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  },
  {
    "path": "datagen.py",
    "content": "from __future__ import print_function\nimport io\nimport os\nimport sys\nimport random\nimport torch\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\nfrom PIL import Image\n\nclass ListDataset(data.Dataset):\n    def __init__(self, root, list_file, transform):\n        '''\n        Args:\n          root: (str) ditectory to images.\n          list_file: (str) path to index file.\n          transform: ([transforms]) image transforms.\n        '''\n        self.root = root\n        self.transform = transform\n\n        self.fname = []\n        self.fiiqa = []\n\n        with io.open(list_file, encoding='gbk') as f:\n            lines = f.readlines()\n            self.num_imgs = len(lines)\n\n        for line in lines:\n            sp = line.strip().split()\n            self.fname.append(sp[0])\n            self.fiiqa.append(int(sp[1]))\n\n    def __getitem__(self, idx):\n        '''Load image.\n\n        Args:\n          idx: (int) image index.\n\n        Returns:\n          img: (tensor) image tensor.\n          fiiqa: (float) fiiqa.\n        '''\n        # Load image and bbox locations.\n        fname = self.fname[idx]\n        fiiqa = self.fiiqa[idx]\n\n        img = Image.open(os.path.join(self.root, fname)).convert('RGB')\n        img = self.transform(img)\n        return img, fiiqa\n\n    def __len__(self):\n        return self.num_imgs\n\n\ndef test():\n    import torchvision\n    transform = transforms.Compose([\n        transforms.RandomSizedCrop(224),\n        transforms.RandomHorizontalFlip(),\n        transforms.ToTensor(),\n        transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n    ])\n    dataset = ListDataset(root='./data/validationset/val-faces/', list_file='./data/validationset/val-faces/new_4people_val_standard.txt', transform=transform)\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True, num_workers=1)\n    for img, fiiqa in dataloader:\n        print(img.size())\n        print(fiiqa.size())\n\n"
  },
  {
    "path": "flops_counter_pytorch/LICENSE",
    "content": "MIT License\n\nCopyright (c) 2018 Vladislav Sovrasov\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "flops_counter_pytorch/README.md",
    "content": "# Flops counter for convolutional networks in pytorch framework\n[![Pypi version](https://img.shields.io/pypi/v/ptflops.svg)](https://pypi.org/project/ptflops/)\n\nThis script is designed to compute the theoretical amount of multiply-add operations\nin convolutional neural networks. It also can compute the number of parameters and\nprint per-layer computational cost of a given network.\n\nSupported layers:\n- Convolution2d (including grouping)\n- BatchNorm2d\n- Activations (ReLU, PReLU, ELU, ReLU6, LeakyReLU)\n- Linear\n- Upsample\n- Poolings (AvgPool2d, MaxPool2d and adaptive ones)\n\nRequirements: Pytorch 0.4.1 or 1.0, torchvision 0.2.1\n\nThanks to @warmspringwinds for the initial version of script.\n\n## Install the latest version\n```bash\npip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git\n```\n\n## Example\n```python\nimport torchvision.models as models\nimport torch\nfrom ptflops import get_model_complexity_info\n\nwith torch.cuda.device(0):\n  net = models.densenet161()\n  flops, params = get_model_complexity_info(net, (224, 224), as_strings=True, print_per_layer_stat=True)\n  print('Flops:  ' + flops)\n  print('Params: ' + params)\n```\n"
  },
  {
    "path": "flops_counter_pytorch/ptflops/__init__.py",
    "content": "from .flops_counter import get_model_complexity_info\n"
  },
  {
    "path": "flops_counter_pytorch/ptflops/flops_counter.py",
    "content": "import torch.nn as nn\nimport torch\nimport numpy as np\n\ndef get_model_complexity_info(model, input_res, print_per_layer_stat=True, as_strings=True,\n                              input_constructor=None):\n    assert type(input_res) is tuple\n    assert len(input_res) == 2\n    flops_model = add_flops_counting_methods(model)\n    flops_model.eval().start_flops_count()\n    if input_constructor:\n        input = input_constructor(input_res)\n        _ = flops_model(**input)\n    else:\n        batch = torch.FloatTensor(1, 3, *input_res)\n        _ = flops_model(batch)\n\n    if print_per_layer_stat:\n        print_model_with_flops(flops_model)\n    flops_count = flops_model.compute_average_flops_cost()\n    params_count = get_model_parameters_number(flops_model)\n    flops_model.stop_flops_count()\n\n    if as_strings:\n        return flops_to_string(flops_count), params_to_string(params_count)\n\n    return flops_count, params_count\n\ndef flops_to_string(flops, units='GMac', precision=12):\n    if units is None:\n        if flops // 10**9 > 0:\n            return str(round(flops / 10.