54:57,245 - mmcls - INFO - Epoch [68][3700/5005] lr: 1.000e-03, eta: 3:57:12, time: 0.086, data_time: 0.016, memory: 928, loss: 4.8881 2023-01-04 23:55:05,844 - mmcls - INFO - Epoch [68][3800/5005] lr: 1.000e-03, eta: 3:57:03, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8873 2023-01-04 23:55:14,881 - mmcls - INFO - Epoch [68][3900/5005] lr: 1.000e-03, eta: 3:56:55, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8843 2023-01-04 23:55:23,500 - mmcls - INFO - Epoch [68][4000/5005] lr: 1.000e-03, eta: 3:56:46, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8890 2023-01-04 23:55:32,287 - mmcls - INFO - Epoch [68][4100/5005] lr: 1.000e-03, eta: 3:56:37, time: 0.088, data_time: 0.030, memory: 928, loss: 4.8805 2023-01-04 23:55:40,880 - mmcls - INFO - Epoch [68][4200/5005] lr: 1.000e-03, eta: 3:56:28, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8844 2023-01-04 23:55:49,411 - mmcls - INFO - Epoch [68][4300/5005] lr: 1.000e-03, eta: 3:56:19, time: 0.085, data_time: 0.024, memory: 928, loss: 4.8895 2023-01-04 23:55:58,194 - mmcls - INFO - Epoch [68][4400/5005] lr: 1.000e-03, eta: 3:56:10, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8899 2023-01-04 23:56:07,164 - mmcls - INFO - Epoch [68][4500/5005] lr: 1.000e-03, eta: 3:56:01, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8638 2023-01-04 23:56:15,691 - mmcls - INFO - Epoch [68][4600/5005] lr: 1.000e-03, eta: 3:55:52, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8600 2023-01-04 23:56:24,460 - mmcls - INFO - Epoch [68][4700/5005] lr: 1.000e-03, eta: 3:55:44, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8878 2023-01-04 23:56:33,249 - mmcls - INFO - Epoch [68][4800/5005] lr: 1.000e-03, eta: 3:55:35, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8801 2023-01-04 23:56:42,020 - mmcls - INFO - Epoch [68][4900/5005] lr: 1.000e-03, eta: 3:55:26, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8783 2023-01-04 23:56:51,069 - mmcls - INFO - Epoch [68][5000/5005] lr: 1.000e-03, eta: 3:55:17, time: 0.091, data_time: 0.008, memory: 928, loss: 4.8874 2023-01-04 23:57:13,756 - mmcls - INFO - Epoch(val) [68][196] accuracy_top-1: 16.9000, accuracy_top-5: 34.5600 2023-01-04 23:57:25,022 - mmcls - INFO - Epoch [69][100/5005] lr: 1.000e-03, eta: 3:55:09, time: 0.112, data_time: 0.041, memory: 928, loss: 4.8800 2023-01-04 23:57:33,656 - mmcls - INFO - Epoch [69][200/5005] lr: 1.000e-03, eta: 3:55:00, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8672 2023-01-04 23:57:42,348 - mmcls - INFO - Epoch [69][300/5005] lr: 1.000e-03, eta: 3:54:51, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8708 2023-01-04 23:57:51,243 - mmcls - INFO - Epoch [69][400/5005] lr: 1.000e-03, eta: 3:54:42, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8780 2023-01-04 23:58:00,122 - mmcls - INFO - Epoch [69][500/5005] lr: 1.000e-03, eta: 3:54:34, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8509 2023-01-04 23:58:08,486 - mmcls - INFO - Epoch [69][600/5005] lr: 1.000e-03, eta: 3:54:25, time: 0.084, data_time: 0.014, memory: 928, loss: 4.8737 2023-01-04 23:58:17,205 - mmcls - INFO - Epoch [69][700/5005] lr: 1.000e-03, eta: 3:54:16, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8714 2023-01-04 23:58:25,876 - mmcls - INFO - Epoch [69][800/5005] lr: 1.000e-03, eta: 3:54:07, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8936 2023-01-04 23:58:34,593 - mmcls - INFO - Epoch [69][900/5005] lr: 1.000e-03, eta: 3:53:58, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8604 2023-01-04 23:58:43,959 - mmcls - INFO - Epoch [69][1000/5005] lr: 1.000e-03, eta: 3:53:49, time: 0.094, data_time: 0.016, memory: 928, loss: 4.8431 2023-01-04 23:58:52,753 - mmcls - INFO - Epoch [69][1100/5005] lr: 1.000e-03, eta: 3:53:41, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8885 2023-01-04 23:59:01,692 - mmcls - INFO - Epoch [69][1200/5005] lr: 1.000e-03, eta: 3:53:32, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8380 2023-01-04 23:59:10,304 - mmcls - INFO - Epoch [69][1300/5005] lr: 1.000e-03, eta: 3:53:23, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8660 2023-01-04 23:59:18,763 - mmcls - INFO - Epoch [69][1400/5005] lr: 1.000e-03, eta: 3:53:14, time: 0.085, data_time: 0.017, memory: 928, loss: 4.8692 2023-01-04 23:59:27,413 - mmcls - INFO - Epoch [69][1500/5005] lr: 1.000e-03, eta: 3:53:05, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8889 2023-01-04 23:59:36,641 - mmcls - INFO - Epoch [69][1600/5005] lr: 1.000e-03, eta: 3:52:56, time: 0.092, data_time: 0.017, memory: 928, loss: 4.8861 2023-01-04 23:59:45,433 - mmcls - INFO - Epoch [69][1700/5005] lr: 1.000e-03, eta: 3:52:48, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8720 2023-01-04 23:59:54,087 - mmcls - INFO - Epoch [69][1800/5005] lr: 1.000e-03, eta: 3:52:39, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8657 2023-01-05 00:00:02,483 - mmcls - INFO - Epoch [69][1900/5005] lr: 1.000e-03, eta: 3:52:30, time: 0.084, data_time: 0.014, memory: 928, loss: 4.8584 2023-01-05 00:00:11,163 - mmcls - INFO - Epoch [69][2000/5005] lr: 1.000e-03, eta: 3:52:21, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8690 2023-01-05 00:00:19,929 - mmcls - INFO - Epoch [69][2100/5005] lr: 1.000e-03, eta: 3:52:12, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8747 2023-01-05 00:00:28,625 - mmcls - INFO - Epoch [69][2200/5005] lr: 1.000e-03, eta: 3:52:03, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8500 2023-01-05 00:00:37,245 - mmcls - INFO - Epoch [69][2300/5005] lr: 1.000e-03, eta: 3:51:54, time: 0.086, data_time: 0.025, memory: 928, loss: 4.8776 2023-01-05 00:00:46,190 - mmcls - INFO - Epoch [69][2400/5005] lr: 1.000e-03, eta: 3:51:45, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8644 2023-01-05 00:00:55,112 - mmcls - INFO - Epoch [69][2500/5005] lr: 1.000e-03, eta: 3:51:37, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8599 2023-01-05 00:01:03,582 - mmcls - INFO - Epoch [69][2600/5005] lr: 1.000e-03, eta: 3:51:28, time: 0.085, data_time: 0.002, memory: 928, loss: 4.9178 2023-01-05 00:01:12,304 - mmcls - INFO - Epoch [69][2700/5005] lr: 1.000e-03, eta: 3:51:19, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8607 2023-01-05 00:01:21,374 - mmcls - INFO - Epoch [69][2800/5005] lr: 1.000e-03, eta: 3:51:10, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8790 2023-01-05 00:01:30,004 - mmcls - INFO - Epoch [69][2900/5005] lr: 1.000e-03, eta: 3:51:01, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8949 2023-01-05 00:01:38,617 - mmcls - INFO - Epoch [69][3000/5005] lr: 1.000e-03, eta: 3:50:52, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8941 2023-01-05 00:01:47,489 - mmcls - INFO - Epoch [69][3100/5005] lr: 1.000e-03, eta: 3:50:44, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8734 2023-01-05 00:01:56,164 - mmcls - INFO - Epoch [69][3200/5005] lr: 1.000e-03, eta: 3:50:35, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8704 2023-01-05 00:02:05,053 - mmcls - INFO - Epoch [69][3300/5005] lr: 1.000e-03, eta: 3:50:26, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8813 2023-01-05 00:02:14,371 - mmcls - INFO - Epoch [69][3400/5005] lr: 1.000e-03, eta: 3:50:17, time: 0.093, data_time: 0.001, memory: 928, loss: 4.8810 2023-01-05 00:02:23,182 - mmcls - INFO - Epoch [69][3500/5005] lr: 1.000e-03, eta: 3:50:08, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8820 2023-01-05 00:02:31,800 - mmcls - INFO - Epoch [69][3600/5005] lr: 1.000e-03, eta: 3:50:00, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8552 2023-01-05 00:02:40,344 - mmcls - INFO - Epoch [69][3700/5005] lr: 1.000e-03, eta: 3:49:51, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8743 2023-01-05 00:02:49,067 - mmcls - INFO - Epoch [69][3800/5005] lr: 1.000e-03, eta: 3:49:42, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8621 2023-01-05 00:02:57,828 - mmcls - INFO - Epoch [69][3900/5005] lr: 1.000e-03, eta: 3:49:33, time: 0.087, data_time: 0.048, memory: 928, loss: 4.8747 2023-01-05 00:03:06,437 - mmcls - INFO - Epoch [69][4000/5005] lr: 1.000e-03, eta: 3:49:24, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8791 2023-01-05 00:03:15,316 - mmcls - INFO - Epoch [69][4100/5005] lr: 1.000e-03, eta: 3:49:15, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8846 2023-01-05 00:03:23,871 - mmcls - INFO - Epoch [69][4200/5005] lr: 1.000e-03, eta: 3:49:06, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8583 2023-01-05 00:03:32,641 - mmcls - INFO - Epoch [69][4300/5005] lr: 1.000e-03, eta: 3:48:57, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8596 2023-01-05 00:03:41,518 - mmcls - INFO - Epoch [69][4400/5005] lr: 1.000e-03, eta: 3:48:49, time: 0.089, data_time: 0.012, memory: 928, loss: 4.8673 2023-01-05 00:03:50,233 - mmcls - INFO - Epoch [69][4500/5005] lr: 1.000e-03, eta: 3:48:40, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8681 2023-01-05 00:03:58,860 - mmcls - INFO - Epoch [69][4600/5005] lr: 1.000e-03, eta: 3:48:31, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8951 2023-01-05 00:04:07,620 - mmcls - INFO - Epoch [69][4700/5005] lr: 1.000e-03, eta: 3:48:22, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8700 2023-01-05 00:04:16,518 - mmcls - INFO - Epoch [69][4800/5005] lr: 1.000e-03, eta: 3:48:13, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8707 2023-01-05 00:04:24,964 - mmcls - INFO - Epoch [69][4900/5005] lr: 1.000e-03, eta: 3:48:04, time: 0.084, data_time: 0.003, memory: 928, loss: 4.8905 2023-01-05 00:04:33,658 - mmcls - INFO - Epoch [69][5000/5005] lr: 1.000e-03, eta: 3:47:55, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8730 2023-01-05 00:04:56,340 - mmcls - INFO - Epoch(val) [69][196] accuracy_top-1: 16.9820, accuracy_top-5: 34.5400 2023-01-05 00:05:07,324 - mmcls - INFO - Epoch [70][100/5005] lr: 1.000e-03, eta: 3:47:47, time: 0.110, data_time: 0.035, memory: 928, loss: 4.8873 2023-01-05 00:05:15,972 - mmcls - INFO - Epoch [70][200/5005] lr: 1.000e-03, eta: 3:47:38, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8527 2023-01-05 00:05:24,888 - mmcls - INFO - Epoch [70][300/5005] lr: 1.000e-03, eta: 3:47:29, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8633 2023-01-05 00:05:33,847 - mmcls - INFO - Epoch [70][400/5005] lr: 1.000e-03, eta: 3:47:21, time: 0.090, data_time: 0.018, memory: 928, loss: 4.8507 2023-01-05 00:05:42,554 - mmcls - INFO - Epoch [70][500/5005] lr: 1.000e-03, eta: 3:47:12, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8924 2023-01-05 00:05:51,301 - mmcls - INFO - Epoch [70][600/5005] lr: 1.000e-03, eta: 3:47:03, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8713 2023-01-05 00:05:59,797 - mmcls - INFO - Epoch [70][700/5005] lr: 1.000e-03, eta: 3:46:54, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8692 2023-01-05 00:06:08,550 - mmcls - INFO - Epoch [70][800/5005] lr: 1.000e-03, eta: 3:46:45, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8720 2023-01-05 00:06:17,063 - mmcls - INFO - Epoch [70][900/5005] lr: 1.000e-03, eta: 3:46:36, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8778 2023-01-05 00:06:25,666 - mmcls - INFO - Epoch [70][1000/5005] lr: 1.000e-03, eta: 3:46:27, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8837 2023-01-05 00:06:34,636 - mmcls - INFO - Epoch [70][1100/5005] lr: 1.000e-03, eta: 3:46:18, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8838 2023-01-05 00:06:43,523 - mmcls - INFO - Epoch [70][1200/5005] lr: 1.000e-03, eta: 3:46:10, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8657 2023-01-05 00:06:52,197 - mmcls - INFO - Epoch [70][1300/5005] lr: 1.000e-03, eta: 3:46:01, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8582 2023-01-05 00:07:00,916 - mmcls - INFO - Epoch [70][1400/5005] lr: 1.000e-03, eta: 3:45:52, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8710 2023-01-05 00:07:09,919 - mmcls - INFO - Epoch [70][1500/5005] lr: 1.000e-03, eta: 3:45:43, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8698 2023-01-05 00:07:18,605 - mmcls - INFO - Epoch [70][1600/5005] lr: 1.000e-03, eta: 3:45:34, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8633 2023-01-05 00:07:27,291 - mmcls - INFO - Epoch [70][1700/5005] lr: 1.000e-03, eta: 3:45:25, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8965 2023-01-05 00:07:35,991 - mmcls - INFO - Epoch [70][1800/5005] lr: 1.000e-03, eta: 3:45:17, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8596 2023-01-05 00:07:44,851 - mmcls - INFO - Epoch [70][1900/5005] lr: 1.000e-03, eta: 3:45:08, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8913 2023-01-05 00:07:53,910 - mmcls - INFO - Epoch [70][2000/5005] lr: 1.000e-03, eta: 3:44:59, time: 0.091, data_time: 0.017, memory: 928, loss: 4.8542 2023-01-05 00:08:02,666 - mmcls - INFO - Epoch [70][2100/5005] lr: 1.000e-03, eta: 3:44:50, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8681 2023-01-05 00:08:11,492 - mmcls - INFO - Epoch [70][2200/5005] lr: 1.000e-03, eta: 3:44:41, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8940 2023-01-05 00:08:20,208 - mmcls - INFO - Epoch [70][2300/5005] lr: 1.000e-03, eta: 3:44:33, time: 0.087, data_time: 0.000, memory: 928, loss: 4.8851 2023-01-05 00:08:29,118 - mmcls - INFO - Epoch [70][2400/5005] lr: 1.000e-03, eta: 3:44:24, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8535 2023-01-05 00:08:37,870 - mmcls - INFO - Epoch [70][2500/5005] lr: 1.000e-03, eta: 3:44:15, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8747 2023-01-05 00:08:46,349 - mmcls - INFO - Epoch [70][2600/5005] lr: 1.000e-03, eta: 3:44:06, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8675 2023-01-05 00:08:55,106 - mmcls - INFO - Epoch [70][2700/5005] lr: 1.000e-03, eta: 3:43:57, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8685 2023-01-05 00:09:03,838 - mmcls - INFO - Epoch [70][2800/5005] lr: 1.000e-03, eta: 3:43:48, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8689 2023-01-05 00:09:12,626 - mmcls - INFO - Epoch [70][2900/5005] lr: 1.000e-03, eta: 3:43:40, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8740 2023-01-05 00:09:21,495 - mmcls - INFO - Epoch [70][3000/5005] lr: 1.000e-03, eta: 3:43:31, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8864 2023-01-05 00:09:30,403 - mmcls - INFO - Epoch [70][3100/5005] lr: 1.000e-03, eta: 3:43:22, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8725 2023-01-05 00:09:39,212 - mmcls - INFO - Epoch [70][3200/5005] lr: 1.000e-03, eta: 3:43:13, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8650 2023-01-05 00:09:47,811 - mmcls - INFO - Epoch [70][3300/5005] lr: 1.000e-03, eta: 3:43:04, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8784 2023-01-05 00:09:56,638 - mmcls - INFO - Epoch [70][3400/5005] lr: 1.000e-03, eta: 3:42:55, time: 0.088, data_time: 0.002, memory: 928, loss: 4.9096 2023-01-05 00:10:05,851 - mmcls - INFO - Epoch [70][3500/5005] lr: 1.000e-03, eta: 3:42:47, time: 0.092, data_time: 0.018, memory: 928, loss: 4.8744 2023-01-05 00:10:14,694 - mmcls - INFO - Epoch [70][3600/5005] lr: 1.000e-03, eta: 3:42:38, time: 0.088, data_time: 0.033, memory: 928, loss: 4.8656 2023-01-05 00:10:23,899 - mmcls - INFO - Epoch [70][3700/5005] lr: 1.000e-03, eta: 3:42:29, time: 0.092, data_time: 0.011, memory: 928, loss: 4.8701 2023-01-05 00:10:32,683 - mmcls - INFO - Epoch [70][3800/5005] lr: 1.000e-03, eta: 3:42:21, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8555 2023-01-05 00:10:41,332 - mmcls - INFO - Epoch [70][3900/5005] lr: 1.000e-03, eta: 3:42:12, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8985 2023-01-05 00:10:50,274 - mmcls - INFO - Epoch [70][4000/5005] lr: 1.000e-03, eta: 3:42:03, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8728 2023-01-05 00:10:59,082 - mmcls - INFO - Epoch [70][4100/5005] lr: 1.000e-03, eta: 3:41:54, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8810 2023-01-05 00:11:07,953 - mmcls - INFO - Epoch [70][4200/5005] lr: 1.000e-03, eta: 3:41:45, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8775 2023-01-05 00:11:16,807 - mmcls - INFO - Epoch [70][4300/5005] lr: 1.000e-03, eta: 3:41:36, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8780 2023-01-05 00:11:25,463 - mmcls - INFO - Epoch [70][4400/5005] lr: 1.000e-03, eta: 3:41:28, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8782 2023-01-05 00:11:34,051 - mmcls - INFO - Epoch [70][4500/5005] lr: 1.000e-03, eta: 3:41:19, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8369 2023-01-05 00:11:42,897 - mmcls - INFO - Epoch [70][4600/5005] lr: 1.000e-03, eta: 3:41:10, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8753 2023-01-05 00:11:51,581 - mmcls - INFO - Epoch [70][4700/5005] lr: 1.000e-03, eta: 3:41:01, time: 0.087, data_time: 0.029, memory: 928, loss: 4.8615 2023-01-05 00:12:00,386 - mmcls - INFO - Epoch [70][4800/5005] lr: 1.000e-03, eta: 3:40:52, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8493 2023-01-05 00:12:09,267 - mmcls - INFO - Epoch [70][4900/5005] lr: 1.000e-03, eta: 3:40:43, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8460 2023-01-05 00:12:17,846 - mmcls - INFO - Epoch [70][5000/5005] lr: 1.000e-03, eta: 3:40:34, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8738 2023-01-05 00:12:40,679 - mmcls - INFO - Epoch(val) [70][196] accuracy_top-1: 17.3400, accuracy_top-5: 35.3940 2023-01-05 00:12:51,694 - mmcls - INFO - Epoch [71][100/5005] lr: 1.000e-03, eta: 3:40:26, time: 0.110, data_time: 0.056, memory: 928, loss: 4.8616 2023-01-05 00:13:00,373 - mmcls - INFO - Epoch [71][200/5005] lr: 1.000e-03, eta: 3:40:17, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8524 2023-01-05 00:13:09,257 - mmcls - INFO - Epoch [71][300/5005] lr: 1.000e-03, eta: 3:40:08, time: 0.089, data_time: 0.005, memory: 928, loss: 4.9004 2023-01-05 00:13:17,864 - mmcls - INFO - Epoch [71][400/5005] lr: 1.000e-03, eta: 3:39:59, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8683 2023-01-05 00:13:26,645 - mmcls - INFO - Epoch [71][500/5005] lr: 1.000e-03, eta: 3:39:51, time: 0.088, data_time: 0.024, memory: 928, loss: 4.8562 2023-01-05 00:13:35,302 - mmcls - INFO - Epoch [71][600/5005] lr: 1.000e-03, eta: 3:39:42, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8668 2023-01-05 00:13:44,070 - mmcls - INFO - Epoch [71][700/5005] lr: 1.000e-03, eta: 3:39:33, time: 0.088, data_time: 0.000, memory: 928, loss: 4.8796 2023-01-05 00:13:52,952 - mmcls - INFO - Epoch [71][800/5005] lr: 1.000e-03, eta: 3:39:24, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8640 2023-01-05 00:14:01,799 - mmcls - INFO - Epoch [71][900/5005] lr: 1.000e-03, eta: 3:39:15, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8672 2023-01-05 00:14:10,391 - mmcls - INFO - Epoch [71][1000/5005] lr: 1.000e-03, eta: 3:39:06, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8831 2023-01-05 00:14:19,151 - mmcls - INFO - Epoch [71][1100/5005] lr: 1.000e-03, eta: 3:38:58, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8725 2023-01-05 00:14:28,175 - mmcls - INFO - Epoch [71][1200/5005] lr: 1.000e-03, eta: 3:38:49, time: 0.090, data_time: 0.005, memory: 928, loss: 4.8448 2023-01-05 00:14:37,008 - mmcls - INFO - Epoch [71][1300/5005] lr: 1.000e-03, eta: 3:38:40, time: 0.088, data_time: 0.037, memory: 928, loss: 4.8750 2023-01-05 00:14:45,955 - mmcls - INFO - Epoch [71][1400/5005] lr: 1.000e-03, eta: 3:38:31, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8685 2023-01-05 00:14:55,157 - mmcls - INFO - Epoch [71][1500/5005] lr: 1.000e-03, eta: 3:38:23, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8683 2023-01-05 00:15:03,813 - mmcls - INFO - Epoch [71][1600/5005] lr: 1.000e-03, eta: 3:38:14, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8777 2023-01-05 00:15:12,476 - mmcls - INFO - Epoch [71][1700/5005] lr: 1.000e-03, eta: 3:38:05, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8487 2023-01-05 00:15:21,545 - mmcls - INFO - Epoch [71][1800/5005] lr: 1.000e-03, eta: 3:37:56, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8679 2023-01-05 00:15:30,241 - mmcls - INFO - Epoch [71][1900/5005] lr: 1.000e-03, eta: 3:37:47, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8487 2023-01-05 00:15:39,010 - mmcls - INFO - Epoch [71][2000/5005] lr: 1.000e-03, eta: 3:37:38, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8539 2023-01-05 00:15:47,698 - mmcls - INFO - Epoch [71][2100/5005] lr: 1.000e-03, eta: 3:37:30, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8679 2023-01-05 00:15:56,695 - mmcls - INFO - Epoch [71][2200/5005] lr: 1.000e-03, eta: 3:37:21, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8665 2023-01-05 00:16:05,537 - mmcls - INFO - Epoch [71][2300/5005] lr: 1.000e-03, eta: 3:37:12, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8411 2023-01-05 00:16:14,011 - mmcls - INFO - Epoch [71][2400/5005] lr: 1.000e-03, eta: 3:37:03, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8707 2023-01-05 00:16:22,753 - mmcls - INFO - Epoch [71][2500/5005] lr: 1.000e-03, eta: 3:36:54, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8691 2023-01-05 00:16:31,792 - mmcls - INFO - Epoch [71][2600/5005] lr: 1.000e-03, eta: 3:36:45, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8571 2023-01-05 00:16:40,500 - mmcls - INFO - Epoch [71][2700/5005] lr: 1.000e-03, eta: 3:36:37, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8647 2023-01-05 00:16:49,553 - mmcls - INFO - Epoch [71][2800/5005] lr: 1.000e-03, eta: 3:36:28, time: 0.090, data_time: 0.008, memory: 928, loss: 4.8495 2023-01-05 00:16:58,200 - mmcls - INFO - Epoch [71][2900/5005] lr: 1.000e-03, eta: 3:36:19, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8764 2023-01-05 00:17:07,008 - mmcls - INFO - Epoch [71][3000/5005] lr: 1.000e-03, eta: 3:36:10, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8741 2023-01-05 00:17:15,776 - mmcls - INFO - Epoch [71][3100/5005] lr: 1.000e-03, eta: 3:36:01, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8768 2023-01-05 00:17:24,575 - mmcls - INFO - Epoch [71][3200/5005] lr: 1.000e-03, eta: 3:35:53, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8824 2023-01-05 00:17:33,804 - mmcls - INFO - Epoch [71][3300/5005] lr: 1.000e-03, eta: 3:35:44, time: 0.092, data_time: 0.007, memory: 928, loss: 4.8552 2023-01-05 00:17:42,909 - mmcls - INFO - Epoch [71][3400/5005] lr: 1.000e-03, eta: 3:35:35, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8745 2023-01-05 00:17:51,721 - mmcls - INFO - Epoch [71][3500/5005] lr: 1.000e-03, eta: 3:35:26, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8744 2023-01-05 00:18:00,268 - mmcls - INFO - Epoch [71][3600/5005] lr: 1.000e-03, eta: 3:35:17, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8521 2023-01-05 00:18:08,920 - mmcls - INFO - Epoch [71][3700/5005] lr: 1.000e-03, eta: 3:35:09, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8563 2023-01-05 00:18:17,823 - mmcls - INFO - Epoch [71][3800/5005] lr: 1.000e-03, eta: 3:35:00, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8897 2023-01-05 00:18:26,251 - mmcls - INFO - Epoch [71][3900/5005] lr: 1.000e-03, eta: 3:34:51, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8614 2023-01-05 00:18:34,914 - mmcls - INFO - Epoch [71][4000/5005] lr: 1.000e-03, eta: 3:34:42, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8802 2023-01-05 00:18:43,983 - mmcls - INFO - Epoch [71][4100/5005] lr: 1.000e-03, eta: 3:34:33, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8715 2023-01-05 00:18:52,679 - mmcls - INFO - Epoch [71][4200/5005] lr: 1.000e-03, eta: 3:34:24, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8739 2023-01-05 00:19:01,360 - mmcls - INFO - Epoch [71][4300/5005] lr: 1.000e-03, eta: 3:34:15, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8918 2023-01-05 00:19:10,101 - mmcls - INFO - Epoch [71][4400/5005] lr: 1.000e-03, eta: 3:34:07, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8827 2023-01-05 00:19:18,574 - mmcls - INFO - Epoch [71][4500/5005] lr: 1.000e-03, eta: 3:33:58, time: 0.085, data_time: 0.016, memory: 928, loss: 4.8876 2023-01-05 00:19:27,760 - mmcls - INFO - Epoch [71][4600/5005] lr: 1.000e-03, eta: 3:33:49, time: 0.092, data_time: 0.008, memory: 928, loss: 4.8749 2023-01-05 00:19:36,381 - mmcls - INFO - Epoch [71][4700/5005] lr: 1.000e-03, eta: 3:33:40, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8509 2023-01-05 00:19:45,093 - mmcls - INFO - Epoch [71][4800/5005] lr: 1.000e-03, eta: 3:33:31, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8691 2023-01-05 00:19:53,904 - mmcls - INFO - Epoch [71][4900/5005] lr: 1.000e-03, eta: 3:33:22, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8734 2023-01-05 00:20:02,691 - mmcls - INFO - Epoch [71][5000/5005] lr: 1.000e-03, eta: 3:33:14, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8400 2023-01-05 00:20:25,525 - mmcls - INFO - Epoch(val) [71][196] accuracy_top-1: 16.8620, accuracy_top-5: 34.5980 2023-01-05 00:20:36,519 - mmcls - INFO - Epoch [72][100/5005] lr: 1.000e-03, eta: 3:33:05, time: 0.110, data_time: 0.048, memory: 928, loss: 4.8613 2023-01-05 00:20:45,421 - mmcls - INFO - Epoch [72][200/5005] lr: 1.000e-03, eta: 3:32:56, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8537 2023-01-05 00:20:54,185 - mmcls - INFO - Epoch [72][300/5005] lr: 1.000e-03, eta: 3:32:47, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8569 2023-01-05 00:21:02,856 - mmcls - INFO - Epoch [72][400/5005] lr: 1.000e-03, eta: 3:32:39, time: 0.087, data_time: 0.027, memory: 928, loss: 4.8578 2023-01-05 00:21:11,259 - mmcls - INFO - Epoch [72][500/5005] lr: 1.000e-03, eta: 3:32:30, time: 0.084, data_time: 0.008, memory: 928, loss: 4.8873 2023-01-05 00:21:20,141 - mmcls - INFO - Epoch [72][600/5005] lr: 1.000e-03, eta: 3:32:21, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8587 2023-01-05 00:21:28,895 - mmcls - INFO - Epoch [72][700/5005] lr: 1.000e-03, eta: 3:32:12, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8640 2023-01-05 00:21:37,596 - mmcls - INFO - Epoch [72][800/5005] lr: 1.000e-03, eta: 3:32:03, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8776 2023-01-05 00:21:46,372 - mmcls - INFO - Epoch [72][900/5005] lr: 1.000e-03, eta: 3:31:54, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8651 2023-01-05 00:21:55,139 - mmcls - INFO - Epoch [72][1000/5005] lr: 1.000e-03, eta: 3:31:45, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8300 2023-01-05 00:22:03,839 - mmcls - INFO - Epoch [72][1100/5005] lr: 1.000e-03, eta: 3:31:37, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8716 2023-01-05 00:22:12,625 - mmcls - INFO - Epoch [72][1200/5005] lr: 1.000e-03, eta: 3:31:28, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8676 2023-01-05 00:22:21,332 - mmcls - INFO - Epoch [72][1300/5005] lr: 1.000e-03, eta: 3:31:19, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8464 2023-01-05 00:22:30,144 - mmcls - INFO - Epoch [72][1400/5005] lr: 1.000e-03, eta: 3:31:10, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8741 2023-01-05 00:22:38,833 - mmcls - INFO - Epoch [72][1500/5005] lr: 1.000e-03, eta: 3:31:01, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8649 2023-01-05 00:22:47,583 - mmcls - INFO - Epoch [72][1600/5005] lr: 1.000e-03, eta: 3:30:52, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8866 2023-01-05 00:22:56,293 - mmcls - INFO - Epoch [72][1700/5005] lr: 1.000e-03, eta: 3:30:44, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8595 2023-01-05 00:23:04,805 - mmcls - INFO - Epoch [72][1800/5005] lr: 1.000e-03, eta: 3:30:35, time: 0.085, data_time: 0.012, memory: 928, loss: 4.8892 2023-01-05 00:23:13,655 - mmcls - INFO - Epoch [72][1900/5005] lr: 1.000e-03, eta: 3:30:26, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8733 2023-01-05 00:23:22,272 - mmcls - INFO - Epoch [72][2000/5005] lr: 1.000e-03, eta: 3:30:17, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8778 2023-01-05 00:23:31,029 - mmcls - INFO - Epoch [72][2100/5005] lr: 1.000e-03, eta: 3:30:08, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8713 2023-01-05 00:23:39,661 - mmcls - INFO - Epoch [72][2200/5005] lr: 1.000e-03, eta: 3:29:59, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8775 2023-01-05 00:23:48,588 - mmcls - INFO - Epoch [72][2300/5005] lr: 1.000e-03, eta: 3:29:50, time: 0.089, data_time: 0.021, memory: 928, loss: 4.8738 2023-01-05 00:23:57,330 - mmcls - INFO - Epoch [72][2400/5005] lr: 1.000e-03, eta: 3:29:42, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8711 2023-01-05 00:24:06,308 - mmcls - INFO - Epoch [72][2500/5005] lr: 1.000e-03, eta: 3:29:33, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8783 2023-01-05 00:24:14,901 - mmcls - INFO - Epoch [72][2600/5005] lr: 1.000e-03, eta: 3:29:24, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8817 2023-01-05 00:24:23,425 - mmcls - INFO - Epoch [72][2700/5005] lr: 1.000e-03, eta: 3:29:15, time: 0.085, data_time: 0.013, memory: 928, loss: 4.8786 2023-01-05 00:24:32,321 - mmcls - INFO - Epoch [72][2800/5005] lr: 1.000e-03, eta: 3:29:06, time: 0.089, data_time: 0.039, memory: 928, loss: 4.8602 2023-01-05 00:24:41,052 - mmcls - INFO - Epoch [72][2900/5005] lr: 1.000e-03, eta: 3:28:57, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8585 2023-01-05 00:24:50,101 - mmcls - INFO - Epoch [72][3000/5005] lr: 1.000e-03, eta: 3:28:49, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8557 2023-01-05 00:24:58,808 - mmcls - INFO - Epoch [72][3100/5005] lr: 1.000e-03, eta: 3:28:40, time: 0.087, data_time: 0.025, memory: 928, loss: 4.8677 2023-01-05 00:25:07,422 - mmcls - INFO - Epoch [72][3200/5005] lr: 1.000e-03, eta: 3:28:31, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8699 2023-01-05 00:25:16,485 - mmcls - INFO - Epoch [72][3300/5005] lr: 1.000e-03, eta: 3:28:22, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8788 2023-01-05 00:25:25,185 - mmcls - INFO - Epoch [72][3400/5005] lr: 1.000e-03, eta: 3:28:13, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8840 2023-01-05 00:25:34,054 - mmcls - INFO - Epoch [72][3500/5005] lr: 1.000e-03, eta: 3:28:05, time: 0.089, data_time: 0.020, memory: 928, loss: 4.8655 2023-01-05 00:25:42,674 - mmcls - INFO - Epoch [72][3600/5005] lr: 1.000e-03, eta: 3:27:56, time: 0.086, data_time: 0.035, memory: 928, loss: 4.8585 2023-01-05 00:25:51,519 - mmcls - INFO - Epoch [72][3700/5005] lr: 1.000e-03, eta: 3:27:47, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8454 2023-01-05 00:26:00,101 - mmcls - INFO - Epoch [72][3800/5005] lr: 1.000e-03, eta: 3:27:38, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8555 2023-01-05 00:26:08,834 - mmcls - INFO - Epoch [72][3900/5005] lr: 1.000e-03, eta: 3:27:29, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8716 2023-01-05 00:26:17,600 - mmcls - INFO - Epoch [72][4000/5005] lr: 1.000e-03, eta: 3:27:20, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8546 2023-01-05 00:26:26,790 - mmcls - INFO - Epoch [72][4100/5005] lr: 1.000e-03, eta: 3:27:12, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8682 2023-01-05 00:26:35,535 - mmcls - INFO - Epoch [72][4200/5005] lr: 1.000e-03, eta: 3:27:03, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8605 2023-01-05 00:26:44,153 - mmcls - INFO - Epoch [72][4300/5005] lr: 1.000e-03, eta: 3:26:54, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8543 2023-01-05 00:26:52,790 - mmcls - INFO - Epoch [72][4400/5005] lr: 1.000e-03, eta: 3:26:45, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8461 2023-01-05 00:27:01,461 - mmcls - INFO - Epoch [72][4500/5005] lr: 1.000e-03, eta: 3:26:36, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8723 2023-01-05 00:27:10,277 - mmcls - INFO - Epoch [72][4600/5005] lr: 1.000e-03, eta: 3:26:27, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8553 2023-01-05 00:27:18,981 - mmcls - INFO - Epoch [72][4700/5005] lr: 1.000e-03, eta: 3:26:18, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8650 2023-01-05 00:27:27,352 - mmcls - INFO - Epoch [72][4800/5005] lr: 1.000e-03, eta: 3:26:09, time: 0.084, data_time: 0.012, memory: 928, loss: 4.8635 2023-01-05 00:27:36,004 - mmcls - INFO - Epoch [72][4900/5005] lr: 1.000e-03, eta: 3:26:01, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8662 2023-01-05 00:27:44,521 - mmcls - INFO - Epoch [72][5000/5005] lr: 1.000e-03, eta: 3:25:52, time: 0.085, data_time: 0.013, memory: 928, loss: 4.8865 2023-01-05 00:28:07,186 - mmcls - INFO - Epoch(val) [72][196] accuracy_top-1: 16.0420, accuracy_top-5: 33.1360 2023-01-05 00:28:18,233 - mmcls - INFO - Epoch [73][100/5005] lr: 1.000e-03, eta: 3:25:43, time: 0.110, data_time: 0.044, memory: 928, loss: 4.8622 2023-01-05 00:28:26,837 - mmcls - INFO - Epoch [73][200/5005] lr: 1.000e-03, eta: 3:25:34, time: 0.086, data_time: 0.032, memory: 928, loss: 4.8472 2023-01-05 00:28:35,647 - mmcls - INFO - Epoch [73][300/5005] lr: 1.000e-03, eta: 3:25:25, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8675 2023-01-05 00:28:44,336 - mmcls - INFO - Epoch [73][400/5005] lr: 1.000e-03, eta: 3:25:16, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8541 2023-01-05 00:28:52,841 - mmcls - INFO - Epoch [73][500/5005] lr: 1.000e-03, eta: 3:25:08, time: 0.085, data_time: 0.014, memory: 928, loss: 4.8728 2023-01-05 00:29:01,827 - mmcls - INFO - Epoch [73][600/5005] lr: 1.000e-03, eta: 3:24:59, time: 0.090, data_time: 0.011, memory: 928, loss: 4.8832 2023-01-05 00:29:10,613 - mmcls - INFO - Epoch [73][700/5005] lr: 1.000e-03, eta: 3:24:50, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8476 2023-01-05 00:29:19,585 - mmcls - INFO - Epoch [73][800/5005] lr: 1.000e-03, eta: 3:24:41, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8668 2023-01-05 00:29:28,126 - mmcls - INFO - Epoch [73][900/5005] lr: 1.000e-03, eta: 3:24:32, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8873 2023-01-05 00:29:36,785 - mmcls - INFO - Epoch [73][1000/5005] lr: 1.000e-03, eta: 3:24:23, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8562 2023-01-05 00:29:45,480 - mmcls - INFO - Epoch [73][1100/5005] lr: 1.000e-03, eta: 3:24:15, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8601 2023-01-05 00:29:53,894 - mmcls - INFO - Epoch [73][1200/5005] lr: 1.000e-03, eta: 3:24:06, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8552 2023-01-05 00:30:02,849 - mmcls - INFO - Epoch [73][1300/5005] lr: 1.000e-03, eta: 3:23:57, time: 0.090, data_time: 0.038, memory: 928, loss: 4.8576 2023-01-05 00:30:11,542 - mmcls - INFO - Epoch [73][1400/5005] lr: 1.000e-03, eta: 3:23:48, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8810 2023-01-05 00:30:20,106 - mmcls - INFO - Epoch [73][1500/5005] lr: 1.000e-03, eta: 3:23:39, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8661 2023-01-05 00:30:28,904 - mmcls - INFO - Epoch [73][1600/5005] lr: 1.000e-03, eta: 3:23:30, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8708 2023-01-05 00:30:37,939 - mmcls - INFO - Epoch [73][1700/5005] lr: 1.000e-03, eta: 3:23:22, time: 0.090, data_time: 0.010, memory: 928, loss: 4.8451 2023-01-05 00:30:46,663 - mmcls - INFO - Epoch [73][1800/5005] lr: 1.000e-03, eta: 3:23:13, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8664 2023-01-05 00:30:55,736 - mmcls - INFO - Epoch [73][1900/5005] lr: 1.000e-03, eta: 3:23:04, time: 0.091, data_time: 0.009, memory: 928, loss: 4.8761 2023-01-05 00:31:04,332 - mmcls - INFO - Epoch [73][2000/5005] lr: 1.000e-03, eta: 3:22:55, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8537 2023-01-05 00:31:12,928 - mmcls - INFO - Epoch [73][2100/5005] lr: 1.000e-03, eta: 3:22:46, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8481 2023-01-05 00:31:21,521 - mmcls - INFO - Epoch [73][2200/5005] lr: 1.000e-03, eta: 3:22:37, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8515 2023-01-05 00:31:30,482 - mmcls - INFO - Epoch [73][2300/5005] lr: 1.000e-03, eta: 3:22:28, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8954 2023-01-05 00:31:39,714 - mmcls - INFO - Epoch [73][2400/5005] lr: 1.000e-03, eta: 3:22:20, time: 0.092, data_time: 0.004, memory: 928, loss: 4.8625 2023-01-05 00:31:48,369 - mmcls - INFO - Epoch [73][2500/5005] lr: 1.000e-03, eta: 3:22:11, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8904 2023-01-05 00:31:56,845 - mmcls - INFO - Epoch [73][2600/5005] lr: 1.000e-03, eta: 3:22:02, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8850 2023-01-05 00:32:05,588 - mmcls - INFO - Epoch [73][2700/5005] lr: 1.000e-03, eta: 3:21:53, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8550 2023-01-05 00:32:14,435 - mmcls - INFO - Epoch [73][2800/5005] lr: 1.000e-03, eta: 3:21:44, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8532 2023-01-05 00:32:23,389 - mmcls - INFO - Epoch [73][2900/5005] lr: 1.000e-03, eta: 3:21:36, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8680 2023-01-05 00:32:32,334 - mmcls - INFO - Epoch [73][3000/5005] lr: 1.000e-03, eta: 3:21:27, time: 0.089, data_time: 0.012, memory: 928, loss: 4.8478 2023-01-05 00:32:40,925 - mmcls - INFO - Epoch [73][3100/5005] lr: 1.000e-03, eta: 3:21:18, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8511 2023-01-05 00:32:49,723 - mmcls - INFO - Epoch [73][3200/5005] lr: 1.000e-03, eta: 3:21:09, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8595 2023-01-05 00:32:58,252 - mmcls - INFO - Epoch [73][3300/5005] lr: 1.000e-03, eta: 3:21:00, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8380 2023-01-05 00:33:07,146 - mmcls - INFO - Epoch [73][3400/5005] lr: 1.000e-03, eta: 3:20:51, time: 0.089, data_time: 0.036, memory: 928, loss: 4.8731 2023-01-05 00:33:15,774 - mmcls - INFO - Epoch [73][3500/5005] lr: 1.000e-03, eta: 3:20:43, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8588 2023-01-05 00:33:24,744 - mmcls - INFO - Epoch [73][3600/5005] lr: 1.000e-03, eta: 3:20:34, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8772 2023-01-05 00:33:33,546 - mmcls - INFO - Epoch [73][3700/5005] lr: 1.000e-03, eta: 3:20:25, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8435 2023-01-05 00:33:42,486 - mmcls - INFO - Epoch [73][3800/5005] lr: 1.000e-03, eta: 3:20:16, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8682 2023-01-05 00:33:51,253 - mmcls - INFO - Epoch [73][3900/5005] lr: 1.000e-03, eta: 3:20:07, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8604 2023-01-05 00:34:00,375 - mmcls - INFO - Epoch [73][4000/5005] lr: 1.000e-03, eta: 3:19:59, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8677 2023-01-05 00:34:09,113 - mmcls - INFO - Epoch [73][4100/5005] lr: 1.000e-03, eta: 3:19:50, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8499 2023-01-05 00:34:17,688 - mmcls - INFO - Epoch [73][4200/5005] lr: 1.000e-03, eta: 3:19:41, time: 0.086, data_time: 0.028, memory: 928, loss: 4.8681 2023-01-05 00:34:26,398 - mmcls - INFO - Epoch [73][4300/5005] lr: 1.000e-03, eta: 3:19:32, time: 0.087, data_time: 0.027, memory: 928, loss: 4.8651 2023-01-05 00:34:35,002 - mmcls - INFO - Epoch [73][4400/5005] lr: 1.000e-03, eta: 3:19:23, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8642 2023-01-05 00:34:43,804 - mmcls - INFO - Epoch [73][4500/5005] lr: 1.000e-03, eta: 3:19:14, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8524 2023-01-05 00:34:52,416 - mmcls - INFO - Epoch [73][4600/5005] lr: 1.000e-03, eta: 3:19:05, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8462 2023-01-05 00:35:01,236 - mmcls - INFO - Epoch [73][4700/5005] lr: 1.000e-03, eta: 3:18:57, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8693 2023-01-05 00:35:10,025 - mmcls - INFO - Epoch [73][4800/5005] lr: 1.000e-03, eta: 3:18:48, time: 0.088, data_time: 0.003, memory: 928, loss: 4.9035 2023-01-05 00:35:18,453 - mmcls - INFO - Epoch [73][4900/5005] lr: 1.000e-03, eta: 3:18:39, time: 0.084, data_time: 0.018, memory: 928, loss: 4.8719 2023-01-05 00:35:27,467 - mmcls - INFO - Epoch [73][5000/5005] lr: 1.000e-03, eta: 3:18:30, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8903 2023-01-05 00:35:50,071 - mmcls - INFO - Epoch(val) [73][196] accuracy_top-1: 16.3260, accuracy_top-5: 33.7100 2023-01-05 00:36:01,155 - mmcls - INFO - Epoch [74][100/5005] lr: 1.000e-03, eta: 3:18:22, time: 0.111, data_time: 0.066, memory: 928, loss: 4.8668 2023-01-05 00:36:10,132 - mmcls - INFO - Epoch [74][200/5005] lr: 1.000e-03, eta: 3:18:13, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8603 2023-01-05 00:36:18,715 - mmcls - INFO - Epoch [74][300/5005] lr: 1.000e-03, eta: 3:18:04, time: 0.086, data_time: 0.034, memory: 928, loss: 4.8595 2023-01-05 00:36:27,450 - mmcls - INFO - Epoch [74][400/5005] lr: 1.000e-03, eta: 3:17:55, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8609 2023-01-05 00:36:36,395 - mmcls - INFO - Epoch [74][500/5005] lr: 1.000e-03, eta: 3:17:46, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8581 2023-01-05 00:36:45,251 - mmcls - INFO - Epoch [74][600/5005] lr: 1.000e-03, eta: 3:17:37, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8786 2023-01-05 00:36:54,027 - mmcls - INFO - Epoch [74][700/5005] lr: 1.000e-03, eta: 3:17:29, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8395 2023-01-05 00:37:03,249 - mmcls - INFO - Epoch [74][800/5005] lr: 1.000e-03, eta: 3:17:20, time: 0.092, data_time: 0.004, memory: 928, loss: 4.8609 2023-01-05 00:37:12,035 - mmcls - INFO - Epoch [74][900/5005] lr: 1.000e-03, eta: 3:17:11, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8656 2023-01-05 00:37:21,021 - mmcls - INFO - Epoch [74][1000/5005] lr: 1.000e-03, eta: 3:17:02, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8665 2023-01-05 00:37:29,489 - mmcls - INFO - Epoch [74][1100/5005] lr: 1.000e-03, eta: 3:16:53, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8716 2023-01-05 00:37:38,129 - mmcls - INFO - Epoch [74][1200/5005] lr: 1.000e-03, eta: 3:16:45, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8764 2023-01-05 00:37:46,887 - mmcls - INFO - Epoch [74][1300/5005] lr: 1.000e-03, eta: 3:16:36, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8489 2023-01-05 00:37:55,566 - mmcls - INFO - Epoch [74][1400/5005] lr: 1.000e-03, eta: 3:16:27, time: 0.087, data_time: 0.028, memory: 928, loss: 4.8737 2023-01-05 00:38:04,443 - mmcls - INFO - Epoch [74][1500/5005] lr: 1.000e-03, eta: 3:16:18, time: 0.089, data_time: 0.026, memory: 928, loss: 4.8751 2023-01-05 00:38:13,006 - mmcls - INFO - Epoch [74][1600/5005] lr: 1.000e-03, eta: 3:16:09, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8728 2023-01-05 00:38:21,761 - mmcls - INFO - Epoch [74][1700/5005] lr: 1.000e-03, eta: 3:16:00, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8722 2023-01-05 00:38:30,429 - mmcls - INFO - Epoch [74][1800/5005] lr: 1.000e-03, eta: 3:15:52, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8598 2023-01-05 00:38:39,223 - mmcls - INFO - Epoch [74][1900/5005] lr: 1.000e-03, eta: 3:15:43, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8960 2023-01-05 00:38:47,821 - mmcls - INFO - Epoch [74][2000/5005] lr: 1.000e-03, eta: 3:15:34, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8601 2023-01-05 00:38:56,499 - mmcls - INFO - Epoch [74][2100/5005] lr: 1.000e-03, eta: 3:15:25, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8469 2023-01-05 00:39:05,421 - mmcls - INFO - Epoch [74][2200/5005] lr: 1.000e-03, eta: 3:15:16, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8542 2023-01-05 00:39:14,130 - mmcls - INFO - Epoch [74][2300/5005] lr: 1.000e-03, eta: 3:15:07, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8574 2023-01-05 00:39:23,225 - mmcls - INFO - Epoch [74][2400/5005] lr: 1.000e-03, eta: 3:14:59, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8574 2023-01-05 00:39:31,925 - mmcls - INFO - Epoch [74][2500/5005] lr: 1.000e-03, eta: 3:14:50, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8370 2023-01-05 00:39:40,731 - mmcls - INFO - Epoch [74][2600/5005] lr: 1.000e-03, eta: 3:14:41, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8645 2023-01-05 00:39:49,460 - mmcls - INFO - Epoch [74][2700/5005] lr: 1.000e-03, eta: 3:14:32, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8629 2023-01-05 00:39:58,108 - mmcls - INFO - Epoch [74][2800/5005] lr: 1.000e-03, eta: 3:14:23, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8659 2023-01-05 00:40:06,805 - mmcls - INFO - Epoch [74][2900/5005] lr: 1.000e-03, eta: 3:14:14, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8387 2023-01-05 00:40:15,674 - mmcls - INFO - Epoch [74][3000/5005] lr: 1.000e-03, eta: 3:14:06, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8633 2023-01-05 00:40:24,317 - mmcls - INFO - Epoch [74][3100/5005] lr: 1.000e-03, eta: 3:13:57, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8790 2023-01-05 00:40:33,190 - mmcls - INFO - Epoch [74][3200/5005] lr: 1.000e-03, eta: 3:13:48, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8566 2023-01-05 00:40:42,495 - mmcls - INFO - Epoch [74][3300/5005] lr: 1.000e-03, eta: 3:13:39, time: 0.093, data_time: 0.010, memory: 928, loss: 4.8403 2023-01-05 00:40:51,134 - mmcls - INFO - Epoch [74][3400/5005] lr: 1.000e-03, eta: 3:13:30, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8479 2023-01-05 00:41:00,108 - mmcls - INFO - Epoch [74][3500/5005] lr: 1.000e-03, eta: 3:13:22, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8878 2023-01-05 00:41:09,033 - mmcls - INFO - Epoch [74][3600/5005] lr: 1.000e-03, eta: 3:13:13, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8683 2023-01-05 00:41:17,792 - mmcls - INFO - Epoch [74][3700/5005] lr: 1.000e-03, eta: 3:13:04, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8910 2023-01-05 00:41:27,070 - mmcls - INFO - Epoch [74][3800/5005] lr: 1.000e-03, eta: 3:12:55, time: 0.093, data_time: 0.026, memory: 928, loss: 4.8694 2023-01-05 00:41:35,781 - mmcls - INFO - Epoch [74][3900/5005] lr: 1.000e-03, eta: 3:12:46, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8537 2023-01-05 00:41:44,534 - mmcls - INFO - Epoch [74][4000/5005] lr: 1.000e-03, eta: 3:12:38, time: 0.087, data_time: 0.028, memory: 928, loss: 4.8638 2023-01-05 00:41:53,287 - mmcls - INFO - Epoch [74][4100/5005] lr: 1.000e-03, eta: 3:12:29, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8543 2023-01-05 00:42:02,096 - mmcls - INFO - Epoch [74][4200/5005] lr: 1.000e-03, eta: 3:12:20, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8955 2023-01-05 00:42:10,735 - mmcls - INFO - Epoch [74][4300/5005] lr: 1.000e-03, eta: 3:12:11, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8578 2023-01-05 00:42:19,128 - mmcls - INFO - Epoch [74][4400/5005] lr: 1.000e-03, eta: 3:12:02, time: 0.084, data_time: 0.008, memory: 928, loss: 4.8647 2023-01-05 00:42:28,118 - mmcls - INFO - Epoch [74][4500/5005] lr: 1.000e-03, eta: 3:11:53, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8614 2023-01-05 00:42:37,060 - mmcls - INFO - Epoch [74][4600/5005] lr: 1.000e-03, eta: 3:11:45, time: 0.089, data_time: 0.026, memory: 928, loss: 4.8679 2023-01-05 00:42:45,847 - mmcls - INFO - Epoch [74][4700/5005] lr: 1.000e-03, eta: 3:11:36, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8627 2023-01-05 00:42:54,716 - mmcls - INFO - Epoch [74][4800/5005] lr: 1.000e-03, eta: 3:11:27, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8784 2023-01-05 00:43:03,432 - mmcls - INFO - Epoch [74][4900/5005] lr: 1.000e-03, eta: 3:11:18, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8597 2023-01-05 00:43:12,102 - mmcls - INFO - Epoch [74][5000/5005] lr: 1.000e-03, eta: 3:11:09, time: 0.087, data_time: 0.031, memory: 928, loss: 4.8712 2023-01-05 00:43:34,868 - mmcls - INFO - Epoch(val) [74][196] accuracy_top-1: 17.3060, accuracy_top-5: 35.3580 2023-01-05 00:43:45,803 - mmcls - INFO - Epoch [75][100/5005] lr: 1.000e-03, eta: 3:11:01, time: 0.109, data_time: 0.060, memory: 928, loss: 4.8869 2023-01-05 00:43:54,176 - mmcls - INFO - Epoch [75][200/5005] lr: 1.000e-03, eta: 3:10:52, time: 0.084, data_time: 0.026, memory: 928, loss: 4.8715 2023-01-05 00:44:02,984 - mmcls - INFO - Epoch [75][300/5005] lr: 1.000e-03, eta: 3:10:43, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8694 2023-01-05 00:44:11,687 - mmcls - INFO - Epoch [75][400/5005] lr: 1.000e-03, eta: 3:10:34, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8737 2023-01-05 00:44:20,689 - mmcls - INFO - Epoch [75][500/5005] lr: 1.000e-03, eta: 3:10:25, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8470 2023-01-05 00:44:29,498 - mmcls - INFO - Epoch [75][600/5005] lr: 1.000e-03, eta: 3:10:16, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8654 2023-01-05 00:44:38,601 - mmcls - INFO - Epoch [75][700/5005] lr: 1.000e-03, eta: 3:10:08, time: 0.091, data_time: 0.031, memory: 928, loss: 4.8678 2023-01-05 00:44:47,019 - mmcls - INFO - Epoch [75][800/5005] lr: 1.000e-03, eta: 3:09:59, time: 0.084, data_time: 0.002, memory: 928, loss: 4.8940 2023-01-05 00:44:55,639 - mmcls - INFO - Epoch [75][900/5005] lr: 1.000e-03, eta: 3:09:50, time: 0.086, data_time: 0.042, memory: 928, loss: 4.8516 2023-01-05 00:45:04,649 - mmcls - INFO - Epoch [75][1000/5005] lr: 1.000e-03, eta: 3:09:41, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8646 2023-01-05 00:45:13,526 - mmcls - INFO - Epoch [75][1100/5005] lr: 1.000e-03, eta: 3:09:32, time: 0.089, data_time: 0.020, memory: 928, loss: 4.8635 2023-01-05 00:45:21,980 - mmcls - INFO - Epoch [75][1200/5005] lr: 1.000e-03, eta: 3:09:23, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8419 2023-01-05 00:45:30,769 - mmcls - INFO - Epoch [75][1300/5005] lr: 1.000e-03, eta: 3:09:15, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8670 2023-01-05 00:45:39,473 - mmcls - INFO - Epoch [75][1400/5005] lr: 1.000e-03, eta: 3:09:06, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8055 2023-01-05 00:45:48,368 - mmcls - INFO - Epoch [75][1500/5005] lr: 1.000e-03, eta: 3:08:57, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8797 2023-01-05 00:45:57,121 - mmcls - INFO - Epoch [75][1600/5005] lr: 1.000e-03, eta: 3:08:48, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8273 2023-01-05 00:46:05,816 - mmcls - INFO - Epoch [75][1700/5005] lr: 1.000e-03, eta: 3:08:39, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8801 2023-01-05 00:46:14,663 - mmcls - INFO - Epoch [75][1800/5005] lr: 1.000e-03, eta: 3:08:30, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8682 2023-01-05 00:46:23,664 - mmcls - INFO - Epoch [75][1900/5005] lr: 1.000e-03, eta: 3:08:22, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8710 2023-01-05 00:46:32,277 - mmcls - INFO - Epoch [75][2000/5005] lr: 1.000e-03, eta: 3:08:13, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8631 2023-01-05 00:46:41,000 - mmcls - INFO - Epoch [75][2100/5005] lr: 1.000e-03, eta: 3:08:04, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8506 2023-01-05 00:46:49,579 - mmcls - INFO - Epoch [75][2200/5005] lr: 1.000e-03, eta: 3:07:55, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8464 2023-01-05 00:46:58,499 - mmcls - INFO - Epoch [75][2300/5005] lr: 1.000e-03, eta: 3:07:46, time: 0.089, data_time: 0.027, memory: 928, loss: 4.8689 2023-01-05 00:47:06,984 - mmcls - INFO - Epoch [75][2400/5005] lr: 1.000e-03, eta: 3:07:37, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8561 2023-01-05 00:47:15,929 - mmcls - INFO - Epoch [75][2500/5005] lr: 1.000e-03, eta: 3:07:29, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8506 2023-01-05 00:47:24,737 - mmcls - INFO - Epoch [75][2600/5005] lr: 1.000e-03, eta: 3:07:20, time: 0.088, data_time: 0.046, memory: 928, loss: 4.8391 2023-01-05 00:47:33,733 - mmcls - INFO - Epoch [75][2700/5005] lr: 1.000e-03, eta: 3:07:11, time: 0.090, data_time: 0.011, memory: 928, loss: 4.8637 2023-01-05 00:47:42,763 - mmcls - INFO - Epoch [75][2800/5005] lr: 1.000e-03, eta: 3:07:02, time: 0.090, data_time: 0.005, memory: 928, loss: 4.8876 2023-01-05 00:47:51,290 - mmcls - INFO - Epoch [75][2900/5005] lr: 1.000e-03, eta: 3:06:53, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8712 2023-01-05 00:48:00,114 - mmcls - INFO - Epoch [75][3000/5005] lr: 1.000e-03, eta: 3:06:45, time: 0.088, data_time: 0.034, memory: 928, loss: 4.8680 2023-01-05 00:48:09,086 - mmcls - INFO - Epoch [75][3100/5005] lr: 1.000e-03, eta: 3:06:36, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8688 2023-01-05 00:48:18,237 - mmcls - INFO - Epoch [75][3200/5005] lr: 1.000e-03, eta: 3:06:27, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8663 2023-01-05 00:48:27,123 - mmcls - INFO - Epoch [75][3300/5005] lr: 1.000e-03, eta: 3:06:18, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8426 2023-01-05 00:48:35,879 - mmcls - INFO - Epoch [75][3400/5005] lr: 1.000e-03, eta: 3:06:09, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8769 2023-01-05 00:48:44,684 - mmcls - INFO - Epoch [75][3500/5005] lr: 1.000e-03, eta: 3:06:01, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8614 2023-01-05 00:48:53,309 - mmcls - INFO - Epoch [75][3600/5005] lr: 1.000e-03, eta: 3:05:52, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8538 2023-01-05 00:49:02,111 - mmcls - INFO - Epoch [75][3700/5005] lr: 1.000e-03, eta: 3:05:43, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8478 2023-01-05 00:49:10,943 - mmcls - INFO - Epoch [75][3800/5005] lr: 1.000e-03, eta: 3:05:34, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8587 2023-01-05 00:49:19,812 - mmcls - INFO - Epoch [75][3900/5005] lr: 1.000e-03, eta: 3:05:25, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8758 2023-01-05 00:49:28,610 - mmcls - INFO - Epoch [75][4000/5005] lr: 1.000e-03, eta: 3:05:17, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8756 2023-01-05 00:49:37,431 - mmcls - INFO - Epoch [75][4100/5005] lr: 1.000e-03, eta: 3:05:08, time: 0.088, data_time: 0.024, memory: 928, loss: 4.8394 2023-01-05 00:49:46,190 - mmcls - INFO - Epoch [75][4200/5005] lr: 1.000e-03, eta: 3:04:59, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8807 2023-01-05 00:49:54,982 - mmcls - INFO - Epoch [75][4300/5005] lr: 1.000e-03, eta: 3:04:50, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8618 2023-01-05 00:50:03,770 - mmcls - INFO - Epoch [75][4400/5005] lr: 1.000e-03, eta: 3:04:41, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8819 2023-01-05 00:50:13,251 - mmcls - INFO - Epoch [75][4500/5005] lr: 1.000e-03, eta: 3:04:33, time: 0.095, data_time: 0.003, memory: 928, loss: 4.8551 2023-01-05 00:50:21,752 - mmcls - INFO - Epoch [75][4600/5005] lr: 1.000e-03, eta: 3:04:24, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8581 2023-01-05 00:50:30,540 - mmcls - INFO - Epoch [75][4700/5005] lr: 1.000e-03, eta: 3:04:15, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8531 2023-01-05 00:50:39,565 - mmcls - INFO - Epoch [75][4800/5005] lr: 1.000e-03, eta: 3:04:06, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8689 2023-01-05 00:50:48,262 - mmcls - INFO - Epoch [75][4900/5005] lr: 1.000e-03, eta: 3:03:57, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8646 2023-01-05 00:50:57,120 - mmcls - INFO - Epoch [75][5000/5005] lr: 1.000e-03, eta: 3:03:49, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8584 2023-01-05 00:51:19,793 - mmcls - INFO - Epoch(val) [75][196] accuracy_top-1: 17.0480, accuracy_top-5: 34.8280 2023-01-05 00:51:31,006 - mmcls - INFO - Epoch [76][100/5005] lr: 1.000e-03, eta: 3:03:40, time: 0.112, data_time: 0.049, memory: 928, loss: 4.8401 2023-01-05 00:51:40,164 - mmcls - INFO - Epoch [76][200/5005] lr: 1.000e-03, eta: 3:03:31, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8603 2023-01-05 00:51:48,865 - mmcls - INFO - Epoch [76][300/5005] lr: 1.000e-03, eta: 3:03:22, time: 0.087, data_time: 0.031, memory: 928, loss: 4.8468 2023-01-05 00:51:57,511 - mmcls - INFO - Epoch [76][400/5005] lr: 1.000e-03, eta: 3:03:14, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8592 2023-01-05 00:52:06,472 - mmcls - INFO - Epoch [76][500/5005] lr: 1.000e-03, eta: 3:03:05, time: 0.090, data_time: 0.024, memory: 928, loss: 4.8757 2023-01-05 00:52:15,319 - mmcls - INFO - Epoch [76][600/5005] lr: 1.000e-03, eta: 3:02:56, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8630 2023-01-05 00:52:24,041 - mmcls - INFO - Epoch [76][700/5005] lr: 1.000e-03, eta: 3:02:47, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8606 2023-01-05 00:52:32,712 - mmcls - INFO - Epoch [76][800/5005] lr: 1.000e-03, eta: 3:02:38, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8624 2023-01-05 00:52:41,255 - mmcls - INFO - Epoch [76][900/5005] lr: 1.000e-03, eta: 3:02:29, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8694 2023-01-05 00:52:50,156 - mmcls - INFO - Epoch [76][1000/5005] lr: 1.000e-03, eta: 3:02:21, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8564 2023-01-05 00:52:58,820 - mmcls - INFO - Epoch [76][1100/5005] lr: 1.000e-03, eta: 3:02:12, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8834 2023-01-05 00:53:08,000 - mmcls - INFO - Epoch [76][1200/5005] lr: 1.000e-03, eta: 3:02:03, time: 0.092, data_time: 0.019, memory: 928, loss: 4.8723 2023-01-05 00:53:16,806 - mmcls - INFO - Epoch [76][1300/5005] lr: 1.000e-03, eta: 3:01:54, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8654 2023-01-05 00:53:25,546 - mmcls - INFO - Epoch [76][1400/5005] lr: 1.000e-03, eta: 3:01:45, time: 0.087, data_time: 0.033, memory: 928, loss: 4.8668 2023-01-05 00:53:34,315 - mmcls - INFO - Epoch [76][1500/5005] lr: 1.000e-03, eta: 3:01:36, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8700 2023-01-05 00:53:43,201 - mmcls - INFO - Epoch [76][1600/5005] lr: 1.000e-03, eta: 3:01:28, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8451 2023-01-05 00:53:51,775 - mmcls - INFO - Epoch [76][1700/5005] lr: 1.000e-03, eta: 3:01:19, time: 0.086, data_time: 0.000, memory: 928, loss: 4.8622 2023-01-05 00:54:00,823 - mmcls - INFO - Epoch [76][1800/5005] lr: 1.000e-03, eta: 3:01:10, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8574 2023-01-05 00:54:09,607 - mmcls - INFO - Epoch [76][1900/5005] lr: 1.000e-03, eta: 3:01:01, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8626 2023-01-05 00:54:18,148 - mmcls - INFO - Epoch [76][2000/5005] lr: 1.000e-03, eta: 3:00:52, time: 0.085, data_time: 0.019, memory: 928, loss: 4.9024 2023-01-05 00:54:27,012 - mmcls - INFO - Epoch [76][2100/5005] lr: 1.000e-03, eta: 3:00:44, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8727 2023-01-05 00:54:36,112 - mmcls - INFO - Epoch [76][2200/5005] lr: 1.000e-03, eta: 3:00:35, time: 0.091, data_time: 0.011, memory: 928, loss: 4.8684 2023-01-05 00:54:45,182 - mmcls - INFO - Epoch [76][2300/5005] lr: 1.000e-03, eta: 3:00:26, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8504 2023-01-05 00:54:53,870 - mmcls - INFO - Epoch [76][2400/5005] lr: 1.000e-03, eta: 3:00:17, time: 0.087, data_time: 0.029, memory: 928, loss: 4.8636 2023-01-05 00:55:02,759 - mmcls - INFO - Epoch [76][2500/5005] lr: 1.000e-03, eta: 3:00:08, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8579 2023-01-05 00:55:11,402 - mmcls - INFO - Epoch [76][2600/5005] lr: 1.000e-03, eta: 3:00:00, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8691 2023-01-05 00:55:20,472 - mmcls - INFO - Epoch [76][2700/5005] lr: 1.000e-03, eta: 2:59:51, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8763 2023-01-05 00:55:29,582 - mmcls - INFO - Epoch [76][2800/5005] lr: 1.000e-03, eta: 2:59:42, time: 0.091, data_time: 0.022, memory: 928, loss: 4.8627 2023-01-05 00:55:38,362 - mmcls - INFO - Epoch [76][2900/5005] lr: 1.000e-03, eta: 2:59:33, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8548 2023-01-05 00:55:47,061 - mmcls - INFO - Epoch [76][3000/5005] lr: 1.000e-03, eta: 2:59:24, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8449 2023-01-05 00:55:55,950 - mmcls - INFO - Epoch [76][3100/5005] lr: 1.000e-03, eta: 2:59:16, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8670 2023-01-05 00:56:04,737 - mmcls - INFO - Epoch [76][3200/5005] lr: 1.000e-03, eta: 2:59:07, time: 0.088, data_time: 0.038, memory: 928, loss: 4.8575 2023-01-05 00:56:13,382 - mmcls - INFO - Epoch [76][3300/5005] lr: 1.000e-03, eta: 2:58:58, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8628 2023-01-05 00:56:22,048 - mmcls - INFO - Epoch [76][3400/5005] lr: 1.000e-03, eta: 2:58:49, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8594 2023-01-05 00:56:30,893 - mmcls - INFO - Epoch [76][3500/5005] lr: 1.000e-03, eta: 2:58:40, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8622 2023-01-05 00:56:39,756 - mmcls - INFO - Epoch [76][3600/5005] lr: 1.000e-03, eta: 2:58:31, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8759 2023-01-05 00:56:48,366 - mmcls - INFO - Epoch [76][3700/5005] lr: 1.000e-03, eta: 2:58:23, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8671 2023-01-05 00:56:57,052 - mmcls - INFO - Epoch [76][3800/5005] lr: 1.000e-03, eta: 2:58:14, time: 0.087, data_time: 0.033, memory: 928, loss: 4.8466 2023-01-05 00:57:05,840 - mmcls - INFO - Epoch [76][3900/5005] lr: 1.000e-03, eta: 2:58:05, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8598 2023-01-05 00:57:14,602 - mmcls - INFO - Epoch [76][4000/5005] lr: 1.000e-03, eta: 2:57:56, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8630 2023-01-05 00:57:23,333 - mmcls - INFO - Epoch [76][4100/5005] lr: 1.000e-03, eta: 2:57:47, time: 0.087, data_time: 0.041, memory: 928, loss: 4.8550 2023-01-05 00:57:32,105 - mmcls - INFO - Epoch [76][4200/5005] lr: 1.000e-03, eta: 2:57:38, time: 0.088, data_time: 0.040, memory: 928, loss: 4.8545 2023-01-05 00:57:41,062 - mmcls - INFO - Epoch [76][4300/5005] lr: 1.000e-03, eta: 2:57:30, time: 0.090, data_time: 0.013, memory: 928, loss: 4.8422 2023-01-05 00:57:49,833 - mmcls - INFO - Epoch [76][4400/5005] lr: 1.000e-03, eta: 2:57:21, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8640 2023-01-05 00:57:58,878 - mmcls - INFO - Epoch [76][4500/5005] lr: 1.000e-03, eta: 2:57:12, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8739 2023-01-05 00:58:07,677 - mmcls - INFO - Epoch [76][4600/5005] lr: 1.000e-03, eta: 2:57:03, time: 0.088, data_time: 0.022, memory: 928, loss: 4.8568 2023-01-05 00:58:16,390 - mmcls - INFO - Epoch [76][4700/5005] lr: 1.000e-03, eta: 2:56:54, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8552 2023-01-05 00:58:24,905 - mmcls - INFO - Epoch [76][4800/5005] lr: 1.000e-03, eta: 2:56:46, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8372 2023-01-05 00:58:33,668 - mmcls - INFO - Epoch [76][4900/5005] lr: 1.000e-03, eta: 2:56:37, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8626 2023-01-05 00:58:42,728 - mmcls - INFO - Epoch [76][5000/5005] lr: 1.000e-03, eta: 2:56:28, time: 0.091, data_time: 0.017, memory: 928, loss: 4.8779 2023-01-05 00:59:05,266 - mmcls - INFO - Epoch(val) [76][196] accuracy_top-1: 17.2600, accuracy_top-5: 35.3860 2023-01-05 00:59:16,234 - mmcls - INFO - Epoch [77][100/5005] lr: 1.000e-03, eta: 2:56:19, time: 0.109, data_time: 0.045, memory: 928, loss: 4.8389 2023-01-05 00:59:25,099 - mmcls - INFO - Epoch [77][200/5005] lr: 1.000e-03, eta: 2:56:10, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8584 2023-01-05 00:59:33,602 - mmcls - INFO - Epoch [77][300/5005] lr: 1.000e-03, eta: 2:56:02, time: 0.085, data_time: 0.024, memory: 928, loss: 4.8558 2023-01-05 00:59:42,324 - mmcls - INFO - Epoch [77][400/5005] lr: 1.000e-03, eta: 2:55:53, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8669 2023-01-05 00:59:51,021 - mmcls - INFO - Epoch [77][500/5005] lr: 1.000e-03, eta: 2:55:44, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8428 2023-01-05 01:00:00,002 - mmcls - INFO - Epoch [77][600/5005] lr: 1.000e-03, eta: 2:55:35, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8525 2023-01-05 01:00:09,111 - mmcls - INFO - Epoch [77][700/5005] lr: 1.000e-03, eta: 2:55:26, time: 0.091, data_time: 0.018, memory: 928, loss: 4.8559 2023-01-05 01:00:18,371 - mmcls - INFO - Epoch [77][800/5005] lr: 1.000e-03, eta: 2:55:18, time: 0.093, data_time: 0.017, memory: 928, loss: 4.8624 2023-01-05 01:00:26,996 - mmcls - INFO - Epoch [77][900/5005] lr: 1.000e-03, eta: 2:55:09, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8912 2023-01-05 01:00:36,034 - mmcls - INFO - Epoch [77][1000/5005] lr: 1.000e-03, eta: 2:55:00, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8897 2023-01-05 01:00:44,972 - mmcls - INFO - Epoch [77][1100/5005] lr: 1.000e-03, eta: 2:54:51, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8813 2023-01-05 01:00:53,566 - mmcls - INFO - Epoch [77][1200/5005] lr: 1.000e-03, eta: 2:54:42, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8551 2023-01-05 01:01:02,226 - mmcls - INFO - Epoch [77][1300/5005] lr: 1.000e-03, eta: 2:54:33, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8341 2023-01-05 01:01:10,918 - mmcls - INFO - Epoch [77][1400/5005] lr: 1.000e-03, eta: 2:54:25, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8846 2023-01-05 01:01:19,896 - mmcls - INFO - Epoch [77][1500/5005] lr: 1.000e-03, eta: 2:54:16, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8643 2023-01-05 01:01:28,584 - mmcls - INFO - Epoch [77][1600/5005] lr: 1.000e-03, eta: 2:54:07, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8657 2023-01-05 01:01:37,400 - mmcls - INFO - Epoch [77][1700/5005] lr: 1.000e-03, eta: 2:53:58, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8532 2023-01-05 01:01:46,214 - mmcls - INFO - Epoch [77][1800/5005] lr: 1.000e-03, eta: 2:53:49, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8582 2023-01-05 01:01:55,038 - mmcls - INFO - Epoch [77][1900/5005] lr: 1.000e-03, eta: 2:53:41, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8489 2023-01-05 01:02:03,603 - mmcls - INFO - Epoch [77][2000/5005] lr: 1.000e-03, eta: 2:53:32, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8591 2023-01-05 01:02:12,464 - mmcls - INFO - Epoch [77][2100/5005] lr: 1.000e-03, eta: 2:53:23, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8561 2023-01-05 01:02:21,296 - mmcls - INFO - Epoch [77][2200/5005] lr: 1.000e-03, eta: 2:53:14, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8689 2023-01-05 01:02:30,160 - mmcls - INFO - Epoch [77][2300/5005] lr: 1.000e-03, eta: 2:53:05, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8436 2023-01-05 01:02:38,875 - mmcls - INFO - Epoch [77][2400/5005] lr: 1.000e-03, eta: 2:52:56, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8752 2023-01-05 01:02:47,598 - mmcls - INFO - Epoch [77][2500/5005] lr: 1.000e-03, eta: 2:52:48, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8406 2023-01-05 01:02:56,189 - mmcls - INFO - Epoch [77][2600/5005] lr: 1.000e-03, eta: 2:52:39, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8608 2023-01-05 01:03:05,032 - mmcls - INFO - Epoch [77][2700/5005] lr: 1.000e-03, eta: 2:52:30, time: 0.089, data_time: 0.015, memory: 928, loss: 4.8772 2023-01-05 01:03:13,776 - mmcls - INFO - Epoch [77][2800/5005] lr: 1.000e-03, eta: 2:52:21, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8623 2023-01-05 01:03:22,599 - mmcls - INFO - Epoch [77][2900/5005] lr: 1.000e-03, eta: 2:52:12, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8576 2023-01-05 01:03:31,415 - mmcls - INFO - Epoch [77][3000/5005] lr: 1.000e-03, eta: 2:52:03, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8425 2023-01-05 01:03:40,301 - mmcls - INFO - Epoch [77][3100/5005] lr: 1.000e-03, eta: 2:51:55, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8339 2023-01-05 01:03:49,008 - mmcls - INFO - Epoch [77][3200/5005] lr: 1.000e-03, eta: 2:51:46, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8443 2023-01-05 01:03:57,886 - mmcls - INFO - Epoch [77][3300/5005] lr: 1.000e-03, eta: 2:51:37, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8360 2023-01-05 01:04:06,927 - mmcls - INFO - Epoch [77][3400/5005] lr: 1.000e-03, eta: 2:51:28, time: 0.090, data_time: 0.013, memory: 928, loss: 4.8546 2023-01-05 01:04:15,718 - mmcls - INFO - Epoch [77][3500/5005] lr: 1.000e-03, eta: 2:51:19, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8761 2023-01-05 01:04:24,492 - mmcls - INFO - Epoch [77][3600/5005] lr: 1.000e-03, eta: 2:51:11, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8493 2023-01-05 01:04:33,094 - mmcls - INFO - Epoch [77][3700/5005] lr: 1.000e-03, eta: 2:51:02, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8656 2023-01-05 01:04:41,595 - mmcls - INFO - Epoch [77][3800/5005] lr: 1.000e-03, eta: 2:50:53, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8492 2023-01-05 01:04:50,482 - mmcls - INFO - Epoch [77][3900/5005] lr: 1.000e-03, eta: 2:50:44, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8776 2023-01-05 01:04:59,126 - mmcls - INFO - Epoch [77][4000/5005] lr: 1.000e-03, eta: 2:50:35, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8510 2023-01-05 01:05:08,119 - mmcls - INFO - Epoch [77][4100/5005] lr: 1.000e-03, eta: 2:50:26, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8685 2023-01-05 01:05:17,088 - mmcls - INFO - Epoch [77][4200/5005] lr: 1.000e-03, eta: 2:50:18, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8318 2023-01-05 01:05:25,742 - mmcls - INFO - Epoch [77][4300/5005] lr: 1.000e-03, eta: 2:50:09, time: 0.086, data_time: 0.021, memory: 928, loss: 4.8442 2023-01-05 01:05:34,643 - mmcls - INFO - Epoch [77][4400/5005] lr: 1.000e-03, eta: 2:50:00, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8761 2023-01-05 01:05:43,259 - mmcls - INFO - Epoch [77][4500/5005] lr: 1.000e-03, eta: 2:49:51, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8608 2023-01-05 01:05:51,859 - mmcls - INFO - Epoch [77][4600/5005] lr: 1.000e-03, eta: 2:49:42, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8584 2023-01-05 01:06:00,370 - mmcls - INFO - Epoch [77][4700/5005] lr: 1.000e-03, eta: 2:49:33, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8456 2023-01-05 01:06:09,034 - mmcls - INFO - Epoch [77][4800/5005] lr: 1.000e-03, eta: 2:49:24, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8744 2023-01-05 01:06:17,889 - mmcls - INFO - Epoch [77][4900/5005] lr: 1.000e-03, eta: 2:49:16, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8703 2023-01-05 01:06:26,615 - mmcls - INFO - Epoch [77][5000/5005] lr: 1.000e-03, eta: 2:49:07, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8373 2023-01-05 01:06:49,397 - mmcls - INFO - Epoch(val) [77][196] accuracy_top-1: 17.0560, accuracy_top-5: 35.1240 2023-01-05 01:07:00,471 - mmcls - INFO - Epoch [78][100/5005] lr: 1.000e-03, eta: 2:48:58, time: 0.111, data_time: 0.049, memory: 928, loss: 4.8439 2023-01-05 01:07:09,349 - mmcls - INFO - Epoch [78][200/5005] lr: 1.000e-03, eta: 2:48:49, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8772 2023-01-05 01:07:18,173 - mmcls - INFO - Epoch [78][300/5005] lr: 1.000e-03, eta: 2:48:40, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8574 2023-01-05 01:07:26,976 - mmcls - INFO - Epoch [78][400/5005] lr: 1.000e-03, eta: 2:48:32, time: 0.088, data_time: 0.032, memory: 928, loss: 4.8356 2023-01-05 01:07:35,537 - mmcls - INFO - Epoch [78][500/5005] lr: 1.000e-03, eta: 2:48:23, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8823 2023-01-05 01:07:44,160 - mmcls - INFO - Epoch [78][600/5005] lr: 1.000e-03, eta: 2:48:14, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8403 2023-01-05 01:07:52,884 - mmcls - INFO - Epoch [78][700/5005] lr: 1.000e-03, eta: 2:48:05, time: 0.087, data_time: 0.039, memory: 928, loss: 4.8555 2023-01-05 01:08:01,728 - mmcls - INFO - Epoch [78][800/5005] lr: 1.000e-03, eta: 2:47:56, time: 0.088, data_time: 0.029, memory: 928, loss: 4.8828 2023-01-05 01:08:10,445 - mmcls - INFO - Epoch [78][900/5005] lr: 1.000e-03, eta: 2:47:47, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8560 2023-01-05 01:08:19,187 - mmcls - INFO - Epoch [78][1000/5005] lr: 1.000e-03, eta: 2:47:39, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8778 2023-01-05 01:08:27,685 - mmcls - INFO - Epoch [78][1100/5005] lr: 1.000e-03, eta: 2:47:30, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8677 2023-01-05 01:08:36,533 - mmcls - INFO - Epoch [78][1200/5005] lr: 1.000e-03, eta: 2:47:21, time: 0.089, data_time: 0.031, memory: 928, loss: 4.8514 2023-01-05 01:08:45,239 - mmcls - INFO - Epoch [78][1300/5005] lr: 1.000e-03, eta: 2:47:12, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8509 2023-01-05 01:08:53,903 - mmcls - INFO - Epoch [78][1400/5005] lr: 1.000e-03, eta: 2:47:03, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8675 2023-01-05 01:09:02,898 - mmcls - INFO - Epoch [78][1500/5005] lr: 1.000e-03, eta: 2:46:54, time: 0.090, data_time: 0.012, memory: 928, loss: 4.8532 2023-01-05 01:09:11,891 - mmcls - INFO - Epoch [78][1600/5005] lr: 1.000e-03, eta: 2:46:46, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8400 2023-01-05 01:09:20,963 - mmcls - INFO - Epoch [78][1700/5005] lr: 1.000e-03, eta: 2:46:37, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8742 2023-01-05 01:09:29,958 - mmcls - INFO - Epoch [78][1800/5005] lr: 1.000e-03, eta: 2:46:28, time: 0.090, data_time: 0.019, memory: 928, loss: 4.8560 2023-01-05 01:09:38,538 - mmcls - INFO - Epoch [78][1900/5005] lr: 1.000e-03, eta: 2:46:19, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8619 2023-01-05 01:09:47,291 - mmcls - INFO - Epoch [78][2000/5005] lr: 1.000e-03, eta: 2:46:10, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8648 2023-01-05 01:09:56,043 - mmcls - INFO - Epoch [78][2100/5005] lr: 1.000e-03, eta: 2:46:02, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8638 2023-01-05 01:10:04,922 - mmcls - INFO - Epoch [78][2200/5005] lr: 1.000e-03, eta: 2:45:53, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8733 2023-01-05 01:10:13,552 - mmcls - INFO - Epoch [78][2300/5005] lr: 1.000e-03, eta: 2:45:44, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8758 2023-01-05 01:10:22,147 - mmcls - INFO - Epoch [78][2400/5005] lr: 1.000e-03, eta: 2:45:35, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8551 2023-01-05 01:10:31,062 - mmcls - INFO - Epoch [78][2500/5005] lr: 1.000e-03, eta: 2:45:26, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8545 2023-01-05 01:10:40,316 - mmcls - INFO - Epoch [78][2600/5005] lr: 1.000e-03, eta: 2:45:18, time: 0.092, data_time: 0.000, memory: 928, loss: 4.8449 2023-01-05 01:10:49,136 - mmcls - INFO - Epoch [78][2700/5005] lr: 1.000e-03, eta: 2:45:09, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8625 2023-01-05 01:10:57,921 - mmcls - INFO - Epoch [78][2800/5005] lr: 1.000e-03, eta: 2:45:00, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8479 2023-01-05 01:11:06,613 - mmcls - INFO - Epoch [78][2900/5005] lr: 1.000e-03, eta: 2:44:51, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8645 2023-01-05 01:11:15,335 - mmcls - INFO - Epoch [78][3000/5005] lr: 1.000e-03, eta: 2:44:42, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8824 2023-01-05 01:11:23,867 - mmcls - INFO - Epoch [78][3100/5005] lr: 1.000e-03, eta: 2:44:33, time: 0.085, data_time: 0.013, memory: 928, loss: 4.8611 2023-01-05 01:11:32,764 - mmcls - INFO - Epoch [78][3200/5005] lr: 1.000e-03, eta: 2:44:25, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8685 2023-01-05 01:11:41,290 - mmcls - INFO - Epoch [78][3300/5005] lr: 1.000e-03, eta: 2:44:16, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8751 2023-01-05 01:11:50,130 - mmcls - INFO - Epoch [78][3400/5005] lr: 1.000e-03, eta: 2:44:07, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8535 2023-01-05 01:11:58,884 - mmcls - INFO - Epoch [78][3500/5005] lr: 1.000e-03, eta: 2:43:58, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8566 2023-01-05 01:12:07,563 - mmcls - INFO - Epoch [78][3600/5005] lr: 1.000e-03, eta: 2:43:49, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8524 2023-01-05 01:12:16,311 - mmcls - INFO - Epoch [78][3700/5005] lr: 1.000e-03, eta: 2:43:40, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8591 2023-01-05 01:12:25,208 - mmcls - INFO - Epoch [78][3800/5005] lr: 1.000e-03, eta: 2:43:32, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8598 2023-01-05 01:12:34,130 - mmcls - INFO - Epoch [78][3900/5005] lr: 1.000e-03, eta: 2:43:23, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8642 2023-01-05 01:12:42,894 - mmcls - INFO - Epoch [78][4000/5005] lr: 1.000e-03, eta: 2:43:14, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8635 2023-01-05 01:12:51,764 - mmcls - INFO - Epoch [78][4100/5005] lr: 1.000e-03, eta: 2:43:05, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8449 2023-01-05 01:13:00,427 - mmcls - INFO - Epoch [78][4200/5005] lr: 1.000e-03, eta: 2:42:56, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8622 2023-01-05 01:13:09,178 - mmcls - INFO - Epoch [78][4300/5005] lr: 1.000e-03, eta: 2:42:47, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8502 2023-01-05 01:13:18,034 - mmcls - INFO - Epoch [78][4400/5005] lr: 1.000e-03, eta: 2:42:39, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8425 2023-01-05 01:13:26,665 - mmcls - INFO - Epoch [78][4500/5005] lr: 1.000e-03, eta: 2:42:30, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8653 2023-01-05 01:13:35,635 - mmcls - INFO - Epoch [78][4600/5005] lr: 1.000e-03, eta: 2:42:21, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8419 2023-01-05 01:13:44,435 - mmcls - INFO - Epoch [78][4700/5005] lr: 1.000e-03, eta: 2:42:12, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8518 2023-01-05 01:13:53,176 - mmcls - INFO - Epoch [78][4800/5005] lr: 1.000e-03, eta: 2:42:03, time: 0.087, data_time: 0.036, memory: 928, loss: 4.8658 2023-01-05 01:14:01,737 - mmcls - INFO - Epoch [78][4900/5005] lr: 1.000e-03, eta: 2:41:54, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8579 2023-01-05 01:14:10,369 - mmcls - INFO - Epoch [78][5000/5005] lr: 1.000e-03, eta: 2:41:46, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8430 2023-01-05 01:14:33,109 - mmcls - INFO - Epoch(val) [78][196] accuracy_top-1: 16.1780, accuracy_top-5: 33.4500 2023-01-05 01:14:44,140 - mmcls - INFO - Epoch [79][100/5005] lr: 1.000e-03, eta: 2:41:37, time: 0.110, data_time: 0.036, memory: 928, loss: 4.8404 2023-01-05 01:14:52,810 - mmcls - INFO - Epoch [79][200/5005] lr: 1.000e-03, eta: 2:41:28, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8484 2023-01-05 01:15:01,575 - mmcls - INFO - Epoch [79][300/5005] lr: 1.000e-03, eta: 2:41:19, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8760 2023-01-05 01:15:10,500 - mmcls - INFO - Epoch [79][400/5005] lr: 1.000e-03, eta: 2:41:10, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8541 2023-01-05 01:15:19,033 - mmcls - INFO - Epoch [79][500/5005] lr: 1.000e-03, eta: 2:41:01, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8657 2023-01-05 01:15:27,876 - mmcls - INFO - Epoch [79][600/5005] lr: 1.000e-03, eta: 2:40:53, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8440 2023-01-05 01:15:36,712 - mmcls - INFO - Epoch [79][700/5005] lr: 1.000e-03, eta: 2:40:44, time: 0.088, data_time: 0.024, memory: 928, loss: 4.8593 2023-01-05 01:15:45,482 - mmcls - INFO - Epoch [79][800/5005] lr: 1.000e-03, eta: 2:40:35, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8615 2023-01-05 01:15:54,299 - mmcls - INFO - Epoch [79][900/5005] lr: 1.000e-03, eta: 2:40:26, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8547 2023-01-05 01:16:02,819 - mmcls - INFO - Epoch [79][1000/5005] lr: 1.000e-03, eta: 2:40:17, time: 0.085, data_time: 0.017, memory: 928, loss: 4.8619 2023-01-05 01:16:11,793 - mmcls - INFO - Epoch [79][1100/5005] lr: 1.000e-03, eta: 2:40:09, time: 0.090, data_time: 0.008, memory: 928, loss: 4.8618 2023-01-05 01:16:20,713 - mmcls - INFO - Epoch [79][1200/5005] lr: 1.000e-03, eta: 2:40:00, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8623 2023-01-05 01:16:29,509 - mmcls - INFO - Epoch [79][1300/5005] lr: 1.000e-03, eta: 2:39:51, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8558 2023-01-05 01:16:38,282 - mmcls - INFO - Epoch [79][1400/5005] lr: 1.000e-03, eta: 2:39:42, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8550 2023-01-05 01:16:47,001 - mmcls - INFO - Epoch [79][1500/5005] lr: 1.000e-03, eta: 2:39:33, time: 0.087, data_time: 0.033, memory: 928, loss: 4.8585 2023-01-05 01:16:55,506 - mmcls - INFO - Epoch [79][1600/5005] lr: 1.000e-03, eta: 2:39:24, time: 0.085, data_time: 0.017, memory: 928, loss: 4.8562 2023-01-05 01:17:04,317 - mmcls - INFO - Epoch [79][1700/5005] lr: 1.000e-03, eta: 2:39:16, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8466 2023-01-05 01:17:13,211 - mmcls - INFO - Epoch [79][1800/5005] lr: 1.000e-03, eta: 2:39:07, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8541 2023-01-05 01:17:21,858 - mmcls - INFO - Epoch [79][1900/5005] lr: 1.000e-03, eta: 2:38:58, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8572 2023-01-05 01:17:30,679 - mmcls - INFO - Epoch [79][2000/5005] lr: 1.000e-03, eta: 2:38:49, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8547 2023-01-05 01:17:39,500 - mmcls - INFO - Epoch [79][2100/5005] lr: 1.000e-03, eta: 2:38:40, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8549 2023-01-05 01:17:48,130 - mmcls - INFO - Epoch [79][2200/5005] lr: 1.000e-03, eta: 2:38:31, time: 0.086, data_time: 0.010, memory: 928, loss: 4.8582 2023-01-05 01:17:57,354 - mmcls - INFO - Epoch [79][2300/5005] lr: 1.000e-03, eta: 2:38:23, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8589 2023-01-05 01:18:06,104 - mmcls - INFO - Epoch [79][2400/5005] lr: 1.000e-03, eta: 2:38:14, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8672 2023-01-05 01:18:14,763 - mmcls - INFO - Epoch [79][2500/5005] lr: 1.000e-03, eta: 2:38:05, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8689 2023-01-05 01:18:23,388 - mmcls - INFO - Epoch [79][2600/5005] lr: 1.000e-03, eta: 2:37:56, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8521 2023-01-05 01:18:32,243 - mmcls - INFO - Epoch [79][2700/5005] lr: 1.000e-03, eta: 2:37:47, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8614 2023-01-05 01:18:41,053 - mmcls - INFO - Epoch [79][2800/5005] lr: 1.000e-03, eta: 2:37:38, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8486 2023-01-05 01:18:49,523 - mmcls - INFO - Epoch [79][2900/5005] lr: 1.000e-03, eta: 2:37:30, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8646 2023-01-05 01:18:58,324 - mmcls - INFO - Epoch [79][3000/5005] lr: 1.000e-03, eta: 2:37:21, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8668 2023-01-05 01:19:07,223 - mmcls - INFO - Epoch [79][3100/5005] lr: 1.000e-03, eta: 2:37:12, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8517 2023-01-05 01:19:16,065 - mmcls - INFO - Epoch [79][3200/5005] lr: 1.000e-03, eta: 2:37:03, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8765 2023-01-05 01:19:24,889 - mmcls - INFO - Epoch [79][3300/5005] lr: 1.000e-03, eta: 2:36:54, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8621 2023-01-05 01:19:33,464 - mmcls - INFO - Epoch [79][3400/5005] lr: 1.000e-03, eta: 2:36:45, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8652 2023-01-05 01:19:42,143 - mmcls - INFO - Epoch [79][3500/5005] lr: 1.000e-03, eta: 2:36:37, time: 0.087, data_time: 0.029, memory: 928, loss: 4.8692 2023-01-05 01:19:50,998 - mmcls - INFO - Epoch [79][3600/5005] lr: 1.000e-03, eta: 2:36:28, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8737 2023-01-05 01:20:00,055 - mmcls - INFO - Epoch [79][3700/5005] lr: 1.000e-03, eta: 2:36:19, time: 0.091, data_time: 0.004, memory: 928, loss: 4.8474 2023-01-05 01:20:08,977 - mmcls - INFO - Epoch [79][3800/5005] lr: 1.000e-03, eta: 2:36:10, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8701 2023-01-05 01:20:17,959 - mmcls - INFO - Epoch [79][3900/5005] lr: 1.000e-03, eta: 2:36:02, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8524 2023-01-05 01:20:26,671 - mmcls - INFO - Epoch [79][4000/5005] lr: 1.000e-03, eta: 2:35:53, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8483 2023-01-05 01:20:35,401 - mmcls - INFO - Epoch [79][4100/5005] lr: 1.000e-03, eta: 2:35:44, time: 0.087, data_time: 0.025, memory: 928, loss: 4.8561 2023-01-05 01:20:44,276 - mmcls - INFO - Epoch [79][4200/5005] lr: 1.000e-03, eta: 2:35:35, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8445 2023-01-05 01:20:52,972 - mmcls - INFO - Epoch [79][4300/5005] lr: 1.000e-03, eta: 2:35:26, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8759 2023-01-05 01:21:01,779 - mmcls - INFO - Epoch [79][4400/5005] lr: 1.000e-03, eta: 2:35:17, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8626 2023-01-05 01:21:10,326 - mmcls - INFO - Epoch [79][4500/5005] lr: 1.000e-03, eta: 2:35:08, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8356 2023-01-05 01:21:18,926 - mmcls - INFO - Epoch [79][4600/5005] lr: 1.000e-03, eta: 2:35:00, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8550 2023-01-05 01:21:27,638 - mmcls - INFO - Epoch [79][4700/5005] lr: 1.000e-03, eta: 2:34:51, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8505 2023-01-05 01:21:36,493 - mmcls - INFO - Epoch [79][4800/5005] lr: 1.000e-03, eta: 2:34:42, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8852 2023-01-05 01:21:45,356 - mmcls - INFO - Epoch [79][4900/5005] lr: 1.000e-03, eta: 2:34:33, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8421 2023-01-05 01:21:54,025 - mmcls - INFO - Epoch [79][5000/5005] lr: 1.000e-03, eta: 2:34:24, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8585 2023-01-05 01:22:16,935 - mmcls - INFO - Epoch(val) [79][196] accuracy_top-1: 17.1500, accuracy_top-5: 35.0720 2023-01-05 01:22:28,055 - mmcls - INFO - Epoch [80][100/5005] lr: 1.000e-03, eta: 2:34:16, time: 0.111, data_time: 0.033, memory: 928, loss: 4.8536 2023-01-05 01:22:36,878 - mmcls - INFO - Epoch [80][200/5005] lr: 1.000e-03, eta: 2:34:07, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8568 2023-01-05 01:22:45,669 - mmcls - INFO - Epoch [80][300/5005] lr: 1.000e-03, eta: 2:33:58, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8524 2023-01-05 01:22:54,427 - mmcls - INFO - Epoch [80][400/5005] lr: 1.000e-03, eta: 2:33:49, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8615 2023-01-05 01:23:03,550 - mmcls - INFO - Epoch [80][500/5005] lr: 1.000e-03, eta: 2:33:40, time: 0.091, data_time: 0.023, memory: 928, loss: 4.8367 2023-01-05 01:23:12,177 - mmcls - INFO - Epoch [80][600/5005] lr: 1.000e-03, eta: 2:33:31, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8312 2023-01-05 01:23:21,138 - mmcls - INFO - Epoch [80][700/5005] lr: 1.000e-03, eta: 2:33:23, time: 0.090, data_time: 0.026, memory: 928, loss: 4.8388 2023-01-05 01:23:29,834 - mmcls - INFO - Epoch [80][800/5005] lr: 1.000e-03, eta: 2:33:14, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8412 2023-01-05 01:23:38,536 - mmcls - INFO - Epoch [80][900/5005] lr: 1.000e-03, eta: 2:33:05, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8760 2023-01-05 01:23:47,347 - mmcls - INFO - Epoch [80][1000/5005] lr: 1.000e-03, eta: 2:32:56, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8657 2023-01-05 01:23:56,024 - mmcls - INFO - Epoch [80][1100/5005] lr: 1.000e-03, eta: 2:32:47, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8731 2023-01-05 01:24:04,669 - mmcls - INFO - Epoch [80][1200/5005] lr: 1.000e-03, eta: 2:32:38, time: 0.086, data_time: 0.016, memory: 928, loss: 4.8590 2023-01-05 01:24:13,244 - mmcls - INFO - Epoch [80][1300/5005] lr: 1.000e-03, eta: 2:32:30, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8586 2023-01-05 01:24:22,202 - mmcls - INFO - Epoch [80][1400/5005] lr: 1.000e-03, eta: 2:32:21, time: 0.089, data_time: 0.027, memory: 928, loss: 4.8513 2023-01-05 01:24:31,179 - mmcls - INFO - Epoch [80][1500/5005] lr: 1.000e-03, eta: 2:32:12, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8440 2023-01-05 01:24:39,875 - mmcls - INFO - Epoch [80][1600/5005] lr: 1.000e-03, eta: 2:32:03, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8456 2023-01-05 01:24:48,579 - mmcls - INFO - Epoch [80][1700/5005] lr: 1.000e-03, eta: 2:31:54, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8488 2023-01-05 01:24:57,675 - mmcls - INFO - Epoch [80][1800/5005] lr: 1.000e-03, eta: 2:31:46, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8474 2023-01-05 01:25:06,265 - mmcls - INFO - Epoch [80][1900/5005] lr: 1.000e-03, eta: 2:31:37, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8617 2023-01-05 01:25:15,005 - mmcls - INFO - Epoch [80][2000/5005] lr: 1.000e-03, eta: 2:31:28, time: 0.087, data_time: 0.027, memory: 928, loss: 4.8506 2023-01-05 01:25:23,557 - mmcls - INFO - Epoch [80][2100/5005] lr: 1.000e-03, eta: 2:31:19, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8667 2023-01-05 01:25:32,447 - mmcls - INFO - Epoch [80][2200/5005] lr: 1.000e-03, eta: 2:31:10, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8536 2023-01-05 01:25:41,449 - mmcls - INFO - Epoch [80][2300/5005] lr: 1.000e-03, eta: 2:31:01, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8593 2023-01-05 01:25:50,165 - mmcls - INFO - Epoch [80][2400/5005] lr: 1.000e-03, eta: 2:30:53, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8626 2023-01-05 01:25:58,989 - mmcls - INFO - Epoch [80][2500/5005] lr: 1.000e-03, eta: 2:30:44, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8520 2023-01-05 01:26:07,581 - mmcls - INFO - Epoch [80][2600/5005] lr: 1.000e-03, eta: 2:30:35, time: 0.086, data_time: 0.016, memory: 928, loss: 4.8702 2023-01-05 01:26:16,573 - mmcls - INFO - Epoch [80][2700/5005] lr: 1.000e-03, eta: 2:30:26, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8350 2023-01-05 01:26:24,978 - mmcls - INFO - Epoch [80][2800/5005] lr: 1.000e-03, eta: 2:30:17, time: 0.084, data_time: 0.021, memory: 928, loss: 4.8634 2023-01-05 01:26:34,149 - mmcls - INFO - Epoch [80][2900/5005] lr: 1.000e-03, eta: 2:30:09, time: 0.092, data_time: 0.005, memory: 928, loss: 4.8503 2023-01-05 01:26:42,791 - mmcls - INFO - Epoch [80][3000/5005] lr: 1.000e-03, eta: 2:30:00, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8634 2023-01-05 01:26:52,070 - mmcls - INFO - Epoch [80][3100/5005] lr: 1.000e-03, eta: 2:29:51, time: 0.093, data_time: 0.005, memory: 928, loss: 4.8677 2023-01-05 01:27:00,788 - mmcls - INFO - Epoch [80][3200/5005] lr: 1.000e-03, eta: 2:29:42, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8438 2023-01-05 01:27:09,599 - mmcls - INFO - Epoch [80][3300/5005] lr: 1.000e-03, eta: 2:29:33, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8509 2023-01-05 01:27:18,428 - mmcls - INFO - Epoch [80][3400/5005] lr: 1.000e-03, eta: 2:29:25, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8776 2023-01-05 01:27:26,992 - mmcls - INFO - Epoch [80][3500/5005] lr: 1.000e-03, eta: 2:29:16, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8878 2023-01-05 01:27:35,835 - mmcls - INFO - Epoch [80][3600/5005] lr: 1.000e-03, eta: 2:29:07, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8599 2023-01-05 01:27:44,400 - mmcls - INFO - Epoch [80][3700/5005] lr: 1.000e-03, eta: 2:28:58, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8541 2023-01-05 01:27:53,074 - mmcls - INFO - Epoch [80][3800/5005] lr: 1.000e-03, eta: 2:28:49, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8593 2023-01-05 01:28:01,458 - mmcls - INFO - Epoch [80][3900/5005] lr: 1.000e-03, eta: 2:28:40, time: 0.084, data_time: 0.003, memory: 928, loss: 4.8701 2023-01-05 01:28:10,299 - mmcls - INFO - Epoch [80][4000/5005] lr: 1.000e-03, eta: 2:28:31, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8443 2023-01-05 01:28:19,070 - mmcls - INFO - Epoch [80][4100/5005] lr: 1.000e-03, eta: 2:28:23, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8559 2023-01-05 01:28:27,881 - mmcls - INFO - Epoch [80][4200/5005] lr: 1.000e-03, eta: 2:28:14, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8453 2023-01-05 01:28:36,928 - mmcls - INFO - Epoch [80][4300/5005] lr: 1.000e-03, eta: 2:28:05, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8524 2023-01-05 01:28:45,820 - mmcls - INFO - Epoch [80][4400/5005] lr: 1.000e-03, eta: 2:27:56, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8564 2023-01-05 01:28:54,486 - mmcls - INFO - Epoch [80][4500/5005] lr: 1.000e-03, eta: 2:27:47, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8528 2023-01-05 01:29:02,956 - mmcls - INFO - Epoch [80][4600/5005] lr: 1.000e-03, eta: 2:27:38, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8334 2023-01-05 01:29:11,490 - mmcls - INFO - Epoch [80][4700/5005] lr: 1.000e-03, eta: 2:27:30, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8453 2023-01-05 01:29:20,234 - mmcls - INFO - Epoch [80][4800/5005] lr: 1.000e-03, eta: 2:27:21, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8783 2023-01-05 01:29:28,988 - mmcls - INFO - Epoch [80][4900/5005] lr: 1.000e-03, eta: 2:27:12, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8544 2023-01-05 01:29:37,649 - mmcls - INFO - Epoch [80][5000/5005] lr: 1.000e-03, eta: 2:27:03, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8479 2023-01-05 01:30:00,215 - mmcls - INFO - Epoch(val) [80][196] accuracy_top-1: 16.7800, accuracy_top-5: 34.2860 2023-01-05 01:30:10,948 - mmcls - INFO - Epoch [81][100/5005] lr: 1.000e-03, eta: 2:26:54, time: 0.107, data_time: 0.060, memory: 928, loss: 4.8361 2023-01-05 01:30:19,805 - mmcls - INFO - Epoch [81][200/5005] lr: 1.000e-03, eta: 2:26:45, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8606 2023-01-05 01:30:28,473 - mmcls - INFO - Epoch [81][300/5005] lr: 1.000e-03, eta: 2:26:36, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8562 2023-01-05 01:30:37,286 - mmcls - INFO - Epoch [81][400/5005] lr: 1.000e-03, eta: 2:26:28, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8588 2023-01-05 01:30:46,216 - mmcls - INFO - Epoch [81][500/5005] lr: 1.000e-03, eta: 2:26:19, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8557 2023-01-05 01:30:54,885 - mmcls - INFO - Epoch [81][600/5005] lr: 1.000e-03, eta: 2:26:10, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8466 2023-01-05 01:31:04,244 - mmcls - INFO - Epoch [81][700/5005] lr: 1.000e-03, eta: 2:26:01, time: 0.094, data_time: 0.002, memory: 928, loss: 4.8486 2023-01-05 01:31:13,078 - mmcls - INFO - Epoch [81][800/5005] lr: 1.000e-03, eta: 2:25:53, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8598 2023-01-05 01:31:21,933 - mmcls - INFO - Epoch [81][900/5005] lr: 1.000e-03, eta: 2:25:44, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8446 2023-01-05 01:31:30,502 - mmcls - INFO - Epoch [81][1000/5005] lr: 1.000e-03, eta: 2:25:35, time: 0.086, data_time: 0.021, memory: 928, loss: 4.8546 2023-01-05 01:31:39,350 - mmcls - INFO - Epoch [81][1100/5005] lr: 1.000e-03, eta: 2:25:26, time: 0.089, data_time: 0.023, memory: 928, loss: 4.8482 2023-01-05 01:31:48,043 - mmcls - INFO - Epoch [81][1200/5005] lr: 1.000e-03, eta: 2:25:17, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8398 2023-01-05 01:31:56,828 - mmcls - INFO - Epoch [81][1300/5005] lr: 1.000e-03, eta: 2:25:08, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8337 2023-01-05 01:32:05,211 - mmcls - INFO - Epoch [81][1400/5005] lr: 1.000e-03, eta: 2:24:59, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8578 2023-01-05 01:32:14,057 - mmcls - INFO - Epoch [81][1500/5005] lr: 1.000e-03, eta: 2:24:51, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8526 2023-01-05 01:32:22,959 - mmcls - INFO - Epoch [81][1600/5005] lr: 1.000e-03, eta: 2:24:42, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8450 2023-01-05 01:32:31,964 - mmcls - INFO - Epoch [81][1700/5005] lr: 1.000e-03, eta: 2:24:33, time: 0.090, data_time: 0.039, memory: 928, loss: 4.8611 2023-01-05 01:32:41,120 - mmcls - INFO - Epoch [81][1800/5005] lr: 1.000e-03, eta: 2:24:24, time: 0.091, data_time: 0.012, memory: 928, loss: 4.8681 2023-01-05 01:32:49,832 - mmcls - INFO - Epoch [81][1900/5005] lr: 1.000e-03, eta: 2:24:16, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8484 2023-01-05 01:32:58,710 - mmcls - INFO - Epoch [81][2000/5005] lr: 1.000e-03, eta: 2:24:07, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8551 2023-01-05 01:33:07,483 - mmcls - INFO - Epoch [81][2100/5005] lr: 1.000e-03, eta: 2:23:58, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8441 2023-01-05 01:33:16,114 - mmcls - INFO - Epoch [81][2200/5005] lr: 1.000e-03, eta: 2:23:49, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8587 2023-01-05 01:33:24,845 - mmcls - INFO - Epoch [81][2300/5005] lr: 1.000e-03, eta: 2:23:40, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8513 2023-01-05 01:33:33,469 - mmcls - INFO - Epoch [81][2400/5005] lr: 1.000e-03, eta: 2:23:31, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8674 2023-01-05 01:33:42,068 - mmcls - INFO - Epoch [81][2500/5005] lr: 1.000e-03, eta: 2:23:22, time: 0.086, data_time: 0.021, memory: 928, loss: 4.8660 2023-01-05 01:33:50,911 - mmcls - INFO - Epoch [81][2600/5005] lr: 1.000e-03, eta: 2:23:14, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8645 2023-01-05 01:33:59,308 - mmcls - INFO - Epoch [81][2700/5005] lr: 1.000e-03, eta: 2:23:05, time: 0.084, data_time: 0.015, memory: 928, loss: 4.8532 2023-01-05 01:34:08,347 - mmcls - INFO - Epoch [81][2800/5005] lr: 1.000e-03, eta: 2:22:56, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8462 2023-01-05 01:34:16,819 - mmcls - INFO - Epoch [81][2900/5005] lr: 1.000e-03, eta: 2:22:47, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8655 2023-01-05 01:34:25,474 - mmcls - INFO - Epoch [81][3000/5005] lr: 1.000e-03, eta: 2:22:38, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8503 2023-01-05 01:34:34,050 - mmcls - INFO - Epoch [81][3100/5005] lr: 1.000e-03, eta: 2:22:29, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8650 2023-01-05 01:34:42,746 - mmcls - INFO - Epoch [81][3200/5005] lr: 1.000e-03, eta: 2:22:21, time: 0.087, data_time: 0.000, memory: 928, loss: 4.8297 2023-01-05 01:34:51,253 - mmcls - INFO - Epoch [81][3300/5005] lr: 1.000e-03, eta: 2:22:12, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8463 2023-01-05 01:34:59,930 - mmcls - INFO - Epoch [81][3400/5005] lr: 1.000e-03, eta: 2:22:03, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8523 2023-01-05 01:35:08,738 - mmcls - INFO - Epoch [81][3500/5005] lr: 1.000e-03, eta: 2:21:54, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8497 2023-01-05 01:35:17,138 - mmcls - INFO - Epoch [81][3600/5005] lr: 1.000e-03, eta: 2:21:45, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8627 2023-01-05 01:35:25,980 - mmcls - INFO - Epoch [81][3700/5005] lr: 1.000e-03, eta: 2:21:36, time: 0.088, data_time: 0.018, memory: 928, loss: 4.8585 2023-01-05 01:35:34,661 - mmcls - INFO - Epoch [81][3800/5005] lr: 1.000e-03, eta: 2:21:27, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8400 2023-01-05 01:35:43,178 - mmcls - INFO - Epoch [81][3900/5005] lr: 1.000e-03, eta: 2:21:19, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8853 2023-01-05 01:35:51,707 - mmcls - INFO - Epoch [81][4000/5005] lr: 1.000e-03, eta: 2:21:10, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8593 2023-01-05 01:36:00,626 - mmcls - INFO - Epoch [81][4100/5005] lr: 1.000e-03, eta: 2:21:01, time: 0.089, data_time: 0.016, memory: 928, loss: 4.8264 2023-01-05 01:36:09,120 - mmcls - INFO - Epoch [81][4200/5005] lr: 1.000e-03, eta: 2:20:52, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8559 2023-01-05 01:36:17,882 - mmcls - INFO - Epoch [81][4300/5005] lr: 1.000e-03, eta: 2:20:43, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8559 2023-01-05 01:36:27,426 - mmcls - INFO - Epoch [81][4400/5005] lr: 1.000e-03, eta: 2:20:34, time: 0.095, data_time: 0.024, memory: 928, loss: 4.8414 2023-01-05 01:36:36,035 - mmcls - INFO - Epoch [81][4500/5005] lr: 1.000e-03, eta: 2:20:26, time: 0.086, data_time: 0.023, memory: 928, loss: 4.8816 2023-01-05 01:36:44,631 - mmcls - INFO - Epoch [81][4600/5005] lr: 1.000e-03, eta: 2:20:17, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8446 2023-01-05 01:36:53,493 - mmcls - INFO - Epoch [81][4700/5005] lr: 1.000e-03, eta: 2:20:08, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8760 2023-01-05 01:37:01,986 - mmcls - INFO - Epoch [81][4800/5005] lr: 1.000e-03, eta: 2:19:59, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8693 2023-01-05 01:37:10,642 - mmcls - INFO - Epoch [81][4900/5005] lr: 1.000e-03, eta: 2:19:50, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8500 2023-01-05 01:37:19,354 - mmcls - INFO - Epoch [81][5000/5005] lr: 1.000e-03, eta: 2:19:41, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8820 2023-01-05 01:37:42,058 - mmcls - INFO - Epoch(val) [81][196] accuracy_top-1: 16.6260, accuracy_top-5: 34.3740 2023-01-05 01:37:52,659 - mmcls - INFO - Epoch [82][100/5005] lr: 1.000e-03, eta: 2:19:32, time: 0.106, data_time: 0.059, memory: 928, loss: 4.8756 2023-01-05 01:38:01,275 - mmcls - INFO - Epoch [82][200/5005] lr: 1.000e-03, eta: 2:19:24, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8492 2023-01-05 01:38:10,143 - mmcls - INFO - Epoch [82][300/5005] lr: 1.000e-03, eta: 2:19:15, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8713 2023-01-05 01:38:18,837 - mmcls - INFO - Epoch [82][400/5005] lr: 1.000e-03, eta: 2:19:06, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8564 2023-01-05 01:38:27,481 - mmcls - INFO - Epoch [82][500/5005] lr: 1.000e-03, eta: 2:18:57, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8299 2023-01-05 01:38:36,151 - mmcls - INFO - Epoch [82][600/5005] lr: 1.000e-03, eta: 2:18:48, time: 0.087, data_time: 0.023, memory: 928, loss: 4.8492 2023-01-05 01:38:45,008 - mmcls - INFO - Epoch [82][700/5005] lr: 1.000e-03, eta: 2:18:39, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8467 2023-01-05 01:38:54,068 - mmcls - INFO - Epoch [82][800/5005] lr: 1.000e-03, eta: 2:18:31, time: 0.091, data_time: 0.023, memory: 928, loss: 4.8575 2023-01-05 01:39:02,918 - mmcls - INFO - Epoch [82][900/5005] lr: 1.000e-03, eta: 2:18:22, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8630 2023-01-05 01:39:11,643 - mmcls - INFO - Epoch [82][1000/5005] lr: 1.000e-03, eta: 2:18:13, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8519 2023-01-05 01:39:20,782 - mmcls - INFO - Epoch [82][1100/5005] lr: 1.000e-03, eta: 2:18:04, time: 0.091, data_time: 0.017, memory: 928, loss: 4.8503 2023-01-05 01:39:29,675 - mmcls - INFO - Epoch [82][1200/5005] lr: 1.000e-03, eta: 2:17:55, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8569 2023-01-05 01:39:38,377 - mmcls - INFO - Epoch [82][1300/5005] lr: 1.000e-03, eta: 2:17:47, time: 0.087, data_time: 0.038, memory: 928, loss: 4.8723 2023-01-05 01:39:47,093 - mmcls - INFO - Epoch [82][1400/5005] lr: 1.000e-03, eta: 2:17:38, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8627 2023-01-05 01:39:55,845 - mmcls - INFO - Epoch [82][1500/5005] lr: 1.000e-03, eta: 2:17:29, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8439 2023-01-05 01:40:04,716 - mmcls - INFO - Epoch [82][1600/5005] lr: 1.000e-03, eta: 2:17:20, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8470 2023-01-05 01:40:13,557 - mmcls - INFO - Epoch [82][1700/5005] lr: 1.000e-03, eta: 2:17:11, time: 0.088, data_time: 0.029, memory: 928, loss: 4.8721 2023-01-05 01:40:22,574 - mmcls - INFO - Epoch [82][1800/5005] lr: 1.000e-03, eta: 2:17:03, time: 0.090, data_time: 0.030, memory: 928, loss: 4.8447 2023-01-05 01:40:31,254 - mmcls - INFO - Epoch [82][1900/5005] lr: 1.000e-03, eta: 2:16:54, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8608 2023-01-05 01:40:40,167 - mmcls - INFO - Epoch [82][2000/5005] lr: 1.000e-03, eta: 2:16:45, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8518 2023-01-05 01:40:48,952 - mmcls - INFO - Epoch [82][2100/5005] lr: 1.000e-03, eta: 2:16:36, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8589 2023-01-05 01:40:57,673 - mmcls - INFO - Epoch [82][2200/5005] lr: 1.000e-03, eta: 2:16:27, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8617 2023-01-05 01:41:06,631 - mmcls - INFO - Epoch [82][2300/5005] lr: 1.000e-03, eta: 2:16:19, time: 0.090, data_time: 0.021, memory: 928, loss: 4.8331 2023-01-05 01:41:15,656 - mmcls - INFO - Epoch [82][2400/5005] lr: 1.000e-03, eta: 2:16:10, time: 0.090, data_time: 0.014, memory: 928, loss: 4.8559 2023-01-05 01:41:25,062 - mmcls - INFO - Epoch [82][2500/5005] lr: 1.000e-03, eta: 2:16:01, time: 0.094, data_time: 0.029, memory: 928, loss: 4.8735 2023-01-05 01:41:33,837 - mmcls - INFO - Epoch [82][2600/5005] lr: 1.000e-03, eta: 2:15:52, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8373 2023-01-05 01:41:42,594 - mmcls - INFO - Epoch [82][2700/5005] lr: 1.000e-03, eta: 2:15:43, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8600 2023-01-05 01:41:51,965 - mmcls - INFO - Epoch [82][2800/5005] lr: 1.000e-03, eta: 2:15:35, time: 0.094, data_time: 0.005, memory: 928, loss: 4.8515 2023-01-05 01:42:00,730 - mmcls - INFO - Epoch [82][2900/5005] lr: 1.000e-03, eta: 2:15:26, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8256 2023-01-05 01:42:09,571 - mmcls - INFO - Epoch [82][3000/5005] lr: 1.000e-03, eta: 2:15:17, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8626 2023-01-05 01:42:18,431 - mmcls - INFO - Epoch [82][3100/5005] lr: 1.000e-03, eta: 2:15:08, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8513 2023-01-05 01:42:27,183 - mmcls - INFO - Epoch [82][3200/5005] lr: 1.000e-03, eta: 2:14:59, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8607 2023-01-05 01:42:36,102 - mmcls - INFO - Epoch [82][3300/5005] lr: 1.000e-03, eta: 2:14:51, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8648 2023-01-05 01:42:45,309 - mmcls - INFO - Epoch [82][3400/5005] lr: 1.000e-03, eta: 2:14:42, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8657 2023-01-05 01:42:54,015 - mmcls - INFO - Epoch [82][3500/5005] lr: 1.000e-03, eta: 2:14:33, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8461 2023-01-05 01:43:02,910 - mmcls - INFO - Epoch [82][3600/5005] lr: 1.000e-03, eta: 2:14:24, time: 0.089, data_time: 0.031, memory: 928, loss: 4.8238 2023-01-05 01:43:11,594 - mmcls - INFO - Epoch [82][3700/5005] lr: 1.000e-03, eta: 2:14:15, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8561 2023-01-05 01:43:20,599 - mmcls - INFO - Epoch [82][3800/5005] lr: 1.000e-03, eta: 2:14:07, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8635 2023-01-05 01:43:29,554 - mmcls - INFO - Epoch [82][3900/5005] lr: 1.000e-03, eta: 2:13:58, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8517 2023-01-05 01:43:38,330 - mmcls - INFO - Epoch [82][4000/5005] lr: 1.000e-03, eta: 2:13:49, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8570 2023-01-05 01:43:46,915 - mmcls - INFO - Epoch [82][4100/5005] lr: 1.000e-03, eta: 2:13:40, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8726 2023-01-05 01:43:55,479 - mmcls - INFO - Epoch [82][4200/5005] lr: 1.000e-03, eta: 2:13:31, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8669 2023-01-05 01:44:04,067 - mmcls - INFO - Epoch [82][4300/5005] lr: 1.000e-03, eta: 2:13:23, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8340 2023-01-05 01:44:12,683 - mmcls - INFO - Epoch [82][4400/5005] lr: 1.000e-03, eta: 2:13:14, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8520 2023-01-05 01:44:21,479 - mmcls - INFO - Epoch [82][4500/5005] lr: 1.000e-03, eta: 2:13:05, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8730 2023-01-05 01:44:30,305 - mmcls - INFO - Epoch [82][4600/5005] lr: 1.000e-03, eta: 2:12:56, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8573 2023-01-05 01:44:38,834 - mmcls - INFO - Epoch [82][4700/5005] lr: 1.000e-03, eta: 2:12:47, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8644 2023-01-05 01:44:47,840 - mmcls - INFO - Epoch [82][4800/5005] lr: 1.000e-03, eta: 2:12:38, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8462 2023-01-05 01:44:56,579 - mmcls - INFO - Epoch [82][4900/5005] lr: 1.000e-03, eta: 2:12:30, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8502 2023-01-05 01:45:05,252 - mmcls - INFO - Epoch [82][5000/5005] lr: 1.000e-03, eta: 2:12:21, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8404 2023-01-05 01:45:28,042 - mmcls - INFO - Epoch(val) [82][196] accuracy_top-1: 17.0900, accuracy_top-5: 34.6640 2023-01-05 01:45:39,105 - mmcls - INFO - Epoch [83][100/5005] lr: 1.000e-03, eta: 2:12:12, time: 0.110, data_time: 0.030, memory: 928, loss: 4.8576 2023-01-05 01:45:47,841 - mmcls - INFO - Epoch [83][200/5005] lr: 1.000e-03, eta: 2:12:03, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8518 2023-01-05 01:45:56,378 - mmcls - INFO - Epoch [83][300/5005] lr: 1.000e-03, eta: 2:11:54, time: 0.085, data_time: 0.016, memory: 928, loss: 4.8433 2023-01-05 01:46:05,521 - mmcls - INFO - Epoch [83][400/5005] lr: 1.000e-03, eta: 2:11:45, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8639 2023-01-05 01:46:14,255 - mmcls - INFO - Epoch [83][500/5005] lr: 1.000e-03, eta: 2:11:37, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8559 2023-01-05 01:46:23,039 - mmcls - INFO - Epoch [83][600/5005] lr: 1.000e-03, eta: 2:11:28, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8550 2023-01-05 01:46:31,416 - mmcls - INFO - Epoch [83][700/5005] lr: 1.000e-03, eta: 2:11:19, time: 0.084, data_time: 0.006, memory: 928, loss: 4.8473 2023-01-05 01:46:40,180 - mmcls - INFO - Epoch [83][800/5005] lr: 1.000e-03, eta: 2:11:10, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8508 2023-01-05 01:46:48,827 - mmcls - INFO - Epoch [83][900/5005] lr: 1.000e-03, eta: 2:11:01, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8509 2023-01-05 01:46:57,665 - mmcls - INFO - Epoch [83][1000/5005] lr: 1.000e-03, eta: 2:10:52, time: 0.088, data_time: 0.022, memory: 928, loss: 4.8363 2023-01-05 01:47:06,399 - mmcls - INFO - Epoch [83][1100/5005] lr: 1.000e-03, eta: 2:10:43, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8617 2023-01-05 01:47:15,274 - mmcls - INFO - Epoch [83][1200/5005] lr: 1.000e-03, eta: 2:10:35, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8492 2023-01-05 01:47:23,992 - mmcls - INFO - Epoch [83][1300/5005] lr: 1.000e-03, eta: 2:10:26, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8754 2023-01-05 01:47:32,586 - mmcls - INFO - Epoch [83][1400/5005] lr: 1.000e-03, eta: 2:10:17, time: 0.086, data_time: 0.013, memory: 928, loss: 4.8604 2023-01-05 01:47:41,532 - mmcls - INFO - Epoch [83][1500/5005] lr: 1.000e-03, eta: 2:10:08, time: 0.090, data_time: 0.012, memory: 928, loss: 4.8756 2023-01-05 01:47:50,687 - mmcls - INFO - Epoch [83][1600/5005] lr: 1.000e-03, eta: 2:09:59, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8536 2023-01-05 01:47:59,448 - mmcls - INFO - Epoch [83][1700/5005] lr: 1.000e-03, eta: 2:09:51, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8519 2023-01-05 01:48:08,086 - mmcls - INFO - Epoch [83][1800/5005] lr: 1.000e-03, eta: 2:09:42, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8343 2023-01-05 01:48:17,001 - mmcls - INFO - Epoch [83][1900/5005] lr: 1.000e-03, eta: 2:09:33, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8706 2023-01-05 01:48:25,598 - mmcls - INFO - Epoch [83][2000/5005] lr: 1.000e-03, eta: 2:09:24, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8421 2023-01-05 01:48:34,214 - mmcls - INFO - Epoch [83][2100/5005] lr: 1.000e-03, eta: 2:09:15, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8620 2023-01-05 01:48:43,039 - mmcls - INFO - Epoch [83][2200/5005] lr: 1.000e-03, eta: 2:09:06, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8349 2023-01-05 01:48:51,710 - mmcls - INFO - Epoch [83][2300/5005] lr: 1.000e-03, eta: 2:08:58, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8471 2023-01-05 01:49:00,408 - mmcls - INFO - Epoch [83][2400/5005] lr: 1.000e-03, eta: 2:08:49, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8593 2023-01-05 01:49:09,250 - mmcls - INFO - Epoch [83][2500/5005] lr: 1.000e-03, eta: 2:08:40, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8465 2023-01-05 01:49:18,014 - mmcls - INFO - Epoch [83][2600/5005] lr: 1.000e-03, eta: 2:08:31, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8618 2023-01-05 01:49:26,607 - mmcls - INFO - Epoch [83][2700/5005] lr: 1.000e-03, eta: 2:08:22, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8526 2023-01-05 01:49:35,603 - mmcls - INFO - Epoch [83][2800/5005] lr: 1.000e-03, eta: 2:08:14, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8453 2023-01-05 01:49:44,566 - mmcls - INFO - Epoch [83][2900/5005] lr: 1.000e-03, eta: 2:08:05, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8427 2023-01-05 01:49:53,236 - mmcls - INFO - Epoch [83][3000/5005] lr: 1.000e-03, eta: 2:07:56, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8574 2023-01-05 01:50:02,105 - mmcls - INFO - Epoch [83][3100/5005] lr: 1.000e-03, eta: 2:07:47, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8392 2023-01-05 01:50:10,626 - mmcls - INFO - Epoch [83][3200/5005] lr: 1.000e-03, eta: 2:07:38, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8485 2023-01-05 01:50:19,659 - mmcls - INFO - Epoch [83][3300/5005] lr: 1.000e-03, eta: 2:07:29, time: 0.090, data_time: 0.020, memory: 928, loss: 4.8363 2023-01-05 01:50:28,333 - mmcls - INFO - Epoch [83][3400/5005] lr: 1.000e-03, eta: 2:07:21, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8672 2023-01-05 01:50:37,102 - mmcls - INFO - Epoch [83][3500/5005] lr: 1.000e-03, eta: 2:07:12, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8524 2023-01-05 01:50:46,274 - mmcls - INFO - Epoch [83][3600/5005] lr: 1.000e-03, eta: 2:07:03, time: 0.092, data_time: 0.007, memory: 928, loss: 4.8450 2023-01-05 01:50:54,935 - mmcls - INFO - Epoch [83][3700/5005] lr: 1.000e-03, eta: 2:06:54, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8435 2023-01-05 01:51:03,566 - mmcls - INFO - Epoch [83][3800/5005] lr: 1.000e-03, eta: 2:06:45, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8591 2023-01-05 01:51:12,332 - mmcls - INFO - Epoch [83][3900/5005] lr: 1.000e-03, eta: 2:06:37, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8554 2023-01-05 01:51:20,968 - mmcls - INFO - Epoch [83][4000/5005] lr: 1.000e-03, eta: 2:06:28, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8601 2023-01-05 01:51:30,187 - mmcls - INFO - Epoch [83][4100/5005] lr: 1.000e-03, eta: 2:06:19, time: 0.092, data_time: 0.021, memory: 928, loss: 4.8652 2023-01-05 01:51:38,864 - mmcls - INFO - Epoch [83][4200/5005] lr: 1.000e-03, eta: 2:06:10, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8544 2023-01-05 01:51:47,489 - mmcls - INFO - Epoch [83][4300/5005] lr: 1.000e-03, eta: 2:06:01, time: 0.086, data_time: 0.046, memory: 928, loss: 4.8589 2023-01-05 01:51:56,343 - mmcls - INFO - Epoch [83][4400/5005] lr: 1.000e-03, eta: 2:05:52, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8714 2023-01-05 01:52:05,141 - mmcls - INFO - Epoch [83][4500/5005] lr: 1.000e-03, eta: 2:05:44, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8463 2023-01-05 01:52:14,208 - mmcls - INFO - Epoch [83][4600/5005] lr: 1.000e-03, eta: 2:05:35, time: 0.091, data_time: 0.030, memory: 928, loss: 4.8600 2023-01-05 01:52:22,705 - mmcls - INFO - Epoch [83][4700/5005] lr: 1.000e-03, eta: 2:05:26, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8701 2023-01-05 01:52:31,437 - mmcls - INFO - Epoch [83][4800/5005] lr: 1.000e-03, eta: 2:05:17, time: 0.087, data_time: 0.023, memory: 928, loss: 4.8813 2023-01-05 01:52:40,396 - mmcls - INFO - Epoch [83][4900/5005] lr: 1.000e-03, eta: 2:05:08, time: 0.090, data_time: 0.017, memory: 928, loss: 4.8450 2023-01-05 01:52:48,996 - mmcls - INFO - Epoch [83][5000/5005] lr: 1.000e-03, eta: 2:04:59, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8343 2023-01-05 01:53:11,891 - mmcls - INFO - Epoch(val) [83][196] accuracy_top-1: 16.6600, accuracy_top-5: 34.1460 2023-01-05 01:53:23,225 - mmcls - INFO - Epoch [84][100/5005] lr: 1.000e-03, eta: 2:04:51, time: 0.113, data_time: 0.034, memory: 928, loss: 4.8309 2023-01-05 01:53:32,045 - mmcls - INFO - Epoch [84][200/5005] lr: 1.000e-03, eta: 2:04:42, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8655 2023-01-05 01:53:40,670 - mmcls - INFO - Epoch [84][300/5005] lr: 1.000e-03, eta: 2:04:33, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8596 2023-01-05 01:53:49,370 - mmcls - INFO - Epoch [84][400/5005] lr: 1.000e-03, eta: 2:04:24, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8489 2023-01-05 01:53:58,131 - mmcls - INFO - Epoch [84][500/5005] lr: 1.000e-03, eta: 2:04:15, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8479 2023-01-05 01:54:07,132 - mmcls - INFO - Epoch [84][600/5005] lr: 1.000e-03, eta: 2:04:07, time: 0.090, data_time: 0.016, memory: 928, loss: 4.8240 2023-01-05 01:54:16,089 - mmcls - INFO - Epoch [84][700/5005] lr: 1.000e-03, eta: 2:03:58, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8626 2023-01-05 01:54:25,034 - mmcls - INFO - Epoch [84][800/5005] lr: 1.000e-03, eta: 2:03:49, time: 0.090, data_time: 0.017, memory: 928, loss: 4.8689 2023-01-05 01:54:33,754 - mmcls - INFO - Epoch [84][900/5005] lr: 1.000e-03, eta: 2:03:40, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8464 2023-01-05 01:54:42,802 - mmcls - INFO - Epoch [84][1000/5005] lr: 1.000e-03, eta: 2:03:31, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8531 2023-01-05 01:54:51,532 - mmcls - INFO - Epoch [84][1100/5005] lr: 1.000e-03, eta: 2:03:23, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8582 2023-01-05 01:55:00,179 - mmcls - INFO - Epoch [84][1200/5005] lr: 1.000e-03, eta: 2:03:14, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8595 2023-01-05 01:55:08,860 - mmcls - INFO - Epoch [84][1300/5005] lr: 1.000e-03, eta: 2:03:05, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8319 2023-01-05 01:55:17,499 - mmcls - INFO - Epoch [84][1400/5005] lr: 1.000e-03, eta: 2:02:56, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8500 2023-01-05 01:55:26,023 - mmcls - INFO - Epoch [84][1500/5005] lr: 1.000e-03, eta: 2:02:47, time: 0.085, data_time: 0.016, memory: 928, loss: 4.8363 2023-01-05 01:55:34,851 - mmcls - INFO - Epoch [84][1600/5005] lr: 1.000e-03, eta: 2:02:38, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8444 2023-01-05 01:55:43,471 - mmcls - INFO - Epoch [84][1700/5005] lr: 1.000e-03, eta: 2:02:29, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8374 2023-01-05 01:55:52,322 - mmcls - INFO - Epoch [84][1800/5005] lr: 1.000e-03, eta: 2:02:21, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8347 2023-01-05 01:56:01,326 - mmcls - INFO - Epoch [84][1900/5005] lr: 1.000e-03, eta: 2:02:12, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8284 2023-01-05 01:56:10,126 - mmcls - INFO - Epoch [84][2000/5005] lr: 1.000e-03, eta: 2:02:03, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8765 2023-01-05 01:56:18,590 - mmcls - INFO - Epoch [84][2100/5005] lr: 1.000e-03, eta: 2:01:54, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8394 2023-01-05 01:56:27,102 - mmcls - INFO - Epoch [84][2200/5005] lr: 1.000e-03, eta: 2:01:45, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8411 2023-01-05 01:56:36,236 - mmcls - INFO - Epoch [84][2300/5005] lr: 1.000e-03, eta: 2:01:37, time: 0.091, data_time: 0.011, memory: 928, loss: 4.8721 2023-01-05 01:56:44,813 - mmcls - INFO - Epoch [84][2400/5005] lr: 1.000e-03, eta: 2:01:28, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8272 2023-01-05 01:56:53,424 - mmcls - INFO - Epoch [84][2500/5005] lr: 1.000e-03, eta: 2:01:19, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8660 2023-01-05 01:57:02,200 - mmcls - INFO - Epoch [84][2600/5005] lr: 1.000e-03, eta: 2:01:10, time: 0.088, data_time: 0.037, memory: 928, loss: 4.8458 2023-01-05 01:57:10,851 - mmcls - INFO - Epoch [84][2700/5005] lr: 1.000e-03, eta: 2:01:01, time: 0.086, data_time: 0.026, memory: 928, loss: 4.8659 2023-01-05 01:57:19,395 - mmcls - INFO - Epoch [84][2800/5005] lr: 1.000e-03, eta: 2:00:52, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8658 2023-01-05 01:57:28,261 - mmcls - INFO - Epoch [84][2900/5005] lr: 1.000e-03, eta: 2:00:43, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8484 2023-01-05 01:57:37,338 - mmcls - INFO - Epoch [84][3000/5005] lr: 1.000e-03, eta: 2:00:35, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8463 2023-01-05 01:57:45,980 - mmcls - INFO - Epoch [84][3100/5005] lr: 1.000e-03, eta: 2:00:26, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8274 2023-01-05 01:57:54,827 - mmcls - INFO - Epoch [84][3200/5005] lr: 1.000e-03, eta: 2:00:17, time: 0.089, data_time: 0.032, memory: 928, loss: 4.8229 2023-01-05 01:58:03,887 - mmcls - INFO - Epoch [84][3300/5005] lr: 1.000e-03, eta: 2:00:08, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8560 2023-01-05 01:58:12,483 - mmcls - INFO - Epoch [84][3400/5005] lr: 1.000e-03, eta: 1:59:59, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8479 2023-01-05 01:58:21,690 - mmcls - INFO - Epoch [84][3500/5005] lr: 1.000e-03, eta: 1:59:51, time: 0.092, data_time: 0.014, memory: 928, loss: 4.8610 2023-01-05 01:58:30,231 - mmcls - INFO - Epoch [84][3600/5005] lr: 1.000e-03, eta: 1:59:42, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8645 2023-01-05 01:58:39,119 - mmcls - INFO - Epoch [84][3700/5005] lr: 1.000e-03, eta: 1:59:33, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8655 2023-01-05 01:58:47,438 - mmcls - INFO - Epoch [84][3800/5005] lr: 1.000e-03, eta: 1:59:24, time: 0.083, data_time: 0.018, memory: 928, loss: 4.8718 2023-01-05 01:58:56,387 - mmcls - INFO - Epoch [84][3900/5005] lr: 1.000e-03, eta: 1:59:15, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8524 2023-01-05 01:59:05,214 - mmcls - INFO - Epoch [84][4000/5005] lr: 1.000e-03, eta: 1:59:07, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8422 2023-01-05 01:59:13,966 - mmcls - INFO - Epoch [84][4100/5005] lr: 1.000e-03, eta: 1:58:58, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8537 2023-01-05 01:59:22,634 - mmcls - INFO - Epoch [84][4200/5005] lr: 1.000e-03, eta: 1:58:49, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8776 2023-01-05 01:59:31,439 - mmcls - INFO - Epoch [84][4300/5005] lr: 1.000e-03, eta: 1:58:40, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8673 2023-01-05 01:59:40,301 - mmcls - INFO - Epoch [84][4400/5005] lr: 1.000e-03, eta: 1:58:31, time: 0.089, data_time: 0.026, memory: 928, loss: 4.8693 2023-01-05 01:59:48,668 - mmcls - INFO - Epoch [84][4500/5005] lr: 1.000e-03, eta: 1:58:22, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8619 2023-01-05 01:59:57,233 - mmcls - INFO - Epoch [84][4600/5005] lr: 1.000e-03, eta: 1:58:13, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8401 2023-01-05 02:00:05,868 - mmcls - INFO - Epoch [84][4700/5005] lr: 1.000e-03, eta: 1:58:05, time: 0.086, data_time: 0.016, memory: 928, loss: 4.8621 2023-01-05 02:00:14,728 - mmcls - INFO - Epoch [84][4800/5005] lr: 1.000e-03, eta: 1:57:56, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8602 2023-01-05 02:00:23,491 - mmcls - INFO - Epoch [84][4900/5005] lr: 1.000e-03, eta: 1:57:47, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8368 2023-01-05 02:00:32,374 - mmcls - INFO - Epoch [84][5000/5005] lr: 1.000e-03, eta: 1:57:38, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8704 2023-01-05 02:00:55,074 - mmcls - INFO - Epoch(val) [84][196] accuracy_top-1: 16.9240, accuracy_top-5: 34.8840 2023-01-05 02:01:06,082 - mmcls - INFO - Epoch [85][100/5005] lr: 1.000e-03, eta: 1:57:29, time: 0.110, data_time: 0.033, memory: 928, loss: 4.8306 2023-01-05 02:01:14,651 - mmcls - INFO - Epoch [85][200/5005] lr: 1.000e-03, eta: 1:57:20, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8531 2023-01-05 02:01:23,495 - mmcls - INFO - Epoch [85][300/5005] lr: 1.000e-03, eta: 1:57:12, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8576 2023-01-05 02:01:32,240 - mmcls - INFO - Epoch [85][400/5005] lr: 1.000e-03, eta: 1:57:03, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8379 2023-01-05 02:01:40,808 - mmcls - INFO - Epoch [85][500/5005] lr: 1.000e-03, eta: 1:56:54, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8471 2023-01-05 02:01:49,550 - mmcls - INFO - Epoch [85][600/5005] lr: 1.000e-03, eta: 1:56:45, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8562 2023-01-05 02:01:58,402 - mmcls - INFO - Epoch [85][700/5005] lr: 1.000e-03, eta: 1:56:36, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8383 2023-01-05 02:02:07,433 - mmcls - INFO - Epoch [85][800/5005] lr: 1.000e-03, eta: 1:56:28, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8558 2023-01-05 02:02:16,487 - mmcls - INFO - Epoch [85][900/5005] lr: 1.000e-03, eta: 1:56:19, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8571 2023-01-05 02:02:25,245 - mmcls - INFO - Epoch [85][1000/5005] lr: 1.000e-03, eta: 1:56:10, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8309 2023-01-05 02:02:33,914 - mmcls - INFO - Epoch [85][1100/5005] lr: 1.000e-03, eta: 1:56:01, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8562 2023-01-05 02:02:42,915 - mmcls - INFO - Epoch [85][1200/5005] lr: 1.000e-03, eta: 1:55:52, time: 0.090, data_time: 0.014, memory: 928, loss: 4.8372 2023-01-05 02:02:51,525 - mmcls - INFO - Epoch [85][1300/5005] lr: 1.000e-03, eta: 1:55:43, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8502 2023-01-05 02:03:00,532 - mmcls - INFO - Epoch [85][1400/5005] lr: 1.000e-03, eta: 1:55:35, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8514 2023-01-05 02:03:09,100 - mmcls - INFO - Epoch [85][1500/5005] lr: 1.000e-03, eta: 1:55:26, time: 0.086, data_time: 0.034, memory: 928, loss: 4.8446 2023-01-05 02:03:17,707 - mmcls - INFO - Epoch [85][1600/5005] lr: 1.000e-03, eta: 1:55:17, time: 0.086, data_time: 0.011, memory: 928, loss: 4.8535 2023-01-05 02:03:26,547 - mmcls - INFO - Epoch [85][1700/5005] lr: 1.000e-03, eta: 1:55:08, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8585 2023-01-05 02:03:35,269 - mmcls - INFO - Epoch [85][1800/5005] lr: 1.000e-03, eta: 1:54:59, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8750 2023-01-05 02:03:43,953 - mmcls - INFO - Epoch [85][1900/5005] lr: 1.000e-03, eta: 1:54:50, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8378 2023-01-05 02:03:52,960 - mmcls - INFO - Epoch [85][2000/5005] lr: 1.000e-03, eta: 1:54:42, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8432 2023-01-05 02:04:01,543 - mmcls - INFO - Epoch [85][2100/5005] lr: 1.000e-03, eta: 1:54:33, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8452 2023-01-05 02:04:10,286 - mmcls - INFO - Epoch [85][2200/5005] lr: 1.000e-03, eta: 1:54:24, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8517 2023-01-05 02:04:19,327 - mmcls - INFO - Epoch [85][2300/5005] lr: 1.000e-03, eta: 1:54:15, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8627 2023-01-05 02:04:28,167 - mmcls - INFO - Epoch [85][2400/5005] lr: 1.000e-03, eta: 1:54:06, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8438 2023-01-05 02:04:36,961 - mmcls - INFO - Epoch [85][2500/5005] lr: 1.000e-03, eta: 1:53:58, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8545 2023-01-05 02:04:45,535 - mmcls - INFO - Epoch [85][2600/5005] lr: 1.000e-03, eta: 1:53:49, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8602 2023-01-05 02:04:54,198 - mmcls - INFO - Epoch [85][2700/5005] lr: 1.000e-03, eta: 1:53:40, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8458 2023-01-05 02:05:02,972 - mmcls - INFO - Epoch [85][2800/5005] lr: 1.000e-03, eta: 1:53:31, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8397 2023-01-05 02:05:11,824 - mmcls - INFO - Epoch [85][2900/5005] lr: 1.000e-03, eta: 1:53:22, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8657 2023-01-05 02:05:20,530 - mmcls - INFO - Epoch [85][3000/5005] lr: 1.000e-03, eta: 1:53:13, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8462 2023-01-05 02:05:29,263 - mmcls - INFO - Epoch [85][3100/5005] lr: 1.000e-03, eta: 1:53:05, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8388 2023-01-05 02:05:37,953 - mmcls - INFO - Epoch [85][3200/5005] lr: 1.000e-03, eta: 1:52:56, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8593 2023-01-05 02:05:46,657 - mmcls - INFO - Epoch [85][3300/5005] lr: 1.000e-03, eta: 1:52:47, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8744 2023-01-05 02:05:55,269 - mmcls - INFO - Epoch [85][3400/5005] lr: 1.000e-03, eta: 1:52:38, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8577 2023-01-05 02:06:04,002 - mmcls - INFO - Epoch [85][3500/5005] lr: 1.000e-03, eta: 1:52:29, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8511 2023-01-05 02:06:13,011 - mmcls - INFO - Epoch [85][3600/5005] lr: 1.000e-03, eta: 1:52:20, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8768 2023-01-05 02:06:21,841 - mmcls - INFO - Epoch [85][3700/5005] lr: 1.000e-03, eta: 1:52:12, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8473 2023-01-05 02:06:30,706 - mmcls - INFO - Epoch [85][3800/5005] lr: 1.000e-03, eta: 1:52:03, time: 0.089, data_time: 0.016, memory: 928, loss: 4.8491 2023-01-05 02:06:39,175 - mmcls - INFO - Epoch [85][3900/5005] lr: 1.000e-03, eta: 1:51:54, time: 0.085, data_time: 0.012, memory: 928, loss: 4.8455 2023-01-05 02:06:47,900 - mmcls - INFO - Epoch [85][4000/5005] lr: 1.000e-03, eta: 1:51:45, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8687 2023-01-05 02:06:56,498 - mmcls - INFO - Epoch [85][4100/5005] lr: 1.000e-03, eta: 1:51:36, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8599 2023-01-05 02:07:05,375 - mmcls - INFO - Epoch [85][4200/5005] lr: 1.000e-03, eta: 1:51:28, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8295 2023-01-05 02:07:14,239 - mmcls - INFO - Epoch [85][4300/5005] lr: 1.000e-03, eta: 1:51:19, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8291 2023-01-05 02:07:22,861 - mmcls - INFO - Epoch [85][4400/5005] lr: 1.000e-03, eta: 1:51:10, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8463 2023-01-05 02:07:31,548 - mmcls - INFO - Epoch [85][4500/5005] lr: 1.000e-03, eta: 1:51:01, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8371 2023-01-05 02:07:40,203 - mmcls - INFO - Epoch [85][4600/5005] lr: 1.000e-03, eta: 1:50:52, time: 0.086, data_time: 0.022, memory: 928, loss: 4.8712 2023-01-05 02:07:49,040 - mmcls - INFO - Epoch [85][4700/5005] lr: 1.000e-03, eta: 1:50:43, time: 0.088, data_time: 0.029, memory: 928, loss: 4.8420 2023-01-05 02:07:57,985 - mmcls - INFO - Epoch [85][4800/5005] lr: 1.000e-03, eta: 1:50:35, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8376 2023-01-05 02:08:06,586 - mmcls - INFO - Epoch [85][4900/5005] lr: 1.000e-03, eta: 1:50:26, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8517 2023-01-05 02:08:15,686 - mmcls - INFO - Epoch [85][5000/5005] lr: 1.000e-03, eta: 1:50:17, time: 0.091, data_time: 0.016, memory: 928, loss: 4.8659 2023-01-05 02:08:38,390 - mmcls - INFO - Epoch(val) [85][196] accuracy_top-1: 16.3500, accuracy_top-5: 33.4720 2023-01-05 02:08:49,295 - mmcls - INFO - Epoch [86][100/5005] lr: 1.000e-03, eta: 1:50:08, time: 0.109, data_time: 0.046, memory: 928, loss: 4.8427 2023-01-05 02:08:57,933 - mmcls - INFO - Epoch [86][200/5005] lr: 1.000e-03, eta: 1:49:59, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8610 2023-01-05 02:09:06,809 - mmcls - INFO - Epoch [86][300/5005] lr: 1.000e-03, eta: 1:49:50, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8347 2023-01-05 02:09:16,144 - mmcls - INFO - Epoch [86][400/5005] lr: 1.000e-03, eta: 1:49:42, time: 0.093, data_time: 0.002, memory: 928, loss: 4.8431 2023-01-05 02:09:24,983 - mmcls - INFO - Epoch [86][500/5005] lr: 1.000e-03, eta: 1:49:33, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8223 2023-01-05 02:09:33,863 - mmcls - INFO - Epoch [86][600/5005] lr: 1.000e-03, eta: 1:49:24, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8497 2023-01-05 02:09:42,169 - mmcls - INFO - Epoch [86][700/5005] lr: 1.000e-03, eta: 1:49:15, time: 0.083, data_time: 0.012, memory: 928, loss: 4.8412 2023-01-05 02:09:50,900 - mmcls - INFO - Epoch [86][800/5005] lr: 1.000e-03, eta: 1:49:06, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8390 2023-01-05 02:09:59,929 - mmcls - INFO - Epoch [86][900/5005] lr: 1.000e-03, eta: 1:48:57, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8618 2023-01-05 02:10:08,685 - mmcls - INFO - Epoch [86][1000/5005] lr: 1.000e-03, eta: 1:48:49, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8592 2023-01-05 02:10:17,506 - mmcls - INFO - Epoch [86][1100/5005] lr: 1.000e-03, eta: 1:48:40, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8448 2023-01-05 02:10:26,071 - mmcls - INFO - Epoch [86][1200/5005] lr: 1.000e-03, eta: 1:48:31, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8635 2023-01-05 02:10:35,180 - mmcls - INFO - Epoch [86][1300/5005] lr: 1.000e-03, eta: 1:48:22, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8285 2023-01-05 02:10:43,986 - mmcls - INFO - Epoch [86][1400/5005] lr: 1.000e-03, eta: 1:48:13, time: 0.088, data_time: 0.039, memory: 928, loss: 4.8579 2023-01-05 02:10:52,789 - mmcls - INFO - Epoch [86][1500/5005] lr: 1.000e-03, eta: 1:48:05, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8585 2023-01-05 02:11:01,331 - mmcls - INFO - Epoch [86][1600/5005] lr: 1.000e-03, eta: 1:47:56, time: 0.085, data_time: 0.018, memory: 928, loss: 4.8560 2023-01-05 02:11:10,452 - mmcls - INFO - Epoch [86][1700/5005] lr: 1.000e-03, eta: 1:47:47, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8557 2023-01-05 02:11:19,215 - mmcls - INFO - Epoch [86][1800/5005] lr: 1.000e-03, eta: 1:47:38, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8595 2023-01-05 02:11:27,873 - mmcls - INFO - Epoch [86][1900/5005] lr: 1.000e-03, eta: 1:47:29, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8340 2023-01-05 02:11:36,772 - mmcls - INFO - Epoch [86][2000/5005] lr: 1.000e-03, eta: 1:47:21, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8510 2023-01-05 02:11:45,513 - mmcls - INFO - Epoch [86][2100/5005] lr: 1.000e-03, eta: 1:47:12, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8462 2023-01-05 02:11:54,081 - mmcls - INFO - Epoch [86][2200/5005] lr: 1.000e-03, eta: 1:47:03, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8541 2023-01-05 02:12:02,956 - mmcls - INFO - Epoch [86][2300/5005] lr: 1.000e-03, eta: 1:46:54, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8818 2023-01-05 02:12:11,711 - mmcls - INFO - Epoch [86][2400/5005] lr: 1.000e-03, eta: 1:46:45, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8672 2023-01-05 02:12:20,306 - mmcls - INFO - Epoch [86][2500/5005] lr: 1.000e-03, eta: 1:46:36, time: 0.086, data_time: 0.026, memory: 928, loss: 4.8486 2023-01-05 02:12:28,928 - mmcls - INFO - Epoch [86][2600/5005] lr: 1.000e-03, eta: 1:46:28, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8654 2023-01-05 02:12:37,535 - mmcls - INFO - Epoch [86][2700/5005] lr: 1.000e-03, eta: 1:46:19, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8551 2023-01-05 02:12:46,264 - mmcls - INFO - Epoch [86][2800/5005] lr: 1.000e-03, eta: 1:46:10, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8320 2023-01-05 02:12:55,103 - mmcls - INFO - Epoch [86][2900/5005] lr: 1.000e-03, eta: 1:46:01, time: 0.088, data_time: 0.034, memory: 928, loss: 4.8586 2023-01-05 02:13:03,937 - mmcls - INFO - Epoch [86][3000/5005] lr: 1.000e-03, eta: 1:45:52, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8419 2023-01-05 02:13:12,753 - mmcls - INFO - Epoch [86][3100/5005] lr: 1.000e-03, eta: 1:45:43, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8466 2023-01-05 02:13:21,331 - mmcls - INFO - Epoch [86][3200/5005] lr: 1.000e-03, eta: 1:45:35, time: 0.086, data_time: 0.013, memory: 928, loss: 4.8532 2023-01-05 02:13:30,193 - mmcls - INFO - Epoch [86][3300/5005] lr: 1.000e-03, eta: 1:45:26, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8275 2023-01-05 02:13:38,992 - mmcls - INFO - Epoch [86][3400/5005] lr: 1.000e-03, eta: 1:45:17, time: 0.088, data_time: 0.042, memory: 928, loss: 4.8447 2023-01-05 02:13:47,687 - mmcls - INFO - Epoch [86][3500/5005] lr: 1.000e-03, eta: 1:45:08, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8503 2023-01-05 02:13:56,459 - mmcls - INFO - Epoch [86][3600/5005] lr: 1.000e-03, eta: 1:44:59, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8554 2023-01-05 02:14:04,991 - mmcls - INFO - Epoch [86][3700/5005] lr: 1.000e-03, eta: 1:44:50, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8330 2023-01-05 02:14:13,542 - mmcls - INFO - Epoch [86][3800/5005] lr: 1.000e-03, eta: 1:44:42, time: 0.086, data_time: 0.029, memory: 928, loss: 4.8545 2023-01-05 02:14:22,270 - mmcls - INFO - Epoch [86][3900/5005] lr: 1.000e-03, eta: 1:44:33, time: 0.087, data_time: 0.014, memory: 928, loss: 4.8726 2023-01-05 02:14:31,188 - mmcls - INFO - Epoch [86][4000/5005] lr: 1.000e-03, eta: 1:44:24, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8371 2023-01-05 02:14:40,238 - mmcls - INFO - Epoch [86][4100/5005] lr: 1.000e-03, eta: 1:44:15, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8509 2023-01-05 02:14:49,033 - mmcls - INFO - Epoch [86][4200/5005] lr: 1.000e-03, eta: 1:44:06, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8492 2023-01-05 02:14:57,845 - mmcls - INFO - Epoch [86][4300/5005] lr: 1.000e-03, eta: 1:43:58, time: 0.088, data_time: 0.021, memory: 928, loss: 4.8347 2023-01-05 02:15:06,962 - mmcls - INFO - Epoch [86][4400/5005] lr: 1.000e-03, eta: 1:43:49, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8423 2023-01-05 02:15:15,594 - mmcls - INFO - Epoch [86][4500/5005] lr: 1.000e-03, eta: 1:43:40, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8684 2023-01-05 02:15:24,509 - mmcls - INFO - Epoch [86][4600/5005] lr: 1.000e-03, eta: 1:43:31, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8756 2023-01-05 02:15:33,581 - mmcls - INFO - Epoch [86][4700/5005] lr: 1.000e-03, eta: 1:43:22, time: 0.091, data_time: 0.029, memory: 928, loss: 4.8588 2023-01-05 02:15:42,259 - mmcls - INFO - Epoch [86][4800/5005] lr: 1.000e-03, eta: 1:43:13, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8442 2023-01-05 02:15:50,804 - mmcls - INFO - Epoch [86][4900/5005] lr: 1.000e-03, eta: 1:43:05, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8566 2023-01-05 02:15:59,888 - mmcls - INFO - Epoch [86][5000/5005] lr: 1.000e-03, eta: 1:42:56, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8488 2023-01-05 02:16:22,643 - mmcls - INFO - Epoch(val) [86][196] accuracy_top-1: 17.6400, accuracy_top-5: 35.9040 2023-01-05 02:16:33,615 - mmcls - INFO - Epoch [87][100/5005] lr: 1.000e-03, eta: 1:42:47, time: 0.109, data_time: 0.045, memory: 928, loss: 4.8608 2023-01-05 02:16:42,438 - mmcls - INFO - Epoch [87][200/5005] lr: 1.000e-03, eta: 1:42:38, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8451 2023-01-05 02:16:51,291 - mmcls - INFO - Epoch [87][300/5005] lr: 1.000e-03, eta: 1:42:29, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8263 2023-01-05 02:16:59,780 - mmcls - INFO - Epoch [87][400/5005] lr: 1.000e-03, eta: 1:42:20, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8432 2023-01-05 02:17:09,144 - mmcls - INFO - Epoch [87][500/5005] lr: 1.000e-03, eta: 1:42:12, time: 0.094, data_time: 0.004, memory: 928, loss: 4.8377 2023-01-05 02:17:17,924 - mmcls - INFO - Epoch [87][600/5005] lr: 1.000e-03, eta: 1:42:03, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8355 2023-01-05 02:17:26,719 - mmcls - INFO - Epoch [87][700/5005] lr: 1.000e-03, eta: 1:41:54, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8458 2023-01-05 02:17:35,443 - mmcls - INFO - Epoch [87][800/5005] lr: 1.000e-03, eta: 1:41:45, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8570 2023-01-05 02:17:44,080 - mmcls - INFO - Epoch [87][900/5005] lr: 1.000e-03, eta: 1:41:36, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8562 2023-01-05 02:17:52,691 - mmcls - INFO - Epoch [87][1000/5005] lr: 1.000e-03, eta: 1:41:28, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8522 2023-01-05 02:18:01,087 - mmcls - INFO - Epoch [87][1100/5005] lr: 1.000e-03, eta: 1:41:19, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8633 2023-01-05 02:18:10,079 - mmcls - INFO - Epoch [87][1200/5005] lr: 1.000e-03, eta: 1:41:10, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8479 2023-01-05 02:18:19,045 - mmcls - INFO - Epoch [87][1300/5005] lr: 1.000e-03, eta: 1:41:01, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8434 2023-01-05 02:18:27,836 - mmcls - INFO - Epoch [87][1400/5005] lr: 1.000e-03, eta: 1:40:52, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8609 2023-01-05 02:18:36,620 - mmcls - INFO - Epoch [87][1500/5005] lr: 1.000e-03, eta: 1:40:43, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8305 2023-01-05 02:18:45,307 - mmcls - INFO - Epoch [87][1600/5005] lr: 1.000e-03, eta: 1:40:35, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8521 2023-01-05 02:18:53,985 - mmcls - INFO - Epoch [87][1700/5005] lr: 1.000e-03, eta: 1:40:26, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8505 2023-01-05 02:19:02,907 - mmcls - INFO - Epoch [87][1800/5005] lr: 1.000e-03, eta: 1:40:17, time: 0.089, data_time: 0.022, memory: 928, loss: 4.8404 2023-01-05 02:19:11,951 - mmcls - INFO - Epoch [87][1900/5005] lr: 1.000e-03, eta: 1:40:08, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8668 2023-01-05 02:19:20,338 - mmcls - INFO - Epoch [87][2000/5005] lr: 1.000e-03, eta: 1:39:59, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8553 2023-01-05 02:19:29,004 - mmcls - INFO - Epoch [87][2100/5005] lr: 1.000e-03, eta: 1:39:50, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8498 2023-01-05 02:19:37,914 - mmcls - INFO - Epoch [87][2200/5005] lr: 1.000e-03, eta: 1:39:42, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8718 2023-01-05 02:19:46,625 - mmcls - INFO - Epoch [87][2300/5005] lr: 1.000e-03, eta: 1:39:33, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8506 2023-01-05 02:19:55,051 - mmcls - INFO - Epoch [87][2400/5005] lr: 1.000e-03, eta: 1:39:24, time: 0.084, data_time: 0.025, memory: 928, loss: 4.8398 2023-01-05 02:20:04,034 - mmcls - INFO - Epoch [87][2500/5005] lr: 1.000e-03, eta: 1:39:15, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8634 2023-01-05 02:20:12,782 - mmcls - INFO - Epoch [87][2600/5005] lr: 1.000e-03, eta: 1:39:06, time: 0.087, data_time: 0.022, memory: 928, loss: 4.8737 2023-01-05 02:20:21,411 - mmcls - INFO - Epoch [87][2700/5005] lr: 1.000e-03, eta: 1:38:57, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8472 2023-01-05 02:20:30,129 - mmcls - INFO - Epoch [87][2800/5005] lr: 1.000e-03, eta: 1:38:49, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8562 2023-01-05 02:20:39,066 - mmcls - INFO - Epoch [87][2900/5005] lr: 1.000e-03, eta: 1:38:40, time: 0.089, data_time: 0.008, memory: 928, loss: 4.8504 2023-01-05 02:20:47,875 - mmcls - INFO - Epoch [87][3000/5005] lr: 1.000e-03, eta: 1:38:31, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8359 2023-01-05 02:20:56,575 - mmcls - INFO - Epoch [87][3100/5005] lr: 1.000e-03, eta: 1:38:22, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8402 2023-01-05 02:21:05,359 - mmcls - INFO - Epoch [87][3200/5005] lr: 1.000e-03, eta: 1:38:13, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8285 2023-01-05 02:21:14,183 - mmcls - INFO - Epoch [87][3300/5005] lr: 1.000e-03, eta: 1:38:05, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8518 2023-01-05 02:21:23,126 - mmcls - INFO - Epoch [87][3400/5005] lr: 1.000e-03, eta: 1:37:56, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8312 2023-01-05 02:21:31,929 - mmcls - INFO - Epoch [87][3500/5005] lr: 1.000e-03, eta: 1:37:47, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8419 2023-01-05 02:21:40,558 - mmcls - INFO - Epoch [87][3600/5005] lr: 1.000e-03, eta: 1:37:38, time: 0.086, data_time: 0.007, memory: 928, loss: 4.8463 2023-01-05 02:21:49,290 - mmcls - INFO - Epoch [87][3700/5005] lr: 1.000e-03, eta: 1:37:29, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8625 2023-01-05 02:21:58,112 - mmcls - INFO - Epoch [87][3800/5005] lr: 1.000e-03, eta: 1:37:21, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8552 2023-01-05 02:22:06,965 - mmcls - INFO - Epoch [87][3900/5005] lr: 1.000e-03, eta: 1:37:12, time: 0.089, data_time: 0.026, memory: 928, loss: 4.8432 2023-01-05 02:22:15,668 - mmcls - INFO - Epoch [87][4000/5005] lr: 1.000e-03, eta: 1:37:03, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8456 2023-01-05 02:22:24,562 - mmcls - INFO - Epoch [87][4100/5005] lr: 1.000e-03, eta: 1:36:54, time: 0.089, data_time: 0.042, memory: 928, loss: 4.8406 2023-01-05 02:22:33,292 - mmcls - INFO - Epoch [87][4200/5005] lr: 1.000e-03, eta: 1:36:45, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8503 2023-01-05 02:22:42,088 - mmcls - INFO - Epoch [87][4300/5005] lr: 1.000e-03, eta: 1:36:36, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8391 2023-01-05 02:22:50,864 - mmcls - INFO - Epoch [87][4400/5005] lr: 1.000e-03, eta: 1:36:28, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8417 2023-01-05 02:22:59,734 - mmcls - INFO - Epoch [87][4500/5005] lr: 1.000e-03, eta: 1:36:19, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8492 2023-01-05 02:23:08,892 - mmcls - INFO - Epoch [87][4600/5005] lr: 1.000e-03, eta: 1:36:10, time: 0.092, data_time: 0.009, memory: 928, loss: 4.8582 2023-01-05 02:23:17,494 - mmcls - INFO - Epoch [87][4700/5005] lr: 1.000e-03, eta: 1:36:01, time: 0.086, data_time: 0.036, memory: 928, loss: 4.8259 2023-01-05 02:23:26,680 - mmcls - INFO - Epoch [87][4800/5005] lr: 1.000e-03, eta: 1:35:52, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8661 2023-01-05 02:23:35,320 - mmcls - INFO - Epoch [87][4900/5005] lr: 1.000e-03, eta: 1:35:44, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8436 2023-01-05 02:23:44,226 - mmcls - INFO - Epoch [87][5000/5005] lr: 1.000e-03, eta: 1:35:35, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8754 2023-01-05 02:24:06,949 - mmcls - INFO - Epoch(val) [87][196] accuracy_top-1: 15.9120, accuracy_top-5: 32.8820 2023-01-05 02:24:17,876 - mmcls - INFO - Epoch [88][100/5005] lr: 1.000e-03, eta: 1:35:26, time: 0.109, data_time: 0.036, memory: 928, loss: 4.8391 2023-01-05 02:24:26,956 - mmcls - INFO - Epoch [88][200/5005] lr: 1.000e-03, eta: 1:35:17, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8376 2023-01-05 02:24:35,655 - mmcls - INFO - Epoch [88][300/5005] lr: 1.000e-03, eta: 1:35:08, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8463 2023-01-05 02:24:44,383 - mmcls - INFO - Epoch [88][400/5005] lr: 1.000e-03, eta: 1:34:59, time: 0.087, data_time: 0.017, memory: 928, loss: 4.8326 2023-01-05 02:24:53,345 - mmcls - INFO - Epoch [88][500/5005] lr: 1.000e-03, eta: 1:34:51, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8497 2023-01-05 02:25:01,787 - mmcls - INFO - Epoch [88][600/5005] lr: 1.000e-03, eta: 1:34:42, time: 0.084, data_time: 0.021, memory: 928, loss: 4.8341 2023-01-05 02:25:10,407 - mmcls - INFO - Epoch [88][700/5005] lr: 1.000e-03, eta: 1:34:33, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8269 2023-01-05 02:25:19,287 - mmcls - INFO - Epoch [88][800/5005] lr: 1.000e-03, eta: 1:34:24, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8325 2023-01-05 02:25:28,115 - mmcls - INFO - Epoch [88][900/5005] lr: 1.000e-03, eta: 1:34:15, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8559 2023-01-05 02:25:36,663 - mmcls - INFO - Epoch [88][1000/5005] lr: 1.000e-03, eta: 1:34:06, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8571 2023-01-05 02:25:45,163 - mmcls - INFO - Epoch [88][1100/5005] lr: 1.000e-03, eta: 1:33:58, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8530 2023-01-05 02:25:54,070 - mmcls - INFO - Epoch [88][1200/5005] lr: 1.000e-03, eta: 1:33:49, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8429 2023-01-05 02:26:02,583 - mmcls - INFO - Epoch [88][1300/5005] lr: 1.000e-03, eta: 1:33:40, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8483 2023-01-05 02:26:11,390 - mmcls - INFO - Epoch [88][1400/5005] lr: 1.000e-03, eta: 1:33:31, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8475 2023-01-05 02:26:20,189 - mmcls - INFO - Epoch [88][1500/5005] lr: 1.000e-03, eta: 1:33:22, time: 0.088, data_time: 0.022, memory: 928, loss: 4.8529 2023-01-05 02:26:28,842 - mmcls - INFO - Epoch [88][1600/5005] lr: 1.000e-03, eta: 1:33:13, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8537 2023-01-05 02:26:37,342 - mmcls - INFO - Epoch [88][1700/5005] lr: 1.000e-03, eta: 1:33:05, time: 0.085, data_time: 0.001, memory: 928, loss: 4.8597 2023-01-05 02:26:45,938 - mmcls - INFO - Epoch [88][1800/5005] lr: 1.000e-03, eta: 1:32:56, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8495 2023-01-05 02:26:54,850 - mmcls - INFO - Epoch [88][1900/5005] lr: 1.000e-03, eta: 1:32:47, time: 0.089, data_time: 0.049, memory: 928, loss: 4.8449 2023-01-05 02:27:03,626 - mmcls - INFO - Epoch [88][2000/5005] lr: 1.000e-03, eta: 1:32:38, time: 0.088, data_time: 0.023, memory: 928, loss: 4.8463 2023-01-05 02:27:12,583 - mmcls - INFO - Epoch [88][2100/5005] lr: 1.000e-03, eta: 1:32:29, time: 0.090, data_time: 0.013, memory: 928, loss: 4.8330 2023-01-05 02:27:21,092 - mmcls - INFO - Epoch [88][2200/5005] lr: 1.000e-03, eta: 1:32:20, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8307 2023-01-05 02:27:29,805 - mmcls - INFO - Epoch [88][2300/5005] lr: 1.000e-03, eta: 1:32:12, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8559 2023-01-05 02:27:38,702 - mmcls - INFO - Epoch [88][2400/5005] lr: 1.000e-03, eta: 1:32:03, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8616 2023-01-05 02:27:47,500 - mmcls - INFO - Epoch [88][2500/5005] lr: 1.000e-03, eta: 1:31:54, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8602 2023-01-05 02:27:56,310 - mmcls - INFO - Epoch [88][2600/5005] lr: 1.000e-03, eta: 1:31:45, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8695 2023-01-05 02:28:04,799 - mmcls - INFO - Epoch [88][2700/5005] lr: 1.000e-03, eta: 1:31:36, time: 0.085, data_time: 0.008, memory: 928, loss: 4.8287 2023-01-05 02:28:13,432 - mmcls - INFO - Epoch [88][2800/5005] lr: 1.000e-03, eta: 1:31:27, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8328 2023-01-05 02:28:21,863 - mmcls - INFO - Epoch [88][2900/5005] lr: 1.000e-03, eta: 1:31:19, time: 0.084, data_time: 0.031, memory: 928, loss: 4.8575 2023-01-05 02:28:30,472 - mmcls - INFO - Epoch [88][3000/5005] lr: 1.000e-03, eta: 1:31:10, time: 0.086, data_time: 0.031, memory: 928, loss: 4.8477 2023-01-05 02:28:39,207 - mmcls - INFO - Epoch [88][3100/5005] lr: 1.000e-03, eta: 1:31:01, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8438 2023-01-05 02:28:47,795 - mmcls - INFO - Epoch [88][3200/5005] lr: 1.000e-03, eta: 1:30:52, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8362 2023-01-05 02:28:56,607 - mmcls - INFO - Epoch [88][3300/5005] lr: 1.000e-03, eta: 1:30:43, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8194 2023-01-05 02:29:05,282 - mmcls - INFO - Epoch [88][3400/5005] lr: 1.000e-03, eta: 1:30:34, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8494 2023-01-05 02:29:14,360 - mmcls - INFO - Epoch [88][3500/5005] lr: 1.000e-03, eta: 1:30:26, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8377 2023-01-05 02:29:23,012 - mmcls - INFO - Epoch [88][3600/5005] lr: 1.000e-03, eta: 1:30:17, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8666 2023-01-05 02:29:31,645 - mmcls - INFO - Epoch [88][3700/5005] lr: 1.000e-03, eta: 1:30:08, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8453 2023-01-05 02:29:40,736 - mmcls - INFO - Epoch [88][3800/5005] lr: 1.000e-03, eta: 1:29:59, time: 0.091, data_time: 0.014, memory: 928, loss: 4.8536 2023-01-05 02:29:49,676 - mmcls - INFO - Epoch [88][3900/5005] lr: 1.000e-03, eta: 1:29:50, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8641 2023-01-05 02:29:58,833 - mmcls - INFO - Epoch [88][4000/5005] lr: 1.000e-03, eta: 1:29:42, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8154 2023-01-05 02:30:07,432 - mmcls - INFO - Epoch [88][4100/5005] lr: 1.000e-03, eta: 1:29:33, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8474 2023-01-05 02:30:16,309 - mmcls - INFO - Epoch [88][4200/5005] lr: 1.000e-03, eta: 1:29:24, time: 0.089, data_time: 0.011, memory: 928, loss: 4.8669 2023-01-05 02:30:25,036 - mmcls - INFO - Epoch [88][4300/5005] lr: 1.000e-03, eta: 1:29:15, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8627 2023-01-05 02:30:34,018 - mmcls - INFO - Epoch [88][4400/5005] lr: 1.000e-03, eta: 1:29:06, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8511 2023-01-05 02:30:42,792 - mmcls - INFO - Epoch [88][4500/5005] lr: 1.000e-03, eta: 1:28:58, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8397 2023-01-05 02:30:51,977 - mmcls - INFO - Epoch [88][4600/5005] lr: 1.000e-03, eta: 1:28:49, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8778 2023-01-05 02:31:00,974 - mmcls - INFO - Epoch [88][4700/5005] lr: 1.000e-03, eta: 1:28:40, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8450 2023-01-05 02:31:10,477 - mmcls - INFO - Epoch [88][4800/5005] lr: 1.000e-03, eta: 1:28:31, time: 0.095, data_time: 0.045, memory: 928, loss: 4.8335 2023-01-05 02:31:19,158 - mmcls - INFO - Epoch [88][4900/5005] lr: 1.000e-03, eta: 1:28:22, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8387 2023-01-05 02:31:28,276 - mmcls - INFO - Epoch [88][5000/5005] lr: 1.000e-03, eta: 1:28:14, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8634 2023-01-05 02:31:51,147 - mmcls - INFO - Epoch(val) [88][196] accuracy_top-1: 17.1040, accuracy_top-5: 35.0520 2023-01-05 02:32:02,257 - mmcls - INFO - Epoch [89][100/5005] lr: 1.000e-03, eta: 1:28:05, time: 0.111, data_time: 0.034, memory: 928, loss: 4.8365 2023-01-05 02:32:11,149 - mmcls - INFO - Epoch [89][200/5005] lr: 1.000e-03, eta: 1:27:56, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8566 2023-01-05 02:32:19,826 - mmcls - INFO - Epoch [89][300/5005] lr: 1.000e-03, eta: 1:27:47, time: 0.087, data_time: 0.028, memory: 928, loss: 4.8039 2023-01-05 02:32:28,497 - mmcls - INFO - Epoch [89][400/5005] lr: 1.000e-03, eta: 1:27:38, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8263 2023-01-05 02:32:37,555 - mmcls - INFO - Epoch [89][500/5005] lr: 1.000e-03, eta: 1:27:29, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8393 2023-01-05 02:32:45,943 - mmcls - INFO - Epoch [89][600/5005] lr: 1.000e-03, eta: 1:27:21, time: 0.084, data_time: 0.002, memory: 928, loss: 4.8505 2023-01-05 02:32:54,867 - mmcls - INFO - Epoch [89][700/5005] lr: 1.000e-03, eta: 1:27:12, time: 0.089, data_time: 0.012, memory: 928, loss: 4.8300 2023-01-05 02:33:03,663 - mmcls - INFO - Epoch [89][800/5005] lr: 1.000e-03, eta: 1:27:03, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8684 2023-01-05 02:33:12,144 - mmcls - INFO - Epoch [89][900/5005] lr: 1.000e-03, eta: 1:26:54, time: 0.085, data_time: 0.001, memory: 928, loss: 4.8442 2023-01-05 02:33:20,702 - mmcls - INFO - Epoch [89][1000/5005] lr: 1.000e-03, eta: 1:26:45, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8350 2023-01-05 02:33:29,675 - mmcls - INFO - Epoch [89][1100/5005] lr: 1.000e-03, eta: 1:26:36, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8267 2023-01-05 02:33:38,433 - mmcls - INFO - Epoch [89][1200/5005] lr: 1.000e-03, eta: 1:26:28, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8348 2023-01-05 02:33:47,322 - mmcls - INFO - Epoch [89][1300/5005] lr: 1.000e-03, eta: 1:26:19, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8056 2023-01-05 02:33:55,892 - mmcls - INFO - Epoch [89][1400/5005] lr: 1.000e-03, eta: 1:26:10, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8377 2023-01-05 02:34:04,651 - mmcls - INFO - Epoch [89][1500/5005] lr: 1.000e-03, eta: 1:26:01, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8557 2023-01-05 02:34:13,345 - mmcls - INFO - Epoch [89][1600/5005] lr: 1.000e-03, eta: 1:25:52, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8528 2023-01-05 02:34:22,339 - mmcls - INFO - Epoch [89][1700/5005] lr: 1.000e-03, eta: 1:25:43, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8382 2023-01-05 02:34:31,562 - mmcls - INFO - Epoch [89][1800/5005] lr: 1.000e-03, eta: 1:25:35, time: 0.092, data_time: 0.002, memory: 928, loss: 4.8376 2023-01-05 02:34:40,079 - mmcls - INFO - Epoch [89][1900/5005] lr: 1.000e-03, eta: 1:25:26, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8416 2023-01-05 02:34:48,618 - mmcls - INFO - Epoch [89][2000/5005] lr: 1.000e-03, eta: 1:25:17, time: 0.085, data_time: 0.023, memory: 928, loss: 4.8645 2023-01-05 02:34:57,570 - mmcls - INFO - Epoch [89][2100/5005] lr: 1.000e-03, eta: 1:25:08, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8560 2023-01-05 02:35:06,131 - mmcls - INFO - Epoch [89][2200/5005] lr: 1.000e-03, eta: 1:24:59, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8378 2023-01-05 02:35:14,827 - mmcls - INFO - Epoch [89][2300/5005] lr: 1.000e-03, eta: 1:24:51, time: 0.087, data_time: 0.030, memory: 928, loss: 4.8586 2023-01-05 02:35:23,571 - mmcls - INFO - Epoch [89][2400/5005] lr: 1.000e-03, eta: 1:24:42, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8531 2023-01-05 02:35:32,309 - mmcls - INFO - Epoch [89][2500/5005] lr: 1.000e-03, eta: 1:24:33, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8440 2023-01-05 02:35:41,158 - mmcls - INFO - Epoch [89][2600/5005] lr: 1.000e-03, eta: 1:24:24, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8390 2023-01-05 02:35:50,141 - mmcls - INFO - Epoch [89][2700/5005] lr: 1.000e-03, eta: 1:24:15, time: 0.090, data_time: 0.023, memory: 928, loss: 4.8443 2023-01-05 02:35:58,737 - mmcls - INFO - Epoch [89][2800/5005] lr: 1.000e-03, eta: 1:24:06, time: 0.086, data_time: 0.032, memory: 928, loss: 4.8515 2023-01-05 02:36:07,504 - mmcls - INFO - Epoch [89][2900/5005] lr: 1.000e-03, eta: 1:23:58, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8325 2023-01-05 02:36:16,590 - mmcls - INFO - Epoch [89][3000/5005] lr: 1.000e-03, eta: 1:23:49, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8525 2023-01-05 02:36:25,128 - mmcls - INFO - Epoch [89][3100/5005] lr: 1.000e-03, eta: 1:23:40, time: 0.086, data_time: 0.028, memory: 928, loss: 4.8629 2023-01-05 02:36:33,637 - mmcls - INFO - Epoch [89][3200/5005] lr: 1.000e-03, eta: 1:23:31, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8501 2023-01-05 02:36:42,271 - mmcls - INFO - Epoch [89][3300/5005] lr: 1.000e-03, eta: 1:23:22, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8373 2023-01-05 02:36:51,156 - mmcls - INFO - Epoch [89][3400/5005] lr: 1.000e-03, eta: 1:23:14, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8604 2023-01-05 02:36:59,978 - mmcls - INFO - Epoch [89][3500/5005] lr: 1.000e-03, eta: 1:23:05, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8558 2023-01-05 02:37:08,646 - mmcls - INFO - Epoch [89][3600/5005] lr: 1.000e-03, eta: 1:22:56, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8420 2023-01-05 02:37:17,354 - mmcls - INFO - Epoch [89][3700/5005] lr: 1.000e-03, eta: 1:22:47, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8343 2023-01-05 02:37:26,015 - mmcls - INFO - Epoch [89][3800/5005] lr: 1.000e-03, eta: 1:22:38, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8641 2023-01-05 02:37:34,825 - mmcls - INFO - Epoch [89][3900/5005] lr: 1.000e-03, eta: 1:22:29, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8482 2023-01-05 02:37:43,426 - mmcls - INFO - Epoch [89][4000/5005] lr: 1.000e-03, eta: 1:22:21, time: 0.086, data_time: 0.027, memory: 928, loss: 4.8735 2023-01-05 02:37:52,161 - mmcls - INFO - Epoch [89][4100/5005] lr: 1.000e-03, eta: 1:22:12, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8382 2023-01-05 02:38:00,782 - mmcls - INFO - Epoch [89][4200/5005] lr: 1.000e-03, eta: 1:22:03, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8433 2023-01-05 02:38:09,522 - mmcls - INFO - Epoch [89][4300/5005] lr: 1.000e-03, eta: 1:21:54, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8633 2023-01-05 02:38:18,347 - mmcls - INFO - Epoch [89][4400/5005] lr: 1.000e-03, eta: 1:21:45, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8445 2023-01-05 02:38:27,252 - mmcls - INFO - Epoch [89][4500/5005] lr: 1.000e-03, eta: 1:21:36, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8596 2023-01-05 02:38:35,995 - mmcls - INFO - Epoch [89][4600/5005] lr: 1.000e-03, eta: 1:21:28, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8359 2023-01-05 02:38:44,835 - mmcls - INFO - Epoch [89][4700/5005] lr: 1.000e-03, eta: 1:21:19, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8624 2023-01-05 02:38:53,486 - mmcls - INFO - Epoch [89][4800/5005] lr: 1.000e-03, eta: 1:21:10, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8367 2023-01-05 02:39:02,203 - mmcls - INFO - Epoch [89][4900/5005] lr: 1.000e-03, eta: 1:21:01, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8603 2023-01-05 02:39:10,956 - mmcls - INFO - Epoch [89][5000/5005] lr: 1.000e-03, eta: 1:20:52, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8591 2023-01-05 02:39:33,920 - mmcls - INFO - Epoch(val) [89][196] accuracy_top-1: 17.4020, accuracy_top-5: 35.2540 2023-01-05 02:39:44,599 - mmcls - INFO - Epoch [90][100/5005] lr: 1.000e-03, eta: 1:20:43, time: 0.107, data_time: 0.038, memory: 928, loss: 4.8325 2023-01-05 02:39:53,528 - mmcls - INFO - Epoch [90][200/5005] lr: 1.000e-03, eta: 1:20:34, time: 0.089, data_time: 0.040, memory: 928, loss: 4.8339 2023-01-05 02:40:02,257 - mmcls - INFO - Epoch [90][300/5005] lr: 1.000e-03, eta: 1:20:26, time: 0.087, data_time: 0.037, memory: 928, loss: 4.8012 2023-01-05 02:40:11,176 - mmcls - INFO - Epoch [90][400/5005] lr: 1.000e-03, eta: 1:20:17, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8500 2023-01-05 02:40:19,655 - mmcls - INFO - Epoch [90][500/5005] lr: 1.000e-03, eta: 1:20:08, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8364 2023-01-05 02:40:28,432 - mmcls - INFO - Epoch [90][600/5005] lr: 1.000e-03, eta: 1:19:59, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8467 2023-01-05 02:40:37,281 - mmcls - INFO - Epoch [90][700/5005] lr: 1.000e-03, eta: 1:19:50, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8232 2023-01-05 02:40:46,123 - mmcls - INFO - Epoch [90][800/5005] lr: 1.000e-03, eta: 1:19:42, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8489 2023-01-05 02:40:54,738 - mmcls - INFO - Epoch [90][900/5005] lr: 1.000e-03, eta: 1:19:33, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8566 2023-01-05 02:41:03,381 - mmcls - INFO - Epoch [90][1000/5005] lr: 1.000e-03, eta: 1:19:24, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8341 2023-01-05 02:41:12,178 - mmcls - INFO - Epoch [90][1100/5005] lr: 1.000e-03, eta: 1:19:15, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8731 2023-01-05 02:41:21,068 - mmcls - INFO - Epoch [90][1200/5005] lr: 1.000e-03, eta: 1:19:06, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8522 2023-01-05 02:41:29,727 - mmcls - INFO - Epoch [90][1300/5005] lr: 1.000e-03, eta: 1:18:57, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8484 2023-01-05 02:41:38,711 - mmcls - INFO - Epoch [90][1400/5005] lr: 1.000e-03, eta: 1:18:49, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8354 2023-01-05 02:41:47,237 - mmcls - INFO - Epoch [90][1500/5005] lr: 1.000e-03, eta: 1:18:40, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8602 2023-01-05 02:41:55,888 - mmcls - INFO - Epoch [90][1600/5005] lr: 1.000e-03, eta: 1:18:31, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8360 2023-01-05 02:42:04,660 - mmcls - INFO - Epoch [90][1700/5005] lr: 1.000e-03, eta: 1:18:22, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8257 2023-01-05 02:42:13,400 - mmcls - INFO - Epoch [90][1800/5005] lr: 1.000e-03, eta: 1:18:13, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8352 2023-01-05 02:42:21,886 - mmcls - INFO - Epoch [90][1900/5005] lr: 1.000e-03, eta: 1:18:04, time: 0.085, data_time: 0.036, memory: 928, loss: 4.8354 2023-01-05 02:42:30,827 - mmcls - INFO - Epoch [90][2000/5005] lr: 1.000e-03, eta: 1:17:56, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8363 2023-01-05 02:42:39,404 - mmcls - INFO - Epoch [90][2100/5005] lr: 1.000e-03, eta: 1:17:47, time: 0.086, data_time: 0.023, memory: 928, loss: 4.8616 2023-01-05 02:42:48,365 - mmcls - INFO - Epoch [90][2200/5005] lr: 1.000e-03, eta: 1:17:38, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8454 2023-01-05 02:42:56,925 - mmcls - INFO - Epoch [90][2300/5005] lr: 1.000e-03, eta: 1:17:29, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8592 2023-01-05 02:43:05,745 - mmcls - INFO - Epoch [90][2400/5005] lr: 1.000e-03, eta: 1:17:20, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8410 2023-01-05 02:43:14,352 - mmcls - INFO - Epoch [90][2500/5005] lr: 1.000e-03, eta: 1:17:12, time: 0.086, data_time: 0.031, memory: 928, loss: 4.8631 2023-01-05 02:43:23,173 - mmcls - INFO - Epoch [90][2600/5005] lr: 1.000e-03, eta: 1:17:03, time: 0.088, data_time: 0.037, memory: 928, loss: 4.8395 2023-01-05 02:43:32,042 - mmcls - INFO - Epoch [90][2700/5005] lr: 1.000e-03, eta: 1:16:54, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8630 2023-01-05 02:43:40,937 - mmcls - INFO - Epoch [90][2800/5005] lr: 1.000e-03, eta: 1:16:45, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8312 2023-01-05 02:43:50,055 - mmcls - INFO - Epoch [90][2900/5005] lr: 1.000e-03, eta: 1:16:36, time: 0.091, data_time: 0.009, memory: 928, loss: 4.8602 2023-01-05 02:43:58,356 - mmcls - INFO - Epoch [90][3000/5005] lr: 1.000e-03, eta: 1:16:27, time: 0.083, data_time: 0.001, memory: 928, loss: 4.8435 2023-01-05 02:44:06,888 - mmcls - INFO - Epoch [90][3100/5005] lr: 1.000e-03, eta: 1:16:19, time: 0.085, data_time: 0.011, memory: 928, loss: 4.8575 2023-01-05 02:44:15,515 - mmcls - INFO - Epoch [90][3200/5005] lr: 1.000e-03, eta: 1:16:10, time: 0.086, data_time: 0.025, memory: 928, loss: 4.8388 2023-01-05 02:44:24,310 - mmcls - INFO - Epoch [90][3300/5005] lr: 1.000e-03, eta: 1:16:01, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8766 2023-01-05 02:44:33,046 - mmcls - INFO - Epoch [90][3400/5005] lr: 1.000e-03, eta: 1:15:52, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8320 2023-01-05 02:44:42,219 - mmcls - INFO - Epoch [90][3500/5005] lr: 1.000e-03, eta: 1:15:43, time: 0.092, data_time: 0.037, memory: 928, loss: 4.8620 