**9, precision)) + ' GMac'\n        elif flops // 10**6 > 0:\n            return str(round(flops / 10.**6, precision)) + ' MMac'\n        elif flops // 10**3 > 0:\n            return str(round(flops / 10.**3, precision)) + ' KMac'\n        else:\n            return str(flops) + ' Mac'\n    else:\n        if units == 'GMac':\n            return str(round(flops / 10.**9, precision)) + ' ' + units\n        elif units == 'MMac':\n            return str(round(flops / 10.**6, precision)) + ' ' + units\n        elif units == 'KMac':\n            return str(round(flops / 10.**3, precision)) + ' ' + units\n        else:\n            return str(flops) + ' Mac'\n\ndef params_to_string(params_num):\n    if params_num // 10 ** 6 > 0:\n        return str(round(params_num / 10 ** 6, 2)) + ' M'\n    elif params_num // 10 ** 3:\n        return str(round(params_num / 10 ** 3, 2)) + ' k'\n\ndef print_model_with_flops(model, units='GMac', precision=3):\n    total_flops = model.compute_average_flops_cost()\n\n    def accumulate_flops(self):\n        if is_supported_instance(self):\n            return self.__flops__ / model.__batch_counter__\n        else:\n            sum = 0\n            for m in self.children():\n                sum += m.accumulate_flops()\n            return sum\n\n    def flops_repr(self):\n        accumulated_flops_cost = self.accumulate_flops()\n        return ', '.join([flops_to_string(accumulated_flops_cost, units=units, precision=precision),\n                          '{:.3%} MACs'.format(accumulated_flops_cost / total_flops),\n                          self.original_extra_repr()])\n\n    def add_extra_repr(m):\n        m.accumulate_flops = accumulate_flops.__get__(m)\n        flops_extra_repr = flops_repr.__get__(m)\n        if m.extra_repr != flops_extra_repr:\n            m.original_extra_repr = m.extra_repr\n            m.extra_repr = flops_extra_repr\n            assert m.extra_repr != m.original_extra_repr\n\n    def del_extra_repr(m):\n        if hasattr(m, 'original_extra_repr'):\n            m.extra_repr = m.original_extra_repr\n            del m.original_extra_repr\n        if hasattr(m, 'accumulate_flops'):\n            del m.accumulate_flops\n\n    model.apply(add_extra_repr)\n    print(model)\n    model.apply(del_extra_repr)\n\ndef get_model_parameters_number(model):\n    params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)\n    return params_num\n\ndef add_flops_counting_methods(net_main_module):\n    # adding additional methods to the existing module object,\n    # this is done this way so that each function has access to self object\n    net_main_module.start_flops_count = start_flops_count.__get__(net_main_module)\n    net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module)\n    net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module)\n    net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(net_main_module)\n\n    net_main_module.reset_flops_count()\n\n    # Adding variables necessary for masked flops computation\n    net_main_module.apply(add_flops_mask_variable_or_reset)\n\n    return net_main_module\n\n\ndef compute_average_flops_cost(self):\n    \"\"\"\n    A method that will be available after add_flops_counting_methods() is called\n    on a desired net object.\n\n    Returns current mean flops consumption per image.\n\n    \"\"\"\n\n    batches_count = self.__batch_counter__\n    flops_sum = 0\n    for module in self.modules():\n        if is_supported_instance(module):\n            flops_sum += module.__flops__\n\n    return flops_sum / batches_count\n\n\ndef start_flops_count(self):\n    \"\"\"\n    A method that will be available after add_flops_counting_methods() is called\n    on a desired net object.\n\n    Activates the computation of mean flops consumption per image.\n    Call it before you run the network.\n\n    \"\"\"\n    add_batch_counter_hook_function(self)\n    self.apply(add_flops_counter_hook_function)\n\n\ndef stop_flops_count(self):\n    \"\"\"\n    A method that will be available after add_flops_counting_methods() is called\n    on a desired net object.\n\n    Stops computing the mean flops consumption per image.\n    Call whenever you want to pause the computation.\n\n    \"\"\"\n    remove_batch_counter_hook_function(self)\n    self.apply(remove_flops_counter_hook_function)\n\n\ndef reset_flops_count(self):\n    \"\"\"\n    A method that will be available after add_flops_counting_methods() is called\n    on a desired net object.