2023-01-05 02:44:51,077 - mmcls - INFO - Epoch [90][3600/5005] lr: 1.000e-03, eta: 1:15:35, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8645 2023-01-05 02:45:00,127 - mmcls - INFO - Epoch [90][3700/5005] lr: 1.000e-03, eta: 1:15:26, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8689 2023-01-05 02:45:08,814 - mmcls - INFO - Epoch [90][3800/5005] lr: 1.000e-03, eta: 1:15:17, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8319 2023-01-05 02:45:17,699 - mmcls - INFO - Epoch [90][3900/5005] lr: 1.000e-03, eta: 1:15:08, time: 0.089, data_time: 0.030, memory: 928, loss: 4.8282 2023-01-05 02:45:26,664 - mmcls - INFO - Epoch [90][4000/5005] lr: 1.000e-03, eta: 1:14:59, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8468 2023-01-05 02:45:35,580 - mmcls - INFO - Epoch [90][4100/5005] lr: 1.000e-03, eta: 1:14:51, time: 0.089, data_time: 0.013, memory: 928, loss: 4.8697 2023-01-05 02:45:44,312 - mmcls - INFO - Epoch [90][4200/5005] lr: 1.000e-03, eta: 1:14:42, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8194 2023-01-05 02:45:52,812 - mmcls - INFO - Epoch [90][4300/5005] lr: 1.000e-03, eta: 1:14:33, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8378 2023-01-05 02:46:01,658 - mmcls - INFO - Epoch [90][4400/5005] lr: 1.000e-03, eta: 1:14:24, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8373 2023-01-05 02:46:10,239 - mmcls - INFO - Epoch [90][4500/5005] lr: 1.000e-03, eta: 1:14:15, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8491 2023-01-05 02:46:19,415 - mmcls - INFO - Epoch [90][4600/5005] lr: 1.000e-03, eta: 1:14:06, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8326 2023-01-05 02:46:28,087 - mmcls - INFO - Epoch [90][4700/5005] lr: 1.000e-03, eta: 1:13:58, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8409 2023-01-05 02:46:36,791 - mmcls - INFO - Epoch [90][4800/5005] lr: 1.000e-03, eta: 1:13:49, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8408 2023-01-05 02:46:45,560 - mmcls - INFO - Epoch [90][4900/5005] lr: 1.000e-03, eta: 1:13:40, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8471 2023-01-05 02:46:54,458 - mmcls - INFO - Epoch [90][5000/5005] lr: 1.000e-03, eta: 1:13:31, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8458 2023-01-05 02:47:17,377 - mmcls - INFO - Epoch(val) [90][196] accuracy_top-1: 16.9780, accuracy_top-5: 34.3680 2023-01-05 02:47:28,615 - mmcls - INFO - Epoch [91][100/5005] lr: 1.000e-04, eta: 1:13:22, time: 0.112, data_time: 0.040, memory: 928, loss: 4.8306 2023-01-05 02:47:37,267 - mmcls - INFO - Epoch [91][200/5005] lr: 1.000e-04, eta: 1:13:13, time: 0.087, data_time: 0.037, memory: 928, loss: 4.8325 2023-01-05 02:47:46,207 - mmcls - INFO - Epoch [91][300/5005] lr: 1.000e-04, eta: 1:13:04, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8458 2023-01-05 02:47:54,867 - mmcls - INFO - Epoch [91][400/5005] lr: 1.000e-04, eta: 1:12:56, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8029 2023-01-05 02:48:03,695 - mmcls - INFO - Epoch [91][500/5005] lr: 1.000e-04, eta: 1:12:47, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8246 2023-01-05 02:48:12,680 - mmcls - INFO - Epoch [91][600/5005] lr: 1.000e-04, eta: 1:12:38, time: 0.090, data_time: 0.005, memory: 928, loss: 4.8345 2023-01-05 02:48:21,650 - mmcls - INFO - Epoch [91][700/5005] lr: 1.000e-04, eta: 1:12:29, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8067 2023-01-05 02:48:30,465 - mmcls - INFO - Epoch [91][800/5005] lr: 1.000e-04, eta: 1:12:20, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8132 2023-01-05 02:48:39,290 - mmcls - INFO - Epoch [91][900/5005] lr: 1.000e-04, eta: 1:12:12, time: 0.088, data_time: 0.017, memory: 928, loss: 4.8067 2023-01-05 02:48:47,918 - mmcls - INFO - Epoch [91][1000/5005] lr: 1.000e-04, eta: 1:12:03, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8180 2023-01-05 02:48:56,687 - mmcls - INFO - Epoch [91][1100/5005] lr: 1.000e-04, eta: 1:11:54, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8156 2023-01-05 02:49:05,617 - mmcls - INFO - Epoch [91][1200/5005] lr: 1.000e-04, eta: 1:11:45, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8042 2023-01-05 02:49:14,290 - mmcls - INFO - Epoch [91][1300/5005] lr: 1.000e-04, eta: 1:11:36, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8372 2023-01-05 02:49:22,791 - mmcls - INFO - Epoch [91][1400/5005] lr: 1.000e-04, eta: 1:11:27, time: 0.085, data_time: 0.010, memory: 928, loss: 4.8038 2023-01-05 02:49:31,849 - mmcls - INFO - Epoch [91][1500/5005] lr: 1.000e-04, eta: 1:11:19, time: 0.091, data_time: 0.025, memory: 928, loss: 4.8461 2023-01-05 02:49:40,564 - mmcls - INFO - Epoch [91][1600/5005] lr: 1.000e-04, eta: 1:11:10, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8343 2023-01-05 02:49:49,333 - mmcls - INFO - Epoch [91][1700/5005] lr: 1.000e-04, eta: 1:11:01, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8366 2023-01-05 02:49:58,119 - mmcls - INFO - Epoch [91][1800/5005] lr: 1.000e-04, eta: 1:10:52, time: 0.088, data_time: 0.020, memory: 928, loss: 4.8079 2023-01-05 02:50:06,988 - mmcls - INFO - Epoch [91][1900/5005] lr: 1.000e-04, eta: 1:10:43, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8062 2023-01-05 02:50:15,973 - mmcls - INFO - Epoch [91][2000/5005] lr: 1.000e-04, eta: 1:10:35, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8197 2023-01-05 02:50:24,793 - mmcls - INFO - Epoch [91][2100/5005] lr: 1.000e-04, eta: 1:10:26, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8111 2023-01-05 02:50:33,610 - mmcls - INFO - Epoch [91][2200/5005] lr: 1.000e-04, eta: 1:10:17, time: 0.088, data_time: 0.021, memory: 928, loss: 4.8034 2023-01-05 02:50:42,394 - mmcls - INFO - Epoch [91][2300/5005] lr: 1.000e-04, eta: 1:10:08, time: 0.088, data_time: 0.010, memory: 928, loss: 4.7990 2023-01-05 02:50:51,050 - mmcls - INFO - Epoch [91][2400/5005] lr: 1.000e-04, eta: 1:09:59, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8190 2023-01-05 02:50:59,738 - mmcls - INFO - Epoch [91][2500/5005] lr: 1.000e-04, eta: 1:09:51, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8012 2023-01-05 02:51:08,344 - mmcls - INFO - Epoch [91][2600/5005] lr: 1.000e-04, eta: 1:09:42, time: 0.086, data_time: 0.035, memory: 928, loss: 4.7962 2023-01-05 02:51:16,892 - mmcls - INFO - Epoch [91][2700/5005] lr: 1.000e-04, eta: 1:09:33, time: 0.085, data_time: 0.026, memory: 928, loss: 4.7916 2023-01-05 02:51:25,907 - mmcls - INFO - Epoch [91][2800/5005] lr: 1.000e-04, eta: 1:09:24, time: 0.090, data_time: 0.002, memory: 928, loss: 4.8051 2023-01-05 02:51:34,493 - mmcls - INFO - Epoch [91][2900/5005] lr: 1.000e-04, eta: 1:09:15, time: 0.086, data_time: 0.004, memory: 928, loss: 4.7996 2023-01-05 02:51:43,563 - mmcls - INFO - Epoch [91][3000/5005] lr: 1.000e-04, eta: 1:09:06, time: 0.091, data_time: 0.001, memory: 928, loss: 4.7972 2023-01-05 02:51:52,485 - mmcls - INFO - Epoch [91][3100/5005] lr: 1.000e-04, eta: 1:08:58, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8008 2023-01-05 02:52:01,348 - mmcls - INFO - Epoch [91][3200/5005] lr: 1.000e-04, eta: 1:08:49, time: 0.089, data_time: 0.013, memory: 928, loss: 4.7929 2023-01-05 02:52:10,008 - mmcls - INFO - Epoch [91][3300/5005] lr: 1.000e-04, eta: 1:08:40, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8345 2023-01-05 02:52:18,479 - mmcls - INFO - Epoch [91][3400/5005] lr: 1.000e-04, eta: 1:08:31, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8183 2023-01-05 02:52:27,555 - mmcls - INFO - Epoch [91][3500/5005] lr: 1.000e-04, eta: 1:08:22, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8089 2023-01-05 02:52:36,233 - mmcls - INFO - Epoch [91][3600/5005] lr: 1.000e-04, eta: 1:08:14, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8167 2023-01-05 02:52:45,086 - mmcls - INFO - Epoch [91][3700/5005] lr: 1.000e-04, eta: 1:08:05, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8070 2023-01-05 02:52:54,045 - mmcls - INFO - Epoch [91][3800/5005] lr: 1.000e-04, eta: 1:07:56, time: 0.090, data_time: 0.024, memory: 928, loss: 4.8132 2023-01-05 02:53:02,556 - mmcls - INFO - Epoch [91][3900/5005] lr: 1.000e-04, eta: 1:07:47, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8166 2023-01-05 02:53:11,725 - mmcls - INFO - Epoch [91][4000/5005] lr: 1.000e-04, eta: 1:07:38, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8275 2023-01-05 02:53:20,037 - mmcls - INFO - Epoch [91][4100/5005] lr: 1.000e-04, eta: 1:07:29, time: 0.083, data_time: 0.003, memory: 928, loss: 4.8297 2023-01-05 02:53:28,908 - mmcls - INFO - Epoch [91][4200/5005] lr: 1.000e-04, eta: 1:07:21, time: 0.089, data_time: 0.020, memory: 928, loss: 4.8247 2023-01-05 02:53:37,464 - mmcls - INFO - Epoch [91][4300/5005] lr: 1.000e-04, eta: 1:07:12, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8146 2023-01-05 02:53:46,200 - mmcls - INFO - Epoch [91][4400/5005] lr: 1.000e-04, eta: 1:07:03, time: 0.087, data_time: 0.051, memory: 928, loss: 4.8200 2023-01-05 02:53:55,021 - mmcls - INFO - Epoch [91][4500/5005] lr: 1.000e-04, eta: 1:06:54, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8209 2023-01-05 02:54:03,655 - mmcls - INFO - Epoch [91][4600/5005] lr: 1.000e-04, eta: 1:06:45, time: 0.086, data_time: 0.007, memory: 928, loss: 4.7911 2023-01-05 02:54:12,319 - mmcls - INFO - Epoch [91][4700/5005] lr: 1.000e-04, eta: 1:06:36, time: 0.087, data_time: 0.021, memory: 928, loss: 4.8074 2023-01-05 02:54:21,215 - mmcls - INFO - Epoch [91][4800/5005] lr: 1.000e-04, eta: 1:06:28, time: 0.089, data_time: 0.001, memory: 928, loss: 4.7989 2023-01-05 02:54:29,938 - mmcls - INFO - Epoch [91][4900/5005] lr: 1.000e-04, eta: 1:06:19, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8099 2023-01-05 02:54:38,613 - mmcls - INFO - Epoch [91][5000/5005] lr: 1.000e-04, eta: 1:06:10, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8069 2023-01-05 02:55:01,557 - mmcls - INFO - Epoch(val) [91][196] accuracy_top-1: 18.5500, accuracy_top-5: 37.0220 2023-01-05 02:55:12,525 - mmcls - INFO - Epoch [92][100/5005] lr: 1.000e-04, eta: 1:06:01, time: 0.109, data_time: 0.035, memory: 928, loss: 4.8098 2023-01-05 02:55:21,407 - mmcls - INFO - Epoch [92][200/5005] lr: 1.000e-04, eta: 1:05:52, time: 0.089, data_time: 0.021, memory: 928, loss: 4.8128 2023-01-05 02:55:30,147 - mmcls - INFO - Epoch [92][300/5005] lr: 1.000e-04, eta: 1:05:43, time: 0.088, data_time: 0.001, memory: 928, loss: 4.7982 2023-01-05 02:55:38,739 - mmcls - INFO - Epoch [92][400/5005] lr: 1.000e-04, eta: 1:05:34, time: 0.086, data_time: 0.017, memory: 928, loss: 4.7963 2023-01-05 02:55:47,389 - mmcls - INFO - Epoch [92][500/5005] lr: 1.000e-04, eta: 1:05:26, time: 0.086, data_time: 0.025, memory: 928, loss: 4.8138 2023-01-05 02:55:56,250 - mmcls - INFO - Epoch [92][600/5005] lr: 1.000e-04, eta: 1:05:17, time: 0.089, data_time: 0.036, memory: 928, loss: 4.8248 2023-01-05 02:56:05,023 - mmcls - INFO - Epoch [92][700/5005] lr: 1.000e-04, eta: 1:05:08, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8066 2023-01-05 02:56:13,569 - mmcls - INFO - Epoch [92][800/5005] lr: 1.000e-04, eta: 1:04:59, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8054 2023-01-05 02:56:22,318 - mmcls - INFO - Epoch [92][900/5005] lr: 1.000e-04, eta: 1:04:50, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8247 2023-01-05 02:56:31,058 - mmcls - INFO - Epoch [92][1000/5005] lr: 1.000e-04, eta: 1:04:42, time: 0.087, data_time: 0.023, memory: 928, loss: 4.8009 2023-01-05 02:56:39,711 - mmcls - INFO - Epoch [92][1100/5005] lr: 1.000e-04, eta: 1:04:33, time: 0.087, data_time: 0.003, memory: 928, loss: 4.7968 2023-01-05 02:56:48,741 - mmcls - INFO - Epoch [92][1200/5005] lr: 1.000e-04, eta: 1:04:24, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8163 2023-01-05 02:56:57,196 - mmcls - INFO - Epoch [92][1300/5005] lr: 1.000e-04, eta: 1:04:15, time: 0.084, data_time: 0.011, memory: 928, loss: 4.7932 2023-01-05 02:57:05,991 - mmcls - INFO - Epoch [92][1400/5005] lr: 1.000e-04, eta: 1:04:06, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8529 2023-01-05 02:57:14,921 - mmcls - INFO - Epoch [92][1500/5005] lr: 1.000e-04, eta: 1:03:57, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8308 2023-01-05 02:57:23,532 - mmcls - INFO - Epoch [92][1600/5005] lr: 1.000e-04, eta: 1:03:49, time: 0.086, data_time: 0.009, memory: 928, loss: 4.7987 2023-01-05 02:57:32,394 - mmcls - INFO - Epoch [92][1700/5005] lr: 1.000e-04, eta: 1:03:40, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8108 2023-01-05 02:57:41,083 - mmcls - INFO - Epoch [92][1800/5005] lr: 1.000e-04, eta: 1:03:31, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8087 2023-01-05 02:57:50,184 - mmcls - INFO - Epoch [92][1900/5005] lr: 1.000e-04, eta: 1:03:22, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8077 2023-01-05 02:57:58,908 - mmcls - INFO - Epoch [92][2000/5005] lr: 1.000e-04, eta: 1:03:13, time: 0.087, data_time: 0.012, memory: 928, loss: 4.8165 2023-01-05 02:58:07,659 - mmcls - INFO - Epoch [92][2100/5005] lr: 1.000e-04, eta: 1:03:05, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8039 2023-01-05 02:58:16,405 - mmcls - INFO - Epoch [92][2200/5005] lr: 1.000e-04, eta: 1:02:56, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8136 2023-01-05 02:58:25,290 - mmcls - INFO - Epoch [92][2300/5005] lr: 1.000e-04, eta: 1:02:47, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8190 2023-01-05 02:58:34,132 - mmcls - INFO - Epoch [92][2400/5005] lr: 1.000e-04, eta: 1:02:38, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8189 2023-01-05 02:58:42,877 - mmcls - INFO - Epoch [92][2500/5005] lr: 1.000e-04, eta: 1:02:29, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7943 2023-01-05 02:58:51,865 - mmcls - INFO - Epoch [92][2600/5005] lr: 1.000e-04, eta: 1:02:20, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8129 2023-01-05 02:59:00,269 - mmcls - INFO - Epoch [92][2700/5005] lr: 1.000e-04, eta: 1:02:12, time: 0.084, data_time: 0.001, memory: 928, loss: 4.7938 2023-01-05 02:59:09,228 - mmcls - INFO - Epoch [92][2800/5005] lr: 1.000e-04, eta: 1:02:03, time: 0.090, data_time: 0.008, memory: 928, loss: 4.8149 2023-01-05 02:59:17,878 - mmcls - INFO - Epoch [92][2900/5005] lr: 1.000e-04, eta: 1:01:54, time: 0.086, data_time: 0.019, memory: 928, loss: 4.8234 2023-01-05 02:59:26,735 - mmcls - INFO - Epoch [92][3000/5005] lr: 1.000e-04, eta: 1:01:45, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8061 2023-01-05 02:59:35,353 - mmcls - INFO - Epoch [92][3100/5005] lr: 1.000e-04, eta: 1:01:36, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8277 2023-01-05 02:59:44,338 - mmcls - INFO - Epoch [92][3200/5005] lr: 1.000e-04, eta: 1:01:28, time: 0.090, data_time: 0.001, memory: 928, loss: 4.7997 2023-01-05 02:59:53,193 - mmcls - INFO - Epoch [92][3300/5005] lr: 1.000e-04, eta: 1:01:19, time: 0.088, data_time: 0.000, memory: 928, loss: 4.7896 2023-01-05 03:00:01,898 - mmcls - INFO - Epoch [92][3400/5005] lr: 1.000e-04, eta: 1:01:10, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8192 2023-01-05 03:00:10,839 - mmcls - INFO - Epoch [92][3500/5005] lr: 1.000e-04, eta: 1:01:01, time: 0.089, data_time: 0.029, memory: 928, loss: 4.7862 2023-01-05 03:00:19,449 - mmcls - INFO - Epoch [92][3600/5005] lr: 1.000e-04, eta: 1:00:52, time: 0.086, data_time: 0.021, memory: 928, loss: 4.8120 2023-01-05 03:00:28,255 - mmcls - INFO - Epoch [92][3700/5005] lr: 1.000e-04, eta: 1:00:43, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8099 2023-01-05 03:00:37,022 - mmcls - INFO - Epoch [92][3800/5005] lr: 1.000e-04, eta: 1:00:35, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8103 2023-01-05 03:00:45,616 - mmcls - INFO - Epoch [92][3900/5005] lr: 1.000e-04, eta: 1:00:26, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7919 2023-01-05 03:00:54,201 - mmcls - INFO - Epoch [92][4000/5005] lr: 1.000e-04, eta: 1:00:17, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8486 2023-01-05 03:01:02,984 - mmcls - INFO - Epoch [92][4100/5005] lr: 1.000e-04, eta: 1:00:08, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8258 2023-01-05 03:01:11,956 - mmcls - INFO - Epoch [92][4200/5005] lr: 1.000e-04, eta: 0:59:59, time: 0.090, data_time: 0.020, memory: 928, loss: 4.8090 2023-01-05 03:01:20,774 - mmcls - INFO - Epoch [92][4300/5005] lr: 1.000e-04, eta: 0:59:51, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7937 2023-01-05 03:01:29,645 - mmcls - INFO - Epoch [92][4400/5005] lr: 1.000e-04, eta: 0:59:42, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8018 2023-01-05 03:01:38,040 - mmcls - INFO - Epoch [92][4500/5005] lr: 1.000e-04, eta: 0:59:33, time: 0.084, data_time: 0.007, memory: 928, loss: 4.8008 2023-01-05 03:01:47,090 - mmcls - INFO - Epoch [92][4600/5005] lr: 1.000e-04, eta: 0:59:24, time: 0.090, data_time: 0.006, memory: 928, loss: 4.8092 2023-01-05 03:01:55,871 - mmcls - INFO - Epoch [92][4700/5005] lr: 1.000e-04, eta: 0:59:15, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8041 2023-01-05 03:02:04,870 - mmcls - INFO - Epoch [92][4800/5005] lr: 1.000e-04, eta: 0:59:07, time: 0.090, data_time: 0.012, memory: 928, loss: 4.8238 2023-01-05 03:02:13,741 - mmcls - INFO - Epoch [92][4900/5005] lr: 1.000e-04, eta: 0:58:58, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8358 2023-01-05 03:02:22,510 - mmcls - INFO - Epoch [92][5000/5005] lr: 1.000e-04, eta: 0:58:49, time: 0.088, data_time: 0.034, memory: 928, loss: 4.8168 2023-01-05 03:02:45,083 - mmcls - INFO - Epoch(val) [92][196] accuracy_top-1: 17.9720, accuracy_top-5: 36.1720 2023-01-05 03:02:56,088 - mmcls - INFO - Epoch [93][100/5005] lr: 1.000e-04, eta: 0:58:40, time: 0.110, data_time: 0.047, memory: 928, loss: 4.8204 2023-01-05 03:03:04,775 - mmcls - INFO - Epoch [93][200/5005] lr: 1.000e-04, eta: 0:58:31, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8055 2023-01-05 03:03:13,625 - mmcls - INFO - Epoch [93][300/5005] lr: 1.000e-04, eta: 0:58:22, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8442 2023-01-05 03:03:22,397 - mmcls - INFO - Epoch [93][400/5005] lr: 1.000e-04, eta: 0:58:13, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8066 2023-01-05 03:03:31,297 - mmcls - INFO - Epoch [93][500/5005] lr: 1.000e-04, eta: 0:58:05, time: 0.089, data_time: 0.012, memory: 928, loss: 4.7997 2023-01-05 03:03:40,117 - mmcls - INFO - Epoch [93][600/5005] lr: 1.000e-04, eta: 0:57:56, time: 0.088, data_time: 0.032, memory: 928, loss: 4.7881 2023-01-05 03:03:49,334 - mmcls - INFO - Epoch [93][700/5005] lr: 1.000e-04, eta: 0:57:47, time: 0.092, data_time: 0.007, memory: 928, loss: 4.8098 2023-01-05 03:03:57,934 - mmcls - INFO - Epoch [93][800/5005] lr: 1.000e-04, eta: 0:57:38, time: 0.086, data_time: 0.005, memory: 928, loss: 4.7972 2023-01-05 03:04:06,986 - mmcls - INFO - Epoch [93][900/5005] lr: 1.000e-04, eta: 0:57:29, time: 0.091, data_time: 0.001, memory: 928, loss: 4.8088 2023-01-05 03:04:15,352 - mmcls - INFO - Epoch [93][1000/5005] lr: 1.000e-04, eta: 0:57:20, time: 0.084, data_time: 0.001, memory: 928, loss: 4.8066 2023-01-05 03:04:24,483 - mmcls - INFO - Epoch [93][1100/5005] lr: 1.000e-04, eta: 0:57:12, time: 0.091, data_time: 0.007, memory: 928, loss: 4.7859 2023-01-05 03:04:33,238 - mmcls - INFO - Epoch [93][1200/5005] lr: 1.000e-04, eta: 0:57:03, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8039 2023-01-05 03:04:41,912 - mmcls - INFO - Epoch [93][1300/5005] lr: 1.000e-04, eta: 0:56:54, time: 0.087, data_time: 0.013, memory: 928, loss: 4.7932 2023-01-05 03:04:50,435 - mmcls - INFO - Epoch [93][1400/5005] lr: 1.000e-04, eta: 0:56:45, time: 0.085, data_time: 0.001, memory: 928, loss: 4.8173 2023-01-05 03:04:59,467 - mmcls - INFO - Epoch [93][1500/5005] lr: 1.000e-04, eta: 0:56:36, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8043 2023-01-05 03:05:08,518 - mmcls - INFO - Epoch [93][1600/5005] lr: 1.000e-04, eta: 0:56:28, time: 0.091, data_time: 0.010, memory: 928, loss: 4.8059 2023-01-05 03:05:17,339 - mmcls - INFO - Epoch [93][1700/5005] lr: 1.000e-04, eta: 0:56:19, time: 0.088, data_time: 0.010, memory: 928, loss: 4.8162 2023-01-05 03:05:25,955 - mmcls - INFO - Epoch [93][1800/5005] lr: 1.000e-04, eta: 0:56:10, time: 0.086, data_time: 0.028, memory: 928, loss: 4.7858 2023-01-05 03:05:34,727 - mmcls - INFO - Epoch [93][1900/5005] lr: 1.000e-04, eta: 0:56:01, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8097 2023-01-05 03:05:43,367 - mmcls - INFO - Epoch [93][2000/5005] lr: 1.000e-04, eta: 0:55:52, time: 0.086, data_time: 0.013, memory: 928, loss: 4.8031 2023-01-05 03:05:51,839 - mmcls - INFO - Epoch [93][2100/5005] lr: 1.000e-04, eta: 0:55:43, time: 0.085, data_time: 0.017, memory: 928, loss: 4.7843 2023-01-05 03:06:00,298 - mmcls - INFO - Epoch [93][2200/5005] lr: 1.000e-04, eta: 0:55:35, time: 0.085, data_time: 0.003, memory: 928, loss: 4.8292 2023-01-05 03:06:09,202 - mmcls - INFO - Epoch [93][2300/5005] lr: 1.000e-04, eta: 0:55:26, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8119 2023-01-05 03:06:17,769 - mmcls - INFO - Epoch [93][2400/5005] lr: 1.000e-04, eta: 0:55:17, time: 0.086, data_time: 0.005, memory: 928, loss: 4.7988 2023-01-05 03:06:27,023 - mmcls - INFO - Epoch [93][2500/5005] lr: 1.000e-04, eta: 0:55:08, time: 0.092, data_time: 0.002, memory: 928, loss: 4.8243 2023-01-05 03:06:35,972 - mmcls - INFO - Epoch [93][2600/5005] lr: 1.000e-04, eta: 0:54:59, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8212 2023-01-05 03:06:44,779 - mmcls - INFO - Epoch [93][2700/5005] lr: 1.000e-04, eta: 0:54:51, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8087 2023-01-05 03:06:53,354 - mmcls - INFO - Epoch [93][2800/5005] lr: 1.000e-04, eta: 0:54:42, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7920 2023-01-05 03:07:02,010 - mmcls - INFO - Epoch [93][2900/5005] lr: 1.000e-04, eta: 0:54:33, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8113 2023-01-05 03:07:10,791 - mmcls - INFO - Epoch [93][3000/5005] lr: 1.000e-04, eta: 0:54:24, time: 0.088, data_time: 0.015, memory: 928, loss: 4.8257 2023-01-05 03:07:19,461 - mmcls - INFO - Epoch [93][3100/5005] lr: 1.000e-04, eta: 0:54:15, time: 0.087, data_time: 0.027, memory: 928, loss: 4.8100 2023-01-05 03:07:28,068 - mmcls - INFO - Epoch [93][3200/5005] lr: 1.000e-04, eta: 0:54:06, time: 0.086, data_time: 0.017, memory: 928, loss: 4.8289 2023-01-05 03:07:36,930 - mmcls - INFO - Epoch [93][3300/5005] lr: 1.000e-04, eta: 0:53:58, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8319 2023-01-05 03:07:45,671 - mmcls - INFO - Epoch [93][3400/5005] lr: 1.000e-04, eta: 0:53:49, time: 0.087, data_time: 0.006, memory: 928, loss: 4.7993 2023-01-05 03:07:54,123 - mmcls - INFO - Epoch [93][3500/5005] lr: 1.000e-04, eta: 0:53:40, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8148 2023-01-05 03:08:03,368 - mmcls - INFO - Epoch [93][3600/5005] lr: 1.000e-04, eta: 0:53:31, time: 0.092, data_time: 0.032, memory: 928, loss: 4.8034 2023-01-05 03:08:12,409 - mmcls - INFO - Epoch [93][3700/5005] lr: 1.000e-04, eta: 0:53:22, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8218 2023-01-05 03:08:21,095 - mmcls - INFO - Epoch [93][3800/5005] lr: 1.000e-04, eta: 0:53:14, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8373 2023-01-05 03:08:30,026 - mmcls - INFO - Epoch [93][3900/5005] lr: 1.000e-04, eta: 0:53:05, time: 0.089, data_time: 0.001, memory: 928, loss: 4.7915 2023-01-05 03:08:38,535 - mmcls - INFO - Epoch [93][4000/5005] lr: 1.000e-04, eta: 0:52:56, time: 0.085, data_time: 0.016, memory: 928, loss: 4.7961 2023-01-05 03:08:47,109 - mmcls - INFO - Epoch [93][4100/5005] lr: 1.000e-04, eta: 0:52:47, time: 0.086, data_time: 0.010, memory: 928, loss: 4.7894 2023-01-05 03:08:56,605 - mmcls - INFO - Epoch [93][4200/5005] lr: 1.000e-04, eta: 0:52:38, time: 0.095, data_time: 0.020, memory: 928, loss: 4.8144 2023-01-05 03:09:04,972 - mmcls - INFO - Epoch [93][4300/5005] lr: 1.000e-04, eta: 0:52:29, time: 0.084, data_time: 0.007, memory: 928, loss: 4.8016 2023-01-05 03:09:13,682 - mmcls - INFO - Epoch [93][4400/5005] lr: 1.000e-04, eta: 0:52:21, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8104 2023-01-05 03:09:22,430 - mmcls - INFO - Epoch [93][4500/5005] lr: 1.000e-04, eta: 0:52:12, time: 0.088, data_time: 0.007, memory: 928, loss: 4.7822 2023-01-05 03:09:31,133 - mmcls - INFO - Epoch [93][4600/5005] lr: 1.000e-04, eta: 0:52:03, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7990 2023-01-05 03:09:40,063 - mmcls - INFO - Epoch [93][4700/5005] lr: 1.000e-04, eta: 0:51:54, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8212 2023-01-05 03:09:48,997 - mmcls - INFO - Epoch [93][4800/5005] lr: 1.000e-04, eta: 0:51:45, time: 0.089, data_time: 0.002, memory: 928, loss: 4.7983 2023-01-05 03:09:57,471 - mmcls - INFO - Epoch [93][4900/5005] lr: 1.000e-04, eta: 0:51:37, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8213 2023-01-05 03:10:06,269 - mmcls - INFO - Epoch [93][5000/5005] lr: 1.000e-04, eta: 0:51:28, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8219 2023-01-05 03:10:29,129 - mmcls - INFO - Epoch(val) [93][196] accuracy_top-1: 18.5320, accuracy_top-5: 36.9080 2023-01-05 03:10:39,918 - mmcls - INFO - Epoch [94][100/5005] lr: 1.000e-04, eta: 0:51:19, time: 0.108, data_time: 0.033, memory: 928, loss: 4.7942 2023-01-05 03:10:48,687 - mmcls - INFO - Epoch [94][200/5005] lr: 1.000e-04, eta: 0:51:10, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8304 2023-01-05 03:10:57,375 - mmcls - INFO - Epoch [94][300/5005] lr: 1.000e-04, eta: 0:51:01, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8012 2023-01-05 03:11:05,937 - mmcls - INFO - Epoch [94][400/5005] lr: 1.000e-04, eta: 0:50:52, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8189 2023-01-05 03:11:14,792 - mmcls - INFO - Epoch [94][500/5005] lr: 1.000e-04, eta: 0:50:43, time: 0.089, data_time: 0.023, memory: 928, loss: 4.7939 2023-01-05 03:11:23,772 - mmcls - INFO - Epoch [94][600/5005] lr: 1.000e-04, eta: 0:50:35, time: 0.090, data_time: 0.037, memory: 928, loss: 4.8085 2023-01-05 03:11:32,450 - mmcls - INFO - Epoch [94][700/5005] lr: 1.000e-04, eta: 0:50:26, time: 0.087, data_time: 0.017, memory: 928, loss: 4.7847 2023-01-05 03:11:41,129 - mmcls - INFO - Epoch [94][800/5005] lr: 1.000e-04, eta: 0:50:17, time: 0.087, data_time: 0.012, memory: 928, loss: 4.7933 2023-01-05 03:11:49,762 - mmcls - INFO - Epoch [94][900/5005] lr: 1.000e-04, eta: 0:50:08, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8108 2023-01-05 03:11:58,580 - mmcls - INFO - Epoch [94][1000/5005] lr: 1.000e-04, eta: 0:49:59, time: 0.088, data_time: 0.008, memory: 928, loss: 4.8027 2023-01-05 03:12:07,234 - mmcls - INFO - Epoch [94][1100/5005] lr: 1.000e-04, eta: 0:49:50, time: 0.087, data_time: 0.041, memory: 928, loss: 4.7987 2023-01-05 03:12:16,186 - mmcls - INFO - Epoch [94][1200/5005] lr: 1.000e-04, eta: 0:49:42, time: 0.089, data_time: 0.015, memory: 928, loss: 4.7938 2023-01-05 03:12:25,070 - mmcls - INFO - Epoch [94][1300/5005] lr: 1.000e-04, eta: 0:49:33, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8410 2023-01-05 03:12:33,545 - mmcls - INFO - Epoch [94][1400/5005] lr: 1.000e-04, eta: 0:49:24, time: 0.085, data_time: 0.001, memory: 928, loss: 4.7984 2023-01-05 03:12:42,655 - mmcls - INFO - Epoch [94][1500/5005] lr: 1.000e-04, eta: 0:49:15, time: 0.091, data_time: 0.024, memory: 928, loss: 4.8265 2023-01-05 03:12:51,544 - mmcls - INFO - Epoch [94][1600/5005] lr: 1.000e-04, eta: 0:49:06, time: 0.089, data_time: 0.006, memory: 928, loss: 4.7939 2023-01-05 03:13:00,390 - mmcls - INFO - Epoch [94][1700/5005] lr: 1.000e-04, eta: 0:48:58, time: 0.088, data_time: 0.030, memory: 928, loss: 4.8089 2023-01-05 03:13:09,061 - mmcls - INFO - Epoch [94][1800/5005] lr: 1.000e-04, eta: 0:48:49, time: 0.087, data_time: 0.010, memory: 928, loss: 4.7918 2023-01-05 03:13:18,075 - mmcls - INFO - Epoch [94][1900/5005] lr: 1.000e-04, eta: 0:48:40, time: 0.090, data_time: 0.012, memory: 928, loss: 4.7989 2023-01-05 03:13:26,913 - mmcls - INFO - Epoch [94][2000/5005] lr: 1.000e-04, eta: 0:48:31, time: 0.088, data_time: 0.001, memory: 928, loss: 4.7927 2023-01-05 03:13:35,824 - mmcls - INFO - Epoch [94][2100/5005] lr: 1.000e-04, eta: 0:48:22, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8310 2023-01-05 03:13:44,695 - mmcls - INFO - Epoch [94][2200/5005] lr: 1.000e-04, eta: 0:48:14, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8045 2023-01-05 03:13:53,702 - mmcls - INFO - Epoch [94][2300/5005] lr: 1.000e-04, eta: 0:48:05, time: 0.090, data_time: 0.007, memory: 928, loss: 4.8083 2023-01-05 03:14:02,195 - mmcls - INFO - Epoch [94][2400/5005] lr: 1.000e-04, eta: 0:47:56, time: 0.085, data_time: 0.002, memory: 928, loss: 4.7988 2023-01-05 03:14:11,439 - mmcls - INFO - Epoch [94][2500/5005] lr: 1.000e-04, eta: 0:47:47, time: 0.092, data_time: 0.002, memory: 928, loss: 4.8233 2023-01-05 03:14:20,081 - mmcls - INFO - Epoch [94][2600/5005] lr: 1.000e-04, eta: 0:47:38, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8073 2023-01-05 03:14:29,140 - mmcls - INFO - Epoch [94][2700/5005] lr: 1.000e-04, eta: 0:47:29, time: 0.091, data_time: 0.019, memory: 928, loss: 4.8062 2023-01-05 03:14:37,856 - mmcls - INFO - Epoch [94][2800/5005] lr: 1.000e-04, eta: 0:47:21, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7926 2023-01-05 03:14:46,581 - mmcls - INFO - Epoch [94][2900/5005] lr: 1.000e-04, eta: 0:47:12, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8382 2023-01-05 03:14:55,212 - mmcls - INFO - Epoch [94][3000/5005] lr: 1.000e-04, eta: 0:47:03, time: 0.086, data_time: 0.003, memory: 928, loss: 4.7977 2023-01-05 03:15:03,862 - mmcls - INFO - Epoch [94][3100/5005] lr: 1.000e-04, eta: 0:46:54, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8157 2023-01-05 03:15:13,094 - mmcls - INFO - Epoch [94][3200/5005] lr: 1.000e-04, eta: 0:46:45, time: 0.092, data_time: 0.003, memory: 928, loss: 4.8025 2023-01-05 03:15:21,711 - mmcls - INFO - Epoch [94][3300/5005] lr: 1.000e-04, eta: 0:46:37, time: 0.086, data_time: 0.001, memory: 928, loss: 4.7945 2023-01-05 03:15:30,608 - mmcls - INFO - Epoch [94][3400/5005] lr: 1.000e-04, eta: 0:46:28, time: 0.089, data_time: 0.025, memory: 928, loss: 4.8084 2023-01-05 03:15:39,284 - mmcls - INFO - Epoch [94][3500/5005] lr: 1.000e-04, eta: 0:46:19, time: 0.087, data_time: 0.016, memory: 928, loss: 4.8113 2023-01-05 03:15:47,832 - mmcls - INFO - Epoch [94][3600/5005] lr: 1.000e-04, eta: 0:46:10, time: 0.085, data_time: 0.005, memory: 928, loss: 4.8000 2023-01-05 03:15:56,329 - mmcls - INFO - Epoch [94][3700/5005] lr: 1.000e-04, eta: 0:46:01, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8043 2023-01-05 03:16:05,235 - mmcls - INFO - Epoch [94][3800/5005] lr: 1.000e-04, eta: 0:45:52, time: 0.089, data_time: 0.016, memory: 928, loss: 4.8157 2023-01-05 03:16:14,095 - mmcls - INFO - Epoch [94][3900/5005] lr: 1.000e-04, eta: 0:45:44, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8005 2023-01-05 03:16:22,683 - mmcls - INFO - Epoch [94][4000/5005] lr: 1.000e-04, eta: 0:45:35, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8124 2023-01-05 03:16:31,274 - mmcls - INFO - Epoch [94][4100/5005] lr: 1.000e-04, eta: 0:45:26, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8335 2023-01-05 03:16:39,946 - mmcls - INFO - Epoch [94][4200/5005] lr: 1.000e-04, eta: 0:45:17, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8224 2023-01-05 03:16:49,055 - mmcls - INFO - Epoch [94][4300/5005] lr: 1.000e-04, eta: 0:45:08, time: 0.091, data_time: 0.002, memory: 928, loss: 4.8104 2023-01-05 03:16:57,939 - mmcls - INFO - Epoch [94][4400/5005] lr: 1.000e-04, eta: 0:45:00, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8015 2023-01-05 03:17:06,772 - mmcls - INFO - Epoch [94][4500/5005] lr: 1.000e-04, eta: 0:44:51, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8274 2023-01-05 03:17:15,546 - mmcls - INFO - Epoch [94][4600/5005] lr: 1.000e-04, eta: 0:44:42, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8102 2023-01-05 03:17:24,079 - mmcls - INFO - Epoch [94][4700/5005] lr: 1.000e-04, eta: 0:44:33, time: 0.085, data_time: 0.015, memory: 928, loss: 4.8074 2023-01-05 03:17:32,772 - mmcls - INFO - Epoch [94][4800/5005] lr: 1.000e-04, eta: 0:44:24, time: 0.087, data_time: 0.003, memory: 928, loss: 4.7868 2023-01-05 03:17:41,223 - mmcls - INFO - Epoch [94][4900/5005] lr: 1.000e-04, eta: 0:44:15, time: 0.085, data_time: 0.012, memory: 928, loss: 4.7958 2023-01-05 03:17:50,013 - mmcls - INFO - Epoch [94][5000/5005] lr: 1.000e-04, eta: 0:44:07, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8064 2023-01-05 03:18:12,756 - mmcls - INFO - Epoch(val) [94][196] accuracy_top-1: 17.7680, accuracy_top-5: 35.9220 2023-01-05 03:18:23,926 - mmcls - INFO - Epoch [95][100/5005] lr: 1.000e-04, eta: 0:43:58, time: 0.112, data_time: 0.046, memory: 928, loss: 4.7896 2023-01-05 03:18:32,572 - mmcls - INFO - Epoch [95][200/5005] lr: 1.000e-04, eta: 0:43:49, time: 0.086, data_time: 0.004, memory: 928, loss: 4.7944 2023-01-05 03:18:41,396 - mmcls - INFO - Epoch [95][300/5005] lr: 1.000e-04, eta: 0:43:40, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8014 2023-01-05 03:18:50,229 - mmcls - INFO - Epoch [95][400/5005] lr: 1.000e-04, eta: 0:43:31, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8127 2023-01-05 03:18:58,992 - mmcls - INFO - Epoch [95][500/5005] lr: 1.000e-04, eta: 0:43:22, time: 0.088, data_time: 0.019, memory: 928, loss: 4.7864 2023-01-05 03:19:07,737 - mmcls - INFO - Epoch [95][600/5005] lr: 1.000e-04, eta: 0:43:13, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8083 2023-01-05 03:19:16,337 - mmcls - INFO - Epoch [95][700/5005] lr: 1.000e-04, eta: 0:43:05, time: 0.086, data_time: 0.014, memory: 928, loss: 4.8056 2023-01-05 03:19:25,039 - mmcls - INFO - Epoch [95][800/5005] lr: 1.000e-04, eta: 0:42:56, time: 0.087, data_time: 0.007, memory: 928, loss: 4.8101 2023-01-05 03:19:33,559 - mmcls - INFO - Epoch [95][900/5005] lr: 1.000e-04, eta: 0:42:47, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8059 2023-01-05 03:19:42,290 - mmcls - INFO - Epoch [95][1000/5005] lr: 1.000e-04, eta: 0:42:38, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8195 2023-01-05 03:19:51,266 - mmcls - INFO - Epoch [95][1100/5005] lr: 1.000e-04, eta: 0:42:29, time: 0.090, data_time: 0.012, memory: 928, loss: 4.8051 2023-01-05 03:20:00,111 - mmcls - INFO - Epoch [95][1200/5005] lr: 1.000e-04, eta: 0:42:21, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8139 2023-01-05 03:20:08,942 - mmcls - INFO - Epoch [95][1300/5005] lr: 1.000e-04, eta: 0:42:12, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8192 2023-01-05 03:20:17,850 - mmcls - INFO - Epoch [95][1400/5005] lr: 1.000e-04, eta: 0:42:03, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8016 2023-01-05 03:20:26,790 - mmcls - INFO - Epoch [95][1500/5005] lr: 1.000e-04, eta: 0:41:54, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8163 2023-01-05 03:20:35,597 - mmcls - INFO - Epoch [95][1600/5005] lr: 1.000e-04, eta: 0:41:45, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8106 2023-01-05 03:20:44,362 - mmcls - INFO - Epoch [95][1700/5005] lr: 1.000e-04, eta: 0:41:36, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8108 2023-01-05 03:20:53,223 - mmcls - INFO - Epoch [95][1800/5005] lr: 1.000e-04, eta: 0:41:28, time: 0.089, data_time: 0.003, memory: 928, loss: 4.7929 2023-01-05 03:21:01,895 - mmcls - INFO - Epoch [95][1900/5005] lr: 1.000e-04, eta: 0:41:19, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8070 2023-01-05 03:21:10,796 - mmcls - INFO - Epoch [95][2000/5005] lr: 1.000e-04, eta: 0:41:10, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8140 2023-01-05 03:21:19,365 - mmcls - INFO - Epoch [95][2100/5005] lr: 1.000e-04, eta: 0:41:01, time: 0.086, data_time: 0.015, memory: 928, loss: 4.8066 2023-01-05 03:21:27,946 - mmcls - INFO - Epoch [95][2200/5005] lr: 1.000e-04, eta: 0:40:52, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8057 2023-01-05 03:21:36,865 - mmcls - INFO - Epoch [95][2300/5005] lr: 1.000e-04, eta: 0:40:44, time: 0.089, data_time: 0.015, memory: 928, loss: 4.8004 2023-01-05 03:21:45,727 - mmcls - INFO - Epoch [95][2400/5005] lr: 1.000e-04, eta: 0:40:35, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8067 2023-01-05 03:21:54,468 - mmcls - INFO - Epoch [95][2500/5005] lr: 1.000e-04, eta: 0:40:26, time: 0.087, data_time: 0.011, memory: 928, loss: 4.8204 2023-01-05 03:22:03,177 - mmcls - INFO - Epoch [95][2600/5005] lr: 1.000e-04, eta: 0:40:17, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8326 2023-01-05 03:22:11,691 - mmcls - INFO - Epoch [95][2700/5005] lr: 1.000e-04, eta: 0:40:08, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8079 2023-01-05 03:22:20,515 - mmcls - INFO - Epoch [95][2800/5005] lr: 1.000e-04, eta: 0:39:59, time: 0.088, data_time: 0.003, memory: 928, loss: 4.8075 2023-01-05 03:22:29,544 - mmcls - INFO - Epoch [95][2900/5005] lr: 1.000e-04, eta: 0:39:51, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8063 2023-01-05 03:22:38,155 - mmcls - INFO - Epoch [95][3000/5005] lr: 1.000e-04, eta: 0:39:42, time: 0.086, data_time: 0.023, memory: 928, loss: 4.8226 2023-01-05 03:22:47,248 - mmcls - INFO - Epoch [95][3100/5005] lr: 1.000e-04, eta: 0:39:33, time: 0.091, data_time: 0.018, memory: 928, loss: 4.8069 2023-01-05 03:22:56,192 - mmcls - INFO - Epoch [95][3200/5005] lr: 1.000e-04, eta: 0:39:24, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8132 2023-01-05 03:23:04,979 - mmcls - INFO - Epoch [95][3300/5005] lr: 1.000e-04, eta: 0:39:15, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8046 2023-01-05 03:23:13,606 - mmcls - INFO - Epoch [95][3400/5005] lr: 1.000e-04, eta: 0:39:07, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8140 2023-01-05 03:23:22,470 - mmcls - INFO - Epoch [95][3500/5005] lr: 1.000e-04, eta: 0:38:58, time: 0.088, data_time: 0.031, memory: 928, loss: 4.7987 2023-01-05 03:23:31,419 - mmcls - INFO - Epoch [95][3600/5005] lr: 1.000e-04, eta: 0:38:49, time: 0.090, data_time: 0.006, memory: 928, loss: 4.7870 2023-01-05 03:23:40,138 - mmcls - INFO - Epoch [95][3700/5005] lr: 1.000e-04, eta: 0:38:40, time: 0.087, data_time: 0.011, memory: 928, loss: 4.7965 2023-01-05 03:23:49,039 - mmcls - INFO - Epoch [95][3800/5005] lr: 1.000e-04, eta: 0:38:31, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8255 2023-01-05 03:23:57,702 - mmcls - INFO - Epoch [95][3900/5005] lr: 1.000e-04, eta: 0:38:23, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8198 2023-01-05 03:24:06,580 - mmcls - INFO - Epoch [95][4000/5005] lr: 1.000e-04, eta: 0:38:14, time: 0.089, data_time: 0.007, memory: 928, loss: 4.8008 2023-01-05 03:24:15,110 - mmcls - INFO - Epoch [95][4100/5005] lr: 1.000e-04, eta: 0:38:05, time: 0.085, data_time: 0.040, memory: 928, loss: 4.8036 2023-01-05 03:24:23,612 - mmcls - INFO - Epoch [95][4200/5005] lr: 1.000e-04, eta: 0:37:56, time: 0.085, data_time: 0.010, memory: 928, loss: 4.7962 2023-01-05 03:24:32,324 - mmcls - INFO - Epoch [95][4300/5005] lr: 1.000e-04, eta: 0:37:47, time: 0.087, data_time: 0.013, memory: 928, loss: 4.7882 2023-01-05 03:24:41,031 - mmcls - INFO - Epoch [95][4400/5005] lr: 1.000e-04, eta: 0:37:38, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8053 2023-01-05 03:24:50,201 - mmcls - INFO - Epoch [95][4500/5005] lr: 1.000e-04, eta: 0:37:30, time: 0.092, data_time: 0.027, memory: 928, loss: 4.8043 2023-01-05 03:24:58,791 - mmcls - INFO - Epoch [95][4600/5005] lr: 1.000e-04, eta: 0:37:21, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8071 2023-01-05 03:25:07,647 - mmcls - INFO - Epoch [95][4700/5005] lr: 1.000e-04, eta: 0:37:12, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8230 2023-01-05 03:25:16,573 - mmcls - INFO - Epoch [95][4800/5005] lr: 1.000e-04, eta: 0:37:03, time: 0.089, data_time: 0.024, memory: 928, loss: 4.7982 2023-01-05 03:25:25,188 - mmcls - INFO - Epoch [95][4900/5005] lr: 1.000e-04, eta: 0:36:54, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8006 2023-01-05 03:25:33,905 - mmcls - INFO - Epoch [95][5000/5005] lr: 1.000e-04, eta: 0:36:46, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8114 2023-01-05 03:25:56,826 - mmcls - INFO - Epoch(val) [95][196] accuracy_top-1: 17.8940, accuracy_top-5: 36.2120 2023-01-05 03:26:07,873 - mmcls - INFO - Epoch [96][100/5005] lr: 1.000e-04, eta: 0:36:36, time: 0.110, data_time: 0.041, memory: 928, loss: 4.8120 2023-01-05 03:26:16,424 - mmcls - INFO - Epoch [96][200/5005] lr: 1.000e-04, eta: 0:36:28, time: 0.086, data_time: 0.018, memory: 928, loss: 4.8125 2023-01-05 03:26:25,242 - mmcls - INFO - Epoch [96][300/5005] lr: 1.000e-04, eta: 0:36:19, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8130 2023-01-05 03:26:33,671 - mmcls - INFO - Epoch [96][400/5005] lr: 1.000e-04, eta: 0:36:10, time: 0.084, data_time: 0.005, memory: 928, loss: 4.8179 2023-01-05 03:26:42,519 - mmcls - INFO - Epoch [96][500/5005] lr: 1.000e-04, eta: 0:36:01, time: 0.088, data_time: 0.016, memory: 928, loss: 4.8182 2023-01-05 03:26:51,123 - mmcls - INFO - Epoch [96][600/5005] lr: 1.000e-04, eta: 0:35:52, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8135 2023-01-05 03:27:00,051 - mmcls - INFO - Epoch [96][700/5005] lr: 1.000e-04, eta: 0:35:43, time: 0.089, data_time: 0.044, memory: 928, loss: 4.7894 2023-01-05 03:27:08,735 - mmcls - INFO - Epoch [96][800/5005] lr: 1.000e-04, eta: 0:35:35, time: 0.087, data_time: 0.026, memory: 928, loss: 4.7824 2023-01-05 03:27:17,658 - mmcls - INFO - Epoch [96][900/5005] lr: 1.000e-04, eta: 0:35:26, time: 0.089, data_time: 0.002, memory: 928, loss: 4.7921 2023-01-05 03:27:26,325 - mmcls - INFO - Epoch [96][1000/5005] lr: 1.000e-04, eta: 0:35:17, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7873 2023-01-05 03:27:34,901 - mmcls - INFO - Epoch [96][1100/5005] lr: 1.000e-04, eta: 0:35:08, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8131 2023-01-05 03:27:43,517 - mmcls - INFO - Epoch [96][1200/5005] lr: 1.000e-04, eta: 0:34:59, time: 0.086, data_time: 0.014, memory: 928, loss: 4.7975 2023-01-05 03:27:52,005 - mmcls - INFO - Epoch [96][1300/5005] lr: 1.000e-04, eta: 0:34:51, time: 0.085, data_time: 0.007, memory: 928, loss: 4.7983 2023-01-05 03:28:00,530 - mmcls - INFO - Epoch [96][1400/5005] lr: 1.000e-04, eta: 0:34:42, time: 0.085, data_time: 0.041, memory: 928, loss: 4.8043 2023-01-05 03:28:09,525 - mmcls - INFO - Epoch [96][1500/5005] lr: 1.000e-04, eta: 0:34:33, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8022 2023-01-05 03:28:18,270 - mmcls - INFO - Epoch [96][1600/5005] lr: 1.000e-04, eta: 0:34:24, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8274 2023-01-05 03:28:27,311 - mmcls - INFO - Epoch [96][1700/5005] lr: 1.000e-04, eta: 0:34:15, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8471 2023-01-05 03:28:35,870 - mmcls - INFO - Epoch [96][1800/5005] lr: 1.000e-04, eta: 0:34:06, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8113 2023-01-05 03:28:44,608 - mmcls - INFO - Epoch [96][1900/5005] lr: 1.000e-04, eta: 0:33:58, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8279 2023-01-05 03:28:53,377 - mmcls - INFO - Epoch [96][2000/5005] lr: 1.000e-04, eta: 0:33:49, time: 0.088, data_time: 0.008, memory: 928, loss: 4.7871 2023-01-05 03:29:02,332 - mmcls - INFO - Epoch [96][2100/5005] lr: 1.000e-04, eta: 0:33:40, time: 0.090, data_time: 0.002, memory: 928, loss: 4.7868 2023-01-05 03:29:11,073 - mmcls - INFO - Epoch [96][2200/5005] lr: 1.000e-04, eta: 0:33:31, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8107 2023-01-05 03:29:19,928 - mmcls - INFO - Epoch [96][2300/5005] lr: 1.000e-04, eta: 0:33:22, time: 0.088, data_time: 0.013, memory: 928, loss: 4.8083 2023-01-05 03:29:28,838 - mmcls - INFO - Epoch [96][2400/5005] lr: 1.000e-04, eta: 0:33:14, time: 0.089, data_time: 0.021, memory: 928, loss: 4.8133 2023-01-05 03:29:37,662 - mmcls - INFO - Epoch [96][2500/5005] lr: 1.000e-04, eta: 0:33:05, time: 0.088, data_time: 0.009, memory: 928, loss: 4.7908 2023-01-05 03:29:46,225 - mmcls - INFO - Epoch [96][2600/5005] lr: 1.000e-04, eta: 0:32:56, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8170 2023-01-05 03:29:54,741 - mmcls - INFO - Epoch [96][2700/5005] lr: 1.000e-04, eta: 0:32:47, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8089 2023-01-05 03:30:03,924 - mmcls - INFO - Epoch [96][2800/5005] lr: 1.000e-04, eta: 0:32:38, time: 0.092, data_time: 0.001, memory: 928, loss: 4.7918 2023-01-05 03:30:12,823 - mmcls - INFO - Epoch [96][2900/5005] lr: 1.000e-04, eta: 0:32:29, time: 0.089, data_time: 0.001, memory: 928, loss: 4.7909 2023-01-05 03:30:21,654 - mmcls - INFO - Epoch [96][3000/5005] lr: 1.000e-04, eta: 0:32:21, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8227 2023-01-05 03:30:30,425 - mmcls - INFO - Epoch [96][3100/5005] lr: 1.000e-04, eta: 0:32:12, time: 0.088, data_time: 0.004, memory: 928, loss: 4.7947 2023-01-05 03:30:38,969 - mmcls - INFO - Epoch [96][3200/5005] lr: 1.000e-04, eta: 0:32:03, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8023 2023-01-05 03:30:47,678 - mmcls - INFO - Epoch [96][3300/5005] lr: 1.000e-04, eta: 0:31:54, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8106 2023-01-05 03:30:56,495 - mmcls - INFO - Epoch [96][3400/5005] lr: 1.000e-04, eta: 0:31:45, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7973 2023-01-05 03:31:05,456 - mmcls - INFO - Epoch [96][3500/5005] lr: 1.000e-04, eta: 0:31:37, time: 0.090, data_time: 0.004, memory: 928, loss: 4.7867 2023-01-05 03:31:14,245 - mmcls - INFO - Epoch [96][3600/5005] lr: 1.000e-04, eta: 0:31:28, time: 0.088, data_time: 0.038, memory: 928, loss: 4.8036 2023-01-05 03:31:22,942 - mmcls - INFO - Epoch [96][3700/5005] lr: 1.000e-04, eta: 0:31:19, time: 0.087, data_time: 0.008, memory: 928, loss: 4.8013 2023-01-05 03:31:31,644 - mmcls - INFO - Epoch [96][3800/5005] lr: 1.000e-04, eta: 0:31:10, time: 0.087, data_time: 0.006, memory: 928, loss: 4.7957 2023-01-05 03:31:40,277 - mmcls - INFO - Epoch [96][3900/5005] lr: 1.000e-04, eta: 0:31:01, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8008 2023-01-05 03:31:49,019 - mmcls - INFO - Epoch [96][4000/5005] lr: 1.000e-04, eta: 0:30:53, time: 0.087, data_time: 0.019, memory: 928, loss: 4.7980 2023-01-05 03:31:57,771 - mmcls - INFO - Epoch [96][4100/5005] lr: 1.000e-04, eta: 0:30:44, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8253 2023-01-05 03:32:06,660 - mmcls - INFO - Epoch [96][4200/5005] lr: 1.000e-04, eta: 0:30:35, time: 0.089, data_time: 0.002, memory: 928, loss: 4.7995 2023-01-05 03:32:15,760 - mmcls - INFO - Epoch [96][4300/5005] lr: 1.000e-04, eta: 0:30:26, time: 0.091, data_time: 0.016, memory: 928, loss: 4.8229 2023-01-05 03:32:24,624 - mmcls - INFO - Epoch [96][4400/5005] lr: 1.000e-04, eta: 0:30:17, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8014 2023-01-05 03:32:33,378 - mmcls - INFO - Epoch [96][4500/5005] lr: 1.000e-04, eta: 0:30:08, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8111 2023-01-05 03:32:42,097 - mmcls - INFO - Epoch [96][4600/5005] lr: 1.000e-04, eta: 0:30:00, time: 0.087, data_time: 0.024, memory: 928, loss: 4.8146 2023-01-05 03:32:50,817 - mmcls - INFO - Epoch [96][4700/5005] lr: 1.000e-04, eta: 0:29:51, time: 0.087, data_time: 0.009, memory: 928, loss: 4.7987 2023-01-05 03:32:59,744 - mmcls - INFO - Epoch [96][4800/5005] lr: 1.000e-04, eta: 0:29:42, time: 0.089, data_time: 0.005, memory: 928, loss: 4.8251 2023-01-05 03:33:08,329 - mmcls - INFO - Epoch [96][4900/5005] lr: 1.000e-04, eta: 0:29:33, time: 0.086, data_time: 0.009, memory: 928, loss: 4.8285 2023-01-05 03:33:16,807 - mmcls - INFO - Epoch [96][5000/5005] lr: 1.000e-04, eta: 0:29:24, time: 0.085, data_time: 0.007, memory: 928, loss: 4.8078 2023-01-05 03:33:39,637 - mmcls - INFO - Epoch(val) [96][196] accuracy_top-1: 18.4520, accuracy_top-5: 36.9060 2023-01-05 03:33:50,749 - mmcls - INFO - Epoch [97][100/5005] lr: 1.000e-04, eta: 0:29:15, time: 0.111, data_time: 0.043, memory: 928, loss: 4.8057 2023-01-05 03:33:59,420 - mmcls - INFO - Epoch [97][200/5005] lr: 1.000e-04, eta: 0:29:06, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7847 2023-01-05 03:34:08,095 - mmcls - INFO - Epoch [97][300/5005] lr: 1.000e-04, eta: 0:28:58, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8017 2023-01-05 03:34:16,800 - mmcls - INFO - Epoch [97][400/5005] lr: 1.000e-04, eta: 0:28:49, time: 0.087, data_time: 0.010, memory: 928, loss: 4.8163 2023-01-05 03:34:25,577 - mmcls - INFO - Epoch [97][500/5005] lr: 1.000e-04, eta: 0:28:40, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8063 2023-01-05 03:34:34,244 - mmcls - INFO - Epoch [97][600/5005] lr: 1.000e-04, eta: 0:28:31, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7935 2023-01-05 03:34:42,900 - mmcls - INFO - Epoch [97][700/5005] lr: 1.000e-04, eta: 0:28:22, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7884 2023-01-05 03:34:51,575 - mmcls - INFO - Epoch [97][800/5005] lr: 1.000e-04, eta: 0:28:13, time: 0.087, data_time: 0.009, memory: 928, loss: 4.7847 2023-01-05 03:35:00,359 - mmcls - INFO - Epoch [97][900/5005] lr: 1.000e-04, eta: 0:28:05, time: 0.088, data_time: 0.023, memory: 928, loss: 4.8012 2023-01-05 03:35:09,145 - mmcls - INFO - Epoch [97][1000/5005] lr: 1.000e-04, eta: 0:27:56, time: 0.088, data_time: 0.010, memory: 928, loss: 4.7932 2023-01-05 03:35:17,881 - mmcls - INFO - Epoch [97][1100/5005] lr: 1.000e-04, eta: 0:27:47, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8130 2023-01-05 03:35:26,367 - mmcls - INFO - Epoch [97][1200/5005] lr: 1.000e-04, eta: 0:27:38, time: 0.085, data_time: 0.005, memory: 928, loss: 4.7939 2023-01-05 03:35:35,092 - mmcls - INFO - Epoch [97][1300/5005] lr: 1.000e-04, eta: 0:27:29, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8184 2023-01-05 03:35:43,946 - mmcls - INFO - Epoch [97][1400/5005] lr: 1.000e-04, eta: 0:27:21, time: 0.089, data_time: 0.029, memory: 928, loss: 4.7932 2023-01-05 03:35:52,534 - mmcls - INFO - Epoch [97][1500/5005] lr: 1.000e-04, eta: 0:27:12, time: 0.086, data_time: 0.008, memory: 928, loss: 4.7885 2023-01-05 03:36:01,313 - mmcls - INFO - Epoch [97][1600/5005] lr: 1.000e-04, eta: 0:27:03, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8089 2023-01-05 03:36:10,199 - mmcls - INFO - Epoch [97][1700/5005] lr: 1.000e-04, eta: 0:26:54, time: 0.089, data_time: 0.001, memory: 928, loss: 4.7953 2023-01-05 03:36:19,043 - mmcls - INFO - Epoch [97][1800/5005] lr: 1.000e-04, eta: 0:26:45, time: 0.088, data_time: 0.009, memory: 928, loss: 4.7979 2023-01-05 03:36:27,796 - mmcls - INFO - Epoch [97][1900/5005] lr: 1.000e-04, eta: 0:26:36, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8199 2023-01-05 03:36:36,356 - mmcls - INFO - Epoch [97][2000/5005] lr: 1.000e-04, eta: 0:26:28, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8167 2023-01-05 03:36:45,418 - mmcls - INFO - Epoch [97][2100/5005] lr: 1.000e-04, eta: 0:26:19, time: 0.091, data_time: 0.004, memory: 928, loss: 4.7934 2023-01-05 03:36:54,155 - mmcls - INFO - Epoch [97][2200/5005] lr: 1.000e-04, eta: 0:26:10, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7985 2023-01-05 03:37:02,622 - mmcls - INFO - Epoch [97][2300/5005] lr: 1.000e-04, eta: 0:26:01, time: 0.085, data_time: 0.009, memory: 928, loss: 4.7916 2023-01-05 03:37:11,473 - mmcls - INFO - Epoch [97][2400/5005] lr: 1.000e-04, eta: 0:25:52, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8093 2023-01-05 03:37:20,007 - mmcls - INFO - Epoch [97][2500/5005] lr: 1.000e-04, eta: 0:25:44, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8249 2023-01-05 03:37:28,538 - mmcls - INFO - Epoch [97][2600/5005] lr: 1.000e-04, eta: 0:25:35, time: 0.085, data_time: 0.004, memory: 928, loss: 4.7859 2023-01-05 03:37:37,177 - mmcls - INFO - Epoch [97][2700/5005] lr: 1.000e-04, eta: 0:25:26, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8140 2023-01-05 03:37:45,994 - mmcls - INFO - Epoch [97][2800/5005] lr: 1.000e-04, eta: 0:25:17, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8023 2023-01-05 03:37:54,853 - mmcls - INFO - Epoch [97][2900/5005] lr: 1.000e-04, eta: 0:25:08, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8099 2023-01-05 03:38:03,667 - mmcls - INFO - Epoch [97][3000/5005] lr: 1.000e-04, eta: 0:24:59, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8126 2023-01-05 03:38:12,356 - mmcls - INFO - Epoch [97][3100/5005] lr: 1.000e-04, eta: 0:24:51, time: 0.087, data_time: 0.004, memory: 928, loss: 4.7859 2023-01-05 03:38:21,151 - mmcls - INFO - Epoch [97][3200/5005] lr: 1.000e-04, eta: 0:24:42, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8095 2023-01-05 03:38:29,934 - mmcls - INFO - Epoch [97][3300/5005] lr: 1.000e-04, eta: 0:24:33, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8119 2023-01-05 03:38:38,933 - mmcls - INFO - Epoch [97][3400/5005] lr: 1.000e-04, eta: 0:24:24, time: 0.090, data_time: 0.002, memory: 928, loss: 4.7874 2023-01-05 03:38:47,849 - mmcls - INFO - Epoch [97][3500/5005] lr: 1.000e-04, eta: 0:24:15, time: 0.089, data_time: 0.001, memory: 928, loss: 4.8024 2023-01-05 03:38:56,975 - mmcls - INFO - Epoch [97][3600/5005] lr: 1.000e-04, eta: 0:24:07, time: 0.091, data_time: 0.002, memory: 928, loss: 4.7848 2023-01-05 03:39:06,080 - mmcls - INFO - Epoch [97][3700/5005] lr: 1.000e-04, eta: 0:23:58, time: 0.091, data_time: 0.014, memory: 928, loss: 4.8113 2023-01-05 03:39:14,514 - mmcls - INFO - Epoch [97][3800/5005] lr: 1.000e-04, eta: 0:23:49, time: 0.084, data_time: 0.017, memory: 928, loss: 4.8099 2023-01-05 03:39:23,234 - mmcls - INFO - Epoch [97][3900/5005] lr: 1.000e-04, eta: 0:23:40, time: 0.087, data_time: 0.005, memory: 928, loss: 4.7995 2023-01-05 03:39:32,195 - mmcls - INFO - Epoch [97][4000/5005] lr: 1.000e-04, eta: 0:23:31, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8082 2023-01-05 03:39:40,852 - mmcls - INFO - Epoch [97][4100/5005] lr: 1.000e-04, eta: 0:23:23, time: 0.087, data_time: 0.039, memory: 928, loss: 4.7936 2023-01-05 03:39:49,683 - mmcls - INFO - Epoch [97][4200/5005] lr: 1.000e-04, eta: 0:23:14, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8125 2023-01-05 03:39:58,544 - mmcls - INFO - Epoch [97][4300/5005] lr: 1.000e-04, eta: 0:23:05, time: 0.089, data_time: 0.014, memory: 928, loss: 4.8047 2023-01-05 03:40:07,449 - mmcls - INFO - Epoch [97][4400/5005] lr: 1.000e-04, eta: 0:22:56, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8048 2023-01-05 03:40:16,004 - mmcls - INFO - Epoch [97][4500/5005] lr: 1.000e-04, eta: 0:22:47, time: 0.086, data_time: 0.009, memory: 928, loss: 4.7977 2023-01-05 03:40:24,709 - mmcls - INFO - Epoch [97][4600/5005] lr: 1.000e-04, eta: 0:22:38, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8375 2023-01-05 03:40:33,240 - mmcls - INFO - Epoch [97][4700/5005] lr: 1.000e-04, eta: 0:22:30, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8009 2023-01-05 03:40:41,847 - mmcls - INFO - Epoch [97][4800/5005] lr: 1.000e-04, eta: 0:22:21, time: 0.086, data_time: 0.001, memory: 928, loss: 4.8044 2023-01-05 03:40:50,437 - mmcls - INFO - Epoch [97][4900/5005] lr: 1.000e-04, eta: 0:22:12, time: 0.086, data_time: 0.002, memory: 928, loss: 4.8204 2023-01-05 03:40:59,522 - mmcls - INFO - Epoch [97][5000/5005] lr: 1.000e-04, eta: 0:22:03, time: 0.091, data_time: 0.004, memory: 928, loss: 4.8015 2023-01-05 03:41:22,377 - mmcls - INFO - Epoch(val) [97][196] accuracy_top-1: 18.5960, accuracy_top-5: 37.2300 2023-01-05 03:41:33,245 - mmcls - INFO - Epoch [98][100/5005] lr: 1.000e-04, eta: 0:21:54, time: 0.108, data_time: 0.046, memory: 928, loss: 4.7892 2023-01-05 03:41:42,277 - mmcls - INFO - Epoch [98][200/5005] lr: 1.000e-04, eta: 0:21:45, time: 0.090, data_time: 0.004, memory: 928, loss: 4.8031 2023-01-05 03:41:50,868 - mmcls - INFO - Epoch [98][300/5005] lr: 1.000e-04, eta: 0:21:36, time: 0.086, data_time: 0.009, memory: 928, loss: 4.7911 2023-01-05 03:41:59,517 - mmcls - INFO - Epoch [98][400/5005] lr: 1.000e-04, eta: 0:21:28, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7772 2023-01-05 03:42:08,254 - mmcls - INFO - Epoch [98][500/5005] lr: 1.000e-04, eta: 0:21:19, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8198 2023-01-05 03:42:17,355 - mmcls - INFO - Epoch [98][600/5005] lr: 1.000e-04, eta: 0:21:10, time: 0.091, data_time: 0.003, memory: 928, loss: 4.8002 2023-01-05 03:42:26,266 - mmcls - INFO - Epoch [98][700/5005] lr: 1.000e-04, eta: 0:21:01, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8111 2023-01-05 03:42:35,101 - mmcls - INFO - Epoch [98][800/5005] lr: 1.000e-04, eta: 0:20:52, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8054 2023-01-05 03:42:43,906 - mmcls - INFO - Epoch [98][900/5005] lr: 1.000e-04, eta: 0:20:44, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7904 2023-01-05 03:42:52,693 - mmcls - INFO - Epoch [98][1000/5005] lr: 1.000e-04, eta: 0:20:35, time: 0.088, data_time: 0.031, memory: 928, loss: 4.8030 2023-01-05 03:43:01,357 - mmcls - INFO - Epoch [98][1100/5005] lr: 1.000e-04, eta: 0:20:26, time: 0.087, data_time: 0.044, memory: 928, loss: 4.8011 2023-01-05 03:43:10,177 - mmcls - INFO - Epoch [98][1200/5005] lr: 1.000e-04, eta: 0:20:17, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8050 2023-01-05 03:43:19,050 - mmcls - INFO - Epoch [98][1300/5005] lr: 1.000e-04, eta: 0:20:08, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8062 2023-01-05 03:43:27,776 - mmcls - INFO - Epoch [98][1400/5005] lr: 1.000e-04, eta: 0:19:59, time: 0.087, data_time: 0.001, memory: 928, loss: 4.7876 2023-01-05 03:43:36,483 - mmcls - INFO - Epoch [98][1500/5005] lr: 1.000e-04, eta: 0:19:51, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8080 2023-01-05 03:43:45,218 - mmcls - INFO - Epoch [98][1600/5005] lr: 1.000e-04, eta: 0:19:42, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8042 2023-01-05 03:43:54,102 - mmcls - INFO - Epoch [98][1700/5005] lr: 1.000e-04, eta: 0:19:33, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8048 2023-01-05 03:44:02,899 - mmcls - INFO - Epoch [98][1800/5005] lr: 1.000e-04, eta: 0:19:24, time: 0.088, data_time: 0.010, memory: 928, loss: 4.7982 2023-01-05 03:44:11,480 - mmcls - INFO - Epoch [98][1900/5005] lr: 1.000e-04, eta: 0:19:15, time: 0.086, data_time: 0.018, memory: 928, loss: 4.7762 2023-01-05 03:44:20,259 - mmcls - INFO - Epoch [98][2000/5005] lr: 1.000e-04, eta: 0:19:07, time: 0.088, data_time: 0.007, memory: 928, loss: 4.7872 2023-01-05 03:44:28,918 - mmcls - INFO - Epoch [98][2100/5005] lr: 1.000e-04, eta: 0:18:58, time: 0.087, data_time: 0.016, memory: 928, loss: 4.7957 2023-01-05 03:44:37,415 - mmcls - INFO - Epoch [98][2200/5005] lr: 1.000e-04, eta: 0:18:49, time: 0.085, data_time: 0.006, memory: 928, loss: 4.8210 2023-01-05 03:44:46,099 - mmcls - INFO - Epoch [98][2300/5005] lr: 1.000e-04, eta: 0:18:40, time: 0.087, data_time: 0.004, memory: 928, loss: 4.8106 2023-01-05 03:44:54,935 - mmcls - INFO - Epoch [98][2400/5005] lr: 1.000e-04, eta: 0:18:31, time: 0.088, data_time: 0.023, memory: 928, loss: 4.8189 2023-01-05 03:45:03,845 - mmcls - INFO - Epoch [98][2500/5005] lr: 1.000e-04, eta: 0:18:22, time: 0.089, data_time: 0.041, memory: 928, loss: 4.7799 2023-01-05 03:45:12,442 - mmcls - INFO - Epoch [98][2600/5005] lr: 1.000e-04, eta: 0:18:14, time: 0.086, data_time: 0.001, memory: 928, loss: 4.7971 2023-01-05 03:45:21,557 - mmcls - INFO - Epoch [98][2700/5005] lr: 1.000e-04, eta: 0:18:05, time: 0.091, data_time: 0.011, memory: 928, loss: 4.8195 2023-01-05 03:45:30,445 - mmcls - INFO - Epoch [98][2800/5005] lr: 1.000e-04, eta: 0:17:56, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8054 2023-01-05 03:45:39,271 - mmcls - INFO - Epoch [98][2900/5005] lr: 1.000e-04, eta: 0:17:47, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8131 2023-01-05 03:45:48,201 - mmcls - INFO - Epoch [98][3000/5005] lr: 1.000e-04, eta: 0:17:38, time: 0.089, data_time: 0.006, memory: 928, loss: 4.8175 2023-01-05 03:45:57,049 - mmcls - INFO - Epoch [98][3100/5005] lr: 1.000e-04, eta: 0:17:30, time: 0.089, data_time: 0.011, memory: 928, loss: 4.7953 2023-01-05 03:46:05,922 - mmcls - INFO - Epoch [98][3200/5005] lr: 1.000e-04, eta: 0:17:21, time: 0.089, data_time: 0.002, memory: 928, loss: 4.7929 2023-01-05 03:46:15,122 - mmcls - INFO - Epoch [98][3300/5005] lr: 1.000e-04, eta: 0:17:12, time: 0.092, data_time: 0.011, memory: 928, loss: 4.8008 2023-01-05 03:46:23,822 - mmcls - INFO - Epoch [98][3400/5005] lr: 1.000e-04, eta: 0:17:03, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7855 2023-01-05 03:46:32,674 - mmcls - INFO - Epoch [98][3500/5005] lr: 1.000e-04, eta: 0:16:54, time: 0.089, data_time: 0.021, memory: 928, loss: 4.7998 2023-01-05 03:46:41,386 - mmcls - INFO - Epoch [98][3600/5005] lr: 1.000e-04, eta: 0:16:46, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8201 2023-01-05 03:46:49,948 - mmcls - INFO - Epoch [98][3700/5005] lr: 1.000e-04, eta: 0:16:37, time: 0.085, data_time: 0.009, memory: 928, loss: 4.8087 2023-01-05 03:46:58,538 - mmcls - INFO - Epoch [98][3800/5005] lr: 1.000e-04, eta: 0:16:28, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8110 2023-01-05 03:47:07,115 - mmcls - INFO - Epoch [98][3900/5005] lr: 1.000e-04, eta: 0:16:19, time: 0.086, data_time: 0.031, memory: 928, loss: 4.8128 2023-01-05 03:47:15,744 - mmcls - INFO - Epoch [98][4000/5005] lr: 1.000e-04, eta: 0:16:10, time: 0.086, data_time: 0.006, memory: 928, loss: 4.7944 2023-01-05 03:47:24,294 - mmcls - INFO - Epoch [98][4100/5005] lr: 1.000e-04, eta: 0:16:01, time: 0.086, data_time: 0.017, memory: 928, loss: 4.7931 2023-01-05 03:47:32,994 - mmcls - INFO - Epoch [98][4200/5005] lr: 1.000e-04, eta: 0:15:53, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8234 2023-01-05 03:47:41,656 - mmcls - INFO - Epoch [98][4300/5005] lr: 1.000e-04, eta: 0:15:44, time: 0.087, data_time: 0.005, memory: 928, loss: 4.8025 2023-01-05 03:47:50,193 - mmcls - INFO - Epoch [98][4400/5005] lr: 1.000e-04, eta: 0:15:35, time: 0.085, data_time: 0.008, memory: 928, loss: 4.7958 2023-01-05 03:47:59,112 - mmcls - INFO - Epoch [98][4500/5005] lr: 1.000e-04, eta: 0:15:26, time: 0.089, data_time: 0.003, memory: 928, loss: 4.8123 2023-01-05 03:48:07,847 - mmcls - INFO - Epoch [98][4600/5005] lr: 1.000e-04, eta: 0:15:17, time: 0.087, data_time: 0.003, memory: 928, loss: 4.7840 2023-01-05 03:48:16,633 - mmcls - INFO - Epoch [98][4700/5005] lr: 1.000e-04, eta: 0:15:09, time: 0.088, data_time: 0.005, memory: 928, loss: 4.8031 2023-01-05 03:48:25,534 - mmcls - INFO - Epoch [98][4800/5005] lr: 1.000e-04, eta: 0:15:00, time: 0.089, data_time: 0.003, memory: 928, loss: 4.7970 2023-01-05 03:48:34,150 - mmcls - INFO - Epoch [98][4900/5005] lr: 1.000e-04, eta: 0:14:51, time: 0.086, data_time: 0.024, memory: 928, loss: 4.8093 2023-01-05 03:48:43,043 - mmcls - INFO - Epoch [98][5000/5005] lr: 1.000e-04, eta: 0:14:42, time: 0.089, data_time: 0.015, memory: 928, loss: 4.8219 2023-01-05 03:49:05,592 - mmcls - INFO - Epoch(val) [98][196] accuracy_top-1: 18.7140, accuracy_top-5: 37.3520 2023-01-05 03:49:16,644 - mmcls - INFO - Epoch [99][100/5005] lr: 1.000e-04, eta: 0:14:33, time: 0.110, data_time: 0.051, memory: 928, loss: 4.7947 2023-01-05 03:49:25,611 - mmcls - INFO - Epoch [99][200/5005] lr: 1.000e-04, eta: 0:14:24, time: 0.090, data_time: 0.033, memory: 928, loss: 4.8044 2023-01-05 03:49:34,332 - mmcls - INFO - Epoch [99][300/5005] lr: 1.000e-04, eta: 0:14:15, time: 0.087, data_time: 0.009, memory: 928, loss: 4.8025 2023-01-05 03:49:43,013 - mmcls - INFO - Epoch [99][400/5005] lr: 1.000e-04, eta: 0:14:06, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8046 2023-01-05 03:49:51,709 - mmcls - INFO - Epoch [99][500/5005] lr: 1.000e-04, eta: 0:13:58, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8021 2023-01-05 03:50:00,520 - mmcls - INFO - Epoch [99][600/5005] lr: 1.000e-04, eta: 0:13:49, time: 0.088, data_time: 0.003, memory: 928, loss: 4.7923 2023-01-05 03:50:09,188 - mmcls - INFO - Epoch [99][700/5005] lr: 1.000e-04, eta: 0:13:40, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8230 2023-01-05 03:50:17,875 - mmcls - INFO - Epoch [99][800/5005] lr: 1.000e-04, eta: 0:13:31, time: 0.087, data_time: 0.033, memory: 928, loss: 4.8027 2023-01-05 03:50:26,409 - mmcls - INFO - Epoch [99][900/5005] lr: 1.000e-04, eta: 0:13:22, time: 0.085, data_time: 0.030, memory: 928, loss: 4.8009 2023-01-05 03:50:35,286 - mmcls - INFO - Epoch [99][1000/5005] lr: 1.000e-04, eta: 0:13:14, time: 0.089, data_time: 0.002, memory: 928, loss: 4.8187 2023-01-05 03:50:44,074 - mmcls - INFO - Epoch [99][1100/5005] lr: 1.000e-04, eta: 0:13:05, time: 0.088, data_time: 0.011, memory: 928, loss: 4.8035 2023-01-05 03:50:53,072 - mmcls - INFO - Epoch [99][1200/5005] lr: 1.000e-04, eta: 0:12:56, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8112 2023-01-05 03:51:01,854 - mmcls - INFO - Epoch [99][1300/5005] lr: 1.000e-04, eta: 0:12:47, time: 0.088, data_time: 0.009, memory: 928, loss: 4.7996 2023-01-05 03:51:10,928 - mmcls - INFO - Epoch [99][1400/5005] lr: 1.000e-04, eta: 0:12:38, time: 0.091, data_time: 0.007, memory: 928, loss: 4.8163 2023-01-05 03:51:19,748 - mmcls - INFO - Epoch [99][1500/5005] lr: 1.000e-04, eta: 0:12:30, time: 0.088, data_time: 0.050, memory: 928, loss: 4.7957 2023-01-05 03:51:28,677 - mmcls - INFO - Epoch [99][1600/5005] lr: 1.000e-04, eta: 0:12:21, time: 0.089, data_time: 0.009, memory: 928, loss: 4.8120 2023-01-05 03:51:37,524 - mmcls - INFO - Epoch [99][1700/5005] lr: 1.000e-04, eta: 0:12:12, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8040 2023-01-05 03:51:46,231 - mmcls - INFO - Epoch [99][1800/5005] lr: 1.000e-04, eta: 0:12:03, time: 0.087, data_time: 0.003, memory: 928, loss: 4.7894 2023-01-05 03:51:54,869 - mmcls - INFO - Epoch [99][1900/5005] lr: 1.000e-04, eta: 0:11:54, time: 0.086, data_time: 0.006, memory: 928, loss: 4.8028 2023-01-05 03:52:03,665 - mmcls - INFO - Epoch [99][2000/5005] lr: 1.000e-04, eta: 0:11:45, time: 0.088, data_time: 0.009, memory: 928, loss: 4.8107 2023-01-05 03:52:12,853 - mmcls - INFO - Epoch [99][2100/5005] lr: 1.000e-04, eta: 0:11:37, time: 0.092, data_time: 0.001, memory: 928, loss: 4.7782 2023-01-05 03:52:21,722 - mmcls - INFO - Epoch [99][2200/5005] lr: 1.000e-04, eta: 0:11:28, time: 0.089, data_time: 0.008, memory: 928, loss: 4.7992 2023-01-05 03:52:30,604 - mmcls - INFO - Epoch [99][2300/5005] lr: 1.000e-04, eta: 0:11:19, time: 0.089, data_time: 0.017, memory: 928, loss: 4.8301 2023-01-05 03:52:39,478 - mmcls - INFO - Epoch [99][2400/5005] lr: 1.000e-04, eta: 0:11:10, time: 0.089, data_time: 0.019, memory: 928, loss: 4.8064 2023-01-05 03:52:48,517 - mmcls - INFO - Epoch [99][2500/5005] lr: 1.000e-04, eta: 0:11:01, time: 0.090, data_time: 0.019, memory: 928, loss: 4.8092 2023-01-05 03:52:57,467 - mmcls - INFO - Epoch [99][2600/5005] lr: 1.000e-04, eta: 0:10:53, time: 0.089, data_time: 0.004, memory: 928, loss: 4.8187 2023-01-05 03:53:06,323 - mmcls - INFO - Epoch [99][2700/5005] lr: 1.000e-04, eta: 0:10:44, time: 0.088, data_time: 0.037, memory: 928, loss: 4.7863 2023-01-05 03:53:14,880 - mmcls - INFO - Epoch [99][2800/5005] lr: 1.000e-04, eta: 0:10:35, time: 0.086, data_time: 0.020, memory: 928, loss: 4.8086 2023-01-05 03:53:23,843 - mmcls - INFO - Epoch [99][2900/5005] lr: 1.000e-04, eta: 0:10:26, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8193 2023-01-05 03:53:32,497 - mmcls - INFO - Epoch [99][3000/5005] lr: 1.000e-04, eta: 0:10:17, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7975 2023-01-05 03:53:41,286 - mmcls - INFO - Epoch [99][3100/5005] lr: 1.000e-04, eta: 0:10:08, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8067 2023-01-05 03:53:49,836 - mmcls - INFO - Epoch [99][3200/5005] lr: 1.000e-04, eta: 0:10:00, time: 0.086, data_time: 0.027, memory: 928, loss: 4.7834 2023-01-05 03:53:58,723 - mmcls - INFO - Epoch [99][3300/5005] lr: 1.000e-04, eta: 0:09:51, time: 0.089, data_time: 0.030, memory: 928, loss: 4.7978 2023-01-05 03:54:07,545 - mmcls - INFO - Epoch [99][3400/5005] lr: 1.000e-04, eta: 0:09:42, time: 0.088, data_time: 0.006, memory: 928, loss: 4.8117 2023-01-05 03:54:16,173 - mmcls - INFO - Epoch [99][3500/5005] lr: 1.000e-04, eta: 0:09:33, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8134 2023-01-05 03:54:24,904 - mmcls - INFO - Epoch [99][3600/5005] lr: 1.000e-04, eta: 0:09:24, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8025 2023-01-05 03:54:33,472 - mmcls - INFO - Epoch [99][3700/5005] lr: 1.000e-04, eta: 0:09:16, time: 0.086, data_time: 0.008, memory: 928, loss: 4.8141 2023-01-05 03:54:42,455 - mmcls - INFO - Epoch [99][3800/5005] lr: 1.000e-04, eta: 0:09:07, time: 0.090, data_time: 0.005, memory: 928, loss: 4.8044 2023-01-05 03:54:51,097 - mmcls - INFO - Epoch [99][3900/5005] lr: 1.000e-04, eta: 0:08:58, time: 0.086, data_time: 0.003, memory: 928, loss: 4.8147 2023-01-05 03:54:59,956 - mmcls - INFO - Epoch [99][4000/5005] lr: 1.000e-04, eta: 0:08:49, time: 0.089, data_time: 0.026, memory: 928, loss: 4.7990 2023-01-05 03:55:08,715 - mmcls - INFO - Epoch [99][4100/5005] lr: 1.000e-04, eta: 0:08:40, time: 0.088, data_time: 0.025, memory: 928, loss: 4.8067 2023-01-05 03:55:17,406 - mmcls - INFO - Epoch [99][4200/5005] lr: 1.000e-04, eta: 0:08:32, time: 0.087, data_time: 0.011, memory: 928, loss: 4.7970 2023-01-05 03:55:26,161 - mmcls - INFO - Epoch [99][4300/5005] lr: 1.000e-04, eta: 0:08:23, time: 0.088, data_time: 0.030, memory: 928, loss: 4.8082 2023-01-05 03:55:34,743 - mmcls - INFO - Epoch [99][4400/5005] lr: 1.000e-04, eta: 0:08:14, time: 0.086, data_time: 0.012, memory: 928, loss: 4.8091 2023-01-05 03:55:43,471 - mmcls - INFO - Epoch [99][4500/5005] lr: 1.000e-04, eta: 0:08:05, time: 0.087, data_time: 0.045, memory: 928, loss: 4.8049 2023-01-05 03:55:52,032 - mmcls - INFO - Epoch [99][4600/5005] lr: 1.000e-04, eta: 0:07:56, time: 0.086, data_time: 0.032, memory: 928, loss: 4.7934 2023-01-05 03:56:00,776 - mmcls - INFO - Epoch [99][4700/5005] lr: 1.000e-04, eta: 0:07:47, time: 0.087, data_time: 0.040, memory: 928, loss: 4.8344 2023-01-05 03:56:09,487 - mmcls - INFO - Epoch [99][4800/5005] lr: 1.000e-04, eta: 0:07:39, time: 0.087, data_time: 0.015, memory: 928, loss: 4.8118 2023-01-05 03:56:17,971 - mmcls - INFO - Epoch [99][4900/5005] lr: 1.000e-04, eta: 0:07:30, time: 0.085, data_time: 0.004, memory: 928, loss: 4.8184 2023-01-05 03:56:26,967 - mmcls - INFO - Epoch [99][5000/5005] lr: 1.000e-04, eta: 0:07:21, time: 0.090, data_time: 0.016, memory: 928, loss: 4.8199 2023-01-05 03:56:49,946 - mmcls - INFO - Epoch(val) [99][196] accuracy_top-1: 18.5760, accuracy_top-5: 37.2920 2023-01-05 03:57:00,684 - mmcls - INFO - Epoch [100][100/5005] lr: 1.000e-04, eta: 0:07:12, time: 0.107, data_time: 0.051, memory: 928, loss: 4.7705 2023-01-05 03:57:09,459 - mmcls - INFO - Epoch [100][200/5005] lr: 1.000e-04, eta: 0:07:03, time: 0.088, data_time: 0.001, memory: 928, loss: 4.8188 2023-01-05 03:57:18,422 - mmcls - INFO - Epoch [100][300/5005] lr: 1.000e-04, eta: 0:06:54, time: 0.090, data_time: 0.038, memory: 928, loss: 4.7933 2023-01-05 03:57:27,452 - mmcls - INFO - Epoch [100][400/5005] lr: 1.000e-04, eta: 0:06:45, time: 0.090, data_time: 0.006, memory: 928, loss: 4.7989 2023-01-05 03:57:36,096 - mmcls - INFO - Epoch [100][500/5005] lr: 1.000e-04, eta: 0:06:37, time: 0.086, data_time: 0.005, memory: 928, loss: 4.8135 2023-01-05 03:57:45,176 - mmcls - INFO - Epoch [100][600/5005] lr: 1.000e-04, eta: 0:06:28, time: 0.091, data_time: 0.006, memory: 928, loss: 4.8263 2023-01-05 03:57:53,810 - mmcls - INFO - Epoch [100][700/5005] lr: 1.000e-04, eta: 0:06:19, time: 0.086, data_time: 0.007, memory: 928, loss: 4.7979 2023-01-05 03:58:02,861 - mmcls - INFO - Epoch [100][800/5005] lr: 1.000e-04, eta: 0:06:10, time: 0.091, data_time: 0.009, memory: 928, loss: 4.7869 2023-01-05 03:58:11,382 - mmcls - INFO - Epoch [100][900/5005] lr: 1.000e-04, eta: 0:06:01, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8078 2023-01-05 03:58:20,174 - mmcls - INFO - Epoch [100][1000/5005] lr: 1.000e-04, eta: 0:05:52, time: 0.088, data_time: 0.002, memory: 928, loss: 4.8131 2023-01-05 03:58:29,005 - mmcls - INFO - Epoch [100][1100/5005] lr: 1.000e-04, eta: 0:05:44, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7880 2023-01-05 03:58:37,743 - mmcls - INFO - Epoch [100][1200/5005] lr: 1.000e-04, eta: 0:05:35, time: 0.087, data_time: 0.002, memory: 928, loss: 4.8248 2023-01-05 03:58:46,700 - mmcls - INFO - Epoch [100][1300/5005] lr: 1.000e-04, eta: 0:05:26, time: 0.090, data_time: 0.005, memory: 928, loss: 4.7793 2023-01-05 03:58:55,524 - mmcls - INFO - Epoch [100][1400/5005] lr: 1.000e-04, eta: 0:05:17, time: 0.088, data_time: 0.017, memory: 928, loss: 4.7909 2023-01-05 03:59:04,159 - mmcls - INFO - Epoch [100][1500/5005] lr: 1.000e-04, eta: 0:05:08, time: 0.086, data_time: 0.005, memory: 928, loss: 4.7871 2023-01-05 03:59:12,866 - mmcls - INFO - Epoch [100][1600/5005] lr: 1.000e-04, eta: 0:05:00, time: 0.087, data_time: 0.026, memory: 928, loss: 4.8125 2023-01-05 03:59:21,751 - mmcls - INFO - Epoch [100][1700/5005] lr: 1.000e-04, eta: 0:04:51, time: 0.089, data_time: 0.018, memory: 928, loss: 4.7996 2023-01-05 03:59:30,704 - mmcls - INFO - Epoch [100][1800/5005] lr: 1.000e-04, eta: 0:04:42, time: 0.090, data_time: 0.009, memory: 928, loss: 4.8011 2023-01-05 03:59:39,691 - mmcls - INFO - Epoch [100][1900/5005] lr: 1.000e-04, eta: 0:04:33, time: 0.090, data_time: 0.019, memory: 928, loss: 4.7970 2023-01-05 03:59:48,366 - mmcls - INFO - Epoch [100][2000/5005] lr: 1.000e-04, eta: 0:04:24, time: 0.087, data_time: 0.019, memory: 928, loss: 4.8005 2023-01-05 03:59:57,024 - mmcls - INFO - Epoch [100][2100/5005] lr: 1.000e-04, eta: 0:04:16, time: 0.087, data_time: 0.001, memory: 928, loss: 4.8135 2023-01-05 04:00:05,469 - mmcls - INFO - Epoch [100][2200/5005] lr: 1.000e-04, eta: 0:04:07, time: 0.084, data_time: 0.006, memory: 928, loss: 4.8023 2023-01-05 04:00:14,680 - mmcls - INFO - Epoch [100][2300/5005] lr: 1.000e-04, eta: 0:03:58, time: 0.092, data_time: 0.006, memory: 928, loss: 4.7886 2023-01-05 04:00:23,155 - mmcls - INFO - Epoch [100][2400/5005] lr: 1.000e-04, eta: 0:03:49, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8179 2023-01-05 04:00:31,891 - mmcls - INFO - Epoch [100][2500/5005] lr: 1.000e-04, eta: 0:03:40, time: 0.087, data_time: 0.002, memory: 928, loss: 4.7970 2023-01-05 04:00:40,654 - mmcls - INFO - Epoch [100][2600/5005] lr: 1.000e-04, eta: 0:03:31, time: 0.088, data_time: 0.015, memory: 928, loss: 4.7952 2023-01-05 04:00:49,413 - mmcls - INFO - Epoch [100][2700/5005] lr: 1.000e-04, eta: 0:03:23, time: 0.088, data_time: 0.007, memory: 928, loss: 4.8035 2023-01-05 04:00:58,226 - mmcls - INFO - Epoch [100][2800/5005] lr: 1.000e-04, eta: 0:03:14, time: 0.088, data_time: 0.046, memory: 928, loss: 4.7887 2023-01-05 04:01:06,990 - mmcls - INFO - Epoch [100][2900/5005] lr: 1.000e-04, eta: 0:03:05, time: 0.088, data_time: 0.019, memory: 928, loss: 4.8201 2023-01-05 04:01:15,865 - mmcls - INFO - Epoch [100][3000/5005] lr: 1.000e-04, eta: 0:02:56, time: 0.089, data_time: 0.007, memory: 928, loss: 4.7942 2023-01-05 04:01:24,381 - mmcls - INFO - Epoch [100][3100/5005] lr: 1.000e-04, eta: 0:02:47, time: 0.085, data_time: 0.002, memory: 928, loss: 4.8035 2023-01-05 04:01:33,103 - mmcls - INFO - Epoch [100][3200/5005] lr: 1.000e-04, eta: 0:02:39, time: 0.087, data_time: 0.020, memory: 928, loss: 4.8038 2023-01-05 04:01:41,526 - mmcls - INFO - Epoch [100][3300/5005] lr: 1.000e-04, eta: 0:02:30, time: 0.084, data_time: 0.009, memory: 928, loss: 4.8017 2023-01-05 04:01:50,540 - mmcls - INFO - Epoch [100][3400/5005] lr: 1.000e-04, eta: 0:02:21, time: 0.090, data_time: 0.001, memory: 928, loss: 4.8016 2023-01-05 04:01:59,397 - mmcls - INFO - Epoch [100][3500/5005] lr: 1.000e-04, eta: 0:02:12, time: 0.088, data_time: 0.012, memory: 928, loss: 4.8070 2023-01-05 04:02:08,247 - mmcls - INFO - Epoch [100][3600/5005] lr: 1.000e-04, eta: 0:02:03, time: 0.089, data_time: 0.010, memory: 928, loss: 4.8186 2023-01-05 04:02:16,812 - mmcls - INFO - Epoch [100][3700/5005] lr: 1.000e-04, eta: 0:01:55, time: 0.086, data_time: 0.020, memory: 928, loss: 4.7825 2023-01-05 04:02:25,621 - mmcls - INFO - Epoch [100][3800/5005] lr: 1.000e-04, eta: 0:01:46, time: 0.088, data_time: 0.002, memory: 928, loss: 4.7748 2023-01-05 04:02:34,349 - mmcls - INFO - Epoch [100][3900/5005] lr: 1.000e-04, eta: 0:01:37, time: 0.087, data_time: 0.003, memory: 928, loss: 4.8049 2023-01-05 04:02:42,995 - mmcls - INFO - Epoch [100][4000/5005] lr: 1.000e-04, eta: 0:01:28, time: 0.086, data_time: 0.029, memory: 928, loss: 4.7859 2023-01-05 04:02:52,005 - mmcls - INFO - Epoch [100][4100/5005] lr: 1.000e-04, eta: 0:01:19, time: 0.090, data_time: 0.003, memory: 928, loss: 4.8139 2023-01-05 04:03:00,776 - mmcls - INFO - Epoch [100][4200/5005] lr: 1.000e-04, eta: 0:01:10, time: 0.088, data_time: 0.004, memory: 928, loss: 4.8102 2023-01-05 04:03:09,332 - mmcls - INFO - Epoch [100][4300/5005] lr: 1.000e-04, eta: 0:01:02, time: 0.086, data_time: 0.004, memory: 928, loss: 4.8002 2023-01-05 04:03:18,503 - mmcls - INFO - Epoch [100][4400/5005] lr: 1.000e-04, eta: 0:00:53, time: 0.092, data_time: 0.001, memory: 928, loss: 4.8026 2023-01-05 04:03:27,258 - mmcls - INFO - Epoch [100][4500/5005] lr: 1.000e-04, eta: 0:00:44, time: 0.088, data_time: 0.014, memory: 928, loss: 4.8206 2023-01-05 04:03:36,024 - mmcls - INFO - Epoch [100][4600/5005] lr: 1.000e-04, eta: 0:00:35, time: 0.087, data_time: 0.018, memory: 928, loss: 4.8141 2023-01-05 04:03:44,622 - mmcls - INFO - Epoch [100][4700/5005] lr: 1.000e-04, eta: 0:00:26, time: 0.086, data_time: 0.002, memory: 928, loss: 4.7949 2023-01-05 04:03:53,354 - mmcls - INFO - Epoch [100][4800/5005] lr: 1.000e-04, eta: 0:00:18, time: 0.087, data_time: 0.006, memory: 928, loss: 4.8127 2023-01-05 04:04:02,081 - mmcls - INFO - Epoch [100][4900/5005] lr: 1.000e-04, eta: 0:00:09, time: 0.087, data_time: 0.013, memory: 928, loss: 4.8232 2023-01-05 04:04:10,928 - mmcls - INFO - Epoch [100][5000/5005] lr: 1.000e-04, eta: 0:00:00, time: 0.089, data_time: 0.011, memory: 928, loss: 4.7976 2023-01-05 04:04:11,341 - mmcls - INFO - Saving checkpoint at 100 epochs 2023-01-05 04:04:33,598 - mmcls - INFO - Epoch(val) [100][196] accuracy_top-1: 18.3480, accuracy_top-5: 36.7180 ================================================ FILE: face_recognition/README.md ================================================ Official Implementation of the experiments on discriminative tasks with polynomial networks as described in the CVPR'20 paper [Π-nets: Deep Polynomial Neural Networks](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chrysos_P-nets_Deep_Polynomial_Neural_Networks_CVPR_2020_paper.pdf) and its [extension](https://arxiv.org/abs/2006.13026). ### Model Training 1. Download the training dataset [`MS1MV3-RetinaFace`](https://ibug.doc.ic.ac.uk/resources/lightweight-face-recognition-challenge-workshop/). The training dataset includes the following 6 files: ```Shell ms1m-retinaface/ train.idx train.rec property lfw.bin cfp_fp.bin agedb_30.bin ``` The first three files are the training dataset while the last three files are verification sets. 2. Edit config file: ```Shell cp sample_config.py config.py vim config.py # edit dataset path etc.. ``` 3. Our experiments were conducted on the 8*2080ti GPU. The training script is like this: ```Shell CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u train_parall.py --network r50 --loss arcface --dataset retina > log_ms1mretina_ploynet50_arcface.txt 2>&1 ``` 4. Compared with the [ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition) code, we only add three lines between line 409 and 412 of the symbol/fresnet.py: ```Shell act2 = Act(data=bn3*shortcut, act_type='tanh', name=name + '_tanh1') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1, 1), stride=(1,1), pad=(0, 0), no_bias=True, workspace=workspace, name=name + '_conv3') bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4') ``` 5. Pre-trained models can be downloaded from [here](https://drive.google.com/file/d/1Y9250PK5aK5oxyrvWVqcQsVdfqjtFL53/view?usp=sharing). 6. Results on [IJB-C](https://github.com/deepinsight/insightface/tree/master/Evaluation/IJB): | Method | TAR@FAR=1e-5 | TAR@FAR=1e-4 | | ------- | ------ | --------- | |ResNet50 | 94.28 | 96.17 | |Prodpoly-ResNet50 | 95.16 | 96.58 | ### Dependencies Requirements: - python 3.x - mxnet - opencv ================================================ FILE: face_recognition/__init__.py ================================================ ================================================ FILE: face_recognition/config.py ================================================ import numpy as np import os from easydict import EasyDict as edict config = edict() config.bn_mom = 0.9 config.workspace = 256 config.emb_size = 512 config.ckpt_embedding = True config.net_se = 0 config.net_act = 'prelu' config.net_unit = 3 config.net_input = 1 config.net_blocks = [1,4,6,2] config.net_output = 'E' config.net_multiplier = 1.0 config.val_targets = ['lfw', 'cfp_fp', 'agedb_30'] config.ce_loss = True config.fc7_lr_mult = 1.0 config.fc7_wd_mult = 1.0 config.fc7_no_bias = False config.max_steps = 0 config.data_rand_mirror = True config.data_cutoff = False config.data_color = 0 config.data_images_filter = 0 config.count_flops = True config.memonger = False #not work now # network settings network = edict() network.r100 = edict() network.r100.net_name = 'fresnet' network.r100.num_layers = 100 network.r100fc = edict() network.r100fc.net_name = 'fresnet' network.r100fc.num_layers = 100 network.r100fc.net_output = 'FC' network.r50 = edict() network.r50.net_name = 'fresnet' network.r50.num_layers = 50 network.r50v1 = edict() network.r50v1.net_name = 'fresnet' network.r50v1.num_layers = 50 network.r50v1.net_unit = 1 network.d169 = edict() network.d169.net_name = 'fdensenet' network.d169.num_layers = 169 network.d169.per_batch_size = 64 network.d169.densenet_dropout = 0.0 network.d201 = edict() network.d201.net_name = 'fdensenet' network.d201.num_layers = 201 network.d201.per_batch_size = 64 network.d201.densenet_dropout = 0.0 network.y1 = edict() network.y1.net_name = 'fmobilefacenet' network.y1.emb_size = 128 network.y1.net_output = 'GDC' network.y2 = edict() network.y2.net_name = 'fmobilefacenet' network.y2.emb_size = 256 network.y2.net_output = 'GDC' network.y2.net_blocks = [2,8,16,4] network.m1 = edict() network.m1.net_name = 'fmobilenet' network.m1.emb_size = 256 network.m1.net_output = 'GDC' network.m1.net_multiplier = 1.0 network.m05 = edict() network.m05.net_name = 'fmobilenet' network.m05.emb_size = 256 network.m05.net_output = 'GDC' network.m05.net_multiplier = 0.5 network.mnas = edict() network.mnas.net_name = 'fmnasnet' network.mnas.emb_size = 256 network.mnas.net_output = 'GDC' network.mnas.net_multiplier = 1.0 network.mnas05 = edict() network.mnas05.net_name = 'fmnasnet' network.mnas05.emb_size = 256 network.mnas05.net_output = 'GDC' network.mnas05.net_multiplier = 0.5 network.mnas025 = edict() network.mnas025.net_name = 'fmnasnet' network.mnas025.emb_size = 256 network.mnas025.net_output = 'GDC' network.mnas025.net_multiplier = 0.25 network.vargfacenet = edict() network.vargfacenet.net_name = 'vargfacenet' network.vargfacenet.net_multiplier = 1.25 network.vargfacenet.emb_size = 512 network.vargfacenet.net_output='J' # dataset settings dataset = edict() dataset.cmsw = edict() dataset.cmsw.dataset = 'cmsw' dataset.cmsw.dataset_path = '/home/dengjiankang/datasets/cmsw' dataset.cmsw.num_classes = 492795 dataset.cmsw.image_shape = (112,112,3) dataset.cmsw.val_targets = ['lfw', 'cfp_fp', 'agedb_30'] dataset.retina = edict() dataset.retina.dataset = 'retina' dataset.retina.dataset_path = '/home/dengjiankang/datasets/ms1m-retinaface' dataset.retina.num_classes = 93431 dataset.retina.image_shape = (112,112,3) dataset.retina.val_targets = ['lfw', 'cfp_fp', 'agedb_30'] loss = edict() loss.softmax = edict() loss.softmax.loss_name = 'softmax' loss.nsoftmax = edict() loss.nsoftmax.loss_name = 'margin_softmax' loss.nsoftmax.loss_s = 64.0 loss.nsoftmax.loss_m1 = 1.0 loss.nsoftmax.loss_m2 = 0.0 loss.nsoftmax.loss_m3 = 0.0 loss.arcface = edict() loss.arcface.loss_name = 'margin_softmax' loss.arcface.loss_s = 64.0 loss.arcface.loss_m1 = 1.0 loss.arcface.loss_m2 = 0.5 loss.arcface.loss_m3 = 0.0 loss.cosface = edict() loss.cosface.loss_name = 'margin_softmax' loss.cosface.loss_s = 64.0 loss.cosface.loss_m1 = 1.0 loss.cosface.loss_m2 = 0.0 loss.cosface.loss_m3 = 0.35 loss.combined = edict() loss.combined.loss_name = 'margin_softmax' loss.combined.loss_s = 64.0 loss.combined.loss_m1 = 1.0 loss.combined.loss_m2 = 0.3 loss.combined.loss_m3 = 0.2 loss.triplet = edict() loss.triplet.loss_name = 'triplet' loss.triplet.images_per_identity = 5 loss.triplet.triplet_alpha = 0.3 loss.triplet.triplet_bag_size = 7200 loss.triplet.triplet_max_ap = 0.0 loss.triplet.per_batch_size = 60 loss.triplet.lr = 0.05 loss.atriplet = edict() loss.atriplet.loss_name = 'atriplet' loss.atriplet.images_per_identity = 5 loss.atriplet.triplet_alpha = 0.35 loss.atriplet.triplet_bag_size = 7200 loss.atriplet.triplet_max_ap = 0.0 loss.atriplet.per_batch_size = 60 loss.atriplet.lr = 0.05 # default settings default = edict() # default network default.network = 'r50' default.pretrained = '' default.pretrained_epoch = 1 # default dataset default.dataset = 'retina' default.loss = 'arcface' default.frequent = 20 default.verbose = 2000 default.kvstore = 'device' default.end_epoch = 10000 default.lr = 0.1 default.wd = 0.0005 default.mom = 0.9 default.per_batch_size = 64 default.ckpt = 2 # default.lr_steps = '300000,450000,600000' default.lr_steps = '150000,240000,330000' # default.lr_steps = '100000,160000,220000' default.models_root = './models' def generate_config(_network, _dataset, _loss): for k, v in loss[_loss].items(): config[k] = v if k in default: default[k] = v for k, v in network[_network].items(): config[k] = v if k in default: default[k] = v for k, v in dataset[_dataset].items(): config[k] = v if k in default: default[k] = v config.loss = _loss config.network = _network config.dataset = _dataset config.num_workers = 1 if 'DMLC_NUM_WORKER' in os.environ: config.num_workers = int(os.environ['DMLC_NUM_WORKER']) ================================================ FILE: face_recognition/data/build_eval_pack.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function #import mxnet as mx #from mxnet import ndarray as nd import argparse import cv2 import pickle import numpy as np import sys import os sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'common')) sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..', 'RetinaFace')) import face_align from retinaface import RetinaFace def to_rgb(img): w, h = img.shape ret = np.empty((w, h, 3), dtype=np.uint8) ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img return ret def IOU(Reframe,GTframe): x1 = Reframe[0]; y1 = Reframe[1]; width1 = Reframe[2]-Reframe[0]; height1 = Reframe[3]-Reframe[1]; x2 = GTframe[0] y2 = GTframe[1] width2 = GTframe[2]-GTframe[0] height2 = GTframe[3]-GTframe[1] endx = max(x1+width1,x2+width2) startx = min(x1,x2) width = width1+width2-(endx-startx) endy = max(y1+height1,y2+height2) starty = min(y1,y2) height = height1+height2-(endy-starty) if width <=0 or height <= 0: ratio = 0 else: Area = width*height Area1 = width1*height1 Area2 = width2*height2 ratio = Area*1./(Area1+Area2-Area) return ratio parser = argparse.ArgumentParser(description='Package eval images') # general parser.add_argument('--data-dir', default='', help='') parser.add_argument('--image-size', type=int, default=112, help='') parser.add_argument('--gpu', type=int, default=0, help='') parser.add_argument('--det-prefix', type=str, default='./model/R50', help='') parser.add_argument('--output', default='./', help='path to save.') parser.add_argument('--align-mode', default='arcface', help='align mode.') args = parser.parse_args() gpu_id = args.gpu detector = RetinaFace(args.det_prefix, 0, gpu_id, network='net3') target_size = 400 max_size = 800 def get_norm_crop(image_path): im = cv2.imread(image_path) im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(target_size) / float(im_size_min) # prevent bigger axis from being more than max_size: if np.round(im_scale * im_size_max) > max_size: im_scale = float(max_size) / float(im_size_max) bbox, landmark = detector.detect(im, threshold=0.5, scales=[im_scale]) #print(im.shape, bbox.shape, landmark.shape) if bbox.shape[0]==0: bbox, landmark = detector.detect(im, threshold=0.05, scales=[im_scale*0.75, im_scale, im_scale*2.0]) print('refine', im.shape, bbox.shape, landmark.shape) nrof_faces = bbox.shape[0] if nrof_faces>0: det = bbox[:,0:4] img_size = np.asarray(im.shape)[0:2] bindex = 0 if nrof_faces>1: bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1]) img_center = img_size / 2 offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ]) offset_dist_squared = np.sum(np.power(offsets,2.0),0) bindex = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering #_bbox = bounding_boxes[bindex, 0:4] _landmark = landmark[bindex] warped = face_align.norm_crop(im, landmark = _landmark, image_size=args.image_size, mode=args.align_mode) return warped else: return None bins = [] issame_list = [] pp = 0 for line in open(os.path.join(args.data_dir, 'pairs_label.txt'), 'r'): pp+=1 if pp%100==0: print('processing', pp) line = line.strip().split() assert len(line)==3 path1 = os.path.join(args.data_dir, line[0]) path2 = os.path.join(args.data_dir, line[1]) im1 = get_norm_crop(path1) im2 = get_norm_crop(path2) issame = True if line[2]=='0': issame = False issame_list.append(issame) for im in [im1, im2]: _, s = cv2.imencode('.jpg', im) bins.append(s) with open(args.output, 'wb') as f: pickle.dump((bins, issame_list), f, 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Sandra_Banning/Sandra_Banning_0001.jpg Wolfgang_Schneiderhan/Wolfgang_Schneiderhan_0001.jpg 0 Scott_Wolf/Scott_Wolf_0002.jpg Troy_Polamalu/Troy_Polamalu_0001.jpg 0 Sergei_Alexandrovitch_Ordzhonikidze/Sergei_Alexandrovitch_Ordzhonikidze_0001.jpg Yolanda_King/Yolanda_King_0001.jpg 0 Shane_Loux/Shane_Loux_0001.jpg Val_Ackerman/Val_Ackerman_0001.jpg 0 Shawn_Marion/Shawn_Marion_0001.jpg Shirley_Jones/Shirley_Jones_0001.jpg 0 Slobodan_Milosevic/Slobodan_Milosevic_0002.jpg Sok_An/Sok_An_0001.jpg 0 ================================================ FILE: face_recognition/data/rec2image.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import mxnet as mx from mxnet import ndarray as nd import random import argparse import cv2 import time import sklearn import numpy as np def main(args): include_datasets = args.include.split(',') rec_list = [] for ds in include_datasets: path_imgrec = os.path.join(ds, 'train.rec') path_imgidx = os.path.join(ds, 'train.idx') imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type rec_list.append(imgrec) if not os.path.exists(args.output): os.makedirs(args.output) for ds_id in range(len(rec_list)): id_list = [] imgrec = rec_list[ds_id] s = imgrec.read_idx(0) header, _ = mx.recordio.unpack(s) assert header.flag>0 print('header0 label', header.label) header0 = (int(header.label[0]), int(header.label[1])) seq_identity = range(int(header.label[0]), int(header.label[1])) pp=0 for identity in seq_identity: id_dir = os.path.join(args.output, "%d_%d"%(ds_id, identity)) os.makedirs(id_dir) pp+=1 if pp%10==0: print('processing id', pp) s = imgrec.read_idx(identity) header, _ = mx.recordio.unpack(s) imgid = 0 for _idx in range(int(header.label[0]), int(header.label[1])): s = imgrec.read_idx(_idx) _header, _img = mx.recordio.unpack(s) _img = mx.image.imdecode(_img).asnumpy()[:,:,::-1] # to bgr image_path = os.path.join(id_dir, "%d.jpg"%imgid) cv2.imwrite(image_path, _img) imgid+=1 if __name__ == '__main__': parser = argparse.ArgumentParser(description='do dataset merge') # general parser.add_argument('--include', default='', type=str, help='') parser.add_argument('--output', default='', type=str, help='') args = parser.parse_args() main(args) ================================================ FILE: face_recognition/eval/lfw.py ================================================ """Helper for evaluation on the Labeled Faces in the Wild dataset """ # MIT License # # Copyright (c) 2016 David Sandberg # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import numpy as np from scipy import misc from sklearn.model_selection import KFold from scipy import interpolate import sklearn from sklearn.decomposition import PCA import mxnet as mx from mxnet import ndarray as nd def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca = 0): assert(embeddings1.shape[0] == embeddings2.shape[0]) assert(embeddings1.shape[1] == embeddings2.shape[1]) nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) nrof_thresholds = len(thresholds) k_fold = KFold(n_splits=nrof_folds, shuffle=False) tprs = np.zeros((nrof_folds,nrof_thresholds)) fprs = np.zeros((nrof_folds,nrof_thresholds)) accuracy = np.zeros((nrof_folds)) indices = np.arange(nrof_pairs) #print('pca', pca) if pca==0: diff = np.subtract(embeddings1, embeddings2) dist = np.sum(np.square(diff),1) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): #print('train_set', train_set) #print('test_set', test_set) if pca>0: print('doing pca on', fold_idx) embed1_train = embeddings1[train_set] embed2_train = embeddings2[train_set] _embed_train = np.concatenate( (embed1_train, embed2_train), axis=0 ) #print(_embed_train.shape) pca_model = PCA(n_components=pca) pca_model.fit(_embed_train) embed1 = pca_model.transform(embeddings1) embed2 = pca_model.transform(embeddings2) embed1 = sklearn.preprocessing.normalize(embed1) embed2 = sklearn.preprocessing.normalize(embed2) #print(embed1.shape, embed2.shape) diff = np.subtract(embed1, embed2) dist = np.sum(np.square(diff),1) # Find the best threshold for the fold acc_train = np.zeros((nrof_thresholds)) for threshold_idx, threshold in enumerate(thresholds): _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set]) best_threshold_index = np.argmax(acc_train) for threshold_idx, threshold in enumerate(thresholds): tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set]) _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set]) tpr = np.mean(tprs,0) fpr = np.mean(fprs,0) return tpr, fpr, accuracy def calculate_accuracy(threshold, dist, actual_issame): predict_issame = np.less(dist, threshold) tp = np.sum(np.logical_and(predict_issame, actual_issame)) fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame))) fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn) fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn) acc = float(tp+tn)/dist.size return tpr, fpr, acc def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10): assert(embeddings1.shape[0] == embeddings2.shape[0]) assert(embeddings1.shape[1] == embeddings2.shape[1]) nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) nrof_thresholds = len(thresholds) k_fold = KFold(n_splits=nrof_folds, shuffle=False) val = np.zeros(nrof_folds) far = np.zeros(nrof_folds) diff = np.subtract(embeddings1, embeddings2) dist = np.sum(np.square(diff),1) indices = np.arange(nrof_pairs) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): # Find the threshold that gives FAR = far_target far_train = np.zeros(nrof_thresholds) for threshold_idx, threshold in enumerate(thresholds): _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set]) if np.max(far_train)>=far_target: f = interpolate.interp1d(far_train, thresholds, kind='slinear') threshold = f(far_target) else: threshold = 0.0 val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set]) val_mean = np.mean(val) far_mean = np.mean(far) val_std = np.std(val) return val_mean, val_std, far_mean def calculate_val_far(threshold, dist, actual_issame): predict_issame = np.less(dist, threshold) true_accept = np.sum(np.logical_and(predict_issame, actual_issame)) false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) n_same = np.sum(actual_issame) n_diff = np.sum(np.logical_not(actual_issame)) val = float(true_accept) / float(n_same) far = float(false_accept) / float(n_diff) return val, far def evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0): # Calculate evaluation metrics thresholds = np.arange(0, 4, 0.01) embeddings1 = embeddings[0::2] embeddings2 = embeddings[1::2] tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), nrof_folds=nrof_folds, pca = pca) thresholds = np.arange(0, 4, 0.001) val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds) return tpr, fpr, accuracy, val, val_std, far def get_paths(lfw_dir, pairs, file_ext): nrof_skipped_pairs = 0 path_list = [] issame_list = [] for pair in pairs: if len(pair) == 3: path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext) path1 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[2])+'.'+file_ext) issame = True elif len(pair) == 4: path0 = os.path.join(lfw_dir, pair[0], pair[0] + '_' + '%04d' % int(pair[1])+'.'+file_ext) path1 = os.path.join(lfw_dir, pair[2], pair[2] + '_' + '%04d' % int(pair[3])+'.'+file_ext) issame = False if os.path.exists(path0) and os.path.exists(path1): # Only add the pair if both paths exist path_list += (path0,path1) issame_list.append(issame) else: print('not exists', path0, path1) nrof_skipped_pairs += 1 if nrof_skipped_pairs>0: print('Skipped %d image pairs' % nrof_skipped_pairs) return path_list, issame_list def read_pairs(pairs_filename): pairs = [] with open(pairs_filename, 'r') as f: for line in f.readlines()[1:]: pair = line.strip().split() pairs.append(pair) return np.array(pairs) def load_dataset(lfw_dir, image_size): lfw_pairs = read_pairs(os.path.join(lfw_dir, 'pairs.txt')) lfw_paths, issame_list = get_paths(lfw_dir, lfw_pairs, 'jpg') lfw_data_list = [] for flip in [0,1]: lfw_data = nd.empty((len(lfw_paths), 3, image_size[0], image_size[1])) lfw_data_list.append(lfw_data) i = 0 for path in lfw_paths: with open(path, 'rb') as fin: _bin = fin.read() img = mx.image.imdecode(_bin) img = nd.transpose(img, axes=(2, 0, 1)) for flip in [0,1]: if flip==1: img = mx.ndarray.flip(data=img, axis=2) lfw_data_list[flip][i][:] = img i+=1 if i%1000==0: print('loading lfw', i) print(lfw_data_list[0].shape) print(lfw_data_list[1].shape) return (lfw_data_list, issame_list) def test(lfw_set, mx_model, batch_size): print('testing lfw..') lfw_data_list = lfw_set[0] issame_list = lfw_set[1] model = mx_model embeddings_list = [] for i in range( len(lfw_data_list) ): lfw_data = lfw_data_list[i] embeddings = None ba = 0 while ba1: self.k_fold = KFold(n_splits = n_splits, shuffle = shuffle) def split(self, indices): if self.n_splits>1: return self.k_fold.split(indices) else: return [(indices, indices)] def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, nrof_folds=10, pca = 0): assert(embeddings1.shape[0] == embeddings2.shape[0]) assert(embeddings1.shape[1] == embeddings2.shape[1]) nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) nrof_thresholds = len(thresholds) k_fold = LFold(n_splits=nrof_folds, shuffle=False) tprs = np.zeros((nrof_folds,nrof_thresholds)) fprs = np.zeros((nrof_folds,nrof_thresholds)) accuracy = np.zeros((nrof_folds)) indices = np.arange(nrof_pairs) #print('pca', pca) if pca==0: diff = np.subtract(embeddings1, embeddings2) dist = np.sum(np.square(diff),1) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): #print('train_set', train_set) #print('test_set', test_set) if pca>0: print('doing pca on', fold_idx) embed1_train = embeddings1[train_set] embed2_train = embeddings2[train_set] _embed_train = np.concatenate( (embed1_train, embed2_train), axis=0 ) #print(_embed_train.shape) pca_model = PCA(n_components=pca) pca_model.fit(_embed_train) embed1 = pca_model.transform(embeddings1) embed2 = pca_model.transform(embeddings2) embed1 = sklearn.preprocessing.normalize(embed1) embed2 = sklearn.preprocessing.normalize(embed2) #print(embed1.shape, embed2.shape) diff = np.subtract(embed1, embed2) dist = np.sum(np.square(diff),1) # Find the best threshold for the fold acc_train = np.zeros((nrof_thresholds)) for threshold_idx, threshold in enumerate(thresholds): _, _, acc_train[threshold_idx] = calculate_accuracy(threshold, dist[train_set], actual_issame[train_set]) best_threshold_index = np.argmax(acc_train) #print('threshold', thresholds[best_threshold_index]) for threshold_idx, threshold in enumerate(thresholds): tprs[fold_idx,threshold_idx], fprs[fold_idx,threshold_idx], _ = calculate_accuracy(threshold, dist[test_set], actual_issame[test_set]) _, _, accuracy[fold_idx] = calculate_accuracy(thresholds[best_threshold_index], dist[test_set], actual_issame[test_set]) tpr = np.mean(tprs,0) fpr = np.mean(fprs,0) return tpr, fpr, accuracy def calculate_accuracy(threshold, dist, actual_issame): predict_issame = np.less(dist, threshold) tp = np.sum(np.logical_and(predict_issame, actual_issame)) fp = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) tn = np.sum(np.logical_and(np.logical_not(predict_issame), np.logical_not(actual_issame))) fn = np.sum(np.logical_and(np.logical_not(predict_issame), actual_issame)) tpr = 0 if (tp+fn==0) else float(tp) / float(tp+fn) fpr = 0 if (fp+tn==0) else float(fp) / float(fp+tn) acc = float(tp+tn)/dist.size return tpr, fpr, acc def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10): assert(embeddings1.shape[0] == embeddings2.shape[0]) assert(embeddings1.shape[1] == embeddings2.shape[1]) nrof_pairs = min(len(actual_issame), embeddings1.shape[0]) nrof_thresholds = len(thresholds) k_fold = LFold(n_splits=nrof_folds, shuffle=False) val = np.zeros(nrof_folds) far = np.zeros(nrof_folds) diff = np.subtract(embeddings1, embeddings2) dist = np.sum(np.square(diff),1) indices = np.arange(nrof_pairs) for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)): # Find the threshold that gives FAR = far_target far_train = np.zeros(nrof_thresholds) for threshold_idx, threshold in enumerate(thresholds): _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set]) if np.max(far_train)>=far_target: f = interpolate.interp1d(far_train, thresholds, kind='slinear') threshold = f(far_target) else: threshold = 0.0 val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set]) val_mean = np.mean(val) far_mean = np.mean(far) val_std = np.std(val) return val_mean, val_std, far_mean def calculate_val_far(threshold, dist, actual_issame): predict_issame = np.less(dist, threshold) true_accept = np.sum(np.logical_and(predict_issame, actual_issame)) false_accept = np.sum(np.logical_and(predict_issame, np.logical_not(actual_issame))) n_same = np.sum(actual_issame) n_diff = np.sum(np.logical_not(actual_issame)) #print(true_accept, false_accept) #print(n_same, n_diff) val = float(true_accept) / float(n_same) far = float(false_accept) / float(n_diff) return val, far def evaluate(embeddings, actual_issame, nrof_folds=10, pca = 0): # Calculate evaluation metrics thresholds = np.arange(0, 4, 0.01) embeddings1 = embeddings[0::2] embeddings2 = embeddings[1::2] tpr, fpr, accuracy = calculate_roc(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), nrof_folds=nrof_folds, pca = pca) thresholds = np.arange(0, 4, 0.001) val, val_std, far = calculate_val(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), 1e-3, nrof_folds=nrof_folds) return tpr, fpr, accuracy, val, val_std, far def load_bin(path, image_size): try: with open(path, 'rb') as f: bins, issame_list = pickle.load(f) #py2 except UnicodeDecodeError as e: with open(path, 'rb') as f: bins, issame_list = pickle.load(f, encoding='bytes') #py3 data_list = [] for flip in [0,1]: data = nd.empty((len(issame_list)*2, 3, image_size[0], image_size[1])) data_list.append(data) for i in range(len(issame_list)*2): _bin = bins[i] img = mx.image.imdecode(_bin) if img.shape[1]!=image_size[0]: img = mx.image.resize_short(img, image_size[0]) img = nd.transpose(img, axes=(2, 0, 1)) for flip in [0,1]: if flip==1: img = mx.ndarray.flip(data=img, axis=2) data_list[flip][i][:] = img if i%1000==0: print('loading bin', i) print(data_list[0].shape) return (data_list, issame_list) def test(data_set, mx_model, batch_size, nfolds=10, data_extra = None, label_shape = None): print('testing verification..') data_list = data_set[0] issame_list = data_set[1] model = mx_model embeddings_list = [] if data_extra is not None: _data_extra = nd.array(data_extra) time_consumed = 0.0 if label_shape is None: _label = nd.ones( (batch_size,) ) else: _label = nd.ones( label_shape ) for i in range( len(data_list) ): data = data_list[i] embeddings = None ba = 0 while ba0.0: imga = data[ida].asnumpy().transpose( (1,2,0) )[...,::-1] #to bgr imgb = data[idb].asnumpy().transpose( (1,2,0) )[...,::-1] #print(imga.shape, imgb.shape, violate, asame, _dist) if asame: pouts.append( (imga, imgb, _dist, best_threshold, ida) ) else: nouts.append( (imga, imgb, _dist, best_threshold, ida) ) tpr = np.mean(tprs,0) fpr = np.mean(fprs,0) acc = np.mean(accuracy) pouts = sorted(pouts, key = lambda x: x[2], reverse=True) nouts = sorted(nouts, key = lambda x: x[2], reverse=False) print(len(pouts), len(nouts)) print('acc', acc) gap = 10 image_shape = (112,224,3) out_dir = "./badcases" if not os.path.exists(out_dir): os.makedirs(out_dir) if len(nouts)>0: threshold = nouts[0][3] else: threshold = pouts[-1][3] for item in [(pouts, 'positive(false_negative).png'), (nouts, 'negative(false_positive).png')]: cols = 4 rows = 8000 outs = item[0] if len(outs)==0: continue #if len(outs)==9: # cols = 3 # rows = 3 _rows = int(math.ceil(len(outs)/cols)) rows = min(rows, _rows) hack = {} if name.startswith('cfp') and item[1].startswith('pos'): hack = {0:'manual/238_13.jpg.jpg', 6:'manual/088_14.jpg.jpg', 10:'manual/470_14.jpg.jpg', 25:'manual/238_13.jpg.jpg', 28:'manual/143_11.jpg.jpg'} filename = item[1] if len(name)>0: filename = name+"_"+filename filename = os.path.join(out_dir, filename) img = np.zeros( (image_shape[0]*rows+20, image_shape[1]*cols+(cols-1)*gap, 3), dtype=np.uint8 ) img[:,:,:] = 255 text_color = (0,0,153) text_color = (255,178,102) text_color = (153,255,51) for outi, out in enumerate(outs): row = outi//cols col = outi%cols if row==rows: break imga = out[0].copy() imgb = out[1].copy() if outi in hack: idx = out[4] print('noise idx',idx) aa = hack[outi] imgb = cv2.imread(aa) #if aa==1: # imgb = cv2.transpose(imgb) # imgb = cv2.flip(imgb, 1) #elif aa==3: # imgb = cv2.transpose(imgb) # imgb = cv2.flip(imgb, 0) #else: # for ii in range(2): # imgb = cv2.transpose(imgb) # imgb = cv2.flip(imgb, 1) dist = out[2] _img = np.concatenate( (imga, imgb), axis=1 ) k = "%.3f"%dist #print(k) font = cv2.FONT_HERSHEY_SIMPLEX cv2.putText(_img,k,(80,image_shape[0]//2+7), font, 0.6, text_color, 2) #_filename = filename+"_%d.png"%outi #cv2.imwrite(_filename, _img) img[row*image_shape[0]:(row+1)*image_shape[0], (col*image_shape[1]+gap*col):((col+1)*image_shape[1]+gap*col),:] = _img #threshold = outs[0][3] font = cv2.FONT_HERSHEY_SIMPLEX k = "threshold: %.3f"%threshold cv2.putText(img,k,(img.shape[1]//2-70,img.shape[0]-5), font, 0.6, text_color, 2) cv2.imwrite(filename, img) def dumpR(data_set, mx_model, batch_size, name='', data_extra = None, label_shape = None): print('dump verification embedding..') data_list = data_set[0] issame_list = data_set[1] model = mx_model embeddings_list = [] if data_extra is not None: _data_extra = nd.array(data_extra) time_consumed = 0.0 if label_shape is None: _label = nd.ones( (batch_size,) ) else: _label = nd.ones( label_shape ) for i in range( len(data_list) ): data = data_list[i] embeddings = None ba = 0 while ba0: _max = [int(x) for x in args.max.split(',')] assert len(_max)==2 if len(epochs)>_max[1]: epochs = epochs[_max[0]:_max[1]] else: epochs = [int(x) for x in vec[1].split('|')] print('model number', len(epochs)) time0 = datetime.datetime.now() for epoch in epochs: print('loading',prefix, epoch) sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) #arg_params, aux_params = ch_dev(arg_params, aux_params, ctx) all_layers = sym.get_internals() sym = all_layers['fc1_output'] model = mx.mod.Module(symbol=sym, context=ctx, label_names = None) #model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))]) model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))]) model.set_params(arg_params, aux_params) nets.append(model) time_now = datetime.datetime.now() diff = time_now - time0 print('model loading time', diff.total_seconds()) ver_list = [] ver_name_list = [] for name in args.target.split(','): path = os.path.join(args.data_dir,name+".bin") if os.path.exists(path): print('loading.. ', name) data_set = load_bin(path, image_size) ver_list.append(data_set) ver_name_list.append(name) if args.mode==0: for i in range(len(ver_list)): results = [] for model in nets: acc1, std1, acc2, std2, xnorm, embeddings_list = test(ver_list[i], model, args.batch_size, args.nfolds) print('[%s]XNorm: %f' % (ver_name_list[i], xnorm)) print('[%s]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], acc1, std1)) print('[%s]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], acc2, std2)) results.append(acc2) print('Max of [%s] is %1.5f' % (ver_name_list[i], np.max(results))) elif args.mode==1: model = nets[0] test_badcase(ver_list[0], model, args.batch_size, args.target) else: model = nets[0] dumpR(ver_list[0], model, args.batch_size, args.target) ================================================ FILE: face_recognition/flops_counter.py ================================================ ''' @author: insightface ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import os import json import argparse import numpy as np import mxnet as mx def is_no_bias(attr): ret = False if 'no_bias' in attr and (attr['no_bias']==True or attr['no_bias']=='True'): ret = True return ret def count_fc_flops(input_filter, output_filter, attr): #print(input_filter, output_filter ,attr) ret = 2*input_filter*output_filter if is_no_bias(attr): ret -= output_filter return int(ret) def count_conv_flops(input_shape, output_shape, attr): kernel = attr['kernel'][1:-1].split(',') kernel = [int(x) for x in kernel] #print('kernel', kernel) if is_no_bias(attr): ret = (2*input_shape[1]*kernel[0]*kernel[1]-1)*output_shape[2]*output_shape[3]*output_shape[1] else: ret = 2*input_shape[1]*kernel[0]*kernel[1]*output_shape[2]*output_shape[3]*output_shape[1] num_group = 1 if 'num_group' in attr: num_group = int(attr['num_group']) ret /= num_group return int(ret) def count_flops(sym, **data_shapes): all_layers = sym.get_internals() #print(all_layers) arg_shapes, out_shapes, aux_shapes = all_layers.infer_shape(**data_shapes) out_shape_dict = dict(zip(all_layers.list_outputs(), out_shapes)) nodes = json.loads(sym.tojson())['nodes'] nodeid_shape = {} for nodeid, node in enumerate(nodes): name = node['name'] layer_name = name+"_output" if layer_name in out_shape_dict: nodeid_shape[nodeid] = out_shape_dict[layer_name] #print(nodeid_shape) FLOPs = 0 for nodeid, node in enumerate(nodes): flops = 0 if node['op']=='Convolution': output_shape = nodeid_shape[nodeid] name = node['name'] attr = node['attrs'] input_nodeid = node['inputs'][0][0] input_shape = nodeid_shape[input_nodeid] flops = count_conv_flops(input_shape, output_shape, attr) elif node['op']=='FullyConnected': attr = node['attrs'] output_shape = nodeid_shape[nodeid] input_nodeid = node['inputs'][0][0] input_shape = nodeid_shape[input_nodeid] output_filter = output_shape[1] input_filter = input_shape[1]*input_shape[2]*input_shape[3] #assert len(input_shape)==4 and input_shape[2]==1 and input_shape[3]==1 flops = count_fc_flops(input_filter, output_filter, attr) #print(node, flops) FLOPs += flops return FLOPs def flops_str(FLOPs): preset = [ (1e12, 'T'), (1e9, 'G'), (1e6, 'M'), (1e3, 'K') ] for p in preset: if FLOPs//p[0]>0: N = FLOPs/p[0] ret = "%.1f%s"%(N, p[1]) return ret ret = "%.1f"%(FLOPs) return ret if __name__ == '__main__': parser = argparse.ArgumentParser(description='flops counter') # general #parser.add_argument('--model', default='../models2/y2-arcface-retinat1/model,1', help='path to load model.') #parser.add_argument('--model', default='../models2/r100fc-arcface-retinaa/model,1', help='path to load model.') parser.add_argument('--model', default='../models2/r50fc-arcface-emore/model,1', help='path to load model.') args = parser.parse_args() _vec = args.model.split(',') assert len(_vec)==2 prefix = _vec[0] epoch = int(_vec[1]) print('loading',prefix, epoch) sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch) all_layers = sym.get_internals() sym = all_layers['fc1_output'] FLOPs = count_flops(sym, data=(1,3,112,112)) print('FLOPs:', FLOPs) ================================================ FILE: face_recognition/image_iter.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import random import logging import sys import numbers import math import sklearn import datetime import numpy as np import cv2 import mxnet as mx from mxnet import ndarray as nd from mxnet import io from mxnet import recordio logger = logging.getLogger() class FaceImageIter(io.DataIter): def __init__(self, batch_size, data_shape, path_imgrec = None, shuffle=False, aug_list=None, mean = None, rand_mirror = False, cutoff = 0, color_jittering = 0, images_filter = 0, data_name='data', label_name='softmax_label', **kwargs): super(FaceImageIter, self).__init__() assert path_imgrec if path_imgrec: logging.info('loading recordio %s...', path_imgrec) path_imgidx = path_imgrec[0:-4]+".idx" self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type s = self.imgrec.read_idx(0) header, _ = recordio.unpack(s) if header.flag>0: print('header0 label', header.label) self.header0 = (int(header.label[0]), int(header.label[1])) #assert(header.flag==1) #self.imgidx = range(1, int(header.label[0])) self.imgidx = [] self.id2range = {} self.seq_identity = range(int(header.label[0]), int(header.label[1])) for identity in self.seq_identity: s = self.imgrec.read_idx(identity) header, _ = recordio.unpack(s) a,b = int(header.label[0]), int(header.label[1]) count = b-a if count= len(self.seq): raise StopIteration idx = self.seq[self.cur] self.cur += 1 if self.imgrec is not None: s = self.imgrec.read_idx(idx) header, img = recordio.unpack(s) label = header.label if not isinstance(label, numbers.Number): label = label[0] return label, img, None, None else: label, fname, bbox, landmark = self.imglist[idx] return label, self.read_image(fname), bbox, landmark else: s = self.imgrec.read() if s is None: raise StopIteration header, img = recordio.unpack(s) return header.label, img, None, None def brightness_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) src *= alpha return src def contrast_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) coef = nd.array([[[0.299, 0.587, 0.114]]]) gray = src * coef gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray) src *= alpha src += gray return src def saturation_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) coef = nd.array([[[0.299, 0.587, 0.114]]]) gray = src * coef gray = nd.sum(gray, axis=2, keepdims=True) gray *= (1.0 - alpha) src *= alpha src += gray return src def color_aug(self, img, x): #augs = [self.brightness_aug, self.contrast_aug, self.saturation_aug] #random.shuffle(augs) #for aug in augs: # #print(img.shape) # img = aug(img, x) # #print(img.shape) #return img return self.CJA(img) def mirror_aug(self, img): _rd = random.randint(0,1) if _rd==1: for c in range(img.shape[2]): img[:,:,c] = np.fliplr(img[:,:,c]) return img def compress_aug(self, img): from PIL import Image from io import BytesIO buf = BytesIO() img = Image.fromarray(img.asnumpy(), 'RGB') q = random.randint(2, 20) img.save(buf, format='JPEG', quality=q) buf = buf.getvalue() img = Image.open(BytesIO(buf)) return nd.array(np.asarray(img, 'float32')) def next(self): if not self.is_init: self.reset() self.is_init = True """Returns the next batch of data.""" #print('in next', self.cur, self.labelcur) self.nbatch+=1 batch_size = self.batch_size c, h, w = self.data_shape batch_data = nd.empty((batch_size, c, h, w)) if self.provide_label is not None: batch_label = nd.empty(self.provide_label[0][1]) i = 0 try: while i < batch_size: label, s, bbox, landmark = self.next_sample() _data = self.imdecode(s) if _data.shape[0]!=self.data_shape[1]: _data = mx.image.resize_short(_data, self.data_shape[1]) if self.rand_mirror: _rd = random.randint(0,1) if _rd==1: _data = mx.ndarray.flip(data=_data, axis=1) if self.color_jittering>0: if self.color_jittering>1: _rd = random.randint(0,1) if _rd==1: _data = self.compress_aug(_data) #print('do color aug') _data = _data.astype('float32', copy=False) #print(_data.__class__) _data = self.color_aug(_data, 0.125) if self.nd_mean is not None: _data = _data.astype('float32', copy=False) _data -= self.nd_mean _data *= 0.0078125 if self.cutoff>0: _rd = random.randint(0,1) if _rd==1: #print('do cutoff aug', self.cutoff) centerh = random.randint(0, _data.shape[0]-1) centerw = random.randint(0, _data.shape[1]-1) half = self.cutoff//2 starth = max(0, centerh-half) endh = min(_data.shape[0], centerh+half) startw = max(0, centerw-half) endw = min(_data.shape[1], centerw+half) #print(starth, endh, startw, endw, _data.shape) _data[starth:endh, startw:endw, :] = 128 data = [_data] try: self.check_valid_image(data) except RuntimeError as e: logging.debug('Invalid image, skipping: %s', str(e)) continue #print('aa',data[0].shape) #data = self.augmentation_transform(data) #print('bb',data[0].shape) for datum in data: assert i < batch_size, 'Batch size must be multiples of augmenter output length' #print(datum.shape) batch_data[i][:] = self.postprocess_data(datum) batch_label[i][:] = label i += 1 except StopIteration: if i>> dataIter.read_image('Face.jpg') # returns decoded raw bytes. """ with open(os.path.join(self.path_root, fname), 'rb') as fin: img = fin.read() return img def augmentation_transform(self, data): """Transforms input data with specified augmentation.""" for aug in self.auglist: data = [ret for src in data for ret in aug(src)] return data def postprocess_data(self, datum): """Final postprocessing step before image is loaded into the batch.""" return nd.transpose(datum, axes=(2, 0, 1)) class FaceImageIterList(io.DataIter): def __init__(self, iter_list): assert len(iter_list)>0 self.provide_data = iter_list[0].provide_data self.provide_label = iter_list[0].provide_label self.iter_list = iter_list self.cur_iter = None def reset(self): self.cur_iter.reset() def next(self): self.cur_iter = random.choice(self.iter_list) while True: try: ret = self.cur_iter.next() except StopIteration: self.cur_iter.reset() continue return ret ================================================ FILE: face_recognition/metric.py ================================================ import numpy as np import mxnet as mx class AccMetric(mx.metric.EvalMetric): def __init__(self): self.axis = 1 super(AccMetric, self).__init__( 'acc', axis=self.axis, output_names=None, label_names=None) self.losses = [] self.count = 0 def update(self, labels, preds): self.count+=1 label = labels[0] pred_label = preds[1] #print('ACC', label.shape, pred_label.shape) if pred_label.shape != label.shape: pred_label = mx.ndarray.argmax(pred_label, axis=self.axis) pred_label = pred_label.asnumpy().astype('int32').flatten() label = label.asnumpy() if label.ndim==2: label = label[:,0] label = label.astype('int32').flatten() assert label.shape==pred_label.shape self.sum_metric += (pred_label.flat == label.flat).sum() self.num_inst += len(pred_label.flat) class LossValueMetric(mx.metric.EvalMetric): def __init__(self): self.axis = 1 super(LossValueMetric, self).__init__( 'lossvalue', axis=self.axis, output_names=None, label_names=None) self.losses = [] def update(self, labels, preds): #label = labels[0].asnumpy() pred = preds[-1].asnumpy() #print('in loss', pred.shape) #print(pred) loss = pred[0] self.sum_metric += loss self.num_inst += 1.0 #gt_label = preds[-2].asnumpy() #print(gt_label) ================================================ FILE: face_recognition/models/MS1Mretina-polynet50-arcface/log_retina_r50newton_arcface.txt ================================================ gpu num: 8 prefix ./models/r50-arcface-retina/model image_size [112, 112] num_classes 93431 Called with argument: Namespace(batch_size=512, ckpt=3, ctx_num=8, ctx_num_classes=11679, dataset='retina', extra_model_name='', frequent=20, image_channel=3, kvstore='device', local_class_start=0, local_num_classes=93432, loss='arcface', lr=0.1, lr_steps='100000,160000,220000', models_root='./models', mom=0.9, network='r50', per_batch_size=64, pretrained='', pretrained_epoch=1, rescale_threshold=0, verbose=2000, wd=0.0005, worker_id=0) {'bn_mom': 0.9, 'workspace': 256, 'emb_size': 512, 'ckpt_embedding': True, 'net_se': 0, 'net_act': 'prelu', 'net_unit': 3, 'net_input': 1, 'net_blocks': [1, 4, 6, 2], 'net_output': 'E', 'net_multiplier': 1.0, 'val_targets': ['lfw', 'cfp_fp', 'agedb_30'], 'ce_loss': True, 'fc7_lr_mult': 1.0, 'fc7_wd_mult': 1.0, 'fc7_no_bias': False, 'max_steps': 0, 'data_rand_mirror': True, 'data_cutoff': False, 'data_color': 0, 'data_images_filter': 0, 'count_flops': True, 'memonger': False, 'loss_name': 'margin_softmax', 'loss_s': 64.0, 'loss_m1': 1.0, 'loss_m2': 0.5, 'loss_m3': 0.0, 'net_name': 'fresnet', 'num_layers': 50, 'dataset': 'retina', 'dataset_path': '/home/dengjiankang/datasets/ms1m-retinaface', 'num_classes': 93431, 'image_shape': [112, 112, 3], 'loss': 'arcface', 'network': 'r50', 'num_workers': 1, 'batch_size': 512, 'per_batch_size': 64} 0 1 E 3 prelu False Network FLOPs: 13.2G INFO:root:loading recordio /home/dengjiankang/datasets/ms1m-retinaface/train.rec... header0 label [5179511. 5272942.] id2range 93431 5179510 rand_mirror True loading bin 0 loading bin 1000 loading bin 2000 loading bin 3000 loading bin 4000 loading bin 5000 loading bin 6000 loading bin 7000 loading bin 8000 loading bin 9000 loading bin 10000 loading bin 11000 (12000, 3, 112, 112) ver lfw loading bin 0 loading bin 1000 loading bin 2000 loading bin 3000 loading bin 4000 loading bin 5000 loading bin 6000 loading bin 7000 loading bin 8000 loading bin 9000 loading bin 10000 loading bin 11000 loading bin 12000 loading bin 13000 (14000, 3, 112, 112) ver cfp_fp loading bin 0 loading bin 1000 loading bin 2000 loading bin 3000 loading bin 4000 loading bin 5000 loading bin 6000 loading bin 7000 loading bin 8000 loading bin 9000 loading bin 10000 loading bin 11000 (12000, 3, 112, 112) ver agedb_30 lr_steps [100000, 160000, 220000] call reset()in_bind False [('data', (512, 3, 112, 112))] [('softmax_label', (512,))] _data_shape (512, 3, 112, 112) [('softmax_label', (512,))] [05:01:09] src/kvstore/././comm.h:744: only 0 out of 56 GPU pairs are enabled direct access. It may affect the performance. You can set MXNET_ENABLE_GPU_P2P=0 to turn it off [05:01:09] src/kvstore/././comm.h:753: ........ [05:01:09] src/kvstore/././comm.h:753: ........ [05:01:09] src/kvstore/././comm.h:753: ........ [05:01:09] src/kvstore/././comm.h:753: ........ [05:01:09] src/kvstore/././comm.h:753: ........ [05:01:09] src/kvstore/././comm.h:753: ........ [05:01:09] src/kvstore/././comm.h:753: ........ [05:01:09] src/kvstore/././comm.h:753: ........ [05:01:09] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... 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(12000, 512) infer time 4.841412 [lfw][2000]XNorm: 18.523469 [lfw][2000]Accuracy-Flip: 0.89367+-0.01192 testing verification.. (14000, 512) infer time 5.474852 [cfp_fp][2000]XNorm: 17.399827 [cfp_fp][2000]Accuracy-Flip: 0.71286+-0.01161 testing verification.. (12000, 512) infer time 3.888363 [agedb_30][2000]XNorm: 17.571481 [agedb_30][2000]Accuracy-Flip: 0.65400+-0.01654 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [2000]Accuracy-Highest: 0.65400 INFO:root:Iter[0] Batch [2000] Speed: 258.75 samples/sec INFO:root:Iter[0] Batch [2020] Speed: 803.29 samples/sec INFO:root:Iter[0] Batch [2040] Speed: 807.90 samples/sec INFO:root:Iter[0] Batch [2060] Speed: 794.24 samples/sec INFO:root:Iter[0] Batch [2080] Speed: 816.99 samples/sec INFO:root:Iter[0] Batch [2100] Speed: 777.10 samples/sec INFO:root:Iter[0] Batch [2120] Speed: 663.56 samples/sec INFO:root:Iter[0] Batch [2140] Speed: 538.26 samples/sec INFO:root:Iter[0] Batch [2160] Speed: 531.56 samples/sec INFO:root:Iter[0] Batch [2180] Speed: 530.37 samples/sec INFO:root:Iter[0] Batch [2200] Speed: 550.65 samples/sec INFO:root:Iter[0] Batch [2220] Speed: 542.38 samples/sec INFO:root:Iter[0] Batch [2240] Speed: 513.84 samples/sec INFO:root:Iter[0] Batch 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0.001953125 lr-batch-epoch: 0.1 3999 0 testing verification.. (12000, 512) infer time 3.8892730000000006 [lfw][4000]XNorm: 23.257066 [lfw][4000]Accuracy-Flip: 0.97817+-0.00634 testing verification.. (14000, 512) infer time 4.4200940000000015 [cfp_fp][4000]XNorm: 20.015906 [cfp_fp][4000]Accuracy-Flip: 0.87514+-0.01527 testing verification.. (12000, 512) infer time 3.727984 [agedb_30][4000]XNorm: 22.074380 [agedb_30][4000]Accuracy-Flip: 0.85417+-0.02240 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [4000]Accuracy-Highest: 0.85417 INFO:root:Iter[0] Batch [4000] Speed: 254.13 samples/sec INFO:root:Iter[0] Batch [4020] Speed: 815.22 samples/sec INFO:root:Iter[0] Batch [4040] Speed: 817.85 samples/sec INFO:root:Iter[0] Batch [4060] Speed: 825.93 samples/sec INFO:root:Iter[0] Batch [4080] Speed: 826.41 samples/sec INFO:root:Iter[0] Batch [4100] Speed: 823.65 samples/sec INFO:root:Iter[0] Batch [4120] Speed: 826.77 samples/sec INFO:root:Iter[0] Batch [4140] Speed: 850.02 samples/sec INFO:root:Iter[0] Batch [4160] Speed: 828.30 samples/sec INFO:root:Iter[0] Batch [4180] Speed: 842.60 samples/sec INFO:root:Iter[0] Batch [4200] Speed: 838.72 samples/sec INFO:root:Iter[0] Batch [4220] Speed: 845.31 samples/sec INFO:root:Iter[0] Batch [4240] Speed: 843.87 samples/sec INFO:root:Iter[0] Batch 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0.02734375 lr-batch-epoch: 0.1 5999 0 testing verification.. (12000, 512) infer time 3.734949 [lfw][6000]XNorm: 22.328517 [lfw][6000]Accuracy-Flip: 0.99017+-0.00376 testing verification.. (14000, 512) infer time 4.430807000000001 [cfp_fp][6000]XNorm: 20.147548 [cfp_fp][6000]Accuracy-Flip: 0.91643+-0.01466 testing verification.. (12000, 512) infer time 4.029299000000001 [agedb_30][6000]XNorm: 21.609999 [agedb_30][6000]Accuracy-Flip: 0.90733+-0.01927 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [6000]Accuracy-Highest: 0.90733 INFO:root:Iter[0] Batch [6000] Speed: 271.42 samples/sec INFO:root:Iter[0] Batch [6020] Speed: 830.59 samples/sec INFO:root:Iter[0] Batch [6040] Speed: 829.11 samples/sec INFO:root:Iter[0] Batch [6060] Speed: 832.60 samples/sec INFO:root:Iter[0] Batch [6080] Speed: 808.61 samples/sec INFO:root:Iter[0] Batch [6100] Speed: 824.98 samples/sec INFO:root:Iter[0] Batch [6120] Speed: 796.80 samples/sec INFO:root:Iter[0] Batch [6140] Speed: 789.76 samples/sec INFO:root:Iter[0] Batch [6160] Speed: 715.62 samples/sec INFO:root:Iter[0] Batch [6180] Speed: 754.30 samples/sec INFO:root:Iter[0] Batch [6200] Speed: 758.06 samples/sec INFO:root:Iter[0] Batch [6220] Speed: 724.40 samples/sec INFO:root:Iter[0] Batch [6240] Speed: 702.80 samples/sec INFO:root:Iter[0] 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0.044921875 lr-batch-epoch: 0.1 7999 0 testing verification.. (12000, 512) infer time 4.798265999999999 [lfw][8000]XNorm: 22.355289 [lfw][8000]Accuracy-Flip: 0.99200+-0.00356 testing verification.. (14000, 512) infer time 5.348635999999999 [cfp_fp][8000]XNorm: 20.345150 [cfp_fp][8000]Accuracy-Flip: 0.93300+-0.01541 testing verification.. (12000, 512) infer time 3.733784 [agedb_30][8000]XNorm: 21.736653 [agedb_30][8000]Accuracy-Flip: 0.93000+-0.01558 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [8000]Accuracy-Highest: 0.93000 INFO:root:Iter[0] Batch [8000] Speed: 224.71 samples/sec INFO:root:Iter[0] Batch [8020] Speed: 832.34 samples/sec INFO:root:Iter[0] Batch [8040] Speed: 829.61 samples/sec INFO:root:Iter[0] Batch [8060] Speed: 818.94 samples/sec INFO:root:Iter[0] Batch [8080] Speed: 850.24 samples/sec INFO:root:Iter[0] Batch [8100] Speed: 840.75 samples/sec INFO:root:Iter[0] Batch [8120] Speed: 829.20 samples/sec INFO:root:Iter[0] Batch [8140] Speed: 830.21 samples/sec INFO:root:Iter[0] Batch [8160] Speed: 804.19 samples/sec INFO:root:Iter[0] Batch [8180] Speed: 782.53 samples/sec INFO:root:Iter[0] Batch [8200] Speed: 783.79 samples/sec INFO:root:Iter[0] Batch [8220] Speed: 736.52 samples/sec INFO:root:Iter[0] Batch [8240] Speed: 747.41 samples/sec INFO:root:Iter[0] Batch 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0.056640625 lr-batch-epoch: 0.1 9999 0 testing verification.. (12000, 512) infer time 4.606075000000001 [lfw][10000]XNorm: 22.007681 [lfw][10000]Accuracy-Flip: 0.99417+-0.00417 testing verification.. (14000, 512) infer time 5.531821999999999 [cfp_fp][10000]XNorm: 20.070973 [cfp_fp][10000]Accuracy-Flip: 0.94029+-0.01353 testing verification.. (12000, 512) infer time 4.114674 [agedb_30][10000]XNorm: 21.293229 [agedb_30][10000]Accuracy-Flip: 0.94233+-0.01319 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [10000]Accuracy-Highest: 0.94233 INFO:root:Iter[0] Batch [10000] Speed: 232.69 samples/sec INFO:root:Iter[0] Batch [10020] Speed: 852.31 samples/sec INFO:root:Iter[0] Batch [10040] Speed: 833.44 samples/sec INFO:root:Iter[0] Batch [10060] Speed: 826.87 samples/sec INFO:root:Iter[0] Batch [10080] Speed: 762.52 samples/sec INFO:root:Iter[0] Batch [10100] Speed: 803.08 samples/sec INFO:root:Epoch[0] Time cost=7414.997 call reset() INFO:root:Iter[1] Batch [20] Speed: 734.91 samples/sec INFO:root:Iter[1] Batch [40] Speed: 738.56 samples/sec INFO:root:Iter[1] Batch [60] Speed: 747.57 samples/sec INFO:root:Iter[1] Batch [80] Speed: 682.84 samples/sec INFO:root:Iter[1] Batch [100] Speed: 669.86 samples/sec INFO:root:Iter[1] Batch [120] Speed: 712.56 samples/sec INFO:root:Iter[1] Batch [140] 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{fc7_acc} 12000 0.064453125 lr-batch-epoch: 0.1 1883 1 testing verification.. (12000, 512) infer time 4.898198000000001 [lfw][12000]XNorm: 22.295786 [lfw][12000]Accuracy-Flip: 0.99500+-0.00342 testing verification.. (14000, 512) infer time 5.716751000000001 [cfp_fp][12000]XNorm: 20.151784 [cfp_fp][12000]Accuracy-Flip: 0.94714+-0.01171 testing verification.. 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0.060546875 lr-batch-epoch: 0.1 3883 1 testing verification.. (12000, 512) infer time 4.7930069999999985 [lfw][14000]XNorm: 22.113240 [lfw][14000]Accuracy-Flip: 0.99500+-0.00307 testing verification.. (14000, 512) infer time 5.646342999999999 [cfp_fp][14000]XNorm: 20.224656 [cfp_fp][14000]Accuracy-Flip: 0.94757+-0.00961 testing verification.. (12000, 512) infer time 4.297239 [agedb_30][14000]XNorm: 21.444361 [agedb_30][14000]Accuracy-Flip: 0.95533+-0.00983 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [14000]Accuracy-Highest: 0.95533 INFO:root:Iter[1] Batch [3900] Speed: 234.59 samples/sec INFO:root:Iter[1] Batch [3920] Speed: 704.59 samples/sec INFO:root:Iter[1] Batch [3940] Speed: 699.22 samples/sec INFO:root:Iter[1] Batch [3960] Speed: 729.53 samples/sec INFO:root:Iter[1] Batch [3980] Speed: 689.91 samples/sec INFO:root:Iter[1] Batch [4000] Speed: 678.71 samples/sec INFO:root:Iter[1] Batch [4020] Speed: 702.99 samples/sec INFO:root:Iter[1] Batch [4040] Speed: 631.42 samples/sec INFO:root:Iter[1] Batch [4060] Speed: 638.87 samples/sec INFO:root:Iter[1] Batch [4080] Speed: 609.54 samples/sec INFO:root:Iter[1] Batch [4100] Speed: 604.52 samples/sec INFO:root:Iter[1] Batch [4120] Speed: 621.41 samples/sec INFO:root:Iter[1] Batch [4140] Speed: 629.25 samples/sec INFO:root:Iter[1] Batch 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0.05859375 lr-batch-epoch: 0.1 5883 1 testing verification.. (12000, 512) infer time 4.644432999999999 [lfw][16000]XNorm: 22.302065 [lfw][16000]Accuracy-Flip: 0.99367+-0.00393 testing verification.. (14000, 512) infer time 5.554006000000001 [cfp_fp][16000]XNorm: 20.218698 [cfp_fp][16000]Accuracy-Flip: 0.94871+-0.01081 testing verification.. (12000, 512) infer time 4.482938999999999 [agedb_30][16000]XNorm: 21.908128 [agedb_30][16000]Accuracy-Flip: 0.95783+-0.00764 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [16000]Accuracy-Highest: 0.95783 INFO:root:Iter[1] Batch [5900] Speed: 247.22 samples/sec INFO:root:Iter[1] Batch [5920] Speed: 757.23 samples/sec INFO:root:Iter[1] Batch [5940] Speed: 683.70 samples/sec INFO:root:Iter[1] Batch [5960] Speed: 653.50 samples/sec INFO:root:Iter[1] Batch [5980] Speed: 644.56 samples/sec INFO:root:Iter[1] Batch [6000] Speed: 621.26 samples/sec INFO:root:Iter[1] Batch [6020] Speed: 621.47 samples/sec INFO:root:Iter[1] Batch [6040] Speed: 597.13 samples/sec INFO:root:Iter[1] Batch [6060] Speed: 606.55 samples/sec INFO:root:Iter[1] Batch [6080] Speed: 615.76 samples/sec INFO:root:Iter[1] Batch [6100] Speed: 605.37 samples/sec INFO:root:Iter[1] Batch [6120] Speed: 624.99 samples/sec INFO:root:Iter[1] Batch [6140] Speed: 601.21 samples/sec 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{fc7_acc} 18000 0.078125 lr-batch-epoch: 0.1 7883 1 testing verification.. (12000, 512) infer time 4.825372 [lfw][18000]XNorm: 23.149973 [lfw][18000]Accuracy-Flip: 0.99500+-0.00316 testing verification.. (14000, 512) infer time 5.187598000000001 [cfp_fp][18000]XNorm: 21.119774 [cfp_fp][18000]Accuracy-Flip: 0.94486+-0.01036 testing verification.. (12000, 512) infer time 4.3640289999999995 [agedb_30][18000]XNorm: 22.523933 [agedb_30][18000]Accuracy-Flip: 0.95467+-0.00994 [18000]Accuracy-Highest: 0.95783 INFO:root:Iter[1] Batch [7900] Speed: 239.39 samples/sec INFO:root:Iter[1] Batch [7920] Speed: 696.87 samples/sec INFO:root:Iter[1] Batch [7940] Speed: 648.38 samples/sec INFO:root:Iter[1] Batch [7960] Speed: 660.44 samples/sec INFO:root:Iter[1] Batch [7980] Speed: 690.09 samples/sec INFO:root:Iter[1] Batch [8000] Speed: 593.65 samples/sec INFO:root:Iter[1] Batch [8020] Speed: 606.86 samples/sec INFO:root:Iter[1] Batch [8040] Speed: 631.02 samples/sec INFO:root:Iter[1] Batch [8060] Speed: 612.15 samples/sec INFO:root:Iter[1] Batch [8080] Speed: 582.09 samples/sec INFO:root:Iter[1] Batch [8100] Speed: 579.10 samples/sec INFO:root:Iter[1] Batch [8120] Speed: 582.92 samples/sec INFO:root:Iter[1] Batch [8140] Speed: 635.35 samples/sec INFO:root:Iter[1] Batch [8160] Speed: 584.87 samples/sec INFO:root:Iter[1] Batch [8180] Speed: 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(12000, 512) infer time 4.868860999999999 [lfw][20000]XNorm: 23.296230 [lfw][20000]Accuracy-Flip: 0.99483+-0.00345 testing verification.. (14000, 512) infer time 4.578794 [cfp_fp][20000]XNorm: 21.180500 [cfp_fp][20000]Accuracy-Flip: 0.94971+-0.01320 testing verification.. (12000, 512) infer time 3.808032 [agedb_30][20000]XNorm: 22.936576 [agedb_30][20000]Accuracy-Flip: 0.95967+-0.00921 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [20000]Accuracy-Highest: 0.95967 INFO:root:Iter[1] Batch [9900] Speed: 259.71 samples/sec INFO:root:Iter[1] Batch [9920] Speed: 813.11 samples/sec INFO:root:Iter[1] Batch [9940] Speed: 695.49 samples/sec INFO:root:Iter[1] Batch [9960] Speed: 589.49 samples/sec INFO:root:Iter[1] Batch [9980] Speed: 625.93 samples/sec INFO:root:Iter[1] Batch [10000] Speed: 609.72 samples/sec INFO:root:Iter[1] Batch [10020] Speed: 619.71 samples/sec INFO:root:Iter[1] Batch [10040] Speed: 613.88 samples/sec INFO:root:Iter[1] Batch [10060] Speed: 597.30 samples/sec INFO:root:Iter[1] Batch [10080] Speed: 606.05 samples/sec INFO:root:Iter[1] Batch [10100] Speed: 597.66 samples/sec INFO:root:Epoch[1] Time cost=8335.413 call reset() INFO:root:Iter[2] Batch [20] Speed: 663.60 samples/sec INFO:root:Iter[2] Batch 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verification.. (12000, 512) infer time 4.725538999999999 [lfw][22000]XNorm: 22.746689 [lfw][22000]Accuracy-Flip: 0.99583+-0.00300 testing verification.. (14000, 512) infer time 5.467440000000002 [cfp_fp][22000]XNorm: 20.407138 [cfp_fp][22000]Accuracy-Flip: 0.95871+-0.01413 testing verification.. (12000, 512) infer time 4.3162590000000005 [agedb_30][22000]XNorm: 22.623622 [agedb_30][22000]Accuracy-Flip: 0.96183+-0.01004 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [22000]Accuracy-Highest: 0.96183 INFO:root:Iter[2] Batch [1780] Speed: 246.31 samples/sec INFO:root:Iter[2] Batch [1800] Speed: 676.48 samples/sec INFO:root:Iter[2] Batch [1820] Speed: 633.35 samples/sec INFO:root:Iter[2] Batch [1840] Speed: 598.07 samples/sec INFO:root:Iter[2] Batch [1860] Speed: 587.19 samples/sec INFO:root:Iter[2] Batch [1880] Speed: 604.09 samples/sec INFO:root:Iter[2] Batch [1900] Speed: 633.83 samples/sec INFO:root:Iter[2] Batch [1920] Speed: 615.23 samples/sec INFO:root:Iter[2] Batch [1940] Speed: 596.03 samples/sec INFO:root:Iter[2] Batch [1960] Speed: 592.33 samples/sec INFO:root:Iter[2] Batch [1980] Speed: 590.61 samples/sec INFO:root:Iter[2] Batch [2000] Speed: 596.36 samples/sec INFO:root:Iter[2] Batch [2020] Speed: 609.37 samples/sec 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{fc7_acc} 24000 0.11328125 lr-batch-epoch: 0.1 3767 2 testing verification.. (12000, 512) infer time 4.962007999999999 [lfw][24000]XNorm: 22.515475 [lfw][24000]Accuracy-Flip: 0.99333+-0.00394 testing verification.. (14000, 512) infer time 5.588909 [cfp_fp][24000]XNorm: 20.317678 [cfp_fp][24000]Accuracy-Flip: 0.95243+-0.01401 testing verification.. (12000, 512) infer time 4.145109999999999 [agedb_30][24000]XNorm: 22.217995 [agedb_30][24000]Accuracy-Flip: 0.95700+-0.01222 [24000]Accuracy-Highest: 0.96183 INFO:root:Iter[2] Batch [3780] Speed: 256.90 samples/sec INFO:root:Iter[2] Batch [3800] Speed: 744.34 samples/sec INFO:root:Iter[2] Batch [3820] Speed: 710.00 samples/sec INFO:root:Iter[2] Batch [3840] Speed: 672.80 samples/sec INFO:root:Iter[2] Batch [3860] Speed: 698.92 samples/sec INFO:root:Iter[2] Batch [3880] Speed: 667.37 samples/sec INFO:root:Iter[2] Batch [3900] Speed: 652.75 samples/sec INFO:root:Iter[2] Batch [3920] Speed: 677.30 samples/sec INFO:root:Iter[2] Batch [3940] Speed: 691.03 samples/sec INFO:root:Iter[2] Batch [3960] Speed: 709.45 samples/sec INFO:root:Iter[2] Batch [3980] Speed: 654.86 samples/sec INFO:root:Iter[2] Batch [4000] Speed: 658.33 samples/sec INFO:root:Iter[2] Batch [4020] Speed: 628.70 samples/sec INFO:root:Iter[2] Batch [4040] Speed: 628.72 samples/sec INFO:root:Iter[2] Batch [4060] Speed: 689.00 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(12000, 512) infer time 4.774698 [lfw][26000]XNorm: 22.003539 [lfw][26000]Accuracy-Flip: 0.99483+-0.00376 testing verification.. (14000, 512) infer time 5.429288 [cfp_fp][26000]XNorm: 19.619027 [cfp_fp][26000]Accuracy-Flip: 0.95143+-0.01346 testing verification.. (12000, 512) infer time 4.688038999999999 [agedb_30][26000]XNorm: 21.687001 [agedb_30][26000]Accuracy-Flip: 0.95900+-0.01221 [26000]Accuracy-Highest: 0.96183 INFO:root:Iter[2] Batch [5780] Speed: 250.94 samples/sec INFO:root:Iter[2] Batch [5800] Speed: 713.31 samples/sec INFO:root:Iter[2] Batch [5820] Speed: 697.54 samples/sec INFO:root:Iter[2] Batch [5840] Speed: 678.42 samples/sec INFO:root:Iter[2] Batch [5860] Speed: 655.14 samples/sec INFO:root:Iter[2] Batch [5880] Speed: 622.79 samples/sec INFO:root:Iter[2] Batch [5900] Speed: 595.00 samples/sec INFO:root:Iter[2] Batch [5920] Speed: 615.00 samples/sec INFO:root:Iter[2] Batch [5940] Speed: 587.69 samples/sec INFO:root:Iter[2] Batch [5960] Speed: 579.74 samples/sec INFO:root:Iter[2] Batch [5980] Speed: 598.34 samples/sec INFO:root:Iter[2] Batch [6000] Speed: 589.52 samples/sec INFO:root:Iter[2] Batch [6020] Speed: 579.19 samples/sec INFO:root:Iter[2] Batch [6040] Speed: 588.38 samples/sec INFO:root:Iter[2] Batch [6060] Speed: 613.76 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(12000, 512) infer time 4.871786999999999 [lfw][28000]XNorm: 21.879433 [lfw][28000]Accuracy-Flip: 0.99567+-0.00291 testing verification.. (14000, 512) infer time 5.464368999999999 [cfp_fp][28000]XNorm: 19.584085 [cfp_fp][28000]Accuracy-Flip: 0.95800+-0.01201 testing verification.. (12000, 512) infer time 4.288126999999999 [agedb_30][28000]XNorm: 21.389323 [agedb_30][28000]Accuracy-Flip: 0.96383+-0.00891 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [28000]Accuracy-Highest: 0.96383 INFO:root:Iter[2] Batch [7780] Speed: 254.44 samples/sec INFO:root:Iter[2] Batch [7800] Speed: 700.19 samples/sec INFO:root:Iter[2] Batch [7820] Speed: 684.50 samples/sec INFO:root:Iter[2] Batch [7840] Speed: 668.50 samples/sec INFO:root:Iter[2] Batch [7860] Speed: 687.52 samples/sec INFO:root:Iter[2] Batch [7880] Speed: 702.14 samples/sec INFO:root:Iter[2] Batch [7900] Speed: 727.88 samples/sec INFO:root:Iter[2] Batch [7920] Speed: 738.92 samples/sec INFO:root:Iter[2] Batch [7940] Speed: 729.91 samples/sec INFO:root:Iter[2] Batch [7960] Speed: 740.86 samples/sec INFO:root:Iter[2] Batch [7980] Speed: 685.65 samples/sec INFO:root:Iter[2] Batch [8000] Speed: 693.87 samples/sec INFO:root:Iter[2] Batch [8020] Speed: 753.02 samples/sec 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{fc7_acc} 30000 0.076171875 lr-batch-epoch: 0.1 9767 2 testing verification.. (12000, 512) infer time 4.890932000000001 [lfw][30000]XNorm: 22.448510 [lfw][30000]Accuracy-Flip: 0.99583+-0.00250 testing verification.. (14000, 512) infer time 5.398857000000001 [cfp_fp][30000]XNorm: 19.899113 [cfp_fp][30000]Accuracy-Flip: 0.96314+-0.00901 testing verification.. (12000, 512) infer time 4.727556 [agedb_30][30000]XNorm: 22.269962 [agedb_30][30000]Accuracy-Flip: 0.96233+-0.01123 [30000]Accuracy-Highest: 0.96383 INFO:root:Iter[2] Batch [9780] Speed: 253.53 samples/sec INFO:root:Iter[2] Batch [9800] Speed: 653.63 samples/sec INFO:root:Iter[2] Batch [9820] Speed: 653.07 samples/sec INFO:root:Iter[2] Batch [9840] Speed: 690.20 samples/sec INFO:root:Iter[2] Batch [9860] Speed: 651.14 samples/sec INFO:root:Iter[2] Batch [9880] Speed: 675.61 samples/sec INFO:root:Iter[2] Batch [9900] Speed: 635.59 samples/sec INFO:root:Iter[2] Batch [9920] Speed: 590.14 samples/sec INFO:root:Iter[2] Batch [9940] Speed: 591.82 samples/sec INFO:root:Iter[2] Batch [9960] Speed: 580.82 samples/sec INFO:root:Iter[2] Batch [9980] Speed: 597.35 samples/sec INFO:root:Iter[2] Batch [10000] Speed: 585.11 samples/sec INFO:root:Iter[2] Batch [10020] Speed: 604.40 samples/sec INFO:root:Iter[2] Batch [10040] Speed: 593.27 samples/sec INFO:root:Iter[2] Batch [10060] Speed: 582.71 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(12000, 512) infer time 4.734977 [lfw][32000]XNorm: 22.637648 [lfw][32000]Accuracy-Flip: 0.99617+-0.00198 testing verification.. (14000, 512) infer time 5.768416000000001 [cfp_fp][32000]XNorm: 20.503982 [cfp_fp][32000]Accuracy-Flip: 0.95471+-0.01197 testing verification.. (12000, 512) infer time 4.381756000000001 [agedb_30][32000]XNorm: 22.300112 [agedb_30][32000]Accuracy-Flip: 0.96233+-0.01116 [32000]Accuracy-Highest: 0.96383 INFO:root:Iter[3] Batch [1660] Speed: 245.89 samples/sec INFO:root:Iter[3] Batch [1680] Speed: 694.82 samples/sec INFO:root:Iter[3] Batch [1700] Speed: 680.23 samples/sec INFO:root:Iter[3] Batch [1720] Speed: 641.97 samples/sec INFO:root:Iter[3] Batch [1740] Speed: 632.02 samples/sec INFO:root:Iter[3] Batch [1760] Speed: 670.64 samples/sec INFO:root:Iter[3] Batch [1780] Speed: 664.55 samples/sec INFO:root:Iter[3] Batch [1800] Speed: 570.41 samples/sec INFO:root:Iter[3] Batch [1820] Speed: 643.97 samples/sec INFO:root:Iter[3] Batch [1840] Speed: 601.03 samples/sec INFO:root:Iter[3] Batch [1860] Speed: 591.94 samples/sec INFO:root:Iter[3] Batch [1880] Speed: 631.79 samples/sec INFO:root:Iter[3] Batch [1900] Speed: 576.86 samples/sec INFO:root:Iter[3] Batch [1920] Speed: 603.66 samples/sec INFO:root:Iter[3] Batch [1940] Speed: 646.98 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(12000, 512) infer time 4.606083999999999 [lfw][34000]XNorm: 22.634147 [lfw][34000]Accuracy-Flip: 0.99633+-0.00277 testing verification.. (14000, 512) infer time 5.361383 [cfp_fp][34000]XNorm: 20.187350 [cfp_fp][34000]Accuracy-Flip: 0.95600+-0.00885 testing verification.. (12000, 512) infer time 4.360931000000001 [agedb_30][34000]XNorm: 22.442505 [agedb_30][34000]Accuracy-Flip: 0.96617+-0.00746 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [34000]Accuracy-Highest: 0.96617 INFO:root:Iter[3] Batch [3660] Speed: 249.27 samples/sec INFO:root:Iter[3] Batch [3680] Speed: 746.09 samples/sec INFO:root:Iter[3] Batch [3700] Speed: 657.22 samples/sec INFO:root:Iter[3] Batch [3720] Speed: 637.64 samples/sec INFO:root:Iter[3] Batch [3740] Speed: 599.38 samples/sec INFO:root:Iter[3] Batch [3760] Speed: 581.59 samples/sec INFO:root:Iter[3] Batch [3780] Speed: 577.96 samples/sec INFO:root:Iter[3] Batch [3800] Speed: 595.29 samples/sec INFO:root:Iter[3] Batch [3820] Speed: 582.04 samples/sec INFO:root:Iter[3] Batch [3840] Speed: 601.86 samples/sec INFO:root:Iter[3] Batch [3860] Speed: 624.82 samples/sec INFO:root:Iter[3] Batch [3880] Speed: 602.42 samples/sec INFO:root:Iter[3] Batch [3900] Speed: 611.90 samples/sec 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{fc7_acc} 36000 0.109375 lr-batch-epoch: 0.1 5651 3 testing verification.. (12000, 512) infer time 4.916956000000001 [lfw][36000]XNorm: 23.379338 [lfw][36000]Accuracy-Flip: 0.99617+-0.00198 testing verification.. (14000, 512) infer time 5.017122999999999 [cfp_fp][36000]XNorm: 21.225156 [cfp_fp][36000]Accuracy-Flip: 0.95486+-0.00924 testing verification.. (12000, 512) infer time 3.730208 [agedb_30][36000]XNorm: 23.175668 [agedb_30][36000]Accuracy-Flip: 0.96150+-0.00886 [36000]Accuracy-Highest: 0.96617 INFO:root:Iter[3] Batch [5660] Speed: 264.82 samples/sec INFO:root:Iter[3] Batch [5680] Speed: 709.99 samples/sec INFO:root:Iter[3] Batch [5700] Speed: 729.75 samples/sec INFO:root:Iter[3] Batch [5720] Speed: 709.60 samples/sec INFO:root:Iter[3] Batch [5740] Speed: 717.84 samples/sec INFO:root:Iter[3] Batch [5760] Speed: 737.12 samples/sec INFO:root:Iter[3] Batch [5780] Speed: 678.15 samples/sec INFO:root:Iter[3] Batch [5800] Speed: 689.04 samples/sec INFO:root:Iter[3] Batch [5820] Speed: 713.31 samples/sec INFO:root:Iter[3] Batch [5840] Speed: 677.56 samples/sec INFO:root:Iter[3] Batch [5860] Speed: 714.71 samples/sec INFO:root:Iter[3] Batch [5880] Speed: 749.11 samples/sec INFO:root:Iter[3] Batch [5900] Speed: 726.57 samples/sec INFO:root:Iter[3] Batch [5920] Speed: 738.44 samples/sec INFO:root:Iter[3] Batch [5940] Speed: 738.66 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(12000, 512) infer time 4.768552000000001 [lfw][38000]XNorm: 21.701570 [lfw][38000]Accuracy-Flip: 0.99467+-0.00287 testing verification.. (14000, 512) infer time 5.669459000000002 [cfp_fp][38000]XNorm: 19.931105 [cfp_fp][38000]Accuracy-Flip: 0.95557+-0.01066 testing verification.. (12000, 512) infer time 4.674008999999999 [agedb_30][38000]XNorm: 21.425217 [agedb_30][38000]Accuracy-Flip: 0.96333+-0.00931 [38000]Accuracy-Highest: 0.96617 INFO:root:Iter[3] Batch [7660] Speed: 260.14 samples/sec INFO:root:Iter[3] Batch [7680] Speed: 746.82 samples/sec INFO:root:Iter[3] Batch [7700] Speed: 732.82 samples/sec INFO:root:Iter[3] Batch [7720] Speed: 729.83 samples/sec INFO:root:Iter[3] Batch [7740] Speed: 721.00 samples/sec INFO:root:Iter[3] Batch [7760] Speed: 713.14 samples/sec INFO:root:Iter[3] Batch [7780] Speed: 722.21 samples/sec INFO:root:Iter[3] Batch [7800] Speed: 707.35 samples/sec INFO:root:Iter[3] Batch [7820] Speed: 693.80 samples/sec INFO:root:Iter[3] Batch [7840] Speed: 728.00 samples/sec INFO:root:Iter[3] Batch [7860] Speed: 681.08 samples/sec INFO:root:Iter[3] Batch [7880] Speed: 722.25 samples/sec INFO:root:Iter[3] Batch [7900] Speed: 670.21 samples/sec INFO:root:Iter[3] Batch [7920] Speed: 717.60 samples/sec INFO:root:Iter[3] Batch [7940] Speed: 691.70 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(12000, 512) infer time 4.503161999999998 [lfw][40000]XNorm: 22.101674 [lfw][40000]Accuracy-Flip: 0.99683+-0.00174 testing verification.. (14000, 512) infer time 5.522584 [cfp_fp][40000]XNorm: 19.765480 [cfp_fp][40000]Accuracy-Flip: 0.95214+-0.01201 testing verification.. (12000, 512) infer time 4.810084 [agedb_30][40000]XNorm: 21.644162 [agedb_30][40000]Accuracy-Flip: 0.96000+-0.00679 [40000]Accuracy-Highest: 0.96617 INFO:root:Iter[3] Batch [9660] Speed: 252.79 samples/sec INFO:root:Iter[3] Batch [9680] Speed: 764.65 samples/sec INFO:root:Iter[3] Batch [9700] Speed: 702.79 samples/sec INFO:root:Iter[3] Batch [9720] Speed: 641.11 samples/sec INFO:root:Iter[3] Batch [9740] Speed: 668.92 samples/sec INFO:root:Iter[3] Batch [9760] Speed: 700.32 samples/sec INFO:root:Iter[3] Batch [9780] Speed: 681.39 samples/sec INFO:root:Iter[3] Batch [9800] Speed: 693.24 samples/sec INFO:root:Iter[3] Batch [9820] Speed: 672.04 samples/sec INFO:root:Iter[3] Batch [9840] Speed: 684.55 samples/sec INFO:root:Iter[3] Batch [9860] Speed: 705.62 samples/sec INFO:root:Iter[3] Batch [9880] Speed: 705.34 samples/sec INFO:root:Iter[3] Batch [9900] Speed: 652.62 samples/sec INFO:root:Iter[3] Batch [9920] Speed: 646.97 samples/sec INFO:root:Iter[3] Batch [9940] Speed: 699.43 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(12000, 512) infer time 4.85494 [lfw][42000]XNorm: 23.056007 [lfw][42000]Accuracy-Flip: 0.99567+-0.00343 testing verification.. (14000, 512) infer time 5.740873000000001 [cfp_fp][42000]XNorm: 20.478953 [cfp_fp][42000]Accuracy-Flip: 0.95786+-0.00937 testing verification.. (12000, 512) infer time 4.565226999999999 [agedb_30][42000]XNorm: 22.902919 [agedb_30][42000]Accuracy-Flip: 0.96333+-0.00983 [42000]Accuracy-Highest: 0.96617 INFO:root:Iter[4] Batch [1540] Speed: 244.33 samples/sec INFO:root:Iter[4] Batch [1560] Speed: 720.75 samples/sec INFO:root:Iter[4] Batch [1580] Speed: 732.19 samples/sec INFO:root:Iter[4] Batch [1600] Speed: 734.91 samples/sec INFO:root:Iter[4] Batch [1620] Speed: 731.75 samples/sec INFO:root:Iter[4] Batch [1640] Speed: 713.34 samples/sec INFO:root:Iter[4] Batch [1660] Speed: 707.02 samples/sec INFO:root:Iter[4] Batch [1680] Speed: 690.41 samples/sec INFO:root:Iter[4] Batch [1700] Speed: 703.15 samples/sec INFO:root:Iter[4] Batch [1720] Speed: 719.68 samples/sec INFO:root:Iter[4] Batch [1740] Speed: 659.91 samples/sec INFO:root:Iter[4] Batch [1760] Speed: 730.54 samples/sec INFO:root:Iter[4] Batch [1780] Speed: 654.12 samples/sec INFO:root:Iter[4] Batch [1800] Speed: 637.16 samples/sec INFO:root:Iter[4] Batch [1820] Speed: 653.01 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(12000, 512) infer time 4.957928999999999 [lfw][44000]XNorm: 22.441999 [lfw][44000]Accuracy-Flip: 0.99517+-0.00462 testing verification.. (14000, 512) infer time 5.646681000000001 [cfp_fp][44000]XNorm: 19.973483 [cfp_fp][44000]Accuracy-Flip: 0.95871+-0.00916 testing verification.. (12000, 512) infer time 4.3998539999999995 [agedb_30][44000]XNorm: 22.337421 [agedb_30][44000]Accuracy-Flip: 0.96383+-0.01011 [44000]Accuracy-Highest: 0.96617 INFO:root:Iter[4] Batch [3540] Speed: 259.27 samples/sec INFO:root:Iter[4] Batch [3560] Speed: 756.99 samples/sec INFO:root:Iter[4] Batch [3580] Speed: 740.12 samples/sec INFO:root:Iter[4] Batch [3600] Speed: 683.67 samples/sec INFO:root:Iter[4] Batch [3620] Speed: 706.82 samples/sec INFO:root:Iter[4] Batch [3640] Speed: 659.39 samples/sec INFO:root:Iter[4] Batch [3660] Speed: 609.70 samples/sec INFO:root:Iter[4] Batch [3680] Speed: 611.43 samples/sec INFO:root:Iter[4] Batch [3700] Speed: 654.61 samples/sec INFO:root:Iter[4] Batch [3720] Speed: 651.19 samples/sec INFO:root:Iter[4] Batch [3740] Speed: 679.30 samples/sec INFO:root:Iter[4] Batch [3760] Speed: 735.90 samples/sec INFO:root:Iter[4] Batch [3780] Speed: 642.95 samples/sec INFO:root:Iter[4] Batch [3800] Speed: 630.91 samples/sec INFO:root:Iter[4] Batch [3820] Speed: 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(12000, 512) infer time 4.8697940000000015 [lfw][46000]XNorm: 22.455550 [lfw][46000]Accuracy-Flip: 0.99550+-0.00269 testing verification.. (14000, 512) infer time 5.620071 [cfp_fp][46000]XNorm: 20.206844 [cfp_fp][46000]Accuracy-Flip: 0.95614+-0.01114 testing verification.. (12000, 512) infer time 4.988168 [agedb_30][46000]XNorm: 22.083596 [agedb_30][46000]Accuracy-Flip: 0.96500+-0.00946 [46000]Accuracy-Highest: 0.96617 INFO:root:Iter[4] Batch [5540] Speed: 254.38 samples/sec INFO:root:Iter[4] Batch [5560] Speed: 684.06 samples/sec INFO:root:Iter[4] Batch [5580] Speed: 665.34 samples/sec INFO:root:Iter[4] Batch [5600] Speed: 707.74 samples/sec INFO:root:Iter[4] Batch [5620] Speed: 720.96 samples/sec INFO:root:Iter[4] Batch [5640] Speed: 719.68 samples/sec INFO:root:Iter[4] Batch [5660] Speed: 708.72 samples/sec INFO:root:Iter[4] Batch [5680] Speed: 676.25 samples/sec INFO:root:Iter[4] Batch [5700] Speed: 715.44 samples/sec INFO:root:Iter[4] Batch [5720] Speed: 706.67 samples/sec INFO:root:Iter[4] Batch [5740] Speed: 691.48 samples/sec INFO:root:Iter[4] Batch [5760] Speed: 694.01 samples/sec INFO:root:Iter[4] Batch [5780] Speed: 678.54 samples/sec INFO:root:Iter[4] Batch [5800] Speed: 715.69 samples/sec INFO:root:Iter[4] Batch [5820] Speed: 686.40 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(12000, 512) infer time 4.962359999999999 [lfw][48000]XNorm: 22.483017 [lfw][48000]Accuracy-Flip: 0.99550+-0.00308 testing verification.. (14000, 512) infer time 5.7092160000000005 [cfp_fp][48000]XNorm: 20.406360 [cfp_fp][48000]Accuracy-Flip: 0.95871+-0.00889 testing verification.. (12000, 512) infer time 4.761514 [agedb_30][48000]XNorm: 22.303764 [agedb_30][48000]Accuracy-Flip: 0.96600+-0.00750 [48000]Accuracy-Highest: 0.96617 INFO:root:Iter[4] Batch [7540] Speed: 248.29 samples/sec INFO:root:Iter[4] Batch [7560] Speed: 683.82 samples/sec INFO:root:Iter[4] Batch [7580] Speed: 712.96 samples/sec INFO:root:Iter[4] Batch [7600] Speed: 672.83 samples/sec INFO:root:Iter[4] Batch [7620] Speed: 627.44 samples/sec INFO:root:Iter[4] Batch [7640] Speed: 664.65 samples/sec INFO:root:Iter[4] Batch [7660] Speed: 677.23 samples/sec INFO:root:Iter[4] Batch [7680] Speed: 666.47 samples/sec INFO:root:Iter[4] Batch [7700] Speed: 658.71 samples/sec INFO:root:Iter[4] Batch [7720] Speed: 738.68 samples/sec INFO:root:Iter[4] Batch [7740] Speed: 647.82 samples/sec INFO:root:Iter[4] Batch [7760] Speed: 720.32 samples/sec INFO:root:Iter[4] Batch [7780] Speed: 733.34 samples/sec INFO:root:Iter[4] Batch [7800] Speed: 717.36 samples/sec INFO:root:Iter[4] Batch [7820] Speed: 680.76 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(12000, 512) infer time 4.890999 [lfw][50000]XNorm: 21.896787 [lfw][50000]Accuracy-Flip: 0.99467+-0.00332 testing verification.. (14000, 512) infer time 5.648433000000001 [cfp_fp][50000]XNorm: 19.663834 [cfp_fp][50000]Accuracy-Flip: 0.95314+-0.01176 testing verification.. (12000, 512) infer time 4.636121000000001 [agedb_30][50000]XNorm: 21.633498 [agedb_30][50000]Accuracy-Flip: 0.96050+-0.01075 [50000]Accuracy-Highest: 0.96617 INFO:root:Iter[4] Batch [9540] Speed: 251.16 samples/sec INFO:root:Iter[4] Batch [9560] Speed: 739.05 samples/sec INFO:root:Iter[4] Batch [9580] Speed: 746.25 samples/sec INFO:root:Iter[4] Batch [9600] Speed: 688.87 samples/sec INFO:root:Iter[4] Batch [9620] Speed: 646.77 samples/sec INFO:root:Iter[4] Batch [9640] Speed: 741.11 samples/sec INFO:root:Iter[4] Batch [9660] Speed: 681.17 samples/sec INFO:root:Iter[4] Batch [9680] Speed: 702.98 samples/sec INFO:root:Iter[4] Batch [9700] Speed: 760.69 samples/sec INFO:root:Iter[4] Batch [9720] Speed: 686.17 samples/sec INFO:root:Iter[4] Batch [9740] Speed: 643.49 samples/sec INFO:root:Iter[4] Batch [9760] Speed: 675.41 samples/sec INFO:root:Iter[4] Batch [9780] Speed: 706.15 samples/sec INFO:root:Iter[4] Batch [9800] Speed: 722.89 samples/sec INFO:root:Iter[4] Batch [9820] Speed: 746.83 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(12000, 512) infer time 4.821573 [lfw][52000]XNorm: 22.992716 [lfw][52000]Accuracy-Flip: 0.99550+-0.00334 testing verification.. (14000, 512) infer time 5.523978999999999 [cfp_fp][52000]XNorm: 20.518771 [cfp_fp][52000]Accuracy-Flip: 0.96271+-0.00946 testing verification.. (12000, 512) infer time 4.667565999999999 [agedb_30][52000]XNorm: 22.412810 [agedb_30][52000]Accuracy-Flip: 0.96483+-0.00851 [52000]Accuracy-Highest: 0.96617 INFO:root:Iter[5] Batch [1420] Speed: 260.35 samples/sec INFO:root:Iter[5] Batch [1440] Speed: 705.83 samples/sec INFO:root:Iter[5] Batch [1460] Speed: 663.50 samples/sec INFO:root:Iter[5] Batch [1480] Speed: 655.93 samples/sec INFO:root:Iter[5] Batch [1500] Speed: 662.17 samples/sec INFO:root:Iter[5] Batch [1520] Speed: 669.20 samples/sec INFO:root:Iter[5] Batch [1540] Speed: 680.43 samples/sec INFO:root:Iter[5] Batch [1560] Speed: 674.15 samples/sec INFO:root:Iter[5] Batch [1580] Speed: 710.38 samples/sec INFO:root:Iter[5] Batch [1600] Speed: 706.43 samples/sec INFO:root:Iter[5] Batch [1620] Speed: 743.47 samples/sec INFO:root:Iter[5] Batch [1640] Speed: 645.52 samples/sec INFO:root:Iter[5] Batch [1660] Speed: 663.84 samples/sec INFO:root:Iter[5] Batch [1680] Speed: 671.56 samples/sec INFO:root:Iter[5] Batch [1700] Speed: 696.93 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(12000, 512) infer time 4.824230000000001 [lfw][54000]XNorm: 22.194393 [lfw][54000]Accuracy-Flip: 0.99667+-0.00224 testing verification.. (14000, 512) infer time 5.711121 [cfp_fp][54000]XNorm: 20.143004 [cfp_fp][54000]Accuracy-Flip: 0.95914+-0.01058 testing verification.. (12000, 512) infer time 4.587525000000001 [agedb_30][54000]XNorm: 21.901776 [agedb_30][54000]Accuracy-Flip: 0.96333+-0.00753 [54000]Accuracy-Highest: 0.96617 INFO:root:Iter[5] Batch [3420] Speed: 256.47 samples/sec INFO:root:Iter[5] Batch [3440] Speed: 662.45 samples/sec INFO:root:Iter[5] Batch [3460] Speed: 763.35 samples/sec INFO:root:Iter[5] Batch [3480] Speed: 678.94 samples/sec INFO:root:Iter[5] Batch [3500] Speed: 696.10 samples/sec INFO:root:Iter[5] Batch [3520] Speed: 670.90 samples/sec INFO:root:Iter[5] Batch [3540] Speed: 660.68 samples/sec INFO:root:Iter[5] Batch [3560] Speed: 690.22 samples/sec INFO:root:Iter[5] Batch [3580] Speed: 657.05 samples/sec INFO:root:Iter[5] Batch [3600] Speed: 680.12 samples/sec INFO:root:Iter[5] Batch [3620] Speed: 625.61 samples/sec INFO:root:Iter[5] Batch [3640] Speed: 711.11 samples/sec INFO:root:Iter[5] Batch [3660] Speed: 627.30 samples/sec INFO:root:Iter[5] Batch [3680] Speed: 667.05 samples/sec INFO:root:Iter[5] Batch [3700] Speed: 667.51 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(12000, 512) infer time 4.629894 [lfw][56000]XNorm: 22.606347 [lfw][56000]Accuracy-Flip: 0.99600+-0.00403 testing verification.. (14000, 512) infer time 5.317968999999999 [cfp_fp][56000]XNorm: 20.260585 [cfp_fp][56000]Accuracy-Flip: 0.95743+-0.01047 testing verification.. (12000, 512) infer time 4.739341999999999 [agedb_30][56000]XNorm: 22.605472 [agedb_30][56000]Accuracy-Flip: 0.96967+-0.00770 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [56000]Accuracy-Highest: 0.96967 INFO:root:Iter[5] Batch [5420] Speed: 242.92 samples/sec INFO:root:Iter[5] Batch [5440] Speed: 795.35 samples/sec INFO:root:Iter[5] Batch [5460] Speed: 751.39 samples/sec INFO:root:Iter[5] Batch [5480] Speed: 709.02 samples/sec INFO:root:Iter[5] Batch [5500] Speed: 657.94 samples/sec INFO:root:Iter[5] Batch [5520] Speed: 670.26 samples/sec INFO:root:Iter[5] Batch [5540] Speed: 644.64 samples/sec INFO:root:Iter[5] Batch [5560] Speed: 668.53 samples/sec INFO:root:Iter[5] Batch [5580] Speed: 659.76 samples/sec INFO:root:Iter[5] Batch [5600] Speed: 672.12 samples/sec INFO:root:Iter[5] Batch [5620] Speed: 660.42 samples/sec INFO:root:Iter[5] Batch [5640] Speed: 660.73 samples/sec INFO:root:Iter[5] Batch [5660] Speed: 660.79 samples/sec 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{fc7_acc} 58000 0.087890625 lr-batch-epoch: 0.1 7419 5 testing verification.. (12000, 512) infer time 3.830012000000001 [lfw][58000]XNorm: 22.272995 [lfw][58000]Accuracy-Flip: 0.99550+-0.00358 testing verification.. (14000, 512) infer time 4.457234999999999 [cfp_fp][58000]XNorm: 19.957929 [cfp_fp][58000]Accuracy-Flip: 0.96014+-0.00686 testing verification.. (12000, 512) infer time 3.8317229999999998 [agedb_30][58000]XNorm: 21.938620 [agedb_30][58000]Accuracy-Flip: 0.96717+-0.00857 [58000]Accuracy-Highest: 0.96967 INFO:root:Iter[5] Batch [7420] Speed: 297.77 samples/sec INFO:root:Iter[5] Batch [7440] Speed: 818.14 samples/sec INFO:root:Iter[5] Batch [7460] Speed: 824.72 samples/sec INFO:root:Iter[5] Batch [7480] Speed: 840.70 samples/sec INFO:root:Iter[5] Batch [7500] Speed: 842.76 samples/sec INFO:root:Iter[5] Batch [7520] Speed: 811.77 samples/sec INFO:root:Iter[5] Batch [7540] Speed: 817.04 samples/sec INFO:root:Iter[5] Batch [7560] Speed: 831.93 samples/sec INFO:root:Iter[5] Batch [7580] Speed: 798.34 samples/sec INFO:root:Iter[5] Batch [7600] Speed: 833.32 samples/sec INFO:root:Iter[5] Batch [7620] Speed: 852.16 samples/sec INFO:root:Iter[5] Batch [7640] Speed: 853.62 samples/sec INFO:root:Iter[5] Batch [7660] Speed: 820.63 samples/sec INFO:root:Iter[5] Batch [7680] Speed: 844.98 samples/sec INFO:root:Iter[5] Batch [7700] Speed: 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(12000, 512) infer time 3.6612720000000003 [lfw][60000]XNorm: 23.032262 [lfw][60000]Accuracy-Flip: 0.99667+-0.00298 testing verification.. (14000, 512) infer time 4.490294 [cfp_fp][60000]XNorm: 20.837502 [cfp_fp][60000]Accuracy-Flip: 0.96014+-0.00689 testing verification.. (12000, 512) infer time 3.863707 [agedb_30][60000]XNorm: 22.595966 [agedb_30][60000]Accuracy-Flip: 0.96783+-0.00691 [60000]Accuracy-Highest: 0.96967 INFO:root:Iter[5] Batch [9420] Speed: 294.77 samples/sec INFO:root:Iter[5] Batch [9440] Speed: 859.22 samples/sec INFO:root:Iter[5] Batch [9460] Speed: 813.44 samples/sec INFO:root:Iter[5] Batch [9480] Speed: 825.34 samples/sec INFO:root:Iter[5] Batch [9500] Speed: 803.46 samples/sec INFO:root:Iter[5] Batch [9520] Speed: 827.76 samples/sec INFO:root:Iter[5] Batch [9540] Speed: 833.46 samples/sec INFO:root:Iter[5] Batch [9560] Speed: 867.79 samples/sec INFO:root:Iter[5] Batch [9580] Speed: 847.49 samples/sec INFO:root:Iter[5] Batch [9600] Speed: 849.00 samples/sec INFO:root:Iter[5] Batch [9620] Speed: 846.94 samples/sec INFO:root:Iter[5] Batch [9640] Speed: 842.64 samples/sec INFO:root:Iter[5] Batch [9660] Speed: 859.35 samples/sec INFO:root:Iter[5] Batch [9680] Speed: 861.80 samples/sec INFO:root:Iter[5] Batch [9700] Speed: 861.56 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samples/sec INFO:root:Iter[6] Batch [1000] Speed: 836.17 samples/sec INFO:root:Iter[6] Batch [1020] Speed: 846.84 samples/sec INFO:root:Iter[6] Batch [1040] Speed: 861.44 samples/sec INFO:root:Iter[6] Batch [1060] Speed: 830.13 samples/sec INFO:root:Iter[6] Batch [1080] Speed: 873.45 samples/sec INFO:root:Iter[6] Batch [1100] Speed: 856.89 samples/sec INFO:root:Iter[6] Batch [1120] Speed: 856.35 samples/sec INFO:root:Iter[6] Batch [1140] Speed: 835.08 samples/sec INFO:root:Iter[6] Batch [1160] Speed: 862.34 samples/sec INFO:root:Iter[6] Batch [1180] Speed: 845.53 samples/sec INFO:root:Iter[6] Batch [1200] Speed: 872.12 samples/sec INFO:root:Iter[6] Batch [1220] Speed: 866.53 samples/sec INFO:root:Iter[6] Batch [1240] Speed: 861.67 samples/sec INFO:root:Iter[6] Batch [1260] Speed: 863.22 samples/sec INFO:root:Iter[6] Batch [1280] Speed: 864.78 samples/sec INFO:root:Iter[6] Batch [1300] Speed: 859.20 samples/sec {fc7_acc} 62000 0.109375 lr-batch-epoch: 0.1 1303 6 testing verification.. (12000, 512) infer time 3.808450999999999 [lfw][62000]XNorm: 23.853313 [lfw][62000]Accuracy-Flip: 0.99683+-0.00174 testing verification.. (14000, 512) infer time 4.3622119999999995 [cfp_fp][62000]XNorm: 21.328320 [cfp_fp][62000]Accuracy-Flip: 0.95514+-0.01143 testing verification.. (12000, 512) infer time 3.711176000000002 [agedb_30][62000]XNorm: 23.250208 [agedb_30][62000]Accuracy-Flip: 0.96700+-0.00912 [62000]Accuracy-Highest: 0.96967 INFO:root:Iter[6] Batch [1320] Speed: 299.25 samples/sec INFO:root:Iter[6] Batch [1340] Speed: 847.39 samples/sec INFO:root:Iter[6] Batch [1360] Speed: 856.22 samples/sec INFO:root:Iter[6] Batch [1380] Speed: 850.51 samples/sec INFO:root:Iter[6] Batch [1400] Speed: 840.30 samples/sec INFO:root:Iter[6] Batch [1420] Speed: 828.44 samples/sec INFO:root:Iter[6] Batch [1440] Speed: 820.92 samples/sec INFO:root:Iter[6] Batch [1460] Speed: 828.98 samples/sec INFO:root:Iter[6] Batch [1480] Speed: 854.07 samples/sec INFO:root:Iter[6] Batch [1500] Speed: 801.66 samples/sec INFO:root:Iter[6] Batch [1520] Speed: 863.91 samples/sec INFO:root:Iter[6] Batch [1540] Speed: 836.93 samples/sec INFO:root:Iter[6] Batch [1560] Speed: 844.92 samples/sec INFO:root:Iter[6] Batch [1580] Speed: 851.55 samples/sec INFO:root:Iter[6] Batch [1600] Speed: 850.89 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Batch [3000] Speed: 869.34 samples/sec INFO:root:Iter[6] Batch [3020] Speed: 890.33 samples/sec INFO:root:Iter[6] Batch [3040] Speed: 893.06 samples/sec INFO:root:Iter[6] Batch [3060] Speed: 879.09 samples/sec INFO:root:Iter[6] Batch [3080] Speed: 865.74 samples/sec INFO:root:Iter[6] Batch [3100] Speed: 872.10 samples/sec INFO:root:Iter[6] Batch [3120] Speed: 830.15 samples/sec INFO:root:Iter[6] Batch [3140] Speed: 850.01 samples/sec INFO:root:Iter[6] Batch [3160] Speed: 872.43 samples/sec INFO:root:Iter[6] Batch [3180] Speed: 849.73 samples/sec INFO:root:Iter[6] Batch [3200] Speed: 827.93 samples/sec INFO:root:Iter[6] Batch [3220] Speed: 867.93 samples/sec INFO:root:Iter[6] Batch [3240] Speed: 889.47 samples/sec INFO:root:Iter[6] Batch [3260] Speed: 880.99 samples/sec INFO:root:Iter[6] Batch [3280] Speed: 853.68 samples/sec INFO:root:Iter[6] Batch [3300] Speed: 857.67 samples/sec {fc7_acc} 64000 0.087890625 lr-batch-epoch: 0.1 3303 6 testing verification.. (12000, 512) infer time 3.726739999999998 [lfw][64000]XNorm: 22.264175 [lfw][64000]Accuracy-Flip: 0.99567+-0.00343 testing verification.. (14000, 512) infer time 4.396396000000002 [cfp_fp][64000]XNorm: 19.941387 [cfp_fp][64000]Accuracy-Flip: 0.96143+-0.00998 testing verification.. (12000, 512) infer time 3.7493549999999995 [agedb_30][64000]XNorm: 21.979496 [agedb_30][64000]Accuracy-Flip: 0.96717+-0.00785 [64000]Accuracy-Highest: 0.96967 INFO:root:Iter[6] Batch [3320] Speed: 292.96 samples/sec INFO:root:Iter[6] Batch [3340] Speed: 806.78 samples/sec INFO:root:Iter[6] Batch [3360] Speed: 810.98 samples/sec INFO:root:Iter[6] Batch [3380] Speed: 832.02 samples/sec INFO:root:Iter[6] Batch [3400] Speed: 832.15 samples/sec INFO:root:Iter[6] Batch [3420] Speed: 852.84 samples/sec INFO:root:Iter[6] Batch [3440] Speed: 837.73 samples/sec INFO:root:Iter[6] Batch [3460] Speed: 837.07 samples/sec INFO:root:Iter[6] Batch [3480] Speed: 822.07 samples/sec INFO:root:Iter[6] Batch [3500] Speed: 814.23 samples/sec INFO:root:Iter[6] Batch [3520] Speed: 813.83 samples/sec INFO:root:Iter[6] Batch [3540] Speed: 828.27 samples/sec INFO:root:Iter[6] Batch [3560] Speed: 832.50 samples/sec INFO:root:Iter[6] Batch [3580] Speed: 844.93 samples/sec INFO:root:Iter[6] Batch [3600] Speed: 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INFO:root:Iter[6] Batch [5000] Speed: 846.63 samples/sec INFO:root:Iter[6] Batch [5020] Speed: 863.47 samples/sec INFO:root:Iter[6] Batch [5040] Speed: 872.64 samples/sec INFO:root:Iter[6] Batch [5060] Speed: 850.41 samples/sec INFO:root:Iter[6] Batch [5080] Speed: 877.95 samples/sec INFO:root:Iter[6] Batch [5100] Speed: 877.89 samples/sec INFO:root:Iter[6] Batch [5120] Speed: 876.80 samples/sec INFO:root:Iter[6] Batch [5140] Speed: 868.09 samples/sec INFO:root:Iter[6] Batch [5160] Speed: 898.60 samples/sec INFO:root:Iter[6] Batch [5180] Speed: 875.18 samples/sec INFO:root:Iter[6] Batch [5200] Speed: 873.39 samples/sec INFO:root:Iter[6] Batch [5220] Speed: 884.71 samples/sec INFO:root:Iter[6] Batch [5240] Speed: 879.29 samples/sec INFO:root:Iter[6] Batch [5260] Speed: 857.51 samples/sec INFO:root:Iter[6] Batch [5280] Speed: 881.79 samples/sec INFO:root:Iter[6] Batch [5300] Speed: 879.87 samples/sec {fc7_acc} 66000 0.09375 lr-batch-epoch: 0.1 5303 6 testing verification.. (12000, 512) infer time 3.7767109999999997 [lfw][66000]XNorm: 22.566646 [lfw][66000]Accuracy-Flip: 0.99650+-0.00311 testing verification.. (14000, 512) infer time 4.356084999999998 [cfp_fp][66000]XNorm: 19.912192 [cfp_fp][66000]Accuracy-Flip: 0.96157+-0.00920 testing verification.. (12000, 512) infer time 3.808288 [agedb_30][66000]XNorm: 22.379414 [agedb_30][66000]Accuracy-Flip: 0.96283+-0.00658 [66000]Accuracy-Highest: 0.96967 INFO:root:Iter[6] Batch [5320] Speed: 299.47 samples/sec INFO:root:Iter[6] Batch [5340] Speed: 861.00 samples/sec INFO:root:Iter[6] Batch [5360] Speed: 843.80 samples/sec INFO:root:Iter[6] Batch [5380] Speed: 823.39 samples/sec INFO:root:Iter[6] Batch [5400] Speed: 832.28 samples/sec INFO:root:Iter[6] Batch [5420] Speed: 835.93 samples/sec INFO:root:Iter[6] Batch [5440] Speed: 849.01 samples/sec INFO:root:Iter[6] Batch [5460] Speed: 839.37 samples/sec INFO:root:Iter[6] Batch [5480] Speed: 852.63 samples/sec INFO:root:Iter[6] Batch [5500] Speed: 826.83 samples/sec INFO:root:Iter[6] Batch [5520] Speed: 829.97 samples/sec INFO:root:Iter[6] Batch [5540] Speed: 823.80 samples/sec INFO:root:Iter[6] Batch [5560] Speed: 842.07 samples/sec INFO:root:Iter[6] Batch [5580] Speed: 850.03 samples/sec INFO:root:Iter[6] Batch [5600] Speed: 852.66 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(12000, 512) infer time 3.7111660000000004 [lfw][68000]XNorm: 22.671821 [lfw][68000]Accuracy-Flip: 0.99533+-0.00314 testing verification.. (14000, 512) infer time 4.371093999999999 [cfp_fp][68000]XNorm: 20.253466 [cfp_fp][68000]Accuracy-Flip: 0.95571+-0.00924 testing verification.. (12000, 512) infer time 3.7734950000000005 [agedb_30][68000]XNorm: 22.363483 [agedb_30][68000]Accuracy-Flip: 0.96433+-0.00746 [68000]Accuracy-Highest: 0.96967 INFO:root:Iter[6] Batch [7320] Speed: 296.74 samples/sec INFO:root:Iter[6] Batch [7340] Speed: 820.03 samples/sec INFO:root:Iter[6] Batch [7360] Speed: 829.31 samples/sec INFO:root:Iter[6] Batch [7380] Speed: 843.79 samples/sec INFO:root:Iter[6] Batch [7400] Speed: 836.62 samples/sec INFO:root:Iter[6] Batch [7420] Speed: 842.28 samples/sec INFO:root:Iter[6] Batch [7440] Speed: 809.64 samples/sec INFO:root:Iter[6] Batch [7460] Speed: 820.88 samples/sec INFO:root:Iter[6] Batch [7480] Speed: 828.41 samples/sec INFO:root:Iter[6] Batch [7500] Speed: 843.22 samples/sec INFO:root:Iter[6] Batch [7520] Speed: 859.39 samples/sec INFO:root:Iter[6] Batch [7540] Speed: 840.14 samples/sec INFO:root:Iter[6] Batch [7560] Speed: 867.77 samples/sec INFO:root:Iter[6] Batch [7580] Speed: 849.46 samples/sec INFO:root:Iter[6] Batch [7600] Speed: 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(12000, 512) infer time 3.7356160000000007 [lfw][70000]XNorm: 22.489358 [lfw][70000]Accuracy-Flip: 0.99600+-0.00300 testing verification.. (14000, 512) infer time 4.402325999999999 [cfp_fp][70000]XNorm: 20.403485 [cfp_fp][70000]Accuracy-Flip: 0.95786+-0.00920 testing verification.. (12000, 512) infer time 3.7562919999999993 [agedb_30][70000]XNorm: 22.425607 [agedb_30][70000]Accuracy-Flip: 0.96717+-0.00879 [70000]Accuracy-Highest: 0.96967 INFO:root:Iter[6] Batch [9320] Speed: 298.76 samples/sec INFO:root:Iter[6] Batch [9340] Speed: 835.24 samples/sec INFO:root:Iter[6] Batch [9360] Speed: 854.31 samples/sec INFO:root:Iter[6] Batch [9380] Speed: 839.02 samples/sec INFO:root:Iter[6] Batch [9400] Speed: 846.47 samples/sec INFO:root:Iter[6] Batch [9420] Speed: 842.41 samples/sec INFO:root:Iter[6] Batch [9440] Speed: 822.78 samples/sec INFO:root:Iter[6] Batch [9460] Speed: 828.21 samples/sec INFO:root:Iter[6] Batch [9480] Speed: 839.31 samples/sec INFO:root:Iter[6] Batch [9500] Speed: 834.42 samples/sec INFO:root:Iter[6] Batch [9520] Speed: 840.37 samples/sec INFO:root:Iter[6] Batch [9540] Speed: 848.63 samples/sec INFO:root:Iter[6] Batch [9560] Speed: 847.35 samples/sec INFO:root:Iter[6] Batch [9580] Speed: 842.15 samples/sec INFO:root:Iter[6] Batch [9600] Speed: 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(12000, 512) infer time 3.756963 [lfw][72000]XNorm: 22.860865 [lfw][72000]Accuracy-Flip: 0.99500+-0.00325 testing verification.. (14000, 512) infer time 4.344826 [cfp_fp][72000]XNorm: 20.004100 [cfp_fp][72000]Accuracy-Flip: 0.96143+-0.00917 testing verification.. (12000, 512) infer time 3.8215990000000013 [agedb_30][72000]XNorm: 22.451080 [agedb_30][72000]Accuracy-Flip: 0.96767+-0.00824 [72000]Accuracy-Highest: 0.96967 INFO:root:Iter[7] Batch [1200] Speed: 296.72 samples/sec INFO:root:Iter[7] Batch [1220] Speed: 829.63 samples/sec INFO:root:Iter[7] Batch [1240] Speed: 840.92 samples/sec INFO:root:Iter[7] Batch [1260] Speed: 829.93 samples/sec INFO:root:Iter[7] Batch [1280] Speed: 834.24 samples/sec INFO:root:Iter[7] Batch [1300] Speed: 800.70 samples/sec INFO:root:Iter[7] Batch [1320] Speed: 819.64 samples/sec INFO:root:Iter[7] Batch [1340] Speed: 847.64 samples/sec INFO:root:Iter[7] Batch [1360] Speed: 817.36 samples/sec INFO:root:Iter[7] Batch [1380] Speed: 844.00 samples/sec INFO:root:Iter[7] Batch [1400] Speed: 838.09 samples/sec INFO:root:Iter[7] Batch [1420] Speed: 825.75 samples/sec INFO:root:Iter[7] Batch [1440] Speed: 830.38 samples/sec INFO:root:Iter[7] Batch [1460] Speed: 850.58 samples/sec INFO:root:Iter[7] Batch [1480] Speed: 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INFO:root:Iter[7] Batch [2880] Speed: 886.81 samples/sec INFO:root:Iter[7] Batch [2900] Speed: 870.68 samples/sec INFO:root:Iter[7] Batch [2920] Speed: 875.61 samples/sec INFO:root:Iter[7] Batch [2940] Speed: 871.59 samples/sec INFO:root:Iter[7] Batch [2960] Speed: 859.68 samples/sec INFO:root:Iter[7] Batch [2980] Speed: 899.55 samples/sec INFO:root:Iter[7] Batch [3000] Speed: 879.79 samples/sec INFO:root:Iter[7] Batch [3020] Speed: 875.33 samples/sec INFO:root:Iter[7] Batch [3040] Speed: 873.85 samples/sec INFO:root:Iter[7] Batch [3060] Speed: 893.82 samples/sec INFO:root:Iter[7] Batch [3080] Speed: 867.73 samples/sec INFO:root:Iter[7] Batch [3100] Speed: 894.57 samples/sec INFO:root:Iter[7] Batch [3120] Speed: 881.47 samples/sec INFO:root:Iter[7] Batch [3140] Speed: 878.96 samples/sec INFO:root:Iter[7] Batch [3160] Speed: 870.47 samples/sec INFO:root:Iter[7] Batch [3180] Speed: 878.30 samples/sec {fc7_acc} 74000 0.12695312 lr-batch-epoch: 0.1 3187 7 testing verification.. (12000, 512) infer time 3.716516 [lfw][74000]XNorm: 22.100995 [lfw][74000]Accuracy-Flip: 0.99633+-0.00256 testing verification.. (14000, 512) infer time 4.350040999999998 [cfp_fp][74000]XNorm: 19.616758 [cfp_fp][74000]Accuracy-Flip: 0.95957+-0.00860 testing verification.. (12000, 512) infer time 3.763639 [agedb_30][74000]XNorm: 21.882216 [agedb_30][74000]Accuracy-Flip: 0.96783+-0.00650 [74000]Accuracy-Highest: 0.96967 INFO:root:Iter[7] Batch [3200] Speed: 299.49 samples/sec INFO:root:Iter[7] Batch [3220] Speed: 870.25 samples/sec INFO:root:Iter[7] Batch [3240] Speed: 839.35 samples/sec INFO:root:Iter[7] Batch [3260] Speed: 833.55 samples/sec INFO:root:Iter[7] Batch [3280] Speed: 831.17 samples/sec INFO:root:Iter[7] Batch [3300] Speed: 852.89 samples/sec INFO:root:Iter[7] Batch [3320] Speed: 812.90 samples/sec INFO:root:Iter[7] Batch [3340] Speed: 841.42 samples/sec INFO:root:Iter[7] Batch [3360] Speed: 857.06 samples/sec INFO:root:Iter[7] Batch [3380] Speed: 874.36 samples/sec INFO:root:Iter[7] Batch [3400] Speed: 809.13 samples/sec INFO:root:Iter[7] Batch [3420] Speed: 864.35 samples/sec INFO:root:Iter[7] Batch [3440] Speed: 858.15 samples/sec INFO:root:Iter[7] Batch [3460] Speed: 841.57 samples/sec INFO:root:Iter[7] Batch [3480] Speed: 843.28 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(12000, 512) infer time 3.664283 [lfw][76000]XNorm: 22.580532 [lfw][76000]Accuracy-Flip: 0.99550+-0.00317 testing verification.. (14000, 512) infer time 4.334086 [cfp_fp][76000]XNorm: 19.965066 [cfp_fp][76000]Accuracy-Flip: 0.95971+-0.01008 testing verification.. (12000, 512) infer time 3.7310489999999996 [agedb_30][76000]XNorm: 22.021819 [agedb_30][76000]Accuracy-Flip: 0.96367+-0.00881 [76000]Accuracy-Highest: 0.96967 INFO:root:Iter[7] Batch [5200] Speed: 301.11 samples/sec INFO:root:Iter[7] Batch [5220] Speed: 839.23 samples/sec INFO:root:Iter[7] Batch [5240] Speed: 845.40 samples/sec INFO:root:Iter[7] Batch [5260] Speed: 862.91 samples/sec INFO:root:Iter[7] Batch [5280] Speed: 867.48 samples/sec INFO:root:Iter[7] Batch [5300] Speed: 874.80 samples/sec INFO:root:Iter[7] Batch [5320] Speed: 844.03 samples/sec INFO:root:Iter[7] Batch [5340] Speed: 841.21 samples/sec INFO:root:Iter[7] Batch [5360] Speed: 873.13 samples/sec INFO:root:Iter[7] Batch [5380] Speed: 872.42 samples/sec INFO:root:Iter[7] Batch [5400] Speed: 861.07 samples/sec INFO:root:Iter[7] Batch [5420] Speed: 875.07 samples/sec INFO:root:Iter[7] Batch [5440] Speed: 864.82 samples/sec INFO:root:Iter[7] Batch [5460] Speed: 856.98 samples/sec INFO:root:Iter[7] Batch [5480] Speed: 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(12000, 512) infer time 3.677184 [lfw][78000]XNorm: 22.794664 [lfw][78000]Accuracy-Flip: 0.99450+-0.00299 testing verification.. (14000, 512) infer time 4.335374999999997 [cfp_fp][78000]XNorm: 20.497834 [cfp_fp][78000]Accuracy-Flip: 0.95300+-0.01107 testing verification.. (12000, 512) infer time 3.7228099999999995 [agedb_30][78000]XNorm: 22.573804 [agedb_30][78000]Accuracy-Flip: 0.96133+-0.00971 [78000]Accuracy-Highest: 0.96967 INFO:root:Iter[7] Batch [7200] Speed: 297.35 samples/sec INFO:root:Iter[7] Batch [7220] Speed: 837.69 samples/sec INFO:root:Iter[7] Batch [7240] Speed: 834.90 samples/sec INFO:root:Iter[7] Batch [7260] Speed: 833.63 samples/sec INFO:root:Iter[7] Batch [7280] Speed: 832.73 samples/sec INFO:root:Iter[7] Batch [7300] Speed: 851.51 samples/sec INFO:root:Iter[7] Batch [7320] Speed: 869.82 samples/sec INFO:root:Iter[7] Batch [7340] Speed: 836.07 samples/sec INFO:root:Iter[7] Batch [7360] Speed: 871.24 samples/sec INFO:root:Iter[7] Batch [7380] Speed: 853.32 samples/sec INFO:root:Iter[7] Batch [7400] Speed: 859.22 samples/sec INFO:root:Iter[7] Batch [7420] Speed: 855.20 samples/sec INFO:root:Iter[7] Batch [7440] Speed: 876.54 samples/sec INFO:root:Iter[7] Batch [7460] Speed: 884.16 samples/sec INFO:root:Iter[7] Batch [7480] Speed: 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(12000, 512) infer time 3.7100259999999996 [lfw][80000]XNorm: 22.025294 [lfw][80000]Accuracy-Flip: 0.99683+-0.00263 testing verification.. (14000, 512) infer time 4.425262 [cfp_fp][80000]XNorm: 19.703620 [cfp_fp][80000]Accuracy-Flip: 0.95771+-0.01107 testing verification.. (12000, 512) infer time 3.7774139999999985 [agedb_30][80000]XNorm: 21.516902 [agedb_30][80000]Accuracy-Flip: 0.96183+-0.00821 [80000]Accuracy-Highest: 0.96967 INFO:root:Iter[7] Batch [9200] Speed: 298.28 samples/sec INFO:root:Iter[7] Batch [9220] Speed: 835.73 samples/sec INFO:root:Iter[7] Batch [9240] Speed: 861.10 samples/sec INFO:root:Iter[7] Batch [9260] Speed: 844.15 samples/sec INFO:root:Iter[7] Batch [9280] Speed: 846.17 samples/sec INFO:root:Iter[7] Batch [9300] Speed: 844.19 samples/sec INFO:root:Iter[7] Batch [9320] Speed: 844.54 samples/sec INFO:root:Iter[7] Batch [9340] Speed: 825.91 samples/sec INFO:root:Iter[7] Batch [9360] Speed: 848.90 samples/sec INFO:root:Iter[7] Batch [9380] Speed: 831.23 samples/sec INFO:root:Iter[7] Batch [9400] Speed: 876.87 samples/sec INFO:root:Iter[7] Batch [9420] Speed: 847.55 samples/sec INFO:root:Iter[7] Batch [9440] Speed: 844.95 samples/sec INFO:root:Iter[7] Batch [9460] Speed: 848.40 samples/sec INFO:root:Iter[7] Batch [9480] Speed: 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samples/sec INFO:root:Iter[8] Batch [780] Speed: 882.19 samples/sec INFO:root:Iter[8] Batch [800] Speed: 852.80 samples/sec INFO:root:Iter[8] Batch [820] Speed: 845.30 samples/sec INFO:root:Iter[8] Batch [840] Speed: 843.04 samples/sec INFO:root:Iter[8] Batch [860] Speed: 876.45 samples/sec INFO:root:Iter[8] Batch [880] Speed: 897.98 samples/sec INFO:root:Iter[8] Batch [900] Speed: 886.20 samples/sec INFO:root:Iter[8] Batch [920] Speed: 853.54 samples/sec INFO:root:Iter[8] Batch [940] Speed: 898.03 samples/sec INFO:root:Iter[8] Batch [960] Speed: 872.76 samples/sec INFO:root:Iter[8] Batch [980] Speed: 889.45 samples/sec INFO:root:Iter[8] Batch [1000] Speed: 881.31 samples/sec INFO:root:Iter[8] Batch [1020] Speed: 892.45 samples/sec INFO:root:Iter[8] Batch [1040] Speed: 894.71 samples/sec INFO:root:Iter[8] Batch [1060] Speed: 875.89 samples/sec {fc7_acc} 82000 0.14257812 lr-batch-epoch: 0.1 1071 8 testing verification.. (12000, 512) infer time 3.682686000000001 [lfw][82000]XNorm: 21.990605 [lfw][82000]Accuracy-Flip: 0.99667+-0.00342 testing verification.. (14000, 512) infer time 4.33785 [cfp_fp][82000]XNorm: 19.757290 [cfp_fp][82000]Accuracy-Flip: 0.96100+-0.00769 testing verification.. (12000, 512) infer time 3.7145340000000004 [agedb_30][82000]XNorm: 21.531648 [agedb_30][82000]Accuracy-Flip: 0.96583+-0.00772 [82000]Accuracy-Highest: 0.96967 INFO:root:Iter[8] Batch [1080] Speed: 301.59 samples/sec INFO:root:Iter[8] Batch [1100] Speed: 842.45 samples/sec INFO:root:Iter[8] Batch [1120] Speed: 839.12 samples/sec INFO:root:Iter[8] Batch [1140] Speed: 830.73 samples/sec INFO:root:Iter[8] Batch [1160] Speed: 850.46 samples/sec INFO:root:Iter[8] Batch [1180] Speed: 857.97 samples/sec INFO:root:Iter[8] Batch [1200] Speed: 817.38 samples/sec INFO:root:Iter[8] Batch [1220] Speed: 836.48 samples/sec INFO:root:Iter[8] Batch [1240] Speed: 834.48 samples/sec INFO:root:Iter[8] Batch [1260] Speed: 861.99 samples/sec INFO:root:Iter[8] Batch [1280] Speed: 840.24 samples/sec INFO:root:Iter[8] Batch [1300] Speed: 847.71 samples/sec INFO:root:Iter[8] Batch [1320] Speed: 833.84 samples/sec INFO:root:Iter[8] Batch [1340] Speed: 885.54 samples/sec INFO:root:Iter[8] Batch [1360] Speed: 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(12000, 512) infer time 3.6849349999999994 [lfw][84000]XNorm: 22.821174 [lfw][84000]Accuracy-Flip: 0.99533+-0.00340 testing verification.. (14000, 512) infer time 4.3632 [cfp_fp][84000]XNorm: 20.602443 [cfp_fp][84000]Accuracy-Flip: 0.95543+-0.00840 testing verification.. (12000, 512) infer time 3.7329799999999995 [agedb_30][84000]XNorm: 22.581731 [agedb_30][84000]Accuracy-Flip: 0.96200+-0.00722 [84000]Accuracy-Highest: 0.96967 INFO:root:Iter[8] Batch [3080] Speed: 301.01 samples/sec INFO:root:Iter[8] Batch [3100] Speed: 838.50 samples/sec INFO:root:Iter[8] Batch [3120] Speed: 814.03 samples/sec INFO:root:Iter[8] Batch [3140] Speed: 854.80 samples/sec INFO:root:Iter[8] Batch [3160] Speed: 866.13 samples/sec INFO:root:Iter[8] Batch [3180] Speed: 791.83 samples/sec INFO:root:Iter[8] Batch [3200] Speed: 816.43 samples/sec INFO:root:Iter[8] Batch [3220] Speed: 828.34 samples/sec INFO:root:Iter[8] Batch [3240] Speed: 837.80 samples/sec INFO:root:Iter[8] Batch [3260] Speed: 821.36 samples/sec INFO:root:Iter[8] Batch [3280] Speed: 805.30 samples/sec INFO:root:Iter[8] Batch [3300] Speed: 834.26 samples/sec INFO:root:Iter[8] Batch [3320] Speed: 812.36 samples/sec INFO:root:Iter[8] Batch [3340] Speed: 820.52 samples/sec INFO:root:Iter[8] Batch [3360] Speed: 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INFO:root:Iter[8] Batch [4760] Speed: 872.57 samples/sec INFO:root:Iter[8] Batch [4780] Speed: 868.61 samples/sec INFO:root:Iter[8] Batch [4800] Speed: 861.28 samples/sec INFO:root:Iter[8] Batch [4820] Speed: 876.35 samples/sec INFO:root:Iter[8] Batch [4840] Speed: 861.43 samples/sec INFO:root:Iter[8] Batch [4860] Speed: 860.44 samples/sec INFO:root:Iter[8] Batch [4880] Speed: 864.27 samples/sec INFO:root:Iter[8] Batch [4900] Speed: 857.62 samples/sec INFO:root:Iter[8] Batch [4920] Speed: 899.76 samples/sec INFO:root:Iter[8] Batch [4940] Speed: 873.83 samples/sec INFO:root:Iter[8] Batch [4960] Speed: 868.90 samples/sec INFO:root:Iter[8] Batch [4980] Speed: 844.40 samples/sec INFO:root:Iter[8] Batch [5000] Speed: 890.38 samples/sec INFO:root:Iter[8] Batch [5020] Speed: 854.16 samples/sec INFO:root:Iter[8] Batch [5040] Speed: 871.69 samples/sec INFO:root:Iter[8] Batch [5060] Speed: 855.69 samples/sec {fc7_acc} 86000 0.1171875 lr-batch-epoch: 0.1 5071 8 testing verification.. (12000, 512) infer time 3.8021619999999996 [lfw][86000]XNorm: 22.179180 [lfw][86000]Accuracy-Flip: 0.99650+-0.00337 testing verification.. (14000, 512) infer time 4.42521 [cfp_fp][86000]XNorm: 19.631991 [cfp_fp][86000]Accuracy-Flip: 0.96157+-0.00692 testing verification.. (12000, 512) infer time 3.733421 [agedb_30][86000]XNorm: 21.554914 [agedb_30][86000]Accuracy-Flip: 0.96450+-0.00898 [86000]Accuracy-Highest: 0.96967 INFO:root:Iter[8] Batch [5080] Speed: 297.24 samples/sec INFO:root:Iter[8] Batch [5100] Speed: 840.42 samples/sec INFO:root:Iter[8] Batch [5120] Speed: 800.81 samples/sec INFO:root:Iter[8] Batch [5140] Speed: 818.34 samples/sec INFO:root:Iter[8] Batch [5160] Speed: 793.70 samples/sec INFO:root:Iter[8] Batch [5180] Speed: 825.48 samples/sec INFO:root:Iter[8] Batch [5200] Speed: 854.91 samples/sec INFO:root:Iter[8] Batch [5220] Speed: 801.93 samples/sec INFO:root:Iter[8] Batch [5240] Speed: 830.44 samples/sec INFO:root:Iter[8] Batch [5260] Speed: 862.37 samples/sec INFO:root:Iter[8] Batch [5280] Speed: 846.06 samples/sec INFO:root:Iter[8] Batch [5300] Speed: 837.58 samples/sec INFO:root:Iter[8] Batch [5320] Speed: 856.54 samples/sec INFO:root:Iter[8] Batch [5340] Speed: 844.53 samples/sec INFO:root:Iter[8] Batch [5360] Speed: 828.21 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(12000, 512) infer time 3.805940999999999 [lfw][88000]XNorm: 22.519524 [lfw][88000]Accuracy-Flip: 0.99667+-0.00401 testing verification.. (14000, 512) infer time 4.384367 [cfp_fp][88000]XNorm: 19.937628 [cfp_fp][88000]Accuracy-Flip: 0.96186+-0.01002 testing verification.. (12000, 512) infer time 3.7039560000000002 [agedb_30][88000]XNorm: 22.299327 [agedb_30][88000]Accuracy-Flip: 0.96533+-0.00819 [88000]Accuracy-Highest: 0.96967 INFO:root:Iter[8] Batch [7080] Speed: 300.38 samples/sec INFO:root:Iter[8] Batch [7100] Speed: 852.67 samples/sec INFO:root:Iter[8] Batch [7120] Speed: 861.93 samples/sec INFO:root:Iter[8] Batch [7140] Speed: 848.86 samples/sec INFO:root:Iter[8] Batch [7160] Speed: 832.99 samples/sec INFO:root:Iter[8] Batch [7180] Speed: 833.93 samples/sec INFO:root:Iter[8] Batch [7200] Speed: 824.25 samples/sec INFO:root:Iter[8] Batch [7220] Speed: 849.48 samples/sec INFO:root:Iter[8] Batch [7240] Speed: 850.97 samples/sec INFO:root:Iter[8] Batch [7260] Speed: 858.75 samples/sec INFO:root:Iter[8] Batch [7280] Speed: 841.30 samples/sec INFO:root:Iter[8] Batch [7300] Speed: 854.18 samples/sec INFO:root:Iter[8] Batch [7320] Speed: 842.63 samples/sec INFO:root:Iter[8] Batch [7340] Speed: 856.01 samples/sec INFO:root:Iter[8] Batch [7360] Speed: 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(12000, 512) infer time 3.741168000000001 [lfw][90000]XNorm: 22.112350 [lfw][90000]Accuracy-Flip: 0.99583+-0.00327 testing verification.. (14000, 512) infer time 4.39933 [cfp_fp][90000]XNorm: 19.809296 [cfp_fp][90000]Accuracy-Flip: 0.96157+-0.00955 testing verification.. (12000, 512) infer time 3.7241239999999998 [agedb_30][90000]XNorm: 22.009123 [agedb_30][90000]Accuracy-Flip: 0.96617+-0.00817 [90000]Accuracy-Highest: 0.96967 INFO:root:Iter[8] Batch [9080] Speed: 297.15 samples/sec INFO:root:Iter[8] Batch [9100] Speed: 840.32 samples/sec INFO:root:Iter[8] Batch [9120] Speed: 852.77 samples/sec INFO:root:Iter[8] Batch [9140] Speed: 810.92 samples/sec INFO:root:Iter[8] Batch [9160] Speed: 820.99 samples/sec INFO:root:Iter[8] Batch [9180] Speed: 796.16 samples/sec INFO:root:Iter[8] Batch [9200] Speed: 855.67 samples/sec INFO:root:Iter[8] Batch [9220] Speed: 819.44 samples/sec INFO:root:Iter[8] Batch [9240] Speed: 842.36 samples/sec INFO:root:Iter[8] Batch [9260] Speed: 832.87 samples/sec INFO:root:Iter[8] Batch [9280] Speed: 817.32 samples/sec INFO:root:Iter[8] Batch [9300] Speed: 837.38 samples/sec INFO:root:Iter[8] Batch [9320] Speed: 869.66 samples/sec INFO:root:Iter[8] Batch [9340] Speed: 857.03 samples/sec INFO:root:Iter[8] Batch [9360] Speed: 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[10060] Speed: 860.99 samples/sec lr-batch-epoch: 0.1 10071 8 INFO:root:Iter[8] Batch [10080] Speed: 829.98 samples/sec INFO:root:Iter[8] Batch [10100] Speed: 843.64 samples/sec INFO:root:Epoch[8] Time cost=6107.461 call reset() INFO:root:Iter[9] Batch [20] Speed: 871.45 samples/sec INFO:root:Iter[9] Batch [40] Speed: 855.01 samples/sec INFO:root:Iter[9] Batch [60] Speed: 877.62 samples/sec INFO:root:Iter[9] Batch [80] Speed: 898.66 samples/sec INFO:root:Iter[9] Batch [100] Speed: 856.11 samples/sec INFO:root:Iter[9] Batch [120] Speed: 874.92 samples/sec INFO:root:Iter[9] Batch [140] Speed: 893.59 samples/sec INFO:root:Iter[9] Batch [160] Speed: 857.28 samples/sec INFO:root:Iter[9] Batch [180] Speed: 878.47 samples/sec INFO:root:Iter[9] Batch [200] Speed: 850.48 samples/sec INFO:root:Iter[9] Batch [220] Speed: 881.43 samples/sec INFO:root:Iter[9] Batch [240] Speed: 904.66 samples/sec INFO:root:Iter[9] Batch [260] Speed: 876.27 samples/sec INFO:root:Iter[9] Batch [280] Speed: 871.15 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(12000, 512) infer time 3.716125999999999 [lfw][92000]XNorm: 22.759317 [lfw][92000]Accuracy-Flip: 0.99700+-0.00306 testing verification.. (14000, 512) infer time 4.400988999999998 [cfp_fp][92000]XNorm: 20.843794 [cfp_fp][92000]Accuracy-Flip: 0.95814+-0.01211 testing verification.. (12000, 512) infer time 3.7838380000000003 [agedb_30][92000]XNorm: 22.467918 [agedb_30][92000]Accuracy-Flip: 0.96483+-0.00987 [92000]Accuracy-Highest: 0.96967 INFO:root:Iter[9] Batch [960] Speed: 297.44 samples/sec INFO:root:Iter[9] Batch [980] Speed: 819.91 samples/sec INFO:root:Iter[9] Batch [1000] Speed: 813.89 samples/sec INFO:root:Iter[9] Batch [1020] Speed: 824.45 samples/sec INFO:root:Iter[9] Batch [1040] Speed: 818.12 samples/sec INFO:root:Iter[9] Batch [1060] Speed: 785.73 samples/sec INFO:root:Iter[9] Batch [1080] Speed: 823.19 samples/sec INFO:root:Iter[9] Batch [1100] Speed: 835.60 samples/sec INFO:root:Iter[9] Batch [1120] Speed: 835.79 samples/sec INFO:root:Iter[9] Batch [1140] Speed: 823.91 samples/sec INFO:root:Iter[9] Batch [1160] Speed: 828.92 samples/sec INFO:root:Iter[9] Batch [1180] Speed: 853.33 samples/sec INFO:root:Iter[9] Batch [1200] Speed: 852.29 samples/sec INFO:root:Iter[9] Batch [1220] Speed: 837.16 samples/sec INFO:root:Iter[9] Batch [1240] Speed: 839.31 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(12000, 512) infer time 3.749378 [lfw][94000]XNorm: 21.911712 [lfw][94000]Accuracy-Flip: 0.99650+-0.00391 testing verification.. (14000, 512) infer time 4.323374999999999 [cfp_fp][94000]XNorm: 19.708941 [cfp_fp][94000]Accuracy-Flip: 0.95957+-0.00913 testing verification.. (12000, 512) infer time 3.773514 [agedb_30][94000]XNorm: 21.656288 [agedb_30][94000]Accuracy-Flip: 0.96550+-0.00533 [94000]Accuracy-Highest: 0.96967 INFO:root:Iter[9] Batch [2960] Speed: 301.42 samples/sec INFO:root:Iter[9] Batch [2980] Speed: 861.14 samples/sec INFO:root:Iter[9] Batch [3000] Speed: 823.62 samples/sec INFO:root:Iter[9] Batch [3020] Speed: 841.66 samples/sec INFO:root:Iter[9] Batch [3040] Speed: 847.26 samples/sec INFO:root:Iter[9] Batch [3060] Speed: 860.69 samples/sec INFO:root:Iter[9] Batch [3080] Speed: 844.05 samples/sec INFO:root:Iter[9] Batch [3100] Speed: 850.56 samples/sec INFO:root:Iter[9] Batch [3120] Speed: 872.27 samples/sec INFO:root:Iter[9] Batch [3140] Speed: 839.15 samples/sec INFO:root:Iter[9] Batch [3160] Speed: 859.11 samples/sec INFO:root:Iter[9] Batch [3180] Speed: 859.68 samples/sec INFO:root:Iter[9] Batch [3200] Speed: 849.32 samples/sec INFO:root:Iter[9] Batch [3220] Speed: 805.79 samples/sec INFO:root:Iter[9] Batch [3240] Speed: 848.46 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(12000, 512) infer time 3.8075829999999997 [lfw][96000]XNorm: 22.600551 [lfw][96000]Accuracy-Flip: 0.99650+-0.00311 testing verification.. (14000, 512) infer time 4.381874999999999 [cfp_fp][96000]XNorm: 20.323492 [cfp_fp][96000]Accuracy-Flip: 0.96214+-0.01094 testing verification.. (12000, 512) infer time 3.735205999999999 [agedb_30][96000]XNorm: 22.485650 [agedb_30][96000]Accuracy-Flip: 0.96717+-0.00803 [96000]Accuracy-Highest: 0.96967 INFO:root:Iter[9] Batch [4960] Speed: 298.60 samples/sec INFO:root:Iter[9] Batch [4980] Speed: 855.96 samples/sec INFO:root:Iter[9] Batch [5000] Speed: 803.62 samples/sec INFO:root:Iter[9] Batch [5020] Speed: 835.51 samples/sec INFO:root:Iter[9] Batch [5040] Speed: 856.57 samples/sec INFO:root:Iter[9] Batch [5060] Speed: 843.23 samples/sec INFO:root:Iter[9] Batch [5080] Speed: 857.34 samples/sec INFO:root:Iter[9] Batch [5100] Speed: 851.91 samples/sec INFO:root:Iter[9] Batch [5120] Speed: 852.15 samples/sec INFO:root:Iter[9] Batch [5140] Speed: 872.75 samples/sec INFO:root:Iter[9] Batch [5160] Speed: 856.06 samples/sec INFO:root:Iter[9] Batch [5180] Speed: 856.48 samples/sec INFO:root:Iter[9] Batch [5200] Speed: 839.10 samples/sec INFO:root:Iter[9] Batch [5220] Speed: 846.60 samples/sec INFO:root:Iter[9] Batch [5240] Speed: 865.28 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(12000, 512) infer time 3.7235989999999997 [lfw][98000]XNorm: 23.360562 [lfw][98000]Accuracy-Flip: 0.99483+-0.00437 testing verification.. (14000, 512) infer time 4.330789 [cfp_fp][98000]XNorm: 21.138974 [cfp_fp][98000]Accuracy-Flip: 0.95914+-0.01310 testing verification.. (12000, 512) infer time 3.7344579999999996 [agedb_30][98000]XNorm: 23.336823 [agedb_30][98000]Accuracy-Flip: 0.96250+-0.00735 [98000]Accuracy-Highest: 0.96967 INFO:root:Iter[9] Batch [6960] Speed: 301.67 samples/sec INFO:root:Iter[9] Batch [6980] Speed: 809.01 samples/sec INFO:root:Iter[9] Batch [7000] Speed: 844.53 samples/sec INFO:root:Iter[9] Batch [7020] Speed: 844.34 samples/sec INFO:root:Iter[9] Batch [7040] Speed: 849.55 samples/sec INFO:root:Iter[9] Batch [7060] Speed: 850.28 samples/sec INFO:root:Iter[9] Batch [7080] Speed: 832.19 samples/sec INFO:root:Iter[9] Batch [7100] Speed: 851.43 samples/sec INFO:root:Iter[9] Batch [7120] Speed: 876.18 samples/sec INFO:root:Iter[9] Batch [7140] Speed: 846.53 samples/sec INFO:root:Iter[9] Batch [7160] Speed: 857.09 samples/sec INFO:root:Iter[9] Batch [7180] Speed: 838.67 samples/sec INFO:root:Iter[9] Batch [7200] Speed: 843.74 samples/sec INFO:root:Iter[9] Batch [7220] Speed: 866.35 samples/sec INFO:root:Iter[9] Batch [7240] Speed: 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0.010000000000000002 8955 9 testing verification.. (12000, 512) infer time 3.7528820000000005 [lfw][100000]XNorm: 22.659202 [lfw][100000]Accuracy-Flip: 0.99583+-0.00327 testing verification.. (14000, 512) infer time 4.345158000000001 [cfp_fp][100000]XNorm: 20.361970 [cfp_fp][100000]Accuracy-Flip: 0.96343+-0.01050 testing verification.. (12000, 512) infer time 3.7719840000000002 [agedb_30][100000]XNorm: 22.329666 [agedb_30][100000]Accuracy-Flip: 0.96767+-0.00904 [100000]Accuracy-Highest: 0.96967 INFO:root:Iter[9] Batch [8960] Speed: 300.17 samples/sec INFO:root:Iter[9] Batch [8980] Speed: 869.36 samples/sec INFO:root:Iter[9] Batch [9000] Speed: 834.94 samples/sec INFO:root:Iter[9] Batch [9020] Speed: 836.38 samples/sec INFO:root:Iter[9] Batch [9040] Speed: 849.44 samples/sec INFO:root:Iter[9] Batch [9060] Speed: 842.72 samples/sec INFO:root:Iter[9] Batch [9080] Speed: 843.35 samples/sec INFO:root:Iter[9] Batch [9100] Speed: 851.28 samples/sec INFO:root:Iter[9] Batch [9120] Speed: 846.48 samples/sec INFO:root:Iter[9] Batch [9140] Speed: 825.15 samples/sec INFO:root:Iter[9] Batch [9160] Speed: 848.60 samples/sec INFO:root:Iter[9] Batch [9180] Speed: 873.84 samples/sec INFO:root:Iter[9] Batch [9200] Speed: 860.19 samples/sec INFO:root:Iter[9] Batch [9220] Speed: 844.46 samples/sec INFO:root:Iter[9] Batch [9240] Speed: 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testing verification.. (12000, 512) infer time 3.67801 [lfw][102000]XNorm: 22.285897 [lfw][102000]Accuracy-Flip: 0.99683+-0.00311 testing verification.. (14000, 512) infer time 4.398971999999999 [cfp_fp][102000]XNorm: 20.829206 [cfp_fp][102000]Accuracy-Flip: 0.97914+-0.00583 testing verification.. (12000, 512) infer time 3.8127229999999988 [agedb_30][102000]XNorm: 22.570539 [agedb_30][102000]Accuracy-Flip: 0.97567+-0.00583 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [102000]Accuracy-Highest: 0.97567 INFO:root:Iter[10] Batch [840] Speed: 294.27 samples/sec INFO:root:Iter[10] Batch [860] Speed: 846.61 samples/sec INFO:root:Iter[10] Batch [880] Speed: 860.79 samples/sec INFO:root:Iter[10] Batch [900] Speed: 827.67 samples/sec INFO:root:Iter[10] Batch [920] Speed: 836.07 samples/sec INFO:root:Iter[10] Batch [940] Speed: 856.60 samples/sec INFO:root:Iter[10] Batch [960] Speed: 848.55 samples/sec INFO:root:Iter[10] Batch [980] Speed: 847.98 samples/sec INFO:root:Iter[10] Batch [1000] Speed: 853.98 samples/sec INFO:root:Iter[10] Batch [1020] Speed: 851.98 samples/sec INFO:root:Iter[10] Batch [1040] Speed: 860.12 samples/sec INFO:root:Iter[10] Batch [1060] Speed: 854.22 samples/sec INFO:root:Iter[10] Batch [1080] Speed: 845.44 samples/sec 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samples/sec INFO:root:Iter[10] Batch [2800] Speed: 880.89 samples/sec INFO:root:Iter[10] Batch [2820] Speed: 854.81 samples/sec {fc7_acc} 104000 0.49804688 lr-batch-epoch: 0.010000000000000002 2839 10 testing verification.. (12000, 512) infer time 3.7476700000000003 [lfw][104000]XNorm: 22.333480 [lfw][104000]Accuracy-Flip: 0.99733+-0.00271 testing verification.. (14000, 512) infer time 4.354682 [cfp_fp][104000]XNorm: 21.109632 [cfp_fp][104000]Accuracy-Flip: 0.98243+-0.00557 testing verification.. (12000, 512) infer time 3.7134919999999996 [agedb_30][104000]XNorm: 22.814853 [agedb_30][104000]Accuracy-Flip: 0.97583+-0.00700 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [104000]Accuracy-Highest: 0.97583 INFO:root:Iter[10] Batch [2840] Speed: 299.86 samples/sec INFO:root:Iter[10] Batch [2860] Speed: 849.56 samples/sec INFO:root:Iter[10] Batch [2880] Speed: 867.13 samples/sec INFO:root:Iter[10] Batch [2900] Speed: 847.29 samples/sec INFO:root:Iter[10] Batch [2920] Speed: 831.29 samples/sec INFO:root:Iter[10] Batch [2940] Speed: 822.39 samples/sec INFO:root:Iter[10] Batch [2960] Speed: 804.93 samples/sec INFO:root:Iter[10] Batch [2980] Speed: 819.86 samples/sec INFO:root:Iter[10] Batch [3000] Speed: 843.67 samples/sec INFO:root:Iter[10] Batch [3020] Speed: 852.39 samples/sec INFO:root:Iter[10] Batch [3040] Speed: 839.75 samples/sec INFO:root:Iter[10] Batch [3060] Speed: 854.86 samples/sec INFO:root:Iter[10] Batch [3080] Speed: 859.86 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samples/sec INFO:root:Iter[10] Batch [4800] Speed: 848.13 samples/sec INFO:root:Iter[10] Batch [4820] Speed: 854.37 samples/sec {fc7_acc} 106000 0.47851562 lr-batch-epoch: 0.010000000000000002 4839 10 testing verification.. (12000, 512) infer time 3.702305 [lfw][106000]XNorm: 22.247230 [lfw][106000]Accuracy-Flip: 0.99767+-0.00238 testing verification.. (14000, 512) infer time 4.377616999999999 [cfp_fp][106000]XNorm: 21.198276 [cfp_fp][106000]Accuracy-Flip: 0.98329+-0.00419 testing verification.. (12000, 512) infer time 3.7258560000000003 [agedb_30][106000]XNorm: 22.986752 [agedb_30][106000]Accuracy-Flip: 0.97817+-0.00705 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [106000]Accuracy-Highest: 0.97817 INFO:root:Iter[10] Batch [4840] Speed: 291.65 samples/sec INFO:root:Iter[10] Batch [4860] Speed: 850.81 samples/sec INFO:root:Iter[10] Batch [4880] Speed: 871.21 samples/sec INFO:root:Iter[10] Batch [4900] Speed: 834.39 samples/sec INFO:root:Iter[10] Batch [4920] Speed: 844.26 samples/sec INFO:root:Iter[10] Batch [4940] Speed: 844.63 samples/sec INFO:root:Iter[10] Batch [4960] Speed: 826.69 samples/sec INFO:root:Iter[10] Batch [4980] Speed: 851.95 samples/sec INFO:root:Iter[10] Batch [5000] Speed: 834.05 samples/sec INFO:root:Iter[10] Batch [5020] Speed: 839.93 samples/sec INFO:root:Iter[10] Batch [5040] Speed: 852.70 samples/sec INFO:root:Iter[10] Batch [5060] Speed: 831.51 samples/sec INFO:root:Iter[10] Batch [5080] Speed: 845.64 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(12000, 512) infer time 3.7759099999999997 [lfw][108000]XNorm: 22.160037 [lfw][108000]Accuracy-Flip: 0.99733+-0.00281 testing verification.. (14000, 512) infer time 4.358632999999999 [cfp_fp][108000]XNorm: 21.238027 [cfp_fp][108000]Accuracy-Flip: 0.98343+-0.00405 testing verification.. (12000, 512) infer time 3.768266999999999 [agedb_30][108000]XNorm: 22.646651 [agedb_30][108000]Accuracy-Flip: 0.97617+-0.00646 [108000]Accuracy-Highest: 0.97817 INFO:root:Iter[10] Batch [6840] Speed: 302.83 samples/sec INFO:root:Iter[10] Batch [6860] Speed: 846.57 samples/sec INFO:root:Iter[10] Batch [6880] Speed: 852.12 samples/sec INFO:root:Iter[10] Batch [6900] Speed: 823.26 samples/sec INFO:root:Iter[10] Batch [6920] Speed: 852.04 samples/sec INFO:root:Iter[10] Batch [6940] Speed: 846.92 samples/sec INFO:root:Iter[10] Batch [6960] Speed: 870.05 samples/sec INFO:root:Iter[10] Batch [6980] Speed: 851.76 samples/sec INFO:root:Iter[10] Batch [7000] Speed: 830.78 samples/sec INFO:root:Iter[10] Batch [7020] Speed: 841.47 samples/sec INFO:root:Iter[10] Batch [7040] Speed: 827.91 samples/sec INFO:root:Iter[10] Batch [7060] Speed: 862.45 samples/sec INFO:root:Iter[10] Batch [7080] Speed: 853.00 samples/sec INFO:root:Iter[10] Batch [7100] Speed: 865.06 samples/sec INFO:root:Iter[10] Batch 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(12000, 512) infer time 3.737724 [lfw][110000]XNorm: 22.443803 [lfw][110000]Accuracy-Flip: 0.99750+-0.00250 testing verification.. (14000, 512) infer time 4.402831 [cfp_fp][110000]XNorm: 21.488400 [cfp_fp][110000]Accuracy-Flip: 0.98214+-0.00488 testing verification.. (12000, 512) infer time 3.718142 [agedb_30][110000]XNorm: 23.123208 [agedb_30][110000]Accuracy-Flip: 0.97967+-0.00730 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [110000]Accuracy-Highest: 0.97967 INFO:root:Iter[10] Batch [8840] Speed: 297.13 samples/sec INFO:root:Iter[10] Batch [8860] Speed: 850.45 samples/sec INFO:root:Iter[10] Batch [8880] Speed: 854.53 samples/sec INFO:root:Iter[10] Batch [8900] Speed: 842.29 samples/sec INFO:root:Iter[10] Batch [8920] Speed: 834.12 samples/sec INFO:root:Iter[10] Batch [8940] Speed: 844.16 samples/sec INFO:root:Iter[10] Batch [8960] Speed: 820.74 samples/sec INFO:root:Iter[10] Batch [8980] Speed: 826.68 samples/sec INFO:root:Iter[10] Batch [9000] Speed: 863.32 samples/sec INFO:root:Iter[10] Batch [9020] Speed: 837.46 samples/sec INFO:root:Iter[10] Batch [9040] Speed: 844.77 samples/sec INFO:root:Iter[10] Batch [9060] Speed: 827.00 samples/sec INFO:root:Iter[10] Batch [9080] Speed: 831.45 samples/sec 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(12000, 512) infer time 3.7096469999999995 [lfw][112000]XNorm: 22.521457 [lfw][112000]Accuracy-Flip: 0.99750+-0.00310 testing verification.. (14000, 512) infer time 4.353513 [cfp_fp][112000]XNorm: 21.524713 [cfp_fp][112000]Accuracy-Flip: 0.98386+-0.00279 testing verification.. (12000, 512) infer time 3.7515240000000003 [agedb_30][112000]XNorm: 23.215881 [agedb_30][112000]Accuracy-Flip: 0.98083+-0.00676 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [112000]Accuracy-Highest: 0.98083 INFO:root:Iter[11] Batch [740] Speed: 291.03 samples/sec INFO:root:Iter[11] Batch [760] Speed: 851.10 samples/sec INFO:root:Iter[11] Batch [780] Speed: 836.79 samples/sec INFO:root:Iter[11] Batch [800] Speed: 847.69 samples/sec INFO:root:Iter[11] Batch [820] Speed: 858.92 samples/sec INFO:root:Iter[11] Batch [840] Speed: 866.72 samples/sec INFO:root:Iter[11] Batch [860] Speed: 857.94 samples/sec INFO:root:Iter[11] Batch [880] Speed: 859.32 samples/sec INFO:root:Iter[11] Batch [900] Speed: 855.35 samples/sec INFO:root:Iter[11] Batch [920] Speed: 837.11 samples/sec INFO:root:Iter[11] Batch [940] Speed: 841.63 samples/sec INFO:root:Iter[11] Batch [960] Speed: 831.63 samples/sec INFO:root:Iter[11] Batch [980] Speed: 853.98 samples/sec 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(12000, 512) infer time 3.734689000000001 [lfw][114000]XNorm: 22.355181 [lfw][114000]Accuracy-Flip: 0.99817+-0.00229 testing verification.. (14000, 512) infer time 4.35048 [cfp_fp][114000]XNorm: 21.261960 [cfp_fp][114000]Accuracy-Flip: 0.98471+-0.00419 testing verification.. (12000, 512) infer time 3.7565320000000004 [agedb_30][114000]XNorm: 23.065941 [agedb_30][114000]Accuracy-Flip: 0.97717+-0.00730 [114000]Accuracy-Highest: 0.98083 INFO:root:Iter[11] Batch [2740] Speed: 303.03 samples/sec INFO:root:Iter[11] Batch [2760] Speed: 848.07 samples/sec INFO:root:Iter[11] Batch [2780] Speed: 843.41 samples/sec INFO:root:Iter[11] Batch [2800] Speed: 863.67 samples/sec INFO:root:Iter[11] Batch [2820] Speed: 845.55 samples/sec INFO:root:Iter[11] Batch [2840] Speed: 829.46 samples/sec INFO:root:Iter[11] Batch [2860] Speed: 843.91 samples/sec INFO:root:Iter[11] Batch [2880] Speed: 833.12 samples/sec INFO:root:Iter[11] Batch [2900] Speed: 864.77 samples/sec INFO:root:Iter[11] Batch [2920] Speed: 827.98 samples/sec INFO:root:Iter[11] Batch [2940] Speed: 855.10 samples/sec INFO:root:Iter[11] Batch [2960] Speed: 863.04 samples/sec INFO:root:Iter[11] Batch [2980] Speed: 856.67 samples/sec INFO:root:Iter[11] Batch [3000] Speed: 849.18 samples/sec INFO:root:Iter[11] Batch 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(12000, 512) infer time 3.781023 [lfw][116000]XNorm: 22.456224 [lfw][116000]Accuracy-Flip: 0.99817+-0.00241 testing verification.. (14000, 512) infer time 4.341352999999999 [cfp_fp][116000]XNorm: 21.461585 [cfp_fp][116000]Accuracy-Flip: 0.98343+-0.00400 testing verification.. (12000, 512) infer time 3.7292699999999988 [agedb_30][116000]XNorm: 23.120616 [agedb_30][116000]Accuracy-Flip: 0.97950+-0.00687 [116000]Accuracy-Highest: 0.98083 INFO:root:Iter[11] Batch [4740] Speed: 295.54 samples/sec INFO:root:Iter[11] Batch [4760] Speed: 835.13 samples/sec INFO:root:Iter[11] Batch [4780] Speed: 844.76 samples/sec INFO:root:Iter[11] Batch [4800] Speed: 816.63 samples/sec INFO:root:Iter[11] Batch [4820] Speed: 841.38 samples/sec INFO:root:Iter[11] Batch [4840] Speed: 844.28 samples/sec INFO:root:Iter[11] Batch [4860] Speed: 835.25 samples/sec INFO:root:Iter[11] Batch [4880] Speed: 856.44 samples/sec INFO:root:Iter[11] Batch [4900] Speed: 860.22 samples/sec INFO:root:Iter[11] Batch [4920] Speed: 842.56 samples/sec INFO:root:Iter[11] Batch [4940] Speed: 871.09 samples/sec INFO:root:Iter[11] Batch [4960] Speed: 828.63 samples/sec INFO:root:Iter[11] Batch [4980] Speed: 852.31 samples/sec INFO:root:Iter[11] Batch [5000] Speed: 847.80 samples/sec INFO:root:Iter[11] Batch 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(12000, 512) infer time 3.858423999999999 [lfw][118000]XNorm: 22.523597 [lfw][118000]Accuracy-Flip: 0.99800+-0.00233 testing verification.. (14000, 512) infer time 4.317844000000003 [cfp_fp][118000]XNorm: 21.405447 [cfp_fp][118000]Accuracy-Flip: 0.98471+-0.00483 testing verification.. (12000, 512) infer time 3.6936679999999997 [agedb_30][118000]XNorm: 23.261558 [agedb_30][118000]Accuracy-Flip: 0.97950+-0.00837 [118000]Accuracy-Highest: 0.98083 INFO:root:Iter[11] Batch [6740] Speed: 299.12 samples/sec INFO:root:Iter[11] Batch [6760] Speed: 833.10 samples/sec INFO:root:Iter[11] Batch [6780] Speed: 814.37 samples/sec INFO:root:Iter[11] Batch [6800] Speed: 835.82 samples/sec INFO:root:Iter[11] Batch [6820] Speed: 859.61 samples/sec INFO:root:Iter[11] Batch [6840] Speed: 823.84 samples/sec INFO:root:Iter[11] Batch [6860] Speed: 819.18 samples/sec INFO:root:Iter[11] Batch [6880] Speed: 824.04 samples/sec INFO:root:Iter[11] Batch [6900] Speed: 856.97 samples/sec INFO:root:Iter[11] Batch [6920] Speed: 841.57 samples/sec INFO:root:Iter[11] Batch [6940] Speed: 843.82 samples/sec INFO:root:Iter[11] Batch [6960] Speed: 826.23 samples/sec INFO:root:Iter[11] Batch [6980] Speed: 814.70 samples/sec INFO:root:Iter[11] Batch [7000] Speed: 838.39 samples/sec INFO:root:Iter[11] Batch 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INFO:root:Iter[11] Batch [8720] Speed: 850.32 samples/sec {fc7_acc} 120000 0.4765625 lr-batch-epoch: 0.010000000000000002 8723 11 testing verification.. (12000, 512) infer time 3.697938 [lfw][120000]XNorm: 22.183467 [lfw][120000]Accuracy-Flip: 0.99750+-0.00250 testing verification.. (14000, 512) infer time 4.338123 [cfp_fp][120000]XNorm: 21.285486 [cfp_fp][120000]Accuracy-Flip: 0.98286+-0.00553 testing verification.. (12000, 512) infer time 3.7352730000000003 [agedb_30][120000]XNorm: 22.709336 [agedb_30][120000]Accuracy-Flip: 0.97700+-0.00819 [120000]Accuracy-Highest: 0.98083 INFO:root:Iter[11] Batch [8740] Speed: 300.88 samples/sec INFO:root:Iter[11] Batch [8760] Speed: 834.21 samples/sec INFO:root:Iter[11] Batch [8780] Speed: 857.26 samples/sec INFO:root:Iter[11] Batch [8800] Speed: 835.58 samples/sec INFO:root:Iter[11] Batch [8820] Speed: 830.24 samples/sec INFO:root:Iter[11] Batch [8840] Speed: 817.66 samples/sec INFO:root:Iter[11] Batch [8860] Speed: 852.25 samples/sec INFO:root:Iter[11] Batch [8880] Speed: 809.76 samples/sec INFO:root:Iter[11] Batch [8900] Speed: 849.62 samples/sec INFO:root:Iter[11] Batch [8920] Speed: 848.21 samples/sec INFO:root:Iter[11] Batch [8940] Speed: 853.39 samples/sec INFO:root:Iter[11] Batch [8960] Speed: 826.92 samples/sec INFO:root:Iter[11] Batch [8980] Speed: 819.01 samples/sec INFO:root:Iter[11] Batch [9000] Speed: 875.47 samples/sec INFO:root:Iter[11] Batch 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{fc7_acc} 122000 0.5136719 lr-batch-epoch: 0.010000000000000002 607 12 testing verification.. (12000, 512) infer time 3.715101 [lfw][122000]XNorm: 22.904237 [lfw][122000]Accuracy-Flip: 0.99833+-0.00183 testing verification.. (14000, 512) infer time 4.372281 [cfp_fp][122000]XNorm: 21.572908 [cfp_fp][122000]Accuracy-Flip: 0.98471+-0.00350 testing verification.. (12000, 512) infer time 3.7970539999999993 [agedb_30][122000]XNorm: 23.371132 [agedb_30][122000]Accuracy-Flip: 0.97900+-0.00735 [122000]Accuracy-Highest: 0.98083 INFO:root:Iter[12] Batch [620] Speed: 299.47 samples/sec INFO:root:Iter[12] Batch [640] Speed: 853.31 samples/sec INFO:root:Iter[12] Batch [660] Speed: 852.44 samples/sec INFO:root:Iter[12] Batch [680] Speed: 849.28 samples/sec INFO:root:Iter[12] Batch [700] Speed: 835.25 samples/sec INFO:root:Iter[12] Batch [720] Speed: 831.74 samples/sec INFO:root:Iter[12] Batch [740] Speed: 845.42 samples/sec INFO:root:Iter[12] Batch [760] Speed: 853.84 samples/sec INFO:root:Iter[12] Batch [780] Speed: 862.70 samples/sec INFO:root:Iter[12] Batch [800] Speed: 840.84 samples/sec INFO:root:Iter[12] Batch [820] Speed: 858.00 samples/sec INFO:root:Iter[12] Batch [840] Speed: 862.52 samples/sec INFO:root:Iter[12] Batch [860] Speed: 884.19 samples/sec INFO:root:Iter[12] Batch [880] Speed: 855.27 samples/sec INFO:root:Iter[12] Batch [900] Speed: 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samples/sec {fc7_acc} 124000 0.44140625 lr-batch-epoch: 0.010000000000000002 2607 12 testing verification.. (12000, 512) infer time 3.721994000000001 [lfw][124000]XNorm: 22.244782 [lfw][124000]Accuracy-Flip: 0.99767+-0.00271 testing verification.. (14000, 512) infer time 4.343334000000001 [cfp_fp][124000]XNorm: 21.402666 [cfp_fp][124000]Accuracy-Flip: 0.98229+-0.00496 testing verification.. (12000, 512) infer time 3.7484390000000007 [agedb_30][124000]XNorm: 22.953952 [agedb_30][124000]Accuracy-Flip: 0.97967+-0.00839 [124000]Accuracy-Highest: 0.98083 INFO:root:Iter[12] Batch [2620] Speed: 295.31 samples/sec INFO:root:Iter[12] Batch [2640] Speed: 872.79 samples/sec INFO:root:Iter[12] Batch [2660] Speed: 838.72 samples/sec INFO:root:Iter[12] Batch [2680] Speed: 835.60 samples/sec INFO:root:Iter[12] Batch [2700] Speed: 828.23 samples/sec INFO:root:Iter[12] Batch [2720] Speed: 823.36 samples/sec INFO:root:Iter[12] Batch [2740] Speed: 830.96 samples/sec INFO:root:Iter[12] Batch [2760] Speed: 835.21 samples/sec INFO:root:Iter[12] Batch [2780] Speed: 834.40 samples/sec INFO:root:Iter[12] Batch [2800] Speed: 835.67 samples/sec INFO:root:Iter[12] Batch [2820] Speed: 833.29 samples/sec INFO:root:Iter[12] Batch [2840] Speed: 816.52 samples/sec INFO:root:Iter[12] Batch [2860] Speed: 841.91 samples/sec INFO:root:Iter[12] Batch [2880] Speed: 884.30 samples/sec INFO:root:Iter[12] Batch 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INFO:root:Iter[12] Batch [4600] Speed: 851.61 samples/sec {fc7_acc} 126000 0.43554688 lr-batch-epoch: 0.010000000000000002 4607 12 testing verification.. (12000, 512) infer time 3.733989999999999 [lfw][126000]XNorm: 23.082340 [lfw][126000]Accuracy-Flip: 0.99733+-0.00327 testing verification.. (14000, 512) infer time 4.3843440000000005 [cfp_fp][126000]XNorm: 22.036275 [cfp_fp][126000]Accuracy-Flip: 0.98386+-0.00424 testing verification.. (12000, 512) infer time 3.746640000000001 [agedb_30][126000]XNorm: 23.554487 [agedb_30][126000]Accuracy-Flip: 0.98017+-0.00664 [126000]Accuracy-Highest: 0.98083 INFO:root:Iter[12] Batch [4620] Speed: 288.16 samples/sec INFO:root:Iter[12] Batch [4640] Speed: 857.27 samples/sec INFO:root:Iter[12] Batch [4660] Speed: 815.38 samples/sec INFO:root:Iter[12] Batch [4680] Speed: 862.63 samples/sec INFO:root:Iter[12] Batch [4700] Speed: 826.28 samples/sec INFO:root:Iter[12] Batch [4720] Speed: 829.75 samples/sec INFO:root:Iter[12] Batch [4740] Speed: 834.81 samples/sec INFO:root:Iter[12] Batch [4760] Speed: 843.76 samples/sec INFO:root:Iter[12] Batch [4780] Speed: 849.68 samples/sec INFO:root:Iter[12] Batch [4800] Speed: 822.79 samples/sec INFO:root:Iter[12] Batch [4820] Speed: 839.11 samples/sec INFO:root:Iter[12] Batch [4840] Speed: 848.24 samples/sec INFO:root:Iter[12] Batch [4860] Speed: 824.23 samples/sec INFO:root:Iter[12] Batch [4880] Speed: 835.80 samples/sec INFO:root:Iter[12] Batch 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INFO:root:Iter[12] Batch [6600] Speed: 860.41 samples/sec {fc7_acc} 128000 0.46289062 lr-batch-epoch: 0.010000000000000002 6607 12 testing verification.. (12000, 512) infer time 3.671902999999999 [lfw][128000]XNorm: 22.872422 [lfw][128000]Accuracy-Flip: 0.99800+-0.00233 testing verification.. (14000, 512) infer time 4.333102000000001 [cfp_fp][128000]XNorm: 21.602315 [cfp_fp][128000]Accuracy-Flip: 0.98314+-0.00526 testing verification.. (12000, 512) infer time 3.7167739999999987 [agedb_30][128000]XNorm: 23.080620 [agedb_30][128000]Accuracy-Flip: 0.97950+-0.00699 [128000]Accuracy-Highest: 0.98083 INFO:root:Iter[12] Batch [6620] Speed: 301.11 samples/sec INFO:root:Iter[12] Batch [6640] Speed: 834.12 samples/sec INFO:root:Iter[12] Batch [6660] Speed: 793.87 samples/sec INFO:root:Iter[12] Batch [6680] Speed: 838.47 samples/sec INFO:root:Iter[12] Batch [6700] Speed: 827.62 samples/sec INFO:root:Iter[12] Batch [6720] Speed: 847.12 samples/sec INFO:root:Iter[12] Batch [6740] Speed: 817.61 samples/sec INFO:root:Iter[12] Batch [6760] Speed: 824.54 samples/sec INFO:root:Iter[12] Batch [6780] Speed: 834.06 samples/sec INFO:root:Iter[12] Batch [6800] Speed: 836.76 samples/sec INFO:root:Iter[12] Batch [6820] Speed: 799.29 samples/sec INFO:root:Iter[12] Batch [6840] Speed: 861.64 samples/sec INFO:root:Iter[12] Batch [6860] Speed: 835.38 samples/sec INFO:root:Iter[12] Batch [6880] Speed: 842.47 samples/sec INFO:root:Iter[12] Batch 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INFO:root:Iter[12] Batch [8600] Speed: 887.61 samples/sec {fc7_acc} 130000 0.44726562 lr-batch-epoch: 0.010000000000000002 8607 12 testing verification.. (12000, 512) infer time 3.6885679999999996 [lfw][130000]XNorm: 21.875819 [lfw][130000]Accuracy-Flip: 0.99733+-0.00327 testing verification.. (14000, 512) infer time 4.310547 [cfp_fp][130000]XNorm: 21.100618 [cfp_fp][130000]Accuracy-Flip: 0.98400+-0.00588 testing verification.. (12000, 512) infer time 3.7245030000000003 [agedb_30][130000]XNorm: 22.150837 [agedb_30][130000]Accuracy-Flip: 0.98017+-0.00815 [130000]Accuracy-Highest: 0.98083 INFO:root:Iter[12] Batch [8620] Speed: 293.36 samples/sec INFO:root:Iter[12] Batch [8640] Speed: 838.69 samples/sec INFO:root:Iter[12] Batch [8660] Speed: 847.73 samples/sec INFO:root:Iter[12] Batch [8680] Speed: 842.76 samples/sec INFO:root:Iter[12] Batch [8700] Speed: 822.13 samples/sec INFO:root:Iter[12] Batch [8720] Speed: 825.32 samples/sec INFO:root:Iter[12] Batch [8740] Speed: 810.77 samples/sec INFO:root:Iter[12] Batch [8760] Speed: 816.47 samples/sec INFO:root:Iter[12] Batch [8780] Speed: 855.92 samples/sec INFO:root:Iter[12] Batch [8800] Speed: 849.55 samples/sec INFO:root:Iter[12] Batch [8820] Speed: 828.76 samples/sec INFO:root:Iter[12] Batch [8840] Speed: 844.90 samples/sec INFO:root:Iter[12] Batch [8860] Speed: 865.53 samples/sec INFO:root:Iter[12] Batch [8880] Speed: 832.33 samples/sec INFO:root:Iter[12] Batch 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{fc7_acc} 132000 0.5097656 lr-batch-epoch: 0.010000000000000002 491 13 testing verification.. (12000, 512) infer time 3.716216 [lfw][132000]XNorm: 22.432684 [lfw][132000]Accuracy-Flip: 0.99767+-0.00249 testing verification.. (14000, 512) infer time 4.324108 [cfp_fp][132000]XNorm: 21.362753 [cfp_fp][132000]Accuracy-Flip: 0.98300+-0.00631 testing verification.. (12000, 512) infer time 3.692070000000001 [agedb_30][132000]XNorm: 22.924560 [agedb_30][132000]Accuracy-Flip: 0.97867+-0.00649 [132000]Accuracy-Highest: 0.98083 INFO:root:Iter[13] Batch [500] Speed: 300.05 samples/sec INFO:root:Iter[13] Batch [520] Speed: 874.55 samples/sec INFO:root:Iter[13] Batch [540] Speed: 848.18 samples/sec INFO:root:Iter[13] Batch [560] Speed: 828.19 samples/sec INFO:root:Iter[13] Batch [580] Speed: 849.05 samples/sec INFO:root:Iter[13] Batch [600] Speed: 827.06 samples/sec INFO:root:Iter[13] Batch [620] Speed: 844.04 samples/sec INFO:root:Iter[13] Batch [640] Speed: 845.55 samples/sec INFO:root:Iter[13] Batch [660] Speed: 859.49 samples/sec INFO:root:Iter[13] Batch [680] Speed: 850.80 samples/sec INFO:root:Iter[13] Batch [700] Speed: 844.90 samples/sec INFO:root:Iter[13] Batch [720] Speed: 849.13 samples/sec INFO:root:Iter[13] Batch [740] Speed: 846.22 samples/sec INFO:root:Iter[13] Batch [760] Speed: 859.95 samples/sec INFO:root:Iter[13] Batch [780] Speed: 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samples/sec {fc7_acc} 134000 0.49609375 lr-batch-epoch: 0.010000000000000002 2491 13 testing verification.. (12000, 512) infer time 3.6866640000000004 [lfw][134000]XNorm: 22.149758 [lfw][134000]Accuracy-Flip: 0.99817+-0.00229 testing verification.. (14000, 512) infer time 4.29976 [cfp_fp][134000]XNorm: 21.457099 [cfp_fp][134000]Accuracy-Flip: 0.98557+-0.00463 testing verification.. (12000, 512) infer time 3.723896 [agedb_30][134000]XNorm: 22.593258 [agedb_30][134000]Accuracy-Flip: 0.97983+-0.00647 [134000]Accuracy-Highest: 0.98083 INFO:root:Iter[13] Batch [2500] Speed: 297.41 samples/sec INFO:root:Iter[13] Batch [2520] Speed: 826.72 samples/sec INFO:root:Iter[13] Batch [2540] Speed: 845.39 samples/sec INFO:root:Iter[13] Batch [2560] Speed: 852.25 samples/sec INFO:root:Iter[13] Batch [2580] Speed: 839.52 samples/sec INFO:root:Iter[13] Batch [2600] Speed: 865.16 samples/sec INFO:root:Iter[13] Batch [2620] Speed: 847.05 samples/sec INFO:root:Iter[13] Batch [2640] Speed: 843.10 samples/sec INFO:root:Iter[13] Batch [2660] Speed: 840.61 samples/sec INFO:root:Iter[13] Batch [2680] Speed: 847.56 samples/sec INFO:root:Iter[13] Batch [2700] Speed: 842.77 samples/sec INFO:root:Iter[13] Batch [2720] Speed: 850.39 samples/sec INFO:root:Iter[13] Batch [2740] Speed: 819.83 samples/sec INFO:root:Iter[13] Batch [2760] Speed: 841.18 samples/sec INFO:root:Iter[13] Batch [2780] 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samples/sec {fc7_acc} 136000 0.421875 lr-batch-epoch: 0.010000000000000002 4491 13 testing verification.. (12000, 512) infer time 3.835394 [lfw][136000]XNorm: 23.280009 [lfw][136000]Accuracy-Flip: 0.99750+-0.00318 testing verification.. (14000, 512) infer time 4.443386999999998 [cfp_fp][136000]XNorm: 22.211812 [cfp_fp][136000]Accuracy-Flip: 0.98371+-0.00547 testing verification.. (12000, 512) infer time 3.7242979999999988 [agedb_30][136000]XNorm: 23.757143 [agedb_30][136000]Accuracy-Flip: 0.97917+-0.00735 [136000]Accuracy-Highest: 0.98083 INFO:root:Iter[13] Batch [4500] Speed: 299.12 samples/sec INFO:root:Iter[13] Batch [4520] Speed: 812.19 samples/sec INFO:root:Iter[13] Batch [4540] Speed: 841.21 samples/sec INFO:root:Iter[13] Batch [4560] Speed: 832.34 samples/sec INFO:root:Iter[13] Batch [4580] Speed: 815.63 samples/sec INFO:root:Iter[13] Batch [4600] Speed: 864.47 samples/sec INFO:root:Iter[13] Batch [4620] Speed: 858.36 samples/sec INFO:root:Iter[13] Batch [4640] Speed: 861.78 samples/sec INFO:root:Iter[13] Batch [4660] Speed: 853.26 samples/sec INFO:root:Iter[13] Batch [4680] Speed: 862.95 samples/sec INFO:root:Iter[13] Batch [4700] Speed: 805.70 samples/sec INFO:root:Iter[13] Batch [4720] Speed: 859.14 samples/sec INFO:root:Iter[13] Batch [4740] Speed: 836.96 samples/sec INFO:root:Iter[13] Batch [4760] Speed: 851.90 samples/sec INFO:root:Iter[13] Batch 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INFO:root:Iter[13] Batch [6480] Speed: 864.27 samples/sec {fc7_acc} 138000 0.4453125 lr-batch-epoch: 0.010000000000000002 6491 13 testing verification.. (12000, 512) infer time 3.7075859999999987 [lfw][138000]XNorm: 22.328348 [lfw][138000]Accuracy-Flip: 0.99800+-0.00233 testing verification.. (14000, 512) infer time 4.397911999999999 [cfp_fp][138000]XNorm: 21.083719 [cfp_fp][138000]Accuracy-Flip: 0.98329+-0.00613 testing verification.. (12000, 512) infer time 3.7072580000000004 [agedb_30][138000]XNorm: 23.075552 [agedb_30][138000]Accuracy-Flip: 0.98017+-0.00776 [138000]Accuracy-Highest: 0.98083 INFO:root:Iter[13] Batch [6500] Speed: 297.84 samples/sec INFO:root:Iter[13] Batch [6520] Speed: 837.97 samples/sec INFO:root:Iter[13] Batch [6540] Speed: 824.20 samples/sec INFO:root:Iter[13] Batch [6560] Speed: 848.24 samples/sec INFO:root:Iter[13] Batch [6580] Speed: 835.96 samples/sec INFO:root:Iter[13] Batch [6600] Speed: 844.29 samples/sec INFO:root:Iter[13] Batch [6620] Speed: 870.24 samples/sec INFO:root:Iter[13] Batch [6640] Speed: 867.66 samples/sec INFO:root:Iter[13] Batch [6660] Speed: 841.22 samples/sec INFO:root:Iter[13] Batch [6680] Speed: 854.88 samples/sec INFO:root:Iter[13] Batch [6700] Speed: 862.57 samples/sec INFO:root:Iter[13] Batch [6720] Speed: 862.45 samples/sec INFO:root:Iter[13] Batch [6740] Speed: 842.80 samples/sec INFO:root:Iter[13] Batch [6760] Speed: 845.31 samples/sec INFO:root:Iter[13] Batch 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INFO:root:Iter[13] Batch [8480] Speed: 886.76 samples/sec {fc7_acc} 140000 0.47265625 lr-batch-epoch: 0.010000000000000002 8491 13 testing verification.. (12000, 512) infer time 3.7134789999999995 [lfw][140000]XNorm: 22.747301 [lfw][140000]Accuracy-Flip: 0.99750+-0.00250 testing verification.. (14000, 512) infer time 4.383935000000001 [cfp_fp][140000]XNorm: 21.669952 [cfp_fp][140000]Accuracy-Flip: 0.98500+-0.00453 testing verification.. (12000, 512) infer time 3.721106 [agedb_30][140000]XNorm: 23.064378 [agedb_30][140000]Accuracy-Flip: 0.97967+-0.00806 [140000]Accuracy-Highest: 0.98083 INFO:root:Iter[13] Batch [8500] Speed: 293.89 samples/sec INFO:root:Iter[13] Batch [8520] Speed: 869.31 samples/sec INFO:root:Iter[13] Batch [8540] Speed: 838.77 samples/sec INFO:root:Iter[13] Batch [8560] Speed: 854.38 samples/sec INFO:root:Iter[13] Batch [8580] Speed: 860.96 samples/sec INFO:root:Iter[13] Batch [8600] Speed: 892.81 samples/sec INFO:root:Iter[13] Batch [8620] Speed: 834.36 samples/sec INFO:root:Iter[13] Batch [8640] Speed: 873.78 samples/sec INFO:root:Iter[13] Batch [8660] Speed: 850.73 samples/sec INFO:root:Iter[13] Batch [8680] Speed: 837.02 samples/sec INFO:root:Iter[13] Batch [8700] Speed: 847.73 samples/sec INFO:root:Iter[13] Batch [8720] Speed: 875.98 samples/sec INFO:root:Iter[13] Batch [8740] Speed: 854.14 samples/sec INFO:root:Iter[13] Batch [8760] Speed: 864.08 samples/sec INFO:root:Iter[13] Batch [8780] 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872.55 samples/sec INFO:root:Iter[14] Batch [40] Speed: 865.32 samples/sec INFO:root:Iter[14] Batch [60] Speed: 860.53 samples/sec INFO:root:Iter[14] Batch [80] Speed: 874.17 samples/sec INFO:root:Iter[14] Batch [100] Speed: 882.52 samples/sec INFO:root:Iter[14] Batch [120] Speed: 859.89 samples/sec INFO:root:Iter[14] Batch [140] Speed: 852.52 samples/sec INFO:root:Iter[14] Batch [160] Speed: 856.52 samples/sec INFO:root:Iter[14] Batch [180] Speed: 854.37 samples/sec INFO:root:Iter[14] Batch [200] Speed: 858.93 samples/sec INFO:root:Iter[14] Batch [220] Speed: 844.43 samples/sec INFO:root:Iter[14] Batch [240] Speed: 861.50 samples/sec INFO:root:Iter[14] Batch [260] Speed: 901.54 samples/sec INFO:root:Iter[14] Batch [280] Speed: 858.86 samples/sec INFO:root:Iter[14] Batch [300] Speed: 849.03 samples/sec INFO:root:Iter[14] Batch [320] Speed: 872.45 samples/sec INFO:root:Iter[14] Batch [340] Speed: 851.95 samples/sec INFO:root:Iter[14] Batch [360] Speed: 875.85 samples/sec {fc7_acc} 142000 0.51171875 lr-batch-epoch: 0.010000000000000002 375 14 testing verification.. (12000, 512) infer time 3.721441 [lfw][142000]XNorm: 23.065543 [lfw][142000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.366748 [cfp_fp][142000]XNorm: 21.987793 [cfp_fp][142000]Accuracy-Flip: 0.98457+-0.00473 testing verification.. (12000, 512) infer time 3.712053 [agedb_30][142000]XNorm: 23.621845 [agedb_30][142000]Accuracy-Flip: 0.97850+-0.00603 [142000]Accuracy-Highest: 0.98083 INFO:root:Iter[14] Batch [380] Speed: 299.99 samples/sec INFO:root:Iter[14] Batch [400] Speed: 851.20 samples/sec INFO:root:Iter[14] Batch [420] Speed: 814.39 samples/sec INFO:root:Iter[14] Batch [440] Speed: 845.00 samples/sec INFO:root:Iter[14] Batch [460] Speed: 866.12 samples/sec INFO:root:Iter[14] Batch [480] Speed: 833.28 samples/sec INFO:root:Iter[14] Batch [500] Speed: 833.90 samples/sec INFO:root:Iter[14] Batch [520] Speed: 817.73 samples/sec INFO:root:Iter[14] Batch [540] Speed: 825.91 samples/sec INFO:root:Iter[14] Batch [560] Speed: 819.91 samples/sec INFO:root:Iter[14] Batch [580] Speed: 862.13 samples/sec INFO:root:Iter[14] Batch [600] Speed: 830.71 samples/sec INFO:root:Iter[14] Batch [620] Speed: 824.90 samples/sec INFO:root:Iter[14] Batch [640] Speed: 842.12 samples/sec INFO:root:Iter[14] Batch [660] Speed: 846.91 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144000 0.48632812 lr-batch-epoch: 0.010000000000000002 2375 14 testing verification.. (12000, 512) infer time 3.752606000000001 [lfw][144000]XNorm: 22.767382 [lfw][144000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 4.3232930000000005 [cfp_fp][144000]XNorm: 21.327395 [cfp_fp][144000]Accuracy-Flip: 0.98514+-0.00479 testing verification.. (12000, 512) infer time 3.7498480000000005 [agedb_30][144000]XNorm: 23.084249 [agedb_30][144000]Accuracy-Flip: 0.97767+-0.00754 [144000]Accuracy-Highest: 0.98083 INFO:root:Iter[14] Batch [2380] Speed: 302.24 samples/sec INFO:root:Iter[14] Batch [2400] Speed: 846.10 samples/sec INFO:root:Iter[14] Batch [2420] Speed: 827.93 samples/sec INFO:root:Iter[14] Batch [2440] Speed: 808.02 samples/sec INFO:root:Iter[14] Batch [2460] Speed: 839.10 samples/sec INFO:root:Iter[14] Batch [2480] Speed: 823.93 samples/sec INFO:root:Iter[14] Batch [2500] Speed: 829.89 samples/sec INFO:root:Iter[14] Batch [2520] Speed: 836.39 samples/sec INFO:root:Iter[14] Batch [2540] Speed: 815.68 samples/sec INFO:root:Iter[14] Batch [2560] Speed: 840.83 samples/sec INFO:root:Iter[14] Batch [2580] Speed: 826.17 samples/sec INFO:root:Iter[14] Batch [2600] Speed: 833.21 samples/sec INFO:root:Iter[14] Batch [2620] Speed: 837.40 samples/sec INFO:root:Iter[14] Batch [2640] Speed: 849.67 samples/sec INFO:root:Iter[14] Batch 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INFO:root:Iter[14] Batch [4360] Speed: 854.46 samples/sec {fc7_acc} 146000 0.5019531 lr-batch-epoch: 0.010000000000000002 4375 14 testing verification.. (12000, 512) infer time 3.708131 [lfw][146000]XNorm: 22.280206 [lfw][146000]Accuracy-Flip: 0.99800+-0.00277 testing verification.. (14000, 512) infer time 4.502179999999999 [cfp_fp][146000]XNorm: 21.293334 [cfp_fp][146000]Accuracy-Flip: 0.98300+-0.00396 testing verification.. (12000, 512) infer time 3.7402799999999994 [agedb_30][146000]XNorm: 22.773426 [agedb_30][146000]Accuracy-Flip: 0.97983+-0.00697 [146000]Accuracy-Highest: 0.98083 INFO:root:Iter[14] Batch [4380] Speed: 298.26 samples/sec INFO:root:Iter[14] Batch [4400] Speed: 812.42 samples/sec INFO:root:Iter[14] Batch [4420] Speed: 822.60 samples/sec INFO:root:Iter[14] Batch [4440] Speed: 816.67 samples/sec INFO:root:Iter[14] Batch [4460] Speed: 829.09 samples/sec INFO:root:Iter[14] Batch [4480] Speed: 858.72 samples/sec INFO:root:Iter[14] Batch [4500] Speed: 830.03 samples/sec INFO:root:Iter[14] Batch [4520] Speed: 834.79 samples/sec INFO:root:Iter[14] Batch [4540] Speed: 862.51 samples/sec INFO:root:Iter[14] Batch [4560] Speed: 818.17 samples/sec INFO:root:Iter[14] Batch [4580] Speed: 854.40 samples/sec INFO:root:Iter[14] Batch [4600] Speed: 858.60 samples/sec INFO:root:Iter[14] Batch [4620] Speed: 857.68 samples/sec INFO:root:Iter[14] Batch [4640] Speed: 826.81 samples/sec INFO:root:Iter[14] Batch 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(12000, 512) infer time 3.7951569999999997 [lfw][148000]XNorm: 22.690602 [lfw][148000]Accuracy-Flip: 0.99800+-0.00233 testing verification.. (14000, 512) infer time 4.391771 [cfp_fp][148000]XNorm: 21.541549 [cfp_fp][148000]Accuracy-Flip: 0.98514+-0.00453 testing verification.. (12000, 512) infer time 3.7491429999999992 [agedb_30][148000]XNorm: 22.928387 [agedb_30][148000]Accuracy-Flip: 0.97933+-0.00775 [148000]Accuracy-Highest: 0.98083 INFO:root:Iter[14] Batch [6380] Speed: 302.79 samples/sec INFO:root:Iter[14] Batch [6400] Speed: 823.58 samples/sec INFO:root:Iter[14] Batch [6420] Speed: 834.15 samples/sec INFO:root:Iter[14] Batch [6440] Speed: 830.91 samples/sec INFO:root:Iter[14] Batch [6460] Speed: 843.05 samples/sec INFO:root:Iter[14] Batch [6480] Speed: 858.59 samples/sec INFO:root:Iter[14] Batch [6500] Speed: 808.85 samples/sec INFO:root:Iter[14] Batch [6520] Speed: 854.37 samples/sec INFO:root:Iter[14] Batch [6540] Speed: 838.07 samples/sec INFO:root:Iter[14] Batch [6560] Speed: 856.10 samples/sec INFO:root:Iter[14] Batch [6580] Speed: 867.21 samples/sec INFO:root:Iter[14] Batch [6600] Speed: 856.37 samples/sec INFO:root:Iter[14] Batch [6620] Speed: 871.36 samples/sec INFO:root:Iter[14] Batch [6640] Speed: 837.81 samples/sec INFO:root:Iter[14] Batch 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(12000, 512) infer time 3.8120849999999997 [lfw][150000]XNorm: 22.216176 [lfw][150000]Accuracy-Flip: 0.99783+-0.00289 testing verification.. (14000, 512) infer time 4.408327 [cfp_fp][150000]XNorm: 21.304477 [cfp_fp][150000]Accuracy-Flip: 0.98600+-0.00455 testing verification.. (12000, 512) infer time 3.7472690000000006 [agedb_30][150000]XNorm: 22.605532 [agedb_30][150000]Accuracy-Flip: 0.97933+-0.00764 [150000]Accuracy-Highest: 0.98083 INFO:root:Iter[14] Batch [8380] Speed: 298.51 samples/sec INFO:root:Iter[14] Batch [8400] Speed: 846.82 samples/sec INFO:root:Iter[14] Batch [8420] Speed: 839.17 samples/sec INFO:root:Iter[14] Batch [8440] Speed: 838.26 samples/sec INFO:root:Iter[14] Batch [8460] Speed: 837.84 samples/sec INFO:root:Iter[14] Batch [8480] Speed: 856.55 samples/sec INFO:root:Iter[14] Batch [8500] Speed: 837.25 samples/sec INFO:root:Iter[14] Batch [8520] Speed: 861.99 samples/sec INFO:root:Iter[14] Batch [8540] Speed: 830.68 samples/sec INFO:root:Iter[14] Batch [8560] Speed: 834.81 samples/sec INFO:root:Iter[14] Batch [8580] Speed: 864.18 samples/sec INFO:root:Iter[14] Batch [8600] Speed: 852.75 samples/sec INFO:root:Iter[14] Batch [8620] Speed: 848.90 samples/sec INFO:root:Iter[14] Batch [8640] Speed: 881.89 samples/sec INFO:root:Iter[14] Batch 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Speed: 853.92 samples/sec {fc7_acc} 152000 0.4765625 lr-batch-epoch: 0.010000000000000002 259 15 testing verification.. (12000, 512) infer time 3.690974 [lfw][152000]XNorm: 22.426722 [lfw][152000]Accuracy-Flip: 0.99817+-0.00241 testing verification.. (14000, 512) infer time 4.395228 [cfp_fp][152000]XNorm: 21.643166 [cfp_fp][152000]Accuracy-Flip: 0.98429+-0.00447 testing verification.. (12000, 512) infer time 3.7295089999999993 [agedb_30][152000]XNorm: 23.070360 [agedb_30][152000]Accuracy-Flip: 0.97900+-0.00633 [152000]Accuracy-Highest: 0.98083 INFO:root:Iter[15] Batch [260] Speed: 304.70 samples/sec INFO:root:Iter[15] Batch [280] Speed: 826.41 samples/sec INFO:root:Iter[15] Batch [300] Speed: 809.87 samples/sec INFO:root:Iter[15] Batch [320] Speed: 844.37 samples/sec INFO:root:Iter[15] Batch [340] Speed: 826.99 samples/sec INFO:root:Iter[15] Batch [360] Speed: 826.59 samples/sec INFO:root:Iter[15] Batch [380] Speed: 830.20 samples/sec INFO:root:Iter[15] Batch [400] Speed: 849.65 samples/sec INFO:root:Iter[15] Batch [420] Speed: 855.44 samples/sec INFO:root:Iter[15] Batch [440] Speed: 885.32 samples/sec INFO:root:Iter[15] Batch [460] Speed: 849.50 samples/sec INFO:root:Iter[15] Batch [480] Speed: 855.08 samples/sec INFO:root:Iter[15] Batch [500] Speed: 840.96 samples/sec INFO:root:Iter[15] Batch [520] Speed: 852.20 samples/sec INFO:root:Iter[15] Batch [540] Speed: 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154000 0.43359375 lr-batch-epoch: 0.010000000000000002 2259 15 testing verification.. (12000, 512) infer time 3.674056 [lfw][154000]XNorm: 22.511655 [lfw][154000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.335099 [cfp_fp][154000]XNorm: 21.350071 [cfp_fp][154000]Accuracy-Flip: 0.98614+-0.00495 testing verification.. (12000, 512) infer time 3.7419060000000006 [agedb_30][154000]XNorm: 22.977380 [agedb_30][154000]Accuracy-Flip: 0.98050+-0.00619 [154000]Accuracy-Highest: 0.98083 INFO:root:Iter[15] Batch [2260] Speed: 303.46 samples/sec INFO:root:Iter[15] Batch [2280] Speed: 853.20 samples/sec INFO:root:Iter[15] Batch [2300] Speed: 840.16 samples/sec INFO:root:Iter[15] Batch [2320] Speed: 860.69 samples/sec INFO:root:Iter[15] Batch [2340] Speed: 847.29 samples/sec INFO:root:Iter[15] Batch [2360] Speed: 840.54 samples/sec INFO:root:Iter[15] Batch [2380] Speed: 848.70 samples/sec INFO:root:Iter[15] Batch [2400] Speed: 851.59 samples/sec INFO:root:Iter[15] Batch [2420] Speed: 854.42 samples/sec INFO:root:Iter[15] Batch [2440] Speed: 832.83 samples/sec INFO:root:Iter[15] Batch [2460] Speed: 853.35 samples/sec INFO:root:Iter[15] Batch [2480] Speed: 839.54 samples/sec INFO:root:Iter[15] Batch [2500] Speed: 854.31 samples/sec INFO:root:Iter[15] Batch [2520] Speed: 852.50 samples/sec INFO:root:Iter[15] Batch 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(12000, 512) infer time 3.7139059999999993 [lfw][156000]XNorm: 21.793911 [lfw][156000]Accuracy-Flip: 0.99750+-0.00310 testing verification.. (14000, 512) infer time 4.353741 [cfp_fp][156000]XNorm: 20.881752 [cfp_fp][156000]Accuracy-Flip: 0.98214+-0.00500 testing verification.. (12000, 512) infer time 3.7686550000000008 [agedb_30][156000]XNorm: 22.799360 [agedb_30][156000]Accuracy-Flip: 0.97900+-0.00779 [156000]Accuracy-Highest: 0.98083 INFO:root:Iter[15] Batch [4260] Speed: 306.47 samples/sec INFO:root:Iter[15] Batch [4280] Speed: 829.79 samples/sec INFO:root:Iter[15] Batch [4300] Speed: 830.73 samples/sec INFO:root:Iter[15] Batch [4320] Speed: 812.62 samples/sec INFO:root:Iter[15] Batch [4340] Speed: 839.89 samples/sec INFO:root:Iter[15] Batch [4360] Speed: 839.29 samples/sec INFO:root:Iter[15] Batch [4380] Speed: 820.88 samples/sec INFO:root:Iter[15] Batch [4400] Speed: 813.13 samples/sec INFO:root:Iter[15] Batch [4420] Speed: 826.48 samples/sec INFO:root:Iter[15] Batch [4440] Speed: 844.36 samples/sec INFO:root:Iter[15] Batch [4460] Speed: 847.13 samples/sec INFO:root:Iter[15] Batch [4480] Speed: 837.20 samples/sec INFO:root:Iter[15] Batch [4500] Speed: 831.87 samples/sec INFO:root:Iter[15] Batch [4520] Speed: 855.29 samples/sec INFO:root:Iter[15] Batch 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INFO:root:Iter[15] Batch [6240] Speed: 853.55 samples/sec {fc7_acc} 158000 0.46875 lr-batch-epoch: 0.010000000000000002 6259 15 testing verification.. (12000, 512) infer time 3.720740000000001 [lfw][158000]XNorm: 23.307812 [lfw][158000]Accuracy-Flip: 0.99817+-0.00241 testing verification.. (14000, 512) infer time 4.387300000000001 [cfp_fp][158000]XNorm: 21.991954 [cfp_fp][158000]Accuracy-Flip: 0.98429+-0.00356 testing verification.. (12000, 512) infer time 3.747420000000001 [agedb_30][158000]XNorm: 23.818229 [agedb_30][158000]Accuracy-Flip: 0.98100+-0.00646 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [158000]Accuracy-Highest: 0.98100 INFO:root:Iter[15] Batch [6260] Speed: 297.85 samples/sec INFO:root:Iter[15] Batch [6280] Speed: 810.00 samples/sec INFO:root:Iter[15] Batch [6300] Speed: 818.38 samples/sec INFO:root:Iter[15] Batch [6320] Speed: 858.55 samples/sec INFO:root:Iter[15] Batch [6340] Speed: 834.14 samples/sec INFO:root:Iter[15] Batch [6360] Speed: 869.71 samples/sec INFO:root:Iter[15] Batch [6380] Speed: 822.73 samples/sec INFO:root:Iter[15] Batch [6400] Speed: 873.70 samples/sec INFO:root:Iter[15] Batch [6420] Speed: 811.39 samples/sec INFO:root:Iter[15] Batch [6440] Speed: 833.71 samples/sec INFO:root:Iter[15] Batch [6460] Speed: 853.26 samples/sec INFO:root:Iter[15] Batch [6480] Speed: 815.40 samples/sec INFO:root:Iter[15] Batch [6500] Speed: 808.27 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(12000, 512) infer time 3.747035999999999 [lfw][160000]XNorm: 22.706179 [lfw][160000]Accuracy-Flip: 0.99817+-0.00241 testing verification.. (14000, 512) infer time 4.444989 [cfp_fp][160000]XNorm: 21.663541 [cfp_fp][160000]Accuracy-Flip: 0.98557+-0.00353 testing verification.. (12000, 512) infer time 3.7130609999999993 [agedb_30][160000]XNorm: 23.358417 [agedb_30][160000]Accuracy-Flip: 0.98200+-0.00869 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [160000]Accuracy-Highest: 0.98200 INFO:root:Iter[15] Batch [8260] Speed: 298.53 samples/sec INFO:root:Iter[15] Batch [8280] Speed: 810.06 samples/sec INFO:root:Iter[15] Batch [8300] Speed: 821.55 samples/sec INFO:root:Iter[15] Batch [8320] Speed: 803.02 samples/sec INFO:root:Iter[15] Batch [8340] Speed: 848.55 samples/sec INFO:root:Iter[15] Batch [8360] Speed: 850.50 samples/sec INFO:root:Iter[15] Batch [8380] Speed: 829.59 samples/sec INFO:root:Iter[15] Batch [8400] Speed: 841.42 samples/sec INFO:root:Iter[15] Batch [8420] Speed: 837.39 samples/sec INFO:root:Iter[15] Batch [8440] Speed: 829.41 samples/sec INFO:root:Iter[15] Batch [8460] Speed: 846.41 samples/sec INFO:root:Iter[15] Batch [8480] Speed: 870.01 samples/sec INFO:root:Iter[15] Batch [8500] Speed: 833.55 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(12000, 512) infer time 3.6968080000000003 [lfw][162000]XNorm: 22.147852 [lfw][162000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.299741000000001 [cfp_fp][162000]XNorm: 21.627607 [cfp_fp][162000]Accuracy-Flip: 0.98700+-0.00341 testing verification.. (12000, 512) infer time 3.82574 [agedb_30][162000]XNorm: 23.081771 [agedb_30][162000]Accuracy-Flip: 0.98283+-0.00789 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [162000]Accuracy-Highest: 0.98283 INFO:root:Iter[16] Batch [160] Speed: 292.24 samples/sec INFO:root:Iter[16] Batch [180] Speed: 872.39 samples/sec INFO:root:Iter[16] Batch [200] Speed: 826.43 samples/sec INFO:root:Iter[16] Batch [220] Speed: 828.96 samples/sec INFO:root:Iter[16] Batch [240] Speed: 862.08 samples/sec INFO:root:Iter[16] Batch [260] Speed: 850.48 samples/sec INFO:root:Iter[16] Batch [280] Speed: 868.20 samples/sec INFO:root:Iter[16] Batch [300] Speed: 816.68 samples/sec INFO:root:Iter[16] Batch [320] Speed: 849.00 samples/sec INFO:root:Iter[16] Batch [340] Speed: 860.62 samples/sec INFO:root:Iter[16] Batch [360] Speed: 843.35 samples/sec INFO:root:Iter[16] Batch [380] Speed: 852.71 samples/sec INFO:root:Iter[16] Batch [400] Speed: 846.45 samples/sec INFO:root:Iter[16] 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(12000, 512) infer time 3.7348539999999986 [lfw][164000]XNorm: 22.316927 [lfw][164000]Accuracy-Flip: 0.99850+-0.00203 testing verification.. (14000, 512) infer time 4.376252 [cfp_fp][164000]XNorm: 21.936219 [cfp_fp][164000]Accuracy-Flip: 0.98686+-0.00408 testing verification.. (12000, 512) infer time 3.8226910000000003 [agedb_30][164000]XNorm: 23.280371 [agedb_30][164000]Accuracy-Flip: 0.98183+-0.00776 [164000]Accuracy-Highest: 0.98283 INFO:root:Iter[16] Batch [2160] Speed: 298.57 samples/sec INFO:root:Iter[16] Batch [2180] Speed: 832.60 samples/sec INFO:root:Iter[16] Batch [2200] Speed: 857.45 samples/sec INFO:root:Iter[16] Batch [2220] Speed: 860.12 samples/sec INFO:root:Iter[16] Batch [2240] Speed: 802.67 samples/sec INFO:root:Iter[16] Batch [2260] Speed: 824.62 samples/sec INFO:root:Iter[16] Batch [2280] Speed: 839.06 samples/sec INFO:root:Iter[16] Batch [2300] Speed: 846.35 samples/sec INFO:root:Iter[16] Batch [2320] Speed: 843.62 samples/sec INFO:root:Iter[16] Batch [2340] Speed: 842.47 samples/sec INFO:root:Iter[16] Batch [2360] Speed: 817.79 samples/sec INFO:root:Iter[16] Batch [2380] Speed: 828.95 samples/sec INFO:root:Iter[16] Batch [2400] Speed: 848.44 samples/sec INFO:root:Iter[16] Batch [2420] Speed: 838.73 samples/sec INFO:root:Iter[16] Batch 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(12000, 512) infer time 3.703957999999999 [lfw][166000]XNorm: 22.156163 [lfw][166000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.353723 [cfp_fp][166000]XNorm: 21.905539 [cfp_fp][166000]Accuracy-Flip: 0.98771+-0.00357 testing verification.. (12000, 512) infer time 3.706925000000001 [agedb_30][166000]XNorm: 23.254591 [agedb_30][166000]Accuracy-Flip: 0.98200+-0.00767 [166000]Accuracy-Highest: 0.98283 INFO:root:Iter[16] Batch [4160] Speed: 300.63 samples/sec INFO:root:Iter[16] Batch [4180] Speed: 839.75 samples/sec INFO:root:Iter[16] Batch [4200] Speed: 836.98 samples/sec INFO:root:Iter[16] Batch [4220] Speed: 832.30 samples/sec INFO:root:Iter[16] Batch [4240] Speed: 855.66 samples/sec INFO:root:Iter[16] Batch [4260] Speed: 853.44 samples/sec INFO:root:Iter[16] Batch [4280] Speed: 862.77 samples/sec INFO:root:Iter[16] Batch [4300] Speed: 821.51 samples/sec INFO:root:Iter[16] Batch [4320] Speed: 862.07 samples/sec INFO:root:Iter[16] Batch [4340] Speed: 852.35 samples/sec INFO:root:Iter[16] Batch [4360] Speed: 867.63 samples/sec INFO:root:Iter[16] Batch [4380] Speed: 845.14 samples/sec INFO:root:Iter[16] Batch [4400] Speed: 849.18 samples/sec INFO:root:Iter[16] Batch [4420] Speed: 858.06 samples/sec INFO:root:Iter[16] Batch 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(12000, 512) infer time 3.689509 [lfw][168000]XNorm: 22.239091 [lfw][168000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.357136 [cfp_fp][168000]XNorm: 22.014172 [cfp_fp][168000]Accuracy-Flip: 0.98829+-0.00262 testing verification.. (12000, 512) infer time 3.7604499999999996 [agedb_30][168000]XNorm: 23.343474 [agedb_30][168000]Accuracy-Flip: 0.98200+-0.00792 [168000]Accuracy-Highest: 0.98283 INFO:root:Iter[16] Batch [6160] Speed: 297.73 samples/sec INFO:root:Iter[16] Batch [6180] Speed: 818.92 samples/sec INFO:root:Iter[16] Batch [6200] Speed: 824.70 samples/sec INFO:root:Iter[16] Batch [6220] Speed: 794.90 samples/sec INFO:root:Iter[16] Batch [6240] Speed: 834.59 samples/sec INFO:root:Iter[16] Batch [6260] Speed: 827.36 samples/sec INFO:root:Iter[16] Batch [6280] Speed: 829.85 samples/sec INFO:root:Iter[16] Batch [6300] Speed: 831.82 samples/sec INFO:root:Iter[16] Batch [6320] Speed: 832.37 samples/sec INFO:root:Iter[16] Batch [6340] Speed: 845.99 samples/sec INFO:root:Iter[16] Batch [6360] Speed: 844.15 samples/sec INFO:root:Iter[16] Batch [6380] Speed: 829.19 samples/sec INFO:root:Iter[16] Batch [6400] Speed: 840.48 samples/sec INFO:root:Iter[16] Batch [6420] Speed: 842.21 samples/sec INFO:root:Iter[16] Batch 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INFO:root:Iter[16] Batch [8140] Speed: 868.78 samples/sec {fc7_acc} 170000 0.63671875 lr-batch-epoch: 0.0010000000000000002 8143 16 testing verification.. (12000, 512) infer time 3.698393 [lfw][170000]XNorm: 22.223124 [lfw][170000]Accuracy-Flip: 0.99850+-0.00203 testing verification.. (14000, 512) infer time 4.359666000000001 [cfp_fp][170000]XNorm: 21.965220 [cfp_fp][170000]Accuracy-Flip: 0.98871+-0.00353 testing verification.. (12000, 512) infer time 3.74349 [agedb_30][170000]XNorm: 23.422694 [agedb_30][170000]Accuracy-Flip: 0.98183+-0.00818 [170000]Accuracy-Highest: 0.98283 INFO:root:Iter[16] Batch [8160] Speed: 297.50 samples/sec INFO:root:Iter[16] Batch [8180] Speed: 847.24 samples/sec INFO:root:Iter[16] Batch [8200] Speed: 815.55 samples/sec INFO:root:Iter[16] Batch [8220] Speed: 820.36 samples/sec INFO:root:Iter[16] Batch [8240] Speed: 822.25 samples/sec INFO:root:Iter[16] Batch [8260] Speed: 829.08 samples/sec INFO:root:Iter[16] Batch [8280] Speed: 858.61 samples/sec INFO:root:Iter[16] Batch [8300] Speed: 854.75 samples/sec INFO:root:Iter[16] Batch [8320] Speed: 856.35 samples/sec INFO:root:Iter[16] Batch [8340] Speed: 844.36 samples/sec INFO:root:Iter[16] Batch [8360] Speed: 847.07 samples/sec INFO:root:Iter[16] Batch [8380] Speed: 843.43 samples/sec INFO:root:Iter[16] Batch [8400] Speed: 871.96 samples/sec INFO:root:Iter[16] Batch [8420] Speed: 829.77 samples/sec INFO:root:Iter[16] Batch [8440] 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samples/sec {fc7_acc} 172000 0.7167969 lr-batch-epoch: 0.0010000000000000002 27 17 testing verification.. (12000, 512) infer time 3.771154 [lfw][172000]XNorm: 22.182886 [lfw][172000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.394113000000001 [cfp_fp][172000]XNorm: 22.053307 [cfp_fp][172000]Accuracy-Flip: 0.98871+-0.00267 testing verification.. (12000, 512) infer time 3.7644230000000007 [agedb_30][172000]XNorm: 23.184985 [agedb_30][172000]Accuracy-Flip: 0.98317+-0.00713 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [172000]Accuracy-Highest: 0.98317 INFO:root:Iter[17] Batch [40] Speed: 296.05 samples/sec INFO:root:Iter[17] Batch [60] Speed: 851.02 samples/sec INFO:root:Iter[17] Batch [80] Speed: 815.15 samples/sec INFO:root:Iter[17] Batch [100] Speed: 832.18 samples/sec INFO:root:Iter[17] Batch [120] Speed: 843.37 samples/sec INFO:root:Iter[17] Batch [140] Speed: 805.32 samples/sec INFO:root:Iter[17] Batch [160] Speed: 817.55 samples/sec INFO:root:Iter[17] Batch [180] Speed: 843.32 samples/sec INFO:root:Iter[17] Batch [200] Speed: 855.43 samples/sec INFO:root:Iter[17] Batch [220] Speed: 823.25 samples/sec INFO:root:Iter[17] Batch [240] Speed: 844.98 samples/sec INFO:root:Iter[17] Batch [260] Speed: 846.58 samples/sec INFO:root:Iter[17] Batch [280] Speed: 857.53 samples/sec 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(12000, 512) infer time 3.66235 [lfw][174000]XNorm: 22.114102 [lfw][174000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.402771000000001 [cfp_fp][174000]XNorm: 22.200148 [cfp_fp][174000]Accuracy-Flip: 0.98771+-0.00363 testing verification.. (12000, 512) infer time 3.755437999999998 [agedb_30][174000]XNorm: 23.364816 [agedb_30][174000]Accuracy-Flip: 0.98217+-0.00749 [174000]Accuracy-Highest: 0.98317 INFO:root:Iter[17] Batch [2040] Speed: 296.93 samples/sec INFO:root:Iter[17] Batch [2060] Speed: 823.87 samples/sec INFO:root:Iter[17] Batch [2080] Speed: 845.96 samples/sec INFO:root:Iter[17] Batch [2100] Speed: 848.40 samples/sec INFO:root:Iter[17] Batch [2120] Speed: 838.55 samples/sec INFO:root:Iter[17] Batch [2140] Speed: 833.08 samples/sec INFO:root:Iter[17] Batch [2160] Speed: 815.49 samples/sec INFO:root:Iter[17] Batch [2180] Speed: 875.74 samples/sec INFO:root:Iter[17] Batch [2200] Speed: 852.55 samples/sec INFO:root:Iter[17] Batch [2220] Speed: 842.08 samples/sec INFO:root:Iter[17] Batch [2240] Speed: 868.95 samples/sec INFO:root:Iter[17] Batch [2260] Speed: 828.17 samples/sec INFO:root:Iter[17] Batch [2280] Speed: 859.05 samples/sec INFO:root:Iter[17] Batch [2300] Speed: 828.12 samples/sec INFO:root:Iter[17] Batch 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INFO:root:Iter[17] Batch [4020] Speed: 887.81 samples/sec {fc7_acc} 176000 0.7050781 lr-batch-epoch: 0.0010000000000000002 4027 17 testing verification.. (12000, 512) infer time 3.6654420000000014 [lfw][176000]XNorm: 21.916601 [lfw][176000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.3355820000000005 [cfp_fp][176000]XNorm: 22.058505 [cfp_fp][176000]Accuracy-Flip: 0.98829+-0.00403 testing verification.. (12000, 512) infer time 3.728072000000001 [agedb_30][176000]XNorm: 23.131193 [agedb_30][176000]Accuracy-Flip: 0.98350+-0.00743 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [176000]Accuracy-Highest: 0.98350 INFO:root:Iter[17] Batch [4040] Speed: 293.78 samples/sec INFO:root:Iter[17] Batch [4060] Speed: 828.61 samples/sec INFO:root:Iter[17] Batch [4080] Speed: 837.63 samples/sec INFO:root:Iter[17] Batch [4100] Speed: 838.96 samples/sec INFO:root:Iter[17] Batch [4120] Speed: 827.80 samples/sec INFO:root:Iter[17] Batch [4140] Speed: 804.14 samples/sec INFO:root:Iter[17] Batch [4160] Speed: 843.41 samples/sec INFO:root:Iter[17] Batch [4180] Speed: 821.78 samples/sec INFO:root:Iter[17] Batch [4200] Speed: 801.45 samples/sec INFO:root:Iter[17] Batch [4220] Speed: 833.78 samples/sec INFO:root:Iter[17] Batch [4240] Speed: 856.74 samples/sec INFO:root:Iter[17] Batch [4260] Speed: 860.55 samples/sec INFO:root:Iter[17] Batch [4280] Speed: 852.10 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samples/sec INFO:root:Iter[17] Batch [6000] Speed: 849.44 samples/sec INFO:root:Iter[17] Batch [6020] Speed: 845.22 samples/sec {fc7_acc} 178000 0.69921875 lr-batch-epoch: 0.0010000000000000002 6027 17 testing verification.. (12000, 512) infer time 3.8389690000000005 [lfw][178000]XNorm: 22.057607 [lfw][178000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.329192999999999 [cfp_fp][178000]XNorm: 22.064286 [cfp_fp][178000]Accuracy-Flip: 0.98957+-0.00279 testing verification.. (12000, 512) infer time 3.7979759999999994 [agedb_30][178000]XNorm: 23.282595 [agedb_30][178000]Accuracy-Flip: 0.98250+-0.00743 [178000]Accuracy-Highest: 0.98350 INFO:root:Iter[17] Batch [6040] Speed: 298.39 samples/sec INFO:root:Iter[17] Batch [6060] Speed: 840.86 samples/sec INFO:root:Iter[17] Batch [6080] Speed: 836.79 samples/sec INFO:root:Iter[17] Batch [6100] Speed: 841.68 samples/sec INFO:root:Iter[17] Batch [6120] Speed: 850.37 samples/sec INFO:root:Iter[17] Batch [6140] Speed: 837.14 samples/sec INFO:root:Iter[17] Batch [6160] Speed: 845.69 samples/sec INFO:root:Iter[17] Batch [6180] Speed: 850.26 samples/sec INFO:root:Iter[17] Batch [6200] Speed: 870.37 samples/sec INFO:root:Iter[17] Batch [6220] Speed: 861.46 samples/sec INFO:root:Iter[17] Batch [6240] Speed: 859.43 samples/sec INFO:root:Iter[17] Batch [6260] Speed: 859.77 samples/sec INFO:root:Iter[17] Batch [6280] Speed: 867.64 samples/sec INFO:root:Iter[17] Batch [6300] Speed: 863.45 samples/sec INFO:root:Iter[17] Batch 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(12000, 512) infer time 3.6735110000000004 [lfw][180000]XNorm: 21.896245 [lfw][180000]Accuracy-Flip: 0.99850+-0.00203 testing verification.. (14000, 512) infer time 4.401587999999999 [cfp_fp][180000]XNorm: 22.106320 [cfp_fp][180000]Accuracy-Flip: 0.98914+-0.00314 testing verification.. (12000, 512) infer time 3.730888000000001 [agedb_30][180000]XNorm: 23.152054 [agedb_30][180000]Accuracy-Flip: 0.98350+-0.00769 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [180000]Accuracy-Highest: 0.98350 INFO:root:Iter[17] Batch [8040] Speed: 295.15 samples/sec INFO:root:Iter[17] Batch [8060] Speed: 837.51 samples/sec INFO:root:Iter[17] Batch [8080] Speed: 839.06 samples/sec INFO:root:Iter[17] Batch [8100] Speed: 845.08 samples/sec INFO:root:Iter[17] Batch [8120] Speed: 861.88 samples/sec INFO:root:Iter[17] Batch [8140] Speed: 824.29 samples/sec INFO:root:Iter[17] Batch [8160] Speed: 823.10 samples/sec INFO:root:Iter[17] Batch [8180] Speed: 869.35 samples/sec INFO:root:Iter[17] Batch [8200] Speed: 836.87 samples/sec INFO:root:Iter[17] Batch [8220] Speed: 847.93 samples/sec INFO:root:Iter[17] Batch [8240] Speed: 850.06 samples/sec INFO:root:Iter[17] Batch [8260] Speed: 859.71 samples/sec INFO:root:Iter[17] Batch [8280] Speed: 852.25 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samples/sec INFO:root:Iter[17] Batch [10000] Speed: 853.26 samples/sec INFO:root:Iter[17] Batch [10020] Speed: 870.16 samples/sec {fc7_acc} 182000 0.7011719 lr-batch-epoch: 0.0010000000000000002 10027 17 testing verification.. (12000, 512) infer time 3.7053710000000013 [lfw][182000]XNorm: 21.997721 [lfw][182000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.341547000000001 [cfp_fp][182000]XNorm: 22.146307 [cfp_fp][182000]Accuracy-Flip: 0.98800+-0.00345 testing verification.. (12000, 512) infer time 3.7414909999999995 [agedb_30][182000]XNorm: 23.285442 [agedb_30][182000]Accuracy-Flip: 0.98217+-0.00723 [182000]Accuracy-Highest: 0.98350 INFO:root:Iter[17] Batch [10040] Speed: 302.70 samples/sec INFO:root:Iter[17] Batch [10060] Speed: 854.66 samples/sec INFO:root:Iter[17] Batch [10080] Speed: 832.66 samples/sec INFO:root:Iter[17] Batch [10100] Speed: 807.32 samples/sec INFO:root:Epoch[17] Time cost=6163.521 call reset() INFO:root:Iter[18] Batch [20] Speed: 859.37 samples/sec INFO:root:Iter[18] Batch [40] Speed: 876.59 samples/sec INFO:root:Iter[18] Batch [60] Speed: 824.00 samples/sec INFO:root:Iter[18] Batch [80] Speed: 843.08 samples/sec INFO:root:Iter[18] Batch [100] Speed: 847.41 samples/sec INFO:root:Iter[18] Batch [120] Speed: 854.27 samples/sec INFO:root:Iter[18] Batch [140] Speed: 836.07 samples/sec INFO:root:Iter[18] Batch [160] Speed: 856.80 samples/sec INFO:root:Iter[18] Batch [180] Speed: 842.64 samples/sec INFO:root:Iter[18] Batch [200] Speed: 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184000 0.7324219 lr-batch-epoch: 0.0010000000000000002 1911 18 testing verification.. (12000, 512) infer time 3.748889 [lfw][184000]XNorm: 21.909514 [lfw][184000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.399809999999999 [cfp_fp][184000]XNorm: 22.299681 [cfp_fp][184000]Accuracy-Flip: 0.98729+-0.00341 testing verification.. (12000, 512) infer time 3.7375390000000004 [agedb_30][184000]XNorm: 23.301223 [agedb_30][184000]Accuracy-Flip: 0.98267+-0.00720 [184000]Accuracy-Highest: 0.98350 INFO:root:Iter[18] Batch [1920] Speed: 301.08 samples/sec INFO:root:Iter[18] Batch [1940] Speed: 841.64 samples/sec INFO:root:Iter[18] Batch [1960] Speed: 849.74 samples/sec INFO:root:Iter[18] Batch [1980] Speed: 881.68 samples/sec INFO:root:Iter[18] Batch [2000] Speed: 831.89 samples/sec INFO:root:Iter[18] Batch [2020] Speed: 861.84 samples/sec INFO:root:Iter[18] Batch [2040] Speed: 852.06 samples/sec INFO:root:Iter[18] Batch [2060] Speed: 851.67 samples/sec INFO:root:Iter[18] Batch [2080] Speed: 851.51 samples/sec INFO:root:Iter[18] Batch [2100] Speed: 849.38 samples/sec INFO:root:Iter[18] Batch [2120] Speed: 857.43 samples/sec INFO:root:Iter[18] Batch [2140] Speed: 868.37 samples/sec INFO:root:Iter[18] Batch [2160] Speed: 835.95 samples/sec INFO:root:Iter[18] Batch [2180] Speed: 857.90 samples/sec INFO:root:Iter[18] Batch 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INFO:root:Iter[18] Batch [3900] Speed: 882.14 samples/sec {fc7_acc} 186000 0.6933594 lr-batch-epoch: 0.0010000000000000002 3911 18 testing verification.. (12000, 512) infer time 3.7265960000000002 [lfw][186000]XNorm: 22.091486 [lfw][186000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.356240999999999 [cfp_fp][186000]XNorm: 22.387087 [cfp_fp][186000]Accuracy-Flip: 0.98843+-0.00347 testing verification.. (12000, 512) infer time 3.756242 [agedb_30][186000]XNorm: 23.237590 [agedb_30][186000]Accuracy-Flip: 0.98333+-0.00785 [186000]Accuracy-Highest: 0.98350 INFO:root:Iter[18] Batch [3920] Speed: 303.19 samples/sec INFO:root:Iter[18] Batch [3940] Speed: 840.15 samples/sec INFO:root:Iter[18] Batch [3960] Speed: 838.52 samples/sec INFO:root:Iter[18] Batch [3980] Speed: 836.82 samples/sec INFO:root:Iter[18] Batch [4000] Speed: 812.55 samples/sec INFO:root:Iter[18] Batch [4020] Speed: 815.06 samples/sec INFO:root:Iter[18] Batch [4040] Speed: 846.37 samples/sec INFO:root:Iter[18] Batch [4060] Speed: 838.62 samples/sec INFO:root:Iter[18] Batch [4080] Speed: 838.95 samples/sec INFO:root:Iter[18] Batch [4100] Speed: 823.55 samples/sec INFO:root:Iter[18] Batch [4120] Speed: 851.62 samples/sec INFO:root:Iter[18] Batch [4140] Speed: 849.17 samples/sec INFO:root:Iter[18] Batch [4160] Speed: 855.81 samples/sec INFO:root:Iter[18] Batch [4180] Speed: 854.75 samples/sec INFO:root:Iter[18] Batch [4200] 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samples/sec {fc7_acc} 188000 0.7109375 lr-batch-epoch: 0.0010000000000000002 5911 18 testing verification.. (12000, 512) infer time 3.7200599999999997 [lfw][188000]XNorm: 22.099083 [lfw][188000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.353227999999998 [cfp_fp][188000]XNorm: 22.428772 [cfp_fp][188000]Accuracy-Flip: 0.98700+-0.00380 testing verification.. (12000, 512) infer time 3.735055 [agedb_30][188000]XNorm: 23.237059 [agedb_30][188000]Accuracy-Flip: 0.98050+-0.00695 [188000]Accuracy-Highest: 0.98350 INFO:root:Iter[18] Batch [5920] Speed: 302.60 samples/sec INFO:root:Iter[18] Batch [5940] Speed: 830.02 samples/sec INFO:root:Iter[18] Batch [5960] Speed: 838.13 samples/sec INFO:root:Iter[18] Batch [5980] Speed: 851.17 samples/sec INFO:root:Iter[18] Batch [6000] Speed: 857.39 samples/sec INFO:root:Iter[18] Batch [6020] Speed: 858.11 samples/sec INFO:root:Iter[18] Batch [6040] Speed: 824.23 samples/sec INFO:root:Iter[18] Batch [6060] Speed: 809.18 samples/sec INFO:root:Iter[18] Batch [6080] Speed: 830.23 samples/sec INFO:root:Iter[18] Batch [6100] Speed: 823.83 samples/sec INFO:root:Iter[18] Batch [6120] Speed: 857.81 samples/sec INFO:root:Iter[18] Batch [6140] Speed: 861.92 samples/sec INFO:root:Iter[18] Batch [6160] Speed: 845.53 samples/sec INFO:root:Iter[18] Batch [6180] Speed: 858.07 samples/sec INFO:root:Iter[18] Batch [6200] 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samples/sec {fc7_acc} 190000 0.6953125 lr-batch-epoch: 0.0010000000000000002 7911 18 testing verification.. (12000, 512) infer time 3.7326740000000007 [lfw][190000]XNorm: 21.922789 [lfw][190000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.463557999999996 [cfp_fp][190000]XNorm: 22.178044 [cfp_fp][190000]Accuracy-Flip: 0.98871+-0.00358 testing verification.. (12000, 512) infer time 3.7665740000000008 [agedb_30][190000]XNorm: 23.114838 [agedb_30][190000]Accuracy-Flip: 0.98317+-0.00740 [190000]Accuracy-Highest: 0.98350 INFO:root:Iter[18] Batch [7920] Speed: 299.78 samples/sec INFO:root:Iter[18] Batch [7940] Speed: 853.05 samples/sec INFO:root:Iter[18] Batch [7960] Speed: 832.77 samples/sec INFO:root:Iter[18] Batch [7980] Speed: 832.41 samples/sec INFO:root:Iter[18] Batch [8000] Speed: 872.54 samples/sec INFO:root:Iter[18] Batch [8020] Speed: 861.89 samples/sec INFO:root:Iter[18] Batch [8040] Speed: 842.42 samples/sec INFO:root:Iter[18] Batch [8060] Speed: 830.96 samples/sec INFO:root:Iter[18] Batch [8080] Speed: 819.68 samples/sec INFO:root:Iter[18] Batch [8100] Speed: 846.21 samples/sec INFO:root:Iter[18] Batch [8120] Speed: 845.88 samples/sec INFO:root:Iter[18] Batch [8140] Speed: 845.50 samples/sec INFO:root:Iter[18] Batch [8160] Speed: 842.43 samples/sec INFO:root:Iter[18] Batch [8180] Speed: 848.74 samples/sec INFO:root:Iter[18] Batch 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INFO:root:Iter[18] Batch [9900] Speed: 866.37 samples/sec {fc7_acc} 192000 0.7285156 lr-batch-epoch: 0.0010000000000000002 9911 18 testing verification.. (12000, 512) infer time 3.7381839999999995 [lfw][192000]XNorm: 21.807910 [lfw][192000]Accuracy-Flip: 0.99850+-0.00203 testing verification.. (14000, 512) infer time 4.359571000000001 [cfp_fp][192000]XNorm: 22.162108 [cfp_fp][192000]Accuracy-Flip: 0.98986+-0.00316 testing verification.. (12000, 512) infer time 3.7468790000000003 [agedb_30][192000]XNorm: 22.953126 [agedb_30][192000]Accuracy-Flip: 0.98283+-0.00768 [192000]Accuracy-Highest: 0.98350 INFO:root:Iter[18] Batch [9920] Speed: 299.97 samples/sec INFO:root:Iter[18] Batch [9940] Speed: 828.93 samples/sec INFO:root:Iter[18] Batch [9960] Speed: 831.73 samples/sec INFO:root:Iter[18] Batch [9980] Speed: 850.49 samples/sec INFO:root:Iter[18] Batch [10000] Speed: 821.87 samples/sec INFO:root:Iter[18] Batch [10020] Speed: 853.82 samples/sec INFO:root:Iter[18] Batch [10040] Speed: 836.37 samples/sec INFO:root:Iter[18] Batch [10060] Speed: 843.41 samples/sec INFO:root:Iter[18] Batch [10080] Speed: 840.26 samples/sec INFO:root:Iter[18] Batch [10100] Speed: 857.26 samples/sec INFO:root:Epoch[18] Time cost=6122.812 call reset() INFO:root:Iter[19] Batch [20] Speed: 840.88 samples/sec INFO:root:Iter[19] Batch [40] Speed: 868.01 samples/sec INFO:root:Iter[19] Batch [60] Speed: 879.87 samples/sec INFO:root:Iter[19] Batch [80] 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194000 0.74609375 lr-batch-epoch: 0.0010000000000000002 1795 19 testing verification.. (12000, 512) infer time 3.7628739999999996 [lfw][194000]XNorm: 21.893979 [lfw][194000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.404064000000001 [cfp_fp][194000]XNorm: 22.231899 [cfp_fp][194000]Accuracy-Flip: 0.98929+-0.00287 testing verification.. (12000, 512) infer time 3.709927 [agedb_30][194000]XNorm: 23.287066 [agedb_30][194000]Accuracy-Flip: 0.98267+-0.00680 [194000]Accuracy-Highest: 0.98350 INFO:root:Iter[19] Batch [1800] Speed: 303.33 samples/sec INFO:root:Iter[19] Batch [1820] Speed: 869.33 samples/sec INFO:root:Iter[19] Batch [1840] Speed: 801.72 samples/sec INFO:root:Iter[19] Batch [1860] Speed: 853.98 samples/sec INFO:root:Iter[19] Batch [1880] Speed: 801.20 samples/sec INFO:root:Iter[19] Batch [1900] Speed: 830.15 samples/sec INFO:root:Iter[19] Batch [1920] Speed: 864.83 samples/sec INFO:root:Iter[19] Batch [1940] Speed: 827.47 samples/sec INFO:root:Iter[19] Batch [1960] Speed: 832.67 samples/sec INFO:root:Iter[19] Batch [1980] Speed: 841.75 samples/sec INFO:root:Iter[19] Batch [2000] Speed: 859.02 samples/sec INFO:root:Iter[19] Batch [2020] Speed: 858.76 samples/sec INFO:root:Iter[19] Batch [2040] Speed: 846.74 samples/sec INFO:root:Iter[19] Batch [2060] Speed: 840.75 samples/sec INFO:root:Iter[19] Batch [2080] 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samples/sec {fc7_acc} 196000 0.7109375 lr-batch-epoch: 0.0010000000000000002 3795 19 testing verification.. (12000, 512) infer time 3.706037 [lfw][196000]XNorm: 22.166811 [lfw][196000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.353313000000001 [cfp_fp][196000]XNorm: 22.409943 [cfp_fp][196000]Accuracy-Flip: 0.98914+-0.00294 testing verification.. (12000, 512) infer time 3.807636 [agedb_30][196000]XNorm: 23.208672 [agedb_30][196000]Accuracy-Flip: 0.98183+-0.00835 [196000]Accuracy-Highest: 0.98350 INFO:root:Iter[19] Batch [3800] Speed: 302.38 samples/sec INFO:root:Iter[19] Batch [3820] Speed: 864.21 samples/sec INFO:root:Iter[19] Batch [3840] Speed: 829.30 samples/sec INFO:root:Iter[19] Batch [3860] Speed: 854.86 samples/sec INFO:root:Iter[19] Batch [3880] Speed: 805.43 samples/sec INFO:root:Iter[19] Batch [3900] Speed: 812.14 samples/sec INFO:root:Iter[19] Batch [3920] Speed: 845.87 samples/sec INFO:root:Iter[19] Batch [3940] Speed: 835.27 samples/sec INFO:root:Iter[19] Batch [3960] Speed: 831.17 samples/sec INFO:root:Iter[19] Batch [3980] Speed: 834.28 samples/sec INFO:root:Iter[19] Batch [4000] Speed: 834.57 samples/sec INFO:root:Iter[19] Batch [4020] Speed: 800.93 samples/sec INFO:root:Iter[19] Batch [4040] Speed: 849.33 samples/sec INFO:root:Iter[19] Batch [4060] Speed: 848.12 samples/sec INFO:root:Iter[19] Batch [4080] 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samples/sec {fc7_acc} 198000 0.7011719 lr-batch-epoch: 0.0010000000000000002 5795 19 testing verification.. (12000, 512) infer time 3.686325 [lfw][198000]XNorm: 21.888298 [lfw][198000]Accuracy-Flip: 0.99817+-0.00203 testing verification.. (14000, 512) infer time 4.398947000000001 [cfp_fp][198000]XNorm: 22.284916 [cfp_fp][198000]Accuracy-Flip: 0.98800+-0.00308 testing verification.. (12000, 512) infer time 3.7760179999999997 [agedb_30][198000]XNorm: 23.060574 [agedb_30][198000]Accuracy-Flip: 0.98283+-0.00695 [198000]Accuracy-Highest: 0.98350 INFO:root:Iter[19] Batch [5800] Speed: 299.24 samples/sec INFO:root:Iter[19] Batch [5820] Speed: 868.98 samples/sec INFO:root:Iter[19] Batch [5840] Speed: 838.38 samples/sec INFO:root:Iter[19] Batch [5860] Speed: 870.59 samples/sec INFO:root:Iter[19] Batch [5880] Speed: 858.07 samples/sec INFO:root:Iter[19] Batch [5900] Speed: 844.21 samples/sec INFO:root:Iter[19] Batch [5920] Speed: 844.50 samples/sec INFO:root:Iter[19] Batch [5940] Speed: 852.09 samples/sec INFO:root:Iter[19] Batch [5960] Speed: 847.07 samples/sec INFO:root:Iter[19] Batch [5980] Speed: 834.13 samples/sec INFO:root:Iter[19] Batch [6000] Speed: 862.63 samples/sec INFO:root:Iter[19] Batch [6020] Speed: 840.06 samples/sec INFO:root:Iter[19] Batch [6040] Speed: 874.99 samples/sec INFO:root:Iter[19] Batch [6060] Speed: 851.57 samples/sec INFO:root:Iter[19] Batch 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INFO:root:Iter[19] Batch [7780] Speed: 872.58 samples/sec {fc7_acc} 200000 0.7265625 lr-batch-epoch: 0.0010000000000000002 7795 19 testing verification.. (12000, 512) infer time 3.6762269999999986 [lfw][200000]XNorm: 22.069662 [lfw][200000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.3890649999999996 [cfp_fp][200000]XNorm: 22.408883 [cfp_fp][200000]Accuracy-Flip: 0.98929+-0.00363 testing verification.. (12000, 512) infer time 3.71312 [agedb_30][200000]XNorm: 23.278020 [agedb_30][200000]Accuracy-Flip: 0.98200+-0.00690 [200000]Accuracy-Highest: 0.98350 INFO:root:Iter[19] Batch [7800] Speed: 300.35 samples/sec INFO:root:Iter[19] Batch [7820] Speed: 843.24 samples/sec INFO:root:Iter[19] Batch [7840] Speed: 832.25 samples/sec INFO:root:Iter[19] Batch [7860] Speed: 852.43 samples/sec INFO:root:Iter[19] Batch [7880] Speed: 861.92 samples/sec INFO:root:Iter[19] Batch [7900] Speed: 855.33 samples/sec INFO:root:Iter[19] Batch [7920] Speed: 848.08 samples/sec INFO:root:Iter[19] Batch [7940] Speed: 835.82 samples/sec INFO:root:Iter[19] Batch [7960] Speed: 847.20 samples/sec INFO:root:Iter[19] Batch [7980] Speed: 843.35 samples/sec INFO:root:Iter[19] Batch [8000] Speed: 856.30 samples/sec INFO:root:Iter[19] Batch [8020] Speed: 825.48 samples/sec INFO:root:Iter[19] Batch [8040] Speed: 835.02 samples/sec INFO:root:Iter[19] Batch [8060] Speed: 866.76 samples/sec INFO:root:Iter[19] Batch [8080] 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samples/sec {fc7_acc} 202000 0.7128906 lr-batch-epoch: 0.0010000000000000002 9795 19 testing verification.. (12000, 512) infer time 3.798563 [lfw][202000]XNorm: 21.842393 [lfw][202000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.371077999999999 [cfp_fp][202000]XNorm: 22.229222 [cfp_fp][202000]Accuracy-Flip: 0.98914+-0.00308 testing verification.. (12000, 512) infer time 3.7532710000000002 [agedb_30][202000]XNorm: 23.075952 [agedb_30][202000]Accuracy-Flip: 0.98267+-0.00797 [202000]Accuracy-Highest: 0.98350 INFO:root:Iter[19] Batch [9800] Speed: 299.95 samples/sec INFO:root:Iter[19] Batch [9820] Speed: 844.02 samples/sec INFO:root:Iter[19] Batch [9840] Speed: 865.42 samples/sec INFO:root:Iter[19] Batch [9860] Speed: 852.32 samples/sec INFO:root:Iter[19] Batch [9880] Speed: 842.29 samples/sec INFO:root:Iter[19] Batch [9900] Speed: 838.75 samples/sec INFO:root:Iter[19] Batch [9920] Speed: 828.85 samples/sec INFO:root:Iter[19] Batch [9940] Speed: 834.52 samples/sec INFO:root:Iter[19] Batch [9960] Speed: 828.05 samples/sec INFO:root:Iter[19] Batch [9980] Speed: 850.52 samples/sec INFO:root:Iter[19] Batch [10000] Speed: 829.04 samples/sec INFO:root:Iter[19] Batch [10020] Speed: 855.20 samples/sec INFO:root:Iter[19] Batch [10040] Speed: 857.52 samples/sec INFO:root:Iter[19] Batch [10060] Speed: 857.02 samples/sec INFO:root:Iter[19] 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204000 0.7480469 lr-batch-epoch: 0.0010000000000000002 1679 20 testing verification.. (12000, 512) infer time 3.705913999999999 [lfw][204000]XNorm: 21.867965 [lfw][204000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.359247999999999 [cfp_fp][204000]XNorm: 22.310416 [cfp_fp][204000]Accuracy-Flip: 0.98800+-0.00308 testing verification.. (12000, 512) infer time 3.7507579999999994 [agedb_30][204000]XNorm: 23.081857 [agedb_30][204000]Accuracy-Flip: 0.98300+-0.00748 [204000]Accuracy-Highest: 0.98350 INFO:root:Iter[20] Batch [1680] Speed: 297.18 samples/sec INFO:root:Iter[20] Batch [1700] Speed: 843.12 samples/sec INFO:root:Iter[20] Batch [1720] Speed: 834.23 samples/sec INFO:root:Iter[20] Batch [1740] Speed: 851.12 samples/sec INFO:root:Iter[20] Batch [1760] Speed: 830.91 samples/sec INFO:root:Iter[20] Batch [1780] Speed: 832.65 samples/sec INFO:root:Iter[20] Batch [1800] Speed: 837.34 samples/sec INFO:root:Iter[20] Batch [1820] Speed: 837.39 samples/sec INFO:root:Iter[20] Batch [1840] Speed: 845.74 samples/sec INFO:root:Iter[20] Batch [1860] Speed: 840.32 samples/sec INFO:root:Iter[20] Batch [1880] Speed: 854.47 samples/sec INFO:root:Iter[20] Batch [1900] Speed: 880.35 samples/sec INFO:root:Iter[20] Batch [1920] Speed: 856.91 samples/sec INFO:root:Iter[20] Batch [1940] Speed: 858.46 samples/sec INFO:root:Iter[20] Batch 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INFO:root:Iter[20] Batch [3660] Speed: 897.91 samples/sec {fc7_acc} 206000 0.76171875 lr-batch-epoch: 0.0010000000000000002 3679 20 testing verification.. (12000, 512) infer time 3.766843 [lfw][206000]XNorm: 22.019147 [lfw][206000]Accuracy-Flip: 0.99800+-0.00194 testing verification.. (14000, 512) infer time 4.346297000000001 [cfp_fp][206000]XNorm: 22.356760 [cfp_fp][206000]Accuracy-Flip: 0.98857+-0.00271 testing verification.. (12000, 512) infer time 3.7549310000000005 [agedb_30][206000]XNorm: 23.148297 [agedb_30][206000]Accuracy-Flip: 0.98450+-0.00667 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [206000]Accuracy-Highest: 0.98450 INFO:root:Iter[20] Batch [3680] Speed: 295.06 samples/sec INFO:root:Iter[20] Batch [3700] Speed: 826.14 samples/sec INFO:root:Iter[20] Batch [3720] Speed: 841.46 samples/sec INFO:root:Iter[20] Batch [3740] Speed: 842.73 samples/sec INFO:root:Iter[20] Batch [3760] Speed: 852.73 samples/sec INFO:root:Iter[20] Batch [3780] Speed: 850.47 samples/sec INFO:root:Iter[20] Batch [3800] Speed: 829.79 samples/sec INFO:root:Iter[20] Batch [3820] Speed: 798.46 samples/sec INFO:root:Iter[20] Batch [3840] Speed: 812.62 samples/sec INFO:root:Iter[20] Batch [3860] Speed: 866.95 samples/sec INFO:root:Iter[20] Batch [3880] Speed: 851.06 samples/sec INFO:root:Iter[20] Batch [3900] Speed: 849.55 samples/sec INFO:root:Iter[20] Batch [3920] Speed: 849.06 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(12000, 512) infer time 3.730286999999999 [lfw][208000]XNorm: 22.090661 [lfw][208000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.361149999999999 [cfp_fp][208000]XNorm: 22.631044 [cfp_fp][208000]Accuracy-Flip: 0.98986+-0.00289 testing verification.. (12000, 512) infer time 3.7666229999999996 [agedb_30][208000]XNorm: 23.267387 [agedb_30][208000]Accuracy-Flip: 0.98217+-0.00753 [208000]Accuracy-Highest: 0.98450 INFO:root:Iter[20] Batch [5680] Speed: 298.89 samples/sec INFO:root:Iter[20] Batch [5700] Speed: 837.73 samples/sec INFO:root:Iter[20] Batch [5720] Speed: 813.02 samples/sec INFO:root:Iter[20] Batch [5740] Speed: 844.33 samples/sec INFO:root:Iter[20] Batch [5760] Speed: 810.52 samples/sec INFO:root:Iter[20] Batch [5780] Speed: 838.38 samples/sec INFO:root:Iter[20] Batch [5800] Speed: 836.68 samples/sec INFO:root:Iter[20] Batch [5820] Speed: 827.81 samples/sec INFO:root:Iter[20] Batch [5840] Speed: 865.49 samples/sec INFO:root:Iter[20] Batch [5860] Speed: 865.14 samples/sec INFO:root:Iter[20] Batch [5880] Speed: 845.95 samples/sec INFO:root:Iter[20] Batch [5900] Speed: 848.76 samples/sec INFO:root:Iter[20] Batch [5920] Speed: 826.70 samples/sec INFO:root:Iter[20] Batch [5940] Speed: 851.55 samples/sec INFO:root:Iter[20] Batch 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(12000, 512) infer time 3.66378 [lfw][210000]XNorm: 22.027636 [lfw][210000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.349364 [cfp_fp][210000]XNorm: 22.562682 [cfp_fp][210000]Accuracy-Flip: 0.98857+-0.00300 testing verification.. (12000, 512) infer time 3.7263399999999995 [agedb_30][210000]XNorm: 23.049446 [agedb_30][210000]Accuracy-Flip: 0.98183+-0.00740 [210000]Accuracy-Highest: 0.98450 INFO:root:Iter[20] Batch [7680] Speed: 298.42 samples/sec INFO:root:Iter[20] Batch [7700] Speed: 817.35 samples/sec INFO:root:Iter[20] Batch [7720] Speed: 809.75 samples/sec INFO:root:Iter[20] Batch [7740] Speed: 848.21 samples/sec INFO:root:Iter[20] Batch [7760] Speed: 859.13 samples/sec INFO:root:Iter[20] Batch [7780] Speed: 840.88 samples/sec INFO:root:Iter[20] Batch [7800] Speed: 837.26 samples/sec INFO:root:Iter[20] Batch [7820] Speed: 855.91 samples/sec INFO:root:Iter[20] Batch [7840] Speed: 854.54 samples/sec INFO:root:Iter[20] Batch [7860] Speed: 848.82 samples/sec INFO:root:Iter[20] Batch [7880] Speed: 854.15 samples/sec INFO:root:Iter[20] Batch [7900] Speed: 808.01 samples/sec INFO:root:Iter[20] Batch [7920] Speed: 832.84 samples/sec INFO:root:Iter[20] Batch [7940] Speed: 841.79 samples/sec INFO:root:Iter[20] Batch 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INFO:root:Iter[20] Batch [9660] Speed: 912.72 samples/sec {fc7_acc} 212000 0.77734375 lr-batch-epoch: 0.0010000000000000002 9679 20 testing verification.. (12000, 512) infer time 3.750807 [lfw][212000]XNorm: 21.816938 [lfw][212000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.363461000000001 [cfp_fp][212000]XNorm: 22.416725 [cfp_fp][212000]Accuracy-Flip: 0.98871+-0.00267 testing verification.. (12000, 512) infer time 3.721793 [agedb_30][212000]XNorm: 23.053230 [agedb_30][212000]Accuracy-Flip: 0.98283+-0.00703 [212000]Accuracy-Highest: 0.98450 INFO:root:Iter[20] Batch [9680] Speed: 302.28 samples/sec INFO:root:Iter[20] Batch [9700] Speed: 822.05 samples/sec INFO:root:Iter[20] Batch [9720] Speed: 840.00 samples/sec INFO:root:Iter[20] Batch [9740] Speed: 841.70 samples/sec INFO:root:Iter[20] Batch [9760] Speed: 822.76 samples/sec INFO:root:Iter[20] Batch [9780] Speed: 854.40 samples/sec INFO:root:Iter[20] Batch [9800] Speed: 855.94 samples/sec INFO:root:Iter[20] Batch [9820] Speed: 855.98 samples/sec INFO:root:Iter[20] Batch [9840] Speed: 818.42 samples/sec INFO:root:Iter[20] Batch [9860] Speed: 797.00 samples/sec INFO:root:Iter[20] Batch [9880] Speed: 831.79 samples/sec INFO:root:Iter[20] Batch [9900] Speed: 837.31 samples/sec INFO:root:Iter[20] Batch [9920] Speed: 809.79 samples/sec INFO:root:Iter[20] Batch [9940] Speed: 839.04 samples/sec INFO:root:Iter[20] Batch [9960] 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Speed: 869.94 samples/sec {fc7_acc} 214000 0.75390625 lr-batch-epoch: 0.0010000000000000002 1563 21 testing verification.. (12000, 512) infer time 3.704817 [lfw][214000]XNorm: 22.064022 [lfw][214000]Accuracy-Flip: 0.99767+-0.00213 testing verification.. (14000, 512) infer time 4.408377 [cfp_fp][214000]XNorm: 22.439286 [cfp_fp][214000]Accuracy-Flip: 0.98871+-0.00353 testing verification.. (12000, 512) infer time 3.767955 [agedb_30][214000]XNorm: 23.068192 [agedb_30][214000]Accuracy-Flip: 0.98267+-0.00731 [214000]Accuracy-Highest: 0.98450 INFO:root:Iter[21] Batch [1580] Speed: 298.52 samples/sec INFO:root:Iter[21] Batch [1600] Speed: 826.24 samples/sec INFO:root:Iter[21] Batch [1620] Speed: 818.58 samples/sec INFO:root:Iter[21] Batch [1640] Speed: 819.83 samples/sec INFO:root:Iter[21] Batch [1660] Speed: 832.70 samples/sec INFO:root:Iter[21] Batch [1680] Speed: 823.29 samples/sec INFO:root:Iter[21] Batch [1700] Speed: 855.89 samples/sec INFO:root:Iter[21] Batch [1720] Speed: 832.53 samples/sec INFO:root:Iter[21] Batch [1740] Speed: 842.52 samples/sec INFO:root:Iter[21] Batch [1760] Speed: 840.04 samples/sec INFO:root:Iter[21] Batch [1780] Speed: 870.98 samples/sec INFO:root:Iter[21] Batch [1800] Speed: 830.56 samples/sec INFO:root:Iter[21] Batch [1820] Speed: 849.68 samples/sec INFO:root:Iter[21] Batch [1840] Speed: 836.96 samples/sec INFO:root:Iter[21] Batch [1860] 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samples/sec {fc7_acc} 216000 0.7890625 lr-batch-epoch: 0.0010000000000000002 3563 21 testing verification.. (12000, 512) infer time 3.7081870000000006 [lfw][216000]XNorm: 22.267854 [lfw][216000]Accuracy-Flip: 0.99800+-0.00194 testing verification.. (14000, 512) infer time 4.331989 [cfp_fp][216000]XNorm: 22.709489 [cfp_fp][216000]Accuracy-Flip: 0.98914+-0.00273 testing verification.. (12000, 512) infer time 3.7441340000000003 [agedb_30][216000]XNorm: 23.379942 [agedb_30][216000]Accuracy-Flip: 0.98283+-0.00687 [216000]Accuracy-Highest: 0.98450 INFO:root:Iter[21] Batch [3580] Speed: 302.19 samples/sec INFO:root:Iter[21] Batch [3600] Speed: 818.69 samples/sec INFO:root:Iter[21] Batch [3620] Speed: 859.35 samples/sec INFO:root:Iter[21] Batch [3640] Speed: 822.18 samples/sec INFO:root:Iter[21] Batch [3660] Speed: 823.81 samples/sec INFO:root:Iter[21] Batch [3680] Speed: 831.39 samples/sec INFO:root:Iter[21] Batch [3700] Speed: 849.94 samples/sec INFO:root:Iter[21] Batch [3720] Speed: 851.80 samples/sec INFO:root:Iter[21] Batch [3740] Speed: 814.07 samples/sec INFO:root:Iter[21] Batch [3760] Speed: 840.77 samples/sec INFO:root:Iter[21] Batch [3780] Speed: 843.37 samples/sec INFO:root:Iter[21] Batch [3800] Speed: 829.21 samples/sec INFO:root:Iter[21] Batch [3820] Speed: 842.93 samples/sec INFO:root:Iter[21] Batch [3840] Speed: 838.81 samples/sec INFO:root:Iter[21] Batch 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INFO:root:Iter[21] Batch [5560] Speed: 871.00 samples/sec {fc7_acc} 218000 0.7578125 lr-batch-epoch: 0.0010000000000000002 5563 21 testing verification.. (12000, 512) infer time 3.785873 [lfw][218000]XNorm: 22.003539 [lfw][218000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.388636999999999 [cfp_fp][218000]XNorm: 22.316060 [cfp_fp][218000]Accuracy-Flip: 0.98814+-0.00320 testing verification.. (12000, 512) infer time 3.7546760000000012 [agedb_30][218000]XNorm: 22.958186 [agedb_30][218000]Accuracy-Flip: 0.98217+-0.00792 [218000]Accuracy-Highest: 0.98450 INFO:root:Iter[21] Batch [5580] Speed: 300.01 samples/sec INFO:root:Iter[21] Batch [5600] Speed: 837.99 samples/sec INFO:root:Iter[21] Batch [5620] Speed: 811.19 samples/sec INFO:root:Iter[21] Batch [5640] Speed: 833.72 samples/sec INFO:root:Iter[21] Batch [5660] Speed: 846.09 samples/sec INFO:root:Iter[21] Batch [5680] Speed: 858.02 samples/sec INFO:root:Iter[21] Batch [5700] Speed: 872.01 samples/sec INFO:root:Iter[21] Batch [5720] Speed: 838.97 samples/sec INFO:root:Iter[21] Batch [5740] Speed: 822.83 samples/sec INFO:root:Iter[21] Batch [5760] Speed: 814.17 samples/sec INFO:root:Iter[21] Batch [5780] Speed: 841.79 samples/sec INFO:root:Iter[21] Batch [5800] Speed: 806.17 samples/sec INFO:root:Iter[21] Batch [5820] Speed: 853.39 samples/sec INFO:root:Iter[21] Batch [5840] Speed: 844.03 samples/sec INFO:root:Iter[21] Batch 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INFO:root:Iter[21] Batch [7560] Speed: 896.95 samples/sec {fc7_acc} 220000 0.7519531 lr change to 0.00010000000000000003 lr-batch-epoch: 0.00010000000000000003 7563 21 testing verification.. (12000, 512) infer time 3.7241689999999985 [lfw][220000]XNorm: 22.111377 [lfw][220000]Accuracy-Flip: 0.99800+-0.00194 testing verification.. (14000, 512) infer time 4.347648999999999 [cfp_fp][220000]XNorm: 22.422131 [cfp_fp][220000]Accuracy-Flip: 0.98929+-0.00224 testing verification.. (12000, 512) infer time 3.7596820000000006 [agedb_30][220000]XNorm: 23.130037 [agedb_30][220000]Accuracy-Flip: 0.98233+-0.00746 [220000]Accuracy-Highest: 0.98450 INFO:root:Iter[21] Batch [7580] Speed: 297.67 samples/sec INFO:root:Iter[21] Batch [7600] Speed: 859.57 samples/sec INFO:root:Iter[21] Batch [7620] Speed: 836.94 samples/sec INFO:root:Iter[21] Batch [7640] Speed: 842.29 samples/sec INFO:root:Iter[21] Batch [7660] Speed: 850.78 samples/sec INFO:root:Iter[21] Batch [7680] Speed: 855.84 samples/sec INFO:root:Iter[21] Batch [7700] Speed: 865.63 samples/sec INFO:root:Iter[21] Batch [7720] Speed: 847.78 samples/sec INFO:root:Iter[21] Batch [7740] Speed: 861.21 samples/sec INFO:root:Iter[21] Batch [7760] Speed: 808.85 samples/sec INFO:root:Iter[21] Batch [7780] Speed: 836.38 samples/sec INFO:root:Iter[21] Batch [7800] Speed: 835.20 samples/sec INFO:root:Iter[21] Batch [7820] Speed: 859.61 samples/sec INFO:root:Iter[21] Batch [7840] Speed: 836.61 samples/sec INFO:root:Iter[21] Batch 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(12000, 512) infer time 3.6726490000000003 [lfw][222000]XNorm: 22.007484 [lfw][222000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.348526 [cfp_fp][222000]XNorm: 22.513464 [cfp_fp][222000]Accuracy-Flip: 0.98914+-0.00321 testing verification.. (12000, 512) infer time 3.774223999999999 [agedb_30][222000]XNorm: 23.126660 [agedb_30][222000]Accuracy-Flip: 0.98267+-0.00672 [222000]Accuracy-Highest: 0.98450 INFO:root:Iter[21] Batch [9580] Speed: 302.15 samples/sec INFO:root:Iter[21] Batch [9600] Speed: 846.44 samples/sec INFO:root:Iter[21] Batch [9620] Speed: 851.63 samples/sec INFO:root:Iter[21] Batch [9640] Speed: 849.92 samples/sec INFO:root:Iter[21] Batch [9660] Speed: 867.64 samples/sec INFO:root:Iter[21] Batch [9680] Speed: 863.18 samples/sec INFO:root:Iter[21] Batch [9700] Speed: 873.05 samples/sec INFO:root:Iter[21] Batch [9720] Speed: 858.14 samples/sec INFO:root:Iter[21] Batch [9740] Speed: 854.30 samples/sec INFO:root:Iter[21] Batch [9760] Speed: 838.76 samples/sec INFO:root:Iter[21] Batch [9780] Speed: 859.61 samples/sec INFO:root:Iter[21] Batch [9800] Speed: 864.28 samples/sec INFO:root:Iter[21] Batch [9820] Speed: 859.30 samples/sec INFO:root:Iter[21] Batch [9840] Speed: 833.25 samples/sec INFO:root:Iter[21] Batch 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lr-batch-epoch: 0.00010000000000000003 1447 22 testing verification.. (12000, 512) infer time 3.6867229999999993 [lfw][224000]XNorm: 21.934377 [lfw][224000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.347269000000001 [cfp_fp][224000]XNorm: 22.580874 [cfp_fp][224000]Accuracy-Flip: 0.98914+-0.00273 testing verification.. (12000, 512) infer time 3.749904 [agedb_30][224000]XNorm: 23.170913 [agedb_30][224000]Accuracy-Flip: 0.98217+-0.00723 [224000]Accuracy-Highest: 0.98450 INFO:root:Iter[22] Batch [1460] Speed: 299.17 samples/sec INFO:root:Iter[22] Batch [1480] Speed: 834.01 samples/sec INFO:root:Iter[22] Batch [1500] Speed: 815.45 samples/sec INFO:root:Iter[22] Batch [1520] Speed: 844.93 samples/sec INFO:root:Iter[22] Batch [1540] Speed: 829.08 samples/sec INFO:root:Iter[22] Batch [1560] Speed: 831.11 samples/sec INFO:root:Iter[22] Batch [1580] Speed: 818.87 samples/sec INFO:root:Iter[22] Batch [1600] Speed: 823.97 samples/sec INFO:root:Iter[22] Batch [1620] Speed: 839.25 samples/sec INFO:root:Iter[22] Batch [1640] Speed: 862.33 samples/sec INFO:root:Iter[22] Batch [1660] Speed: 846.83 samples/sec INFO:root:Iter[22] Batch [1680] Speed: 830.82 samples/sec INFO:root:Iter[22] Batch [1700] Speed: 847.63 samples/sec INFO:root:Iter[22] Batch [1720] Speed: 864.55 samples/sec INFO:root:Iter[22] Batch [1740] 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samples/sec {fc7_acc} 226000 0.78125 lr-batch-epoch: 0.00010000000000000003 3447 22 testing verification.. (12000, 512) infer time 3.7637460000000003 [lfw][226000]XNorm: 21.935862 [lfw][226000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.395764000000002 [cfp_fp][226000]XNorm: 22.548207 [cfp_fp][226000]Accuracy-Flip: 0.98871+-0.00329 testing verification.. (12000, 512) infer time 3.723211999999999 [agedb_30][226000]XNorm: 23.080109 [agedb_30][226000]Accuracy-Flip: 0.98217+-0.00738 [226000]Accuracy-Highest: 0.98450 INFO:root:Iter[22] Batch [3460] Speed: 299.78 samples/sec INFO:root:Iter[22] Batch [3480] Speed: 837.66 samples/sec INFO:root:Iter[22] Batch [3500] Speed: 847.78 samples/sec INFO:root:Iter[22] Batch [3520] Speed: 853.29 samples/sec INFO:root:Iter[22] Batch [3540] Speed: 834.48 samples/sec INFO:root:Iter[22] Batch [3560] Speed: 835.29 samples/sec INFO:root:Iter[22] Batch [3580] Speed: 838.80 samples/sec INFO:root:Iter[22] Batch [3600] Speed: 869.98 samples/sec INFO:root:Iter[22] Batch [3620] Speed: 856.23 samples/sec INFO:root:Iter[22] Batch [3640] Speed: 843.10 samples/sec INFO:root:Iter[22] Batch [3660] Speed: 845.58 samples/sec INFO:root:Iter[22] Batch [3680] Speed: 844.78 samples/sec INFO:root:Iter[22] Batch [3700] Speed: 859.87 samples/sec INFO:root:Iter[22] Batch [3720] Speed: 857.71 samples/sec INFO:root:Iter[22] Batch 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(12000, 512) infer time 3.810872 [lfw][228000]XNorm: 21.942227 [lfw][228000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.343376 [cfp_fp][228000]XNorm: 22.636391 [cfp_fp][228000]Accuracy-Flip: 0.98914+-0.00308 testing verification.. (12000, 512) infer time 3.7501360000000004 [agedb_30][228000]XNorm: 23.121338 [agedb_30][228000]Accuracy-Flip: 0.98217+-0.00775 [228000]Accuracy-Highest: 0.98450 INFO:root:Iter[22] Batch [5460] Speed: 299.37 samples/sec INFO:root:Iter[22] Batch [5480] Speed: 831.15 samples/sec INFO:root:Iter[22] Batch [5500] Speed: 844.73 samples/sec INFO:root:Iter[22] Batch [5520] Speed: 843.66 samples/sec INFO:root:Iter[22] Batch [5540] Speed: 836.32 samples/sec INFO:root:Iter[22] Batch [5560] Speed: 839.34 samples/sec INFO:root:Iter[22] Batch [5580] Speed: 807.09 samples/sec INFO:root:Iter[22] Batch [5600] Speed: 819.10 samples/sec INFO:root:Iter[22] Batch [5620] Speed: 823.23 samples/sec INFO:root:Iter[22] Batch [5640] Speed: 810.74 samples/sec INFO:root:Iter[22] Batch [5660] Speed: 816.92 samples/sec INFO:root:Iter[22] Batch [5680] Speed: 838.67 samples/sec INFO:root:Iter[22] Batch [5700] Speed: 849.78 samples/sec INFO:root:Iter[22] Batch [5720] Speed: 841.16 samples/sec INFO:root:Iter[22] Batch 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(12000, 512) infer time 3.7571829999999995 [lfw][230000]XNorm: 21.938767 [lfw][230000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.333293999999999 [cfp_fp][230000]XNorm: 22.620853 [cfp_fp][230000]Accuracy-Flip: 0.98886+-0.00343 testing verification.. (12000, 512) infer time 3.705837000000001 [agedb_30][230000]XNorm: 23.173331 [agedb_30][230000]Accuracy-Flip: 0.98417+-0.00625 [230000]Accuracy-Highest: 0.98450 INFO:root:Iter[22] Batch [7460] Speed: 296.31 samples/sec INFO:root:Iter[22] Batch [7480] Speed: 840.70 samples/sec INFO:root:Iter[22] Batch [7500] Speed: 857.12 samples/sec INFO:root:Iter[22] Batch [7520] Speed: 824.66 samples/sec INFO:root:Iter[22] Batch [7540] Speed: 840.25 samples/sec INFO:root:Iter[22] Batch [7560] Speed: 808.85 samples/sec INFO:root:Iter[22] Batch [7580] Speed: 814.26 samples/sec INFO:root:Iter[22] Batch [7600] Speed: 792.21 samples/sec INFO:root:Iter[22] Batch [7620] Speed: 855.31 samples/sec INFO:root:Iter[22] Batch [7640] Speed: 857.99 samples/sec INFO:root:Iter[22] Batch [7660] Speed: 858.53 samples/sec INFO:root:Iter[22] Batch [7680] Speed: 829.33 samples/sec INFO:root:Iter[22] Batch [7700] Speed: 852.02 samples/sec INFO:root:Iter[22] Batch [7720] Speed: 843.15 samples/sec INFO:root:Iter[22] Batch 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(12000, 512) infer time 3.812969999999999 [lfw][232000]XNorm: 21.993114 [lfw][232000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.375048000000001 [cfp_fp][232000]XNorm: 22.690120 [cfp_fp][232000]Accuracy-Flip: 0.98986+-0.00347 testing verification.. (12000, 512) infer time 3.773423 [agedb_30][232000]XNorm: 23.211998 [agedb_30][232000]Accuracy-Flip: 0.98400+-0.00659 [232000]Accuracy-Highest: 0.98450 INFO:root:Iter[22] Batch [9460] Speed: 295.57 samples/sec INFO:root:Iter[22] Batch [9480] Speed: 819.16 samples/sec INFO:root:Iter[22] Batch [9500] Speed: 842.00 samples/sec INFO:root:Iter[22] Batch [9520] Speed: 840.88 samples/sec INFO:root:Iter[22] Batch [9540] Speed: 827.89 samples/sec INFO:root:Iter[22] Batch [9560] Speed: 838.01 samples/sec INFO:root:Iter[22] Batch [9580] Speed: 834.50 samples/sec INFO:root:Iter[22] Batch [9600] Speed: 827.81 samples/sec INFO:root:Iter[22] Batch [9620] Speed: 837.43 samples/sec INFO:root:Iter[22] Batch [9640] Speed: 845.20 samples/sec INFO:root:Iter[22] Batch [9660] Speed: 844.67 samples/sec INFO:root:Iter[22] Batch [9680] Speed: 871.26 samples/sec INFO:root:Iter[22] Batch [9700] Speed: 815.92 samples/sec INFO:root:Iter[22] Batch [9720] Speed: 839.01 samples/sec INFO:root:Iter[22] Batch [9740] 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0.00010000000000000003 1331 23 testing verification.. (12000, 512) infer time 3.7026880000000006 [lfw][234000]XNorm: 21.844835 [lfw][234000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.399933000000001 [cfp_fp][234000]XNorm: 22.596082 [cfp_fp][234000]Accuracy-Flip: 0.98857+-0.00271 testing verification.. (12000, 512) infer time 3.6841390000000005 [agedb_30][234000]XNorm: 23.070350 [agedb_30][234000]Accuracy-Flip: 0.98233+-0.00720 [234000]Accuracy-Highest: 0.98450 INFO:root:Iter[23] Batch [1340] Speed: 301.91 samples/sec INFO:root:Iter[23] Batch [1360] Speed: 833.35 samples/sec INFO:root:Iter[23] Batch [1380] Speed: 840.73 samples/sec INFO:root:Iter[23] Batch [1400] Speed: 852.95 samples/sec INFO:root:Iter[23] Batch [1420] Speed: 865.11 samples/sec INFO:root:Iter[23] Batch [1440] Speed: 850.11 samples/sec INFO:root:Iter[23] Batch [1460] Speed: 840.71 samples/sec INFO:root:Iter[23] Batch [1480] Speed: 838.55 samples/sec INFO:root:Iter[23] Batch [1500] Speed: 850.26 samples/sec INFO:root:Iter[23] Batch [1520] Speed: 862.28 samples/sec INFO:root:Iter[23] Batch [1540] Speed: 839.67 samples/sec INFO:root:Iter[23] Batch [1560] Speed: 850.64 samples/sec INFO:root:Iter[23] Batch [1580] Speed: 826.26 samples/sec INFO:root:Iter[23] Batch [1600] Speed: 870.56 samples/sec INFO:root:Iter[23] Batch 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INFO:root:Iter[23] Batch [3320] Speed: 869.76 samples/sec {fc7_acc} 236000 0.8105469 lr-batch-epoch: 0.00010000000000000003 3331 23 testing verification.. (12000, 512) infer time 3.7903420000000008 [lfw][236000]XNorm: 21.998338 [lfw][236000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.425785999999999 [cfp_fp][236000]XNorm: 22.693386 [cfp_fp][236000]Accuracy-Flip: 0.98900+-0.00313 testing verification.. (12000, 512) infer time 3.7499080000000005 [agedb_30][236000]XNorm: 23.162510 [agedb_30][236000]Accuracy-Flip: 0.98317+-0.00713 [236000]Accuracy-Highest: 0.98450 INFO:root:Iter[23] Batch [3340] Speed: 297.79 samples/sec INFO:root:Iter[23] Batch [3360] Speed: 850.04 samples/sec INFO:root:Iter[23] Batch [3380] Speed: 839.11 samples/sec INFO:root:Iter[23] Batch [3400] Speed: 825.39 samples/sec INFO:root:Iter[23] Batch [3420] Speed: 822.54 samples/sec INFO:root:Iter[23] Batch [3440] Speed: 858.84 samples/sec INFO:root:Iter[23] Batch [3460] Speed: 853.66 samples/sec INFO:root:Iter[23] Batch [3480] Speed: 821.82 samples/sec INFO:root:Iter[23] Batch [3500] Speed: 833.08 samples/sec INFO:root:Iter[23] Batch [3520] Speed: 841.99 samples/sec INFO:root:Iter[23] Batch [3540] Speed: 832.78 samples/sec INFO:root:Iter[23] Batch [3560] Speed: 880.02 samples/sec INFO:root:Iter[23] Batch [3580] Speed: 800.46 samples/sec INFO:root:Iter[23] Batch [3600] Speed: 848.49 samples/sec INFO:root:Iter[23] Batch 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INFO:root:Iter[23] Batch [5320] Speed: 897.41 samples/sec {fc7_acc} 238000 0.7910156 lr-batch-epoch: 0.00010000000000000003 5331 23 testing verification.. (12000, 512) infer time 3.6796300000000004 [lfw][238000]XNorm: 21.851398 [lfw][238000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.369123000000001 [cfp_fp][238000]XNorm: 22.648261 [cfp_fp][238000]Accuracy-Flip: 0.98900+-0.00307 testing verification.. (12000, 512) infer time 3.7493249999999994 [agedb_30][238000]XNorm: 23.063459 [agedb_30][238000]Accuracy-Flip: 0.98433+-0.00638 [238000]Accuracy-Highest: 0.98450 INFO:root:Iter[23] Batch [5340] Speed: 291.58 samples/sec INFO:root:Iter[23] Batch [5360] Speed: 834.72 samples/sec INFO:root:Iter[23] Batch [5380] Speed: 816.17 samples/sec INFO:root:Iter[23] Batch [5400] Speed: 824.40 samples/sec INFO:root:Iter[23] Batch [5420] Speed: 834.58 samples/sec INFO:root:Iter[23] Batch [5440] Speed: 846.88 samples/sec INFO:root:Iter[23] Batch [5460] Speed: 839.98 samples/sec INFO:root:Iter[23] Batch [5480] Speed: 859.27 samples/sec INFO:root:Iter[23] Batch [5500] Speed: 854.88 samples/sec INFO:root:Iter[23] Batch [5520] Speed: 843.78 samples/sec INFO:root:Iter[23] Batch [5540] Speed: 829.83 samples/sec INFO:root:Iter[23] Batch [5560] Speed: 842.20 samples/sec INFO:root:Iter[23] Batch [5580] Speed: 862.95 samples/sec INFO:root:Iter[23] Batch [5600] Speed: 840.84 samples/sec INFO:root:Iter[23] Batch 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(12000, 512) infer time 3.754494 [lfw][240000]XNorm: 21.897407 [lfw][240000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.449599999999999 [cfp_fp][240000]XNorm: 22.651103 [cfp_fp][240000]Accuracy-Flip: 0.98871+-0.00303 testing verification.. (12000, 512) infer time 3.6943240000000004 [agedb_30][240000]XNorm: 23.113330 [agedb_30][240000]Accuracy-Flip: 0.98267+-0.00772 [240000]Accuracy-Highest: 0.98450 INFO:root:Iter[23] Batch [7340] Speed: 299.86 samples/sec INFO:root:Iter[23] Batch [7360] Speed: 868.01 samples/sec INFO:root:Iter[23] Batch [7380] Speed: 830.50 samples/sec INFO:root:Iter[23] Batch [7400] Speed: 852.50 samples/sec INFO:root:Iter[23] Batch [7420] Speed: 840.86 samples/sec INFO:root:Iter[23] Batch [7440] Speed: 853.64 samples/sec INFO:root:Iter[23] Batch [7460] Speed: 838.28 samples/sec INFO:root:Iter[23] Batch [7480] Speed: 842.82 samples/sec INFO:root:Iter[23] Batch [7500] Speed: 824.66 samples/sec INFO:root:Iter[23] Batch [7520] Speed: 831.44 samples/sec INFO:root:Iter[23] Batch [7540] Speed: 845.87 samples/sec INFO:root:Iter[23] Batch [7560] Speed: 820.42 samples/sec INFO:root:Iter[23] Batch [7580] Speed: 849.20 samples/sec INFO:root:Iter[23] Batch [7600] Speed: 859.25 samples/sec INFO:root:Iter[23] Batch 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INFO:root:Iter[23] Batch [9320] Speed: 874.09 samples/sec {fc7_acc} 242000 0.796875 lr-batch-epoch: 0.00010000000000000003 9331 23 testing verification.. (12000, 512) infer time 3.7637289999999997 [lfw][242000]XNorm: 21.847019 [lfw][242000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.389742 [cfp_fp][242000]XNorm: 22.568605 [cfp_fp][242000]Accuracy-Flip: 0.98886+-0.00305 testing verification.. (12000, 512) infer time 3.7907069999999994 [agedb_30][242000]XNorm: 23.054570 [agedb_30][242000]Accuracy-Flip: 0.98300+-0.00694 [242000]Accuracy-Highest: 0.98450 INFO:root:Iter[23] Batch [9340] Speed: 294.39 samples/sec INFO:root:Iter[23] Batch [9360] Speed: 848.01 samples/sec INFO:root:Iter[23] Batch [9380] Speed: 814.14 samples/sec INFO:root:Iter[23] Batch [9400] Speed: 818.51 samples/sec INFO:root:Iter[23] Batch [9420] Speed: 835.22 samples/sec INFO:root:Iter[23] Batch [9440] Speed: 854.07 samples/sec INFO:root:Iter[23] Batch [9460] Speed: 842.23 samples/sec INFO:root:Iter[23] Batch [9480] Speed: 847.01 samples/sec INFO:root:Iter[23] Batch [9500] Speed: 860.69 samples/sec INFO:root:Iter[23] Batch [9520] Speed: 847.83 samples/sec INFO:root:Iter[23] Batch [9540] Speed: 854.49 samples/sec INFO:root:Iter[23] Batch [9560] Speed: 847.42 samples/sec INFO:root:Iter[23] Batch [9580] Speed: 828.22 samples/sec INFO:root:Iter[23] Batch [9600] Speed: 874.02 samples/sec INFO:root:Iter[23] Batch 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0.8027344 lr-batch-epoch: 0.00010000000000000003 1215 24 testing verification.. (12000, 512) infer time 3.7307539999999992 [lfw][244000]XNorm: 21.928902 [lfw][244000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.300034 [cfp_fp][244000]XNorm: 22.647776 [cfp_fp][244000]Accuracy-Flip: 0.98829+-0.00305 testing verification.. (12000, 512) infer time 3.7173160000000003 [agedb_30][244000]XNorm: 23.141310 [agedb_30][244000]Accuracy-Flip: 0.98300+-0.00591 [244000]Accuracy-Highest: 0.98450 INFO:root:Iter[24] Batch [1220] Speed: 305.04 samples/sec INFO:root:Iter[24] Batch [1240] Speed: 864.33 samples/sec INFO:root:Iter[24] Batch [1260] Speed: 818.13 samples/sec INFO:root:Iter[24] Batch [1280] Speed: 827.49 samples/sec INFO:root:Iter[24] Batch [1300] Speed: 822.76 samples/sec INFO:root:Iter[24] Batch [1320] Speed: 855.65 samples/sec INFO:root:Iter[24] Batch [1340] Speed: 831.63 samples/sec INFO:root:Iter[24] Batch [1360] Speed: 825.88 samples/sec INFO:root:Iter[24] Batch [1380] Speed: 843.24 samples/sec INFO:root:Iter[24] Batch [1400] Speed: 829.43 samples/sec INFO:root:Iter[24] Batch [1420] Speed: 842.80 samples/sec INFO:root:Iter[24] Batch [1440] Speed: 825.18 samples/sec INFO:root:Iter[24] Batch [1460] Speed: 870.25 samples/sec INFO:root:Iter[24] Batch [1480] Speed: 828.85 samples/sec INFO:root:Iter[24] Batch 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(12000, 512) infer time 3.770176 [lfw][246000]XNorm: 21.929517 [lfw][246000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.438579000000001 [cfp_fp][246000]XNorm: 22.636126 [cfp_fp][246000]Accuracy-Flip: 0.98957+-0.00293 testing verification.. (12000, 512) infer time 3.788399 [agedb_30][246000]XNorm: 23.138202 [agedb_30][246000]Accuracy-Flip: 0.98250+-0.00739 [246000]Accuracy-Highest: 0.98450 INFO:root:Iter[24] Batch [3220] Speed: 298.77 samples/sec INFO:root:Iter[24] Batch [3240] Speed: 860.37 samples/sec INFO:root:Iter[24] Batch [3260] Speed: 842.16 samples/sec INFO:root:Iter[24] Batch [3280] Speed: 846.08 samples/sec INFO:root:Iter[24] Batch [3300] Speed: 801.91 samples/sec INFO:root:Iter[24] Batch [3320] Speed: 827.72 samples/sec INFO:root:Iter[24] Batch [3340] Speed: 835.26 samples/sec INFO:root:Iter[24] Batch [3360] Speed: 833.26 samples/sec INFO:root:Iter[24] Batch [3380] Speed: 853.29 samples/sec INFO:root:Iter[24] Batch [3400] Speed: 842.39 samples/sec INFO:root:Iter[24] Batch [3420] Speed: 864.26 samples/sec INFO:root:Iter[24] Batch [3440] Speed: 852.56 samples/sec INFO:root:Iter[24] Batch [3460] Speed: 867.77 samples/sec INFO:root:Iter[24] Batch [3480] Speed: 834.26 samples/sec INFO:root:Iter[24] Batch [3500] 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samples/sec {fc7_acc} 248000 0.7988281 lr-batch-epoch: 0.00010000000000000003 5215 24 testing verification.. (12000, 512) infer time 3.700053 [lfw][248000]XNorm: 21.923726 [lfw][248000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.365951 [cfp_fp][248000]XNorm: 22.648089 [cfp_fp][248000]Accuracy-Flip: 0.98900+-0.00344 testing verification.. (12000, 512) infer time 3.7687120000000003 [agedb_30][248000]XNorm: 23.093899 [agedb_30][248000]Accuracy-Flip: 0.98383+-0.00646 [248000]Accuracy-Highest: 0.98450 INFO:root:Iter[24] Batch [5220] Speed: 302.63 samples/sec INFO:root:Iter[24] Batch [5240] Speed: 843.74 samples/sec INFO:root:Iter[24] Batch [5260] Speed: 832.26 samples/sec INFO:root:Iter[24] Batch [5280] Speed: 861.72 samples/sec INFO:root:Iter[24] Batch [5300] Speed: 825.12 samples/sec INFO:root:Iter[24] Batch [5320] Speed: 787.62 samples/sec INFO:root:Iter[24] Batch [5340] Speed: 808.17 samples/sec INFO:root:Iter[24] Batch [5360] Speed: 816.05 samples/sec INFO:root:Iter[24] Batch [5380] Speed: 828.55 samples/sec INFO:root:Iter[24] Batch [5400] Speed: 816.23 samples/sec INFO:root:Iter[24] Batch [5420] Speed: 850.75 samples/sec INFO:root:Iter[24] Batch [5440] Speed: 825.08 samples/sec INFO:root:Iter[24] Batch [5460] Speed: 826.88 samples/sec INFO:root:Iter[24] Batch [5480] Speed: 855.06 samples/sec INFO:root:Iter[24] Batch 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INFO:root:Iter[24] Batch [7200] Speed: 828.21 samples/sec {fc7_acc} 250000 0.8027344 lr-batch-epoch: 0.00010000000000000003 7215 24 testing verification.. (12000, 512) infer time 3.714597000000001 [lfw][250000]XNorm: 21.970235 [lfw][250000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.357260000000001 [cfp_fp][250000]XNorm: 22.676156 [cfp_fp][250000]Accuracy-Flip: 0.98986+-0.00259 testing verification.. (12000, 512) infer time 3.744320000000001 [agedb_30][250000]XNorm: 23.151715 [agedb_30][250000]Accuracy-Flip: 0.98317+-0.00724 [250000]Accuracy-Highest: 0.98450 INFO:root:Iter[24] Batch [7220] Speed: 298.42 samples/sec INFO:root:Iter[24] Batch [7240] Speed: 830.45 samples/sec INFO:root:Iter[24] Batch [7260] Speed: 828.49 samples/sec INFO:root:Iter[24] Batch [7280] Speed: 820.84 samples/sec INFO:root:Iter[24] Batch [7300] Speed: 831.58 samples/sec INFO:root:Iter[24] Batch [7320] Speed: 811.52 samples/sec INFO:root:Iter[24] Batch [7340] Speed: 837.27 samples/sec INFO:root:Iter[24] Batch [7360] Speed: 851.59 samples/sec INFO:root:Iter[24] Batch [7380] Speed: 802.49 samples/sec INFO:root:Iter[24] Batch [7400] Speed: 822.51 samples/sec INFO:root:Iter[24] Batch [7420] Speed: 822.16 samples/sec INFO:root:Iter[24] Batch [7440] Speed: 851.28 samples/sec INFO:root:Iter[24] Batch [7460] Speed: 846.75 samples/sec INFO:root:Iter[24] Batch [7480] Speed: 817.21 samples/sec INFO:root:Iter[24] Batch 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(12000, 512) infer time 3.74506 [lfw][252000]XNorm: 21.983731 [lfw][252000]Accuracy-Flip: 0.99817+-0.00203 testing verification.. (14000, 512) infer time 4.359591999999999 [cfp_fp][252000]XNorm: 22.686943 [cfp_fp][252000]Accuracy-Flip: 0.98971+-0.00254 testing verification.. (12000, 512) infer time 3.7850639999999998 [agedb_30][252000]XNorm: 23.180454 [agedb_30][252000]Accuracy-Flip: 0.98283+-0.00719 [252000]Accuracy-Highest: 0.98450 INFO:root:Iter[24] Batch [9220] Speed: 299.40 samples/sec INFO:root:Iter[24] Batch [9240] Speed: 851.83 samples/sec INFO:root:Iter[24] Batch [9260] Speed: 832.51 samples/sec INFO:root:Iter[24] Batch [9280] Speed: 826.59 samples/sec INFO:root:Iter[24] Batch [9300] Speed: 845.25 samples/sec INFO:root:Iter[24] Batch [9320] Speed: 854.16 samples/sec INFO:root:Iter[24] Batch [9340] Speed: 862.06 samples/sec INFO:root:Iter[24] Batch [9360] Speed: 834.58 samples/sec INFO:root:Iter[24] Batch [9380] Speed: 851.93 samples/sec INFO:root:Iter[24] Batch [9400] Speed: 835.63 samples/sec INFO:root:Iter[24] Batch [9420] Speed: 851.91 samples/sec INFO:root:Iter[24] Batch [9440] Speed: 847.77 samples/sec INFO:root:Iter[24] Batch [9460] Speed: 849.29 samples/sec INFO:root:Iter[24] Batch [9480] Speed: 849.95 samples/sec INFO:root:Iter[24] Batch 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0.00010000000000000003 1099 25 testing verification.. (12000, 512) infer time 3.6918850000000005 [lfw][254000]XNorm: 21.915973 [lfw][254000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.328554000000001 [cfp_fp][254000]XNorm: 22.677482 [cfp_fp][254000]Accuracy-Flip: 0.98886+-0.00284 testing verification.. (12000, 512) infer time 3.730802 [agedb_30][254000]XNorm: 23.157218 [agedb_30][254000]Accuracy-Flip: 0.98367+-0.00702 [254000]Accuracy-Highest: 0.98450 INFO:root:Iter[25] Batch [1100] Speed: 305.15 samples/sec INFO:root:Iter[25] Batch [1120] Speed: 835.38 samples/sec INFO:root:Iter[25] Batch [1140] Speed: 839.24 samples/sec INFO:root:Iter[25] Batch [1160] Speed: 832.51 samples/sec INFO:root:Iter[25] Batch [1180] Speed: 863.43 samples/sec INFO:root:Iter[25] Batch [1200] Speed: 818.65 samples/sec INFO:root:Iter[25] Batch [1220] Speed: 829.45 samples/sec INFO:root:Iter[25] Batch [1240] Speed: 839.71 samples/sec INFO:root:Iter[25] Batch [1260] Speed: 838.14 samples/sec INFO:root:Iter[25] Batch [1280] Speed: 874.73 samples/sec INFO:root:Iter[25] Batch [1300] Speed: 841.17 samples/sec INFO:root:Iter[25] Batch [1320] Speed: 865.11 samples/sec INFO:root:Iter[25] Batch [1340] Speed: 851.79 samples/sec INFO:root:Iter[25] Batch [1360] Speed: 859.42 samples/sec INFO:root:Iter[25] Batch [1380] 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samples/sec {fc7_acc} 256000 0.7871094 lr-batch-epoch: 0.00010000000000000003 3099 25 testing verification.. (12000, 512) infer time 3.783379999999999 [lfw][256000]XNorm: 21.975894 [lfw][256000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.453872000000001 [cfp_fp][256000]XNorm: 22.729450 [cfp_fp][256000]Accuracy-Flip: 0.98914+-0.00351 testing verification.. (12000, 512) infer time 3.807198999999999 [agedb_30][256000]XNorm: 23.156534 [agedb_30][256000]Accuracy-Flip: 0.98233+-0.00700 [256000]Accuracy-Highest: 0.98450 INFO:root:Iter[25] Batch [3100] Speed: 298.21 samples/sec INFO:root:Iter[25] Batch [3120] Speed: 837.13 samples/sec INFO:root:Iter[25] Batch [3140] Speed: 876.55 samples/sec INFO:root:Iter[25] Batch [3160] Speed: 836.04 samples/sec INFO:root:Iter[25] Batch [3180] Speed: 824.92 samples/sec INFO:root:Iter[25] Batch [3200] Speed: 863.48 samples/sec INFO:root:Iter[25] Batch [3220] Speed: 820.43 samples/sec INFO:root:Iter[25] Batch [3240] Speed: 861.56 samples/sec INFO:root:Iter[25] Batch [3260] Speed: 895.53 samples/sec INFO:root:Iter[25] Batch [3280] Speed: 836.92 samples/sec INFO:root:Iter[25] Batch [3300] Speed: 833.29 samples/sec INFO:root:Iter[25] Batch [3320] Speed: 854.12 samples/sec INFO:root:Iter[25] Batch [3340] Speed: 838.69 samples/sec INFO:root:Iter[25] Batch [3360] Speed: 832.91 samples/sec INFO:root:Iter[25] Batch 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(12000, 512) infer time 3.673021999999999 [lfw][258000]XNorm: 21.910349 [lfw][258000]Accuracy-Flip: 0.99800+-0.00194 testing verification.. (14000, 512) infer time 4.464505 [cfp_fp][258000]XNorm: 22.681882 [cfp_fp][258000]Accuracy-Flip: 0.98971+-0.00254 testing verification.. (12000, 512) infer time 3.7419109999999995 [agedb_30][258000]XNorm: 23.101694 [agedb_30][258000]Accuracy-Flip: 0.98267+-0.00764 [258000]Accuracy-Highest: 0.98450 INFO:root:Iter[25] Batch [5100] Speed: 300.03 samples/sec INFO:root:Iter[25] Batch [5120] Speed: 842.71 samples/sec INFO:root:Iter[25] Batch [5140] Speed: 858.01 samples/sec INFO:root:Iter[25] Batch [5160] Speed: 834.20 samples/sec INFO:root:Iter[25] Batch [5180] Speed: 857.90 samples/sec INFO:root:Iter[25] Batch [5200] Speed: 848.13 samples/sec INFO:root:Iter[25] Batch [5220] Speed: 830.11 samples/sec INFO:root:Iter[25] Batch [5240] Speed: 838.71 samples/sec INFO:root:Iter[25] Batch [5260] Speed: 837.31 samples/sec INFO:root:Iter[25] Batch [5280] Speed: 868.22 samples/sec INFO:root:Iter[25] Batch [5300] Speed: 870.07 samples/sec INFO:root:Iter[25] Batch [5320] Speed: 826.16 samples/sec INFO:root:Iter[25] Batch [5340] Speed: 857.30 samples/sec INFO:root:Iter[25] Batch [5360] Speed: 847.85 samples/sec INFO:root:Iter[25] Batch 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INFO:root:Iter[25] Batch [7080] Speed: 889.81 samples/sec {fc7_acc} 260000 0.79296875 lr-batch-epoch: 0.00010000000000000003 7099 25 testing verification.. (12000, 512) infer time 3.6920349999999993 [lfw][260000]XNorm: 21.901894 [lfw][260000]Accuracy-Flip: 0.99800+-0.00194 testing verification.. (14000, 512) infer time 4.378443999999999 [cfp_fp][260000]XNorm: 22.698878 [cfp_fp][260000]Accuracy-Flip: 0.99014+-0.00289 testing verification.. (12000, 512) infer time 3.727699 [agedb_30][260000]XNorm: 23.116018 [agedb_30][260000]Accuracy-Flip: 0.98233+-0.00720 [260000]Accuracy-Highest: 0.98450 INFO:root:Iter[25] Batch [7100] Speed: 308.66 samples/sec INFO:root:Iter[25] Batch [7120] Speed: 833.95 samples/sec INFO:root:Iter[25] Batch [7140] Speed: 824.00 samples/sec INFO:root:Iter[25] Batch [7160] Speed: 835.48 samples/sec INFO:root:Iter[25] Batch [7180] Speed: 826.46 samples/sec INFO:root:Iter[25] Batch [7200] Speed: 816.49 samples/sec INFO:root:Iter[25] Batch [7220] Speed: 802.84 samples/sec INFO:root:Iter[25] Batch [7240] Speed: 850.99 samples/sec INFO:root:Iter[25] Batch [7260] Speed: 869.58 samples/sec INFO:root:Iter[25] Batch [7280] Speed: 860.60 samples/sec INFO:root:Iter[25] Batch [7300] Speed: 842.24 samples/sec INFO:root:Iter[25] Batch [7320] Speed: 843.94 samples/sec INFO:root:Iter[25] Batch [7340] Speed: 825.28 samples/sec INFO:root:Iter[25] Batch [7360] Speed: 840.30 samples/sec INFO:root:Iter[25] Batch [7380] 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samples/sec {fc7_acc} 262000 0.79296875 lr-batch-epoch: 0.00010000000000000003 9099 25 testing verification.. (12000, 512) infer time 3.7036189999999993 [lfw][262000]XNorm: 21.923383 [lfw][262000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.372299999999999 [cfp_fp][262000]XNorm: 22.652397 [cfp_fp][262000]Accuracy-Flip: 0.98957+-0.00279 testing verification.. (12000, 512) infer time 3.7132700000000005 [agedb_30][262000]XNorm: 23.112127 [agedb_30][262000]Accuracy-Flip: 0.98267+-0.00800 [262000]Accuracy-Highest: 0.98450 INFO:root:Iter[25] Batch [9100] Speed: 299.70 samples/sec INFO:root:Iter[25] Batch [9120] Speed: 856.03 samples/sec INFO:root:Iter[25] Batch [9140] Speed: 826.38 samples/sec INFO:root:Iter[25] Batch [9160] Speed: 822.84 samples/sec INFO:root:Iter[25] Batch [9180] Speed: 855.18 samples/sec INFO:root:Iter[25] Batch [9200] Speed: 829.14 samples/sec INFO:root:Iter[25] Batch [9220] Speed: 843.14 samples/sec INFO:root:Iter[25] Batch [9240] Speed: 818.12 samples/sec INFO:root:Iter[25] Batch [9260] Speed: 839.94 samples/sec INFO:root:Iter[25] Batch [9280] Speed: 845.42 samples/sec INFO:root:Iter[25] Batch [9300] Speed: 846.77 samples/sec INFO:root:Iter[25] Batch [9320] Speed: 871.01 samples/sec INFO:root:Iter[25] Batch [9340] Speed: 880.26 samples/sec INFO:root:Iter[25] Batch [9360] Speed: 870.84 samples/sec INFO:root:Iter[25] Batch 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Speed: 852.38 samples/sec {fc7_acc} 264000 0.828125 lr-batch-epoch: 0.00010000000000000003 983 26 testing verification.. (12000, 512) infer time 3.6828010000000004 [lfw][264000]XNorm: 21.909757 [lfw][264000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.348984000000001 [cfp_fp][264000]XNorm: 22.671618 [cfp_fp][264000]Accuracy-Flip: 0.98957+-0.00286 testing verification.. (12000, 512) infer time 3.7288200000000002 [agedb_30][264000]XNorm: 23.125675 [agedb_30][264000]Accuracy-Flip: 0.98433+-0.00606 [264000]Accuracy-Highest: 0.98450 INFO:root:Iter[26] Batch [1000] Speed: 296.34 samples/sec INFO:root:Iter[26] Batch [1020] Speed: 834.22 samples/sec INFO:root:Iter[26] Batch [1040] Speed: 825.41 samples/sec INFO:root:Iter[26] Batch [1060] Speed: 832.69 samples/sec INFO:root:Iter[26] Batch [1080] Speed: 862.23 samples/sec INFO:root:Iter[26] Batch [1100] Speed: 862.25 samples/sec INFO:root:Iter[26] Batch [1120] Speed: 859.79 samples/sec INFO:root:Iter[26] Batch [1140] Speed: 859.40 samples/sec INFO:root:Iter[26] Batch [1160] Speed: 861.67 samples/sec INFO:root:Iter[26] Batch [1180] Speed: 837.88 samples/sec INFO:root:Iter[26] Batch [1200] Speed: 858.71 samples/sec INFO:root:Iter[26] Batch [1220] Speed: 851.45 samples/sec INFO:root:Iter[26] Batch [1240] Speed: 843.08 samples/sec INFO:root:Iter[26] Batch [1260] Speed: 853.66 samples/sec INFO:root:Iter[26] Batch 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INFO:root:Iter[26] Batch [2980] Speed: 880.84 samples/sec {fc7_acc} 266000 0.8046875 lr-batch-epoch: 0.00010000000000000003 2983 26 testing verification.. (12000, 512) infer time 3.742849 [lfw][266000]XNorm: 21.873783 [lfw][266000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.341927000000001 [cfp_fp][266000]XNorm: 22.639698 [cfp_fp][266000]Accuracy-Flip: 0.98914+-0.00280 testing verification.. (12000, 512) infer time 3.7589659999999996 [agedb_30][266000]XNorm: 23.096936 [agedb_30][266000]Accuracy-Flip: 0.98283+-0.00727 [266000]Accuracy-Highest: 0.98450 INFO:root:Iter[26] Batch [3000] Speed: 295.89 samples/sec INFO:root:Iter[26] Batch [3020] Speed: 850.58 samples/sec INFO:root:Iter[26] Batch [3040] Speed: 839.13 samples/sec INFO:root:Iter[26] Batch [3060] Speed: 852.19 samples/sec INFO:root:Iter[26] Batch [3080] Speed: 818.86 samples/sec INFO:root:Iter[26] Batch [3100] Speed: 832.82 samples/sec INFO:root:Iter[26] Batch [3120] Speed: 833.99 samples/sec INFO:root:Iter[26] Batch [3140] Speed: 845.40 samples/sec INFO:root:Iter[26] Batch [3160] Speed: 838.52 samples/sec INFO:root:Iter[26] Batch [3180] Speed: 840.62 samples/sec INFO:root:Iter[26] Batch [3200] Speed: 828.96 samples/sec INFO:root:Iter[26] Batch [3220] Speed: 854.40 samples/sec INFO:root:Iter[26] Batch [3240] Speed: 863.68 samples/sec INFO:root:Iter[26] Batch [3260] Speed: 834.54 samples/sec INFO:root:Iter[26] Batch 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INFO:root:Iter[26] Batch [4980] Speed: 902.10 samples/sec {fc7_acc} 268000 0.7910156 lr-batch-epoch: 0.00010000000000000003 4983 26 testing verification.. (12000, 512) infer time 3.8172699999999997 [lfw][268000]XNorm: 21.935035 [lfw][268000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.361105 [cfp_fp][268000]XNorm: 22.722444 [cfp_fp][268000]Accuracy-Flip: 0.98986+-0.00259 testing verification.. (12000, 512) infer time 3.81046 [agedb_30][268000]XNorm: 23.161427 [agedb_30][268000]Accuracy-Flip: 0.98167+-0.00711 [268000]Accuracy-Highest: 0.98450 INFO:root:Iter[26] Batch [5000] Speed: 299.57 samples/sec INFO:root:Iter[26] Batch [5020] Speed: 833.84 samples/sec INFO:root:Iter[26] Batch [5040] Speed: 817.34 samples/sec INFO:root:Iter[26] Batch [5060] Speed: 819.03 samples/sec INFO:root:Iter[26] Batch [5080] Speed: 859.69 samples/sec INFO:root:Iter[26] Batch [5100] Speed: 835.19 samples/sec INFO:root:Iter[26] Batch [5120] Speed: 821.24 samples/sec INFO:root:Iter[26] Batch [5140] Speed: 859.57 samples/sec INFO:root:Iter[26] Batch [5160] Speed: 865.16 samples/sec INFO:root:Iter[26] Batch [5180] Speed: 815.40 samples/sec INFO:root:Iter[26] Batch [5200] Speed: 843.50 samples/sec INFO:root:Iter[26] Batch [5220] Speed: 824.59 samples/sec INFO:root:Iter[26] Batch [5240] Speed: 855.64 samples/sec INFO:root:Iter[26] Batch [5260] Speed: 847.17 samples/sec INFO:root:Iter[26] Batch [5280] 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samples/sec {fc7_acc} 270000 0.7910156 lr-batch-epoch: 0.00010000000000000003 6983 26 testing verification.. (12000, 512) infer time 3.7880840000000005 [lfw][270000]XNorm: 21.875213 [lfw][270000]Accuracy-Flip: 0.99817+-0.00203 testing verification.. (14000, 512) infer time 4.334204 [cfp_fp][270000]XNorm: 22.686974 [cfp_fp][270000]Accuracy-Flip: 0.98871+-0.00329 testing verification.. (12000, 512) infer time 3.768275 [agedb_30][270000]XNorm: 23.119831 [agedb_30][270000]Accuracy-Flip: 0.98417+-0.00611 [270000]Accuracy-Highest: 0.98450 INFO:root:Iter[26] Batch [7000] Speed: 295.13 samples/sec INFO:root:Iter[26] Batch [7020] Speed: 831.24 samples/sec INFO:root:Iter[26] Batch [7040] Speed: 848.00 samples/sec INFO:root:Iter[26] Batch [7060] Speed: 817.50 samples/sec INFO:root:Iter[26] Batch [7080] Speed: 816.68 samples/sec INFO:root:Iter[26] Batch [7100] Speed: 809.23 samples/sec INFO:root:Iter[26] Batch [7120] Speed: 820.92 samples/sec INFO:root:Iter[26] Batch [7140] Speed: 835.98 samples/sec INFO:root:Iter[26] Batch [7160] Speed: 833.63 samples/sec INFO:root:Iter[26] Batch [7180] Speed: 844.69 samples/sec INFO:root:Iter[26] Batch [7200] Speed: 842.69 samples/sec INFO:root:Iter[26] Batch [7220] Speed: 803.25 samples/sec INFO:root:Iter[26] Batch [7240] Speed: 839.11 samples/sec INFO:root:Iter[26] Batch [7260] Speed: 855.79 samples/sec INFO:root:Iter[26] Batch [7280] 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samples/sec {fc7_acc} 272000 0.76953125 lr-batch-epoch: 0.00010000000000000003 8983 26 testing verification.. (12000, 512) infer time 3.8053709999999996 [lfw][272000]XNorm: 21.857190 [lfw][272000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.3597670000000015 [cfp_fp][272000]XNorm: 22.653217 [cfp_fp][272000]Accuracy-Flip: 0.98929+-0.00273 testing verification.. (12000, 512) infer time 3.7299980000000006 [agedb_30][272000]XNorm: 23.098476 [agedb_30][272000]Accuracy-Flip: 0.98317+-0.00721 [272000]Accuracy-Highest: 0.98450 INFO:root:Iter[26] Batch [9000] Speed: 297.07 samples/sec INFO:root:Iter[26] Batch [9020] Speed: 868.25 samples/sec INFO:root:Iter[26] Batch [9040] Speed: 865.19 samples/sec INFO:root:Iter[26] Batch [9060] Speed: 805.76 samples/sec INFO:root:Iter[26] Batch [9080] Speed: 819.62 samples/sec INFO:root:Iter[26] Batch [9100] Speed: 834.06 samples/sec INFO:root:Iter[26] Batch [9120] Speed: 840.23 samples/sec INFO:root:Iter[26] Batch [9140] Speed: 837.40 samples/sec INFO:root:Iter[26] Batch [9160] Speed: 842.93 samples/sec INFO:root:Iter[26] Batch [9180] Speed: 827.74 samples/sec INFO:root:Iter[26] Batch [9200] Speed: 839.96 samples/sec INFO:root:Iter[26] Batch [9220] Speed: 845.93 samples/sec INFO:root:Iter[26] Batch [9240] Speed: 866.17 samples/sec INFO:root:Iter[26] Batch [9260] Speed: 873.50 samples/sec INFO:root:Iter[26] Batch 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lr-batch-epoch: 0.00010000000000000003 867 27 testing verification.. (12000, 512) infer time 3.7452680000000007 [lfw][274000]XNorm: 21.876404 [lfw][274000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.341177 [cfp_fp][274000]XNorm: 22.699434 [cfp_fp][274000]Accuracy-Flip: 0.98943+-0.00287 testing verification.. (12000, 512) infer time 3.773223 [agedb_30][274000]XNorm: 23.086900 [agedb_30][274000]Accuracy-Flip: 0.98217+-0.00771 [274000]Accuracy-Highest: 0.98450 INFO:root:Iter[27] Batch [880] Speed: 296.67 samples/sec INFO:root:Iter[27] Batch [900] Speed: 863.67 samples/sec INFO:root:Iter[27] Batch [920] Speed: 843.65 samples/sec INFO:root:Iter[27] Batch [940] Speed: 855.37 samples/sec INFO:root:Iter[27] Batch [960] Speed: 858.12 samples/sec INFO:root:Iter[27] Batch [980] Speed: 858.05 samples/sec INFO:root:Iter[27] Batch [1000] Speed: 849.66 samples/sec INFO:root:Iter[27] Batch [1020] Speed: 893.77 samples/sec INFO:root:Iter[27] Batch [1040] Speed: 847.87 samples/sec INFO:root:Iter[27] Batch [1060] Speed: 851.22 samples/sec INFO:root:Iter[27] Batch [1080] Speed: 872.90 samples/sec INFO:root:Iter[27] Batch [1100] Speed: 854.38 samples/sec INFO:root:Iter[27] Batch [1120] Speed: 823.99 samples/sec INFO:root:Iter[27] Batch [1140] Speed: 827.69 samples/sec INFO:root:Iter[27] Batch [1160] Speed: 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samples/sec {fc7_acc} 276000 0.80078125 lr-batch-epoch: 0.00010000000000000003 2867 27 testing verification.. (12000, 512) infer time 3.7080830000000002 [lfw][276000]XNorm: 21.905753 [lfw][276000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.397842000000001 [cfp_fp][276000]XNorm: 22.672234 [cfp_fp][276000]Accuracy-Flip: 0.98886+-0.00325 testing verification.. (12000, 512) infer time 3.7725990000000005 [agedb_30][276000]XNorm: 23.100533 [agedb_30][276000]Accuracy-Flip: 0.98233+-0.00750 [276000]Accuracy-Highest: 0.98450 INFO:root:Iter[27] Batch [2880] Speed: 297.01 samples/sec INFO:root:Iter[27] Batch [2900] Speed: 832.39 samples/sec INFO:root:Iter[27] Batch [2920] Speed: 830.55 samples/sec INFO:root:Iter[27] Batch [2940] Speed: 831.65 samples/sec INFO:root:Iter[27] Batch [2960] Speed: 838.61 samples/sec INFO:root:Iter[27] Batch [2980] Speed: 823.05 samples/sec INFO:root:Iter[27] Batch [3000] Speed: 829.91 samples/sec INFO:root:Iter[27] Batch [3020] Speed: 845.96 samples/sec INFO:root:Iter[27] Batch [3040] Speed: 831.29 samples/sec INFO:root:Iter[27] Batch [3060] Speed: 813.96 samples/sec INFO:root:Iter[27] Batch [3080] Speed: 827.99 samples/sec INFO:root:Iter[27] Batch [3100] Speed: 840.60 samples/sec INFO:root:Iter[27] Batch [3120] Speed: 826.88 samples/sec INFO:root:Iter[27] Batch [3140] Speed: 844.95 samples/sec INFO:root:Iter[27] Batch 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INFO:root:Iter[27] Batch [4860] Speed: 901.09 samples/sec {fc7_acc} 278000 0.76953125 lr-batch-epoch: 0.00010000000000000003 4867 27 testing verification.. (12000, 512) infer time 3.7760820000000006 [lfw][278000]XNorm: 21.893752 [lfw][278000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.312944999999999 [cfp_fp][278000]XNorm: 22.721322 [cfp_fp][278000]Accuracy-Flip: 0.98943+-0.00294 testing verification.. (12000, 512) infer time 3.754520999999999 [agedb_30][278000]XNorm: 23.110464 [agedb_30][278000]Accuracy-Flip: 0.98400+-0.00696 [278000]Accuracy-Highest: 0.98450 INFO:root:Iter[27] Batch [4880] Speed: 298.88 samples/sec INFO:root:Iter[27] Batch [4900] Speed: 863.27 samples/sec INFO:root:Iter[27] Batch [4920] Speed: 837.60 samples/sec INFO:root:Iter[27] Batch [4940] Speed: 858.00 samples/sec INFO:root:Iter[27] Batch [4960] Speed: 831.06 samples/sec INFO:root:Iter[27] Batch [4980] Speed: 844.91 samples/sec INFO:root:Iter[27] Batch [5000] Speed: 855.06 samples/sec INFO:root:Iter[27] Batch [5020] Speed: 852.16 samples/sec INFO:root:Iter[27] Batch [5040] Speed: 811.33 samples/sec INFO:root:Iter[27] Batch [5060] Speed: 841.36 samples/sec INFO:root:Iter[27] Batch [5080] Speed: 856.19 samples/sec INFO:root:Iter[27] Batch [5100] Speed: 851.12 samples/sec INFO:root:Iter[27] Batch [5120] Speed: 830.31 samples/sec INFO:root:Iter[27] Batch [5140] Speed: 862.77 samples/sec INFO:root:Iter[27] Batch 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(12000, 512) infer time 3.729013999999999 [lfw][280000]XNorm: 21.902808 [lfw][280000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.353605999999998 [cfp_fp][280000]XNorm: 22.691652 [cfp_fp][280000]Accuracy-Flip: 0.98986+-0.00289 testing verification.. (12000, 512) infer time 3.7437699999999987 [agedb_30][280000]XNorm: 23.121240 [agedb_30][280000]Accuracy-Flip: 0.98367+-0.00730 [280000]Accuracy-Highest: 0.98450 INFO:root:Iter[27] Batch [6880] Speed: 300.01 samples/sec INFO:root:Iter[27] Batch [6900] Speed: 833.84 samples/sec INFO:root:Iter[27] Batch [6920] Speed: 816.43 samples/sec INFO:root:Iter[27] Batch [6940] Speed: 834.88 samples/sec INFO:root:Iter[27] Batch [6960] Speed: 878.96 samples/sec INFO:root:Iter[27] Batch [6980] Speed: 831.47 samples/sec INFO:root:Iter[27] Batch [7000] Speed: 853.98 samples/sec INFO:root:Iter[27] Batch [7020] Speed: 828.51 samples/sec INFO:root:Iter[27] Batch [7040] Speed: 841.61 samples/sec INFO:root:Iter[27] Batch [7060] Speed: 818.58 samples/sec INFO:root:Iter[27] Batch [7080] Speed: 851.61 samples/sec INFO:root:Iter[27] Batch [7100] Speed: 822.64 samples/sec INFO:root:Iter[27] Batch [7120] Speed: 822.83 samples/sec INFO:root:Iter[27] Batch [7140] Speed: 834.10 samples/sec INFO:root:Iter[27] Batch 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INFO:root:Iter[27] Batch [8860] Speed: 853.79 samples/sec {fc7_acc} 282000 0.8261719 lr-batch-epoch: 0.00010000000000000003 8867 27 testing verification.. (12000, 512) infer time 3.7327110000000006 [lfw][282000]XNorm: 21.879665 [lfw][282000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.576282000000001 [cfp_fp][282000]XNorm: 22.726157 [cfp_fp][282000]Accuracy-Flip: 0.98971+-0.00270 testing verification.. (12000, 512) infer time 3.729272 [agedb_30][282000]XNorm: 23.085668 [agedb_30][282000]Accuracy-Flip: 0.98500+-0.00587 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [282000]Accuracy-Highest: 0.98500 INFO:root:Iter[27] Batch [8880] Speed: 287.97 samples/sec INFO:root:Iter[27] Batch [8900] Speed: 822.76 samples/sec INFO:root:Iter[27] Batch [8920] Speed: 855.90 samples/sec INFO:root:Iter[27] Batch [8940] Speed: 851.11 samples/sec INFO:root:Iter[27] Batch [8960] Speed: 830.83 samples/sec INFO:root:Iter[27] Batch [8980] Speed: 858.67 samples/sec INFO:root:Iter[27] Batch [9000] Speed: 844.99 samples/sec INFO:root:Iter[27] Batch [9020] Speed: 857.52 samples/sec INFO:root:Iter[27] Batch [9040] Speed: 845.61 samples/sec INFO:root:Iter[27] Batch [9060] Speed: 846.32 samples/sec INFO:root:Iter[27] Batch [9080] Speed: 839.02 samples/sec INFO:root:Iter[27] Batch [9100] Speed: 864.33 samples/sec INFO:root:Iter[27] Batch [9120] Speed: 860.97 samples/sec 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Speed: 851.54 samples/sec INFO:root:Iter[28] Batch [740] Speed: 874.94 samples/sec {fc7_acc} 284000 0.84375 lr-batch-epoch: 0.00010000000000000003 751 28 testing verification.. (12000, 512) infer time 3.7761050000000007 [lfw][284000]XNorm: 21.892307 [lfw][284000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.367002000000002 [cfp_fp][284000]XNorm: 22.690597 [cfp_fp][284000]Accuracy-Flip: 0.98843+-0.00289 testing verification.. (12000, 512) infer time 3.7245760000000003 [agedb_30][284000]XNorm: 23.100871 [agedb_30][284000]Accuracy-Flip: 0.98383+-0.00746 [284000]Accuracy-Highest: 0.98500 INFO:root:Iter[28] Batch [760] Speed: 299.53 samples/sec INFO:root:Iter[28] Batch [780] Speed: 821.72 samples/sec INFO:root:Iter[28] Batch [800] Speed: 834.34 samples/sec INFO:root:Iter[28] Batch [820] Speed: 810.24 samples/sec INFO:root:Iter[28] Batch [840] Speed: 833.03 samples/sec INFO:root:Iter[28] Batch [860] Speed: 859.35 samples/sec INFO:root:Iter[28] Batch [880] Speed: 845.94 samples/sec INFO:root:Iter[28] Batch [900] Speed: 837.70 samples/sec INFO:root:Iter[28] Batch [920] Speed: 829.30 samples/sec INFO:root:Iter[28] Batch [940] Speed: 853.91 samples/sec INFO:root:Iter[28] Batch [960] Speed: 837.03 samples/sec INFO:root:Iter[28] Batch [980] Speed: 842.59 samples/sec INFO:root:Iter[28] Batch [1000] Speed: 828.27 samples/sec INFO:root:Iter[28] Batch [1020] Speed: 829.43 samples/sec INFO:root:Iter[28] Batch [1040] Speed: 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samples/sec {fc7_acc} 286000 0.7832031 lr-batch-epoch: 0.00010000000000000003 2751 28 testing verification.. (12000, 512) infer time 3.761543 [lfw][286000]XNorm: 21.925858 [lfw][286000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.305725000000002 [cfp_fp][286000]XNorm: 22.651198 [cfp_fp][286000]Accuracy-Flip: 0.98971+-0.00262 testing verification.. (12000, 512) infer time 3.721898000000001 [agedb_30][286000]XNorm: 23.125365 [agedb_30][286000]Accuracy-Flip: 0.98383+-0.00699 [286000]Accuracy-Highest: 0.98500 INFO:root:Iter[28] Batch [2760] Speed: 301.97 samples/sec INFO:root:Iter[28] Batch [2780] Speed: 859.26 samples/sec INFO:root:Iter[28] Batch [2800] Speed: 819.57 samples/sec INFO:root:Iter[28] Batch [2820] Speed: 846.35 samples/sec INFO:root:Iter[28] Batch [2840] Speed: 853.90 samples/sec INFO:root:Iter[28] Batch [2860] Speed: 833.31 samples/sec INFO:root:Iter[28] Batch [2880] Speed: 852.92 samples/sec INFO:root:Iter[28] Batch [2900] Speed: 857.82 samples/sec INFO:root:Iter[28] Batch [2920] Speed: 840.81 samples/sec INFO:root:Iter[28] Batch [2940] Speed: 872.65 samples/sec INFO:root:Iter[28] Batch [2960] Speed: 869.07 samples/sec INFO:root:Iter[28] Batch [2980] Speed: 816.85 samples/sec INFO:root:Iter[28] Batch [3000] Speed: 825.65 samples/sec INFO:root:Iter[28] Batch [3020] Speed: 835.14 samples/sec INFO:root:Iter[28] Batch 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(12000, 512) infer time 3.699165 [lfw][288000]XNorm: 21.932219 [lfw][288000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.337257 [cfp_fp][288000]XNorm: 22.748199 [cfp_fp][288000]Accuracy-Flip: 0.98914+-0.00357 testing verification.. (12000, 512) infer time 3.9130559999999983 [agedb_30][288000]XNorm: 23.160412 [agedb_30][288000]Accuracy-Flip: 0.98317+-0.00724 [288000]Accuracy-Highest: 0.98500 INFO:root:Iter[28] Batch [4760] Speed: 296.11 samples/sec INFO:root:Iter[28] Batch [4780] Speed: 839.54 samples/sec INFO:root:Iter[28] Batch [4800] Speed: 847.43 samples/sec INFO:root:Iter[28] Batch [4820] Speed: 814.58 samples/sec INFO:root:Iter[28] Batch [4840] Speed: 852.55 samples/sec INFO:root:Iter[28] Batch [4860] Speed: 853.28 samples/sec INFO:root:Iter[28] Batch [4880] Speed: 825.32 samples/sec INFO:root:Iter[28] Batch [4900] Speed: 820.03 samples/sec INFO:root:Iter[28] Batch [4920] Speed: 825.72 samples/sec INFO:root:Iter[28] Batch [4940] Speed: 850.65 samples/sec INFO:root:Iter[28] Batch [4960] Speed: 856.26 samples/sec INFO:root:Iter[28] Batch [4980] Speed: 843.59 samples/sec INFO:root:Iter[28] Batch [5000] Speed: 849.93 samples/sec INFO:root:Iter[28] Batch [5020] Speed: 850.30 samples/sec INFO:root:Iter[28] Batch 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INFO:root:Iter[28] Batch [6740] Speed: 878.17 samples/sec {fc7_acc} 290000 0.7910156 lr-batch-epoch: 0.00010000000000000003 6751 28 testing verification.. (12000, 512) infer time 3.735802 [lfw][290000]XNorm: 21.800522 [lfw][290000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.347777000000002 [cfp_fp][290000]XNorm: 22.599933 [cfp_fp][290000]Accuracy-Flip: 0.98871+-0.00234 testing verification.. (12000, 512) infer time 3.7770970000000004 [agedb_30][290000]XNorm: 23.014746 [agedb_30][290000]Accuracy-Flip: 0.98500+-0.00619 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [290000]Accuracy-Highest: 0.98500 INFO:root:Iter[28] Batch [6760] Speed: 293.33 samples/sec INFO:root:Iter[28] Batch [6780] Speed: 857.73 samples/sec INFO:root:Iter[28] Batch [6800] Speed: 839.10 samples/sec INFO:root:Iter[28] Batch [6820] Speed: 842.95 samples/sec INFO:root:Iter[28] Batch [6840] Speed: 847.79 samples/sec INFO:root:Iter[28] Batch [6860] Speed: 821.85 samples/sec INFO:root:Iter[28] Batch [6880] Speed: 834.02 samples/sec INFO:root:Iter[28] Batch [6900] Speed: 844.64 samples/sec INFO:root:Iter[28] Batch [6920] Speed: 853.06 samples/sec INFO:root:Iter[28] Batch [6940] Speed: 833.21 samples/sec INFO:root:Iter[28] Batch [6960] Speed: 841.62 samples/sec INFO:root:Iter[28] Batch [6980] Speed: 831.08 samples/sec INFO:root:Iter[28] Batch [7000] Speed: 860.80 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(12000, 512) infer time 3.7129899999999996 [lfw][292000]XNorm: 21.905070 [lfw][292000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.352374999999999 [cfp_fp][292000]XNorm: 22.720670 [cfp_fp][292000]Accuracy-Flip: 0.98986+-0.00274 testing verification.. (12000, 512) infer time 3.7249089999999994 [agedb_30][292000]XNorm: 23.079613 [agedb_30][292000]Accuracy-Flip: 0.98483+-0.00584 [292000]Accuracy-Highest: 0.98500 INFO:root:Iter[28] Batch [8760] Speed: 302.74 samples/sec INFO:root:Iter[28] Batch [8780] Speed: 842.75 samples/sec INFO:root:Iter[28] Batch [8800] Speed: 821.02 samples/sec INFO:root:Iter[28] Batch [8820] Speed: 833.34 samples/sec INFO:root:Iter[28] Batch [8840] Speed: 838.65 samples/sec INFO:root:Iter[28] Batch [8860] Speed: 834.95 samples/sec INFO:root:Iter[28] Batch [8880] Speed: 828.35 samples/sec INFO:root:Iter[28] Batch [8900] Speed: 846.55 samples/sec INFO:root:Iter[28] Batch [8920] Speed: 830.90 samples/sec INFO:root:Iter[28] Batch [8940] Speed: 874.14 samples/sec INFO:root:Iter[28] Batch [8960] Speed: 827.06 samples/sec INFO:root:Iter[28] Batch [8980] Speed: 858.79 samples/sec INFO:root:Iter[28] Batch [9000] Speed: 861.53 samples/sec INFO:root:Iter[28] Batch [9020] Speed: 842.83 samples/sec INFO:root:Iter[28] Batch 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{fc7_acc} 294000 0.8027344 lr-batch-epoch: 0.00010000000000000003 635 29 testing verification.. (12000, 512) infer time 3.654581999999999 [lfw][294000]XNorm: 21.881306 [lfw][294000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.359620999999999 [cfp_fp][294000]XNorm: 22.690348 [cfp_fp][294000]Accuracy-Flip: 0.99000+-0.00239 testing verification.. (12000, 512) infer time 3.7572239999999995 [agedb_30][294000]XNorm: 23.074623 [agedb_30][294000]Accuracy-Flip: 0.98383+-0.00628 [294000]Accuracy-Highest: 0.98500 INFO:root:Iter[29] Batch [640] Speed: 302.83 samples/sec INFO:root:Iter[29] Batch [660] Speed: 850.39 samples/sec INFO:root:Iter[29] Batch [680] Speed: 840.85 samples/sec INFO:root:Iter[29] Batch [700] Speed: 827.04 samples/sec INFO:root:Iter[29] Batch [720] Speed: 828.17 samples/sec INFO:root:Iter[29] Batch [740] Speed: 842.83 samples/sec INFO:root:Iter[29] Batch [760] Speed: 814.55 samples/sec INFO:root:Iter[29] Batch [780] Speed: 842.28 samples/sec INFO:root:Iter[29] Batch [800] Speed: 843.94 samples/sec INFO:root:Iter[29] Batch [820] Speed: 833.81 samples/sec INFO:root:Iter[29] Batch [840] Speed: 829.67 samples/sec INFO:root:Iter[29] Batch [860] Speed: 850.42 samples/sec INFO:root:Iter[29] Batch [880] Speed: 814.92 samples/sec INFO:root:Iter[29] Batch [900] Speed: 829.91 samples/sec INFO:root:Iter[29] Batch [920] Speed: 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samples/sec {fc7_acc} 296000 0.828125 lr-batch-epoch: 0.00010000000000000003 2635 29 testing verification.. (12000, 512) infer time 3.6961730000000004 [lfw][296000]XNorm: 21.879025 [lfw][296000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.317145999999999 [cfp_fp][296000]XNorm: 22.689612 [cfp_fp][296000]Accuracy-Flip: 0.98971+-0.00262 testing verification.. (12000, 512) infer time 3.729677999999999 [agedb_30][296000]XNorm: 23.111305 [agedb_30][296000]Accuracy-Flip: 0.98350+-0.00689 [296000]Accuracy-Highest: 0.98500 INFO:root:Iter[29] Batch [2640] Speed: 301.29 samples/sec INFO:root:Iter[29] Batch [2660] Speed: 838.45 samples/sec INFO:root:Iter[29] Batch [2680] Speed: 813.15 samples/sec INFO:root:Iter[29] Batch [2700] Speed: 835.59 samples/sec INFO:root:Iter[29] Batch [2720] Speed: 845.71 samples/sec INFO:root:Iter[29] Batch [2740] Speed: 817.01 samples/sec INFO:root:Iter[29] Batch [2760] Speed: 819.73 samples/sec INFO:root:Iter[29] Batch [2780] Speed: 840.17 samples/sec INFO:root:Iter[29] Batch [2800] Speed: 835.34 samples/sec INFO:root:Iter[29] Batch [2820] Speed: 861.13 samples/sec INFO:root:Iter[29] Batch [2840] Speed: 809.56 samples/sec INFO:root:Iter[29] Batch [2860] Speed: 848.45 samples/sec INFO:root:Iter[29] Batch [2880] Speed: 840.67 samples/sec INFO:root:Iter[29] Batch [2900] Speed: 876.68 samples/sec INFO:root:Iter[29] Batch 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INFO:root:Iter[29] Batch [4620] Speed: 891.37 samples/sec {fc7_acc} 298000 0.8183594 lr-batch-epoch: 0.00010000000000000003 4635 29 testing verification.. (12000, 512) infer time 3.8088550000000008 [lfw][298000]XNorm: 21.873101 [lfw][298000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.373542 [cfp_fp][298000]XNorm: 22.706805 [cfp_fp][298000]Accuracy-Flip: 0.98971+-0.00277 testing verification.. (12000, 512) infer time 3.769575 [agedb_30][298000]XNorm: 23.052678 [agedb_30][298000]Accuracy-Flip: 0.98283+-0.00699 [298000]Accuracy-Highest: 0.98500 INFO:root:Iter[29] Batch [4640] Speed: 301.17 samples/sec INFO:root:Iter[29] Batch [4660] Speed: 829.12 samples/sec INFO:root:Iter[29] Batch [4680] Speed: 839.83 samples/sec INFO:root:Iter[29] Batch [4700] Speed: 855.99 samples/sec INFO:root:Iter[29] Batch [4720] Speed: 847.85 samples/sec INFO:root:Iter[29] Batch [4740] Speed: 824.63 samples/sec INFO:root:Iter[29] Batch [4760] Speed: 839.46 samples/sec INFO:root:Iter[29] Batch [4780] Speed: 817.44 samples/sec INFO:root:Iter[29] Batch [4800] Speed: 838.03 samples/sec INFO:root:Iter[29] Batch [4820] Speed: 845.45 samples/sec INFO:root:Iter[29] Batch [4840] Speed: 828.80 samples/sec INFO:root:Iter[29] Batch [4860] Speed: 820.54 samples/sec INFO:root:Iter[29] Batch [4880] Speed: 816.34 samples/sec INFO:root:Iter[29] Batch [4900] Speed: 840.37 samples/sec INFO:root:Iter[29] Batch [4920] 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samples/sec {fc7_acc} 300000 0.7871094 lr-batch-epoch: 0.00010000000000000003 6635 29 testing verification.. (12000, 512) infer time 3.7695109999999996 [lfw][300000]XNorm: 21.956001 [lfw][300000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.4344719999999995 [cfp_fp][300000]XNorm: 22.729357 [cfp_fp][300000]Accuracy-Flip: 0.98886+-0.00254 testing verification.. (12000, 512) infer time 3.802524 [agedb_30][300000]XNorm: 23.180751 [agedb_30][300000]Accuracy-Flip: 0.98300+-0.00795 [300000]Accuracy-Highest: 0.98500 INFO:root:Iter[29] Batch [6640] Speed: 301.10 samples/sec INFO:root:Iter[29] Batch [6660] Speed: 843.46 samples/sec INFO:root:Iter[29] Batch [6680] Speed: 844.95 samples/sec INFO:root:Iter[29] Batch [6700] Speed: 826.19 samples/sec INFO:root:Iter[29] Batch [6720] Speed: 819.86 samples/sec INFO:root:Iter[29] Batch [6740] Speed: 832.92 samples/sec INFO:root:Iter[29] Batch [6760] Speed: 831.61 samples/sec INFO:root:Iter[29] Batch [6780] Speed: 835.29 samples/sec INFO:root:Iter[29] Batch [6800] Speed: 839.02 samples/sec INFO:root:Iter[29] Batch [6820] Speed: 850.00 samples/sec INFO:root:Iter[29] Batch [6840] Speed: 850.10 samples/sec INFO:root:Iter[29] Batch [6860] Speed: 846.07 samples/sec INFO:root:Iter[29] Batch [6880] Speed: 862.98 samples/sec INFO:root:Iter[29] Batch [6900] Speed: 863.62 samples/sec INFO:root:Iter[29] Batch [6920] 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samples/sec {fc7_acc} 302000 0.7714844 lr-batch-epoch: 0.00010000000000000003 8635 29 testing verification.. (12000, 512) infer time 3.7187090000000005 [lfw][302000]XNorm: 21.959273 [lfw][302000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.29964 [cfp_fp][302000]XNorm: 22.805852 [cfp_fp][302000]Accuracy-Flip: 0.99014+-0.00259 testing verification.. (12000, 512) infer time 3.7633059999999996 [agedb_30][302000]XNorm: 23.149359 [agedb_30][302000]Accuracy-Flip: 0.98183+-0.00732 [302000]Accuracy-Highest: 0.98500 INFO:root:Iter[29] Batch [8640] Speed: 300.97 samples/sec INFO:root:Iter[29] Batch [8660] Speed: 845.98 samples/sec INFO:root:Iter[29] Batch [8680] Speed: 841.99 samples/sec INFO:root:Iter[29] Batch [8700] Speed: 846.30 samples/sec INFO:root:Iter[29] Batch [8720] Speed: 853.36 samples/sec INFO:root:Iter[29] Batch [8740] Speed: 831.62 samples/sec INFO:root:Iter[29] Batch [8760] Speed: 840.97 samples/sec INFO:root:Iter[29] Batch [8780] Speed: 839.18 samples/sec INFO:root:Iter[29] Batch [8800] Speed: 858.27 samples/sec INFO:root:Iter[29] Batch [8820] Speed: 824.76 samples/sec INFO:root:Iter[29] Batch [8840] Speed: 841.74 samples/sec INFO:root:Iter[29] Batch [8860] Speed: 838.51 samples/sec INFO:root:Iter[29] Batch [8880] Speed: 850.67 samples/sec INFO:root:Iter[29] Batch [8900] Speed: 857.39 samples/sec INFO:root:Iter[29] Batch 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{fc7_acc} 304000 0.7910156 lr-batch-epoch: 0.00010000000000000003 519 30 testing verification.. (12000, 512) infer time 3.7023049999999995 [lfw][304000]XNorm: 21.941813 [lfw][304000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.408518 [cfp_fp][304000]XNorm: 22.784374 [cfp_fp][304000]Accuracy-Flip: 0.98943+-0.00241 testing verification.. (12000, 512) infer time 3.7619719999999996 [agedb_30][304000]XNorm: 23.106112 [agedb_30][304000]Accuracy-Flip: 0.98350+-0.00660 [304000]Accuracy-Highest: 0.98500 INFO:root:Iter[30] Batch [520] Speed: 304.62 samples/sec INFO:root:Iter[30] Batch [540] Speed: 824.18 samples/sec INFO:root:Iter[30] Batch [560] Speed: 827.29 samples/sec INFO:root:Iter[30] Batch [580] Speed: 836.71 samples/sec INFO:root:Iter[30] Batch [600] Speed: 830.24 samples/sec INFO:root:Iter[30] Batch [620] Speed: 826.58 samples/sec INFO:root:Iter[30] Batch [640] Speed: 830.32 samples/sec INFO:root:Iter[30] Batch [660] Speed: 841.78 samples/sec INFO:root:Iter[30] Batch [680] Speed: 845.93 samples/sec INFO:root:Iter[30] Batch [700] Speed: 865.75 samples/sec INFO:root:Iter[30] Batch [720] Speed: 843.97 samples/sec INFO:root:Iter[30] Batch [740] Speed: 841.38 samples/sec INFO:root:Iter[30] Batch [760] Speed: 792.21 samples/sec INFO:root:Iter[30] Batch [780] Speed: 818.70 samples/sec INFO:root:Iter[30] Batch [800] Speed: 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samples/sec {fc7_acc} 306000 0.7949219 lr-batch-epoch: 0.00010000000000000003 2519 30 testing verification.. (12000, 512) infer time 3.774218 [lfw][306000]XNorm: 21.916169 [lfw][306000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.416122999999998 [cfp_fp][306000]XNorm: 22.788253 [cfp_fp][306000]Accuracy-Flip: 0.98971+-0.00262 testing verification.. (12000, 512) infer time 3.734230000000001 [agedb_30][306000]XNorm: 23.135613 [agedb_30][306000]Accuracy-Flip: 0.98283+-0.00749 [306000]Accuracy-Highest: 0.98500 INFO:root:Iter[30] Batch [2520] Speed: 302.08 samples/sec INFO:root:Iter[30] Batch [2540] Speed: 841.41 samples/sec INFO:root:Iter[30] Batch [2560] Speed: 823.83 samples/sec INFO:root:Iter[30] Batch [2580] Speed: 822.46 samples/sec INFO:root:Iter[30] Batch [2600] Speed: 852.63 samples/sec INFO:root:Iter[30] Batch [2620] Speed: 842.97 samples/sec INFO:root:Iter[30] Batch [2640] Speed: 830.93 samples/sec INFO:root:Iter[30] Batch [2660] Speed: 846.42 samples/sec INFO:root:Iter[30] Batch [2680] Speed: 855.61 samples/sec INFO:root:Iter[30] Batch [2700] Speed: 828.36 samples/sec INFO:root:Iter[30] Batch [2720] Speed: 812.24 samples/sec INFO:root:Iter[30] Batch [2740] Speed: 833.43 samples/sec INFO:root:Iter[30] Batch [2760] Speed: 837.86 samples/sec INFO:root:Iter[30] Batch [2780] Speed: 817.70 samples/sec INFO:root:Iter[30] Batch 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INFO:root:Iter[30] Batch [4500] Speed: 886.40 samples/sec {fc7_acc} 308000 0.7910156 lr-batch-epoch: 0.00010000000000000003 4519 30 testing verification.. (12000, 512) infer time 3.7549949999999996 [lfw][308000]XNorm: 21.896138 [lfw][308000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.360628 [cfp_fp][308000]XNorm: 22.747291 [cfp_fp][308000]Accuracy-Flip: 0.98986+-0.00259 testing verification.. (12000, 512) infer time 3.8158220000000003 [agedb_30][308000]XNorm: 23.112907 [agedb_30][308000]Accuracy-Flip: 0.98333+-0.00699 [308000]Accuracy-Highest: 0.98500 INFO:root:Iter[30] Batch [4520] Speed: 301.67 samples/sec INFO:root:Iter[30] Batch [4540] Speed: 843.74 samples/sec INFO:root:Iter[30] Batch [4560] Speed: 815.45 samples/sec INFO:root:Iter[30] Batch [4580] Speed: 849.54 samples/sec INFO:root:Iter[30] Batch [4600] Speed: 775.76 samples/sec INFO:root:Iter[30] Batch [4620] Speed: 821.93 samples/sec INFO:root:Iter[30] Batch [4640] Speed: 822.81 samples/sec INFO:root:Iter[30] Batch [4660] Speed: 859.69 samples/sec INFO:root:Iter[30] Batch [4680] Speed: 800.44 samples/sec INFO:root:Iter[30] Batch [4700] Speed: 829.89 samples/sec INFO:root:Iter[30] Batch [4720] Speed: 829.25 samples/sec INFO:root:Iter[30] Batch [4740] Speed: 865.18 samples/sec INFO:root:Iter[30] Batch [4760] Speed: 852.01 samples/sec INFO:root:Iter[30] Batch [4780] Speed: 842.16 samples/sec INFO:root:Iter[30] Batch 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INFO:root:Iter[30] Batch [6500] Speed: 863.34 samples/sec {fc7_acc} 310000 0.80078125 lr-batch-epoch: 0.00010000000000000003 6519 30 testing verification.. (12000, 512) infer time 3.7481359999999992 [lfw][310000]XNorm: 21.928433 [lfw][310000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.462405999999999 [cfp_fp][310000]XNorm: 22.752242 [cfp_fp][310000]Accuracy-Flip: 0.98857+-0.00271 testing verification.. (12000, 512) infer time 3.8540369999999995 [agedb_30][310000]XNorm: 23.129698 [agedb_30][310000]Accuracy-Flip: 0.98417+-0.00659 [310000]Accuracy-Highest: 0.98500 INFO:root:Iter[30] Batch [6520] Speed: 299.62 samples/sec INFO:root:Iter[30] Batch [6540] Speed: 881.55 samples/sec INFO:root:Iter[30] Batch [6560] Speed: 854.44 samples/sec INFO:root:Iter[30] Batch [6580] Speed: 834.49 samples/sec INFO:root:Iter[30] Batch [6600] Speed: 860.34 samples/sec INFO:root:Iter[30] Batch [6620] Speed: 839.81 samples/sec INFO:root:Iter[30] Batch [6640] Speed: 853.63 samples/sec INFO:root:Iter[30] Batch [6660] Speed: 841.09 samples/sec INFO:root:Iter[30] Batch [6680] Speed: 857.41 samples/sec INFO:root:Iter[30] Batch [6700] Speed: 865.57 samples/sec INFO:root:Iter[30] Batch [6720] Speed: 853.92 samples/sec INFO:root:Iter[30] Batch [6740] Speed: 864.82 samples/sec INFO:root:Iter[30] Batch [6760] Speed: 821.14 samples/sec INFO:root:Iter[30] Batch [6780] Speed: 840.57 samples/sec INFO:root:Iter[30] Batch 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INFO:root:Iter[30] Batch [8500] Speed: 865.02 samples/sec {fc7_acc} 312000 0.80078125 lr-batch-epoch: 0.00010000000000000003 8519 30 testing verification.. (12000, 512) infer time 3.763192 [lfw][312000]XNorm: 21.858937 [lfw][312000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.400868999999999 [cfp_fp][312000]XNorm: 22.703332 [cfp_fp][312000]Accuracy-Flip: 0.98957+-0.00271 testing verification.. (12000, 512) infer time 3.7762369999999996 [agedb_30][312000]XNorm: 23.098977 [agedb_30][312000]Accuracy-Flip: 0.98283+-0.00723 [312000]Accuracy-Highest: 0.98500 INFO:root:Iter[30] Batch [8520] Speed: 299.23 samples/sec INFO:root:Iter[30] Batch [8540] Speed: 864.65 samples/sec INFO:root:Iter[30] Batch [8560] Speed: 847.59 samples/sec INFO:root:Iter[30] Batch [8580] Speed: 861.83 samples/sec INFO:root:Iter[30] Batch [8600] Speed: 877.85 samples/sec INFO:root:Iter[30] Batch [8620] Speed: 858.81 samples/sec INFO:root:Iter[30] Batch [8640] Speed: 844.39 samples/sec INFO:root:Iter[30] Batch [8660] Speed: 851.83 samples/sec INFO:root:Iter[30] Batch [8680] Speed: 840.68 samples/sec INFO:root:Iter[30] Batch [8700] Speed: 851.52 samples/sec INFO:root:Iter[30] Batch [8720] Speed: 867.40 samples/sec INFO:root:Iter[30] Batch [8740] Speed: 825.53 samples/sec INFO:root:Iter[30] Batch [8760] Speed: 888.95 samples/sec INFO:root:Iter[30] Batch [8780] Speed: 848.51 samples/sec INFO:root:Iter[30] Batch 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Speed: 845.35 samples/sec INFO:root:Iter[31] Batch [400] Speed: 890.14 samples/sec {fc7_acc} 314000 0.8261719 lr-batch-epoch: 0.00010000000000000003 403 31 testing verification.. (12000, 512) infer time 3.7908149999999994 [lfw][314000]XNorm: 21.934977 [lfw][314000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.372041 [cfp_fp][314000]XNorm: 22.788260 [cfp_fp][314000]Accuracy-Flip: 0.98929+-0.00249 testing verification.. (12000, 512) infer time 3.8102860000000014 [agedb_30][314000]XNorm: 23.119900 [agedb_30][314000]Accuracy-Flip: 0.98233+-0.00708 [314000]Accuracy-Highest: 0.98500 INFO:root:Iter[31] Batch [420] Speed: 296.27 samples/sec INFO:root:Iter[31] Batch [440] Speed: 838.32 samples/sec INFO:root:Iter[31] Batch [460] Speed: 831.27 samples/sec INFO:root:Iter[31] Batch [480] Speed: 822.42 samples/sec INFO:root:Iter[31] Batch [500] Speed: 822.35 samples/sec INFO:root:Iter[31] Batch [520] Speed: 829.03 samples/sec INFO:root:Iter[31] Batch [540] Speed: 857.80 samples/sec INFO:root:Iter[31] Batch [560] Speed: 865.05 samples/sec INFO:root:Iter[31] Batch [580] Speed: 878.14 samples/sec INFO:root:Iter[31] Batch [600] Speed: 834.75 samples/sec INFO:root:Iter[31] Batch [620] Speed: 854.42 samples/sec INFO:root:Iter[31] Batch [640] Speed: 866.59 samples/sec INFO:root:Iter[31] Batch [660] Speed: 874.53 samples/sec INFO:root:Iter[31] Batch [680] Speed: 867.81 samples/sec INFO:root:Iter[31] Batch [700] Speed: 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samples/sec {fc7_acc} 316000 0.75390625 lr-batch-epoch: 0.00010000000000000003 2403 31 testing verification.. (12000, 512) infer time 3.7555349999999996 [lfw][316000]XNorm: 21.865513 [lfw][316000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.381695 [cfp_fp][316000]XNorm: 22.704524 [cfp_fp][316000]Accuracy-Flip: 0.98900+-0.00264 testing verification.. (12000, 512) infer time 3.6915660000000003 [agedb_30][316000]XNorm: 23.048283 [agedb_30][316000]Accuracy-Flip: 0.98483+-0.00669 [316000]Accuracy-Highest: 0.98500 INFO:root:Iter[31] Batch [2420] Speed: 299.07 samples/sec INFO:root:Iter[31] Batch [2440] Speed: 835.25 samples/sec INFO:root:Iter[31] Batch [2460] Speed: 844.41 samples/sec INFO:root:Iter[31] Batch [2480] Speed: 849.35 samples/sec INFO:root:Iter[31] Batch [2500] Speed: 851.19 samples/sec INFO:root:Iter[31] Batch [2520] Speed: 833.69 samples/sec INFO:root:Iter[31] Batch [2540] Speed: 808.93 samples/sec INFO:root:Iter[31] Batch [2560] Speed: 826.35 samples/sec INFO:root:Iter[31] Batch [2580] Speed: 808.29 samples/sec INFO:root:Iter[31] Batch [2600] Speed: 822.00 samples/sec INFO:root:Iter[31] Batch [2620] Speed: 831.87 samples/sec INFO:root:Iter[31] Batch [2640] Speed: 871.34 samples/sec INFO:root:Iter[31] Batch [2660] Speed: 853.90 samples/sec INFO:root:Iter[31] Batch [2680] Speed: 843.87 samples/sec INFO:root:Iter[31] Batch 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INFO:root:Iter[31] Batch [4400] Speed: 864.78 samples/sec {fc7_acc} 318000 0.8222656 lr-batch-epoch: 0.00010000000000000003 4403 31 testing verification.. (12000, 512) infer time 3.780258 [lfw][318000]XNorm: 21.891169 [lfw][318000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.301933000000002 [cfp_fp][318000]XNorm: 22.791667 [cfp_fp][318000]Accuracy-Flip: 0.98943+-0.00287 testing verification.. (12000, 512) infer time 3.747726 [agedb_30][318000]XNorm: 23.099151 [agedb_30][318000]Accuracy-Flip: 0.98333+-0.00738 [318000]Accuracy-Highest: 0.98500 INFO:root:Iter[31] Batch [4420] Speed: 299.44 samples/sec INFO:root:Iter[31] Batch [4440] Speed: 828.11 samples/sec INFO:root:Iter[31] Batch [4460] Speed: 814.41 samples/sec INFO:root:Iter[31] Batch [4480] Speed: 818.17 samples/sec INFO:root:Iter[31] Batch [4500] Speed: 835.62 samples/sec INFO:root:Iter[31] Batch [4520] Speed: 827.39 samples/sec INFO:root:Iter[31] Batch [4540] Speed: 813.16 samples/sec INFO:root:Iter[31] Batch [4560] Speed: 820.05 samples/sec INFO:root:Iter[31] Batch [4580] Speed: 828.38 samples/sec INFO:root:Iter[31] Batch [4600] Speed: 858.07 samples/sec INFO:root:Iter[31] Batch [4620] Speed: 857.09 samples/sec INFO:root:Iter[31] Batch [4640] Speed: 833.69 samples/sec INFO:root:Iter[31] Batch [4660] Speed: 852.72 samples/sec INFO:root:Iter[31] Batch [4680] Speed: 843.57 samples/sec INFO:root:Iter[31] Batch [4700] 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samples/sec {fc7_acc} 320000 0.8183594 lr-batch-epoch: 0.00010000000000000003 6403 31 testing verification.. (12000, 512) infer time 3.6790059999999993 [lfw][320000]XNorm: 21.884770 [lfw][320000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.430515 [cfp_fp][320000]XNorm: 22.729294 [cfp_fp][320000]Accuracy-Flip: 0.98900+-0.00286 testing verification.. (12000, 512) infer time 3.77228 [agedb_30][320000]XNorm: 23.095152 [agedb_30][320000]Accuracy-Flip: 0.98283+-0.00742 [320000]Accuracy-Highest: 0.98500 INFO:root:Iter[31] Batch [6420] Speed: 296.25 samples/sec INFO:root:Iter[31] Batch [6440] Speed: 844.60 samples/sec INFO:root:Iter[31] Batch [6460] Speed: 839.39 samples/sec INFO:root:Iter[31] Batch [6480] Speed: 864.15 samples/sec INFO:root:Iter[31] Batch [6500] Speed: 860.42 samples/sec INFO:root:Iter[31] Batch [6520] Speed: 860.01 samples/sec INFO:root:Iter[31] Batch [6540] Speed: 869.49 samples/sec INFO:root:Iter[31] Batch [6560] Speed: 860.78 samples/sec INFO:root:Iter[31] Batch [6580] Speed: 842.73 samples/sec INFO:root:Iter[31] Batch [6600] Speed: 837.46 samples/sec INFO:root:Iter[31] Batch [6620] Speed: 833.67 samples/sec INFO:root:Iter[31] Batch [6640] Speed: 847.35 samples/sec INFO:root:Iter[31] Batch [6660] Speed: 861.83 samples/sec INFO:root:Iter[31] Batch [6680] Speed: 849.23 samples/sec INFO:root:Iter[31] Batch [6700] 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samples/sec {fc7_acc} 322000 0.80859375 lr-batch-epoch: 0.00010000000000000003 8403 31 testing verification.. (12000, 512) infer time 3.6762140000000003 [lfw][322000]XNorm: 21.845539 [lfw][322000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.329363999999999 [cfp_fp][322000]XNorm: 22.696256 [cfp_fp][322000]Accuracy-Flip: 0.98929+-0.00241 testing verification.. (12000, 512) infer time 3.7518580000000004 [agedb_30][322000]XNorm: 23.121985 [agedb_30][322000]Accuracy-Flip: 0.98250+-0.00672 [322000]Accuracy-Highest: 0.98500 INFO:root:Iter[31] Batch [8420] Speed: 304.24 samples/sec INFO:root:Iter[31] Batch [8440] Speed: 819.30 samples/sec INFO:root:Iter[31] Batch [8460] Speed: 831.77 samples/sec INFO:root:Iter[31] Batch [8480] Speed: 830.13 samples/sec INFO:root:Iter[31] Batch [8500] Speed: 862.83 samples/sec INFO:root:Iter[31] Batch [8520] Speed: 853.82 samples/sec INFO:root:Iter[31] Batch [8540] Speed: 845.44 samples/sec INFO:root:Iter[31] Batch [8560] Speed: 825.07 samples/sec INFO:root:Iter[31] Batch [8580] Speed: 883.20 samples/sec INFO:root:Iter[31] Batch [8600] Speed: 831.54 samples/sec INFO:root:Iter[31] Batch [8620] Speed: 840.89 samples/sec INFO:root:Iter[31] Batch [8640] Speed: 825.25 samples/sec INFO:root:Iter[31] Batch [8660] Speed: 865.89 samples/sec INFO:root:Iter[31] Batch [8680] Speed: 834.69 samples/sec INFO:root:Iter[31] Batch 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Speed: 853.95 samples/sec {fc7_acc} 324000 0.7871094 lr-batch-epoch: 0.00010000000000000003 287 32 testing verification.. (12000, 512) infer time 3.7533279999999998 [lfw][324000]XNorm: 21.889460 [lfw][324000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.360629000000001 [cfp_fp][324000]XNorm: 22.721149 [cfp_fp][324000]Accuracy-Flip: 0.98971+-0.00254 testing verification.. (12000, 512) infer time 3.7445629999999994 [agedb_30][324000]XNorm: 23.027542 [agedb_30][324000]Accuracy-Flip: 0.98483+-0.00584 [324000]Accuracy-Highest: 0.98500 INFO:root:Iter[32] Batch [300] Speed: 302.55 samples/sec INFO:root:Iter[32] Batch [320] Speed: 847.30 samples/sec INFO:root:Iter[32] Batch [340] Speed: 861.35 samples/sec INFO:root:Iter[32] Batch [360] Speed: 849.77 samples/sec INFO:root:Iter[32] Batch [380] Speed: 835.54 samples/sec INFO:root:Iter[32] Batch [400] Speed: 855.14 samples/sec INFO:root:Iter[32] Batch [420] Speed: 850.02 samples/sec INFO:root:Iter[32] Batch [440] Speed: 860.63 samples/sec INFO:root:Iter[32] Batch [460] Speed: 825.34 samples/sec INFO:root:Iter[32] Batch [480] Speed: 854.61 samples/sec INFO:root:Iter[32] Batch [500] Speed: 822.61 samples/sec INFO:root:Iter[32] Batch [520] Speed: 820.35 samples/sec INFO:root:Iter[32] Batch [540] Speed: 849.20 samples/sec INFO:root:Iter[32] Batch [560] Speed: 852.73 samples/sec INFO:root:Iter[32] Batch [580] Speed: 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samples/sec {fc7_acc} 326000 0.8125 lr-batch-epoch: 0.00010000000000000003 2287 32 testing verification.. (12000, 512) infer time 3.69318 [lfw][326000]XNorm: 21.893645 [lfw][326000]Accuracy-Flip: 0.99817+-0.00203 testing verification.. (14000, 512) infer time 4.330982 [cfp_fp][326000]XNorm: 22.745846 [cfp_fp][326000]Accuracy-Flip: 0.98886+-0.00246 testing verification.. (12000, 512) infer time 3.7934330000000016 [agedb_30][326000]XNorm: 23.061304 [agedb_30][326000]Accuracy-Flip: 0.98467+-0.00609 [326000]Accuracy-Highest: 0.98500 INFO:root:Iter[32] Batch [2300] Speed: 302.85 samples/sec INFO:root:Iter[32] Batch [2320] Speed: 850.11 samples/sec INFO:root:Iter[32] Batch [2340] Speed: 841.64 samples/sec INFO:root:Iter[32] Batch [2360] Speed: 832.44 samples/sec INFO:root:Iter[32] Batch [2380] Speed: 845.26 samples/sec INFO:root:Iter[32] Batch [2400] Speed: 817.58 samples/sec INFO:root:Iter[32] Batch [2420] Speed: 818.21 samples/sec INFO:root:Iter[32] Batch [2440] Speed: 827.31 samples/sec INFO:root:Iter[32] Batch [2460] Speed: 854.99 samples/sec INFO:root:Iter[32] Batch [2480] Speed: 852.61 samples/sec INFO:root:Iter[32] Batch [2500] Speed: 837.28 samples/sec INFO:root:Iter[32] Batch [2520] Speed: 852.86 samples/sec INFO:root:Iter[32] Batch [2540] Speed: 862.16 samples/sec INFO:root:Iter[32] Batch [2560] Speed: 864.22 samples/sec INFO:root:Iter[32] Batch 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INFO:root:Iter[32] Batch [4280] Speed: 861.76 samples/sec {fc7_acc} 328000 0.7734375 lr-batch-epoch: 0.00010000000000000003 4287 32 testing verification.. (12000, 512) infer time 3.77291 [lfw][328000]XNorm: 21.808073 [lfw][328000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.334163 [cfp_fp][328000]XNorm: 22.676845 [cfp_fp][328000]Accuracy-Flip: 0.98957+-0.00231 testing verification.. (12000, 512) infer time 3.755013999999999 [agedb_30][328000]XNorm: 23.063853 [agedb_30][328000]Accuracy-Flip: 0.98233+-0.00790 [328000]Accuracy-Highest: 0.98500 INFO:root:Iter[32] Batch [4300] Speed: 298.04 samples/sec INFO:root:Iter[32] Batch [4320] Speed: 843.11 samples/sec INFO:root:Iter[32] Batch [4340] Speed: 829.45 samples/sec INFO:root:Iter[32] Batch [4360] Speed: 825.85 samples/sec INFO:root:Iter[32] Batch [4380] Speed: 839.10 samples/sec INFO:root:Iter[32] Batch [4400] Speed: 840.46 samples/sec INFO:root:Iter[32] Batch [4420] Speed: 859.64 samples/sec INFO:root:Iter[32] Batch [4440] Speed: 805.87 samples/sec INFO:root:Iter[32] Batch [4460] Speed: 854.15 samples/sec INFO:root:Iter[32] Batch [4480] Speed: 822.09 samples/sec INFO:root:Iter[32] Batch [4500] Speed: 822.87 samples/sec INFO:root:Iter[32] Batch [4520] Speed: 817.07 samples/sec INFO:root:Iter[32] Batch [4540] Speed: 827.97 samples/sec INFO:root:Iter[32] Batch [4560] Speed: 829.35 samples/sec INFO:root:Iter[32] Batch 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INFO:root:Iter[32] Batch [6280] Speed: 892.03 samples/sec {fc7_acc} 330000 0.83984375 lr-batch-epoch: 0.00010000000000000003 6287 32 testing verification.. (12000, 512) infer time 3.7011299999999996 [lfw][330000]XNorm: 21.876165 [lfw][330000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.39658 [cfp_fp][330000]XNorm: 22.720489 [cfp_fp][330000]Accuracy-Flip: 0.98871+-0.00243 testing verification.. (12000, 512) infer time 3.8188219999999995 [agedb_30][330000]XNorm: 23.116132 [agedb_30][330000]Accuracy-Flip: 0.98383+-0.00730 [330000]Accuracy-Highest: 0.98500 INFO:root:Iter[32] Batch [6300] Speed: 300.86 samples/sec INFO:root:Iter[32] Batch [6320] Speed: 858.64 samples/sec INFO:root:Iter[32] Batch [6340] Speed: 855.01 samples/sec INFO:root:Iter[32] Batch [6360] Speed: 844.34 samples/sec INFO:root:Iter[32] Batch [6380] Speed: 834.69 samples/sec INFO:root:Iter[32] Batch [6400] Speed: 867.08 samples/sec INFO:root:Iter[32] Batch [6420] Speed: 838.74 samples/sec INFO:root:Iter[32] Batch [6440] Speed: 848.97 samples/sec INFO:root:Iter[32] Batch [6460] Speed: 833.02 samples/sec INFO:root:Iter[32] Batch [6480] Speed: 856.80 samples/sec INFO:root:Iter[32] Batch [6500] Speed: 852.72 samples/sec INFO:root:Iter[32] Batch [6520] Speed: 879.64 samples/sec INFO:root:Iter[32] Batch [6540] Speed: 839.07 samples/sec INFO:root:Iter[32] Batch [6560] Speed: 836.00 samples/sec INFO:root:Iter[32] Batch 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INFO:root:Iter[32] Batch [8280] Speed: 855.11 samples/sec {fc7_acc} 332000 0.8027344 lr-batch-epoch: 0.00010000000000000003 8287 32 testing verification.. (12000, 512) infer time 3.7399149999999994 [lfw][332000]XNorm: 21.856096 [lfw][332000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.397185 [cfp_fp][332000]XNorm: 22.754394 [cfp_fp][332000]Accuracy-Flip: 0.98971+-0.00254 testing verification.. (12000, 512) infer time 3.7877290000000006 [agedb_30][332000]XNorm: 23.070906 [agedb_30][332000]Accuracy-Flip: 0.98283+-0.00683 [332000]Accuracy-Highest: 0.98500 INFO:root:Iter[32] Batch [8300] Speed: 298.24 samples/sec INFO:root:Iter[32] Batch [8320] Speed: 823.22 samples/sec INFO:root:Iter[32] Batch [8340] Speed: 815.95 samples/sec INFO:root:Iter[32] Batch [8360] Speed: 821.10 samples/sec INFO:root:Iter[32] Batch [8380] Speed: 815.25 samples/sec INFO:root:Iter[32] Batch [8400] Speed: 836.28 samples/sec INFO:root:Iter[32] Batch [8420] Speed: 846.27 samples/sec INFO:root:Iter[32] Batch [8440] Speed: 836.21 samples/sec INFO:root:Iter[32] Batch [8460] Speed: 871.88 samples/sec INFO:root:Iter[32] Batch [8480] Speed: 855.73 samples/sec INFO:root:Iter[32] Batch [8500] Speed: 846.76 samples/sec INFO:root:Iter[32] Batch [8520] Speed: 858.23 samples/sec INFO:root:Iter[32] Batch [8540] Speed: 860.67 samples/sec INFO:root:Iter[32] Batch [8560] Speed: 853.71 samples/sec INFO:root:Iter[32] Batch 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[160] Speed: 890.59 samples/sec {fc7_acc} 334000 0.796875 lr-batch-epoch: 0.00010000000000000003 171 33 testing verification.. (12000, 512) infer time 3.713485000000001 [lfw][334000]XNorm: 21.902251 [lfw][334000]Accuracy-Flip: 0.99850+-0.00189 testing verification.. (14000, 512) infer time 4.407966 [cfp_fp][334000]XNorm: 22.761501 [cfp_fp][334000]Accuracy-Flip: 0.98957+-0.00256 testing verification.. (12000, 512) infer time 3.752524 [agedb_30][334000]XNorm: 23.075723 [agedb_30][334000]Accuracy-Flip: 0.98333+-0.00723 [334000]Accuracy-Highest: 0.98500 INFO:root:Iter[33] Batch [180] Speed: 300.54 samples/sec INFO:root:Iter[33] Batch [200] Speed: 826.63 samples/sec INFO:root:Iter[33] Batch [220] Speed: 830.93 samples/sec INFO:root:Iter[33] Batch [240] Speed: 852.26 samples/sec INFO:root:Iter[33] Batch [260] Speed: 833.66 samples/sec INFO:root:Iter[33] Batch [280] Speed: 811.72 samples/sec INFO:root:Iter[33] Batch [300] Speed: 814.71 samples/sec INFO:root:Iter[33] Batch [320] Speed: 837.22 samples/sec INFO:root:Iter[33] Batch [340] Speed: 830.62 samples/sec INFO:root:Iter[33] Batch [360] Speed: 846.98 samples/sec INFO:root:Iter[33] Batch [380] Speed: 855.75 samples/sec INFO:root:Iter[33] Batch [400] Speed: 874.71 samples/sec INFO:root:Iter[33] Batch [420] Speed: 835.87 samples/sec INFO:root:Iter[33] Batch [440] Speed: 828.00 samples/sec INFO:root:Iter[33] Batch [460] Speed: 850.95 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336000 0.80859375 lr-batch-epoch: 0.00010000000000000003 2171 33 testing verification.. (12000, 512) infer time 3.7277899999999997 [lfw][336000]XNorm: 21.972699 [lfw][336000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.376143999999998 [cfp_fp][336000]XNorm: 22.795769 [cfp_fp][336000]Accuracy-Flip: 0.99029+-0.00262 testing verification.. (12000, 512) infer time 3.717962 [agedb_30][336000]XNorm: 23.141228 [agedb_30][336000]Accuracy-Flip: 0.98450+-0.00650 [336000]Accuracy-Highest: 0.98500 INFO:root:Iter[33] Batch [2180] Speed: 298.45 samples/sec INFO:root:Iter[33] Batch [2200] Speed: 800.90 samples/sec INFO:root:Iter[33] Batch [2220] Speed: 845.96 samples/sec INFO:root:Iter[33] Batch [2240] Speed: 832.28 samples/sec INFO:root:Iter[33] Batch [2260] Speed: 827.44 samples/sec INFO:root:Iter[33] Batch [2280] Speed: 842.05 samples/sec INFO:root:Iter[33] Batch [2300] Speed: 868.15 samples/sec INFO:root:Iter[33] Batch [2320] Speed: 865.21 samples/sec INFO:root:Iter[33] Batch [2340] Speed: 892.57 samples/sec INFO:root:Iter[33] Batch [2360] Speed: 837.88 samples/sec INFO:root:Iter[33] Batch [2380] Speed: 828.17 samples/sec INFO:root:Iter[33] Batch [2400] Speed: 866.26 samples/sec INFO:root:Iter[33] Batch [2420] Speed: 854.68 samples/sec INFO:root:Iter[33] Batch [2440] Speed: 846.11 samples/sec INFO:root:Iter[33] Batch [2460] 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samples/sec {fc7_acc} 338000 0.8222656 lr-batch-epoch: 0.00010000000000000003 4171 33 testing verification.. (12000, 512) infer time 3.733961 [lfw][338000]XNorm: 21.949603 [lfw][338000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.339886000000002 [cfp_fp][338000]XNorm: 22.744562 [cfp_fp][338000]Accuracy-Flip: 0.99014+-0.00259 testing verification.. (12000, 512) infer time 3.6746089999999993 [agedb_30][338000]XNorm: 23.133407 [agedb_30][338000]Accuracy-Flip: 0.98367+-0.00714 [338000]Accuracy-Highest: 0.98500 INFO:root:Iter[33] Batch [4180] Speed: 300.28 samples/sec INFO:root:Iter[33] Batch [4200] Speed: 847.51 samples/sec INFO:root:Iter[33] Batch [4220] Speed: 867.63 samples/sec INFO:root:Iter[33] Batch [4240] Speed: 828.82 samples/sec INFO:root:Iter[33] Batch [4260] Speed: 827.97 samples/sec INFO:root:Iter[33] Batch [4280] Speed: 837.21 samples/sec INFO:root:Iter[33] Batch [4300] Speed: 832.39 samples/sec INFO:root:Iter[33] Batch [4320] Speed: 829.12 samples/sec INFO:root:Iter[33] Batch [4340] Speed: 850.43 samples/sec INFO:root:Iter[33] Batch [4360] Speed: 871.79 samples/sec INFO:root:Iter[33] Batch [4380] Speed: 856.45 samples/sec INFO:root:Iter[33] Batch [4400] Speed: 853.55 samples/sec INFO:root:Iter[33] Batch [4420] Speed: 854.54 samples/sec INFO:root:Iter[33] Batch [4440] Speed: 843.69 samples/sec INFO:root:Iter[33] Batch 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INFO:root:Iter[33] Batch [6160] Speed: 842.14 samples/sec {fc7_acc} 340000 0.80078125 lr-batch-epoch: 0.00010000000000000003 6171 33 testing verification.. (12000, 512) infer time 3.674583 [lfw][340000]XNorm: 21.893978 [lfw][340000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 4.373347999999998 [cfp_fp][340000]XNorm: 22.776294 [cfp_fp][340000]Accuracy-Flip: 0.98943+-0.00333 testing verification.. (12000, 512) infer time 3.726589 [agedb_30][340000]XNorm: 23.173668 [agedb_30][340000]Accuracy-Flip: 0.98250+-0.00761 [340000]Accuracy-Highest: 0.98500 INFO:root:Iter[33] Batch [6180] Speed: 300.06 samples/sec INFO:root:Iter[33] Batch [6200] Speed: 814.46 samples/sec INFO:root:Iter[33] Batch [6220] Speed: 835.37 samples/sec INFO:root:Iter[33] Batch [6240] Speed: 847.02 samples/sec INFO:root:Iter[33] Batch [6260] Speed: 844.20 samples/sec INFO:root:Iter[33] Batch [6280] Speed: 832.63 samples/sec INFO:root:Iter[33] Batch [6300] Speed: 850.58 samples/sec INFO:root:Iter[33] Batch [6320] Speed: 819.78 samples/sec INFO:root:Iter[33] Batch [6340] Speed: 855.78 samples/sec INFO:root:Iter[33] Batch [6360] Speed: 840.36 samples/sec INFO:root:Iter[33] Batch [6380] Speed: 845.81 samples/sec INFO:root:Iter[33] Batch [6400] Speed: 839.16 samples/sec INFO:root:Iter[33] Batch [6420] Speed: 836.84 samples/sec INFO:root:Iter[33] Batch [6440] Speed: 844.08 samples/sec INFO:root:Iter[33] Batch [6460] 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samples/sec {fc7_acc} 342000 0.8339844 lr-batch-epoch: 0.00010000000000000003 8171 33 testing verification.. (12000, 512) infer time 3.6822339999999993 [lfw][342000]XNorm: 21.761155 [lfw][342000]Accuracy-Flip: 0.99800+-0.00194 testing verification.. (14000, 512) infer time 4.399541 [cfp_fp][342000]XNorm: 22.673288 [cfp_fp][342000]Accuracy-Flip: 0.98986+-0.00274 testing verification.. (12000, 512) infer time 3.8138129999999997 [agedb_30][342000]XNorm: 22.957931 [agedb_30][342000]Accuracy-Flip: 0.98383+-0.00730 [342000]Accuracy-Highest: 0.98500 INFO:root:Iter[33] Batch [8180] Speed: 296.88 samples/sec INFO:root:Iter[33] Batch [8200] Speed: 847.64 samples/sec INFO:root:Iter[33] Batch [8220] Speed: 820.67 samples/sec INFO:root:Iter[33] Batch [8240] Speed: 811.66 samples/sec INFO:root:Iter[33] Batch [8260] Speed: 842.09 samples/sec INFO:root:Iter[33] Batch [8280] Speed: 870.67 samples/sec INFO:root:Iter[33] Batch [8300] Speed: 833.99 samples/sec INFO:root:Iter[33] Batch [8320] Speed: 835.02 samples/sec INFO:root:Iter[33] Batch [8340] Speed: 796.11 samples/sec INFO:root:Iter[33] Batch [8360] Speed: 855.68 samples/sec INFO:root:Iter[33] Batch [8380] Speed: 832.30 samples/sec INFO:root:Iter[33] Batch [8400] Speed: 832.54 samples/sec INFO:root:Iter[33] Batch [8420] Speed: 838.59 samples/sec INFO:root:Iter[33] Batch [8440] Speed: 872.45 samples/sec INFO:root:Iter[33] Batch [8460] Speed: 840.11 samples/sec INFO:root:Iter[33] Batch [8480] Speed: 854.34 samples/sec INFO:root:Iter[33] Batch [8500] Speed: 837.61 samples/sec INFO:root:Iter[33] Batch [8520] Speed: 848.03 samples/sec INFO:root:Iter[33] Batch [8540] Speed: 850.99 samples/sec INFO:root:Iter[33] Batch [8560] Speed: 838.42 samples/sec INFO:root:Iter[33] Batch [8580] Speed: 857.56 samples/sec INFO:root:Iter[33] Batch [8600] Speed: 844.20 samples/sec INFO:root:Iter[33] Batch [8620] Speed: 829.50 samples/sec INFO:root:Iter[33] Batch [8640] Speed: 874.40 samples/sec INFO:root:Iter[33] Batch [8660] Speed: 835.48 samples/sec INFO:root:Iter[33] Batch [8680] Speed: 860.04 samples/sec INFO:root:Iter[33] Batch [8700] Speed: 839.82 samples/sec Traceback (most recent call last): File "train_parall.py", line 353, in main() File "train_parall.py", line 350, in main train_net(args) File "train_parall.py", line 345, in train_net epoch_end_callback = epoch_cb ) File "/DATA/disk1/jiankangdeng/mxnet/insightface/recognition/parall_module_local_v1.py", line 475, in fit self.update() File "/DATA/disk1/jiankangdeng/mxnet/insightface/recognition/parall_module_local_v1.py", line 323, in update self._curr_module.update() File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/module/module.py", line 666, in update self._kvstore, self._exec_group.param_names) File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/model.py", line 160, in _update_params_on_kvstore kvstore.pull(name, arg_list, priority=-index) File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/kvstore.py", line 301, in pull ckeys, cvals, use_str_keys = _ctype_key_value(key, out) File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/kvstore.py", line 65, in _ctype_key_value return (c_keys, c_handle_array(vals), use_str_keys) File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/base.py", line 409, in c_handle_array arr[:] = [o.handle for o in objs] File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/base.py", line 409, in arr[:] = [o.handle for o in objs] KeyboardInterrupt ================================================ FILE: face_recognition/models/MS1Mretina-polynet50-arcface/model-symbol.json ================================================ { "nodes": [ { "op": "null", "name": "data", "inputs": [] }, { "op": "_copy", "name": "id", "inputs": [[0, 0, 0]] }, { "op": "_minus_scalar", "name": "_minusscalar0", "attrs": {"scalar": "127.5"}, "inputs": [[1, 0, 0]] }, { "op": "_mul_scalar", "name": "_mulscalar0", "attrs": {"scalar": "0.0078125"}, "inputs": [[2, 0, 0]] }, { "op": "null", "name": "conv0_weight", "attrs": { "kernel": "(3, 3)", "no_bias": "True", "num_filter": "64", "pad": "(1, 1)", "stride": "(1, 1)", "workspace": "256" }, "inputs": [] }, 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709, 711, 713, 714, 715, 716, 718, 720, 722, 723, 724, 725, 727, 729, 730, 731, 732, 737, 739, 740, 741, 742, 745, 746, 747, 748, 750, 752, 753, 754, 755, 757, 759, 761, 762, 763, 764, 769, 771, 772, 773, 774, 777, 778, 779, 780, 782, 784, 785, 786, 787, 789, 791, 793, 794, 795, 796, 801, 803, 804, 805, 806, 809, 810, 811, 812, 815, 816, 818, 819, 820, 821 ], "node_row_ptr": [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 42, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 67, 68, 71, 72, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 103, 104, 105, 106, 107, 108, 111, 112, 113, 114, 115, 116, 117, 120, 121, 122, 123, 124, 125, 126, 127, 128, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 143, 144, 145, 146, 147, 148, 151, 152, 153, 154, 155, 156, 157, 160, 161, 162, 163, 164, 165, 166, 167, 168, 171, 172, 173, 174, 175, 176, 177, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 197, 200, 201, 202, 203, 204, 205, 206, 209, 210, 211, 212, 213, 214, 215, 216, 217, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 232, 233, 234, 235, 236, 237, 240, 241, 242, 243, 244, 245, 246, 249, 250, 251, 252, 253, 254, 255, 256, 257, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 272, 273, 274, 275, 276, 277, 280, 281, 282, 283, 284, 285, 286, 289, 290, 291, 292, 293, 294, 295, 296, 297, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 312, 313, 314, 315, 316, 317, 320, 321, 322, 323, 324, 325, 326, 329, 330, 331, 332, 333, 334, 335, 336, 337, 340, 341, 342, 343, 344, 345, 346, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 361, 362, 363, 364, 365, 366, 369, 370, 371, 372, 373, 374, 375, 378, 379, 380, 381, 382, 383, 384, 385, 386, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 401, 402, 403, 404, 405, 406, 409, 410, 411, 412, 413, 414, 415, 418, 419, 420, 421, 422, 423, 424, 425, 426, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 441, 442, 443, 444, 445, 446, 449, 450, 451, 452, 453, 454, 455, 458, 459, 460, 461, 462, 463, 464, 465, 466, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 481, 482, 483, 484, 485, 486, 489, 490, 491, 492, 493, 494, 495, 498, 499, 500, 501, 502, 503, 504, 505, 506, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 521, 522, 523, 524, 525, 526, 529, 530, 531, 532, 533, 534, 535, 538, 539, 540, 541, 542, 543, 544, 545, 546, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 561, 562, 563, 564, 565, 566, 569, 570, 571, 572, 573, 574, 575, 578, 579, 580, 581, 582, 583, 584, 585, 586, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 601, 602, 603, 604, 605, 606, 609, 610, 611, 612, 613, 614, 615, 618, 619, 620, 621, 622, 623, 624, 625, 626, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 641, 642, 643, 644, 645, 646, 649, 650, 651, 652, 653, 654, 655, 658, 659, 660, 661, 662, 663, 664, 665, 666, 669, 670, 671, 672, 673, 674, 675, 676, 677, 678, 681, 682, 683, 684, 685, 686, 689, 690, 691, 692, 693, 694, 695, 698, 699, 700, 701, 702, 703, 704, 705, 706, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 721, 722, 723, 724, 725, 726, 729, 730, 731, 732, 733, 734, 735, 738, 739, 740, 741, 742, 743, 744, 745, 746, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 761, 762, 763, 764, 765, 766, 769, 770, 771, 772, 773, 774, 775, 778, 779, 780, 781, 782, 783, 784, 785, 786, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 801, 802, 803, 804, 805, 806, 809, 810, 811, 812, 813, 814, 815, 818, 819, 820, 821, 822, 823, 824, 825, 826, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 841, 842, 843, 844, 845, 846, 849, 850, 851, 852, 853, 854, 855, 858, 859, 860, 861, 862, 863, 864, 865, 866, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 881, 882, 883, 884, 885, 886, 889, 890, 891, 892, 893, 894, 895, 898, 899, 900, 901, 902, 903, 904, 905, 906, 909, 910, 911, 912, 913, 914, 915, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 930, 931, 932, 933, 934, 935, 938, 939, 940, 941, 942, 943, 944, 947, 948, 949, 950, 951, 952, 953, 954, 955, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 970, 971, 972, 973, 974, 975, 978, 979, 980, 981, 982, 983, 984, 987, 988, 989, 990, 991, 992, 993, 994, 995, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1010, 1011, 1012, 1013, 1014, 1015, 1018, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1030 ], "heads": [[822, 0, 0]], "attrs": {"mxnet_version": ["int", 10600]} } ================================================ FILE: face_recognition/models/MS1Mretina-resnet50-arcface/log_retina_r50_arcface.txt ================================================ gpu num: 8 prefix ./models/r50-arcface-retina/model image_size [112, 112] num_classes 93431 Called with argument: Namespace(batch_size=512, ckpt=3, ctx_num=8, ctx_num_classes=11679, dataset='retina', extra_model_name='', frequent=20, image_channel=3, kvstore='device', local_class_start=0, local_num_classes=93432, loss='arcface', lr=0.1, lr_steps='100000,160000,220000', models_root='./models', mom=0.9, network='r50', per_batch_size=64, pretrained='', pretrained_epoch=1, rescale_threshold=0, verbose=2000, wd=0.0005, worker_id=0) {'bn_mom': 0.9, 'workspace': 256, 'emb_size': 512, 'ckpt_embedding': True, 'net_se': 0, 'net_act': 'prelu', 'net_unit': 3, 'net_input': 1, 'net_blocks': [1, 4, 6, 2], 'net_output': 'E', 'net_multiplier': 1.0, 'val_targets': ['lfw', 'cfp_fp', 'agedb_30'], 'ce_loss': True, 'fc7_lr_mult': 1.0, 'fc7_wd_mult': 1.0, 'fc7_no_bias': False, 'max_steps': 0, 'data_rand_mirror': True, 'data_cutoff': False, 'data_color': 0, 'data_images_filter': 0, 'count_flops': True, 'memonger': False, 'loss_name': 'margin_softmax', 'loss_s': 64.0, 'loss_m1': 1.0, 'loss_m2': 0.5, 'loss_m3': 0.0, 'net_name': 'fresnet', 'num_layers': 50, 'dataset': 'retina', 'dataset_path': '../datasets/ms1m-retinaface', 'num_classes': 93431, 'image_shape': [112, 112, 3], 'loss': 'arcface', 'network': 'r50', 'num_workers': 1, 'batch_size': 512, 'per_batch_size': 64} 0 1 E 3 prelu False Network FLOPs: 12.6G INFO:root:loading recordio ../datasets/ms1m-retinaface/train.rec... header0 label [5179511. 5272942.] id2range 93431 5179510 rand_mirror True loading bin 0 loading bin 1000 loading bin 2000 loading bin 3000 loading bin 4000 loading bin 5000 loading bin 6000 loading bin 7000 loading bin 8000 loading bin 9000 loading bin 10000 loading bin 11000 (12000, 3, 112, 112) ver lfw loading bin 0 loading bin 1000 loading bin 2000 loading bin 3000 loading bin 4000 loading bin 5000 loading bin 6000 loading bin 7000 loading bin 8000 loading bin 9000 loading bin 10000 loading bin 11000 loading bin 12000 loading bin 13000 (14000, 3, 112, 112) ver cfp_fp loading bin 0 loading bin 1000 loading bin 2000 loading bin 3000 loading bin 4000 loading bin 5000 loading bin 6000 loading bin 7000 loading bin 8000 loading bin 9000 loading bin 10000 loading bin 11000 (12000, 3, 112, 112) ver agedb_30 lr_steps [100000, 160000, 220000] call reset()in_bind False [('data', (512, 3, 112, 112))] [('softmax_label', (512,))] _data_shape (512, 3, 112, 112) [('softmax_label', (512,))] [18:18:28] src/kvstore/././comm.h:744: only 0 out of 56 GPU pairs are enabled direct access. It may affect the performance. You can set MXNET_ENABLE_GPU_P2P=0 to turn it off [18:18:28] src/kvstore/././comm.h:753: ........ [18:18:28] src/kvstore/././comm.h:753: ........ [18:18:28] src/kvstore/././comm.h:753: ........ [18:18:28] src/kvstore/././comm.h:753: ........ [18:18:28] src/kvstore/././comm.h:753: ........ [18:18:28] src/kvstore/././comm.h:753: ........ [18:18:28] src/kvstore/././comm.h:753: ........ [18:18:28] src/kvstore/././comm.h:753: ........ [18:18:29] src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (set the environment variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable) INFO:root:Iter[0] Batch [20] Speed: 523.28 samples/sec INFO:root:Iter[0] Batch [40] Speed: 580.59 samples/sec INFO:root:Iter[0] Batch [60] Speed: 591.03 samples/sec INFO:root:Iter[0] Batch [80] Speed: 604.40 samples/sec INFO:root:Iter[0] Batch [100] Speed: 568.77 samples/sec INFO:root:Iter[0] Batch [120] Speed: 556.98 samples/sec INFO:root:Iter[0] Batch [140] Speed: 558.32 samples/sec INFO:root:Iter[0] Batch [160] Speed: 578.28 samples/sec INFO:root:Iter[0] Batch [180] Speed: 581.61 samples/sec INFO:root:Iter[0] Batch [200] Speed: 579.75 samples/sec INFO:root:Iter[0] Batch [220] Speed: 535.07 samples/sec INFO:root:Iter[0] Batch [240] Speed: 553.90 samples/sec INFO:root:Iter[0] Batch [260] Speed: 576.64 samples/sec INFO:root:Iter[0] Batch [280] Speed: 560.29 samples/sec INFO:root:Iter[0] Batch [300] Speed: 571.43 samples/sec INFO:root:Iter[0] Batch [320] Speed: 593.56 samples/sec INFO:root:Iter[0] Batch [340] 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(12000, 512) infer time 4.409407000000002 [lfw][2000]XNorm: 21.780299 [lfw][2000]Accuracy-Flip: 0.93867+-0.00945 testing verification.. (14000, 512) infer time 4.746598999999999 [cfp_fp][2000]XNorm: 18.694772 [cfp_fp][2000]Accuracy-Flip: 0.74800+-0.01314 testing verification.. (12000, 512) infer time 4.061450000000001 [agedb_30][2000]XNorm: 20.538235 [agedb_30][2000]Accuracy-Flip: 0.77350+-0.02001 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [2000]Accuracy-Highest: 0.77350 INFO:root:Iter[0] Batch [2000] Speed: 265.37 samples/sec INFO:root:Iter[0] Batch [2020] Speed: 855.82 samples/sec INFO:root:Iter[0] Batch [2040] Speed: 786.01 samples/sec INFO:root:Iter[0] Batch [2060] Speed: 842.24 samples/sec INFO:root:Iter[0] Batch [2080] Speed: 789.51 samples/sec INFO:root:Iter[0] Batch [2100] Speed: 815.07 samples/sec INFO:root:Iter[0] Batch [2120] Speed: 862.04 samples/sec INFO:root:Iter[0] Batch [2140] Speed: 857.85 samples/sec INFO:root:Iter[0] Batch [2160] Speed: 846.04 samples/sec INFO:root:Iter[0] Batch [2180] Speed: 849.94 samples/sec INFO:root:Iter[0] Batch [2200] Speed: 823.70 samples/sec INFO:root:Iter[0] Batch [2220] Speed: 824.38 samples/sec INFO:root:Iter[0] Batch [2240] Speed: 830.58 samples/sec INFO:root:Iter[0] 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0.013671875 lr-batch-epoch: 0.1 3999 0 testing verification.. (12000, 512) infer time 4.053942000000001 [lfw][4000]XNorm: 23.800846 [lfw][4000]Accuracy-Flip: 0.97967+-0.00446 testing verification.. (14000, 512) infer time 4.779448 [cfp_fp][4000]XNorm: 20.779606 [cfp_fp][4000]Accuracy-Flip: 0.87714+-0.01268 testing verification.. (12000, 512) infer time 4.052049000000002 [agedb_30][4000]XNorm: 23.027685 [agedb_30][4000]Accuracy-Flip: 0.87617+-0.02197 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [4000]Accuracy-Highest: 0.87617 INFO:root:Iter[0] Batch [4000] Speed: 259.69 samples/sec INFO:root:Iter[0] Batch [4020] Speed: 745.99 samples/sec INFO:root:Iter[0] Batch [4040] Speed: 800.61 samples/sec INFO:root:Iter[0] Batch [4060] Speed: 826.08 samples/sec INFO:root:Iter[0] Batch [4080] Speed: 832.46 samples/sec INFO:root:Iter[0] Batch [4100] Speed: 830.25 samples/sec INFO:root:Iter[0] Batch [4120] Speed: 833.37 samples/sec INFO:root:Iter[0] Batch [4140] Speed: 829.63 samples/sec INFO:root:Iter[0] Batch [4160] Speed: 864.07 samples/sec INFO:root:Iter[0] Batch [4180] Speed: 793.88 samples/sec INFO:root:Iter[0] Batch [4200] Speed: 859.77 samples/sec INFO:root:Iter[0] Batch [4220] Speed: 849.39 samples/sec INFO:root:Iter[0] Batch [4240] Speed: 847.61 samples/sec INFO:root:Iter[0] 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0.025390625 lr-batch-epoch: 0.1 5999 0 testing verification.. (12000, 512) infer time 4.165312999999999 [lfw][6000]XNorm: 23.531083 [lfw][6000]Accuracy-Flip: 0.98750+-0.00501 testing verification.. (14000, 512) infer time 4.743952000000001 [cfp_fp][6000]XNorm: 20.458699 [cfp_fp][6000]Accuracy-Flip: 0.91186+-0.01517 testing verification.. (12000, 512) infer time 4.213140999999999 [agedb_30][6000]XNorm: 22.713725 [agedb_30][6000]Accuracy-Flip: 0.91267+-0.01862 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [6000]Accuracy-Highest: 0.91267 INFO:root:Iter[0] Batch [6000] Speed: 263.88 samples/sec INFO:root:Iter[0] Batch [6020] Speed: 758.07 samples/sec INFO:root:Iter[0] Batch [6040] Speed: 793.61 samples/sec INFO:root:Iter[0] Batch [6060] Speed: 785.33 samples/sec INFO:root:Iter[0] Batch [6080] Speed: 804.22 samples/sec INFO:root:Iter[0] Batch [6100] Speed: 790.91 samples/sec INFO:root:Iter[0] Batch [6120] Speed: 782.15 samples/sec INFO:root:Iter[0] Batch [6140] Speed: 824.61 samples/sec INFO:root:Iter[0] Batch [6160] Speed: 782.52 samples/sec INFO:root:Iter[0] Batch [6180] Speed: 817.55 samples/sec INFO:root:Iter[0] Batch [6200] Speed: 788.84 samples/sec INFO:root:Iter[0] Batch [6220] Speed: 819.53 samples/sec INFO:root:Iter[0] Batch [6240] Speed: 830.23 samples/sec INFO:root:Iter[0] 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0.044921875 lr-batch-epoch: 0.1 7999 0 testing verification.. (12000, 512) infer time 4.145141 [lfw][8000]XNorm: 23.539455 [lfw][8000]Accuracy-Flip: 0.99033+-0.00645 testing verification.. (14000, 512) infer time 4.8725 [cfp_fp][8000]XNorm: 20.860535 [cfp_fp][8000]Accuracy-Flip: 0.92086+-0.01406 testing verification.. (12000, 512) infer time 4.031148000000001 [agedb_30][8000]XNorm: 22.862032 [agedb_30][8000]Accuracy-Flip: 0.92100+-0.01759 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [8000]Accuracy-Highest: 0.92100 INFO:root:Iter[0] Batch [8000] Speed: 266.31 samples/sec INFO:root:Iter[0] Batch [8020] Speed: 778.89 samples/sec INFO:root:Iter[0] Batch [8040] Speed: 817.96 samples/sec INFO:root:Iter[0] Batch [8060] Speed: 817.35 samples/sec INFO:root:Iter[0] Batch [8080] Speed: 826.14 samples/sec INFO:root:Iter[0] Batch [8100] Speed: 718.73 samples/sec INFO:root:Iter[0] Batch [8120] Speed: 783.13 samples/sec INFO:root:Iter[0] Batch [8140] Speed: 838.81 samples/sec INFO:root:Iter[0] Batch [8160] Speed: 749.82 samples/sec INFO:root:Iter[0] Batch [8180] Speed: 799.18 samples/sec INFO:root:Iter[0] Batch [8200] Speed: 778.74 samples/sec INFO:root:Iter[0] Batch [8220] Speed: 809.19 samples/sec INFO:root:Iter[0] Batch [8240] Speed: 784.66 samples/sec INFO:root:Iter[0] 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0.03515625 lr-batch-epoch: 0.1 9999 0 testing verification.. (12000, 512) infer time 4.092543 [lfw][10000]XNorm: 22.585020 [lfw][10000]Accuracy-Flip: 0.99267+-0.00403 testing verification.. (14000, 512) infer time 4.912218 [cfp_fp][10000]XNorm: 20.205368 [cfp_fp][10000]Accuracy-Flip: 0.92986+-0.01353 testing verification.. (12000, 512) infer time 3.9060079999999995 [agedb_30][10000]XNorm: 21.708579 [agedb_30][10000]Accuracy-Flip: 0.93217+-0.01812 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [10000]Accuracy-Highest: 0.93217 INFO:root:Iter[0] Batch [10000] Speed: 271.64 samples/sec INFO:root:Iter[0] Batch [10020] Speed: 836.86 samples/sec INFO:root:Iter[0] Batch [10040] Speed: 805.85 samples/sec INFO:root:Iter[0] Batch [10060] Speed: 837.04 samples/sec INFO:root:Iter[0] Batch [10080] Speed: 813.73 samples/sec INFO:root:Iter[0] Batch [10100] Speed: 839.30 samples/sec INFO:root:Epoch[0] Time cost=6434.179 call reset() INFO:root:Iter[1] Batch [20] Speed: 779.23 samples/sec INFO:root:Iter[1] Batch [40] Speed: 811.31 samples/sec INFO:root:Iter[1] Batch [60] Speed: 833.41 samples/sec INFO:root:Iter[1] Batch [80] Speed: 864.87 samples/sec INFO:root:Iter[1] Batch [100] Speed: 852.35 samples/sec INFO:root:Iter[1] Batch [120] Speed: 863.64 samples/sec INFO:root:Iter[1] Batch 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{fc7_acc} 12000 0.044921875 lr-batch-epoch: 0.1 1883 1 testing verification.. (12000, 512) infer time 4.1605810000000005 [lfw][12000]XNorm: 22.699552 [lfw][12000]Accuracy-Flip: 0.99167+-0.00387 testing verification.. (14000, 512) infer time 5.022702 [cfp_fp][12000]XNorm: 20.320430 [cfp_fp][12000]Accuracy-Flip: 0.93171+-0.01430 testing verification.. (12000, 512) infer time 4.043082 [agedb_30][12000]XNorm: 22.693639 [agedb_30][12000]Accuracy-Flip: 0.93800+-0.01542 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [12000]Accuracy-Highest: 0.93800 INFO:root:Iter[1] Batch [1900] Speed: 263.49 samples/sec INFO:root:Iter[1] Batch [1920] Speed: 845.92 samples/sec INFO:root:Iter[1] Batch [1940] Speed: 818.03 samples/sec INFO:root:Iter[1] Batch [1960] Speed: 820.49 samples/sec INFO:root:Iter[1] Batch [1980] Speed: 798.45 samples/sec INFO:root:Iter[1] Batch [2000] Speed: 867.20 samples/sec INFO:root:Iter[1] Batch [2020] Speed: 776.90 samples/sec INFO:root:Iter[1] Batch [2040] Speed: 854.13 samples/sec INFO:root:Iter[1] Batch [2060] Speed: 852.99 samples/sec INFO:root:Iter[1] Batch [2080] Speed: 836.88 samples/sec INFO:root:Iter[1] Batch [2100] Speed: 820.63 samples/sec INFO:root:Iter[1] Batch [2120] Speed: 839.35 samples/sec INFO:root:Iter[1] Batch [2140] Speed: 807.12 samples/sec INFO:root:Iter[1] Batch 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0.056640625 lr-batch-epoch: 0.1 3883 1 testing verification.. (12000, 512) infer time 4.549033000000001 [lfw][14000]XNorm: 22.442470 [lfw][14000]Accuracy-Flip: 0.99350+-0.00462 testing verification.. (14000, 512) infer time 5.4271970000000005 [cfp_fp][14000]XNorm: 19.555188 [cfp_fp][14000]Accuracy-Flip: 0.92771+-0.01299 testing verification.. (12000, 512) infer time 4.669950000000001 [agedb_30][14000]XNorm: 21.719965 [agedb_30][14000]Accuracy-Flip: 0.94217+-0.00960 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [14000]Accuracy-Highest: 0.94217 INFO:root:Iter[1] Batch [3900] Speed: 219.08 samples/sec INFO:root:Iter[1] Batch [3920] Speed: 616.46 samples/sec INFO:root:Iter[1] Batch [3940] Speed: 626.72 samples/sec INFO:root:Iter[1] Batch [3960] Speed: 589.08 samples/sec INFO:root:Iter[1] Batch [3980] Speed: 606.04 samples/sec INFO:root:Iter[1] Batch [4000] Speed: 606.42 samples/sec INFO:root:Iter[1] Batch [4020] Speed: 599.35 samples/sec INFO:root:Iter[1] Batch [4040] Speed: 594.28 samples/sec INFO:root:Iter[1] Batch [4060] Speed: 582.69 samples/sec INFO:root:Iter[1] Batch [4080] Speed: 590.06 samples/sec INFO:root:Iter[1] Batch [4100] Speed: 600.70 samples/sec INFO:root:Iter[1] Batch [4120] Speed: 597.78 samples/sec INFO:root:Iter[1] Batch [4140] Speed: 580.92 samples/sec 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{fc7_acc} 16000 0.046875 lr-batch-epoch: 0.1 5883 1 testing verification.. (12000, 512) infer time 4.801693 [lfw][16000]XNorm: 22.462053 [lfw][16000]Accuracy-Flip: 0.99367+-0.00476 testing verification.. (14000, 512) infer time 5.950698 [cfp_fp][16000]XNorm: 19.873526 [cfp_fp][16000]Accuracy-Flip: 0.93757+-0.01132 testing verification.. (12000, 512) infer time 4.886003999999999 [agedb_30][16000]XNorm: 21.748005 [agedb_30][16000]Accuracy-Flip: 0.94433+-0.01114 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [16000]Accuracy-Highest: 0.94433 INFO:root:Iter[1] Batch [5900] Speed: 206.21 samples/sec INFO:root:Iter[1] Batch [5920] Speed: 582.62 samples/sec INFO:root:Iter[1] Batch [5940] Speed: 592.99 samples/sec INFO:root:Iter[1] Batch [5960] Speed: 577.09 samples/sec INFO:root:Iter[1] Batch [5980] Speed: 550.13 samples/sec INFO:root:Iter[1] Batch [6000] Speed: 547.59 samples/sec INFO:root:Iter[1] Batch [6020] Speed: 570.81 samples/sec INFO:root:Iter[1] Batch [6040] Speed: 558.78 samples/sec INFO:root:Iter[1] Batch [6060] Speed: 576.39 samples/sec INFO:root:Iter[1] Batch [6080] Speed: 570.58 samples/sec INFO:root:Iter[1] Batch [6100] Speed: 595.25 samples/sec INFO:root:Iter[1] Batch [6120] Speed: 566.25 samples/sec INFO:root:Iter[1] Batch [6140] Speed: 577.28 samples/sec 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{fc7_acc} 18000 0.060546875 lr-batch-epoch: 0.1 7883 1 testing verification.. (12000, 512) infer time 4.166773999999998 [lfw][18000]XNorm: 22.816428 [lfw][18000]Accuracy-Flip: 0.99317+-0.00353 testing verification.. (14000, 512) infer time 4.774588000000001 [cfp_fp][18000]XNorm: 19.944414 [cfp_fp][18000]Accuracy-Flip: 0.93400+-0.00947 testing verification.. (12000, 512) infer time 4.207226 [agedb_30][18000]XNorm: 21.934844 [agedb_30][18000]Accuracy-Flip: 0.94533+-0.01154 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [18000]Accuracy-Highest: 0.94533 INFO:root:Iter[1] Batch [7900] Speed: 255.73 samples/sec INFO:root:Iter[1] Batch [7920] Speed: 779.82 samples/sec INFO:root:Iter[1] Batch [7940] Speed: 811.37 samples/sec INFO:root:Iter[1] Batch [7960] Speed: 861.39 samples/sec INFO:root:Iter[1] Batch [7980] Speed: 779.83 samples/sec INFO:root:Iter[1] Batch [8000] Speed: 793.77 samples/sec INFO:root:Iter[1] Batch [8020] Speed: 808.72 samples/sec INFO:root:Iter[1] Batch [8040] Speed: 741.58 samples/sec INFO:root:Iter[1] Batch [8060] Speed: 829.93 samples/sec INFO:root:Iter[1] Batch [8080] Speed: 780.91 samples/sec INFO:root:Iter[1] Batch [8100] Speed: 820.40 samples/sec INFO:root:Iter[1] Batch [8120] Speed: 847.79 samples/sec INFO:root:Iter[1] Batch [8140] Speed: 827.28 samples/sec INFO:root:Iter[1] Batch 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0.06640625 lr-batch-epoch: 0.1 9883 1 testing verification.. (12000, 512) infer time 4.192165999999999 [lfw][20000]XNorm: 22.648036 [lfw][20000]Accuracy-Flip: 0.99533+-0.00306 testing verification.. (14000, 512) infer time 4.5796090000000005 [cfp_fp][20000]XNorm: 19.903187 [cfp_fp][20000]Accuracy-Flip: 0.93871+-0.00970 testing verification.. (12000, 512) infer time 4.028409 [agedb_30][20000]XNorm: 21.739618 [agedb_30][20000]Accuracy-Flip: 0.94233+-0.01096 [20000]Accuracy-Highest: 0.94533 INFO:root:Iter[1] Batch [9900] Speed: 263.84 samples/sec INFO:root:Iter[1] Batch [9920] Speed: 789.38 samples/sec INFO:root:Iter[1] Batch [9940] Speed: 817.50 samples/sec INFO:root:Iter[1] Batch [9960] Speed: 791.13 samples/sec INFO:root:Iter[1] Batch [9980] Speed: 804.17 samples/sec INFO:root:Iter[1] Batch [10000] Speed: 789.24 samples/sec INFO:root:Iter[1] Batch [10020] Speed: 849.90 samples/sec INFO:root:Iter[1] Batch [10040] Speed: 820.89 samples/sec INFO:root:Iter[1] Batch [10060] Speed: 825.35 samples/sec INFO:root:Iter[1] Batch [10080] Speed: 814.04 samples/sec INFO:root:Iter[1] Batch [10100] Speed: 853.61 samples/sec INFO:root:Epoch[1] Time cost=7318.968 call reset() INFO:root:Iter[2] Batch [20] Speed: 831.28 samples/sec INFO:root:Iter[2] Batch [40] Speed: 835.25 samples/sec INFO:root:Iter[2] Batch [60] Speed: 852.28 samples/sec 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(12000, 512) infer time 4.2735069999999995 [lfw][22000]XNorm: 23.013188 [lfw][22000]Accuracy-Flip: 0.99467+-0.00364 testing verification.. (14000, 512) infer time 4.466018999999999 [cfp_fp][22000]XNorm: 20.236656 [cfp_fp][22000]Accuracy-Flip: 0.94243+-0.00994 testing verification.. (12000, 512) infer time 4.0087909999999995 [agedb_30][22000]XNorm: 22.554223 [agedb_30][22000]Accuracy-Flip: 0.94900+-0.01170 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [22000]Accuracy-Highest: 0.94900 INFO:root:Iter[2] Batch [1780] Speed: 270.17 samples/sec INFO:root:Iter[2] Batch [1800] Speed: 831.71 samples/sec INFO:root:Iter[2] Batch [1820] Speed: 773.04 samples/sec INFO:root:Iter[2] Batch [1840] Speed: 819.99 samples/sec INFO:root:Iter[2] Batch [1860] Speed: 819.84 samples/sec INFO:root:Iter[2] Batch [1880] Speed: 811.04 samples/sec INFO:root:Iter[2] Batch [1900] Speed: 867.82 samples/sec INFO:root:Iter[2] Batch [1920] Speed: 818.59 samples/sec INFO:root:Iter[2] Batch [1940] Speed: 873.88 samples/sec INFO:root:Iter[2] Batch [1960] Speed: 796.25 samples/sec INFO:root:Iter[2] Batch [1980] Speed: 816.62 samples/sec INFO:root:Iter[2] Batch [2000] Speed: 762.56 samples/sec INFO:root:Iter[2] Batch [2020] Speed: 848.69 samples/sec 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{fc7_acc} 24000 0.052734375 lr-batch-epoch: 0.1 3767 2 testing verification.. (12000, 512) infer time 4.230650999999999 [lfw][24000]XNorm: 23.275828 [lfw][24000]Accuracy-Flip: 0.99467+-0.00332 testing verification.. (14000, 512) infer time 4.937892000000001 [cfp_fp][24000]XNorm: 20.798728 [cfp_fp][24000]Accuracy-Flip: 0.93729+-0.01450 testing verification.. (12000, 512) infer time 4.073869999999999 [agedb_30][24000]XNorm: 22.581437 [agedb_30][24000]Accuracy-Flip: 0.95000+-0.00978 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [24000]Accuracy-Highest: 0.95000 INFO:root:Iter[2] Batch [3780] Speed: 276.79 samples/sec INFO:root:Iter[2] Batch [3800] Speed: 838.27 samples/sec INFO:root:Iter[2] Batch [3820] Speed: 818.48 samples/sec INFO:root:Iter[2] Batch [3840] Speed: 827.46 samples/sec INFO:root:Iter[2] Batch [3860] Speed: 799.75 samples/sec INFO:root:Iter[2] Batch [3880] Speed: 856.55 samples/sec INFO:root:Iter[2] Batch [3900] Speed: 770.05 samples/sec INFO:root:Iter[2] Batch [3920] Speed: 784.62 samples/sec INFO:root:Iter[2] Batch [3940] Speed: 799.55 samples/sec INFO:root:Iter[2] Batch [3960] Speed: 825.59 samples/sec INFO:root:Iter[2] Batch [3980] Speed: 789.92 samples/sec INFO:root:Iter[2] Batch [4000] Speed: 867.29 samples/sec INFO:root:Iter[2] Batch [4020] Speed: 802.15 samples/sec 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{fc7_acc} 26000 0.06640625 lr-batch-epoch: 0.1 5767 2 testing verification.. (12000, 512) infer time 4.176133000000001 [lfw][26000]XNorm: 22.679174 [lfw][26000]Accuracy-Flip: 0.99450+-0.00317 testing verification.. (14000, 512) infer time 5.202191999999999 [cfp_fp][26000]XNorm: 20.499744 [cfp_fp][26000]Accuracy-Flip: 0.94714+-0.01343 testing verification.. (12000, 512) infer time 4.120784999999999 [agedb_30][26000]XNorm: 22.318011 [agedb_30][26000]Accuracy-Flip: 0.94817+-0.01177 [26000]Accuracy-Highest: 0.95000 INFO:root:Iter[2] Batch [5780] Speed: 272.21 samples/sec INFO:root:Iter[2] Batch [5800] Speed: 837.34 samples/sec INFO:root:Iter[2] Batch [5820] Speed: 784.70 samples/sec INFO:root:Iter[2] Batch [5840] Speed: 811.51 samples/sec INFO:root:Iter[2] Batch [5860] Speed: 842.68 samples/sec INFO:root:Iter[2] Batch [5880] Speed: 839.30 samples/sec INFO:root:Iter[2] Batch [5900] Speed: 807.52 samples/sec INFO:root:Iter[2] Batch [5920] Speed: 837.61 samples/sec INFO:root:Iter[2] Batch [5940] Speed: 831.86 samples/sec INFO:root:Iter[2] Batch [5960] Speed: 835.63 samples/sec INFO:root:Iter[2] Batch [5980] Speed: 747.05 samples/sec INFO:root:Iter[2] Batch [6000] Speed: 803.95 samples/sec INFO:root:Iter[2] Batch [6020] Speed: 791.80 samples/sec INFO:root:Iter[2] Batch [6040] Speed: 854.23 samples/sec INFO:root:Iter[2] Batch [6060] Speed: 788.64 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(12000, 512) infer time 4.194047999999999 [lfw][28000]XNorm: 22.609090 [lfw][28000]Accuracy-Flip: 0.99533+-0.00348 testing verification.. (14000, 512) infer time 4.431404000000001 [cfp_fp][28000]XNorm: 20.000019 [cfp_fp][28000]Accuracy-Flip: 0.94443+-0.01074 testing verification.. (12000, 512) infer time 4.2142599999999995 [agedb_30][28000]XNorm: 22.088261 [agedb_30][28000]Accuracy-Flip: 0.95350+-0.00935 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [28000]Accuracy-Highest: 0.95350 INFO:root:Iter[2] Batch [7780] Speed: 278.45 samples/sec INFO:root:Iter[2] Batch [7800] Speed: 823.65 samples/sec INFO:root:Iter[2] Batch [7820] Speed: 811.92 samples/sec INFO:root:Iter[2] Batch [7840] Speed: 842.80 samples/sec INFO:root:Iter[2] Batch [7860] Speed: 841.53 samples/sec INFO:root:Iter[2] Batch [7880] Speed: 765.07 samples/sec INFO:root:Iter[2] Batch [7900] Speed: 807.82 samples/sec INFO:root:Iter[2] Batch [7920] Speed: 815.32 samples/sec INFO:root:Iter[2] Batch [7940] Speed: 826.90 samples/sec INFO:root:Iter[2] Batch [7960] Speed: 827.97 samples/sec INFO:root:Iter[2] Batch [7980] Speed: 833.22 samples/sec INFO:root:Iter[2] Batch [8000] Speed: 854.56 samples/sec INFO:root:Iter[2] Batch [8020] Speed: 828.77 samples/sec 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{fc7_acc} 30000 0.078125 lr-batch-epoch: 0.1 9767 2 testing verification.. (12000, 512) infer time 4.295065000000001 [lfw][30000]XNorm: 22.880937 [lfw][30000]Accuracy-Flip: 0.99417+-0.00359 testing verification.. (14000, 512) infer time 4.956051 [cfp_fp][30000]XNorm: 19.910640 [cfp_fp][30000]Accuracy-Flip: 0.94057+-0.01388 testing verification.. (12000, 512) infer time 4.0597259999999995 [agedb_30][30000]XNorm: 22.301977 [agedb_30][30000]Accuracy-Flip: 0.94900+-0.00929 [30000]Accuracy-Highest: 0.95350 INFO:root:Iter[2] Batch [9780] Speed: 272.69 samples/sec INFO:root:Iter[2] Batch [9800] Speed: 812.06 samples/sec INFO:root:Iter[2] Batch [9820] Speed: 796.84 samples/sec INFO:root:Iter[2] Batch [9840] Speed: 818.32 samples/sec INFO:root:Iter[2] Batch [9860] Speed: 821.46 samples/sec INFO:root:Iter[2] Batch [9880] Speed: 813.55 samples/sec INFO:root:Iter[2] Batch [9900] Speed: 857.27 samples/sec INFO:root:Iter[2] Batch [9920] Speed: 825.73 samples/sec INFO:root:Iter[2] Batch [9940] Speed: 822.68 samples/sec INFO:root:Iter[2] Batch [9960] Speed: 821.00 samples/sec INFO:root:Iter[2] Batch [9980] Speed: 841.64 samples/sec INFO:root:Iter[2] Batch [10000] Speed: 858.68 samples/sec INFO:root:Iter[2] Batch [10020] Speed: 850.14 samples/sec INFO:root:Iter[2] Batch [10040] Speed: 830.63 samples/sec INFO:root:Iter[2] Batch [10060] Speed: 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(12000, 512) infer time 4.564897 [lfw][32000]XNorm: 22.508208 [lfw][32000]Accuracy-Flip: 0.99367+-0.00400 testing verification.. (14000, 512) infer time 4.919637000000002 [cfp_fp][32000]XNorm: 20.102147 [cfp_fp][32000]Accuracy-Flip: 0.94571+-0.01082 testing verification.. (12000, 512) infer time 4.472847000000001 [agedb_30][32000]XNorm: 21.779467 [agedb_30][32000]Accuracy-Flip: 0.95133+-0.01152 [32000]Accuracy-Highest: 0.95350 INFO:root:Iter[3] Batch [1660] Speed: 270.47 samples/sec INFO:root:Iter[3] Batch [1680] Speed: 836.25 samples/sec INFO:root:Iter[3] Batch [1700] Speed: 791.37 samples/sec INFO:root:Iter[3] Batch [1720] Speed: 834.43 samples/sec INFO:root:Iter[3] Batch [1740] Speed: 827.91 samples/sec INFO:root:Iter[3] Batch [1760] Speed: 831.80 samples/sec INFO:root:Iter[3] Batch [1780] Speed: 824.40 samples/sec INFO:root:Iter[3] Batch [1800] Speed: 849.40 samples/sec INFO:root:Iter[3] Batch [1820] Speed: 896.65 samples/sec INFO:root:Iter[3] Batch [1840] Speed: 810.56 samples/sec INFO:root:Iter[3] Batch [1860] Speed: 866.34 samples/sec INFO:root:Iter[3] Batch [1880] Speed: 805.19 samples/sec INFO:root:Iter[3] Batch [1900] Speed: 842.71 samples/sec INFO:root:Iter[3] Batch [1920] Speed: 811.06 samples/sec INFO:root:Iter[3] Batch [1940] Speed: 851.85 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(12000, 512) infer time 3.763038000000001 [lfw][34000]XNorm: 23.386487 [lfw][34000]Accuracy-Flip: 0.99517+-0.00383 testing verification.. (14000, 512) infer time 4.394195 [cfp_fp][34000]XNorm: 20.504604 [cfp_fp][34000]Accuracy-Flip: 0.94557+-0.01161 testing verification.. (12000, 512) infer time 3.763016999999999 [agedb_30][34000]XNorm: 22.813826 [agedb_30][34000]Accuracy-Flip: 0.95300+-0.00936 [34000]Accuracy-Highest: 0.95350 INFO:root:Iter[3] Batch [3660] Speed: 288.02 samples/sec INFO:root:Iter[3] Batch [3680] Speed: 865.61 samples/sec INFO:root:Iter[3] Batch [3700] Speed: 854.18 samples/sec INFO:root:Iter[3] Batch [3720] Speed: 879.91 samples/sec INFO:root:Iter[3] Batch [3740] Speed: 830.61 samples/sec INFO:root:Iter[3] Batch [3760] Speed: 846.46 samples/sec INFO:root:Iter[3] Batch [3780] Speed: 821.91 samples/sec INFO:root:Iter[3] Batch [3800] Speed: 840.93 samples/sec INFO:root:Iter[3] Batch [3820] Speed: 827.24 samples/sec INFO:root:Iter[3] Batch [3840] Speed: 843.60 samples/sec INFO:root:Iter[3] Batch [3860] Speed: 835.56 samples/sec INFO:root:Iter[3] Batch [3880] Speed: 838.93 samples/sec INFO:root:Iter[3] Batch [3900] Speed: 845.41 samples/sec INFO:root:Iter[3] Batch [3920] Speed: 848.27 samples/sec INFO:root:Iter[3] Batch [3940] Speed: 844.61 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(12000, 512) infer time 3.6454990000000005 [lfw][36000]XNorm: 23.038620 [lfw][36000]Accuracy-Flip: 0.99383+-0.00299 testing verification.. (14000, 512) infer time 4.416512 [cfp_fp][36000]XNorm: 20.391173 [cfp_fp][36000]Accuracy-Flip: 0.94357+-0.01138 testing verification.. (12000, 512) infer time 3.8413839999999997 [agedb_30][36000]XNorm: 22.327823 [agedb_30][36000]Accuracy-Flip: 0.95633+-0.00605 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [36000]Accuracy-Highest: 0.95633 INFO:root:Iter[3] Batch [5660] Speed: 287.45 samples/sec INFO:root:Iter[3] Batch [5680] Speed: 831.73 samples/sec INFO:root:Iter[3] Batch [5700] Speed: 811.55 samples/sec INFO:root:Iter[3] Batch [5720] Speed: 836.44 samples/sec INFO:root:Iter[3] Batch [5740] Speed: 871.32 samples/sec INFO:root:Iter[3] Batch [5760] Speed: 889.43 samples/sec INFO:root:Iter[3] Batch [5780] Speed: 849.81 samples/sec INFO:root:Iter[3] Batch [5800] Speed: 865.13 samples/sec INFO:root:Iter[3] Batch [5820] Speed: 850.69 samples/sec INFO:root:Iter[3] Batch [5840] Speed: 826.39 samples/sec INFO:root:Iter[3] Batch [5860] Speed: 848.70 samples/sec INFO:root:Iter[3] Batch [5880] Speed: 851.69 samples/sec INFO:root:Iter[3] Batch [5900] Speed: 848.30 samples/sec 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{fc7_acc} 38000 0.08203125 lr-batch-epoch: 0.1 7651 3 testing verification.. (12000, 512) infer time 3.649617000000001 [lfw][38000]XNorm: 22.975183 [lfw][38000]Accuracy-Flip: 0.99450+-0.00308 testing verification.. (14000, 512) infer time 4.339734999999999 [cfp_fp][38000]XNorm: 20.547486 [cfp_fp][38000]Accuracy-Flip: 0.94486+-0.01140 testing verification.. (12000, 512) infer time 3.6477309999999985 [agedb_30][38000]XNorm: 22.862426 [agedb_30][38000]Accuracy-Flip: 0.95400+-0.00712 [38000]Accuracy-Highest: 0.95633 INFO:root:Iter[3] Batch [7660] Speed: 296.24 samples/sec INFO:root:Iter[3] Batch [7680] Speed: 821.86 samples/sec INFO:root:Iter[3] Batch [7700] Speed: 854.55 samples/sec INFO:root:Iter[3] Batch [7720] Speed: 823.37 samples/sec INFO:root:Iter[3] Batch [7740] Speed: 832.89 samples/sec INFO:root:Iter[3] Batch [7760] Speed: 849.00 samples/sec INFO:root:Iter[3] Batch [7780] Speed: 843.96 samples/sec INFO:root:Iter[3] Batch [7800] Speed: 828.27 samples/sec INFO:root:Iter[3] Batch [7820] Speed: 852.04 samples/sec INFO:root:Iter[3] Batch [7840] Speed: 799.21 samples/sec INFO:root:Iter[3] Batch [7860] Speed: 800.78 samples/sec INFO:root:Iter[3] Batch [7880] Speed: 818.62 samples/sec INFO:root:Iter[3] Batch [7900] Speed: 842.11 samples/sec INFO:root:Iter[3] Batch [7920] Speed: 824.27 samples/sec INFO:root:Iter[3] Batch [7940] Speed: 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(12000, 512) infer time 3.7630409999999994 [lfw][40000]XNorm: 23.362323 [lfw][40000]Accuracy-Flip: 0.99483+-0.00398 testing verification.. (14000, 512) infer time 4.468310999999999 [cfp_fp][40000]XNorm: 20.334348 [cfp_fp][40000]Accuracy-Flip: 0.94529+-0.01096 testing verification.. (12000, 512) infer time 3.8544110000000007 [agedb_30][40000]XNorm: 22.479364 [agedb_30][40000]Accuracy-Flip: 0.95050+-0.01152 [40000]Accuracy-Highest: 0.95633 INFO:root:Iter[3] Batch [9660] Speed: 285.32 samples/sec INFO:root:Iter[3] Batch [9680] Speed: 710.58 samples/sec INFO:root:Iter[3] Batch [9700] Speed: 770.23 samples/sec INFO:root:Iter[3] Batch [9720] Speed: 756.44 samples/sec INFO:root:Iter[3] Batch [9740] Speed: 816.53 samples/sec INFO:root:Iter[3] Batch [9760] Speed: 782.93 samples/sec INFO:root:Iter[3] Batch [9780] Speed: 723.29 samples/sec INFO:root:Iter[3] Batch [9800] Speed: 835.71 samples/sec INFO:root:Iter[3] Batch [9820] Speed: 845.12 samples/sec INFO:root:Iter[3] Batch [9840] Speed: 782.35 samples/sec INFO:root:Iter[3] Batch [9860] Speed: 815.22 samples/sec INFO:root:Iter[3] Batch [9880] Speed: 835.18 samples/sec INFO:root:Iter[3] Batch [9900] Speed: 826.85 samples/sec INFO:root:Iter[3] Batch [9920] Speed: 727.33 samples/sec INFO:root:Iter[3] Batch [9940] Speed: 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(12000, 512) infer time 3.819207 [lfw][42000]XNorm: 22.373976 [lfw][42000]Accuracy-Flip: 0.99417+-0.00250 testing verification.. (14000, 512) infer time 4.614007999999999 [cfp_fp][42000]XNorm: 19.673297 [cfp_fp][42000]Accuracy-Flip: 0.93843+-0.01426 testing verification.. (12000, 512) infer time 3.6721259999999996 [agedb_30][42000]XNorm: 21.892939 [agedb_30][42000]Accuracy-Flip: 0.95450+-0.00823 [42000]Accuracy-Highest: 0.95633 INFO:root:Iter[4] Batch [1540] Speed: 285.06 samples/sec INFO:root:Iter[4] Batch [1560] Speed: 851.66 samples/sec INFO:root:Iter[4] Batch [1580] Speed: 814.57 samples/sec INFO:root:Iter[4] Batch [1600] Speed: 849.03 samples/sec INFO:root:Iter[4] Batch [1620] Speed: 809.96 samples/sec INFO:root:Iter[4] Batch [1640] Speed: 836.82 samples/sec INFO:root:Iter[4] Batch [1660] Speed: 843.60 samples/sec INFO:root:Iter[4] Batch [1680] Speed: 815.64 samples/sec INFO:root:Iter[4] Batch [1700] Speed: 791.09 samples/sec INFO:root:Iter[4] Batch [1720] Speed: 859.05 samples/sec INFO:root:Iter[4] Batch [1740] Speed: 855.71 samples/sec INFO:root:Iter[4] Batch [1760] Speed: 860.97 samples/sec INFO:root:Iter[4] Batch [1780] Speed: 844.93 samples/sec INFO:root:Iter[4] Batch [1800] Speed: 839.58 samples/sec INFO:root:Iter[4] Batch [1820] Speed: 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(12000, 512) infer time 3.8005329999999997 [lfw][44000]XNorm: 23.132230 [lfw][44000]Accuracy-Flip: 0.99333+-0.00447 testing verification.. (14000, 512) infer time 4.672949999999999 [cfp_fp][44000]XNorm: 20.529375 [cfp_fp][44000]Accuracy-Flip: 0.94900+-0.00973 testing verification.. (12000, 512) infer time 3.4992150000000004 [agedb_30][44000]XNorm: 22.356605 [agedb_30][44000]Accuracy-Flip: 0.95567+-0.00867 [44000]Accuracy-Highest: 0.95633 INFO:root:Iter[4] Batch [3540] Speed: 289.80 samples/sec INFO:root:Iter[4] Batch [3560] Speed: 769.86 samples/sec INFO:root:Iter[4] Batch [3580] Speed: 782.56 samples/sec INFO:root:Iter[4] Batch [3600] Speed: 812.31 samples/sec INFO:root:Iter[4] Batch [3620] Speed: 819.95 samples/sec INFO:root:Iter[4] Batch [3640] Speed: 801.69 samples/sec INFO:root:Iter[4] Batch [3660] Speed: 755.35 samples/sec INFO:root:Iter[4] Batch [3680] Speed: 780.40 samples/sec INFO:root:Iter[4] Batch [3700] Speed: 861.26 samples/sec INFO:root:Iter[4] Batch [3720] Speed: 791.32 samples/sec INFO:root:Iter[4] Batch [3740] Speed: 842.45 samples/sec INFO:root:Iter[4] Batch [3760] Speed: 819.81 samples/sec INFO:root:Iter[4] Batch [3780] Speed: 848.16 samples/sec INFO:root:Iter[4] Batch [3800] Speed: 857.90 samples/sec INFO:root:Iter[4] Batch [3820] Speed: 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(12000, 512) infer time 4.728242999999999 [lfw][46000]XNorm: 22.230601 [lfw][46000]Accuracy-Flip: 0.99600+-0.00396 testing verification.. (14000, 512) infer time 5.419311 [cfp_fp][46000]XNorm: 19.848105 [cfp_fp][46000]Accuracy-Flip: 0.94957+-0.01200 testing verification.. (12000, 512) infer time 4.575854000000001 [agedb_30][46000]XNorm: 21.293841 [agedb_30][46000]Accuracy-Flip: 0.95467+-0.01147 [46000]Accuracy-Highest: 0.95633 INFO:root:Iter[4] Batch [5540] Speed: 202.67 samples/sec INFO:root:Iter[4] Batch [5560] Speed: 631.66 samples/sec INFO:root:Iter[4] Batch [5580] Speed: 634.20 samples/sec INFO:root:Iter[4] Batch [5600] Speed: 633.03 samples/sec INFO:root:Iter[4] Batch [5620] Speed: 617.04 samples/sec INFO:root:Iter[4] Batch [5640] Speed: 632.00 samples/sec INFO:root:Iter[4] Batch [5660] Speed: 625.92 samples/sec INFO:root:Iter[4] Batch [5680] Speed: 633.91 samples/sec INFO:root:Iter[4] Batch [5700] Speed: 612.36 samples/sec INFO:root:Iter[4] Batch [5720] Speed: 621.06 samples/sec INFO:root:Iter[4] Batch [5740] Speed: 605.71 samples/sec INFO:root:Iter[4] Batch [5760] Speed: 620.71 samples/sec INFO:root:Iter[4] Batch [5780] Speed: 624.09 samples/sec INFO:root:Iter[4] Batch [5800] Speed: 613.69 samples/sec INFO:root:Iter[4] Batch [5820] Speed: 618.78 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(12000, 512) infer time 4.709712 [lfw][48000]XNorm: 22.687105 [lfw][48000]Accuracy-Flip: 0.99500+-0.00325 testing verification.. (14000, 512) infer time 5.618719999999999 [cfp_fp][48000]XNorm: 19.560554 [cfp_fp][48000]Accuracy-Flip: 0.94729+-0.01017 testing verification.. (12000, 512) infer time 4.760356000000001 [agedb_30][48000]XNorm: 21.950171 [agedb_30][48000]Accuracy-Flip: 0.95650+-0.00841 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [48000]Accuracy-Highest: 0.95650 INFO:root:Iter[4] Batch [7540] Speed: 227.81 samples/sec INFO:root:Iter[4] Batch [7560] Speed: 595.00 samples/sec INFO:root:Iter[4] Batch [7580] Speed: 599.40 samples/sec INFO:root:Iter[4] Batch [7600] Speed: 583.48 samples/sec INFO:root:Iter[4] Batch [7620] Speed: 595.17 samples/sec INFO:root:Iter[4] Batch [7640] Speed: 581.63 samples/sec INFO:root:Iter[4] Batch [7660] Speed: 570.78 samples/sec INFO:root:Iter[4] Batch [7680] Speed: 571.22 samples/sec INFO:root:Iter[4] Batch [7700] Speed: 586.20 samples/sec INFO:root:Iter[4] Batch [7720] Speed: 573.97 samples/sec INFO:root:Iter[4] Batch [7740] Speed: 585.83 samples/sec INFO:root:Iter[4] Batch [7760] Speed: 579.27 samples/sec INFO:root:Iter[4] Batch [7780] Speed: 584.59 samples/sec 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{fc7_acc} 50000 0.0625 lr-batch-epoch: 0.1 9535 4 testing verification.. (12000, 512) infer time 4.852609000000001 [lfw][50000]XNorm: 22.629978 [lfw][50000]Accuracy-Flip: 0.99433+-0.00396 testing verification.. (14000, 512) infer time 5.5267420000000005 [cfp_fp][50000]XNorm: 20.376531 [cfp_fp][50000]Accuracy-Flip: 0.95286+-0.01105 testing verification.. (12000, 512) infer time 4.656258 [agedb_30][50000]XNorm: 21.906240 [agedb_30][50000]Accuracy-Flip: 0.95417+-0.01121 [50000]Accuracy-Highest: 0.95650 INFO:root:Iter[4] Batch [9540] Speed: 234.66 samples/sec INFO:root:Iter[4] Batch [9560] Speed: 569.52 samples/sec INFO:root:Iter[4] Batch [9580] Speed: 566.94 samples/sec INFO:root:Iter[4] Batch [9600] Speed: 575.16 samples/sec INFO:root:Iter[4] Batch [9620] Speed: 552.53 samples/sec INFO:root:Iter[4] Batch [9640] Speed: 565.37 samples/sec INFO:root:Iter[4] Batch [9660] Speed: 572.58 samples/sec INFO:root:Iter[4] Batch [9680] Speed: 566.19 samples/sec INFO:root:Iter[4] Batch [9700] Speed: 568.30 samples/sec INFO:root:Iter[4] Batch [9720] Speed: 574.53 samples/sec INFO:root:Iter[4] Batch [9740] Speed: 569.49 samples/sec INFO:root:Iter[4] Batch [9760] Speed: 590.69 samples/sec INFO:root:Iter[4] Batch [9780] Speed: 557.92 samples/sec INFO:root:Iter[4] Batch [9800] Speed: 589.29 samples/sec INFO:root:Iter[4] Batch [9820] Speed: 570.19 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(12000, 512) infer time 4.001041000000001 [lfw][52000]XNorm: 22.575653 [lfw][52000]Accuracy-Flip: 0.99333+-0.00316 testing verification.. (14000, 512) infer time 4.46646 [cfp_fp][52000]XNorm: 19.582342 [cfp_fp][52000]Accuracy-Flip: 0.94157+-0.01425 testing verification.. (12000, 512) infer time 3.5978720000000006 [agedb_30][52000]XNorm: 21.807126 [agedb_30][52000]Accuracy-Flip: 0.95283+-0.00943 [52000]Accuracy-Highest: 0.95650 INFO:root:Iter[5] Batch [1420] Speed: 288.33 samples/sec INFO:root:Iter[5] Batch [1440] Speed: 715.62 samples/sec INFO:root:Iter[5] Batch [1460] Speed: 741.85 samples/sec INFO:root:Iter[5] Batch [1480] Speed: 753.95 samples/sec INFO:root:Iter[5] Batch [1500] Speed: 752.15 samples/sec INFO:root:Iter[5] Batch [1520] Speed: 803.22 samples/sec INFO:root:Iter[5] Batch [1540] Speed: 749.07 samples/sec INFO:root:Iter[5] Batch [1560] Speed: 799.99 samples/sec INFO:root:Iter[5] Batch [1580] Speed: 831.30 samples/sec INFO:root:Iter[5] Batch [1600] Speed: 874.79 samples/sec INFO:root:Iter[5] Batch [1620] Speed: 816.20 samples/sec INFO:root:Iter[5] Batch [1640] Speed: 784.35 samples/sec INFO:root:Iter[5] Batch [1660] Speed: 811.52 samples/sec INFO:root:Iter[5] Batch [1680] Speed: 838.38 samples/sec INFO:root:Iter[5] Batch [1700] Speed: 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(12000, 512) infer time 3.795184000000001 [lfw][54000]XNorm: 23.058491 [lfw][54000]Accuracy-Flip: 0.99350+-0.00411 testing verification.. (14000, 512) infer time 4.127266 [cfp_fp][54000]XNorm: 20.112438 [cfp_fp][54000]Accuracy-Flip: 0.94800+-0.01197 testing verification.. (12000, 512) infer time 3.4141529999999993 [agedb_30][54000]XNorm: 22.430016 [agedb_30][54000]Accuracy-Flip: 0.95050+-0.01006 [54000]Accuracy-Highest: 0.95650 INFO:root:Iter[5] Batch [3420] Speed: 273.62 samples/sec INFO:root:Iter[5] Batch [3440] Speed: 805.37 samples/sec INFO:root:Iter[5] Batch [3460] Speed: 754.21 samples/sec INFO:root:Iter[5] Batch [3480] Speed: 733.14 samples/sec INFO:root:Iter[5] Batch [3500] Speed: 735.47 samples/sec INFO:root:Iter[5] Batch [3520] Speed: 775.04 samples/sec INFO:root:Iter[5] Batch [3540] Speed: 841.27 samples/sec INFO:root:Iter[5] Batch [3560] Speed: 801.23 samples/sec INFO:root:Iter[5] Batch [3580] Speed: 841.53 samples/sec INFO:root:Iter[5] Batch [3600] Speed: 743.14 samples/sec INFO:root:Iter[5] Batch [3620] Speed: 742.13 samples/sec INFO:root:Iter[5] Batch [3640] Speed: 697.37 samples/sec INFO:root:Iter[5] Batch [3660] Speed: 741.69 samples/sec INFO:root:Iter[5] Batch [3680] Speed: 744.01 samples/sec INFO:root:Iter[5] Batch [3700] Speed: 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(12000, 512) infer time 4.645127 [lfw][56000]XNorm: 23.188329 [lfw][56000]Accuracy-Flip: 0.99550+-0.00342 testing verification.. (14000, 512) infer time 5.24816 [cfp_fp][56000]XNorm: 20.403209 [cfp_fp][56000]Accuracy-Flip: 0.94443+-0.01226 testing verification.. (12000, 512) infer time 4.516985 [agedb_30][56000]XNorm: 22.817443 [agedb_30][56000]Accuracy-Flip: 0.95650+-0.00740 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [56000]Accuracy-Highest: 0.95650 INFO:root:Iter[5] Batch [5420] Speed: 235.91 samples/sec INFO:root:Iter[5] Batch [5440] Speed: 700.29 samples/sec INFO:root:Iter[5] Batch [5460] Speed: 661.12 samples/sec INFO:root:Iter[5] Batch [5480] Speed: 628.28 samples/sec INFO:root:Iter[5] Batch [5500] Speed: 621.31 samples/sec INFO:root:Iter[5] Batch [5520] Speed: 631.07 samples/sec INFO:root:Iter[5] Batch [5540] Speed: 628.21 samples/sec INFO:root:Iter[5] Batch [5560] Speed: 651.23 samples/sec INFO:root:Iter[5] Batch [5580] Speed: 615.59 samples/sec INFO:root:Iter[5] Batch [5600] Speed: 622.79 samples/sec INFO:root:Iter[5] Batch [5620] Speed: 660.09 samples/sec INFO:root:Iter[5] Batch [5640] Speed: 648.79 samples/sec INFO:root:Iter[5] Batch [5660] Speed: 618.08 samples/sec INFO:root:Iter[5] Batch 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0.0859375 lr-batch-epoch: 0.1 7419 5 testing verification.. (12000, 512) infer time 4.587228 [lfw][58000]XNorm: 22.670233 [lfw][58000]Accuracy-Flip: 0.99467+-0.00356 testing verification.. (14000, 512) infer time 5.4709010000000005 [cfp_fp][58000]XNorm: 20.033374 [cfp_fp][58000]Accuracy-Flip: 0.94800+-0.00778 testing verification.. (12000, 512) infer time 4.518572999999999 [agedb_30][58000]XNorm: 21.928634 [agedb_30][58000]Accuracy-Flip: 0.95250+-0.00851 [58000]Accuracy-Highest: 0.95650 INFO:root:Iter[5] Batch [7420] Speed: 237.16 samples/sec INFO:root:Iter[5] Batch [7440] Speed: 613.26 samples/sec INFO:root:Iter[5] Batch [7460] Speed: 611.88 samples/sec INFO:root:Iter[5] Batch [7480] Speed: 598.99 samples/sec INFO:root:Iter[5] Batch [7500] Speed: 582.82 samples/sec INFO:root:Iter[5] Batch [7520] Speed: 580.25 samples/sec INFO:root:Iter[5] Batch [7540] Speed: 593.82 samples/sec INFO:root:Iter[5] Batch [7560] Speed: 590.68 samples/sec INFO:root:Iter[5] Batch [7580] Speed: 591.28 samples/sec INFO:root:Iter[5] Batch [7600] Speed: 605.57 samples/sec INFO:root:Iter[5] Batch [7620] Speed: 590.24 samples/sec INFO:root:Iter[5] Batch [7640] Speed: 593.97 samples/sec INFO:root:Iter[5] Batch [7660] Speed: 608.94 samples/sec INFO:root:Iter[5] Batch [7680] Speed: 610.18 samples/sec INFO:root:Iter[5] Batch [7700] Speed: 593.36 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(12000, 512) infer time 4.670974 [lfw][60000]XNorm: 23.003152 [lfw][60000]Accuracy-Flip: 0.99517+-0.00398 testing verification.. (14000, 512) infer time 5.491356 [cfp_fp][60000]XNorm: 19.923724 [cfp_fp][60000]Accuracy-Flip: 0.94957+-0.01532 testing verification.. (12000, 512) infer time 4.441644 [agedb_30][60000]XNorm: 22.432797 [agedb_30][60000]Accuracy-Flip: 0.95250+-0.00860 [60000]Accuracy-Highest: 0.95650 INFO:root:Iter[5] Batch [9420] Speed: 237.70 samples/sec INFO:root:Iter[5] Batch [9440] Speed: 592.41 samples/sec INFO:root:Iter[5] Batch [9460] Speed: 582.41 samples/sec INFO:root:Iter[5] Batch [9480] Speed: 584.13 samples/sec INFO:root:Iter[5] Batch [9500] Speed: 574.60 samples/sec INFO:root:Iter[5] Batch [9520] Speed: 579.08 samples/sec INFO:root:Iter[5] Batch [9540] Speed: 569.49 samples/sec INFO:root:Iter[5] Batch [9560] Speed: 575.10 samples/sec INFO:root:Iter[5] Batch [9580] Speed: 557.26 samples/sec INFO:root:Iter[5] Batch [9600] Speed: 562.25 samples/sec INFO:root:Iter[5] Batch [9620] Speed: 573.80 samples/sec INFO:root:Iter[5] Batch [9640] Speed: 593.76 samples/sec INFO:root:Iter[5] Batch [9660] Speed: 590.40 samples/sec INFO:root:Iter[5] Batch [9680] Speed: 544.21 samples/sec INFO:root:Iter[5] Batch [9700] Speed: 576.00 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verification.. (12000, 512) infer time 4.7667639999999984 [lfw][62000]XNorm: 23.052353 [lfw][62000]Accuracy-Flip: 0.99450+-0.00388 testing verification.. (14000, 512) infer time 5.5434660000000004 [cfp_fp][62000]XNorm: 19.993222 [cfp_fp][62000]Accuracy-Flip: 0.95129+-0.00905 testing verification.. (12000, 512) infer time 4.806905 [agedb_30][62000]XNorm: 22.355372 [agedb_30][62000]Accuracy-Flip: 0.95333+-0.00925 [62000]Accuracy-Highest: 0.95650 INFO:root:Iter[6] Batch [1320] Speed: 229.83 samples/sec INFO:root:Iter[6] Batch [1340] Speed: 562.12 samples/sec INFO:root:Iter[6] Batch [1360] Speed: 555.58 samples/sec INFO:root:Iter[6] Batch [1380] Speed: 572.41 samples/sec INFO:root:Iter[6] Batch [1400] Speed: 569.57 samples/sec INFO:root:Iter[6] Batch [1420] Speed: 555.47 samples/sec INFO:root:Iter[6] Batch [1440] Speed: 548.05 samples/sec INFO:root:Iter[6] Batch [1460] Speed: 560.66 samples/sec INFO:root:Iter[6] Batch [1480] Speed: 552.17 samples/sec INFO:root:Iter[6] Batch [1500] Speed: 575.44 samples/sec INFO:root:Iter[6] Batch [1520] Speed: 574.34 samples/sec INFO:root:Iter[6] Batch [1540] Speed: 577.22 samples/sec INFO:root:Iter[6] Batch [1560] Speed: 555.12 samples/sec INFO:root:Iter[6] Batch [1580] Speed: 565.25 samples/sec INFO:root:Iter[6] Batch [1600] Speed: 567.36 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(12000, 512) infer time 4.703465000000001 [lfw][64000]XNorm: 22.648013 [lfw][64000]Accuracy-Flip: 0.99333+-0.00350 testing verification.. (14000, 512) infer time 5.4488259999999995 [cfp_fp][64000]XNorm: 19.591941 [cfp_fp][64000]Accuracy-Flip: 0.94886+-0.00962 testing verification.. (12000, 512) infer time 4.627840999999999 [agedb_30][64000]XNorm: 21.858337 [agedb_30][64000]Accuracy-Flip: 0.95433+-0.00889 [64000]Accuracy-Highest: 0.95650 INFO:root:Iter[6] Batch [3320] Speed: 234.58 samples/sec INFO:root:Iter[6] Batch [3340] Speed: 567.92 samples/sec INFO:root:Iter[6] Batch [3360] Speed: 563.07 samples/sec INFO:root:Iter[6] Batch [3380] Speed: 567.99 samples/sec INFO:root:Iter[6] Batch [3400] Speed: 558.28 samples/sec INFO:root:Iter[6] Batch [3420] Speed: 563.19 samples/sec INFO:root:Iter[6] Batch [3440] Speed: 554.34 samples/sec INFO:root:Iter[6] Batch [3460] Speed: 581.66 samples/sec INFO:root:Iter[6] Batch [3480] Speed: 573.93 samples/sec INFO:root:Iter[6] Batch [3500] Speed: 570.62 samples/sec INFO:root:Iter[6] Batch [3520] Speed: 562.12 samples/sec INFO:root:Iter[6] Batch [3540] Speed: 560.27 samples/sec INFO:root:Iter[6] Batch [3560] Speed: 567.76 samples/sec INFO:root:Iter[6] Batch [3580] Speed: 583.08 samples/sec INFO:root:Iter[6] Batch [3600] Speed: 566.10 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(12000, 512) infer time 4.900481000000002 [lfw][66000]XNorm: 22.626668 [lfw][66000]Accuracy-Flip: 0.99617+-0.00358 testing verification.. (14000, 512) infer time 5.5791340000000025 [cfp_fp][66000]XNorm: 19.670671 [cfp_fp][66000]Accuracy-Flip: 0.94771+-0.01127 testing verification.. (12000, 512) infer time 4.826205000000001 [agedb_30][66000]XNorm: 21.799397 [agedb_30][66000]Accuracy-Flip: 0.95883+-0.00873 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [66000]Accuracy-Highest: 0.95883 INFO:root:Iter[6] Batch [5320] Speed: 190.55 samples/sec INFO:root:Iter[6] Batch [5340] Speed: 568.29 samples/sec INFO:root:Iter[6] Batch [5360] Speed: 558.49 samples/sec INFO:root:Iter[6] Batch [5380] Speed: 555.72 samples/sec INFO:root:Iter[6] Batch [5400] Speed: 562.08 samples/sec INFO:root:Iter[6] Batch [5420] Speed: 561.61 samples/sec INFO:root:Iter[6] Batch [5440] Speed: 555.94 samples/sec INFO:root:Iter[6] Batch [5460] Speed: 551.72 samples/sec INFO:root:Iter[6] Batch [5480] Speed: 545.08 samples/sec INFO:root:Iter[6] Batch [5500] Speed: 560.38 samples/sec INFO:root:Iter[6] Batch [5520] Speed: 563.89 samples/sec INFO:root:Iter[6] Batch [5540] Speed: 579.11 samples/sec INFO:root:Iter[6] Batch [5560] Speed: 558.46 samples/sec 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{fc7_acc} 68000 0.078125 lr-batch-epoch: 0.1 7303 6 testing verification.. (12000, 512) infer time 4.90637 [lfw][68000]XNorm: 22.579679 [lfw][68000]Accuracy-Flip: 0.99417+-0.00239 testing verification.. (14000, 512) infer time 5.567666999999999 [cfp_fp][68000]XNorm: 19.797303 [cfp_fp][68000]Accuracy-Flip: 0.94629+-0.00829 testing verification.. (12000, 512) infer time 4.715268999999999 [agedb_30][68000]XNorm: 21.722864 [agedb_30][68000]Accuracy-Flip: 0.95667+-0.00960 [68000]Accuracy-Highest: 0.95883 INFO:root:Iter[6] Batch [7320] Speed: 227.04 samples/sec INFO:root:Iter[6] Batch [7340] Speed: 569.15 samples/sec INFO:root:Iter[6] Batch [7360] Speed: 558.33 samples/sec INFO:root:Iter[6] Batch [7380] Speed: 569.46 samples/sec INFO:root:Iter[6] Batch [7400] Speed: 551.37 samples/sec INFO:root:Iter[6] Batch [7420] Speed: 568.33 samples/sec INFO:root:Iter[6] Batch [7440] Speed: 568.59 samples/sec INFO:root:Iter[6] Batch [7460] Speed: 548.85 samples/sec INFO:root:Iter[6] Batch [7480] Speed: 571.32 samples/sec INFO:root:Iter[6] Batch [7500] Speed: 556.30 samples/sec INFO:root:Iter[6] Batch [7520] Speed: 557.33 samples/sec INFO:root:Iter[6] Batch [7540] Speed: 562.68 samples/sec INFO:root:Iter[6] Batch [7560] Speed: 553.00 samples/sec INFO:root:Iter[6] Batch [7580] Speed: 569.07 samples/sec INFO:root:Iter[6] Batch [7600] Speed: 578.73 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(12000, 512) infer time 4.405543 [lfw][70000]XNorm: 22.802909 [lfw][70000]Accuracy-Flip: 0.99483+-0.00411 testing verification.. (14000, 512) infer time 5.295956000000001 [cfp_fp][70000]XNorm: 19.729091 [cfp_fp][70000]Accuracy-Flip: 0.94857+-0.01387 testing verification.. (12000, 512) infer time 4.127823000000001 [agedb_30][70000]XNorm: 21.874365 [agedb_30][70000]Accuracy-Flip: 0.95783+-0.00792 [70000]Accuracy-Highest: 0.95883 INFO:root:Iter[6] Batch [9320] Speed: 263.53 samples/sec INFO:root:Iter[6] Batch [9340] Speed: 746.46 samples/sec INFO:root:Iter[6] Batch [9360] Speed: 711.10 samples/sec INFO:root:Iter[6] Batch [9380] Speed: 641.68 samples/sec INFO:root:Iter[6] Batch [9400] Speed: 685.72 samples/sec INFO:root:Iter[6] Batch [9420] Speed: 641.26 samples/sec INFO:root:Iter[6] Batch [9440] Speed: 660.65 samples/sec INFO:root:Iter[6] Batch [9460] Speed: 633.66 samples/sec INFO:root:Iter[6] Batch [9480] Speed: 712.24 samples/sec INFO:root:Iter[6] Batch [9500] Speed: 772.05 samples/sec INFO:root:Iter[6] Batch [9520] Speed: 793.29 samples/sec INFO:root:Iter[6] Batch [9540] Speed: 857.96 samples/sec INFO:root:Iter[6] Batch [9560] Speed: 776.11 samples/sec INFO:root:Iter[6] Batch [9580] Speed: 751.56 samples/sec INFO:root:Iter[6] Batch [9600] Speed: 763.21 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(12000, 512) infer time 4.267507999999999 [lfw][72000]XNorm: 22.385195 [lfw][72000]Accuracy-Flip: 0.99383+-0.00388 testing verification.. (14000, 512) infer time 4.3935080000000015 [cfp_fp][72000]XNorm: 19.587729 [cfp_fp][72000]Accuracy-Flip: 0.94986+-0.01230 testing verification.. (12000, 512) infer time 3.5034419999999997 [agedb_30][72000]XNorm: 21.556726 [agedb_30][72000]Accuracy-Flip: 0.95850+-0.00643 [72000]Accuracy-Highest: 0.95883 INFO:root:Iter[7] Batch [1200] Speed: 272.12 samples/sec INFO:root:Iter[7] Batch [1220] Speed: 820.11 samples/sec INFO:root:Iter[7] Batch [1240] Speed: 753.16 samples/sec INFO:root:Iter[7] Batch [1260] Speed: 675.90 samples/sec INFO:root:Iter[7] Batch [1280] Speed: 657.56 samples/sec INFO:root:Iter[7] Batch [1300] Speed: 642.13 samples/sec INFO:root:Iter[7] Batch [1320] Speed: 649.91 samples/sec INFO:root:Iter[7] Batch [1340] Speed: 622.54 samples/sec INFO:root:Iter[7] Batch [1360] Speed: 623.95 samples/sec INFO:root:Iter[7] Batch [1380] Speed: 613.92 samples/sec INFO:root:Iter[7] Batch [1400] Speed: 643.57 samples/sec INFO:root:Iter[7] Batch [1420] Speed: 668.48 samples/sec INFO:root:Iter[7] Batch [1440] Speed: 641.60 samples/sec INFO:root:Iter[7] Batch [1460] Speed: 660.03 samples/sec INFO:root:Iter[7] Batch [1480] Speed: 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(12000, 512) infer time 4.164616 [lfw][74000]XNorm: 22.751835 [lfw][74000]Accuracy-Flip: 0.99550+-0.00350 testing verification.. (14000, 512) infer time 4.263693 [cfp_fp][74000]XNorm: 20.281441 [cfp_fp][74000]Accuracy-Flip: 0.94143+-0.01400 testing verification.. (12000, 512) infer time 3.4792840000000007 [agedb_30][74000]XNorm: 22.427244 [agedb_30][74000]Accuracy-Flip: 0.95633+-0.00966 [74000]Accuracy-Highest: 0.95883 INFO:root:Iter[7] Batch [3200] Speed: 281.47 samples/sec INFO:root:Iter[7] Batch [3220] Speed: 847.78 samples/sec INFO:root:Iter[7] Batch [3240] Speed: 850.60 samples/sec INFO:root:Iter[7] Batch [3260] Speed: 798.61 samples/sec INFO:root:Iter[7] Batch [3280] Speed: 828.16 samples/sec INFO:root:Iter[7] Batch [3300] Speed: 747.45 samples/sec INFO:root:Iter[7] Batch [3320] Speed: 770.77 samples/sec INFO:root:Iter[7] Batch [3340] Speed: 802.78 samples/sec INFO:root:Iter[7] Batch [3360] Speed: 738.80 samples/sec INFO:root:Iter[7] Batch [3380] Speed: 693.33 samples/sec INFO:root:Iter[7] Batch [3400] Speed: 678.09 samples/sec INFO:root:Iter[7] Batch [3420] Speed: 652.38 samples/sec INFO:root:Iter[7] Batch [3440] Speed: 699.40 samples/sec INFO:root:Iter[7] Batch [3460] Speed: 688.19 samples/sec INFO:root:Iter[7] Batch [3480] Speed: 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INFO:root:Iter[7] Batch [4880] Speed: 617.30 samples/sec INFO:root:Iter[7] Batch [4900] Speed: 638.73 samples/sec INFO:root:Iter[7] Batch [4920] Speed: 634.19 samples/sec INFO:root:Iter[7] Batch [4940] Speed: 683.60 samples/sec INFO:root:Iter[7] Batch [4960] Speed: 636.42 samples/sec INFO:root:Iter[7] Batch [4980] Speed: 610.63 samples/sec INFO:root:Iter[7] Batch [5000] Speed: 603.58 samples/sec INFO:root:Iter[7] Batch [5020] Speed: 619.87 samples/sec INFO:root:Iter[7] Batch [5040] Speed: 607.93 samples/sec INFO:root:Iter[7] Batch [5060] Speed: 616.99 samples/sec INFO:root:Iter[7] Batch [5080] Speed: 596.81 samples/sec INFO:root:Iter[7] Batch [5100] Speed: 614.22 samples/sec INFO:root:Iter[7] Batch [5120] Speed: 617.22 samples/sec INFO:root:Iter[7] Batch [5140] Speed: 627.57 samples/sec INFO:root:Iter[7] Batch [5160] Speed: 609.16 samples/sec INFO:root:Iter[7] Batch [5180] Speed: 593.15 samples/sec {fc7_acc} 76000 0.095703125 lr-batch-epoch: 0.1 5187 7 testing verification.. (12000, 512) infer time 4.371621000000001 [lfw][76000]XNorm: 22.632687 [lfw][76000]Accuracy-Flip: 0.99317+-0.00450 testing verification.. (14000, 512) infer time 4.771504999999999 [cfp_fp][76000]XNorm: 20.210529 [cfp_fp][76000]Accuracy-Flip: 0.94714+-0.01340 testing verification.. (12000, 512) infer time 3.7277709999999997 [agedb_30][76000]XNorm: 22.074968 [agedb_30][76000]Accuracy-Flip: 0.95833+-0.01065 [76000]Accuracy-Highest: 0.95883 INFO:root:Iter[7] Batch [5200] Speed: 261.69 samples/sec INFO:root:Iter[7] Batch [5220] Speed: 859.68 samples/sec INFO:root:Iter[7] Batch [5240] Speed: 807.00 samples/sec INFO:root:Iter[7] Batch [5260] Speed: 782.47 samples/sec INFO:root:Iter[7] Batch [5280] Speed: 751.76 samples/sec INFO:root:Iter[7] Batch [5300] Speed: 671.79 samples/sec INFO:root:Iter[7] Batch [5320] Speed: 708.34 samples/sec INFO:root:Iter[7] Batch [5340] Speed: 729.27 samples/sec INFO:root:Iter[7] Batch [5360] Speed: 690.39 samples/sec INFO:root:Iter[7] Batch [5380] Speed: 721.38 samples/sec INFO:root:Iter[7] Batch [5400] Speed: 671.96 samples/sec INFO:root:Iter[7] Batch [5420] Speed: 705.46 samples/sec INFO:root:Iter[7] Batch [5440] Speed: 662.90 samples/sec INFO:root:Iter[7] Batch [5460] Speed: 687.35 samples/sec INFO:root:Iter[7] Batch [5480] Speed: 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(12000, 512) infer time 4.424833 [lfw][78000]XNorm: 22.629479 [lfw][78000]Accuracy-Flip: 0.99517+-0.00302 testing verification.. (14000, 512) infer time 4.404696999999998 [cfp_fp][78000]XNorm: 19.828022 [cfp_fp][78000]Accuracy-Flip: 0.94457+-0.01697 testing verification.. (12000, 512) infer time 3.69298 [agedb_30][78000]XNorm: 21.993378 [agedb_30][78000]Accuracy-Flip: 0.95600+-0.00810 [78000]Accuracy-Highest: 0.95883 INFO:root:Iter[7] Batch [7200] Speed: 265.50 samples/sec INFO:root:Iter[7] Batch [7220] Speed: 789.56 samples/sec INFO:root:Iter[7] Batch [7240] Speed: 808.34 samples/sec INFO:root:Iter[7] Batch [7260] Speed: 801.36 samples/sec INFO:root:Iter[7] Batch [7280] Speed: 828.74 samples/sec INFO:root:Iter[7] Batch [7300] Speed: 737.97 samples/sec INFO:root:Iter[7] Batch [7320] Speed: 747.35 samples/sec INFO:root:Iter[7] Batch [7340] Speed: 719.92 samples/sec INFO:root:Iter[7] Batch [7360] Speed: 761.72 samples/sec INFO:root:Iter[7] Batch [7380] Speed: 846.00 samples/sec INFO:root:Iter[7] Batch [7400] Speed: 746.06 samples/sec INFO:root:Iter[7] Batch [7420] Speed: 712.79 samples/sec INFO:root:Iter[7] Batch [7440] Speed: 735.28 samples/sec INFO:root:Iter[7] Batch [7460] Speed: 688.36 samples/sec INFO:root:Iter[7] Batch [7480] Speed: 657.88 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(12000, 512) infer time 3.9701770000000005 [lfw][80000]XNorm: 22.040490 [lfw][80000]Accuracy-Flip: 0.99467+-0.00371 testing verification.. (14000, 512) infer time 4.25541 [cfp_fp][80000]XNorm: 19.008887 [cfp_fp][80000]Accuracy-Flip: 0.94857+-0.01192 testing verification.. (12000, 512) infer time 3.5005100000000002 [agedb_30][80000]XNorm: 21.189791 [agedb_30][80000]Accuracy-Flip: 0.95417+-0.01091 [80000]Accuracy-Highest: 0.95883 INFO:root:Iter[7] Batch [9200] Speed: 267.27 samples/sec INFO:root:Iter[7] Batch [9220] Speed: 803.73 samples/sec INFO:root:Iter[7] Batch [9240] Speed: 794.38 samples/sec INFO:root:Iter[7] Batch [9260] Speed: 750.81 samples/sec INFO:root:Iter[7] Batch [9280] Speed: 765.42 samples/sec INFO:root:Iter[7] Batch [9300] Speed: 665.43 samples/sec INFO:root:Iter[7] Batch [9320] Speed: 661.91 samples/sec INFO:root:Iter[7] Batch [9340] Speed: 731.25 samples/sec INFO:root:Iter[7] Batch [9360] Speed: 673.47 samples/sec INFO:root:Iter[7] Batch [9380] Speed: 704.17 samples/sec INFO:root:Iter[7] Batch [9400] Speed: 685.36 samples/sec INFO:root:Iter[7] Batch [9420] Speed: 640.18 samples/sec INFO:root:Iter[7] Batch [9440] Speed: 665.01 samples/sec INFO:root:Iter[7] Batch [9460] Speed: 646.75 samples/sec INFO:root:Iter[7] Batch [9480] Speed: 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samples/sec INFO:root:Iter[8] Batch [780] Speed: 612.91 samples/sec INFO:root:Iter[8] Batch [800] Speed: 637.52 samples/sec INFO:root:Iter[8] Batch [820] Speed: 619.56 samples/sec INFO:root:Iter[8] Batch [840] Speed: 691.57 samples/sec INFO:root:Iter[8] Batch [860] Speed: 618.69 samples/sec INFO:root:Iter[8] Batch [880] Speed: 626.22 samples/sec INFO:root:Iter[8] Batch [900] Speed: 623.06 samples/sec INFO:root:Iter[8] Batch [920] Speed: 618.89 samples/sec INFO:root:Iter[8] Batch [940] Speed: 604.30 samples/sec INFO:root:Iter[8] Batch [960] Speed: 603.19 samples/sec INFO:root:Iter[8] Batch [980] Speed: 611.52 samples/sec INFO:root:Iter[8] Batch [1000] Speed: 621.02 samples/sec INFO:root:Iter[8] Batch [1020] Speed: 602.88 samples/sec INFO:root:Iter[8] Batch [1040] Speed: 610.03 samples/sec INFO:root:Iter[8] Batch [1060] Speed: 611.49 samples/sec {fc7_acc} 82000 0.083984375 lr-batch-epoch: 0.1 1071 8 testing verification.. (12000, 512) infer time 4.3395969999999995 [lfw][82000]XNorm: 22.954974 [lfw][82000]Accuracy-Flip: 0.99600+-0.00200 testing verification.. (14000, 512) infer time 4.19632 [cfp_fp][82000]XNorm: 20.056604 [cfp_fp][82000]Accuracy-Flip: 0.94814+-0.01249 testing verification.. (12000, 512) infer time 3.5939480000000006 [agedb_30][82000]XNorm: 22.490925 [agedb_30][82000]Accuracy-Flip: 0.95850+-0.00804 [82000]Accuracy-Highest: 0.95883 INFO:root:Iter[8] Batch [1080] Speed: 260.10 samples/sec INFO:root:Iter[8] Batch [1100] Speed: 721.59 samples/sec INFO:root:Iter[8] Batch [1120] Speed: 810.02 samples/sec INFO:root:Iter[8] Batch [1140] Speed: 782.24 samples/sec INFO:root:Iter[8] Batch [1160] Speed: 761.20 samples/sec INFO:root:Iter[8] Batch [1180] Speed: 781.46 samples/sec INFO:root:Iter[8] Batch [1200] Speed: 715.27 samples/sec INFO:root:Iter[8] Batch [1220] Speed: 716.09 samples/sec INFO:root:Iter[8] Batch [1240] Speed: 722.55 samples/sec INFO:root:Iter[8] Batch [1260] Speed: 738.15 samples/sec INFO:root:Iter[8] Batch [1280] Speed: 737.97 samples/sec INFO:root:Iter[8] Batch [1300] Speed: 703.12 samples/sec INFO:root:Iter[8] Batch [1320] Speed: 713.63 samples/sec INFO:root:Iter[8] Batch [1340] Speed: 705.35 samples/sec INFO:root:Iter[8] Batch [1360] Speed: 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(12000, 512) infer time 4.671614 [lfw][84000]XNorm: 22.702071 [lfw][84000]Accuracy-Flip: 0.99567+-0.00327 testing verification.. (14000, 512) infer time 5.309511999999997 [cfp_fp][84000]XNorm: 20.334103 [cfp_fp][84000]Accuracy-Flip: 0.94786+-0.01189 testing verification.. (12000, 512) infer time 4.166418 [agedb_30][84000]XNorm: 22.029201 [agedb_30][84000]Accuracy-Flip: 0.95417+-0.00949 [84000]Accuracy-Highest: 0.95883 INFO:root:Iter[8] Batch [3080] Speed: 254.97 samples/sec INFO:root:Iter[8] Batch [3100] Speed: 737.57 samples/sec INFO:root:Iter[8] Batch [3120] Speed: 702.37 samples/sec INFO:root:Iter[8] Batch [3140] Speed: 683.80 samples/sec INFO:root:Iter[8] Batch [3160] Speed: 687.94 samples/sec INFO:root:Iter[8] Batch [3180] Speed: 661.53 samples/sec INFO:root:Iter[8] Batch [3200] Speed: 629.29 samples/sec INFO:root:Iter[8] Batch [3220] Speed: 642.79 samples/sec INFO:root:Iter[8] Batch [3240] Speed: 636.41 samples/sec INFO:root:Iter[8] Batch [3260] Speed: 631.86 samples/sec INFO:root:Iter[8] Batch [3280] Speed: 651.56 samples/sec INFO:root:Iter[8] Batch [3300] Speed: 649.25 samples/sec INFO:root:Iter[8] Batch [3320] Speed: 632.71 samples/sec INFO:root:Iter[8] Batch [3340] Speed: 626.05 samples/sec INFO:root:Iter[8] Batch [3360] Speed: 641.17 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(12000, 512) infer time 4.63958 [lfw][86000]XNorm: 22.475337 [lfw][86000]Accuracy-Flip: 0.99567+-0.00271 testing verification.. (14000, 512) infer time 5.524654999999999 [cfp_fp][86000]XNorm: 19.521985 [cfp_fp][86000]Accuracy-Flip: 0.94943+-0.01163 testing verification.. (12000, 512) infer time 4.538101 [agedb_30][86000]XNorm: 22.040609 [agedb_30][86000]Accuracy-Flip: 0.95867+-0.01030 [86000]Accuracy-Highest: 0.95883 INFO:root:Iter[8] Batch [5080] Speed: 244.87 samples/sec INFO:root:Iter[8] Batch [5100] Speed: 700.02 samples/sec INFO:root:Iter[8] Batch [5120] Speed: 692.61 samples/sec INFO:root:Iter[8] Batch [5140] Speed: 676.74 samples/sec INFO:root:Iter[8] Batch [5160] Speed: 685.91 samples/sec INFO:root:Iter[8] Batch [5180] Speed: 666.88 samples/sec INFO:root:Iter[8] Batch [5200] Speed: 670.17 samples/sec INFO:root:Iter[8] Batch [5220] Speed: 654.97 samples/sec INFO:root:Iter[8] Batch [5240] Speed: 659.38 samples/sec INFO:root:Iter[8] Batch [5260] Speed: 665.71 samples/sec INFO:root:Iter[8] Batch [5280] Speed: 688.00 samples/sec INFO:root:Iter[8] Batch [5300] Speed: 649.35 samples/sec INFO:root:Iter[8] Batch [5320] Speed: 652.13 samples/sec INFO:root:Iter[8] Batch [5340] Speed: 685.79 samples/sec INFO:root:Iter[8] Batch [5360] Speed: 647.82 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(12000, 512) infer time 4.367806999999999 [lfw][88000]XNorm: 23.170552 [lfw][88000]Accuracy-Flip: 0.99500+-0.00325 testing verification.. (14000, 512) infer time 5.4657690000000025 [cfp_fp][88000]XNorm: 20.544067 [cfp_fp][88000]Accuracy-Flip: 0.95329+-0.00836 testing verification.. (12000, 512) infer time 4.403173999999999 [agedb_30][88000]XNorm: 22.175344 [agedb_30][88000]Accuracy-Flip: 0.95667+-0.00910 [88000]Accuracy-Highest: 0.95883 INFO:root:Iter[8] Batch [7080] Speed: 241.78 samples/sec INFO:root:Iter[8] Batch [7100] Speed: 706.02 samples/sec INFO:root:Iter[8] Batch [7120] Speed: 691.04 samples/sec INFO:root:Iter[8] Batch [7140] Speed: 666.48 samples/sec INFO:root:Iter[8] Batch [7160] Speed: 639.43 samples/sec INFO:root:Iter[8] Batch [7180] Speed: 662.04 samples/sec INFO:root:Iter[8] Batch [7200] Speed: 649.64 samples/sec INFO:root:Iter[8] Batch [7220] Speed: 675.67 samples/sec INFO:root:Iter[8] Batch [7240] Speed: 671.40 samples/sec INFO:root:Iter[8] Batch [7260] Speed: 635.14 samples/sec INFO:root:Iter[8] Batch [7280] Speed: 654.34 samples/sec INFO:root:Iter[8] Batch [7300] Speed: 634.28 samples/sec INFO:root:Iter[8] Batch [7320] Speed: 609.07 samples/sec INFO:root:Iter[8] Batch [7340] Speed: 637.59 samples/sec INFO:root:Iter[8] Batch [7360] Speed: 648.66 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(12000, 512) infer time 4.335816 [lfw][90000]XNorm: 22.704983 [lfw][90000]Accuracy-Flip: 0.99433+-0.00374 testing verification.. (14000, 512) infer time 5.101187999999999 [cfp_fp][90000]XNorm: 19.777959 [cfp_fp][90000]Accuracy-Flip: 0.94900+-0.00867 testing verification.. (12000, 512) infer time 4.40356 [agedb_30][90000]XNorm: 21.785644 [agedb_30][90000]Accuracy-Flip: 0.95433+-0.00895 [90000]Accuracy-Highest: 0.95883 INFO:root:Iter[8] Batch [9080] Speed: 245.96 samples/sec INFO:root:Iter[8] Batch [9100] Speed: 672.30 samples/sec INFO:root:Iter[8] Batch [9120] Speed: 665.29 samples/sec INFO:root:Iter[8] Batch [9140] Speed: 641.99 samples/sec INFO:root:Iter[8] Batch [9160] Speed: 653.08 samples/sec INFO:root:Iter[8] Batch [9180] Speed: 636.79 samples/sec INFO:root:Iter[8] Batch [9200] Speed: 648.64 samples/sec INFO:root:Iter[8] Batch [9220] Speed: 645.54 samples/sec INFO:root:Iter[8] Batch [9240] Speed: 675.37 samples/sec INFO:root:Iter[8] Batch [9260] Speed: 663.23 samples/sec INFO:root:Iter[8] Batch [9280] Speed: 644.93 samples/sec INFO:root:Iter[8] Batch [9300] Speed: 685.86 samples/sec INFO:root:Iter[8] Batch [9320] Speed: 643.03 samples/sec INFO:root:Iter[8] Batch [9340] Speed: 647.38 samples/sec INFO:root:Iter[8] Batch [9360] Speed: 692.42 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(12000, 512) infer time 4.42685 [lfw][92000]XNorm: 23.396403 [lfw][92000]Accuracy-Flip: 0.99467+-0.00414 testing verification.. (14000, 512) infer time 5.159983 [cfp_fp][92000]XNorm: 20.448512 [cfp_fp][92000]Accuracy-Flip: 0.95186+-0.00821 testing verification.. (12000, 512) infer time 4.405628 [agedb_30][92000]XNorm: 22.349678 [agedb_30][92000]Accuracy-Flip: 0.95583+-0.00754 [92000]Accuracy-Highest: 0.95883 INFO:root:Iter[9] Batch [960] Speed: 250.49 samples/sec INFO:root:Iter[9] Batch [980] Speed: 676.03 samples/sec INFO:root:Iter[9] Batch [1000] Speed: 651.09 samples/sec INFO:root:Iter[9] Batch [1020] Speed: 665.16 samples/sec INFO:root:Iter[9] Batch [1040] Speed: 677.69 samples/sec INFO:root:Iter[9] Batch [1060] Speed: 632.00 samples/sec INFO:root:Iter[9] Batch [1080] Speed: 646.19 samples/sec INFO:root:Iter[9] Batch [1100] Speed: 680.11 samples/sec INFO:root:Iter[9] Batch [1120] Speed: 673.55 samples/sec INFO:root:Iter[9] Batch [1140] Speed: 656.53 samples/sec INFO:root:Iter[9] Batch [1160] Speed: 643.08 samples/sec INFO:root:Iter[9] Batch [1180] Speed: 661.06 samples/sec INFO:root:Iter[9] Batch [1200] Speed: 645.38 samples/sec INFO:root:Iter[9] Batch [1220] Speed: 656.62 samples/sec INFO:root:Iter[9] Batch [1240] Speed: 657.47 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(12000, 512) infer time 4.613936000000001 [lfw][94000]XNorm: 23.264539 [lfw][94000]Accuracy-Flip: 0.99633+-0.00245 testing verification.. (14000, 512) infer time 5.363396000000001 [cfp_fp][94000]XNorm: 20.385478 [cfp_fp][94000]Accuracy-Flip: 0.94957+-0.01343 testing verification.. (12000, 512) infer time 4.4818370000000005 [agedb_30][94000]XNorm: 22.547079 [agedb_30][94000]Accuracy-Flip: 0.95050+-0.00981 [94000]Accuracy-Highest: 0.95883 INFO:root:Iter[9] Batch [2960] Speed: 243.27 samples/sec INFO:root:Iter[9] Batch [2980] Speed: 646.29 samples/sec INFO:root:Iter[9] Batch [3000] Speed: 690.85 samples/sec INFO:root:Iter[9] Batch [3020] Speed: 672.16 samples/sec INFO:root:Iter[9] Batch [3040] Speed: 680.44 samples/sec INFO:root:Iter[9] Batch [3060] Speed: 667.36 samples/sec INFO:root:Iter[9] Batch [3080] Speed: 672.80 samples/sec INFO:root:Iter[9] Batch [3100] Speed: 640.37 samples/sec INFO:root:Iter[9] Batch [3120] Speed: 656.53 samples/sec INFO:root:Iter[9] Batch [3140] Speed: 652.17 samples/sec INFO:root:Iter[9] Batch [3160] Speed: 668.30 samples/sec INFO:root:Iter[9] Batch [3180] Speed: 646.79 samples/sec INFO:root:Iter[9] Batch [3200] Speed: 650.52 samples/sec INFO:root:Iter[9] Batch [3220] Speed: 626.58 samples/sec INFO:root:Iter[9] Batch [3240] Speed: 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INFO:root:Iter[9] Batch [4640] Speed: 617.15 samples/sec INFO:root:Iter[9] Batch [4660] Speed: 613.00 samples/sec INFO:root:Iter[9] Batch [4680] Speed: 624.89 samples/sec INFO:root:Iter[9] Batch [4700] Speed: 613.45 samples/sec INFO:root:Iter[9] Batch [4720] Speed: 592.79 samples/sec INFO:root:Iter[9] Batch [4740] Speed: 611.88 samples/sec INFO:root:Iter[9] Batch [4760] Speed: 609.09 samples/sec INFO:root:Iter[9] Batch [4780] Speed: 609.91 samples/sec INFO:root:Iter[9] Batch [4800] Speed: 617.80 samples/sec INFO:root:Iter[9] Batch [4820] Speed: 619.05 samples/sec INFO:root:Iter[9] Batch [4840] Speed: 609.01 samples/sec INFO:root:Iter[9] Batch [4860] Speed: 596.09 samples/sec INFO:root:Iter[9] Batch [4880] Speed: 605.81 samples/sec INFO:root:Iter[9] Batch [4900] Speed: 616.34 samples/sec INFO:root:Iter[9] Batch [4920] Speed: 621.47 samples/sec INFO:root:Iter[9] Batch [4940] Speed: 599.84 samples/sec {fc7_acc} 96000 0.080078125 lr-batch-epoch: 0.1 4955 9 testing verification.. (12000, 512) infer time 4.699489999999999 [lfw][96000]XNorm: 22.486703 [lfw][96000]Accuracy-Flip: 0.99550+-0.00358 testing verification.. (14000, 512) infer time 5.520715 [cfp_fp][96000]XNorm: 19.776191 [cfp_fp][96000]Accuracy-Flip: 0.94829+-0.01374 testing verification.. (12000, 512) infer time 4.662697 [agedb_30][96000]XNorm: 21.752647 [agedb_30][96000]Accuracy-Flip: 0.95767+-0.00854 [96000]Accuracy-Highest: 0.95883 INFO:root:Iter[9] Batch [4960] Speed: 240.05 samples/sec INFO:root:Iter[9] Batch [4980] Speed: 721.45 samples/sec INFO:root:Iter[9] Batch [5000] Speed: 653.07 samples/sec INFO:root:Iter[9] Batch [5020] Speed: 676.12 samples/sec INFO:root:Iter[9] Batch [5040] Speed: 653.24 samples/sec INFO:root:Iter[9] Batch [5060] Speed: 664.57 samples/sec INFO:root:Iter[9] Batch [5080] Speed: 643.40 samples/sec INFO:root:Iter[9] Batch [5100] Speed: 631.01 samples/sec INFO:root:Iter[9] Batch [5120] Speed: 649.21 samples/sec INFO:root:Iter[9] Batch [5140] Speed: 640.69 samples/sec INFO:root:Iter[9] Batch [5160] Speed: 647.05 samples/sec INFO:root:Iter[9] Batch [5180] Speed: 612.56 samples/sec INFO:root:Iter[9] Batch [5200] Speed: 656.82 samples/sec INFO:root:Iter[9] Batch [5220] Speed: 632.81 samples/sec INFO:root:Iter[9] Batch [5240] Speed: 640.40 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(12000, 512) infer time 4.393353999999999 [lfw][98000]XNorm: 22.800965 [lfw][98000]Accuracy-Flip: 0.99417+-0.00344 testing verification.. (14000, 512) infer time 5.386346 [cfp_fp][98000]XNorm: 19.854595 [cfp_fp][98000]Accuracy-Flip: 0.94743+-0.01158 testing verification.. (12000, 512) infer time 4.428126 [agedb_30][98000]XNorm: 22.078042 [agedb_30][98000]Accuracy-Flip: 0.95533+-0.00770 [98000]Accuracy-Highest: 0.95883 INFO:root:Iter[9] Batch [6960] Speed: 244.95 samples/sec INFO:root:Iter[9] Batch [6980] Speed: 642.64 samples/sec INFO:root:Iter[9] Batch [7000] Speed: 661.48 samples/sec INFO:root:Iter[9] Batch [7020] Speed: 641.14 samples/sec INFO:root:Iter[9] Batch [7040] Speed: 645.39 samples/sec INFO:root:Iter[9] Batch [7060] Speed: 652.02 samples/sec INFO:root:Iter[9] Batch [7080] Speed: 648.40 samples/sec INFO:root:Iter[9] Batch [7100] Speed: 623.89 samples/sec INFO:root:Iter[9] Batch [7120] Speed: 647.10 samples/sec INFO:root:Iter[9] Batch [7140] Speed: 628.24 samples/sec INFO:root:Iter[9] Batch [7160] Speed: 634.34 samples/sec INFO:root:Iter[9] Batch [7180] Speed: 676.15 samples/sec INFO:root:Iter[9] Batch [7200] Speed: 620.78 samples/sec INFO:root:Iter[9] Batch [7220] Speed: 652.76 samples/sec INFO:root:Iter[9] Batch [7240] Speed: 631.57 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testing verification.. (12000, 512) infer time 4.673266999999999 [lfw][100000]XNorm: 22.902755 [lfw][100000]Accuracy-Flip: 0.99450+-0.00380 testing verification.. (14000, 512) infer time 5.385035999999999 [cfp_fp][100000]XNorm: 19.945270 [cfp_fp][100000]Accuracy-Flip: 0.95343+-0.00984 testing verification.. (12000, 512) infer time 4.576235 [agedb_30][100000]XNorm: 22.041748 [agedb_30][100000]Accuracy-Flip: 0.95250+-0.00655 [100000]Accuracy-Highest: 0.95883 INFO:root:Iter[9] Batch [8960] Speed: 240.38 samples/sec INFO:root:Iter[9] Batch [8980] Speed: 655.47 samples/sec INFO:root:Iter[9] Batch [9000] Speed: 624.13 samples/sec INFO:root:Iter[9] Batch [9020] Speed: 616.39 samples/sec INFO:root:Iter[9] Batch [9040] Speed: 637.04 samples/sec INFO:root:Iter[9] Batch [9060] Speed: 613.04 samples/sec INFO:root:Iter[9] Batch [9080] Speed: 628.90 samples/sec INFO:root:Iter[9] Batch [9100] Speed: 621.43 samples/sec INFO:root:Iter[9] Batch [9120] Speed: 629.61 samples/sec INFO:root:Iter[9] Batch [9140] Speed: 630.92 samples/sec INFO:root:Iter[9] Batch [9160] Speed: 632.19 samples/sec INFO:root:Iter[9] Batch [9180] Speed: 630.56 samples/sec INFO:root:Iter[9] Batch [9200] Speed: 634.74 samples/sec INFO:root:Iter[9] Batch [9220] Speed: 621.27 samples/sec INFO:root:Iter[9] Batch [9240] Speed: 616.27 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verification.. (12000, 512) infer time 4.631664999999999 [lfw][102000]XNorm: 22.595342 [lfw][102000]Accuracy-Flip: 0.99600+-0.00300 testing verification.. (14000, 512) infer time 5.414955000000002 [cfp_fp][102000]XNorm: 20.712817 [cfp_fp][102000]Accuracy-Flip: 0.97500+-0.00937 testing verification.. (12000, 512) infer time 4.582382 [agedb_30][102000]XNorm: 22.292107 [agedb_30][102000]Accuracy-Flip: 0.97117+-0.00727 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [102000]Accuracy-Highest: 0.97117 INFO:root:Iter[10] Batch [840] Speed: 232.44 samples/sec INFO:root:Iter[10] Batch [860] Speed: 650.83 samples/sec INFO:root:Iter[10] Batch [880] Speed: 610.13 samples/sec INFO:root:Iter[10] Batch [900] Speed: 614.90 samples/sec INFO:root:Iter[10] Batch [920] Speed: 635.46 samples/sec INFO:root:Iter[10] Batch [940] Speed: 625.42 samples/sec INFO:root:Iter[10] Batch [960] Speed: 628.77 samples/sec INFO:root:Iter[10] Batch [980] Speed: 612.24 samples/sec INFO:root:Iter[10] Batch [1000] Speed: 621.31 samples/sec INFO:root:Iter[10] Batch [1020] Speed: 619.51 samples/sec INFO:root:Iter[10] Batch [1040] Speed: 619.12 samples/sec INFO:root:Iter[10] Batch [1060] Speed: 611.40 samples/sec INFO:root:Iter[10] Batch [1080] Speed: 638.01 samples/sec 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(12000, 512) infer time 4.616122 [lfw][104000]XNorm: 22.682730 [lfw][104000]Accuracy-Flip: 0.99633+-0.00296 testing verification.. (14000, 512) infer time 5.3092549999999985 [cfp_fp][104000]XNorm: 20.676239 [cfp_fp][104000]Accuracy-Flip: 0.97571+-0.00777 testing verification.. (12000, 512) infer time 4.519714999999999 [agedb_30][104000]XNorm: 22.374658 [agedb_30][104000]Accuracy-Flip: 0.97133+-0.00819 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [104000]Accuracy-Highest: 0.97133 INFO:root:Iter[10] Batch [2840] Speed: 232.68 samples/sec INFO:root:Iter[10] Batch [2860] Speed: 650.80 samples/sec INFO:root:Iter[10] Batch [2880] Speed: 622.89 samples/sec INFO:root:Iter[10] Batch [2900] Speed: 651.37 samples/sec INFO:root:Iter[10] Batch [2920] Speed: 618.27 samples/sec INFO:root:Iter[10] Batch [2940] Speed: 609.52 samples/sec INFO:root:Iter[10] Batch [2960] Speed: 616.85 samples/sec INFO:root:Iter[10] Batch [2980] Speed: 616.88 samples/sec INFO:root:Iter[10] Batch [3000] Speed: 587.67 samples/sec INFO:root:Iter[10] Batch [3020] Speed: 611.16 samples/sec INFO:root:Iter[10] Batch [3040] Speed: 624.07 samples/sec INFO:root:Iter[10] Batch [3060] Speed: 611.53 samples/sec INFO:root:Iter[10] Batch [3080] Speed: 592.98 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(12000, 512) infer time 4.637902 [lfw][106000]XNorm: 22.688633 [lfw][106000]Accuracy-Flip: 0.99617+-0.00325 testing verification.. (14000, 512) infer time 5.468170999999998 [cfp_fp][106000]XNorm: 20.875697 [cfp_fp][106000]Accuracy-Flip: 0.98057+-0.00520 testing verification.. (12000, 512) infer time 4.584187999999999 [agedb_30][106000]XNorm: 22.632446 [agedb_30][106000]Accuracy-Flip: 0.97367+-0.00666 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [106000]Accuracy-Highest: 0.97367 INFO:root:Iter[10] Batch [4840] Speed: 229.86 samples/sec INFO:root:Iter[10] Batch [4860] Speed: 608.26 samples/sec INFO:root:Iter[10] Batch [4880] Speed: 607.58 samples/sec INFO:root:Iter[10] Batch [4900] Speed: 608.56 samples/sec INFO:root:Iter[10] Batch [4920] Speed: 613.73 samples/sec INFO:root:Iter[10] Batch [4940] Speed: 624.88 samples/sec INFO:root:Iter[10] Batch [4960] Speed: 604.76 samples/sec INFO:root:Iter[10] Batch [4980] Speed: 585.59 samples/sec INFO:root:Iter[10] Batch [5000] Speed: 613.24 samples/sec INFO:root:Iter[10] Batch [5020] Speed: 583.24 samples/sec INFO:root:Iter[10] Batch [5040] Speed: 593.99 samples/sec INFO:root:Iter[10] Batch [5060] Speed: 593.04 samples/sec INFO:root:Iter[10] Batch [5080] Speed: 587.73 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(12000, 512) infer time 4.731368 [lfw][108000]XNorm: 22.365977 [lfw][108000]Accuracy-Flip: 0.99750+-0.00250 testing verification.. (14000, 512) infer time 5.642403000000001 [cfp_fp][108000]XNorm: 20.626727 [cfp_fp][108000]Accuracy-Flip: 0.97814+-0.00775 testing verification.. (12000, 512) infer time 4.796688 [agedb_30][108000]XNorm: 22.335527 [agedb_30][108000]Accuracy-Flip: 0.97200+-0.00733 [108000]Accuracy-Highest: 0.97367 INFO:root:Iter[10] Batch [6840] Speed: 234.11 samples/sec INFO:root:Iter[10] Batch [6860] Speed: 584.44 samples/sec INFO:root:Iter[10] Batch [6880] Speed: 582.28 samples/sec INFO:root:Iter[10] Batch [6900] Speed: 558.58 samples/sec INFO:root:Iter[10] Batch [6920] Speed: 575.50 samples/sec INFO:root:Iter[10] Batch [6940] Speed: 574.07 samples/sec INFO:root:Iter[10] Batch [6960] Speed: 561.04 samples/sec INFO:root:Iter[10] Batch [6980] Speed: 571.47 samples/sec INFO:root:Iter[10] Batch [7000] Speed: 561.85 samples/sec INFO:root:Iter[10] Batch [7020] Speed: 569.77 samples/sec INFO:root:Iter[10] Batch [7040] Speed: 576.78 samples/sec INFO:root:Iter[10] Batch [7060] Speed: 590.88 samples/sec INFO:root:Iter[10] Batch [7080] Speed: 580.72 samples/sec INFO:root:Iter[10] Batch [7100] Speed: 578.01 samples/sec INFO:root:Iter[10] Batch [7120] 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samples/sec {fc7_acc} 110000 0.43359375 lr-batch-epoch: 0.010000000000000002 8839 10 testing verification.. (12000, 512) infer time 4.741572000000001 [lfw][110000]XNorm: 22.297012 [lfw][110000]Accuracy-Flip: 0.99683+-0.00263 testing verification.. (14000, 512) infer time 5.615647 [cfp_fp][110000]XNorm: 20.665292 [cfp_fp][110000]Accuracy-Flip: 0.98057+-0.00561 testing verification.. (12000, 512) infer time 4.741881000000001 [agedb_30][110000]XNorm: 22.270407 [agedb_30][110000]Accuracy-Flip: 0.97650+-0.00736 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [110000]Accuracy-Highest: 0.97650 INFO:root:Iter[10] Batch [8840] Speed: 229.47 samples/sec INFO:root:Iter[10] Batch [8860] Speed: 574.92 samples/sec INFO:root:Iter[10] Batch [8880] Speed: 569.74 samples/sec INFO:root:Iter[10] Batch [8900] Speed: 580.66 samples/sec INFO:root:Iter[10] Batch [8920] Speed: 577.36 samples/sec INFO:root:Iter[10] Batch [8940] Speed: 588.25 samples/sec INFO:root:Iter[10] Batch [8960] Speed: 583.13 samples/sec INFO:root:Iter[10] Batch [8980] Speed: 585.73 samples/sec INFO:root:Iter[10] Batch [9000] Speed: 564.11 samples/sec INFO:root:Iter[10] Batch [9020] Speed: 548.37 samples/sec INFO:root:Iter[10] Batch [9040] Speed: 580.43 samples/sec INFO:root:Iter[10] Batch [9060] Speed: 581.47 samples/sec INFO:root:Iter[10] Batch [9080] Speed: 575.86 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Speed: 591.79 samples/sec INFO:root:Iter[11] Batch [700] Speed: 619.48 samples/sec INFO:root:Iter[11] Batch [720] Speed: 598.17 samples/sec {fc7_acc} 112000 0.39257812 lr-batch-epoch: 0.010000000000000002 723 11 testing verification.. (12000, 512) infer time 4.677223000000001 [lfw][112000]XNorm: 22.671680 [lfw][112000]Accuracy-Flip: 0.99733+-0.00271 testing verification.. (14000, 512) infer time 5.295327999999999 [cfp_fp][112000]XNorm: 21.033314 [cfp_fp][112000]Accuracy-Flip: 0.98229+-0.00617 testing verification.. (12000, 512) infer time 4.698333000000001 [agedb_30][112000]XNorm: 22.653178 [agedb_30][112000]Accuracy-Flip: 0.97367+-0.00799 [112000]Accuracy-Highest: 0.97650 INFO:root:Iter[11] Batch [740] Speed: 238.94 samples/sec INFO:root:Iter[11] Batch [760] Speed: 606.35 samples/sec INFO:root:Iter[11] Batch [780] Speed: 594.88 samples/sec INFO:root:Iter[11] Batch [800] Speed: 598.95 samples/sec INFO:root:Iter[11] Batch [820] Speed: 603.97 samples/sec INFO:root:Iter[11] Batch [840] Speed: 591.64 samples/sec INFO:root:Iter[11] Batch [860] Speed: 597.79 samples/sec INFO:root:Iter[11] Batch [880] Speed: 587.20 samples/sec INFO:root:Iter[11] Batch [900] Speed: 594.20 samples/sec INFO:root:Iter[11] Batch [920] Speed: 582.27 samples/sec INFO:root:Iter[11] Batch [940] Speed: 584.67 samples/sec INFO:root:Iter[11] Batch [960] Speed: 587.48 samples/sec INFO:root:Iter[11] Batch [980] Speed: 595.87 samples/sec INFO:root:Iter[11] Batch [1000] Speed: 594.16 samples/sec INFO:root:Iter[11] Batch [1020] Speed: 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samples/sec {fc7_acc} 114000 0.40039062 lr-batch-epoch: 0.010000000000000002 2723 11 testing verification.. (12000, 512) infer time 4.673833000000001 [lfw][114000]XNorm: 22.625018 [lfw][114000]Accuracy-Flip: 0.99600+-0.00300 testing verification.. (14000, 512) infer time 5.501331 [cfp_fp][114000]XNorm: 20.882728 [cfp_fp][114000]Accuracy-Flip: 0.98100+-0.00685 testing verification.. (12000, 512) infer time 4.765035999999999 [agedb_30][114000]XNorm: 22.438218 [agedb_30][114000]Accuracy-Flip: 0.97333+-0.00756 [114000]Accuracy-Highest: 0.97650 INFO:root:Iter[11] Batch [2740] Speed: 231.13 samples/sec INFO:root:Iter[11] Batch [2760] Speed: 562.69 samples/sec INFO:root:Iter[11] Batch [2780] Speed: 567.35 samples/sec INFO:root:Iter[11] Batch [2800] Speed: 563.88 samples/sec INFO:root:Iter[11] Batch [2820] Speed: 559.20 samples/sec INFO:root:Iter[11] Batch [2840] Speed: 553.83 samples/sec INFO:root:Iter[11] Batch [2860] Speed: 555.25 samples/sec INFO:root:Iter[11] Batch [2880] Speed: 574.78 samples/sec INFO:root:Iter[11] Batch [2900] Speed: 577.53 samples/sec INFO:root:Iter[11] Batch [2920] Speed: 580.82 samples/sec INFO:root:Iter[11] Batch [2940] Speed: 563.58 samples/sec INFO:root:Iter[11] Batch [2960] Speed: 569.39 samples/sec INFO:root:Iter[11] Batch [2980] Speed: 566.55 samples/sec INFO:root:Iter[11] Batch [3000] Speed: 565.98 samples/sec INFO:root:Iter[11] Batch 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(12000, 512) infer time 4.780187 [lfw][116000]XNorm: 22.739926 [lfw][116000]Accuracy-Flip: 0.99683+-0.00283 testing verification.. (14000, 512) infer time 5.4961920000000015 [cfp_fp][116000]XNorm: 20.876933 [cfp_fp][116000]Accuracy-Flip: 0.98114+-0.00494 testing verification.. (12000, 512) infer time 4.836881000000001 [agedb_30][116000]XNorm: 22.618780 [agedb_30][116000]Accuracy-Flip: 0.97600+-0.00870 [116000]Accuracy-Highest: 0.97650 INFO:root:Iter[11] Batch [4740] Speed: 228.79 samples/sec INFO:root:Iter[11] Batch [4760] Speed: 563.10 samples/sec INFO:root:Iter[11] Batch [4780] Speed: 565.80 samples/sec INFO:root:Iter[11] Batch [4800] Speed: 567.49 samples/sec INFO:root:Iter[11] Batch [4820] Speed: 566.11 samples/sec INFO:root:Iter[11] Batch [4840] Speed: 555.39 samples/sec INFO:root:Iter[11] Batch [4860] Speed: 550.19 samples/sec INFO:root:Iter[11] Batch [4880] Speed: 574.35 samples/sec INFO:root:Iter[11] Batch [4900] Speed: 577.96 samples/sec INFO:root:Iter[11] Batch [4920] Speed: 566.24 samples/sec INFO:root:Iter[11] Batch [4940] Speed: 567.04 samples/sec INFO:root:Iter[11] Batch [4960] Speed: 571.48 samples/sec INFO:root:Iter[11] Batch [4980] Speed: 562.62 samples/sec INFO:root:Iter[11] Batch [5000] Speed: 581.67 samples/sec INFO:root:Iter[11] Batch 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(12000, 512) infer time 4.751512000000001 [lfw][118000]XNorm: 22.677340 [lfw][118000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.538938999999998 [cfp_fp][118000]XNorm: 20.836081 [cfp_fp][118000]Accuracy-Flip: 0.98029+-0.00663 testing verification.. (12000, 512) infer time 4.806184 [agedb_30][118000]XNorm: 22.510237 [agedb_30][118000]Accuracy-Flip: 0.97417+-0.00704 [118000]Accuracy-Highest: 0.97650 INFO:root:Iter[11] Batch [6740] Speed: 228.41 samples/sec INFO:root:Iter[11] Batch [6760] Speed: 577.42 samples/sec INFO:root:Iter[11] Batch [6780] Speed: 566.35 samples/sec INFO:root:Iter[11] Batch [6800] Speed: 563.56 samples/sec INFO:root:Iter[11] Batch [6820] Speed: 538.48 samples/sec INFO:root:Iter[11] Batch [6840] Speed: 569.54 samples/sec INFO:root:Iter[11] Batch [6860] Speed: 563.91 samples/sec INFO:root:Iter[11] Batch [6880] Speed: 571.61 samples/sec INFO:root:Iter[11] Batch [6900] Speed: 568.29 samples/sec INFO:root:Iter[11] Batch [6920] Speed: 569.89 samples/sec INFO:root:Iter[11] Batch [6940] Speed: 559.01 samples/sec INFO:root:Iter[11] Batch [6960] Speed: 560.94 samples/sec INFO:root:Iter[11] Batch [6980] Speed: 551.60 samples/sec INFO:root:Iter[11] Batch [7000] Speed: 589.77 samples/sec INFO:root:Iter[11] Batch [7020] 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samples/sec {fc7_acc} 120000 0.38085938 lr-batch-epoch: 0.010000000000000002 8723 11 testing verification.. (12000, 512) infer time 4.724269999999998 [lfw][120000]XNorm: 22.784078 [lfw][120000]Accuracy-Flip: 0.99617+-0.00350 testing verification.. (14000, 512) infer time 5.586721999999998 [cfp_fp][120000]XNorm: 21.051435 [cfp_fp][120000]Accuracy-Flip: 0.98086+-0.00778 testing verification.. (12000, 512) infer time 4.851052000000002 [agedb_30][120000]XNorm: 22.748555 [agedb_30][120000]Accuracy-Flip: 0.97350+-0.00717 [120000]Accuracy-Highest: 0.97650 INFO:root:Iter[11] Batch [8740] Speed: 229.28 samples/sec INFO:root:Iter[11] Batch [8760] Speed: 557.76 samples/sec INFO:root:Iter[11] Batch [8780] Speed: 549.97 samples/sec INFO:root:Iter[11] Batch [8800] Speed: 559.49 samples/sec INFO:root:Iter[11] Batch [8820] Speed: 565.04 samples/sec INFO:root:Iter[11] Batch [8840] Speed: 568.90 samples/sec INFO:root:Iter[11] Batch [8860] Speed: 554.20 samples/sec INFO:root:Iter[11] Batch [8880] Speed: 561.59 samples/sec INFO:root:Iter[11] Batch [8900] Speed: 566.21 samples/sec INFO:root:Iter[11] Batch [8920] Speed: 578.65 samples/sec INFO:root:Iter[11] Batch [8940] Speed: 565.03 samples/sec INFO:root:Iter[11] Batch [8960] Speed: 566.82 samples/sec INFO:root:Iter[11] Batch [8980] Speed: 571.60 samples/sec INFO:root:Iter[11] Batch [9000] Speed: 581.14 samples/sec INFO:root:Iter[11] Batch 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{fc7_acc} 122000 0.390625 lr-batch-epoch: 0.010000000000000002 607 12 testing verification.. (12000, 512) infer time 4.466574 [lfw][122000]XNorm: 22.985673 [lfw][122000]Accuracy-Flip: 0.99733+-0.00271 testing verification.. (14000, 512) infer time 5.522776 [cfp_fp][122000]XNorm: 21.301214 [cfp_fp][122000]Accuracy-Flip: 0.97986+-0.00551 testing verification.. (12000, 512) infer time 4.732594 [agedb_30][122000]XNorm: 22.473095 [agedb_30][122000]Accuracy-Flip: 0.97550+-0.00898 [122000]Accuracy-Highest: 0.97650 INFO:root:Iter[12] Batch [620] Speed: 237.37 samples/sec INFO:root:Iter[12] Batch [640] Speed: 570.74 samples/sec INFO:root:Iter[12] Batch [660] Speed: 576.02 samples/sec INFO:root:Iter[12] Batch [680] Speed: 566.22 samples/sec INFO:root:Iter[12] Batch [700] Speed: 572.50 samples/sec INFO:root:Iter[12] Batch [720] Speed: 559.78 samples/sec INFO:root:Iter[12] Batch [740] Speed: 566.02 samples/sec INFO:root:Iter[12] Batch [760] Speed: 558.75 samples/sec INFO:root:Iter[12] Batch [780] Speed: 579.32 samples/sec INFO:root:Iter[12] Batch [800] Speed: 577.62 samples/sec INFO:root:Iter[12] Batch [820] Speed: 592.47 samples/sec INFO:root:Iter[12] Batch [840] Speed: 565.17 samples/sec INFO:root:Iter[12] Batch [860] Speed: 575.02 samples/sec INFO:root:Iter[12] Batch [880] Speed: 582.79 samples/sec INFO:root:Iter[12] Batch [900] Speed: 576.47 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samples/sec {fc7_acc} 124000 0.38085938 lr-batch-epoch: 0.010000000000000002 2607 12 testing verification.. (12000, 512) infer time 4.610068000000001 [lfw][124000]XNorm: 22.729069 [lfw][124000]Accuracy-Flip: 0.99600+-0.00396 testing verification.. (14000, 512) infer time 5.534431000000001 [cfp_fp][124000]XNorm: 20.905895 [cfp_fp][124000]Accuracy-Flip: 0.98014+-0.00773 testing verification.. (12000, 512) infer time 4.760185000000001 [agedb_30][124000]XNorm: 22.677550 [agedb_30][124000]Accuracy-Flip: 0.97467+-0.00670 [124000]Accuracy-Highest: 0.97650 INFO:root:Iter[12] Batch [2620] Speed: 232.39 samples/sec INFO:root:Iter[12] Batch [2640] Speed: 560.10 samples/sec INFO:root:Iter[12] Batch [2660] Speed: 569.12 samples/sec INFO:root:Iter[12] Batch [2680] Speed: 574.07 samples/sec INFO:root:Iter[12] Batch [2700] Speed: 565.43 samples/sec INFO:root:Iter[12] Batch [2720] Speed: 549.31 samples/sec INFO:root:Iter[12] Batch [2740] Speed: 589.04 samples/sec INFO:root:Iter[12] Batch [2760] Speed: 567.31 samples/sec INFO:root:Iter[12] Batch [2780] Speed: 549.61 samples/sec INFO:root:Iter[12] Batch [2800] Speed: 564.00 samples/sec INFO:root:Iter[12] Batch [2820] Speed: 555.34 samples/sec INFO:root:Iter[12] Batch [2840] Speed: 566.37 samples/sec INFO:root:Iter[12] Batch [2860] Speed: 588.59 samples/sec INFO:root:Iter[12] Batch [2880] Speed: 574.10 samples/sec INFO:root:Iter[12] Batch 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INFO:root:Iter[12] Batch [4600] Speed: 579.30 samples/sec {fc7_acc} 126000 0.37304688 lr-batch-epoch: 0.010000000000000002 4607 12 testing verification.. (12000, 512) infer time 4.772605 [lfw][126000]XNorm: 22.605249 [lfw][126000]Accuracy-Flip: 0.99650+-0.00302 testing verification.. (14000, 512) infer time 5.571069000000001 [cfp_fp][126000]XNorm: 20.937761 [cfp_fp][126000]Accuracy-Flip: 0.97700+-0.00978 testing verification.. (12000, 512) infer time 4.811676999999999 [agedb_30][126000]XNorm: 22.363817 [agedb_30][126000]Accuracy-Flip: 0.97600+-0.00779 [126000]Accuracy-Highest: 0.97650 INFO:root:Iter[12] Batch [4620] Speed: 230.60 samples/sec INFO:root:Iter[12] Batch [4640] Speed: 542.84 samples/sec INFO:root:Iter[12] Batch [4660] Speed: 561.52 samples/sec INFO:root:Iter[12] Batch [4680] Speed: 562.75 samples/sec INFO:root:Iter[12] Batch [4700] Speed: 542.24 samples/sec INFO:root:Iter[12] Batch [4720] Speed: 551.82 samples/sec INFO:root:Iter[12] Batch [4740] Speed: 552.51 samples/sec INFO:root:Iter[12] Batch [4760] Speed: 577.86 samples/sec INFO:root:Iter[12] Batch [4780] Speed: 563.43 samples/sec INFO:root:Iter[12] Batch [4800] Speed: 575.22 samples/sec INFO:root:Iter[12] Batch [4820] Speed: 577.46 samples/sec INFO:root:Iter[12] Batch [4840] Speed: 567.22 samples/sec INFO:root:Iter[12] Batch [4860] Speed: 560.98 samples/sec INFO:root:Iter[12] Batch [4880] Speed: 566.92 samples/sec INFO:root:Iter[12] Batch 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Batch [6600] Speed: 91.46 samples/sec {fc7_acc} 128000 0.3828125 lr-batch-epoch: 0.010000000000000002 6607 12 testing verification.. (12000, 512) infer time 5.707871000000002 [lfw][128000]XNorm: 22.870186 [lfw][128000]Accuracy-Flip: 0.99633+-0.00233 testing verification.. (14000, 512) infer time 5.441767999999999 [cfp_fp][128000]XNorm: 21.229069 [cfp_fp][128000]Accuracy-Flip: 0.98286+-0.00546 testing verification.. (12000, 512) infer time 4.7241 [agedb_30][128000]XNorm: 22.650301 [agedb_30][128000]Accuracy-Flip: 0.97583+-0.00793 [128000]Accuracy-Highest: 0.97650 INFO:root:Iter[12] Batch [6620] Speed: 145.79 samples/sec INFO:root:Iter[12] Batch [6640] Speed: 571.87 samples/sec INFO:root:Iter[12] Batch [6660] Speed: 566.59 samples/sec INFO:root:Iter[12] Batch [6680] Speed: 570.51 samples/sec INFO:root:Iter[12] Batch [6700] Speed: 566.87 samples/sec INFO:root:Iter[12] Batch [6720] Speed: 577.21 samples/sec INFO:root:Iter[12] Batch [6740] Speed: 566.88 samples/sec INFO:root:Iter[12] Batch [6760] Speed: 581.46 samples/sec INFO:root:Iter[12] Batch [6780] Speed: 590.16 samples/sec INFO:root:Iter[12] Batch [6800] Speed: 576.43 samples/sec INFO:root:Iter[12] Batch [6820] Speed: 580.59 samples/sec INFO:root:Iter[12] Batch [6840] Speed: 579.75 samples/sec INFO:root:Iter[12] Batch [6860] Speed: 579.60 samples/sec INFO:root:Iter[12] Batch [6880] Speed: 574.98 samples/sec INFO:root:Iter[12] Batch [6900] Speed: 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samples/sec {fc7_acc} 130000 0.41992188 lr-batch-epoch: 0.010000000000000002 8607 12 testing verification.. (12000, 512) infer time 4.650144999999998 [lfw][130000]XNorm: 22.419189 [lfw][130000]Accuracy-Flip: 0.99667+-0.00269 testing verification.. (14000, 512) infer time 5.601025000000001 [cfp_fp][130000]XNorm: 20.753087 [cfp_fp][130000]Accuracy-Flip: 0.97743+-0.00687 testing verification.. (12000, 512) infer time 4.795422999999998 [agedb_30][130000]XNorm: 22.193764 [agedb_30][130000]Accuracy-Flip: 0.97467+-0.00802 [130000]Accuracy-Highest: 0.97650 INFO:root:Iter[12] Batch [8620] Speed: 231.91 samples/sec INFO:root:Iter[12] Batch [8640] Speed: 575.57 samples/sec INFO:root:Iter[12] Batch [8660] Speed: 571.65 samples/sec INFO:root:Iter[12] Batch [8680] Speed: 574.25 samples/sec INFO:root:Iter[12] Batch [8700] Speed: 564.55 samples/sec INFO:root:Iter[12] Batch [8720] Speed: 569.72 samples/sec INFO:root:Iter[12] Batch [8740] Speed: 572.86 samples/sec INFO:root:Iter[12] Batch [8760] Speed: 566.03 samples/sec INFO:root:Iter[12] Batch [8780] Speed: 566.62 samples/sec INFO:root:Iter[12] Batch [8800] Speed: 563.75 samples/sec INFO:root:Iter[12] Batch [8820] Speed: 561.88 samples/sec INFO:root:Iter[12] Batch [8840] Speed: 577.31 samples/sec INFO:root:Iter[12] Batch [8860] Speed: 573.56 samples/sec INFO:root:Iter[12] Batch [8880] Speed: 570.23 samples/sec INFO:root:Iter[12] Batch 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{fc7_acc} 132000 0.35351562 lr-batch-epoch: 0.010000000000000002 491 13 testing verification.. (12000, 512) infer time 4.707742000000001 [lfw][132000]XNorm: 22.218325 [lfw][132000]Accuracy-Flip: 0.99683+-0.00241 testing verification.. (14000, 512) infer time 5.509950000000001 [cfp_fp][132000]XNorm: 21.045709 [cfp_fp][132000]Accuracy-Flip: 0.97929+-0.00767 testing verification.. (12000, 512) infer time 4.672350000000001 [agedb_30][132000]XNorm: 21.962242 [agedb_30][132000]Accuracy-Flip: 0.97600+-0.00834 [132000]Accuracy-Highest: 0.97650 INFO:root:Iter[13] Batch [500] Speed: 233.76 samples/sec INFO:root:Iter[13] Batch [520] Speed: 591.64 samples/sec INFO:root:Iter[13] Batch [540] Speed: 578.78 samples/sec INFO:root:Iter[13] Batch [560] Speed: 581.04 samples/sec INFO:root:Iter[13] Batch [580] Speed: 580.68 samples/sec INFO:root:Iter[13] Batch [600] Speed: 562.22 samples/sec INFO:root:Iter[13] Batch [620] Speed: 544.36 samples/sec INFO:root:Iter[13] Batch [640] Speed: 559.96 samples/sec INFO:root:Iter[13] Batch [660] Speed: 564.47 samples/sec INFO:root:Iter[13] Batch [680] Speed: 563.48 samples/sec INFO:root:Iter[13] Batch [700] Speed: 582.19 samples/sec INFO:root:Iter[13] Batch [720] Speed: 569.53 samples/sec INFO:root:Iter[13] Batch [740] Speed: 585.08 samples/sec INFO:root:Iter[13] Batch [760] Speed: 580.02 samples/sec INFO:root:Iter[13] Batch [780] Speed: 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samples/sec {fc7_acc} 134000 0.35351562 lr-batch-epoch: 0.010000000000000002 2491 13 testing verification.. (12000, 512) infer time 4.402627000000001 [lfw][134000]XNorm: 21.994982 [lfw][134000]Accuracy-Flip: 0.99733+-0.00351 testing verification.. (14000, 512) infer time 5.678553 [cfp_fp][134000]XNorm: 20.484188 [cfp_fp][134000]Accuracy-Flip: 0.98086+-0.00520 testing verification.. (12000, 512) infer time 4.694464000000001 [agedb_30][134000]XNorm: 21.956600 [agedb_30][134000]Accuracy-Flip: 0.97650+-0.00747 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [134000]Accuracy-Highest: 0.97650 INFO:root:Iter[13] Batch [2500] Speed: 232.36 samples/sec INFO:root:Iter[13] Batch [2520] Speed: 579.60 samples/sec INFO:root:Iter[13] Batch [2540] Speed: 577.99 samples/sec INFO:root:Iter[13] Batch [2560] Speed: 563.34 samples/sec INFO:root:Iter[13] Batch [2580] Speed: 557.64 samples/sec INFO:root:Iter[13] Batch [2600] Speed: 560.16 samples/sec INFO:root:Iter[13] Batch [2620] Speed: 568.21 samples/sec INFO:root:Iter[13] Batch [2640] Speed: 582.11 samples/sec INFO:root:Iter[13] Batch [2660] Speed: 582.89 samples/sec INFO:root:Iter[13] Batch [2680] Speed: 565.30 samples/sec INFO:root:Iter[13] Batch [2700] Speed: 572.37 samples/sec INFO:root:Iter[13] Batch [2720] Speed: 580.29 samples/sec INFO:root:Iter[13] Batch [2740] Speed: 588.48 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samples/sec INFO:root:Iter[13] Batch [4460] Speed: 591.25 samples/sec INFO:root:Iter[13] Batch [4480] Speed: 600.22 samples/sec {fc7_acc} 136000 0.40429688 lr-batch-epoch: 0.010000000000000002 4491 13 testing verification.. (12000, 512) infer time 4.5775060000000005 [lfw][136000]XNorm: 22.293095 [lfw][136000]Accuracy-Flip: 0.99667+-0.00316 testing verification.. (14000, 512) infer time 5.651526 [cfp_fp][136000]XNorm: 20.721541 [cfp_fp][136000]Accuracy-Flip: 0.97771+-0.00583 testing verification.. (12000, 512) infer time 4.746907999999999 [agedb_30][136000]XNorm: 22.334272 [agedb_30][136000]Accuracy-Flip: 0.97467+-0.00836 [136000]Accuracy-Highest: 0.97650 INFO:root:Iter[13] Batch [4500] Speed: 232.44 samples/sec INFO:root:Iter[13] Batch [4520] Speed: 569.62 samples/sec INFO:root:Iter[13] Batch [4540] Speed: 560.93 samples/sec INFO:root:Iter[13] Batch [4560] Speed: 565.42 samples/sec INFO:root:Iter[13] Batch [4580] Speed: 562.45 samples/sec INFO:root:Iter[13] Batch [4600] Speed: 555.72 samples/sec INFO:root:Iter[13] Batch [4620] Speed: 561.38 samples/sec INFO:root:Iter[13] Batch [4640] Speed: 558.57 samples/sec INFO:root:Iter[13] Batch [4660] Speed: 576.43 samples/sec INFO:root:Iter[13] Batch [4680] Speed: 562.50 samples/sec INFO:root:Iter[13] Batch [4700] Speed: 581.37 samples/sec INFO:root:Iter[13] Batch [4720] Speed: 564.62 samples/sec INFO:root:Iter[13] Batch [4740] Speed: 564.91 samples/sec INFO:root:Iter[13] Batch [4760] Speed: 554.13 samples/sec INFO:root:Iter[13] Batch 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(12000, 512) infer time 4.7785709999999995 [lfw][138000]XNorm: 23.316420 [lfw][138000]Accuracy-Flip: 0.99750+-0.00300 testing verification.. (14000, 512) infer time 5.579112999999999 [cfp_fp][138000]XNorm: 21.238623 [cfp_fp][138000]Accuracy-Flip: 0.97857+-0.00714 testing verification.. (12000, 512) infer time 4.751777000000001 [agedb_30][138000]XNorm: 23.007418 [agedb_30][138000]Accuracy-Flip: 0.97350+-0.00769 [138000]Accuracy-Highest: 0.97650 INFO:root:Iter[13] Batch [6500] Speed: 232.56 samples/sec INFO:root:Iter[13] Batch [6520] Speed: 562.77 samples/sec INFO:root:Iter[13] Batch [6540] Speed: 578.80 samples/sec INFO:root:Iter[13] Batch [6560] Speed: 558.17 samples/sec INFO:root:Iter[13] Batch [6580] Speed: 567.70 samples/sec INFO:root:Iter[13] Batch [6600] Speed: 551.64 samples/sec INFO:root:Iter[13] Batch [6620] Speed: 576.20 samples/sec INFO:root:Iter[13] Batch [6640] Speed: 560.33 samples/sec INFO:root:Iter[13] Batch [6660] Speed: 552.19 samples/sec INFO:root:Iter[13] Batch [6680] Speed: 566.19 samples/sec INFO:root:Iter[13] Batch [6700] Speed: 566.69 samples/sec INFO:root:Iter[13] Batch [6720] Speed: 589.58 samples/sec INFO:root:Iter[13] Batch [6740] Speed: 566.97 samples/sec INFO:root:Iter[13] Batch [6760] Speed: 573.89 samples/sec INFO:root:Iter[13] Batch 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(12000, 512) infer time 4.464169000000001 [lfw][140000]XNorm: 22.765040 [lfw][140000]Accuracy-Flip: 0.99733+-0.00271 testing verification.. (14000, 512) infer time 5.378337999999999 [cfp_fp][140000]XNorm: 21.119776 [cfp_fp][140000]Accuracy-Flip: 0.98129+-0.00380 testing verification.. (12000, 512) infer time 4.235718 [agedb_30][140000]XNorm: 22.779174 [agedb_30][140000]Accuracy-Flip: 0.97383+-0.00624 [140000]Accuracy-Highest: 0.97650 INFO:root:Iter[13] Batch [8500] Speed: 246.11 samples/sec INFO:root:Iter[13] Batch [8520] Speed: 738.28 samples/sec INFO:root:Iter[13] Batch [8540] Speed: 699.77 samples/sec INFO:root:Iter[13] Batch [8560] Speed: 665.02 samples/sec INFO:root:Iter[13] Batch [8580] Speed: 682.81 samples/sec INFO:root:Iter[13] Batch [8600] Speed: 625.42 samples/sec INFO:root:Iter[13] Batch [8620] Speed: 605.97 samples/sec INFO:root:Iter[13] Batch [8640] Speed: 608.79 samples/sec INFO:root:Iter[13] Batch [8660] Speed: 699.41 samples/sec INFO:root:Iter[13] Batch [8680] Speed: 633.95 samples/sec INFO:root:Iter[13] Batch [8700] Speed: 596.39 samples/sec INFO:root:Iter[13] Batch [8720] Speed: 630.53 samples/sec INFO:root:Iter[13] Batch [8740] Speed: 635.97 samples/sec INFO:root:Iter[13] Batch [8760] Speed: 661.69 samples/sec INFO:root:Iter[13] Batch [8780] 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626.94 samples/sec INFO:root:Iter[14] Batch [40] Speed: 615.39 samples/sec INFO:root:Iter[14] Batch [60] Speed: 597.92 samples/sec INFO:root:Iter[14] Batch [80] Speed: 666.18 samples/sec INFO:root:Iter[14] Batch [100] Speed: 608.45 samples/sec INFO:root:Iter[14] Batch [120] Speed: 647.13 samples/sec INFO:root:Iter[14] Batch [140] Speed: 606.38 samples/sec INFO:root:Iter[14] Batch [160] Speed: 629.25 samples/sec INFO:root:Iter[14] Batch [180] Speed: 657.49 samples/sec INFO:root:Iter[14] Batch [200] Speed: 632.38 samples/sec INFO:root:Iter[14] Batch [220] Speed: 664.56 samples/sec INFO:root:Iter[14] Batch [240] Speed: 705.77 samples/sec INFO:root:Iter[14] Batch [260] Speed: 642.48 samples/sec INFO:root:Iter[14] Batch [280] Speed: 642.78 samples/sec INFO:root:Iter[14] Batch [300] Speed: 650.18 samples/sec INFO:root:Iter[14] Batch [320] Speed: 635.47 samples/sec INFO:root:Iter[14] Batch [340] Speed: 631.54 samples/sec INFO:root:Iter[14] Batch [360] Speed: 623.20 samples/sec {fc7_acc} 142000 0.46484375 lr-batch-epoch: 0.010000000000000002 375 14 testing verification.. (12000, 512) infer time 4.517025000000001 [lfw][142000]XNorm: 22.687477 [lfw][142000]Accuracy-Flip: 0.99767+-0.00300 testing verification.. (14000, 512) infer time 5.512318000000001 [cfp_fp][142000]XNorm: 21.092077 [cfp_fp][142000]Accuracy-Flip: 0.98386+-0.00487 testing verification.. (12000, 512) infer time 3.6180989999999986 [agedb_30][142000]XNorm: 22.350091 [agedb_30][142000]Accuracy-Flip: 0.97633+-0.00767 [142000]Accuracy-Highest: 0.97650 INFO:root:Iter[14] Batch [380] Speed: 252.09 samples/sec INFO:root:Iter[14] Batch [400] Speed: 817.76 samples/sec INFO:root:Iter[14] Batch [420] Speed: 873.41 samples/sec INFO:root:Iter[14] Batch [440] Speed: 809.39 samples/sec INFO:root:Iter[14] Batch [460] Speed: 804.94 samples/sec INFO:root:Iter[14] Batch [480] Speed: 772.48 samples/sec INFO:root:Iter[14] Batch [500] Speed: 684.69 samples/sec INFO:root:Iter[14] Batch [520] Speed: 650.00 samples/sec INFO:root:Iter[14] Batch [540] Speed: 730.06 samples/sec INFO:root:Iter[14] Batch [560] Speed: 632.49 samples/sec INFO:root:Iter[14] Batch [580] Speed: 608.67 samples/sec INFO:root:Iter[14] Batch [600] Speed: 632.10 samples/sec INFO:root:Iter[14] Batch [620] Speed: 670.64 samples/sec INFO:root:Iter[14] Batch [640] Speed: 639.73 samples/sec INFO:root:Iter[14] Batch [660] Speed: 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samples/sec {fc7_acc} 144000 0.38085938 lr-batch-epoch: 0.010000000000000002 2375 14 testing verification.. (12000, 512) infer time 4.659367 [lfw][144000]XNorm: 22.737742 [lfw][144000]Accuracy-Flip: 0.99667+-0.00365 testing verification.. (14000, 512) infer time 5.400553000000001 [cfp_fp][144000]XNorm: 21.160134 [cfp_fp][144000]Accuracy-Flip: 0.98014+-0.00627 testing verification.. (12000, 512) infer time 4.036646999999999 [agedb_30][144000]XNorm: 22.582316 [agedb_30][144000]Accuracy-Flip: 0.97750+-0.00638 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [144000]Accuracy-Highest: 0.97750 INFO:root:Iter[14] Batch [2380] Speed: 242.91 samples/sec INFO:root:Iter[14] Batch [2400] Speed: 772.32 samples/sec INFO:root:Iter[14] Batch [2420] Speed: 735.79 samples/sec INFO:root:Iter[14] Batch [2440] Speed: 701.44 samples/sec INFO:root:Iter[14] Batch [2460] Speed: 709.79 samples/sec INFO:root:Iter[14] Batch [2480] Speed: 726.18 samples/sec INFO:root:Iter[14] Batch [2500] Speed: 649.76 samples/sec INFO:root:Iter[14] Batch [2520] Speed: 694.57 samples/sec INFO:root:Iter[14] Batch [2540] Speed: 648.24 samples/sec INFO:root:Iter[14] Batch [2560] Speed: 650.62 samples/sec INFO:root:Iter[14] Batch [2580] Speed: 625.59 samples/sec INFO:root:Iter[14] Batch [2600] Speed: 680.34 samples/sec INFO:root:Iter[14] Batch [2620] Speed: 648.09 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samples/sec INFO:root:Iter[14] Batch [4340] Speed: 601.70 samples/sec INFO:root:Iter[14] Batch [4360] Speed: 602.31 samples/sec {fc7_acc} 146000 0.34375 lr-batch-epoch: 0.010000000000000002 4375 14 testing verification.. (12000, 512) infer time 4.611288 [lfw][146000]XNorm: 22.803929 [lfw][146000]Accuracy-Flip: 0.99767+-0.00260 testing verification.. (14000, 512) infer time 5.253866999999999 [cfp_fp][146000]XNorm: 20.878355 [cfp_fp][146000]Accuracy-Flip: 0.98000+-0.00753 testing verification.. (12000, 512) infer time 4.520581999999999 [agedb_30][146000]XNorm: 22.532789 [agedb_30][146000]Accuracy-Flip: 0.97517+-0.00776 [146000]Accuracy-Highest: 0.97750 INFO:root:Iter[14] Batch [4380] Speed: 246.95 samples/sec INFO:root:Iter[14] Batch [4400] Speed: 765.97 samples/sec INFO:root:Iter[14] Batch [4420] Speed: 774.84 samples/sec INFO:root:Iter[14] Batch [4440] Speed: 764.36 samples/sec INFO:root:Iter[14] Batch [4460] Speed: 734.91 samples/sec INFO:root:Iter[14] Batch [4480] Speed: 770.62 samples/sec INFO:root:Iter[14] Batch [4500] Speed: 712.66 samples/sec INFO:root:Iter[14] Batch [4520] Speed: 662.42 samples/sec INFO:root:Iter[14] Batch [4540] Speed: 615.05 samples/sec INFO:root:Iter[14] Batch [4560] Speed: 645.27 samples/sec INFO:root:Iter[14] Batch [4580] Speed: 631.30 samples/sec INFO:root:Iter[14] Batch [4600] Speed: 641.42 samples/sec INFO:root:Iter[14] Batch [4620] Speed: 647.55 samples/sec INFO:root:Iter[14] Batch [4640] Speed: 623.55 samples/sec INFO:root:Iter[14] Batch 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INFO:root:Iter[14] Batch [6360] Speed: 596.75 samples/sec {fc7_acc} 148000 0.3671875 lr-batch-epoch: 0.010000000000000002 6375 14 testing verification.. (12000, 512) infer time 4.334922999999998 [lfw][148000]XNorm: 22.301018 [lfw][148000]Accuracy-Flip: 0.99633+-0.00267 testing verification.. (14000, 512) infer time 5.348769999999998 [cfp_fp][148000]XNorm: 20.621164 [cfp_fp][148000]Accuracy-Flip: 0.98100+-0.00572 testing verification.. (12000, 512) infer time 4.167936999999999 [agedb_30][148000]XNorm: 22.039533 [agedb_30][148000]Accuracy-Flip: 0.97717+-0.00863 [148000]Accuracy-Highest: 0.97750 INFO:root:Iter[14] Batch [6380] Speed: 247.86 samples/sec INFO:root:Iter[14] Batch [6400] Speed: 767.74 samples/sec INFO:root:Iter[14] Batch [6420] Speed: 749.39 samples/sec INFO:root:Iter[14] Batch [6440] Speed: 743.62 samples/sec INFO:root:Iter[14] Batch [6460] Speed: 790.96 samples/sec INFO:root:Iter[14] Batch [6480] Speed: 761.43 samples/sec INFO:root:Iter[14] Batch [6500] Speed: 714.95 samples/sec INFO:root:Iter[14] Batch [6520] Speed: 690.99 samples/sec INFO:root:Iter[14] Batch [6540] Speed: 625.02 samples/sec INFO:root:Iter[14] Batch [6560] Speed: 676.31 samples/sec INFO:root:Iter[14] Batch [6580] Speed: 676.95 samples/sec INFO:root:Iter[14] Batch [6600] Speed: 693.41 samples/sec INFO:root:Iter[14] Batch [6620] Speed: 676.39 samples/sec INFO:root:Iter[14] Batch [6640] Speed: 748.89 samples/sec INFO:root:Iter[14] Batch 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INFO:root:Iter[14] Batch [8360] Speed: 604.30 samples/sec {fc7_acc} 150000 0.41601562 lr-batch-epoch: 0.010000000000000002 8375 14 testing verification.. (12000, 512) infer time 4.411530999999999 [lfw][150000]XNorm: 22.436894 [lfw][150000]Accuracy-Flip: 0.99767+-0.00300 testing verification.. (14000, 512) infer time 5.157344000000002 [cfp_fp][150000]XNorm: 20.822673 [cfp_fp][150000]Accuracy-Flip: 0.98357+-0.00448 testing verification.. (12000, 512) infer time 4.42706 [agedb_30][150000]XNorm: 22.297208 [agedb_30][150000]Accuracy-Flip: 0.97583+-0.00684 [150000]Accuracy-Highest: 0.97750 INFO:root:Iter[14] Batch [8380] Speed: 249.81 samples/sec INFO:root:Iter[14] Batch [8400] Speed: 721.34 samples/sec INFO:root:Iter[14] Batch [8420] Speed: 673.45 samples/sec INFO:root:Iter[14] Batch [8440] Speed: 703.03 samples/sec INFO:root:Iter[14] Batch [8460] Speed: 715.62 samples/sec INFO:root:Iter[14] Batch [8480] Speed: 654.69 samples/sec INFO:root:Iter[14] Batch [8500] Speed: 695.18 samples/sec INFO:root:Iter[14] Batch [8520] Speed: 717.88 samples/sec INFO:root:Iter[14] Batch [8540] Speed: 720.18 samples/sec INFO:root:Iter[14] Batch [8560] Speed: 700.27 samples/sec INFO:root:Iter[14] Batch [8580] Speed: 656.83 samples/sec INFO:root:Iter[14] Batch [8600] Speed: 672.02 samples/sec INFO:root:Iter[14] Batch [8620] Speed: 656.96 samples/sec INFO:root:Iter[14] Batch [8640] Speed: 707.66 samples/sec INFO:root:Iter[14] Batch [8660] 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{fc7_acc} 152000 0.42578125 lr-batch-epoch: 0.010000000000000002 259 15 testing verification.. (12000, 512) infer time 4.533885999999999 [lfw][152000]XNorm: 22.718659 [lfw][152000]Accuracy-Flip: 0.99667+-0.00224 testing verification.. (14000, 512) infer time 5.375025000000001 [cfp_fp][152000]XNorm: 21.040871 [cfp_fp][152000]Accuracy-Flip: 0.97943+-0.00351 testing verification.. (12000, 512) infer time 4.419113 [agedb_30][152000]XNorm: 22.557287 [agedb_30][152000]Accuracy-Flip: 0.97317+-0.00762 [152000]Accuracy-Highest: 0.97750 INFO:root:Iter[15] Batch [260] Speed: 243.67 samples/sec INFO:root:Iter[15] Batch [280] Speed: 739.08 samples/sec INFO:root:Iter[15] Batch [300] Speed: 705.24 samples/sec INFO:root:Iter[15] Batch [320] Speed: 703.33 samples/sec INFO:root:Iter[15] Batch [340] Speed: 721.66 samples/sec INFO:root:Iter[15] Batch [360] Speed: 668.58 samples/sec INFO:root:Iter[15] Batch [380] Speed: 699.63 samples/sec INFO:root:Iter[15] Batch [400] Speed: 626.95 samples/sec INFO:root:Iter[15] Batch [420] Speed: 639.57 samples/sec INFO:root:Iter[15] Batch [440] Speed: 604.06 samples/sec INFO:root:Iter[15] Batch [460] Speed: 702.10 samples/sec INFO:root:Iter[15] Batch [480] Speed: 620.24 samples/sec INFO:root:Iter[15] Batch [500] Speed: 656.17 samples/sec INFO:root:Iter[15] Batch [520] Speed: 630.02 samples/sec INFO:root:Iter[15] Batch [540] Speed: 625.94 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INFO:root:Iter[15] Batch [1920] Speed: 600.46 samples/sec INFO:root:Iter[15] Batch [1940] Speed: 618.07 samples/sec INFO:root:Iter[15] Batch [1960] Speed: 616.30 samples/sec INFO:root:Iter[15] Batch [1980] Speed: 625.19 samples/sec INFO:root:Iter[15] Batch [2000] Speed: 610.99 samples/sec INFO:root:Iter[15] Batch [2020] Speed: 589.88 samples/sec INFO:root:Iter[15] Batch [2040] Speed: 616.73 samples/sec INFO:root:Iter[15] Batch [2060] Speed: 603.95 samples/sec INFO:root:Iter[15] Batch [2080] Speed: 581.30 samples/sec INFO:root:Iter[15] Batch [2100] Speed: 624.19 samples/sec INFO:root:Iter[15] Batch [2120] Speed: 609.03 samples/sec INFO:root:Iter[15] Batch [2140] Speed: 596.80 samples/sec INFO:root:Iter[15] Batch [2160] Speed: 606.65 samples/sec INFO:root:Iter[15] Batch [2180] Speed: 632.86 samples/sec INFO:root:Iter[15] Batch [2200] Speed: 616.23 samples/sec INFO:root:Iter[15] Batch [2220] Speed: 619.14 samples/sec INFO:root:Iter[15] Batch [2240] Speed: 617.83 samples/sec {fc7_acc} 154000 0.40234375 lr-batch-epoch: 0.010000000000000002 2259 15 testing verification.. (12000, 512) infer time 4.531496999999999 [lfw][154000]XNorm: 22.857261 [lfw][154000]Accuracy-Flip: 0.99733+-0.00271 testing verification.. (14000, 512) infer time 5.384439999999999 [cfp_fp][154000]XNorm: 21.188341 [cfp_fp][154000]Accuracy-Flip: 0.98129+-0.00551 testing verification.. (12000, 512) infer time 4.588539 [agedb_30][154000]XNorm: 22.740998 [agedb_30][154000]Accuracy-Flip: 0.97500+-0.00573 [154000]Accuracy-Highest: 0.97750 INFO:root:Iter[15] Batch [2260] Speed: 242.46 samples/sec INFO:root:Iter[15] Batch [2280] Speed: 784.09 samples/sec INFO:root:Iter[15] Batch [2300] Speed: 700.41 samples/sec INFO:root:Iter[15] Batch [2320] Speed: 730.27 samples/sec INFO:root:Iter[15] Batch [2340] Speed: 702.01 samples/sec INFO:root:Iter[15] Batch [2360] Speed: 715.49 samples/sec INFO:root:Iter[15] Batch [2380] Speed: 712.09 samples/sec INFO:root:Iter[15] Batch [2400] Speed: 655.37 samples/sec INFO:root:Iter[15] Batch [2420] Speed: 716.18 samples/sec INFO:root:Iter[15] Batch [2440] Speed: 725.27 samples/sec INFO:root:Iter[15] Batch [2460] Speed: 666.24 samples/sec INFO:root:Iter[15] Batch [2480] Speed: 682.78 samples/sec INFO:root:Iter[15] Batch [2500] Speed: 680.88 samples/sec INFO:root:Iter[15] Batch [2520] Speed: 660.72 samples/sec INFO:root:Iter[15] Batch [2540] 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samples/sec {fc7_acc} 156000 0.375 lr-batch-epoch: 0.010000000000000002 4259 15 testing verification.. (12000, 512) infer time 4.581127 [lfw][156000]XNorm: 22.779744 [lfw][156000]Accuracy-Flip: 0.99683+-0.00293 testing verification.. (14000, 512) infer time 4.8275549999999985 [cfp_fp][156000]XNorm: 21.021297 [cfp_fp][156000]Accuracy-Flip: 0.98171+-0.00605 testing verification.. (12000, 512) infer time 4.0952459999999995 [agedb_30][156000]XNorm: 22.744132 [agedb_30][156000]Accuracy-Flip: 0.97467+-0.00799 [156000]Accuracy-Highest: 0.97750 INFO:root:Iter[15] Batch [4260] Speed: 255.52 samples/sec INFO:root:Iter[15] Batch [4280] Speed: 672.18 samples/sec INFO:root:Iter[15] Batch [4300] Speed: 700.64 samples/sec INFO:root:Iter[15] Batch [4320] Speed: 721.43 samples/sec INFO:root:Iter[15] Batch [4340] Speed: 642.59 samples/sec INFO:root:Iter[15] Batch [4360] Speed: 679.53 samples/sec INFO:root:Iter[15] Batch [4380] Speed: 656.92 samples/sec INFO:root:Iter[15] Batch [4400] Speed: 692.32 samples/sec INFO:root:Iter[15] Batch [4420] Speed: 639.73 samples/sec INFO:root:Iter[15] Batch [4440] Speed: 608.80 samples/sec INFO:root:Iter[15] Batch [4460] Speed: 648.66 samples/sec INFO:root:Iter[15] Batch [4480] Speed: 633.71 samples/sec INFO:root:Iter[15] Batch [4500] Speed: 649.33 samples/sec INFO:root:Iter[15] Batch [4520] Speed: 631.38 samples/sec INFO:root:Iter[15] Batch 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INFO:root:Iter[15] Batch [6240] Speed: 607.16 samples/sec {fc7_acc} 158000 0.40234375 lr-batch-epoch: 0.010000000000000002 6259 15 testing verification.. (12000, 512) infer time 4.522163 [lfw][158000]XNorm: 23.036473 [lfw][158000]Accuracy-Flip: 0.99650+-0.00293 testing verification.. (14000, 512) infer time 4.972971 [cfp_fp][158000]XNorm: 21.051040 [cfp_fp][158000]Accuracy-Flip: 0.98029+-0.00645 testing verification.. (12000, 512) infer time 4.255795 [agedb_30][158000]XNorm: 22.765480 [agedb_30][158000]Accuracy-Flip: 0.97700+-0.00547 [158000]Accuracy-Highest: 0.97750 INFO:root:Iter[15] Batch [6260] Speed: 243.15 samples/sec INFO:root:Iter[15] Batch [6280] Speed: 686.37 samples/sec INFO:root:Iter[15] Batch [6300] Speed: 742.46 samples/sec INFO:root:Iter[15] Batch [6320] Speed: 743.51 samples/sec INFO:root:Iter[15] Batch [6340] Speed: 689.34 samples/sec INFO:root:Iter[15] Batch [6360] Speed: 712.59 samples/sec INFO:root:Iter[15] Batch [6380] Speed: 692.81 samples/sec INFO:root:Iter[15] Batch [6400] Speed: 671.08 samples/sec INFO:root:Iter[15] Batch [6420] Speed: 658.74 samples/sec INFO:root:Iter[15] Batch [6440] Speed: 662.54 samples/sec INFO:root:Iter[15] Batch [6460] Speed: 671.30 samples/sec INFO:root:Iter[15] Batch [6480] Speed: 712.59 samples/sec INFO:root:Iter[15] Batch [6500] Speed: 674.61 samples/sec INFO:root:Iter[15] Batch [6520] Speed: 675.52 samples/sec INFO:root:Iter[15] Batch [6540] 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samples/sec {fc7_acc} 160000 0.3828125 lr change to 0.0010000000000000002 lr-batch-epoch: 0.0010000000000000002 8259 15 testing verification.. (12000, 512) infer time 4.515715999999998 [lfw][160000]XNorm: 23.017197 [lfw][160000]Accuracy-Flip: 0.99633+-0.00340 testing verification.. (14000, 512) infer time 5.373546000000002 [cfp_fp][160000]XNorm: 21.182743 [cfp_fp][160000]Accuracy-Flip: 0.97843+-0.00677 testing verification.. (12000, 512) infer time 4.3391280000000005 [agedb_30][160000]XNorm: 22.763711 [agedb_30][160000]Accuracy-Flip: 0.97717+-0.00768 [160000]Accuracy-Highest: 0.97750 INFO:root:Iter[15] Batch [8260] Speed: 245.18 samples/sec INFO:root:Iter[15] Batch [8280] Speed: 703.27 samples/sec INFO:root:Iter[15] Batch [8300] Speed: 706.13 samples/sec INFO:root:Iter[15] Batch [8320] Speed: 688.15 samples/sec INFO:root:Iter[15] Batch [8340] Speed: 744.14 samples/sec INFO:root:Iter[15] Batch [8360] Speed: 658.73 samples/sec INFO:root:Iter[15] Batch [8380] Speed: 664.55 samples/sec INFO:root:Iter[15] Batch [8400] Speed: 627.41 samples/sec INFO:root:Iter[15] Batch [8420] Speed: 643.90 samples/sec INFO:root:Iter[15] Batch [8440] Speed: 608.25 samples/sec INFO:root:Iter[15] Batch [8460] Speed: 635.42 samples/sec INFO:root:Iter[15] Batch [8480] Speed: 657.39 samples/sec INFO:root:Iter[15] Batch [8500] Speed: 642.66 samples/sec INFO:root:Iter[15] Batch [8520] Speed: 613.45 samples/sec INFO:root:Iter[15] Batch 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(12000, 512) infer time 4.580578000000002 [lfw][162000]XNorm: 22.549730 [lfw][162000]Accuracy-Flip: 0.99667+-0.00365 testing verification.. (14000, 512) infer time 5.459996999999999 [cfp_fp][162000]XNorm: 21.397352 [cfp_fp][162000]Accuracy-Flip: 0.98614+-0.00320 testing verification.. (12000, 512) infer time 4.389001999999998 [agedb_30][162000]XNorm: 22.674503 [agedb_30][162000]Accuracy-Flip: 0.97833+-0.00715 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [162000]Accuracy-Highest: 0.97833 INFO:root:Iter[16] Batch [160] Speed: 246.51 samples/sec INFO:root:Iter[16] Batch [180] Speed: 751.21 samples/sec INFO:root:Iter[16] Batch [200] Speed: 717.94 samples/sec INFO:root:Iter[16] Batch [220] Speed: 711.24 samples/sec INFO:root:Iter[16] Batch [240] Speed: 701.99 samples/sec INFO:root:Iter[16] Batch [260] Speed: 713.27 samples/sec INFO:root:Iter[16] Batch [280] Speed: 658.50 samples/sec INFO:root:Iter[16] Batch [300] Speed: 656.12 samples/sec INFO:root:Iter[16] Batch [320] Speed: 669.99 samples/sec INFO:root:Iter[16] Batch [340] Speed: 692.28 samples/sec INFO:root:Iter[16] Batch [360] Speed: 702.52 samples/sec INFO:root:Iter[16] Batch [380] Speed: 640.89 samples/sec INFO:root:Iter[16] Batch [400] Speed: 644.79 samples/sec 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(12000, 512) infer time 4.534564 [lfw][164000]XNorm: 22.454099 [lfw][164000]Accuracy-Flip: 0.99800+-0.00221 testing verification.. (14000, 512) infer time 5.473105000000001 [cfp_fp][164000]XNorm: 21.505601 [cfp_fp][164000]Accuracy-Flip: 0.98571+-0.00373 testing verification.. (12000, 512) infer time 4.781446 [agedb_30][164000]XNorm: 22.757843 [agedb_30][164000]Accuracy-Flip: 0.97883+-0.00711 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [164000]Accuracy-Highest: 0.97883 INFO:root:Iter[16] Batch [2160] Speed: 244.47 samples/sec INFO:root:Iter[16] Batch [2180] Speed: 718.18 samples/sec INFO:root:Iter[16] Batch [2200] Speed: 621.95 samples/sec INFO:root:Iter[16] Batch [2220] Speed: 665.88 samples/sec INFO:root:Iter[16] Batch [2240] Speed: 678.97 samples/sec INFO:root:Iter[16] Batch [2260] Speed: 674.64 samples/sec INFO:root:Iter[16] Batch [2280] Speed: 665.88 samples/sec INFO:root:Iter[16] Batch [2300] Speed: 666.06 samples/sec INFO:root:Iter[16] Batch [2320] Speed: 649.19 samples/sec INFO:root:Iter[16] Batch [2340] Speed: 609.33 samples/sec INFO:root:Iter[16] Batch [2360] Speed: 648.84 samples/sec INFO:root:Iter[16] Batch [2380] Speed: 642.22 samples/sec INFO:root:Iter[16] Batch [2400] Speed: 626.52 samples/sec 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samples/sec INFO:root:Iter[16] Batch [4120] Speed: 649.78 samples/sec INFO:root:Iter[16] Batch [4140] Speed: 623.27 samples/sec {fc7_acc} 166000 0.62109375 lr-batch-epoch: 0.0010000000000000002 4143 16 testing verification.. (12000, 512) infer time 4.581860999999999 [lfw][166000]XNorm: 22.590786 [lfw][166000]Accuracy-Flip: 0.99767+-0.00238 testing verification.. (14000, 512) infer time 5.272571000000002 [cfp_fp][166000]XNorm: 21.677773 [cfp_fp][166000]Accuracy-Flip: 0.98714+-0.00271 testing verification.. (12000, 512) infer time 4.427877 [agedb_30][166000]XNorm: 22.724973 [agedb_30][166000]Accuracy-Flip: 0.97817+-0.00717 [166000]Accuracy-Highest: 0.97883 INFO:root:Iter[16] Batch [4160] Speed: 253.29 samples/sec INFO:root:Iter[16] Batch [4180] Speed: 692.72 samples/sec INFO:root:Iter[16] Batch [4200] Speed: 664.88 samples/sec INFO:root:Iter[16] Batch [4220] Speed: 637.52 samples/sec INFO:root:Iter[16] Batch [4240] Speed: 642.40 samples/sec INFO:root:Iter[16] Batch [4260] Speed: 614.27 samples/sec INFO:root:Iter[16] Batch [4280] Speed: 623.61 samples/sec INFO:root:Iter[16] Batch [4300] Speed: 662.40 samples/sec INFO:root:Iter[16] Batch [4320] Speed: 610.89 samples/sec INFO:root:Iter[16] Batch [4340] Speed: 625.47 samples/sec INFO:root:Iter[16] Batch [4360] Speed: 639.18 samples/sec INFO:root:Iter[16] Batch [4380] Speed: 629.22 samples/sec INFO:root:Iter[16] Batch [4400] Speed: 612.20 samples/sec INFO:root:Iter[16] Batch [4420] Speed: 619.70 samples/sec INFO:root:Iter[16] Batch [4440] 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samples/sec {fc7_acc} 168000 0.60546875 lr-batch-epoch: 0.0010000000000000002 6143 16 testing verification.. (12000, 512) infer time 4.629511 [lfw][168000]XNorm: 22.362449 [lfw][168000]Accuracy-Flip: 0.99717+-0.00269 testing verification.. (14000, 512) infer time 5.129416 [cfp_fp][168000]XNorm: 21.610070 [cfp_fp][168000]Accuracy-Flip: 0.98700+-0.00426 testing verification.. (12000, 512) infer time 4.524317999999999 [agedb_30][168000]XNorm: 22.744910 [agedb_30][168000]Accuracy-Flip: 0.98067+-0.00739 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [168000]Accuracy-Highest: 0.98067 INFO:root:Iter[16] Batch [6160] Speed: 243.97 samples/sec INFO:root:Iter[16] Batch [6180] Speed: 651.98 samples/sec INFO:root:Iter[16] Batch [6200] Speed: 672.77 samples/sec INFO:root:Iter[16] Batch [6220] Speed: 647.30 samples/sec INFO:root:Iter[16] Batch [6240] Speed: 610.09 samples/sec INFO:root:Iter[16] Batch [6260] Speed: 648.35 samples/sec INFO:root:Iter[16] Batch [6280] Speed: 603.03 samples/sec INFO:root:Iter[16] Batch [6300] Speed: 616.18 samples/sec INFO:root:Iter[16] Batch [6320] Speed: 655.83 samples/sec INFO:root:Iter[16] Batch [6340] Speed: 635.69 samples/sec INFO:root:Iter[16] Batch [6360] Speed: 626.54 samples/sec INFO:root:Iter[16] Batch [6380] Speed: 610.22 samples/sec INFO:root:Iter[16] Batch [6400] Speed: 627.86 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(12000, 