\n\n    Resets statistics computed so far.\n\n    \"\"\"\n    add_batch_counter_variables_or_reset(self)\n    self.apply(add_flops_counter_variable_or_reset)\n\n\ndef add_flops_mask(module, mask):\n    def add_flops_mask_func(module):\n        if isinstance(module, torch.nn.Conv2d):\n            module.__mask__ = mask\n    module.apply(add_flops_mask_func)\n\n\ndef remove_flops_mask(module):\n    module.apply(add_flops_mask_variable_or_reset)\n\n\n# ---- Internal functions\ndef is_supported_instance(module):\n    if isinstance(module, (torch.nn.Conv2d, torch.nn.ReLU, torch.nn.PReLU, torch.nn.ELU, \\\n                           torch.nn.LeakyReLU, torch.nn.ReLU6, torch.nn.Linear, \\\n                           torch.nn.MaxPool2d, torch.nn.AvgPool2d, torch.nn.BatchNorm2d, \\\n                           torch.nn.Upsample, nn.AdaptiveMaxPool2d, nn.AdaptiveAvgPool2d)):\n        return True\n\n    return False\n\n\ndef empty_flops_counter_hook(module, input, output):\n    module.__flops__ += 0\n\n\ndef upsample_flops_counter_hook(module, input, output):\n    output_size = output[0]\n    batch_size = output_size.shape[0]\n    output_elements_count = batch_size\n    for val in output_size.shape[1:]:\n        output_elements_count *= val\n    module.__flops__ += int(output_elements_count)\n\n\ndef relu_flops_counter_hook(module, input, output):\n    active_elements_count = output.numel()\n    module.__flops__ += int(active_elements_count)\n\n\ndef linear_flops_counter_hook(module, input, output):\n    input = input[0]\n    batch_size = input.shape[0]\n    module.__flops__ += int(batch_size * input.shape[1] * output.shape[1])\n\n\ndef pool_flops_counter_hook(module, input, output):\n    input = input[0]\n    module.__flops__ += int(np.prod(input.shape))\n\ndef bn_flops_counter_hook(module, input, output):\n    module.affine\n    input = input[0]\n\n    batch_flops = np.prod(input.shape)\n    if module.affine:\n        batch_flops *= 2\n    module.__flops__ += int(batch_flops)\n\ndef conv_flops_counter_hook(conv_module, input, output):\n    # Can have multiple inputs, getting the first one\n    input = input[0]\n\n    batch_size = input.shape[0]\n    output_height, output_width = output.shape[2:]\n\n    kernel_height, kernel_width = conv_module.kernel_size\n    in_channels = conv_module.in_channels\n    out_channels = conv_module.out_channels\n    groups = conv_module.groups\n\n    filters_per_channel = out_channels // groups\n    conv_per_position_flops = kernel_height * kernel_width * in_channels * filters_per_channel\n\n    active_elements_count = batch_size * output_height * output_width\n\n    if conv_module.__mask__ is not None:\n        # (b, 1, h, w)\n        flops_mask = conv_module.__mask__.expand(batch_size, 1, output_height, output_width)\n        active_elements_count = flops_mask.sum()\n\n    overall_conv_flops = conv_per_position_flops * active_elements_count\n\n    bias_flops = 0\n\n    if conv_module.bias is not None:\n\n        bias_flops = out_channels * active_elements_count\n\n    overall_flops = overall_conv_flops + bias_flops\n\n    conv_module.__flops__ += int(overall_flops)\n\n\ndef batch_counter_hook(module, input, output):\n    batch_size = 1\n    if len(input) > 0:\n        # Can have multiple inputs, getting the first one\n        input = input[0]\n        batch_size = len(input)\n    else:\n        pass\n        print('Warning! No positional inputs found for a module, assuming batch size is 1.')\n    module.__batch_counter__ += batch_size\n\n\ndef add_batch_counter_variables_or_reset(module):\n\n    module.__batch_counter__ = 0\n\n\ndef add_batch_counter_hook_function(module):\n    if hasattr(module, '__batch_counter_handle__'):\n        return\n\n    handle = module.register_forward_hook(batch_counter_hook)\n    module.__batch_counter_handle__ = handle\n\n\ndef remove_batch_counter_hook_function(module):\n    if hasattr(module, '__batch_counter_handle__'):\n        module.__batch_counter_handle__.remove()\n        del module.__batch_counter_handle__\n\n\ndef add_flops_counter_variable_or_reset(module):\n    if is_supported_instance(module):\n        module.