512) infer time 4.613684 [lfw][170000]XNorm: 22.287266 [lfw][170000]Accuracy-Flip: 0.99750+-0.00239 testing verification.. (14000, 512) infer time 5.279231000000001 [cfp_fp][170000]XNorm: 21.554854 [cfp_fp][170000]Accuracy-Flip: 0.98671+-0.00286 testing verification.. (12000, 512) infer time 4.576589 [agedb_30][170000]XNorm: 22.741497 [agedb_30][170000]Accuracy-Flip: 0.97950+-0.00633 [170000]Accuracy-Highest: 0.98067 INFO:root:Iter[16] Batch [8160] Speed: 253.85 samples/sec INFO:root:Iter[16] Batch [8180] Speed: 699.55 samples/sec INFO:root:Iter[16] Batch [8200] Speed: 648.99 samples/sec INFO:root:Iter[16] Batch [8220] Speed: 661.29 samples/sec INFO:root:Iter[16] Batch [8240] Speed: 660.31 samples/sec INFO:root:Iter[16] Batch [8260] Speed: 663.09 samples/sec INFO:root:Iter[16] Batch [8280] Speed: 646.71 samples/sec INFO:root:Iter[16] Batch [8300] Speed: 677.71 samples/sec INFO:root:Iter[16] Batch [8320] Speed: 618.88 samples/sec INFO:root:Iter[16] Batch [8340] Speed: 611.26 samples/sec INFO:root:Iter[16] Batch [8360] Speed: 615.66 samples/sec INFO:root:Iter[16] Batch [8380] Speed: 614.38 samples/sec INFO:root:Iter[16] Batch [8400] Speed: 600.21 samples/sec INFO:root:Iter[16] Batch [8420] Speed: 633.03 samples/sec INFO:root:Iter[16] Batch [8440] 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samples/sec {fc7_acc} 172000 0.6269531 lr-batch-epoch: 0.0010000000000000002 27 17 testing verification.. (12000, 512) infer time 4.626301999999999 [lfw][172000]XNorm: 22.221275 [lfw][172000]Accuracy-Flip: 0.99750+-0.00239 testing verification.. (14000, 512) infer time 5.449152 [cfp_fp][172000]XNorm: 21.575128 [cfp_fp][172000]Accuracy-Flip: 0.98771+-0.00339 testing verification.. (12000, 512) infer time 4.733766000000001 [agedb_30][172000]XNorm: 22.667121 [agedb_30][172000]Accuracy-Flip: 0.98117+-0.00699 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [172000]Accuracy-Highest: 0.98117 INFO:root:Iter[17] Batch [40] Speed: 238.65 samples/sec INFO:root:Iter[17] Batch [60] Speed: 633.26 samples/sec INFO:root:Iter[17] Batch [80] Speed: 623.28 samples/sec INFO:root:Iter[17] Batch [100] Speed: 637.71 samples/sec INFO:root:Iter[17] Batch [120] Speed: 674.50 samples/sec INFO:root:Iter[17] Batch [140] Speed: 652.26 samples/sec INFO:root:Iter[17] Batch [160] Speed: 616.79 samples/sec INFO:root:Iter[17] Batch [180] Speed: 628.91 samples/sec INFO:root:Iter[17] Batch [200] Speed: 639.29 samples/sec INFO:root:Iter[17] Batch [220] Speed: 639.44 samples/sec INFO:root:Iter[17] Batch [240] Speed: 635.07 samples/sec INFO:root:Iter[17] Batch [260] Speed: 607.55 samples/sec INFO:root:Iter[17] Batch [280] Speed: 619.11 samples/sec 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samples/sec INFO:root:Iter[17] Batch [2020] Speed: 613.28 samples/sec {fc7_acc} 174000 0.6113281 lr-batch-epoch: 0.0010000000000000002 2027 17 testing verification.. (12000, 512) infer time 4.5834280000000005 [lfw][174000]XNorm: 22.422947 [lfw][174000]Accuracy-Flip: 0.99700+-0.00277 testing verification.. (14000, 512) infer time 5.488603 [cfp_fp][174000]XNorm: 21.798188 [cfp_fp][174000]Accuracy-Flip: 0.98757+-0.00286 testing verification.. (12000, 512) infer time 4.576129 [agedb_30][174000]XNorm: 22.900268 [agedb_30][174000]Accuracy-Flip: 0.98050+-0.00667 [174000]Accuracy-Highest: 0.98117 INFO:root:Iter[17] Batch [2040] Speed: 242.15 samples/sec INFO:root:Iter[17] Batch [2060] Speed: 614.09 samples/sec INFO:root:Iter[17] Batch [2080] Speed: 622.00 samples/sec INFO:root:Iter[17] Batch [2100] Speed: 616.44 samples/sec INFO:root:Iter[17] Batch [2120] Speed: 592.90 samples/sec INFO:root:Iter[17] Batch [2140] Speed: 601.55 samples/sec INFO:root:Iter[17] Batch [2160] Speed: 582.08 samples/sec INFO:root:Iter[17] Batch [2180] Speed: 580.97 samples/sec INFO:root:Iter[17] Batch [2200] Speed: 589.92 samples/sec INFO:root:Iter[17] Batch [2220] Speed: 579.11 samples/sec INFO:root:Iter[17] Batch [2240] Speed: 589.60 samples/sec INFO:root:Iter[17] Batch [2260] Speed: 601.88 samples/sec INFO:root:Iter[17] Batch [2280] Speed: 565.99 samples/sec INFO:root:Iter[17] Batch [2300] Speed: 595.63 samples/sec INFO:root:Iter[17] Batch [2320] 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samples/sec {fc7_acc} 176000 0.6230469 lr-batch-epoch: 0.0010000000000000002 4027 17 testing verification.. (12000, 512) infer time 4.625084 [lfw][176000]XNorm: 22.324362 [lfw][176000]Accuracy-Flip: 0.99750+-0.00239 testing verification.. (14000, 512) infer time 5.5593379999999994 [cfp_fp][176000]XNorm: 21.744170 [cfp_fp][176000]Accuracy-Flip: 0.98757+-0.00264 testing verification.. (12000, 512) infer time 4.696576000000001 [agedb_30][176000]XNorm: 22.776666 [agedb_30][176000]Accuracy-Flip: 0.97950+-0.00628 [176000]Accuracy-Highest: 0.98117 INFO:root:Iter[17] Batch [4040] Speed: 235.22 samples/sec INFO:root:Iter[17] Batch [4060] Speed: 594.11 samples/sec INFO:root:Iter[17] Batch [4080] Speed: 593.21 samples/sec INFO:root:Iter[17] Batch [4100] Speed: 606.52 samples/sec INFO:root:Iter[17] Batch [4120] Speed: 574.16 samples/sec INFO:root:Iter[17] Batch [4140] Speed: 599.71 samples/sec INFO:root:Iter[17] Batch [4160] Speed: 594.27 samples/sec INFO:root:Iter[17] Batch [4180] Speed: 589.79 samples/sec INFO:root:Iter[17] Batch [4200] Speed: 561.12 samples/sec INFO:root:Iter[17] Batch [4220] Speed: 580.34 samples/sec INFO:root:Iter[17] Batch [4240] Speed: 564.94 samples/sec INFO:root:Iter[17] Batch [4260] Speed: 579.47 samples/sec INFO:root:Iter[17] Batch [4280] Speed: 579.93 samples/sec INFO:root:Iter[17] Batch [4300] Speed: 608.16 samples/sec INFO:root:Iter[17] Batch 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INFO:root:Iter[17] Batch [6020] Speed: 601.46 samples/sec {fc7_acc} 178000 0.6425781 lr-batch-epoch: 0.0010000000000000002 6027 17 testing verification.. (12000, 512) infer time 4.678597999999999 [lfw][178000]XNorm: 22.304220 [lfw][178000]Accuracy-Flip: 0.99767+-0.00260 testing verification.. (14000, 512) infer time 5.311802000000001 [cfp_fp][178000]XNorm: 21.801386 [cfp_fp][178000]Accuracy-Flip: 0.98886+-0.00237 testing verification.. (12000, 512) infer time 4.776193000000002 [agedb_30][178000]XNorm: 22.941455 [agedb_30][178000]Accuracy-Flip: 0.97983+-0.00660 [178000]Accuracy-Highest: 0.98117 INFO:root:Iter[17] Batch [6040] Speed: 239.56 samples/sec INFO:root:Iter[17] Batch [6060] Speed: 591.58 samples/sec INFO:root:Iter[17] Batch [6080] Speed: 580.12 samples/sec INFO:root:Iter[17] Batch [6100] Speed: 608.82 samples/sec INFO:root:Iter[17] Batch [6120] Speed: 574.21 samples/sec INFO:root:Iter[17] Batch [6140] Speed: 589.29 samples/sec INFO:root:Iter[17] Batch [6160] Speed: 584.42 samples/sec INFO:root:Iter[17] Batch [6180] Speed: 582.58 samples/sec INFO:root:Iter[17] Batch [6200] Speed: 556.61 samples/sec INFO:root:Iter[17] Batch [6220] Speed: 585.64 samples/sec INFO:root:Iter[17] Batch [6240] Speed: 595.86 samples/sec INFO:root:Iter[17] Batch [6260] Speed: 581.23 samples/sec INFO:root:Iter[17] Batch [6280] Speed: 584.18 samples/sec INFO:root:Iter[17] Batch [6300] Speed: 577.23 samples/sec INFO:root:Iter[17] Batch 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INFO:root:Iter[17] Batch [8020] Speed: 610.54 samples/sec {fc7_acc} 180000 0.61328125 lr-batch-epoch: 0.0010000000000000002 8027 17 testing verification.. (12000, 512) infer time 4.7814960000000015 [lfw][180000]XNorm: 22.351871 [lfw][180000]Accuracy-Flip: 0.99733+-0.00271 testing verification.. (14000, 512) infer time 5.510505 [cfp_fp][180000]XNorm: 21.734676 [cfp_fp][180000]Accuracy-Flip: 0.98771+-0.00357 testing verification.. (12000, 512) infer time 4.710077 [agedb_30][180000]XNorm: 22.746195 [agedb_30][180000]Accuracy-Flip: 0.98133+-0.00702 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [180000]Accuracy-Highest: 0.98133 INFO:root:Iter[17] Batch [8040] Speed: 230.37 samples/sec INFO:root:Iter[17] Batch [8060] Speed: 597.39 samples/sec INFO:root:Iter[17] Batch [8080] Speed: 592.24 samples/sec INFO:root:Iter[17] Batch [8100] Speed: 582.32 samples/sec INFO:root:Iter[17] Batch [8120] Speed: 595.58 samples/sec INFO:root:Iter[17] Batch [8140] Speed: 602.41 samples/sec INFO:root:Iter[17] Batch [8160] Speed: 575.49 samples/sec INFO:root:Iter[17] Batch [8180] Speed: 596.14 samples/sec INFO:root:Iter[17] Batch [8200] Speed: 586.75 samples/sec INFO:root:Iter[17] Batch [8220] Speed: 575.66 samples/sec INFO:root:Iter[17] Batch [8240] Speed: 580.23 samples/sec INFO:root:Iter[17] Batch [8260] Speed: 577.71 samples/sec INFO:root:Iter[17] Batch [8280] Speed: 566.98 samples/sec 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samples/sec INFO:root:Iter[17] Batch [10000] Speed: 613.78 samples/sec INFO:root:Iter[17] Batch [10020] Speed: 601.25 samples/sec {fc7_acc} 182000 0.6582031 lr-batch-epoch: 0.0010000000000000002 10027 17 testing verification.. (12000, 512) infer time 4.629121 [lfw][182000]XNorm: 22.344922 [lfw][182000]Accuracy-Flip: 0.99750+-0.00239 testing verification.. (14000, 512) infer time 5.51079 [cfp_fp][182000]XNorm: 21.834040 [cfp_fp][182000]Accuracy-Flip: 0.98829+-0.00318 testing verification.. (12000, 512) infer time 4.641132 [agedb_30][182000]XNorm: 22.648604 [agedb_30][182000]Accuracy-Flip: 0.97950+-0.00707 [182000]Accuracy-Highest: 0.98133 INFO:root:Iter[17] Batch [10040] Speed: 234.47 samples/sec INFO:root:Iter[17] Batch [10060] Speed: 597.09 samples/sec INFO:root:Iter[17] Batch [10080] Speed: 569.12 samples/sec INFO:root:Iter[17] Batch [10100] Speed: 585.20 samples/sec INFO:root:Epoch[17] Time cost=8786.149 call reset() INFO:root:Iter[18] Batch [20] Speed: 582.14 samples/sec INFO:root:Iter[18] Batch [40] Speed: 582.11 samples/sec INFO:root:Iter[18] Batch [60] Speed: 577.53 samples/sec INFO:root:Iter[18] Batch [80] Speed: 577.94 samples/sec INFO:root:Iter[18] Batch [100] Speed: 580.23 samples/sec INFO:root:Iter[18] Batch [120] Speed: 575.64 samples/sec INFO:root:Iter[18] Batch [140] Speed: 572.11 samples/sec INFO:root:Iter[18] Batch [160] Speed: 579.75 samples/sec INFO:root:Iter[18] Batch [180] Speed: 585.63 samples/sec INFO:root:Iter[18] Batch [200] Speed: 582.03 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0.0010000000000000002 1911 18 testing verification.. (12000, 512) infer time 4.597079 [lfw][184000]XNorm: 22.310604 [lfw][184000]Accuracy-Flip: 0.99733+-0.00249 testing verification.. (14000, 512) infer time 5.438942999999999 [cfp_fp][184000]XNorm: 21.871057 [cfp_fp][184000]Accuracy-Flip: 0.98943+-0.00301 testing verification.. (12000, 512) infer time 4.699032000000001 [agedb_30][184000]XNorm: 22.751367 [agedb_30][184000]Accuracy-Flip: 0.97767+-0.00841 [184000]Accuracy-Highest: 0.98133 INFO:root:Iter[18] Batch [1920] Speed: 234.34 samples/sec INFO:root:Iter[18] Batch [1940] Speed: 567.01 samples/sec INFO:root:Iter[18] Batch [1960] Speed: 565.32 samples/sec INFO:root:Iter[18] Batch [1980] Speed: 573.61 samples/sec INFO:root:Iter[18] Batch [2000] Speed: 563.82 samples/sec INFO:root:Iter[18] Batch [2020] Speed: 580.88 samples/sec INFO:root:Iter[18] Batch [2040] Speed: 575.87 samples/sec INFO:root:Iter[18] Batch [2060] Speed: 560.19 samples/sec INFO:root:Iter[18] Batch [2080] Speed: 562.10 samples/sec INFO:root:Iter[18] Batch [2100] Speed: 563.22 samples/sec INFO:root:Iter[18] Batch [2120] Speed: 569.46 samples/sec INFO:root:Iter[18] Batch [2140] Speed: 559.35 samples/sec INFO:root:Iter[18] Batch [2160] Speed: 553.12 samples/sec INFO:root:Iter[18] Batch [2180] Speed: 567.73 samples/sec INFO:root:Iter[18] Batch 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INFO:root:Iter[18] Batch [3900] Speed: 579.89 samples/sec {fc7_acc} 186000 0.62109375 lr-batch-epoch: 0.0010000000000000002 3911 18 testing verification.. (12000, 512) infer time 4.626253999999999 [lfw][186000]XNorm: 22.377109 [lfw][186000]Accuracy-Flip: 0.99833+-0.00211 testing verification.. (14000, 512) infer time 5.640823 [cfp_fp][186000]XNorm: 21.916055 [cfp_fp][186000]Accuracy-Flip: 0.98786+-0.00395 testing verification.. (12000, 512) infer time 4.812139000000001 [agedb_30][186000]XNorm: 22.909451 [agedb_30][186000]Accuracy-Flip: 0.98000+-0.00601 [186000]Accuracy-Highest: 0.98133 INFO:root:Iter[18] Batch [3920] Speed: 231.48 samples/sec INFO:root:Iter[18] Batch [3940] Speed: 563.36 samples/sec INFO:root:Iter[18] Batch [3960] Speed: 559.18 samples/sec INFO:root:Iter[18] Batch [3980] Speed: 577.24 samples/sec INFO:root:Iter[18] Batch [4000] Speed: 568.78 samples/sec INFO:root:Iter[18] Batch [4020] Speed: 559.39 samples/sec INFO:root:Iter[18] Batch [4040] Speed: 584.36 samples/sec INFO:root:Iter[18] Batch [4060] Speed: 552.38 samples/sec INFO:root:Iter[18] Batch [4080] Speed: 579.01 samples/sec INFO:root:Iter[18] Batch [4100] Speed: 569.91 samples/sec INFO:root:Iter[18] Batch [4120] Speed: 570.12 samples/sec INFO:root:Iter[18] Batch [4140] Speed: 581.11 samples/sec INFO:root:Iter[18] Batch [4160] Speed: 582.25 samples/sec INFO:root:Iter[18] Batch [4180] Speed: 561.92 samples/sec INFO:root:Iter[18] Batch 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INFO:root:Iter[18] Batch [5900] Speed: 122.16 samples/sec {fc7_acc} 188000 0.61328125 lr-batch-epoch: 0.0010000000000000002 5911 18 testing verification.. (12000, 512) infer time 5.982574999999998 [lfw][188000]XNorm: 22.435840 [lfw][188000]Accuracy-Flip: 0.99717+-0.00279 testing verification.. (14000, 512) infer time 5.648836999999998 [cfp_fp][188000]XNorm: 22.102128 [cfp_fp][188000]Accuracy-Flip: 0.98886+-0.00413 testing verification.. (12000, 512) infer time 4.590596 [agedb_30][188000]XNorm: 22.899462 [agedb_30][188000]Accuracy-Flip: 0.97933+-0.00731 [188000]Accuracy-Highest: 0.98133 INFO:root:Iter[18] Batch [5920] Speed: 94.78 samples/sec INFO:root:Iter[18] Batch [5940] Speed: 572.67 samples/sec INFO:root:Iter[18] Batch [5960] Speed: 572.47 samples/sec INFO:root:Iter[18] Batch [5980] Speed: 575.82 samples/sec INFO:root:Iter[18] Batch [6000] Speed: 548.46 samples/sec INFO:root:Iter[18] Batch [6020] Speed: 545.22 samples/sec INFO:root:Iter[18] Batch [6040] Speed: 571.43 samples/sec INFO:root:Iter[18] Batch [6060] Speed: 550.75 samples/sec INFO:root:Iter[18] Batch [6080] Speed: 547.77 samples/sec INFO:root:Iter[18] Batch [6100] Speed: 562.98 samples/sec INFO:root:Iter[18] Batch [6120] Speed: 548.48 samples/sec INFO:root:Iter[18] Batch [6140] Speed: 582.82 samples/sec INFO:root:Iter[18] Batch [6160] Speed: 562.73 samples/sec INFO:root:Iter[18] Batch [6180] Speed: 566.76 samples/sec INFO:root:Iter[18] Batch [6200] 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samples/sec {fc7_acc} 190000 0.6425781 lr-batch-epoch: 0.0010000000000000002 7911 18 testing verification.. (12000, 512) infer time 4.5763219999999984 [lfw][190000]XNorm: 22.539247 [lfw][190000]Accuracy-Flip: 0.99717+-0.00308 testing verification.. (14000, 512) infer time 5.132697 [cfp_fp][190000]XNorm: 21.985819 [cfp_fp][190000]Accuracy-Flip: 0.98771+-0.00301 testing verification.. (12000, 512) infer time 4.303090999999999 [agedb_30][190000]XNorm: 22.969587 [agedb_30][190000]Accuracy-Flip: 0.98100+-0.00742 [190000]Accuracy-Highest: 0.98133 INFO:root:Iter[18] Batch [7920] Speed: 247.21 samples/sec INFO:root:Iter[18] Batch [7940] Speed: 808.09 samples/sec INFO:root:Iter[18] Batch [7960] Speed: 728.53 samples/sec INFO:root:Iter[18] Batch [7980] Speed: 665.81 samples/sec INFO:root:Iter[18] Batch [8000] Speed: 674.39 samples/sec INFO:root:Iter[18] Batch [8020] Speed: 651.23 samples/sec INFO:root:Iter[18] Batch [8040] Speed: 671.73 samples/sec INFO:root:Iter[18] Batch [8060] Speed: 605.62 samples/sec INFO:root:Iter[18] Batch [8080] Speed: 635.74 samples/sec INFO:root:Iter[18] Batch [8100] Speed: 625.34 samples/sec INFO:root:Iter[18] Batch [8120] Speed: 608.72 samples/sec INFO:root:Iter[18] Batch [8140] Speed: 632.05 samples/sec INFO:root:Iter[18] Batch [8160] Speed: 620.25 samples/sec INFO:root:Iter[18] Batch [8180] Speed: 628.55 samples/sec INFO:root:Iter[18] Batch 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INFO:root:Iter[18] Batch [9900] Speed: 619.03 samples/sec {fc7_acc} 192000 0.6425781 lr-batch-epoch: 0.0010000000000000002 9911 18 testing verification.. (12000, 512) infer time 4.5447310000000005 [lfw][192000]XNorm: 22.254041 [lfw][192000]Accuracy-Flip: 0.99717+-0.00279 testing verification.. (14000, 512) infer time 5.435789 [cfp_fp][192000]XNorm: 21.851157 [cfp_fp][192000]Accuracy-Flip: 0.98771+-0.00194 testing verification.. (12000, 512) infer time 4.524665000000001 [agedb_30][192000]XNorm: 22.842779 [agedb_30][192000]Accuracy-Flip: 0.97983+-0.00713 [192000]Accuracy-Highest: 0.98133 INFO:root:Iter[18] Batch [9920] Speed: 248.37 samples/sec INFO:root:Iter[18] Batch [9940] Speed: 720.23 samples/sec INFO:root:Iter[18] Batch [9960] Speed: 744.11 samples/sec INFO:root:Iter[18] Batch [9980] Speed: 686.80 samples/sec INFO:root:Iter[18] Batch [10000] Speed: 810.14 samples/sec INFO:root:Iter[18] Batch [10020] Speed: 676.31 samples/sec INFO:root:Iter[18] Batch [10040] Speed: 669.94 samples/sec INFO:root:Iter[18] Batch [10060] Speed: 629.29 samples/sec INFO:root:Iter[18] Batch [10080] Speed: 607.67 samples/sec INFO:root:Iter[18] Batch [10100] Speed: 622.01 samples/sec INFO:root:Epoch[18] Time cost=10248.955 call reset() INFO:root:Iter[19] Batch [20] Speed: 704.20 samples/sec INFO:root:Iter[19] Batch [40] Speed: 616.36 samples/sec INFO:root:Iter[19] Batch [60] Speed: 658.70 samples/sec INFO:root:Iter[19] Batch [80] 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194000 0.671875 lr-batch-epoch: 0.0010000000000000002 1795 19 testing verification.. (12000, 512) infer time 4.464005000000001 [lfw][194000]XNorm: 22.373273 [lfw][194000]Accuracy-Flip: 0.99750+-0.00250 testing verification.. (14000, 512) infer time 5.328418 [cfp_fp][194000]XNorm: 21.998590 [cfp_fp][194000]Accuracy-Flip: 0.98757+-0.00356 testing verification.. (12000, 512) infer time 4.226246 [agedb_30][194000]XNorm: 22.850469 [agedb_30][194000]Accuracy-Flip: 0.98067+-0.00742 [194000]Accuracy-Highest: 0.98133 INFO:root:Iter[19] Batch [1800] Speed: 247.54 samples/sec INFO:root:Iter[19] Batch [1820] Speed: 807.65 samples/sec INFO:root:Iter[19] Batch [1840] Speed: 786.28 samples/sec INFO:root:Iter[19] Batch [1860] Speed: 811.91 samples/sec INFO:root:Iter[19] Batch [1880] Speed: 777.39 samples/sec INFO:root:Iter[19] Batch [1900] Speed: 721.63 samples/sec INFO:root:Iter[19] Batch [1920] Speed: 675.03 samples/sec INFO:root:Iter[19] Batch [1940] Speed: 661.44 samples/sec INFO:root:Iter[19] Batch [1960] Speed: 678.78 samples/sec INFO:root:Iter[19] Batch [1980] Speed: 646.74 samples/sec INFO:root:Iter[19] Batch [2000] Speed: 626.44 samples/sec INFO:root:Iter[19] Batch [2020] Speed: 611.92 samples/sec INFO:root:Iter[19] Batch [2040] Speed: 623.36 samples/sec INFO:root:Iter[19] Batch [2060] Speed: 643.94 samples/sec INFO:root:Iter[19] Batch [2080] 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samples/sec {fc7_acc} 196000 0.6699219 lr-batch-epoch: 0.0010000000000000002 3795 19 testing verification.. (12000, 512) infer time 4.539581000000001 [lfw][196000]XNorm: 22.349757 [lfw][196000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 4.614454 [cfp_fp][196000]XNorm: 22.001451 [cfp_fp][196000]Accuracy-Flip: 0.98857+-0.00306 testing verification.. (12000, 512) infer time 3.4799790000000006 [agedb_30][196000]XNorm: 22.699597 [agedb_30][196000]Accuracy-Flip: 0.98000+-0.00691 [196000]Accuracy-Highest: 0.98133 INFO:root:Iter[19] Batch [3800] Speed: 254.40 samples/sec INFO:root:Iter[19] Batch [3820] Speed: 787.10 samples/sec INFO:root:Iter[19] Batch [3840] Speed: 763.04 samples/sec INFO:root:Iter[19] Batch [3860] Speed: 702.55 samples/sec INFO:root:Iter[19] Batch [3880] Speed: 683.56 samples/sec INFO:root:Iter[19] Batch [3900] Speed: 637.00 samples/sec INFO:root:Iter[19] Batch [3920] Speed: 650.42 samples/sec INFO:root:Iter[19] Batch [3940] Speed: 611.40 samples/sec INFO:root:Iter[19] Batch [3960] Speed: 631.12 samples/sec INFO:root:Iter[19] Batch [3980] Speed: 629.90 samples/sec INFO:root:Iter[19] Batch [4000] Speed: 622.54 samples/sec INFO:root:Iter[19] Batch [4020] Speed: 596.75 samples/sec INFO:root:Iter[19] Batch [4040] Speed: 607.83 samples/sec INFO:root:Iter[19] Batch [4060] Speed: 650.23 samples/sec INFO:root:Iter[19] Batch 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INFO:root:Iter[19] Batch [5780] Speed: 629.72 samples/sec {fc7_acc} 198000 0.6503906 lr-batch-epoch: 0.0010000000000000002 5795 19 testing verification.. (12000, 512) infer time 4.315464 [lfw][198000]XNorm: 22.327605 [lfw][198000]Accuracy-Flip: 0.99733+-0.00271 testing verification.. (14000, 512) infer time 4.364106999999999 [cfp_fp][198000]XNorm: 21.851095 [cfp_fp][198000]Accuracy-Flip: 0.98929+-0.00321 testing verification.. (12000, 512) infer time 3.81614 [agedb_30][198000]XNorm: 22.886056 [agedb_30][198000]Accuracy-Flip: 0.97967+-0.00816 [198000]Accuracy-Highest: 0.98133 INFO:root:Iter[19] Batch [5800] Speed: 257.39 samples/sec INFO:root:Iter[19] Batch [5820] Speed: 759.87 samples/sec INFO:root:Iter[19] Batch [5840] Speed: 779.88 samples/sec INFO:root:Iter[19] Batch [5860] Speed: 787.17 samples/sec INFO:root:Iter[19] Batch [5880] Speed: 668.12 samples/sec INFO:root:Iter[19] Batch [5900] Speed: 737.19 samples/sec INFO:root:Iter[19] Batch [5920] Speed: 748.75 samples/sec INFO:root:Iter[19] Batch [5940] Speed: 666.74 samples/sec INFO:root:Iter[19] Batch [5960] Speed: 663.23 samples/sec INFO:root:Iter[19] Batch [5980] Speed: 693.39 samples/sec INFO:root:Iter[19] Batch [6000] Speed: 619.96 samples/sec INFO:root:Iter[19] Batch [6020] Speed: 671.35 samples/sec INFO:root:Iter[19] Batch [6040] Speed: 686.49 samples/sec INFO:root:Iter[19] Batch [6060] Speed: 645.95 samples/sec INFO:root:Iter[19] Batch [6080] 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samples/sec {fc7_acc} 200000 0.6933594 lr-batch-epoch: 0.0010000000000000002 7795 19 testing verification.. (12000, 512) infer time 4.379664999999998 [lfw][200000]XNorm: 22.540620 [lfw][200000]Accuracy-Flip: 0.99767+-0.00226 testing verification.. (14000, 512) infer time 5.399257999999999 [cfp_fp][200000]XNorm: 22.015812 [cfp_fp][200000]Accuracy-Flip: 0.98857+-0.00286 testing verification.. (12000, 512) infer time 4.267128 [agedb_30][200000]XNorm: 22.860042 [agedb_30][200000]Accuracy-Flip: 0.98067+-0.00688 [200000]Accuracy-Highest: 0.98133 INFO:root:Iter[19] Batch [7800] Speed: 245.35 samples/sec INFO:root:Iter[19] Batch [7820] Speed: 778.82 samples/sec INFO:root:Iter[19] Batch [7840] Speed: 744.54 samples/sec INFO:root:Iter[19] Batch [7860] Speed: 777.41 samples/sec INFO:root:Iter[19] Batch [7880] Speed: 694.13 samples/sec INFO:root:Iter[19] Batch [7900] Speed: 754.40 samples/sec INFO:root:Iter[19] Batch [7920] Speed: 688.05 samples/sec INFO:root:Iter[19] Batch [7940] Speed: 671.86 samples/sec INFO:root:Iter[19] Batch [7960] Speed: 697.36 samples/sec INFO:root:Iter[19] Batch [7980] Speed: 695.93 samples/sec INFO:root:Iter[19] Batch [8000] Speed: 667.62 samples/sec INFO:root:Iter[19] Batch [8020] Speed: 686.99 samples/sec INFO:root:Iter[19] Batch [8040] Speed: 645.13 samples/sec INFO:root:Iter[19] Batch [8060] Speed: 633.42 samples/sec INFO:root:Iter[19] Batch [8080] 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samples/sec {fc7_acc} 202000 0.6425781 lr-batch-epoch: 0.0010000000000000002 9795 19 testing verification.. (12000, 512) infer time 4.562984000000001 [lfw][202000]XNorm: 22.523423 [lfw][202000]Accuracy-Flip: 0.99767+-0.00238 testing verification.. (14000, 512) infer time 5.3992830000000005 [cfp_fp][202000]XNorm: 22.015804 [cfp_fp][202000]Accuracy-Flip: 0.98814+-0.00320 testing verification.. (12000, 512) infer time 4.2082619999999995 [agedb_30][202000]XNorm: 22.882395 [agedb_30][202000]Accuracy-Flip: 0.97833+-0.00789 [202000]Accuracy-Highest: 0.98133 INFO:root:Iter[19] Batch [9800] Speed: 238.43 samples/sec INFO:root:Iter[19] Batch [9820] Speed: 709.83 samples/sec INFO:root:Iter[19] Batch [9840] Speed: 752.47 samples/sec INFO:root:Iter[19] Batch [9860] Speed: 751.69 samples/sec INFO:root:Iter[19] Batch [9880] Speed: 719.43 samples/sec INFO:root:Iter[19] Batch [9900] Speed: 715.47 samples/sec INFO:root:Iter[19] Batch [9920] Speed: 628.81 samples/sec INFO:root:Iter[19] Batch [9940] Speed: 687.84 samples/sec INFO:root:Iter[19] Batch [9960] Speed: 706.66 samples/sec INFO:root:Iter[19] Batch [9980] Speed: 688.73 samples/sec INFO:root:Iter[19] Batch [10000] Speed: 639.39 samples/sec INFO:root:Iter[19] Batch [10020] Speed: 692.35 samples/sec INFO:root:Iter[19] Batch [10040] Speed: 676.24 samples/sec INFO:root:Iter[19] Batch [10060] Speed: 669.29 samples/sec INFO:root:Iter[19] 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204000 0.6738281 lr-batch-epoch: 0.0010000000000000002 1679 20 testing verification.. (12000, 512) infer time 4.6051090000000015 [lfw][204000]XNorm: 22.524374 [lfw][204000]Accuracy-Flip: 0.99750+-0.00239 testing verification.. (14000, 512) infer time 5.069748000000001 [cfp_fp][204000]XNorm: 22.121963 [cfp_fp][204000]Accuracy-Flip: 0.98929+-0.00363 testing verification.. (12000, 512) infer time 4.34192 [agedb_30][204000]XNorm: 23.020326 [agedb_30][204000]Accuracy-Flip: 0.98000+-0.00860 [204000]Accuracy-Highest: 0.98133 INFO:root:Iter[20] Batch [1680] Speed: 244.37 samples/sec INFO:root:Iter[20] Batch [1700] Speed: 730.35 samples/sec INFO:root:Iter[20] Batch [1720] Speed: 759.24 samples/sec INFO:root:Iter[20] Batch [1740] Speed: 759.28 samples/sec INFO:root:Iter[20] Batch [1760] Speed: 712.70 samples/sec INFO:root:Iter[20] Batch [1780] Speed: 742.00 samples/sec INFO:root:Iter[20] Batch [1800] Speed: 675.71 samples/sec INFO:root:Iter[20] Batch [1820] Speed: 604.13 samples/sec INFO:root:Iter[20] Batch [1840] Speed: 655.22 samples/sec INFO:root:Iter[20] Batch [1860] Speed: 649.49 samples/sec INFO:root:Iter[20] Batch [1880] Speed: 711.64 samples/sec INFO:root:Iter[20] Batch [1900] Speed: 639.39 samples/sec INFO:root:Iter[20] Batch [1920] Speed: 638.82 samples/sec INFO:root:Iter[20] Batch [1940] Speed: 623.60 samples/sec INFO:root:Iter[20] Batch [1960] 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samples/sec {fc7_acc} 206000 0.64453125 lr-batch-epoch: 0.0010000000000000002 3679 20 testing verification.. (12000, 512) infer time 4.544575 [lfw][206000]XNorm: 22.154274 [lfw][206000]Accuracy-Flip: 0.99767+-0.00238 testing verification.. (14000, 512) infer time 5.448837000000001 [cfp_fp][206000]XNorm: 22.123268 [cfp_fp][206000]Accuracy-Flip: 0.98771+-0.00301 testing verification.. (12000, 512) infer time 4.583459000000001 [agedb_30][206000]XNorm: 22.821958 [agedb_30][206000]Accuracy-Flip: 0.97950+-0.00827 [206000]Accuracy-Highest: 0.98133 INFO:root:Iter[20] Batch [3680] Speed: 243.08 samples/sec INFO:root:Iter[20] Batch [3700] Speed: 743.72 samples/sec INFO:root:Iter[20] Batch [3720] Speed: 747.88 samples/sec INFO:root:Iter[20] Batch [3740] Speed: 751.33 samples/sec INFO:root:Iter[20] Batch [3760] Speed: 688.25 samples/sec INFO:root:Iter[20] Batch [3780] Speed: 645.94 samples/sec INFO:root:Iter[20] Batch [3800] Speed: 704.27 samples/sec INFO:root:Iter[20] Batch [3820] Speed: 608.53 samples/sec INFO:root:Iter[20] Batch [3840] Speed: 644.21 samples/sec INFO:root:Iter[20] Batch [3860] Speed: 609.80 samples/sec INFO:root:Iter[20] Batch [3880] Speed: 635.22 samples/sec INFO:root:Iter[20] Batch [3900] Speed: 617.69 samples/sec INFO:root:Iter[20] Batch [3920] Speed: 613.62 samples/sec INFO:root:Iter[20] Batch [3940] Speed: 644.43 samples/sec INFO:root:Iter[20] Batch 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INFO:root:Iter[20] Batch [5660] Speed: 619.15 samples/sec {fc7_acc} 208000 0.6484375 lr-batch-epoch: 0.0010000000000000002 5679 20 testing verification.. (12000, 512) infer time 4.531166000000002 [lfw][208000]XNorm: 22.524271 [lfw][208000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.203143999999998 [cfp_fp][208000]XNorm: 22.094445 [cfp_fp][208000]Accuracy-Flip: 0.98886+-0.00343 testing verification.. (12000, 512) infer time 4.541568 [agedb_30][208000]XNorm: 23.003073 [agedb_30][208000]Accuracy-Flip: 0.98083+-0.00664 [208000]Accuracy-Highest: 0.98133 INFO:root:Iter[20] Batch [5680] Speed: 242.75 samples/sec INFO:root:Iter[20] Batch [5700] Speed: 758.34 samples/sec INFO:root:Iter[20] Batch [5720] Speed: 687.06 samples/sec INFO:root:Iter[20] Batch [5740] Speed: 688.01 samples/sec INFO:root:Iter[20] Batch [5760] Speed: 724.33 samples/sec INFO:root:Iter[20] Batch [5780] Speed: 660.43 samples/sec INFO:root:Iter[20] Batch [5800] Speed: 667.38 samples/sec INFO:root:Iter[20] Batch [5820] Speed: 660.97 samples/sec INFO:root:Iter[20] Batch [5840] Speed: 606.81 samples/sec INFO:root:Iter[20] Batch [5860] Speed: 628.53 samples/sec INFO:root:Iter[20] Batch [5880] Speed: 631.52 samples/sec INFO:root:Iter[20] Batch [5900] Speed: 629.30 samples/sec INFO:root:Iter[20] Batch [5920] Speed: 637.70 samples/sec INFO:root:Iter[20] Batch [5940] Speed: 633.69 samples/sec INFO:root:Iter[20] Batch [5960] 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samples/sec {fc7_acc} 210000 0.6875 lr-batch-epoch: 0.0010000000000000002 7679 20 testing verification.. (12000, 512) infer time 4.460148000000001 [lfw][210000]XNorm: 22.654044 [lfw][210000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.1507819999999995 [cfp_fp][210000]XNorm: 22.314327 [cfp_fp][210000]Accuracy-Flip: 0.98929+-0.00374 testing verification.. (12000, 512) infer time 4.5117709999999995 [agedb_30][210000]XNorm: 22.986626 [agedb_30][210000]Accuracy-Flip: 0.97967+-0.00710 [210000]Accuracy-Highest: 0.98133 INFO:root:Iter[20] Batch [7680] Speed: 239.79 samples/sec INFO:root:Iter[20] Batch [7700] Speed: 694.28 samples/sec INFO:root:Iter[20] Batch [7720] Speed: 705.27 samples/sec INFO:root:Iter[20] Batch [7740] Speed: 708.43 samples/sec INFO:root:Iter[20] Batch [7760] Speed: 687.46 samples/sec INFO:root:Iter[20] Batch [7780] Speed: 671.28 samples/sec INFO:root:Iter[20] Batch [7800] Speed: 656.26 samples/sec INFO:root:Iter[20] Batch [7820] Speed: 655.38 samples/sec INFO:root:Iter[20] Batch [7840] Speed: 643.56 samples/sec INFO:root:Iter[20] Batch [7860] Speed: 679.49 samples/sec INFO:root:Iter[20] Batch [7880] Speed: 689.62 samples/sec INFO:root:Iter[20] Batch [7900] Speed: 677.32 samples/sec INFO:root:Iter[20] Batch [7920] Speed: 668.12 samples/sec INFO:root:Iter[20] Batch [7940] Speed: 691.63 samples/sec INFO:root:Iter[20] Batch 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INFO:root:Iter[20] Batch [9660] Speed: 623.10 samples/sec {fc7_acc} 212000 0.6347656 lr-batch-epoch: 0.0010000000000000002 9679 20 testing verification.. (12000, 512) infer time 4.1624539999999985 [lfw][212000]XNorm: 22.124231 [lfw][212000]Accuracy-Flip: 0.99767+-0.00281 testing verification.. (14000, 512) infer time 5.076325000000001 [cfp_fp][212000]XNorm: 21.802282 [cfp_fp][212000]Accuracy-Flip: 0.98814+-0.00344 testing verification.. (12000, 512) infer time 4.502375000000001 [agedb_30][212000]XNorm: 22.621845 [agedb_30][212000]Accuracy-Flip: 0.97967+-0.00714 [212000]Accuracy-Highest: 0.98133 INFO:root:Iter[20] Batch [9680] Speed: 246.54 samples/sec INFO:root:Iter[20] Batch [9700] Speed: 664.67 samples/sec INFO:root:Iter[20] Batch [9720] Speed: 636.37 samples/sec INFO:root:Iter[20] Batch [9740] Speed: 662.28 samples/sec INFO:root:Iter[20] Batch [9760] Speed: 629.78 samples/sec INFO:root:Iter[20] Batch [9780] Speed: 635.68 samples/sec INFO:root:Iter[20] Batch [9800] Speed: 626.22 samples/sec INFO:root:Iter[20] Batch [9820] Speed: 627.01 samples/sec INFO:root:Iter[20] Batch [9840] Speed: 630.70 samples/sec INFO:root:Iter[20] Batch [9860] Speed: 606.12 samples/sec INFO:root:Iter[20] Batch [9880] Speed: 630.94 samples/sec INFO:root:Iter[20] Batch [9900] Speed: 643.14 samples/sec INFO:root:Iter[20] Batch [9920] Speed: 598.87 samples/sec INFO:root:Iter[20] Batch [9940] Speed: 620.12 samples/sec INFO:root:Iter[20] Batch 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Speed: 606.34 samples/sec {fc7_acc} 214000 0.6796875 lr-batch-epoch: 0.0010000000000000002 1563 21 testing verification.. (12000, 512) infer time 4.7345679999999986 [lfw][214000]XNorm: 21.938307 [lfw][214000]Accuracy-Flip: 0.99817+-0.00229 testing verification.. (14000, 512) infer time 5.486679 [cfp_fp][214000]XNorm: 21.835499 [cfp_fp][214000]Accuracy-Flip: 0.98843+-0.00289 testing verification.. (12000, 512) infer time 4.709214 [agedb_30][214000]XNorm: 22.549564 [agedb_30][214000]Accuracy-Flip: 0.97867+-0.00640 [214000]Accuracy-Highest: 0.98133 INFO:root:Iter[21] Batch [1580] Speed: 244.26 samples/sec INFO:root:Iter[21] Batch [1600] Speed: 637.33 samples/sec INFO:root:Iter[21] Batch [1620] Speed: 639.86 samples/sec INFO:root:Iter[21] Batch [1640] Speed: 588.06 samples/sec INFO:root:Iter[21] Batch [1660] Speed: 598.87 samples/sec INFO:root:Iter[21] Batch [1680] Speed: 622.80 samples/sec INFO:root:Iter[21] Batch [1700] Speed: 608.89 samples/sec INFO:root:Iter[21] Batch [1720] Speed: 598.49 samples/sec INFO:root:Iter[21] Batch [1740] Speed: 596.32 samples/sec INFO:root:Iter[21] Batch [1760] Speed: 608.56 samples/sec INFO:root:Iter[21] Batch [1780] Speed: 604.40 samples/sec INFO:root:Iter[21] Batch [1800] Speed: 606.82 samples/sec INFO:root:Iter[21] Batch [1820] Speed: 596.08 samples/sec INFO:root:Iter[21] Batch [1840] Speed: 604.10 samples/sec INFO:root:Iter[21] Batch [1860] 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samples/sec {fc7_acc} 216000 0.6640625 lr-batch-epoch: 0.0010000000000000002 3563 21 testing verification.. (12000, 512) infer time 4.621801 [lfw][216000]XNorm: 22.304107 [lfw][216000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.508092 [cfp_fp][216000]XNorm: 21.812644 [cfp_fp][216000]Accuracy-Flip: 0.98900+-0.00271 testing verification.. (12000, 512) infer time 4.541527999999999 [agedb_30][216000]XNorm: 22.649629 [agedb_30][216000]Accuracy-Flip: 0.98183+-0.00669 saving 1 INFO:root:Saved checkpoint to "./models/r50-arcface-retina/model-0001.params" [216000]Accuracy-Highest: 0.98183 INFO:root:Iter[21] Batch [3580] Speed: 245.02 samples/sec INFO:root:Iter[21] Batch [3600] Speed: 727.45 samples/sec INFO:root:Iter[21] Batch [3620] Speed: 681.04 samples/sec INFO:root:Iter[21] Batch [3640] Speed: 698.83 samples/sec INFO:root:Iter[21] Batch [3660] Speed: 673.84 samples/sec INFO:root:Iter[21] Batch [3680] Speed: 642.68 samples/sec INFO:root:Iter[21] Batch [3700] Speed: 678.11 samples/sec INFO:root:Iter[21] Batch [3720] Speed: 645.54 samples/sec INFO:root:Iter[21] Batch [3740] Speed: 626.54 samples/sec INFO:root:Iter[21] Batch [3760] Speed: 607.60 samples/sec INFO:root:Iter[21] Batch [3780] Speed: 624.56 samples/sec INFO:root:Iter[21] Batch [3800] Speed: 625.47 samples/sec INFO:root:Iter[21] Batch [3820] Speed: 629.30 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(12000, 512) infer time 4.83396 [lfw][218000]XNorm: 22.407999 [lfw][218000]Accuracy-Flip: 0.99767+-0.00281 testing verification.. (14000, 512) infer time 5.695028000000002 [cfp_fp][218000]XNorm: 21.979488 [cfp_fp][218000]Accuracy-Flip: 0.98843+-0.00282 testing verification.. 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INFO:root:Iter[21] Batch [7560] Speed: 573.89 samples/sec {fc7_acc} 220000 0.6699219 lr change to 0.00010000000000000003 lr-batch-epoch: 0.00010000000000000003 7563 21 testing verification.. (12000, 512) infer time 4.749623 [lfw][220000]XNorm: 22.098433 [lfw][220000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.6617700000000015 [cfp_fp][220000]XNorm: 21.816499 [cfp_fp][220000]Accuracy-Flip: 0.98900+-0.00231 testing verification.. (12000, 512) infer time 4.896090999999999 [agedb_30][220000]XNorm: 22.659617 [agedb_30][220000]Accuracy-Flip: 0.97883+-0.00637 [220000]Accuracy-Highest: 0.98183 INFO:root:Iter[21] Batch [7580] Speed: 226.92 samples/sec INFO:root:Iter[21] Batch [7600] Speed: 562.88 samples/sec INFO:root:Iter[21] Batch [7620] Speed: 560.13 samples/sec INFO:root:Iter[21] Batch [7640] Speed: 551.49 samples/sec INFO:root:Iter[21] Batch [7660] Speed: 555.68 samples/sec INFO:root:Iter[21] Batch [7680] Speed: 570.80 samples/sec INFO:root:Iter[21] Batch [7700] Speed: 570.86 samples/sec INFO:root:Iter[21] Batch [7720] Speed: 562.52 samples/sec INFO:root:Iter[21] Batch [7740] Speed: 553.97 samples/sec INFO:root:Iter[21] Batch [7760] Speed: 579.64 samples/sec INFO:root:Iter[21] Batch [7780] Speed: 580.43 samples/sec INFO:root:Iter[21] Batch [7800] Speed: 579.13 samples/sec INFO:root:Iter[21] Batch [7820] Speed: 589.12 samples/sec INFO:root:Iter[21] Batch [7840] Speed: 568.75 samples/sec INFO:root:Iter[21] Batch 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{fc7_acc} 222000 0.7050781 lr-batch-epoch: 0.00010000000000000003 9563 21 testing verification.. (12000, 512) infer time 20.386584999999997 [lfw][222000]XNorm: 22.312716 [lfw][222000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 19.155909 [cfp_fp][222000]XNorm: 22.068246 [cfp_fp][222000]Accuracy-Flip: 0.98914+-0.00363 testing verification.. (12000, 512) infer time 18.964722 [agedb_30][222000]XNorm: 22.806046 [agedb_30][222000]Accuracy-Flip: 0.98100+-0.00655 [222000]Accuracy-Highest: 0.98183 INFO:root:Iter[21] Batch [9580] Speed: 76.11 samples/sec INFO:root:Iter[21] Batch [9600] Speed: 130.94 samples/sec INFO:root:Iter[21] Batch [9620] Speed: 98.68 samples/sec INFO:root:Iter[21] Batch [9640] Speed: 142.86 samples/sec INFO:root:Iter[21] Batch [9660] Speed: 104.56 samples/sec INFO:root:Iter[21] Batch [9680] Speed: 126.66 samples/sec INFO:root:Iter[21] Batch [9700] Speed: 124.52 samples/sec INFO:root:Iter[21] Batch [9720] Speed: 109.78 samples/sec INFO:root:Iter[21] Batch [9740] Speed: 107.64 samples/sec INFO:root:Iter[21] Batch [9760] Speed: 123.29 samples/sec INFO:root:Iter[21] Batch [9780] Speed: 102.48 samples/sec INFO:root:Iter[21] Batch [9800] Speed: 106.52 samples/sec INFO:root:Iter[21] Batch [9820] Speed: 102.51 samples/sec INFO:root:Iter[21] Batch [9840] Speed: 82.37 samples/sec INFO:root:Iter[21] Batch [9860] Speed: 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lr-batch-epoch: 0.00010000000000000003 1447 22 testing verification.. (12000, 512) infer time 4.766961000000001 [lfw][224000]XNorm: 22.229276 [lfw][224000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.425170000000001 [cfp_fp][224000]XNorm: 22.101161 [cfp_fp][224000]Accuracy-Flip: 0.98900+-0.00332 testing verification.. (12000, 512) infer time 4.778857000000001 [agedb_30][224000]XNorm: 22.862776 [agedb_30][224000]Accuracy-Flip: 0.97967+-0.00694 [224000]Accuracy-Highest: 0.98183 INFO:root:Iter[22] Batch [1460] Speed: 236.62 samples/sec INFO:root:Iter[22] Batch [1480] Speed: 584.73 samples/sec INFO:root:Iter[22] Batch [1500] Speed: 585.12 samples/sec INFO:root:Iter[22] Batch [1520] Speed: 591.52 samples/sec INFO:root:Iter[22] Batch [1540] Speed: 596.84 samples/sec INFO:root:Iter[22] Batch [1560] Speed: 582.17 samples/sec INFO:root:Iter[22] Batch [1580] Speed: 583.75 samples/sec INFO:root:Iter[22] Batch [1600] Speed: 566.57 samples/sec INFO:root:Iter[22] Batch [1620] Speed: 588.54 samples/sec INFO:root:Iter[22] Batch [1640] Speed: 564.63 samples/sec INFO:root:Iter[22] Batch [1660] Speed: 575.95 samples/sec INFO:root:Iter[22] Batch [1680] Speed: 586.77 samples/sec INFO:root:Iter[22] Batch [1700] Speed: 572.40 samples/sec INFO:root:Iter[22] Batch [1720] Speed: 579.45 samples/sec INFO:root:Iter[22] Batch 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INFO:root:Iter[22] Batch [3440] Speed: 584.00 samples/sec {fc7_acc} 226000 0.69140625 lr-batch-epoch: 0.00010000000000000003 3447 22 testing verification.. (12000, 512) infer time 4.607984999999998 [lfw][226000]XNorm: 22.210446 [lfw][226000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.510592999999999 [cfp_fp][226000]XNorm: 22.104044 [cfp_fp][226000]Accuracy-Flip: 0.98857+-0.00263 testing verification.. (12000, 512) infer time 4.8148729999999995 [agedb_30][226000]XNorm: 22.803399 [agedb_30][226000]Accuracy-Flip: 0.97950+-0.00734 [226000]Accuracy-Highest: 0.98183 INFO:root:Iter[22] Batch [3460] Speed: 233.34 samples/sec INFO:root:Iter[22] Batch [3480] Speed: 569.46 samples/sec INFO:root:Iter[22] Batch [3500] Speed: 582.84 samples/sec INFO:root:Iter[22] Batch [3520] Speed: 555.17 samples/sec INFO:root:Iter[22] Batch [3540] Speed: 584.00 samples/sec INFO:root:Iter[22] Batch [3560] Speed: 568.53 samples/sec INFO:root:Iter[22] Batch [3580] Speed: 569.99 samples/sec INFO:root:Iter[22] Batch [3600] Speed: 590.02 samples/sec INFO:root:Iter[22] Batch [3620] Speed: 583.46 samples/sec INFO:root:Iter[22] Batch [3640] Speed: 583.62 samples/sec INFO:root:Iter[22] Batch [3660] Speed: 578.30 samples/sec INFO:root:Iter[22] Batch [3680] Speed: 568.82 samples/sec INFO:root:Iter[22] Batch [3700] Speed: 560.21 samples/sec INFO:root:Iter[22] Batch [3720] Speed: 572.57 samples/sec INFO:root:Iter[22] Batch 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INFO:root:Iter[22] Batch [5440] Speed: 90.09 samples/sec {fc7_acc} 228000 0.6933594 lr-batch-epoch: 0.00010000000000000003 5447 22 testing verification.. (12000, 512) infer time 17.463659999999997 [lfw][228000]XNorm: 22.205749 [lfw][228000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 17.650339999999993 [cfp_fp][228000]XNorm: 22.087392 [cfp_fp][228000]Accuracy-Flip: 0.98971+-0.00262 testing verification.. (12000, 512) infer time 17.603612999999996 [agedb_30][228000]XNorm: 22.789488 [agedb_30][228000]Accuracy-Flip: 0.98000+-0.00764 [228000]Accuracy-Highest: 0.98183 INFO:root:Iter[22] Batch [5460] Speed: 77.29 samples/sec INFO:root:Iter[22] Batch [5480] Speed: 167.08 samples/sec INFO:root:Iter[22] Batch [5500] Speed: 138.69 samples/sec INFO:root:Iter[22] Batch [5520] Speed: 184.38 samples/sec INFO:root:Iter[22] Batch [5540] Speed: 127.05 samples/sec INFO:root:Iter[22] Batch [5560] Speed: 281.72 samples/sec INFO:root:Iter[22] Batch [5580] Speed: 117.60 samples/sec INFO:root:Iter[22] Batch [5600] Speed: 139.93 samples/sec INFO:root:Iter[22] Batch [5620] Speed: 479.29 samples/sec INFO:root:Iter[22] Batch [5640] Speed: 597.51 samples/sec INFO:root:Iter[22] Batch [5660] Speed: 610.08 samples/sec INFO:root:Iter[22] Batch [5680] Speed: 614.98 samples/sec INFO:root:Iter[22] Batch [5700] Speed: 611.19 samples/sec INFO:root:Iter[22] Batch [5720] Speed: 630.27 samples/sec INFO:root:Iter[22] Batch 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INFO:root:Iter[22] Batch [7440] Speed: 616.93 samples/sec {fc7_acc} 230000 0.69140625 lr-batch-epoch: 0.00010000000000000003 7447 22 testing verification.. (12000, 512) infer time 4.340886 [lfw][230000]XNorm: 22.185961 [lfw][230000]Accuracy-Flip: 0.99733+-0.00271 testing verification.. (14000, 512) infer time 4.818808 [cfp_fp][230000]XNorm: 22.146706 [cfp_fp][230000]Accuracy-Flip: 0.98957+-0.00248 testing verification.. (12000, 512) infer time 4.002055999999999 [agedb_30][230000]XNorm: 22.772595 [agedb_30][230000]Accuracy-Flip: 0.97967+-0.00662 [230000]Accuracy-Highest: 0.98183 INFO:root:Iter[22] Batch [7460] Speed: 260.56 samples/sec INFO:root:Iter[22] Batch [7480] Speed: 726.11 samples/sec INFO:root:Iter[22] Batch [7500] Speed: 701.87 samples/sec INFO:root:Iter[22] Batch [7520] Speed: 665.51 samples/sec INFO:root:Iter[22] Batch [7540] Speed: 733.96 samples/sec INFO:root:Iter[22] Batch [7560] Speed: 662.40 samples/sec INFO:root:Iter[22] Batch [7580] Speed: 645.10 samples/sec INFO:root:Iter[22] Batch [7600] Speed: 689.69 samples/sec INFO:root:Iter[22] Batch [7620] Speed: 690.49 samples/sec INFO:root:Iter[22] Batch [7640] Speed: 668.99 samples/sec INFO:root:Iter[22] Batch [7660] Speed: 682.20 samples/sec INFO:root:Iter[22] Batch [7680] Speed: 676.57 samples/sec INFO:root:Iter[22] Batch [7700] Speed: 689.84 samples/sec INFO:root:Iter[22] Batch [7720] Speed: 657.26 samples/sec INFO:root:Iter[22] Batch 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INFO:root:Iter[22] Batch [9440] Speed: 591.52 samples/sec {fc7_acc} 232000 0.71875 lr-batch-epoch: 0.00010000000000000003 9447 22 testing verification.. (12000, 512) infer time 4.739927000000001 [lfw][232000]XNorm: 22.188972 [lfw][232000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.622777000000001 [cfp_fp][232000]XNorm: 22.142333 [cfp_fp][232000]Accuracy-Flip: 0.98886+-0.00291 testing verification.. (12000, 512) infer time 4.707344 [agedb_30][232000]XNorm: 22.799411 [agedb_30][232000]Accuracy-Flip: 0.97967+-0.00730 [232000]Accuracy-Highest: 0.98183 INFO:root:Iter[22] Batch [9460] Speed: 231.71 samples/sec INFO:root:Iter[22] Batch [9480] Speed: 575.52 samples/sec INFO:root:Iter[22] Batch [9500] Speed: 555.67 samples/sec INFO:root:Iter[22] Batch [9520] Speed: 572.21 samples/sec INFO:root:Iter[22] Batch [9540] Speed: 565.93 samples/sec INFO:root:Iter[22] Batch [9560] Speed: 585.12 samples/sec INFO:root:Iter[22] Batch [9580] Speed: 558.25 samples/sec INFO:root:Iter[22] Batch [9600] Speed: 567.09 samples/sec INFO:root:Iter[22] Batch [9620] Speed: 573.73 samples/sec INFO:root:Iter[22] Batch [9640] Speed: 577.03 samples/sec INFO:root:Iter[22] Batch [9660] Speed: 545.87 samples/sec INFO:root:Iter[22] Batch [9680] Speed: 572.61 samples/sec INFO:root:Iter[22] Batch [9700] Speed: 571.51 samples/sec INFO:root:Iter[22] Batch [9720] Speed: 577.60 samples/sec INFO:root:Iter[22] Batch [9740] 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0.00010000000000000003 1331 23 testing verification.. (12000, 512) infer time 4.746484000000001 [lfw][234000]XNorm: 22.245343 [lfw][234000]Accuracy-Flip: 0.99783+-0.00289 testing verification.. (14000, 512) infer time 5.7090960000000015 [cfp_fp][234000]XNorm: 22.200148 [cfp_fp][234000]Accuracy-Flip: 0.98943+-0.00280 testing verification.. (12000, 512) infer time 4.733142999999999 [agedb_30][234000]XNorm: 22.816560 [agedb_30][234000]Accuracy-Flip: 0.98133+-0.00737 [234000]Accuracy-Highest: 0.98183 INFO:root:Iter[23] Batch [1340] Speed: 234.31 samples/sec INFO:root:Iter[23] Batch [1360] Speed: 585.11 samples/sec INFO:root:Iter[23] Batch [1380] Speed: 580.90 samples/sec INFO:root:Iter[23] Batch [1400] Speed: 569.28 samples/sec INFO:root:Iter[23] Batch [1420] Speed: 546.63 samples/sec INFO:root:Iter[23] Batch [1440] Speed: 578.46 samples/sec INFO:root:Iter[23] Batch [1460] Speed: 570.87 samples/sec INFO:root:Iter[23] Batch [1480] Speed: 569.02 samples/sec INFO:root:Iter[23] Batch [1500] Speed: 566.03 samples/sec INFO:root:Iter[23] Batch [1520] Speed: 571.03 samples/sec INFO:root:Iter[23] Batch [1540] Speed: 569.85 samples/sec INFO:root:Iter[23] Batch [1560] Speed: 575.15 samples/sec INFO:root:Iter[23] Batch [1580] Speed: 573.17 samples/sec INFO:root:Iter[23] Batch [1600] Speed: 564.79 samples/sec INFO:root:Iter[23] Batch 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(12000, 512) infer time 4.680164000000001 [lfw][236000]XNorm: 22.220247 [lfw][236000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.523248000000001 [cfp_fp][236000]XNorm: 22.125186 [cfp_fp][236000]Accuracy-Flip: 0.98843+-0.00322 testing verification.. (12000, 512) infer time 4.869905000000001 [agedb_30][236000]XNorm: 22.762994 [agedb_30][236000]Accuracy-Flip: 0.97983+-0.00709 [236000]Accuracy-Highest: 0.98183 INFO:root:Iter[23] Batch [3340] Speed: 235.66 samples/sec INFO:root:Iter[23] Batch [3360] Speed: 576.22 samples/sec INFO:root:Iter[23] Batch [3380] Speed: 572.45 samples/sec INFO:root:Iter[23] Batch [3400] Speed: 564.51 samples/sec INFO:root:Iter[23] Batch [3420] Speed: 555.82 samples/sec INFO:root:Iter[23] Batch [3440] Speed: 561.08 samples/sec INFO:root:Iter[23] Batch [3460] Speed: 554.21 samples/sec INFO:root:Iter[23] Batch [3480] Speed: 566.25 samples/sec INFO:root:Iter[23] Batch [3500] Speed: 582.99 samples/sec INFO:root:Iter[23] Batch [3520] Speed: 575.22 samples/sec INFO:root:Iter[23] Batch [3540] Speed: 564.68 samples/sec INFO:root:Iter[23] Batch [3560] Speed: 565.35 samples/sec INFO:root:Iter[23] Batch [3580] Speed: 564.83 samples/sec INFO:root:Iter[23] Batch [3600] Speed: 580.35 samples/sec INFO:root:Iter[23] Batch 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INFO:root:Iter[23] Batch [5320] Speed: 606.57 samples/sec {fc7_acc} 238000 0.66796875 lr-batch-epoch: 0.00010000000000000003 5331 23 testing verification.. (12000, 512) infer time 4.528449999999999 [lfw][238000]XNorm: 22.150623 [lfw][238000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.045224999999999 [cfp_fp][238000]XNorm: 22.138149 [cfp_fp][238000]Accuracy-Flip: 0.98929+-0.00265 testing verification.. (12000, 512) infer time 4.369397 [agedb_30][238000]XNorm: 22.835673 [agedb_30][238000]Accuracy-Flip: 0.98000+-0.00723 [238000]Accuracy-Highest: 0.98183 INFO:root:Iter[23] Batch [5340] Speed: 248.29 samples/sec INFO:root:Iter[23] Batch [5360] Speed: 685.07 samples/sec INFO:root:Iter[23] Batch [5380] Speed: 635.29 samples/sec INFO:root:Iter[23] Batch [5400] Speed: 660.06 samples/sec INFO:root:Iter[23] Batch [5420] Speed: 644.66 samples/sec INFO:root:Iter[23] Batch [5440] Speed: 650.98 samples/sec INFO:root:Iter[23] Batch [5460] Speed: 603.44 samples/sec INFO:root:Iter[23] Batch [5480] Speed: 604.97 samples/sec INFO:root:Iter[23] Batch [5500] Speed: 584.53 samples/sec INFO:root:Iter[23] Batch [5520] Speed: 604.81 samples/sec INFO:root:Iter[23] Batch [5540] Speed: 594.61 samples/sec INFO:root:Iter[23] Batch [5560] Speed: 616.65 samples/sec INFO:root:Iter[23] Batch [5580] Speed: 616.63 samples/sec INFO:root:Iter[23] Batch [5600] Speed: 633.80 samples/sec INFO:root:Iter[23] Batch [5620] 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samples/sec {fc7_acc} 240000 0.7519531 lr-batch-epoch: 0.00010000000000000003 7331 23 testing verification.. (12000, 512) infer time 4.702042 [lfw][240000]XNorm: 22.185911 [lfw][240000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.327238999999999 [cfp_fp][240000]XNorm: 22.154494 [cfp_fp][240000]Accuracy-Flip: 0.98800+-0.00308 testing verification.. (12000, 512) infer time 4.678689 [agedb_30][240000]XNorm: 22.823256 [agedb_30][240000]Accuracy-Flip: 0.98033+-0.00745 [240000]Accuracy-Highest: 0.98183 INFO:root:Iter[23] Batch [7340] Speed: 242.08 samples/sec INFO:root:Iter[23] Batch [7360] Speed: 625.64 samples/sec INFO:root:Iter[23] Batch [7380] Speed: 608.30 samples/sec INFO:root:Iter[23] Batch [7400] Speed: 608.54 samples/sec INFO:root:Iter[23] Batch [7420] Speed: 625.70 samples/sec INFO:root:Iter[23] Batch [7440] Speed: 617.63 samples/sec INFO:root:Iter[23] Batch [7460] Speed: 614.42 samples/sec INFO:root:Iter[23] Batch [7480] Speed: 594.19 samples/sec INFO:root:Iter[23] Batch [7500] Speed: 591.76 samples/sec INFO:root:Iter[23] Batch [7520] Speed: 620.28 samples/sec INFO:root:Iter[23] Batch [7540] Speed: 590.44 samples/sec INFO:root:Iter[23] Batch [7560] Speed: 597.01 samples/sec INFO:root:Iter[23] Batch [7580] Speed: 625.99 samples/sec INFO:root:Iter[23] Batch [7600] Speed: 607.05 samples/sec INFO:root:Iter[23] Batch [7620] 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samples/sec {fc7_acc} 242000 0.7109375 lr-batch-epoch: 0.00010000000000000003 9331 23 testing verification.. (12000, 512) infer time 4.673104000000001 [lfw][242000]XNorm: 22.201944 [lfw][242000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.349182000000001 [cfp_fp][242000]XNorm: 22.191180 [cfp_fp][242000]Accuracy-Flip: 0.98943+-0.00287 testing verification.. (12000, 512) infer time 4.02694 [agedb_30][242000]XNorm: 22.828381 [agedb_30][242000]Accuracy-Flip: 0.97950+-0.00715 [242000]Accuracy-Highest: 0.98183 INFO:root:Iter[23] Batch [9340] Speed: 244.70 samples/sec INFO:root:Iter[23] Batch [9360] Speed: 903.85 samples/sec INFO:root:Iter[23] Batch [9380] Speed: 868.72 samples/sec INFO:root:Iter[23] Batch [9400] Speed: 852.75 samples/sec INFO:root:Iter[23] Batch [9420] Speed: 747.27 samples/sec INFO:root:Iter[23] Batch [9440] Speed: 742.54 samples/sec INFO:root:Iter[23] Batch [9460] Speed: 640.09 samples/sec INFO:root:Iter[23] Batch [9480] Speed: 649.35 samples/sec INFO:root:Iter[23] Batch [9500] Speed: 599.75 samples/sec INFO:root:Iter[23] Batch [9520] Speed: 558.36 samples/sec INFO:root:Iter[23] Batch [9540] Speed: 532.56 samples/sec INFO:root:Iter[23] Batch [9560] Speed: 536.38 samples/sec INFO:root:Iter[23] Batch [9580] Speed: 530.03 samples/sec INFO:root:Iter[23] Batch [9600] Speed: 563.13 samples/sec INFO:root:Iter[23] Batch [9620] 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0.7128906 lr-batch-epoch: 0.00010000000000000003 1215 24 testing verification.. (12000, 512) infer time 4.521699000000001 [lfw][244000]XNorm: 22.170035 [lfw][244000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.451293 [cfp_fp][244000]XNorm: 22.177032 [cfp_fp][244000]Accuracy-Flip: 0.98914+-0.00301 testing verification.. (12000, 512) infer time 4.076279 [agedb_30][244000]XNorm: 22.858804 [agedb_30][244000]Accuracy-Flip: 0.97983+-0.00697 [244000]Accuracy-Highest: 0.98183 INFO:root:Iter[24] Batch [1220] Speed: 235.11 samples/sec INFO:root:Iter[24] Batch [1240] Speed: 870.49 samples/sec INFO:root:Iter[24] Batch [1260] Speed: 793.61 samples/sec INFO:root:Iter[24] Batch [1280] Speed: 691.14 samples/sec INFO:root:Iter[24] Batch [1300] Speed: 678.57 samples/sec INFO:root:Iter[24] Batch [1320] Speed: 736.20 samples/sec INFO:root:Iter[24] Batch [1340] Speed: 609.20 samples/sec INFO:root:Iter[24] Batch [1360] Speed: 639.88 samples/sec INFO:root:Iter[24] Batch [1380] Speed: 557.95 samples/sec INFO:root:Iter[24] Batch [1400] Speed: 556.28 samples/sec INFO:root:Iter[24] Batch [1420] Speed: 551.65 samples/sec INFO:root:Iter[24] Batch [1440] Speed: 520.65 samples/sec INFO:root:Iter[24] Batch [1460] Speed: 535.45 samples/sec INFO:root:Iter[24] Batch [1480] Speed: 542.67 samples/sec INFO:root:Iter[24] Batch [1500] 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samples/sec {fc7_acc} 246000 0.72265625 lr-batch-epoch: 0.00010000000000000003 3215 24 testing verification.. (12000, 512) infer time 4.654249 [lfw][246000]XNorm: 22.184556 [lfw][246000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.252119999999999 [cfp_fp][246000]XNorm: 22.141661 [cfp_fp][246000]Accuracy-Flip: 0.98957+-0.00271 testing verification.. (12000, 512) infer time 3.7945889999999998 [agedb_30][246000]XNorm: 22.773084 [agedb_30][246000]Accuracy-Flip: 0.98083+-0.00724 [246000]Accuracy-Highest: 0.98183 INFO:root:Iter[24] Batch [3220] Speed: 243.18 samples/sec INFO:root:Iter[24] Batch [3240] Speed: 841.33 samples/sec INFO:root:Iter[24] Batch [3260] Speed: 858.42 samples/sec INFO:root:Iter[24] Batch [3280] Speed: 852.52 samples/sec INFO:root:Iter[24] Batch [3300] Speed: 690.43 samples/sec INFO:root:Iter[24] Batch [3320] Speed: 603.20 samples/sec INFO:root:Iter[24] Batch [3340] Speed: 587.99 samples/sec INFO:root:Iter[24] Batch [3360] Speed: 548.50 samples/sec INFO:root:Iter[24] Batch [3380] Speed: 522.19 samples/sec INFO:root:Iter[24] Batch [3400] Speed: 594.24 samples/sec INFO:root:Iter[24] Batch [3420] Speed: 540.34 samples/sec INFO:root:Iter[24] Batch [3440] Speed: 515.68 samples/sec INFO:root:Iter[24] Batch [3460] Speed: 564.58 samples/sec INFO:root:Iter[24] Batch [3480] Speed: 546.40 samples/sec INFO:root:Iter[24] Batch 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INFO:root:Iter[24] Batch [5200] Speed: 502.01 samples/sec {fc7_acc} 248000 0.7402344 lr-batch-epoch: 0.00010000000000000003 5215 24 testing verification.. (12000, 512) infer time 4.786943 [lfw][248000]XNorm: 22.160875 [lfw][248000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 4.848508999999999 [cfp_fp][248000]XNorm: 22.194315 [cfp_fp][248000]Accuracy-Flip: 0.98914+-0.00333 testing verification.. (12000, 512) infer time 4.182132 [agedb_30][248000]XNorm: 22.779809 [agedb_30][248000]Accuracy-Flip: 0.98050+-0.00757 [248000]Accuracy-Highest: 0.98183 INFO:root:Iter[24] Batch [5220] Speed: 234.24 samples/sec INFO:root:Iter[24] Batch [5240] Speed: 799.90 samples/sec INFO:root:Iter[24] Batch [5260] Speed: 746.43 samples/sec INFO:root:Iter[24] Batch [5280] Speed: 696.74 samples/sec INFO:root:Iter[24] Batch [5300] Speed: 641.73 samples/sec INFO:root:Iter[24] Batch [5320] Speed: 626.20 samples/sec INFO:root:Iter[24] Batch [5340] Speed: 505.32 samples/sec INFO:root:Iter[24] Batch [5360] Speed: 501.70 samples/sec INFO:root:Iter[24] Batch [5380] Speed: 591.65 samples/sec INFO:root:Iter[24] Batch [5400] Speed: 544.50 samples/sec INFO:root:Iter[24] Batch [5420] Speed: 551.25 samples/sec INFO:root:Iter[24] Batch [5440] Speed: 513.10 samples/sec INFO:root:Iter[24] Batch [5460] Speed: 538.78 samples/sec INFO:root:Iter[24] Batch [5480] Speed: 524.58 samples/sec INFO:root:Iter[24] Batch [5500] 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samples/sec {fc7_acc} 250000 0.7636719 lr-batch-epoch: 0.00010000000000000003 7215 24 testing verification.. (12000, 512) infer time 4.699861 [lfw][250000]XNorm: 22.197182 [lfw][250000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.4772820000000015 [cfp_fp][250000]XNorm: 22.164313 [cfp_fp][250000]Accuracy-Flip: 0.98929+-0.00280 testing verification.. (12000, 512) infer time 4.196889000000001 [agedb_30][250000]XNorm: 22.784449 [agedb_30][250000]Accuracy-Flip: 0.98050+-0.00730 [250000]Accuracy-Highest: 0.98183 INFO:root:Iter[24] Batch [7220] Speed: 232.85 samples/sec INFO:root:Iter[24] Batch [7240] Speed: 833.71 samples/sec INFO:root:Iter[24] Batch [7260] Speed: 804.97 samples/sec INFO:root:Iter[24] Batch [7280] Speed: 814.69 samples/sec INFO:root:Iter[24] Batch [7300] Speed: 711.30 samples/sec INFO:root:Iter[24] Batch [7320] Speed: 648.94 samples/sec INFO:root:Iter[24] Batch [7340] Speed: 626.85 samples/sec INFO:root:Iter[24] Batch [7360] Speed: 661.87 samples/sec INFO:root:Iter[24] Batch [7380] Speed: 638.89 samples/sec INFO:root:Iter[24] Batch [7400] Speed: 626.70 samples/sec INFO:root:Iter[24] Batch [7420] Speed: 629.35 samples/sec INFO:root:Iter[24] Batch [7440] Speed: 642.33 samples/sec INFO:root:Iter[24] Batch [7460] Speed: 589.39 samples/sec INFO:root:Iter[24] Batch [7480] Speed: 577.34 samples/sec INFO:root:Iter[24] Batch 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INFO:root:Iter[24] Batch [9200] Speed: 510.71 samples/sec {fc7_acc} 252000 0.7050781 lr-batch-epoch: 0.00010000000000000003 9215 24 testing verification.. (12000, 512) infer time 4.655111000000001 [lfw][252000]XNorm: 22.191859 [lfw][252000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.374650000000001 [cfp_fp][252000]XNorm: 22.168860 [cfp_fp][252000]Accuracy-Flip: 0.98914+-0.00400 testing verification.. (12000, 512) infer time 3.824371 [agedb_30][252000]XNorm: 22.720397 [agedb_30][252000]Accuracy-Flip: 0.97950+-0.00746 [252000]Accuracy-Highest: 0.98183 INFO:root:Iter[24] Batch [9220] Speed: 237.21 samples/sec INFO:root:Iter[24] Batch [9240] Speed: 876.50 samples/sec INFO:root:Iter[24] Batch [9260] Speed: 858.38 samples/sec INFO:root:Iter[24] Batch [9280] Speed: 864.17 samples/sec INFO:root:Iter[24] Batch [9300] Speed: 836.35 samples/sec INFO:root:Iter[24] Batch [9320] Speed: 815.82 samples/sec INFO:root:Iter[24] Batch [9340] Speed: 867.31 samples/sec INFO:root:Iter[24] Batch [9360] Speed: 810.06 samples/sec INFO:root:Iter[24] Batch [9380] Speed: 737.31 samples/sec INFO:root:Iter[24] Batch [9400] Speed: 710.61 samples/sec INFO:root:Iter[24] Batch [9420] Speed: 624.27 samples/sec INFO:root:Iter[24] Batch [9440] Speed: 590.79 samples/sec INFO:root:Iter[24] Batch [9460] Speed: 549.59 samples/sec INFO:root:Iter[24] Batch [9480] Speed: 553.25 samples/sec INFO:root:Iter[24] Batch [9500] 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0.00010000000000000003 1099 25 testing verification.. (12000, 512) infer time 4.605824000000001 [lfw][254000]XNorm: 22.217558 [lfw][254000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.527154000000002 [cfp_fp][254000]XNorm: 22.233232 [cfp_fp][254000]Accuracy-Flip: 0.98900+-0.00256 testing verification.. (12000, 512) infer time 4.1545619999999985 [agedb_30][254000]XNorm: 22.791359 [agedb_30][254000]Accuracy-Flip: 0.98133+-0.00770 [254000]Accuracy-Highest: 0.98183 INFO:root:Iter[25] Batch [1100] Speed: 226.66 samples/sec INFO:root:Iter[25] Batch [1120] Speed: 834.89 samples/sec INFO:root:Iter[25] Batch [1140] Speed: 863.32 samples/sec INFO:root:Iter[25] Batch [1160] Speed: 719.10 samples/sec INFO:root:Iter[25] Batch [1180] Speed: 678.57 samples/sec INFO:root:Iter[25] Batch [1200] Speed: 694.21 samples/sec INFO:root:Iter[25] Batch [1220] Speed: 638.92 samples/sec INFO:root:Iter[25] Batch [1240] Speed: 533.98 samples/sec INFO:root:Iter[25] Batch [1260] Speed: 516.70 samples/sec INFO:root:Iter[25] Batch [1280] Speed: 507.60 samples/sec INFO:root:Iter[25] Batch [1300] Speed: 532.11 samples/sec INFO:root:Iter[25] Batch [1320] Speed: 539.13 samples/sec INFO:root:Iter[25] Batch [1340] Speed: 516.54 samples/sec INFO:root:Iter[25] Batch [1360] Speed: 531.74 samples/sec INFO:root:Iter[25] Batch 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INFO:root:Iter[25] Batch [3080] Speed: 494.58 samples/sec {fc7_acc} 256000 0.75390625 lr-batch-epoch: 0.00010000000000000003 3099 25 testing verification.. (12000, 512) infer time 4.695453 [lfw][256000]XNorm: 22.166511 [lfw][256000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.504968000000001 [cfp_fp][256000]XNorm: 22.218946 [cfp_fp][256000]Accuracy-Flip: 0.98900+-0.00286 testing verification.. (12000, 512) infer time 3.993035 [agedb_30][256000]XNorm: 22.860274 [agedb_30][256000]Accuracy-Flip: 0.97900+-0.00731 [256000]Accuracy-Highest: 0.98183 INFO:root:Iter[25] Batch [3100] Speed: 223.57 samples/sec INFO:root:Iter[25] Batch [3120] Speed: 789.51 samples/sec INFO:root:Iter[25] Batch [3140] Speed: 779.43 samples/sec INFO:root:Iter[25] Batch [3160] Speed: 651.61 samples/sec INFO:root:Iter[25] Batch [3180] Speed: 592.46 samples/sec INFO:root:Iter[25] Batch [3200] Speed: 691.35 samples/sec INFO:root:Iter[25] Batch [3220] Speed: 609.07 samples/sec INFO:root:Iter[25] Batch [3240] Speed: 579.18 samples/sec INFO:root:Iter[25] Batch [3260] Speed: 638.11 samples/sec INFO:root:Iter[25] Batch [3280] Speed: 663.77 samples/sec INFO:root:Iter[25] Batch [3300] Speed: 519.76 samples/sec INFO:root:Iter[25] Batch [3320] Speed: 559.04 samples/sec INFO:root:Iter[25] Batch [3340] Speed: 623.59 samples/sec INFO:root:Iter[25] Batch [3360] Speed: 554.76 samples/sec INFO:root:Iter[25] Batch [3380] 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samples/sec {fc7_acc} 258000 0.6953125 lr-batch-epoch: 0.00010000000000000003 5099 25 testing verification.. (12000, 512) infer time 4.753545 [lfw][258000]XNorm: 22.163911 [lfw][258000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.647091000000001 [cfp_fp][258000]XNorm: 22.192050 [cfp_fp][258000]Accuracy-Flip: 0.98929+-0.00249 testing verification.. (12000, 512) infer time 4.351289999999999 [agedb_30][258000]XNorm: 22.791663 [agedb_30][258000]Accuracy-Flip: 0.98117+-0.00753 [258000]Accuracy-Highest: 0.98183 INFO:root:Iter[25] Batch [5100] Speed: 221.87 samples/sec INFO:root:Iter[25] Batch [5120] Speed: 850.88 samples/sec INFO:root:Iter[25] Batch [5140] Speed: 892.21 samples/sec INFO:root:Iter[25] Batch [5160] Speed: 765.60 samples/sec INFO:root:Iter[25] Batch [5180] Speed: 759.00 samples/sec INFO:root:Iter[25] Batch [5200] Speed: 615.28 samples/sec INFO:root:Iter[25] Batch [5220] Speed: 567.76 samples/sec INFO:root:Iter[25] Batch [5240] Speed: 594.36 samples/sec INFO:root:Iter[25] Batch [5260] Speed: 559.99 samples/sec INFO:root:Iter[25] Batch [5280] Speed: 531.28 samples/sec INFO:root:Iter[25] Batch [5300] Speed: 553.95 samples/sec INFO:root:Iter[25] Batch [5320] Speed: 590.75 samples/sec INFO:root:Iter[25] Batch [5340] Speed: 600.91 samples/sec INFO:root:Iter[25] Batch [5360] Speed: 581.12 samples/sec INFO:root:Iter[25] Batch 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INFO:root:Iter[25] Batch [7080] Speed: 506.71 samples/sec {fc7_acc} 260000 0.7089844 lr-batch-epoch: 0.00010000000000000003 7099 25 testing verification.. (12000, 512) infer time 4.606140000000001 [lfw][260000]XNorm: 22.217250 [lfw][260000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.467195 [cfp_fp][260000]XNorm: 22.221342 [cfp_fp][260000]Accuracy-Flip: 0.98857+-0.00271 testing verification.. (12000, 512) infer time 4.03759 [agedb_30][260000]XNorm: 22.830286 [agedb_30][260000]Accuracy-Flip: 0.98083+-0.00761 [260000]Accuracy-Highest: 0.98183 INFO:root:Iter[25] Batch [7100] Speed: 227.75 samples/sec INFO:root:Iter[25] Batch [7120] Speed: 877.02 samples/sec INFO:root:Iter[25] Batch [7140] Speed: 855.62 samples/sec INFO:root:Iter[25] Batch [7160] Speed: 812.04 samples/sec INFO:root:Iter[25] Batch [7180] Speed: 797.33 samples/sec INFO:root:Iter[25] Batch [7200] Speed: 760.42 samples/sec INFO:root:Iter[25] Batch [7220] Speed: 710.77 samples/sec INFO:root:Iter[25] Batch [7240] Speed: 699.72 samples/sec INFO:root:Iter[25] Batch [7260] Speed: 656.20 samples/sec INFO:root:Iter[25] Batch [7280] Speed: 629.52 samples/sec INFO:root:Iter[25] Batch [7300] Speed: 554.66 samples/sec INFO:root:Iter[25] Batch [7320] Speed: 556.15 samples/sec INFO:root:Iter[25] Batch [7340] Speed: 591.81 samples/sec INFO:root:Iter[25] Batch [7360] Speed: 570.47 samples/sec INFO:root:Iter[25] Batch [7380] 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samples/sec {fc7_acc} 262000 0.71484375 lr-batch-epoch: 0.00010000000000000003 9099 25 testing verification.. (12000, 512) infer time 4.740588000000002 [lfw][262000]XNorm: 22.190214 [lfw][262000]Accuracy-Flip: 0.99750+-0.00318 testing verification.. (14000, 512) infer time 5.162668 [cfp_fp][262000]XNorm: 22.245973 [cfp_fp][262000]Accuracy-Flip: 0.98914+-0.00249 testing verification.. (12000, 512) infer time 4.211380000000001 [agedb_30][262000]XNorm: 22.792154 [agedb_30][262000]Accuracy-Flip: 0.98117+-0.00715 [262000]Accuracy-Highest: 0.98183 INFO:root:Iter[25] Batch [9100] Speed: 222.79 samples/sec INFO:root:Iter[25] Batch [9120] Speed: 847.92 samples/sec INFO:root:Iter[25] Batch [9140] Speed: 817.06 samples/sec INFO:root:Iter[25] Batch [9160] Speed: 839.64 samples/sec INFO:root:Iter[25] Batch [9180] Speed: 670.40 samples/sec INFO:root:Iter[25] Batch [9200] Speed: 708.05 samples/sec INFO:root:Iter[25] Batch [9220] Speed: 626.71 samples/sec INFO:root:Iter[25] Batch [9240] Speed: 583.73 samples/sec INFO:root:Iter[25] Batch [9260] Speed: 589.35 samples/sec INFO:root:Iter[25] Batch [9280] Speed: 618.14 samples/sec INFO:root:Iter[25] Batch [9300] Speed: 582.41 samples/sec INFO:root:Iter[25] Batch [9320] Speed: 641.33 samples/sec INFO:root:Iter[25] Batch [9340] Speed: 569.40 samples/sec INFO:root:Iter[25] Batch [9360] Speed: 592.10 samples/sec INFO:root:Iter[25] Batch 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samples/sec INFO:root:Iter[25] Batch [10080] Speed: 490.66 samples/sec lr-batch-epoch: 0.00010000000000000003 10099 25 INFO:root:Iter[25] Batch [10100] Speed: 483.54 samples/sec INFO:root:Epoch[25] Time cost=10029.221 call reset() INFO:root:Iter[26] Batch [20] Speed: 522.93 samples/sec INFO:root:Iter[26] Batch [40] Speed: 526.17 samples/sec INFO:root:Iter[26] Batch [60] Speed: 533.09 samples/sec INFO:root:Iter[26] Batch [80] Speed: 526.69 samples/sec INFO:root:Iter[26] Batch [100] Speed: 521.59 samples/sec INFO:root:Iter[26] Batch [120] Speed: 500.68 samples/sec INFO:root:Iter[26] Batch [140] Speed: 496.79 samples/sec INFO:root:Iter[26] Batch [160] Speed: 512.36 samples/sec INFO:root:Iter[26] Batch [180] Speed: 512.00 samples/sec INFO:root:Iter[26] Batch [200] Speed: 523.07 samples/sec INFO:root:Iter[26] Batch [220] Speed: 520.23 samples/sec INFO:root:Iter[26] Batch [240] Speed: 504.60 samples/sec INFO:root:Iter[26] Batch [260] Speed: 521.28 samples/sec INFO:root:Iter[26] Batch [280] 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Speed: 515.83 samples/sec {fc7_acc} 264000 0.7246094 lr-batch-epoch: 0.00010000000000000003 983 26 testing verification.. (12000, 512) infer time 4.693481 [lfw][264000]XNorm: 22.162829 [lfw][264000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.240375 [cfp_fp][264000]XNorm: 22.162430 [cfp_fp][264000]Accuracy-Flip: 0.98957+-0.00271 testing verification.. (12000, 512) infer time 3.8327310000000008 [agedb_30][264000]XNorm: 22.825919 [agedb_30][264000]Accuracy-Flip: 0.97850+-0.00769 [264000]Accuracy-Highest: 0.98183 INFO:root:Iter[26] Batch [1000] Speed: 268.07 samples/sec INFO:root:Iter[26] Batch [1020] Speed: 777.35 samples/sec INFO:root:Iter[26] Batch [1040] Speed: 795.18 samples/sec INFO:root:Iter[26] Batch [1060] Speed: 648.30 samples/sec INFO:root:Iter[26] Batch [1080] Speed: 564.02 samples/sec INFO:root:Iter[26] Batch [1100] Speed: 524.55 samples/sec INFO:root:Iter[26] Batch [1120] Speed: 607.94 samples/sec INFO:root:Iter[26] Batch [1140] Speed: 555.01 samples/sec INFO:root:Iter[26] Batch [1160] Speed: 580.63 samples/sec INFO:root:Iter[26] Batch [1180] Speed: 552.36 samples/sec INFO:root:Iter[26] Batch [1200] Speed: 566.22 samples/sec INFO:root:Iter[26] Batch [1220] Speed: 509.58 samples/sec INFO:root:Iter[26] Batch [1240] Speed: 517.91 samples/sec INFO:root:Iter[26] Batch [1260] Speed: 533.44 samples/sec INFO:root:Iter[26] Batch 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INFO:root:Iter[26] Batch [2980] Speed: 484.36 samples/sec {fc7_acc} 266000 0.76953125 lr-batch-epoch: 0.00010000000000000003 2983 26 testing verification.. (12000, 512) infer time 4.561841000000002 [lfw][266000]XNorm: 22.252474 [lfw][266000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 4.859440000000001 [cfp_fp][266000]XNorm: 22.260743 [cfp_fp][266000]Accuracy-Flip: 0.98914+-0.00265 testing verification.. (12000, 512) infer time 3.6261570000000005 [agedb_30][266000]XNorm: 22.850891 [agedb_30][266000]Accuracy-Flip: 0.98033+-0.00690 [266000]Accuracy-Highest: 0.98183 INFO:root:Iter[26] Batch [3000] Speed: 270.64 samples/sec INFO:root:Iter[26] Batch [3020] Speed: 746.12 samples/sec INFO:root:Iter[26] Batch [3040] Speed: 650.23 samples/sec INFO:root:Iter[26] Batch [3060] Speed: 738.43 samples/sec INFO:root:Iter[26] Batch [3080] Speed: 596.03 samples/sec INFO:root:Iter[26] Batch [3100] Speed: 564.15 samples/sec INFO:root:Iter[26] Batch [3120] Speed: 616.52 samples/sec INFO:root:Iter[26] Batch [3140] Speed: 640.29 samples/sec INFO:root:Iter[26] Batch [3160] Speed: 656.78 samples/sec INFO:root:Iter[26] Batch [3180] Speed: 567.06 samples/sec INFO:root:Iter[26] Batch [3200] Speed: 527.10 samples/sec INFO:root:Iter[26] Batch [3220] Speed: 587.34 samples/sec INFO:root:Iter[26] Batch [3240] Speed: 557.38 samples/sec INFO:root:Iter[26] Batch [3260] Speed: 534.28 samples/sec INFO:root:Iter[26] Batch 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INFO:root:Iter[26] Batch [4980] Speed: 502.73 samples/sec {fc7_acc} 268000 0.7285156 lr-batch-epoch: 0.00010000000000000003 4983 26 testing verification.. (12000, 512) infer time 4.649915999999999 [lfw][268000]XNorm: 22.194020 [lfw][268000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 4.468873 [cfp_fp][268000]XNorm: 22.238437 [cfp_fp][268000]Accuracy-Flip: 0.98943+-0.00265 testing verification.. (12000, 512) infer time 3.3612589999999996 [agedb_30][268000]XNorm: 22.730904 [agedb_30][268000]Accuracy-Flip: 0.97967+-0.00686 [268000]Accuracy-Highest: 0.98183 INFO:root:Iter[26] Batch [5000] Speed: 274.63 samples/sec INFO:root:Iter[26] Batch [5020] Speed: 907.90 samples/sec INFO:root:Iter[26] Batch [5040] Speed: 806.27 samples/sec INFO:root:Iter[26] Batch [5060] Speed: 746.20 samples/sec INFO:root:Iter[26] Batch [5080] Speed: 654.04 samples/sec INFO:root:Iter[26] Batch [5100] Speed: 701.45 samples/sec INFO:root:Iter[26] Batch [5120] Speed: 702.15 samples/sec INFO:root:Iter[26] Batch [5140] Speed: 641.51 samples/sec INFO:root:Iter[26] Batch [5160] Speed: 536.76 samples/sec INFO:root:Iter[26] Batch [5180] Speed: 555.81 samples/sec INFO:root:Iter[26] Batch [5200] Speed: 554.14 samples/sec INFO:root:Iter[26] Batch [5220] Speed: 557.36 samples/sec INFO:root:Iter[26] Batch [5240] Speed: 528.95 samples/sec INFO:root:Iter[26] Batch [5260] Speed: 565.38 samples/sec INFO:root:Iter[26] Batch 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INFO:root:Iter[26] Batch [6980] Speed: 488.59 samples/sec {fc7_acc} 270000 0.69140625 lr-batch-epoch: 0.00010000000000000003 6983 26 testing verification.. (12000, 512) infer time 4.5939559999999995 [lfw][270000]XNorm: 22.176709 [lfw][270000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.352843000000002 [cfp_fp][270000]XNorm: 22.194317 [cfp_fp][270000]Accuracy-Flip: 0.99014+-0.00274 testing verification.. (12000, 512) infer time 3.7528879999999996 [agedb_30][270000]XNorm: 22.801659 [agedb_30][270000]Accuracy-Flip: 0.98133+-0.00718 [270000]Accuracy-Highest: 0.98183 INFO:root:Iter[26] Batch [7000] Speed: 259.82 samples/sec INFO:root:Iter[26] Batch [7020] Speed: 716.54 samples/sec INFO:root:Iter[26] Batch [7040] Speed: 723.22 samples/sec INFO:root:Iter[26] Batch [7060] Speed: 609.26 samples/sec INFO:root:Iter[26] Batch [7080] Speed: 616.12 samples/sec INFO:root:Iter[26] Batch [7100] Speed: 592.40 samples/sec INFO:root:Iter[26] Batch [7120] Speed: 575.03 samples/sec INFO:root:Iter[26] Batch [7140] Speed: 555.45 samples/sec INFO:root:Iter[26] Batch [7160] Speed: 548.99 samples/sec INFO:root:Iter[26] Batch [7180] Speed: 568.99 samples/sec INFO:root:Iter[26] Batch [7200] Speed: 580.21 samples/sec INFO:root:Iter[26] Batch [7220] Speed: 598.11 samples/sec INFO:root:Iter[26] Batch [7240] Speed: 577.82 samples/sec INFO:root:Iter[26] Batch [7260] Speed: 588.53 samples/sec INFO:root:Iter[26] Batch 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INFO:root:Iter[26] Batch [8980] Speed: 511.49 samples/sec {fc7_acc} 272000 0.7421875 lr-batch-epoch: 0.00010000000000000003 8983 26 testing verification.. (12000, 512) infer time 4.514492000000001 [lfw][272000]XNorm: 22.251933 [lfw][272000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.3308659999999986 [cfp_fp][272000]XNorm: 22.282492 [cfp_fp][272000]Accuracy-Flip: 0.98857+-0.00319 testing verification.. (12000, 512) infer time 4.388996000000001 [agedb_30][272000]XNorm: 22.901113 [agedb_30][272000]Accuracy-Flip: 0.97917+-0.00743 [272000]Accuracy-Highest: 0.98183 INFO:root:Iter[26] Batch [9000] Speed: 252.07 samples/sec INFO:root:Iter[26] Batch [9020] Speed: 674.53 samples/sec INFO:root:Iter[26] Batch [9040] Speed: 692.19 samples/sec INFO:root:Iter[26] Batch [9060] Speed: 599.68 samples/sec INFO:root:Iter[26] Batch [9080] Speed: 594.54 samples/sec INFO:root:Iter[26] Batch [9100] Speed: 611.47 samples/sec INFO:root:Iter[26] Batch [9120] Speed: 549.98 samples/sec INFO:root:Iter[26] Batch [9140] Speed: 571.72 samples/sec INFO:root:Iter[26] Batch [9160] Speed: 570.31 samples/sec INFO:root:Iter[26] Batch [9180] Speed: 546.60 samples/sec INFO:root:Iter[26] Batch [9200] Speed: 533.73 samples/sec INFO:root:Iter[26] Batch [9220] Speed: 521.78 samples/sec INFO:root:Iter[26] Batch [9240] Speed: 531.78 samples/sec INFO:root:Iter[26] Batch [9260] Speed: 564.83 samples/sec INFO:root:Iter[26] Batch 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lr-batch-epoch: 0.00010000000000000003 867 27 testing verification.. (12000, 512) infer time 4.716957000000001 [lfw][274000]XNorm: 22.176560 [lfw][274000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.6571539999999985 [cfp_fp][274000]XNorm: 22.210855 [cfp_fp][274000]Accuracy-Flip: 0.98914+-0.00249 testing verification.. (12000, 512) infer time 4.489292000000002 [agedb_30][274000]XNorm: 22.768113 [agedb_30][274000]Accuracy-Flip: 0.97983+-0.00681 [274000]Accuracy-Highest: 0.98183 INFO:root:Iter[27] Batch [880] Speed: 247.92 samples/sec INFO:root:Iter[27] Batch [900] Speed: 873.48 samples/sec INFO:root:Iter[27] Batch [920] Speed: 833.23 samples/sec INFO:root:Iter[27] Batch [940] Speed: 696.91 samples/sec INFO:root:Iter[27] Batch [960] Speed: 606.21 samples/sec INFO:root:Iter[27] Batch [980] Speed: 630.02 samples/sec INFO:root:Iter[27] Batch [1000] Speed: 657.73 samples/sec INFO:root:Iter[27] Batch [1020] Speed: 666.47 samples/sec INFO:root:Iter[27] Batch [1040] Speed: 619.02 samples/sec INFO:root:Iter[27] Batch [1060] Speed: 628.31 samples/sec INFO:root:Iter[27] Batch [1080] Speed: 573.18 samples/sec INFO:root:Iter[27] Batch [1100] Speed: 530.79 samples/sec INFO:root:Iter[27] Batch [1120] Speed: 550.28 samples/sec INFO:root:Iter[27] Batch [1140] Speed: 565.90 samples/sec INFO:root:Iter[27] Batch [1160] 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samples/sec {fc7_acc} 276000 0.6796875 lr-batch-epoch: 0.00010000000000000003 2867 27 testing verification.. (12000, 512) infer time 4.504569000000001 [lfw][276000]XNorm: 22.230377 [lfw][276000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.610185000000001 [cfp_fp][276000]XNorm: 22.226020 [cfp_fp][276000]Accuracy-Flip: 0.98843+-0.00274 testing verification.. (12000, 512) infer time 4.116058999999998 [agedb_30][276000]XNorm: 22.812712 [agedb_30][276000]Accuracy-Flip: 0.98033+-0.00741 [276000]Accuracy-Highest: 0.98183 INFO:root:Iter[27] Batch [2880] Speed: 241.93 samples/sec INFO:root:Iter[27] Batch [2900] Speed: 793.07 samples/sec INFO:root:Iter[27] Batch [2920] Speed: 695.07 samples/sec INFO:root:Iter[27] Batch [2940] Speed: 679.02 samples/sec INFO:root:Iter[27] Batch [2960] Speed: 661.46 samples/sec INFO:root:Iter[27] Batch [2980] Speed: 614.18 samples/sec INFO:root:Iter[27] Batch [3000] Speed: 630.03 samples/sec INFO:root:Iter[27] Batch [3020] Speed: 632.91 samples/sec INFO:root:Iter[27] Batch [3040] Speed: 552.88 samples/sec INFO:root:Iter[27] Batch [3060] Speed: 541.59 samples/sec INFO:root:Iter[27] Batch [3080] Speed: 540.92 samples/sec INFO:root:Iter[27] Batch [3100] Speed: 547.30 samples/sec INFO:root:Iter[27] Batch [3120] Speed: 505.75 samples/sec INFO:root:Iter[27] Batch [3140] Speed: 569.61 samples/sec INFO:root:Iter[27] Batch 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INFO:root:Iter[27] Batch [4860] Speed: 512.65 samples/sec {fc7_acc} 278000 0.7558594 lr-batch-epoch: 0.00010000000000000003 4867 27 testing verification.. (12000, 512) infer time 4.085320000000001 [lfw][278000]XNorm: 22.150309 [lfw][278000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.318088000000001 [cfp_fp][278000]XNorm: 22.138545 [cfp_fp][278000]Accuracy-Flip: 0.98914+-0.00249 testing verification.. (12000, 512) infer time 4.121637 [agedb_30][278000]XNorm: 22.740057 [agedb_30][278000]Accuracy-Flip: 0.98083+-0.00708 [278000]Accuracy-Highest: 0.98183 INFO:root:Iter[27] Batch [4880] Speed: 255.82 samples/sec INFO:root:Iter[27] Batch [4900] Speed: 906.86 samples/sec INFO:root:Iter[27] Batch [4920] Speed: 850.26 samples/sec INFO:root:Iter[27] Batch [4940] Speed: 710.79 samples/sec INFO:root:Iter[27] Batch [4960] Speed: 607.49 samples/sec INFO:root:Iter[27] Batch [4980] Speed: 647.63 samples/sec INFO:root:Iter[27] Batch [5000] Speed: 668.78 samples/sec INFO:root:Iter[27] Batch [5020] Speed: 632.54 samples/sec INFO:root:Iter[27] Batch [5040] Speed: 545.13 samples/sec INFO:root:Iter[27] Batch [5060] Speed: 556.18 samples/sec INFO:root:Iter[27] Batch [5080] Speed: 526.28 samples/sec INFO:root:Iter[27] Batch [5100] Speed: 510.70 samples/sec INFO:root:Iter[27] Batch [5120] Speed: 521.52 samples/sec INFO:root:Iter[27] Batch [5140] Speed: 509.71 samples/sec INFO:root:Iter[27] Batch [5160] 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samples/sec {fc7_acc} 280000 0.6953125 lr-batch-epoch: 0.00010000000000000003 6867 27 testing verification.. (12000, 512) infer time 4.658848999999998 [lfw][280000]XNorm: 22.178855 [lfw][280000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.500916000000002 [cfp_fp][280000]XNorm: 22.228011 [cfp_fp][280000]Accuracy-Flip: 0.98957+-0.00307 testing verification.. (12000, 512) infer time 4.498231 [agedb_30][280000]XNorm: 22.730727 [agedb_30][280000]Accuracy-Flip: 0.98133+-0.00714 [280000]Accuracy-Highest: 0.98183 INFO:root:Iter[27] Batch [6880] Speed: 246.82 samples/sec INFO:root:Iter[27] Batch [6900] Speed: 905.84 samples/sec INFO:root:Iter[27] Batch [6920] Speed: 861.96 samples/sec INFO:root:Iter[27] Batch [6940] Speed: 720.77 samples/sec INFO:root:Iter[27] Batch [6960] Speed: 644.31 samples/sec INFO:root:Iter[27] Batch [6980] Speed: 596.09 samples/sec INFO:root:Iter[27] Batch [7000] Speed: 654.85 samples/sec INFO:root:Iter[27] Batch [7020] Speed: 569.48 samples/sec INFO:root:Iter[27] Batch [7040] Speed: 598.75 samples/sec INFO:root:Iter[27] Batch [7060] Speed: 535.31 samples/sec INFO:root:Iter[27] Batch [7080] Speed: 577.52 samples/sec INFO:root:Iter[27] Batch [7100] Speed: 530.75 samples/sec INFO:root:Iter[27] Batch [7120] Speed: 524.68 samples/sec INFO:root:Iter[27] Batch [7140] Speed: 548.45 samples/sec INFO:root:Iter[27] Batch [7160] 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samples/sec {fc7_acc} 282000 0.71484375 lr-batch-epoch: 0.00010000000000000003 8867 27 testing verification.. (12000, 512) infer time 4.674663000000002 [lfw][282000]XNorm: 22.173334 [lfw][282000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.254767999999999 [cfp_fp][282000]XNorm: 22.203611 [cfp_fp][282000]Accuracy-Flip: 0.98886+-0.00229 testing verification.. (12000, 512) infer time 4.556896 [agedb_30][282000]XNorm: 22.774687 [agedb_30][282000]Accuracy-Flip: 0.98083+-0.00716 [282000]Accuracy-Highest: 0.98183 INFO:root:Iter[27] Batch [8880] Speed: 251.56 samples/sec INFO:root:Iter[27] Batch [8900] Speed: 819.39 samples/sec INFO:root:Iter[27] Batch [8920] Speed: 782.93 samples/sec INFO:root:Iter[27] Batch [8940] Speed: 822.97 samples/sec INFO:root:Iter[27] Batch [8960] Speed: 827.31 samples/sec INFO:root:Iter[27] Batch [8980] Speed: 692.44 samples/sec INFO:root:Iter[27] Batch [9000] Speed: 722.54 samples/sec INFO:root:Iter[27] Batch [9020] Speed: 613.92 samples/sec INFO:root:Iter[27] Batch [9040] Speed: 654.81 samples/sec INFO:root:Iter[27] Batch [9060] Speed: 571.61 samples/sec INFO:root:Iter[27] Batch [9080] Speed: 570.78 samples/sec INFO:root:Iter[27] Batch [9100] Speed: 545.87 samples/sec INFO:root:Iter[27] Batch [9120] Speed: 549.12 samples/sec INFO:root:Iter[27] Batch [9140] Speed: 515.46 samples/sec INFO:root:Iter[27] Batch [9160] 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lr-batch-epoch: 0.00010000000000000003 751 28 testing verification.. (12000, 512) infer time 4.720783999999999 [lfw][284000]XNorm: 22.171831 [lfw][284000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 4.896747000000002 [cfp_fp][284000]XNorm: 22.254207 [cfp_fp][284000]Accuracy-Flip: 0.98857+-0.00256 testing verification.. (12000, 512) infer time 4.269107 [agedb_30][284000]XNorm: 22.778362 [agedb_30][284000]Accuracy-Flip: 0.97917+-0.00724 [284000]Accuracy-Highest: 0.98183 INFO:root:Iter[28] Batch [760] Speed: 242.55 samples/sec INFO:root:Iter[28] Batch [780] Speed: 761.18 samples/sec INFO:root:Iter[28] Batch [800] Speed: 718.25 samples/sec INFO:root:Iter[28] Batch [820] Speed: 696.39 samples/sec INFO:root:Iter[28] Batch [840] Speed: 658.73 samples/sec INFO:root:Iter[28] Batch [860] Speed: 741.59 samples/sec INFO:root:Iter[28] Batch [880] Speed: 663.39 samples/sec INFO:root:Iter[28] Batch [900] Speed: 661.69 samples/sec INFO:root:Iter[28] Batch [920] Speed: 663.65 samples/sec INFO:root:Iter[28] Batch [940] Speed: 638.82 samples/sec INFO:root:Iter[28] Batch [960] Speed: 682.43 samples/sec INFO:root:Iter[28] Batch [980] Speed: 659.31 samples/sec INFO:root:Iter[28] Batch [1000] Speed: 672.95 samples/sec INFO:root:Iter[28] Batch [1020] Speed: 698.11 samples/sec INFO:root:Iter[28] Batch [1040] Speed: 751.04 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samples/sec {fc7_acc} 286000 0.7285156 lr-batch-epoch: 0.00010000000000000003 2751 28 testing verification.. (12000, 512) infer time 4.608176 [lfw][286000]XNorm: 22.150140 [lfw][286000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.3960550000000005 [cfp_fp][286000]XNorm: 22.206329 [cfp_fp][286000]Accuracy-Flip: 0.98986+-0.00251 testing verification.. (12000, 512) infer time 4.261118999999999 [agedb_30][286000]XNorm: 22.812511 [agedb_30][286000]Accuracy-Flip: 0.97883+-0.00734 [286000]Accuracy-Highest: 0.98183 INFO:root:Iter[28] Batch [2760] Speed: 254.60 samples/sec INFO:root:Iter[28] Batch [2780] Speed: 871.01 samples/sec INFO:root:Iter[28] Batch [2800] Speed: 837.81 samples/sec INFO:root:Iter[28] Batch [2820] Speed: 863.56 samples/sec INFO:root:Iter[28] Batch [2840] Speed: 749.99 samples/sec INFO:root:Iter[28] Batch [2860] Speed: 712.87 samples/sec INFO:root:Iter[28] Batch [2880] Speed: 661.59 samples/sec INFO:root:Iter[28] Batch [2900] Speed: 744.87 samples/sec INFO:root:Iter[28] Batch [2920] Speed: 661.05 samples/sec INFO:root:Iter[28] Batch [2940] Speed: 721.51 samples/sec INFO:root:Iter[28] Batch [2960] Speed: 647.09 samples/sec INFO:root:Iter[28] Batch [2980] Speed: 642.21 samples/sec INFO:root:Iter[28] Batch [3000] Speed: 652.33 samples/sec INFO:root:Iter[28] Batch [3020] Speed: 610.04 samples/sec INFO:root:Iter[28] Batch 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INFO:root:Iter[28] Batch [4740] Speed: 610.92 samples/sec {fc7_acc} 288000 0.7519531 lr-batch-epoch: 0.00010000000000000003 4751 28 testing verification.. (12000, 512) infer time 4.238507 [lfw][288000]XNorm: 22.159188 [lfw][288000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.399526000000001 [cfp_fp][288000]XNorm: 22.224319 [cfp_fp][288000]Accuracy-Flip: 0.98886+-0.00262 testing verification.. (12000, 512) infer time 4.416805 [agedb_30][288000]XNorm: 22.765968 [agedb_30][288000]Accuracy-Flip: 0.98050+-0.00723 [288000]Accuracy-Highest: 0.98183 INFO:root:Iter[28] Batch [4760] Speed: 259.38 samples/sec INFO:root:Iter[28] Batch [4780] Speed: 889.92 samples/sec INFO:root:Iter[28] Batch [4800] Speed: 857.58 samples/sec INFO:root:Iter[28] Batch [4820] Speed: 777.03 samples/sec INFO:root:Iter[28] Batch [4840] Speed: 725.87 samples/sec INFO:root:Iter[28] Batch [4860] Speed: 611.78 samples/sec INFO:root:Iter[28] Batch [4880] Speed: 680.80 samples/sec INFO:root:Iter[28] Batch [4900] Speed: 673.46 samples/sec INFO:root:Iter[28] Batch [4920] Speed: 639.99 samples/sec INFO:root:Iter[28] Batch [4940] Speed: 663.75 samples/sec INFO:root:Iter[28] Batch [4960] Speed: 617.85 samples/sec INFO:root:Iter[28] Batch [4980] Speed: 674.35 samples/sec INFO:root:Iter[28] Batch [5000] Speed: 600.52 samples/sec INFO:root:Iter[28] Batch [5020] Speed: 646.28 samples/sec INFO:root:Iter[28] Batch [5040] 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samples/sec {fc7_acc} 290000 0.73046875 lr-batch-epoch: 0.00010000000000000003 6751 28 testing verification.. (12000, 512) infer time 4.027936 [lfw][290000]XNorm: 22.156969 [lfw][290000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.324678000000001 [cfp_fp][290000]XNorm: 22.212051 [cfp_fp][290000]Accuracy-Flip: 0.98829+-0.00254 testing verification.. (12000, 512) infer time 4.737529999999998 [agedb_30][290000]XNorm: 22.797386 [agedb_30][290000]Accuracy-Flip: 0.97983+-0.00776 [290000]Accuracy-Highest: 0.98183 INFO:root:Iter[28] Batch [6760] Speed: 252.45 samples/sec INFO:root:Iter[28] Batch [6780] Speed: 804.94 samples/sec INFO:root:Iter[28] Batch [6800] Speed: 743.36 samples/sec INFO:root:Iter[28] Batch [6820] Speed: 695.62 samples/sec INFO:root:Iter[28] Batch [6840] Speed: 680.59 samples/sec INFO:root:Iter[28] Batch [6860] Speed: 641.82 samples/sec INFO:root:Iter[28] Batch [6880] Speed: 595.43 samples/sec INFO:root:Iter[28] Batch [6900] Speed: 663.38 samples/sec INFO:root:Iter[28] Batch [6920] Speed: 665.84 samples/sec INFO:root:Iter[28] Batch [6940] Speed: 661.38 samples/sec INFO:root:Iter[28] Batch [6960] Speed: 678.06 samples/sec INFO:root:Iter[28] Batch [6980] Speed: 713.81 samples/sec INFO:root:Iter[28] Batch [7000] Speed: 664.01 samples/sec INFO:root:Iter[28] Batch [7020] Speed: 617.72 samples/sec INFO:root:Iter[28] Batch 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INFO:root:Iter[28] Batch [8740] Speed: 625.31 samples/sec {fc7_acc} 292000 0.75 lr-batch-epoch: 0.00010000000000000003 8751 28 testing verification.. (12000, 512) infer time 4.575980999999999 [lfw][292000]XNorm: 22.271433 [lfw][292000]Accuracy-Flip: 0.99767+-0.00327 testing verification.. (14000, 512) infer time 5.374421000000001 [cfp_fp][292000]XNorm: 22.251917 [cfp_fp][292000]Accuracy-Flip: 0.98829+-0.00270 testing verification.. (12000, 512) infer time 3.878468999999998 [agedb_30][292000]XNorm: 22.818390 [agedb_30][292000]Accuracy-Flip: 0.98017+-0.00669 [292000]Accuracy-Highest: 0.98183 INFO:root:Iter[28] Batch [8760] Speed: 256.33 samples/sec INFO:root:Iter[28] Batch [8780] Speed: 797.56 samples/sec INFO:root:Iter[28] Batch [8800] Speed: 664.18 samples/sec INFO:root:Iter[28] Batch [8820] Speed: 620.28 samples/sec INFO:root:Iter[28] Batch [8840] Speed: 622.86 samples/sec INFO:root:Iter[28] Batch [8860] Speed: 651.41 samples/sec INFO:root:Iter[28] Batch [8880] Speed: 648.01 samples/sec INFO:root:Iter[28] Batch [8900] Speed: 659.30 samples/sec INFO:root:Iter[28] Batch [8920] Speed: 624.20 samples/sec INFO:root:Iter[28] Batch [8940] Speed: 638.76 samples/sec INFO:root:Iter[28] Batch [8960] Speed: 615.76 samples/sec INFO:root:Iter[28] Batch [8980] Speed: 623.39 samples/sec INFO:root:Iter[28] Batch [9000] Speed: 623.59 samples/sec INFO:root:Iter[28] Batch [9020] Speed: 664.51 samples/sec INFO:root:Iter[28] Batch 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{fc7_acc} 294000 0.7265625 lr-batch-epoch: 0.00010000000000000003 635 29 testing verification.. (12000, 512) infer time 4.576885000000001 [lfw][294000]XNorm: 22.178729 [lfw][294000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.218273 [cfp_fp][294000]XNorm: 22.228354 [cfp_fp][294000]Accuracy-Flip: 0.98914+-0.00301 testing verification.. (12000, 512) infer time 3.576464 [agedb_30][294000]XNorm: 22.813746 [agedb_30][294000]Accuracy-Flip: 0.98017+-0.00732 [294000]Accuracy-Highest: 0.98183 INFO:root:Iter[29] Batch [640] Speed: 256.77 samples/sec INFO:root:Iter[29] Batch [660] Speed: 901.76 samples/sec INFO:root:Iter[29] Batch [680] Speed: 882.47 samples/sec INFO:root:Iter[29] Batch [700] Speed: 838.43 samples/sec INFO:root:Iter[29] Batch [720] Speed: 720.62 samples/sec INFO:root:Iter[29] Batch [740] Speed: 783.47 samples/sec INFO:root:Iter[29] Batch [760] Speed: 715.55 samples/sec INFO:root:Iter[29] Batch [780] Speed: 731.84 samples/sec INFO:root:Iter[29] Batch [800] Speed: 661.18 samples/sec INFO:root:Iter[29] Batch [820] Speed: 641.67 samples/sec INFO:root:Iter[29] Batch [840] Speed: 642.38 samples/sec INFO:root:Iter[29] Batch [860] Speed: 636.35 samples/sec INFO:root:Iter[29] Batch [880] Speed: 652.18 samples/sec INFO:root:Iter[29] Batch [900] Speed: 642.66 samples/sec INFO:root:Iter[29] Batch [920] Speed: 626.44 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samples/sec {fc7_acc} 296000 0.76171875 lr-batch-epoch: 0.00010000000000000003 2635 29 testing verification.. (12000, 512) infer time 4.637565999999999 [lfw][296000]XNorm: 22.111761 [lfw][296000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.322678999999999 [cfp_fp][296000]XNorm: 22.226446 [cfp_fp][296000]Accuracy-Flip: 0.98929+-0.00258 testing verification.. (12000, 512) infer time 4.116315 [agedb_30][296000]XNorm: 22.718974 [agedb_30][296000]Accuracy-Flip: 0.97983+-0.00776 [296000]Accuracy-Highest: 0.98183 INFO:root:Iter[29] Batch [2640] Speed: 251.99 samples/sec INFO:root:Iter[29] Batch [2660] Speed: 785.47 samples/sec INFO:root:Iter[29] Batch [2680] Speed: 813.63 samples/sec INFO:root:Iter[29] Batch [2700] Speed: 804.55 samples/sec INFO:root:Iter[29] Batch [2720] Speed: 698.94 samples/sec INFO:root:Iter[29] Batch [2740] Speed: 736.34 samples/sec INFO:root:Iter[29] Batch [2760] Speed: 641.14 samples/sec INFO:root:Iter[29] Batch [2780] Speed: 639.92 samples/sec INFO:root:Iter[29] Batch [2800] Speed: 709.75 samples/sec INFO:root:Iter[29] Batch [2820] Speed: 679.59 samples/sec INFO:root:Iter[29] Batch [2840] Speed: 691.33 samples/sec INFO:root:Iter[29] Batch [2860] Speed: 672.03 samples/sec INFO:root:Iter[29] Batch [2880] Speed: 655.32 samples/sec INFO:root:Iter[29] Batch [2900] Speed: 651.64 samples/sec INFO:root:Iter[29] Batch [2920] 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samples/sec {fc7_acc} 298000 0.68359375 lr-batch-epoch: 0.00010000000000000003 4635 29 testing verification.. (12000, 512) infer time 4.512219999999999 [lfw][298000]XNorm: 22.254004 [lfw][298000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.331285 [cfp_fp][298000]XNorm: 22.313146 [cfp_fp][298000]Accuracy-Flip: 0.98857+-0.00286 testing verification.. (12000, 512) infer time 4.689254999999999 [agedb_30][298000]XNorm: 22.890555 [agedb_30][298000]Accuracy-Flip: 0.98100+-0.00688 [298000]Accuracy-Highest: 0.98183 INFO:root:Iter[29] Batch [4640] Speed: 246.49 samples/sec INFO:root:Iter[29] Batch [4660] Speed: 888.39 samples/sec INFO:root:Iter[29] Batch [4680] Speed: 821.16 samples/sec INFO:root:Iter[29] Batch [4700] Speed: 712.64 samples/sec INFO:root:Iter[29] Batch [4720] Speed: 636.92 samples/sec INFO:root:Iter[29] Batch [4740] Speed: 619.96 samples/sec INFO:root:Iter[29] Batch [4760] Speed: 648.95 samples/sec INFO:root:Iter[29] Batch [4780] Speed: 604.70 samples/sec INFO:root:Iter[29] Batch [4800] Speed: 600.40 samples/sec INFO:root:Iter[29] Batch [4820] Speed: 625.76 samples/sec INFO:root:Iter[29] Batch [4840] Speed: 617.41 samples/sec INFO:root:Iter[29] Batch [4860] Speed: 640.99 samples/sec INFO:root:Iter[29] Batch [4880] Speed: 654.88 samples/sec INFO:root:Iter[29] Batch [4900] Speed: 613.23 samples/sec INFO:root:Iter[29] Batch 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INFO:root:Iter[29] Batch [6620] Speed: 592.61 samples/sec {fc7_acc} 300000 0.734375 lr-batch-epoch: 0.00010000000000000003 6635 29 testing verification.. (12000, 512) infer time 4.403759 [lfw][300000]XNorm: 22.193812 [lfw][300000]Accuracy-Flip: 0.99733+-0.00309 testing verification.. (14000, 512) infer time 5.627749999999999 [cfp_fp][300000]XNorm: 22.231463 [cfp_fp][300000]Accuracy-Flip: 0.98914+-0.00232 testing verification.. (12000, 512) infer time 4.36394 [agedb_30][300000]XNorm: 22.719524 [agedb_30][300000]Accuracy-Flip: 0.98100+-0.00696 [300000]Accuracy-Highest: 0.98183 INFO:root:Iter[29] Batch [6640] Speed: 249.82 samples/sec INFO:root:Iter[29] Batch [6660] Speed: 890.87 samples/sec INFO:root:Iter[29] Batch [6680] Speed: 880.59 samples/sec INFO:root:Iter[29] Batch [6700] Speed: 860.13 samples/sec INFO:root:Iter[29] Batch [6720] Speed: 754.88 samples/sec INFO:root:Iter[29] Batch [6740] Speed: 783.56 samples/sec INFO:root:Iter[29] Batch [6760] Speed: 708.31 samples/sec INFO:root:Iter[29] Batch [6780] Speed: 712.52 samples/sec INFO:root:Iter[29] Batch [6800] Speed: 691.34 samples/sec INFO:root:Iter[29] Batch [6820] Speed: 728.11 samples/sec INFO:root:Iter[29] Batch [6840] Speed: 649.35 samples/sec INFO:root:Iter[29] Batch [6860] Speed: 644.89 samples/sec INFO:root:Iter[29] Batch [6880] Speed: 649.43 samples/sec INFO:root:Iter[29] Batch [6900] Speed: 689.26 samples/sec INFO:root:Iter[29] Batch [6920] 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samples/sec {fc7_acc} 302000 0.7265625 lr-batch-epoch: 0.00010000000000000003 8635 29 testing verification.. (12000, 512) infer time 4.646906 [lfw][302000]XNorm: 22.176247 [lfw][302000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.441162 [cfp_fp][302000]XNorm: 22.226028 [cfp_fp][302000]Accuracy-Flip: 0.98886+-0.00262 testing verification.. (12000, 512) infer time 4.365901000000001 [agedb_30][302000]XNorm: 22.799321 [agedb_30][302000]Accuracy-Flip: 0.98050+-0.00683 [302000]Accuracy-Highest: 0.98183 INFO:root:Iter[29] Batch [8640] Speed: 245.86 samples/sec INFO:root:Iter[29] Batch [8660] Speed: 796.70 samples/sec INFO:root:Iter[29] Batch [8680] Speed: 774.36 samples/sec INFO:root:Iter[29] Batch [8700] Speed: 764.55 samples/sec INFO:root:Iter[29] Batch [8720] Speed: 668.14 samples/sec INFO:root:Iter[29] Batch [8740] Speed: 639.61 samples/sec INFO:root:Iter[29] Batch [8760] Speed: 640.40 samples/sec INFO:root:Iter[29] Batch [8780] Speed: 652.35 samples/sec INFO:root:Iter[29] Batch [8800] Speed: 629.70 samples/sec INFO:root:Iter[29] Batch [8820] Speed: 657.36 samples/sec INFO:root:Iter[29] Batch [8840] Speed: 612.52 samples/sec INFO:root:Iter[29] Batch [8860] Speed: 640.76 samples/sec INFO:root:Iter[29] Batch [8880] Speed: 598.84 samples/sec INFO:root:Iter[29] Batch [8900] Speed: 616.32 samples/sec INFO:root:Iter[29] Batch 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{fc7_acc} 304000 0.7324219 lr-batch-epoch: 0.00010000000000000003 519 30 testing verification.. (12000, 512) infer time 4.620367000000001 [lfw][304000]XNorm: 22.140491 [lfw][304000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.109844999999999 [cfp_fp][304000]XNorm: 22.201819 [cfp_fp][304000]Accuracy-Flip: 0.98929+-0.00258 testing verification.. (12000, 512) infer time 3.796861 [agedb_30][304000]XNorm: 22.771274 [agedb_30][304000]Accuracy-Flip: 0.98117+-0.00738 [304000]Accuracy-Highest: 0.98183 INFO:root:Iter[30] Batch [520] Speed: 251.15 samples/sec INFO:root:Iter[30] Batch [540] Speed: 837.06 samples/sec INFO:root:Iter[30] Batch [560] Speed: 881.90 samples/sec INFO:root:Iter[30] Batch [580] Speed: 836.69 samples/sec INFO:root:Iter[30] Batch [600] Speed: 789.56 samples/sec INFO:root:Iter[30] Batch [620] Speed: 743.41 samples/sec INFO:root:Iter[30] Batch [640] Speed: 645.82 samples/sec INFO:root:Iter[30] Batch [660] Speed: 777.92 samples/sec INFO:root:Iter[30] Batch [680] Speed: 662.41 samples/sec INFO:root:Iter[30] Batch [700] Speed: 675.88 samples/sec INFO:root:Iter[30] Batch [720] Speed: 601.67 samples/sec INFO:root:Iter[30] Batch [740] Speed: 661.68 samples/sec INFO:root:Iter[30] Batch [760] Speed: 635.48 samples/sec INFO:root:Iter[30] Batch [780] Speed: 643.28 samples/sec INFO:root:Iter[30] Batch [800] Speed: 634.32 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samples/sec {fc7_acc} 306000 0.7207031 lr-batch-epoch: 0.00010000000000000003 2519 30 testing verification.. (12000, 512) infer time 4.551702000000001 [lfw][306000]XNorm: 22.234881 [lfw][306000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.402669000000002 [cfp_fp][306000]XNorm: 22.258805 [cfp_fp][306000]Accuracy-Flip: 0.98971+-0.00254 testing verification.. (12000, 512) infer time 3.8932400000000005 [agedb_30][306000]XNorm: 22.830463 [agedb_30][306000]Accuracy-Flip: 0.98000+-0.00719 [306000]Accuracy-Highest: 0.98183 INFO:root:Iter[30] Batch [2520] Speed: 246.00 samples/sec INFO:root:Iter[30] Batch [2540] Speed: 732.93 samples/sec INFO:root:Iter[30] Batch [2560] Speed: 808.97 samples/sec INFO:root:Iter[30] Batch [2580] Speed: 752.62 samples/sec INFO:root:Iter[30] Batch [2600] Speed: 682.24 samples/sec INFO:root:Iter[30] Batch [2620] Speed: 691.25 samples/sec INFO:root:Iter[30] Batch [2640] Speed: 660.08 samples/sec INFO:root:Iter[30] Batch [2660] Speed: 649.10 samples/sec INFO:root:Iter[30] Batch [2680] Speed: 608.64 samples/sec INFO:root:Iter[30] Batch [2700] Speed: 644.08 samples/sec INFO:root:Iter[30] Batch [2720] Speed: 612.02 samples/sec INFO:root:Iter[30] Batch [2740] Speed: 621.23 samples/sec INFO:root:Iter[30] Batch [2760] Speed: 608.27 samples/sec INFO:root:Iter[30] Batch [2780] Speed: 626.78 samples/sec INFO:root:Iter[30] Batch 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INFO:root:Iter[30] Batch [4500] Speed: 610.96 samples/sec {fc7_acc} 308000 0.7265625 lr-batch-epoch: 0.00010000000000000003 4519 30 testing verification.. (12000, 512) infer time 4.580029000000001 [lfw][308000]XNorm: 22.128091 [lfw][308000]Accuracy-Flip: 0.99717+-0.00350 testing verification.. (14000, 512) infer time 5.193554999999999 [cfp_fp][308000]XNorm: 22.273708 [cfp_fp][308000]Accuracy-Flip: 0.98800+-0.00314 testing verification.. (12000, 512) infer time 4.093954 [agedb_30][308000]XNorm: 22.772271 [agedb_30][308000]Accuracy-Flip: 0.98133+-0.00726 [308000]Accuracy-Highest: 0.98183 INFO:root:Iter[30] Batch [4520] Speed: 244.44 samples/sec INFO:root:Iter[30] Batch [4540] Speed: 861.28 samples/sec INFO:root:Iter[30] Batch [4560] Speed: 760.23 samples/sec INFO:root:Iter[30] Batch [4580] Speed: 779.37 samples/sec INFO:root:Iter[30] Batch [4600] Speed: 667.40 samples/sec INFO:root:Iter[30] Batch [4620] Speed: 706.68 samples/sec INFO:root:Iter[30] Batch [4640] Speed: 737.36 samples/sec INFO:root:Iter[30] Batch [4660] Speed: 669.23 samples/sec INFO:root:Iter[30] Batch [4680] Speed: 647.97 samples/sec INFO:root:Iter[30] Batch [4700] Speed: 628.64 samples/sec INFO:root:Iter[30] Batch [4720] Speed: 652.80 samples/sec INFO:root:Iter[30] Batch [4740] Speed: 678.41 samples/sec INFO:root:Iter[30] Batch [4760] Speed: 673.04 samples/sec INFO:root:Iter[30] Batch [4780] Speed: 691.40 samples/sec INFO:root:Iter[30] Batch [4800] 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samples/sec {fc7_acc} 310000 0.7207031 lr-batch-epoch: 0.00010000000000000003 6519 30 testing verification.. (12000, 512) infer time 4.664497000000002 [lfw][310000]XNorm: 22.195260 [lfw][310000]Accuracy-Flip: 0.99767+-0.00238 testing verification.. (14000, 512) infer time 5.131363999999999 [cfp_fp][310000]XNorm: 22.227274 [cfp_fp][310000]Accuracy-Flip: 0.98871+-0.00282 testing verification.. 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(12000, 512) infer time 4.41821 [lfw][312000]XNorm: 22.227343 [lfw][312000]Accuracy-Flip: 0.99750+-0.00271 testing verification.. (14000, 512) infer time 5.421950999999999 [cfp_fp][312000]XNorm: 22.290448 [cfp_fp][312000]Accuracy-Flip: 0.98829+-0.00254 testing verification.. (12000, 512) infer time 4.520631999999999 [agedb_30][312000]XNorm: 22.811532 [agedb_30][312000]Accuracy-Flip: 0.98033+-0.00710 [312000]Accuracy-Highest: 0.98183 INFO:root:Iter[30] Batch [8520] Speed: 239.88 samples/sec INFO:root:Iter[30] Batch [8540] Speed: 754.95 samples/sec INFO:root:Iter[30] Batch [8560] Speed: 691.97 samples/sec INFO:root:Iter[30] Batch [8580] Speed: 720.37 samples/sec INFO:root:Iter[30] Batch [8600] Speed: 751.83 samples/sec INFO:root:Iter[30] Batch [8620] Speed: 667.73 samples/sec INFO:root:Iter[30] Batch [8640] Speed: 702.57 samples/sec INFO:root:Iter[30] Batch [8660] Speed: 622.52 samples/sec INFO:root:Iter[30] Batch [8680] Speed: 621.62 samples/sec INFO:root:Iter[30] Batch [8700] Speed: 634.28 samples/sec INFO:root:Iter[30] Batch [8720] Speed: 639.92 samples/sec INFO:root:Iter[30] Batch [8740] Speed: 604.52 samples/sec INFO:root:Iter[30] Batch [8760] Speed: 629.80 samples/sec INFO:root:Iter[30] Batch [8780] Speed: 627.05 samples/sec INFO:root:Iter[30] Batch 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586.76 samples/sec INFO:root:Iter[30] Batch [9160] Speed: 610.48 samples/sec INFO:root:Iter[30] Batch [9180] Speed: 619.90 samples/sec INFO:root:Iter[30] Batch [9200] Speed: 601.62 samples/sec INFO:root:Iter[30] Batch [9220] Speed: 632.28 samples/sec INFO:root:Iter[30] Batch [9240] Speed: 615.17 samples/sec Traceback (most recent call last): File "train_parall.py", line 353, in main() File "train_parall.py", line 350, in main train_net(args) File "train_parall.py", line 345, in train_net epoch_end_callback = epoch_cb ) File "/DATA/disk1/jiankangdeng/mxnet/insightface/recognition/parall_module_local_v1.py", line 497, in fit next_data_batch = next(data_iter) File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/io/io.py", line 230, in __next__ return self.next() File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/io/io.py", line 473, in next if self.iter_next(): File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/site-packages/mxnet/io/io.py", line 451, in iter_next i.wait() File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/threading.py", line 552, in wait signaled = self._cond.wait(timeout) File "/home/dengjiankang/miniconda3/envs/pytorchenv/lib/python3.7/threading.py", line 296, in wait waiter.acquire() KeyboardInterrupt ================================================ FILE: face_recognition/models/MS1Mretina-resnet50-arcface/model-symbol.json ================================================ { "nodes": [ { "op": "null", "name": "data", "inputs": [] }, { "op": "_copy", "name": "id", "inputs": [[0, 0, 0]] }, { "op": "_minus_scalar", "name": "_minusscalar0", "attrs": {"scalar": "127.5"}, "inputs": [[1, 0, 0]] }, { "op": "_mul_scalar", "name": "_mulscalar0", "attrs": {"scalar": "0.0078125"}, "inputs": [[2, 0, 0]] }, { "op": "null", "name": "conv0_weight", "attrs": { "kernel": "(3, 3)", "no_bias": "True", "num_filter": "64", "pad": "(1, 1)", "stride": "(1, 1)", "workspace": "256" }, "inputs": [] }, { "op": "Convolution", "name": "conv0", "attrs": { "kernel": "(3, 3)", "no_bias": "True", "num_filter": "64", "pad": "(1, 1)", "stride": "(1, 1)", "workspace": "256" }, "inputs": [[3, 0, 0], [4, 0, 0]] }, { "op": "null", "name": "bn0_gamma", "attrs": { "eps": "2e-05", "fix_gamma": "False", "momentum": "0.9" }, "inputs": [] }, { "op": "null", "name": "bn0_beta", "attrs": { "eps": "2e-05", "fix_gamma": "False", "momentum": "0.9" }, "inputs": [] }, { "op": "null", "name": "bn0_moving_mean", "attrs": { "__init__": "[\"zero\", {}]", "eps": "2e-05", "fix_gamma": "False", "momentum": "0.9" }, "inputs": [] }, { "op": "null", "name": "bn0_moving_var", "attrs": { "__init__": "[\"one\", {}]", "eps": "2e-05", "fix_gamma": "False", "momentum": "0.9" }, "inputs": [] }, { "op": "BatchNorm", "name": "bn0", "attrs": { "eps": "2e-05", "fix_gamma": "False", "momentum": "0.9" }, "inputs": [[5, 0, 0], [6, 0, 0], [7, 0, 0], [8, 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config class ParallModule(BaseModule): def __init__(self, symbol, data_names, label_names, logger=logging, context=ctx.cpu(), work_load_list=None, asymbol = None, args = None): super(ParallModule, self).__init__(logger=logger) self._symbol = symbol self._asymbol = asymbol self._data_names = data_names self._label_names = label_names self._context = context self._work_load_list = work_load_list self._num_classes = config.num_classes self._batch_size = args.batch_size self._verbose = args.verbose self._emb_size = config.emb_size self._local_class_start = args.local_class_start self._iter = 0 self._curr_module = None self._num_workers = config.num_workers self._num_ctx = len(self._context) self._ctx_num_classes = args.ctx_num_classes self._nd_cache = {} self._ctx_cpu = mx.cpu() self._ctx_single_gpu = self._context[-1] self._fixed_param_names = None self._curr_module = Module(self._symbol, self._data_names, self._label_names, logger=self.logger, context=self._context, work_load_list=self._work_load_list, fixed_param_names=self._fixed_param_names) self._arcface_modules = [] self._ctx_class_start = [] for i in range(len(self._context)): args._ctxid = i _module = Module(self._asymbol(args), self._data_names, self._label_names, logger=self.logger, context=mx.gpu(i), work_load_list=self._work_load_list, fixed_param_names=self._fixed_param_names) self._arcface_modules.append(_module) _c = args.local_class_start + i*args.ctx_num_classes self._ctx_class_start.append(_c) self._usekv = False if self._usekv: self._distkv = mx.kvstore.create('dist_sync') self._kvinit = {} def _reset_bind(self): self.binded = False self._curr_module = None @property def data_names(self): return self._data_names @property def output_names(self): return self._symbol.list_outputs() @property def data_shapes(self): assert self.binded return self._curr_module.data_shapes @property def label_shapes(self): assert self.binded return self._curr_module.label_shapes @property def output_shapes(self): assert self.binded return self._curr_module.output_shapes def get_export_params(self): assert self.binded and self.params_initialized _g, _x = self._curr_module.get_params() g = _g.copy() x = _x.copy() return g, x def get_params(self): assert self.binded and self.params_initialized _g, _x = self._curr_module.get_params() g = _g.copy() x = _x.copy() for _module in self._arcface_modules: _g, _x = _module.get_params() ag = _g.copy() ax = _x.copy() g.update(ag) x.update(ax) return g, x def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True, allow_extra=False): g = arg_params x = aux_params #ag = {} #ax = {} rk = [] for k in g: v = g[k] if k.startswith('fc7'): p1 = k.find('_') p2 = k.rfind('_') _ctxid = int(k[p1+1:p2]) self._arcface_modules[_ctxid].set_params({k:v}, {}) rk.append(k) for k in rk: del g[k] self._curr_module.set_params(g, x) #self._arcface_module.set_params(ag, ax) def init_params(self, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_init=False, allow_extra=False): if self.params_initialized and not force_init: return assert self.binded, 'call bind before initializing the parameters' #TODO init the same weights with all work nodes self._curr_module.init_params(initializer=initializer, arg_params=None, aux_params=None, allow_missing=allow_missing, force_init=force_init, allow_extra=allow_extra) for _module in self._arcface_modules: #_initializer = initializer _initializer = mx.init.Normal(0.01) _module.init_params(initializer=_initializer, arg_params=None, aux_params=None, allow_missing=allow_missing, force_init=force_init, allow_extra=allow_extra) self.params_initialized = True def bind(self, data_shapes, label_shapes=None, for_training=True, inputs_need_grad=False, force_rebind=False, shared_module=None): print('in_bind', self.params_initialized, data_shapes, label_shapes) if self.params_initialized: arg_params, aux_params = self.get_params() # force rebinding is typically used when one want to switch from # training to prediction phase. if force_rebind: self._reset_bind() if self.binded: self.logger.warning('Already binded, ignoring bind()') return assert shared_module is None, 'shared_module for MutableModule is not supported' self.for_training = for_training self.inputs_need_grad = inputs_need_grad self.binded = True self._curr_module.bind(data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind=False, shared_module=None) _data_shape = data_shapes[0][1] print('_data_shape', _data_shape, label_shapes) for _module in self._arcface_modules: _module.bind([('data', (_data_shape[0]*self._num_workers, self._emb_size))], [('softmax_label', (_data_shape[0]*self._num_workers,))], for_training, True, force_rebind=False, shared_module=None) if self.params_initialized: self.set_params(arg_params, aux_params) def init_optimizer(self, kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), force_init=False): assert self.binded and self.params_initialized if self.optimizer_initialized and not force_init: self.logger.warning('optimizer already initialized, ignoring.') return self._curr_module.init_optimizer(kvstore, optimizer, optimizer_params, force_init=force_init) for _module in self._arcface_modules: _module.init_optimizer(kvstore, optimizer, optimizer_params, force_init=force_init) self.optimizer_initialized = True def kv_push(self, key, value): #if value.context!=mx.cpu(): # value = value.as_in_context(mx.cpu()) if not key in self._kvinit: self._distkv.init(key, nd.zeros_like(value)) self._kvinit[key] = 1 self._distkv.push(key, value) #get fc1 and partial fc7 def forward(self, data_batch, is_train=None): #g,x = self.get_params() #print('{fc7_weight[0][0]}', self._iter, g['fc7_0_weight'].asnumpy()[0][0]) #print('{pre_fc1_weight[0][0]}', self._iter, g['pre_fc1_weight'].asnumpy()[0][0]) assert self.binded and self.params_initialized self._curr_module.forward(data_batch, is_train=is_train) if is_train: self._iter+=1 fc1, label = self._curr_module.get_outputs(merge_multi_context=True) global_fc1 = fc1 self.global_label = label.as_in_context(self._ctx_cpu) for i, _module in enumerate(self._arcface_modules): _label = self.global_label - self._ctx_class_start[i] db_global_fc1 = io.DataBatch([global_fc1], [_label]) _module.forward(db_global_fc1) #fc7 with margin #print('forward end') def get_ndarray(self, context, name, shape): key = "%s_%s"%(name, context) #print(key) if not key in self._nd_cache: v = nd.zeros( shape=shape, ctx = context) self._nd_cache[key] = v else: v = self._nd_cache[key] return v def get_ndarray2(self, context, name, arr): key = "%s_%s"%(name, context) #print(key) if not key in self._nd_cache: v = nd.zeros( shape=arr.shape, ctx = context) self._nd_cache[key] = v else: v = self._nd_cache[key] arr.copyto(v) return v def backward(self, out_grads=None): #print('in backward') assert self.binded and self.params_initialized #tmp_ctx = self._ctx_cpu tmp_ctx = self._ctx_single_gpu fc7_outs = [] ctx_fc7_max = self.get_ndarray(tmp_ctx, 'ctx_fc7_max', (self._batch_size, len(self._context))) #local_fc7_max = nd.zeros( (self.global_label.shape[0],1), ctx=mx.cpu()) for i, _module in enumerate(self._arcface_modules): _fc7 = _module.get_outputs(merge_multi_context=True)[0] fc7_outs.append(_fc7) _fc7_max = nd.max(_fc7, axis=1).as_in_context(tmp_ctx) ctx_fc7_max[:,i] = _fc7_max local_fc7_max = self.get_ndarray(tmp_ctx, 'local_fc7_max', (self._batch_size, 1)) nd.max(ctx_fc7_max, axis=1, keepdims=True, out=local_fc7_max) global_fc7_max = local_fc7_max #local_fc7_sum = None local_fc7_sum = self.get_ndarray(tmp_ctx, 'local_fc7_sum', (self._batch_size,1)) local_fc7_sum[:,:] = 0.0 for i, _module in enumerate(self._arcface_modules): _max = self.get_ndarray2(fc7_outs[i].context, 'fc7_max', global_fc7_max) fc7_outs[i] = nd.broadcast_sub(fc7_outs[i], _max) fc7_outs[i] = nd.exp(fc7_outs[i]) _sum = nd.sum(fc7_outs[i], axis=1, keepdims=True).as_in_context(tmp_ctx) local_fc7_sum += _sum global_fc7_sum = local_fc7_sum if self._iter%self._verbose==0: #_ctx = self._context[-1] _ctx = self._ctx_cpu _probs = [] for i, _module in enumerate(self._arcface_modules): _prob = self.get_ndarray2(_ctx, '_fc7_prob_%d'%i, fc7_outs[i]) _probs.append(_prob) fc7_prob = self.get_ndarray(_ctx, 'test_fc7_prob', (self._batch_size, self._ctx_num_classes*len(self._context))) nd.concat(*_probs, dim=1, out=fc7_prob) fc7_pred = nd.argmax(fc7_prob, axis=1) local_label = self.global_label - self._local_class_start #local_label = self.get_ndarray2(_ctx, 'test_label', local_label) _pred = nd.equal(fc7_pred, local_label) print('{fc7_acc}', self._iter, nd.mean(_pred).asnumpy()[0]) #local_fc1_grad = [] #fc1_grad_ctx = self._ctx_cpu fc1_grad_ctx = self._ctx_single_gpu local_fc1_grad = self.get_ndarray(fc1_grad_ctx, 'local_fc1_grad', (self._batch_size,self._emb_size)) local_fc1_grad[:,:] = 0.0 for i, _module in enumerate(self._arcface_modules): _sum = self.get_ndarray2(fc7_outs[i].context, 'fc7_sum', global_fc7_sum) fc7_outs[i] = nd.broadcast_div(fc7_outs[i], _sum) a = i*self._ctx_num_classes b = (i+1)*self._ctx_num_classes _label = self.global_label - self._ctx_class_start[i] _label = self.get_ndarray2(fc7_outs[i].context, 'label', _label) onehot_label = self.get_ndarray(fc7_outs[i].context, 'label_onehot', (self._batch_size, self._ctx_num_classes)) nd.one_hot(_label, depth=self._ctx_num_classes, on_value = 1.0, off_value = 0.0, out=onehot_label) fc7_outs[i] -= onehot_label _module.backward(out_grads = [fc7_outs[i]]) #ctx_fc1_grad = _module.get_input_grads()[0].as_in_context(mx.cpu()) ctx_fc1_grad = self.get_ndarray2(fc1_grad_ctx, 'ctx_fc1_grad_%d'%i, _module.get_input_grads()[0]) local_fc1_grad += ctx_fc1_grad global_fc1_grad = local_fc1_grad self._curr_module.backward(out_grads = [global_fc1_grad]) def update(self): assert self.binded and self.params_initialized and self.optimizer_initialized self._curr_module.update() for i, _module in enumerate(self._arcface_modules): _module.update() mx.nd.waitall() def get_outputs(self, merge_multi_context=True): assert self.binded and self.params_initialized return self._curr_module.get_outputs(merge_multi_context=merge_multi_context) #return self._arcface_module.get_outputs(merge_multi_context=merge_multi_context) def get_input_grads(self, merge_multi_context=True): assert self.binded and self.params_initialized and self.inputs_need_grad return self._curr_module.get_input_grads(merge_multi_context=merge_multi_context) def update_metric(self, eval_metric, labels): assert self.binded and self.params_initialized #self._curr_module.update_metric(eval_metric, labels) #label = labels[0] #print(label.shape) #self._arcface_module.update_metric(eval_metric, labels) def install_monitor(self, mon): """ Install monitor on all executors """ assert self.binded self._curr_module.install_monitor(mon) def forward_backward(self, data_batch): """A convenient function that calls both ``forward`` and ``backward``.""" self.forward(data_batch, is_train=True) # get fc1 and partial fc7 self.backward() def fit(self, train_data, eval_data=None, eval_metric='acc', epoch_end_callback=None, batch_end_callback=None, kvstore='local', optimizer='sgd', optimizer_params=(('learning_rate', 0.01),), eval_end_callback=None, eval_batch_end_callback=None, initializer=Uniform(0.01), arg_params=None, aux_params=None, allow_missing=False, force_rebind=False, force_init=False, begin_epoch=0, num_epoch=None, validation_metric=None, monitor=None, sparse_row_id_fn=None): """Trains the module parameters. Checkout `Module Tutorial `_ to see a end-to-end use-case. Parameters ---------- train_data : DataIter Train DataIter. eval_data : DataIter If not ``None``, will be used as validation set and the performance after each epoch will be evaluated. eval_metric : str or EvalMetric Defaults to 'accuracy'. The performance measure used to display during training. Other possible predefined metrics are: 'ce' (CrossEntropy), 'f1', 'mae', 'mse', 'rmse', 'top_k_accuracy'. epoch_end_callback : function or list of functions Each callback will be called with the current `epoch`, `symbol`, `arg_params` and `aux_params`. batch_end_callback : function or list of function Each callback will be called with a `BatchEndParam`. kvstore : str or KVStore Defaults to 'local'. optimizer : str or Optimizer Defaults to 'sgd'. optimizer_params : dict Defaults to ``(('learning_rate', 0.01),)``. The parameters for the optimizer constructor. The default value is not a dict, just to avoid pylint warning on dangerous default values. eval_end_callback : function or list of function These will be called at the end of each full evaluation, with the metrics over the entire evaluation set. eval_batch_end_callback : function or list of function These will be called at the end of each mini-batch during evaluation. initializer : Initializer The initializer is called to initialize the module parameters when they are not already initialized. arg_params : dict Defaults to ``None``, if not ``None``, should be existing parameters from a trained model or loaded from a checkpoint (previously saved model). In this case, the value here will be used to initialize the module parameters, unless they are already initialized by the user via a call to `init_params` or `fit`. `arg_params` has a higher priority than `initializer`. aux_params : dict Defaults to ``None``. Similar to `arg_params`, except for auxiliary states. allow_missing : bool Defaults to ``False``. Indicates whether to allow missing parameters when `arg_params` and `aux_params` are not ``None``. If this is ``True``, then the missing parameters will be initialized via the `initializer`. force_rebind : bool Defaults to ``False``. Whether to force rebinding the executors if already bound. force_init : bool Defaults to ``False``. Indicates whether to force initialization even if the parameters are already initialized. begin_epoch : int Defaults to 0. Indicates the starting epoch. Usually, if resumed from a checkpoint saved at a previous training phase at epoch N, then this value should be N+1. num_epoch : int Number of epochs for training. sparse_row_id_fn : A callback function The function takes `data_batch` as an input and returns a dict of str -> NDArray. The resulting dict is used for pulling row_sparse parameters from the kvstore, where the str key is the name of the param, and the value is the row id of the param to pull. Examples -------- >>> # An example of using fit for training. >>> # Assume training dataIter and validation dataIter are ready >>> # Assume loading a previously checkpointed model >>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, 3) >>> mod.fit(train_data=train_dataiter, eval_data=val_dataiter, optimizer='sgd', ... optimizer_params={'learning_rate':0.01, 'momentum': 0.9}, ... arg_params=arg_params, aux_params=aux_params, ... eval_metric='acc', num_epoch=10, begin_epoch=3) """ assert num_epoch is not None, 'please specify number of epochs' assert arg_params is None and aux_params is None self.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label, for_training=True, force_rebind=force_rebind) if monitor is not None: self.install_monitor(monitor) self.init_params(initializer=initializer, arg_params=arg_params, aux_params=aux_params, allow_missing=allow_missing, force_init=force_init) self.init_optimizer(kvstore=kvstore, optimizer=optimizer, optimizer_params=optimizer_params) if validation_metric is None: validation_metric = eval_metric if not isinstance(eval_metric, metric.EvalMetric): eval_metric = metric.create(eval_metric) epoch_eval_metric = copy.deepcopy(eval_metric) ################################################################################ # training loop ################################################################################ for epoch in range(begin_epoch, num_epoch): tic = time.time() eval_metric.reset() epoch_eval_metric.reset() nbatch = 0 data_iter = iter(train_data) end_of_batch = False next_data_batch = next(data_iter) while not end_of_batch: data_batch = next_data_batch if monitor is not None: monitor.tic() self.forward_backward(data_batch) self.update() assert not isinstance(data_batch, list) #if isinstance(data_batch, list): # #print('XXX') # self.update_metric(eval_metric, # [db.label for db in data_batch], # pre_sliced=True) # self.update_metric(epoch_eval_metric, # [db.label for db in data_batch], # pre_sliced=True) #else: # #print('before update metric') # self.update_metric(eval_metric, data_batch.label) # self.update_metric(epoch_eval_metric, data_batch.label) #labels = data_batch.label #labels = [self.global_label] #self.update_metric(eval_metric, labels) #self.update_metric(epoch_eval_metric, labels) try: # pre fetch next batch next_data_batch = next(data_iter) self.prepare(next_data_batch, sparse_row_id_fn=sparse_row_id_fn) except StopIteration: end_of_batch = True if monitor is not None: monitor.toc_print() #if end_of_batch: # eval_name_vals = epoch_eval_metric.get_name_value() if batch_end_callback is not None: batch_end_params = BatchEndParam(epoch=epoch, nbatch=nbatch, eval_metric=None, locals=locals()) batch_end_callback(batch_end_params) #for callback in _as_list(batch_end_callback): # callback(batch_end_params) nbatch += 1 # one epoch of training is finished #for name, val in eval_name_vals: # self.logger.info('Epoch[%d] Train-%s=%f', epoch, name, val) toc = time.time() self.logger.info('Epoch[%d] Time cost=%.3f', epoch, (toc-tic)) # sync aux params across devices arg_params, aux_params = self.get_params() self.set_params(arg_params, aux_params) # end of 1 epoch, reset the data-iter for another epoch train_data.reset() ================================================ FILE: face_recognition/sample_config.py ================================================ import numpy as np import os from easydict import EasyDict as edict config = edict() config.bn_mom = 0.9 config.workspace = 256 config.emb_size = 512 config.ckpt_embedding = True config.net_se = 0 config.net_act = 'prelu' config.net_unit = 3 config.net_input = 1 config.net_blocks = [1,4,6,2] config.net_output = 'E' config.net_multiplier = 1.0 config.val_targets = ['lfw', 'cfp_fp', 'agedb_30'] config.ce_loss = True config.fc7_lr_mult = 1.0 config.fc7_wd_mult = 1.0 config.fc7_no_bias = False config.max_steps = 0 config.data_rand_mirror = True config.data_cutoff = False config.data_color = 0 config.data_images_filter = 0 config.count_flops = True config.memonger = False #not work now # network settings network = edict() network.r100 = edict() network.r100.net_name = 'fresnet' network.r100.num_layers = 100 network.r100fc = edict() network.r100fc.net_name = 'fresnet' network.r100fc.num_layers = 100 network.r100fc.net_output = 'FC' network.r50 = edict() network.r50.net_name = 'fresnet' network.r50.num_layers = 50 network.r50v1 = edict() network.r50v1.net_name = 'fresnet' network.r50v1.num_layers = 50 network.r50v1.net_unit = 1 network.d169 = edict() network.d169.net_name = 'fdensenet' network.d169.num_layers = 169 network.d169.per_batch_size = 64 network.d169.densenet_dropout = 0.0 network.d201 = edict() network.d201.net_name = 'fdensenet' network.d201.num_layers = 201 network.d201.per_batch_size = 64 network.d201.densenet_dropout = 0.0 network.y1 = edict() network.y1.net_name = 'fmobilefacenet' network.y1.emb_size = 128 network.y1.net_output = 'GDC' network.y2 = edict() network.y2.net_name = 'fmobilefacenet' network.y2.emb_size = 256 network.y2.net_output = 'GDC' network.y2.net_blocks = [2,8,16,4] network.m1 = edict() network.m1.net_name = 'fmobilenet' network.m1.emb_size = 256 network.m1.net_output = 'GDC' network.m1.net_multiplier = 1.0 network.m05 = edict() network.m05.net_name = 'fmobilenet' network.m05.emb_size = 256 network.m05.net_output = 'GDC' network.m05.net_multiplier = 0.5 network.mnas = edict() network.mnas.net_name = 'fmnasnet' network.mnas.emb_size = 256 network.mnas.net_output = 'GDC' network.mnas.net_multiplier = 1.0 network.mnas05 = edict() network.mnas05.net_name = 'fmnasnet' network.mnas05.emb_size = 256 network.mnas05.net_output = 'GDC' network.mnas05.net_multiplier = 0.5 network.mnas025 = edict() network.mnas025.net_name = 'fmnasnet' network.mnas025.emb_size = 256 network.mnas025.net_output = 'GDC' network.mnas025.net_multiplier = 0.25 network.vargfacenet = edict() network.vargfacenet.net_name = 'vargfacenet' network.vargfacenet.net_multiplier = 1.25 network.vargfacenet.emb_size = 512 network.vargfacenet.net_output='J' # dataset settings dataset = edict() dataset.emore = edict() dataset.emore.dataset = 'emore' dataset.emore.dataset_path = '../datasets/faces_emore' dataset.emore.num_classes = 85742 dataset.emore.image_shape = (112,112,3) dataset.emore.val_targets = ['lfw', 'cfp_fp', 'agedb_30'] dataset.retina = edict() dataset.retina.dataset = 'retina' dataset.retina.dataset_path = '../datasets/ms1m-retinaface-t1' dataset.retina.num_classes = 93431 dataset.retina.image_shape = (112,112,3) dataset.retina.val_targets = ['lfw', 'cfp_fp', 'agedb_30'] loss = edict() loss.softmax = edict() loss.softmax.loss_name = 'softmax' loss.nsoftmax = edict() loss.nsoftmax.loss_name = 'margin_softmax' loss.nsoftmax.loss_s = 64.0 loss.nsoftmax.loss_m1 = 1.0 loss.nsoftmax.loss_m2 = 0.0 loss.nsoftmax.loss_m3 = 0.0 loss.arcface = edict() loss.arcface.loss_name = 'margin_softmax' loss.arcface.loss_s = 64.0 loss.arcface.loss_m1 = 1.0 loss.arcface.loss_m2 = 0.5 loss.arcface.loss_m3 = 0.0 loss.cosface = edict() loss.cosface.loss_name = 'margin_softmax' loss.cosface.loss_s = 64.0 loss.cosface.loss_m1 = 1.0 loss.cosface.loss_m2 = 0.0 loss.cosface.loss_m3 = 0.35 loss.combined = edict() loss.combined.loss_name = 'margin_softmax' loss.combined.loss_s = 64.0 loss.combined.loss_m1 = 1.0 loss.combined.loss_m2 = 0.3 loss.combined.loss_m3 = 0.2 loss.triplet = edict() loss.triplet.loss_name = 'triplet' loss.triplet.images_per_identity = 5 loss.triplet.triplet_alpha = 0.3 loss.triplet.triplet_bag_size = 7200 loss.triplet.triplet_max_ap = 0.0 loss.triplet.per_batch_size = 60 loss.triplet.lr = 0.05 loss.atriplet = edict() loss.atriplet.loss_name = 'atriplet' loss.atriplet.images_per_identity = 5 loss.atriplet.triplet_alpha = 0.35 loss.atriplet.triplet_bag_size = 7200 loss.atriplet.triplet_max_ap = 0.0 loss.atriplet.per_batch_size = 60 loss.atriplet.lr = 0.05 # default settings default = edict() # default network default.network = 'r100' default.pretrained = '' default.pretrained_epoch = 1 # default dataset default.dataset = 'emore' default.loss = 'arcface' default.frequent = 20 default.verbose = 2000 default.kvstore = 'device' default.end_epoch = 10000 default.lr = 0.1 default.wd = 0.0005 default.mom = 0.9 default.per_batch_size = 128 default.ckpt = 3 default.lr_steps = '100000,160000,220000' default.models_root = './models' def generate_config(_network, _dataset, _loss): for k, v in loss[_loss].items(): config[k] = v if k in default: default[k] = v for k, v in network[_network].items(): config[k] = v if k in default: default[k] = v for k, v in dataset[_dataset].items(): config[k] = v if k in default: default[k] = v config.loss = _loss config.network = _network config.dataset = _dataset config.num_workers = 1 if 'DMLC_NUM_WORKER' in os.environ: config.num_workers = int(os.environ['DMLC_NUM_WORKER']) ================================================ FILE: face_recognition/symbol/fdensenet.py ================================================ # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. # coding: utf-8 # pylint: disable= arguments-differ """DenseNet, implemented in Gluon.""" import sys import os import mxnet as mx import mxnet.ndarray as nd import mxnet.gluon as gluon import mxnet.gluon.nn as nn import mxnet.autograd as ag import symbol_utils sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from config import config def Act(): if config.net_act=='prelu': return nn.PReLU() else: return nn.Activation(config.net_act) # Helpers def _make_dense_block(num_layers, bn_size, growth_rate, dropout, stage_index): out = nn.HybridSequential(prefix='stage%d_'%stage_index) with out.name_scope(): for _ in range(num_layers): out.add(_make_dense_layer(growth_rate, bn_size, dropout)) return out def _make_dense_layer(growth_rate, bn_size, dropout): new_features = nn.HybridSequential(prefix='') new_features.add(nn.BatchNorm()) #new_features.add(nn.Activation('relu')) new_features.add(Act()) new_features.add(nn.Conv2D(bn_size * growth_rate, kernel_size=1, use_bias=False)) new_features.add(nn.BatchNorm()) #new_features.add(nn.Activation('relu')) new_features.add(Act()) new_features.add(nn.Conv2D(growth_rate, kernel_size=3, padding=1, use_bias=False)) if dropout: new_features.add(nn.Dropout(dropout)) out = gluon.contrib.nn.HybridConcurrent(axis=1, prefix='') out.add(gluon.contrib.nn.Identity()) out.add(new_features) return out def _make_transition(num_output_features): out = nn.HybridSequential(prefix='') out.add(nn.BatchNorm()) #out.add(nn.Activation('relu')) out.add(Act()) out.add(nn.Conv2D(num_output_features, kernel_size=1, use_bias=False)) out.add(nn.AvgPool2D(pool_size=2, strides=2)) return out # Net class DenseNet(nn.HybridBlock): r"""Densenet-BC model from the `"Densely Connected Convolutional Networks" `_ paper. Parameters ---------- num_init_features : int Number of filters to learn in the first convolution layer. growth_rate : int Number of filters to add each layer (`k` in the paper). block_config : list of int List of integers for numbers of layers in each pooling block. bn_size : int, default 4 Multiplicative factor for number of bottle neck layers. (i.e. bn_size * k features in the bottleneck layer) dropout : float, default 0 Rate of dropout after each dense layer. classes : int, default 1000 Number of classification classes. """ def __init__(self, num_init_features, growth_rate, block_config, bn_size=4, dropout=0, classes=1000, **kwargs): super(DenseNet, self).__init__(**kwargs) with self.name_scope(): self.features = nn.HybridSequential(prefix='') self.features.add(nn.Conv2D(num_init_features, kernel_size=3, strides=1, padding=1, use_bias=False)) self.features.add(nn.BatchNorm()) self.features.add(nn.Activation('relu')) self.features.add(nn.MaxPool2D(pool_size=3, strides=2, padding=1)) # Add dense blocks num_features = num_init_features for i, num_layers in enumerate(block_config): self.features.add(_make_dense_block(num_layers, bn_size, growth_rate, dropout, i+1)) num_features = num_features + num_layers * growth_rate if i != len(block_config) - 1: self.features.add(_make_transition(num_features // 2)) num_features = num_features // 2 self.features.add(nn.BatchNorm()) self.features.add(nn.Activation('relu')) #self.features.add(nn.AvgPool2D(pool_size=7)) #self.features.add(nn.Flatten()) #self.output = nn.Dense(classes) def hybrid_forward(self, F, x): x = self.features(x) #x = self.output(x) return x # Specification densenet_spec = {121: (64, 32, [6, 12, 24, 16]), 161: (96, 48, [6, 12, 36, 24]), 169: (64, 32, [6, 12, 32, 32]), 201: (64, 32, [6, 12, 48, 32])} # Constructor def get_symbol(): num_layers = config.num_layers num_init_features, growth_rate, block_config = densenet_spec[num_layers] net = DenseNet(num_init_features, growth_rate, block_config, dropout = config.densenet_dropout) data = mx.sym.Variable(name='data') data = data-127.5 data = data*0.0078125 body = net(data) fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output) return fc1 ================================================ FILE: face_recognition/symbol/fmnasnet.py ================================================ import sys import os import mxnet as mx import mxnet.ndarray as nd import mxnet.gluon as gluon import mxnet.gluon.nn as nn import mxnet.autograd as ag import symbol_utils sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from config import config def Act(): if config.net_act=='prelu': return nn.PReLU() else: return nn.Activation(config.net_act) def ConvBlock(channels, kernel_size, strides, **kwargs): out = nn.HybridSequential(**kwargs) with out.name_scope(): out.add( nn.Conv2D(channels, kernel_size, strides=strides, padding=1, use_bias=False), nn.BatchNorm(scale=True), Act() #nn.Activation('relu') ) return out def Conv1x1(channels, is_linear=False, **kwargs): out = nn.HybridSequential(**kwargs) with out.name_scope(): out.add( nn.Conv2D(channels, 1, padding=0, use_bias=False), nn.BatchNorm(scale=True) ) if not is_linear: #out.add(nn.Activation('relu')) out.add(Act()) return out def DWise(channels, strides, kernel_size=3, **kwargs): out = nn.HybridSequential(**kwargs) with out.name_scope(): out.add( nn.Conv2D(channels, kernel_size, strides=strides, padding=kernel_size // 2, groups=channels, use_bias=False), nn.BatchNorm(scale=True), Act() #nn.Activation('relu') ) return out class SepCONV(nn.HybridBlock): def __init__(self, inp, output, kernel_size, depth_multiplier=1, with_bn=True, **kwargs): super(SepCONV, self).__init__(**kwargs) with self.name_scope(): self.net = nn.HybridSequential() cn = int(inp*depth_multiplier) if output is None: self.net.add( nn.Conv2D(in_channels=inp, channels=cn, groups=inp, kernel_size=kernel_size, strides=(1,1), padding=kernel_size // 2 , use_bias=not with_bn) ) else: self.net.add( nn.Conv2D(in_channels=inp, channels=cn, groups=inp, kernel_size=kernel_size, strides=(1,1), padding=kernel_size // 2 , use_bias=False), nn.BatchNorm(), Act(), #nn.Activation('relu'), nn.Conv2D(in_channels=cn, channels=output, kernel_size=(1,1), strides=(1,1) , use_bias=not with_bn) ) self.with_bn = with_bn self.act = Act() #self.act = nn.Activation('relu') if with_bn: self.bn = nn.BatchNorm() def hybrid_forward(self, F ,x): x = self.net(x) if self.with_bn: x = self.bn(x) if self.act is not None: x = self.act(x) return x class ExpandedConv(nn.HybridBlock): def __init__(self, inp, oup, t, strides, kernel=3, same_shape=True, **kwargs): super(ExpandedConv, self).__init__(**kwargs) self.same_shape = same_shape self.strides = strides with self.name_scope(): self.bottleneck = nn.HybridSequential() self.bottleneck.add( Conv1x1(inp*t, prefix="expand_"), DWise(inp*t, self.strides, kernel, prefix="dwise_"), Conv1x1(oup, is_linear=True, prefix="linear_") ) def hybrid_forward(self, F, x): out = self.bottleneck(x) if self.strides == 1 and self.same_shape: out = F.elemwise_add(out, x) return out def ExpandedConvSequence(t, k, inp, oup, repeats, first_strides, **kwargs): seq = nn.HybridSequential(**kwargs) with seq.name_scope(): seq.add(ExpandedConv(inp, oup, t, first_strides, k, same_shape=False)) curr_inp = oup for i in range(1, repeats): seq.add(ExpandedConv(curr_inp, oup, t, 1)) curr_inp = oup return seq class MNasNet(nn.HybridBlock): def __init__(self, m=1.0, **kwargs): super(MNasNet, self).__init__(**kwargs) self.first_oup = int(32*m) self.second_oup = int(16*m) #self.second_oup = int(32*m) self.interverted_residual_setting = [ # t, c, n, s, k [3, int(24*m), 3, 2, 3, "stage2_"], # -> 56x56 [3, int(40*m), 3, 2, 5, "stage3_"], # -> 28x28 [6, int(80*m), 3, 2, 5, "stage4_1_"], # -> 14x14 [6, int(96*m), 2, 1, 3, "stage4_2_"], # -> 14x14 [6, int(192*m), 4, 2, 5, "stage5_1_"], # -> 7x7 [6, int(320*m), 1, 1, 3, "stage5_2_"], # -> 7x7 ] self.last_channels = int(1024*m) with self.name_scope(): self.features = nn.HybridSequential() self.features.add(ConvBlock(self.first_oup, 3, 1, prefix="stage1_conv0_")) self.features.add(SepCONV(self.first_oup, self.second_oup, 3, prefix="stage1_sepconv0_")) inp = self.second_oup for i, (t, c, n, s, k, prefix) in enumerate(self.interverted_residual_setting): oup = c self.features.add(ExpandedConvSequence(t, k, inp, oup, n, s, prefix=prefix)) inp = oup self.features.add(Conv1x1(self.last_channels, prefix="stage5_3_")) #self.features.add(nn.GlobalAvgPool2D()) #self.features.add(nn.Flatten()) #self.output = nn.Dense(num_classes) def hybrid_forward(self, F, x): x = self.features(x) #x = self.output(x) return x def num_output_channel(self): return self.last_channels def get_symbol(): net = MNasNet(config.net_multiplier) data = mx.sym.Variable(name='data') data = data-127.5 data = data*0.0078125 body = net(data) fc1 = symbol_utils.get_fc1(body, config.emb_size, config.net_output, input_channel=net.num_output_channel()) return fc1 ================================================ FILE: face_recognition/symbol/fmobilefacenet.py ================================================ import sys import os import mxnet as mx import symbol_utils sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from config import config def Act(data, act_type, name): #ignore param act_type, set it in this function if act_type=='prelu': body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name) else: body = mx.sym.Activation(data=data, act_type=act_type, name=name) return body def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''): conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=config.bn_mom) act = Act(data=bn, act_type=config.net_act, name='%s%s_relu' %(name, suffix)) return act def Linear(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''): conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=config.bn_mom) return bn def ConvOnly(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''): conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) return conv def DResidual(data, num_out=1, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=1, name=None, suffix=''): conv = Conv(data=data, num_filter=num_group, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name='%s%s_conv_sep' %(name, suffix)) conv_dw = Conv(data=conv, num_filter=num_group, num_group=num_group, kernel=kernel, pad=pad, stride=stride, name='%s%s_conv_dw' %(name, suffix)) proj = Linear(data=conv_dw, num_filter=num_out, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name='%s%s_conv_proj' %(name, suffix)) return proj def Residual(data, num_block=1, num_out=1, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=1, name=None, suffix=''): identity=data for i in range(num_block): shortcut=identity conv=DResidual(data=identity, num_out=num_out, kernel=kernel, stride=stride, pad=pad, num_group=num_group, name='%s%s_block' %(name, suffix), suffix='%d'%i) identity=conv+shortcut return identity def get_symbol(): num_classes = config.emb_size print('in_network', config) fc_type = config.net_output data = mx.symbol.Variable(name="data") data = data-127.5 data = data*0.0078125 blocks = config.net_blocks conv_1 = Conv(data, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") if blocks[0]==1: conv_2_dw = Conv(conv_1, num_group=64, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") else: conv_2_dw = Residual(conv_1, num_block=blocks[0], num_out=64, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=64, name="res_2") conv_23 = DResidual(conv_2_dw, num_out=64, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=128, name="dconv_23") conv_3 = Residual(conv_23, num_block=blocks[1], num_out=64, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=128, name="res_3") conv_34 = DResidual(conv_3, num_out=128, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=256, name="dconv_34") conv_4 = Residual(conv_34, num_block=blocks[2], num_out=128, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=256, name="res_4") conv_45 = DResidual(conv_4, num_out=128, kernel=(3, 3), stride=(2, 2), pad=(1, 1), num_group=512, name="dconv_45") conv_5 = Residual(conv_45, num_block=blocks[3], num_out=128, kernel=(3, 3), stride=(1, 1), pad=(1, 1), num_group=256, name="res_5") conv_6_sep = Conv(conv_5, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_6sep") fc1 = symbol_utils.get_fc1(conv_6_sep, num_classes, fc_type) return fc1 ================================================ FILE: face_recognition/symbol/fmobilenet.py ================================================ # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import sys import os import mxnet as mx import symbol_utils sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from config import config def Act(data, act_type, name): #ignore param act_type, set it in this function if act_type=='prelu': body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name) else: body = mx.sym.Activation(data=data, act_type=act_type, name=name) return body def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''): conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True) act = Act(data=bn, act_type=config.net_act, name='%s%s_relu' %(name, suffix)) return act def ConvOnly(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''): conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) return conv def get_symbol(): num_classes = config.emb_size bn_mom = config.bn_mom workspace = config.workspace data = mx.symbol.Variable(name="data") # 224 data = data-127.5 data = data*0.0078125 fc_type = config.net_output bf = int(32*config.net_multiplier) if config.net_input==0: conv_1 = Conv(data, num_filter=bf, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 224/112 else: conv_1 = Conv(data, num_filter=bf, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_1") # 224/112 conv_2_dw = Conv(conv_1, num_group=bf, num_filter=bf, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112 conv_2 = Conv(conv_2_dw, num_filter=bf*2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 112/112 conv_3_dw = Conv(conv_2, num_group=bf*2, num_filter=bf*2, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_3_dw") # 112/56 conv_3 = Conv(conv_3_dw, num_filter=bf*4, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_3") # 56/56 conv_4_dw = Conv(conv_3, num_group=bf*4, num_filter=bf*4, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_4_dw") # 56/56 conv_4 = Conv(conv_4_dw, num_filter=bf*4, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_4") # 56/56 conv_5_dw = Conv(conv_4, num_group=bf*4, num_filter=bf*4, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_5_dw") # 56/28 conv_5 = Conv(conv_5_dw, num_filter=bf*8, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_5") # 28/28 conv_6_dw = Conv(conv_5, num_group=bf*8, num_filter=bf*8, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_6_dw") # 28/28 conv_6 = Conv(conv_6_dw, num_filter=bf*8, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_6") # 28/28 conv_7_dw = Conv(conv_6, num_group=bf*8, num_filter=bf*8, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_7_dw") # 28/14 conv_7 = Conv(conv_7_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_7") # 14/14 conv_8_dw = Conv(conv_7, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_8_dw") # 14/14 conv_8 = Conv(conv_8_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_8") # 14/14 conv_9_dw = Conv(conv_8, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_9_dw") # 14/14 conv_9 = Conv(conv_9_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_9") # 14/14 conv_10_dw = Conv(conv_9, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_10_dw") # 14/14 conv_10 = Conv(conv_10_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_10") # 14/14 conv_11_dw = Conv(conv_10, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_11_dw") # 14/14 conv_11 = Conv(conv_11_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_11") # 14/14 conv_12_dw = Conv(conv_11, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_12_dw") # 14/14 conv_12 = Conv(conv_12_dw, num_filter=bf*16, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_12") # 14/14 conv_13_dw = Conv(conv_12, num_group=bf*16, num_filter=bf*16, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_13_dw") # 14/7 conv_13 = Conv(conv_13_dw, num_filter=bf*32, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_13") # 7/7 conv_14_dw = Conv(conv_13, num_group=bf*32, num_filter=bf*32, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_14_dw") # 7/7 conv_14 = Conv(conv_14_dw, num_filter=bf*32, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_14") # 7/7 body = conv_14 fc1 = symbol_utils.get_fc1(body, num_classes, fc_type) return fc1 ================================================ FILE: face_recognition/symbol/fresnet.py ================================================ # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. ''' Adapted from https://github.com/tornadomeet/ResNet/blob/master/symbol_resnet.py Original author Wei Wu Implemented the following paper: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Identity Mappings in Deep Residual Networks" ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys import os import mxnet as mx import numpy as np import symbol_utils import memonger import sklearn sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from config import config def Conv(**kwargs): #name = kwargs.get('name') #_weight = mx.symbol.Variable(name+'_weight') #_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0) #body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs) body = mx.sym.Convolution(**kwargs) return body def Act(data, act_type, name): if act_type=='prelu': body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name) else: body = mx.symbol.Activation(data=data, act_type=act_type, name=name) return body def residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') #print('in unit1') if bottle_neck: conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') if use_se: #se begin body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn3 = mx.symbol.broadcast_mul(bn3, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3') else: conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2') if use_se: #se begin body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn2 = mx.symbol.broadcast_mul(bn2, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3') def residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') #print('in unit1') if bottle_neck: conv1 = Conv(data=data, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') bn3 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') if use_se: #se begin body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn3 = mx.symbol.broadcast_mul(bn3, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return Act(data=bn3 + shortcut, act_type=act_type, name=name + '_relu3') else: conv1 = Conv(data=data, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn2 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2') if use_se: #se begin body = mx.sym.Pooling(data=bn2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn2 = mx.symbol.broadcast_mul(bn2, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return Act(data=bn2 + shortcut, act_type=act_type, name=name + '_relu3') def residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') #print('in unit2') if bottle_neck: # the same as https://github.com/facebook/fb.resnet.torch#notes, a bit difference with origin paper bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv1 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') conv2 = Conv(data=act2, num_filter=int(num_filter*0.25), kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') act3 = Act(data=bn3, act_type=act_type, name=name + '_relu3') conv3 = Conv(data=act3, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') if use_se: #se begin body = mx.sym.Pooling(data=conv3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") conv3 = mx.symbol.broadcast_mul(conv3, body) if dim_match: shortcut = data else: shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_sc') if memonger: shortcut._set_attr(mirror_stage='True') return conv3 + shortcut else: bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn1') act1 = Act(data=bn1, act_type=act_type, name=name + '_relu1') conv1 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_bn2') act2 = Act(data=bn2, act_type=act_type, name=name + '_relu2') conv2 = Conv(data=act2, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') if use_se: #se begin body = mx.sym.Pooling(data=conv2, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") conv2 = mx.symbol.broadcast_mul(conv2, body) if dim_match: shortcut = data else: shortcut = Conv(data=act1, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_sc') if memonger: shortcut._set_attr(mirror_stage='True') return conv2 + shortcut def residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNet Unit symbol for building ResNet Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') #print('in unit3') if bottle_neck: bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') conv1 = Conv(data=bn1, num_filter=int(num_filter*0.25), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=int(num_filter*0.25), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') act2 = Act(data=bn3, act_type=act_type, name=name + '_relu2') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4') if use_se: #se begin body = mx.sym.Pooling(data=bn4, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn4 = mx.symbol.broadcast_mul(bn4, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return bn4 + shortcut else: bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') conv1 = Conv(data=bn1, num_filter=num_filter, kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_filter=num_filter, kernel=(3,3), stride=stride, pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') if use_se: #se begin body = mx.sym.Pooling(data=bn3, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn3 = mx.symbol.broadcast_mul(bn3, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, momentum=bn_mom, eps=2e-5, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') act2 = Act(data=bn3*shortcut, act_type='tanh', name=name + '_tanh1') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1, 1), stride=(1,1), pad=(0, 0), no_bias=True, workspace=workspace, name=name + '_conv3') bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4') return bn3 + shortcut + bn4 def residual_unit_v3_x(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): """Return ResNeXt Unit symbol for building ResNeXt Parameters ---------- data : str Input data num_filter : int Number of output channels bnf : int Bottle neck channels factor with regard to num_filter stride : tuple Stride used in convolution dim_match : Boolean True means channel number between input and output is the same, otherwise means differ name : str Base name of the operators workspace : int Workspace used in convolution operator """ assert(bottle_neck) use_se = kwargs.get('version_se', 1) bn_mom = kwargs.get('bn_mom', 0.9) workspace = kwargs.get('workspace', 256) memonger = kwargs.get('memonger', False) act_type = kwargs.get('version_act', 'prelu') num_group = 32 #print('in unit3') bn1 = mx.sym.BatchNorm(data=data, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn1') conv1 = Conv(data=bn1, num_group=num_group, num_filter=int(num_filter*0.5), kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv1') bn2 = mx.sym.BatchNorm(data=conv1, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn2') act1 = Act(data=bn2, act_type=act_type, name=name + '_relu1') conv2 = Conv(data=act1, num_group=num_group, num_filter=int(num_filter*0.5), kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, workspace=workspace, name=name + '_conv2') bn3 = mx.sym.BatchNorm(data=conv2, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn3') act2 = Act(data=bn3, act_type=act_type, name=name + '_relu2') conv3 = Conv(data=act2, num_filter=num_filter, kernel=(1,1), stride=stride, pad=(0,0), no_bias=True, workspace=workspace, name=name + '_conv3') bn4 = mx.sym.BatchNorm(data=conv3, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_bn4') if use_se: #se begin body = mx.sym.Pooling(data=bn4, global_pool=True, kernel=(7, 7), pool_type='avg', name=name+'_se_pool1') body = Conv(data=body, num_filter=num_filter//16, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv1", workspace=workspace) body = Act(data=body, act_type=act_type, name=name+'_se_relu1') body = Conv(data=body, num_filter=num_filter, kernel=(1,1), stride=(1,1), pad=(0,0), name=name+"_se_conv2", workspace=workspace) body = mx.symbol.Activation(data=body, act_type='sigmoid', name=name+"_se_sigmoid") bn4 = mx.symbol.broadcast_mul(bn4, body) #se end if dim_match: shortcut = data else: conv1sc = Conv(data=data, num_filter=num_filter, kernel=(1,1), stride=stride, no_bias=True, workspace=workspace, name=name+'_conv1sc') shortcut = mx.sym.BatchNorm(data=conv1sc, fix_gamma=False, eps=2e-5, momentum=bn_mom, name=name + '_sc') if memonger: shortcut._set_attr(mirror_stage='True') return bn4 + shortcut def residual_unit(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs): uv = kwargs.get('version_unit', 3) version_input = kwargs.get('version_input', 1) # print(uv, version_input) if uv==1: if version_input==0: return residual_unit_v1(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) else: return residual_unit_v1_L(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) elif uv==2: return residual_unit_v2(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) elif uv==4: return residual_unit_v4(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) else: return residual_unit_v3(data, num_filter, stride, dim_match, name, bottle_neck, **kwargs) def resnet(units, num_stages, filter_list, num_classes, bottle_neck): bn_mom = config.bn_mom workspace = config.workspace kwargs = {'version_se' : config.net_se, 'version_input': config.net_input, 'version_output': config.net_output, 'version_unit': config.net_unit, 'version_act': config.net_act, 'bn_mom': bn_mom, 'workspace': workspace, 'memonger': config.memonger, } """Return ResNet symbol of Parameters ---------- units : list Number of units in each stage num_stages : int Number of stage filter_list : list Channel size of each stage num_classes : int Ouput size of symbol dataset : str Dataset type, only cifar10 and imagenet supports workspace : int Workspace used in convolution operator """ version_se = kwargs.get('version_se', 1) version_input = kwargs.get('version_input', 1) assert version_input>=0 version_output = kwargs.get('version_output', 'E') fc_type = version_output version_unit = kwargs.get('version_unit', 3) act_type = kwargs.get('version_act', 'prelu') memonger = kwargs.get('memonger', False) print(version_se, version_input, version_output, version_unit, act_type, memonger) num_unit = len(units) assert(num_unit == num_stages) data = mx.sym.Variable(name='data') if version_input==0: #data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') data = mx.sym.identity(data=data, name='id') data = data-127.5 data = data*0.0078125 body = Conv(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = Act(data=body, act_type=act_type, name='relu0') #body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') elif version_input==2: data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') body = Conv(data=data, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1,1), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = Act(data=body, act_type=act_type, name='relu0') else: data = mx.sym.identity(data=data, name='id') data = data-127.5 data = data*0.0078125 body = data body = Conv(data=body, num_filter=filter_list[0], kernel=(3,3), stride=(1,1), pad=(1, 1), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = Act(data=body, act_type=act_type, name='relu0') for i in range(num_stages): #if version_input==0: # body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False, # name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs) #else: # body = residual_unit(body, filter_list[i+1], (2, 2), False, # name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs) body = residual_unit(body, filter_list[i+1], (2, 2), False, name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, **kwargs) for j in range(units[i]-1): body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i+1, j+2), bottle_neck=bottle_neck, **kwargs) if bottle_neck: body = Conv(data=body, num_filter=512, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, name="convd", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bnd') body = Act(data=body, act_type=act_type, name='relud') fc1 = symbol_utils.get_fc1(body, num_classes, fc_type) return fc1 def get_symbol(): """ Adapted from https://github.com/tornadomeet/ResNet/blob/master/train_resnet.py Original author Wei Wu """ num_classes = config.emb_size num_layers = config.num_layers if num_layers >= 500: filter_list = [64, 256, 512, 1024, 2048] bottle_neck = True else: filter_list = [64, 64, 128, 256, 512] bottle_neck = False num_stages = 4 if num_layers == 18: units = [2, 2, 2, 2] elif num_layers == 34: units = [3, 4, 6, 3] elif num_layers == 49: units = [3, 4, 14, 3] elif num_layers == 50: units = [3, 4, 14, 3] elif num_layers == 74: units = [3, 6, 24, 3] elif num_layers == 90: units = [3, 8, 30, 3] elif num_layers == 98: units = [3, 4, 38, 3] elif num_layers == 99: units = [3, 8, 35, 3] elif num_layers == 100: units = [3, 13, 30, 3] elif num_layers == 134: units = [3, 10, 50, 3] elif num_layers == 136: units = [3, 13, 48, 3] elif num_layers == 140: units = [3, 15, 48, 3] elif num_layers == 124: units = [3, 13, 40, 5] elif num_layers == 160: units = [3, 24, 49, 3] elif num_layers == 101: units = [3, 4, 23, 3] elif num_layers == 152: units = [3, 8, 36, 3] elif num_layers == 200: units = [3, 24, 36, 3] elif num_layers == 269: units = [3, 30, 48, 8] else: raise ValueError("no experiments done on num_layers {}, you can do it yourself".format(num_layers)) net = resnet(units = units, num_stages = num_stages, filter_list = filter_list, num_classes = num_classes, bottle_neck = bottle_neck) if config.memonger: dshape = (config.per_batch_size, config.image_shape[2], config.image_shape[0], config.image_shape[1]) net_mem_planned = memonger.search_plan(net, data=dshape) old_cost = memonger.get_cost(net, data=dshape) new_cost = memonger.get_cost(net_mem_planned, data=dshape) print('Old feature map cost=%d MB' % old_cost) print('New feature map cost=%d MB' % new_cost) net = net_mem_planned return net ================================================ FILE: face_recognition/symbol/memonger.py ================================================ import mxnet as mx import math def prod(shape): """Get product of the shape. """ ret = 1 for s in shape: ret *= s return ret def is_param(name): """Quick script to check if name is a parameter. """ if name == 'data': return False if name.endswith('weight'): return True if name.endswith('bias'): return True if name.endswith('beta'): return True if name.endswith('gamma'): return True return False def make_mirror_plan(sym, threshold, plan_info=None, **kwargs): """Memory allocation planner with a given threshold. The user can pass in a network configuration, a threshold that limits memory per block. And input shape configurations. Parameters ---------- sym : symbol Input configuration of symbols. The user need to pre-mark the attribute "mirror_stage" on the nodes that can be book-kept as stage The algorithm will decide whether to disbale mirror on the stage nodes. threshold: integer A tuning parameter to tune the approximate size of each stage blocks plan_info: dict, optional Used to hold plan information. **kwargs: The arguments to infer shape. Returns ------- alloc_sym: symbol A symbol with force mirror tagged on the nodes for better allocation. """ threshold = threshold << 20 sym = sym.