__flops__ = 0\n\n\ndef add_flops_counter_hook_function(module):\n    if is_supported_instance(module):\n        if hasattr(module, '__flops_handle__'):\n            return\n\n        if isinstance(module, torch.nn.Conv2d):\n            handle = module.register_forward_hook(conv_flops_counter_hook)\n        elif isinstance(module, (torch.nn.ReLU, torch.nn.PReLU, torch.nn.ELU, \\\n                                 torch.nn.LeakyReLU, torch.nn.ReLU6)):\n            handle = module.register_forward_hook(relu_flops_counter_hook)\n        elif isinstance(module, torch.nn.Linear):\n            handle = module.register_forward_hook(linear_flops_counter_hook)\n        elif isinstance(module, (torch.nn.AvgPool2d, torch.nn.MaxPool2d, nn.AdaptiveMaxPool2d, \\\n                                 nn.AdaptiveAvgPool2d)):\n            handle = module.register_forward_hook(pool_flops_counter_hook)\n        elif isinstance(module, torch.nn.BatchNorm2d):\n            handle = module.register_forward_hook(bn_flops_counter_hook)\n        elif isinstance(module, torch.nn.Upsample):\n            handle = module.register_forward_hook(upsample_flops_counter_hook)\n        else:\n            handle = module.register_forward_hook(empty_flops_counter_hook)\n        module.__flops_handle__ = handle\n\n\ndef remove_flops_counter_hook_function(module):\n    if is_supported_instance(module):\n        if hasattr(module, '__flops_handle__'):\n            module.__flops_handle__.remove()\n            del module.__flops_handle__\n# --- Masked flops counting\n\n\n# Also being run in the initialization\ndef add_flops_mask_variable_or_reset(module):\n    if is_supported_instance(module):\n        module.__mask__ = None\n"
  },
  {
    "path": "flops_counter_pytorch/sample.py",
    "content": "import argparse\nimport torchvision.models as models\nimport torch\nfrom ptflops import get_model_complexity_info\n\npt_models = { 'resnet18': models.resnet18, 'resnet50': models.resnet50,\n              'alexnet': models.alexnet,\n              'vgg16': models.vgg16, 'squeezenet': models.squeezenet1_0,\n              'densenet': models.densenet161}\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Evaluation script for Face Recognition in PyTorch')\n    parser.add_argument('--device', type=int, default=-1, help='Device to store the model.')\n    parser.add_argument('--model', choices=list(pt_models.keys()), type=str, default='resnet18')\n    args = parser.parse_args()\n\n    with torch.cuda.device(args.device):\n        net = pt_models[args.model]()\n        flops, params = get_model_complexity_info(net, (224, 224), as_strings=True, print_per_layer_stat=True)\n        print('Flops: ' + flops)\n        print('Params: ' + params)\n"
  },
  {
    "path": "flops_counter_pytorch/setup.py",
    "content": "import os\nimport shutil\nimport sys\nfrom setuptools import setup, find_packages\n\nreadme = open('README.md').read()\n\nVERSION = '0.5'\n\nrequirements = [\n    'torch',\n]\n\nsetup(\n    # Metadata\n    name='ptflops',\n    version=VERSION,\n    author='Vladislav Sovrasov',\n    author_email='sovrasov.vlad@gmail.com',\n    url='https://github.com/sovrasov/flops-counter.pytorch',\n    description='Flops counter for convolutional networks in pytorch framework',\n    long_description=readme,\n    license='MIT',\n\n    # Package info\n    packages=find_packages(exclude=('*test*',)),\n\n    #\n    zip_safe=True,\n    install_requires=requirements,\n\n    # Classifiers\n    classifiers=[\n        'Programming Language :: Python :: 3',\n    ],\n)\n"
  },
  {
    "path": "loss.py",
    "content": "from __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.autograd import Variable\n\n\nclass FIIQALoss(nn.Module):\n    def __init__(self):\n        super(AGLoss, self).__init__()\n\n    def forward(self, fiiqa_preds, fiiqa_targets):\n        '''Compute loss (fiiqa_preds, fiiqa_targets) .\n\n        Args:\n          fiiqa_preds: (tensor) predicted fiiqa, sized [batch_size,100].\n          fiiqa_targets: (tensor) target fiiqa, sized [batch_size,].\n\n        loss:\n          (tensor) loss = SmoothL1Loss(fiiqa_preds, fiiqa_targets)\n        '''\n        #使用分类概率和估计值相乘再求和来求期望的方法,比直接分类和直接回归的效果更好。\n        #先求得分类概率\n        fiiqa_prob = F.softmax(fiiqa_preds,dim=1)\n        #利用分类概率与对应预测值相乘后累加求和，求得期望值\n        fiiqa_expect = torch.sum(Variable(torch.arange(0,200)).cuda().float()*fiiqa_prob, 1)\n        #loss是期望值与ground trouth 之间的误差\n        fiiqa_loss = F.smooth_l1_loss(fiiqa_expect, fiiqa_targets.float())\n        return fiiqa_loss \n"
  },
  {
    "path": "model/haarcascade_frontalface_default.xml",