__copy__() internals = sym.get_internals() _, out_shapes, _ = internals.infer_shape(**kwargs) shape_dict = list(zip(internals.list_outputs(), out_shapes)) total_size = 0 param_size = 0 local_size = 0 save_size = 0 max_size = 0 last_sb = None last_local = 0 period = 1 last_stage = '' stage_decision = '' for idx, item in enumerate(shape_dict): sb = internals[idx] name, shape = item if is_param(name): param_size += prod(shape) * 4 continue else: total_size += prod(shape) * 4 local_size += prod(shape) * 4 sb._set_attr(force_mirroring='True') if sb.attr('mirror_stage') is not None: stage = sb.attr('mirror_stage') if stage == 'True' or stage != last_stage: if local_size > threshold: save_size += prod(shape) * 4 max_size = max(max_size, local_size) local_size = 0 stage_decision = 'False' sb._set_attr(force_mirroring=stage_decision) else: stage_decision = 'True' pass last_stage = stage elif stage == last_stage and stage_decision == 'False': save_size += prod(shape) * 4 sb._set_attr(force_mirroring=stage_decision) if plan_info is not None: plan_info['max_size'] = max_size plan_info['save_size'] = save_size return sym def get_cost(sym, type_dict=None, **kwargs): """Get the cost of the current symbolic plan by running bind on CPU. sym : Symbolic Variable """ texec = sym.simple_bind(ctx=mx.gpu(), grad_req='write', type_dict=type_dict, **kwargs) return int(texec.debug_str().split('\n')[-3].split()[1]) def search_plan(sym, ntrial=6, type_dict=None, **kwargs): """Quickly heurestic search over possible plans to find good memory plan. Parameters ---------- sym : symbolic Symbolic configurations ntrial: integer Additional grid search steps """ history = [] threshold = 0 min_threshold = None min_cost = None nbegin = 3 for k in range(nbegin): info = {} sym = make_mirror_plan(sym, threshold=threshold, plan_info=info, **kwargs) cost = get_cost(sym, type_dict, **kwargs) save_size = info['save_size'] >> 20 local_size = info['max_size'] >> 20 guess = int(math.sqrt(save_size * local_size / 2)) if min_cost is None or min_cost > cost: min_cost = cost if min_threshold is None or local_size < min_threshold: min_threshold = local_size print ("Search threshold=%d MB, cost=%d MB" % (threshold, cost)) history.append((cost, threshold, sym)) threshold = guess max_threshold = threshold * math.sqrt(2) step = int((max_threshold - min_threshold) / ntrial) threshold = min_threshold + step if step > 0: for k in range(ntrial): sym = make_mirror_plan(sym, threshold=threshold, plan_info=info, **kwargs) cost = get_cost(sym, type_dict, **kwargs) print ("Search threshold=%d MB, cost=%d MB" % (threshold, cost)) history.append((cost, threshold, sym)) threshold += step history.sort(key = lambda x: x[0]) cost, threshold, sym = history[0] print('Find best plan with threshold=%d, cost=%d MB' % (threshold, cost)) return sym ================================================ FILE: face_recognition/symbol/symbol_utils.py ================================================ import sys import os import mxnet as mx sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from config import config def Conv(**kwargs): #name = kwargs.get('name') #_weight = mx.symbol.Variable(name+'_weight') #_bias = mx.symbol.Variable(name+'_bias', lr_mult=2.0, wd_mult=0.0) #body = mx.sym.Convolution(weight = _weight, bias = _bias, **kwargs) body = mx.sym.Convolution(**kwargs) return body def Act(data, act_type, name): #ignore param act_type, set it in this function if act_type=='prelu': body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name) else: body = mx.sym.Activation(data=data, act_type=act_type, name=name) return body bn_mom = config.bn_mom def Linear(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''): conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=False,momentum=bn_mom) return bn def get_fc1(last_conv, num_classes, fc_type, input_channel=512): body = last_conv if fc_type=='Z': body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') body = mx.symbol.Dropout(data=body, p=0.4) fc1 = body elif fc_type=='E': body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') body = mx.symbol.Dropout(data=body, p=0.4) fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1') fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1') elif fc_type=='FC': body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1') fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1') elif fc_type=='SFC': body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') body = Conv(data=body, num_filter=input_channel, kernel=(3,3), stride=(2,2), pad=(1,1), no_bias=True, name="convf", num_group = input_channel) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bnf') body = Act(data=body, act_type=config.net_act, name='reluf') body = Conv(data=body, num_filter=input_channel, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="convf2") body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bnf2') body = Act(data=body, act_type=config.net_act, name='reluf2') fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1') fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1') elif fc_type=='GAP': bn1 = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn1') relu1 = Act(data=bn1, act_type=config.net_act, name='relu1') # Although kernel is not used here when global_pool=True, we should put one pool1 = mx.sym.Pooling(data=relu1, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1') flat = mx.sym.Flatten(data=pool1) fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1') fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='fc1') elif fc_type=='GNAP': #mobilefacenet++ filters_in = 512 # param in mobilefacenet if num_classes>filters_in: body = mx.sym.Convolution(data=last_conv, num_filter=num_classes, kernel=(1,1), stride=(1,1), pad=(0,0), no_bias=True, name='convx') body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=0.9, name='convx_bn') body = Act(data=body, act_type=config.net_act, name='convx_relu') filters_in = num_classes else: body = last_conv body = mx.sym.BatchNorm(data=body, fix_gamma=True, eps=2e-5, momentum=0.9, name='bn6f') spatial_norm=body*body spatial_norm=mx.sym.sum(data=spatial_norm, axis=1, keepdims=True) spatial_sqrt=mx.sym.sqrt(spatial_norm) #spatial_mean=mx.sym.mean(spatial_sqrt, axis=(1,2,3), keepdims=True) spatial_mean=mx.sym.mean(spatial_sqrt) spatial_div_inverse=mx.sym.broadcast_div(spatial_mean, spatial_sqrt) spatial_attention_inverse=mx.symbol.tile(spatial_div_inverse, reps=(1,filters_in,1,1)) body=body*spatial_attention_inverse #body = mx.sym.broadcast_mul(body, spatial_div_inverse) fc1 = mx.sym.Pooling(body, kernel=(7, 7), global_pool=True, pool_type='avg') if num_classes= 2, 'stage is {}, stage must be set >=2'.format(stage) data = vargnet_branch_merge_block(data=data, n_out_ch1=in_channels, n_out_ch2=out_channels, n_out_ch3=out_channels, setting_params=setting_params, factor=factor, dim_match=False, multiplier=multiplier, kernel=kernel, stride=stride, dilate=dilate, with_dilate=with_dilate, name=name + '_stage_{}_unit_1'.format(stage)) for i in range(units - 1): data = vargnet_block(data=data, n_out_ch1=out_channels, n_out_ch2=out_channels, n_out_ch3=out_channels, setting_params=setting_params, factor=factor, dim_match=True, multiplier=multiplier, kernel=kernel, stride=(1, 1), dilate=dilate, with_dilate=with_dilate, name=name + '_stage_{}_unit_{}'.format(stage, i + 2)) return data def add_head_block(data, num_filter, setting_params, multiplier, head_pooling=False, kernel=(3, 3), stride=(2, 2), pad=(1, 1), name=None): bn_mom = setting_params['bn_mom'] bn_eps = setting_params['bn_eps'] fix_gamma = setting_params['fix_gamma'] use_global_stats = setting_params['use_global_stats'] workspace = setting_params['workspace'] act_type = setting_params['act_type'] channels = int(num_filter * multiplier) conv1 = mx.sym.Convolution(data=data, num_filter=channels, kernel=kernel, pad=pad, stride=stride, no_bias=True, num_group=1, workspace=workspace, name=name + '_conv1') bn1 = mx.sym.BatchNorm(data=conv1, fix_gamma=fix_gamma, eps=bn_eps, momentum=bn_mom, use_global_stats=use_global_stats, name=name + '_conv1_bn') act1 = Act(data=bn1, act_type=act_type, name=name + '_conv1_act') if head_pooling: head_data = mx.symbol.Pooling(data=act1, kernel=(3, 3), stride=(2, 2), pad=(1, 1), pool_type='max', name=name + '_max_pooling') else: head_data = vargnet_block(data=act1, n_out_ch1=num_filter, n_out_ch2=num_filter, n_out_ch3=num_filter, setting_params=setting_params, factor=1, dim_match=False, multiplier=multiplier, kernel=kernel, stride=(2, 2), dilate=1, with_dilate=False, name=name + '_head_pooling') return head_data def add_emb_block(data, input_channels, last_channels, emb_size, fc_type, setting_params, bias=False, name=None): bn_mom = setting_params['bn_mom'] bn_eps = setting_params['bn_eps'] fix_gamma = setting_params['fix_gamma'] use_global_stats = setting_params['use_global_stats'] workspace = setting_params['workspace'] act_type = setting_params['act_type'] group_base = setting_params['group_base'] # last channels if input_channels != last_channels: data = mx.sym.Convolution(data=data, num_filter=last_channels, kernel=(1, 1), pad=(0, 0), stride=(1, 1), no_bias=False if bias else True, workspace=workspace, name=name + '_convx') data = mx.sym.BatchNorm(data=data, fix_gamma=fix_gamma, eps=bn_eps, momentum=bn_mom, use_global_stats=use_global_stats, name=name + '_convx_bn') data = Act(data=data, act_type=act_type, name=name + '_convx_act') # depthwise convx_depthwise = mx.sym.Convolution(data=data, num_filter=last_channels, num_group=int(last_channels / group_base), kernel=(7, 7), pad=(0, 0), stride=(1, 1), no_bias=False if bias else True, workspace=workspace, name=name + '_convx_depthwise') convx_depthwise = mx.sym.BatchNorm(data=convx_depthwise, fix_gamma=fix_gamma, eps=bn_eps, momentum=bn_mom, use_global_stats=use_global_stats, name=name + '_convx_depthwise_bn') # pointwise convx_pointwise = mx.sym.Convolution(data=convx_depthwise, num_filter=last_channels // 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), no_bias=False if bias else True, workspace=workspace, name=name + '_convx_pointwise') convx_pointwise = mx.sym.BatchNorm(data=convx_pointwise, fix_gamma=fix_gamma, eps=bn_eps, momentum=bn_mom, use_global_stats=use_global_stats, name=name + '_convx_pointwise_bn') convx_pointwise = Act(data=convx_pointwise, act_type=act_type, name=name + '_convx_pointwise_act') fc1 = symbol_utils.get_fc1(convx_pointwise, emb_size, fc_type) return fc1 def get_symbol(): multiplier = config.net_multiplier emb_size = config.emb_size fc_type = config.net_output kwargs = {'use_se' : config.net_se, 'act_type': config.net_act, 'bn_mom': config.bn_mom, 'workspace': config.workspace, } setting_params = get_setting_params(**kwargs) factor = 2 head_pooling = False num_stage = 3 stage_list = [2, 3, 4] units = [3, 7, 4] filter_list = [32, 64, 128, 256] last_channels = 1024 dilate_list = [1, 1, 1] with_dilate_list = [False, False, False] data = mx.sym.Variable(name='data') data = mx.sym.identity(data=data, name='id') data = data - 127.5 data = data * 0.0078125 body = add_head_block(data=data, num_filter=filter_list[0], setting_params=setting_params, multiplier=multiplier, head_pooling=head_pooling, kernel=(3, 3), stride=(1, 1), pad=(1, 1), name="vargface_head") for i in range(num_stage): body = add_vargnet_conv_block(data=body, stage=stage_list[i], units=units[i], in_channels=filter_list[i], out_channels=filter_list[i + 1], setting_params=setting_params, kernel=(3, 3), stride=(2, 2), multiplier=multiplier, factor=factor, dilate=dilate_list[i], with_dilate=with_dilate_list[i], name="vargface") emb_feat = add_emb_block(data=body, input_channels=filter_list[3], last_channels=last_channels, emb_size=emb_size, fc_type=fc_type, setting_params=setting_params, bias=False, name='embed') return emb_feat if __name__ == '__main__': get_symbol() ================================================ FILE: face_recognition/train.py ================================================ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import math import random import logging import sklearn import pickle import numpy as np import mxnet as mx from mxnet import ndarray as nd import argparse import mxnet.optimizer as optimizer from config import config, default, generate_config from metric import * sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common')) import flops_counter sys.path.append(os.path.join(os.path.dirname(__file__), 'eval')) import verification sys.path.append(os.path.join(os.path.dirname(__file__), 'symbol')) import fresnet import fmobilefacenet import fmobilenet import fmnasnet import fdensenet import vargfacenet logger = logging.getLogger() logger.setLevel(logging.INFO) args = None def parse_args(): parser = argparse.ArgumentParser(description='Train face network') # general parser.add_argument('--dataset', default=default.dataset, help='dataset config') parser.add_argument('--network', default=default.network, help='network config') parser.add_argument('--loss', default=default.loss, help='loss config') args, rest = parser.parse_known_args() generate_config(args.network, args.dataset, args.loss) parser.add_argument('--models-root', default=default.models_root, help='root directory to save model.') parser.add_argument('--pretrained', default=default.pretrained, help='pretrained model to load') parser.add_argument('--pretrained-epoch', type=int, default=default.pretrained_epoch, help='pretrained epoch to load') parser.add_argument('--ckpt', type=int, default=default.ckpt, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save') parser.add_argument('--verbose', type=int, default=default.verbose, help='do verification testing and model saving every verbose batches') parser.add_argument('--lr', type=float, default=default.lr, help='start learning rate') parser.add_argument('--lr-steps', type=str, default=default.lr_steps, help='steps of lr changing') parser.add_argument('--wd', type=float, default=default.wd, help='weight decay') parser.add_argument('--mom', type=float, default=default.mom, help='momentum') parser.add_argument('--frequent', type=int, default=default.frequent, help='') parser.add_argument('--per-batch-size', type=int, default=default.per_batch_size, help='batch size in each context') parser.add_argument('--kvstore', type=str, default=default.kvstore, help='kvstore setting') args = parser.parse_args() return args def get_symbol(args): embedding = eval(config.net_name).get_symbol() all_label = mx.symbol.Variable('softmax_label') gt_label = all_label is_softmax = True if config.loss_name=='softmax': #softmax _weight = mx.symbol.Variable("fc7_weight", shape=(config.num_classes, config.emb_size), lr_mult=config.fc7_lr_mult, wd_mult=config.fc7_wd_mult, init=mx.init.Normal(0.01)) if config.fc7_no_bias: fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, no_bias = True, num_hidden=config.num_classes, name='fc7') else: _bias = mx.symbol.Variable('fc7_bias', lr_mult=2.0, wd_mult=0.0) fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, bias = _bias, num_hidden=config.num_classes, name='fc7') elif config.loss_name=='margin_softmax': _weight = mx.symbol.Variable("fc7_weight", shape=(config.num_classes, config.emb_size), lr_mult=config.fc7_lr_mult, wd_mult=config.fc7_wd_mult, init=mx.init.Normal(0.01)) s = config.loss_s _weight = mx.symbol.L2Normalization(_weight, mode='instance') nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=config.num_classes, name='fc7') if config.loss_m1!=1.0 or config.loss_m2!=0.0 or config.loss_m3!=0.0: if config.loss_m1==1.0 and config.loss_m2==0.0: s_m = s*config.loss_m3 gt_one_hot = mx.sym.one_hot(gt_label, depth = config.num_classes, on_value = s_m, off_value = 0.0) fc7 = fc7-gt_one_hot else: zy = mx.sym.pick(fc7, gt_label, axis=1) cos_t = zy/s t = mx.sym.arccos(cos_t) if config.loss_m1!=1.0: t = t*config.loss_m1 if config.loss_m2>0.0: t = t+config.loss_m2 body = mx.sym.cos(t) if config.loss_m3>0.0: body = body - config.loss_m3 new_zy = body*s diff = new_zy - zy diff = mx.sym.expand_dims(diff, 1) gt_one_hot = mx.sym.one_hot(gt_label, depth = config.num_classes, on_value = 1.0, off_value = 0.0) body = mx.sym.broadcast_mul(gt_one_hot, diff) fc7 = fc7+body elif config.loss_name.find('triplet')>=0: is_softmax = False nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n') anchor = mx.symbol.slice_axis(nembedding, axis=0, begin=0, end=args.per_batch_size//3) positive = mx.symbol.slice_axis(nembedding, axis=0, begin=args.per_batch_size//3, end=2*args.per_batch_size//3) negative = mx.symbol.slice_axis(nembedding, axis=0, begin=2*args.per_batch_size//3, end=args.per_batch_size) if config.loss_name=='triplet': ap = anchor - positive an = anchor - negative ap = ap*ap an = an*an ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1) an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1) triplet_loss = mx.symbol.Activation(data = (ap-an+config.triplet_alpha), act_type='relu') triplet_loss = mx.symbol.mean(triplet_loss) else: ap = anchor*positive an = anchor*negative ap = mx.symbol.sum(ap, axis=1, keepdims=1) #(T,1) an = mx.symbol.sum(an, axis=1, keepdims=1) #(T,1) ap = mx.sym.arccos(ap) an = mx.sym.arccos(an) triplet_loss = mx.symbol.Activation(data = (ap-an+config.triplet_alpha), act_type='relu') triplet_loss = mx.symbol.mean(triplet_loss) triplet_loss = mx.symbol.MakeLoss(triplet_loss) out_list = [mx.symbol.BlockGrad(embedding)] if is_softmax: softmax = mx.symbol.SoftmaxOutput(data=fc7, label = gt_label, name='softmax', normalization='valid') out_list.append(softmax) if config.ce_loss: #ce_loss = mx.symbol.softmax_cross_entropy(data=fc7, label = gt_label, name='ce_loss')/args.per_batch_size body = mx.symbol.SoftmaxActivation(data=fc7) body = mx.symbol.log(body) _label = mx.sym.one_hot(gt_label, depth = config.num_classes, on_value = -1.0, off_value = 0.0) body = body*_label ce_loss = mx.symbol.sum(body)/args.per_batch_size out_list.append(mx.symbol.BlockGrad(ce_loss)) else: out_list.append(mx.sym.BlockGrad(gt_label)) out_list.append(triplet_loss) out = mx.symbol.Group(out_list) return out def train_net(args): ctx = [] cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip() if len(cvd)>0: for i in range(len(cvd.split(','))): ctx.append(mx.gpu(i)) if len(ctx)==0: ctx = [mx.cpu()] print('use cpu') else: print('gpu num:', len(ctx)) prefix = os.path.join(args.models_root, '%s-%s-%s'%(args.network, args.loss, args.dataset), 'model') prefix_dir = os.path.dirname(prefix) print('prefix', prefix) if not os.path.exists(prefix_dir): os.makedirs(prefix_dir) args.ctx_num = len(ctx) args.batch_size = args.per_batch_size*args.ctx_num args.rescale_threshold = 0 args.image_channel = config.image_shape[2] config.batch_size = args.batch_size config.per_batch_size = args.per_batch_size data_dir = config.dataset_path path_imgrec = None path_imglist = None image_size = config.image_shape[0:2] assert len(image_size)==2 assert image_size[0]==image_size[1] print('image_size', image_size) print('num_classes', config.num_classes) path_imgrec = os.path.join(data_dir, "train.rec") print('Called with argument:', args, config) data_shape = (args.image_channel,image_size[0],image_size[1]) mean = None begin_epoch = 0 if len(args.pretrained)==0: arg_params = None aux_params = None sym = get_symbol(args) if config.net_name=='spherenet': data_shape_dict = {'data' : (args.per_batch_size,)+data_shape} spherenet.init_weights(sym, data_shape_dict, args.num_layers) else: print('loading', args.pretrained, args.pretrained_epoch) _, arg_params, aux_params = mx.model.load_checkpoint(args.pretrained, args.pretrained_epoch) sym = get_symbol(args) if config.count_flops: all_layers = sym.get_internals() _sym = all_layers['fc1_output'] FLOPs = flops_counter.count_flops(_sym, data=(1,3,image_size[0],image_size[1])) _str = flops_counter.flops_str(FLOPs) print('Network FLOPs: %s'%_str) #label_name = 'softmax_label' #label_shape = (args.batch_size,) model = mx.mod.Module( context = ctx, symbol = sym, ) val_dataiter = None if config.loss_name.find('triplet')>=0: from triplet_image_iter import FaceImageIter triplet_params = [config.triplet_bag_size, config.triplet_alpha, config.triplet_max_ap] train_dataiter = FaceImageIter( batch_size = args.batch_size, data_shape = data_shape, path_imgrec = path_imgrec, shuffle = True, rand_mirror = config.data_rand_mirror, mean = mean, cutoff = config.data_cutoff, ctx_num = args.ctx_num, images_per_identity = config.images_per_identity, triplet_params = triplet_params, mx_model = model, ) _metric = LossValueMetric() eval_metrics = [mx.metric.create(_metric)] else: from image_iter import FaceImageIter train_dataiter = FaceImageIter( batch_size = args.batch_size, data_shape = data_shape, path_imgrec = path_imgrec, shuffle = True, rand_mirror = config.data_rand_mirror, mean = mean, cutoff = config.data_cutoff, color_jittering = config.data_color, images_filter = config.data_images_filter, ) metric1 = AccMetric() eval_metrics = [mx.metric.create(metric1)] if config.ce_loss: metric2 = LossValueMetric() eval_metrics.append( mx.metric.create(metric2) ) if config.net_name=='fresnet' or config.net_name=='fmobilefacenet': initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="out", magnitude=2) #resnet style else: initializer = mx.init.Xavier(rnd_type='uniform', factor_type="in", magnitude=2) #initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="out", magnitude=2) #resnet style _rescale = 1.0/args.ctx_num opt = optimizer.SGD(learning_rate=args.lr, momentum=args.mom, wd=args.wd, rescale_grad=_rescale) _cb = mx.callback.Speedometer(args.batch_size, args.frequent) ver_list = [] ver_name_list = [] for name in config.val_targets: path = os.path.join(data_dir,name+".bin") if os.path.exists(path): data_set = verification.load_bin(path, image_size) ver_list.append(data_set) ver_name_list.append(name) print('ver', name) def ver_test(nbatch): results = [] for i in range(len(ver_list)): acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(ver_list[i], model, args.batch_size, 10, None, None) print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm)) #print('[%s][%d]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc1, std1)) print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2)) results.append(acc2) return results highest_acc = [0.0, 0.0] #lfw and target #for i in range(len(ver_list)): # highest_acc.append(0.0) global_step = [0] save_step = [0] lr_steps = [int(x) for x in args.lr_steps.split(',')] print('lr_steps', lr_steps) def _batch_callback(param): #global global_step global_step[0]+=1 mbatch = global_step[0] for step in lr_steps: if mbatch==step: opt.lr *= 0.1 print('lr change to', opt.lr) break _cb(param) if mbatch%1000==0: print('lr-batch-epoch:',opt.lr,param.nbatch,param.epoch) if mbatch>=0 and mbatch%args.verbose==0: acc_list = ver_test(mbatch) save_step[0]+=1 msave = save_step[0] do_save = False is_highest = False if len(acc_list)>0: #lfw_score = acc_list[0] #if lfw_score>highest_acc[0]: # highest_acc[0] = lfw_score # if lfw_score>=0.998: # do_save = True score = sum(acc_list) if acc_list[-1]>=highest_acc[-1]: if acc_list[-1]>highest_acc[-1]: is_highest = True else: if score>=highest_acc[0]: is_highest = True highest_acc[0] = score highest_acc[-1] = acc_list[-1] #if lfw_score>=0.99: # do_save = True if is_highest: do_save = True if args.ckpt==0: do_save = False elif args.ckpt==2: do_save = True elif args.ckpt==3: msave = 1 if do_save: print('saving', msave) arg, aux = model.get_params() if config.ckpt_embedding: all_layers = model.symbol.get_internals() _sym = all_layers['fc1_output'] _arg = {} for k in arg: if not k.startswith('fc7'): _arg[k] = arg[k] mx.model.save_checkpoint(prefix, msave, _sym, _arg, aux) else: mx.model.save_checkpoint(prefix, msave, model.symbol, arg, aux) print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1])) if config.max_steps>0 and mbatch>config.max_steps: sys.exit(0) epoch_cb = None train_dataiter = mx.io.PrefetchingIter(train_dataiter) model.fit(train_dataiter, begin_epoch = begin_epoch, num_epoch = 999999, eval_data = val_dataiter, eval_metric = eval_metrics, kvstore = args.kvstore, optimizer = opt, #optimizer_params = optimizer_params, initializer = initializer, arg_params = arg_params, aux_params = aux_params, allow_missing = True, batch_end_callback = _batch_callback, epoch_end_callback = epoch_cb ) def main(): global args args = parse_args() train_net(args) if __name__ == '__main__': main() ================================================ FILE: face_recognition/train_parall.py ================================================ ''' @author: insightface ''' from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import math import random import logging import pickle import sklearn import numpy as np from image_iter import FaceImageIter import mxnet as mx from mxnet import ndarray as nd import argparse import mxnet.optimizer as optimizer sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'common')) import flops_counter from config import config, default, generate_config sys.path.append(os.path.join(os.path.dirname(__file__), 'eval')) import verification sys.path.append(os.path.join(os.path.dirname(__file__), 'symbol')) import fresnet import fmobilefacenet import fmobilenet import fmnasnet import fdensenet import vargfacenet logger = logging.getLogger() logger.setLevel(logging.INFO) args = None def parse_args(): parser = argparse.ArgumentParser(description='Train parall face network') # general parser.add_argument('--dataset', default=default.dataset, help='dataset config') parser.add_argument('--network', default=default.network, help='network config') parser.add_argument('--loss', default=default.loss, help='loss config') args, rest = parser.parse_known_args() generate_config(args.network, args.dataset, args.loss) parser.add_argument('--models-root', default=default.models_root, help='root directory to save model.') parser.add_argument('--pretrained', default=default.pretrained, help='pretrained model to load') parser.add_argument('--pretrained-epoch', type=int, default=default.pretrained_epoch, help='pretrained epoch to load') parser.add_argument('--ckpt', type=int, default=default.ckpt, help='checkpoint saving option. 0: discard saving. 1: save when necessary. 2: always save') parser.add_argument('--verbose', type=int, default=default.verbose, help='do verification testing and model saving every verbose batches') parser.add_argument('--lr', type=float, default=default.lr, help='start learning rate') parser.add_argument('--lr-steps', type=str, default=default.lr_steps, help='steps of lr changing') parser.add_argument('--wd', type=float, default=default.wd, help='weight decay') parser.add_argument('--mom', type=float, default=default.mom, help='momentum') parser.add_argument('--frequent', type=int, default=default.frequent, help='') parser.add_argument('--per-batch-size', type=int, default=default.per_batch_size, help='batch size in each context') parser.add_argument('--kvstore', type=str, default=default.kvstore, help='kvstore setting') parser.add_argument('--worker-id', type=int, default=0, help='worker id for dist training, starts from 0') parser.add_argument('--extra-model-name', type=str, default='', help='extra model name') args = parser.parse_args() return args def get_symbol_embedding(): embedding = eval(config.net_name).get_symbol() all_label = mx.symbol.Variable('softmax_label') #embedding = mx.symbol.BlockGrad(embedding) all_label = mx.symbol.BlockGrad(all_label) out_list = [embedding, all_label] out = mx.symbol.Group(out_list) return out def get_symbol_arcface(args): embedding = mx.symbol.Variable('data') all_label = mx.symbol.Variable('softmax_label') gt_label = all_label is_softmax = True #print('call get_sym_arcface with', args, config) _weight = mx.symbol.Variable("fc7_%d_weight"%args._ctxid, shape=(args.ctx_num_classes, config.emb_size), lr_mult=config.fc7_lr_mult, wd_mult=config.fc7_wd_mult) if config.loss_name=='softmax': #softmax fc7 = mx.sym.FullyConnected(data=embedding, weight = _weight, no_bias = True, num_hidden=args.ctx_num_classes, name='fc7_%d'%args._ctxid) elif config.loss_name=='margin_softmax': _weight = mx.symbol.L2Normalization(_weight, mode='instance') nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n_%d'%args._ctxid) fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.ctx_num_classes, name='fc7_%d'%args._ctxid) if config.loss_m1!=1.0 or config.loss_m2!=0.0 or config.loss_m3!=0.0: gt_one_hot = mx.sym.one_hot(gt_label, depth = args.ctx_num_classes, on_value = 1.0, off_value = 0.0) if config.loss_m1==1.0 and config.loss_m2==0.0: _one_hot = gt_one_hot*args.margin_b fc7 = fc7-_one_hot else: fc7_onehot = fc7 * gt_one_hot cos_t = fc7_onehot t = mx.sym.arccos(cos_t) if config.loss_m1!=1.0: t = t*config.loss_m1 if config.loss_m2!=0.0: t = t+config.loss_m2 margin_cos = mx.sym.cos(t) if config.loss_m3!=0.0: margin_cos = margin_cos - config.loss_m3 margin_fc7 = margin_cos margin_fc7_onehot = margin_fc7 * gt_one_hot diff = margin_fc7_onehot - fc7_onehot fc7 = fc7+diff fc7 = fc7*config.loss_s out_list = [] out_list.append(fc7) if config.loss_name=='softmax': #softmax out_list.append(gt_label) out = mx.symbol.Group(out_list) return out def train_net(args): #_seed = 727 #random.seed(_seed) #np.random.seed(_seed) #mx.random.seed(_seed) ctx = [] cvd = os.environ['CUDA_VISIBLE_DEVICES'].strip() if len(cvd)>0: for i in range(len(cvd.split(','))): ctx.append(mx.gpu(i)) if len(ctx)==0: ctx = [mx.cpu()] print('use cpu') else: print('gpu num:', len(ctx)) if len(args.extra_model_name)==0: prefix = os.path.join(args.models_root, '%s-%s-%s'%(args.network, args.loss, args.dataset), 'model') else: prefix = os.path.join(args.models_root, '%s-%s-%s-%s'%(args.network, args.loss, args.dataset, args.extra_model_name), 'model') prefix_dir = os.path.dirname(prefix) print('prefix', prefix) if not os.path.exists(prefix_dir): os.makedirs(prefix_dir) args.ctx_num = len(ctx) if args.per_batch_size==0: args.per_batch_size = 128 args.batch_size = args.per_batch_size*args.ctx_num args.rescale_threshold = 0 args.image_channel = config.image_shape[2] config.batch_size = args.batch_size config.per_batch_size = args.per_batch_size data_dir = config.dataset_path path_imgrec = None path_imglist = None image_size = config.image_shape[0:2] assert len(image_size)==2 assert image_size[0]==image_size[1] print('image_size', image_size) print('num_classes', config.num_classes) path_imgrec = os.path.join(data_dir, "train.rec") data_shape = (args.image_channel,image_size[0],image_size[1]) num_workers = config.num_workers global_num_ctx = num_workers * args.ctx_num if config.num_classes%global_num_ctx==0: args.ctx_num_classes = config.num_classes//global_num_ctx else: args.ctx_num_classes = config.num_classes//global_num_ctx+1 args.local_num_classes = args.ctx_num_classes * args.ctx_num args.local_class_start = args.local_num_classes * args.worker_id #if len(args.partial)==0: # local_classes_range = (0, args.num_classes) #else: # _vec = args.partial.split(',') # local_classes_range = (int(_vec[0]), int(_vec[1])) #args.partial_num_classes = local_classes_range[1] - local_classes_range[0] #args.partial_start = local_classes_range[0] print('Called with argument:', args, config) mean = None begin_epoch = 0 base_lr = args.lr base_wd = args.wd base_mom = args.mom arg_params = None aux_params = None if len(args.pretrained)==0: esym = get_symbol_embedding() asym = get_symbol_arcface else: assert False if config.count_flops: all_layers = esym.get_internals() _sym = all_layers['fc1_output'] FLOPs = flops_counter.count_flops(_sym, data=(1,3,image_size[0],image_size[1])) _str = flops_counter.flops_str(FLOPs) print('Network FLOPs: %s'%_str) if config.num_workers==1: from parall_module_local_v1 import ParallModule else: from parall_module_dist import ParallModule model = ParallModule( context = ctx, symbol = esym, data_names = ['data'], label_names = ['softmax_label'], asymbol = asym, args = args, ) val_dataiter = None train_dataiter = FaceImageIter( batch_size = args.batch_size, data_shape = data_shape, path_imgrec = path_imgrec, shuffle = True, rand_mirror = config.data_rand_mirror, mean = mean, cutoff = config.data_cutoff, color_jittering = config.data_color, images_filter = config.data_images_filter, ) if config.net_name=='fresnet' or config.net_name=='fmobilefacenet': initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="out", magnitude=2) #resnet style else: initializer = mx.init.Xavier(rnd_type='uniform', factor_type="in", magnitude=2) _rescale = 1.0/args.batch_size opt = optimizer.SGD(learning_rate=base_lr, momentum=base_mom, wd=base_wd, rescale_grad=_rescale) _cb = mx.callback.Speedometer(args.batch_size, args.frequent) ver_list = [] ver_name_list = [] for name in config.val_targets: path = os.path.join(data_dir,name+".bin") if os.path.exists(path): data_set = verification.load_bin(path, image_size) ver_list.append(data_set) ver_name_list.append(name) print('ver', name) def ver_test(nbatch): results = [] for i in range(len(ver_list)): acc1, std1, acc2, std2, xnorm, embeddings_list = verification.test(ver_list[i], model, args.batch_size, 10, None, None) print('[%s][%d]XNorm: %f' % (ver_name_list[i], nbatch, xnorm)) #print('[%s][%d]Accuracy: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc1, std1)) print('[%s][%d]Accuracy-Flip: %1.5f+-%1.5f' % (ver_name_list[i], nbatch, acc2, std2)) results.append(acc2) return results highest_acc = [0.0, 0.0] #lfw and target #for i in range(len(ver_list)): # highest_acc.append(0.0) global_step = [0] save_step = [0] lr_steps = [int(x) for x in args.lr_steps.split(',')] print('lr_steps', lr_steps) def _batch_callback(param): #global global_step global_step[0]+=1 mbatch = global_step[0] for step in lr_steps: if mbatch==step: opt.lr *= 0.1 print('lr change to', opt.lr) break _cb(param) if mbatch%1000==0: print('lr-batch-epoch:',opt.lr,param.nbatch,param.epoch) if mbatch>=0 and mbatch%args.verbose==0: acc_list = ver_test(mbatch) save_step[0]+=1 msave = save_step[0] do_save = False is_highest = False if len(acc_list)>0: #lfw_score = acc_list[0] #if lfw_score>highest_acc[0]: # highest_acc[0] = lfw_score # if lfw_score>=0.998: # do_save = True score = sum(acc_list) if acc_list[-1]>=highest_acc[-1]: if acc_list[-1]>highest_acc[-1]: is_highest = True else: if score>=highest_acc[0]: is_highest = True highest_acc[0] = score highest_acc[-1] = acc_list[-1] #if lfw_score>=0.99: # do_save = True if is_highest: do_save = True if args.ckpt==0: do_save = False elif args.ckpt==2: do_save = True elif args.ckpt==3: msave = 1 if do_save: print('saving', msave) arg, aux = model.get_export_params() all_layers = model.symbol.get_internals() _sym = all_layers['fc1_output'] mx.model.save_checkpoint(prefix, msave, _sym, arg, aux) print('[%d]Accuracy-Highest: %1.5f'%(mbatch, highest_acc[-1])) if config.max_steps>0 and mbatch>config.max_steps: sys.exit(0) epoch_cb = None train_dataiter = mx.io.PrefetchingIter(train_dataiter) model.fit(train_dataiter, begin_epoch = begin_epoch, num_epoch = 999999, eval_data = val_dataiter, #eval_metric = eval_metrics, kvstore = args.kvstore, optimizer = opt, #optimizer_params = optimizer_params, initializer = initializer, arg_params = arg_params, aux_params = aux_params, allow_missing = True, batch_end_callback = _batch_callback, epoch_end_callback = epoch_cb ) def main(): global args args = parse_args() train_net(args) if __name__ == '__main__': main() ================================================ FILE: face_recognition/triplet_image_iter.py ================================================ # THIS FILE IS FOR EXPERIMENTS, USE image_iter.py FOR NORMAL IMAGE LOADING. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import random import logging import sys import numbers import math import sklearn import datetime import numpy as np import cv2 import mxnet as mx from mxnet import ndarray as nd #from . import _ndarray_internal as _internal #from mxnet._ndarray_internal import _cvimresize as imresize #from ._ndarray_internal import _cvcopyMakeBorder as copyMakeBorder from mxnet import io from mxnet import recordio sys.path.append(os.path.join(os.path.dirname(__file__), 'common')) import face_preprocess logger = logging.getLogger() class FaceImageIter(io.DataIter): def __init__(self, batch_size, data_shape, path_imgrec = None, shuffle=False, aug_list=None, rand_mirror = False, cutoff = 0, ctx_num = 0, images_per_identity = 0, triplet_params = None, mx_model = None, data_name='data', label_name='softmax_label', **kwargs): super(FaceImageIter, self).__init__() assert path_imgrec assert shuffle logging.info('loading recordio %s...', path_imgrec) path_imgidx = path_imgrec[0:-4]+".idx" self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type s = self.imgrec.read_idx(0) header, _ = recordio.unpack(s) assert header.flag>0 print('header0 label', header.label) self.header0 = (int(header.label[0]), int(header.label[1])) #assert(header.flag==1) self.imgidx = range(1, int(header.label[0])) self.id2range = {} self.seq_identity = range(int(header.label[0]), int(header.label[1])) for identity in self.seq_identity: s = self.imgrec.read_idx(identity) header, _ = recordio.unpack(s) a,b = int(header.label[0]), int(header.label[1]) self.id2range[identity] = (a,b) print('id2range', len(self.id2range)) self.seq = self.imgidx print(len(self.seq)) self.check_data_shape(data_shape) self.provide_data = [(data_name, (batch_size,) + data_shape)] self.batch_size = batch_size self.data_shape = data_shape self.shuffle = shuffle self.image_size = '%d,%d'%(data_shape[1],data_shape[2]) self.rand_mirror = rand_mirror print('rand_mirror', rand_mirror) self.cutoff = cutoff #self.cast_aug = mx.image.CastAug() #self.color_aug = mx.image.ColorJitterAug(0.4, 0.4, 0.4) self.ctx_num = ctx_num self.per_batch_size = int(self.batch_size/self.ctx_num) self.images_per_identity = images_per_identity if self.images_per_identity>0: self.identities = int(self.per_batch_size/self.images_per_identity) self.per_identities = self.identities self.repeat = 3000000.0/(self.images_per_identity*len(self.id2range)) self.repeat = int(self.repeat) print(self.images_per_identity, self.identities, self.repeat) self.mx_model = mx_model self.triplet_params = triplet_params self.triplet_mode = False #self.provide_label = None self.provide_label = [(label_name, (batch_size,))] if self.triplet_params is not None: assert self.images_per_identity>0 assert self.mx_model is not None self.triplet_bag_size = self.triplet_params[0] self.triplet_alpha = self.triplet_params[1] self.triplet_max_ap = self.triplet_params[2] assert self.triplet_bag_size>0 assert self.triplet_alpha>=0.0 assert self.triplet_alpha<=1.0 self.triplet_mode = True self.triplet_cur = 0 self.triplet_seq = [] self.triplet_reset() self.seq_min_size = self.batch_size*2 self.cur = 0 self.nbatch = 0 self.is_init = False self.times = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0] #self.reset() def pairwise_dists(self, embeddings): nd_embedding_list = [] for i in range(self.ctx_num): nd_embedding = mx.nd.array(embeddings, mx.gpu(i)) nd_embedding_list.append(nd_embedding) nd_pdists = [] pdists = [] for idx in range(embeddings.shape[0]): emb_idx = idx%self.ctx_num nd_embedding = nd_embedding_list[emb_idx] a_embedding = nd_embedding[idx] body = mx.nd.broadcast_sub(a_embedding, nd_embedding) body = body*body body = mx.nd.sum_axis(body, axis=1) nd_pdists.append(body) if len(nd_pdists)==self.ctx_num or idx==embeddings.shape[0]-1: for x in nd_pdists: pdists.append(x.asnumpy()) nd_pdists = [] return pdists def pick_triplets(self, embeddings, nrof_images_per_class): emb_start_idx = 0 triplets = [] people_per_batch = len(nrof_images_per_class) #self.time_reset() pdists = self.pairwise_dists(embeddings) #self.times[3] += self.time_elapsed() for i in range(people_per_batch): nrof_images = int(nrof_images_per_class[i]) for j in range(1,nrof_images): #self.time_reset() a_idx = emb_start_idx + j - 1 #neg_dists_sqr = np.sum(np.square(embeddings[a_idx] - embeddings), 1) neg_dists_sqr = pdists[a_idx] #self.times[3] += self.time_elapsed() for pair in range(j, nrof_images): # For every possible positive pair. p_idx = emb_start_idx + pair #self.time_reset() pos_dist_sqr = np.sum(np.square(embeddings[a_idx]-embeddings[p_idx])) #self.times[4] += self.time_elapsed() #self.time_reset() neg_dists_sqr[emb_start_idx:emb_start_idx+nrof_images] = np.NaN if self.triplet_max_ap>0.0: if pos_dist_sqr>self.triplet_max_ap: continue all_neg = np.where(np.logical_and(neg_dists_sqr-pos_dist_sqr0: rnd_idx = np.random.randint(nrof_random_negs) n_idx = all_neg[rnd_idx] triplets.append( (a_idx, p_idx, n_idx) ) emb_start_idx += nrof_images np.random.shuffle(triplets) return triplets def triplet_reset(self): #reset self.oseq by identities seq self.triplet_cur = 0 ids = [] for k in self.id2range: ids.append(k) random.shuffle(ids) self.triplet_seq = [] for _id in ids: v = self.id2range[_id] _list = range(*v) random.shuffle(_list) if len(_list)>self.images_per_identity: _list = _list[0:self.images_per_identity] self.triplet_seq += _list print('triplet_seq', len(self.triplet_seq)) assert len(self.triplet_seq)>=self.triplet_bag_size def time_reset(self): self.time_now = datetime.datetime.now() def time_elapsed(self): time_now = datetime.datetime.now() diff = time_now - self.time_now return diff.total_seconds() def select_triplets(self): self.seq = [] while len(self.seq)len(self.triplet_seq): self.triplet_reset() #bag_size = min(bag_size, len(self.triplet_seq)) print('eval %d images..'%bag_size, self.triplet_cur) self.times[0] += self.time_elapsed() self.time_reset() #print(data.shape) data = nd.zeros( self.provide_data[0][1] ) label = None if self.provide_label is not None: label = nd.zeros( self.provide_label[0][1] ) ba = 0 while True: bb = min(ba+batch_size, bag_size) if ba>=bb: break _count = bb-ba #data = nd.zeros( (_count,)+self.data_shape ) #_batch = self.data_iter.next() #_data = _batch.data[0].asnumpy() #print(_data.shape) #_label = _batch.label[0].asnumpy() #data[ba:bb,:,:,:] = _data #label[ba:bb] = _label for i in range(ba, bb): #print(ba, bb, self.triplet_cur, i, len(self.triplet_seq)) _idx = self.triplet_seq[i+self.triplet_cur] s = self.imgrec.read_idx(_idx) header, img = recordio.unpack(s) img = self.imdecode(img) data[i-ba][:] = self.postprocess_data(img) _label = header.label if not isinstance(_label, numbers.Number): _label = _label[0] if label is not None: label[i-ba][:] = _label tag.append( ( int(_label), _idx) ) #idx[i] = _idx db = mx.io.DataBatch(data=(data,)) self.mx_model.forward(db, is_train=False) net_out = self.mx_model.get_outputs() #print('eval for selecting triplets',ba,bb) #print(net_out) #print(len(net_out)) #print(net_out[0].asnumpy()) net_out = net_out[0].asnumpy() #print(net_out) #print('net_out', net_out.shape) if embeddings is None: embeddings = np.zeros( (bag_size, net_out.shape[1])) embeddings[ba:bb,:] = net_out ba = bb assert len(tag)==bag_size self.triplet_cur+=bag_size embeddings = sklearn.preprocessing.normalize(embeddings) self.times[1] += self.time_elapsed() self.time_reset() nrof_images_per_class = [1] for i in range(1, bag_size): if tag[i][0]==tag[i-1][0]: nrof_images_per_class[-1]+=1 else: nrof_images_per_class.append(1) triplets = self.pick_triplets(embeddings, nrof_images_per_class) # shape=(T,3) print('found triplets', len(triplets)) ba = 0 while True: bb = ba+self.per_batch_size//3 if bb>len(triplets): break _triplets = triplets[ba:bb] for i in range(3): for triplet in _triplets: _pos = triplet[i] _idx = tag[_pos][1] self.seq.append(_idx) ba = bb self.times[2] += self.time_elapsed() def hard_mining_reset(self): #import faiss from annoy import AnnoyIndex data = nd.zeros( self.provide_data[0][1] ) label = nd.zeros( self.provide_label[0][1] ) #label = np.zeros( self.provide_label[0][1] ) X = None ba = 0 batch_num = 0 while ba0: if self.triplet_mode: self.select_triplets() elif not self.hard_mining: self.seq = [] idlist = [] for _id in self.id2range: v = self.id2range[_id] idlist.append((_id,range(*v))) for r in range(self.repeat): if r%10==0: print('repeat', r) if self.shuffle: random.shuffle(idlist) for item in idlist: _id = item[0] _list = item[1] #random.shuffle(_list) if len(_list)= len(self.seq): raise StopIteration idx = self.seq[self.cur] self.cur += 1 s = self.imgrec.read_idx(idx) header, img = recordio.unpack(s) label = header.label if not isinstance(label, numbers.Number): label = label[0] return label, img, None, None def brightness_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) src *= alpha return src def contrast_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) coef = np.array([[[0.299, 0.587, 0.114]]]) gray = src * coef gray = (3.0 * (1.0 - alpha) / gray.size) * np.sum(gray) src *= alpha src += gray return src def saturation_aug(self, src, x): alpha = 1.0 + random.uniform(-x, x) coef = np.array([[[0.299, 0.587, 0.114]]]) gray = src * coef gray = np.sum(gray, axis=2, keepdims=True) gray *= (1.0 - alpha) src *= alpha src += gray return src def color_aug(self, img, x): augs = [self.brightness_aug, self.contrast_aug, self.saturation_aug] random.shuffle(augs) for aug in augs: #print(img.shape) img = aug(img, x) #print(img.shape) return img def mirror_aug(self, img): _rd = random.randint(0,1) if _rd==1: for c in range(img.shape[2]): img[:,:,c] = np.fliplr(img[:,:,c]) return img def next(self): if not self.is_init: self.reset() self.is_init = True """Returns the next batch of data.""" #print('in next', self.cur, self.labelcur) self.nbatch+=1 batch_size = self.batch_size c, h, w = self.data_shape batch_data = nd.empty((batch_size, c, h, w)) if self.provide_label is not None: batch_label = nd.empty(self.provide_label[0][1]) i = 0 try: while i < batch_size: label, s, bbox, landmark = self.next_sample() _data = self.imdecode(s) if self.rand_mirror: _rd = random.randint(0,1) if _rd==1: _data = mx.ndarray.flip(data=_data, axis=1) if self.cutoff>0: centerh = random.randint(0, _data.shape[0]-1) centerw = random.randint(0, _data.shape[1]-1) half = self.cutoff//2 starth = max(0, centerh-half) endh = min(_data.shape[0], centerh+half) startw = max(0, centerw-half) endw = min(_data.shape[1], centerw+half) _data = _data.astype('float32') #print(starth, endh, startw, endw, _data.shape) _data[starth:endh, startw:endw, :] = 127.5 #_npdata = _data.asnumpy() #if landmark is not None: # _npdata = face_preprocess.preprocess(_npdata, bbox = bbox, landmark=landmark, image_size=self.image_size) #if self.rand_mirror: # _npdata = self.mirror_aug(_npdata) #if self.mean is not None: # _npdata = _npdata.astype(np.float32) # _npdata -= self.mean # _npdata *= 0.0078125 #nimg = np.zeros(_npdata.shape, dtype=np.float32) #nimg[self.patch[1]:self.patch[3],self.patch[0]:self.patch[2],:] = _npdata[self.patch[1]:self.patch[3], self.patch[0]:self.patch[2], :] #_data = mx.nd.array(nimg) data = [_data] try: self.check_valid_image(data) except RuntimeError as e: logging.debug('Invalid image, skipping: %s', str(e)) continue #print('aa',data[0].shape) #data = self.augmentation_transform(data) #print('bb',data[0].shape) for datum in data: assert i < batch_size, 'Batch size must be multiples of augmenter output length' #print(datum.shape) batch_data[i][:] = self.postprocess_data(datum) if self.provide_label is not None: batch_label[i][:] = label i += 1 except StopIteration: if i>> dataIter.read_image('Face.jpg') # returns decoded raw bytes. """ with open(os.path.join(self.path_root, fname), 'rb') as fin: img = fin.read() return img def augmentation_transform(self, data): """Transforms input data with specified augmentation.""" for aug in self.auglist: data = [ret for src in data for ret in aug(src)] return data def postprocess_data(self, datum): """Final postprocessing step before image is loaded into the batch.""" return nd.transpose(datum, axes=(2, 0, 1)) class FaceImageIterList(io.DataIter): def __init__(self, iter_list): assert len(iter_list)>0 self.provide_data = iter_list[0].provide_data self.provide_label = iter_list[0].provide_label self.iter_list = iter_list self.cur_iter = None def reset(self): self.cur_iter.reset() def next(self): self.cur_iter = random.choice(self.iter_list) while True: try: ret = self.cur_iter.next() except StopIteration: self.cur_iter.reset() continue return ret ================================================ FILE: image_generation_chainer/__init__.py ================================================ ================================================ FILE: image_generation_chainer/datasets/mnist.py ================================================ from os.path import isdir import chainer import numpy as np class MnistDb(chainer.dataset.DatasetMixin): def __init__(self, path, ims_suff='train_ims.npy', labels_suff=None, internal_perm=False, seed=9, permute=False, n_samples=None, train=True, **kwargs): # # load the images (loaded all in ram). if isdir(path): self.base = np.load(path + ims_suff) else: print('Path not found. Loading fashion-mnist by default.') trainset, _ = chainer.datasets.get_fashion_mnist(withlabel=True, scale=1, ndim=3) ims = np.array([np.pad(samp[0], ((0, 0), (2, 2), (2, 2)), 'constant') for samp in trainset], dtype=np.float32) self.base = ims * 2 - 1 if self.base[0].ndim == 2: # # extend to avoid issues. Channels in front in chainer!!! sh1 = self.base.shape self.base = np.reshape(self.base, (sh1[0], 1, sh1[1], sh1[2])) elif self.base.shape[-1] == 3 and self.base.ndim == 4: self.base = self.base.transpose((0, 3, 1, 2)) if n_samples is not None: self.base = self.base[:n_samples] # # load the labels if the appropriate npy is provided. if labels_suff is not None: self.labels = np.load(path + labels_suff) if n_samples is not None: self.labels = self.labels[:n_samples] if internal_perm: # # do a random permutation of the data. np.random.seed(seed) perm = np.random.permutation(range(self.base.shape[0])) self.base = self.base[perm] if labels_suff is not None: self.labels = self.labels[perm] self.n_classes = len(set(self.labels)) if (labels_suff is not None) else 0 def __len__(self): return len(self.base) def get_example(self, i): image = self.base[i] if hasattr(self, 'labels'): return image, self.labels[i] else: return image, 1 ================================================ FILE: image_generation_chainer/dis_models/__init__.py ================================================ ================================================ FILE: image_generation_chainer/dis_models/resblocks_dis.py ================================================ import math import chainer from source.links.sn_convolution_2d import SNConvolution2D from chainer import functions as F from chainer import links as L def _downsample(x): # Downsample (Mean Avg Pooling with 2x2 kernel) return F.average_pooling_2d(x, 2) class Block(chainer.Chain): def __init__(self, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1, activation=F.relu, downsample=False, dropout_ratio=0.0, sn=False, gate=False): super(Block, self).__init__() initializer = chainer.initializers.GlorotUniform(math.sqrt(2)) initializer_sc = chainer.initializers.GlorotUniform() self.dropout_ratio = dropout_ratio self.activation = activation self.downsample = downsample self.learnable_sc = (in_channels != out_channels) or downsample self.gate = gate if sn: Conv = SNConvolution2D else: Conv = L.Convolution2D hidden_channels = in_channels if hidden_channels is None else hidden_channels with self.init_scope(): self.c1 = Conv(in_channels, hidden_channels, ksize=ksize, pad=pad, initialW=initializer) self.c2 = Conv(hidden_channels, out_channels, ksize=ksize, pad=pad, initialW=initializer) if self.learnable_sc: self.c_sc = Conv(in_channels, out_channels, ksize=1, pad=0, initialW=initializer_sc) if gate: self.a = chainer.Parameter(initializer=chainer.initializers.Zero(), shape=(1)) def residual(self, x): h = x h = self.activation(h) h = self.c1(h) h = self.activation(h) h = self.c2(h) if self.downsample: h = _downsample(h) return h def shortcut(self, x): if self.learnable_sc: x = self.c_sc(x) if self.downsample: return _downsample(x) else: return x else: return x def __call__(self, x): if self.gate: res = self.residual(x) sh = self.shortcut(x) sigm_a = F.broadcast_to(F.sigmoid(self.a)[:, None, None, None], res.shape) return 2 * ((1 - sigm_a) * res + sigm_a * sh) else: return self.residual(x) + self.shortcut(x) class OptimizedBlock(chainer.Chain): def __init__(self, in_channels, out_channels, ksize=3, pad=1, activation=F.relu, sn=False, gate=False): super(OptimizedBlock, self).__init__() initializer = chainer.initializers.GlorotUniform(math.sqrt(2)) initializer_sc = chainer.initializers.GlorotUniform() self.activation = activation self.gate = gate if sn: Conv = SNConvolution2D else: Conv = L.Convolution2D with self.init_scope(): self.c1 = Conv(in_channels, out_channels, ksize=ksize, pad=pad, initialW=initializer) self.c2 = Conv(out_channels, out_channels, ksize=ksize, pad=pad, initialW=initializer) self.c_sc = Conv(in_channels, out_channels, ksize=1, pad=0, initialW=initializer_sc) if gate: self.a = chainer.Parameter(initializer=chainer.initializers.Zero(), shape=(1)) def residual(self, x): h = x h = self.c1(h) h = self.activation(h) # Conv -> Pool h = self.c2(h) h = _downsample(h) return h def shortcut(self, x): # Pool -> Conv return self.c_sc(_downsample(x)) def __call__(self, x): if self.gate: res = self.residual(x) sh = self.shortcut(x) sigm_a = F.broadcast_to(F.sigmoid(self.a)[:, None, None, None], res.shape) return 2 * ((1 - sigm_a) * res + sigm_a * sh) else: return self.residual(x) + self.shortcut(x) ================================================ FILE: image_generation_chainer/dis_models/snresnet_32.py ================================================ import chainer from chainer import functions as F from source.links.sn_embed_id import SNEmbedID from source.links.sn_linear import SNLinear from dis_models.resblocks_dis import Block, OptimizedBlock class SNResNetProjectionDiscriminator(chainer.Chain): def __init__(self, ch=128, n_classes=0, activation=F.relu, sn=False, ch_input=3): super(SNResNetProjectionDiscriminator, self).__init__() self.activation = activation self.n_classes = n_classes with self.init_scope(): self.block1 = OptimizedBlock(ch_input, ch, sn=sn) self.block2 = Block(ch, ch, activation=activation, downsample=True, sn=sn) self.block3 = Block(ch, ch, activation=activation, downsample=False, sn=sn) self.block4 = Block(ch, ch, activation=activation, downsample=False, sn=sn) self.l5 = SNLinear(ch, 1, initialW=chainer.initializers.GlorotUniform(), nobias=True) if n_classes > 0: self.l_y = SNEmbedID(n_classes, ch, initialW=chainer.initializers.GlorotUniform()) def __call__(self, x, y=None, return_feature=False): h = x h = self.block1(h) h = self.block2(h) h = self.block3(h) h = self.block4(h) h = self.activation(h) # Global average pooling h = F.sum(h, axis=(2, 3)) output = self.l5(h) if return_feature: return output, h if y is not None and self.n_classes: w_y = self.l_y(y) output += F.sum(w_y * h, axis=1, keepdims=True) return output ================================================ FILE: image_generation_chainer/evaluations/__init__.py ================================================ ================================================ FILE: image_generation_chainer/evaluations/extensions.py ================================================ import os import sys import math import numpy as np from PIL import Image import scipy.linalg import chainer import chainer.cuda from chainer import Variable from chainer import serializers from chainer import cuda import chainer.functions as F from chainer.training import extensions sys.path.append(os.path.dirname(__file__)) sys.path.append('../') from source.inception.inception_score import inception_score, Inception from source.links.sn_convolution_2d import SNConvolution2D from source.functions.max_sv import max_singular_value from numpy.linalg import svd try: from skl_groups.divergences import KNNDivergenceEstimator except ImportError: pass # # even though NDB is imported, its evaluation was not used for this # # project; it remains here for legacy mode. from evaluations.ndb import NDB # # define as globals since the trainer does not have a memory. best_min_metric, best_inception, best_fid = 100000000, 0, 1000000 best_min_ndb, ndb = 100000000, None def gen_images(gen, n=50000, batchsize=100, seed=None, z=None): ims = [] xp = gen.xp if seed is not None: xp.random.seed(seed=seed) for i in range(0, n, batchsize): with chainer.using_config('train', False), chainer.using_config('enable_backprop', False): x = gen(batchsize, z=z) x.unchain_backward() x_cpu = chainer.cuda.to_cpu(x.data) del x x_cpu = np.asarray(np.clip(x_cpu * 127.5 + 127.5, 0.0, 255.0), dtype=np.uint8) ims.append(x_cpu) ims = np.asarray(ims) # # the line below is only for images; synthetic might differ in number of dimensions. _, _, c, h, w = ims.shape ims = ims.reshape((n, c, h, w)) return ims def gen_images_with_condition(gen, c=0, n=500, batchsize=100, z=None): # # used to synthesize images with condition c, i.e. pass to the generator the # # variable c. ims = [] xp = gen.xp for i in range(0, n, batchsize): with chainer.using_config('train', False), chainer.using_config('enable_backprop', False): if isinstance(c, np.ndarray) and len(c.shape) == 2 and c.shape[0] == batchsize: y = xp.asarray(c, dtype=xp.int32) else: y = xp.asarray([c] * batchsize, dtype=xp.int32) x = gen(batchsize, y=y, z=z) x = chainer.cuda.to_cpu(x.data) x = np.asarray(np.clip(x * 127.5 + 127.5, 0.0, 255.0), dtype=np.uint8) ims.append(x) ims = np.asarray(ims) _, _, c, h, w = ims.shape ims = ims.reshape((n, c, h, w)) return ims def sample_generate_light(gen, dst, rows=5, cols=5, batchsize=5, seed=0): @chainer.training.make_extension() def make_image(trainer): np.random.seed(seed) n_images = rows * cols x = gen_images(gen, n_images, batchsize=batchsize) _, c, H, W = x.shape x = x.reshape((rows, cols, c, H, W)) x = x.transpose(0, 3, 1, 4, 2) x = x.reshape((rows * H, cols * W, c)) if c == 1: x = np.concatenate([x, x, x], axis=2) preview_dir = '{}/preview'.format(dst) preview_path = preview_dir + '/image_latest.png' if not os.path.exists(preview_dir): os.makedirs(preview_dir) Image.fromarray(x).save(preview_path) return make_image def sample_generate(gen, dst, rows=10, cols=10, batchsize=10, seed=0): @chainer.training.make_extension() def make_image(trainer): print('in sample generation: ', trainer.updater.iteration) np.random.seed(seed) n_images = rows * cols x = gen_images(gen, n_images, batchsize=batchsize) _, c, h, w = x.shape x = x.reshape((rows, cols, c, h, w)) x = x.transpose(0, 3, 1, 4, 2) x = x.reshape((rows * h, cols * w, c)) if c == 1: x = np.concatenate([x, x, x], axis=2) if c > 3: x = x[:, :, :3] preview_dir = '{}/preview'.format(dst) preview_path = preview_dir + '/image{:0>8}.png'.format(trainer.updater.iteration) if not os.path.exists(preview_dir): os.makedirs(preview_dir) Image.fromarray(x).save(preview_path) return make_image def sample_generate_conditional(gen, dst, rows=10, n_classes=1000, seed=0, classes=None): """Visualization of rows*cols images randomly generated by the generator.""" if classes is None: classes = np.arange(10) * (n_classes / 10) print('Sample visualization classes: ', classes) # # update the rows if less than the clases. rows = min(rows, len(classes)) @chainer.training.make_extension() def make_image(trainer=None): np.random.seed(seed) xp = gen.xp with chainer.using_config('train', False), chainer.using_config('enable_backprop', False): x = [] for c in classes: x.append(gen_images_with_condition(gen, c=c, n=rows, batchsize=rows)) x = np.concatenate(x, 0) _, c, h, w = x.shape x = x.reshape((rows, len(classes), c, h, w)) x = x.transpose(0, 3, 1, 4, 2) x = x.reshape((rows * h, len(classes) * w, c)) if c == 1: x = np.concatenate([x, x, x], axis=2) preview_dir = '{}/preview'.format(dst) preview_path = preview_dir + '/image{:0>8}.png'.format( trainer.updater.iteration if trainer is not None else None) if not os.path.exists(preview_dir): os.makedirs(preview_dir) Image.fromarray(x).save(preview_path) return make_image def divergence_gen(gen, gt_db, batch=1000, metric='kl', normalize=False, n_bins=100, whitening=True, classes=None, **kwargs): """ Given a generator and the gt function (the one generator tries to approximate), we measure the discrepancy of the generated from the gt signals. """ # # generate some samples. batch = gt_db.shape[0] if classes is None: gen_samples = gen_images(gen, n=batch, batchsize=batch) else: # # conditional case. gen_csamples, n_ms = [], int(batch // len(classes) + 10) for cl in classes: x = gen_images_with_condition(gen, n=n_ms, c=cl, batchsize=n_ms) gen_csamples.append(x) gen_csamples = np.concatenate(gen_csamples, 0) gen_samples = gen_csamples[:gt_db.shape[0]] if len(gt_db.shape) != 2: gt_db = gt_db.reshape((batch, -1)) if len(gen_samples.shape) != 2: gen_samples = gen_samples.reshape((batch, -1)) if gen_samples.dtype == np.uint8: gen_samples = gen_samples.astype(np.float32) if normalize: # # Given that gen_images have a range [0, 255], normalize # # the images in the [-1, 1] range for the KNN. gen_samples1 = gen_samples / 127.5 - 1 else: gen_samples1 = gen_samples if metric == 'ndb': global ndb if ndb is None: ndb = NDB(training_data=gt_db, number_of_bins=n_bins, whitening=whitening) metric_val = ndb.evaluate(gen_samples) chainer.reporter.report({'ndb': metric_val['NDB']}) chainer.reporter.report({'JS': metric_val['JS']}) diver = metric_val['NDB'] else: # # define an estimator (e.g. KL divergence). est = KNNDivergenceEstimator(div_funcs=[metric], Ks=[3], clamp=False) # # fit and return the result. res_diver = est.fit_transform([gt_db, gen_samples]) try: diver = res_diver[0, 1] except: diver = res_diver[0][0][0, 1] chainer.reporter.report({'kl': diver}) return diver def divergence_trainer(gen, db, metric='kl', export_best=True, **kwargs): """ Chainer extension for evaluating the divergence of the generator from gt. """ @chainer.training.make_extension() def evaluation(trainer=None): for met in metric: metric_val = divergence_gen(gen, db, metric=met, **kwargs) if export_best and trainer is not None: # # compare with the best score (per metric). global best_min_metric, best_min_ndb if 'kl' in met and metric_val < best_min_metric: # # export if it has the best score so far (kl). best_min_metric = metric_val ext = extensions.snapshot_object(gen, '{}_best.npz'.format(gen.__class__.__name__)) ext(trainer) elif 'ndb' in met and metric_val < best_min_ndb: # # export if it has the best score so far (ndb). best_min_ndb = metric_val ext = extensions.snapshot_object(gen, '{}_best_ndb.npz'.format(gen.__class__.__name__)) ext(trainer) if isinstance(metric, str): metric = [metric] return evaluation def load_inception_model(path=None, gpu=0): path = path if path is not None else "%s/inception/inception_score.model" % os.path.dirname(__file__) model = Inception() serializers.load_hdf5(path, model) if gpu >= 0: model.to_gpu() return model def calc_inception(gen, batchsize=10, dst=None, path=None, n_ims=50000, splits=10): @chainer.training.make_extension() def evaluation(trainer=None): model = load_inception_model(path) ims = gen_images(gen, n_ims, batchsize=batchsize).astype("f") mean, std = inception_score(model, ims, splits=splits) chainer.reporter.report({ 'inception_mean': mean, 'inception_std': std }) if dst is not None: preview_dir = '{}/stats'.format(dst) preview_path = preview_dir + '/inception_score_{:0>8}.txt'.format( trainer.updater.iteration if trainer is not None else None) np.savetxt(preview_path, np.array([mean, std])) # # export the best model. global best_inception if trainer is not None and best_inception < mean: ext = extensions.snapshot_object(gen, gen.__class__.__name__ + '_best_inception.npz') ext(trainer) best_inception = mean return evaluation def get_mean_cov(model, ims, batch_size=100, verbose=False): n, c, w, h = ims.shape n_batches = int(math.ceil(float(n) / float(batch_size))) xp = model.xp ys = xp.empty((n, 2048), dtype=xp.float32) for i in range(n_batches): if verbose: print('Running batch', i + 1, '/', n_batches, '...') batch_start = (i * batch_size) batch_end = min((i + 1) * batch_size, n) ims_batch = ims[batch_start:batch_end] ims_batch = Variable(xp.asarray(ims_batch, dtype=xp.float32)) # Resize image to the shape expected by the inception module if (w, h) != (299, 299): ims_batch = F.resize_images(ims_batch, (299, 299)) # bilinear # Feed images to the inception module to get the features with chainer.using_config('train', False), chainer.using_config('enable_backprop', False): y = model(ims_batch, get_feature=True) ys[batch_start:batch_end] = y.data mean = xp.mean(ys, axis=0).get() # cov = F.cross_covariance(ys, ys, reduce="no").datasets.get() cov = np.cov(ys.get().T) return mean, cov def monitor_largest_singular_values(dis, dst): @chainer.training.make_extension() def evaluation(trainer=None): def _l2normalize(v, eps=1e-12): return v / (((v ** 2).sum()) ** 0.5 + eps) xp = dis.xp links = [[name, link] for name, link in sorted(dis.namedlinks())] sigmas = [] for name, link in links: if isinstance(link, SNConvolution2D): W, u = link.W, link.u W_mat = W.reshape(W.shape[0], -1) sigma, _, _ = max_singular_value(W_mat, u) W_bar = cuda.to_cpu((W_mat.data / xp.squeeze(sigma.data))) _, s, _ = svd(W_bar) _sigma = s[0] print(name.strip('/'), _sigma) sigmas.append([name.strip('/'), _sigma]) if dst is not None: preview_dir = '{}/sigmas'.format(dst) if not os.path.exists(preview_dir): os.makedirs(preview_dir) preview_path = preview_dir + '/sigmas_{:0>8}.txt'.format( trainer.updater.iteration if trainer is not None else None) with open(preview_path, 'wb') as f: np.savetxt(f, np.array(sigmas, dtype=np.str), delimiter=" ", fmt="%s") return evaluation def FID(m0, c0, m1, c1): ret = 0 ret += np.sum((m0 - m1) ** 2) ret += np.trace(c0 + c1 - 2.0 * scipy.linalg.sqrtm(np.dot(c0, c1))) return np.real(ret) def calc_FID(gen, batchsize=100, stat_file="%s/cifar-10-fid.npz" % os.path.dirname(__file__), dst=None, path=None, n_ims=5000): """Frechet Inception Distance proposed by https://arxiv.org/abs/1706.08500""" @chainer.training.make_extension() def evaluation(trainer=None): model = load_inception_model(path) stat = np.load(stat_file) ims = gen_images(gen, n_ims, batchsize=batchsize).astype("f") with chainer.using_config('train', False), chainer.using_config('enable_backprop', False): mean, cov = get_mean_cov(model, ims) fid = FID(stat["mean"], stat["cov"], mean, cov) chainer.reporter.report({'FID': fid}) if dst is not None: preview_dir = '{}/stats'.format(dst) preview_path = preview_dir + '/fid_{:0>8}.txt'.format( trainer.updater.iteration if trainer is not None else None) np.savetxt(preview_path, np.array([fid])) # # export the best model. global best_fid if trainer is not None and best_fid > fid: ext = extensions.snapshot_object(gen, gen.__class__.__name__ + '_best_fid.npz') ext(trainer) best_fid = fid return evaluation # For classification def get_batch(iterator, xp): batch = iterator.next() batchsize = len(batch) x, y = [], [] for j in range(batchsize): _x = batch[j][0] _y = batch[j][1] if isinstance(_x, (list, tuple)): for k in range(len(_x)): x.append(np.asarray(_x[k]).astype("f")) y.append(np.asarray(_y[k]).astype(np.int32)) else: x.append(np.asarray(batch[j][0]).astype("f")) y.append(np.asarray(batch[j][1]).astype(np.int32)) x = xp.asarray(x) y = xp.asarray(y, dtype=xp.int32) return x, y def validation_loss_and_acc(cls, iterator, n=50000, dis=None): @chainer.training.make_extension() def _evaluate(trainer): iterator.reset() losses = [] accs = [] for i in range(0, n, iterator.batch_size): x, y = get_batch(iterator, cls.xp) with chainer.using_config('train', False), chainer.using_config('enable_backprop', False): if dis is not None: _, x = dis(x, return_feature=True) logit = cls(x) loss = F.softmax_cross_entropy(logit, y) acc = F.accuracy(logit, y) losses.append(chainer.cuda.to_cpu(loss.array)) accs.append(chainer.cuda.to_cpu(acc.array)) chainer.reporter.report({ 'val_loss': np.mean(np.asarray(losses)), 'val_acc': np.mean(np.asarray(accs)) }) return _evaluate ================================================ FILE: image_generation_chainer/evaluations/ndb.py ================================================ import os import numpy as np from sklearn.cluster import KMeans from scipy.stats import norm from matplotlib import pyplot as plt import pickle as pkl class NDB: def __init__(self, training_data=None, number_of_bins=100, significance_level=0.05, z_threshold=None, whitening=False, max_dims=None, cache_folder=None, verbose=False): """ Original code: https://github.com/eitanrich/gans-n-gmms/blob/master/utils/ndb.py Publication: https://arxiv.org/abs/1805.12462 . NDB Evaluation Class :param training_data: Optional - the training samples - array of m x d floats (m samples of dimension d) :param number_of_bins: Number of bins (clusters) default=100 :param significance_level: The statistical significance level for the two-sample test :param z_threshold: Allow defining a threshold in terms of difference/SE for defining a bin as statistically different :param whitening: Perform data whitening - subtract mean and divide by per-dimension std :param max_dims: Max dimensions to use in K-means. By default derived automatically from d :param bins_file: Optional - file to write / read-from the clusters (to avoid re-calculation) """ self.number_of_bins = number_of_bins self.significance_level = significance_level self.z_threshold = z_threshold self.whitening = whitening self.ndb_eps = 1e-6 self.training_mean = 0.0 self.training_std = 1.0 self.max_dims = max_dims self.cache_folder = cache_folder self.bin_centers = None self.bin_proportions = None self.ref_sample_size = None self.used_d_indices = None self.results_file = None self.verbose = verbose self.test_name = 'ndb_{}_bins_{}'.format(self.number_of_bins, 'whiten' if self.whitening else 'orig') self.cached_results = {} if self.cache_folder: self.results_file = os.path.join(cache_folder, self.test_name+'_results.pkl') if os.path.isfile(self.results_file): # print('Loading previous results from', self.results_file, ':') self.cached_results = pkl.load(open(self.results_file, 'rb')) # print(self.cached_results.keys()) if training_data is not None or cache_folder is not None: bins_file = None if cache_folder: os.makedirs(cache_folder, exist_ok=True) bins_file = os.path.join(cache_folder, self.test_name+'.pkl') self.construct_bins(training_data, bins_file) def construct_bins(self, training_samples, bins_file): """ Performs K-means clustering of the training samples :param training_samples: An array of m x d floats (m samples of dimension d) """ if self.__read_from_bins_file(bins_file): return n, d = training_samples.shape k = self.number_of_bins if self.whitening: self.training_mean = np.mean(training_samples, axis=0) self.training_std = np.std(training_samples, axis=0) + self.ndb_eps if self.max_dims is None and d > 1000: # To ran faster, perform binning on sampled data dimension (i.e. don't use all channels of all pixels) self.max_dims = d//6 whitened_samples = (training_samples-self.training_mean)/self.training_std d_used = d if self.max_dims is None else min(d, self.max_dims) self.used_d_indices = np.random.choice(d, d_used, replace=False) print('Performing K-Means clustering of {} samples in dimension {} / {} to {} clusters ...'.format(n, d_used, d, k)) #print('Can take a couple of minutes...') if n//k > 1000: print('Training data size should be ~500 times the number of bins (for reasonable speed and accuracy)') clusters = KMeans(n_clusters=k, max_iter=100, n_jobs=-1).fit(whitened_samples[:, self.used_d_indices]) bin_centers = np.zeros([k, d]) for i in range(k): bin_centers[i, :] = np.mean(whitened_samples[clusters.labels_ == i, :], axis=0) # Organize bins by size label_vals, label_counts = np.unique(clusters.labels_, return_counts=True) bin_order = np.argsort(-label_counts) self.bin_proportions = label_counts[bin_order] / np.sum(label_counts) self.bin_centers = bin_centers[bin_order, :] self.ref_sample_size = n self.__write_to_bins_file(bins_file) #print('Done.') def evaluate(self, query_samples, model_label=None): """ Assign each sample to the nearest bin center (in L2). Pre-whiten if required. and calculate the NDB (Number of statistically Different Bins) and JS divergence scores. :param query_samples: An array of m x d floats (m samples of dimension d) :param model_label: optional label string for the evaluated model, allows plotting results of multiple models :return: results dictionary containing NDB and JS scores and array of labels (assigned bin for each query sample) """ n = query_samples.shape[0] query_bin_proportions, query_bin_assignments = self.__calculate_bin_proportions(query_samples) # print(query_bin_proportions) different_bins = NDB.two_proportions_z_test(self.bin_proportions, self.ref_sample_size, query_bin_proportions, n, significance_level=self.significance_level, z_threshold=self.z_threshold) ndb = np.count_nonzero(different_bins) js = NDB.jensen_shannon_divergence(self.bin_proportions, query_bin_proportions) results = {'NDB': ndb, 'JS': js, 'Proportions': query_bin_proportions, 'N': n, 'Bin-Assignment': query_bin_assignments, 'Different-Bins': different_bins} if model_label: print('Results for {} samples from {}: '.format(n, model_label), end='') self.cached_results[model_label] = results if self.results_file: # print('Storing result to', self.results_file) pkl.dump(self.cached_results, open(self.results_file, 'wb')) if self.verbose: print('NDB =', ndb, 'NDB/K =', ndb/self.number_of_bins, ', JS =', js) return results def print_results(self): print('NSB results (K={}{}):'.format(self.number_of_bins, ', data whitening' if self.whitening else '')) for model in sorted(list(self.cached_results.keys())): res = self.cached_results[model] print('%s: NDB = %d, NDB/K = %.3f, JS = %.4f' % (model, res['NDB'], res['NDB']/self.number_of_bins, res['JS'])) def plot_results(self, models_to_plot=None): """ Plot the binning proportions of different methods :param models_to_plot: optional list of model labels to plot """ K = self.number_of_bins w = 1.0 / (len(self.cached_results)+1) assert K == self.bin_proportions.size assert self.cached_results # Used for plotting only def calc_se(p1, n1, p2, n2): p = (p1 * n1 + p2 * n2) / (n1 + n2) return np.sqrt(p * (1 - p) * (1/n1 + 1/n2)) if not models_to_plot: models_to_plot = sorted(list(self.cached_results.keys())) # Visualize the standard errors using the train proportions and size and query sample size train_se = calc_se(self.bin_proportions, self.ref_sample_size, self.bin_proportions, self.cached_results[models_to_plot[0]]['N']) plt.bar(np.arange(0, K)+0.5, height=train_se*2.0, bottom=self.bin_proportions-train_se, width=1.0, label='Train$\pm$SE', color='gray') ymax = 0.0 for i, model in enumerate(models_to_plot): results = self.cached_results[model] label = '%s (%i : %.4f)' % (model, results['NDB'], results['JS']) ymax = max(ymax, np.max(results['Proportions'])) if K <= 70: plt.bar(np.arange(0, K)+(i+1.0)*w, results['Proportions'], width=w, label=label) else: plt.plot(np.arange(0, K)+0.5, results['Proportions'], '--*', label=label) plt.legend(loc='best') plt.ylim((0.0, min(ymax, np.max(self.bin_proportions)*4.0))) plt.grid(True) plt.title('Binning Proportions Evaluation Results for {} bins (NDB : JS)'.format(K)) plt.show() def __calculate_bin_proportions(self, samples): if self.bin_centers is None: print('First run construct_bins on samples from the reference training data') assert samples.shape[1] == self.bin_centers.shape[1] n, d = samples.shape k = self.bin_centers.shape[0] D = np.zeros([n, k], dtype=samples.dtype) if self.verbose: print('Calculating bin assignments for {} samples...'.format(n)) whitened_samples = (samples-self.training_mean)/self.training_std for i in range(k): if self.verbose: print('.', end='', flush=True) D[:, i] = np.linalg.norm(whitened_samples[:, self.used_d_indices] - self.bin_centers[i, self.used_d_indices], ord=2, axis=1) if self.verbose: print() labels = np.argmin(D, axis=1) probs = np.zeros([k]) label_vals, label_counts = np.unique(labels, return_counts=True) probs[label_vals] = label_counts / n return probs, labels def __read_from_bins_file(self, bins_file): if bins_file and os.path.isfile(bins_file): print('Loading binning results from', bins_file) bins_data = pkl.load(open(bins_file,'rb')) self.bin_proportions = bins_data['proportions'] self.bin_centers = bins_data['centers'] self.ref_sample_size = bins_data['n'] self.training_mean = bins_data['mean'] self.training_std = bins_data['std'] self.used_d_indices = bins_data['d_indices'] return True return False def __write_to_bins_file(self, bins_file): if bins_file: print('Caching binning results to', bins_file) bins_data = {'proportions': self.bin_proportions, 'centers': self.bin_centers, 'n': self.ref_sample_size, 'mean': self.training_mean, 'std': self.training_std, 'd_indices': self.used_d_indices} pkl.dump(bins_data, open(bins_file, 'wb')) @staticmethod def two_proportions_z_test(p1, n1, p2, n2, significance_level, z_threshold=None): # Per http://stattrek.com/hypothesis-test/difference-in-proportions.aspx # See also http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/binotest.htm p = (p1 * n1 + p2 * n2) / (n1 + n2) se = np.sqrt(p * (1 - p) * (1/n1 + 1/n2)) z = (p1 - p2) / se # Allow defining a threshold in terms as Z (difference relative to the SE) rather than in p-values. if z_threshold is not None: return abs(z) > z_threshold p_values = 2.0 * norm.cdf(-1.0 * np.abs(z)) # Two-tailed test return p_values < significance_level @staticmethod def jensen_shannon_divergence(p, q): """ Calculates the symmetric Jensen–Shannon divergence between the two PDFs """ m = (p + q) * 0.5 return 0.5 * (NDB.kl_divergence(p, m) + NDB.kl_divergence(q, m)) @staticmethod def kl_divergence(p, q): """ The Kullback–Leibler divergence. Defined only if q != 0 whenever p != 0. """ assert np.all(np.isfinite(p)) assert np.all(np.isfinite(q)) assert not np.any(np.logical_and(p != 0, q == 0)) p_pos = (p > 0) return np.sum(p[p_pos] * np.log(p[p_pos] / q[p_pos])) if __name__ == "__main__": dim=100 k=100 n_train = k*100 n_test = k*10 train_samples = np.random.uniform(size=[n_train, dim]) ndb = NDB(training_data=train_samples, number_of_bins=k, whitening=True) test_samples = np.random.uniform(high=1.0, size=[n_test, dim]) ndb.evaluate(test_samples, model_label='Test') test_samples = np.random.uniform(high=0.9, size=[n_test, dim]) ndb.evaluate(test_samples, model_label='Good') test_samples = np.random.uniform(high=0.75, size=[n_test, dim]) ndb.evaluate(test_samples, model_label='Bad') ndb.plot_results(models_to_plot=['Test', 'Good', 'Bad']) ================================================ FILE: image_generation_chainer/gen_models/cnn_gen_custom_prodpoly.py ================================================ import numpy as np import chainer import chainer.functions as F import chainer.links as L from source.links.sn_linear import SNLinear from source.links.sn_convolution_2d import SNConvolution2D from source.links.categorical_conditional_batch_normalization import CategoricalConditionalBatchNormalization try: from source.links.instance_normalization import InstanceNormalization except ImportError: #print('The instance norm is not implemented or imported in the generator.') pass from functools import partial from source.miscs.random_samples import sample_continuous, sample_categorical def return_norm(norm): if isinstance(norm, str): return norm if norm == 0: return 'batch' elif norm == 1: return 'instance' elif norm == 2: return 'cinstance' def _upsample(x): h, w = x.shape[2:] return F.unpooling_2d(x, 2, outsize=(h * 2, w * 2)) class ProdPolyConvGenerator(chainer.Chain): def __init__(self, layer_d, use_bn=False, sn=False, out_ch=2, mult_lat=True, distribution='uniform', ksizes=None, strides=None, paddings=None, activ=None, bottom_width=1, use_localz=True, n_classes=0, use_out_act=False, channels=None, power_poly=[4, 3], derivfc=0, activ_prod=0, use_bias=True, dim_z=128, train=True, add_h_injection=False, add_h_poly=False, norm_after_poly=False, normalize_preinject=False, use_act_zgl=False, allow_conv=False, repeat_poly=1, share_first=False, type_norm='batch', typen_poly='batch', skip_rep=False, order_out_poly=None, use_activ=False, thresh_skip=None, use_sec_recurs=False, sign_srec=-1): """ Initializes the product polynomial generator. :param layer_d: List of all layers' inputs/outputs channels (including the input to the last output). :param use_bn: Bool, whether to use batch normalization. :param sn: Bool, if True use spectral normalization. :param out_ch: int, Output channels of the generator. :param ksizes: List of the kernel sizes per layer. :param strides: List of the stride per layer. :param paddings: List of the padding per layer (int). :param activ: str or None: The activation to use. :param bottom_width: int, spatial resolution to reshape the init noise. :param use_localz: bool, if True, then use 'local' transformations (affine) to transform from the original z to the current shape. :param use_out_act: bool, if True, it uses a tanh as output activation. :param repeat_poly: int or list: List of how many times to repeat each polynomial (original with FC layers, the last is NOT repeated). :param share_first: bool. If True, it shares the first FC layer in the FC polynomials. It assumes the respective FC layers are of the same size. :param type_norm: str. The type of normalization to be used, i.e. 'batch' for BN, 'instance' for instance norm. :param typen_poly: str. The type of normalization to be used ONLY for the FC layers. :param skip_rep: bool. If True, it uses a skip connection before the layer to the next, i.e. in practice it directly skips the convolution/fc plus hadamard product of this layer. See derivation 3. :param order_out_poly: int or None. If int, a polynomial of the order is used in the output, i.e. before the final convolution. :param use_activ: bool. If True, use activations in the main deconvolutional part. :param thresh_skip: None, int or list. It represents the probability of skipping the hadamard product in the deconvolution part (i.e. main polynomial). This is per layer; e.g. if 0.3, this means that approx. 30% of the time this hadamard will be skipped. It should have values in the [0, 1]. In inference, it should be deactivated. :param use_sec_recurs: bool, default False. If True, it uses the second recursive term, i.e. x_(n+1)=f(x_n, x_(n-1)). This is used only in the derivation 3 setting. Also, for practical reasons, use only for the FC layers when their order > 3. """ super(ProdPolyConvGenerator, self).__init__() self.n_l = n_l = len(layer_d) - 1 w = chainer.initializers.GlorotUniform() if sn: Conv = SNConvolution2D Linear = SNLinear raise RuntimeError('Not implemented!') else: Conv = L.Deconvolution2D Linear = L.Linear Conv0 = L.Convolution2D # # initialize args not provided. if ksizes is None: ksizes = [4] * n_l if strides is None: strides = [2] * n_l if paddings is None: paddings = [1] * n_l # # the length of the ksizes and the strides is only for the conv layers, hence # # it should be one number short of the layer_d. assert len(ksizes) == len(strides) == len(paddings) == n_l # # save in self, several useful properties. self.use_bn = use_bn self.n_channels = layer_d self.mult_lat = mult_lat self.distribution = distribution # # Define dim_z. assert isinstance(dim_z, int) self.dim_z = dim_z self.train = train # # bottom_width: The default starting spatial resolution of the convolutions. self.bottom_width = bottom_width self.use_localz = use_localz self.n_classes = n_classes if activ is not None: activ = getattr(F, activ) elif use_activ: activ = F.relu else: activ = lambda x: x self.activ = self.activation = activ self.out_act = F.tanh if use_out_act else lambda x: x # # Set the add_one to True out of consistency with the other # # implementation (resnet-based). self.add_one = 1 self.add_h_injection = add_h_injection self.normalize_preinject = normalize_preinject self.use_bias = use_bias self.add_h_poly = add_h_poly self.power_poly = power_poly if channels is None: # # Initialize in the dimension of z. channels = [dim_z] * len(self.power_poly) self.channels = channels if isinstance(activ_prod, int): activ_prod = [activ_prod] * len(self.power_poly) self.activ_prod = activ_prod if isinstance(repeat_poly, int): repeat_poly = [repeat_poly] * len(self.power_poly) self.repeat_poly = repeat_poly assert len(self.power_poly) == len(self.activ_prod) == len(self.channels) assert len(self.power_poly) == len(self.repeat_poly) # # If True, it compensates for python open interval in the end of range. self.derivfc = derivfc self.norm_after_poly = norm_after_poly # # whether to use an activation in the global transformation. self.use_act_zgl = use_act_zgl and use_activ self.share_first = share_first self.type_norm = return_norm(type_norm) self.typen_poly = return_norm(typen_poly) self.skip_rep = skip_rep # # optionally adding one 'output polynomial', before making the final output convolution. self.order_out_poly = order_out_poly # # set the threshold for skipping the injection in the deconvolution part. if isinstance(thresh_skip, float) and self.train: self.thresh_skip = [0] + [thresh_skip] * (n_l - 1) print('[Gen] Skip probabilities: {}.'.format(self.thresh_skip)) else: self.thresh_skip = thresh_skip self.use_sec_recurs = use_sec_recurs and skip_rep self.sign_srec = sign_srec with self.init_scope(): if self.n_classes == 0: bn1 = partial(L.BatchNormalization, use_gamma=True, use_beta=False) elif type_norm == 'batch': print('[Gen] Categorical batch normalization in the generator.') bn1 = partial(CategoricalConditionalBatchNormalization, n_cat=n_classes) elif type_norm == 'instance': print('[Gen] Instance normalization in the generator.') bn1 = InstanceNormalization if self.typen_poly != self.type_norm and self.typen_poly == 'instance': print('[Gen] Using instance normalization for the FC layers *ONLY*.') bn2 = InstanceNormalization else: bn2 = bn1 # # inpc_pol: the input size to the current polynomial; initialize on dimz. inpc_pol = dim_z # # iterate over all the polynomials (in the fully-connected part). print('[Gen] Number of product polynomials: {}.'.format(len(self.power_poly))) for id_poly in range(len(self.power_poly)): chp = self.channels[id_poly] m1 = '[Gen] Channels of the polynomial {}: {}, depth: {}.' print(m1.format(id_poly, chp, self.power_poly[id_poly])) # ensure that the current input channels match the expected. setattr(self, 'has_rsz{}'.format(id_poly), inpc_pol != chp) if inpc_pol != chp: setattr(self, 'resize{}'.format(id_poly), Linear(inpc_pol, chp, nobias=not use_bias, initialW=w)) # # now build the current polynomial (id_poly). for l in range(1, self.power_poly[id_poly] + self.add_one): if l == 1 and id_poly > 0 and self.share_first: # # in this case the layer will be shared with the first polynomial's # # the first layer. continue setattr(self, 'l{}_{}'.format(id_poly, l), Linear(chp, chp, nobias=not use_bias, initialW=w)) # # define the activation for this polynomial. actp = F.relu if self.activ_prod[id_poly] else lambda x: x setattr(self, 'activ{}'.format(id_poly), actp) if self.norm_after_poly: # # define different batch normalizations for each polynomial repeated. for repeat in range(self.repeat_poly[id_poly]): setattr(self, 'bnp{}_r{}'.format(id_poly, repeat), bn2(chp)) # # update input for the next polynomial. inpc_pol = int(chp) if bottom_width > 1: # # make a linear layer to transform to this shape. setattr(self, 'lin0', Linear(inpc_pol, inpc_pol * bottom_width ** 2, initialW=w)) # # iterate over all layers (till the last) and save in self. for l in range(1, n_l + 1): # # define the input and the output names. ni, no = layer_d[l - 1], layer_d[l] Conv_sel = Conv0 if strides[l - 1] == 1 and ksizes[l - 1] == 3 and allow_conv else Conv conv_i = partial(Conv_sel, initialW=w, ksize=ksizes[l - 1], stride=strides[l - 1], pad=paddings[l - 1]) # # save the self.layer. setattr(self, 'l{}'.format(l), conv_i(ni, no)) if self.skip_rep: # # define some 1x1 convolutions iff the channels are not the same in # # the input and the output. Otherwise, use the identity mapping. func = (lambda x: x) if ni == no else Conv(ni, no, ksize=1) setattr(self, 'skipch{}'.format(l), func) if self.order_out_poly is not None: ch1 = layer_d[n_l] for i in range(1, self.order_out_poly): setattr(self, 'oro{}'.format(i + 1), Conv0(ch1, ch1, ksize=3, stride=1, pad=1, initialW=w)) # # save the last layer. # # In Deconv, we need to define a ksize (otherwise the dimensions unchanged). This # # last layer leaves the spatial dimensions untouched. setattr(self, 'l{}'.format(n_l + 1), Conv(layer_d[n_l], out_ch, ksize=3, pad=1, initialW=w)) self.n_channels.append(out_ch) if use_bn: # # set the batch norm (applied before the first layer conv). setattr(self, 'bn{}'.format(1), bn1(layer_d[0])) for l in range(2, self.n_l + 1): # # set the batch norm for the layer. setattr(self, 'bn{}'.format(l), bn1(layer_d[l - 1])) if self.mult_lat and use_localz: # # define the 'local' transformations of z (z local Linear). for l in range(1, n_l + 1): ni, no = layer_d[0], layer_d[l] setattr(self, 'locz{}'.format(l), Linear(ni, no, initialW=w)) # # the condition to add a residual skip to the representation. self.skip1 = self.add_h_injection or self.add_h_poly or self.skip_rep def return_injected(self, h, z, n_layer, mult_until_exec=None): """ Performs the Hadamard products with z. """ # # check whether to skip the hadamard. skip_injection = False if self.thresh_skip is not None and self.thresh_skip[n_layer-1] > 0: # # skip the hadamard, iff the random number is smaller than the threshold. skip_injection = np.random.uniform() < self.thresh_skip[n_layer-1] if not skip_injection and mult_until_exec is not None: skip_injection = mult_until_exec <= n_layer if self.mult_lat and not skip_injection: if self.use_localz: # # apply local transformation. z1 = getattr(self, 'locz{}'.format(n_layer))(z) else: z1 = z # # appropriately reshape z for the elementwise multiplication. sh = h.shape z1 = F.reshape(z1, (sh[0], sh[1], 1)) if self.normalize_preinject: z1 /= F.sqrt(F.mean(z1 * z1, axis=1, keepdims=True) + 1e-8) z2 = F.repeat(z1, sh[3] * sh[2], axis=2) z2 = F.reshape(z2, sh) ret = h * z2 + h if self.add_h_injection else h * z2 return ret return h def prod_poly_FC(self, z, mult_until_exec, batchsize=None, y=None): """ Performs the products of polynomials for the fully-connected part. """ # # input_poly: the input variable to each polynomial; for the # # first, simply z, i.e. the noise vector. input_poly = z + 0 # # iterate over all the polynomials (length of channels many). for id_poly in range(len(self.channels)): # # ensure that the channels from previous polynomial are of the # # appropriate size. if getattr(self, 'has_rsz{}'.format(id_poly)): input_poly = getattr(self, 'resize{}'.format(id_poly))(input_poly) # # id_first: The index of the first FC layer; if we share it, it should # # be that of l0_1; otherwise l[id_poly]_1. id_first = id_poly if not self.share_first else 0 # # order of the current polynomial. order = self.power_poly[id_poly] + self.add_one # # condition to use the second recursive term. sec_rec = order > 2 and self.skip1 and self.use_sec_recurs # # Repeat each polynomial repeat_poly times (if repeat==1, then only running once). for repeat in range(self.repeat_poly[id_poly]): h = getattr(self, 'l{}_1'.format(id_first))(input_poly) if sec_rec: all_reps = [] # # loop over the current polynomial layers and compute the # # output (for this polynomial). for layer in range(2, order): if layer <= mult_until_exec: # # step 1: perform the hadamard product. if self.derivfc == 0: z1 = getattr(self, 'l{}_{}'.format(id_poly, layer))(input_poly) if self.skip1: if sec_rec: all_reps.append(h + 0) h += z1 * h else: h = z1 * h elif self.derivfc == 1: # # In this case, we assume that both A_i, B_i of # # the original polygan derivation are identity matrices. h1 = getattr(self, 'l{}_{}'.format(id_poly, layer))(h) if self.skip1: if sec_rec: all_reps.append(h + 0) h += h1 * input_poly else: h = h1 * input_poly if sec_rec and layer > 2: h = h + self.sign_srec * all_reps[-2] # # step 2: activation. h = getattr(self, 'activ{}'.format(id_poly))(h) # # if we included the second recursive term, add back in the final representation. if sec_rec: for rep in all_reps: h = h - self.sign_srec * rep if self.norm_after_poly: # # apply a different BN for every repeat time of the poly. if y is None or self.typen_poly == 'instance': h = getattr(self, 'bnp{}_r{}'.format(id_poly, repeat))(h) else: h = getattr(self, 'bnp{}_r{}'.format(id_poly, repeat))(h, y) # # update the input for the next polynomial. input_poly = h + 0 # # use the output of the products above as z. z = input_poly if not self.use_act_zgl else self.activation(input_poly) return z def __call__(self, batchsize=None, y=None, z=None, mult_until_exec=None, **kwargs): if z is None: z = sample_continuous(self.dim_z, batchsize, distribution=self.distribution, xp=self.xp) if y is None: y = sample_categorical(self.n_classes, batchsize, distribution="uniform", xp=self.xp) if self.n_classes > 0 else None activ = self.activation # # mult_until_exec: If set, we perform the multiplications until that layer. # # In the product of polynomials, it applies the same rule for *every* # # polynomial. E.g. if mult_until_exec == 2 it will perform the hadamard # # products until second order terms in every polynomial. if mult_until_exec is None: mult_until_exec = 10000 z = self.prod_poly_FC(z, mult_until_exec, batchsize=batchsize, y=y) h = z + 0 if self.bottom_width > 1: h = getattr(self, 'lin0')(h) h = F.reshape(h, (h.shape[0], -1, self.bottom_width, self.bottom_width)) # # loop over the layers and get the layers along with the # # normalizations per layer. for l in range(1, self.n_l + 1): if self.skip_rep: h_hold = h + 0 if self.use_bn and y is None: h = getattr(self, 'bn{}'.format(l))(h) elif self.use_bn: h = getattr(self, 'bn{}'.format(l))(h, y) h = activ(getattr(self, 'l{}'.format(l))(h)) h = self.return_injected(h, z, l, mult_until_exec=mult_until_exec) if self.skip_rep: # # transform the channels of h_hold if required. h_hold = getattr(self, 'skipch{}'.format(l))(h_hold) # # upsample if required. if h_hold.shape[-1] != h.shape[-1]: h_hold = _upsample(h_hold) h += h_hold if self.order_out_poly is not None: z0 = h + 0 for i in range(1, self.order_out_poly): h1 = getattr(self, 'oro{}'.format(i + 1))(h) if self.skip_rep: # # model3 polynomial. h += z0 * h1 else: h = z0 * h1 # # last layer (no activation). output = getattr(self, 'l{}'.format(self.n_l + 1))(h) out = self.out_act(output) return out def __str__(self, **kwargs): m1 = 'Layers: {}.\t Info for channels: {}.' str1 = m1.format(self.n_l, self.n_channels) return str1 ================================================ FILE: image_generation_chainer/jobs/pinet/fashionmnist_cnn_prodpoly_linear.yml ================================================ batchsize: 64 iteration: 50000 iteration_decay_start: 0 seed: 0 display_interval: 200 snapshot_interval: 5000 evaluation_interval: 2000 models: generator: fn: gen_models/cnn_gen_custom_prodpoly.py name: ProdPolyConvGenerator args: dim_z: 128 distribution: uniform out_ch: 1 power_poly: [1, 3] activ_prod: 0 add_h_poly: True layer_d: [128, 128, 128, 256, 256, 256, 256, 256, 64] strides: [2, 2, 1, 2, 1, 2, 2, 1] paddings: [1, 1, 1, 1, 1, 1, 1, 1] ksizes: [4, 4, 3, 4, 3, 4, 4, 3] use_bn: True use_act_zgl: False normalize_preinject: 0 add_h_injection: 0 derivfc: 1 order_out_poly: 2 use_out_act: True discriminator: fn: dis_models/snresnet_32.py name: SNResNetProjectionDiscriminator args: ch: 80 n_classes: 0 sn: True ch_input: 1 dataset: dataset_fn: datasets/mnist.py dataset_name: MnistDb args: path: /vol/deform/grigoris/databases/fashion_mnist/ labels_suff: train_labels.npy adam: alpha: 0.0002 beta1: 0.0 beta2: 0.9 updater: fn: updaters/gans/updater.py name: Updater args: n_dis: 1 n_gen_samples: 512 conditional: False loss_type: hinge ================================================ FILE: image_generation_chainer/jobs/pinet/train_mn_mnist.py ================================================ import os, sys import numpy as np from socket import gethostname from time import strftime import argparse import chainer from chainer import training from chainer.training import extensions, extension import chainermn import multiprocessing base = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(base, '../../')) sys.path.append(base) from evaluations.extensions import divergence_trainer, sample_generate, sample_generate_conditional import yaml import source.yaml_utils as yaml_utils from source.miscs.model_moiving_average import ModelMovingAverage from source.misc_train_utils import create_result_dir, load_models, ensure_config_paths, plot_losses_log def make_optimizer(model, comm, alpha=0.001, beta1=0.9, beta2=0.999, chmn=False, weight_decay_rate=0): # # 12/2018: problem in minoas, probably related with openmpi. if chmn: optimizer = chainermn.create_multi_node_optimizer( chainer.optimizers.Adam(alpha=alpha, beta1=beta1, beta2=beta2, weight_decay_rate=weight_decay_rate), comm) else: optimizer = chainer.optimizers.Adam(alpha=alpha, beta1=beta1, beta2=beta2, weight_decay_rate=weight_decay_rate) optimizer.setup(model) return optimizer def main(): parser = argparse.ArgumentParser() parser.add_argument('--config_path', type=str, default='jobs/pinet/fashionmnist_cnn_prodpoly_linear.yml') parser.add_argument('--n_devices', type=int) parser.add_argument('--test', action='store_true', default=False) parser.add_argument('--communicator', type=str, default='hierarchical', help='Type of communicator') parser.add_argument('--results_dir', type=str, default='results_polynomial') parser.add_argument('--inception_model_path', type=str, default='/home/user/inception/inception.model') parser.add_argument('--resume', type=str, default='') parser.add_argument('--gen_snapshot', type=str, default=None, help='path to the generator snapshot') parser.add_argument('--dis_snapshot', type=str, default=None, help='path to the discriminator snapshot') parser.add_argument('--loaderjob', type=int, help='Number of parallel data loading processes') parser.add_argument('--multiprocessing', action='store_true', default=False) parser.add_argument('--label', type=str, default='synth') parser.add_argument('--batch_val', type=int, default=1000) args = parser.parse_args() config = yaml_utils.Config(yaml.load(open(args.config_path))) # # ensure that the paths of the config are correct. config = ensure_config_paths(config) try: comm = chainermn.create_communicator(args.communicator) except: comm = chainermn.create_communicator() device = comm.intra_rank chainer.cuda.get_device_from_id(device).use() # # get the pc name, e.g. for chainerui. pcname = gethostname() print('Init on pc: {}.'.format(pcname)) if comm.rank == 0: print('==========================================') print('Using {} communicator'.format(args.communicator)) print('==========================================') gen, dis = load_models(config) gen.to_gpu() dis.to_gpu() mma = ModelMovingAverage(0.999, gen) models = {"gen": gen, "dis": dis} if args.gen_snapshot is not None: print('Loading generator: {}.'.format(args.gen_snapshot)) chainer.serializers.load_npz(args.gen_snapshot, gen) if args.dis_snapshot is not None: print('Loading discriminator: {}.'.format(args.dis_snapshot)) chainer.serializers.load_npz(args.dis_snapshot, dis) # Optimizer # # convenience function for optimizer: func_opt = lambda net, alpha, wdr0=0: make_optimizer(net, comm, chmn=args.multiprocessing, alpha=alpha, beta1=config.adam['beta1'], beta2=config.adam['beta2'], weight_decay_rate=wdr0) # Optimizer wdr = 0 if 'weight_decay_rate' not in config.updater['args'] else config.updater['args']['weight_decay_rate_gener'] opt_gen = func_opt(gen, config.adam['alpha'], wdr0=wdr) keydopt = 'alphad' if 'alphad' in config.adam.keys() else 'alpha' opt_dis = func_opt(dis, config.adam[keydopt]) opts = {"opt_gen": opt_gen, "opt_dis": opt_dis} if hasattr(dis, 'fix_last') and hasattr(dis, 'lin') and dis.fix_last: # # This should be used with care. It fixes the linear layer that # # makes the classification in the discriminator. print('Fixing the linear layer of the discriminator!') dis.disable_update() # Dataset if comm.rank == 0: dataset = yaml_utils.load_dataset(config) # # even though not new samples, use as proxy for iid validation ones. if hasattr(dataset, 'n_concats') and dataset.n_concats == 1: valid_samples = (dataset.base[:args.batch_val] + 1) * 127.5 else: valid_samples = [(dataset.get_example(i)[0] + 1) * 127.5 for i in range(args.batch_val)] # # convert the validation to an array as required by the kl script. valid_samples = np.array(valid_samples, dtype=np.float32) else: _ = yaml_utils.load_dataset(config) # Dummy, for adding path to the dataset module dataset = None dataset = chainermn.scatter_dataset(dataset, comm) # Iterator multiprocessing.set_start_method('forkserver') if args.multiprocessing: # # In minoas this might fail with the forkserver.py error. iterator = chainer.iterators.MultiprocessIterator(dataset, config.batchsize, n_processes=args.loaderjob) else: iterator = chainer.iterators.SerialIterator(dataset, config.batchsize) kwargs = config.updater['args'] if 'args' in config.updater else {} kwargs.update({ 'models': models, 'iterator': iterator, 'optimizer': opts, 'device': device, 'mma': mma, }) updater = yaml_utils.load_updater_class(config) updater = updater(**kwargs) if not args.test: mainf = '{}_{}'.format(strftime('%Y_%m_%d__%H_%M_%S'), args.label) out = os.path.join(args.results_dir, mainf, '') else: out = 'results/test' if comm.rank == 0: create_result_dir(out, args.config_path, config) trainer = training.Trainer(updater, (config.iteration, 'iteration'), out=out) report_keys = ['loss_dis', 'loss_gen', 'kl', 'ndb', 'JS', 'dis_real', 'dis_fake'] if comm.rank == 0: # Set up logging # trainer.extend(extensions.snapshot(), trigger=(config.snapshot_interval, 'iteration')) for m in models.values(): trainer.extend(extensions.snapshot_object( m, m.__class__.__name__ + '_{.updater.iteration}.npz'), trigger=(config.snapshot_interval, 'iteration')) trainer.extend(extensions.LogReport(trigger=(config.display_interval, 'iteration'))) trainer.extend(extensions.PrintReport(report_keys), trigger=(config.display_interval, 'iteration')) trainer.extend(extensions.ProgressBar(update_interval=config.display_interval)) if gen.n_classes == 0: trainer.extend(sample_generate(mma.avg_model, out), trigger=(config.evaluation_interval, 'iteration'), priority=extension.PRIORITY_WRITER) print('unconditional image generation extension added.') else: trainer.extend(sample_generate_conditional(mma.avg_model, out, n_classes=gen.n_classes), trigger=(config.evaluation_interval, 'iteration'), priority=extension.PRIORITY_WRITER) trainer.extend(divergence_trainer(gen, valid_samples, metric=['kl', 'ndb'], batch=args.batch_val), trigger=(config.evaluation_interval, 'iteration'), priority=extension.PRIORITY_WRITER) # # convenience function for linearshift in optimizer: func_opt_shift = lambda optim1: extensions.LinearShift('alpha', (config.adam['alpha'], 0.), (config.iteration_decay_start, config.iteration), optim1) # # define the actual extensions (for optimizer shift). trainer.extend(func_opt_shift(opt_gen)) trainer.extend(func_opt_shift(opt_dis)) if args.resume: print("Resume Trainer") chainer.serializers.load_npz(args.resume, trainer) m1 = 'Generator params: {}. Discriminator params: {}.' print(m1.format(gen.count_params(), dis.count_params())) # Run the training print("start training") trainer.run() print('The output dir was {}.'.format(out)) plot_losses_log(out, savefig=True) if __name__ == '__main__': main() ================================================ FILE: image_generation_chainer/readme.rst ================================================ ======================================= Π-nets: Deep Polynomial Neural Networks ======================================= Chainer implementation of the image generation as described on the CVPR'20 paper "**Π-nets: Deep Polynomial Neural Networks**". Specifically, we include the code for image generation on fashion images. Browsing the folders ==================== The folder structure is the following: * ``gen_models``: The folder for the generator models; this is the primary folder with the polynomial generator(s). * ``dis_models``: The folder for the discriminator models; do not modify the optional arguments, unless you know what you are doing. * ``updater``: The folder that contains the core code for the updater, i.e. the chunk of code that runs in every iteration to update the modules. * ``jobs``: It contains a) the yml with the hyper-parameter setting, b) the main command to load the models and run the training (train_mn*.py). * ``source``: It contains auxiliary code, you should probably not modify any of that code. * ``evaluations``: It contains the code for validation (either during training or offline). * ``datasets``: Each file in the folder describes a parser for a dataset; normally you should not modify it for provided dataset(s). Train the network ================= To train the network, you can execute the following command:: python jobs/pinet/train_mn_mnist.py --label my_experiment The yml file describes the modules/dataset to train on. The default dataset used is `Fashion-mnist `_; other datasets can be used by modifying the respective paths in the yml. The hyper-parameters are included in the yml, no need to hardcode them in the files. Probably, you should modify the path of the dataset (in the yml, find the `dataset:` part) to match the one in your local pc. The results are exported in the directory defined in `train_mn_mnist.py` (default `results_polynomial`). The directory with the results includes folders named as [start_date][label]; inside each respective folder the pretrained weights and few indicative images are exported. Requirements ============ The code requires the chainer library. Tested on a Linux machine with: * chainer=4.0.0, chainercv=0.9.0, * chainer=5.2.0, chainercv=0.12.0. * chainer=6.1.0, chainercv=0.13.1. The code is highly influenced by [1]_. Apart from Chainer, the code depends on Pyaml [2]_ and [3]_ (for the evaluation). References ========== .. [1] https://github.com/grigorisg9gr/rocgan/ .. [2] https://pypi.org/project/pyaml/ .. [3] https://github.com/djsutherland/skl-groups ================================================ FILE: image_generation_chainer/source/__init__.py ================================================ ================================================ FILE: image_generation_chainer/source/functions/__init__.py ================================================ ================================================ FILE: image_generation_chainer/source/functions/cond_bn_wrapper.py ================================================ import chainer from chainer import configuration from chainer import cuda from chainer.functions.normalization import batch_normalization from chainer import initializers from chainer import link from chainer.utils import argument from chainer import variable from chainer.links import EmbedID import chainer.functions as F def cond_bn_wrapper(x, c, bn, gamma, beta): h = bn(x) shape = h.shape ndim = len(shape) gamma_c = gamma(c) beta_c = beta(c) gamma_c = F.broadcast_to(F.reshape(gamma_c, list(gamma_c.shape) + [1] * (ndim - 2)), shape) beta_c = F.broadcast_to(F.reshape(beta_c, list(beta_c.shape) + [1] * (ndim - 2)), shape) return gamma_c * h + beta_c ================================================ FILE: image_generation_chainer/source/functions/losses.py ================================================ import numpy as np import chainer import chainer.functions as F from chainer import Variable def _tensor_to_matrix(tensor, axis=0): """ Reshapes a 4-dimensional tensor to 2D matrix. """ if tensor.ndim == 4: # # reshape to a 2D matrix. matr = F.reshape(tensor, (tensor.shape[axis], -1)) elif tensor.ndim == 2: matr = tensor else: matr = F.reshape(tensor, (-1, 1)) return matr def cosine_loss(tens1, tens2, absol=True): """ Computes the cosine loss between two representations. The cos is computed per element, i.e. assumed that tens1[i] and tens2[i] correspond to the representations of which we want to compute the cos. Works only on chainer 5.x, because of the einsum. """ mat1 = _tensor_to_matrix(tens1, axis=0) mat2 = _tensor_to_matrix(tens2, axis=0) # # compute the inner product. prod = F.einsum('ij,ij->i', mat1, mat2) # # compute the norms. norm1 = F.batch_l2_norm_squared(mat1) norm2 = F.batch_l2_norm_squared(mat2) # # compute the final cosine (per element). cos = prod / F.matmul(norm1, norm2) if absol: # # We restrict the angles to [-90, 90] effectively. # # That is, we allow only positive cos. cos = F.absolute(cos) return F.mean(cos) def decov_loss(tensor, xp=None, axis=1): """ Decov loss as described in https://arxiv.org/pdf/1511.06068.pdf 'Reducing Overfitting In Deep Networks by Decorrelating Representation'. This version implements the loss in the variable format. ARGS: axis: (int, optional) If the tensor is 4-dim, it is reshaped into 2-dim; axis is the first dimension. """ if xp is None: # # get xp module if not provided. xp = chainer.cuda.get_array_module(tensor.data) # # reshape to a 2D matrix. matr = _tensor_to_matrix(tensor, axis=axis) # # subtract the mean. centered = F.bias(matr, -F.mean(matr)) # # compute the covariance. cov = F.matmul(centered, F.transpose(centered)) # # compute the frombenius norm. frob_norm = F.sum(F.square(cov)) # # get the norm of diagonal elements. # # in chainer 5.x this should work. # corr_diag_sqr = F.sum(F.square(F.diagonal(cov1))) corr_diag_sqr = F.sum(F.square(cov * xp.eye(cov.shape[0], dtype=cov.dtype))) loss = 0.5 * (frob_norm - corr_diag_sqr) return loss def decov_loss_matrix(tensor, xp=None, axis=1): """ Decov loss as described in https://arxiv.org/pdf/1511.06068.pdf 'Reducing Overfitting In Deep Networks by Decorrelating Representation'. This version implements the matrix case, i.e. Variables are converted into matrices/tensors. ARGS: axis: (int, optional) If the tensor is 4-dim, it is reshaped into 2-dim; axis is the first dimension. """ if type(tensor, Variable): tensor = tensor.data if xp is None: # # get xp module if not provided. xp = chainer.cuda.get_array_module(tensor) if tensor.ndim == 4: # # reshape to a 2D matrix. matr = tensor.reshape((tensor.shape[axis], -1)) elif tensor.ndim == 2: matr = tensor.copy() # # subtract the mean. mean1 = matr.mean() matr -= mean1 # # compute the covariance. cov = xp.matmul(matr, matr.T) # # compute the frombenius norm. frob_norm = xp.sum(xp.square(cov)) # # get the norm of diagonal elements. corr_diag_sqr = xp.sum(xp.square(xp.diag(cov))) loss = 0.5 * (frob_norm - corr_diag_sqr) return loss def loss_revKL_dis(dis_fake, dis_real): L1 = F.mean(F.softplus(-dis_real)) L2 = F.mean(F.softplus(dis_fake)) loss = L1 + L2 return loss def loss_revKL_gen(dis_fake): loss = -F.mean(dis_fake) return loss # Classic Adversarial Loss def loss_dcgan_dis(dis_fake, dis_real): L1 = F.mean(F.softplus(-dis_real)) L2 = F.mean(F.softplus(dis_fake)) loss = L1 + L2 return loss def loss_dcgan_gen(dis_fake): loss = F.mean(F.softplus(-dis_fake)) return loss # Hinge Loss def loss_hinge_dis(dis_fake, dis_real): loss = F.mean(F.relu(1. - dis_real)) loss += F.mean(F.relu(1. + dis_fake)) return loss def loss_hinge_gen(dis_fake): loss = -F.mean(dis_fake) return loss # WGAN Loss def loss_wgan_dis(dis_fake, dis_real): loss = - F.mean(dis_real) + F.mean(dis_fake) return loss def loss_wgan_gen(dis_fake): loss = - F.mean(dis_fake) return loss def gradient_penalty(dis_output, x): grad_x, = chainer.grad([dis_output], [x], enable_double_backprop=True) norm_grad_x = F.mean(F.sum(grad_x * grad_x, axis=(1, 2, 3))) return norm_grad_x def gradient_penalty_wgangp(dis_output, x, lipnorm): grad_x, = chainer.grad([dis_output], [x], enable_double_backprop=True) norm_grad_x = F.sqrt(F.sum(grad_x * grad_x, axis=(1, 2, 3))) xp = norm_grad_x.xp return F.mean_squared_error(norm_grad_x, lipnorm * xp.ones_like(norm_grad_x.array)) ================================================ FILE: image_generation_chainer/source/functions/max_sv.py ================================================ import chainer from chainer import cuda from chainer.functions.math import sum from chainer.functions.connection import linear from chainer.functions.array import transpose import numpy as np def _l2normalize_gpu(v, eps=1e-12): norm = cuda.reduce('T x', 'T out', 'x * x', 'a + b', 'out = sqrt(a)', 0, 'norm_sn') div = cuda.elementwise('T x, T norm, T eps', 'T out', 'out = x / (norm + eps)', 'div_sn') return div(v, norm(v), eps) def _l2normalize_cpu(v, eps=1e-12): return v / (((v ** 2).sum()) ** 0.5 + eps) def max_singular_value(W, u=None, Ip=1): """ Apply power iteration for the weight parameter """ xp = cuda.get_array_module(W.data) if u is None: u = xp.random.normal(size=(1, W.shape[0])).astype(xp.float32) _u = u if xp == np: _l2normalize = _l2normalize_cpu else: _l2normalize = _l2normalize_gpu for _ in range(Ip): _v = _l2normalize(xp.dot(_u, W.data), eps=1e-12) _u = _l2normalize(xp.dot(_v, W.data.transpose()), eps=1e-12) sigma = sum.sum(linear.linear(_u, transpose.transpose(W)) * _v) return sigma, _u, _v ================================================ FILE: image_generation_chainer/source/inception/__init__.py ================================================ ================================================ FILE: image_generation_chainer/source/inception/download.py ================================================ """ Including code from the official implementation by OpenAI found at https://github.com/openai/improved-gan """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import os.path import sys import tarfile import numpy as np import tensorflow as tf import glob import scipy.misc import math from six.moves import urllib from chainer import serializers from inception_score import Inception MODEL_DIR = '/tmp/imagenet' DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--outfile', type=str, default='inception_score.model') return parser.parse_args() def copy_conv(sess, tftensor, layer): """Copy TensorFlow convolution layer weights to Chainer model.""" W = sess.graph.get_tensor_by_name('{}/conv2d_params:0'.format(tftensor)).eval() W = W.transpose((3, 2, 0, 1)) assert W.shape == layer.W.data.shape layer.W.data = W def copy_bn(sess, tftensor, layer): """Copy TensorFlow batch normalization params weights to Chainer model.""" gamma = sess.graph.get_tensor_by_name('{}/gamma:0'.format(tftensor)).eval() beta = sess.graph.get_tensor_by_name('{}/beta:0'.format(tftensor)).eval() avg_mean = sess.graph.get_tensor_by_name('{}/moving_mean:0'.format(tftensor)).eval() avg_var = sess.graph.get_tensor_by_name('{}/moving_variance:0'.format(tftensor)).eval() eps = sess.graph.get_operation_by_name(tftensor).get_attr('variance_epsilon') assert layer.beta.data.shape == beta.shape assert layer.gamma.data.shape == gamma.shape assert layer.avg_mean.shape == avg_mean.shape assert layer.avg_var.shape == avg_var.shape assert eps > 0.0 layer.beta.data = beta layer.gamma.data = gamma layer.avg_mean = avg_mean layer.avg_var = avg_var layer.eps = eps def copy_inception(sess, model): """Copy weights and params from the graph in the given TensorFlow session to the Chainer chain.""" print('Copying first layers ...') copy_conv(sess, 'conv', model.conv) copy_bn(sess, 'conv/batchnorm', model.bn_conv) copy_conv(sess, 'conv_1', model.conv_1) copy_bn(sess, 'conv_1/batchnorm', model.bn_conv_1) copy_conv(sess, 'conv_2', model.conv_2) copy_bn(sess, 'conv_2/batchnorm', model.bn_conv_2) copy_conv(sess, 'conv_3', model.conv_3) copy_bn(sess, 'conv_3/batchnorm', model.bn_conv_3) copy_conv(sess, 'conv_4', model.conv_4) copy_bn(sess, 'conv_4/batchnorm', model.bn_conv_4) for m in ['mixed', 'mixed_1', 'mixed_2']: print('Copying ', m, '...') copy_conv(sess, '{}/conv'.format(m), getattr(model, m).conv.conv) copy_bn(sess, '{}/conv/batchnorm'.format(m), getattr(model, m).conv.bn_conv) for t in ['tower', 'tower_1', 'tower_2']: copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv) copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv) if t == 'tower' or t == 'tower_1': copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1) copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1) if t == 'tower_1': copy_conv(sess, '{}/{}/conv_2'.format(m, t), getattr(getattr(model, m), t).conv_2) copy_bn(sess, '{}/{}/conv_2/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_2) for m in ['mixed_3']: print('Copying ', m, '...') copy_conv(sess, '{}/conv'.format(m), getattr(model, m).conv.conv) copy_bn(sess, '{}/conv/batchnorm'.format(m), getattr(model, m).conv.bn_conv) for t in ['tower']: copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv) copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv) copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1) copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1) copy_conv(sess, '{}/{}/conv_2'.format(m, t), getattr(getattr(model, m), t).conv_2) copy_bn(sess, '{}/{}/conv_2/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_2) for m in ['mixed_4', 'mixed_5', 'mixed_6', 'mixed_7']: print('Copying ', m, '...') copy_conv(sess, '{}/conv'.format(m), getattr(model, m).conv.conv) copy_bn(sess, '{}/conv/batchnorm'.format(m), getattr(model, m).conv.bn_conv) for t in ['tower', 'tower_1', 'tower_2']: copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv) copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv) if t == 'tower' or t == 'tower_1': copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1) copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1) copy_conv(sess, '{}/{}/conv_2'.format(m, t), getattr(getattr(model, m), t).conv_2) copy_bn(sess, '{}/{}/conv_2/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_2) if t == 'tower_1': copy_conv(sess, '{}/{}/conv_3'.format(m, t), getattr(getattr(model, m), t).conv_3) copy_bn(sess, '{}/{}/conv_3/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_3) copy_conv(sess, '{}/{}/conv_4'.format(m, t), getattr(getattr(model, m), t).conv_4) copy_bn(sess, '{}/{}/conv_4/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_4) for m in ['mixed_8']: print('Copying ', m, '...') for t in ['tower', 'tower_1']: copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv) copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv) copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1) copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1) if t == 'tower_1': copy_conv(sess, '{}/{}/conv_2'.format(m, t), getattr(getattr(model, m), t).conv_2) copy_bn(sess, '{}/{}/conv_2/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_2) copy_conv(sess, '{}/{}/conv_3'.format(m, t), getattr(getattr(model, m), t).conv_3) copy_bn(sess, '{}/{}/conv_3/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_3) for m in ['mixed_9', 'mixed_10']: print('Copying ', m, '...') copy_conv(sess, '{}/conv'.format(m), getattr(model, m).conv.conv) copy_bn(sess, '{}/conv/batchnorm'.format(m), getattr(model, m).conv.bn_conv) for t in ['tower', 'tower_1', 'tower_2']: copy_conv(sess, '{}/{}/conv'.format(m, t), getattr(getattr(model, m), t).conv) copy_bn(sess, '{}/{}/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv) if t == 'tower' or t == 'tower_1': copy_conv(sess, '{}/{}/mixed/conv'.format(m, t), getattr(getattr(model, m), t).mixed.conv.conv) copy_bn(sess, '{}/{}/mixed/conv/batchnorm'.format(m, t), getattr(getattr(model, m), t).mixed.conv.bn_conv) copy_conv(sess, '{}/{}/mixed/conv_1'.format(m, t), getattr(getattr(model, m), t).mixed.conv_1.conv_1) copy_bn(sess, '{}/{}/mixed/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).mixed.conv_1.bn_conv_1) if t == 'tower_1': copy_conv(sess, '{}/{}/conv_1'.format(m, t), getattr(getattr(model, m), t).conv_1) copy_bn(sess, '{}/{}/conv_1/batchnorm'.format(m, t), getattr(getattr(model, m), t).bn_conv_1) print('Copying logit...') w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1].eval() b = sess.graph.get_tensor_by_name("softmax/biases:0").eval() assert w.T.shape == model.logit.W.shape assert b.shape == model.logit.b.shape model.logit.W.data = w.T model.logit.b.data = b def download_tf_params(): """Download and extract pretrained TensorFlow inception model params.""" if not os.path.exists(MODEL_DIR): os.makedirs(MODEL_DIR) filename = DATA_URL.split('/')[-1] filepath = os.path.join(MODEL_DIR, filename) if not os.path.exists(filepath): def _progress(count, block_size, total_size): sys.stdout.write('\r>> Downloading %s %.1f%%' % ( filename, float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress) statinfo = os.stat(filepath) print() print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR) def set_tf_params(model, write_graph=False): """Update the parameters of the given chainer model with the downloaded TensorFlow model.""" with tf.gfile.FastGFile(os.path.join( MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(graph_def, name='') if write_graph: # Write graph to file so that it can be visualized using TensorBoard summary_writer = tf.summary.FileWriter('data', graph=graph_def) config = tf.ConfigProto() config.gpu_options.allow_growth = True # Do not allocale the whole GPU mem with tf.Session(config=config) as sess: copy_inception(sess, model) def main(args): outfile = args.outfile # Download pretrained TensorFlow model download_tf_params() # Create empty Chainer inception model model = Inception() # Update parameters of Chainer model with pretrained TensorFlow model set_tf_params(model) # TODO(hvy): Test score similarity with the original implementation print('Saving ', outfile, '...') serializers.save_hdf5(outfile, model) if __name__ == '__main__': args = parse_args() main(args) ================================================ FILE: image_generation_chainer/source/inception/example.py ================================================ import argparse import numpy as np from chainer import cuda from chainer import datasets from chainer import serializers from inception_score import Inception from inception_score import inception_score def parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0) parser.add_argument('--samples', type=int, default=-1) parser.add_argument('--model', type=str, default='inception_score.model') return parser.parse_args() def main(args): # Load trained model model = Inception() serializers.load_hdf5(args.model, model) if args.gpu >= 0: cuda.get_device(args.gpu).use() model.to_gpu() # Load images if 0: train, test = datasets.get_cifar10(ndim=3, withlabel=False, scale=255.0) else: train, test = datasets.get_mnist(ndim=3, rgb_format=True, scale=255.0, withlabel=False) # Use all 60000 images, unless the number of samples are specified ims = np.concatenate((train, test)) if args.samples > 0: ims = ims[:args.samples] mean, std = inception_score(model, ims) print('Inception score mean:', mean) print('Inception score std:', std) if __name__ == '__main__': args = parse_args() main(args) ================================================ FILE: image_generation_chainer/source/inception/inception_score.py ================================================ import math import chainer from chainer import Chain from chainer import functions as F from chainer import links as L from chainer import Variable def inception_forward(model, ims, batch_size): """ Run the inception model (forward pass). """ n, c, w, h = ims.shape n_batches = int(math.ceil(float(n) / float(batch_size))) xp = model.xp # Compute the softmax predicitions for for all images, split into batches # in order to fit in memory ys = xp.empty((n, 1008), dtype=xp.float32) # Softmax container for i in range(n_batches): batch_start = (i * batch_size) batch_end = min((i + 1) * batch_size, n) ims_batch = ims[batch_start:batch_end] ims_batch = xp.asarray(ims_batch) # To GPU if using CuPy ims_batch = Variable(ims_batch) # Resize image to the shape expected by the inception module if (w, h) != (299, 299): ims_batch = F.resize_images(ims_batch, (299, 299)) # bilinear # Feed images to the inception module to get the softmax predictions with chainer.using_config('train', False), chainer.using_config('enable_backprop', False): y = model(ims_batch) ys[batch_start:batch_end] = y.data # # grigoris: trained on imagenet, so 1k classes(?) ys = ys[:, 1:1001] #0 and 1001-1008 are the dummies return ys def inception_score(model, ims, batch_size=100, splits=10, verbose=True): """Compute the inception score for given images. Default batch_size is 100 and split size is 10. Please refer to the official implementation. It is recommended to to use at least 50000 images to obtain a reliable score. Reference: https://github.com/openai/improved-gan/blob/master/inception_score/model.py """ n, c, w, h = ims.shape n_batches = int(math.ceil(float(n) / float(batch_size))) xp = model.xp if c == 1: ims = xp.repeat(ims, 3, axis=1) if verbose: print('Inception: batch_size: {}, n_ims: {}, n_batches: {}'.format(batch_size, n, n_batches)) ys = inception_forward(model, ims, batch_size) # Compute the inception score based on the softmax predictions of the # inception module. scores = xp.empty((splits), dtype=xp.float32) # Split inception scores for i in range(splits): # get n//split representations (from inception features). part = ys[(i * n // splits):((i + 1) * n // splits), :] # # grigoris, compute the KL: KL(p(y|x)||p(y)) where y is the classes and x the image. kl = part * (xp.log(part) - xp.log(xp.expand_dims(xp.mean(part, 0), 0))) kl = xp.mean(xp.sum(kl, 1)) scores[i] = xp.exp(kl) return xp.mean(scores), xp.std(scores) def inception_accuracy(model, ims, labels, batch_size=100, splits=10, lab_range=None): """Compute the inception score for given images. Default batch_size is 100 and split size is 10. Please refer to the official implementation. It is recommended to to use at least 50000 images to obtain a reliable score. Reference: https://github.com/openai/improved-gan/blob/master/inception_score/model.py """ if isinstance(ims, (list, tuple)): ims_list = ims ys_list = [] for ims in ims_list: n, c, w, h = ims.shape n_batches = int(math.ceil(float(n) / float(batch_size))) xp = model.xp print('batch_size:{}, n_ims{}, n_batches{}'.format(batch_size, n, n_batches)) print('Calculating inception accuracy...') ys = inception_forward(model, ims, batch_size) ys_list.append(ys) ys = sum(ys_list) / len(ys_list) else: n, c, w, h = ims.shape n_batches = int(math.ceil(float(n) / float(batch_size))) xp = model.xp print('batch_size:{}, n_ims{}, n_batches{}'.format(batch_size, n, n_batches)) print('Calculating inception accuracy...') ys = inception_forward(model, ims, batch_size) if lab_range is not None: ys = ys[np.arange(len(ys)), lab_range] return F.accuracy(ys, labels).data class Mixed(Chain): def __init__(self, trunk): super().__init__() for name, link in trunk: self.add_link(name, link) self.trunk = trunk def __call__(self, x): hs = [] for name, _ in self.trunk: h = getattr(self, name)(x) hs.append(h) return F.concat(hs) class Tower(Chain): def __init__(self, trunk): super().__init__() for name, link in trunk: if not name.startswith('_'): self.add_link(name, link) self.trunk = trunk def __call__(self, x): h = x for name, f in self.trunk: if not name.startswith('_'): # Link if 'bn' in name: h = getattr(self, name)(h) else: h = getattr(self, name)(h) else: # AveragePooling2D, MaxPooling2D or ReLU h = f(h) return h def _average_pooling_2d(h): return F.average_pooling_2d(h, 3, 1, 1) def _max_pooling_2d(h): return F.max_pooling_2d(h, 3, 1, 1) def _max_pooling_2d_320(h): return F.max_pooling_2d(h, 3, 2, 0) class Inception(Chain): def __init__(self): super().__init__( conv=L.Convolution2D(3, 32, 3, stride=2, pad=0), conv_1=L.Convolution2D(32, 32, 3, stride=1, pad=0), conv_2=L.Convolution2D(32, 64, 3, stride=1, pad=1), conv_3=L.Convolution2D(64, 80, 1, stride=1, pad=0), conv_4=L.Convolution2D(80, 192, 3, stride=1, pad=0), bn_conv=L.BatchNormalization(32), bn_conv_1=L.BatchNormalization(32), bn_conv_2=L.BatchNormalization(64), bn_conv_3=L.BatchNormalization(80), bn_conv_4=L.BatchNormalization(192), mixed=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(192, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu) ])), ('tower', Tower([ ('conv', L.Convolution2D(192, 48, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(48)), ('_relu', F.relu), ('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)), ('bn_conv_1', L.BatchNormalization(64)), ('_relu_1', F.relu) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(192, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu), ('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)), ('bn_conv_1', L.BatchNormalization(96)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)), ('bn_conv_2', L.BatchNormalization(96)), ('_relu_2', F.relu) ])), ('tower_2', Tower([ ('_pooling', _average_pooling_2d), ('conv', L.Convolution2D(192, 32, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(32)), ('_relu', F.relu) ])) ]), mixed_1=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu) ])), ('tower', Tower([ ('conv', L.Convolution2D(256, 48, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(48)), ('_relu', F.relu), ('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)), ('bn_conv_1', L.BatchNormalization(64)), ('_relu_1', F.relu) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu), ('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)), ('bn_conv_1', L.BatchNormalization(96)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)), ('bn_conv_2', L.BatchNormalization(96)), ('_relu_2', F.relu) ])), ('tower_2', Tower([ ('_pooling', _average_pooling_2d), ('conv', L.Convolution2D(256, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu) ])) ]), mixed_2=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu) ])), ('tower', Tower([ ('conv', L.Convolution2D(288, 48, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(48)), ('_relu', F.relu), ('conv_1', L.Convolution2D(48, 64, 5, stride=1, pad=2)), ('bn_conv_1', L.BatchNormalization(64)), ('_relu_1', F.relu) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu), ('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)), ('bn_conv_1', L.BatchNormalization(96)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(96, 96, 3, stride=1, pad=1)), ('bn_conv_2', L.BatchNormalization(96)), ('_relu_2', F.relu) ])), ('tower_2', Tower([ ('_pooling', _average_pooling_2d), ('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu) ])) ]), mixed_3=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(288, 384, 3, stride=2, pad=0)), ('bn_conv', L.BatchNormalization(384)), ('_relu', F.relu) ])), ('tower', Tower([ ('conv', L.Convolution2D(288, 64, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(64)), ('_relu', F.relu), ('conv_1', L.Convolution2D(64, 96, 3, stride=1, pad=1)), ('bn_conv_1', L.BatchNormalization(96)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(96, 96, 3, stride=2, pad=0)), ('bn_conv_2', L.BatchNormalization(96)), ('_relu_2', F.relu) ])), ('pool', Tower([ ('_pooling', _max_pooling_2d_320) ])) ]), mixed_4=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])), ('tower', Tower([ ('conv', L.Convolution2D(768, 128, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(128)), ('_relu', F.relu), ('conv_1', L.Convolution2D(128, 128, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_1', L.BatchNormalization(128)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(128, 192, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_2', L.BatchNormalization(192)), ('_relu_2', F.relu) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(768, 128, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(128)), ('_relu', F.relu), ('conv_1', L.Convolution2D(128, 128, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_1', L.BatchNormalization(128)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(128, 128, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_2', L.BatchNormalization(128)), ('_relu_2', F.relu), ('conv_3', L.Convolution2D(128, 128, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_3', L.BatchNormalization(128)), ('_relu_3', F.relu), ('conv_4', L.Convolution2D(128, 192, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_4', L.BatchNormalization(192)), ('_relu_4', F.relu) ])), ('tower_2', Tower([ ('_pooling', _average_pooling_2d), ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])) ]), mixed_5=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])), ('tower', Tower([ ('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(160)), ('_relu', F.relu), ('conv_1', L.Convolution2D(160, 160, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_1', L.BatchNormalization(160)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(160, 192, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_2', L.BatchNormalization(192)), ('_relu_2', F.relu) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(160)), ('_relu', F.relu), ('conv_1', L.Convolution2D(160, 160, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_1', L.BatchNormalization(160)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(160, 160, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_2', L.BatchNormalization(160)), ('_relu_2', F.relu), ('conv_3', L.Convolution2D(160, 160, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_3', L.BatchNormalization(160)), ('_relu_3', F.relu), ('conv_4', L.Convolution2D(160, 192, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_4', L.BatchNormalization(192)), ('_relu_4', F.relu) ])), ('tower_2', Tower([ ('_pooling', _average_pooling_2d), ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])) ]), mixed_6=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])), ('tower', Tower([ ('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(160)), ('_relu', F.relu), ('conv_1', L.Convolution2D(160, 160, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_1', L.BatchNormalization(160)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(160, 192, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_2', L.BatchNormalization(192)), ('_relu_2', F.relu) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(768, 160, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(160)), ('_relu', F.relu), ('conv_1', L.Convolution2D(160, 160, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_1', L.BatchNormalization(160)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(160, 160, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_2', L.BatchNormalization(160)), ('_relu_2', F.relu), ('conv_3', L.Convolution2D(160, 160, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_3', L.BatchNormalization(160)), ('_relu_3', F.relu), ('conv_4', L.Convolution2D(160, 192, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_4', L.BatchNormalization(192)), ('_relu_4', F.relu) ])), ('tower_2', Tower([ ('_pooling', _average_pooling_2d), ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])) ]), mixed_7=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])), ('tower', Tower([ ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu), ('conv_1', L.Convolution2D(192, 192, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_1', L.BatchNormalization(192)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(192, 192, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_2', L.BatchNormalization(192)), ('_relu_2', F.relu) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu), ('conv_1', L.Convolution2D(192, 192, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_1', L.BatchNormalization(192)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(192, 192, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_2', L.BatchNormalization(192)), ('_relu_2', F.relu), ('conv_3', L.Convolution2D(192, 192, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_3', L.BatchNormalization(192)), ('_relu_3', F.relu), ('conv_4', L.Convolution2D(192, 192, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_4', L.BatchNormalization(192)), ('_relu_4', F.relu) ])), ('tower_2', Tower([ ('_pooling', _average_pooling_2d), ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])) ]), mixed_8=Mixed([ ('tower', Tower([ ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu), ('conv_1', L.Convolution2D(192, 320, 3, stride=2, pad=0)), ('bn_conv_1', L.BatchNormalization(320)), ('_relu_1', F.relu) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(768, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu), ('conv_1', L.Convolution2D(192, 192, (1, 7), stride=1, pad=(0, 3))), ('bn_conv_1', L.BatchNormalization(192)), ('_relu_1', F.relu), ('conv_2', L.Convolution2D(192, 192, (7, 1), stride=1, pad=(3, 0))), ('bn_conv_2', L.BatchNormalization(192)), ('_relu_2', F.relu), ('conv_3', L.Convolution2D(192, 192, 3, stride=2, pad=0)), ('bn_conv_3', L.BatchNormalization(192)), ('_relu_3', F.relu) ])), ('pool', Tower([ ('_pooling', _max_pooling_2d_320) ])) ]), mixed_9=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(1280, 320, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(320)), ('_relu', F.relu), ])), ('tower', Tower([ ('conv', L.Convolution2D(1280, 384, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(384)), ('_relu', F.relu), ('mixed', Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(384, 384, (1, 3), stride=1, pad=(0, 1))), ('bn_conv', L.BatchNormalization(384)), ('_relu', F.relu), ])), ('conv_1', Tower([ ('conv_1', L.Convolution2D(384, 384, (3, 1), stride=1, pad=(1, 0))), ('bn_conv_1', L.BatchNormalization(384)), ('_relu_1', F.relu), ])) ])) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(1280, 448, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(448)), ('_relu', F.relu), ('conv_1', L.Convolution2D(448, 384, 3, stride=1, pad=1)), ('bn_conv_1', L.BatchNormalization(384)), ('_relu_1', F.relu), ('mixed', Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(384, 384, (1, 3), stride=1, pad=(0, 1))), ('bn_conv', L.BatchNormalization(384)), ('_relu', F.relu), ])), ('conv_1', Tower([ ('conv_1', L.Convolution2D(384, 384, (3, 1), stride=1, pad=(1, 0))), ('bn_conv_1', L.BatchNormalization(384)), ('_relu_1', F.relu), ])) ])) ])), ('tower_2', Tower([ ('_pooling', _average_pooling_2d), ('conv', L.Convolution2D(1280, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])) ]), mixed_10=Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(2048, 320, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(320)), ('_relu', F.relu), ])), ('tower', Tower([ ('conv', L.Convolution2D(2048, 384, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(384)), ('_relu', F.relu), ('mixed', Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(384, 384, (1, 3), stride=1, pad=(0, 1))), ('bn_conv', L.BatchNormalization(384)), ('_relu', F.relu), ])), ('conv_1', Tower([ ('conv_1', L.Convolution2D(384, 384, (3, 1), stride=1, pad=(1, 0))), ('bn_conv_1', L.BatchNormalization(384)), ('_relu_1', F.relu), ])) ])) ])), ('tower_1', Tower([ ('conv', L.Convolution2D(2048, 448, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(448)), ('_relu', F.relu), ('conv_1', L.Convolution2D(448, 384, 3, stride=1, pad=1)), ('bn_conv_1', L.BatchNormalization(384)), ('_relu_1', F.relu), ('mixed', Mixed([ ('conv', Tower([ ('conv', L.Convolution2D(384, 384, (1, 3), stride=1, pad=(0, 1))), ('bn_conv', L.BatchNormalization(384)), ('_relu', F.relu) ])), ('conv_1', Tower([ ('conv_1', L.Convolution2D(384, 384, (3, 1), stride=1, pad=(1, 0))), ('bn_conv_1', L.BatchNormalization(384)), ('_relu_1', F.relu) ])) ])) ])), ('tower_2', Tower([ ('_pooling', _max_pooling_2d), ('conv', L.Convolution2D(2048, 192, 1, stride=1, pad=0)), ('bn_conv', L.BatchNormalization(192)), ('_relu', F.relu) ])) ]), logit=L.Linear(2048, 1008) ) def __call__(self, x, get_feature=False): """Input dims are (batch_size, 3, 299, 299).""" # assert x.shape[1:] == (3, 299, 299) x -= 128.0 x *= 0.0078125 h = F.relu(self.bn_conv(self.conv(x))) # assert h.shape[1:] == (32, 149, 149) h = F.relu(self.bn_conv_1(self.conv_1(h))) # assert h.shape[1:] == (32, 147, 147) h = F.relu(self.bn_conv_2(self.conv_2(h))) # assert h.shape[1:] == (64, 147, 147) h = F.max_pooling_2d(h, 3, stride=2, pad=0) # assert h.shape[1:] == (64, 73, 73) h = F.relu(self.bn_conv_3(self.conv_3(h))) # assert h.shape[1:] == (80, 73, 73) h = F.relu(self.bn_conv_4(self.conv_4(h))) # assert h.shape[1:] == (192, 71, 71) h = F.max_pooling_2d(h, 3, stride=2, pad=0) # assert h.shape[1:] == (192, 35, 35) h = self.mixed(h) # assert h.shape[1:] == (256, 35, 35) h = self.mixed_1(h) # assert h.shape[1:] == (288, 35, 35) h = self.mixed_2(h) # assert h.shape[1:] == (288, 35, 35) h = self.mixed_3(h) # assert h.shape[1:] == (768, 17, 17) h = self.mixed_4(h) # assert h.shape[1:] == (768, 17, 17) h = self.mixed_5(h) # assert h.shape[1:] == (768, 17, 17) h = self.mixed_6(h) # assert h.shape[1:] == (768, 17, 17) h = self.mixed_7(h) # assert h.shape[1:] == (768, 17, 17) h = self.mixed_8(h) # assert h.shape[1:] == (1280, 8, 8) h = self.mixed_9(h) # assert h.shape[1:] == (2048, 8, 8) h = self.mixed_10(h) # assert h.shape[1:] == (2048, 8, 8) h = F.average_pooling_2d(h, 8, 1) # assert h.shape[1:] == (2048, 1, 1) h = F.reshape(h, (-1, 2048)) if get_feature: return h else: h = self.logit(h) h = F.softmax(h) # assert h.shape[1:] == (1008,) return h ================================================ FILE: image_generation_chainer/source/inception/inception_score_tf.py ================================================ # Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py from __future__ import absolute_import from __future__ import division from __future__ import print_function import os.path import tarfile import numpy as np from six.moves import urllib import tensorflow as tf import scipy.misc import math import sys from chainer import functions as F MODEL_DIR = '/tmp/imagenet' DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' softmax = None last_layer = None config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.3 def inception_forward(images, layer): assert (type(images[0]) == np.ndarray) assert (len(images[0].shape) == 3) try: assert (np.max(images[0]) > 10), 'image in the [0, 1] interval (or too bad)?' except AssertionError: print('Either image [0, 1] interval or too bad generation.') assert (np.min(images[0]) >= 0.0) bs = 100 images = images.transpose(0, 2, 3, 1) with tf.Session(config=config) as sess: preds = [] n_batches = int(math.ceil(float(len(images)) / float(bs))) for i in range(n_batches): sys.stdout.write(".") sys.stdout.flush() inp = images[(i * bs):min((i + 1) * bs, len(images))] pred = sess.run(layer, {'ExpandDims:0': inp}) preds.append(pred) preds = np.concatenate(preds, 0) return preds def get_mean_and_cov(images): before_preds = inception_forward(images, last_layer) m = np.mean(before_preds, 0) cov = np.cov(before_preds, rowvar=False) return m, cov def get_fid(images, ref_stats=None, images_ref=None, splits=10): before_preds = inception_forward(images, last_layer) if ref_stats is None: if images_ref is None: raise ValueError('images_ref should be provided if ref_stats is None') m_ref, cov_ref = get_mean_and_cov(images_ref) fids = [] for i in range(splits): part = before_preds[(i * before_preds.shape[0] // splits):((i + 1) * before_preds.shape[0] // splits), :] m_gen = np.mean(part, 0) cov_gen = np.cov(part, rowvar=False) fid = np.sum((m_ref - m_gen) ** 2) + np.trace( cov_ref + cov_gen - 2 * scipy.linalg.sqrtm(np.dot(cov_ref, cov_gen))) fids.append(fid) return np.mean(fids), np.std(fids) # Call this function with list of images. Each of elements should be a # numpy array with values ranging from 0 to 255. def get_inception_score(images, splits=10): preds = inception_forward(images, softmax) scores = [] for i in range(splits): part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :] kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0))) kl = np.mean(np.sum(kl, 1)) scores.append(np.exp(kl)) return np.mean(scores), np.std(scores) # Call this function with list of images. Each of elements should be a # numpy array with values ranging from 0 to 255. def get_inception_accuracy(images, labels, lab_range=None): batch_size = 100 if isinstance(images, (list, tuple)): ims_list = images ys_list = [] for ims in ims_list: n, _, _, _ = ims.shape n_batches = int(math.ceil(float(n) / float(batch_size))) print('batch_size:{}, n_ims{}, n_batches{}'.format(batch_size, n, n_batches)) print('Calculating inception accuracy...') ys = inception_forward(ims, softmax)[:, 1:1001] ys_list.append(ys) ys = sum(ys_list) / len(ys_list) else: n, _, _, _, = images.shape n_batches = int(math.ceil(float(n) / float(batch_size))) print('batch_size:{}, n_ims{}, n_batches{}'.format(batch_size, n, n_batches)) print('Calculating inception accuracy...') ys = inception_forward(images, softmax)[:, 1:1001] if lab_range is not None: ys = ys[:, lab_range] print(ys, labels) return F.accuracy(ys, labels).data # This function is called automatically. def _init_inception(): global softmax global last_layer if not os.path.exists(MODEL_DIR): os.makedirs(MODEL_DIR) filename = DATA_URL.split('/')[-1] filepath = os.path.join(MODEL_DIR, filename) if not os.path.exists(filepath): def _progress(count, block_size, total_size): sys.stdout.write('\r>> Downloading %s %.1f%%' % ( filename, float(count * block_size) / float(total_size) * 100.0)) sys.stdout.flush() filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress) print() statinfo = os.stat(filepath) print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR) with tf.gfile.FastGFile(os.path.join( MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(graph_def, name='') # Works with an arbitrary minibatch size. with tf.Session(config=config) as sess: pool3 = sess.graph.get_tensor_by_name('pool_3:0') ops = pool3.graph.get_operations() for op_idx, op in enumerate(ops): for o in op.outputs: shape = o.get_shape() shape = [s.value for s in shape] new_shape = [] for j, s in enumerate(shape): if s == 1 and j == 0: new_shape.append(None) else: new_shape.append(s) o._shape = tf.TensorShape(new_shape) w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1] last_layer = tf.squeeze(pool3) logits = tf.matmul(last_layer, w) softmax = tf.nn.softmax(logits) if softmax is None: _init_inception() ================================================ FILE: image_generation_chainer/source/links/__init__.py ================================================ ================================================ FILE: image_generation_chainer/source/links/categorical_conditional_batch_normalization.py ================================================ import numpy import chainer from chainer import configuration from chainer import cuda from chainer.functions.normalization import batch_normalization from chainer import initializers from chainer import link from chainer.utils import argument from chainer import variable from chainer.links import EmbedID import chainer.functions as F from source.links.conditional_batch_normalization import ConditionalBatchNormalization class CategoricalConditionalBatchNormalization(ConditionalBatchNormalization): """ Conditional Batch Normalization Args: size (int or tuple of ints): Size (or shape) of channel dimensions. n_cat (int): the number of categories of categorical variable. decay (float): Decay rate of moving average. It is used on training. eps (float): Epsilon value for numerical stability. dtype (numpy.dtype): Type to use in computing. use_gamma (bool): If ``True``, use scaling parameter. Otherwise, use unit(1) which makes no effect. use_beta (bool): If ``True``, use shifting parameter. Otherwise, use unit(0) which makes no effect. See: `Batch Normalization: Accelerating Deep Network Training by Reducing\ Internal Covariate Shift `_ .. seealso:: :func:`~chainer.functions.batch_normalization`, :func:`~chainer.functions.fixed_batch_normalization` Attributes: gamma (~chainer.Variable): Scaling parameter. beta (~chainer.Variable): Shifting parameter. avg_mean (numpy.ndarray or cupy.ndarray): Population mean. avg_var (numpy.ndarray or cupy.ndarray): Population variance. N (int): Count of batches given for fine-tuning. decay (float): Decay rate of moving average. It is used on training. eps (float): Epsilon value for numerical stability. This value is added to the batch variances. """ def __init__(self, size, n_cat, decay=0.9, eps=2e-5, dtype=numpy.float32, initial_gamma=None, initial_beta=None, comm=None): super(CategoricalConditionalBatchNormalization, self).__init__( size=size, n_cat=n_cat, decay=decay, eps=eps, dtype=dtype, comm=comm) with self.init_scope(): if initial_gamma is None: initial_gamma = 1 initial_gamma = initializers._get_initializer(initial_gamma) initial_gamma.dtype = dtype self.gammas = EmbedID(n_cat, size, initialW=initial_gamma) if initial_beta is None: initial_beta = 0 initial_beta = initializers._get_initializer(initial_beta) initial_beta.dtype = dtype self.betas = EmbedID(n_cat, size, initialW=initial_beta) def __call__(self, x, c, **kwargs): """__call__(self, x, c, finetune=False) Invokes the forward propagation of BatchNormalization. In training mode, the BatchNormalization computes moving averages of mean and variance for evaluatino during training, and normalizes the input using batch statistics. .. warning:: ``test`` argument is not supported anymore since v2. Instead, use ``chainer.using_config('train', train)``. See :func:`chainer.using_config`. Args: x (Variable): Input variable. c (Variable): Input variable for conditioning gamma and beta finetune (bool): If it is in the training mode and ``finetune`` is ``True``, BatchNormalization runs in fine-tuning mode; it accumulates the input array to compute population statistics for normalization, and normalizes the input using batch statistics. """ weights, = argument.parse_kwargs(kwargs, ('weights', None)) if isinstance(c, (list, tuple)): gamma_cs = [] beta_cs = [] for _c, w in zip(c, weights): _gamma_c = self.gammas(_c) _beta_c = self.betas(_c) # print(F.expand_dims(w, 1).shape, _gamma_c.shape) # print(F.broadcast_to(F.expand_dims(w, 1), _gamma_c).shape) gamma_cs.append(F.broadcast_to(F.expand_dims(w, 1), _gamma_c.shape) * _gamma_c) beta_cs.append(F.broadcast_to(F.expand_dims(w, 1), _beta_c.shape) * _beta_c) gamma_c = sum(gamma_cs) beta_c = sum(beta_cs) else: gamma_c = self.gammas(c) beta_c = self.betas(c) return super(CategoricalConditionalBatchNormalization, self).__call__(x, gamma_c, beta_c) def start_finetuning(self): """Resets the population count for collecting population statistics. This method can be skipped if it is the first time to use the fine-tuning mode. Otherwise, this method should be called before starting the fine-tuning mode again. """ self.N = 0 ================================================ FILE: image_generation_chainer/source/links/conditional_batch_normalization.py ================================================ import numpy import chainer from chainer import configuration from chainer import cuda from chainer.functions.normalization import batch_normalization from chainer import initializers from chainer import link from chainer.utils import argument from chainer import variable from chainer.links import EmbedID import chainer.functions as F class ConditionalBatchNormalization(chainer.Chain): """ Conditional Batch Normalization Args: size (int or tuple of ints): Size (or shape) of channel dimensions. n_cat (int): the number of categories of categorical variable. decay (float): Decay rate of moving average. It is used on training. eps (float): Epsilon value for numerical stability. dtype (numpy.dtype): Type to use in computing. use_gamma (bool): If ``True``, use scaling parameter. Otherwise, use unit(1) which makes no effect. use_beta (bool): If ``True``, use shifting parameter. Otherwise, use unit(0) which makes no effect. See: `Batch Normalization: Accelerating Deep Network Training by Reducing\ Internal Covariate Shift `_ .. seealso:: :func:`~chainer.functions.batch_normalization`, :func:`~chainer.functions.fixed_batch_normalization` Attributes: gamma (~chainer.Variable): Scaling parameter. beta (~chainer.Variable): Shifting parameter. avg_mean (numpy.ndarray or cupy.ndarray): Population mean. avg_var (numpy.ndarray or cupy.ndarray): Population variance. N (int): Count of batches given for fine-tuning. decay (float): Decay rate of moving average. It is used on training. eps (float): Epsilon value for numerical stability. This value is added to the batch variances. """ def __init__(self, size, n_cat, decay=0.9, eps=2e-5, dtype=numpy.float32, comm=None): super(ConditionalBatchNormalization, self).__init__() self.avg_mean = numpy.zeros(size, dtype=dtype) self.register_persistent('avg_mean') self.avg_var = numpy.zeros(size, dtype=dtype) self.register_persistent('avg_var') self.N = 0 self.register_persistent('N') self.decay = decay self.eps = eps self.n_cat = n_cat self.comm = comm if self.comm is not None: # # only enable this feature in gpu machines. from chainermn.functions.batch_normalization import MultiNodeBatchNormalizationFunction def __call__(self, x, gamma, beta, **kwargs): """__call__(self, x, c, finetune=False) Invokes the forward propagation of BatchNormalization. In training mode, the BatchNormalization computes moving averages of mean and variance for evaluatino during training, and normalizes the input using batch statistics. .. warning:: ``test`` argument is not supported anymore since v2. Instead, use ``chainer.using_config('train', train)``. See :func:`chainer.using_config`. Args: x (Variable): Input variable. gamma (Variable): Input variable of gamma of shape finetune (bool): If it is in the training mode and ``finetune`` is ``True``, BatchNormalization runs in fine-tuning mode; it accumulates the input array to compute population statistics for normalization, and normalizes the input using batch statistics. """ argument.check_unexpected_kwargs( kwargs, test='test argument is not supported anymore. ' 'Use chainer.using_config') finetune, = argument.parse_kwargs(kwargs, ('finetune', False)) with cuda.get_device_from_id(self._device_id): _gamma = variable.Variable(self.xp.ones( self.avg_mean.shape, dtype=x.dtype)) with cuda.get_device_from_id(self._device_id): _beta = variable.Variable(self.xp.zeros( self.avg_mean.shape, dtype=x.dtype)) if configuration.config.train: if finetune: self.N += 1 decay = 1. - 1. / self.N else: decay = self.decay if self.comm is not None: func = MultiNodeBatchNormalizationFunction( self.comm, self.eps, self.avg_mean, self.avg_var, decay) ret = func(x, _gamma, _beta) self.avg_mean[:] = func.running_mean self.avg_var[:] = func.running_var else: ret = chainer.functions.batch_normalization(x, _gamma, _beta, eps=self.eps, running_mean=self.avg_mean, running_var=self.avg_var, decay=decay) else: # Use running average statistics or fine-tuned statistics. mean = variable.Variable(self.avg_mean) var = variable.Variable(self.avg_var) ret = batch_normalization.fixed_batch_normalization( x, _gamma, _beta, mean, var, self.eps) shape = ret.shape ndim = len(shape) gamma = F.broadcast_to(F.reshape(gamma, list(gamma.shape) + [1] * (ndim - len(gamma.shape))), shape) beta = F.broadcast_to(F.reshape(beta, list(beta.shape) + [1] * (ndim - len(beta.shape))), shape) return gamma * ret + beta def start_finetuning(self): """Resets the population count for collecting population statistics. This method can be skipped if it is the first time to use the fine-tuning mode. Otherwise, this method should be called before starting the fine-tuning mode again. """ self.N = 0 ================================================ FILE: image_generation_chainer/source/links/resnet_layer.py ================================================ from __future__ import print_function import collections import os import numpy try: from PIL import Image available = True except ImportError as e: available = False _import_error = e from chainer.dataset.convert import concat_examples from chainer.dataset import download from chainer import function from chainer.functions.activation.relu import relu from chainer.functions.activation.softmax import softmax from chainer.functions.array.reshape import reshape from chainer.functions.math.sum import sum from chainer.functions.pooling.average_pooling_2d import average_pooling_2d from chainer.functions.pooling.max_pooling_2d import max_pooling_2d from chainer.initializers import constant from chainer.initializers import normal from chainer import link from chainer.links.connection.convolution_2d import Convolution2D from chainer.links.connection.linear import Linear from chainer.links.normalization.batch_normalization import BatchNormalization from chainer.serializers import npz from chainer.utils import argument from chainer.utils import imgproc from chainer.variable import Variable class ResNetLayers(link.Chain): """A pre-trained CNN model provided by MSRA. When you specify the path of the pre-trained chainer model serialized as a ``.npz`` file in the constructor, this chain model automatically initializes all the parameters with it. This model would be useful when you want to extract a semantic feature vector per image, or fine-tune the model on a different dataset. Note that unlike ``VGG16Layers``, it does not automatically download a pre-trained caffemodel. This caffemodel can be downloaded at `GitHub `_. If you want to manually convert the pre-trained caffemodel to a chainer model that can be specified in the constructor, please use ``convert_caffemodel_to_npz`` classmethod instead. See: K. He et. al., `Deep Residual Learning for Image Recognition `_ Args: pretrained_model (str): the destination of the pre-trained chainer model serialized as a ``.npz`` file. If this argument is specified as ``auto``, it automatically loads and converts the caffemodel from ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-{n-layers}-model.caffemodel``, where ``$CHAINER_DATASET_ROOT`` is set as ``$HOME/.chainer/dataset`` unless you specify another value by modifying the environment variable and {n_layers} is replaced with the specified number of layers given as the first argment to this costructor. Note that in this case the converted chainer model is stored on the same directory and automatically used from the next time. If this argument is specified as ``None``, all the parameters are not initialized by the pre-trained model, but the default initializer used in the original paper, i.e., ``chainer.initializers.HeNormal(scale=1.0)``. n_layers (int): The number of layers of this model. It should be either 50, 101, or 152. Attributes: available_layers (list of str): The list of available layer names used by ``__call__`` and ``extract`` methods. """ def __init__(self, pretrained_model, n_layers): super(ResNetLayers, self).__init__() if pretrained_model: # As a sampling process is time-consuming, # we employ a zero initializer for faster computation. kwargs = {'initialW': constant.Zero()} else: # employ default initializers used in the original paper kwargs = {'initialW': normal.HeNormal(scale=1.0)} if n_layers == 50: block = [3, 4, 6, 3] elif n_layers == 101: block = [3, 4, 23, 3] elif n_layers == 152: block = [3, 8, 36, 3] else: raise ValueError('The n_layers argument should be either 50, 101,' ' or 152, but {} was given.'.format(n_layers)) with self.init_scope(): self.conv1 = Convolution2D(3, 64, 7, 2, 3, **kwargs) self.bn1 = BatchNormalization(64) self.res2 = BuildingBlock(block[0], 64, 64, 256, 1, **kwargs) self.res3 = BuildingBlock(block[1], 256, 128, 512, 2, **kwargs) self.res4 = BuildingBlock(block[2], 512, 256, 1024, 2, **kwargs) self.res5 = BuildingBlock(block[3], 1024, 512, 2048, 2, **kwargs) self.fc6 = Linear(2048, 1000) if pretrained_model and pretrained_model.endswith('.caffemodel'): _retrieve(n_layers, 'ResNet-{}-model.npz'.format(n_layers), pretrained_model, self) elif pretrained_model: npz.load_npz(pretrained_model, self) @property def functions(self): return collections.OrderedDict([ ('conv1', [self.conv1, self.bn1, relu]), ('pool1', [lambda x: max_pooling_2d(x, ksize=3, stride=2)]), ('res2', [self.res2]), ('res3', [self.res3]), ('res4', [self.res4]), ('res5', [self.res5]), ('pool5', [_global_average_pooling_2d]), ('fc6', [self.fc6]), ('prob', [softmax]), ]) @property def available_layers(self): return list(self.functions.keys()) @classmethod def convert_caffemodel_to_npz(cls, path_caffemodel, path_npz, n_layers=50): """Converts a pre-trained caffemodel to a chainer model. Args: path_caffemodel (str): Path of the pre-trained caffemodel. path_npz (str): Path of the converted chainer model. """ # As CaffeFunction uses shortcut symbols, # we import CaffeFunction here. from chainer.links.caffe.caffe_function import CaffeFunction caffemodel = CaffeFunction(path_caffemodel) chainermodel = ResNetLayers(pretrained_model=None, n_layers=n_layers) if n_layers == 50: _transfer_resnet50(caffemodel, chainermodel) elif n_layers == 101: _transfer_resnet101(caffemodel, chainermodel) elif n_layers == 152: _transfer_resnet152(caffemodel, chainermodel) else: raise ValueError('The n_layers argument should be either 50, 101,' ' or 152, but {} was given.'.format(n_layers)) npz.save_npz(path_npz, chainermodel, compression=False) def __call__(self, x, layers=['prob'], **kwargs): """__call__(self, x, layers=['prob']) Computes all the feature maps specified by ``layers``. .. warning:: ``test`` argument is not supported anymore since v2. Instead, use ``chainer.using_config('train', train)``. See :func:`chainer.using_config`. Args: x (~chainer.Variable): Input variable. layers (list of str): The list of layer names you want to extract. Returns: Dictionary of ~chainer.Variable: A directory in which the key contains the layer name and the value contains the corresponding feature map variable. """ argument.check_unexpected_kwargs( kwargs, test='test argument is not supported anymore. ' 'Use chainer.using_config') argument.assert_kwargs_empty(kwargs) h = x activations = {} target_layers = set(layers) for key, funcs in self.functions.items(): if len(target_layers) == 0: break for func in funcs: h = func(h) if key in target_layers: activations[key] = h target_layers.remove(key) return activations def extract(self, images, layers=['pool5'], size=(224, 224), **kwargs): """extract(self, images, layers=['pool5'], size=(224, 224)) Extracts all the feature maps of given images. The difference of directly executing ``__call__`` is that it directly accepts images as an input and automatically transforms them to a proper variable. That is, it is also interpreted as a shortcut method that implicitly calls ``prepare`` and ``__call__`` functions. .. warning:: ``test`` and ``volatile`` arguments are not supported anymore since v2. Instead, use ``chainer.using_config('train', train)`` and ``chainer.using_config('enable_backprop', not volatile)`` respectively. See :func:`chainer.using_config`. Args: images (iterable of PIL.Image or numpy.ndarray): Input images. layers (list of str): The list of layer names you want to extract. size (pair of ints): The resolution of resized images used as an input of CNN. All the given images are not resized if this argument is ``None``, but the resolutions of all the images should be the same. Returns: Dictionary of ~chainer.Variable: A directory in which the key contains the layer name and the value contains the corresponding feature map variable. """ argument.check_unexpected_kwargs( kwargs, test='test argument is not supported anymore. ' 'Use chainer.using_config', volatile='volatile argument is not supported anymore. ' 'Use chainer.using_config') argument.assert_kwargs_empty(kwargs) x = concat_examples([prepare(img, size=size) for img in images]) x = Variable(self.xp.asarray(x)) return self(x, layers=layers) def predict(self, images, oversample=True): """Computes all the probabilities of given images. Args: images (iterable of PIL.Image or numpy.ndarray): Input images. oversample (bool): If ``True``, it averages results across center, corners, and mirrors. Otherwise, it uses only the center. Returns: ~chainer.Variable: Output that contains the class probabilities of given images. """ x = concat_examples([prepare(img, size=(256, 256)) for img in images]) if oversample: x = imgproc.oversample(x, crop_dims=(224, 224)) else: x = x[:, :, 16:240, 16:240] # Use no_backprop_mode to reduce memory consumption with function.no_backprop_mode(): x = Variable(self.xp.asarray(x)) y = self(x, layers=['prob'])['prob'] if oversample: n = y.data.shape[0] // 10 y_shape = y.data.shape[1:] y = reshape(y, (n, 10) + y_shape) y = sum(y, axis=1) / 10 return y class ResNet50Layers(ResNetLayers): """A pre-trained CNN model with 50 layers provided by MSRA. When you specify the path of the pre-trained chainer model serialized as a ``.npz`` file in the constructor, this chain model automatically initializes all the parameters with it. This model would be useful when you want to extract a semantic feature vector per image, or fine-tune the model on a different dataset. Note that unlike ``VGG16Layers``, it does not automatically download a pre-trained caffemodel. This caffemodel can be downloaded at `GitHub `_. If you want to manually convert the pre-trained caffemodel to a chainer model that can be specified in the constructor, please use ``convert_caffemodel_to_npz`` classmethod instead. ResNet50 has 25,557,096 trainable parameters, and it's 58% and 43% fewer than ResNet101 and ResNet152, respectively. On the other hand, the top-5 classification accuracy on ImageNet dataset drops only 0.7% and 1.1% from ResNet101 and ResNet152, respectively. Therefore, ResNet50 may have the best balance between the accuracy and the model size. It would be basically just enough for many cases, but some advanced models for object detection or semantic segmentation use deeper ones as their building blocks, so these deeper ResNets are here for making reproduction work easier. See: K. He et. al., `Deep Residual Learning for Image Recognition `_ Args: pretrained_model (str): the destination of the pre-trained chainer model serialized as a ``.npz`` file. If this argument is specified as ``auto``, it automatically loads and converts the caffemodel from ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-50-model.caffemodel``, where ``$CHAINER_DATASET_ROOT`` is set as ``$HOME/.chainer/dataset`` unless you specify another value by modifying the environment variable. Note that in this case the converted chainer model is stored on the same directory and automatically used from the next time. If this argument is specified as ``None``, all the parameters are not initialized by the pre-trained model, but the default initializer used in the original paper, i.e., ``chainer.initializers.HeNormal(scale=1.0)``. Attributes: available_layers (list of str): The list of available layer names used by ``__call__`` and ``extract`` methods. """ def __init__(self, pretrained_model='auto'): if pretrained_model == 'auto': pretrained_model = 'ResNet-50-model.caffemodel' super(ResNet50Layers, self).__init__(pretrained_model, 50) class ResNet101Layers(ResNetLayers): """A pre-trained CNN model with 101 layers provided by MSRA. When you specify the path of the pre-trained chainer model serialized as a ``.npz`` file in the constructor, this chain model automatically initializes all the parameters with it. This model would be useful when you want to extract a semantic feature vector per image, or fine-tune the model on a different dataset. Note that unlike ``VGG16Layers``, it does not automatically download a pre-trained caffemodel. This caffemodel can be downloaded at `GitHub `_. If you want to manually convert the pre-trained caffemodel to a chainer model that can be specified in the constructor, please use ``convert_caffemodel_to_npz`` classmethod instead. ResNet101 has 44,549,224 trainable parameters, and it's 43% fewer than ResNet152 model, while the top-5 classification accuracy on ImageNet dataset drops 1.1% from ResNet152. For many cases, ResNet50 may have the best balance between the accuracy and the model size. See: K. He et. al., `Deep Residual Learning for Image Recognition `_ Args: pretrained_model (str): the destination of the pre-trained chainer model serialized as a ``.npz`` file. If this argument is specified as ``auto``, it automatically loads and converts the caffemodel from ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-101-model.caffemodel``, where ``$CHAINER_DATASET_ROOT`` is set as ``$HOME/.chainer/dataset`` unless you specify another value by modifying the environment variable. Note that in this case the converted chainer model is stored on the same directory and automatically used from the next time. If this argument is specified as ``None``, all the parameters are not initialized by the pre-trained model, but the default initializer used in the original paper, i.e., ``chainer.initializers.HeNormal(scale=1.0)``. Attributes: available_layers (list of str): The list of available layer names used by ``__call__`` and ``extract`` methods. """ def __init__(self, pretrained_model='auto'): if pretrained_model == 'auto': pretrained_model = 'ResNet-101-model.caffemodel' super(ResNet101Layers, self).__init__(pretrained_model, 101) class ResNet152Layers(ResNetLayers): """A pre-trained CNN model with 152 layers provided by MSRA. When you specify the path of the pre-trained chainer model serialized as a ``.npz`` file in the constructor, this chain model automatically initializes all the parameters with it. This model would be useful when you want to extract a semantic feature vector per image, or fine-tune the model on a different dataset. Note that unlike ``VGG16Layers``, it does not automatically download a pre-trained caffemodel. This caffemodel can be downloaded at `GitHub `_. If you want to manually convert the pre-trained caffemodel to a chainer model that can be specified in the constructor, please use ``convert_caffemodel_to_npz`` classmethod instead. ResNet152 has 60,192,872 trainable parameters, and it's the deepest ResNet model and it achieves the best result on ImageNet classification task in `ILSVRC 2015 `_. See: K. He et. al., `Deep Residual Learning for Image Recognition `_ Args: pretrained_model (str): the destination of the pre-trained chainer model serialized as a ``.npz`` file. If this argument is specified as ``auto``, it automatically loads and converts the caffemodel from ``$CHAINER_DATASET_ROOT/pfnet/chainer/models/ResNet-152-model.caffemodel``, where ``$CHAINER_DATASET_ROOT`` is set as ``$HOME/.chainer/dataset`` unless you specify another value by modifying the environment variable. Note that in this case the converted chainer model is stored on the same directory and automatically used from the next time. If this argument is specified as ``None``, all the parameters are not initialized by the pre-trained model, but the default initializer used in the original paper, i.e., ``chainer.initializers.HeNormal(scale=1.0)``. Attributes: available_layers (list of str): The list of available layer names used by ``__call__`` and ``extract`` methods. """ def __init__(self, pretrained_model='auto'): if pretrained_model == 'auto': pretrained_model = 'ResNet-152-model.caffemodel' super(ResNet152Layers, self).__init__(pretrained_model, 152) def prepare(image, size=(224, 224)): """Converts the given image to the numpy array for ResNets. Note that you have to call this method before ``__call__`` because the pre-trained resnet model requires to resize the given image, covert the RGB to the BGR, subtract the mean, and permute the dimensions before calling. Args: image (PIL.Image or numpy.ndarray): Input image. If an input is ``numpy.ndarray``, its shape must be ``(height, width)``, ``(height, width, channels)``, or ``(channels, height, width)``, and the order of the channels must be RGB. size (pair of ints): Size of converted images. If ``None``, the given image is not resized. Returns: numpy.ndarray: The converted output array. """ if not available: raise ImportError('PIL cannot be loaded. Install Pillow!\n' 'The actual import error is as follows:\n' + str(_import_error)) if isinstance(image, numpy.ndarray): if image.ndim == 3: if image.shape[0] == 1: image = image[0, :, :] elif image.shape[0] == 3: image = image.transpose((1, 2, 0)) image = Image.fromarray(image.astype(numpy.uint8)) image = image.convert('RGB') if size: image = image.resize(size) image = numpy.asarray(image, dtype=numpy.float32) image = image[:, :, ::-1] # NOTE: in the original paper they subtract a fixed mean image, # however, in order to support arbitrary size we instead use the # mean pixel (rather than mean image) as with VGG team. The mean # value used in ResNet is slightly different from that of VGG16. image -= numpy.array( [103.063, 115.903, 123.152], dtype=numpy.float32) image = image.transpose((2, 0, 1)) return image class BuildingBlock(link.Chain): """A building block that consists of several Bottleneck layers. Args: n_layer (int): Number of layers used in the building block. in_channels (int): Number of channels of input arrays. mid_channels (int): Number of channels of intermediate arrays. out_channels (int): Number of channels of output arrays. stride (int or tuple of ints): Stride of filter application. initialW (4-D array): Initial weight value used in the convolutional layers. """ def __init__(self, n_layer, in_channels, mid_channels, out_channels, stride, initialW=None): super(BuildingBlock, self).__init__() with self.init_scope(): self.a = BottleneckA( in_channels, mid_channels, out_channels, stride, initialW) self._forward = ["a"] for i in range(n_layer - 1): name = 'b{}'.format(i + 1) bottleneck = BottleneckB(out_channels, mid_channels, initialW) setattr(self, name, bottleneck) self._forward.append(name) def __call__(self, x): for name in self._forward: l = getattr(self, name) x = l(x) return x @property def forward(self): return [getattr(self, name) for name in self._forward] class BottleneckA(link.Chain): """A bottleneck layer that reduces the resolution of the feature map. Args: in_channels (int): Number of channels of input arrays. mid_channels (int): Number of channels of intermediate arrays. out_channels (int): Number of channels of output arrays. stride (int or tuple of ints): Stride of filter application. initialW (4-D array): Initial weight value used in the convolutional layers. """ def __init__(self, in_channels, mid_channels, out_channels, stride=2, initialW=None): super(BottleneckA, self).__init__() with self.init_scope(): self.conv1 = Convolution2D( in_channels, mid_channels, 1, stride, 0, initialW=initialW, nobias=True) self.bn1 = BatchNormalization(mid_channels) self.conv2 = Convolution2D( mid_channels, mid_channels, 3, 1, 1, initialW=initialW, nobias=True) self.bn2 = BatchNormalization(mid_channels) self.conv3 = Convolution2D( mid_channels, out_channels, 1, 1, 0, initialW=initialW, nobias=True) self.bn3 = BatchNormalization(out_channels) self.conv4 = Convolution2D( in_channels, out_channels, 1, stride, 0, initialW=initialW, nobias=True) self.bn4 = BatchNormalization(out_channels) def __call__(self, x): h1 = relu(self.bn1(self.conv1(x))) h1 = relu(self.bn2(self.conv2(h1))) h1 = self.bn3(self.conv3(h1)) h2 = self.bn4(self.conv4(x)) return relu(h1 + h2) class BottleneckB(link.Chain): """A bottleneck layer that maintains the resolution of the feature map. Args: in_channels (int): Number of channels of input and output arrays. mid_channels (int): Number of channels of intermediate arrays. initialW (4-D array): Initial weight value used in the convolutional layers. """ def __init__(self, in_channels, mid_channels, initialW=None): super(BottleneckB, self).__init__() with self.init_scope(): self.conv1 = Convolution2D( in_channels, mid_channels, 1, 1, 0, initialW=initialW, nobias=True) self.bn1 = BatchNormalization(mid_channels) self.conv2 = Convolution2D( mid_channels, mid_channels, 3, 1, 1, initialW=initialW, nobias=True) self.bn2 = BatchNormalization(mid_channels) self.conv3 = Convolution2D( mid_channels, in_channels, 1, 1, 0, initialW=initialW, nobias=True) self.bn3 = BatchNormalization(in_channels) def __call__(self, x): h = relu(self.bn1(self.conv1(x))) h = relu(self.bn2(self.conv2(h))) h = self.bn3(self.conv3(h)) return relu(h + x) def _global_average_pooling_2d(x): n, channel, rows, cols = x.data.shape h = average_pooling_2d(x, (rows, cols), stride=1) h = reshape(h, (n, channel)) return h def _transfer_components(src, dst_conv, dst_bn, bname, cname): src_conv = getattr(src, 'res{}_branch{}'.format(bname, cname)) src_bn = getattr(src, 'bn{}_branch{}'.format(bname, cname)) src_scale = getattr(src, 'scale{}_branch{}'.format(bname, cname)) dst_conv.W.data[:] = src_conv.W.data dst_bn.avg_mean[:] = src_bn.avg_mean dst_bn.avg_var[:] = src_bn.avg_var dst_bn.gamma.data[:] = src_scale.W.data dst_bn.beta.data[:] = src_scale.bias.b.data def _transfer_bottleneckA(src, dst, name): _transfer_components(src, dst.conv1, dst.bn1, name, '2a') _transfer_components(src, dst.conv2, dst.bn2, name, '2b') _transfer_components(src, dst.conv3, dst.bn3, name, '2c') _transfer_components(src, dst.conv4, dst.bn4, name, '1') def _transfer_bottleneckB(src, dst, name): _transfer_components(src, dst.conv1, dst.bn1, name, '2a') _transfer_components(src, dst.conv2, dst.bn2, name, '2b') _transfer_components(src, dst.conv3, dst.bn3, name, '2c') def _transfer_block(src, dst, names): _transfer_bottleneckA(src, dst.a, names[0]) for i, name in enumerate(names[1:]): dst_bottleneckB = getattr(dst, 'b{}'.format(i + 1)) _transfer_bottleneckB(src, dst_bottleneckB, name) def _transfer_resnet50(src, dst): dst.conv1.W.data[:] = src.conv1.W.data dst.conv1.b.data[:] = src.conv1.b.data dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean dst.bn1.avg_var[:] = src.bn_conv1.avg_var dst.bn1.gamma.data[:] = src.scale_conv1.W.data dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data _transfer_block(src, dst.res2, ['2a', '2b', '2c']) _transfer_block(src, dst.res3, ['3a', '3b', '3c', '3d']) _transfer_block(src, dst.res4, ['4a', '4b', '4c', '4d', '4e', '4f']) _transfer_block(src, dst.res5, ['5a', '5b', '5c']) dst.fc6.W.data[:] = src.fc1000.W.data dst.fc6.b.data[:] = src.fc1000.b.data def _transfer_resnet101(src, dst): dst.conv1.W.data[:] = src.conv1.W.data dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean dst.bn1.avg_var[:] = src.bn_conv1.avg_var dst.bn1.gamma.data[:] = src.scale_conv1.W.data dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data _transfer_block(src, dst.res2, ['2a', '2b', '2c']) _transfer_block(src, dst.res3, ['3a', '3b1', '3b2', '3b3']) _transfer_block(src, dst.res4, ['4a'] + ['4b{}'.format(i) for i in range(1, 23)]) _transfer_block(src, dst.res5, ['5a', '5b', '5c']) dst.fc6.W.data[:] = src.fc1000.W.data dst.fc6.b.data[:] = src.fc1000.b.data def _transfer_resnet152(src, dst): dst.conv1.W.data[:] = src.conv1.W.data dst.bn1.avg_mean[:] = src.bn_conv1.avg_mean dst.bn1.avg_var[:] = src.bn_conv1.avg_var dst.bn1.gamma.data[:] = src.scale_conv1.W.data dst.bn1.beta.data[:] = src.scale_conv1.bias.b.data _transfer_block(src, dst.res2, ['2a', '2b', '2c']) _transfer_block(src, dst.res3, ['3a'] + ['3b{}'.format(i) for i in range(1, 8)]) _transfer_block(src, dst.res4, ['4a'] + ['4b{}'.format(i) for i in range(1, 36)]) _transfer_block(src, dst.res5, ['5a', '5b', '5c']) dst.fc6.W.data[:] = src.fc1000.W.data dst.fc6.b.data[:] = src.fc1000.b.data def _make_npz(path_npz, path_caffemodel, model, n_layers): print('Now loading caffemodel (usually it may take few minutes)') if not os.path.exists(path_caffemodel): raise IOError( 'The pre-trained caffemodel does not exist. Please download it ' 'from \'https://github.com/KaimingHe/deep-residual-networks\', ' 'and place it on {}'.format(path_caffemodel)) if n_layers == 50: ResNet50Layers.convert_caffemodel_to_npz(path_caffemodel, path_npz, 50) elif n_layers == 101: ResNet101Layers.convert_caffemodel_to_npz( path_caffemodel, path_npz, 101) elif n_layers == 152: ResNet152Layers.convert_caffemodel_to_npz( path_caffemodel, path_npz, 152) npz.load_npz(path_npz, model) return model def _retrieve(n_layers, name_npz, name_caffemodel, model): root = download.get_dataset_directory('pfnet/chainer/models/') path = os.path.join(root, name_npz) path_caffemodel = os.path.join(root, name_caffemodel) return download.cache_or_load_file( path, lambda path: _make_npz(path, path_caffemodel, model, n_layers), lambda path: npz.load_npz(path, model)) ================================================ FILE: image_generation_chainer/source/links/sn_convolution_2d.py ================================================ import chainer import numpy as np from chainer import cuda from chainer.functions.array.broadcast import broadcast_to from chainer.functions.connection import convolution_2d from chainer.links.connection.convolution_2d import Convolution2D from source.functions.max_sv import max_singular_value class SNConvolution2D(Convolution2D): """Two-dimensional convolutional layer with spectral normalization. This link wraps the :func:`~chainer.functions.convolution_2d` function and holds the filter weight and bias vector as parameters. Args: in_channels (int): Number of channels of input arrays. If ``None``, parameter initialization will be deferred until the first forward datasets pass at which time the size will be determined. out_channels (int): Number of channels of output arrays. ksize (int or pair of ints): Size of filters (a.k.a. kernels). ``ksize=k`` and ``ksize=(k, k)`` are equivalent. stride (int or pair of ints): Stride of filter applications. ``stride=s`` and ``stride=(s, s)`` are equivalent. pad (int or pair of ints): Spatial padding width for input arrays. ``pad=p`` and ``pad=(p, p)`` are equivalent. wscale (float): Scaling factor of the initial weight. bias (float): Initial bias value. nobias (bool): If ``True``, then this link does not use the bias term. initialW (4-D array): Initial weight value. If ``None``, then this function uses to initialize ``wscale``. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. initial_bias (1-D array): Initial bias value. If ``None``, then this function uses to initialize ``bias``. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. use_gamma (bool): If true, apply scalar multiplication to the normalized weight (i.e. reparameterize). Ip (int): The number of power iteration for calculating the spcetral norm of the weights. factor (float) : constant factor to adjust spectral norm of W_bar. .. seealso:: See :func:`chainer.functions.convolution_2d` for the definition of two-dimensional convolution. Attributes: W (~chainer.Variable): Weight parameter. W_bar (~chainer.Variable): Spectrally normalized weight parameter. b (~chainer.Variable): Bias parameter. u (~numpy.array): Current estimation of the right largest singular vector of W. (optional) gamma (~chainer.Variable): the multiplier parameter. (optional) factor (float): constant factor to adjust spectral norm of W_bar. """ def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0, nobias=False, initialW=None, initial_bias=None, use_gamma=False, Ip=1, factor=None): self.Ip = Ip self.use_gamma = use_gamma super(SNConvolution2D, self).__init__( in_channels, out_channels, ksize, stride, pad, nobias, initialW, initial_bias) self.u = np.random.normal(size=(1, out_channels)).astype(dtype="f") self.register_persistent('u') self.factor = factor @property def W_bar(self): """ Spectrally Normalized Weight """ W_mat = self.W.reshape(self.W.shape[0], -1) sigma, _u, _ = max_singular_value(W_mat, self.u, self.Ip) if self.factor: sigma = sigma / self.factor sigma = broadcast_to(sigma.reshape((1, 1, 1, 1)), self.W.shape) if chainer.config.train: # Update estimated 1st singular vector self.u[:] = _u if hasattr(self, 'gamma'): return broadcast_to(self.gamma, self.W.shape) * self.W / sigma else: return self.W / sigma def _initialize_params(self, in_size): super(SNConvolution2D, self)._initialize_params(in_size) if self.use_gamma: W_mat = self.W.data.reshape(self.W.shape[0], -1) _, s, _ = np.linalg.svd(W_mat) with self.init_scope(): self.gamma = chainer.Parameter(s[0], (1, 1, 1, 1)) def __call__(self, x): """Applies the convolution layer. Args: x (~chainer.Variable): Input image. Returns: ~chainer.Variable: Output of the convolution. """ if self.W.data is None: self._initialize_params(x.shape[1]) return convolution_2d.convolution_2d( x, self.W_bar, self.b, self.stride, self.pad) ================================================ FILE: image_generation_chainer/source/links/sn_convolution_nd.py ================================================ import numpy as np from chainer.functions.connection import convolution_nd from chainer import initializers from chainer import link from chainer.utils import conv_nd from chainer import variable from chainer.functions.array.broadcast import broadcast_to from source.functions.max_sv import max_singular_value class SNConvolutionND(link.Link): """N-dimensional convolution layer. This link wraps the :func:`~chainer.functions.convolution_nd` function and holds the filter weight and bias vector as parameters. Args: ndim (int): Number of spatial dimensions. in_channels (int): Number of channels of input arrays. out_channels (int): Number of channels of output arrays. ksize (int or tuple of ints): Size of filters (a.k.a. kernels). ``ksize=k`` and ``ksize=(k, k, ..., k)`` are equivalent. stride (int or tuple of ints): Stride of filter application. ``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent. pad (int or tuple of ints): Spatial padding width for input arrays. ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent. nobias (bool): If ``True``, then this function does not use the bias. initialW (array): Initial weight array. If ``None``, the default initializer is used. May be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. initial_bias (array): Initial bias vector. If ``None``, the bias is set to zero. May be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. cover_all (bool): If ``True``, all spatial locations are convoluted into some output pixels. It may make the output size larger. ``cover_all`` needs to be ``False`` if you want to use cuDNN. use_gamma (bool): If true, apply scalar multiplication to the normalized weight (i.e. reparameterize). Ip (int): The number of power iteration for calculating the spcetral norm of the weights. factor (float) : constant factor to adjust spectral norm of W_bar. .. seealso:: See :func:`~chainer.functions.convolution_nd` for the definition of N-dimensional convolution. See :func:`~chainer.functions.convolution_2d` for the definition of two-dimensional convolution. Attributes: W (~chainer.Variable): Weight parameter. W_bar (~chainer.Variable): Spectrally normalized weight parameter. b (~chainer.Variable): Bias parameter. If ``initial_bias`` is ``None``, set to ``None``. u (~numpy.array): Current estimation of the right largest singular vector of W. (optional) gamma (~chainer.Variable): the multiplier parameter. (optional) factor (float): constant factor to adjust spectral norm of W_bar. """ def __init__(self, ndim, in_channels, out_channels, ksize, stride=1, pad=0, nobias=False, initialW=None, initial_bias=None, cover_all=False, use_gamma=False, Ip=1, factor=None): super(SNConvolutionND, self).__init__() ksize = conv_nd.as_tuple(ksize, ndim) self.stride = stride self.pad = pad self.cover_all = cover_all self.use_gamma = use_gamma self.Ip = Ip self.u = np.random.normal(size=(1, out_channels)).astype(dtype="f") self.register_persistent('u') self.factor = factor with self.init_scope(): W_shape = (out_channels, in_channels) + ksize self.W = variable.Parameter( initializers._get_initializer(initialW), W_shape) if nobias: self.b = None else: if initial_bias is None: initial_bias = 0 initial_bias = initializers._get_initializer(initial_bias) self.b = variable.Parameter(initial_bias, out_channels) if self.use_gamma: W_mat = self.W.data.reshape(self.W.shape[0], -1) _, s, _ = np.linalg.svd(W_mat) self.gamma = variable.Parameter(s[0], (1,) * len(self.W.shape)) @property def W_bar(self): """ Spectral Normalized Weight """ W_mat = self.W.reshape(self.W.shape[0], -1) sigma, _u, _ = max_singular_value(W_mat, self.u, self.Ip) if self.factor: sigma = sigma / self.factor sigma = broadcast_to(sigma.reshape([1] * len(self.W.shape)), self.W.shape) self.u[:] = _u if hasattr(self, 'gamma'): return broadcast_to(self.gamma, self.W.shape) * self.W / sigma else: return self.W / sigma def __call__(self, x): """Applies N-dimensional convolution layer. Args: x (~chainer.Variable): Input image. Returns: ~chainer.Variable: Output of convolution. """ return convolution_nd.convolution_nd( x, self.W_bar, self.b, self.stride, self.pad, cover_all=self.cover_all) ================================================ FILE: image_generation_chainer/source/links/sn_dilated_convolution_2d.py ================================================ import chainer import numpy as np from chainer import cuda from chainer.functions.array.broadcast import broadcast_to from chainer.functions.connection import convolution_2d from chainer.links.connection.convolution_2d import Convolution2D from source.functions.max_sv import max_singular_value from chainer.functions.connection import dilated_convolution_2d from chainer import initializers from chainer import link from chainer import variable from chainer.links.connection.dilated_convolution_2d import DilatedConvolution2D class SNDilatedConvolution2D(DilatedConvolution2D): """Two-dimensional dilated convolutional layer with spectral normalization. Please see the original DilatedConv implementation: https://github.com/chainer/chainer/blob/v3.2.0/chainer/links/connection/dilated_convolution_2d.py """ def __init__(self, in_channels, out_channels, ksize=None, stride=1, pad=0, dilate=1, nobias=False, initialW=None, initial_bias=None, use_gamma=False, Ip=1, adjust_snorm_for_convfilter=False): if adjust_snorm_for_convfilter is not False: self.ratio = ksize // stride else: self.ratio = None if ksize is None: out_channels, ksize, in_channels = in_channels, out_channels, None self.ksize = ksize self.stride = _pair(stride) self.pad = _pair(pad) self.dilate = _pair(dilate) self.out_channels = out_channels self.use_gamma = use_gamma self.Ip = Ip self.u = None super(SNDilatedConvolution2D, self).__init__( in_channels, out_channels, ksize, stride, pad, dilate, nobias, initialW, initial_bias) @property def W_bar(self): """ Spectral Normalized Weight """ xp = cuda.get_array_module(self.W.data) W_mat = self.W.reshape(self.W.shape[0], -1) sigma, _u, _ = max_singular_value(W_mat, self.u, self.Ip) sigma = self.ratio * sigma if self.ratio is not None else sigma sigma = broadcast_to(sigma.reshape((1, 1, 1, 1)), self.W.shape) self.u = _u if hasattr(self, 'gamma'): return broadcast_to(self.gamma, self.W.shape) * self.W / sigma else: return self.W / sigma def _initialize_params(self, in_channels): super(SNDilatedConvolution2D, self)._initialize_params(in_channels) if self.use_gamma: W_mat = self.W.data.reshape(self.W.shape[0], -1) _, s, _ = np.linalg.svd(W_mat) with self.init_scope(): self.gamma = chainer.Parameter(s[0], (1, 1, 1, 1)) def __call__(self, x): """Applies the convolution layer. Args: x (~chainer.Variable): Input image. Returns: ~chainer.Variable: Output of the convolution. """ if self.W.data is None: self._initialize_params(x.shape[1]) return dilated_convolution_2d.dilated_convolution_2d( x, self.W_bar, self.b, self.stride, self.pad, self.dilate) def _pair(x): if hasattr(x, '__getitem__'): return x return x, x ================================================ FILE: image_generation_chainer/source/links/sn_embed_id.py ================================================ import chainer from chainer.functions.connection import embed_id from chainer.initializers import normal from chainer import link from chainer import variable from chainer.functions.array.broadcast import broadcast_to from source.functions.max_sv import max_singular_value import numpy as np class SNEmbedID(link.Link): """Efficient linear layer for one-hot input. This is a link that wraps the :func:`~chainer.functions.embed_id` function. This link holds the ID (word) embedding matrix ``W`` as a parameter. Args: in_size (int): Number of different identifiers (a.k.a. vocabulary size). out_size (int): Size of embedding vector. initialW (2-D array): Initial weight value. If ``None``, then the matrix is initialized from the standard normal distribution. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. ignore_label (int or None): If ``ignore_label`` is an int value, ``i``-th column of return value is filled with ``0``. Ip (int): The number of power iteration for calculating the spcetral norm of the weights. factor (float) : constant factor to adjust spectral norm of W_bar. .. seealso:: :func:`chainer.functions.embed_id` Attributes: W (~chainer.Variable): Embedding parameter matrix. W_bar (~chainer.Variable): Spectrally normalized weight parameter. u (~numpy.array): Current estimation of the right largest singular vector of W. (optional) gamma (~chainer.Variable): the multiplier parameter. (optional) factor (float): constant factor to adjust spectral norm of W_bar. """ ignore_label = None def __init__(self, in_size, out_size, initialW=None, ignore_label=None, Ip=1, factor=None): super(SNEmbedID, self).__init__() self.ignore_label = ignore_label self.Ip = Ip with self.init_scope(): if initialW is None: initialW = normal.Normal(1.0) self.W = variable.Parameter(initialW, (in_size, out_size)) self.u = np.random.normal(size=(1, in_size)).astype(dtype="f") self.register_persistent('u') self.factor = factor @property def W_bar(self): """ Spectral Normalized Weight """ sigma, _u, _ = max_singular_value(self.W, self.u, self.Ip) if chainer.config.train: # Update estimated 1st singular vector self.u[:] = _u if self.factor: sigma = sigma / self.factor sigma = broadcast_to(sigma.reshape((1, 1)), self.W.shape) return self.W / sigma def __call__(self, x): """Extracts the word embedding of given IDs. Args: x (~chainer.Variable): Batch vectors of IDs. Returns: ~chainer.Variable: Batch of corresponding embeddings. """ return embed_id.embed_id(x, self.W_bar, ignore_label=self.ignore_label) ================================================ FILE: image_generation_chainer/source/links/sn_linear.py ================================================ import chainer import numpy as np from chainer.functions.array.broadcast import broadcast_to from chainer.functions.connection import linear from chainer.links.connection.linear import Linear from source.functions.max_sv import max_singular_value class SNLinear(Linear): """Linear layer with Spectral Normalization. Args: in_size (int): Dimension of input vectors. If ``None``, parameter initialization will be deferred until the first forward datasets pass at which time the size will be determined. out_size (int): Dimension of output vectors. wscale (float): Scaling factor of the weight matrix. bias (float): Initial bias value. nobias (bool): If ``True``, then this function does not use the bias. initialW (2-D array): Initial weight value. If ``None``, then this function uses to initialize ``wscale``. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. initial_bias (1-D array): Initial bias value. If ``None``, then this function uses to initialize ``bias``. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. use_gamma (bool): If true, apply scalar multiplication to the normalized weight (i.e. reparameterize). Ip (int): The number of power iteration for calculating the spcetral norm of the weights. factor (float) : constant factor to adjust spectral norm of W_bar. .. seealso:: :func:`~chainer.functions.linear` Attributes: W (~chainer.Variable): Weight parameter. W_bar (~chainer.Variable): Spectrally normalized weight parameter. b (~chainer.Variable): Bias parameter. u (~numpy.array): Current estimation of the right largest singular vector of W. (optional) gamma (~chainer.Variable): the multiplier parameter. (optional) factor (float): constant factor to adjust spectral norm of W_bar. """ def __init__(self, in_size, out_size, use_gamma=False, nobias=False, initialW=None, initial_bias=None, Ip=1, factor=None): self.Ip = Ip self.use_gamma = use_gamma super(SNLinear, self).__init__( in_size, out_size, nobias, initialW, initial_bias ) self.u = np.random.normal(size=(1, out_size)).astype(dtype="f") self.register_persistent('u') self.factor = factor @property def W_bar(self): """ Spectral Normalized Weight """ sigma, _u, _ = max_singular_value(self.W, self.u, self.Ip) if self.factor: sigma = sigma / self.factor sigma = broadcast_to(sigma.reshape((1, 1)), self.W.shape) if chainer.config.train: # Update estimated 1st singular vector self.u[:] = _u if hasattr(self, 'gamma'): return broadcast_to(self.gamma, self.W.shape) * self.W / sigma else: return self.W / sigma def _initialize_params(self, in_size): super(SNLinear, self)._initialize_params(in_size) if self.use_gamma: _, s, _ = np.linalg.svd(self.W.data) with self.init_scope(): self.gamma = chainer.Parameter(s[0], (1, 1)) def __call__(self, x): """Applies the linear layer. Args: x (~chainer.Variable): Batch of input vectors. Returns: ~chainer.Variable: Output of the linear layer. """ if self.W.data is None: self._initialize_params(x.size // x.shape[0]) return linear.linear(x, self.W_bar, self.b) ================================================ FILE: image_generation_chainer/source/links/sn_symmetric_linear.py ================================================ import chainer import numpy as np from chainer.functions.array.broadcast import broadcast_to from chainer.functions.connection import linear from chainer.links.connection.linear import Linear from source.functions.max_sv import max_singular_value class SNSymmetricLinear(Linear): """Symmetric Linear layer with Spectral Normalization. Args: in_size (int): Dimension of input vectors. If ``None``, parameter initialization will be deferred until the first forward datasets pass at which time the size will be determined. out_size (int): Dimension of output vectors. wscale (float): Scaling factor of the weight matrix. bias (float): Initial bias value. nobias (bool): If ``True``, then this function does not use the bias. initialW (2-D array): Initial weight value. If ``None``, then this function uses to initialize ``wscale``. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. initial_bias (1-D array): Initial bias value. If ``None``, then this function uses to initialize ``bias``. May also be a callable that takes ``numpy.ndarray`` or ``cupy.ndarray`` and edits its value. use_gamma (bool): If true, apply scalar multiplication to the normalized weight (i.e. reparameterize). Ip (int): The number of power iteration for calculating the spcetral norm of the weights. .. seealso:: :func:`~chainer.functions.linear` Attributes: W (~chainer.Variable): Weight parameter. W_bar (~chainer.Variable): Normalized (Reparametrized) weight parameter. b (~chainer.Variable): Bias parameter. u (~array): Current estimation of the right largest singular vector of W. (optional) gamma (~chainer.Variable): the multiplier parameter. """ def __init__(self, in_size, out_size, use_gamma=False, nobias=False, initialW=None, initial_bias=None, Ip=1): self.Ip = Ip self.u = None self.use_gamma = use_gamma super(SNLinear, self).__init__( in_size, out_size, nobias, initialW, initial_bias ) @property def W_sym(self): return self.W + F.transpose(self.W) @property def W_bar(self): """ Spectral Normalized Weight """ sigma, _u, _ = max_singular_value(self.W_sym, self.u, self.Ip) sigma = broadcast_to(sigma.reshape((1, 1)), self.W_sym.shape) self.u = _u if hasattr(self, 'gamma'): return broadcast_to(self.gamma, self.W_sym.shape) * self.W_sym / sigma else: return self.W_sym / sigma def _initialize_params(self, in_size): super(SNLinear, self)._initialize_params(in_size) if self.use_gamma: _, s, _ = np.linalg.svd(self.W_sym.data) with self.init_scope(): self.gamma = chainer.Parameter(s[0], (1, 1)) def __call__(self, x): """Applies the linear layer. Args: x (~chainer.Variable): Batch of input vectors. Returns: ~chainer.Variable: Output of the linear layer. """ if self.W.data is None: self._initialize_params(x.size // x.shape[0]) return linear.linear(x, self.W_bar, self.b) ================================================ FILE: image_generation_chainer/source/misc_train_utils.py ================================================ import matplotlib as mpl mpl.use('Agg') from os import makedirs, getcwd from os.path import basename, exists, join, isfile, dirname, isdir import numpy as np import shutil from pathlib import Path from functools import reduce import operator from time import strftime import json from textwrap import wrap import matplotlib.pyplot as plt import source.yaml_utils as yaml_utils def printtime(msg, time_format='%a %d/%m %H:%M:%S'): """ Wraps the typical print(msg) with a time to display the time. """ print('[{}] {}'.format(strftime(time_format), msg)) def get_by_nested_path(root, items): """Access a nested object in root by item sequence.""" return reduce(operator.getitem, items, root) def get_config_value(config, path, default=None): """ Fetches the value of a nested key or returns default value. Wrapper around get_by_nested_path(). """ error = False try: value = get_by_nested_path(config, path) except KeyError: value, error = default, True return value, error def create_result_dir(result_dir, config_path, config): if not exists(result_dir): makedirs(result_dir) def copy_to_result_dir(fn, result_dir): shutil.copy(fn, '{}/{}'.format(result_dir, basename(fn))) copy_to_result_dir(config_path, result_dir) # # define the paths we want to copy from. Some of those might # # not exist; copy only the existing ones. pool = [['models', 'generator', 'fn'], ['models', 'encoder', 'fn'], ['models', 'decoder', 'fn'], ['models', 'discriminator', 'fn']] for path1 in pool: val, err = get_config_value(config, path1) if not err: copy_to_result_dir(val, result_dir) copy_to_result_dir( config.dataset['dataset_fn'], result_dir) copy_to_result_dir( config.updater['fn'], result_dir) def load_models(config): gen_conf = config.models['generator'] gen = yaml_utils.load_model(gen_conf['fn'], gen_conf['name'], gen_conf['args']) dis_conf = config.models['discriminator'] dis = yaml_utils.load_model(dis_conf['fn'], dis_conf['name'], dis_conf['args']) return gen, dis def load_models_cgan(config, rocgan=False): """ Load all the network models based on the yml config. """ # # convenience function for loading a net: loadnet = lambda net_conf: yaml_utils.load_model(net_conf['fn'], net_conf['name'], net_conf['args']) # # load the encoder/decoder and discriminator. enc = loadnet(config.models['encoder']) dec = loadnet(config.models['decoder']) dis = loadnet(config.models['discriminator']) if not rocgan: return enc, dec, dis else: # # load the second encoder (ae pathway) in this case. enc_ae = loadnet(config.models['encoder_ae']) return enc, dec, dis, enc_ae def ensure_config_paths(config, pb=None, verbose=True): """ Parses the config (i.e. a dict) and ensures the paths with label 'fn' exist, or tries to replace it with local paths. """ def _get_key(config, key): try: config[key] return True except (AttributeError, KeyError): return False # # set the base name (if the modules should change dir). If pb is provided, # # use that, otherwise use the current working directory. pbase = pb if pb is not None and isdir(pb) else getcwd() # # boolean to understand if something is changed. changed = False if _get_key(config, 'models'): # # iterate over all models and change paths. for key in config['models'].keys(): m_conf = config['models'][key] if 'fn' in m_conf.keys() and not isfile(m_conf['fn']): # # modify the path (since the yml is for deepmux.) modelp = Path(m_conf['fn']) m_conf['fn'] = join(pbase, modelp.parts[-2], modelp.parts[-1]) changed = True # # similarly change the dataset path. if _get_key(config, 'dataset'): dbp = Path(config['dataset']['dataset_fn']) if not dbp.exists(): config['dataset']['dataset_fn'] = join(pbase, dbp.parts[-2], dbp.parts[-1]) changed = True if _get_key(config, 'updater'): updp = Path(config['updater']['fn']) if not updp.exists(): config['updater']['fn'] = join(pbase, updp.parts[-3], updp.parts[-2], updp.parts[-1]) changed = True if verbose and changed: print('Changed the paths in config to base: {}.'.format(pbase)) return config def moving_average(values, n=3): """ Moving average in numpy (values is a list/array). """ ret = np.cumsum(values, dtype=float) ret[n:] = ret[n:] - ret[:-n] return ret[n - 1:] / n def plot_losses_log(pout, savefig=True, title_len=30, name_out='losses.png', enable_ma=False, ma_n=5, interp_short=False): """ Plots a host of losses (hardcoded) based on the log. The log and the output dir are assumed to be in pout (convenient for running time). ARGS: enable_ma: (bool, optional) If True, the moving average of the saved values is plotted. interp_short: (bool, optional) If True, the lists shorter than the log, e.g. ones not exported frequently are interpolated. """ # # Naming conventions: l[name] is loss of [name], vl[n] is validation # # loss of [n] var, bp[var] is best position of [var]. printtime('Plotting losses.') # # convenience function if moving average is requested. ma = lambda x: moving_average(x, n=ma_n) if enable_ma else x # # convenience function to set the subtitle length to many lines. set_title = lambda ax, text, length=title_len: ax.set_title('\n'.join(wrap(text, length))) # # log1: The list with the values exported per iteration. # # Each element of the list is (expected to be) a dictionary. log1 = json.load(open(join(pout, 'log'), 'r')) total_lines = len(log1) # # empty lists for the losses. # # ldis: loss discriminator, lgadv: adversarial loss of generator, ldis, lgadv, ll1 = [], [], [] vlmssim, vlmmae, vlsssim, bpssim, vfid = [], [], [], [], [] # # additional vars to save. d_fake, d_real, real_min, fake_max = [], [], [], [] for cnt, dict_iter in enumerate(log1): # # plot the training losses. ldis.append(dict_iter['loss_dis']) if 'lgen_adv' in dict_iter.keys(): lgadv.append(dict_iter['lgen_adv']) if 'loss_l1' in dict_iter.keys(): ll1.append(dict_iter['loss_l1']) # # plot the dis_fake/real. d_fake.append(dict_iter['dis_fake']) d_real.append(dict_iter['dis_real']) if 'real_min' in dict_iter.keys(): real_min.append(dict_iter['real_min']) fake_max.append(dict_iter['fake_max']) # # append the validation losses. if 'mssim' in dict_iter.keys(): vlmssim.append(dict_iter['mssim']) vlsssim.append(dict_iter['sdssim']) if 'mmae' in dict_iter.keys(): vlmmae.append(dict_iter['mmae']) if 'bssim' in dict_iter.keys(): bpssim.append(dict_iter['bssim']) if 'FID' in dict_iter.keys(): vfid.append(dict_iter['FID']) # # get the export frequency from log. freq = log1[1]['iteration'] - log1[0]['iteration'] pool_to_plot = [ (ldis, 'Discriminator adversarial loss', []), (lgadv, 'Generator adversarial loss', []), (ll1, 'Training loss L1', []), (d_real, 'Real vs fake (red)', d_fake), (vlmssim, '(Valid) Mean ssim', []), (vlsssim, '(Valid) Std ssim', []), (vlmmae, '(Valid) Mean MAE', []), (real_min, 'Real min vs fake max (red)', fake_max), (bpssim, 'Best valid (ssim) iter', []), (vfid, 'Frechet Inception Distance', []), ] # # define the maximum rows and columns to support. max_rows, max_cols = 3, 4 f, axarr = plt.subplots(max_rows, max_cols, sharex=True, figsize=(5 * max_cols, 7 * max_rows)) # # c1: counter for the plots added. c1, total = 0, max_rows * max_cols # # iterate over the pool above and plot them. for cnt1, (list1, str1, l2) in enumerate(pool_to_plot): if len(list1) > 0: # # select the subplot. ax = axarr[c1 // max_cols, c1 % max_cols] if interp_short and len(list1) < total_lines // 4: # # in this case, we interpolate (staircase) the # # variable that was exported infrequently. list1 = np.repeat(list1, int(total_lines // len(list1))) ax.plot(ma(list1)) set_title(ax, str1) if len(l2) > 0: # # in this case, add a second variable to plot. ax.plot(ma(l2), c='r') # # increase the counter for the plots made. c1 += 1 if savefig: plt.savefig(pout + name_out, bbox_inches='tight', pad_inches=0.) return f ================================================ FILE: image_generation_chainer/source/miscs/__init__.py ================================================ ================================================ FILE: image_generation_chainer/source/miscs/model_moiving_average.py ================================================ import copy import chainer def copypersistents(src, dst): assert isinstance(src, chainer.Link) assert isinstance(dst, chainer.Link) dst._persistent = list(src._persistent) for name in dst._persistent: dst.__dict__[name] = copy.deepcopy(src.__dict__[name]) if isinstance(src, chainer.Chain): assert isinstance(dst, chainer.Chain) for name in src._children: copypersistents(src.__dict__[name], dst.__dict__[name]) def namedpersistents(src): assert isinstance(src, chainer.Link) for lname, link in src.namedlinks(): for pname in link._persistent: yield lname + '/' + pname, link.__dict__[pname] def apply_exponential_averaging(src_model, dst_model, alpha): src_params = [ param for _, param in sorted(src_model.namedparams())] dst_params = [ param for _, param in sorted(dst_model.namedparams())] assert (len(src_params) == len(dst_params)) for p, avg_p in zip(src_params, dst_params): avg_p.data[:] += float(1 - alpha) * (p.data - avg_p.data) src_persistents = [ param for _, param in sorted(namedpersistents(src_model))] dst_persistents = [ param for _, param in sorted(namedpersistents(dst_model))] assert (len(src_persistents) == len(dst_persistents)) for src, dst in zip(src_persistents, dst_persistents): dst *= alpha dst += src * (1 - alpha) class ModelMovingAverage(object): name = "ModelMovingAverage" def __init__(self, alpha, model=None): self.alpha = alpha self.avg_model = copy.deepcopy(model) if model is not None else None def update(self, src_model): if self.avg_model is None: self.avg_model = copy.deepcopy(src_model) apply_exponential_averaging(src_model, self.avg_model, self.alpha) def copy_avg_model_to(self, model): if self.avg_model is None: raise Exception("self.avg_model is None") apply_exponential_averaging(self.avg_model, model, 0) def set_avg_model(self, model): self.avg_model = copy.deepcopy(model) ================================================ FILE: image_generation_chainer/source/miscs/random_samples.py ================================================ import numpy as np import chainer def sample_continuous(dim, batchsize, distribution='normal', xp=np): if distribution == "normal": return xp.random.randn(batchsize, dim) \ .astype(xp.float32) elif distribution == "laplace": if xp == np: return np.random.laplace(0, 1, size=(batchsize, dim)).astype(xp.float32) else: # TODO: cuda kernel implementation return xp.asarray(np.random.laplace(0, 1, size=(batchsize, dim)).astype(np.float32)) elif distribution == "uniform": return xp.random.uniform(-1, 1, (batchsize, dim)) \ .astype(xp.float32) else: raise NotImplementedError def sample_categorical(n_cat, batchsize, distribution='uniform', xp=np, a=None): if a is not None: return xp.random.choice(a, size=(batchsize)).astype(xp.int32) if distribution == 'uniform': return xp.random.randint(low=0, high=n_cat, size=(batchsize)).astype(xp.int32) else: raise NotImplementedError def sample_from_categorical_distribution(batch_probs): """Sample a batch of actions from a batch of action probabilities. Args: batch_probs (ndarray): batch of action probabilities BxA Returns: ndarray consisting of sampled action indices """ xp = chainer.cuda.get_array_module(batch_probs) return xp.argmax( xp.log(batch_probs) + xp.random.gumbel(size=batch_probs.shape), axis=1).astype(np.int32, copy=False) ================================================ FILE: image_generation_chainer/source/yaml_utils.py ================================================ # !/usr/bin/env python # -*- coding: utf-8 -*- from os.path import splitext, basename, dirname, isfile, join from os import getcwd import sys import time from pathlib import Path from copy import deepcopy import yaml # Copy from tgans repo. class Config(object): def __init__(self, config_dict): self.config = config_dict def __getattr__(self, key): if key in self.config: return self.config[key] else: raise AttributeError(key) def __getitem__(self, key): return self.config[key] def __repr__(self): return yaml.dump(self.config, default_flow_style=False) def load_dataset(config, validation=False, valid_path=None): args = deepcopy(config.dataset['args']) dataset = load_module(config.dataset['dataset_fn'], config.dataset['dataset_name']) if validation: # # Modify the path for the validation db. if 'path' in args.keys(): p1 = args['path'] if not isfile(p1): # # try one more time by appending the current path. p1 = join(getcwd(), args['path']) if valid_path is not None: args['path'] = Path(p1).with_name(valid_path).as_posix() else: # # e.g. used in cifar10/svhn. args['train'] = False return dataset(**args) def load_module(fn, name): mod_name = splitext(basename(fn))[0] mod_path = dirname(fn) sys.path.insert(0, mod_path) return getattr(__import__(mod_name), name) def load_model(model_fn, model_name, args=None, GPU=0): model = load_module(model_fn, model_name) model1 = model(**args) if args else model() if GPU: model1.to_gpu() return model1 def load_updater_class(config): return load_module(config.updater['fn'], config.updater['name']) ================================================ FILE: image_generation_chainer/updaters/gans/updater.py ================================================ import numpy as np import chainer import chainer.functions as F from chainer import Variable import source.functions.losses as losses from source.miscs.random_samples import sample_continuous, sample_categorical class Updater(chainer.training.StandardUpdater): def __init__(self, *args, iter_incndis=None, iter_ndis=1, start_ndisone=False, mma=None, mma_dis=None, update_gener=True, **kwargs): self.mma, self.mma_dis, self.update_gener = mma, mma_dis, update_gener self.iter_incndis, self.iter_ndis, self.start_ndisone = iter_incndis, iter_ndis, start_ndisone self.models = kwargs.pop('models') self.n_dis = kwargs.pop('n_dis') self.loss_type = kwargs.pop('loss_type') self.conditional = kwargs.pop('conditional') self.n_gen_samples = kwargs.pop('n_gen_samples') # # iter_incndis: The iteration(s) to increase n_dis, e.g. in the later stages of the training. self.idx_inc, self.ndis_inc = 0, kwargs.pop('ndis_inc') if 'ndis_inc' in kwargs else self.n_dis if self.iter_incndis is not None and isinstance(self.iter_incndis, int): self.iter_incndis = [(self.iter_incndis, self.ndis_inc)] if self.loss_type == 'softplus': self.loss_gen = losses.loss_dcgan_gen self.loss_dis = losses.loss_dcgan_dis elif self.loss_type == 'hinge': self.loss_gen = losses.loss_hinge_gen self.loss_dis = losses.loss_hinge_dis elif self.loss_type == 'revKL': self.loss_gen = losses.loss_revKL_gen self.loss_dis = losses.loss_revKL_dis elif self.loss_type == 'wgan': self.loss_gen = losses.loss_wgan_gen self.loss_dis = losses.loss_wgan_dis super(Updater, self).__init__(*args, **kwargs) def _generate_samples(self, n_gen_samples=None): if n_gen_samples is None: n_gen_samples = self.n_gen_samples gen = self.models['gen'] if self.conditional: y = sample_categorical(gen.n_classes, n_gen_samples, xp=gen.xp) else: y = None x_fake = gen(n_gen_samples, y=y) return x_fake, y def get_batch(self, xp): batch = self.get_iterator('main').next() batchsize = len(batch) x, y = [], [] for j in range(batchsize): x.append(np.asarray(batch[j][0]).astype("f")) y.append(np.asarray(batch[j][1]).astype(np.int32)) x_real = Variable(xp.asarray(x)) y_real = Variable(xp.asarray(y, dtype=xp.int32)) if self.conditional else None return x_real, y_real def update_core(self): gen = self.models['gen'] dis = self.models['dis'] gen_optimizer = self.get_optimizer('opt_gen') dis_optimizer = self.get_optimizer('opt_dis') xp = gen.xp if self.start_ndisone: if self.iteration == 0: # # save the original n_dis and make it 1 in the first few iterations. self.org_ndis = int(self.n_dis) self.n_dis = 1 print('Updater (iter={}): Setting n_dis={}.'.format(self.iteration, self.n_dis)) elif self.iteration == self.iter_ndis: # # make the normal n_dis for the rest of the training. self.n_dis = int(self.org_ndis) # # set the original flag to false to avoid doing the if's. self.start_ndisone = False print('Updater (iter={}): Setting n_dis={}.'.format(self.iteration, self.n_dis)) if self.iter_incndis is not None: if self.iteration < self.iter_incndis[self.idx_inc][0]: self.n_dis = self.iter_incndis[self.idx_inc][1] else: self.idx_inc += 1 if len(self.iter_incndis) < self.idx_inc: # # set to None, since we do not have any more parts to change. self.iter_incndis = None m1 = 'Updater (iter={}): Was n_dis={} ({}).' print(m1.format(self.iteration, self.n_dis, self.iter_incndis)) for i in range(self.n_dis): x_real, y_real = self.get_batch(xp) batchsize = len(x_real) dis_real = dis(x_real, y=y_real) x_fake, y_fake = self._generate_samples(n_gen_samples=batchsize) dis_fake = dis(x_fake, y=y_fake) x_fake.unchain_backward() fake_arr, real_arr = dis_fake.array, dis_real.array chainer.reporter.report({'dis_fake': fake_arr.mean()}) chainer.reporter.report({'dis_real': real_arr.mean()}) chainer.reporter.report({'fake_max': fake_arr.max()}) chainer.reporter.report({'real_min': real_arr.min()}) loss_dis = self.loss_dis(dis_fake=dis_fake, dis_real=dis_real) dis.cleargrads() loss_dis.backward() dis_optimizer.update() loss_dis.unchain_backward() chainer.reporter.report({'loss_dis': loss_dis.array}) del loss_dis if i == 0 and self.update_gener: x_fake, y_fake = self._generate_samples() dis_fake = dis(x_fake, y=y_fake) loss_gen = self.loss_gen(dis_fake=dis_fake) assert not xp.isnan(loss_gen.data) gen.cleargrads() loss_gen.backward() gen_optimizer.update() loss_gen.unchain_backward() chainer.reporter.report({'loss_gen': loss_gen.array}) del loss_gen self.mma.update(gen) if self.mma is not None else None ================================================ FILE: image_generation_pytorch/FID/LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: image_generation_pytorch/FID/README.md ================================================ # Fréchet Inception Distance (FID score) in PyTorch This is a port of the official implementation of [Fréchet Inception Distance](https://arxiv.org/abs/1706.08500) to PyTorch. See [https://github.com/bioinf-jku/TTUR](https://github.com/bioinf-jku/TTUR) for the original implementation using Tensorflow. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the [Fréchet distance](https://en.wikipedia.org/wiki/Fr%C3%A9chet_distance) between two Gaussians fitted to feature representations of the Inception network. Further insights and an independent evaluation of the FID score can be found in [Are GANs Created Equal? A Large-Scale Study](https://arxiv.org/abs/1711.10337). **Note that the official implementation most likely gives slightly different scores**, as it uses pretrained weights from Tensorflow 's Inception, not PyTorch's Inception as used here. If you report FID scores in your paper, and you want them to be comparable to FID scores reported in other papers, you should use [the official Tensorflow implementation](https://github.com/bioinf-jku/TTUR). You can still use this version if you want a quick FID estimate without installing Tensorflow. ## Usage Requirements: - python3 - pytorch - torchvision - numpy - scipy To compute the FID score between two datasets, where images of each dataset are contained in an individual folder: ``` ./fid_score.py path/to/dataset1 path/to/dataset2 ``` To run the evaluation on GPU, use the flag `--gpu N`, where `N` is the index of the GPU to use. ### Using different layers for feature maps In difference to the official implementation, you can choose to use a different feature layer of the Inception network instead of the default `pool3` layer. As the lower layer features still have spatial extent, the features are first global average pooled to a vector before estimating mean and covariance. This might be useful if the datasets you want to compare have less than the otherwise required 2048 images. Note that this changes the magnitude of the FID score and you can not compare them against scores calculated on another dimensionality. The resulting scores might also no longer correlate with visual quality. You can select the dimensionality of features to use with the flag `--dims N`, where N is the dimensionality of features. The choices are: - 64: first max pooling features - 192: second max pooling featurs - 768: pre-aux classifier features - 2048: final average pooling features (this is the default) ## License This implementation is licensed under the Apache License 2.0. FID was introduced by Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler and Sepp Hochreiter in "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium", see [https://arxiv.org/abs/1706.08500](https://arxiv.org/abs/1706.08500) The original implementation is by the Institute of Bioinformatics, JKU Linz, licensed under the Apache License 2.0. See [https://github.com/bioinf-jku/TTUR](https://github.com/bioinf-jku/TTUR). ================================================ FILE: image_generation_pytorch/FID/__init__.py ================================================ ================================================ FILE: image_generation_pytorch/FID/fid_score.py ================================================ #!/usr/bin/env python3 """Calculates the Frechet Inception Distance (FID) to evalulate GANs The FID metric calculates the distance between two distributions of images. Typically, we have summary statistics (mean & covariance matrix) of one of these distributions, while the 2nd distribution is given by a GAN. When run as a stand-alone program, it compares the distribution of images that are stored as PNG/JPEG at a specified location with a distribution given by summary statistics (in pickle format). The FID is calculated by assuming that X_1 and X_2 are the activations of the pool_3 layer of the inception net for generated samples and real world samples respectively. See --help to see further details. Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead of Tensorflow Copyright 2018 Institute of Bioinformatics, JKU Linz Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import os import pathlib from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter import numpy as np import torch import pickle from scipy import linalg from scipy.misc import imread from torch.nn.functional import adaptive_avg_pool2d try: from tqdm import tqdm except ImportError: # If not tqdm is not available, provide a mock version of it def tqdm(x): return x from .inception import InceptionV3 parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter) parser.add_argument('path', type=str, nargs=2, help=('Path to the generated images or ' 'to .npz statistic files')) parser.add_argument('--batch-size', type=int, default=50, help='Batch size to use') parser.add_argument('--dims', type=int, default=2048, choices=list(InceptionV3.BLOCK_INDEX_BY_DIM), help=('Dimensionality of Inception features to use. ' 'By default, uses pool3 features')) parser.add_argument('-c', '--gpu', default='', type=str, help='GPU to use (leave blank for CPU only)') def get_activations(files, model, batch_size=50, dims=2048, cuda=True, verbose=False): """Calculates the activations of the pool_3 layer for all images. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : Batch size of images for the model to process at once. Make sure that the number of samples is a multiple of the batch size, otherwise some samples are ignored. This behavior is retained to match the original FID score implementation. -- dims : Dimensionality of features returned by Inception -- cuda : If set to True, use GPU -- verbose : If set to True and parameter out_step is given, the number of calculated batches is reported. Returns: -- A numpy array of dimension (num images, dims) that contains the activations of the given tensor when feeding inception with the query tensor. """ model.eval() if len(files) % batch_size != 0: print(('Warning: number of images is not a multiple of the ' 'batch size. Some samples are going to be ignored.')) if batch_size > len(files): print(('Warning: batch size is bigger than the data size. ' 'Setting batch size to data size')) batch_size = len(files) n_batches = len(files) // batch_size n_used_imgs = n_batches * batch_size pred_arr = np.empty((n_used_imgs, dims)) for i in tqdm(range(n_batches)): if verbose: print('\rPropagating batch %d/%d' % (i + 1, n_batches), end='', flush=True) start = i * batch_size end = start + batch_size images = np.array([imread(str(f)).astype(np.float32) for f in files[start:end]]) # Reshape to (n_images, 3, height, width) images = images.transpose((0, 3, 1, 2)) images /= 255 batch = torch.from_numpy(images).type(torch.FloatTensor) if cuda: batch = batch.cuda() pred = model(batch)[0] # If model output is not scalar, apply global spatial average pooling. # This happens if you choose a dimensionality not equal 2048. if pred.shape[2] != 1 or pred.shape[3] != 1: pred = adaptive_avg_pool2d(pred, output_size=(1, 1)) pred_arr[start:end] = pred.cpu().data.numpy().reshape(batch_size, -1) if verbose: print(' done') return pred_arr def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params: -- mu1 : Numpy array containing the activations of a layer of the inception net (like returned by the function 'get_predictions') for generated samples. -- mu2 : The sample mean over activations, precalculated on an representative data set. -- sigma1: The covariance matrix over activations for generated samples. -- sigma2: The covariance matrix over activations, precalculated on an representative data set. Returns: -- : The Frechet Distance. """ mu1 = np.atleast_1d(mu1) mu2 = np.atleast_1d(mu2) sigma1 = np.atleast_2d(sigma1) sigma2 = np.atleast_2d(sigma2) assert mu1.shape == mu2.shape, \ 'Training and test mean vectors have different lengths' assert sigma1.shape == sigma2.shape, \ 'Training and test covariances have different dimensions' diff = mu1 - mu2 # Product might be almost singular covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) if not np.isfinite(covmean).all(): msg = ('fid calculation produces singular product; ' 'adding %s to diagonal of cov estimates') % eps print(msg) offset = np.eye(sigma1.shape[0]) * eps covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) # Numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError('Imaginary component {}'.format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) return (diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean) def calculate_activation_statistics(files, model, batch_size=50, dims=2048, cuda=False, verbose=False): """Calculation of the statistics used by the FID. Params: -- files : List of image files paths -- model : Instance of inception model -- batch_size : The images numpy array is split into batches with batch size batch_size. A reasonable batch size depends on the hardware. -- dims : Dimensionality of features returned by Inception -- cuda : If set to True, use GPU -- verbose : If set to True and parameter out_step is given, the number of calculated batches is reported. Returns: -- mu : The mean over samples of the activations of the pool_3 layer of the inception model. -- sigma : The covariance matrix of the activations of the pool_3 layer of the inception model. """ act = get_activations(files, model, batch_size, dims, cuda, verbose) mu = np.mean(act, axis=0) sigma = np.cov(act, rowvar=False) return mu, sigma def _compute_statistics_of_path(path, model, batch_size, dims, cuda): if path.endswith('.pkl'): f = pickle.load(open(path, 'rb')) m, s = f['mu'][:], f['sigma'][:] else: path = pathlib.Path(path) files = list(path.glob('*.jpg')) + list(path.glob('*.png')) m, s = calculate_activation_statistics(files, model, batch_size, dims, cuda) return m, s def calculate_fid_given_paths(paths, batch_size, cuda, dims): """Calculates the FID of two paths""" for p in paths: if not os.path.exists(p): raise RuntimeError('Invalid path: %s' % p) block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims] model = InceptionV3([block_idx]) if cuda: model.cuda() m1, s1 = _compute_statistics_of_path(paths[0], model, batch_size, dims, cuda) m2, s2 = _compute_statistics_of_path(paths[1], model, batch_size, dims, cuda) fid_value = calculate_frechet_distance(m1, s1, m2, s2) return fid_value if __name__ == '__main__': args = parser.parse_args() # os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu fid_value = calculate_fid_given_paths(args.path, args.batch_size, True, args.dims) print('FID: ', fid_value) ================================================ FILE: image_generation_pytorch/FID/inception.py ================================================ import torch.nn as nn import torch.nn.functional as F from torchvision import models class InceptionV3(nn.Module): """Pretrained InceptionV3 network returning feature maps""" # Index of default block of inception to return, # corresponds to output of final average pooling DEFAULT_BLOCK_INDEX = 3 # Maps feature dimensionality to their output blocks indices BLOCK_INDEX_BY_DIM = { 64: 0, # First max pooling features 192: 1, # Second max pooling featurs 768: 2, # Pre-aux classifier features 2048: 3 # Final average pooling features } def __init__(self, output_blocks=[DEFAULT_BLOCK_INDEX], resize_input=True, normalize_input=True, requires_grad=False): """Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return features of. Possible values are: - 0: corresponds to output of first max pooling - 1: corresponds to output of second max pooling - 2: corresponds to output which is fed to aux classifier - 3: corresponds to output of final average pooling resize_input : bool If true, bilinearly resizes input to width and height 299 before feeding input to model. As the network without fully connected layers is fully convolutional, it should be able to handle inputs of arbitrary size, so resizing might not be strictly needed normalize_input : bool If true, scales the input from range (0, 1) to the range the pretrained Inception network expects, namely (-1, 1) requires_grad : bool If true, parameters of the model require gradient. Possibly useful for finetuning the network """ super(InceptionV3, self).__init__() self.resize_input = resize_input self.normalize_input = normalize_input self.output_blocks = sorted(output_blocks) self.last_needed_block = max(output_blocks) assert self.last_needed_block <= 3, \ 'Last possible output block index is 3' self.blocks = nn.ModuleList() inception = models.inception_v3(pretrained=True) # Block 0: input to maxpool1 block0 = [ inception.Conv2d_1a_3x3, inception.Conv2d_2a_3x3, inception.Conv2d_2b_3x3, nn.MaxPool2d(kernel_size=3, stride=2) ] self.blocks.append(nn.Sequential(*block0)) # Block 1: maxpool1 to maxpool2 if self.last_needed_block >= 1: block1 = [ inception.Conv2d_3b_1x1, inception.Conv2d_4a_3x3, nn.MaxPool2d(kernel_size=3, stride=2) ] self.blocks.append(nn.Sequential(*block1)) # Block 2: maxpool2 to aux classifier if self.last_needed_block >= 2: block2 = [ inception.Mixed_5b, inception.Mixed_5c, inception.Mixed_5d, inception.Mixed_6a, inception.Mixed_6b, inception.Mixed_6c, inception.Mixed_6d, inception.Mixed_6e, ] self.blocks.append(nn.Sequential(*block2)) # Block 3: aux classifier to final avgpool if self.last_needed_block >= 3: block3 = [ inception.Mixed_7a, inception.Mixed_7b, inception.Mixed_7c, nn.AdaptiveAvgPool2d(output_size=(1, 1)) ] self.blocks.append(nn.Sequential(*block3)) for param in self.parameters(): param.requires_grad = requires_grad def forward(self, inp): """Get Inception feature maps Parameters ---------- inp : torch.autograd.Variable Input tensor of shape Bx3xHxW. Values are expected to be in range (0, 1) Returns ------- List of torch.autograd.Variable, corresponding to the selected output block, sorted ascending by index """ outp = [] x = inp if self.resize_input: x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=False) if self.normalize_input: x = 2 * x - 1 # Scale from range (0, 1) to range (-1, 1) for idx, block in enumerate(self.blocks): x = block(x) if idx in self.output_blocks: outp.append(x) if idx == self.last_needed_block: break return outp ================================================ FILE: image_generation_pytorch/IS/LICENSE.md ================================================ Copyright 2017 Shane T. Barratt Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: image_generation_pytorch/IS/README.md ================================================ # Inception Score Pytorch Pytorch was lacking code to calculate the Inception Score for GANs. This repository fills this gap. However, we do not recommend using the Inception Score to evaluate generative models, see [our note](https://arxiv.org/abs/1801.01973) for why. ## Getting Started Clone the repository and navigate to it: ``` $ git clone git@github.com:sbarratt/inception-score-pytorch.git $ cd inception-score-pytorch ``` To generate random 64x64 images and calculate the inception score, do the following: ``` $ python inception_score.py ``` The only function is `inception_score`. It takes a list of numpy images normalized to the range [0,1] and a set of arguments and then calculates the inception score. Please assure your images are 299x299x3 and if not (e.g. your GAN was trained on CIFAR), pass `resize=True` to the function to have it automatically resize using bilinear interpolation before passing the images to the inception network. ```python def inception_score(imgs, cuda=True, batch_size=32, resize=False): """Computes the inception score of the generated images imgs imgs -- list of (HxWx3) numpy images normalized in the range [0,1] cuda -- whether or not to run on GPU batch_size -- batch size to feed into inception """ ``` ### Prerequisites You will need [torch](http://pytorch.org/), [torchvision](https://github.com/pytorch/vision), [numpy/scipy](https://scipy.org/). ## License This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details ## Acknowledgments * Inception Score from [Improved Techniques for Training GANs](https://arxiv.org/abs/1606.03498) ================================================ FILE: image_generation_pytorch/IS/inception_score.py ================================================ import torch from torch import nn from torch.autograd import Variable from torch.nn import functional as F import torch.utils.data from torchvision.models.inception import inception_v3 import numpy as np from scipy.stats import entropy def inception_score(imgs, cuda=True, batch_size=1, resize=True, splits=1): """Computes the inception score of the generated images imgs imgs -- Torch dataset of (3xHxW) numpy images normalized in the range [-1, 1] cuda -- whether or not to run on GPU batch_size -- batch size for feeding into Inception v3 splits -- number of splits """ N = len(imgs) assert batch_size > 0 assert N > batch_size # Set up dtype if cuda: dtype = torch.cuda.FloatTensor else: if torch.cuda.is_available(): print("WARNING: You have a CUDA device, so you should probably set cuda=True") dtype = torch.FloatTensor # Set up dataloader # dataloader = torch.utils.data.DataLoader(imgs, batch_size=batch_size) # Load inception model inception_model = inception_v3(pretrained=True, transform_input=False).type(dtype) inception_model.eval() # up = nn.functional.interpolate(size=(299, 299), mode='bilinear', align_corners=True).type(dtype) def get_pred(x): if resize: # x = up(x) x = nn.functional.interpolate(x, size=(299, 299), mode='bilinear', align_corners=True).type(dtype) x = inception_model(x) return F.softmax(x, dim=1).data.cpu().numpy() # Get predictions preds = np.zeros((N, 1000)) # for i, batch in enumerate(dataloader, 0): # for i, batch in enumerate(imgs): for i in range(N//batch_size): batch = imgs[batch_size*i:batch_size*(i+1), :, :, :] batch = torch.from_numpy(batch) batch = batch.type(dtype) batchv = Variable(batch) # batchv = batchv.unsqueeze(0) batch_size_i = batch.size()[0] preds[i*batch_size:i*batch_size + batch_size_i] = get_pred(batchv) # Now compute the mean kl-div split_scores = [] for k in range(splits): part = preds[k * (N // splits): (k+1) * (N // splits), :] py = np.mean(part, axis=0) scores = [] for i in range(part.shape[0]): pyx = part[i, :] scores.append(entropy(pyx, py)) split_scores.append(np.exp(np.mean(scores))) return np.mean(split_scores), np.std(split_scores) ================================================ FILE: image_generation_pytorch/README.rst ================================================ ======================================= Π-nets: Deep Polynomial Neural Networks ======================================= Pytorch implementation of the image generation as described on the CVPR'20 paper "**Π-nets: Deep Polynomial Neural Networks**". Specifically, we include the code for image generation on Cifar10 images. Browsing the folders ==================== The folder structure is the following: * ``data``: The folder that contains the data for Cifar10. If it has not been downloaded and extracted already, it will be downloaded during the first run. * ``FID``: The folder that contains the core code for computing the FID scores. * ``IS``: The folder that contains the core code for computing the IS scores. Train the network ================= First download the following file: https://www.doc.ic.ac.uk/~sm3515/cifar_10_stats.pkl We will need this file to calculate the FID score for Cifar10. We have pre-computed the first/second order statistics for Cifar10 database to expedite the training process (so that we do not need to process and extract statistics from the Cifar10 images each time we need to compute the FID score during the validation). To train the network, you can execute the following command: python main.py The file main.py contains many hyper-parameters which can be either passed as arguments upon the execution of the command above or you can edit the file itself with the hyper-parameters of your liking. If you will be using the default hyper-parameters, the experiments will be saved in the folder dcgan_inject, in the current working directory. Requirements ============ The code was tested on a Linux machine and requires the following packages/versions to run. * python 3.5 with cuda toolkit and cuda 10 (if you intend on running on a GPU) * tensorflow 1.15 * pytorch 1.2 * torchvision 0.4 * scipy 1.1.0 Snippets of the code are borrowed by the official pytorch DCGAN implementation [1]. The codes for the IS and FID scores are borrowed from [2] and [3], respectively. We should point out that the FID/IS scores that are mentioned in the paper may be slightly different to the ones calculated here since for the paper scores, after having extracted the best network, we run the official Tensorflow FID/IS scores implementations and report those, in order to be consistent throughout the whole set of experiments and frameworks (chainer, pytorch, etc.). References ========== .. [1] https://github.com/pytorch/examples/tree/master/dcgan .. [2] https://github.com/sbarratt/inception-score-pytorch .. [3] https://github.com/mseitzer/pytorch-fid ================================================ FILE: image_generation_pytorch/data_loader.py ================================================ import torch import torchvision import torchvision.transforms as transforms def get_loader(data_path, batch_size, mode, num_workers=4): transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) if mode == 'train': dataset = torchvision.datasets.CIFAR10(root=data_path, train=True, download=True, transform=transform) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers) else: dataset = torchvision.datasets.CIFAR10(root=data_path, train=False, download=True, transform=transform) dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers) return dataloader ================================================ FILE: image_generation_pytorch/logger.py ================================================ # Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514 import tensorflow as tf import numpy as np import scipy.misc try: from StringIO import StringIO # Python 2.7 except ImportError: from io import BytesIO # Python 3.x class Logger(object): def __init__(self, log_dir): """Create a summary writer logging to log_dir.""" self.writer = tf.summary.FileWriter(log_dir) def scalar_summary(self, tag, value, step): """Log a scalar variable.""" summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)]) self.writer.add_summary(summary, step) def image_summary(self, tag, images, step): """Log a list of images.""" img_summaries = [] for i, img in enumerate(images): # Write the image to a string try: s = StringIO() except: s = BytesIO() scipy.misc.toimage(img).save(s, format="png") # Create an Image object img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(), height=img.shape[0], width=img.shape[1]) # Create a Summary value img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum)) # Create and write Summary summary = tf.Summary(value=img_summaries) self.writer.add_summary(summary, step) def histo_summary(self, tag, values, step, bins=1000): """Log a histogram of the tensor of values.""" # Create a histogram using numpy counts, bin_edges = np.histogram(values, bins=bins) # Fill the fields of the histogram proto hist = tf.HistogramProto() hist.min = float(np.min(values)) hist.max = float(np.max(values)) hist.num = int(np.prod(values.shape)) hist.sum = float(np.sum(values)) hist.sum_squares = float(np.sum(values**2)) # Drop the start of the first bin bin_edges = bin_edges[1:] # Add bin edges and counts for edge in bin_edges: hist.bucket_limit.append(edge) for c in counts: hist.bucket.append(c) # Create and write Summary summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)]) self.writer.add_summary(summary, step) self.writer.flush() ================================================ FILE: image_generation_pytorch/main.py ================================================ import argparse import os from solver import Solver from data_loader import get_loader from torch.backends import cudnn from datetime import datetime import json def save_config(config, shared_path): save_path = os.path.join(shared_path, "params.json") with open(save_path, 'w') as fp: json.dump(config.__dict__, fp, indent=4, sort_keys=True) def str2bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') def get_time(): return datetime.now().strftime("%d%m_%H%M%S") def main(config): cudnn.benchmark = True config.time_now = get_time() if config.mode == 'train': data_loader = get_loader(data_path=config.data_path, batch_size=config.batch_size, mode=config.mode, num_workers=config.num_workers) shared_path = '{}{}'.format(config.base_path, config.time_now) config.base_path = shared_path config.model_path = "{}/{}".format(shared_path, config.model_path) config.sample_path = "{}/{}".format(shared_path, config.sample_path) config.logs_path = "{}/{}".format(shared_path, config.logs_path) config.validation_path = "{}/{}".format(shared_path, config.validation_path) if not os.path.exists(config.model_path): os.makedirs(config.model_path) if not os.path.exists(config.sample_path): os.makedirs(config.sample_path) if not os.path.exists(config.logs_path): os.makedirs(config.logs_path) if not os.path.exists(config.validation_path): os.makedirs(config.validation_path) save_config(config, shared_path) else: data_loader = None # for i in range(400, 705, 100): # config.seed = i solver = Solver(config, data_loader) if config.mode == 'train': solver.train() elif config.mode == 'test' or config.mode == 'sample': data_loader = None if config.mode == 'test': solver.test() else: solver.sample(config.n_samples) if __name__ == '__main__': parser = argparse.ArgumentParser() # training hyper-parameters parser.add_argument('--num_epochs', type=int, default=100) parser.add_argument('--batch_size', type=int, default=64) parser.add_argument('--sample_size', type=int, default=100) parser.add_argument('--num_workers', type=int, default=16) parser.add_argument('--lr', type=float, default=0.0002) parser.add_argument('--beta1', type=float, default=0.5) parser.add_argument('--beta2', type=float, default=0.99) parser.add_argument('--g_layers', type=int, nargs='+', default=[100, 512, 256, 128, 64, 3]) # network structure of the Generator. First layer (100) is the size of the noise, the last layer (3) is the output size (RGB image in our case) parser.add_argument('--d_layers', type=int, nargs='+', default=[3, 64, 128, 256, 512, 1]) # network structure of the Discriminator. First layer (3) is the input size (RGB image in our case) and the last layer (1) corresponds to the output parser.add_argument('--activation_fn', type=str2bool, default=True) parser.add_argument('--inject_z', type=str2bool, default=True) parser.add_argument('--concat_injection', type=str2bool, default=False) # whether the injection will be concatenated or multiplied parser.add_argument('--loss', type=str, default='original') # can be changed to Wassterstein GAN with GP. Just put 'wgan-gp' parser.add_argument('--gp_weight', type=float, default=10) parser.add_argument('--seed', type=int, default=0) parser.add_argument('--pc_name', type=str, default='neumann') parser.add_argument('--spectral_norm', type=str2bool, default=False) parser.add_argument('--transform_z', type=str2bool, default=False) # whether to have an MLP before injection parser.add_argument('--transform_rep', type=int, default=1) # if transform_z is ``True'', how many MLPs to have in a row before injection parser.add_argument('--norm', type=str, default='batch') # can be changed to ``instance'' to activate the instance normalization # misc parser.add_argument('--mode', type=str, default='train', choices=['train', 'sample']) parser.add_argument('--model_path', type=str, default='models') parser.add_argument('--sample_path', type=str, default='samples') parser.add_argument('--logs_path', type=str, default="logs") parser.add_argument('--data_path', type=str, default='./data') parser.add_argument('--base_path', type=str, default='./dcgan_inject/') # where to save the experiment results parser.add_argument('--ckpt_gen_path', type=str, default='') parser.add_argument('--log_step', type=int, default=50) parser.add_argument('--sample_step', type=int, default=350) parser.add_argument('--validation_step', type=int, default=350) parser.add_argument('--validation_path', type=str, default="validation") parser.add_argument('--cifar10_path', type=str, default='./cifar_10_stats.pkl') # the path for the cifar_10_stats.pkl file parser.add_argument('--FID_images', type=int, default=2048) parser.add_argument('--num_imgs_val', type=int, default=2048) parser.add_argument('--max_score', type=float, default=0) parser.add_argument('--save_every', type=int, default=5) parser.add_argument('--n_samples', type=int, default=100000) # number of samples to generate under the ``sample'' scenario config = parser.parse_args() print(config) main(config) ================================================ FILE: image_generation_pytorch/model.py ================================================ import torch.nn as nn import torch.nn.functional as F from torch.nn.utils import spectral_norm as SN from numpy import sqrt import torch from torch.nn.functional import linear class Generator(nn.Module): def __init__(self, g_layers=[], activation_fn=True, inject_z=True, transform_rep=6, transform_z=False, concat_injection=False, norm='instance'): super(Generator, self).__init__() self.activation_fn = activation_fn self.g_layers = g_layers self.inject_z = inject_z self.num_layers = len(self.g_layers) - 1 # minus the input/output sizes self.transform_rep = transform_rep self.transform_z = transform_z self.concat_injection = concat_injection self.norm = norm if self.transform_z: for i in range(self.transform_rep): setattr(self, "global{}".format(i), nn.Sequential( nn.Linear(self.g_layers[0], self.g_layers[0]), nn.ReLU())) for i in range(self.num_layers): if i == 0: if not self.activation_fn: if self.norm == 'instance': setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 1, 0, bias=False), nn.InstanceNorm2d(self.g_layers[i+1]))) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 1, 0, bias=False), nn.BatchNorm2d(self.g_layers[i+1]))) else: if self.norm == 'instance': setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 1, 0, bias=False), nn.InstanceNorm2d(self.g_layers[i+1]), nn.ReLU())) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 1, 0, bias=False), nn.BatchNorm2d(self.g_layers[i+1]), nn.ReLU())) elif i == self.num_layers - 1: if not self.activation_fn: setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 3, 1, 1, bias=False), nn.Tanh())) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 3, 1, 1, bias=False), nn.Tanh())) else: if self.inject_z: if not self.activation_fn: setattr(self, "inject{}".format(i), nn.Linear(self.g_layers[0], self.g_layers[i])) else: setattr(self, "inject{}".format(i), nn.Sequential( nn.Linear(self.g_layers[0], self.g_layers[i]), nn.ReLU())) if not self.activation_fn: if self.norm == 'instance': setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), nn.InstanceNorm2d(self.g_layers[i+1]))) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), nn.BatchNorm2d(self.g_layers[i+1]))) if self.concat_injection: if self.norm == 'instance': setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(2*self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), nn.InstanceNorm2d(self.g_layers[i+1]))) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(2*self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), nn.BatchNorm2d(self.g_layers[i+1]))) else: if self.norm == 'instance': setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), nn.InstanceNorm2d(self.g_layers[i+1]), nn.ReLU())) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), nn.BatchNorm2d(self.g_layers[i+1]), nn.ReLU())) if self.concat_injection: if self.norm == 'instance': setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(2*self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), nn.InstanceNorm2d(self.g_layers[i+1]), nn.ReLU())) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.ConvTranspose2d(2*self.g_layers[i], self.g_layers[i+1], 4, 2, 1, bias=False), nn.BatchNorm2d(self.g_layers[i+1]), nn.ReLU())) def forward(self, x): z = x.squeeze(3).squeeze(2) if self.transform_z: for i in range(self.transform_rep): z = getattr(self, "global{}".format(i))(z) x = z.unsqueeze(2).unsqueeze(3) for i in range(self.num_layers): if self.inject_z: if i > 0 and i < self.num_layers - 1: a = getattr(self, "inject{}".format(i))(z) a = a.unsqueeze(2).unsqueeze(3).expand(x.size(0), x.size(1), x.size(2), x.size(3)) if self.concat_injection: x = torch.cat((x, a), dim=1) else: x *= a x = getattr(self, "layer{}".format(i))(x) return x class Discriminator(nn.Module): def __init__(self, d_layers=[], activation_fn=True, spectral_norm=False): super(Discriminator, self).__init__() self.activation_fn = activation_fn self.d_layers = d_layers self.num_layers = len(self.d_layers) - 1 # minus the input/output sizes for i in range(self.num_layers): if i == 0: if not spectral_norm: if not self.activation_fn: setattr(self, "layer{}".format(i), nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False)) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False), nn.LeakyReLU(0.2, inplace=True))) else: if not self.activation_fn: setattr(self, "layer{}".format(i), spectral_norm(nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False))) else: setattr(self, "layer{}".format(i), nn.Sequential( spectral_norm(nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False)), nn.LeakyReLU(0.2, inplace=True))) elif i == self.num_layers - 1: if not spectral_norm: setattr(self, "layer{}".format(i), nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False)) else: setattr(self, "layer{}".format(i), spectral_norm(nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False))) else: if not spectral_norm: if not self.activation_fn: setattr(self, "layer{}".format(i), nn.Sequential( nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False), nn.BatchNorm2d(self.d_layers[i+1]))) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False), nn.BatchNorm2d(self.d_layers[i+1]), nn.LeakyReLU(0.2, inplace=True))) else: if not self.activation_fn: setattr(self, "layer{}".format(i), nn.Sequential( spectral_norm(nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False)), nn.BatchNorm2d(self.d_layers[i+1]))) else: setattr(self, "layer{}".format(i), nn.Sequential( nn.Conv2d(self.d_layers[i], self.d_layers[i+1], 4, 2, 1, bias=False), nn.BatchNorm2d(self.d_layers[i+1]), nn.LeakyReLU(0.2, inplace=True))) def forward(self, x): for i in range(self.num_layers): x = getattr(self, "layer{}".format(i))(x) return x ================================================ FILE: image_generation_pytorch/solver.py ================================================ import torch import torch.nn as nn import os from torch import optim from torch.autograd import Variable from model import Discriminator from model import Generator from logger import Logger import numpy as np from torchvision.utils import save_image from torch.autograd import grad as torch_grad from collections import OrderedDict from IS.inception_score import inception_score as IS torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False import json from FID.fid_score import calculate_fid_given_paths as FID def save_is(base_path, score): save_file = os.path.join(base_path, "scores.json") with open(save_file, 'a+') as fp: json.dump(score, fp, indent=4) fp.write("\n") fp.close() def denorm(x): x = 0.5 * (x + 1) x = x.clamp(0, 1) return x def to_cuda(x): if torch.cuda.is_available(): x = x.cuda() return x def to_numpy(x): if torch.cuda.is_available(): x = x.data.cpu() return x.numpy() class Solver(object): def __init__(self, config, data_loader): self.generator = None self.discriminator = None self.g_optimizer = None self.d_optimizer = None self.pc_name = config.pc_name self.base_path = config.base_path self.time_now = config.time_now self.inject_z = config.inject_z self.data_loader = data_loader self.num_epochs = config.num_epochs self.sample_size = config.sample_size self.logs_path = config.logs_path self.save_every = config.save_every self.activation_fn = config.activation_fn self.max_score = config.max_score self.lr = config.lr self.beta1 = config.beta1 self.beta2 = config.beta2 self.log_step = config.log_step self.sample_step = config.sample_step self.validation_step = config.validation_step self.sample_path = config.sample_path self.model_path = config.model_path self.g_layers = config.g_layers self.d_layers = config.d_layers self.z_dim = self.g_layers[0] self.num_imgs_val = config.num_imgs_val self.criterion = nn.BCEWithLogitsLoss() self.ckpt_gen_path = config.ckpt_gen_path self.gp_weight = config.gp_weight self.loss = config.loss self.seed = config.seed self.validation_path = config.validation_path self.FID_images = config.FID_images self.transform_rep = config.transform_rep self.transform_z = config.transform_z self.spectral_norm = config.spectral_norm self.cifar10_path = config.cifar10_path self.fid_score = 100000 self.concat_injection = config.concat_injection self.norm = config.norm self.build_model() def build_model(self): torch.manual_seed(self.seed) self.generator = Generator(g_layers=self.g_layers, activation_fn=self.activation_fn, inject_z=self.inject_z, transform_rep=self.transform_rep, transform_z=self.transform_z, concat_injection=self.concat_injection, norm=self.norm) self.discriminator = Discriminator(d_layers=self.d_layers, activation_fn=self.activation_fn, spectral_norm=self.spectral_norm) self.generator.apply(self.weights_init) self.discriminator.apply(self.weights_init) self.g_optimizer = optim.Adam(self.generator.parameters(), self.lr, betas=(self.beta1, self.beta2)) self.d_optimizer = optim.Adam(self.discriminator.parameters(), self.lr, betas=(self.beta1, self.beta2)) self.logger = Logger(self.logs_path) self.gen_params = sum(p.numel() for p in self.generator.parameters() if p.requires_grad) self.disc_params = sum(p.numel() for p in self.discriminator.parameters() if p.requires_grad) self.total_params = self.gen_params + self.disc_params print("Generator params: {}".format(self.gen_params)) print("Discrimintor params: {}".format(self.disc_params)) print("Total params: {}".format(self.total_params)) if torch.cuda.is_available(): self.generator.cuda() self.discriminator.cuda() def reset_grad(self): self.discriminator.zero_grad() self.generator.zero_grad() # custom weights initialization called on netG and netD def weights_init(self, m): classname = m.__class__.__name__ if classname.find('Conv') != -1: m.weight.data.normal_(0.0, 0.02) elif classname.find('BatchNorm') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) def gradient_penalty(self, real_data, generated_data): batch_size = real_data.size()[0] # Calculate interpolation alpha = torch.rand(batch_size, 1) alpha = alpha.expand_as(real_data) alpha = alpha.cuda() interpolated = alpha * real_data.data + (1 - alpha) * generated_data.data interpolated = Variable(interpolated, requires_grad=True) interpolated = interpolated.cuda() # Calculate probability of interpolated examples prob_interpolated = self.discriminator(interpolated) # Calculate gradients of probabilities with respect to examples gradients = torch_grad(outputs=prob_interpolated, inputs=interpolated, grad_outputs=torch.ones(prob_interpolated.size()).cuda(), create_graph=True, retain_graph=True)[0] # Gradients have shape (batch_size, num_channels, height, width), # so flatten to easily take norm per example in batch gradients = gradients.view(batch_size, -1) # Derivatives of the gradient close to 0 can cause problems because of # the square root, so manually calculate norm and add epsilon gradients_norm = torch.sqrt(torch.sum(gradients ** 2, dim=1) + 1e-12) # Return gradient penalty return self.gp_weight*((gradients_norm - 1) ** 2).mean() def train(self): total_step = len(self.data_loader) for epoch in range(self.num_epochs): for i, (data, _) in enumerate(self.data_loader): batch_size = data.size(0) # train Discriminator data = data.type(torch.FloatTensor) data = to_cuda(data) real_labels = to_cuda(torch.ones(batch_size, self.d_layers[-1])) fake_labels = to_cuda(torch.zeros(batch_size, self.d_layers[-1])) outputs_real = self.discriminator(data) z = to_cuda(torch.randn(batch_size, self.z_dim, 1, 1)) fake_data = self.generator(z) outputs_fake = self.discriminator(fake_data) if self.loss == 'original': d_loss_real = self.criterion(outputs_real.squeeze(), real_labels.squeeze()) d_loss_fake = self.criterion(outputs_fake.squeeze(), fake_labels.squeeze()) d_loss = d_loss_real + d_loss_fake elif self.loss == 'wgan-gp': gradient_penalty = self.gradient_penalty(data, fake_data) d_loss = - outputs_real.mean() + outputs_fake.mean() + gradient_penalty self.reset_grad() d_loss.backward() self.d_optimizer.step() # train Generator z = to_cuda(torch.randn(batch_size, self.z_dim, 1, 1)) fake_data = self.generator(z) outputs_fake = self.discriminator(fake_data) if self.loss == 'original': g_loss = self.criterion(outputs_fake.squeeze(), real_labels.squeeze()) elif self.loss == 'wgan-gp': g_loss = -outputs_fake.mean() self.reset_grad() g_loss.backward() self.g_optimizer.step() if (i+1) % self.log_step == 0: print('Epoch [{0:d}/{1:d}], Step [{2:d}/{3:d}], d_real_loss: {4:.4f}, ' ' g_loss: {5:.4f}'.format(epoch+1, self.num_epochs, i+1, total_step, d_loss.item(), g_loss.item())) # log scalars in tensorboard info = { 'd_real_loss': d_loss.item(), 'g_loss': g_loss.item(), 'inception_score': self.max_score } for tag, value in info.items(): self.logger.scalar_summary(tag, value, epoch*total_step + i + 1) if (i + 1) % self.sample_step == 0: save_image(denorm(fake_data).cpu(), self.sample_path + "/epoch_{}_{}.png".format(i + 1, epoch + 1), nrow=8) if (i + 1) % self.validation_step == 0: fake_data_all = np.zeros((self.num_imgs_val, fake_data.size(1), fake_data.size(2), fake_data.size(3))) for j in range(self.num_imgs_val // batch_size): fake_data_all[j*batch_size:(j+1)*batch_size] = to_numpy(fake_data) npy_path = os.path.join(self.model_path, '{}_{}_val_data.pkl'.format(epoch + 1, i + 1)) np.save(npy_path, fake_data_all) score, _ = IS(fake_data_all, cuda=True, batch_size=batch_size) if score > self.max_score: print("Found new best IS score: {}".format(score)) self.max_score = score data = "IS " + str(self.seed) + " " + str(epoch + 1) + " " + str(i + 1) + " " + str(self.max_score) save_is(self.base_path, data) g_path = os.path.join(self.model_path, 'generator-best.pkl') d_path = os.path.join(self.model_path, 'discriminator-best.pkl') torch.save(self.generator.state_dict(), g_path) torch.save(self.discriminator.state_dict(), d_path) for j in range(self.FID_images): z = to_cuda(torch.randn(1, self.z_dim, 1, 1)) fake_datum = self.generator(z) save_image(denorm(fake_datum.squeeze()).cpu(), self.validation_path + "/" + str(j) + ".png") fid_value = FID([self.validation_path, self.cifar10_path], 64, True, 2048) if fid_value < self.fid_score: self.fid_score = fid_value print("Found new best FID score: {}".format(self.fid_score)) data = "FID " + str(self.seed) + " " + str(epoch + 1) + " " + str(i + 1) + " " + str(self.fid_score) save_is(self.base_path, data) g_path = os.path.join(self.model_path, 'generator-best-fid.pkl') d_path = os.path.join(self.model_path, 'discriminator-best-fid.pkl') torch.save(self.generator.state_dict(), g_path) torch.save(self.discriminator.state_dict(), d_path) if (epoch + 1) % self.save_every == 0: g_path = os.path.join(self.model_path, 'generator-{}.pkl'.format(epoch+1)) d_path = os.path.join(self.model_path, 'discriminator-{}.pkl'.format(epoch+1)) torch.save(self.generator.state_dict(), g_path) torch.save(self.discriminator.state_dict(), d_path) def sample(self, n_samples): self.n_samples = n_samples self.generator = Generator(g_layers=self.g_layers, inject_z=self.inject_z) self.generator.load_state_dict(torch.load(self.ckpt_gen_path)) if torch.cuda.is_available(): self.generator.cuda() self.generator.eval() z_samples = to_cuda(torch.randn(n_samples, self.z_dim, 1, 1)) generated_samples = self.generator(z_samples) generated_samples = to_numpy(generated_samples) np.save('./saved/generated_samples.npy', generated_samples) z_samples = to_numpy(z_samples) np.save('./saved/z_samples.npy', z_samples) ================================================ FILE: mesh_pi_nets/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class .idea/ # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook *.ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ ================================================ FILE: mesh_pi_nets/README.md ================================================ Official Implementation of the experiments on mesh representation learning with polynomial networks as described in the CVPR'20 paper [Π-nets: Deep Polynomial Neural Networks](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chrysos_P-nets_Deep_Polynomial_Neural_Networks_CVPR_2020_paper.pdf) and its [extension](https://arxiv.org/abs/2006.13026). This repository is based on Neural3DMM [[paper](https://arxiv.org/abs/1905.02876), [code](https://github.com/gbouritsas/Neural3DMM)]. Please refer to the Neural3DMM repository for technical details (e.g. regarding the local vertex orderings or the mesh downsampling operations) and for explanations of the data organisation/folder hierarchy. ## Data We provide the templates (mesh topology) as well as their downsampled versions for the two datasets that we experimented with, namely [COMA](https://coma.is.tue.mpg.de/) and [DFAUST](http://dfaust.is.tue.mpg.de/). Unfortunately, we cannot provide the data in this repository, but they can be easily obtained from the authors of the datasets. Then, follow the instructions in the [Neural3DMM repo](https://github.com/gbouritsas/Neural3DMM) to organise the data as expected from the code. For COMA we use the splits provided by the authors (in the paper we report the results on the 'sliced' version of the dataset - interpolation experiment). If you would like to train on the same splits for DFAUST, please contact us. ## Important parameters ``` --injection: if set to True runs the polynomial version of SpiralNet. Alternatively, plain SpiralNet is created. --order: the order of the polynomial --model:'full' creates the polynomial that NCP decomposition yields, 'simple' creates the parameter efficient polynomial (element-wise multiplications akin to polynomial activation functions) --normalize: 'final' (only the highest order term is normalised), '2nd' (normalises the 2nd order term of each iterate of the NCP recursive formulation), 'all' (normalises the output of each iterate of the NCP recursive formulation) --residual: if True, it invokes the NCP-skip decomposition --activation: if set to identity, no activation functions are used (linear w.r.t. weights) ``` ## Training Example usage: **COMA** ``` python spiral_pi_nets.py --root_dir ./datasets --name sliced --dataset COMA --mode train --order 3 --model full --normalize 2nd --activation identity --residual True --results_folder 3rd_order_full_norm_2nd_residual_linear --device_idx 0 --batch_size 16 ``` **DFAUST** ``` python spiral_pi_nets.py --root_dir ./datasets --dataset DFAUST --mode train --order 3 --model full --normalize 2nd --activation identity --residual True --results_folder 3rd_order_full_norm_2nd_residual_linear --device_idx 0 --batch_size 16 ``` ## Testing Assign to the _results_folder_ argument the value of the folder where the pretrained network is saved. By setting the _mm_constant_ you can obtain measurements in milimiters. For COMA and DFAUST this value is 10^3. Example usage: **COMA** ``` python spiral_pi_nets.py --root_dir ./datasets --name sliced --dataset COMA --mode test --order 3 --model full --normalize 2nd --activation identity --residual True --results_folder 3rd_order_full_norm_2nd_residual_linear --device_idx 0 --batch_size 16 --mm_constant 1000 ``` **DFAUST** ``` python spiral_pi_nets.py --root_dir ./datasets --dataset DFAUST --mode test --order 3 --model full --normalize 2nd --activation identity --residual True --results_folder 3rd_order_full_norm_2nd_residual_linear --device_idx 0 --batch_size 16 --mm_constant 1000 ``` ## Dependencies and Installation Instructions Requirements: - python 3.7 - pytorch>=1.2.0 - cudatoolkit>=9.2 - scipy - trimesh - sklearn - tqdm - tensorboardX Recommended installation instructions ``` conda create --name pi_spirals python=3.7 conda activate pi_spirals conda install pytorch==1.2.0 cudatoolkit=9.2 -c pytorch pip install scipy pip install trimesh pip install sklearn pip install tqdm pip install tensorboardX ``` ================================================ FILE: mesh_pi_nets/autoencoder_dataset.py ================================================ from torch.utils.data import Dataset import torch import numpy as np import os class autoencoder_dataset(Dataset): def __init__(self, root_dir, points_dataset, shapedata, normalization=True, dummy_node=True): self.shapedata = shapedata self.normalization = normalization self.root_dir = root_dir self.points_dataset = points_dataset self.dummy_node = dummy_node self.paths = np.load(os.path.join(root_dir, 'paths_'+points_dataset+'.npy')) def __len__(self): return len(self.paths) def __getitem__(self, idx): basename = self.paths[idx] verts_init = np.load(os.path.join(self.root_dir, 'points'+'_'+self.points_dataset, basename+'.npy')) if self.normalization: verts_init = verts_init - self.shapedata.mean verts_init = verts_init/self.shapedata.std verts_init[np.where(np.isnan(verts_init))]=0.0 verts_init = verts_init.astype('float32') if self.dummy_node: verts = np.zeros((verts_init.shape[0]+1, verts_init.shape[1]), dtype=np.float32) verts[:-1, :] = verts_init verts_init = verts verts = torch.Tensor(verts_init) sample = {'points': verts} return sample ================================================ FILE: mesh_pi_nets/datasets/COMA/templates/COMA_downsample/downsampling_matrices.pkl ================================================ (dp0 S'A' p1 (lp2 ccopy_reg _reconstructor p3 (cscipy.sparse.csc csc_matrix p4 c__builtin__ object p5 Ntp6 Rp7 (dp8 S'indices' p9 cnumpy.core.multiarray _reconstruct p10 (cnumpy ndarray p11 (I0 tp12 S'b' p13 tp14 Rp15 (I1 (I29990 tp16 cnumpy dtype p17 (S'i4' p18 I0 I1 tp19 Rp20 (I3 S'<' p21 NNNI-1 I-1 I0 tp22 bI00 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p98 tp99 bsg25 g10 (g11 (I0 tp100 g13 tp101 Rp102 (I1 (I80 tp103 g20 I00 S'\x00\x00\x00\x00\x07\x00\x00\x00\n\x00\x00\x00\x0e\x00\x00\x00\x16\x00\x00\x00\x1c\x00\x00\x00"\x00\x00\x00&\x00\x00\x00.\x00\x00\x001\x00\x00\x005\x00\x00\x00=\x00\x00\x00D\x00\x00\x00J\x00\x00\x00P\x00\x00\x00W\x00\x00\x00`\x00\x00\x00d\x00\x00\x00h\x00\x00\x00p\x00\x00\x00v\x00\x00\x00{\x00\x00\x00\x80\x00\x00\x00\x86\x00\x00\x00\x8c\x00\x00\x00\x92\x00\x00\x00\x98\x00\x00\x00\x9c\x00\x00\x00\xa1\x00\x00\x00\xa8\x00\x00\x00\xae\x00\x00\x00\xb4\x00\x00\x00\xba\x00\x00\x00\xc0\x00\x00\x00\xc6\x00\x00\x00\xce\x00\x00\x00\xd4\x00\x00\x00\xda\x00\x00\x00\xde\x00\x00\x00\xe3\x00\x00\x00\xe9\x00\x00\x00\xf0\x00\x00\x00\xf7\x00\x00\x00\xfa\x00\x00\x00\xfe\x00\x00\x00\x02\x01\x00\x00\x08\x01\x00\x00\r\x01\x00\x00\x13\x01\x00\x00\x1b\x01\x00\x00 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p120 tp121 bsg25 g10 (g11 (I0 tp122 g13 tp123 Rp124 (I1 (I21 tp125 g20 I00 S'\x00\x00\x00\x00\x06\x00\x00\x00\t\x00\x00\x00\x0e\x00\x00\x00\x12\x00\x00\x00\x18\x00\x00\x00\x1d\x00\x00\x00"\x00\x00\x00\'\x00\x00\x00,\x00\x00\x002\x00\x00\x008\x00\x00\x00<\x00\x00\x00A\x00\x00\x00G\x00\x00\x00L\x00\x00\x00Q\x00\x00\x00U\x00\x00\x00[\x00\x00\x00a\x00\x00\x00h\x00\x00\x00' p126 tp127 bsg32 I50 sg33 (I20 I20 tp128 sg35 g10 (g11 (I0 tp129 g13 tp130 Rp131 (I1 (I104 tp132 g42 I00 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p363 tp364 bsbag3 (g4 g5 Ntp365 Rp366 (dp367 g9 g10 (g11 (I0 tp368 g13 tp369 Rp370 (I1 (I20 tp371 g20 I00 S'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00\x06\x00\x00\x00\x07\x00\x00\x00\x08\x00\x00\x00\t\x00\x00\x00\n\x00\x00\x00\x0b\x00\x00\x00\x0c\x00\x00\x00\r\x00\x00\x00\x0e\x00\x00\x00\x0f\x00\x00\x00\x10\x00\x00\x00\x11\x00\x00\x00\x12\x00\x00\x00\x13\x00\x00\x00' p372 tp373 bsg35 g10 (g11 (I0 tp374 g13 tp375 Rp376 (I1 (I20 tp377 g42 I00 S'\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?' p378 tp379 bsg32 I50 sg33 (I20 I79 tp380 sg25 g10 (g11 (I0 tp381 g13 tp382 Rp383 (I1 (I80 tp384 g20 I00 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1194 f 2094 2075 2093 f 1920 1919 1915 f 453 1912 1911 f 1915 1918 1916 f 3882 2106 3901 f 1655 3531 1868 f 1916 1926 1925 f 704 3561 703 f 278 1925 1900 f 3565 1818 1820 f 1097 1115 1114 f 1927 727 1923 f 3578 3654 3758 f 1920 307 1929 f 1929 48 728 f 503 1497 1509 f 1902 787 796 f 1816 3820 3826 f 1925 1923 1924 f 1535 1568 1534 f 254 258 255 f 1509 366 367 f 119 35 36 f 219 293 294 f 141 47 431 f 542 244 245 f 1940 430 431 f 1779 1827 1790 f 1701 3915 3919 f 1661 3515 1667 f 270 754 647 f 197 749 196 f 1727 1212 1211 f 53 232 754 f 1465 1479 1478 f 1284 3854 1293 f 1951 412 413 f 168 1554 1553 f 1486 206 207 f 506 78 361 f 994 1007 993 f 951 963 964 f 1013 3861 1351 f 3826 1820 1818 f 1498 1954 1482 f 299 543 132 f 1754 1756 1753 f 1681 1683 1682 f 1696 1695 1697 f 13 738 737 f 473 475 474 f 482 768 1640 f 204 30 205 f 1761 820 819 f 611 1125 612 f 1501 1908 1909 f 1074 711 699 f 1960 82 83 f 120 1960 123 f 35 87 34 f 1773 1025 1031 f 1965 1569 1535 f 494 471 479 f 1966 1969 1968 f 446 1971 1967 f 174 125 126 f 1580 1578 1577 f 1785 1787 1784 f 1972 304 305 f 1254 875 999 f 1973 342 514 f 321 1973 509 f 1910 275 1907 f 509 514 508 f 1486 1527 1485 f 1974 804 1975 f 1977 1974 1978 f 1980 1966 1981 f 379 2 295 f 1718 1710 1701 f 884 992 883 f 1680 1678 1679 f 902 525 524 f 344 256 299 f 135 137 136 f 236 228 333 f 448 1967 1966 f 589 604 584 f 910 908 907 f 1981 1968 1987 f 1982 1987 1979 f 1988 1987 1989 f 1990 1989 1991 f 1991 1993 1992 f 322 320 319 f 992 830 883 f 1144 1086 1143 f 1995 1978 1994 f 1133 1118 1126 f 1997 1994 1996 f 870 1335 1489 f 805 1982 1998 f 727 2000 1999 f 1996 1988 1990 f 1994 1979 1988 f 1829 792 1273 f 1830 941 983 f 798 800 799 f 729 731 730 f 2011 2023 2022 f 1834 846 1159 f 402 284 347 f 2022 2024 2025 f 1838 989 988 f 810 952 809 f 1823 1840 644 f 1430 1432 1431 f 1215 1841 1079 f 149 176 177 f 1316 873 872 f 2112 1384 2106 f 1842 1844 1845 f 1159 845 1160 f 837 1846 1120 f 1971 2030 2027 f 517 370 189 f 858 833 812 f 1471 2027 1888 f 3551 626 627 f 2174 770 490 f 935 1854 1010 f 1613 1611 1618 f 1231 1225 1295 f 1856 891 892 f 1300 748 741 f 1150 1858 1859 f 1643 696 1642 f 1215 1264 1216 f 1320 902 901 f 1956 1944 1960 f 2031 669 1474 f 2034 1310 1299 f 1879 1656 1657 f 40 242 39 f 1992 2039 1456 f 888 957 889 f 1882 1996 1990 f 1078 1255 1077 f 1871 1192 1090 f 294 218 219 f 1323 2026 1471 f 123 83 1484 f 1324 2041 1323 f 384 359 352 f 1324 2038 2036 f 1883 1885 1829 f 287 114 107 f 1096 1134 661 f 915 931 916 f 806 1981 1982 f 3533 584 604 f 1425 2042 1018 f 1018 1948 1017 f 3562 740 3519 f 1948 649 646 f 1881 1990 1991 f 1900 130 278 f 992 994 993 f 923 925 924 f 3878 2047 3876 f 1643 625 606 f 2050 2051 2049 f 2045 1404 2047 f 3876 945 3885 f 1139 1079 1841 f 1037 892 982 f 2055 2057 2056 f 1198 718 717 f 1619 1612 1611 f 2058 1606 2059 f 2059 1607 2058 f 2058 1624 1605 f 71 179 37 f 405 1557 382 f 976 1006 977 f 322 1972 1911 f 2061 2063 2062 f 2066 2067 1825 f 2052 2064 2051 f 1609 2066 1647 f 1515 152 1516 f 1009 993 1007 f 1644 3815 2118 f 279 1916 278 f 1285 3862 1284 f 1647 1825 1649 f 2056 2067 2065 f 319 1953 1952 f 667 1913 1914 f 2070 625 2048 f 2052 2055 2063 f 2038 1655 1458 f 1606 2073 2072 f 1235 1270 1236 f 2072 2075 2074 f 2059 2072 2062 f 2074 2077 2076 f 1601 2078 2073 f 2049 1643 1645 f 1000 1001 994 f 1089 684 689 f 1931 1306 1930 f 1462 1021 1022 f 1932 1934 1935 f 875 1846 998 f 1936 1937 1938 f 44 46 92 f 1534 1956 120 f 1541 317 391 f 2081 2084 2083 f 146 1959 300 f 1360 1942 1361 f 985 1032 1033 f 1995 1892 2043 f 2079 2082 2081 f 2095 2076 2077 f 2081 2078 2079 f 2055 2054 2053 f 501 2095 500 f 2097 2099 2098 f 2098 2094 2083 f 2140 3893 3866 f 2141 3894 3893 f 2051 2048 2049 f 592 2101 1576 f 2071 2076 2096 f 2102 2098 2101 f 593 2102 592 f 1019 746 747 f 1950 12 1327 f 725 2096 501 f 1385 2104 1379 f 1379 2106 1384 f 2107 2100 2097 f 3908 502 3896 f 3896 499 3901 f 940 2109 944 f 928 1170 973 f 3868 684 3899 f 1962 1964 1961 f 3878 573 3897 f 2039 1968 1969 f 1389 2110 2109 f 1398 2113 1397 f 3757 3753 3743 f 3911 2107 2103 f 949 924 1115 f 260 324 326 f 1390 2113 2110 f 2070 2064 2071 f 3867 2103 593 f 2105 1421 2114 f 2114 1423 2115 f 3892 2115 3903 f 3908 2114 3892 f 554 1902 553 f 972 967 966 f 1765 1573 1574 f 1984 1986 1983 f 2053 2069 2057 f 3573 724 714 f 1625 1674 1673 f 3881 2119 2122 f 3903 2123 3904 f 56 1906 55 f 626 3879 629 f 2001 2003 2004 f 3912 2127 3907 f 2006 2008 2009 f 2127 1412 2128 f 2014 2016 2017 f 2018 2020 2021 f 1495 1947 197 f 1379 1386 1385 f 3814 1680 1618 f 2029 1884 2028 f 3902 2120 3887 f 629 3914 3872 f 682 943 1089 f 724 2121 716 f 1757 3821 3845 f 765 3844 3809 f 3851 715 3852 f 706 3870 3872 f 981 987 980 f 1457 1656 1880 f 2017 2032 2033 f 1458 1868 1457 f 1776 1792 1793 f 1959 1946 1958 f 3663 3725 3661 f 3913 2122 3850 f 1093 689 1088 f 966 968 972 f 3852 3881 3913 f 2172 2170 2171 f 670 2137 669 f 3761 1903 233 f 1429 28 1449 f 964 978 971 f 3841 1413 2127 f 1473 669 2137 f 3834 1359 1358 f 2138 1870 2137 f 1872 2137 1870 f 715 3888 3881 f 1881 1992 1880 f 3843 739 740 f 1837 3513 3514 f 253 59 85 f 1808 3840 1807 f 1647 3818 1609 f 3865 671 668 f 2112 3883 2111 f 1405 2044 1438 f 3829 1364 1366 f 273 391 249 f 576 3520 3555 f 3875 668 2031 f 1430 2140 2139 f 2136 3860 28 f 1430 18 19 f 3893 570 695 f 1442 2046 2141 f 1431 2141 2140 f 3869 3850 2136 f 3808 1817 766 f 3866 695 671 f 2142 798 2138 f 571 2142 694 f 3819 1618 1611 f 2143 2025 2142 f 694 2138 670 f 681 2144 572 f 2144 2022 2143 f 328 457 290 f 1572 2011 2144 f 630 3903 624 f 2085 2087 2088 f 2090 2092 2089 f 572 2143 571 f 1875 2153 2149 f 1873 2149 2154 f 2103 2097 2102 f 2157 2155 2156 f 2128 1414 2120 f 3874 589 582 f 3872 628 629 f 2149 2158 2159 f 2159 3905 3890 f 698 3902 705 f 3823 3827 1817 f 2158 3874 3905 f 1438 2046 1439 f 2035 2160 2161 f 2161 3889 3886 f 3871 605 589 f 3873 18 3875 f 370 61 451 f 649 413 648 f 2153 2156 2158 f 1428 1449 1450 f 1490 73 1488 f 3890 710 633 f 2155 3891 3871 f 1456 1880 1992 f 2125 2087 2124 f 2160 3890 3889 f 300 505 323 f 1064 1046 1065 f 2043 1891 2155 f 2129 2004 2003 f 2131 2133 2134 f 3848 1450 1449 f 1998 804 805 f 3879 3864 3914 f 474 490 770 f 1164 1182 1177 f 707 3909 3870 f 708 2162 707 f 2162 3886 3909 f 548 1905 458 f 1145 1114 1171 f 705 1650 704 f 2043 1977 1995 f 737 554 559 f 1034 1191 1023 f 458 1907 275 f 195 232 233 f 2040 1969 1970 f 2145 2147 2148 f 2054 2049 1645 f 3832 1639 3818 f 9 11 10 f 252 506 258 f 472 479 471 f 1865 3791 3503 f 900 910 907 f 1941 2151 1942 f 1921 1927 1919 f 2163 1898 2116 f 271 153 178 f 1904 12 9 f 282 264 460 f 1872 2154 2035 f 1326 1897 2163 f 628 693 627 f 1724 1719 1723 f 1549 436 172 f 1582 1730 1729 f 1921 1929 1928 f 3563 10 11 f 2152 1950 1949 f 475 774 771 f 769 2171 473 f 2171 476 473 f 476 772 475 f 1791 464 1760 f 2166 2168 2169 f 595 372 98 f 483 1641 482 f 762 2173 470 f 771 484 492 f 486 1809 483 f 297 1517 296 f 763 2174 762 f 776 759 489 f 184 2176 2000 f 412 303 414 f 2005 185 224 f 3811 1682 3846 f 2164 756 764 f 1472 2161 2162 f 950 964 965 f 2177 2179 2180 f 227 1510 333 f 764 467 2164 f 2182 2184 2181 f 2186 2018 2185 f 1985 2181 1986 f 214 729 1531 f 1545 1546 1569 f 865 842 866 f 849 1357 1362 f 452 1558 454 f 559 3188 737 f 16 737 3188 f 560 498 374 f 3193 3195 3194 f 3200 3198 3199 f 3211 3213 3212 f 3215 3217 3216 f 2187 2002 2001 f 2169 2189 2190 f 2192 2085 2191 f 2193 2195 2196 f 2198 2200 2197 f 1998 1979 1975 f 3664 3668 3671 f 2116 1903 1902 f 1859 2009 1150 f 3904 2126 3906 f 1135 1087 1130 f 2201 2015 2014 f 1149 110 1858 f 3221 3218 3215 f 2203 2205 2206 f 579 1895 1019 f 1755 2208 1756 f 3891 1893 605 f 1211 2150 1727 f 868 2210 2209 f 944 930 940 f 2130 2202 2201 f 233 1898 195 f 665 750 666 f 1167 999 618 f 2211 2213 2214 f 2215 2217 2218 f 3867 590 3868 f 2220 220 2219 f 823 948 1094 f 3225 3227 3226 f 266 455 265 f 3882 2107 3883 f 2221 3519 3517 f 2223 2225 2226 f 2207 2124 2208 f 673 3869 3864 f 817 996 815 f 2227 2146 2145 f 1947 646 749 f 3900 17 3873 f 2229 1638 1381 f 1679 1618 1680 f 1637 2232 2230 f 2233 1617 1616 f 2231 2236 2237 f 1381 2238 1382 f 3586 2242 3587 f 1630 2233 1631 f 1681 472 469 f 1408 2243 2244 f 3236 3238 3237 f 3232 3240 3235 f 1637 2238 1638 f 3237 3233 3234 f 1409 1634 2245 f 1380 2247 2246 f 1628 2245 1621 f 2249 1622 1633 f 1632 1408 2244 f 2248 1631 2250 f 2235 1637 1636 f 3245 3199 3198 f 631 2071 725 f 515 52 49 f 782 2251 1251 f 2253 2255 2252 f 397 459 784 f 1456 2040 1455 f 2040 2036 1455 f 1401 2111 1398 f 2041 1970 2026 f 1535 414 1965 f 1586 2080 2079 f 2256 1132 1886 f 1700 3918 3919 f 3815 1686 3817 f 1263 1886 1096 f 598 844 599 f 2257 2259 2260 f 2258 2262 2259 f 2259 2263 2216 f 2260 2216 2215 f 1967 1970 1969 f 2212 1963 2213 f 922 2218 2217 f 1312 1101 2264 f 1307 1286 789 f 1548 1546 1972 f 2266 2268 2265 f 2265 2270 2269 f 2272 2269 2271 f 2273 2265 2272 f 2275 2277 2274 f 2278 2277 2279 f 2284 3617 3639 f 2287 2286 2281 f 2288 2283 2287 f 2290 3639 3620 f 2291 2293 2294 f 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f 2378 2380 2381 f 2382 2381 2383 f 2384 2378 2382 f 2385 2379 2378 f 2388 2390 2387 f 2387 2392 2391 f 2393 2387 2391 f 2394 2388 2387 f 2186 2402 2400 f 2403 2327 2326 f 2272 2326 2273 f 2271 2403 2272 f 2405 2404 2403 f 2404 2408 2327 f 2409 2411 2412 f 2409 2271 2269 f 2413 2269 2270 f 2413 2410 2409 f 2417 2416 2418 f 2417 2406 2405 f 2412 2405 2271 f 2411 2417 2412 f 2406 2407 2404 f 3569 632 713 f 2420 2397 2421 f 2420 2423 2422 f 2424 2422 2419 f 2424 2398 2420 f 2425 2392 2426 f 2421 2426 2423 f 2397 2425 2421 f 2396 2391 2425 f 2427 2315 2314 f 2427 2408 2428 f 2430 2428 2429 f 2343 2427 2430 f 2432 2430 2431 f 2431 2429 2433 f 2435 2433 2434 f 2436 2431 2435 f 2428 2407 2437 f 2422 2407 2419 f 2438 2422 2423 f 2429 2437 2438 f 2440 2433 2439 f 2441 2433 2429 f 2442 2439 2441 f 2443 2440 2439 f 2445 2444 2443 f 2447 2443 2442 f 2383 2445 2447 f 2381 2446 2445 f 2448 2423 2426 f 2449 2426 2392 f 2441 2449 2442 f 2441 2438 2448 f 2449 2390 2450 f 2450 2389 2451 f 2447 2451 2383 f 2442 2450 2447 f 2451 2453 2452 f 2452 2455 2454 f 2382 2454 2384 f 2383 2452 2382 f 2456 2373 2372 f 2459 2461 2458 f 2463 2465 2462 f 2462 2285 2284 f 2466 2284 2290 f 2467 2462 2466 f 2461 3703 3701 f 2468 2470 2471 f 2472 2471 2473 f 2474 3701 3688 f 2475 2477 2478 f 2458 2453 2479 f 2479 2389 2388 f 2395 2479 2388 f 2480 2458 2479 f 2481 2483 2478 f 2484 2485 221 f 2471 2464 2463 f 2473 2463 2467 f 2487 2466 2486 f 2486 2290 2488 f 2490 2488 2489 f 2491 2486 2490 f 2492 2467 2487 f 2493 2487 2491 f 2494 2492 2493 f 2495 2473 2492 f 3690 2474 3688 f 3691 2496 3690 f 2498 2493 2499 f 2500 2499 2501 f 3700 2498 2500 f 3687 2494 2498 f 2306 2278 2300 f 2305 2504 2306 f 2504 2507 2506 f 2506 2288 2508 f 2274 2508 2275 f 2278 2506 2274 f 3633 2489 2488 f 3620 2488 2290 f 2507 2289 2288 f 2505 2509 2507 f 2508 2287 2511 f 2511 2281 2280 f 2370 2280 2282 f 2275 2511 2370 f 2512 2489 2513 f 2514 2513 2515 f 2517 2514 2516 f 2517 2490 2512 f 2499 2491 2517 f 2499 2516 2501 f 2510 2519 2518 f 2518 2521 2520 f 3616 2520 3621 f 3632 2518 3616 f 2522 2303 2310 f 2523 2310 2309 f 2305 2522 2524 f 2524 2523 2525 f 2519 2525 2521 f 2505 2524 2519 f 2308 2527 2526 f 2526 2529 2528 f 2531 2528 2530 f 2309 2526 2531 f 1214 2151 1211 f 2419 2418 2424 f 2533 2349 2317 f 2534 2317 2320 f 2535 2533 2534 f 2536 2345 2533 f 2537 2346 2536 f 2538 2536 2535 f 2539 2537 2538 f 2540 2351 2537 f 2312 2541 2527 f 2527 2542 2529 f 2543 2434 2544 f 2545 2544 2546 f 2319 2543 2545 f 2318 2435 2543 f 2514 2548 2547 f 2547 2550 2549 f 2552 2549 2551 f 2552 2514 2547 f 2520 2554 2553 f 2553 2556 2555 f 3590 2555 3593 f 3621 2553 3590 f 2523 2531 2557 f 2557 2530 2558 f 2525 2557 2559 f 2559 2558 2560 f 2554 2560 2556 f 2521 2559 2554 f 2561 2007 2006 f 2376 2225 2377 f 2564 2566 2563 f 2563 338 2567 f 2569 2567 2568 f 2570 2563 2569 f 2572 2574 2571 f 2571 2576 2575 f 2544 2575 2546 f 2544 2572 2571 f 2578 2569 2577 f 2579 2569 2568 f 2574 2579 2576 f 2573 2577 2574 f 2581 2583 2580 f 2584 2583 2585 f 3697 2580 2584 f 3676 2580 3675 f 2582 2552 2583 f 2583 2551 2585 f 2500 2589 2588 f 2590 2589 2591 f 3693 2588 2590 f 3695 2500 2588 f 2501 2591 2589 f 2592 2594 2595 f 2386 2503 2502 f 2596 2386 2374 f 2596 2373 2597 f 2599 2597 2598 f 2599 2379 2596 f 2600 2380 2599 f 2600 2598 2601 f 2603 2601 2602 f 2603 2446 2600 f 2604 2602 2605 f 2440 2605 2434 f 2444 2604 2440 f 2606 2581 2607 f 2606 2590 2591 f 2591 2582 2606 f 3676 2456 3689 f 3689 2372 3693 f 2478 2608 2481 f 2609 2611 2199 f 2564 2613 2612 f 2612 2598 2597 f 2457 2597 2373 f 2565 2612 2457 f 2614 2570 2578 f 2615 2578 2573 f 2602 2614 2615 f 2601 2613 2614 f 2572 2605 2616 f 2616 2602 2615 f 2573 2616 2615 f 1459 1045 1021 f 1265 1079 1082 f 1612 1609 1611 f 2067 2069 2068 f 1135 1136 1148 f 1938 2618 2617 f 95 458 128 f 2058 1608 1612 f 1454 1445 1367 f 655 1048 2619 f 722 2621 723 f 1131 1033 1134 f 562 366 1497 f 1723 1725 1591 f 1786 1122 1787 f 267 323 266 f 1325 1654 2038 f 1468 1435 1479 f 658 529 528 f 8 247 7 f 382 1559 3 f 788 1307 789 f 940 929 928 f 911 2623 912 f 2621 655 654 f 2624 2625 2626 f 998 27 26 f 635 2626 2625 f 2627 644 1840 f 2628 1840 2629 f 1200 2627 2628 f 1199 641 2627 f 1520 49 477 f 1452 1349 1451 f 2630 1859 1858 f 644 1335 1823 f 1197 717 2632 f 2632 719 1166 f 2628 1166 1200 f 2629 2632 2628 f 1762 1050 821 f 1823 873 1839 f 2634 794 652 f 1028 1027 1161 f 1042 2635 1043 f 93 1901 1924 f 2036 1458 1455 f 1527 1525 1526 f 877 1274 1277 f 134 797 1544 f 1328 931 1329 f 607 1091 1161 f 1043 2637 2636 f 2636 2639 2638 f 1275 2638 1276 f 1275 1043 2636 f 1272 2626 2640 f 2640 638 637 f 1110 2640 637 f 536 2640 2641 f 1472 1474 1473 f 2042 1951 649 f 1058 1105 1059 f 2642 2643 2644 f 1192 2644 1193 f 912 2642 1192 f 3898 3526 3786 f 1049 2620 2621 f 616 1885 613 f 1331 666 750 f 990 1198 525 f 1911 321 322 f 1298 1205 2645 f 76 1504 75 f 1502 421 422 f 610 658 691 f 1146 659 938 f 2646 914 936 f 1280 1238 1279 f 975 2647 2648 f 2647 975 974 f 1453 1452 1451 f 1514 158 151 f 835 1843 1842 f 1271 1173 1289 f 2649 813 2650 f 1893 1826 1894 f 1050 1937 1051 f 2651 814 813 f 1006 1281 977 f 2653 2654 2655 f 1001 1003 1002 f 585 679 558 f 2624 621 620 f 555 2028 556 f 1207 1197 2629 f 1626 478 472 f 426 419 1567 f 1030 1773 1031 f 3898 634 3880 f 534 138 262 f 1125 1042 1041 f 333 88 227 f 569 561 730 f 1489 871 870 f 1857 620 891 f 1673 3770 3763 f 622 864 1308 f 2647 974 2657 f 619 891 620 f 261 545 262 f 393 1556 404 f 2658 2647 2657 f 1075 2657 917 f 1081 2658 1075 f 1080 2659 2658 f 2644 1141 2660 f 2660 1140 1336 f 642 1336 643 f 1193 2660 642 f 679 555 558 f 1503 1500 1502 f 1318 1101 1100 f 2661 1139 1142 f 2661 2663 2662 f 2662 2659 2661 f 2662 2622 2664 f 2664 911 2646 f 2648 2646 1146 f 2648 2662 2664 f 2083 2093 2081 f 1442 1437 1439 f 519 330 51 f 1885 1209 613 f 799 805 804 f 1946 1952 1953 f 1302 1253 1252 f 566 224 533 f 1389 928 1388 f 683 681 680 f 836 1842 2665 f 2665 1845 1303 f 2666 1303 1302 f 24 2665 2666 f 831 2650 832 f 1845 1304 1303 f 2010 2012 2011 f 1051 2667 2668 f 1522 1511 1523 f 531 441 522 f 782 1251 1250 f 1250 2625 2669 f 2669 1857 1856 f 783 1856 1037 f 782 2669 783 f 720 2671 721 f 1055 1930 1056 f 1333 1251 2251 f 619 1308 1307 f 652 2619 2634 f 521 1539 1540 f 1838 537 2672 f 2672 536 2641 f 1113 2641 1110 f 1113 1838 2672 f 1016 1351 1045 f 3897 570 3894 f 226 104 101 f 3564 3827 1818 f 1036 1884 1883 f 2674 1304 2673 f 2673 1844 2675 f 1334 2675 1039 f 1333 2673 1334 f 2676 1039 2675 f 1843 2675 1844 f 942 2676 1843 f 1074 702 2676 f 811 859 858 f 752 1055 753 f 1200 656 1026 f 1120 874 2677 f 2677 877 1052 f 1241 1052 1005 f 1119 2677 1241 f 1560 151 1515 f 1208 611 523 f 215 736 214 f 1460 1462 1461 f 1208 1839 873 f 854 1282 1297 f 72 182 179 f 544 1565 512 f 217 456 457 f 2047 930 945 f 1204 1937 2633 f 913 686 685 f 840 1113 1112 f 1531 730 561 f 2678 1283 801 f 601 1914 602 f 1537 459 460 f 1819 3509 3527 f 991 862 538 f 2679 1073 1830 f 2680 983 2256 f 779 2256 1263 f 778 2680 779 f 2678 1830 2680 f 180 245 40 f 1886 1131 1134 f 1138 1274 1255 f 1841 1140 1139 f 75 314 74 f 193 420 192 f 2681 1099 1098 f 170 1565 169 f 349 417 432 f 1121 2684 2683 f 547 409 302 f 123 121 120 f 2643 1142 1141 f 1542 513 1543 f 899 907 919 f 1210 676 867 f 791 982 892 f 24 837 836 f 893 932 931 f 1860 863 862 f 535 862 861 f 832 813 812 f 2650 813 832 f 1569 1547 1568 f 181 542 180 f 660 937 688 f 2013 568 2012 f 887 2631 2630 f 1524 361 78 f 520 162 1529 f 2653 854 2685 f 1238 2655 2686 f 718 527 530 f 1492 518 1491 f 792 615 793 f 599 1835 596 f 290 456 175 f 312 1552 425 f 1248 1179 1249 f 153 177 178 f 1240 1222 1221 f 1257 1235 1071 f 1291 1083 1222 f 1052 1277 1053 f 743 2618 1204 f 444 430 443 f 23 1172 1187 f 2687 1180 1179 f 102 409 134 f 1283 610 801 f 373 650 651 f 1180 1084 1291 f 855 2655 1237 f 1173 1917 1174 f 2689 853 2688 f 2688 1108 1279 f 2686 1279 1238 f 2687 2688 2686 f 2691 1252 2690 f 2690 850 2689 f 1248 2689 2687 f 1248 2691 2690 f 2692 24 2666 f 2691 2666 1302 f 1247 2692 2691 f 1258 617 2692 f 394 1555 393 f 2693 1259 1223 f 1072 1262 1261 f 1257 1168 1258 f 398 1555 1554 f 871 1314 872 f 1178 1249 1179 f 1224 1173 1172 f 1010 2618 742 f 1175 1292 1269 f 2654 2686 2655 f 2694 1216 1264 f 71 1487 70 f 701 1111 1038 f 1072 2649 2650 f 1085 22 21 f 895 1264 896 f 1601 1605 1604 f 1259 1271 1289 f 21 1220 1083 f 988 538 537 f 598 1336 1140 f 820 1762 821 f 1050 818 821 f 1298 1761 2652 f 1168 618 617 f 858 1256 833 f 39 516 1536 f 2652 819 814 f 313 315 314 f 2026 1971 2027 f 2656 596 1835 f 814 818 811 f 2696 813 2649 f 1260 2649 1261 f 2693 2696 1260 f 2697 2651 2696 f 381 390 380 f 3818 1611 1609 f 1475 1428 1227 f 1292 1917 1235 f 1551 354 1518 f 1526 1523 1511 f 1593 1595 1573 f 1232 1246 1249 f 2695 1223 22 f 2698 1296 2697 f 2698 2693 2695 f 2685 2695 1085 f 2685 1297 2698 f 562 271 272 f 2652 1296 1298 f 1365 1364 1363 f 1958 1953 1957 f 585 2210 997 f 1561 1515 435 f 975 1146 976 f 347 68 346 f 1038 636 1068 f 2700 780 2699 f 2699 1036 1883 f 2701 1883 1829 f 851 2699 2701 f 2674 2251 2702 f 2702 781 2700 f 1253 2700 851 f 1301 2702 1253 f 1519 388 1550 f 1273 1280 2703 f 2703 1107 852 f 2701 852 851 f 1829 2703 2701 f 91 326 90 f 1784 2656 1785 f 2683 2705 2704 f 604 3499 3533 f 1669 3548 1662 f 2062 2060 2059 f 621 861 622 f 2023 732 2024 f 686 1263 687 f 857 2668 1328 f 883 882 879 f 637 1111 1110 f 2668 893 1328 f 2667 894 2668 f 1039 1068 1334 f 313 1504 1505 f 1911 305 453 f 1936 1964 2212 f 2707 2401 2183 f 2708 2183 2182 f 738 2116 554 f 2349 2432 2436 f 2711 2020 2019 f 2400 2019 2186 f 2714 2716 2713 f 2717 2719 2720 f 2721 2723 2724 f 2726 2728 2725 f 2725 2730 2729 f 2731 2729 2709 f 2732 2725 2731 f 2734 2736 2733 f 2738 2740 2737 f 2737 2741 2742 f 2744 2746 2743 f 2745 2748 2747 f 2749 2747 2751 f 2753 2751 2752 f 2250 2246 2248 f 2754 1614 2755 f 3598 2755 3609 f 3608 2747 3609 f 1617 2755 1614 f 2242 1620 2757 f 2191 2760 2759 f 2761 2760 2762 f 3648 2761 3649 f 3623 2759 3648 f 3209 3223 3224 f 1395 2236 2235 f 1395 1636 1615 f 1616 2229 2233 f 1617 2763 2756 f 2756 3628 3608 f 2765 2234 1630 f 2766 3628 3622 f 3202 3246 3247 f 1516 435 1515 f 2768 3552 3553 f 1495 578 1424 f 2135 28 17 f 3526 2773 2774 f 1690 3926 3925 f 2778 2780 2777 f 2777 2781 2782 f 2784 2777 2783 f 3555 575 576 f 1494 9 540 f 3529 2015 3566 f 451 505 450 f 2786 2788 2785 f 2779 2785 2780 f 2789 2741 2740 f 2790 2752 2789 f 2792 2794 2791 f 2793 2796 2794 f 2796 2750 2749 f 2794 2749 2753 f 2798 1396 2754 f 3591 2754 3598 f 2798 2800 1393 f 3591 2799 2798 f 2797 2795 2801 f 2791 2753 2790 f 216 232 54 f 3804 2807 3803 f 3805 2804 3803 f 2769 3557 3552 f 183 184 97 f 2811 2775 2776 f 2804 2776 2805 f 2809 2811 2804 f 2812 2813 2814 f 3805 2816 3795 f 3796 2815 3795 f 3798 2820 2821 f 3793 2819 3798 f 2824 2735 2819 f 2823 2824 2819 f 2825 2826 2724 f 3793 2827 3792 f 3796 2826 3792 f 2713 2829 2828 f 2828 2831 2830 f 2822 2828 2830 f 2821 2713 2828 f 2832 2822 2830 f 2833 2830 2831 f 2834 2832 2833 f 2835 2827 2832 f 2837 2839 2836 f 2836 2841 2840 f 2842 2840 2727 f 2843 2836 2842 f 2840 2845 2844 f 2718 2845 2719 f 2730 2844 2718 f 2727 2844 2728 f 2846 2719 2845 f 2846 2841 2847 f 2829 2847 2831 f 2829 2716 2846 f 2847 2839 2848 f 2848 2838 2849 f 2833 2849 2834 f 2831 2848 2833 f 2850 2851 2852 f 2853 2852 2854 f 2783 2782 2855 f 2785 2853 2856 f 2780 2856 2781 f 2857 2855 2782 f 2859 2782 2781 f 2861 2842 2860 f 2860 2727 2726 f 2862 2726 2732 f 2863 2860 2862 f 2861 2858 2857 f 2843 2857 2859 f 2856 2854 2864 f 2781 2864 2859 f 2837 2859 2864 f 2838 2864 2854 f 2852 2834 2849 f 2854 2849 2838 f 2865 2818 2835 f 2851 2835 2834 f 2813 2815 2865 f 2814 2865 2851 f 2866 2814 2850 f 2788 2850 2853 f 2866 2787 2867 f 2812 2867 2775 f 1892 1890 1891 f 2720 2846 2870 f 298 256 257 f 2872 2713 2820 f 3794 2735 2734 f 2729 2874 2873 f 2846 2715 2870 f 2876 2878 2875 f 2733 2878 2734 f 3806 2879 2880 f 3806 2872 3794 f 2882 2884 2881 f 2881 2715 2714 f 2880 2714 2872 f 2879 2881 2880 f 2885 2887 2888 f 2888 2890 2885 f 2885 2892 2891 f 2893 2885 2891 f 1942 2532 2894 f 2895 2720 2870 f 2884 2870 2715 f 2883 2895 2884 f 2896 2892 2895 f 2889 2717 2890 f 2890 2720 2892 f 2897 2891 2896 f 2898 2896 2883 f 2899 2883 2882 f 2900 2882 2879 f 3797 2879 3807 f 2876 2901 2877 f 2901 2904 2903 f 1785 1835 1834 f 2905 2899 2900 f 2907 2897 2898 f 3797 2903 3799 f 632 577 633 f 3532 2867 2787 f 434 550 350 f 2768 3640 3643 f 2910 1623 2249 f 2241 2758 2242 f 3640 2913 3652 f 1623 2757 1620 f 3587 2757 3652 f 2229 1380 2250 f 2765 1629 2914 f 2915 1629 2912 f 3642 2914 2915 f 3641 2765 2914 f 2919 3642 3613 f 2918 2912 2241 f 3614 2241 3586 f 3618 3613 3670 f 3541 2180 3496 f 3521 2188 3506 f 3705 714 3851 f 2925 2917 2924 f 2924 2916 2919 f 3610 2919 3618 f 3611 2924 3610 f 2926 2766 2925 f 3658 2925 3611 f 1715 1705 1710 f 785 498 796 f 1387 565 1791 f 374 1899 560 f 3573 2206 3556 f 2929 2886 2893 f 2932 2934 2931 f 1905 1908 1907 f 2930 2936 2937 f 2938 2930 2929 f 1493 517 518 f 1603 1586 1602 f 2939 2929 2908 f 2940 2908 2907 f 3665 1600 3661 f 2886 2937 2887 f 2906 2898 2899 f 2908 2893 2897 f 2942 2933 2941 f 2941 2937 2936 f 3501 2936 3513 f 3498 2941 3501 f 2943 2710 2873 f 2943 2874 2889 f 2945 2889 2888 f 2946 2943 2945 f 2932 2888 2887 f 2931 2945 2932 f 727 1929 728 f 2903 2947 2948 f 545 42 262 f 1480 533 251 f 2907 2949 2940 f 2906 2950 2949 f 1558 215 202 f 3799 2948 3801 f 2054 1644 2118 f 3705 2203 3573 f 1828 1849 1827 f 2953 2955 2952 f 2952 2957 2956 f 2956 2258 2952 f 2953 2258 2257 f 2454 2474 2496 f 3747 2959 3766 f 3747 2131 3737 f 2961 2963 2958 f 2958 2964 2959 f 222 2485 2965 f 2477 2966 2608 f 2631 2006 1859 f 2384 2496 2497 f 2967 2959 2964 f 2968 2970 2969 f 3759 2971 3784 f 3784 2960 3766 f 2972 2033 2032 f 2007 2973 2008 f 3560 2134 2133 f 1836 1832 1831 f 3506 2090 3551 f 955 959 956 f 672 674 673 f 633 713 632 f 2024 733 803 f 2025 803 798 f 425 1550 388 f 2975 888 887 f 1319 1157 2694 f 1212 2977 1213 f 889 2228 2227 f 1978 1975 1979 f 1138 1329 915 f 751 2979 2978 f 1619 1625 1624 f 2252 1728 1727 f 1157 845 1216 f 2706 1938 2617 f 2980 2254 2981 f 2982 887 2630 f 802 711 2679 f 678 2029 2028 f 527 690 528 f 1839 2629 1840 f 2983 602 1914 f 2983 1913 1331 f 2985 1331 1054 f 2986 2983 2985 f 2987 2635 2634 f 2985 2639 2986 f 2990 1318 2989 f 2991 2993 2994 f 2995 2994 2996 f 1106 2991 2995 f 1058 2992 2991 f 1415 1365 1367 f 1317 2217 2997 f 1932 2255 2980 f 926 2999 664 f 3001 2992 3000 f 3000 1057 2978 f 2999 2978 2979 f 2998 3000 2999 f 3002 2209 3003 f 3002 2996 2994 f 2993 3002 2994 f 3001 2998 3002 f 1103 2995 3004 f 3004 2996 3003 f 2209 3004 3003 f 588 3004 2210 f 295 433 381 f 2638 2988 3005 f 3005 1056 1930 f 1305 1930 1306 f 1276 3005 1305 f 1987 1993 1989 f 2633 1761 2645 f 1423 1417 1419 f 3561 739 703 f 2671 2704 2705 f 1473 2035 1472 f 922 2694 895 f 1401 1378 1384 f 556 1884 1037 f 842 3825 3836 f 1854 2617 1010 f 933 1081 1075 f 1440 1436 1437 f 529 719 530 f 1031 1027 1028 f 777 912 1871 f 2645 1204 2633 f 441 1538 1537 f 790 587 586 f 2065 1610 1607 f 1860 990 902 f 60 87 59 f 1424 1948 1947 f 332 1507 331 f 261 138 135 f 187 449 507 f 1719 1726 1725 f 1931 1059 3006 f 3006 1105 1104 f 1287 1104 587 f 1286 3006 1287 f 3007 3009 3010 f 3009 653 795 f 618 26 25 f 2029 1210 1209 f 957 600 2228 f 1049 2228 600 f 2631 3011 2561 f 890 2227 3011 f 3011 2145 3012 f 2561 3012 2375 f 1026 609 608 f 2706 1961 1964 f 3013 2485 3014 f 2148 1267 3015 f 3015 1266 1311 f 2997 1101 1317 f 642 1773 1193 f 186 461 511 f 1041 1320 901 f 1121 3017 1122 f 2984 2987 3018 f 3018 2634 2619 f 1048 3018 2619 f 603 2984 3018 f 2679 801 802 f 2062 2074 2061 f 2061 2076 2064 f 414 1545 1965 f 3016 2683 3019 f 3019 2704 2670 f 3020 2670 720 f 3007 3019 3020 f 1871 1091 777 f 3021 1122 3017 f 3022 3017 3010 f 1314 3022 1315 f 1784 3021 1314 f 1316 1763 611 f 3023 3010 3009 f 3023 795 1092 f 1763 1092 1125 f 1315 3023 1763 f 3016 3010 3017 f 752 2978 1057 f 2986 2637 2987 f 3024 653 3008 f 3020 3008 3007 f 720 3024 3020 f 723 654 3024 f 2681 1268 3025 f 2671 1268 721 f 2705 3025 2671 f 1313 3027 1311 f 3026 3029 3027 f 3027 3030 3031 f 1311 3031 3015 f 2012 731 2023 f 917 974 977 f 743 856 744 f 1888 2030 1425 f 2682 2684 3032 f 3032 1124 2989 f 1100 2989 1318 f 1099 3032 1100 f 1123 2989 1124 f 3033 1159 1158 f 2990 1158 1319 f 1123 3033 2990 f 1786 1834 3033 f 1054 2988 2985 f 2979 664 2999 f 2623 2663 2643 f 1095 688 687 f 3034 2261 2956 f 3036 2956 2957 f 3037 3034 3036 f 3038 3035 3034 f 3040 3026 1313 f 2263 3041 3040 f 3042 3043 3041 f 3041 3028 3026 f 3044 3043 3039 f 3046 3045 3044 f 3047 3049 3045 f 3043 3049 3028 f 3038 3051 3050 f 3050 3053 3052 f 3052 3044 3050 f 3039 3050 3044 f 3054 3039 3042 f 3042 2262 3054 f 2261 3054 2262 f 2216 2997 2217 f 2212 2667 1936 f 3056 2209 3055 f 934 1961 1854 f 2013 3757 567 f 3058 3060 3061 f 3059 3063 3060 f 3060 2213 1963 f 3061 1963 1962 f 3063 2214 2213 f 3062 3064 3063 f 3065 2260 3064 f 3064 2215 2214 f 3066 3031 3030 f 2608 3067 3066 f 2966 3068 3067 f 3031 3068 3015 f 2455 2461 2474 f 2476 2226 2477 f 3551 2089 3525 f 3526 3769 3786 f 433 355 381 f 2115 1419 2123 f 3509 2922 3527 f 3769 2131 2134 f 3069 2167 2166 f 2971 2928 2960 f 2961 2773 2962 f 3070 2478 2483 f 2610 3071 2954 f 2502 2374 2386 f 3072 2595 3073 f 2964 3052 2967 f 3048 3074 3049 f 2593 3075 3074 f 3075 3029 3074 f 3074 3028 3049 f 3072 3075 2592 f 3076 2482 2481 f 2481 3066 3076 f 3076 3030 3075 f 2967 3053 2970 f 2482 3073 2483 f 479 493 494 f 706 3873 709 f 3561 2221 3517 f 2013 3754 3755 f 3533 2016 3529 f 3562 2222 2974 f 1171 1155 1176 f 2586 2565 2587 f 2982 113 2484 f 2562 3077 3068 f 2376 3012 3077 f 3012 2148 3077 f 3077 3015 3068 f 2963 3046 2964 f 3051 2970 3053 f 1683 491 775 f 3850 1447 3860 f 1986 3734 3768 f 3079 2546 2575 f 3081 2575 2576 f 1983 3768 3765 f 3082 2319 2545 f 3080 2545 2546 f 3734 3082 3080 f 3732 2323 3082 f 1309 1888 748 f 3073 2772 2771 f 2760 3084 2762 f 2760 2088 3083 f 3085 3086 3087 f 3089 3090 3088 f 3089 3084 3085 f 3092 3090 3091 f 3093 2744 2743 f 2751 2748 3093 f 3093 2752 2751 f 2742 3093 3094 f 3094 2743 3095 f 3096 3098 3087 f 3099 3087 3086 f 3097 3095 3098 f 3100 2746 3101 f 3101 3102 3100 f 2764 2927 3101 f 2745 3101 2746 f 3102 3091 3100 f 3100 3095 2743 f 2869 3592 3634 f 3102 3104 3092 f 3658 2869 3634 f 2927 3103 3102 f 3098 3091 3090 f 3087 3090 3085 f 2088 3105 3083 f 3083 3086 3084 f 2483 2771 3070 f 2401 2185 2321 f 3099 3105 3106 f 2125 3106 3105 f 3108 2742 3107 f 3107 3094 3097 f 3110 3107 3109 f 3109 3097 3096 f 3111 3096 3099 f 3112 3109 3111 f 3113 2737 3108 f 3115 2722 3114 f 3117 3114 3116 f 3119 3116 3118 f 3118 3120 3121 f 3116 3113 3120 f 3122 3111 3123 f 3122 1755 1754 f 3124 3126 3127 f 3125 3121 3126 f 3128 3118 3125 f 3129 3125 3124 f 2020 3128 3129 f 3853 1346 1352 f 1065 1047 1066 f 1814 3830 1649 f 2021 3129 3130 f 3111 3106 3123 f 3123 2207 1755 f 2377 2224 2973 f 3014 113 112 f 1756 3782 3767 f 3131 2530 2528 f 3133 2528 2529 f 1753 3767 3775 f 3134 3127 3135 f 2327 2314 2313 f 2436 2317 2349 f 2018 3711 3712 f 2534 3137 2535 f 2320 3136 2534 f 2185 3712 3771 f 3138 2542 2539 f 3134 3744 3745 f 3785 3134 3745 f 3140 2539 2538 f 2535 3140 2538 f 3711 3130 3785 f 2558 3132 3141 f 3782 3141 3132 f 2560 3141 3142 f 3760 3142 3141 f 958 2976 2981 f 1933 2980 3143 f 2399 2418 2416 f 2348 2343 2432 f 2322 2319 2323 f 2718 2874 2730 f 2709 2873 2710 f 2894 1361 1942 f 346 176 347 f 1876 1882 1881 f 2802 3607 3592 f 3104 3088 3092 f 2974 3649 3603 f 3089 2761 2762 f 3088 3145 3089 f 2803 3603 3607 f 3870 692 628 f 2123 1418 2126 f 1365 1368 1367 f 1581 1782 1783 f 3569 2129 3520 f 3555 2002 3521 f 3146 2551 3147 f 3751 3147 3750 f 3742 3146 3751 f 3149 2585 3146 f 3196 3197 3195 f 3604 2191 3623 f 3142 2556 2560 f 3777 3150 3142 f 3646 2550 3593 f 3150 2555 2556 f 3778 3151 3150 f 3788 3152 3646 f 3153 2551 2549 f 3152 2549 2550 f 3781 3153 3152 f 3776 3147 3153 f 3148 2206 2205 f 2183 2321 2184 f 2132 2928 2133 f 3684 2204 2203 f 3719 2197 3721 f 2594 2773 2951 f 2595 2951 2772 f 1870 799 1869 f 3713 2178 3706 f 3706 3736 3790 f 336 3774 3722 f 339 3154 336 f 3789 3149 3742 f 3683 2584 3149 f 2963 3048 3047 f 3722 337 336 f 339 2566 2586 f 3156 2168 2167 f 2610 2167 2611 f 2954 3156 2610 f 3157 2953 2257 f 3707 2169 3709 f 2567 3159 2568 f 337 2567 338 f 3730 2166 3707 f 3764 1983 3765 f 2579 3081 2576 f 2568 3160 2579 f 3709 2190 3764 f 3065 3162 3161 f 3161 1984 2189 f 3157 2189 2168 f 2257 3161 3157 f 734 1922 806 f 3059 2708 3163 f 3163 2182 1985 f 3162 1985 1984 f 3062 3163 3162 f 3203 3205 3204 f 2122 1448 1447 f 3817 1685 3812 f 139 444 69 f 1570 566 533 f 2957 3165 3036 f 2955 3164 2957 f 1912 341 1973 f 3130 3124 3134 f 1548 319 1547 f 1949 1327 1887 f 2969 3078 3167 f 3167 3169 3168 f 3724 2017 3720 f 3720 2033 3759 f 3168 3165 3170 f 3170 3164 3171 f 3779 3171 3756 f 3724 3170 3779 f 3171 3166 3172 f 3172 3071 3173 f 3752 3173 3749 f 3756 3172 3752 f 3169 3036 3165 f 2593 2962 2594 f 3173 2609 3174 f 3174 2199 2198 f 3783 2198 3719 f 3749 3174 3783 f 2190 1984 1983 f 2196 2767 3148 f 2119 1414 1448 f 1464 1466 1465 f 2199 3069 2200 f 2457 2587 2565 f 2226 2562 2966 f 481 1627 1639 f 333 235 236 f 3078 3037 3169 f 3040 2997 2263 f 2219 1933 2220 f 2385 2497 2503 f 221 3175 2484 f 2220 3143 3175 f 3175 2976 2975 f 2484 2975 2982 f 2954 3166 2955 f 2630 110 2982 f 1547 1952 1955 f 2091 2188 2187 f 3733 2178 2177 f 1918 1927 1926 f 3563 111 3511 f 955 960 954 f 2037 112 3563 f 3143 2981 2976 f 2218 2214 2215 f 672 3787 3541 f 2152 3014 2037 f 1147 1143 1145 f 2248 1409 2245 f 1615 1635 1616 f 1410 2246 2247 f 3241 3242 3240 f 3177 1383 1382 f 2238 3176 1382 f 3176 2230 3178 f 1410 3179 1407 f 3179 2243 1407 f 1633 2800 2249 f 1621 2758 2912 f 1622 2245 1634 f 2236 3181 3182 f 2237 3182 3183 f 2250 2233 2229 f 2912 1628 1621 f 135 1564 1563 f 1567 512 1565 f 1085 2653 2685 f 3184 868 3056 f 869 3057 934 f 1266 1098 1312 f 1267 722 721 f 937 2646 936 f 2146 722 2147 f 2211 921 2667 f 1957 507 449 f 1313 2264 3040 f 1496 1493 1492 f 2681 2705 2682 f 2887 2933 2932 f 1687 1659 1694 f 145 1498 81 f 1317 1319 2694 f 37 418 71 f 1935 3185 2977 f 104 301 103 f 2101 2083 2084 f 543 360 207 f 1934 3186 3185 f 234 212 237 f 3845 1822 1824 f 1976 1874 1974 f 223 2219 220 f 3655 3605 3612 f 468 773 2165 f 1935 1728 1932 f 3240 3226 3235 f 3261 3250 3260 f 3215 3244 3264 f 3265 3215 3264 f 3267 3198 3196 f 3205 3269 3255 f 3214 3265 3270 f 3271 3214 3192 f 3251 3239 3236 f 3222 3266 3272 f 3232 3234 3233 f 3216 3243 3244 f 3196 3268 3267 f 3244 3254 3255 f 3238 3247 3246 f 3264 3255 3269 f 3253 3216 3217 f 3211 3221 3265 f 3257 3218 3256 f 3258 3272 3259 f 3274 3280 3273 f 3228 3262 3263 f 3275 3281 3280 f 3226 3273 3280 f 3236 3260 3251 f 3281 3236 3237 f 3287 3192 3189 f 3235 3280 3234 f 3234 3281 3237 f 3288 3259 3272 f 3260 3276 3251 f 3289 3278 3284 f 3210 3224 3283 f 3199 3201 3200 f 3228 3230 3229 f 3290 3285 3286 f 3282 3271 3287 f 3260 3262 3261 f 3243 3268 3254 f 3267 3241 3232 f 3194 3208 3193 f 3191 3269 3206 f 3270 3192 3214 f 3275 3230 3231 f 3264 3270 3265 f 3252 3276 3291 f 3248 3291 3223 f 3213 3279 3277 f 3272 3289 3288 f 3277 3212 3213 f 3228 3281 3231 f 3220 3253 3219 f 3219 3217 3257 f 3256 3222 3258 f 3247 3252 3248 f 3245 3232 3233 f 3266 3212 3289 f 3208 3255 3254 f 3288 3284 3285 f 3204 3208 3207 f 560 3187 559 f 3209 3201 3202 f 3189 3191 3190 f 448 807 447 f 647 122 270 f 1833 1780 1832 f 510 508 507 f 3210 1887 3201 f 1887 3200 3201 f 15 3197 3200 f 16 3195 3197 f 3195 3187 3194 f 3194 1899 3207 f 3207 375 3204 f 387 126 127 f 3189 1760 3287 f 3577 757 758 f 763 2172 2175 f 3583 3580 3581 f 3581 3579 3578 f 3594 3596 3595 f 3400 3602 3401 f 3626 3625 3627 f 3631 3599 3601 f 3636 3595 3596 f 3647 3588 3645 f 3650 3584 3647 f 3653 3580 3651 f 3653 3654 3579 f 1801 3656 3655 f 3651 3582 3650 f 3644 3595 3637 f 1767 3659 3660 f 1597 3661 1600 f 1708 3665 3664 f 3671 3667 3666 f 3666 3673 3672 f 1588 3664 1587 f 2080 3666 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3329 3331 3332 f 3333 3335 3336 f 3337 3339 3340 f 3333 3341 3342 f 3342 3343 3344 f 3319 3344 3343 f 3338 3334 3339 f 3346 3302 3347 f 3305 3347 3302 f 3312 3223 3349 f 3350 3340 3351 f 3312 3348 3305 f 3352 3341 3326 f 3311 3354 3308 f 3341 3327 3326 f 3359 3361 3362 f 3360 3250 3249 f 3318 3345 3319 f 3364 3318 3324 f 3314 3324 3365 f 3366 3301 3346 f 3299 3367 3296 f 3308 3368 3309 f 3369 3295 3294 f 3317 3364 3314 f 3370 3317 3316 f 3325 3365 3324 f 3352 3319 3343 f 3296 3353 3311 f 3297 3311 3310 f 3363 3354 3345 f 3345 3353 3344 f 3344 3367 3342 f 3366 3342 3367 f 3347 3334 3346 f 3339 3348 3340 f 3348 3351 3340 f 3363 3369 3368 f 3294 3368 3369 f 3293 3309 3294 f 3331 3374 3332 f 3346 3333 3366 f 3323 3352 3326 f 3356 3321 3320 f 3355 3325 3321 f 3357 3371 3325 f 3322 3320 3352 f 3361 3329 3362 f 3373 3375 3374 f 3374 3376 3332 f 3329 3376 3362 f 3327 3372 3328 f 3336 3375 3327 f 3335 3376 3375 f 3376 3337 3362 f 3337 3359 3362 f 3359 3276 3250 f 3377 3295 3370 f 3277 3315 3278 f 3371 3379 3365 f 3378 3358 3380 f 3365 3315 3314 f 3316 3279 3370 f 3282 3370 3279 f 3313 3224 3312 f 3378 3284 3379 f 3380 3285 3378 f 3379 3278 3315 f 3276 3351 3291 f 3291 3349 3223 f 3313 2965 3013 f 2965 3303 222 f 222 3300 223 f 223 3298 3186 f 3298 3185 3186 f 3297 2977 3185 f 3310 1213 2977 f 3307 1214 1213 f 2532 3306 3293 f 2894 3293 3292 f 2871 3292 3377 f 3253 3240 3242 f 3283 3013 2152 f 1997 1889 1892 f 1237 744 855 f 1535 120 121 f 1531 201 214 f 1119 1032 984 f 946 824 947 f 163 5 162 f 3589 3723 3741 f 1155 1162 1164 f 427 327 424 f 553 796 498 f 3381 2231 2237 f 3381 2230 2232 f 2237 3180 3381 f 3383 3385 3382 f 3384 3387 3385 f 3386 3389 3387 f 3388 3391 3389 f 3392 3394 3395 f 3395 3396 3397 f 3398 3397 3396 f 3400 3399 3398 f 3403 3405 3402 f 3406 3404 3403 f 3408 3407 3406 f 3410 3409 3408 f 3412 3411 3410 f 3415 3398 3414 f 3414 3396 3416 f 3416 3394 3417 f 3417 3393 3418 f 3393 3420 3418 f 3419 3421 3420 f 3390 3422 3421 f 3388 3423 3422 f 3386 3424 3423 f 3384 3425 3424 f 3425 3426 3184 f 2904 3428 2947 f 3429 2904 2902 f 3430 2902 2876 f 2875 3430 2876 f 3432 2733 2736 f 2825 3435 2824 f 3437 3439 3436 f 3441 3436 3440 f 2724 3434 2825 f 3436 3115 3117 f 3440 3117 3119 f 3443 3414 3442 f 3442 3416 3444 f 3444 3417 3445 f 3445 3418 3446 f 3446 3420 3447 f 3420 3448 3447 f 3421 3449 3448 f 3422 3450 3449 f 3423 3451 3450 f 3424 3452 3451 f 3425 3056 3452 f 3451 2253 2252 f 3450 2252 1727 f 3449 1727 2150 f 3448 2150 1941 f 3453 3448 1941 f 3454 3447 3453 f 3455 3446 3454 f 3456 3445 3455 f 3442 3456 3457 f 3443 3457 3458 f 3459 3458 3457 f 3457 3455 3459 f 3459 3454 3461 f 3462 3459 3461 f 3461 3453 3463 f 3464 3461 3463 f 3463 1941 1360 f 3404 3401 3405 f 3407 3399 3404 f 3409 3397 3407 f 3411 3395 3409 f 3465 3413 3412 f 3467 3469 3466 f 3465 3431 3433 f 3470 3466 3465 f 3438 3432 3435 f 3467 3438 3437 f 3468 3437 3441 f 3471 3468 3472 f 3472 3441 3473 f 3473 3440 2712 f 2712 3119 3128 f 3475 3472 3474 f 3474 3473 3476 f 3476 2712 2711 f 3477 2711 2400 f 3478 3476 3477 f 3479 3474 3478 f 3392 3413 3480 f 3466 3480 3413 f 3419 3392 3480 f 3390 3480 3391 f 3469 3391 3480 f 3389 3471 3475 f 3387 3475 3479 f 3385 3479 3481 f 3482 2400 2402 f 3483 3477 3482 f 3481 3478 3483 f 3484 2402 2707 f 3058 2707 2708 f 3061 3484 3058 f 3485 3482 3484 f 3382 3481 3486 f 3486 3483 3485 f 3426 3382 3487 f 3487 3486 1962 f 3486 3061 1962 f 1961 3487 1962 f 3057 3426 3487 f 3428 3402 3488 f 3427 3403 3428 f 3429 3406 3427 f 3430 3408 3429 f 3431 3410 3430 f 3470 3433 3432 f 3434 3438 3435 f 2723 3439 3434 f 2875 3433 3431 f 2736 3435 3432 f 2947 3488 3500 f 3464 1360 3497 f 1900 1924 1901 f 1736 1718 1750 f 1955 1943 1956 f 1671 1735 1751 f 1696 1698 1692 f 1800 1864 1801 f 2063 2056 2060 f 1737 1717 1736 f 1751 1697 1695 f 943 3899 684 f 3656 3402 3605 f 1577 1764 1703 f 3917 1599 3923 f 1945 82 1944 f 68 7 248 f 147 1543 148 f 1109 1133 1102 f 1503 1907 1908 f 1499 1909 1498 f 452 735 215 f 1824 3835 3845 f 1825 2068 1824 f 1463 1022 1194 f 841 1009 1011 f 1568 1955 1534 f 1926 1927 1923 f 1910 1502 1939 f 1939 422 442 f 309 1940 308 f 1804 1866 1803 f 269 1954 56 f 2065 2060 2056 f 3812 1758 3835 f 280 552 1940 f 1875 1869 1874 f 1739 1734 1737 f 1778 1790 1788 f 310 1915 279 f 1909 1906 1954 f 1789 1778 1788 f 978 980 979 f 1584 1731 1583 f 425 311 312 f 148 115 116 f 2044 3894 2046 f 277 1939 551 f 1943 1945 1944 f 1461 1463 1464 f 2253 3056 3055 f 2981 3489 958 f 2209 3489 3055 f 2254 3055 3489 f 2998 958 3489 f 2826 2721 2724 f 3490 2721 2817 f 2816 3490 2817 f 2334 2296 2335 f 2298 2334 2336 f 2301 2336 2338 f 2311 2338 2354 f 2312 2354 2355 f 2359 2361 2360 f 2335 2359 2360 f 2414 2365 2364 f 2413 2361 2365 f 2355 2540 2541 f 2541 2539 2542 f 2722 2739 2738 f 3114 2738 3113 f 3110 3121 3120 f 3108 3120 3113 f 3122 3127 3126 f 3112 3126 3121 f 3127 1753 3135 f 3139 2529 2542 f 3744 3133 3139 f 3490 2740 2739 f 3491 2789 3490 f 228 91 88 f 245 243 242 f 248 247 191 f 2 296 295 f 145 300 267 f 327 210 328 f 329 331 330 f 250 228 236 f 239 240 230 f 373 375 374 f 178 176 175 f 1218 1228 1321 f 3862 1295 3854 f 808 824 823 f 3858 1013 1014 f 3813 1411 1413 f 2154 2159 2160 f 3845 1758 1757 f 306 1922 452 f 2157 1976 1977 f 708 2031 1474 f 1450 1227 1428 f 1672 1605 1624 f 1813 1821 1816 f 1717 1715 1718 f 176 402 347 f 1710 3916 3915 f 1712 1577 1711 f 1705 1711 1704 f 971 979 970 f 822 1094 1102 f 2909 2793 2792 f 2810 2795 2793 f 2799 2249 2800 f 3662 2910 2799 f 2769 2801 2795 f 3177 3178 3179 f 3178 3180 3179 f 3181 1393 2800 f 3181 1632 3182 f 3182 2244 3183 f 3183 2243 3180 f 3492 2790 3491 f 2808 3491 2816 f 2239 3572 2240 f 1149 3571 3570 f 1150 3567 3571 f 2202 3566 2015 f 2921 3565 2923 f 3568 2133 2928 f 1777 1759 1792 f 1201 1183 1184 f 3561 2195 2194 f 3553 2770 2768 f 3927 1687 1692 f 1516 154 503 f 3549 2974 2803 f 3550 2779 2778 f 2771 3575 3546 f 3070 3546 3558 f 3547 2775 2867 f 1376 1370 1372 f 2767 3556 2206 f 2195 3543 2767 f 2775 3542 2776 f 3377 3545 2871 f 3804 3539 2806 f 2776 3538 2805 f 2240 3535 2920 f 2920 3669 3619 f 3548 2786 2779 f 1805 1851 1804 f 3534 2787 2786 f 2951 3540 3531 f 2772 3531 3575 f 2973 3530 3536 f 2224 3537 3530 f 1957 320 510 f 306 304 303 f 2770 3528 2913 f 2913 3574 2239 f 2923 3527 2922 f 308 431 47 f 207 132 543 f 1879 1881 1880 f 3733 3525 2089 f 2475 3558 3524 f 2476 3524 3523 f 2223 3523 3537 f 3522 2803 2802 f 2037 11 1950 f 1959 450 505 f 2003 3520 2129 f 9 334 540 f 2009 3518 3567 f 2008 3536 3518 f 3877 673 3879 f 3515 2778 2784 f 2935 3513 2936 f 111 3570 3511 f 3510 2935 2938 f 683 3885 943 f 3508 2810 2909 f 736 732 729 f 1795 1803 1796 f 3505 2938 2939 f 3507 2939 2940 f 2950 3504 2949 f 2940 3504 3507 f 3801 3512 2950 f 2868 3502 2869 f 2869 3522 2802 f 1024 1023 1020 f 3500 2948 2947 f 551 442 443 f 2972 3568 2928 f 2032 3499 2972 f 216 1924 1923 f 203 201 200 f 1828 1847 1837 f 1958 449 450 f 272 178 175 f 2871 3495 1361 f 3619 3564 2921 f 2801 3643 3662 f 2005 3654 2176 f 960 899 962 f 3657 3612 3624 f 3780 3646 3151 f 3151 3593 2555 f 2193 3623 2194 f 3607 3145 3144 f 3603 2761 3145 f 3592 3144 3104 f 2926 3634 3103 f 3634 3104 3103 f 3755 3746 3753 f 566 3758 2005 f 3488 1864 3500 f 1602 2079 2078 f 2868 3611 2922 f 2922 3610 2923 f 2923 3618 2921 f 3670 2918 3614 f 3619 3614 2920 f 2921 3670 3619 f 2920 3586 2240 f 3613 2915 2918 f 2917 3622 3641 f 2916 3641 3642 f 2239 3652 2913 f 2911 3652 2757 f 3643 2911 2910 f 2797 3662 3591 f 2797 3598 2750 f 3622 2763 2765 f 3608 2764 2745 f 2194 3648 2221 f 2221 3649 2222 f 2756 3609 2755 f 2750 3609 2747 f 3669 1817 3827 f 3763 1691 1699 f 2515 3590 2548 f 2548 3593 2550 f 2513 3632 3616 f 2513 3621 2515 f 2509 3620 2289 f 2510 3633 2509 f 2289 3639 2283 f 2283 3617 2286 f 2240 3587 2239 f 3659 3624 3626 f 716 3887 3888 f 727 3761 1923 f 2165 2170 2164 f 3660 3626 3629 f 3743 3578 3757 f 2175 769 770 f 2100 2095 2099 f 3758 3757 3578 f 640 3568 1826 f 1653 3553 1651 f 1895 3530 746 f 746 3537 741 f 1653 3528 3576 f 3762 1604 1672 f 2093 2073 2078 f 959 995 884 f 962 919 986 f 1400 1398 1399 f 1000 1127 1004 f 956 884 885 f 1662 3550 1661 f 2034 3546 1654 f 3926 3539 3802 f 3535 766 1817 f 3572 765 766 f 334 3571 335 f 1868 3540 1656 f 1684 1652 1678 f 1682 775 1807 f 1585 3762 1700 f 3763 1672 1673 f 2173 490 491 f 1658 3547 1752 f 3556 1650 724 f 1571 2010 1572 f 1587 3671 2080 f 2099 2077 2094 f 787 1999 786 f 2000 786 1999 f 3155 3697 3683 f 3713 3683 3789 f 2180 3685 3684 f 3496 3684 3705 f 1684 3840 1651 f 1760 3495 3545 f 2607 3693 2590 f 2581 3689 2607 f 2371 3700 3695 f 2371 3693 2372 f 2587 3675 2586 f 2586 3697 339 f 2497 3687 2503 f 2502 3687 3700 f 2494 3690 2495 f 3690 2472 2495 f 3688 2468 2472 f 3701 2469 2468 f 726 3892 630 f 581 3518 580 f 10 3570 334 f 1837 3510 1849 f 741 3523 1300 f 1299 3558 2034 f 1851 3504 1866 f 3527 1820 1819 f 580 3536 1895 f 1758 3522 3502 f 1693 3508 1806 f 3504 1867 1866 f 1894 3568 3499 f 2126 3813 3841 f 3906 3841 3912 f 2113 3867 2110 f 2179 3789 3685 f 624 3904 623 f 2192 3788 3780 f 2177 3787 3733 f 1364 3833 1338 f 3858 1470 1453 f 3135 3775 3744 f 2069 3835 2068 f 1351 3853 1352 f 2108 3901 499 f 2089 3736 3733 f 3828 1358 866 f 2001 3783 2187 f 2187 3719 2091 f 2130 3752 2004 f 2004 3749 2001 f 2014 3779 2201 f 2201 3756 2130 f 3167 3759 2969 f 3168 3720 3167 f 3159 3764 3160 f 3160 3765 3081 f 3730 3158 337 f 3158 3709 3159 f 2200 3730 3722 f 3685 3742 2204 f 3722 2197 2200 f 3790 2092 3721 f 3774 3721 2197 f 3154 3790 3774 f 3155 3706 3154 f 2091 3721 2092 f 2196 3750 3776 f 2193 3776 3781 f 3604 3781 3788 f 2086 3780 3778 f 2087 3778 3777 f 2204 3751 2205 f 2205 3750 3148 f 3909 574 692 f 2124 3777 3760 f 2208 3760 3782 f 3711 3140 3137 f 3785 3138 3140 f 3745 3139 3138 f 3771 3136 2322 f 3712 3137 3136 f 3775 3131 3133 f 3767 3132 3131 f 1813 3838 1814 f 1293 3856 1344 f 2184 3748 3732 f 2181 3732 3734 f 3765 3079 3081 f 3768 3080 3079 f 709 3875 2031 f 2774 3737 3769 f 3786 2134 3559 f 3809 766 765 f 2968 3766 2959 f 2969 3784 2968 f 2958 3737 2961 f 2132 3766 2960 f 2321 3771 3748 f 3860 1449 28 f 3905 582 710 f 3886 577 574 f 3847 1818 3827 f 705 3887 2121 f 3816 3907 698 f 2111 3911 2113 f 3810 1366 1411 f 1812 3822 1815 f 675 3852 674 f 3496 3851 675 f 2110 3868 2109 f 2104 3901 2106 f 2109 3899 944 f 3842 703 739 f 1680 3846 1684 f 1639 3819 3818 f 623 3906 697 f 2139 3866 3865 f 1808 3844 3837 f 3820 1819 1820 f 3872 3900 706 f 1646 3830 1648 f 3889 633 577 f 2139 3875 18 f 2156 3871 3874 f 674 3913 3869 f 2117 3812 2069 f 1815 3847 3823 f 3855 1444 1470 f 697 3912 3816 f 866 3836 3828 f 3864 2136 2135 f 3585 3726 3589 f 3583 3731 3585 f 3753 3738 3739 f 3740 3581 3743 f 1088 3746 1093 f 3739 3743 3753 f 2791 3493 2792 f 3492 2807 3493 f 2792 3494 2909 f 3493 2806 3494 f 3508 3494 3516 f 3516 2806 3539 f 2948 3791 3801 f 2105 3896 2104 f 3538 3804 2805 f 680 3876 683 f 1891 3910 3891 f 2905 3801 2950 f 2900 3799 2905 f 2901 3807 2877 f 2734 3806 3794 f 2877 3806 2878 f 2820 3794 2872 f 2818 3792 2827 f 2823 3792 2826 f 2822 3798 2821 f 2819 3800 3798 f 2817 3795 2816 f 2809 3795 2815 f 2808 3803 2807 f 2805 3803 2804 f 1702 3923 1599 f 3922 1797 1802 f 3915 1706 1709 f 3918 1692 1698 f 1657 3880 1879 f 1890 3895 3910 f 3772 1674 1675 f 1689 3772 3773 f 3926 3538 1688 f 3516 1689 3773 f 3917 1703 1764 f 1598 3928 1770 f 3880 1876 1879 f 1691 3925 3927 f 3925 1688 1687 f 1117 1118 3930 f 1066 1322 3930 f 1243 847 3930 f 3825 848 3930 f 1128 1117 3930 f 1129 1130 3930 f 841 1011 3930 f 1230 3863 3930 f 1242 1243 3930 f 1322 1229 3930 f 1064 1065 3930 f 1003 1128 3930 f 1229 1230 3930 f 3863 1285 3930 f 1285 1288 3930 f 848 839 3930 f 847 3825 3930 f 1118 1129 3930 f 1065 1066 3930 f 1087 1086 3930 f 1008 1002 3930 f 1011 1008 3930 f 1002 1003 3930 f 1076 1069 3930 f 838 841 3930 f 839 838 3930 f 1288 1242 3930 f 1130 1087 3930 f 1086 1076 3930 f 1069 1064 3930 f 2410 2414 3931 f 2411 2410 3931 f 3617 2285 3931 f 2394 2393 3931 f 2393 2396 3931 f 2281 2286 3931 f 2282 2280 3931 f 2398 2399 3931 f 2465 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3981 3984 3985 f 3982 3985 3986 f 3987 3990 3991 f 3984 3989 3985 f 3985 3990 3986 f 3991 3994 3995 f 3989 3992 3993 f 3990 3993 3994 f 3995 3998 3999 f 3992 3997 3993 f 3993 3998 3994 f 3998 4003 3999 f 3997 4000 4001 f 3998 4001 4002 f 4002 4007 4003 f 4000 4005 4001 f 4001 4006 4002 f 4006 4011 4007 f 4005 4008 4009 f 4006 4009 4010 f 4011 4014 4015 f 4008 4013 4009 f 4009 4014 4010 f 4014 4019 4015 f 4013 4016 4017 f 4014 4017 4018 f 4019 4022 4023 f 4016 4021 4017 f 4017 4022 4018 f 4023 4026 4027 f 4021 4024 4025 f 4022 4025 4026 f 4027 4030 4031 f 4024 4029 4025 f 4025 4030 4026 f 4031 4034 4035 f 4029 4032 4033 f 4030 4033 4034 f 4034 4039 4035 f 4032 4037 4033 f 4033 4038 4034 f 4038 4043 4039 f 4037 4040 4041 f 4038 4041 4042 f 4043 4046 4047 f 4040 4045 4041 f 4041 4046 4042 f 4046 4051 4047 f 4045 4048 4049 f 4046 4049 4050 f 4051 4055 4056 f 4048 4054 4049 f 4049 4055 4050 f 4056 4059 4060 f 4053 4058 4054 f 4055 4058 4059 f 3932 4052 3936 f 3936 4052 3940 f 3940 4052 3944 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4099 f 4096 4110 4097 f 4095 4107 4108 f 4092 4106 4093 f 4090 4104 4091 f 4089 4101 4102 f 4097 4111 4098 f 4095 4109 4096 f 4094 4106 4107 f 4092 4104 4105 f 4103 4115 4116 f 4100 4114 4101 f 4112 4124 4125 f 4110 4122 4123 f 4107 4121 4108 f 4105 4119 4106 f 4103 4117 4104 f 4101 4115 4102 f 4110 4124 4111 f 4109 4121 4122 f 4107 4119 4120 f 4105 4117 4118 f 4115 4129 4116 f 4114 4126 4127 f 4124 4138 4125 f 4123 4135 4136 f 4120 4134 4121 f 4119 4131 4132 f 4117 4129 4130 f 4114 4128 4115 f 4124 4136 4137 f 4122 4134 4135 f 4119 4133 4120 f 4117 4131 4118 f 4129 4141 4142 f 4126 4140 4127 f 4138 4150 4151 f 4136 4148 4149 f 4133 4147 4134 f 4131 4145 4132 f 4130 4142 4143 f 4127 4141 4128 f 4137 4149 4150 f 4135 4147 4148 f 4132 4146 4133 f 4131 4143 4144 f 4142 4154 4155 f 4140 4152 4153 f 4150 4164 4151 f 4149 4161 4162 f 4146 4160 4147 f 4144 4158 4145 f 4142 4156 4143 f 4140 4154 4141 f 4149 4163 4150 f 4148 4160 4161 f 4146 4158 4159 f 4144 4156 4157 f 4155 4167 4168 f 4153 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1752 f 485 1808 1807 f 3809 3844 1640 f 3837 3844 1810 f 485 482 1640 f 1690 1691 3770 f 487 488 1648 f 3839 1813 1816 f 3838 1811 1648 f 3839 1812 1811 f 1812 1809 487 f 1641 1809 767 f 465 466 755 f 767 1815 3823 f 3822 1816 3826 f 1809 1812 1815 f 3837 1653 1651 f 675 672 3541 f 1814 1649 1822 f 1821 1822 3821 f 1824 1822 1649 f 1827 1849 1850 f 1668 1669 1662 f 1779 1780 1848 f 1790 1827 1805 f 1833 1836 1853 f 3521 3506 693 f 1614 1615 1616 f 1852 1853 3498 f 1620 1621 1622 f 1855 1765 1574 f 1847 1852 3501 f 1766 1765 1855 f 1629 1630 1631 f 1633 1622 1634 f 1635 1636 1637 f 1848 1833 1852 f 1855 1861 1863 f 1803 1866 1867 f 1862 1863 1831 f 1782 1855 1862 f 3922 3924 1865 f 1873 1872 1870 f 1874 1869 799 f 2045 3878 3897 f 1219 1345 1295 f 1615 1614 1396 f 3884 3895 1877 f 645 3898 3786 f 1345 1219 1035 f 634 639 3884 f 15 1887 1327 f 3910 3895 1878 f 1894 604 605 f 3506 3551 627 f 3560 640 645 f 340 341 454 f 1897 194 195 f 1889 1882 1877 f 1901 94 129 f 196 749 754 f 1705 1704 3921 f 340 378 166 f 1570 1480 563 f 1904 1494 194 f 1781 1862 1832 f 1905 549 55 f 1000 994 995 f 986 919 920 f 1191 1185 1190 f 2094 2077 2075 f 1920 1921 1919 f 453 454 1912 f 1915 1919 1918 f 3882 2112 2106 f 1655 3575 3531 f 1916 1918 1926 f 704 3554 3561 f 278 1916 1925 f 3565 3564 1818 f 1097 949 1115 f 1927 1928 727 f 3578 3579 3654 f 1920 310 307 f 1929 307 48 f 503 504 1497 f 1902 1903 787 f 1816 1821 3820 f 1925 1926 1923 f 1535 1569 1568 f 254 252 258 f 1509 1497 366 f 119 86 35 f 219 288 293 f 141 46 47 f 542 205 244 f 1940 552 430 f 1779 1828 1827 f 1701 1710 3915 f 1661 3550 3515 f 270 53 754 f 197 1947 749 f 1727 1728 1212 f 53 54 232 f 1465 1468 1479 f 1284 3862 3854 f 1951 446 412 f 168 398 1554 f 1486 1485 206 f 506 77 78 f 994 1001 1007 f 951 953 963 f 1013 3859 3861 f 3826 3820 1820 f 1498 1909 1954 f 299 298 543 f 1754 1755 1756 f 1681 469 1683 f 1696 1694 1695 f 13 14 738 f 473 476 475 f 482 1641 768 f 204 62 30 f 1761 1762 820 f 611 1763 1125 f 1501 1503 1908 f 1074 1073 711 f 1960 1944 82 f 120 1956 1960 f 35 86 87 f 1773 1199 1025 f 1965 1545 1569 f 494 761 471 f 1966 1967 1969 f 446 1951 1971 f 174 368 125 f 1580 1579 1578 f 1785 1786 1787 f 1972 1546 304 f 1254 876 875 f 1973 341 342 f 321 1912 1973 f 1910 277 275 f 509 1973 514 f 1486 1507 1527 f 1974 1874 804 f 1977 1976 1974 f 1980 448 1966 f 379 1 2 f 1718 1715 1710 f 884 995 992 f 1680 1684 1678 f 902 990 525 f 344 255 256 f 135 138 137 f 448 446 1967 f 589 605 604 f 910 909 908 f 1981 1966 1968 f 1982 1981 1987 f 1988 1979 1987 f 1990 1988 1989 f 1991 1989 1993 f 322 321 320 f 992 828 830 f 1144 1076 1086 f 1995 1977 1978 f 1133 1129 1118 f 1997 1995 1994 f 870 1823 1335 f 805 806 1982 f 727 184 2000 f 1996 1994 1988 f 1994 1978 1979 f 1829 616 792 f 1830 1073 941 f 798 803 800 f 729 732 731 f 2011 2012 2023 f 1834 1835 846 f 402 283 284 f 2022 2023 2024 f 1838 718 989 f 810 955 952 f 1823 1839 1840 f 1430 1426 1432 f 1215 844 1841 f 1316 1208 873 f 2112 1401 1384 f 1842 1843 1844 f 1159 846 845 f 837 27 1846 f 1971 1951 2030 f 517 369 370 f 1471 2026 2027 f 3551 3525 626 f 2174 2175 770 f 935 934 1854 f 1613 1619 1611 f 1231 1228 1225 f 1856 1857 891 f 1300 1309 748 f 1150 1149 1858 f 1643 606 696 f 1215 1265 1264 f 1320 1860 902 f 1956 1943 1944 f 2031 668 669 f 2034 1325 1310 f 1879 1880 1656 f 40 245 242 f 1992 1993 2039 f 888 958 957 f 1882 1889 1996 f 1078 1138 1255 f 1871 912 1192 f 294 411 218 f 1323 2041 2026 f 123 1960 83 f 1324 2036 2041 f 384 358 359 f 1324 1325 2038 f 1883 1884 1885 f 287 1540 114 f 1096 1886 1134 f 915 1329 931 f 806 1980 1981 f 3533 3529 584 f 1425 2030 2042 f 1018 2042 1948 f 3562 1896 740 f 1948 2042 649 f 1881 1882 1990 f 1900 129 130 f 992 995 994 f 923 927 925 f 3878 2045 2047 f 1643 2048 625 f 2050 2052 2051 f 2045 1405 1404 f 3876 2047 945 f 1139 1080 1079 f 1037 1856 892 f 2055 2053 2057 f 1198 989 718 f 1619 2058 1612 f 2058 1605 1606 f 2059 2060 1607 f 2058 1619 1624 f 71 72 179 f 405 406 1557 f 322 1548 1972 f 2061 2052 2063 f 2066 2065 2067 f 2052 2061 2064 f 1609 1610 2066 f 1515 151 152 f 1009 827 993 f 1644 3843 3815 f 279 1915 1916 f 1285 3863 3862 f 1647 2066 1825 f 2056 2057 2067 f 319 320 1953 f 667 666 1913 f 2070 631 625 f 2052 2050 2055 f 2038 1654 1655 f 1606 1601 2073 f 1235 1917 1270 f 2072 2073 2075 f 2059 1606 2072 f 2074 2075 2077 f 1601 1602 2078 f 2049 2048 1643 f 1000 1004 1001 f 1089 943 684 f 1931 1286 1306 f 1462 1459 1021 f 1932 1933 1934 f 875 874 1846 f 1936 1051 1937 f 44 45 46 f 1534 1955 1956 f 1541 318 317 f 2081 2082 2084 f 146 1945 1959 f 1360 1941 1942 f 985 984 1032 f 1995 1997 1892 f 2079 2080 2082 f 2095 2096 2076 f 2081 2093 2078 f 2055 2050 2054 f 501 2096 2095 f 2097 2100 2099 f 2098 2099 2094 f 2140 2141 3893 f 2141 2046 3894 f 2051 2070 2048 f 592 2102 2101 f 2071 2064 2076 f 2102 2097 2098 f 593 2103 2102 f 1019 1895 746 f 1950 11 12 f 725 2071 2096 f 1385 2105 2104 f 1379 2104 2106 f 2107 2108 2100 f 3908 726 502 f 3896 502 499 f 940 1389 2109 f 928 929 1170 f 3868 590 684 f 1962 1963 1964 f 3878 680 573 f 2039 1993 1968 f 1389 1390 2110 f 1398 2111 2113 f 3757 3755 3753 f 3911 3883 2107 f 949 923 924 f 260 241 324 f 1390 1397 2113 f 2070 2051 2064 f 3867 3911 2103 f 2105 1385 1421 f 2114 1421 1423 f 3892 2114 2115 f 3908 2105 2114 f 554 2116 1902 f 972 1163 967 f 1765 1593 1573 f 1984 1985 1986 f 2053 2117 2069 f 3573 3556 724 f 1625 1677 1674 f 3881 3888 2119 f 3903 2115 2123 f 56 1954 1906 f 626 3877 3879 f 2001 2002 2003 f 3912 3841 2127 f 2006 2007 2008 f 2127 1413 1412 f 2014 2015 2016 f 2018 2019 2020 f 1495 1424 1947 f 1379 1377 1386 f 3814 3811 1680 f 2029 1885 1884 f 3902 2128 2120 f 629 3879 3914 f 682 683 943 f 724 1650 2121 f 1757 1819 3821 f 765 1810 3844 f 3851 714 715 f 706 707 3870 f 981 986 987 f 1457 1868 1656 f 2017 2016 2032 f 1458 1655 1868 f 1776 1777 1792 f 1959 1945 1946 f 3663 3715 3725 f 3913 3881 2122 f 1093 1089 689 f 966 965 968 f 3852 715 3881 f 2172 3577 2170 f 670 2138 2137 f 3761 787 1903 f 1429 19 28 f 964 963 978 f 3841 3813 1413 f 1473 1474 669 f 3834 3833 1359 f 2138 798 1870 f 1872 1473 2137 f 715 716 3888 f 1881 1991 1992 f 3843 3842 739 f 1837 1847 3513 f 253 58 59 f 1808 3837 3840 f 1647 3832 3818 f 3865 3866 671 f 2112 3882 3883 f 1405 2045 2044 f 3829 3824 1364 f 273 1541 391 f 576 632 3520 f 3875 3865 668 f 1430 1431 2140 f 2136 3850 3860 f 1430 2139 18 f 3893 3894 570 f 1442 1439 2046 f 1431 1442 2141 f 3869 3913 3850 f 3808 3823 1817 f 3866 3893 695 f 2142 2025 798 f 571 2143 2142 f 3819 3814 1618 f 2143 2022 2025 f 694 2142 2138 f 681 1572 2144 f 2144 2011 2022 f 328 210 457 f 1572 2010 2011 f 630 3892 3903 f 2085 2086 2087 f 2090 2091 2092 f 572 2144 2143 f 1875 1976 2153 f 1873 1875 2149 f 2103 2107 2097 f 2157 2043 2155 f 2128 1412 1414 f 3874 3871 589 f 3872 3870 628 f 2149 2153 2158 f 2159 2158 3905 f 698 3907 3902 f 3823 3847 3827 f 2158 2156 3874 f 1438 2044 2046 f 2035 2154 2160 f 2161 2160 3889 f 3871 3891 605 f 3873 17 18 f 370 29 61 f 649 1951 413 f 2153 2157 2156 f 1428 1429 1449 f 1490 76 73 f 3890 3905 710 f 2155 1891 3891 f 1456 1457 1880 f 2125 2088 2087 f 2160 2159 3890 f 300 1959 505 f 1064 1062 1046 f 2043 1892 1891 f 2129 2130 2004 f 2131 2132 2133 f 3848 3849 1450 f 1998 1975 804 f 3879 673 3864 f 474 492 490 f 1164 1165 1182 f 707 2162 3909 f 708 1472 2162 f 2162 2161 3886 f 548 549 1905 f 1145 1136 1114 f 705 2121 1650 f 2043 2157 1977 f 737 738 554 f 1034 1195 1191 f 458 1905 1907 f 195 196 232 f 2040 2039 1969 f 2145 2146 2147 f 2054 2050 2049 f 3832 1646 1639 f 9 12 11 f 252 364 506 f 472 478 479 f 1865 3924 3791 f 900 903 910 f 1941 2150 2151 f 1921 1928 1927 f 2163 1897 1898 f 271 156 153 f 1904 1326 12 f 282 263 264 f 1872 1873 2154 f 1326 1904 1897 f 628 692 693 f 1724 1722 1719 f 1549 1562 436 f 1582 1583 1730 f 1921 1920 1929 f 3563 3511 10 f 2152 2037 1950 f 475 772 774 f 769 2172 2171 f 2171 2165 476 f 476 773 772 f 1791 565 464 f 2166 2167 2168 f 595 371 372 f 483 1809 1641 f 762 2174 2173 f 771 483 484 f 486 487 1809 f 297 1549 1517 f 763 2175 2174 f 776 755 759 f 184 185 2176 f 412 447 303 f 2005 2176 185 f 3811 1681 1682 f 2164 2170 756 f 1472 2035 2161 f 950 951 964 f 2177 2178 2179 f 227 226 1510 f 2182 2183 2184 f 2186 2019 2018 f 1985 2182 2181 f 214 736 729 f 865 849 842 f 849 865 1357 f 559 3187 3188 f 16 13 737 f 560 553 498 f 3193 3196 3195 f 3200 3197 3198 f 3211 3214 3213 f 3215 3218 3217 f 2187 2188 2002 f 2169 2168 2189 f 2192 2086 2085 f 2193 2194 2195 f 2198 2199 2200 f 1998 1982 1979 f 3664 3665 3668 f 2116 1898 1903 f 1859 2006 2009 f 3904 2123 2126 f 1135 1148 1087 f 2201 2202 2015 f 1149 111 110 f 3221 3222 3218 f 2203 2204 2205 f 579 580 1895 f 1755 2207 2208 f 3891 3910 1893 f 1211 2151 2150 f 868 997 2210 f 944 945 930 f 2130 2129 2202 f 233 1903 1898 f 665 751 750 f 1167 1254 999 f 2211 2212 2213 f 2215 2216 2217 f 3867 593 590 f 2220 221 220 f 823 824 948 f 3225 3220 3227 f 266 204 455 f 3882 2108 2107 f 2221 2222 3519 f 2223 2224 2225 f 2207 2125 2124 f 673 674 3869 f 817 1000 996 f 2227 2228 2146 f 1947 1948 646 f 3900 2135 17 f 2229 1635 1638 f 1679 1613 1618 f 1637 2231 2232 f 2233 2234 1617 f 2231 2235 2236 f 1381 1638 2238 f 3586 2241 2242 f 1630 2234 2233 f 1681 1626 472 f 1408 1407 2243 f 3236 3239 3238 f 3232 3241 3240 f 1637 2230 2238 f 3237 3238 3233 f 1409 1408 1634 f 1380 1383 2247 f 1628 2248 2245 f 2249 1623 1622 f 1632 1634 1408 f 2248 1628 1631 f 2235 2231 1637 f 3245 3246 3199 f 631 2070 2071 f 515 167 52 f 782 781 2251 f 2253 2254 2255 f 397 281 459 f 1456 2039 2040 f 2040 2041 2036 f 1401 2112 2111 f 2041 2040 1970 f 1535 121 414 f 1586 1587 2080 f 2256 983 1132 f 1700 1699 3918 f 3815 1896 1686 f 1263 2256 1886 f 598 1841 844 f 2257 2258 2259 f 2258 2261 2262 f 2259 2262 2263 f 2260 2259 2216 f 1967 1971 1970 f 2212 1964 1963 f 922 921 2218 f 1312 1098 1101 f 1307 1306 1286 f 1548 1569 1546 f 2266 2267 2268 f 2265 2268 2270 f 2272 2265 2269 f 2273 2266 2265 f 2275 2276 2277 f 2278 2274 2277 f 2284 2285 3617 f 2287 2283 2286 f 2288 2289 2283 f 2290 2284 3639 f 2291 2292 2293 f 2295 2291 2294 f 2296 2297 2291 f 2297 2298 2292 f 2294 2293 2299 f 2279 2294 2300 f 2298 2301 2302 f 2292 2302 2303 f 2293 2303 2304 f 2304 2305 2306 f 2308 2309 2310 f 2302 2307 2310 f 2301 2311 2307 f 2312 2308 2307 f 2313 2314 2315 f 2318 2319 2320 f 3748 3771 2322 f 2324 2325 2273 f 2313 2324 2326 f 2328 2324 2313 f 2329 2325 2324 f 2331 2332 2333 f 2330 2333 2267 f 2325 2330 2266 f 2329 2331 2330 f 2333 2334 2335 f 2336 2334 2333 f 2338 2336 2332 f 2331 2339 2337 f 2329 2340 2339 f 2328 2341 2340 f 2316 2342 2341 f 2342 2316 2315 f 2344 2345 2346 f 2341 2344 2347 f 2342 2348 2344 f 2348 2349 2345 f 2350 2351 2352 f 2339 2350 2353 f 2340 2347 2350 f 2347 2346 2351 f 2337 2353 2354 f 2353 2352 2355 f 2356 2277 2276 f 2356 2357 2358 f 2356 2359 2296 f 2279 2277 2356 f 2360 2361 2270 f 2267 2335 2360 f 2362 2363 2364 f 2362 2365 2361 f 2366 2362 2358 f 2366 2367 2363 f 2357 2276 2368 f 2366 2368 2369 f 2276 2275 2370 f 2368 2370 2282 f 2372 2373 2374 f 2375 2376 2377 f 2378 2379 2380 f 2382 2378 2381 f 2384 2385 2378 f 2385 2386 2379 f 2388 2389 2390 f 2387 2390 2392 f 2393 2394 2387 f 2394 2395 2388 f 2186 2401 2402 f 2403 2404 2327 f 2272 2403 2326 f 2271 2405 2403 f 2405 2406 2404 f 2404 2407 2408 f 2409 2410 2411 f 2409 2412 2271 f 2413 2409 2269 f 2413 2414 2410 f 2417 2415 2416 f 2417 2418 2406 f 2412 2417 2405 f 2411 2415 2417 f 2406 2419 2407 f 3569 3520 632 f 2420 2398 2397 f 2420 2421 2423 f 2424 2420 2422 f 2424 2399 2398 f 2425 2391 2392 f 2421 2425 2426 f 2397 2396 2425 f 2396 2393 2391 f 2427 2314 2408 f 2430 2427 2428 f 2343 2315 2427 f 2432 2343 2430 f 2431 2430 2429 f 2435 2431 2433 f 2436 2432 2431 f 2428 2408 2407 f 2422 2437 2407 f 2438 2437 2422 f 2429 2428 2437 f 2440 2434 2433 f 2441 2439 2433 f 2442 2443 2439 f 2443 2444 2440 f 2445 2446 2444 f 2447 2445 2443 f 2383 2381 2445 f 2381 2380 2446 f 2448 2438 2423 f 2449 2448 2426 f 2441 2448 2449 f 2441 2429 2438 f 2449 2392 2390 f 2450 2390 2389 f 2447 2450 2451 f 2442 2449 2450 f 2451 2389 2453 f 2452 2453 2455 f 2382 2452 2454 f 2383 2451 2452 f 2456 2457 2373 f 2459 2460 2461 f 2463 2464 2465 f 2462 2465 2285 f 2466 2462 2284 f 2467 2463 2462 f 2461 2460 3703 f 2468 2469 2470 f 2472 2468 2471 f 2474 2461 3701 f 2475 2476 2477 f 2458 2455 2453 f 2479 2453 2389 f 2395 2480 2479 f 2480 2459 2458 f 2481 2482 2483 f 2484 113 2485 f 2471 2470 2464 f 2473 2471 2463 f 2487 2467 2466 f 2486 2466 2290 f 2490 2486 2488 f 2491 2487 2486 f 2492 2473 2467 f 2493 2492 2487 f 2494 2495 2492 f 2495 2472 2473 f 3690 2496 2474 f 3691 2497 2496 f 2498 2494 2493 f 2500 2498 2499 f 3700 3687 2498 f 3687 3691 2494 f 2306 2504 2278 f 2305 2505 2504 f 2504 2505 2507 f 2506 2507 2288 f 2274 2506 2508 f 2278 2504 2506 f 3633 3632 2489 f 3620 3633 2488 f 2507 2509 2289 f 2505 2510 2509 f 2508 2288 2287 f 2511 2287 2281 f 2370 2511 2280 f 2275 2508 2511 f 2512 2490 2489 f 2514 2512 2513 f 2517 2512 2514 f 2517 2491 2490 f 2499 2493 2491 f 2499 2517 2516 f 2510 2505 2519 f 2518 2519 2521 f 3616 2518 2520 f 3632 2510 2518 f 2522 2304 2303 f 2523 2522 2310 f 2305 2304 2522 f 2524 2522 2523 f 2519 2524 2525 f 2505 2305 2524 f 2308 2312 2527 f 2526 2527 2529 f 2531 2526 2528 f 2309 2308 2526 f 1214 2532 2151 f 2419 2406 2418 f 2533 2345 2349 f 2534 2533 2317 f 2535 2536 2533 f 2536 2346 2345 f 2537 2351 2346 f 2538 2537 2536 f 2539 2540 2537 f 2540 2352 2351 f 2312 2355 2541 f 2527 2541 2542 f 2543 2435 2434 f 2545 2543 2544 f 2319 2318 2543 f 2318 2436 2435 f 2514 2515 2548 f 2547 2548 2550 f 2552 2547 2549 f 2552 2516 2514 f 2520 2521 2554 f 2553 2554 2556 f 3590 2553 2555 f 3621 2520 2553 f 2523 2309 2531 f 2557 2531 2530 f 2525 2523 2557 f 2559 2557 2558 f 2554 2559 2560 f 2521 2525 2559 f 2561 2375 2007 f 2376 2562 2225 f 2564 2565 2566 f 2563 2566 338 f 2569 2563 2567 f 2570 2564 2563 f 2572 2573 2574 f 2571 2574 2576 f 2544 2571 2575 f 2544 2434 2572 f 2578 2570 2569 f 2579 2577 2569 f 2574 2577 2579 f 2573 2578 2577 f 2581 2582 2583 f 2584 2580 2583 f 3697 3675 2580 f 3676 2581 2580 f 2582 2516 2552 f 2583 2552 2551 f 2500 2501 2589 f 2590 2588 2589 f 3693 3695 2588 f 3695 3700 2500 f 2501 2516 2591 f 2592 2593 2594 f 2386 2385 2503 f 2596 2379 2386 f 2596 2374 2373 f 2599 2596 2597 f 2599 2380 2379 f 2600 2446 2380 f 2600 2599 2598 f 2603 2600 2601 f 2603 2444 2446 f 2604 2603 2602 f 2440 2604 2605 f 2444 2603 2604 f 2606 2582 2581 f 2606 2607 2590 f 2591 2516 2582 f 3676 2587 2456 f 3689 2456 2372 f 2478 2477 2608 f 2609 2610 2611 f 2564 2570 2613 f 2612 2613 2598 f 2457 2612 2597 f 2565 2564 2612 f 2614 2613 2570 f 2615 2614 2578 f 2602 2601 2614 f 2601 2598 2613 f 2572 2434 2605 f 2616 2605 2602 f 2573 2572 2616 f 1459 1016 1045 f 1265 1215 1079 f 1612 1608 1609 f 2067 2057 2069 f 1135 1109 1136 f 1938 1937 2618 f 95 548 458 f 2058 1607 1608 f 1454 1469 1445 f 655 2620 1048 f 722 1049 2621 f 1131 985 1033 f 562 1481 366 f 1723 1719 1725 f 1786 1123 1122 f 267 300 323 f 1325 2034 1654 f 1468 1165 1435 f 658 657 529 f 8 532 247 f 382 1557 1559 f 788 619 1307 f 940 930 929 f 911 2622 2623 f 2621 2620 655 f 2624 1857 2625 f 998 1846 27 f 635 638 2626 f 2627 641 644 f 2628 2627 1840 f 1200 1199 2627 f 1199 1773 641 f 1520 515 49 f 1452 1350 1349 f 2630 2631 1859 f 644 643 1335 f 1197 1198 717 f 2632 717 719 f 2628 2632 1166 f 2629 1197 2632 f 1762 2633 1050 f 1823 870 873 f 2634 2635 794 f 1042 794 2635 f 93 94 1901 f 2036 2038 1458 f 1527 332 1525 f 877 876 1274 f 134 409 797 f 1328 893 931 f 607 777 1091 f 1043 2635 2637 f 2636 2637 2639 f 1275 2636 2638 f 1275 1040 1043 f 1272 2624 2626 f 2640 2626 638 f 1110 2641 2640 f 536 1272 2640 f 1472 708 1474 f 2042 2030 1951 f 1058 1106 1105 f 2642 2623 2643 f 1192 2642 2644 f 912 2623 2642 f 3898 1657 3526 f 1049 1048 2620 f 616 1829 1885 f 1331 1913 666 f 990 989 1198 f 1911 1912 321 f 1298 1282 1205 f 76 1490 1504 f 1502 1500 421 f 610 609 658 f 1146 937 659 f 2646 911 914 f 1280 1239 1238 f 1453 1454 1452 f 835 942 1843 f 1271 1270 1173 f 1893 1878 1826 f 1050 2633 1937 f 2651 2652 814 f 1006 1053 1281 f 2653 1084 2654 f 1001 1004 1003 f 585 997 679 f 2624 1272 621 f 555 678 2028 f 1207 526 1197 f 1626 1627 478 f 426 389 419 f 1030 1193 1773 f 3898 645 634 f 534 1533 138 f 1125 1092 1042 f 333 228 88 f 569 1570 561 f 1489 2656 871 f 1857 2624 620 f 1673 1674 3770 f 622 861 864 f 619 788 891 f 261 544 545 f 393 1555 1556 f 2658 2659 2647 f 1075 2658 2657 f 1081 1080 2658 f 1080 1139 2659 f 2644 2643 1141 f 2660 1141 1140 f 642 2660 1336 f 1193 2644 2660 f 679 678 555 f 1503 1501 1500 f 1318 1317 1101 f 2661 2659 1139 f 2661 1142 2663 f 2662 2647 2659 f 2662 2663 2622 f 2664 2622 911 f 2648 2664 2646 f 2648 2647 2662 f 2083 2094 2093 f 1442 1440 1437 f 519 329 330 f 1885 2029 1209 f 799 800 805 f 1946 1943 1952 f 1302 1301 1253 f 566 2005 224 f 1389 940 928 f 683 682 681 f 836 835 1842 f 2665 1842 1845 f 2666 2665 1303 f 24 836 2665 f 831 1072 2650 f 1845 1844 1304 f 2010 2013 2012 f 1051 1936 2667 f 1522 1521 1511 f 531 440 441 f 1250 635 2625 f 2669 2625 1857 f 783 2669 1856 f 782 1250 2669 f 720 2670 2671 f 1055 1931 1930 f 1333 1067 1251 f 619 622 1308 f 652 655 2619 f 521 522 1539 f 1838 988 537 f 2672 537 536 f 1113 2672 2641 f 1113 527 1838 f 1016 1013 1351 f 3897 573 570 f 226 227 104 f 3564 3669 3827 f 1036 1037 1884 f 2674 1301 1304 f 2673 1304 1844 f 1334 2673 2675 f 1333 2674 2673 f 2676 702 1039 f 1843 2676 2675 f 942 1074 2676 f 1074 699 702 f 752 1057 1055 f 1200 1166 656 f 1120 1846 874 f 2677 874 877 f 1241 2677 1052 f 1119 1120 2677 f 1560 1513 151 f 1208 1316 611 f 215 735 736 f 1460 1459 1462 f 1208 1207 1839 f 854 856 1282 f 72 445 182 f 544 1563 1565 f 217 200 456 f 2047 1404 930 f 1204 2618 1937 f 913 777 686 f 840 527 1113 f 1531 729 730 f 2678 778 1283 f 601 667 1914 f 1537 1538 459 f 1819 1757 3509 f 991 1860 862 f 2679 711 1073 f 2680 1830 983 f 779 2680 2256 f 778 2678 2680 f 2678 2679 1830 f 180 542 245 f 1886 1132 1131 f 1138 1137 1274 f 1841 598 1140 f 75 313 314 f 193 423 420 f 2681 2682 1099 f 170 1567 1565 f 349 416 417 f 1121 1124 2684 f 547 797 409 f 123 122 121 f 2643 2663 1142 f 1542 1544 513 f 899 900 907 f 1210 677 676 f 791 663 982 f 24 27 837 f 893 896 932 f 1860 1320 863 f 535 538 862 f 1569 1548 1547 f 181 455 542 f 660 659 937 f 2013 567 568 f 887 890 2631 f 1524 1508 361 f 520 161 162 f 2653 855 854 f 1238 1237 2655 f 718 1838 527 f 1492 1493 518 f 792 616 615 f 599 846 1835 f 290 457 456 f 312 171 1552 f 1248 2687 1179 f 153 149 177 f 1240 1181 1222 f 1257 1292 1235 f 1291 1084 1083 f 1052 877 1277 f 743 742 2618 f 444 140 430 f 23 1224 1172 f 2687 2654 1180 f 102 103 409 f 1283 607 610 f 373 497 650 f 1180 2654 1084 f 855 2653 2655 f 1173 1270 1917 f 2689 850 853 f 2688 853 1108 f 2686 2688 1279 f 2687 2689 2688 f 2691 1302 1252 f 2690 1252 850 f 1248 2690 2689 f 1248 1247 2691 f 2692 617 24 f 2691 2692 2666 f 1247 1258 2692 f 1258 1168 617 f 394 1554 1555 f 2693 1260 1259 f 1072 1236 1262 f 1257 1071 1168 f 398 439 1555 f 871 1784 1314 f 1178 1232 1249 f 1224 1289 1173 f 1010 2617 2618 f 1175 1174 1292 f 2654 2687 2686 f 2694 1157 1216 f 71 418 1487 f 701 1112 1111 f 1072 1261 2649 f 1085 2695 22 f 895 2694 1264 f 1601 1606 1605 f 1259 1262 1271 f 21 20 1220 f 988 991 538 f 598 597 1336 f 1050 860 818 f 1298 2645 1761 f 1168 1167 618 f 858 1168 1256 f 39 242 516 f 2652 1761 819 f 313 316 315 f 2026 1970 1971 f 2656 1489 596 f 814 819 818 f 2696 2651 813 f 1260 2696 2649 f 2693 2697 2696 f 2697 1296 2651 f 381 355 390 f 3818 3819 1611 f 1475 1427 1428 f 1292 1174 1917 f 1551 353 354 f 1526 1525 1523 f 1593 1589 1595 f 1232 1269 1246 f 2695 2693 1223 f 2698 1297 1296 f 