    "content": "<?xml version=\"1.0\"?>\n<!--\n    Stump-based 24x24 discrete(?) adaboost frontal face detector.\n    Created by Rainer Lienhart.\n\n////////////////////////////////////////////////////////////////////////////////////////\n\n  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.\n\n  By downloading, copying, installing or using the software you agree to this license.\n  If you do not agree to this license, do not download, install,\n  copy or use the software.\n\n\n                        Intel License Agreement\n                For Open Source Computer Vision Library\n\n Copyright (C) 2000, Intel Corporation, all rights reserved.\n Third party copyrights are property of their respective owners.\n\n Redistribution and use in source and binary forms, with or without modification,\n are permitted provided that the following conditions are met:\n\n   * Redistribution's of source code must retain the above copyright notice,\n     this list of conditions and the following disclaimer.\n\n   * Redistribution's in binary form must reproduce the above copyright notice,\n     this list of conditions and the following disclaimer in the documentation\n     and/or other materials provided with the distribution.\n\n   * The name of Intel Corporation may not be used to endorse or promote products\n     derived from this software without specific prior written permission.\n\n This software is provided by the copyright holders and contributors \"as is\" and\n any express or implied warranties, including, but not limited to, the implied\n warranties of merchantability and fitness for a particular purpose are disclaimed.\n In no event shall the Intel Corporation or contributors be liable for any direct,\n indirect, incidental, special, exemplary, or consequential damages\n (including, but not limited to, procurement of substitute goods or services;\n loss of use, data, or profits; or business interruption) however caused\n and on any theory of liability, whether in contract, strict liability,\n or tort (including negligence or otherwise) arising in any way out of\n the use of this software, even if advised of the possibility of such damage.\n-->\n<opencv_storage>\n<cascade type_id=\"opencv-cascade-classifier\"><stageType>BOOST</stageType>\n  <featureType>HAAR</featureType>\n  <height>24</height>\n  <width>24</width>\n  <stageParams>\n    <maxWeakCount>211</maxWeakCount></stageParams>\n  <featureParams>\n    <maxCatCount>0</maxCatCount></featureParams>\n  <stageNum>25</stageNum>\n  <stages>\n    <_>\n      <maxWeakCount>9</maxWeakCount>\n      <stageThreshold>-5.0425500869750977e+00</stageThreshold>\n      <weakClassifiers>\n        <_>\n          <internalNodes>\n            0 -1 0 -3.1511999666690826e-02</internalNodes>\n          <leafValues>\n            2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_>\n        <_>\n          <internalNodes>\n            0 -1 1 1.2396000325679779e-02</internalNodes>\n          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  },
  {
    "path": "net.py",
    "content": "'''Predict age in one shot.'''\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, in_planes, planes, stride=1):\n        super(BasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(planes)\n\n        self.downsample = nn.Sequential()\n        if stride != 1 or in_planes != self.expansion*planes:\n            self.downsample = nn.Sequential(\n                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(self.expansion*planes)\n            )\n\n    def forward(self, x):\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        out += self.downsample(x)\n        out = F.relu(out)\n        return out\n\n    def freeze_bn(self):\n        '''Freeze BatchNorm layers.'''\n        for layer in self.modules():\n            if isinstance(layer, nn.BatchNorm2d):\n                layer.training = False\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(self, in_planes, planes, stride=1):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(self.expansion*planes)\n\n        self.downsample = nn.Sequential()\n        if stride != 1 or in_planes != self.expansion*planes:\n            self.downsample = nn.Sequential(\n                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(self.expansion*planes)\n            )\n\n    def forward(self, x):\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = F.relu(self.bn2(self.conv2(out)))\n        out = self.bn3(self.conv3(out))\n        out += self.downsample(x)\n        out = F.relu(out)\n        return out\n\n\nclass ResNet(nn.Module):\n    def __init__(self, block, num_blocks):\n        super(ResNet, self).__init__()\n        self.in_planes = 64\n\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(block,  64, num_blocks[0], stride=1)\n        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)\n        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)\n        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)\n        # self.avg_pool = nn.AvgPool2d(kernel_size=7)\n\n    def _make_layer(self, block, planes, num_blocks, stride):\n        strides = [stride] + [1]*(num_blocks-1)\n        layers = []\n        for stride in strides:\n            layers.append(block(self.in_planes, planes, stride))\n            self.in_planes = planes * block.expansion\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = self.max_pool(out)\n        out = self.layer1(out)\n        out = self.layer2(out)\n        out = self.layer3(out)\n        out = self.layer4(out)\n        return out\n\ndef ResNet18():\n    return ResNet(BasicBlock, [2,2,2,2])\n\ndef ResNet50():\n    return ResNet(Bottleneck, [3,4,6,3])\n\ndef ResNet101():\n    return ResNet(Bottleneck, [2,4,23,3])\n\n\nclass AGNet(nn.Module):\n    def __init__(self):\n        super(AGNet, self).__init__()\n        self.backbone = ResNet18()\n        self.age_head = self._make_head()\n        self.linear1 = nn.Linear(256,200)\n\n    def _make_head(self):\n        return nn.Sequential(\n            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(True),\n            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(True),\n            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),\n        )\n\n    def forward(self, x):\n        N = x.size(0)\n        y = self.backbone(x)\n        y = F.avg_pool2d(y,5)\n        # Age head\n        y_age = self.age_head(y)\n        y_age = self.linear1(y_age.view(N,-1))\n\n        return y_age\n\n\ndef test():\n    net = AGNet()\n    age = net(Variable(torch.randn(2,3,150,150)))\n    print(age.size())\n\n\n# test()\n"
  },
  {
    "path": "readme.md",
    "content": "-----------------------------------------------------------------\nFace Image Illumination Quality Assessment  implements by pytorch\n-----------------------------------------------------------------\nThis is not the official version of Illumination Quality Assessment for Face Images: A Benchmark and A Convolutional Neural Networks Based Model ，original website:https://github.com/zhanglijun95/FIIQA\n\n\n\n\nOverview:\n![image](https://github.com/yangyuke001/FIIQA_pytorch/blob/master/image/1679052023.jpg)\n![image](https://github.com/yangyuke001/FIIQA_pytorch/blob/master/image/996012808.jpg)\n\n\nDependencies\n------------------------------------------------------------------------------------\n*python 3.6+\n\n*pytorch 1.0.1+\n\n\nUsage\n------------------------------------------------------------------------------------\n1.cloning the respository\ngit clone https://github.com/yangyuke001/FIIQA-PyTorch\n\ncd FIIQA-PyTorch\n\n2.Downloading the dataset\n\nFace Image Illumination Quality Dataset 1.0\n\nWe have established a large-scale benchmark dataset, in which face images under various illumination patterns with associated illumination quality scores were constructed by making use of illumination transfer. Thus, we firstly collected an image set containing face images with various real-world illumination patterns, namely source illumination patterns set, and evaluated their illumination quality scores by subjective judgements. And after construction, this dataset is divided into three subsets for DCNN, the training set, the validation set and the testing set.\n2.1 Source illumination patterns set (http://pan.baidu.com/s/1hrYayXI)\n\nUnzip ZIP files, \"illumination patterns.zip\". In the \"illumination patterns\" folder, there are 200 images with various real-world illumination patterns, and for each image pattern, the associated illumination quality scores are given in the \"patternsScores.mat\", the sorted ranks, which are the class labels of those patterns, are given in the \"patternsRank.mat\".\n2.2 Training Set (http://pan.baidu.com/s/1mhFBusg)\n\nUnzip 7Z files, \"trainingset.7z\". In \"train-faces\" folder, there are 159159 images with various illumination patterns, and for each image the rank label of the associated illumination quality are given in the \"train_standard.txt\".\n2.3 Validation Set (http://pan.baidu.com/s/1miMDkt6)\n\nUnzip ZIP files, \"validationset.zip\". In \"val-faces\" folder, there are 30930 images with various illumination patterns, and for each image the rank label of the associated illumination quality are given in the \"val_standard.txt\".\n2.4 Testing Set (http://pan.baidu.com/s/1nuXQjH3)\n\nUnzip 7Z files, \"testingset.7z\". In \"test-faces\" folder, there are 34644 images with various illumination patterns, and for each image the rank label of the associated illumination quality are given in the \"test_standard.txt\".\n\n3. Training\n\nTo train FIIQA , run the script below:\n\n\npython train.py\n\n4. Testing\n\nTo test on FIIQA,run the script below:\n\npython test.py\n\n\n\n"
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  },