2698 2697 2693 f 2685 2698 2695 f 2685 854 1297 f 562 1497 271 f 2652 2651 1296 f 1365 1366 1364 f 1958 1946 1953 f 585 588 2210 f 1561 1560 1515 f 975 2648 1146 f 347 67 68 f 1038 1111 636 f 2700 781 780 f 2699 780 1036 f 2701 2699 1883 f 851 2700 2699 f 2674 1333 2251 f 2702 2251 781 f 1253 2702 2700 f 1301 2674 2702 f 1519 387 388 f 1273 1239 1280 f 2703 1280 1107 f 2701 2703 852 f 1829 1273 2703 f 91 260 326 f 1784 871 2656 f 2683 2684 2705 f 604 1894 3499 f 1669 3534 3548 f 2062 2063 2060 f 621 535 861 f 2023 731 732 f 686 779 1263 f 857 1051 2668 f 883 830 882 f 637 636 1111 f 2668 894 893 f 2667 921 894 f 1039 1038 1068 f 313 75 1504 f 1911 1972 305 f 1936 2706 1964 f 2707 2402 2401 f 2708 2707 2183 f 738 2163 2116 f 2349 2348 2432 f 2711 2712 2020 f 2400 2711 2019 f 2714 2715 2716 f 2717 2718 2719 f 2721 2722 2723 f 2726 2727 2728 f 2725 2728 2730 f 2731 2725 2729 f 2732 2726 2725 f 2734 2735 2736 f 2738 2739 2740 f 2737 2740 2741 f 2744 2745 2746 f 2745 2744 2748 f 2749 2750 2747 f 2753 2749 2751 f 2250 1380 2246 f 2754 1396 1614 f 3598 2754 2755 f 3608 2745 2747 f 1617 2756 2755 f 2242 2758 1620 f 2191 2085 2760 f 2761 2759 2760 f 3648 2759 2761 f 3623 2191 2759 f 3209 3248 3223 f 1395 1394 2236 f 1395 2235 1636 f 1616 1635 2229 f 1617 2234 2763 f 2756 2763 3628 f 2765 2763 2234 f 2766 2764 3628 f 3202 3199 3246 f 1516 437 435 f 2768 2769 3552 f 1495 541 578 f 2135 2136 28 f 3526 3540 2773 f 1690 1689 3926 f 2778 2779 2780 f 2777 2780 2781 f 2784 2778 2777 f 3555 3521 575 f 1494 1904 9 f 3529 2016 2015 f 451 61 505 f 2786 2787 2788 f 2779 2786 2785 f 2789 2752 2741 f 2790 2753 2752 f 2792 2793 2794 f 2793 2795 2796 f 2796 2797 2750 f 2794 2796 2749 f 2798 1393 1396 f 3591 2798 2754 f 2798 2799 2800 f 3591 3662 2799 f 2797 2796 2795 f 2791 2794 2753 f 216 233 232 f 3804 2806 2807 f 3805 2809 2804 f 2769 2810 3557 f 183 185 184 f 2811 2812 2775 f 2804 2811 2776 f 2809 2813 2811 f 2812 2811 2813 f 3805 2808 2816 f 3796 2818 2815 f 3798 3800 2820 f 3793 2823 2819 f 2824 2736 2735 f 2823 2825 2824 f 2825 2823 2826 f 3793 2822 2827 f 3796 2817 2826 f 2713 2716 2829 f 2828 2829 2831 f 2822 2821 2828 f 2821 2820 2713 f 2832 2827 2822 f 2833 2832 2830 f 2834 2835 2832 f 2835 2818 2827 f 2837 2838 2839 f 2836 2839 2841 f 2842 2836 2840 f 2843 2837 2836 f 2840 2841 2845 f 2718 2844 2845 f 2730 2728 2844 f 2727 2840 2844 f 2846 2845 2841 f 2829 2846 2847 f 2847 2841 2839 f 2848 2839 2838 f 2833 2848 2849 f 2831 2847 2848 f 2850 2814 2851 f 2853 2850 2852 f 2783 2777 2782 f 2785 2788 2853 f 2780 2785 2856 f 2857 2858 2855 f 2859 2857 2782 f 2861 2843 2842 f 2860 2842 2727 f 2862 2860 2726 f 2863 2861 2860 f 2861 2863 2858 f 2843 2861 2857 f 2856 2853 2854 f 2781 2856 2864 f 2837 2843 2859 f 2838 2837 2864 f 2852 2851 2834 f 2854 2852 2849 f 2865 2815 2818 f 2851 2865 2835 f 2813 2809 2815 f 2814 2813 2865 f 2866 2812 2814 f 2788 2866 2850 f 2866 2788 2787 f 2812 2866 2867 f 1892 1889 1890 f 2720 2719 2846 f 298 299 256 f 2872 2714 2713 f 3794 3800 2735 f 2729 2730 2874 f 2846 2716 2715 f 2876 2877 2878 f 2733 2875 2878 f 3806 3807 2879 f 3806 2880 2872 f 2882 2883 2884 f 2881 2884 2715 f 2880 2881 2714 f 2879 2882 2881 f 2885 2886 2887 f 2888 2889 2890 f 2885 2890 2892 f 2893 2886 2885 f 1942 2151 2532 f 2895 2892 2720 f 2884 2895 2870 f 2883 2896 2895 f 2896 2891 2892 f 2889 2874 2717 f 2890 2717 2720 f 2897 2893 2891 f 2898 2897 2896 f 2899 2898 2883 f 2900 2899 2882 f 3797 2900 2879 f 2876 2902 2901 f 2901 2902 2904 f 1785 2656 1835 f 2905 2906 2899 f 2907 2908 2897 f 3797 2901 2903 f 632 576 577 f 3532 3547 2867 f 434 385 550 f 2768 2770 3640 f 2910 2911 1623 f 2241 2912 2758 f 3640 2770 2913 f 1623 2911 2757 f 3587 2242 2757 f 2229 1381 1380 f 2765 1630 1629 f 2915 2914 1629 f 3642 3641 2914 f 3641 3622 2765 f 2919 2916 3642 f 2918 2915 2912 f 3614 2918 2241 f 3618 2919 3613 f 3541 2177 2180 f 3521 2002 2188 f 3705 3573 714 f 2925 2766 2917 f 2924 2917 2916 f 3610 2924 2919 f 3611 2925 2924 f 2926 2927 2766 f 3658 2926 2925 f 1715 1713 1705 f 785 497 498 f 1387 651 565 f 374 375 1899 f 3573 2203 2206 f 2929 2930 2886 f 2932 2933 2934 f 1905 1906 1908 f 2930 2935 2936 f 2938 2935 2930 f 1493 369 517 f 1603 1585 1586 f 2939 2938 2929 f 2940 2939 2908 f 3665 1707 1600 f 2886 2930 2937 f 2906 2907 2898 f 2908 2929 2893 f 2942 2934 2933 f 2941 2933 2937 f 3501 2941 2936 f 3498 2942 2941 f 2943 2944 2710 f 2943 2873 2874 f 2945 2943 2889 f 2946 2944 2943 f 2932 2945 2888 f 2931 2946 2945 f 727 1928 1929 f 2903 2904 2947 f 545 594 42 f 1480 1570 533 f 2907 2906 2949 f 2906 2905 2950 f 1558 452 215 f 3799 2903 2948 f 2054 1645 1644 f 3705 3684 2203 f 1828 1837 1849 f 2953 2954 2955 f 2952 2955 2957 f 2956 2261 2258 f 2953 2952 2258 f 2454 2455 2474 f 3747 2958 2959 f 3747 2132 2131 f 2961 2962 2963 f 2958 2963 2964 f 222 221 2485 f 2477 2226 2966 f 2631 2561 2006 f 2384 2454 2496 f 2967 2968 2959 f 2968 2967 2970 f 3759 2033 2971 f 3784 2971 2960 f 2972 2971 2033 f 2007 2377 2973 f 3560 3559 2134 f 1836 1833 1832 f 3506 2188 2090 f 955 810 959 f 672 675 674 f 633 710 713 f 2024 732 733 f 2025 2024 803 f 425 1552 1550 f 2975 2976 888 f 1319 1158 1157 f 1212 1935 2977 f 889 957 2228 f 1978 1974 1975 f 1138 1078 1329 f 751 665 2979 f 1619 1613 1625 f 2252 2255 1728 f 1157 1160 845 f 2706 1936 1938 f 2980 2255 2254 f 2982 2975 887 f 802 712 711 f 678 677 2029 f 527 840 690 f 1839 1207 2629 f 2983 2984 602 f 2983 1914 1913 f 2985 2983 1331 f 2986 2984 2983 f 2987 2637 2635 f 2985 2988 2639 f 2990 1319 1318 f 2991 2992 2993 f 2995 2991 2994 f 1106 1058 2991 f 1058 1057 2992 f 1415 1406 1365 f 1317 922 2217 f 1932 1728 2255 f 926 2998 2999 f 3001 2993 2992 f 3000 2992 1057 f 2999 3000 2978 f 2998 3001 3000 f 3002 2998 2209 f 3002 3003 2996 f 2993 3001 3002 f 1103 1106 2995 f 3004 2995 2996 f 2209 2210 3004 f 588 1103 3004 f 295 296 433 f 2638 2639 2988 f 3005 2988 1056 f 1305 3005 1930 f 1276 2638 3005 f 1987 1968 1993 f 2633 1762 1761 f 1423 1422 1417 f 3561 3517 739 f 2671 2670 2704 f 1473 1872 2035 f 922 1317 2694 f 1401 1400 1378 f 556 2028 1884 f 842 848 3825 f 1854 2706 2617 f 933 1082 1081 f 1440 1441 1436 f 529 657 719 f 1031 1025 1027 f 777 913 912 f 2645 1205 1204 f 441 438 1538 f 790 1287 587 f 2065 2066 1610 f 1860 991 990 f 60 106 87 f 1424 1017 1948 f 332 1527 1507 f 261 262 138 f 187 188 449 f 1719 1720 1726 f 1931 1055 1059 f 3006 1059 1105 f 1287 3006 1104 f 1286 1931 3006 f 3007 3008 3009 f 3009 3008 653 f 618 999 26 f 2029 677 1210 f 957 601 600 f 1049 2146 2228 f 2631 890 3011 f 890 889 2227 f 3011 2227 2145 f 2561 3011 3012 f 1026 656 609 f 2706 1854 1961 f 3013 2965 2485 f 2148 2147 1267 f 3015 1267 1266 f 2997 2264 1101 f 642 641 1773 f 186 477 461 f 1041 1040 1320 f 1121 3016 3017 f 2984 2986 2987 f 3018 2987 2634 f 1048 603 3018 f 603 602 2984 f 2679 2678 801 f 2062 2072 2074 f 2061 2074 2076 f 414 303 1545 f 3016 1121 2683 f 3019 2683 2704 f 3020 3019 2670 f 3007 3016 3019 f 1871 1090 1091 f 3021 1787 1122 f 3022 3021 3017 f 1314 3021 3022 f 1784 1787 3021 f 1316 1315 1763 f 3023 3022 3010 f 3023 3009 795 f 1763 3023 1092 f 1315 3022 3023 f 3016 3007 3010 f 752 751 2978 f 2986 2639 2637 f 3024 654 653 f 3020 3024 3008 f 720 723 3024 f 723 2621 654 f 2681 1098 1268 f 2671 3025 1268 f 2705 2681 3025 f 1313 3026 3027 f 3026 3028 3029 f 3027 3029 3030 f 1311 3027 3031 f 2012 568 731 f 917 2657 974 f 743 1206 856 f 1888 2027 2030 f 2682 2705 2684 f 3032 2684 1124 f 1100 3032 2989 f 1099 2682 3032 f 1123 2990 2989 f 3033 1834 1159 f 2990 3033 1158 f 1123 1786 3033 f 1786 1785 1834 f 1054 1056 2988 f 2979 665 664 f 2623 2622 2663 f 1095 660 688 f 3034 3035 2261 f 3036 3034 2956 f 3037 3038 3034 f 3038 3039 3035 f 3040 3041 3026 f 2263 3042 3041 f 3042 3039 3043 f 3041 3043 3028 f 3044 3045 3043 f 3046 3047 3045 f 3047 3048 3049 f 3043 3045 3049 f 3038 3037 3051 f 3050 3051 3053 f 3052 3046 3044 f 3039 3038 3050 f 3054 3035 3039 f 3042 2263 2262 f 2261 3035 3054 f 2216 2263 2997 f 2212 2211 2667 f 3056 868 2209 f 934 3057 1961 f 2013 3755 3757 f 3058 3059 3060 f 3059 3062 3063 f 3060 3063 2213 f 3061 3060 1963 f 3063 3064 2214 f 3062 3065 3064 f 3065 2257 2260 f 3064 2260 2215 f 3066 3067 3031 f 2608 2966 3067 f 2966 2562 3068 f 3031 3067 3068 f 2455 2458 2461 f 2476 2223 2226 f 3551 2090 2089 f 3526 2774 3769 f 433 354 355 f 2115 1423 1419 f 3509 2868 2922 f 3769 3737 2131 f 3069 2611 2167 f 2971 2972 2928 f 2961 2774 2773 f 3070 2475 2478 f 2610 2609 3071 f 2502 2371 2374 f 3072 2592 2595 f 2964 3046 3052 f 3048 2593 3074 f 2593 2592 3075 f 3075 3030 3029 f 3074 3029 3028 f 3072 3076 3075 f 3076 3072 2482 f 2481 2608 3066 f 3076 3066 3030 f 2967 3052 3053 f 2482 3072 3073 f 479 480 493 f 706 3900 3873 f 3561 2194 2221 f 2013 2010 3754 f 3533 2032 2016 f 3562 3519 2222 f 1171 1154 1155 f 2586 2566 2565 f 2982 110 113 f 2562 2376 3077 f 2376 2375 3012 f 3012 2145 2148 f 3077 2148 3015 f 2963 3047 3046 f 3051 3078 2970 f 1683 2173 491 f 3850 2122 1447 f 1986 2181 3734 f 3079 3080 2546 f 3081 3079 2575 f 1983 1986 3768 f 3082 2323 2319 f 3080 3082 2545 f 3734 3732 3082 f 3732 3748 2323 f 1309 1471 1888 f 3073 2595 2772 f 2760 3083 3084 f 2760 2085 2088 f 3085 3084 3086 f 3089 3085 3090 f 3089 2762 3084 f 3092 3088 3090 f 3093 2748 2744 f 2751 2747 2748 f 3093 2741 2752 f 2742 2741 3093 f 3094 3093 2743 f 3096 3097 3098 f 3099 3096 3087 f 3097 3094 3095 f 3100 2743 2746 f 3101 2927 3102 f 2764 2766 2927 f 2745 2764 3101 f 3102 3092 3091 f 3100 3091 3095 f 2869 2802 3592 f 3102 3103 3104 f 3658 2868 2869 f 2927 2926 3103 f 3098 3095 3091 f 3087 3098 3090 f 2088 2125 3105 f 3083 3105 3086 f 2483 3073 2771 f 2401 2186 2185 f 3099 3086 3105 f 2125 2207 3106 f 3108 2737 2742 f 3107 2742 3094 f 3110 3108 3107 f 3109 3107 3097 f 3111 3109 3096 f 3112 3110 3109 f 3113 2738 2737 f 3115 2723 2722 f 3117 3115 3114 f 3119 3117 3116 f 3118 3116 3120 f 3116 3114 3113 f 3122 3112 3111 f 3122 3123 1755 f 3124 3125 3126 f 3125 3118 3121 f 3128 3119 3118 f 3129 3128 3125 f 2020 2712 3128 f 3853 3857 1346 f 1065 1046 1047 f 1814 3838 3830 f 2021 2020 3129 f 3111 3099 3106 f 3123 3106 2207 f 2377 2225 2224 f 3014 2485 113 f 1756 2208 3782 f 3131 3132 2530 f 3133 3131 2528 f 1753 1756 3767 f 3134 3124 3127 f 2327 2408 2314 f 2436 2318 2317 f 2018 2021 3711 f 2534 3136 3137 f 2320 2322 3136 f 2185 2018 3712 f 3138 3139 2542 f 3134 3135 3744 f 3785 3130 3134 f 3140 3138 2539 f 2535 3137 3140 f 3711 2021 3130 f 2558 2530 3132 f 3782 3760 3141 f 2560 2558 3141 f 3760 3777 3142 f 958 888 2976 f 1933 1932 2980 f 2399 2424 2418 f 2348 2342 2343 f 2322 2320 2319 f 2718 2717 2874 f 2709 2729 2873 f 2894 2871 1361 f 346 289 176 f 1876 1877 1882 f 2802 2803 3607 f 3104 3144 3088 f 2974 2222 3649 f 3089 3145 2761 f 3088 3144 3145 f 2803 2974 3603 f 3870 3909 692 f 2123 1419 1418 f 1365 1363 1368 f 1581 1766 1782 f 3569 2202 2129 f 3555 2003 2002 f 3146 2585 2551 f 3751 3146 3147 f 3742 3149 3146 f 3149 2584 2585 f 3196 3198 3197 f 3604 2192 2191 f 3142 3150 2556 f 3777 3778 3150 f 3646 3152 2550 f 3150 3151 2555 f 3778 3780 3151 f 3788 3781 3152 f 3153 3147 2551 f 3152 3153 2549 f 3781 3776 3153 f 3776 3750 3147 f 3148 2767 2206 f 2183 2401 2321 f 2132 2960 2928 f 3684 3685 2204 f 3719 2198 2197 f 2594 2962 2773 f 2595 2594 2951 f 1870 798 799 f 3713 2179 2178 f 3706 2178 3736 f 336 3154 3774 f 339 3155 3154 f 3789 3683 3149 f 3683 3697 2584 f 2963 2962 3048 f 3722 3730 337 f 339 338 2566 f 3156 3157 2168 f 2610 3156 2167 f 2954 2953 3156 f 3157 3156 2953 f 3707 2166 2169 f 2567 3158 3159 f 337 3158 2567 f 3730 3069 2166 f 3764 2190 1983 f 2579 3160 3081 f 2568 3159 3160 f 3709 2169 2190 f 3065 3062 3162 f 3161 3162 1984 f 3157 3161 2189 f 2257 3065 3161 f 734 735 1922 f 3059 3058 2708 f 3163 2708 2182 f 3162 3163 1985 f 3062 3059 3163 f 3203 3206 3205 f 2122 2119 1448 f 3817 1686 1685 f 139 140 444 f 1570 569 566 f 2957 3164 3165 f 2955 3166 3164 f 1912 454 341 f 3130 3129 3124 f 1548 322 319 f 1949 1950 1327 f 2969 2970 3078 f 3167 3078 3169 f 3724 2014 2017 f 3720 2017 2033 f 3168 3169 3165 f 3170 3165 3164 f 3779 3170 3171 f 3724 3168 3170 f 3171 3164 3166 f 3172 3166 3071 f 3752 3172 3173 f 3756 3171 3172 f 3169 3037 3036 f 2593 3048 2962 f 3173 3071 2609 f 3174 2609 2199 f 3783 3174 2198 f 3749 3173 3174 f 2190 2189 1984 f 2196 2195 2767 f 2119 2120 1414 f 1464 1463 1466 f 2199 2611 3069 f 2457 2456 2587 f 2226 2225 2562 f 481 478 1627 f 333 1512 235 f 3078 3051 3037 f 3040 2264 2997 f 2219 1934 1933 f 2385 2384 2497 f 221 2220 3175 f 2220 1933 3143 f 3175 3143 2976 f 2484 3175 2975 f 2954 3071 3166 f 2630 1858 110 f 1547 319 1952 f 2091 2090 2188 f 3733 3736 2178 f 1918 1919 1927 f 3563 112 111 f 955 956 960 f 2037 3014 112 f 3143 2980 2981 f 2218 2211 2214 f 672 3877 3787 f 2152 3013 3014 f 1147 1144 1143 f 2248 2246 1409 f 1615 1636 1635 f 1410 1409 2246 f 3241 3243 3242 f 1382 3176 3177 f 3177 1410 2247 f 2247 1383 3177 f 3176 2238 2230 f 1410 3177 3179 f 3179 3180 2243 f 1633 3181 2800 f 1621 1620 2758 f 1622 1621 2245 f 2236 1394 3181 f 2237 2236 3182 f 2250 1631 2233 f 2912 1629 1628 f 135 136 1564 f 1567 419 512 f 1085 1084 2653 f 3184 869 868 f 869 3184 3057 f 1266 1268 1098 f 1267 2147 722 f 937 1146 2646 f 2146 1049 722 f 2211 2218 921 f 1957 510 507 f 1313 1312 2264 f 1496 362 1493 f 2887 2937 2933 f 1687 1688 1659 f 145 1499 1498 f 37 38 418 f 1935 1934 3185 f 104 89 301 f 2101 2098 2083 f 543 298 360 f 1934 2219 3186 f 234 1521 212 f 3845 3821 1822 f 1976 1875 1874 f 223 3186 2219 f 3655 3656 3605 f 2165 2164 467 f 467 468 2165 f 773 476 2165 f 1935 1212 1728 f 3240 3225 3226 f 3261 3249 3250 f 3215 3216 3244 f 3265 3221 3215 f 3267 3245 3198 f 3205 3206 3269 f 3214 3211 3265 f 3271 3213 3214 f 3251 3252 3239 f 3222 3221 3266 f 3232 3235 3234 f 3216 3242 3243 f 3196 3193 3268 f 3244 3243 3254 f 3238 3239 3247 f 3264 3244 3255 f 3253 3242 3216 f 3211 3266 3221 f 3257 3217 3218 f 3258 3222 3272 f 3274 3275 3280 f 3228 3229 3262 f 3275 3231 3281 f 3226 3227 3273 f 3236 3263 3260 f 3281 3263 3236 f 3287 3271 3192 f 3235 3226 3280 f 3234 3280 3281 f 3288 3290 3259 f 3260 3250 3276 f 3289 3212 3278 f 3210 3209 3224 f 3199 3202 3201 f 3228 3231 3230 f 3290 3288 3285 f 3282 3279 3271 f 3260 3263 3262 f 3243 3241 3268 f 3267 3268 3241 f 3194 3207 3208 f 3191 3270 3269 f 3270 3191 3192 f 3275 3274 3230 f 3264 3269 3270 f 3252 3251 3276 f 3248 3252 3291 f 3213 3271 3279 f 3272 3266 3289 f 3277 3278 3212 f 3228 3263 3281 f 3220 3225 3253 f 3219 3253 3217 f 3256 3218 3222 f 3247 3239 3252 f 3245 3267 3232 f 3266 3211 3212 f 3208 3205 3255 f 3288 3289 3284 f 3204 3205 3208 f 560 1899 3187 f 3209 3210 3201 f 3189 3192 3191 f 448 1980 807 f 647 648 122 f 1833 1848 1780 f 510 509 508 f 3210 1949 1887 f 1887 15 3200 f 15 16 3197 f 16 3188 3195 f 3195 3188 3187 f 3194 3187 1899 f 3207 1899 375 f 387 173 126 f 3189 1791 1760 f 3577 763 757 f 763 3577 2172 f 3583 3582 3580 f 3581 3580 3579 f 3594 3597 3596 f 3400 3601 3602 f 3626 3624 3625 f 3631 3635 3599 f 3636 3637 3595 f 3647 3584 3588 f 3650 3582 3584 f 3653 3579 3580 f 3653 2176 3654 f 1801 1864 3656 f 3651 3580 3582 f 3644 3638 3595 f 1767 1768 3659 f 1597 3663 3661 f 1708 1707 3665 f 3671 3668 3667 f 3666 3667 3673 f 1588 1708 3664 f 2080 3671 3666 f 2082 3666 3672 f 3674 3677 3635 f 3677 3678 3636 f 3682 3647 3645 f 3692 3651 3650 f 3681 3645 3644 f 786 3694 3692 f 3694 3653 3651 f 785 3692 3686 f 497 3686 3682 f 650 3682 3681 f 3696 564 3681 f 3405 3606 3605 f 3702 3699 3678 f 3699 3698 3679 f 3645 3588 3638 f 1799 3657 3659 f 3708 3704 3458 f 3462 3710 3708 f 3714 463 564 f 3615 3606 3602 f 3710 3714 3696 f 3635 3636 3596 f 3629 3626 3627 f 3597 3600 3599 f 3630 3716 3715 f 3627 3625 3597 f 3717 3716 3723 f 3715 3663 3660 f 3716 3717 3725 f 3668 3665 3661 f 3668 3725 3717 f 3667 3717 3718 f 3727 3728 3673 f 3728 1576 3672 f 3726 3727 3718 f 1576 2101 2084 f 3727 3726 3731 f 3203 376 1387 f 3190 1387 1791 f 3283 2152 1949 f 317 240 259 f 2000 2176 3653 f 3601 3599 3600 f 3589 3588 3584 f 3577 758 756 f 3585 3584 3582 f 3678 3679 3637 f 3204 375 376 f 1801 3655 3657 f 3729 3735 3728 f 1575 3735 3738 f 3735 1575 1576 f 1776 1795 1796 f 1293 1294 1290 f 1326 14 1327 f 1294 1245 1234 f 878 1244 1330 f 497 373 374 f 122 648 413 f 3699 3702 3704 f 3606 3405 3401 f 3708 3710 3698 f 3464 3714 3710 f 3704 3674 3443 f 439 398 399 f 1512 1528 1526 f 1668 1741 1738 f 839 849 1362 f 1362 1357 826 f 3843 740 1896 f 2053 2054 2118 f 3888 3887 2120 f 696 697 3816 f 1826 1878 639 f 3740 3739 3729 f 956 885 886 f 1980 806 1922 f 33 34 105 f 1343 1344 3856 f 3907 2127 2128 f 1766 1581 1582 f 14 1326 2163 f 595 98 99 f 3233 3238 3246 f 1346 1035 1044 f 748 1888 1425 f 3193 3208 3254 f 3190 3191 3206 f 3612 3605 3606 f 3624 3612 3615 f 3631 3415 3443 f 3679 3680 3644 f 3686 3650 3647 f 3702 3677 3674 f 3625 3615 3600 f 3601 3400 3415 f 3739 3738 3735 f 3630 3627 3594 f 3741 3723 3716 f 1284 1290 1288 f 3698 3696 3680 f 1778 1781 1780 f 1792 1759 1584 f 695 570 571 f 3638 3741 3594 f 3741 3638 3588 f 3247 3248 3209 f 3292 3293 3294 f 3296 3297 3298 f 3299 3298 3300 f 3302 3303 3304 f 3306 3307 3308 f 3307 3310 3311 f 3303 3302 3301 f 3312 3305 3304 f 3314 3315 3316 f 3318 3319 3320 f 3324 3318 3321 f 3326 3327 3328 f 3329 3330 3331 f 3333 3334 3335 f 3337 3338 3339 f 3333 3336 3341 f 3342 3341 3343 f 3319 3345 3344 f 3338 3335 3334 f 3346 3301 3302 f 3305 3348 3347 f 3312 3224 3223 f 3350 3337 3340 f 3312 3349 3348 f 3352 3343 3341 f 3311 3353 3354 f 3341 3336 3327 f 3359 3360 3361 f 3360 3359 3250 f 3318 3363 3345 f 3364 3363 3318 f 3314 3364 3324 f 3366 3299 3301 f 3299 3366 3367 f 3308 3354 3368 f 3369 3317 3295 f 3317 3369 3364 f 3370 3295 3317 f 3325 3371 3365 f 3352 3320 3319 f 3296 3367 3353 f 3297 3296 3311 f 3363 3368 3354 f 3345 3354 3353 f 3344 3353 3367 f 3366 3333 3342 f 3347 3339 3334 f 3339 3347 3348 f 3348 3349 3351 f 3363 3364 3369 f 3294 3309 3368 f 3293 3306 3309 f 3331 3373 3374 f 3346 3334 3333 f 3323 3322 3352 f 3356 3355 3321 f 3355 3357 3325 f 3357 3358 3371 f 3322 3356 3320 f 3361 3330 3329 f 3373 3372 3375 f 3374 3375 3376 f 3329 3332 3376 f 3327 3375 3372 f 3336 3335 3375 f 3335 3338 3376 f 3376 3338 3337 f 3337 3350 3359 f 3359 3350 3276 f 3377 3292 3295 f 3277 3316 3315 f 3371 3378 3379 f 3378 3371 3358 f 3365 3379 3315 f 3316 3277 3279 f 3282 3377 3370 f 3313 3283 3224 f 3378 3285 3284 f 3380 3286 3285 f 3379 3284 3278 f 3276 3350 3351 f 3291 3351 3349 f 3313 3304 2965 f 2965 3304 3303 f 222 3303 3300 f 223 3300 3298 f 3298 3297 3185 f 3297 3310 2977 f 3310 3307 1213 f 3307 3306 1214 f 2532 1214 3306 f 2894 2532 3293 f 2871 2894 3292 f 3253 3225 3240 f 3283 3313 3013 f 1997 1996 1889 f 1237 745 744 f 1535 1534 120 f 1531 1532 201 f 1119 1241 1032 f 946 948 824 f 163 8 5 f 3589 3726 3723 f 1155 1156 1162 f 427 213 327 f 553 1902 796 f 3381 2232 2231 f 3381 3178 2230 f 2237 3183 3180 f 3383 3384 3385 f 3384 3386 3387 f 3386 3388 3389 f 3388 3390 3391 f 3392 3393 3394 f 3395 3394 3396 f 3398 3399 3397 f 3400 3401 3399 f 3403 3404 3405 f 3406 3407 3404 f 3408 3409 3407 f 3410 3411 3409 f 3412 3413 3411 f 3415 3400 3398 f 3414 3398 3396 f 3416 3396 3394 f 3417 3394 3393 f 3393 3419 3420 f 3419 3390 3421 f 3390 3388 3422 f 3388 3386 3423 f 3386 3384 3424 f 3384 3383 3425 f 3425 3383 3426 f 2904 3427 3428 f 3429 3427 2904 f 3430 3429 2902 f 2875 3431 3430 f 3432 3433 2733 f 2825 3434 3435 f 3437 3438 3439 f 3441 3437 3436 f 2724 2723 3434 f 3436 3439 3115 f 3440 3436 3117 f 3443 3415 3414 f 3442 3414 3416 f 3444 3416 3417 f 3445 3417 3418 f 3446 3418 3420 f 3420 3421 3448 f 3421 3422 3449 f 3422 3423 3450 f 3423 3424 3451 f 3424 3425 3452 f 3425 3184 3056 f 3451 3452 2253 f 3450 3451 2252 f 3449 3450 1727 f 3448 3449 2150 f 3453 3447 3448 f 3454 3446 3447 f 3455 3445 3446 f 3456 3444 3445 f 3442 3444 3456 f 3443 3442 3457 f 3459 3460 3458 f 3457 3456 3455 f 3459 3455 3454 f 3462 3460 3459 f 3461 3454 3453 f 3464 3462 3461 f 3463 3453 1941 f 3404 3399 3401 f 3407 3397 3399 f 3409 3395 3397 f 3411 3392 3395 f 3465 3466 3413 f 3467 3468 3469 f 3465 3412 3431 f 3470 3467 3466 f 3438 3470 3432 f 3467 3470 3438 f 3468 3467 3437 f 3471 3469 3468 f 3472 3468 3441 f 3473 3441 3440 f 2712 3440 3119 f 3475 3471 3472 f 3474 3472 3473 f 3476 3473 2712 f 3477 3476 2711 f 3478 3474 3476 f 3479 3475 3474 f 3392 3411 3413 f 3466 3469 3480 f 3419 3393 3392 f 3390 3419 3480 f 3469 3471 3391 f 3389 3391 3471 f 3387 3389 3475 f 3385 3387 3479 f 3482 3477 2400 f 3483 3478 3477 f 3481 3479 3478 f 3484 3482 2402 f 3058 3484 2707 f 3061 3485 3484 f 3485 3483 3482 f 3382 3385 3481 f 3486 3481 3483 f 3426 3383 3382 f 3487 3382 3486 f 3486 3485 3061 f 1961 3057 3487 f 3057 3184 3426 f 3428 3403 3402 f 3427 3406 3403 f 3429 3408 3406 f 3430 3410 3408 f 3431 3412 3410 f 3470 3465 3433 f 3434 3439 3438 f 2723 3115 3439 f 2875 2733 3433 f 2736 2824 3435 f 2947 3428 3488 f 3464 3463 1360 f 1900 1925 1924 f 1736 1717 1718 f 1955 1952 1943 f 1671 1738 1735 f 1696 1750 1698 f 1800 1865 1864 f 2063 2055 2056 f 1737 1734 1717 f 1751 1735 1697 f 943 3885 3899 f 3656 3488 3402 f 1577 1578 1764 f 3917 1598 1599 f 1945 146 82 f 68 6 7 f 147 1542 1543 f 1109 1135 1133 f 1503 1910 1907 f 1499 1501 1909 f 452 1922 735 f 1824 2068 3835 f 1825 2067 2068 f 1463 1462 1022 f 841 1012 1009 f 1568 1547 1955 f 1910 1503 1502 f 1939 1502 422 f 309 280 1940 f 1804 1851 1866 f 269 1482 1954 f 2065 1607 2060 f 3812 1685 1758 f 280 276 552 f 1875 1873 1869 f 1739 1721 1734 f 1778 1779 1790 f 310 1920 1915 f 1909 1908 1906 f 1789 1783 1778 f 978 981 980 f 1584 1759 1731 f 425 426 311 f 148 1543 115 f 2044 3897 3894 f 277 1910 1939 f 1943 1946 1945 f 1461 1462 1463 f 2253 3452 3056 f 2981 2254 3489 f 2209 2998 3489 f 2254 2253 3055 f 2998 926 958 f 2826 2817 2721 f 3490 2739 2721 f 2816 3491 3490 f 2334 2297 2296 f 2298 2297 2334 f 2301 2298 2336 f 2311 2301 2338 f 2312 2311 2354 f 2359 2358 2361 f 2335 2296 2359 f 2414 2413 2365 f 2413 2270 2361 f 2355 2352 2540 f 2541 2540 2539 f 2722 2721 2739 f 3114 2722 2738 f 3110 3112 3121 f 3108 3110 3120 f 3122 1754 3127 f 3112 3122 3126 f 3127 1754 1753 f 3139 3133 2529 f 3744 3775 3133 f 3490 2789 2740 f 3491 2790 2789 f 228 229 91 f 245 244 243 f 248 7 247 f 2 297 296 f 145 146 300 f 327 213 210 f 329 332 331 f 239 241 240 f 373 376 375 f 178 177 176 f 1218 1225 1228 f 3862 1231 1295 f 808 809 824 f 3858 3859 1013 f 3813 3810 1411 f 2154 2149 2159 f 3845 3835 1758 f 306 807 1922 f 2157 2153 1976 f 708 709 2031 f 1450 1226 1227 f 1672 1604 1605 f 1813 1814 1821 f 1717 1716 1715 f 176 159 402 f 1710 1705 3916 f 1712 1580 1577 f 1705 1713 1711 f 971 978 979 f 822 823 1094 f 2909 2810 2793 f 2810 2769 2795 f 2799 2910 2249 f 3662 3643 2910 f 2769 2768 2801 f 3177 3176 3178 f 3178 3381 3180 f 3181 1394 1393 f 3181 1633 1632 f 3182 1632 2244 f 3183 2244 2243 f 3492 2791 2790 f 2808 3492 3491 f 2239 3574 3572 f 1149 1150 3571 f 1150 2009 3567 f 2202 3569 3566 f 2921 3564 3565 f 3568 3560 2133 f 1777 1579 1759 f 1201 1196 1183 f 3561 3554 2195 f 3553 3576 2770 f 3927 3925 1687 f 1516 152 154 f 3549 3562 2974 f 3550 3548 2779 f 2771 2772 3575 f 3070 2771 3546 f 3547 3544 2775 f 1376 1375 1370 f 2767 3543 3556 f 2195 3554 3543 f 2775 3544 3542 f 3377 3282 3545 f 3804 3802 3539 f 2776 3542 3538 f 2240 3572 3535 f 2920 3535 3669 f 3548 3534 2786 f 1805 1850 1851 f 3534 3532 2787 f 2951 2773 3540 f 2772 2951 3531 f 2973 2224 3530 f 2224 2223 3537 f 1957 1953 320 f 306 305 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2079 f 2868 3658 3611 f 2922 3611 3610 f 2923 3610 3618 f 3670 3613 2918 f 3619 3670 3614 f 2921 3618 3670 f 2920 3614 3586 f 3613 3642 2915 f 2917 2766 3622 f 2916 2917 3641 f 2239 3587 3652 f 2911 3640 3652 f 3643 3640 2911 f 2797 2801 3662 f 2797 3591 3598 f 3622 3628 2763 f 3608 3628 2764 f 2194 3623 3648 f 2221 3648 3649 f 2756 3608 3609 f 2750 3598 3609 f 3669 3535 1817 f 3763 3770 1691 f 2515 3621 3590 f 2548 3590 3593 f 2513 2489 3632 f 2513 3616 3621 f 2509 3633 3620 f 2510 3632 3633 f 2289 3620 3639 f 2283 3639 3617 f 2240 3586 3587 f 3659 3657 3624 f 716 2121 3887 f 727 1999 3761 f 2165 2171 2170 f 3660 3659 3626 f 3743 3581 3578 f 2175 2172 769 f 2100 500 2095 f 3758 567 3757 f 640 3560 3568 f 1653 3576 3553 f 1895 3536 3530 f 746 3530 3537 f 1653 1810 3528 f 3762 1603 1604 f 2093 2075 2073 f 959 996 995 f 962 899 919 f 1400 1401 1398 f 1000 817 1127 f 956 959 884 f 1662 3548 3550 f 2034 3558 3546 f 3926 1689 3539 f 3535 3572 766 f 3572 3574 765 f 334 3570 3571 f 1868 3531 3540 f 1684 1651 1652 f 1682 1683 775 f 1585 1603 3762 f 3763 3762 1672 f 2173 2174 490 f 1658 3544 3547 f 3556 3543 1650 f 1571 3754 2010 f 1587 3664 3671 f 2099 2095 2077 f 787 3761 1999 f 2000 3694 786 f 3155 339 3697 f 3713 3155 3683 f 2180 2179 3685 f 3496 2180 3684 f 1684 3846 3840 f 1760 464 3495 f 2607 3689 3693 f 2581 3676 3689 f 2371 2502 3700 f 2371 3695 3693 f 2587 3676 3675 f 2586 3675 3697 f 2497 3691 3687 f 2502 2503 3687 f 2494 3691 3690 f 3690 3688 2472 f 3688 3701 2468 f 3701 3703 2469 f 726 3908 3892 f 581 3567 3518 f 10 3511 3570 f 1837 3514 3510 f 741 3537 3523 f 1299 3524 3558 f 1851 3507 3504 f 3527 3565 1820 f 580 3518 3536 f 1758 1685 3522 f 1693 3557 3508 f 3504 3512 1867 f 1894 1826 3568 f 2126 1418 3813 f 3906 2126 3841 f 2113 3911 3867 f 2179 3713 3789 f 624 3903 3904 f 2192 3604 3788 f 2177 3541 3787 f 1364 3824 3833 f 3858 3855 1470 f 3135 1753 3775 f 2069 3812 3835 f 1351 3861 3853 f 2108 3882 3901 f 2089 2092 3736 f 3828 3834 1358 f 2001 3749 3783 f 2187 3783 3719 f 2130 3756 3752 f 2004 3752 3749 f 2014 3724 3779 f 2201 3779 3756 f 3167 3720 3759 f 3168 3724 3720 f 3159 3709 3764 f 3160 3764 3765 f 3730 3707 3158 f 3158 3707 3709 f 2200 3069 3730 f 3685 3789 3742 f 3722 3774 2197 f 3790 3736 2092 f 3774 3790 3721 f 3154 3706 3790 f 3155 3713 3706 f 2091 3719 3721 f 2196 3148 3750 f 2193 2196 3776 f 3604 2193 3781 f 2086 2192 3780 f 2087 2086 3778 f 2204 3742 3751 f 2205 3751 3750 f 3909 3886 574 f 2124 2087 3777 f 2208 2124 3760 f 3711 3785 3140 f 3785 3745 3138 f 3745 3744 3139 f 3771 3712 3136 f 3712 3711 3137 f 3775 3767 3131 f 3767 3782 3132 f 1813 3839 3838 f 1293 3854 3856 f 2184 2321 3748 f 2181 2184 3732 f 3765 3768 3079 f 3768 3734 3080 f 709 3873 3875 f 2774 2961 3737 f 3786 3769 2134 f 3809 3808 766 f 2968 3784 3766 f 2969 3759 3784 f 2958 3747 3737 f 2132 3747 3766 f 2321 2185 3771 f 3860 3848 1449 f 3905 3874 582 f 3886 3889 577 f 3847 3826 1818 f 705 3902 3887 f 3816 3912 3907 f 2111 3883 3911 f 3810 3829 1366 f 1812 3839 3822 f 675 3851 3852 f 3496 3705 3851 f 2110 3867 3868 f 2104 3896 3901 f 2109 3868 3899 f 3842 3831 703 f 1680 3811 3846 f 1639 1627 3819 f 623 3904 3906 f 2139 2140 3866 f 1808 1640 3844 f 3820 3821 1819 f 3872 3914 3900 f 1646 3832 3830 f 3889 3890 633 f 2139 3865 3875 f 2156 2155 3871 f 674 3852 3913 f 2117 3817 3812 f 1815 3822 3847 f 3855 3849 1444 f 697 3906 3912 f 866 842 3836 f 3864 3869 2136 f 3585 3731 3726 f 3583 3740 3731 f 3753 3746 3738 f 3740 3583 3581 f 1088 3738 3746 f 3739 3740 3743 f 2791 3492 3493 f 3492 2808 2807 f 2792 3493 3494 f 3493 2807 2806 f 3508 2909 3494 f 3516 3494 2806 f 2948 3503 3791 f 2105 3908 3896 f 3538 3802 3804 f 680 3878 3876 f 1891 1890 3910 f 2905 3799 3801 f 2900 3797 3799 f 2901 3797 3807 f 2734 2878 3806 f 2877 3807 3806 f 2820 3800 3794 f 2818 3796 3792 f 2823 3793 3792 f 2822 3793 3798 f 2819 2735 3800 f 2817 3796 3795 f 2809 3805 3795 f 2808 3805 3803 f 2805 3804 3803 f 1702 3921 3923 f 3922 3920 1797 f 3915 3916 1706 f 3918 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3993 3997 3998 f 3998 4002 4003 f 3997 3996 4000 f 3998 3997 4001 f 4002 4006 4007 f 4000 4004 4005 f 4001 4005 4006 f 4006 4010 4011 f 4005 4004 4008 f 4006 4005 4009 f 4011 4010 4014 f 4008 4012 4013 f 4009 4013 4014 f 4014 4018 4019 f 4013 4012 4016 f 4014 4013 4017 f 4019 4018 4022 f 4016 4020 4021 f 4017 4021 4022 f 4023 4022 4026 f 4021 4020 4024 f 4022 4021 4025 f 4027 4026 4030 f 4024 4028 4029 f 4025 4029 4030 f 4031 4030 4034 f 4029 4028 4032 f 4030 4029 4033 f 4034 4038 4039 f 4032 4036 4037 f 4033 4037 4038 f 4038 4042 4043 f 4037 4036 4040 f 4038 4037 4041 f 4043 4042 4046 f 4040 4044 4045 f 4041 4045 4046 f 4046 4050 4051 f 4045 4044 4048 f 4046 4045 4049 f 4051 4050 4055 f 4048 4053 4054 f 4049 4054 4055 f 4056 4055 4059 f 4053 4057 4058 f 4055 4054 4058 f 4060 4059 3934 f 4058 4057 3932 f 4058 3933 3934 f 4069 4068 4081 f 4066 4079 4080 f 4065 4064 4077 f 4063 4062 4075 f 4071 4084 4085 f 4069 4082 4083 f 4067 4080 4081 f 4066 4065 4078 f 4063 4076 4077 f 4061 4074 4075 f 4072 4085 4086 f 4071 4070 4083 f 4076 4089 4090 f 4074 4087 4088 f 4086 4085 4098 f 4083 4096 4097 f 4082 4081 4094 f 4079 4092 4093 f 4078 4077 4090 f 4076 4075 4088 f 4085 4084 4097 f 4082 4095 4096 f 4080 4093 4094 f 4079 4078 4091 f 4089 4102 4103 f 4088 4087 4100 f 4098 4111 4112 f 4096 4109 4110 f 4095 4094 4107 f 4092 4105 4106 f 4090 4103 4104 f 4089 4088 4101 f 4097 4110 4111 f 4095 4108 4109 f 4094 4093 4106 f 4092 4091 4104 f 4103 4102 4115 f 4100 4113 4114 f 4112 4111 4124 f 4110 4109 4122 f 4107 4120 4121 f 4105 4118 4119 f 4103 4116 4117 f 4101 4114 4115 f 4110 4123 4124 f 4109 4108 4121 f 4107 4106 4119 f 4105 4104 4117 f 4115 4128 4129 f 4114 4113 4126 f 4124 4137 4138 f 4123 4122 4135 f 4120 4133 4134 f 4119 4118 4131 f 4117 4116 4129 f 4114 4127 4128 f 4124 4123 4136 f 4122 4121 4134 f 4119 4132 4133 f 4117 4130 4131 f 4129 4128 4141 f 4126 4139 4140 f 4138 4137 4150 f 4136 4135 4148 f 4133 4146 4147 f 4131 4144 4145 f 4130 4129 4142 f 4127 4140 4141 f 4137 4136 4149 f 4135 4134 4147 f 4132 4145 4146 f 4131 4130 4143 f 4142 4141 4154 f 4140 4139 4152 f 4150 4163 4164 f 4149 4148 4161 f 4146 4159 4160 f 4144 4157 4158 f 4142 4155 4156 f 4140 4153 4154 f 4149 4162 4163 f 4148 4147 4160 f 4146 4145 4158 f 4144 4143 4156 f 4155 4154 4167 f 4153 4152 4165 f 4163 4176 4177 f 4161 4174 4175 f 4159 4172 4173 f 4158 4157 4170 f 4155 4168 4169 f 4153 4166 4167 f 4163 4162 4175 f 4161 4160 4173 f 4159 4158 4171 f 4156 4169 4170 f 4167 4180 4181 f 4165 4178 4179 f 4176 4189 4190 f 4175 4174 4187 f 4173 4172 4185 f 4170 4183 4184 f 4169 4168 4181 f 4167 4166 4179 f 4175 4188 4189 f 4173 4186 4187 f 4171 4184 4185 f 4170 4169 4182 f 4180 4193 4194 f 4178 4191 4192 f 4190 4189 4202 f 4187 4200 4201 f 4186 4185 4198 f 4183 4196 4197 f 4182 4181 4194 f 4180 4179 4192 f 4189 4188 4201 f 4186 4199 4200 f 4184 4197 4198 f 4183 4182 4195 f 4193 4206 4207 f 4192 4191 4204 f 4202 4215 4216 f 4200 4213 4214 f 4199 4198 4211 f 4196 4209 4210 f 4194 4207 4208 f 4193 4192 4205 f 4201 4214 4215 f 4199 4212 4213 f 4198 4197 4210 f 4196 4195 4208 f 4207 4206 4219 f 4204 4217 4218 f 4216 4215 4228 f 4213 4226 4227 f 4211 4224 4225 f 4209 4222 4223 f 4207 4220 4221 f 4205 4218 4219 f 4214 4227 4228 f 4213 4212 4225 f 4211 4210 4223 f 4209 4208 4221 f 4219 4232 4233 f 4218 4217 4230 f 4228 4241 4242 f 4226 4239 4240 f 4225 4224 4237 f 4223 4222 4235 f 4221 4220 4233 f 4218 4231 4232 f 4228 4227 4240 f 4226 4225 4238 f 4223 4236 4237 f 4221 4234 4235 f 4233 4232 4245 f 4230 4243 4244 f 4242 4241 4254 f 4240 4239 4252 f 4237 4250 4251 f 4235 4248 4249 f 4234 4233 4246 f 4231 4244 4245 f 4241 4240 4253 f 4239 4238 4251 f 4236 4249 4250 f 4235 4234 4247 f 4246 4245 4258 f 4244 4243 4256 f 4254 4267 4268 f 4253 4252 4265 f 4250 4263 4264 f 4248 4261 4262 f 4246 4259 4260 f 4244 4257 4258 f 4253 4266 4267 f 4252 4251 4264 f 4250 4249 4262 f 4248 4247 4260 f 4259 4258 4271 f 4257 4256 4269 f 4267 4280 4281 f 4265 4278 4279 f 4263 4276 4277 f 4262 4261 4274 f 4259 4272 4273 f 4257 4270 4271 f 4266 4279 4280 f 4265 4264 4277 f 4263 4262 4275 f 4260 4273 4274 f 4271 4284 4285 f 4269 4282 4283 f 4281 4280 4293 f 4279 4278 4291 f 4277 4276 4289 f 4274 4287 4288 f 4273 4272 4285 f 4271 4270 4283 f 4280 4279 4292 f 4277 4290 4291 f 4275 4288 4289 f 4274 4273 4286 f 4284 4297 4298 f 4282 4295 4296 f 4294 4293 4306 f 4291 4304 4305 f 4290 4289 4302 f 4287 4300 4301 f 4286 4285 4298 f 4284 4283 4296 f 4293 4292 4305 f 4290 4303 4304 f 4288 4301 4302 f 4287 4286 4299 f 4297 4310 4311 f 4296 4295 4308 f 4306 4319 4320 f 4304 4317 4318 f 4303 4302 4315 f 4300 4313 4314 f 4298 4311 4312 f 4297 4296 4309 f 4305 4318 4319 f 4304 4303 4316 f 4302 4301 4314 f 4300 4299 4312 f 4310 4323 4324 f 4308 4321 4322 f 4320 4319 4332 f 4318 4317 4330 f 4316 4315 4328 f 4313 4326 4327 f 4311 4324 4325 f 4310 4309 4322 f 4318 4331 4332 f 4316 4329 4330 f 4315 4314 4327 f 4312 4325 4326 f 4323 4336 4337 f 4322 4321 4334 f 4332 4345 4346 f 4330 4343 4344 f 4329 4328 4341 f 4327 4326 4339 f 4325 4324 4337 f 4322 4335 4336 f 4332 4331 4344 f 4330 4329 4342 f 4327 4340 4341 f 4325 4338 4339 f 4337 4336 4350 f 4334 4348 4349 f 4346 4345 4359 f 4344 4343 4357 f 4341 4355 4356 f 4340 4339 4353 f 4338 4337 4351 f 4335 4349 4350 f 4345 4344 4358 f 4343 4342 4356 f 4340 4354 4355 f 4338 4352 4353 f 4351 4350 4363 f 4349 4348 4361 f 4359 4372 4373 f 4358 4357 4370 f 4355 4368 4369 f 4354 4353 4366 f 4351 4364 4365 f 4349 4362 4363 f 4358 4371 4372 f 4357 4356 4369 f 4355 4354 4367 f 4352 4365 4366 f 4364 4363 4376 f 4362 4361 4374 f 4373 4372 4385 f 4370 4383 4384 f 4368 4381 4382 f 4367 4366 4379 f 4364 4377 4378 f 4362 4375 4376 f 4372 4371 4384 f 4370 4369 4382 f 4368 4367 4380 f 4365 4378 4379 f 4376 4389 4390 f 4374 4387 4388 f 4385 4398 4399 f 4384 4383 4396 f 4382 4381 4394 f 4379 4392 4393 f 4378 4377 4390 f 4376 4375 4388 f 4384 4397 4398 f 4382 4395 4396 f 4380 4393 4394 f 4379 4378 4391 f 4389 4402 4403 f 4387 4400 4401 f 4399 4398 4411 f 4396 4409 4410 f 4395 4394 4407 f 4393 4392 4405 f 4391 4390 4403 f 4389 4388 4401 f 4398 4397 4410 f 4395 4408 4409 f 4393 4406 4407 f 4391 4404 4405 f 4402 4415 4416 f 4401 4400 4413 f 4411 4424 4425 f 4409 4422 4423 f 4408 4407 4420 f 4406 4405 4418 f 4403 4416 4417 f 4402 4401 4414 f 4410 4423 4424 f 4408 4421 4422 f 4407 4406 4419 f 4404 4417 4418 f 4416 4415 4428 f 4413 4426 4427 f 4425 4424 4437 f 4423 4422 4435 f 4421 4420 4433 f 4418 4431 4432 f 4416 4429 4430 f 4415 4414 4427 f 4423 4436 4437 f 4421 4434 4435 f 4420 4419 4432 f 4418 4417 4430 f 4428 4441 4442 f 4426 4439 4440 f 4437 4450 4451 f 4435 4448 4449 f 4434 4433 4446 f 4432 4431 4444 f 4430 4429 4442 f 4427 4440 4441 f 4437 4436 4449 f 4435 4434 4447 f 4432 4445 4446 f 4430 4443 4444 f 4442 4441 4454 f 4439 4452 4453 f 4451 4450 4463 f 4449 4448 4461 f 4446 4459 4460 f 4444 4457 4458 f 4443 4442 4455 f 4440 4453 4454 f 4450 4449 4462 f 4447 4460 4461 f 4445 4458 4459 f 4444 4443 4456 f 4455 4454 4467 f 4453 4452 4465 f 4463 4476 4477 f 4462 4461 4474 f 4459 4472 4473 f 4457 4470 4471 f 4455 4468 4469 f 4453 4466 4467 f 4462 4475 4476 f 4461 4460 4473 f 4459 4458 4471 f 4457 4456 4469 f 4468 4467 4063 f 4466 4465 4061 f 4477 4476 4072 f 4474 4070 4071 f 4472 4068 4069 f 4471 4470 4066 f 4468 4064 4065 f 4466 4062 4063 f 4476 4475 4071 f 4474 4473 4069 f 4472 4471 4067 f 4469 4065 4066 f 3935 4061 4465 f 4465 4452 4056 f 4452 4439 4051 f 4439 4426 4047 f 4047 4426 4413 f 4043 4413 4400 f 4039 4400 4387 f 4387 4374 4031 f 4031 4374 4361 f 4361 4348 4023 f 4348 4334 4019 f 4334 4321 4015 f 4015 4321 4308 f 4011 4308 4295 f 4007 4295 4282 f 4282 4269 3999 f 3999 4269 4256 f 4256 4243 3991 f 4243 4230 3987 f 4230 4217 3983 f 3983 4217 4204 f 3979 4204 4191 f 3975 4191 4178 f 4178 4165 3967 f 3967 4165 4152 f 4152 4139 3959 f 4139 4126 3955 f 4126 4113 3951 f 3951 4113 4100 f 3947 4100 4087 f 3943 4087 4074 f 4074 4061 3935 f 4480 4479 4483 f 4480 4484 4485 f 4479 4478 4482 f 4484 4488 4489 f 4482 4486 4487 f 4483 4487 4488 f 4488 4492 4493 f 4487 4486 4490 f 4488 4487 4491 f 4493 4492 4496 f 4490 4494 4495 f 4491 4495 4496 f 4496 4500 4501 f 4495 4494 4498 f 4496 4495 4499 f 4501 4500 4504 f 4498 4502 4503 f 4499 4503 4504 f 4505 4504 4508 f 4503 4502 4506 f 4504 4503 4507 f 4509 4508 4512 f 4506 4510 4511 f 4507 4511 4512 f 4513 4512 4516 f 4511 4510 4514 f 4512 4511 4515 f 4516 4520 4521 f 4514 4518 4519 f 4515 4519 4520 f 4520 4524 4525 f 4518 4522 4523 f 4520 4519 4523 f 4525 4524 4528 f 4522 4526 4527 f 4523 4527 4528 f 4528 4532 4533 f 4527 4526 4530 f 4528 4527 4531 f 4533 4532 4536 f 4530 4534 4535 f 4531 4535 4536 f 4537 4536 4540 f 4534 4538 4539 f 4536 4535 4539 f 4541 4540 4544 f 4539 4538 4542 f 4539 4543 4544 f 4544 4548 4549 f 4542 4546 4547 f 4544 4543 4547 f 4548 4552 4553 f 4547 4546 4550 f 4547 4551 4552 f 4552 4556 4557 f 4551 4550 4554 f 4552 4551 4555 f 4557 4556 4560 f 4554 4558 4559 f 4555 4559 4560 f 4560 4564 4565 f 4559 4558 4562 f 4560 4559 4563 f 4565 4564 4568 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p120 tp121 bsg25 g10 (g11 (I0 tp122 g13 tp123 Rp124 (I1 (I28 tp125 g20 I00 S'\x00\x00\x00\x00\x07\x00\x00\x00\n\x00\x00\x00\x11\x00\x00\x00\x16\x00\x00\x00\x1c\x00\x00\x00!\x00\x00\x00)\x00\x00\x00.\x00\x00\x003\x00\x00\x008\x00\x00\x00:\x00\x00\x00A\x00\x00\x00E\x00\x00\x00I\x00\x00\x00P\x00\x00\x00W\x00\x00\x00_\x00\x00\x00f\x00\x00\x00m\x00\x00\x00v\x00\x00\x00z\x00\x00\x00}\x00\x00\x00\x81\x00\x00\x00\x87\x00\x00\x00\x8d\x00\x00\x00\x90\x00\x00\x00\x94\x00\x00\x00' p126 tp127 bsg32 I50 sg33 (I27 I27 tp128 sg35 g10 (g11 (I0 tp129 g13 tp130 Rp131 (I1 (I148 tp132 g42 I00 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p363 tp364 bsbag3 (g4 g5 Ntp365 Rp366 (dp367 g9 g10 (g11 (I0 tp368 g13 tp369 Rp370 (I1 (I27 tp371 g20 I00 S'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00\x06\x00\x00\x00\x07\x00\x00\x00\x08\x00\x00\x00\t\x00\x00\x00\n\x00\x00\x00\x0b\x00\x00\x00\x0c\x00\x00\x00\r\x00\x00\x00\x0e\x00\x00\x00\x0f\x00\x00\x00\x10\x00\x00\x00\x11\x00\x00\x00\x12\x00\x00\x00\x13\x00\x00\x00\x14\x00\x00\x00\x15\x00\x00\x00\x16\x00\x00\x00\x17\x00\x00\x00\x18\x00\x00\x00\x19\x00\x00\x00\x1a\x00\x00\x00' p372 tp373 bsg35 g10 (g11 (I0 tp374 g13 tp375 Rp376 (I1 (I27 tp377 g42 I00 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1950 1929 f 1966 1967 1593 f 1593 1967 1590 f 1969 1967 1968 f 1968 1967 1964 f 1962 1921 1970 f 1970 1921 1971 f 1963 1945 1964 f 1964 1945 1968 f 1972 1948 1922 f 1922 1948 1919 f 1966 1972 1961 f 1961 1972 1922 f 1958 1953 1587 f 1587 1953 1560 f 1974 1975 1973 f 1973 1975 1976 f 1978 1979 1977 f 1977 1979 1691 f 1979 1975 1691 f 1691 1975 1586 f 1938 1942 1939 f 1939 1942 1980 f 1952 1947 1557 f 1557 1947 1694 f 1969 1978 1981 f 1981 1978 1977 f 1983 1984 1982 f 1982 1984 1985 f 1986 1987 1989 f 1989 1987 1988 f 1990 1991 1983 f 1983 1991 1984 f 1990 1993 1992 f 1992 1993 1994 f 1995 1996 1994 f 1994 1996 1997 f 1999 2000 1998 f 1998 2000 2001 f 2003 2004 2002 f 2002 2004 2005 f 2007 2008 2006 f 2006 2008 2009 f 2011 2012 2010 f 2010 2012 2013 f 2014 2015 2017 f 2017 2015 2016 f 2018 2019 2021 f 2021 2019 2020 f 2022 1989 2024 f 2024 1989 2023 f 1989 1988 2023 f 2023 1988 2025 f 2026 2027 2017 f 2017 2027 2014 f 2029 2030 2028 f 2028 2030 2031 f 2033 2034 2032 f 2032 2034 2035 f 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f 2026 2101 2102 f 2100 2099 2104 f 2104 2099 2103 f 2104 2105 2100 f 2100 2105 2101 f 2105 2106 2101 f 2101 2106 2102 f 2108 2061 2107 f 2107 2061 2109 f 2111 2112 2110 f 2110 2112 2020 f 2020 2074 2110 f 2110 2074 2113 f 2115 2116 2114 f 2114 2116 2117 f 2118 2119 1995 f 1995 2119 2120 f 2121 2122 2027 f 2027 2122 2014 f 2047 2075 2044 f 2044 2075 2123 f 2124 2125 2127 f 2127 2125 2126 f 2129 2130 2128 f 2128 2130 2131 f 2132 2133 2057 f 2057 2133 2058 f 2134 1999 2136 f 2136 1999 2135 f 2055 2138 2137 f 2137 2138 2139 f 2141 2142 2140 f 2140 2142 2143 f 2083 2082 2064 f 2064 2082 2065 f 2082 2144 2065 f 2065 2144 2145 f 2040 2146 2041 f 2041 2146 2147 f 2060 2090 2148 f 2148 2090 2149 f 2048 2051 2137 f 2137 2051 2150 f 2151 1997 2135 f 2135 1997 1996 f 2152 2150 2077 f 2077 2150 2051 f 2046 2045 2118 f 2118 2045 2119 f 2154 2155 2153 f 2153 2155 2156 f 2158 2159 2157 f 2157 2159 2160 f 2162 2163 2161 f 2161 2163 2164 f 2164 2163 2165 f 2165 2163 2166 f 2168 2169 2167 f 2167 2169 2170 f 2003 2172 2171 f 2171 2172 2173 f 2174 2175 2002 f 2002 2175 2176 f 2145 2177 2065 f 2065 2177 2062 f 2178 2179 2180 f 2180 2179 2134 f 2181 2052 2152 f 2152 2052 2150 f 2040 2127 2146 f 2146 2127 2182 f 2130 2162 2131 f 2131 2162 2161 f 2052 2055 2150 f 2150 2055 2137 f 2184 2185 2183 f 2183 2185 2186 f 2159 2030 2160 f 2160 2030 2187 f 2146 2182 2029 f 2029 2182 2188 f 2189 2190 2192 f 2192 2190 2191 f 2172 2003 2176 f 2176 2003 2002 f 1998 2151 1999 f 1999 2151 2135 f 2177 2172 2062 f 2062 2172 2176 f 2172 2177 2173 f 2173 2177 2193 f 2063 2194 2064 f 2064 2194 2195 f 2066 2197 2196 f 2196 2197 2198 f 2002 2005 2174 f 2174 2005 2199 f 2004 2003 2200 f 2200 2003 2171 f 2198 2201 2171 f 2171 2201 2200 f 2197 2202 2198 f 2198 2202 2201 f 2202 2197 2203 f 2203 2197 2204 f 2196 2198 2173 f 2173 2198 2171 f 2205 2069 2170 f 2170 2069 2206 f 2197 2066 2204 f 2204 2066 2067 f 2107 2207 2108 f 2108 2207 2208 f 2210 2208 2209 f 2209 2208 2207 f 2185 2211 2212 f 2213 2214 2211 f 2211 2214 2212 f 1998 2001 2213 f 2213 2001 2214 f 2134 2215 1999 f 1999 2215 2000 f 2216 2217 2219 f 2219 2217 2218 f 2206 2220 2170 f 2170 2220 2167 f 2069 2068 2206 f 2206 2068 2221 f 2151 1998 2222 f 2222 1998 2213 f 2124 2039 2205 f 2205 2039 2069 f 2142 2223 2143 f 2143 2223 2128 f 2203 2186 2202 f 2202 2186 2185 f 2185 2184 2211 f 2211 2184 2224 f 2224 2225 2222 f 2222 2225 2226 f 2071 2227 2072 f 2072 2227 2228 f 2229 2074 2230 f 2230 2074 2073 f 2074 2229 2113 f 2113 2229 2231 f 2232 2225 2227 f 2227 2225 2224 f 2233 2234 2031 f 2031 2234 2028 f 2236 2231 2235 f 2235 2231 2229 f 2230 2183 2229 f 2229 2183 2235 f 2237 1987 2096 f 2096 1987 1986 f 2152 2238 2181 f 2181 2238 2239 f 2080 2240 2081 f 2081 2240 2241 f 2236 2242 1971 f 1971 2242 1970 f 1925 2113 1923 f 1923 2113 2231 f 2110 1927 2111 f 2111 1927 1928 f 2103 2111 1931 f 1931 2111 1928 f 1931 2243 2103 f 2103 2243 2104 f 2105 2104 2244 f 2244 2104 2243 f 1939 1980 2148 f 2148 1980 2109 f 1980 1944 2109 f 2109 1944 2107 f 1946 2245 2207 f 2207 2245 2209 f 2245 1970 2209 f 2209 1970 2242 f 2231 2236 1923 f 1923 2236 1971 f 2113 1925 2110 f 2110 1925 1927 f 2234 2246 2028 f 2028 2246 2147 f 2247 2248 2058 f 2058 2248 2059 f 2114 2117 2249 f 2249 2117 2250 f 2252 2253 2251 f 2251 2253 2254 f 2094 2015 2091 f 2091 2015 2255 f 2256 2255 2014 f 2014 2255 2015 f 2257 2145 2258 f 2258 2145 2144 f 2131 2161 2123 f 2123 2161 2044 f 2045 2044 2164 f 2164 2044 2161 f 2045 2164 2119 f 2119 2164 2165 f 2259 2120 2165 f 2165 2120 2119 f 2260 2261 2259 f 2259 2261 2120 f 2205 2262 2124 f 2124 2262 2125 f 2180 2263 2178 f 2178 2263 2264 f 2265 2266 2092 f 2092 2266 2093 f 1992 2267 2268 f 2184 2228 2224 f 2224 2228 2227 f 2048 2269 2049 f 2049 2269 2141 f 2129 2168 2157 f 2157 2168 2167 f 2049 2141 2075 f 2075 2141 2140 f 2199 2215 2270 f 2270 2215 2271 f 2142 2141 2272 f 2272 2141 2269 f 2089 2026 2090 f 2090 2026 2102 f 2146 2029 2147 f 2147 2029 2028 f 2111 2103 2112 f 2112 2103 2099 f 2061 2060 2109 f 2109 2060 2148 f 2273 2274 2041 f 2041 2274 2038 f 2260 2275 2261 f 2261 2275 2136 f 2057 2138 2132 f 2132 2138 2276 f 2272 2269 2056 f 2056 2269 2139 f 2277 2278 2270 f 2270 2278 2199 f 2029 2188 2030 f 2030 2188 2187 f 2040 2039 2127 f 2127 2039 2124 f 2064 2195 2083 f 2083 2195 2279 f 2056 2139 2057 f 2057 2139 2138 f 2278 2277 2247 f 2247 2277 2248 f 2062 2176 2063 f 2063 2176 2175 f 2280 2210 2242 f 2242 2210 2209 f 2282 2233 2281 f 2281 2233 2031 f 2216 2219 2283 f 2283 2219 2221 f 2158 2220 2218 f 2218 2220 2219 f 2217 2282 2218 f 2218 2282 2281 f 2220 2158 2167 f 2167 2158 2157 f 2223 2264 2128 f 2128 2264 2263 f 2261 1996 2120 f 2120 1996 1995 f 2213 2211 2222 f 2222 2211 2224 f 2283 2221 2284 f 2284 2221 2068 f 2135 1996 2136 f 2136 1996 2261 f 2144 2081 2258 f 2258 2081 2241 f 2235 2280 2236 f 2236 2280 2242 f 2246 2273 2147 f 2147 2273 2041 f 2036 2285 2032 f 2032 2285 2088 f 2077 2286 2152 f 2152 2286 2238 f 1937 1960 1973 f 1973 1960 1959 f 2070 2232 2071 f 2071 2232 2227 f 2244 2287 2105 f 2105 2287 2106 f 1936 1939 2149 f 2149 1939 2148 f 1944 1946 2107 f 2107 1946 2207 f 2285 2121 2088 f 2088 2121 2027 f 2253 2249 2254 f 2254 2249 2250 f 2006 2009 2077 f 2077 2009 2286 f 2289 2086 2288 f 2288 2086 2087 f 2223 2142 2290 f 2290 2142 2272 f 2128 2131 2143 f 2143 2131 2123 f 2156 2291 2153 f 2153 2291 2175 f 2292 2293 2278 f 2278 2293 2294 f 2075 2140 2123 f 2123 2140 2143 f 2262 2169 2275 f 2275 2169 2180 f 2275 2180 2136 f 2136 2180 2134 f 2274 2284 2038 f 2038 2284 2068 f 2127 2126 2182 f 2182 2126 2166 f 2094 2093 2098 f 2098 2093 2021 f 2098 2021 2099 f 2099 2021 2112 f 2018 2021 2266 f 2266 2021 2093 f 2251 2289 2252 f 2252 2289 2288 f 1997 2151 2226 f 2226 2151 2222 f 2206 2221 2220 f 2220 2221 2219 f 2269 2048 2139 f 2139 2048 2137 f 2159 2158 2281 f 2281 2158 2218 f 2156 2155 2190 f 2190 2155 2191 f 1974 1965 1583 f 1583 1965 1751 f 2088 2027 2089 f 2089 2027 2026 f 2030 2159 2031 f 2031 2159 2281 f 2106 2149 2102 f 2102 2149 2090 f 2287 1936 2106 f 2106 1936 2149 f 2293 2154 2294 f 2294 2154 2153 f 2021 2020 2112 f 2247 2295 2278 f 2278 2295 2292 f 2284 2296 2283 f 2283 2296 2297 f 2298 2296 2274 f 2274 2296 2284 f 2273 2299 2274 f 2274 2299 2298 f 2246 2300 2273 f 2273 2300 2299 f 2283 2297 2216 f 2216 2297 2301 f 2216 2301 2217 f 2217 2301 2302 f 2302 2303 2217 f 2217 2303 2282 f 2304 2301 2305 f 2305 2301 2297 f 2307 2308 2306 f 2306 2308 2309 f 2306 2310 2307 f 2307 2310 2311 f 2307 2312 2308 f 2308 2312 2313 f 2314 2315 2312 f 2312 2315 2316 f 2316 2317 2312 f 2312 2317 2313 f 2317 2305 2313 f 2313 2305 2318 f 2314 2312 2311 f 2311 2312 2307 f 2319 2320 2322 f 2322 2320 2321 f 2322 2311 2319 f 2319 2311 2310 f 2313 2318 2308 f 2308 2318 2309 f 2296 2298 2318 f 2318 2298 2309 f 2309 2298 2323 f 2323 2298 2299 f 2306 2309 2324 f 2324 2309 2323 f 2310 2306 2325 f 2325 2306 2324 f 2296 2318 2297 f 2297 2318 2305 f 2319 2310 2326 f 2326 2310 2325 f 2328 2325 2327 f 2327 2325 2324 f 2325 2328 2326 f 2326 2328 2329 f 2331 2328 2330 f 2330 2328 2327 f 2332 2333 2327 f 2327 2333 2330 f 2328 2331 2329 f 2329 2331 2334 f 2324 2323 2327 f 2327 2323 2332 f 2300 2332 2299 f 2299 2332 2323 f 2326 2335 2319 f 2319 2335 2320 f 2305 2317 2336 f 2336 2317 2316 f 2336 2316 2337 f 2337 2316 2315 f 2338 2304 2336 f 2336 2304 2305 f 2337 2339 2336 f 2336 2339 2338 f 2337 2315 2341 f 2341 2315 2340 f 2342 2300 2234 f 2234 2300 2246 f 2303 2343 2282 f 2282 2343 2233 f 2234 2233 2342 f 2342 2233 2343 f 2342 2343 2333 f 2333 2343 2344 f 2333 2344 2330 f 2330 2344 2345 f 2346 2344 2303 f 2303 2344 2343 f 2332 2300 2333 f 2333 2300 2342 f 2347 2346 2302 f 2302 2346 2303 f 2301 2304 2302 f 2302 2304 2347 f 2304 2338 2347 f 2347 2338 2348 f 2348 2349 2347 f 2347 2349 2346 f 2350 2351 2348 f 2348 2351 2349 f 2349 2345 2346 f 2346 2345 2344 f 2345 2349 2352 f 2352 2349 2351 f 2338 2339 2348 f 2348 2339 2350 f 2350 2339 2353 f 2353 2339 2354 f 2356 2357 2355 f 2355 2357 2353 f 2351 2350 2357 f 2357 2350 2353 f 2353 2354 2355 f 2355 2354 2335 f 2320 2335 2341 f 2341 2335 2354 f 2339 2337 2354 f 2354 2337 2341 f 2352 2331 2345 f 2345 2331 2330 f 2356 2334 2352 f 2352 2334 2331 f 2356 2352 2357 f 2357 2352 2351 f 2355 2329 2356 f 2356 2329 2334 f 2335 2326 2355 f 2355 2326 2329 f 2321 2314 2322 f 2322 2314 2311 f 2321 2320 2340 f 2340 2320 2341 f 2315 2314 2340 f 2340 2314 2321 f 2125 2260 2126 f 2126 2260 2259 f 2157 2160 2129 f 2129 2160 2130 f 2188 2163 2187 f 2187 2163 2162 f 2182 2166 2188 f 2188 2166 2163 f 2180 2169 2263 f 2263 2169 2168 f 2160 2187 2130 f 2130 2187 2162 f 2275 2260 2262 f 2262 2260 2125 f 2128 2263 2129 f 2129 2263 2168 f 2262 2205 2169 f 2169 2205 2170 f 2165 2166 2259 f 2259 2166 2126 f 2359 2360 2358 f 2358 2360 2361 f 2363 2364 2362 f 2362 2364 2365 f 2367 2363 2366 f 2366 2363 2362 f 2368 2369 2371 f 2371 2369 2370 f 2372 2373 2248 f 2248 2373 2059 f 2364 2374 2365 f 2365 2374 2375 f 2377 2378 2376 f 2376 2378 2379 f 2290 2380 2223 f 2223 2380 2381 f 2223 2381 2264 f 2264 2381 2382 f 2384 2385 2383 f 2383 2385 2386 f 2364 2371 2374 f 2374 2371 2387 f 2059 2373 2290 f 2290 2373 2380 f 2378 2360 2379 f 2379 2360 2388 f 2374 2387 2359 f 2359 2387 2389 f 2390 2367 2386 f 2386 2367 2391 f 2393 2394 2392 f 2392 2394 2395 f 2386 2391 2383 f 2383 2391 2396 f 2367 2366 2391 f 2391 2366 2397 f 2363 2367 2368 f 2368 2367 2390 f 2398 2399 2361 f 2361 2399 2358 f 2399 2400 2358 f 2358 2400 2375 f 2390 2401 2368 f 2368 2401 2369 f 2372 2384 2376 f 2376 2384 2383 f 2374 2359 2375 f 2375 2359 2358 f 2402 2403 2365 f 2365 2403 2362 f 2359 2389 2360 f 2360 2389 2388 f 2364 2363 2371 f 2371 2363 2368 f 2405 2398 2404 f 2404 2398 2361 f 2392 2395 2406 f 2406 2395 2397 f 2377 2396 2394 f 2394 2396 2395 f 2394 2393 2404 f 2404 2393 2405 f 2396 2377 2383 f 2383 2377 2376 f 2406 2397 2407 f 2407 2397 2366 f 2400 2402 2375 f 2375 2402 2365 f 2385 2270 2401 f 2401 2270 2271 f 2403 2407 2362 f 2362 2407 2366 f 2371 2370 2387 f 2387 2370 2382 f 2391 2397 2396 f 2396 2397 2395 f 2378 2377 2404 f 2404 2377 2394 f 2360 2378 2361 f 2361 2378 2404 f 2407 2408 2406 f 2406 2408 2409 f 2410 2408 2403 f 2403 2408 2407 f 2411 2410 2402 f 2402 2410 2403 f 2412 2411 2400 f 2400 2411 2402 f 2406 2409 2392 f 2392 2409 2413 f 2392 2413 2393 f 2393 2413 2414 f 2415 2405 2414 f 2414 2405 2393 f 2416 2413 2417 f 2417 2413 2409 f 2419 2420 2418 f 2418 2420 2421 f 2420 2422 2421 f 2421 2422 2423 f 2420 2424 2422 f 2422 2424 2425 f 2426 2427 2424 f 2424 2427 2428 f 2428 2429 2424 f 2424 2429 2425 f 2426 2424 2419 f 2419 2424 2420 f 2419 2418 2431 f 2431 2418 2430 f 2425 2432 2422 f 2422 2432 2423 f 2408 2410 2432 f 2432 2410 2423 f 2423 2410 2433 f 2433 2410 2411 f 2421 2423 2434 f 2434 2423 2433 f 2435 2418 2434 f 2434 2418 2421 f 2429 2417 2425 f 2425 2417 2432 f 2408 2432 2409 f 2409 2432 2417 f 2418 2435 2430 f 2430 2435 2436 f 2438 2435 2437 f 2437 2435 2434 f 2435 2438 2436 f 2436 2438 2439 f 2441 2438 2440 f 2440 2438 2437 f 2442 2443 2437 f 2437 2443 2440 f 2439 2438 2444 f 2444 2438 2441 f 2433 2442 2434 f 2434 2442 2437 f 2412 2442 2411 f 2411 2442 2433 f 2436 2445 2430 f 2430 2445 2446 f 2430 2446 2431 f 2431 2446 2447 f 2417 2429 2448 f 2448 2429 2428 f 2427 2449 2428 f 2428 2449 2448 f 2450 2416 2448 f 2448 2416 2417 f 2451 2450 2449 f 2449 2450 2448 f 2427 2452 2449 f 2449 2452 2453 f 2447 2446 2452 f 2452 2446 2453 f 2454 2412 2399 f 2399 2412 2400 f 2415 2455 2405 f 2405 2455 2398 f 2399 2398 2454 f 2454 2398 2455 f 2454 2455 2443 f 2443 2455 2456 f 2443 2456 2440 f 2440 2456 2457 f 2458 2456 2415 f 2415 2456 2455 f 2443 2442 2454 f 2454 2442 2412 f 2459 2458 2414 f 2414 2458 2415 f 2413 2416 2414 f 2414 2416 2459 f 2459 2416 2460 f 2460 2416 2450 f 2460 2461 2459 f 2459 2461 2458 f 2462 2463 2460 f 2460 2463 2461 f 2461 2457 2458 f 2458 2457 2456 f 2457 2461 2464 f 2464 2461 2463 f 2451 2462 2450 f 2450 2462 2460 f 2462 2451 2465 f 2465 2451 2466 f 2468 2469 2467 f 2467 2469 2465 f 2463 2462 2469 f 2469 2462 2465 f 2465 2466 2467 f 2467 2466 2445 f 2446 2445 2453 f 2453 2445 2466 f 2451 2449 2466 f 2466 2449 2453 f 2464 2441 2457 f 2457 2441 2440 f 2468 2444 2464 f 2464 2444 2441 f 2468 2464 2469 f 2469 2464 2463 f 2467 2439 2468 f 2468 2439 2444 f 2445 2436 2467 f 2467 2436 2439 f 2447 2426 2431 f 2431 2426 2419 f 2427 2426 2452 f 2452 2426 2447 f 2369 2179 2370 f 2370 2179 2178 f 2376 2379 2372 f 2372 2379 2373 f 2389 2381 2388 f 2388 2381 2380 f 2387 2382 2389 f 2389 2382 2381 f 2270 2385 2277 f 2277 2385 2384 f 2379 2388 2373 f 2373 2388 2380 f 2401 2271 2369 f 2369 2271 2179 f 2248 2277 2372 f 2372 2277 2384 f 2401 2390 2385 f 2385 2390 2386 f 2264 2382 2178 f 2178 2382 2370 f 2215 2134 2271 f 2271 2134 2179 f 2005 2000 2199 f 2199 2000 2215 f 2004 2001 2005 f 2005 2001 2000 f 2200 2214 2004 f 2004 2214 2001 f 2470 2471 2473 f 2473 2471 2472 f 2475 2476 2474 f 2474 2476 2477 f 2479 2475 2478 f 2478 2475 2474 f 2481 2482 2480 f 2480 2482 2483 f 2476 2484 2477 f 2477 2484 2485 f 2487 2488 2486 f 2486 2488 2489 f 2133 2490 2058 f 2058 2490 2491 f 2058 2491 2247 f 2247 2491 2492 f 2494 2495 2493 f 2493 2495 2496 f 2476 2483 2484 f 2484 2483 2497 f 2472 2498 2488 f 2488 2498 2489 f 2484 2497 2471 f 2471 2497 2499 f 2500 2479 2496 f 2496 2479 2501 f 2502 2503 2505 f 2505 2503 2504 f 2496 2501 2493 f 2493 2501 2506 f 2479 2478 2501 f 2501 2478 2507 f 2475 2479 2480 f 2480 2479 2500 f 2508 2509 2473 f 2473 2509 2470 f 2509 2510 2470 f 2470 2510 2485 f 2511 2481 2500 f 2500 2481 2480 f 2512 2494 2486 f 2486 2494 2493 f 2485 2484 2470 f 2470 2484 2471 f 2513 2514 2477 f 2477 2514 2474 f 2471 2499 2472 f 2472 2499 2498 f 2476 2475 2483 f 2483 2475 2480 f 2516 2508 2515 f 2515 2508 2473 f 2502 2505 2517 f 2517 2505 2507 f 2504 2487 2505 f 2505 2487 2506 f 2503 2516 2504 f 2504 2516 2515 f 2506 2487 2493 f 2493 2487 2486 f 2517 2507 2518 f 2518 2507 2478 f 2510 2513 2485 f 2485 2513 2477 f 2511 2495 2293 f 2293 2495 2154 f 2514 2518 2474 f 2474 2518 2478 f 2483 2482 2497 f 2497 2482 2492 f 2501 2507 2506 f 2506 2507 2505 f 2488 2487 2515 f 2515 2487 2504 f 2472 2488 2473 f 2473 2488 2515 f 2518 2519 2517 f 2517 2519 2520 f 2521 2519 2514 f 2514 2519 2518 f 2522 2521 2513 f 2513 2521 2514 f 2523 2522 2510 f 2510 2522 2513 f 2517 2520 2502 f 2502 2520 2524 f 2502 2524 2503 f 2503 2524 2525 f 2525 2526 2503 f 2503 2526 2516 f 2527 2524 2528 f 2528 2524 2520 f 2530 2531 2529 f 2529 2531 2532 f 2531 2533 2532 f 2532 2533 2534 f 2531 2535 2533 f 2533 2535 2536 f 2537 2538 2535 f 2535 2538 2539 f 2539 2540 2535 f 2535 2540 2536 f 2537 2535 2530 f 2530 2535 2531 f 2529 2541 2530 f 2530 2541 2542 f 2543 2534 2536 f 2536 2534 2533 f 2519 2521 2543 f 2543 2521 2534 f 2534 2521 2544 f 2544 2521 2522 f 2532 2534 2545 f 2545 2534 2544 f 2529 2532 2546 f 2546 2532 2545 f 2536 2540 2543 f 2543 2540 2528 f 2519 2543 2520 f 2520 2543 2528 f 2541 2529 2547 f 2547 2529 2546 f 2548 2549 2545 f 2545 2549 2546 f 2549 2550 2546 f 2546 2550 2547 f 2551 2552 2548 f 2548 2552 2549 f 2548 2553 2551 f 2551 2553 2554 f 2550 2549 2555 f 2555 2549 2552 f 2545 2544 2548 f 2548 2544 2553 f 2523 2553 2522 f 2522 2553 2544 f 2547 2556 2541 f 2541 2556 2557 f 2541 2557 2542 f 2542 2557 2558 f 2528 2540 2559 f 2559 2540 2539 f 2538 2560 2539 f 2539 2560 2559 f 2561 2527 2559 f 2559 2527 2528 f 2560 2562 2559 f 2559 2562 2561 f 2560 2538 2564 f 2564 2538 2563 f 2558 2557 2563 f 2563 2557 2564 f 2565 2523 2509 f 2509 2523 2510 f 2526 2566 2516 f 2516 2566 2508 f 2509 2508 2565 f 2565 2508 2566 f 2565 2566 2554 f 2554 2566 2567 f 2554 2567 2551 f 2551 2567 2568 f 2569 2567 2526 f 2526 2567 2566 f 2553 2523 2554 f 2554 2523 2565 f 2570 2569 2525 f 2525 2569 2526 f 2524 2527 2525 f 2525 2527 2570 f 2527 2561 2570 f 2570 2561 2571 f 2571 2572 2570 f 2570 2572 2569 f 2573 2574 2571 f 2571 2574 2572 f 2572 2568 2569 f 2569 2568 2567 f 2568 2572 2575 f 2575 2572 2574 f 2562 2573 2561 f 2561 2573 2571 f 2573 2562 2576 f 2576 2562 2577 f 2579 2580 2578 f 2578 2580 2576 f 2574 2573 2580 f 2580 2573 2576 f 2576 2577 2578 f 2578 2577 2556 f 2557 2556 2564 f 2564 2556 2577 f 2562 2560 2577 f 2577 2560 2564 f 2575 2552 2568 f 2568 2552 2551 f 2579 2555 2575 f 2575 2555 2552 f 2579 2575 2580 f 2580 2575 2574 f 2550 2555 2578 f 2578 2555 2579 f 2556 2547 2578 f 2578 2547 2550 f 2558 2537 2542 f 2542 2537 2530 f 2538 2537 2563 f 2563 2537 2558 f 2292 2295 2481 f 2481 2295 2482 f 2486 2489 2512 f 2512 2489 2581 f 2499 2491 2498 f 2498 2491 2490 f 2497 2492 2499 f 2499 2492 2491 f 2155 2154 2494 f 2494 2154 2495 f 2581 2489 2490 f 2490 2489 2498 f 2293 2292 2511 f 2511 2292 2481 f 2191 2155 2512 f 2512 2155 2494 f 2500 2496 2511 f 2511 2496 2495 f 2247 2492 2295 f 2295 2492 2482 f 2583 2584 2582 f 2582 2584 2585 f 2587 2588 2586 f 2586 2588 2589 f 2591 2587 2590 f 2590 2587 2586 f 2593 2594 2592 f 2592 2594 2595 f 2596 2597 2599 f 2599 2597 2598 f 2588 2600 2589 f 2589 2600 2601 f 2602 2603 2605 f 2605 2603 2604 f 2606 2607 2609 f 2609 2607 2608 f 2609 2608 2190 f 2190 2608 2610 f 2611 2612 2614 f 2614 2612 2613 f 2600 2588 2615 f 2615 2588 2595 f 2599 2598 2606 f 2606 2598 2607 f 2584 2616 2604 f 2604 2616 2605 f 2600 2615 2583 f 2583 2615 2617 f 2591 2619 2618 f 2618 2619 2614 f 2621 2622 2620 f 2620 2622 2623 f 2619 2624 2614 f 2614 2624 2611 f 2591 2590 2619 f 2619 2590 2625 f 2587 2591 2592 f 2592 2591 2618 f 2626 2627 2585 f 2585 2627 2582 f 2627 2628 2582 f 2582 2628 2601 f 2629 2593 2618 f 2618 2593 2592 f 2602 2597 2611 f 2611 2597 2612 f 2600 2583 2601 f 2601 2583 2582 f 2630 2631 2589 f 2589 2631 2586 f 2583 2617 2584 f 2584 2617 2616 f 2588 2587 2595 f 2595 2587 2592 f 2633 2626 2632 f 2632 2626 2585 f 2620 2623 2634 f 2634 2623 2625 f 2622 2603 2623 f 2623 2603 2624 f 2621 2633 2622 f 2622 2633 2632 f 2611 2624 2602 f 2602 2624 2603 f 2634 2625 2635 f 2635 2625 2590 f 2628 2630 2601 f 2601 2630 2589 f 2613 2195 2629 f 2629 2195 2194 f 2631 2635 2586 f 2586 2635 2590 f 2595 2594 2615 f 2615 2594 2610 f 2619 2625 2624 f 2624 2625 2623 f 2632 2604 2622 f 2622 2604 2603 f 2585 2584 2632 f 2632 2584 2604 f 2635 2636 2634 f 2634 2636 2637 f 2638 2636 2631 f 2631 2636 2635 f 2639 2638 2630 f 2630 2638 2631 f 2640 2639 2628 f 2628 2639 2630 f 2634 2637 2620 f 2620 2637 2641 f 2620 2641 2621 f 2621 2641 2642 f 2642 2643 2621 f 2621 2643 2633 f 2641 2637 2644 f 2644 2637 2645 f 2646 2647 2649 f 2649 2647 2648 f 2648 2650 2649 f 2649 2650 2651 f 2648 2652 2650 f 2650 2652 2653 f 2652 2654 2656 f 2656 2654 2655 f 2656 2657 2652 f 2652 2657 2653 f 2654 2652 2647 f 2647 2652 2648 f 2647 2646 2659 f 2659 2646 2658 f 2660 2651 2653 f 2653 2651 2650 f 2636 2638 2660 f 2660 2638 2651 f 2651 2638 2661 f 2661 2638 2639 f 2651 2661 2649 f 2649 2661 2662 f 2663 2646 2662 f 2662 2646 2649 f 2653 2657 2660 f 2660 2657 2645 f 2636 2660 2637 f 2637 2660 2645 f 2646 2663 2658 f 2658 2663 2664 f 2665 2666 2662 f 2662 2666 2663 f 2666 2667 2663 f 2663 2667 2664 f 2668 2669 2665 f 2665 2669 2666 f 2665 2670 2668 f 2668 2670 2671 f 2667 2666 2672 f 2672 2666 2669 f 2662 2661 2665 f 2665 2661 2670 f 2640 2670 2639 f 2639 2670 2661 f 2673 2674 2664 f 2664 2674 2658 f 2658 2674 2659 f 2659 2674 2675 f 2645 2657 2676 f 2676 2657 2656 f 2677 2676 2655 f 2655 2676 2656 f 2676 2678 2645 f 2645 2678 2644 f 2677 2679 2676 f 2676 2679 2678 f 2655 2680 2677 f 2677 2680 2681 f 2675 2674 2680 f 2680 2674 2681 f 2682 2640 2627 f 2627 2640 2628 f 2643 2683 2633 f 2633 2683 2626 f 2682 2627 2683 f 2683 2627 2626 f 2683 2684 2682 f 2682 2684 2671 f 2684 2685 2671 f 2671 2685 2668 f 2686 2684 2643 f 2643 2684 2683 f 2670 2640 2671 f 2671 2640 2682 f 2687 2686 2642 f 2642 2686 2643 f 2642 2641 2687 f 2687 2641 2644 f 2687 2644 2688 f 2688 2644 2678 f 2688 2689 2687 f 2687 2689 2686 f 2690 2691 2688 f 2688 2691 2689 f 2689 2685 2686 f 2686 2685 2684 f 2685 2689 2692 f 2692 2689 2691 f 2679 2690 2678 f 2678 2690 2688 f 2690 2679 2693 f 2693 2679 2694 f 2696 2697 2695 f 2695 2697 2693 f 2691 2690 2697 f 2697 2690 2693 f 2693 2694 2695 f 2695 2694 2673 f 2674 2673 2681 f 2681 2673 2694 f 2679 2677 2694 f 2694 2677 2681 f 2685 2692 2668 f 2668 2692 2669 f 2696 2672 2692 f 2692 2672 2669 f 2696 2692 2697 f 2697 2692 2691 f 2695 2667 2696 f 2696 2667 2672 f 2673 2664 2695 f 2695 2664 2667 f 2675 2654 2659 f 2659 2654 2647 f 2655 2654 2680 f 2680 2654 2675 f 2291 2156 2593 f 2593 2156 2594 f 2597 2602 2598 f 2598 2602 2605 f 2616 2617 2607 f 2607 2617 2608 f 2615 2610 2617 f 2617 2610 2608 f 2597 2596 2612 f 2612 2596 2698 f 2598 2605 2607 f 2607 2605 2616 f 2194 2291 2629 f 2629 2291 2593 f 2629 2618 2613 f 2613 2618 2614 f 2190 2610 2156 f 2156 2610 2594 f 2700 2010 2699 f 2699 2010 2086 f 2267 2701 2116 f 2116 2701 2702 f 2116 2702 2117 f 2117 2702 2703 f 2704 2705 2250 f 2250 2705 2254 f 2011 2706 2267 f 2267 2706 2701 f 2699 2086 2707 f 2707 2086 2289 f 2010 2700 2011 f 2011 2700 2706 f 2708 2707 2251 f 2251 2707 2289 f 2703 2704 2117 f 2117 2704 2250 f 2705 2708 2254 f 2254 2708 2251 f 2709 2710 2708 f 2708 2710 2707 f 2711 2709 2705 f 2705 2709 2708 f 2712 2711 2704 f 2704 2711 2705 f 2713 2712 2703 f 2703 2712 2704 f 2707 2710 2699 f 2699 2710 2714 f 2699 2714 2700 f 2700 2714 2715 f 2716 2706 2715 f 2715 2706 2700 f 2714 2710 2717 f 2717 2710 2718 f 2720 2721 2719 f 2719 2721 2722 f 2721 2723 2722 f 2722 2723 2724 f 2721 2725 2723 f 2723 2725 2726 f 2727 2728 2725 f 2725 2728 2726 f 2729 2725 2720 f 2720 2725 2721 f 2731 2729 2730 f 2730 2729 2720 f 2719 2732 2720 f 2720 2732 2730 f 2726 2733 2723 f 2723 2733 2724 f 2709 2711 2733 f 2733 2711 2724 f 2724 2711 2734 f 2734 2711 2712 f 2722 2724 2735 f 2735 2724 2734 f 2719 2722 2736 f 2736 2722 2735 f 2726 2728 2733 f 2733 2728 2718 f 2709 2733 2710 f 2710 2733 2718 f 2732 2719 2737 f 2737 2719 2736 f 2738 2739 2735 f 2735 2739 2736 f 2739 2740 2736 f 2736 2740 2737 f 2741 2742 2738 f 2738 2742 2739 f 2738 2743 2741 f 2741 2743 2744 f 2740 2739 2745 f 2745 2739 2742 f 2735 2734 2738 f 2738 2734 2743 f 2713 2743 2712 f 2712 2743 2734 f 2737 2746 2732 f 2732 2746 2747 f 2732 2747 2730 f 2730 2747 2731 f 2718 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f 2195 2613 2698 f 2698 2613 2612 f 2596 2279 2698 f 2698 2279 2195 f 2054 2777 2276 f 2276 2777 2778 f 2777 2054 2776 f 2776 2054 2053 f 2053 2779 2776 f 2776 2779 2079 f 2779 2181 2079 f 2079 2181 2080 f 2181 2239 2080 f 2080 2239 2240 f 2122 2256 2014 f 2042 1982 2076 f 2076 1982 2007 f 1982 1985 2007 f 2007 1985 2008 f 2095 2097 2265 f 2265 2097 2266 f 2076 2050 2042 f 2042 2050 2047 f 2046 2043 2047 f 2047 2043 2042 f 2118 1993 2046 f 2046 1993 2043 f 1995 1994 2118 f 2118 1994 1993 f 2267 1992 2011 f 2011 1992 2012 f 1997 2012 1994 f 1994 2012 1992 f 2226 2013 1997 f 1997 2013 2012 f 2225 2087 2226 f 2226 2087 2013 f 2225 2232 2087 f 2087 2232 2288 f 2232 2070 2288 f 2288 2070 2252 f 2070 2025 2252 f 2252 2025 2253 f 1988 2249 2025 f 2025 2249 2253 f 2116 2115 2267 f 2267 2115 2268 f 2096 1984 2237 f 2237 1984 1991 f 2095 1985 2096 f 2096 1985 1984 f 2265 2008 2095 f 2095 2008 1985 f 2092 2009 2265 f 2265 2009 2008 f 2091 2286 2092 f 2092 2286 2009 f 2255 2238 2091 f 2091 2238 2286 f 2256 2239 2255 f 2255 2239 2238 f 2122 2240 2256 f 2256 2240 2239 f 2121 2241 2122 f 2122 2241 2240 f 2022 2018 2097 f 2097 2018 2266 f 2022 2024 2018 f 2018 2024 2019 f 2024 2072 2019 f 2019 2072 2073 f 2072 2228 2073 f 2073 2228 2230 f 2228 2184 2230 f 2230 2184 2183 f 2121 2285 2241 f 2241 2285 2258 f 2285 2036 2258 f 2258 2036 2257 f 2177 2145 2193 f 2193 2145 2257 f 2173 2193 2196 f 2196 2193 2037 f 2036 2037 2257 f 2257 2037 2193 f 2196 2037 2066 f 2066 2037 2035 f 2061 2108 2034 f 2034 2108 2067 f 2108 2208 2067 f 2067 2208 2204 f 2208 2210 2204 f 2204 2210 2203 f 2210 2280 2203 f 2203 2280 2186 f 2183 2186 2235 f 2235 2186 2280 f 1987 2114 1988 f 1988 2114 2249 f 2237 2115 1987 f 1987 2115 2114 f 1991 2268 2237 f 2237 2268 2115 f 1991 1990 2268 f 2268 1990 1992 f 107 110 2781 f 2781 110 2780 f 117 2782 118 f 118 2782 2783 f 119 2784 120 f 120 2784 2785 f 125 2786 126 f 126 2786 2787 f 127 2788 128 f 128 2788 2789 f 127 132 2788 f 2788 132 2790 f 231 230 2792 f 2792 230 2791 f 118 2783 119 f 119 2783 2784 f 241 243 2794 f 2794 243 2793 f 2781 2795 107 f 107 2795 352 f 120 2785 437 f 437 2785 2796 f 126 2787 110 f 110 2787 2780 f 455 2797 456 f 456 2797 2798 f 128 2789 457 f 457 2789 2799 f 456 2798 117 f 117 2798 2782 f 2794 2797 241 f 241 2797 455 f 457 2799 125 f 125 2799 2786 f 132 231 2790 f 2790 231 2792 f 230 437 2791 f 2791 437 2796 f 2792 2789 2790 f 2790 2789 2788 f 2785 2784 2801 f 2801 2784 2800 f 2786 2802 2787 f 2787 2802 2803 f 2792 2791 2805 f 2805 2791 2804 f 2784 2783 2800 f 2800 2783 2806 f 2785 2801 2796 f 2796 2801 2807 f 2787 2803 2780 f 2780 2803 2808 f 2789 2809 2799 f 2799 2809 2810 f 2799 2810 2786 f 2786 2810 2802 f 2791 2796 2804 f 2804 2796 2807 f 2789 2792 2809 f 2809 2792 2805 f 2812 2797 2811 f 2811 2797 2794 f 2809 2805 2810 f 2810 2805 2804 f 2810 2804 2802 f 2802 2804 2807 f 2802 2807 2803 f 2803 2807 2801 f 2808 2803 2800 f 2800 2803 2801 f 2814 699 2813 f 2813 699 698 f 2808 2815 2780 f 2780 2815 2781 f 2817 2782 2816 f 2816 2782 2798 f 2815 2818 2781 f 2781 2818 2795 f 2797 2812 2798 f 2798 2812 2816 f 2783 2782 2806 f 2806 2782 2817 f 2800 2806 2808 f 2808 2806 2815 f 2794 2793 2811 f 2811 2793 2819 f 174 201 384 f 384 201 362 f 200 1766 390 f 390 1766 1777 f 1777 1778 390 f 390 1778 398 f 1778 361 398 f 398 361 222 f 2820 2821 628 f 628 2821 629 f 1406 2820 790 f 790 2820 628 f 794 1415 1233 f 1233 1415 1234 f 1406 790 1407 f 1407 790 1312 f 1407 1312 2822 f 2822 1312 1316 f 2823 1380 1543 f 1543 1380 1379 f 1385 1738 1382 f 1382 1738 1383 f 1381 791 1382 f 1382 791 1386 f 791 794 1386 f 1386 794 1233 f 1259 1390 1260 f 1260 1390 1389 f 1905 1542 1394 f 1394 1542 1395 f 1410 1413 1397 f 1397 1413 1398 f 2821 1410 629 f 629 1410 1397 f 2823 2822 1380 f 1380 2822 1316 f 1392 1395 1378 f 1378 1395 1379 f 1738 1399 1383 f 1383 1399 1398 f 1388 1235 1389 f 1389 1235 1234 f 1393 1391 1394 f 1394 1391 1390 f 1381 1384 779 f 779 1384 780 f 1282 788 792 f 792 788 793 f 1431 1262 1432 f 1432 1262 1261 f 1400 1342 1408 f 1408 1342 1409 f 1508 1544 1411 f 1411 1544 1412 f 788 787 793 f 793 787 1414 f 2821 2820 1429 f 1429 2820 1430 f 2822 2823 1506 f 1506 2823 1507 f 778 1282 779 f 779 1282 792 f 2820 1406 1430 f 1430 1406 1409 f 1410 2821 1411 f 1411 2821 1429 f 1407 2822 1408 f 1408 2822 1506 f 1544 781 1412 f 1412 781 780 f 1260 1415 1261 f 1261 1415 1414 f 1334 2824 923 f 923 2824 924 f 2826 1268 2825 f 2825 1268 1267 f 769 1212 2827 f 2827 1212 2828 f 1492 1190 1266 f 1266 1190 1267 f 1488 2829 1489 f 1489 2829 2830 f 922 1329 923 f 923 1329 1328 f 1484 2831 1485 f 1485 2831 2832 f 1482 2833 1485 f 1485 2833 1486 f 1324 1493 1489 f 1489 1493 1490 f 1265 1491 1266 f 1266 1491 1490 f 2835 2836 2834 f 2834 2836 2837 f 1250 2838 2827 f 2827 2838 2839 f 1488 1491 2840 f 2840 1491 2841 f 1268 2826 2842 f 2842 2826 2843 f 2844 2845 1495 f 1495 2845 1496 f 1427 2847 2846 f 2846 2847 926 f 2829 1488 2848 f 2848 1488 2840 f 1329 922 2849 f 2849 922 927 f 2850 2851 768 f 768 2851 1213 f 1265 1268 2852 f 2852 1268 2842 f 1491 1265 2841 f 2841 1265 2852 f 2854 2855 2853 f 2853 2855 2856 f 2855 2857 2856 f 2856 2857 598 f 2857 2858 598 f 598 2858 595 f 2858 2859 595 f 595 2859 626 f 649 652 2860 f 2860 652 2861 f 639 642 2862 f 2862 642 2863 f 2864 705 2862 f 2862 705 639 f 2865 1418 2864 f 2864 1418 705 f 2866 2867 2865 f 2865 2867 1418 f 1760 1348 1761 f 1761 1348 2867 f 1764 2868 1197 f 1197 2868 1198 f 2869 611 2868 f 2868 611 1198 f 2870 612 2869 f 2869 612 611 f 1352 1350 944 f 944 1350 688 f 1350 1256 688 f 688 1256 685 f 1256 1255 685 f 685 1255 692 f 1255 2871 692 f 692 2871 2872 f 701 2873 698 f 698 2873 2813 f 2872 1349 692 f 692 1349 693 f 1696 1744 1574 f 1574 1744 1575 f 1753 1618 1577 f 1577 1618 1578 f 1729 1594 1608 f 1608 1594 1609 f 1725 1630 1611 f 1611 1630 1612 f 1604 1607 1625 f 1625 1607 1626 f 1624 1703 1625 f 1625 1703 1912 f 1703 1702 1912 f 1912 1702 1913 f 1702 1753 1913 f 1913 1753 1577 f 1598 1569 1915 f 1915 1569 1578 f 1616 1615 1915 f 1915 1615 1916 f 1615 1696 1916 f 1916 1696 1574 f 1744 1745 1575 f 1575 1745 1917 f 1745 1565 1917 f 1917 1565 1609 f 1568 1725 1608 f 1608 1725 1611 f 2874 1613 1918 f 1918 1613 1612 f 1607 2874 1626 f 1626 2874 1918 f 1610 1613 1562 f 1562 1613 1563 f 1547 1550 1570 f 1570 1550 1571 f 1573 1576 1580 f 1580 1576 1581 f 1553 1556 1595 f 1595 1556 1596 f 1556 1582 1596 f 1596 1582 1581 f 1551 1547 1599 f 1599 1547 1570 f 1731 1914 1601 f 1601 1914 1602 f 1686 1689 1605 f 1605 1689 1606 f 1598 1743 1599 f 1599 1743 1692 f 2874 1607 1723 f 1723 1607 1606 f 1727 1553 1730 f 1730 1553 1595 f 1600 1686 1601 f 1601 1686 1605 f 1914 1572 1602 f 1602 1572 1571 f 1579 1693 1580 f 1580 1693 1692 f 1613 2874 1563 f 1563 2874 1723 f 1561 1727 1562 f 1562 1727 1730 f 1949 1742 1548 f 1548 1742 1549 f 1694 1949 1552 f 1552 1949 1548 f 1954 1589 1554 f 1554 1589 1555 f 1693 1579 1558 f 1558 1579 1559 f 1561 1564 1584 f 1584 1564 1585 f 1582 1556 1588 f 1588 1556 1555 f 1600 1603 1591 f 1591 1603 1592 f 1981 1977 1687 f 1687 1977 1688 f 1564 1722 1585 f 1585 1722 1690 f 1551 1693 1552 f 1552 1693 1558 f 1751 1954 1728 f 1728 1954 1554 f 1590 1981 1591 f 1591 1981 1687 f 1603 1550 1592 f 1592 1550 1549 f 1977 1691 1688 f 1688 1691 1690 f 1587 1560 1588 f 1588 1560 1559 f 1727 1561 1728 f 1728 1561 1584 f 2875 1919 1947 f 1947 1919 1948 f 1557 1560 1952 f 1952 1560 1953 f 1933 1932 1955 f 1955 1932 1956 f 1587 1589 1958 f 1958 1589 1956 f 1959 1933 1965 f 1965 1933 1955 f 1961 1964 1966 f 1966 1964 1967 f 1981 1590 1969 f 1969 1590 1967 f 1742 1949 1972 f 1972 1949 1948 f 1593 1742 1966 f 1966 1742 1972 f 1957 1950 1958 f 1958 1950 1953 f 1583 1586 1974 f 1974 1586 1975 f 1940 1943 1978 f 1978 1943 1979 f 1943 1976 1979 f 1979 1976 1975 f 1951 2875 1952 f 1952 2875 1947 f 1968 1940 1969 f 1969 1940 1978 f 1973 1959 1974 f 1974 1959 1965 f 1923 1971 1920 f 1920 1971 1921 f 1919 2875 1920 f 1920 2875 1924 f 2875 1951 1924 f 1924 1951 1926 f 1950 1957 1929 f 1929 1957 1930 f 2244 2243 1934 f 1934 2243 1935 f 1973 1976 1937 f 1937 1976 1938 f 1944 1980 1941 f 1941 1980 1942 f 1940 1968 1941 f 1941 1968 1945 f 2287 2244 1960 f 1960 2244 1934 f 1970 2245 1962 f 1962 2245 1963 f 2243 1931 1935 f 1935 1931 1930 f 1927 1926 1928 f 1928 1926 1929 f 1961 1922 1962 f 1962 1922 1921 f 2245 1946 1963 f 1963 1946 1945 f 1976 1943 1938 f 1938 1943 1942 f 1936 2287 1937 f 1937 2287 1960 f 743 1238 608 f 608 1238 609 f 607 1811 608 f 608 1811 744 f 1304 2876 1305 f 1305 2876 2877 f 586 585 1298 f 1298 585 1299 f 585 702 1299 f 1299 702 1300 f 702 1428 1300 f 1300 1428 1301 f 1238 586 609 f 609 586 1298 f 2878 2879 1218 f 1218 2879 2877 f 2880 2881 1812 f 1812 2881 1819 f 2883 1821 2882 f 2882 1821 1820 f 2840 2841 620 f 620 2841 621 f 2842 2843 795 f 795 2843 796 f 892 895 2844 f 2844 895 2845 f 2848 2840 942 f 942 2840 620 f 2849 927 1202 f 1202 927 1203 f 2852 2842 1270 f 1270 2842 795 f 2841 2852 621 f 621 2852 1270 f 941 1759 942 f 942 1759 2884 f 659 929 1427 f 1427 929 2847 f 1237 2851 751 f 751 2851 2850 f 1216 2885 1217 f 1217 2885 1754 f 2887 734 2886 f 2886 734 733 f 2889 1816 2888 f 2888 1816 1815 f 2890 1245 2889 f 2889 1245 1816 f 1824 2891 1825 f 1825 2891 1246 f 2892 2893 2895 f 2895 2893 2894 f 1854 1833 1851 f 1851 1833 1832 f 1851 1832 1884 f 1884 1832 1876 f 1884 1876 1441 f 1441 1876 1443 f 1441 1443 1442 f 1442 1443 1446 f 1442 1446 2896 f 2896 1446 1507 f 2897 2823 2898 f 2898 2823 1543 f 1542 2899 1543 f 1543 2899 2898 f 1542 1905 2899 f 2899 1905 2900 f 1905 1259 2900 f 2900 1259 2901 f 1262 1431 2902 f 2902 1431 1904 f 1431 1434 1904 f 1904 1434 1902 f 1902 1434 2903 f 2903 1434 719 f 2903 719 2904 f 2904 719 718 f 1869 1881 718 f 718 1881 2904 f 1881 1869 1880 f 1880 1869 756 f 1880 756 2905 f 2905 756 755 f 2905 755 2906 f 2906 755 758 f 762 2907 763 f 763 2907 2908 f 724 723 2909 f 2909 723 894 f 796 895 723 f 723 895 894 f 2843 2845 796 f 796 2845 895 f 2826 1496 2843 f 2843 1496 2845 f 2825 1497 2826 f 2826 1497 1496 f 1189 2836 2825 f 2825 2836 1497 f 797 891 798 f 798 891 2837 f 802 888 797 f 797 888 891 f 2910 2911 2913 f 2913 2911 2912 f 1780 1783 2915 f 2915 1783 2914 f 2916 2917 831 f 831 2917 832 f 2918 2919 845 f 845 2919 846 f 2920 2916 847 f 847 2916 831 f 2921 2922 857 f 857 2922 858 f 2923 2924 860 f 860 2924 861 f 2919 2912 846 f 846 2912 887 f 2925 2920 940 f 940 2920 847 f 2924 2926 861 f 861 2926 1204 f 2917 2918 832 f 832 2918 845 f 2927 2928 1449 f 1449 2928 1450 f 2929 2930 889 f 889 2930 2931 f 2896 1507 2897 f 2897 1507 2823 f 2902 2901 1262 f 1262 2901 1259 f 2932 2933 750 f 750 2933 1236 f 2933 2934 1236 f 1236 2934 1835 f 2936 2937 2935 f 2935 2937 2938 f 2940 2941 2939 f 2939 2941 2942 f 2943 2944 1841 f 1841 2944 1842 f 2945 1860 2944 f 2944 1860 1842 f 2945 2946 1860 f 1860 2946 1859 f 1889 2947 1890 f 1890 2947 1856 f 1856 2947 1857 f 1857 2947 2948 f 2950 709 2949 f 2949 709 1829 f 2952 730 2951 f 2951 730 729 f 2866 2953 2867 f 2867 2953 1761 f 2953 2954 1761 f 1761 2954 1420 f 2954 2955 1420 f 1420 2955 742 f 2955 2956 742 f 742 2956 739 f 2956 2957 739 f 739 2957 760 f 2957 2958 760 f 760 2958 761 f 2958 2959 761 f 761 2959 2960 f 2959 2961 2960 f 2960 2961 2962 f 2963 2964 1839 f 1839 2964 1840 f 2964 2965 1840 f 1840 2965 2942 f 2966 2950 2939 f 2939 2950 2949 f 2935 2967 2936 f 2936 2967 2968 f 2969 2970 736 f 736 2970 735 f 734 2887 1815 f 1815 2887 2888 f 2887 2882 2888 f 2888 2882 2881 f 1819 1820 1817 f 1817 1820 748 f 1195 1757 1197 f 1197 1757 1424 f 1757 647 1424 f 1424 647 646 f 647 648 646 f 646 648 643 f 648 1271 643 f 643 1271 737 f 1271 725 737 f 737 725 765 f 725 724 765 f 765 724 762 f 762 724 2907 f 2907 724 2909 f 1254 1437 2909 f 2909 1437 2907 f 1438 1253 1836 f 1836 1253 1835 f 1836 1835 2938 f 2938 1835 2934 f 2971 2972 2935 f 2935 2972 2967 f 897 896 2969 f 2969 896 2970 f 1815 1814 734 f 734 1814 731 f 728 1215 896 f 896 1215 732 f 2885 1216 733 f 733 1216 2886 f 1216 1219 2886 f 2886 1219 2883 f 2876 1304 1821 f 1821 1304 749 f 1304 1303 749 f 749 1303 746 f 1818 2973 1817 f 1817 2973 1810 f 747 2974 1818 f 1818 2974 2973 f 747 746 2974 f 2974 746 1303 f 2976 2977 2975 f 2975 2977 2978 f 2976 2979 2901 f 2901 2979 2900 f 2980 2981 2982 f 2982 2981 2979 f 2981 2980 2983 f 2983 2980 2984 f 2985 2897 2983 f 2983 2897 2898 f 2987 2988 2986 f 2986 2988 2985 f 2896 2988 1442 f 1442 2988 1439 f 1903 2977 1904 f 1904 2977 2902 f 1856 1855 1890 f 1890 1855 2989 f 2989 2990 1890 f 1890 2990 1359 f 2990 2991 1359 f 1359 2991 1356 f 2946 2992 1859 f 1859 2992 1893 f 2992 2993 1893 f 1893 2993 775 f 2993 2994 775 f 775 2994 776 f 2986 1356 2987 f 2987 1356 2991 f 2975 2978 776 f 776 2978 777 f 2982 2994 2980 f 2980 2994 2995 f 1357 2984 2995 f 2995 2984 2980 f 2995 2996 1357 f 1357 2996 1358 f 2996 2997 1358 f 1358 2997 1889 f 2998 2999 2932 f 2932 2999 2933 f 2999 2971 2933 f 2933 2971 2934 f 1837 1836 2937 f 2937 1836 2938 f 2943 1841 2941 f 2941 1841 1838 f 3001 2944 3000 f 3000 2944 2943 f 3001 3002 2944 f 2944 3002 2945 f 3002 3003 2945 f 2945 3003 2946 f 2997 3003 1889 f 1889 3003 2947 f 2947 3003 2948 f 2948 3003 3002 f 3001 3004 3002 f 3002 3004 2948 f 3000 3005 3001 f 3001 3005 3004 f 2940 3006 3000 f 3000 3006 3005 f 2999 3007 2971 f 2971 3007 2972 f 2998 2951 2999 f 2999 2951 3007 f 3008 2952 2998 f 2998 2952 2951 f 2966 2965 2936 f 2936 2965 2937 f 2935 2938 2971 f 2971 2938 2934 f 3003 2997 2946 f 2946 2997 2992 f 2997 2996 2992 f 2992 2996 2993 f 2996 2995 2993 f 2993 2995 2994 f 1356 2986 1357 f 1357 2986 2984 f 2982 2975 2994 f 2994 2975 776 f 1844 1843 1899 f 1899 1843 3009 f 1843 638 3009 f 3009 638 3010 f 638 637 3010 f 3010 637 3011 f 637 1830 3011 f 3011 1830 3012 f 1830 1833 3012 f 3012 1833 2894 f 1833 1854 2894 f 2894 1854 2895 f 1854 1853 2895 f 2895 1853 1888 f 1858 1857 1885 f 1885 1857 1892 f 1899 3009 1898 f 1898 3009 1874 f 3009 3010 1874 f 1874 3010 1865 f 3010 3011 1865 f 1865 3011 1862 f 3011 3012 1862 f 1862 3012 1882 f 3012 2894 1882 f 1882 2894 2893 f 1850 2890 782 f 782 2890 2889 f 783 2880 1811 f 1811 2880 1812 f 744 1810 745 f 745 1810 2973 f 745 2973 805 f 805 2973 806 f 2973 2974 806 f 806 2974 815 f 1303 812 2974 f 2974 812 815 f 1303 1302 812 f 812 1302 1221 f 1307 3013 1302 f 1302 3013 1221 f 1734 3014 1633 f 1633 3014 1634 f 1646 1649 1636 f 1636 1649 1637 f 1654 1657 1641 f 1641 1657 1642 f 1639 1736 1636 f 1636 1736 1684 f 1716 1713 1698 f 1698 1713 1699 f 1718 1721 1705 f 1705 1721 1706 f 1653 1654 1709 f 1709 1654 1641 f 1620 1623 1714 f 1714 1623 1699 f 1712 1682 1714 f 1714 1682 1740 f 1668 1671 1746 f 1746 1671 1747 f 1752 1650 1749 f 1749 1650 1750 f 1750 1650 1709 f 1709 1650 1653 f 1632 1668 1633 f 1633 1668 1746 f 1649 1752 1637 f 1637 1752 1749 f 1682 1643 1740 f 1740 1643 1642 f 3014 1685 1634 f 1634 1685 1684 f 1704 1748 1705 f 1705 1748 1747 f 1697 1707 1698 f 1698 1707 1706 f 1739 1701 1621 f 1621 1701 1622 f 1567 1566 1647 f 1647 1566 1648 f 1664 1695 1651 f 1651 1695 1652 f 1614 1617 1655 f 1655 1617 1656 f 1704 1707 1659 f 1659 1707 1660 f 3014 1734 1662 f 1662 1734 1663 f 1650 1752 1651 f 1651 1752 1665 f 1741 1657 1667 f 1667 1657 1656 f 1695 1614 1652 f 1652 1614 1655 f 1724 1567 1726 f 1726 1567 1647 f 1645 1628 1733 f 1733 1628 1663 f 1685 3014 1726 f 1726 3014 1662 f 1620 1741 1621 f 1621 1741 1667 f 1566 1666 1648 f 1648 1666 1665 f 1658 1737 1659 f 1659 1737 1901 f 1911 1661 1910 f 1910 1661 1660 f 1701 1911 1622 f 1622 1911 1910 f 1732 1748 1733 f 1733 1748 1901 f 1857 2948 1892 f 1892 2948 3004 f 1892 3004 1891 f 1891 3004 3005 f 1828 3006 1829 f 1829 3006 2949 f 2967 736 2968 f 2968 736 710 f 2972 2969 2967 f 2967 2969 736 f 2969 2972 897 f 897 2972 3007 f 897 3007 729 f 729 3007 2951 f 3016 753 3015 f 3015 753 752 f 3018 767 3017 f 3017 767 766 f 3019 3020 1249 f 1249 3020 3021 f 2915 2914 3019 f 3019 2914 3020 f 3023 2914 3022 f 3022 2914 1783 f 2827 2839 769 f 769 2839 766 f 3025 2838 3024 f 3024 2838 3021 f 768 767 2850 f 2850 767 3026 f 2850 3026 751 f 751 3026 752 f 750 753 2932 f 2932 753 3027 f 3008 2998 3027 f 3027 2998 2932 f 3028 3029 3008 f 3008 3029 2952 f 3029 3030 2952 f 2952 3030 730 f 3032 3033 3031 f 3031 3033 3034 f 3036 3037 3035 f 3035 3037 3038 f 2870 3039 612 f 612 3039 3040 f 3041 3042 3031 f 3031 3042 3038 f 3034 1352 3043 f 3043 1352 944 f 612 3040 613 f 613 3040 3044 f 613 3044 1425 f 1425 3044 1422 f 3045 3042 669 f 669 3042 670 f 3041 3043 599 f 599 3043 600 f 772 771 1544 f 1544 771 781 f 1503 771 1866 f 1866 771 770 f 771 1503 781 f 781 1503 778 f 61 281 416 f 416 281 1907 f 283 103 3046 f 3046 103 3047 f 76 79 3048 f 3048 79 3049 f 280 76 1906 f 1906 76 3048 f 3051 335 3050 f 3050 335 334 f 3053 417 3052 f 3052 417 416 f 3055 435 3054 f 3054 435 434 f 428 435 419 f 419 435 1908 f 420 1909 293 f 293 1909 443 f 79 294 3049 f 3049 294 444 f 3057 444 3056 f 3056 444 443 f 3051 3058 335 f 335 3058 452 f 216 215 1332 f 1332 215 452 f 308 283 462 f 462 283 3046 f 3050 334 3059 f 3059 334 462 f 103 62 3047 f 3047 62 417 f 313 312 434 f 434 312 463 f 3061 574 3060 f 3060 574 573 f 1310 3062 1428 f 1428 3062 3063 f 1258 3064 1255 f 1255 3064 2871 f 932 3065 2853 f 2853 3065 3066 f 699 2814 931 f 931 2814 3067 f 3068 1301 3063 f 3063 1301 1428 f 571 574 1310 f 1310 574 3062 f 3060 573 3069 f 3069 573 1332 f 3070 3071 1906 f 1906 3071 1907 f 3072 3073 1908 f 1908 3073 1909 f 3075 2814 3074 f 3074 2814 2813 f 2854 3076 1258 f 1258 3076 3064 f 2871 3077 2872 f 2872 3077 3078 f 3074 2813 3079 f 3079 2813 2873 f 2872 3078 1349 f 1349 3078 3080 f 2873 701 3068 f 3068 701 1301 f 1330 1201 3080 f 3080 1201 1349 f 1264 1263 3081 f 3081 1263 3082 f 3083 3084 3082 f 3082 3084 3081 f 811 1448 3086 f 3086 1448 3085 f 808 811 3087 f 3087 811 3086 f 3088 3083 833 f 833 3083 3082 f 884 3089 885 f 885 3089 3090 f 3087 1790 808 f 808 1790 816 f 1790 3087 1787 f 1787 3087 3091 f 3092 3091 3086 f 3086 3091 3087 f 3093 3094 3084 f 3084 3094 3095 f 3089 3096 3090 f 3090 3096 3088 f 3097 3093 3083 f 3083 3093 3084 f 3099 3100 3098 f 3098 3100 1786 f 3101 1788 2913 f 2913 1788 3102 f 838 837 885 f 885 837 886 f 886 837 3103 f 3103 837 1541 f 3094 3093 3104 f 3104 3093 3097 f 3105 3106 3089 f 3089 3106 3096 f 3108 3109 3107 f 3107 3109 3110 f 3111 3112 3091 f 3091 3112 1787 f 3114 3108 3113 f 3113 3108 3107 f 3114 3115 3112 f 3112 3115 3102 f 3091 3092 3111 f 3111 3092 3116 f 839 838 835 f 835 838 836 f 885 3090 838 f 838 3090 836 f 3110 3117 1247 f 1247 3117 3118 f 1247 3118 1248 f 1248 3118 3119 f 3120 3121 3119 f 3119 3121 1248 f 1247 844 3110 f 3110 844 3107 f 844 843 3107 f 3107 843 3113 f 843 3099 3113 f 3113 3099 3122 f 1781 3123 3098 f 3098 3123 3124 f 2915 2931 1780 f 1780 2931 2930 f 3019 3125 2915 f 2915 3125 2931 f 890 3125 2834 f 2834 3125 1252 f 3125 3019 1252 f 1252 3019 1249 f 907 908 946 f 946 908 947 f 1185 1188 1369 f 1369 1188 948 f 954 957 951 f 951 957 952 f 910 965 953 f 953 965 950 f 905 958 960 f 960 958 961 f 3126 978 949 f 949 978 962 f 936 935 967 f 967 935 968 f 994 993 969 f 969 993 966 f 902 904 971 f 971 904 972 f 983 985 973 f 973 985 970 f 906 905 974 f 974 905 960 f 977 981 959 f 959 981 975 f 911 910 1143 f 1143 910 953 f 952 957 1142 f 1142 957 1145 f 911 1143 1366 f 1366 1143 1368 f 1185 1369 1145 f 1145 1369 1142 f 993 1519 966 f 966 1519 1456 f 965 1454 950 f 950 1454 1457 f 900 899 1459 f 1459 899 1460 f 988 991 1461 f 1461 991 1458 f 899 963 1460 f 1460 963 1462 f 985 987 970 f 970 987 1463 f 904 906 972 f 972 906 974 f 981 983 975 f 975 983 973 f 935 900 968 f 968 900 1459 f 991 994 1458 f 1458 994 969 f 1154 1526 1088 f 1088 1526 1089 f 1109 1112 1092 f 1092 1112 1093 f 1083 1098 1084 f 1084 1098 1097 f 1078 1471 1100 f 1100 1471 1101 f 1075 1078 1104 f 1104 1078 1100 f 1152 1154 1156 f 1156 1154 1088 f 1156 1155 1372 f 1372 1155 1373 f 1372 1373 1104 f 1104 1373 1075 f 1467 1109 1465 f 1465 1109 1092 f 1465 1182 1468 f 1468 1182 1184 f 1474 1470 1096 f 1096 1470 1097 f 1102 1101 1083 f 1083 1101 1098 f 1112 1531 1093 f 1093 1531 1529 f 1102 1073 1099 f 1099 1073 1072 f 1526 1154 1079 f 1079 1154 1080 f 1081 1091 1082 f 1082 1091 1094 f 1069 1068 1085 f 1085 1068 1086 f 1153 1151 1080 f 1080 1151 1058 f 1371 1177 1179 f 1179 1177 1176 f 1099 1072 1103 f 1103 1072 1176 f 1068 1071 1086 f 1086 1071 1073 f 1085 1084 1180 f 1180 1084 1469 f 1066 1065 1183 f 1183 1065 1181 f 1153 1152 1177 f 1177 1152 1178 f 1091 1081 1182 f 1182 1081 1183 f 1057 1525 1079 f 1079 1525 1527 f 1120 1117 1076 f 1076 1117 1077 f 1087 1090 1107 f 1107 1090 1108 f 1127 1126 1110 f 1110 1126 1111 f 1095 1116 1096 f 1096 1116 1115 f 1471 1078 1119 f 1119 1078 1077 f 1087 1107 1155 f 1155 1107 1158 f 1376 1374 1157 f 1157 1374 1158 f 1076 1075 1374 f 1374 1075 1373 f 1109 1467 1110 f 1110 1467 1472 f 1467 1466 1472 f 1472 1466 1473 f 1135 1475 1114 f 1114 1475 1115 f 1471 1119 1095 f 1095 1119 1116 f 1126 1532 1111 f 1111 1532 1530 f 914 1276 1480 f 1480 1276 1517 f 3127 3128 1223 f 1223 3128 1224 f 824 933 1455 f 1455 933 3129 f 3131 1448 3130 f 3130 1448 1447 f 3132 3133 1452 f 1452 3133 1453 f 3130 1447 1322 f 1322 1447 1455 f 933 1514 3129 f 3129 1514 3134 f 1161 3135 1480 f 1480 3135 1481 f 3128 3136 1224 f 1224 3136 1499 f 3133 1162 1453 f 1453 1162 1517 f 1515 3127 1535 f 1535 3127 1223 f 3095 3085 3131 f 3131 3085 1448 f 3137 3092 3085 f 3085 3092 3086 f 3138 3116 3137 f 3137 3116 3092 f 3110 3109 3117 f 3117 3109 3139 f 3105 3140 3117 f 3117 3140 3118 f 3140 3141 3118 f 3118 3141 3119 f 3142 3120 3141 f 3141 3120 3119 f 1498 3143 1277 f 1277 3143 882 f 3144 3145 1340 f 1340 3145 1339 f 1340 3146 3144 f 3144 3146 3147 f 3145 3144 3143 f 3143 3144 3148 f 871 901 872 f 872 901 1362 f 3149 3148 3147 f 3147 3148 3144 f 1208 3150 1209 f 3098 1786 1781 f 1781 1786 1782 f 2930 2929 3123 f 3123 2929 2911 f 1794 1791 2916 f 2916 1791 2917 f 1793 3101 2918 f 2918 3101 2919 f 1796 1794 2920 f 2920 1794 2916 f 3151 3152 2921 f 2921 3152 2922 f 3153 3154 2923 f 2923 3154 2924 f 2919 3101 2912 f 2912 3101 2913 f 3155 1796 2925 f 2925 1796 2920 f 3154 3156 2924 f 2924 3156 2926 f 1791 1793 2917 f 2917 1793 2918 f 3157 3158 2927 f 2927 3158 2928 f 1780 2930 1781 f 1781 2930 3123 f 2825 1267 1189 f 1189 1267 1190 f 2827 2828 1250 f 1250 2828 1251 f 1489 2830 1324 f 1324 2830 1325 f 923 1328 1334 f 1334 1328 1335 f 1485 2832 1482 f 1482 2832 1483 f 1266 1490 1492 f 1492 1490 1493 f 1333 1346 1334 f 1334 1346 2824 f 722 799 1492 f 1492 799 1190 f 664 657 1482 f 1482 657 2833 f 631 634 1324 f 1324 634 1493 f 2828 1494 1251 f 1251 1494 2835 f 1249 3021 1250 f 1250 3021 2838 f 798 2837 1189 f 1189 2837 2836 f 3100 842 3160 f 3160 842 3159 f 1788 3101 1789 f 1789 3101 1793 f 1792 816 1789 f 1789 816 1790 f 817 816 1795 f 1795 816 1792 f 938 817 1797 f 1797 817 1795 f 1796 3155 1797 f 1797 3155 1798 f 3155 3158 1798 f 1798 3158 1799 f 3157 3152 1801 f 1801 3152 1802 f 3151 3156 1803 f 1803 3156 1804 f 3154 3153 1805 f 1805 3153 1806 f 3153 3161 1806 f 1806 3161 1807 f 3099 843 3100 f 3100 843 842 f 1788 1787 3102 f 3102 1787 3112 f 2910 3115 3124 f 3124 3115 3122 f 3099 3098 3122 f 3122 3098 3124 f 416 1907 3052 f 3052 1907 3071 f 3046 3047 3163 f 3163 3047 3162 f 249 445 259 f 259 445 3164 f 425 3165 424 f 424 3165 461 f 408 410 329 f 329 410 330 f 39 331 40 f 40 331 332 f 305 304 423 f 423 304 464 f 385 254 458 f 458 254 392 f 250 415 251 f 251 415 412 f 233 337 234 f 234 337 3166 f 435 3055 1908 f 1908 3055 3072 f 443 1909 3056 f 3056 1909 3073 f 3167 3049 3057 f 3057 3049 444 f 452 3058 1332 f 1332 3058 3069 f 234 3166 346 f 346 3166 411 f 3046 3163 462 f 462 3163 3059 f 259 3164 260 f 260 3164 336 f 3047 417 3162 f 3162 417 3053 f 463 3168 434 f 434 3168 3054 f 454 830 425 f 425 830 3165 f 3062 3169 3063 f 3063 3169 3170 f 1355 1354 1534 f 1534 1354 3171 f 3063 3170 3068 f 3068 3170 3172 f 574 3061 3062 f 3062 3061 3169 f 1534 3171 1535 f 1535 3171 3173 f 3079 2873 3172 f 3172 2873 3068 f 2838 3025 2839 f 2839 3025 3174 f 3176 3177 3175 f 3175 3177 3178 f 3180 1125 3179 f 3179 1125 1128 f 3182 3183 3181 f 3181 3183 3184 f 1134 3185 1130 f 1130 3185 3182 f 3186 3187 3189 f 3189 3187 3188 f 3191 3189 3190 f 3190 3189 3188 f 1137 1122 3190 f 3190 1122 3192 f 3175 3178 3193 f 3193 3178 3194 f 3181 3184 3179 f 3179 3184 3195 f 3197 3198 3196 f 3196 3198 3180 f 3200 3178 3199 f 3199 3178 3177 f 3202 3196 3201 f 3201 3196 3195 f 3204 3184 3203 f 3203 3184 3183 f 3206 3207 3205 f 3205 3207 3186 f 3205 3186 3208 f 3208 3186 3189 f 3209 3208 3191 f 3191 3208 3189 f 3210 3194 3200 f 3200 3194 3178 f 3201 3195 3204 f 3204 3195 3184 f 3197 3211 3177 f 3177 3211 3199 f 3213 3214 3212 f 3212 3214 3215 f 3217 3218 3216 f 3216 3218 3219 f 3221 3222 3220 f 3220 3222 3223 f 3225 3226 3224 f 3224 3226 3227 f 3229 3230 3228 f 3228 3230 3231 f 3233 3234 3232 f 3232 3234 3235 f 3237 3238 3236 f 3236 3238 3239 f 3238 3240 3239 f 3239 3240 3241 f 3232 3235 3242 f 3242 3235 3243 f 3245 3246 3244 f 3244 3246 3247 f 3248 3249 3245 f 3245 3249 3246 f 3251 3252 3250 f 3250 3252 3253 f 3255 3252 3254 f 3254 3252 3251 f 3256 3257 3259 f 3259 3257 3258 f 3261 3262 3260 f 3260 3262 3263 f 3265 3263 3264 f 3264 3263 3262 f 3256 3259 3266 f 3266 3259 3267 f 3269 3270 3268 f 3268 3270 3271 f 3269 3272 3270 f 3270 3272 3273 f 3274 3275 3277 f 3277 3275 3276 f 3278 3279 3276 f 3276 3279 3277 f 3281 3282 3280 f 3280 3282 3283 f 3280 3284 3281 f 3281 3284 3285 f 3287 3288 3286 f 3286 3288 3289 f 3290 3291 3288 f 3288 3291 3289 f 3234 3218 3235 f 3235 3218 3217 f 3232 3292 3233 f 3233 3292 3293 f 3239 3294 3236 f 3236 3294 3219 f 3233 3293 3237 f 3237 3293 3295 f 3296 3294 3241 f 3241 3294 3239 f 3240 3238 3297 f 3297 3238 3298 f 3217 3216 3235 f 3235 3216 3243 f 3297 3299 3240 f 3240 3299 3242 f 3246 3300 3247 f 3247 3300 3223 f 3301 3302 3254 f 3254 3302 3244 f 3249 3303 3246 f 3246 3303 3300 f 3245 3304 3248 f 3248 3304 3292 f 3252 3221 3253 f 3253 3221 3220 f 3250 3248 3299 f 3299 3248 3292 f 3222 3221 3255 f 3255 3221 3252 f 3297 3301 3251 f 3251 3301 3254 f 3258 3214 3259 f 3259 3214 3213 f 3257 3256 3304 f 3304 3256 3305 f 3263 3306 3260 f 3260 3306 3215 f 3261 3257 3302 f 3302 3257 3304 f 3307 3306 3265 f 3265 3306 3263 f 3262 3301 3264 f 3264 3301 3308 f 3259 3213 3267 f 3267 3213 3212 f 3308 3309 3264 f 3264 3309 3266 f 3270 3310 3271 f 3271 3310 3231 f 3311 3312 3278 f 3278 3312 3268 f 3270 3273 3310 f 3310 3273 3313 f 3272 3269 3305 f 3305 3269 3314 f 3277 3229 3274 f 3274 3229 3228 f 3275 3272 3309 f 3309 3272 3305 f 3279 3230 3277 f 3277 3230 3229 f 3276 3308 3278 f 3278 3308 3311 f 3281 3315 3282 f 3282 3315 3227 f 3290 3316 3283 f 3283 3316 3317 f 3281 3285 3315 f 3315 3285 3318 f 3284 3280 3314 f 3314 3280 3319 f 3289 3225 3286 f 3286 3225 3224 f 3287 3284 3312 f 3284 3314 3312 f 3291 3226 3289 f 3289 3226 3225 f 3288 3311 3290 f 3290 3311 3316 f 3212 3215 3307 f 3307 3215 3306 f 3294 3296 3219 f 3219 3296 3216 f 3303 3220 3300 f 3300 3220 3223 f 3224 3227 3318 f 3318 3227 3315 f 3228 3231 3313 f 3313 3231 3310 f 3234 3233 3236 f 3236 3233 3237 f 3243 3241 3242 f 3242 3241 3240 f 3247 3255 3244 f 3244 3255 3254 f 3253 3249 3250 f 3250 3249 3248 f 3260 3258 3261 f 3261 3258 3257 f 3266 3267 3264 f 3264 3267 3265 f 3268 3271 3278 f 3278 3271 3279 f 3274 3273 3275 f 3275 3273 3272 f 3283 3282 3290 f 3290 3282 3291 f 3286 3285 3287 f 3287 3285 3284 f 3218 3234 3219 f 3219 3234 3236 f 3295 3298 3237 f 3237 3298 3238 f 3216 3296 3243 f 3243 3296 3241 f 3242 3299 3232 f 3232 3299 3292 f 3223 3222 3247 f 3247 3222 3255 f 3244 3302 3245 f 3245 3302 3304 f 3303 3249 3220 f 3220 3249 3253 f 3297 3251 3299 f 3299 3251 3250 f 3215 3214 3260 f 3260 3214 3258 f 3301 3262 3302 f 3302 3262 3261 f 3212 3307 3267 f 3267 3307 3265 f 3266 3309 3256 f 3256 3309 3305 f 3271 3231 3279 f 3279 3231 3230 f 3268 3312 3269 f 3269 3312 3314 f 3228 3313 3274 f 3274 3313 3273 f 3308 3276 3309 f 3309 3276 3275 f 3282 3227 3291 f 3291 3227 3226 f 3317 3319 3283 f 3283 3319 3280 f 3224 3318 3286 f 3286 3318 3285 f 3312 3311 3287 f 3311 3288 3287 f 3320 3321 3194 f 3194 3321 3193 f 1375 3321 1121 f 1121 3321 3322 f 3323 3324 3322 f 3322 3324 3192 f 3175 1124 3176 f 3176 1124 1123 f 3179 3195 3180 f 3180 3195 3196 f 3181 1131 3182 f 3182 1131 1130 f 3207 3185 3186 f 3186 3185 3187 f 1136 3188 1133 f 1133 3188 3187 f 3190 3188 1137 f 1137 3188 1136 f 3193 1159 3175 f 3175 1159 1124 f 3179 1128 3181 f 3181 1128 1131 f 1533 3198 1123 f 1123 3198 3176 f 3321 1375 3193 f 3193 1375 1159 f 3321 3320 3322 f 3322 3320 3323 f 1122 1121 3192 f 3192 1121 3322 f 3194 3210 3320 f 3320 3210 3325 f 3324 3323 3327 f 3327 3323 3326 f 3320 3325 3323 f 3323 3325 3326 f 3208 3328 3205 f 3205 3328 3329 f 3330 3206 3329 f 3329 3206 3205 f 3203 3331 3204 f 3204 3331 3332 f 3210 3333 3325 f 3325 3333 3334 f 3325 3334 3326 f 3326 3334 3335 f 3327 3326 3336 f 3336 3326 3335 f 3327 3209 3324 f 3324 3209 3191 f 3190 3192 3191 f 3191 3192 3324 f 3338 3339 3337 f 3337 3339 3340 f 3338 3341 3339 f 3339 3341 3342 f 3341 3343 3342 f 3342 3343 3344 f 3343 3345 3344 f 3344 3345 3346 f 3345 3347 3346 f 3346 3347 3348 f 3347 3349 3348 f 3348 3349 3350 f 3337 3340 3351 f 3351 3340 3352 f 3351 3352 3353 f 3353 3352 3354 f 3353 3354 3355 f 3355 3354 3356 f 3350 3349 3357 f 3357 3349 3358 f 3355 3356 3359 f 3359 3356 3360 f 3359 3360 3361 f 3361 3360 3362 f 3361 3362 3363 f 3363 3362 3364 f 3363 3364 3358 f 3358 3364 3357 f 3339 3342 3365 f 3365 3342 3366 f 3342 3344 3366 f 3366 3344 3367 f 3344 3346 3367 f 3367 3346 3368 f 3346 3348 3368 f 3368 3348 3369 f 3348 3350 3369 f 3369 3350 3370 f 3368 3369 3371 f 3371 3369 3372 f 3369 3370 3372 f 3372 3370 3373 f 3373 3374 3372 f 3372 3374 3375 f 3377 3378 3376 f 3376 3378 3379 f 3377 3375 3378 f 3378 3375 3374 f 3375 3380 3372 f 3372 3380 3371 f 3377 3381 3375 f 3375 3381 3380 f 3376 3382 3377 f 3377 3382 3381 f 3384 3385 3383 f 3383 3385 3386 f 3384 3383 3388 f 3388 3383 3387 f 3388 3387 3389 f 3389 3387 3390 f 3392 3385 3391 f 3391 3385 3384 f 3388 3393 3384 f 3384 3393 3391 f 3389 3394 3388 f 3388 3394 3393 f 3389 3395 3394 f 3394 3395 3396 f 3395 3389 3397 f 3397 3389 3390 f 3339 3365 3340 f 3340 3365 3398 f 3340 3398 3352 f 3352 3398 3399 f 3371 3400 3368 f 3368 3400 3367 f 3400 3401 3367 f 3367 3401 3366 f 3366 3401 3365 f 3365 3401 3402 f 3398 3365 3403 f 3403 3365 3402 f 3398 3403 3399 f 3399 3403 3404 f 3403 3405 3404 f 3404 3405 3406 f 3352 3399 3354 f 3354 3399 3407 f 3399 3404 3407 f 3407 3404 3408 f 3404 3406 3408 f 3408 3406 3409 f 3405 3410 3406 f 3406 3410 3411 f 3410 3412 3411 f 3411 3412 3413 f 3397 3414 3395 f 3395 3414 3415 f 3417 3415 3416 f 3416 3415 3412 f 3410 3418 3412 f 3412 3418 3416 f 3410 3405 3418 f 3418 3405 3419 f 3405 3403 3419 f 3419 3403 3402 f 3370 3350 3420 f 3420 3350 3357 f 3373 3370 3421 f 3421 3370 3420 f 3374 3373 3422 f 3422 3373 3421 f 3378 3374 3423 f 3423 3374 3422 f 3378 3423 3379 f 3379 3423 3424 f 3424 3425 3379 f 3379 3425 3386 f 3383 3426 3387 f 3387 3426 3427 f 3387 3427 3390 f 3390 3427 3428 f 3390 3428 3397 f 3397 3428 3429 f 3429 3430 3397 f 3397 3430 3414 f 3411 3413 3431 f 3431 3413 3432 f 3406 3411 3409 f 3409 3411 3431 f 3401 3400 3335 f 3335 3400 3336 f 3401 3335 3402 f 3402 3335 3334 f 3402 3334 3419 f 3419 3334 3333 f 3419 3333 3418 f 3418 3333 3433 f 3418 3433 3416 f 3416 3433 3434 f 3435 3417 3434 f 3434 3417 3416 f 3394 3396 3436 f 3436 3396 3437 f 3393 3394 3332 f 3332 3394 3436 f 3393 3332 3391 f 3391 3332 3331 f 3330 3392 3331 f 3331 3392 3391 f 3382 3329 3381 f 3381 3329 3328 f 3354 3407 3356 f 3356 3407 3438 f 3356 3438 3360 f 3360 3438 3439 f 3360 3439 3362 f 3362 3439 3440 f 3362 3440 3364 f 3364 3440 3441 f 3364 3441 3357 f 3357 3441 3420 f 3421 3420 3442 f 3442 3420 3441 f 3442 3441 3443 f 3443 3441 3440 f 3443 3440 3444 f 3444 3440 3439 f 3444 3439 3445 f 3445 3439 3438 f 3445 3438 3408 f 3408 3438 3407 f 3422 3421 3446 f 3446 3421 3442 f 3446 3442 3447 f 3447 3442 3443 f 3447 3443 3448 f 3448 3443 3444 f 3448 3444 3449 f 3449 3444 3445 f 3449 3445 3409 f 3409 3445 3408 f 3409 3431 3449 f 3449 3431 3450 f 3449 3450 3448 f 3448 3450 3451 f 3451 3452 3448 f 3448 3452 3447 f 3447 3452 3446 f 3446 3452 3453 f 3446 3453 3422 f 3422 3453 3423 f 3424 3423 3454 f 3454 3423 3453 f 3452 3455 3453 f 3453 3455 3454 f 3452 3451 3455 f 3455 3451 3456 f 3450 3457 3451 f 3451 3457 3456 f 3431 3432 3450 f 3450 3432 3457 f 3454 3458 3424 f 3424 3458 3425 f 3427 3426 3459 f 3459 3426 3460 f 3428 3427 3461 f 3461 3427 3459 f 3428 3461 3429 f 3429 3461 3462 f 3462 3463 3429 f 3429 3463 3430 f 3464 3465 3462 f 3462 3465 3463 f 3455 3466 3454 f 3454 3466 3458 f 3465 3464 3466 f 3466 3464 3467 f 3461 3459 3468 f 3468 3459 3469 f 3468 3469 3464 f 3464 3469 3467 f 3464 3462 3468 f 3468 3462 3461 f 3459 3460 3469 f 3469 3460 3467 f 3295 3351 3298 f 3298 3351 3353 f 3353 3355 3298 f 3298 3355 3297 f 3297 3355 3301 f 3301 3355 3359 f 3301 3359 3308 f 3308 3359 3361 f 3308 3361 3311 f 3311 3361 3363 f 3311 3363 3316 f 3316 3363 3358 f 3316 3358 3317 f 3317 3358 3349 f 3349 3347 3317 f 3317 3347 3319 f 3347 3345 3319 f 3319 3345 3314 f 3345 3343 3314 f 3314 3343 3305 f 3343 3341 3305 f 3305 3341 3304 f 3304 3341 3292 f 3292 3341 3338 f 3292 3338 3293 f 3293 3338 3337 f 3293 3337 3295 f 3295 3337 3351 f 3437 3202 3436 f 3436 3202 3201 f 3436 3201 3332 f 3332 3201 3204 f 3433 3200 3434 f 3434 3200 3199 f 3211 3435 3199 f 3199 3435 3434 f 3333 3210 3433 f 3433 3210 3200 f 3380 3470 3371 f 3400 3371 3336 f 3336 3371 3470 f 3381 3328 3380 f 3380 3328 3470 f 3470 3328 3209 f 3209 3328 3208 f 3336 3470 3327 f 3327 3470 3209 f 1545 1763 1546 f 1546 1763 1762 f 1403 1546 773 f 773 1546 770 f 1221 3013 1220 f 1220 3013 3471 f 1305 2877 1306 f 1306 2877 2879 f 1220 3471 828 f 828 3471 829 f 1217 1754 3472 f 3472 1754 1756 f 1049 3473 1048 f 1048 3473 3474 f 1371 1179 3473 f 3473 1179 3474 f 1074 3475 1179 f 1179 3475 3474 f 3474 3475 1048 f 1048 3475 1056 f 919 666 1345 f 1345 666 665 f 856 680 919 f 919 680 666 f 679 680 1449 f 1449 680 858 f 1367 878 1500 f 1500 878 877 f 1366 908 1367 f 1367 908 878 f 1512 916 1513 f 1513 916 915 f 947 1368 948 f 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924 3477 f 3477 924 2824 f 927 926 1203 f 1203 926 2847 f 3484 2859 3032 f 3032 2859 3033 f 3472 2878 1217 f 1217 2878 1218 f 2889 2888 2880 f 2880 2888 2881 f 2886 2883 2887 f 2887 2883 2882 f 929 928 2847 f 2847 928 1203 f 857 1204 2921 f 2921 1204 2926 f 1820 1819 2882 f 2882 1819 2881 f 1821 2883 2876 f 2876 2883 1219 f 782 2889 783 f 783 2889 2880 f 1352 3034 1353 f 1353 3034 3033 f 3031 3034 3041 f 3041 3034 3043 f 944 943 3043 f 3043 943 600 f 1339 3145 1498 f 1498 3145 3143 f 1208 882 3148 f 3148 882 3143 f 2921 2926 3151 f 3151 2926 3156 f 3477 2824 1345 f 1345 2824 1346 f 1805 1804 3154 f 3154 1804 3156 f 886 3103 883 f 883 3103 3482 f 841 1248 3485 f 3485 1248 3121 f 2811 2819 2812 f 2812 2819 2816 f 2818 2815 2817 f 2817 2815 2806 f 2817 2816 2818 f 2818 2816 2819 f 2793 2795 2819 f 2819 2795 2818 f 243 352 2793 f 2793 352 2795 f 242 353 243 f 243 353 352 f 359 191 242 f 242 191 353 f 247 192 359 f 359 192 191 f 157 193 247 f 247 193 192 f 156 194 157 f 157 194 193 f 378 261 156 f 156 261 194 f 378 381 261 f 261 381 195 f 195 381 196 f 196 381 376 f 196 376 198 f 198 376 375 f 375 1776 198 f 198 1776 197 f 1776 1767 197 f 197 1767 274 f 181 274 199 f 199 274 1767 f 568 566 564 f 564 566 563 f 568 569 566 f 566 569 567 f 501 1773 1771 f 1771 1773 578 f 547 579 1773 f 1773 579 578 f 3486 470 474 f 474 470 469 f 450 582 3487 f 3487 582 3488 f 582 583 3488 f 3488 583 3489 f 583 584 3489 f 3489 584 3490 f 3490 584 3491 f 3491 584 543 f 470 3486 466 f 466 3486 467 f 3489 3490 503 f 503 3490 504 f 503 3492 3489 f 3489 3492 3488 f 3492 503 3493 f 3493 503 502 f 3495 3492 3494 f 3494 3492 3493 f 3487 3488 3495 f 3495 3488 3492 f 468 1774 465 f 465 1774 570 f 3494 3493 1775 f 1775 3493 1771 f 3490 3491 504 f 504 3491 1772 f 3491 543 1772 f 1772 543 548 f 450 3487 474 f 474 3487 3486 f 3495 467 3487 f 3487 467 3486 f 3494 468 3495 f 3495 468 467 f 468 3494 1774 f 1774 3494 1775 f 501 1771 502 f 502 1771 3493 f 527 366 384 f 384 366 365 f 522 524 527 f 527 524 366 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4203 5341 f 5341 4203 6364 f 4219 4222 6429 f 6429 4222 4383 f 4319 4322 6506 f 6506 4322 4747 f 4340 4714 4341 f 4341 4714 4384 f 4375 4377 6388 f 6388 4377 6546 f 4378 4381 6311 f 6311 4381 4720 f 4449 4341 4387 f 4387 4341 4384 f 4473 4472 4477 f 4477 4472 4474 f 4492 4491 4487 f 4487 4491 4488 f 4523 4491 4522 f 4522 4491 4490 f 4492 4538 4493 f 4493 4538 4555 f 4538 4553 4555 f 4555 4553 4556 f 4620 4615 4621 f 4621 4615 4618 f 4652 4651 4368 f 4368 4651 4365 f 4523 4655 4524 f 4524 4655 4656 f 4553 4667 4556 f 4556 4667 4571 f 4651 1210 4365 f 4365 1210 1209 f 4698 4697 5223 f 5223 4697 6344 f 4340 4339 4322 f 4322 4339 4747 f 4726 4729 5287 f 5287 4729 4198 f 4381 4380 4737 f 4737 4380 6368 f 4836 4835 4451 f 4451 4835 4709 f 4910 6367 4911 f 4911 6367 6366 f 4912 4915 6450 f 6450 4915 6449 f 4927 4836 4450 f 4450 4836 4451 f 4930 4929 4439 f 4439 4929 4436 f 4936 4935 4459 f 4459 4935 4456 f 4473 4655 4470 f 4470 4655 4510 f 4942 4583 4943 f 4943 4583 4572 f 4948 4601 4947 f 4947 4601 4584 f 4949 4835 4625 f 4625 4835 4626 f 4966 4969 6289 f 6289 4969 4695 f 4971 4652 4926 f 4926 4652 4368 f 4203 4977 6364 f 6364 4977 6365 f 4990 4989 4443 f 4443 4989 4440 f 4990 4991 4478 f 4478 4991 4512 f 4992 4991 4528 f 4528 4991 4515 f 4992 4993 4527 f 4527 4993 4485 f 4994 4993 4502 f 4502 4993 4503 f 4994 4995 4482 f 4482 4995 4550 f 4543 4996 4544 f 4544 4996 4995 f 4999 4998 4567 f 4567 4998 4578 f 5000 4998 4575 f 4575 4998 4997 f 5002 5001 4592 f 4592 5001 4574 f 5003 5004 4614 f 4614 5004 4611 f 3483 4215 1755 f 1755 4215 4698 f 5274 5277 4195 f 4195 5277 4223 f 4726 5295 4727 f 4727 5295 6350 f 5309 5342 5310 f 5310 5342 6363 f 5315 5314 5316 f 5316 5314 5317 f 5339 6362 5340 f 5340 6362 5363 f 5310 6363 5339 f 5339 6363 6362 f 4204 5309 4201 f 4201 5309 5308 f 4915 5345 6449 f 6449 5345 6448 f 5345 5315 6448 f 6448 5315 5316 f 5348 5347 6351 f 6351 5347 6352 f 5365 4263 5340 f 5340 4263 4262 f 6295 6298 4733 f 4733 6298 4734 f 4969 6306 4695 f 4695 6306 4696 f 6306 4378 4696 f 4696 4378 6311 f 6369 6372 6544 f 6544 6372 6545 f 4375 6390 4373 f 4373 6390 4374 f 6421 6422 4911 f 4911 6422 4908 f 4910 4909 4737 f 4737 4909 4736 f 5277 4219 4223 f 4223 4219 6429 f 4697 4222 6344 f 6344 4222 4221 f 6436 6361 6437 f 6437 6361 6360 f 6438 6440 6437 f 6437 6440 6434 f 6438 6359 6441 f 6441 6359 6358 f 6442 6441 6357 f 6357 6441 6358 f 6442 6444 6443 f 6443 6444 6445 f 6446 6444 6355 f 6355 6444 6356 f 6446 4912 6447 f 6447 4912 6450 f 6436 6435 5365 f 5365 6435 4263 f 5314 5347 5317 f 5317 5347 5346 f 4319 6512 4320 f 4320 6512 6513 f 6520 6512 6521 f 6521 6512 6508 f 6528 6520 6527 f 6527 6520 6521 f 6532 6539 6533 f 6533 6539 6535 f 6533 6535 6537 f 6537 6535 6534 f 6538 6543 6537 f 6537 6543 6536 f 6295 4377 6296 f 6296 4377 4376 f 4989 4930 4440 f 4440 4930 4439 f 4927 4929 4422 f 4422 4929 4455 f 4449 4936 4388 f 4388 4936 4459 f 4472 4935 4474 f 4474 4935 4932 f 4942 4940 4947 f 4947 4940 4939 f 5001 5000 4574 f 4574 5000 4575 f 4669 4943 4666 f 4666 4943 4667 f 4996 4999 4547 f 4547 4999 4567 f 4948 4946 4620 f 4620 4946 4615 f 5003 5002 4591 f 4591 5002 4592 f 6557 4971 6554 f 6554 4971 4926 f 6532 6560 6539 f 6539 6560 6559 f 6390 6372 4374 f 4374 6372 6371 f 4966 6298 4967 f 4967 6298 6297 f 6538 6526 6369 f 6369 6526 6370 f 6585 6589 4621 f 4621 6589 4619 f 6597 6578 6598 f 6598 6578 6577 f 6606 6583 6607 f 6607 6583 6603 f 6611 6602 6598 f 6598 6602 6596 f 6585 6584 6606 f 6606 6584 6583 f 6597 6580 5004 f 5004 6580 4611 f 6607 6603 6730 f 6730 6603 6732 f 6776 6785 6777 f 6777 6785 6784 f 6785 6776 6792 f 6792 6776 6782 f 6815 6814 6812 f 6812 6814 6813 f 6817 6814 6795 f 6795 6814 6796 f 6825 6784 6827 f 6827 6784 6783 f 6830 6815 6832 f 6832 6815 6812 f 6817 6795 6835 f 6835 6795 6837 f 6792 6782 6730 f 6730 6782 6728 f 6858 6825 6860 f 6860 6825 6827 f 6863 6830 6857 f 6857 6830 6832 f 6864 6863 6856 f 6856 6863 6857 f 6866 6858 6867 f 6867 6858 6860 f 6866 6864 6855 f 6855 6864 6856 f 6835 6837 6611 f 6611 6837 6602 f 6560 6528 6559 f 6559 6528 6527 f 4949 6557 4710 f 4710 6557 6554 f 4430 4090 4175 f 4175 4090 4089 f 4777 4113 4155 f 4155 4113 4086 f 4175 4089 4176 f 4176 4089 4142 f 4199 4271 4200 f 4200 4271 4798 f 4199 4095 4272 f 4272 4095 5273 f 4406 4405 4404 f 4404 4405 4346 f 4410 4409 6877 f 6877 4409 6876 f 4409 4413 6876 f 6876 4413 6307 f 4415 4148 4416 f 4416 4148 4147 f 4690 4405 4344 f 4344 4405 4342 f 4693 4692 4352 f 4352 4692 4349 f 4701 4700 6337 f 6337 4700 6338 f 4778 4777 4081 f 4081 4777 4155 f 4821 4406 4809 f 4809 4406 4404 f 4176 4142 4181 f 4181 4142 4415 f 4760 4970 4399 f 4399 4970 4692 f 4200 5007 4096 f 4096 5007 4078 f 5266 4693 5269 f 5269 4693 4352 f 5270 5007 5361 f 5361 5007 4797 f 4778 5270 4779 f 4779 5270 5361 f 6285 4410 6875 f 6875 4410 6877 f 6308 4413 4687 f 4687 4413 4412 f 6480 6320 6481 f 6481 6320 6880 f 4701 2878 4702 f 4702 2878 3472 f 6339 6347 6340 f 6340 6347 6348 f 6341 6342 6346 f 6346 6342 6345 f 4416 6308 4414 f 4414 6308 4687 f 6385 4690 6381 f 6381 4690 4344 f 5282 6341 5280 f 5280 6341 6339 f 4700 6342 6338 f 6338 6342 5281 f 6340 6348 4272 f 4272 6348 4270 f 6480 6479 4828 f 4828 6479 4829 f 6490 6479 6489 f 6489 6479 6478 f 4090 4430 6490 f 6490 4430 4429 f 6563 6564 4970 f 4970 6564 4815 f 4694 6566 4366 f 4366 6566 6563 f 6572 6385 6568 f 6568 6385 6381 f 4821 6285 4822 f 4822 6285 6875 f 6572 5266 6569 f 6569 5266 5269 f 3482 3103 4369 f 4369 3103 4370 f 3121 4730 3485 f 3485 4730 4327 f 6277 6280 6273 f 6273 6280 6272 f 6267 6276 6278 f 6278 6276 6279 f 6278 6279 6277 f 6277 6279 6280 f 6279 6256 6280 f 6280 6256 6255 f 6256 3857 6255 f 6255 3857 3754 f 3857 3856 3754 f 3754 3856 3755 f 3856 3703 3755 f 3755 3703 3863 f 3703 3702 3863 f 3863 3702 3759 f 3702 3706 3759 f 3759 3706 3670 f 3706 3705 3670 f 3670 3705 3668 f 3705 3773 3668 f 3668 3773 3882 f 3882 3773 3883 f 3883 3773 3707 f 3707 3708 3883 f 3883 3708 3880 f 3708 3710 3880 f 3880 3710 3881 f 3709 5241 3710 f 3710 5241 3881 f 5241 3709 5234 f 5234 3709 3786 f 3693 3712 3786 f 3786 3712 5234 f 4051 4055 4052 f 4052 4055 4056 f 4056 4055 4057 f 4057 4055 4054 f 4066 5238 5236 f 5236 5238 3989 f 4066 4069 5238 f 5238 4069 4035 f 6881 3962 3960 f 3960 3962 3957 f 6883 4070 6882 f 6882 4070 3941 f 4070 6883 4071 f 4071 6883 6884 f 4071 6884 4072 f 4072 6884 6885 f 6885 6886 4072 f 4072 6886 4031 f 3955 6881 3956 f 3956 6881 3960 f 6884 3991 6885 f 6885 3991 3990 f 6883 6887 6884 f 6884 6887 3991 f 6887 6888 3991 f 3991 6888 3992 f 6888 6887 6889 f 6889 6887 6890 f 6887 6883 6890 f 6890 6883 6882 f 4058 5240 3953 f 3953 5240 3954 f 5236 6888 5239 f 5239 6888 6889 f 5237 6886 3990 f 3990 6886 6885 f 4034 4031 5237 f 5237 4031 6886 f 6881 6882 3962 f 3962 6882 3941 f 6881 3955 6882 f 6882 3955 6890 f 3955 3954 6890 f 6890 3954 6889 f 5239 6889 5240 f 5240 6889 3954 f 6888 5236 3992 f 3992 5236 3989 f 3869 3868 3888 f 3888 3868 4015 f 3868 4012 4015 f 4015 4012 4010 f 4012 4022 4010 f 4010 4022 4009 f 4022 4021 4009 f 4009 4021 4018 f 4020 3974 4001 f 4001 3974 3996 f 4021 4020 4029 f 4029 4020 4001 f 4085 4156 4086 f 4086 4156 4155 f 4113 4777 4114 f 4114 4777 4776 f 4130 4129 4094 f 4094 4129 4091 f 4139 4773 4140 f 4140 4773 4772 f 4149 4178 4150 f 4150 4178 5225 f 4160 4159 4087 f 4087 4159 4088 f 4149 4148 4179 f 4179 4148 4141 f 4085 4084 4183 f 4183 4084 6315 f 4179 4141 4159 f 4159 4141 4088 f 4192 4130 4162 f 4162 4130 4094 f 4197 5288 4198 f 4198 5288 5287 f 4229 4766 4230 f 4230 4766 4771 f 4245 4246 4242 f 4242 4246 4249 f 4183 6315 4418 f 4418 6315 6314 f 4679 4178 4680 f 4680 4178 4177 f 4724 4798 4725 f 4725 4798 4271 f 4728 5275 4729 f 4729 5275 5274 f 4230 4771 4249 f 4249 4771 4242 f 4724 4723 4799 f 4799 4723 5355 f 4823 4824 4891 f 4891 4824 6327 f 4823 4192 4161 f 4161 4192 4162 f 4893 4767 4229 f 4229 4767 4766 f 4894 4895 4898 f 4898 4895 4899 f 4106 4150 4103 f 4103 4150 5225 f 4680 4894 4677 f 4677 4894 4898 f 6420 4977 5301 f 5301 4977 4976 f 4679 4678 5226 f 5226 4678 4427 f 5228 5008 4893 f 4893 5008 4767 f 5285 6354 5286 f 5286 6354 5343 f 5289 5352 5290 f 5290 5352 5349 f 5290 5349 5295 f 5295 5349 5348 f 5302 5299 4202 f 4202 5299 4976 f 4725 4271 5311 f 5311 4271 4270 f 5325 4723 5326 f 5326 4723 4722 f 5335 5355 5325 f 5325 5355 4723 f 5326 4722 5286 f 5286 4722 5283 f 4799 5357 4797 f 4797 5357 5361 f 5359 5356 5335 f 5335 5356 5355 f 4775 4774 5356 f 5356 4774 5357 f 5359 5358 4092 f 4092 5358 4093 f 6316 4740 6317 f 6317 4740 4739 f 6318 4827 6316 f 6316 4827 4740 f 6319 4828 6318 f 6318 4828 4827 f 6320 6480 6319 f 6319 6480 4828 f 6484 6322 6485 f 6485 6322 6321 f 6487 6324 6331 f 6331 6324 6323 f 6331 6323 6330 f 6330 6323 6325 f 6325 6326 6330 f 6330 6326 6329 f 6326 6328 6329 f 6329 6328 5231 f 4824 4685 5228 f 5228 4685 5008 f 5226 6343 5225 f 5225 6343 4103 f 4728 4727 6349 f 6349 4727 5284 f 6350 6351 5285 f 5285 6351 6354 f 4247 4246 6418 f 6418 4246 6365 f 6400 6402 6401 f 6401 6402 6459 f 4197 4196 6409 f 6409 4196 6426 f 6412 4897 6328 f 6328 4897 5231 f 6413 4133 6412 f 6412 4133 4897 f 6414 4134 6413 f 6413 4134 4133 f 6415 4225 6414 f 6414 4225 4134 f 6416 4252 6415 f 6415 4252 4225 f 6417 4253 6416 f 6416 4253 4252 f 4253 6417 6366 f 6366 6417 6419 f 6366 6419 4911 f 4911 6419 6421 f 6422 6423 4908 f 4908 6423 5298 f 5298 6423 6396 f 6396 6423 6424 f 6409 6426 6425 f 6425 6426 6397 f 5300 5299 6399 f 6399 5299 6400 f 6465 6408 6401 f 6401 6408 6398 f 6424 6399 6425 f 6425 6399 6398 f 6349 5284 5311 f 5311 5284 5283 f 6465 6464 5289 f 5289 6464 5352 f 6488 6483 6492 f 6492 6483 6482 f 6486 6491 6482 f 6482 6491 6492 f 6491 4895 6492 f 6492 4895 4157 f 6488 4160 6489 f 6489 4160 4087 f 6501 6502 4418 f 4418 6502 4417 f 6504 6500 6317 f 6317 6500 6314 f 3497 3496 6501 f 6501 3496 6502 f 3499 3498 6504 f 6504 3498 6500 f 4729 5274 4198 f 4198 5274 4195 f 6418 6365 6420 f 6420 6365 4977 f 5295 5348 6350 f 6350 5348 6351 f 5301 6422 6420 f 6420 6422 6421 f 4113 4112 4086 f 4086 4112 4083 f 6320 4112 6880 f 6880 4112 4111 f 4091 4772 4092 f 4092 4772 4775 f 4129 4140 4091 f 4091 4140 4772 f 4137 4140 4128 f 4128 4140 4129 f 4128 6324 4137 f 4137 6324 6321 f 6324 6487 6321 f 6321 6487 6485 f 6487 6486 6485 f 6485 6486 6482 f 4774 4773 4779 f 4779 4773 4776 f 4773 4139 4776 f 4776 4139 4114 f 4139 4138 4114 f 4114 4138 4111 f 4138 6322 4111 f 4111 6322 6880 f 6322 6484 6880 f 6880 6484 6481 f 6484 6483 6481 f 6481 6483 6478 f 6879 6343 6309 f 6309 6343 4428 f 4106 4105 4900 f 4900 4105 6307 f 3500 3167 6495 f 6495 3167 6496 f 3500 6495 3070 f 3070 6495 5368 f 3502 5235 3501 f 3501 5235 4813 f 3024 3503 6473 f 6473 3503 6474 f 3503 3023 6474 f 6474 3023 6374 f 3504 1770 4348 f 4348 1770 4345 f 3502 3505 5235 f 5235 3505 4810 f 3498 3497 6500 f 6500 3497 6501 f 3496 3075 6502 f 6502 3075 6275 f 6299 4254 3174 f 3174 4254 3017 f 1769 3501 4814 f 4814 3501 4813 f 3506 3018 6476 f 6476 3018 4257 f 3507 3499 6499 f 6499 3499 6504 f 3030 4216 3483 f 3483 4216 4215 f 3077 3507 6333 f 6333 3507 6499 f 1784 3160 5249 f 5249 3160 6523 f 3015 3506 4240 f 4240 3506 6476 f 3508 3504 6383 f 6383 3504 4348 f 3509 3016 6477 f 6477 3016 4239 f 3028 3509 6467 f 6467 3509 6477 f 3022 1785 5246 f 5246 1785 5247 f 3161 3508 6570 f 6570 3508 6383 f 3150 3149 4694 f 4694 3149 6566 f 1327 4807 3510 f 3510 4807 4811 f 3510 4811 3505 f 3505 4811 4810 f 3146 3511 4816 f 4816 3511 4350 f 3511 1808 4350 f 4350 1808 4351 f 3485 4327 3159 f 3159 4327 4330 f 1326 3512 4806 f 4806 3512 6310 f 3512 1331 6310 f 6310 1331 4688 ================================================ FILE: mesh_pi_nets/mesh_sampling.py ================================================ ### Code obtained and modified from https://github.com/anuragranj/coma, Copyright (c) 2018 Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black and the Max Planck Gesellschaft import math import heapq import numpy as np import os import scipy.sparse as sp from scipy import spatial import pickle import trimesh try: import psbody.mesh found = True except ImportError: found = False if found: from psbody.mesh import Mesh def compute_downsampling(downsample_directory, downsample_method, shapedata, ds_factors): if not os.path.exists(os.path.join(downsample_directory, 'downsampling_matrices.pkl')): if shapedata.meshpackage == 'trimesh': raise NotImplementedError print("Generating Transform Matrices ..") if downsample_method == 'COMA_downsample': M, A, D, U, F = generate_transform_matrices(shapedata.reference_mesh, ds_factors) else: raise NotImplementedError(downsample_method) with open(os.path.join(downsample_directory, 'downsampling_matrices.pkl'), 'wb') as fp: M_verts_faces = [(M[i].v, M[i].f) for i in range(len(M))] pickle.dump({'M_verts_faces': M_verts_faces, 'A': A, 'D': D, 'U': U, 'F': F}, fp) else: print("Loading Transform Matrices ..") with open(os.path.join(downsample_directory, 'downsampling_matrices.pkl'), 'rb') as fp: downsampling_matrices = pickle.load(fp, encoding='latin1') M_verts_faces = downsampling_matrices['M_verts_faces'] if shapedata.meshpackage == 'mpi-mesh': M = [Mesh(v=M_verts_faces[i][0], f=M_verts_faces[i][1]) for i in range(len(M_verts_faces))] elif shapedata.meshpackage == 'trimesh': M = [trimesh.base.Trimesh(vertices=M_verts_faces[i][0], faces=M_verts_faces[i][1], process=False) for i in range(len(M_verts_faces))] A = downsampling_matrices['A'] D = downsampling_matrices['D'] U = downsampling_matrices['U'] F = downsampling_matrices['F'] return M, A, D, U, F def vertex_quadrics(mesh): """Computes a quadric for each vertex in the Mesh. Returns: v_quadrics: an (N x 4 x 4) array, where N is # vertices. """ # Allocate quadrics v_quadrics = np.zeros((len(mesh.v), 4, 4,)) # For each face... for f_idx in range(len(mesh.f)): # Compute normalized plane equation for that face vert_idxs = mesh.f[f_idx] verts = np.hstack((mesh.v[vert_idxs], np.array([1, 1, 1]).reshape(-1, 1))) u, s, v = np.linalg.svd(verts) eq = v[-1, :].reshape(-1, 1) eq = eq / (np.linalg.norm(eq[0:3])) # Add the outer product of the plane equation to the # quadrics of the vertices for this face for k in range(3): v_quadrics[mesh.f[f_idx, k], :, :] += np.outer(eq, eq) return v_quadrics def setup_deformation_transfer(source, target, use_normals=False): rows = np.zeros(3 * target.v.shape[0]) cols = np.zeros(3 * target.v.shape[0]) coeffs_v = np.zeros(3 * target.v.shape[0]) coeffs_n = np.zeros(3 * target.v.shape[0]) nearest_faces, nearest_parts, nearest_vertices = source.compute_aabb_tree().nearest(target.v, True) nearest_faces = nearest_faces.ravel().astype(np.int64) nearest_parts = nearest_parts.ravel().astype(np.int64) nearest_vertices = nearest_vertices.ravel() for i in range(target.v.shape[0]): # Closest triangle index f_id = nearest_faces[i] # Closest triangle vertex ids nearest_f = source.f[f_id] # Closest surface point nearest_v = nearest_vertices[3 * i:3 * i + 3] # Distance vector to the closest surface point dist_vec = target.v[i] - nearest_v rows[3 * i:3 * i + 3] = i * np.ones(3) cols[3 * i:3 * i + 3] = nearest_f n_id = nearest_parts[i] if n_id == 0: # Closest surface point in triangle A = np.vstack((source.v[nearest_f])).T coeffs_v[3 * i:3 * i + 3] = np.linalg.lstsq(A, nearest_v)[0] elif n_id > 0 and n_id <= 3: # Closest surface point on edge A = np.vstack((source.v[nearest_f[n_id - 1]], source.v[nearest_f[n_id % 3]])).T tmp_coeffs = np.linalg.lstsq(A, target.v[i])[0] coeffs_v[3 * i + n_id - 1] = tmp_coeffs[0] coeffs_v[3 * i + n_id % 3] = tmp_coeffs[1] else: # Closest surface point a vertex coeffs_v[3 * i + n_id - 4] = 1.0 # if use_normals: # A = np.vstack((vn[nearest_f])).T # coeffs_n[3 * i:3 * i + 3] = np.linalg.lstsq(A, dist_vec)[0] # coeffs = np.hstack((coeffs_v, coeffs_n)) # rows = np.hstack((rows, rows)) # cols = np.hstack((cols, source.v.shape[0] + cols)) matrix = sp.csc_matrix((coeffs_v, (rows, cols)), shape=(target.v.shape[0], source.v.shape[0])) return matrix def qslim_decimator_transformer(mesh, factor=None, n_verts_desired=None): from opendr.topology import get_vertices_per_edge """Return a simplified version of this mesh. A Qslim-style approach is used here. :param factor: fraction of the original vertices to retain :param n_verts_desired: number of the original vertices to retain :returns: new_faces: An Fx3 array of faces, mtx: Transformation matrix """ if factor is None and n_verts_desired is None: raise Exception('Need either factor or n_verts_desired.') if n_verts_desired is None: n_verts_desired = math.ceil(len(mesh.v) * factor) Qv = vertex_quadrics(mesh) # fill out a sparse matrix indicating vertex-vertex adjacency # from psbody.mesh.topology.connectivity import get_vertices_per_edge vert_adj = get_vertices_per_edge(mesh.v, mesh.f) # vert_adj = sp.lil_matrix((len(mesh.v), len(mesh.v))) # for f_idx in range(len(mesh.f)): # vert_adj[mesh.f[f_idx], mesh.f[f_idx]] = 1 vert_adj = sp.csc_matrix((vert_adj[:, 0] * 0 + 1, (vert_adj[:, 0], vert_adj[:, 1])), shape=(len(mesh.v), len(mesh.v))) vert_adj = vert_adj + vert_adj.T vert_adj = vert_adj.tocoo() def collapse_cost(Qv, r, c, v): Qsum = Qv[r, :, :] + Qv[c, :, :] p1 = np.vstack((v[r].reshape(-1, 1), np.array([1]).reshape(-1, 1))) p2 = np.vstack((v[c].reshape(-1, 1), np.array([1]).reshape(-1, 1))) destroy_c_cost = p1.T.dot(Qsum).dot(p1) destroy_r_cost = p2.T.dot(Qsum).dot(p2) result = { 'destroy_c_cost': destroy_c_cost, 'destroy_r_cost': destroy_r_cost, 'collapse_cost': min([destroy_c_cost, destroy_r_cost]), 'Qsum': Qsum} return result # construct a queue of edges with costs queue = [] for k in range(vert_adj.nnz): r = vert_adj.row[k] c = vert_adj.col[k] if r > c: continue cost = collapse_cost(Qv, r, c, mesh.v)['collapse_cost'] heapq.heappush(queue, (cost, (r, c))) # decimate collapse_list = [] nverts_total = len(mesh.v) faces = mesh.f.copy() while nverts_total > n_verts_desired: e = heapq.heappop(queue) r = e[1][0] c = e[1][1] if r == c: continue cost = collapse_cost(Qv, r, c, mesh.v) if cost['collapse_cost'] > e[0]: heapq.heappush(queue, (cost['collapse_cost'], e[1])) # print 'found outdated cost, %.2f < %.2f' % (e[0], cost['collapse_cost']) continue else: # update old vert idxs to new one, # in queue and in face list if cost['destroy_c_cost'] < cost['destroy_r_cost']: to_destroy = c to_keep = r else: to_destroy = r to_keep = c collapse_list.append([to_keep, to_destroy]) # in our face array, replace "to_destroy" vertidx with "to_keep" vertidx np.place(faces, faces == to_destroy, to_keep) # same for queue which1 = [idx for idx in range(len(queue)) if queue[idx][1][0] == to_destroy] which2 = [idx for idx in range(len(queue)) if queue[idx][1][1] == to_destroy] for k in which1: queue[k] = (queue[k][0], (to_keep, queue[k][1][1])) for k in which2: queue[k] = (queue[k][0], (queue[k][1][0], to_keep)) Qv[r, :, :] = cost['Qsum'] Qv[c, :, :] = cost['Qsum'] a = faces[:, 0] == faces[:, 1] b = faces[:, 1] == faces[:, 2] c = faces[:, 2] == faces[:, 0] # remove degenerate faces def logical_or3(x, y, z): return np.logical_or(x, np.logical_or(y, z)) faces_to_keep = np.logical_not(logical_or3(a, b, c)) faces = faces[faces_to_keep, :].copy() nverts_total = (len(np.unique(faces.flatten()))) new_faces, mtx = _get_sparse_transform(faces, len(mesh.v)) return new_faces, mtx def _get_sparse_transform(faces, num_original_verts): verts_left = np.unique(faces.flatten()) IS = np.arange(len(verts_left)) JS = verts_left data = np.ones(len(JS)) mp = np.arange(0, np.max(faces.flatten()) + 1) mp[JS] = IS new_faces = mp[faces.copy().flatten()].reshape((-1, 3)) ij = np.vstack((IS.flatten(), JS.flatten())) mtx = sp.csc_matrix((data, ij), shape=(len(verts_left), num_original_verts)) return (new_faces, mtx) def generate_transform_matrices(mesh, factors): from opendr.topology import get_vert_connectivity """Generates len(factors) meshes, each of them is scaled by factors[i] and computes the transformations between them. Returns: M: a set of meshes downsampled from mesh by a factor specified in factors. A: Adjacency matrix for each of the meshes D: Downsampling transforms between each of the meshes U: Upsampling transforms between each of the meshes """ factors = map(lambda x: 1.0 / x, factors) M, A, D, U = [], [], [], [] ## sergey code F = [] ## A.append(get_vert_connectivity(mesh.v, mesh.f)) M.append(mesh) i = 0 for factor in factors: ds_f, ds_D = qslim_decimator_transformer(M[-1], factor=factor) D.append(ds_D) ## F.append(ds_f) ## new_mesh_v = ds_D.dot(M[-1].v) new_mesh = Mesh(v=new_mesh_v, f=ds_f) M.append(new_mesh) A.append(get_vert_connectivity(new_mesh.v, new_mesh.f)) U.append(setup_deformation_transfer(M[-1], M[-2])) print('decimation %d by factor %.2f finished' % (i, factor)) i += 1 return M, A, D, U, F def generate_transform_matrices_given_downsamples(mesh, downsample_directory, num_downsamples=4): from opendr.topology import get_vert_connectivity M, A, D, U, F = [], [], [], [], [] A.append(get_vert_connectivity(mesh.v, mesh.f)) M.append(mesh) for i in range(1, num_downsamples + 1): # import trimesh # cur_M = trimesh.load(os.path.join(downsample_directory,'template_d{0}.obj'.format(i))) cur_M = Mesh(filename=os.path.join(downsample_directory, 'template_d{0}.obj'.format(i))) cur_D = np.zeros((cur_M.v.shape[0], M[-1].v.shape[0])) kd = spatial.KDTree(np.array(M[-1].v)) for vi in range(cur_M.v.shape[0]): _, u = kd.query(cur_M.v[vi]) cur_D[vi, u] = 1.0 M.append(cur_M) D.append(sp.csr_matrix(cur_D)) F.append(cur_M.f) A.append(get_vert_connectivity(cur_M.v, cur_M.f)) U.append(setup_deformation_transfer(M[-1], M[-2])) return M, A, D, U, F ================================================ FILE: mesh_pi_nets/models.py ================================================ import torch import torch.nn as nn from torch.nn.parameter import Parameter import math class SpiralPoly(nn.Module): def __init__(self, in_c, spiral_size, out_c, activation='elu', bias=True, device=None, injection = False, residual = False, num_points = None, order = 1, normalize = 'final', model = 'full'): super(SpiralPoly, self).__init__() self.in_c = in_c self.out_c = out_c self.device = device self.injection = injection self.residual = residual self.order = order self.normalize = normalize self.model = model if bias: # constant parameter of the polynomial self.bias = Parameter(torch.Tensor(out_c)) bound = 1 / math.sqrt(in_c * spiral_size) torch.nn.init.uniform_(self.bias, -bound, bound) else: self.register_parameter('bias', None) if self.injection: if self.model == 'full': # conva and convs correspond to A and S matrices of the NCP decomposition (the constant matrices B are abosorbed by A) self.conva = nn.ModuleList([nn.Linear(in_c*spiral_size,out_c,bias=False) for k in range(0,self.order-1)]) self.convs = nn.ModuleList([nn.Linear(in_c*spiral_size,out_c,bias=False)] + [nn.Linear(out_c*spiral_size,out_c,bias=False) for k in range(0,self.order-2)]) elif self.model == 'simple': # this is the compact version of the model, where we use a single parameter matrix and perform element-wise multiplications self.convs = nn.Linear(in_c*spiral_size,out_c,bias=False) if self.normalize == 'final': # normalise only the higher order term of the entire polynomial self.normalizer = nn.BatchNorm1d((num_points+1) * out_c, affine= True) else: # normalise once for every recursion step self.normalizer = nn.ModuleList([nn.BatchNorm1d((num_points+1) * out_c, affine= True) for k in range(0,self.order-1)]) else: if self.model == 'mlp': self.fc1 = nn.Linear(in_c*spiral_size, out_c, bias = True) self.fc2 = nn. Linear(out_c, out_c, bias = True) elif self.model == 'linear': self.conv = nn.Linear(in_c * spiral_size, out_c, bias=False) if activation == 'relu': self.activation = nn.ReLU() elif activation == 'elu': self.activation = nn.ELU() elif activation == 'leaky_relu': self.activation = nn.LeakyReLU(0.02) elif activation == 'sigmoid': self.activation = nn.Sigmoid() elif activation == 'tanh': self.activation = nn.Tanh() elif activation == 'identity': self.activation = lambda x: x else: raise NotImplementedError() def forward(self, x, spiral_adj): bsize, num_pts, feats = x.size() _, _, spiral_size = spiral_adj.size() spirals_index = spiral_adj.view(bsize * num_pts * spiral_size) # [1d array of batch,vertx,vertx-adj] batch_index = torch.arange(bsize, device=self.device).view(-1, 1).repeat([1, num_pts * spiral_size]).view( -1).long() # [0*numpt,1*numpt,etc.] spirals = x[batch_index, spirals_index, :].view(bsize * num_pts, spiral_size * feats) # [bsize*numpt, spiral*feats] if self.injection: if self.model == 'full': spirals_x1 = spirals # input z spirals_xk_1 = spirals # output of previous order term xk_1 for k in range(0, self.order - 1): a_xk = self.conva[k](spirals_x1) s_xk = self.convs[k](spirals_xk_1) if self.normalize == 'all': # normalise the output of the recursion if k >= 1 and self.residual: out_k = a_xk * s_xk + a_xk + out_k else: out_k = a_xk * s_xk + a_xk out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1]) out_k = self.normalizer[k](out_k).reshape(bsize * num_pts, -1) spirals_xk_1 = out_k.view(bsize, num_pts, self.out_c) \ [batch_index, spirals_index, :].view(bsize * num_pts, spiral_size * self.out_c) elif self.normalize == '2nd': # normalise only the 2nd order term in the recursive formulation if k >= 1 and self.residual: out_k_1 = out_k out_k = a_xk * s_xk out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1]) out_k = self.normalizer[k](out_k).reshape(bsize * num_pts, -1) + a_xk + out_k_1 else: out_k = a_xk * s_xk out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1]) out_k = self.normalizer[k](out_k).reshape(bsize * num_pts, -1) + a_xk spirals_xk_1 = out_k.view(bsize, num_pts, self.out_c) \ [batch_index, spirals_index, :].view(bsize * num_pts, spiral_size * self.out_c) elif self.normalize == 'final': # normalise only the higher order term of the entire polynomial if k >= 1 and self.residual: if k == self.order - 2: out_k_1 = out_k out_k = a_xk * s_xk out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1]) out_k = self.normalizer(out_k).reshape(bsize * num_pts, -1) + a_xk + out_k_1 else: out_k = a_xk * s_xk + a_xk + out_k else: if k == self.order - 2: out_k = a_xk * s_xk out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1]) out_k = self.normalizer(out_k).reshape(bsize * num_pts, -1) + a_xk else: out_k = a_xk * s_xk + a_xk spirals_xk_1 = out_k.view(bsize, num_pts, self.out_c) \ [batch_index, spirals_index, :].view(bsize * num_pts, spiral_size * self.out_c) out_feat = out_k + self.bias elif self.model == 'simple': out_1 = self.convs(spirals) out_feat = out_1 for k in range(0, self.order - 1): out_k = out_1 ** (k + 2) if self.normalize == '2nd': out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1]) out_k = self.normalizer[k](out_k).reshape(bsize * num_pts, -1) elif self.normalize == 'final': if k == self.order - 2: out_k = out_k.reshape(bsize, num_pts * out_k.shape[-1]) out_k = self.normalizer(out_k).reshape(bsize * num_pts, -1) out_feat = out_k + out_feat out_feat = out_feat + self.bias else: if self.model == 'mlp': out_feat = self.fc2(nn.ReLU()(self.fc1(spirals))) elif self.model == 'linear': out_feat = self.conv(spirals) + self.bias out_feat = self.activation(out_feat) out_feat = out_feat.view(bsize, num_pts, self.out_c) zero_padding = torch.ones((1, x.size(1), 1), device=self.device) zero_padding[0, -1, 0] = 0.0 out_feat = out_feat * zero_padding return out_feat class SpiralPolyAE(nn.Module): def __init__(self, filters_enc, filters_dec, latent_size, mesh_sizes, spiral_sizes, spirals, D, U, device, activation='elu', injection=False, residual=False, order=1, normalize=None, model=None): super(SpiralPolyAE, self).__init__() self.filters_enc = filters_enc self.filters_dec = filters_dec self.spirals = spirals self.mesh_sizes = mesh_sizes self.spiral_sizes = spiral_sizes self.D = D self.U = U self.device = device self.conv = [] input_size = filters_enc[0][0] for i in range(len(filters_enc)): for j in range(1, len(filters_enc[i])): self.conv.append(SpiralPoly(input_size, spiral_sizes[i], filters_enc[i][j], activation=activation, device=device, injection=injection, residual=residual, num_points=mesh_sizes[i], order=order, normalize=normalize, model=model).to(device)) input_size = filters_enc[i][j] if i < len(filters_enc) - 1: self.conv.append(SpiralPoly(input_size, spiral_sizes[i], filters_enc[i + 1][0], activation=activation, device=device, injection=injection, residual=residual, num_points=mesh_sizes[i], order=order, normalize=normalize, model=model).to(device)) input_size = filters_enc[i + 1][0] self.conv = nn.ModuleList(self.conv) self.fc_latent_enc = nn.Linear((mesh_sizes[-1] + 1) * input_size, latent_size) self.fc_latent_dec = nn.Linear(latent_size, (mesh_sizes[-1] + 1) * filters_dec[0][0]) self.dconv = [] input_size = filters_dec[0][0] for i in range(len(filters_dec)): for j in range(1, len(filters_dec[i])): out_activation = 'identity' if i == len(filters_dec) - 1 and j == len( filters_dec[-1]) - 1 else activation self.dconv.append(SpiralPoly(input_size, spiral_sizes[-2 - i], filters_dec[i][j], activation=out_activation, device=device, injection=injection, residual=residual, num_points=mesh_sizes[-2 - i], order=order, normalize=normalize, model=model).to(device)) input_size = filters_dec[i][j] if i < len(filters_dec) - 1: out_activation = 'identity' if i == len(filters_dec) - 2 and len( filters_dec[-1]) == 1 else activation self.dconv.append(SpiralPoly(input_size, spiral_sizes[-2 - i], filters_dec[i + 1][0], activation=out_activation, device=device, injection=injection, residual=residual, num_points=mesh_sizes[-2 - i], order=order, normalize=normalize, model=model).to(device)) input_size = filters_dec[i + 1][0] self.dconv = nn.ModuleList(self.dconv) def encode(self, x): bsize = x.size(0) j = 0 for i in range(len(self.filters_enc) - 1): for k in range(len(self.filters_enc[i])): x = self.conv[j](x, self.spirals[i].repeat(bsize, 1, 1)) j += 1 x = torch.matmul(self.D[i], x) for k in range(1, len(self.filters_enc[-1])): x = self.conv[j](x, self.spirals[len(self.filters_enc) - 1].repeat(bsize, 1, 1)) j += 1 x = x.view(bsize, -1) out = self.fc_latent_enc(x) return out def decode(self, z): bsize = z.shape[0] x = self.fc_latent_dec(z) x = x.view(bsize, self.mesh_sizes[-1] + 1, -1) j = 0 for i in range(len(self.filters_dec) - 1): x = torch.matmul(self.U[-1 - i], x) for k in range(len(self.filters_dec[i])): x = self.dconv[j](x, self.spirals[-2 - i].repeat(bsize, 1, 1)) j += 1 for k in range(1, len(self.filters_dec[-1])): x = self.dconv[j](x, self.spirals[0].repeat(bsize, 1, 1)) j += 1 return x def forward(self, x): z = self.encode(x) x = self.decode(z) return x ================================================ FILE: mesh_pi_nets/shape_data.py ================================================ ### Code obtained and modified from https://github.com/anuragranj/coma, Copyright (c) 2018 Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, Michael J. Black and the Max Planck Gesellschaft import os import numpy as np try: import psbody.mesh found = True except ImportError: found = False if found: from psbody.mesh import Mesh import trimesh class ShapeData(object): def __init__(self, nVal, train_file, test_file, reference_mesh_file, normalization=True, meshpackage='mpi-mesh', load_flag=True, mean_subtraction_only=False): self.nVal = nVal self.train_file = train_file self.test_file = test_file self.vertices_train = None self.vertices_val = None self.vertices_test = None self.n_vertex = None self.n_features = None self.normalization = normalization self.meshpackage = meshpackage self.load_flag = load_flag self.mean_subtraction_only = mean_subtraction_only if self.load_flag: self.load() if self.meshpackage == 'trimesh': self.reference_mesh = trimesh.load(reference_mesh_file, process=False) elif self.meshpackage == 'mpi-mesh': self.reference_mesh = Mesh(filename=reference_mesh_file) if self.load_flag: self.mean = np.mean(self.vertices_train, axis=0) self.std = np.std(self.vertices_train, axis=0) else: self.mean = None self.std = None self.normalize() def load(self): vertices_train = np.load(self.train_file) self.vertices_train = vertices_train[:-self.nVal] self.vertices_val = vertices_train[-self.nVal:] self.n_vertex = self.vertices_train.shape[1] self.n_features = self.vertices_train.shape[2] if os.path.exists(self.test_file): self.vertices_test = np.load(self.test_file) self.vertices_test = self.vertices_test def normalize(self): if self.load_flag: if self.normalization: if self.mean_subtraction_only: self.std = np.ones_like((self.std)) self.vertices_train = self.vertices_train - self.mean self.vertices_train = self.vertices_train / self.std self.vertices_train[np.where(np.isnan(self.vertices_train))] = 0.0 self.vertices_val = self.vertices_val - self.mean self.vertices_val = self.vertices_val / self.std self.vertices_val[np.where(np.isnan(self.vertices_val))] = 0.0 if self.vertices_test is not None: self.vertices_test = self.vertices_test - self.mean self.vertices_test = self.vertices_test / self.std self.vertices_test[np.where(np.isnan(self.vertices_test))] = 0.0 self.N = self.vertices_train.shape[0] print('Vertices normalized') else: print('Vertices not normalized') def save_meshes(self, filename, meshes, mesh_indices): for i in range(meshes.shape[0]): if self.normalization: vertices = meshes[i].reshape((self.n_vertex, self.n_features)) * self.std + self.mean else: vertices = meshes[i].reshape((self.n_vertex, self.n_features)) if self.meshpackage == 'trimesh': new_mesh = self.reference_mesh if self.n_features == 3: new_mesh.vertices = vertices elif self.n_features == 6: new_mesh.vertices = vertices[:, 0:3] colors = vertices[:, 3:] colors[np.where(colors < 0)] = 0 colors[np.where(colors > 1)] = 1 vertices[:, 3:] = colors new_mesh.visual = trimesh.visual.create_visual(vertex_colors=vertices[:, 3:]) else: raise NotImplementedError new_mesh.export(filename + '.' + str(mesh_indices[i]).zfill(6) + '.ply', 'ply') elif self.meshpackage == 'mpi-mesh': if self.n_features == 3: mesh = Mesh(v=vertices, f=self.reference_mesh.f) mesh.write_ply(filename + '.' + str(mesh_indices[i]).zfill(6) + '.ply') else: raise NotImplementedError return 0 ================================================ FILE: mesh_pi_nets/spiral_pi_nets.py ================================================ import argparse import sys sys.path.append('../') import numpy as np import json import os import copy from shape_data import ShapeData from mesh_sampling import compute_downsampling try: import psbody.mesh found = True except ImportError: found = False if found: pass from autoencoder_dataset import autoencoder_dataset from torch.utils.data import DataLoader from spiral_utils import get_adj_trigs, generate_spirals from models import SpiralPolyAE from test_funcs import test_autoencoder_dataloader from train_funcs import train_autoencoder_dataloader import torch from tensorboardX import SummaryWriter from sklearn.metrics.pairwise import euclidean_distances meshpackage = 'trimesh' def str2bool(v): if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') def str2list2int(v): return [int(c) for c in v.split(',')] def str2ListOfLists2int(v): return [[[] if c == ' ' else int(c) for c in vi.split(',')] for vi in v.split(',,')] def str2list2float(v): return [float(c) for c in v.split(',')] def str2list2bool(v): return [str2bool(c) for c in v.split(',')] def str2ListOfLists2bool(v): return [[[] if c == ' ' else str2bool(c) for c in vi.split(',')] for vi in v.split(',,')] def loss_l1(outputs, targets): L = torch.abs(outputs - targets).mean() return L def main(args): ## Set seeds and invoke device torch.cuda.get_device_name(args['device_idx']) torch.manual_seed(args['seed']) torch.backends.cudnn.deterministic = True torch.cuda.manual_seed(args['seed']) torch.backends.cudnn.benchmark = False np.random.seed(args['seed']) torch.set_num_threads(args['num_threads']) if args['GPU']: device = torch.device("cuda:" + str(args['device_idx']) if torch.cuda.is_available() else "cpu") else: device = torch.device("cpu") print(device) ## Set paths path = os.path.join(args['root_dir'], args['dataset']) args['data'] = os.path.join(path, 'preprocessed', args['name']) args['reference_mesh_file'] = os.path.join(path, 'templates/template.obj') args['downsample_directory'] = os.path.join(path, 'templates', args['downsample_method']) args['results_folder'] = os.path.join(path, 'results', 'polynomial_autoencoder', args['downsample_method'], args['results_folder'], 'latent_' + str(args['nz'])) ## Create folders summary_path = os.path.join(args['results_folder'], 'summaries', args['name']) checkpoint_path = os.path.join(args['results_folder'], 'checkpoints', args['name']) samples_path = os.path.join(args['results_folder'], 'samples', args['name']) prediction_path = os.path.join(args['results_folder'], 'predictions', args['name']) if not os.path.exists(args['downsample_directory']): os.makedirs(args['downsample_directory']) if args['mode'] == 'train': if not os.path.exists(os.path.join(args['results_folder'])): os.makedirs(os.path.join(args['results_folder'])) if not os.path.exists(summary_path): os.makedirs(summary_path) if not os.path.exists(checkpoint_path): os.makedirs(checkpoint_path) if not os.path.exists(samples_path): os.makedirs(samples_path) if not os.path.exists(prediction_path): os.makedirs(prediction_path) ## Set reference points (refer to Neural3DMM paper for the discussion on reference points - tl;dr you can set them arbitrarily). Here chosen on the top of the head. if args['dataset'] == 'COMA': reference_points = [[3567, 4051, 4597]] elif args['dataset'] == 'mein3d': reference_points = [[23822]] elif args['dataset'] == 'DFAUST': reference_points = [[414]] ## Initialise dataset print("Loading data .. ") load_flag = True if not os.path.exists(args['data'] + '/mean.npy') or not os.path.exists( args['data'] + '/std.npy') else False shapedata = ShapeData(nVal=args['nVal'], train_file=args['data'] + '/train.npy', test_file=args['data'] + '/test.npy', reference_mesh_file=args['reference_mesh_file'], normalization=args['data_normalization'], meshpackage=meshpackage, load_flag=load_flag) if load_flag: np.save(args['data'] + '/mean.npy', shapedata.mean) np.save(args['data'] + '/std.npy', shapedata.std) else: shapedata.mean = np.load(args['data'] + '/mean.npy') shapedata.std = np.load(args['data'] + '/std.npy') shapedata.n_vertex = shapedata.mean.shape[0] shapedata.n_features = shapedata.mean.shape[1] ## Load downsampling/upsampling matrices or compute them using the Mesh package (please refer to the Neural3DMM repository for more information) M, A, D, U, F = compute_downsampling(args['downsample_directory'], downsample_method=args['downsample_method'], shapedata=shapedata, ds_factors=args['ds_factors']) ## Add dummy node to downsampling/upsampling matrices and move the to GPU tD = [] tU = [] for i in range(len(D)): d = np.zeros((1, D[i].shape[0] + 1, D[i].shape[1] + 1)) u = np.zeros((1, U[i].shape[0] + 1, U[i].shape[1] + 1)) d[0, :-1, :-1] = D[i].todense() u[0, :-1, :-1] = U[i].todense() d[0, -1, -1] = 1 u[0, -1, -1] = 1 d = torch.from_numpy(d).float().to(device) u = torch.from_numpy(u).float().to(device) tD.append(d) tU.append(u) ## Compute reference points for downsampled meshes print("Calculating reference points for downsampled versions..") for i in range(len(args['ds_factors'])): if shapedata.meshpackage == 'mpi-mesh': dist = euclidean_distances(M[i + 1].v, M[0].v[reference_points[0]]) elif shapedata.meshpackage == 'trimesh': dist = euclidean_distances(M[i + 1].vertices, M[0].vertices[reference_points[0]]) reference_points.append(np.argmin(dist, axis=0).tolist()) ## Compute local node orderings mesh_sizes = [x.v.shape[0] for x in M] if shapedata.meshpackage == 'mpi-mesh' else [x.vertices.shape[0] for x in M] Adj, Trigs = get_adj_trigs(A, F, shapedata.reference_mesh, meshpackage=shapedata.meshpackage) spirals_np, spiral_sizes, spirals = generate_spirals(args['step_sizes'], M, Adj, Trigs, reference_points=reference_points, dilation=args['dilation'], random=False, meshpackage=shapedata.meshpackage, counter_clockwise=True) tspirals = [torch.from_numpy(s).long().to(device) for s in spirals_np] ## Initialise dataloaders if args['mode'] == 'train': dataset_train = autoencoder_dataset(root_dir=args['data'], points_dataset='train', shapedata=shapedata, normalization=args['data_normalization']) dataloader_train = DataLoader(dataset_train, batch_size=args['batch_size'], shuffle=args['shuffle'], num_workers=args['num_workers']) dataset_val = autoencoder_dataset(root_dir=args['data'], points_dataset='val', shapedata=shapedata, normalization=args['data_normalization']) dataloader_val = DataLoader(dataset_val, batch_size=args['batch_size'], shuffle=False, num_workers=args['num_workers']) dataset_test = autoencoder_dataset(root_dir=args['data'], points_dataset=args['test_set'], shapedata=shapedata, normalization=args['data_normalization']) dataloader_test = DataLoader(dataset_test, batch_size=args['batch_size'], shuffle=False, num_workers=args['num_workers']) ## Initialise the model model = SpiralPolyAE(filters_enc=args['filter_sizes_enc'], filters_dec=args['filter_sizes_dec'], latent_size=args['nz'], mesh_sizes=mesh_sizes, spiral_sizes=spiral_sizes, spirals=tspirals, D=tD, U=tU, device=device, injection=args['injection'], residual=args['residual'], order=args['order'], normalize=args['normalize'], model=args['model'], activation=args['activation']).to(device) ## Initialise optimiser, scheduler and set loss function if args['mode'] == 'train': optim = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['regularization']) if args['scheduler']: scheduler = torch.optim.lr_scheduler.StepLR(optim, args['decay_steps'], gamma=args['decay_rate']) else: scheduler = None if args['loss'] == 'l1': loss_fn = loss_l1 ## parameters params = sum(p.numel() for p in model.parameters() if p.requires_grad) print("Total number of parameters is: {}".format(params)) print(model) ############## ---------------- TRAINING LOOP ---------------- ############## if args['mode'] == 'train': ## configure logging and save hypeparams writer = SummaryWriter(summary_path) with open(os.path.join(args['results_folder'], 'checkpoints', args['name'] + '_params.json'), 'w') as fp: saveparams = copy.deepcopy(args) json.dump(saveparams, fp) if args['resume']: print('loading checkpoint from file %s' % (os.path.join(checkpoint_path, args['checkpoint_file']))) checkpoint_dict = torch.load(os.path.join(checkpoint_path, args['checkpoint_file'] + '.pth.tar'), map_location=device) start_epoch = checkpoint_dict['epoch'] + 1 model.load_state_dict(checkpoint_dict['autoencoder_state_dict']) optim.load_state_dict(checkpoint_dict['optimizer_state_dict']) scheduler.load_state_dict(checkpoint_dict['scheduler_state_dict']) print('Resuming from epoch %s' % (str(start_epoch))) else: start_epoch = 0 train_autoencoder_dataloader(dataloader_train, dataloader_val, device, model, optim, loss_fn, bsize=args['batch_size'], start_epoch=start_epoch, n_epochs=args['num_epochs'], eval_freq=args['eval_frequency'], scheduler=scheduler, writer=writer, save_recons=args['save_recons'], shapedata=shapedata, metadata_dir=checkpoint_path, samples_dir=samples_path, checkpoint_path=args['checkpoint_file']) elif args['mode'] == 'test': print('loading checkpoint from file %s' % (os.path.join(checkpoint_path, args['checkpoint_file'] + '.pth.tar'))) checkpoint_dict = torch.load(os.path.join(checkpoint_path, args['checkpoint_file'] + '.pth.tar'), map_location=device) model.load_state_dict(checkpoint_dict['autoencoder_state_dict']) predictions, norm_l1_loss, l2_loss = test_autoencoder_dataloader(device, model, dataloader_test, shapedata, mm_constant=args['mm_constant']) np.save(os.path.join(prediction_path, 'predictions'), predictions) print('autoencoder: normalized loss', norm_l1_loss) print('autoencoder: euclidean distance in mm=', l2_loss) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--seed', type=int, default=2) parser.add_argument('--num_workers', type=int, default=4) parser.add_argument('--num_threads', type=int, default=1) # paths, data etc. parser.add_argument('--root_dir', type=str, default='./datasets') parser.add_argument('--name', type=str, default='') parser.add_argument('--dataset', type=str, default='DFAUST') parser.add_argument('--downsample_method', type=str, default='COMA_downsample') parser.add_argument('--results_folder', type=str, default='temp') parser.add_argument('--checkpoint_file', type=str, default='checkpoint') parser.add_argument('--data_normalization', type=str2bool, default=True) \ # multiply with this constant to get your mesh measurements in milimiters (dataset dependent) parser.add_argument('--mm_constant', type=int, default=1000) # optimisation and training parameters parser.add_argument('--batch_size', type=int, default=16) parser.add_argument('--num_epochs', type=int, default=300) parser.add_argument('--eval_frequency', type=int, default=200) parser.add_argument('--lr', type=float, default=1e-3) parser.add_argument('--regularization', type=float, default=5e-5) parser.add_argument('--scheduler', type=str2bool, default=True) parser.add_argument('--decay_rate', type=float, default=0.99) parser.add_argument('--decay_steps', type=int, default=1) parser.add_argument('--loss', type=str, default='l1') parser.add_argument('--shuffle', type=str2bool, default=True) parser.add_argument('--nVal', type=int, default=100) # model hyperparameters parser.add_argument('--filter_sizes_enc', type=str2ListOfLists2int, default=[[3], [16], [16], [16], [32]]) parser.add_argument('--filter_sizes_dec', type=str2ListOfLists2int, default=[[32], [32], [16], [16], [3]]) parser.add_argument('--nz', type=int, default=16) parser.add_argument('--ds_factors', type=str2list2int, default=[4, 4, 4, 4]) parser.add_argument('--step_sizes', type=str2list2int, default=[1, 1, 1, 1, 1]) parser.add_argument('--dilation', type=str2list2int, default=None) parser.add_argument('--activation', type=str, default='elu') # hyperparameters related to the polynomial parser.add_argument('--injection', type=str2bool, default=True) parser.add_argument('--order', type=int, default=2) parser.add_argument('--model', type=str, default='full') parser.add_argument('--normalize', type=str, default='final') parser.add_argument('--residual', type=str2bool, default=False) # misc parser.add_argument('--resume', type=str2bool, default=False) parser.add_argument('--save_recons', type=str2bool, default=True) # save reconstructions every epoch parser.add_argument('--mode', type=str, default='train') parser.add_argument('--test_set', type=str, default='test') # specify the set on which to test # hardware parser.add_argument('--GPU', type=str2bool, default=True) parser.add_argument('--device_idx', type=int, default=0) args = parser.parse_args() print(args) main(vars(args)) ================================================ FILE: mesh_pi_nets/spiral_utils.py ================================================ import numpy as np from random import choice ## Note: Non-smooth surfaces or bad triangulations may lead to non-spiral orderings of the vertices. ## Common issue in badly triangulated surfaces is that there exist some edges that belong to more than two triangles. In this ## case the mathematical definition of the spiral is insufficient. In this case, in this version of the code, we randomly ## choose two triangles in order to continue the inductive assignment of the order to the rest of the vertices. def get_adj_trigs(A, F, reference_mesh, meshpackage = 'mpi-mesh'): Adj = [] for x in A: adj_x = [] dx = x.todense() for i in range(x.shape[0]): adj_x.append(dx[i].nonzero()[1]) Adj.append(adj_x) if meshpackage =='trimesh': mesh_faces = reference_mesh.faces elif meshpackage =='mpi-mesh': mesh_faces = reference_mesh.f # Create Triangles List trigs_full = [[] for i in range(len(Adj[0]))] for t in mesh_faces: u, v, w = t trigs_full[u].append((u,v,w)) trigs_full[v].append((u,v,w)) trigs_full[w].append((u,v,w)) Trigs = [trigs_full] for i,T in enumerate(F): trigs_down = [[] for i in range(len(Adj[i+1]))] for u,v,w in T: trigs_down[u].append((u,v,w)) trigs_down[v].append((u,v,w)) trigs_down[w].append((u,v,w)) Trigs.append(trigs_down) return Adj, Trigs def generate_spirals(step_sizes, M, Adj, Trigs, reference_points, dilation=None, random=False, meshpackage = 'mpi-mesh', counter_clockwise = True, nb_stds = 2): Adj_spirals = [] for i in range(len(Adj)): if meshpackage =='trimesh': mesh_vertices = M[i].vertices elif meshpackage =='mpi-mesh': mesh_vertices = M[i].v sp = get_spirals(mesh_vertices, Adj[i],Trigs[i],reference_points[i], n_steps=step_sizes[i],\ padding='zero', counter_clockwise = counter_clockwise, random = random) Adj_spirals.append(sp) print('spiral generation for hierarchy %d (%d vertices) finished' %(i,len(Adj_spirals[-1]))) ## Dilated convolution if dilation: for i in range(len(dilation)): dil = dilation[i] dil_spirals = [] for j in range(len(Adj_spirals[i])): s = Adj_spirals[i][j][:1] + Adj_spirals[i][j][1::dil] dil_spirals.append(s) Adj_spirals[i] = dil_spirals # Calculate the lengths of spirals # Use mean + 2 * std_dev, to capture 97% of data L = [] for i in range(len(Adj_spirals)): L.append([]) for j in range(len(Adj_spirals[i])): L[i].append(len(Adj_spirals[i][j])) L[i] = np.array(L[i]) spiral_sizes = [] for i in range(len(L)): sz = L[i].mean() + nb_stds*L[i].std() spiral_sizes.append(int(sz)) print('spiral sizes for hierarchy %d: %d' %(i,spiral_sizes[-1])) # 1) fill with -1 (index to the dummy vertex, i.e the zero padding) the spirals with length smaller than the chosen one # 2) Truncate larger spirals spirals_np = [] for i in range(len(spiral_sizes)): #len(Adj_spirals)): S = np.zeros((1,len(Adj_spirals[i])+1,spiral_sizes[i])) - 1 for j in range(len(Adj_spirals[i])): S[0,j,:len(Adj_spirals[i][j])] = Adj_spirals[i][j][:spiral_sizes[i]] #spirals_np.append(np.repeat(S,args['batch_size'],axis=0)) spirals_np.append(S) return spirals_np, spiral_sizes, Adj_spirals def distance(v,w): return np.sqrt(np.sum(np.square(v-w))) def single_source_shortest_path(V,E,source,dist=None,prev=None): import heapq if dist == None: dist = [None for i in range(len(V))] prev = [None for i in range(len(V))] q = [] seen = set() heapq.heappush(q,(0,source,None)) while len(q) > 0 and len(seen) < len(V): d_,v,p = heapq.heappop(q) if v in seen: continue seen.add(v) prev[v] = p dist[v] = d_ for w in E[v]: if w in seen: continue dw = d_ + distance(V[v],V[w]) heapq.heappush(q,(dw,w,v)) return prev, dist def get_spirals(mesh, adj, trig, reference_points, n_steps=1, padding='zero', counter_clockwise = True, random = False): spirals = [] if not random: heat_path = None dist = None for reference_point in reference_points: heat_path,dist = single_source_shortest_path(mesh,adj,reference_point, dist, heat_path) heat_source = reference_points for i in range(mesh.shape[0]): seen = set(); seen.add(i) trig_central = list(trig[i]); A = adj[i]; spiral = [i] # 1) Frist degree of freedom - choose starting pooint: if not random: if i in heat_source: # choose closest neighbor shortest_dist = np.inf init_vert = None for neighbor in A: d = np.sum(np.square(mesh[i] - mesh[neighbor])) if d < shortest_dist: shortest_dist = d init_vert = neighbor else: # on the shortest path to the reference point init_vert = heat_path[i] else: # choose starting point: # random for first ring init_vert = choice(A) # first ring if init_vert is not None: ring = [init_vert]; seen.add(init_vert) else: ring = [] while len(trig_central) > 0 and init_vert is not None: cur_v = ring[-1] cur_t = [t for t in trig_central if t in trig[cur_v]] if len(ring) == 1: orientation_0 = (cur_t[0][0]==i and cur_t[0][1]==cur_v) \ or (cur_t[0][1]==i and cur_t[0][2]==cur_v) \ or (cur_t[0][2]==i and cur_t[0][0]==cur_v) if not counter_clockwise: orientation_0 = not orientation_0 # 2) Second degree of freedom - 2nd point/orientation ambiguity if len(cur_t) >=2: # Choose the triangle that will direct the spiral counter-clockwise if orientation_0: # Third point in the triangle - next vertex in the spiral third = [p for p in cur_t[0] if p!=i and p!=cur_v][0] trig_central.remove(cur_t[0]) else: third = [p for p in cur_t[1] if p!=i and p!=cur_v][0] trig_central.remove(cur_t[1]) ring.append(third) seen.add(third) # 3) Stop if the spiral hits the boundary in the first point elif len(cur_t) == 1: break else: # 4) Unique ordering for the rest of the points (3rd onwards) if len(cur_t) >= 1: # Third point in the triangle - next vertex in the spiral third = [p for p in cur_t[0] if p!= cur_v and p!=i][0] # Don't append the spiral if the vertex has been visited already # (happens when the first ring is completed and the spiral returns to the central vertex) if third not in seen: ring.append(third) seen.add(third) trig_central.remove(cur_t[0]) # 4) Stop when the spiral hits the boundary (the already visited triangle is no longer in the list): First half of the spiral elif len(cur_t) == 0: break rev_i = len(ring) if init_vert is not None: v = init_vert if orientation_0 and len(ring)==1: reverse_order = False else: reverse_order = True need_padding = False # 5) If on the boundary: restart from the initial vertex towards the other direction, # but put the vertices in reverse order: Second half of the spiral # One exception if the starting point is on the boundary + 2nd point towards the desired direction while len(trig_central) > 0 and init_vert is not None: cur_t = [t for t in trig_central if t in trig[v]] if len(cur_t) != 1: break else: need_padding = True third = [p for p in cur_t[0] if p!=v and p!=i][0] trig_central.remove(cur_t[0]) if third not in seen: ring.insert(rev_i,third) seen.add(third) if not reverse_order: rev_i = len(ring) v = third # Add a dummy vertex between the first half of the spiral and the second half - similar to zero padding in a 2d grid if need_padding: ring.insert(rev_i,-1) """ ring_copy = list(ring[1:]) rev_i = rev_i - 1 for z in range(len(ring_copy)-2): if padding == 'zero': ring.insert(rev_i,-1) # -1 is our sink node elif padding == 'mirror': ring.insert(rev_i,ring_copy[rev_i-z-1]) """ spiral += ring # Next rings: for step in range(n_steps-1): next_ring = set([]); next_trigs = set([]); if len(ring) == 0: break base_triangle = None init_vert = None # Find next hop neighbors for w in ring: if w!=-1: for u in adj[w]: if u not in seen: next_ring.add(u) # Find triangles that contain two outer ring nodes. That way one can folllow the spiral ordering in the same way # as done in the first ring: by simply discarding the already visited triangles+nodes. for u in next_ring: for tr in trig[u]: if len([x for x in tr if x in seen]) == 1: next_trigs.add(tr) elif ring[0] in tr and ring[-1] in tr: base_triangle = tr # Normal case: starting point in the second ring -> # the 3rd point in the triangle that connects the 1st and the last point in the 1st ring with the 2nd ring if base_triangle is not None: init_vert = [x for x in base_triangle if x != ring[0] and x != ring[-1]] # Make sure that the the initial point is appropriate for starting the spiral, # i.e it is connected to at least one of the next candidate vertices if len(list(next_trigs.intersection(set(trig[init_vert[0]]))))==0: init_vert = None # If no such triangle exists (one of the vertices is dummy, # or both the first and the last vertex take part in a specific type of boundary) # or the init vertex is not connected with the rest of the ring --> # Find the relative point in the the triangle that connects the 1st point with the 2nd, or the 2nd with the 3rd # and so on and so forth. Note: This is a slight abuse of the spiral topology if init_vert is None: for r in range(len(ring)-1): if ring[r] !=-1 and ring[r+1]!=-1: tr = [t for t in trig[ring[r]] if t in trig[ring[r+1]]] for t in tr: init_vert = [v for v in t if v not in seen] # make sure that the next vertex is appropriate to start the spiral ordering in the next ring if len(init_vert)>0 and len(list(next_trigs.intersection(set(trig[init_vert[0]]))))>0: break else: init_vert = [] if len(init_vert)>0 and len(list(next_trigs.intersection(set(trig[init_vert[0]]))))>0: break else: init_vert = [] # The rest of the procedure is the same as the first ring if init_vert is None: init_vert = [] if len(init_vert)>0: init_vert = init_vert[0] ring = [init_vert] seen.add(init_vert) else: init_vert = None ring = [] # if i == 57: # import pdb;pdb.set_trace() while len(next_trigs) > 0 and init_vert is not None: cur_v = ring[-1] cur_t = list(next_trigs.intersection(set(trig[cur_v]))) if len(ring) == 1: try: orientation_0 = (cur_t[0][0] in seen and cur_t[0][1]==cur_v) \ or (cur_t[0][1] in seen and cur_t[0][2]==cur_v) \ or (cur_t[0][2] in seen and cur_t[0][0]==cur_v) except: import pdb;pdb.set_trace() if not counter_clockwise: orientation_0 = not orientation_0 # 1) orientation ambiguity for the next ring if len(cur_t) >=2: # Choose the triangle that will direct the spiral counter-clockwise if orientation_0: # Third point in the triangle - next vertex in the spiral third = [p for p in cur_t[0] if p not in seen and p!=cur_v][0] next_trigs.remove(cur_t[0]) else: third = [p for p in cur_t[1] if p not in seen and p!=cur_v][0] next_trigs.remove(cur_t[1]) ring.append(third) seen.add(third) # 2) Stop if the spiral hits the boundary in the first point elif len(cur_t) == 1: break else: # 3) Unique ordering for the rest of the points if len(cur_t) >= 1: third = [p for p in cur_t[0] if p != v and p not in seen] next_trigs.remove(cur_t[0]) if len(third)>0: third = third[0] if third not in seen: ring.append(third) seen.add(third) else: break # 4) Stop when the spiral hits the boundary # (the already visited triangle is no longer in the list): First half of the spiral elif len(cur_t) == 0: break rev_i = len(ring) if init_vert is not None: v = init_vert if orientation_0 and len(ring)==1: reverse_order = False else: reverse_order = True need_padding = False while len(next_trigs) > 0 and init_vert is not None: cur_t = [t for t in next_trigs if t in trig[v]] if len(cur_t) != 1: break else: need_padding = True third = [p for p in cur_t[0] if p!=v and p not in seen] next_trigs.remove(cur_t[0]) if len(third)>0: third = third[0] if third not in seen: ring.insert(rev_i,third) seen.add(third) if not reverse_order: rev_i = len(ring) v = third if need_padding: ring.insert(rev_i,-1) """ ring_copy = list(ring[1:]) rev_i = rev_i - 1 for z in range(len(ring_copy)-2): if padding == 'zero': ring.insert(rev_i,-1) # -1 is our sink node elif padding == 'mirror': ring.insert(rev_i,ring_copy[rev_i-z-1]) """ spiral += ring spirals.append(spiral) return spirals ================================================ FILE: mesh_pi_nets/test_funcs.py ================================================ import torch import copy from tqdm import tqdm, tqdm_notebook outlier_threshold = 1 def test_autoencoder_dataloader(device, model, dataloader_test, shapedata, mm_constant=1000): model.eval() l1_loss = 0 l2_loss = 0 shapedata_mean = torch.Tensor(shapedata.mean).to(device) shapedata_std = torch.Tensor(shapedata.std).to(device) with torch.no_grad(): count = 0 for i, sample_dict in enumerate(tqdm(dataloader_test)): tx = sample_dict['points'].to(device) prediction = model(tx) if i == 0: predictions = copy.deepcopy(prediction) else: predictions = torch.cat([predictions, prediction], 0) if dataloader_test.dataset.dummy_node: x_recon = prediction[:, :-1] x = tx[:, :-1] else: x_recon = prediction x = tx if torch.isnan(torch.mean(torch.abs(x_recon - x))) or torch.isinf(torch.mean(torch.abs(x_recon - x))): # The poly explodes print('warning, nan output') elif torch.mean(torch.abs(x_recon - x)) > outlier_threshold: # There are OUT-OF-DISTRIBUTION datapoints in COMA (completely distorted meshes) print('warning, outlier') else: l1_loss += torch.mean(torch.abs(x_recon - x)) * x.shape[0] x_recon = (x_recon * shapedata_std + shapedata_mean) * mm_constant x = (x * shapedata_std + shapedata_mean) * mm_constant l2_loss += torch.mean(torch.sqrt(torch.sum((x_recon - x) ** 2, dim=2))) * x.shape[0] count += x.shape[0] l1_loss = l1_loss / count l2_loss = l2_loss / count predictions = predictions.cpu().numpy() l1_loss = l1_loss.item() l2_loss = l2_loss.item() return predictions, l1_loss, l2_loss ================================================ FILE: mesh_pi_nets/train_funcs.py ================================================ def isnotebook(): try: shell = get_ipython().__class__.__name__ if shell == 'ZMQInteractiveShell': return True # Jupyter notebook or qtconsole elif shell == 'TerminalInteractiveShell': return False # Terminal running IPython else: return False # Other type (?) except NameError: return False # Probably standard Python interpreter if isnotebook(): from tqdm import tqdm_notebook as tqdm else: from tqdm import tqdm import os import torch def train_autoencoder_dataloader(dataloader_train, dataloader_val, device, model, optim, loss_fn, bsize, start_epoch, n_epochs, eval_freq, scheduler = None, writer=None, save_recons=True, shapedata=None, metadata_dir=None, samples_dir = None, checkpoint_path = None): if not shapedata.normalization: shapedata_mean = torch.Tensor(shapedata.mean).to(device) shapedata_std = torch.Tensor(shapedata.std).to(device) total_steps = start_epoch*len(dataloader_train) for epoch in range(start_epoch, n_epochs): model.train() tloss = [] for b, sample_dict in enumerate(tqdm(dataloader_train)): optim.zero_grad() tx = sample_dict['points'].to(device) cur_bsize = tx.shape[0] tx_hat = model(tx) loss = loss_fn(tx, tx_hat) loss.backward() optim.step() # always measure the loss after normalising the data to be able to compare if shapedata.normalization: tloss.append(cur_bsize * loss.item()) else: with torch.no_grad(): if shapedata.mean.shape[0]!=tx.shape[1]: # remove the dummy node tx_norm = tx[:,:-1,:] tx_hat_norm = tx_hat[:,:-1,:] else: tx_norm = tx tx_hat_norm = tx_hat tx_norm = (tx_norm - shapedata_mean)/shapedata_std tx_norm = torch.cat((tx_norm, torch.zeros(tx.shape[0], 1, tx.shape[2]).to(device)), 1) tx_hat_norm = (tx_hat_norm - shapedata_mean)/shapedata_std tx_hat_norm = torch.cat((tx_hat_norm, torch.zeros(tx.shape[0], 1, tx.shape[2]).to(device)), 1) loss_norm = loss_fn(tx_norm, tx_hat_norm) tloss.append(cur_bsize * loss_norm.item()) if writer and total_steps % eval_freq == 0: writer.add_scalar('loss/loss/data_loss', loss.item(), total_steps) writer.add_scalar('training/learning_rate', optim.param_groups[0]['lr'], total_steps) total_steps += 1 # validate model.eval() vloss = [] with torch.no_grad(): for b, sample_dict in enumerate(tqdm(dataloader_val)): tx = sample_dict['points'].to(device) cur_bsize = tx.shape[0] tx_hat = model(tx) loss = loss_fn(tx, tx_hat) if shapedata.normalization: vloss.append(cur_bsize * loss.item()) else: with torch.no_grad(): if shapedata.mean.shape[0]!=tx.shape[1]: tx_norm = tx[:, :-1, :] tx_hat_norm = tx_hat[:, :-1, :] else: tx_norm = tx tx_hat_norm = tx_hat tx_norm = (tx_norm - shapedata_mean)/shapedata_std tx_norm = torch.cat((tx_norm, torch.zeros(tx.shape[0], 1, tx.shape[2]).to(device)), 1) tx_hat_norm = (tx_hat_norm -shapedata_mean)/shapedata_std tx_hat_norm = torch.cat((tx_hat_norm, torch.zeros(tx.shape[0], 1, tx.shape[2]).to(device)), 1) loss_norm = loss_fn(tx_norm, tx_hat_norm) vloss.append(cur_bsize * loss_norm.item()) if scheduler: scheduler.step() epoch_tloss = sum(tloss) / float(len(dataloader_train.dataset)) writer.add_scalar('avg_epoch_train_loss', epoch_tloss, epoch) if len(dataloader_val.dataset) > 0: epoch_vloss = sum(vloss) / float(len(dataloader_val.dataset)) writer.add_scalar('avg_epoch_valid_loss', epoch_vloss, epoch) print('epoch {0} | tr {1} | val {2}'.format(epoch, epoch_tloss, epoch_vloss)) else: print('epoch {0} | tr {1} '.format(epoch, epoch_tloss)) model = model.cpu() torch.save({'epoch': epoch, 'autoencoder_state_dict': model.state_dict(), 'optimizer_state_dict' : optim.state_dict(), 'scheduler_state_dict': scheduler.state_dict(), }, os.path.join(metadata_dir, checkpoint_path+'.pth.tar')) if epoch % 10 == 0: torch.save({'epoch': epoch, 'autoencoder_state_dict': model.state_dict(), 'optimizer_state_dict' : optim.state_dict(), 'scheduler_state_dict': scheduler.state_dict(), }, os.path.join(metadata_dir, checkpoint_path+'%s.pth.tar'%(epoch))) model = model.to(device) if save_recons: with torch.no_grad(): if epoch == 0: mesh_ind = [0] msh = tx[mesh_ind[0]:1,0:-1,:].detach().cpu().numpy() shapedata.save_meshes(os.path.join(samples_dir, 'input_epoch_{0}'.format(epoch)), msh, mesh_ind) mesh_ind = [0] msh = tx_hat[mesh_ind[0]:1,0:-1,:].detach().cpu().numpy() shapedata.save_meshes(os.path.join(samples_dir, 'epoch_{0}'.format(epoch)), msh, mesh_ind) print('~FIN~') ================================================ FILE: readme.rst ================================================ ======================================= Π-nets: Deep Polynomial Neural Networks ======================================= .. image:: https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg :target: https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg :alt: License .. image:: https://img.shields.io/badge/Preprint-ArXiv-blue.svg :target: https://arxiv.org/abs/2006.13026 :alt: ArXiv .. image:: https://img.shields.io/badge/BlogPost-site-red.svg :target: https://grigorisg9gr.github.io/polynomial-nets/ :alt: Blogpost Official implementation of several experiments in the paper `"**Π-nets: Deep Polynomial Neural Networks**" `_ (CVPR'20) and its `extension `_ (T-PAMI'21; also available `here `_ ). Each folder contains a different experiment. Please follow the instructions in the respective folder on how to run the experiments and reproduce the results. `This repository `_ contains implementations in `MXNet `_, `PyTorch `_ and `Chainer `_. Browsing the experiments ======================== The folder structure is the following: * ``face_recognition``: The folder contains the code for the `face verification and identification experiments `_. * ``image_generation_chainer``: The folder contains the `image generation experiment on Chainer `_; specifically the experiment without activation functions between the layers. * ``image_generation_pytorch``: The folder contains the `image generation experiment on PyTorch `_; specifically the conversion of a DCGAN-like generator into a polynomial generator. * ``mesh_pi_nets``: The folder contains the code for `mesh representation learning `_ with polynomial networks. * ``classification-NO-activation-function``: The folder contains the code for Π-nets without activation functions on classification (e.g. ImageNet). More information on Π-nets ========================== A one-minute pitch of the paper is uploaded `here `_. We describe there what generation results can be obtained even without activation functions between the layers of the generator. Π-nets do not rely on a single architecture, but enable diverse architectures to be built; the architecture is defined by the form of the resursive formula that constructs it. For instance, we visualize below two different Π-net architectures. .. image:: figures/model_intro_.png :width: 200 :alt: Different architectures enables by Π-nets. Results ======= The evaluation in the paper [1]_ suggests that Π-nets can improve state-of-the-art methods. Below, we visualize results in image generation and errors in mesh representation learning. .. image:: figures/prodpoly_generation_ffhq.png :width: 400 :alt: Generation results by Π-nets when trained on FFHQ. The image above shows synthesizes faces. The generator is a Π-net, and more specifically a product of polynomials. .. image:: figures/dfaust.png :width: 400 :alt: Per vertex reconstruction error on an exemplary human body mesh. Color coded results of the per vertex reconstruction error on an exemplary human body mesh. From left to right: ground truth mesh, first order SpiralGNN, second, third and fourth order base polynomial in Π-nets. Dark colors depict a larger error; notice that the (upper and lower) limbs have larger error with first order SpiralGNN. Citing ====== If you use this code, please cite [1]_ or (and) [2]_: *BibTeX*:: @inproceedings{ poly2020, title={$\Pi-$nets: Deep Polynomial Neural Networks}, author={Chrysos, Grigorios and Moschoglou, Stylianos and Bouritsas, Giorgos and Panagakis, Yannis and Deng, Jiankang and Zafeiriou, Stefanos}, booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)}, pages={7325--7335}, year={2020} } *BibTeX*:: @article{poly2021, author={Chrysos, Grigorios and Moschoglou, Stylianos and Bouritsas, Giorgos and Deng, Jiankang and Panagakis, Yannis and Zafeiriou, Stefanos}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, title={Deep Polynomial Neural Networks}, volume={44}, number={8}, pages={4021--4034}, year={2021}, doi={10.1109/TPAMI.2021.3058891}} References ========== .. [1] Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng and Stefanos Zafeiriou, **Π-nets: Deep Polynomial Neural Networks**, *Conference on Computer Vision and Pattern Recognition (CVPR)*, 2020. .. [2] Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Jiankang Deng, Yannis Panagakis and Stefanos Zafeiriou, **Deep Polynomial Neural Networks**, *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 2021.