  {
    "path": "scripts/get_state_dict.py",
    "content": "'''Init AGNet18 with pretrained ResNet18 model.'''\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.init as init\n\nfrom net import AGNet, ResNet18\n\n\nprint('Loading pretrained ResNet18 model..')\nd = torch.load('./model/resnet18.pth')\n\nprint('Loading into backbone..')\nbackbone = ResNet18()\ndd = backbone.state_dict()\nfor k in d.keys():\n    if not k.startswith('fc'):  # skip fc layers\n        dd[k] = d[k]\n\nprint('Saving AGNet..')\nnet = AGNet()\nnet.backbone.load_state_dict(dd)\ntorch.save(net.state_dict(), 'net.pth')\nprint('Done!')\n"
  },
  {
    "path": "shufflenetv2.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom collections import OrderedDict\nfrom torch.nn import init\nimport math\n\n\ndef conv_bn(inp, oup, stride):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n        nn.BatchNorm2d(oup),\n        nn.ReLU(inplace=True)\n    )\n\n\ndef conv_1x1_bn(inp, oup):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        nn.BatchNorm2d(oup),\n        nn.ReLU(inplace=True)\n    )\n\n\ndef channel_shuffle(x, groups):\n    batchsize, num_channels, height, width = x.data.size()\n\n    channels_per_group = num_channels // groups\n\n    # reshape\n    x = x.view(batchsize, groups, channels_per_group, height, width)\n\n    x = torch.transpose(x, 1, 2).contiguous()\n\n    # flatten\n    x = x.view(batchsize, -1, height, width)\n\n    return x\n\n\nclass InvertedResidual(nn.Module):\n    def __init__(self, inp, oup, stride, benchmodel):\n        super(InvertedResidual, self).__init__()\n        self.benchmodel = benchmodel\n        self.stride = stride\n        assert stride in [1, 2]\n\n        oup_inc = oup // 2\n\n        if self.benchmodel == 1:\n            # assert inp == oup_inc\n            self.banch2 = nn.Sequential(\n                # pw\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n                # dw\n                nn.Conv2d(oup_inc, oup_inc, 3, stride,\n                          1, groups=oup_inc, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                # pw-linear\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n        else:\n            self.banch1 = nn.Sequential(\n                # dw\n                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n                nn.BatchNorm2d(inp),\n                # pw-linear\n                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n\n            self.banch2 = nn.Sequential(\n                # pw\n                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n                # dw\n                nn.Conv2d(oup_inc, oup_inc, 3, stride,\n                          1, groups=oup_inc, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                # pw-linear\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n\n    @staticmethod\n    def _concat(x, out):\n        # concatenate along channel axis\n        return torch.cat((x, out), 1)\n\n    def forward(self, x):\n        if 1 == self.benchmodel:\n            # x1 = x[:, :(x.shape[1] // 2), :, :]\n            # x2 = x[:, (x.shape[1] // 2):, :, :]\n            x1, x2 = torch.split(x, x.shape[1] // 2, dim=1)\n            out = self._concat(x1, self.banch2(x2))\n        elif 2 == self.benchmodel:\n            out = self._concat(self.banch1(x), self.banch2(x))\n\n        return channel_shuffle(out, 2)\n\n\nclass ShuffleNetV2(nn.Module):\n    def __init__(self, n_class=200, input_size=160, width_mult=1): #1,0\n        super(ShuffleNetV2, self).__init__()\n\n        assert input_size % 32 == 0\n\n        self.stage_repeats = [4, 8, 4]\n        # index 0 is invalid and should never be called.\n        # only used for indexing convenience.\n        if width_mult == 0.5:\n            self.stage_out_channels = [-1, 24, 48, 96, 192, 192] #175\n        elif width_mult == 1.0:\n            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024] # 1024\n        elif width_mult == 1.5:\n            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]\n        elif width_mult == 2.0:\n            self.stage_out_channels = [-1, 24, 224, 488, 976, 2048]\n        else:\n            raise ValueError(\n                \"\"\"width_mult = {} is not supported\"\"\".format(width_mult))\n\n        # building first layer\n        input_channel = self.stage_out_channels[1]\n        self.conv1 = conv_bn(3, input_channel, 2)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        self.features = []\n        # building inverted residual blocks\n        for idxstage in range(len(self.stage_repeats)):\n            numrepeat = self.stage_repeats[idxstage]\n            output_channel = self.stage_out_channels[idxstage + 2]\n            for i in range(numrepeat):\n                if i == 0:\n                    # inp, oup, stride, benchmodel):\n                    self.features.append(InvertedResidual(\n                        input_channel, output_channel, 2, 2))\n                else:\n                    self.features.append(InvertedResidual(\n                        input_channel, output_channel, 1, 1))\n                input_channel = output_channel\n\n        # make it nn.Sequential\n        self.features = nn.Sequential(*self.features)\n\n        # building last several layers\n        self.conv_last = conv_1x1_bn(\n            input_channel, self.stage_out_channels[-1])\n        self.globalpool = nn.Sequential(nn.AvgPool2d(int(input_size / 32)))\n\n        # building classifier\n        self.classifier = nn.Sequential(\n            nn.Linear(self.stage_out_channels[-1], n_class))\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.maxpool(x)\n        x = self.features(x)\n        x = self.conv_last(x)\n        x = self.globalpool(x)\n        x = x.view(-1, self.stage_out_channels[-1])\n        x = self.classifier(x)\n        return x\n\n\ndef shufflenetv2(width_mult=1.):\n    model = ShuffleNetV2(width_mult=width_mult)\n    return model\n\n\nif __name__ == \"__main__\":\n    \"\"\"Testing\n    \"\"\"\n    model = ShuffleNetV2()\n    print(model)\n"
  },
  {
    "path": "summary/__init__.py",
    "content": "#-*- coding:utf-8 -*-\nfrom summary.model_summary import model_summary\n"
  },
  {
    "path": "summary/example.py",
    "content": "import torch\nfrom misc.summary import model_summary\nfrom model.MobileNetV2 import MobileNetV2\n\n\nif __name__ == '__main__':\n    net = MobileNetV2()\n    model_summary(net, input_size=(3, 224, 224))"
  },
  {
    "path": "summary/model_hook.py",
    "content": "# -*- coding: utf-8 -*-\n\nimport time\nfrom collections import OrderedDict\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom summary.module_mac import compute_module_mac\n\n\nclass CModelHook(object):\n    def __init__(self, model, input_size):\n        assert isinstance(model, nn.Module)\n        assert isinstance(input_size, (list, tuple))\n\n        self._model = model\n        self._input_size = input_size\n        self._origin_call = dict()  # sub module call hook\n\n        self._hook_model()\n        self._model.eval()\n        self._model(torch.rand(1, *self._input_size))\n\n    @staticmethod\n    def _register_buffer(module):\n        assert isinstance(module, nn.Module)\n\n        if len(list(module.children())) > 0:\n            return\n\n        module.register_buffer('input_shape', torch.zeros(3).int())\n        module.register_buffer('output_shape', torch.zeros(3).int())\n        module.register_buffer('parameter_quantity', torch.zeros(1).int())\n        module.register_buffer('inference_memory', torch.zeros(1).long())\n        module.register_buffer('MAdd', torch.zeros(1).long())\n        module.register_buffer('duration', torch.zeros(1).float())\n\n    def _sub_module_call_hook(self):\n        def wrap_call(module, *input, **kwargs):\n            assert module.__class__ in self._origin_call\n\n            start = time.time()\n            output = self._origin_call[module.__class__](module, *input, **kwargs)\n            end = time.time()\n            module.duration = torch.from_numpy(\n                np.array([end - start], dtype=np.float32))\n\n            module.input_shape = torch.from_numpy(\n                np.array(input[0].size()[1:], dtype=np.int32))\n            module.output_shape = torch.from_numpy(\n                np.array(output.size()[1:], dtype=np.int32))\n\n            parameter_quantity = 0\n            # iterate through parameters and count num params\n            for name, p in module._parameters.items():\n                parameter_quantity += (0 if p is None else torch.numel(p.data))\n            module.parameter_quantity = torch.from_numpy(\n                np.array([parameter_quantity], dtype=np.long))\n\n            inference_memory = 1\n            for s in output.size()[1:]:\n                inference_memory *= s\n            # memory += parameters_number  # exclude parameter memory\n            inference_memory = inference_memory * 4 / (1024 ** 2)  # shown as MB unit\n            module.inference_memory = torch.from_numpy(\n                np.array([inference_memory], dtype=np.float32))\n\n            madd = compute_module_mac(module, input[0], output)\n            module.MAdd = torch.from_numpy(\n                np.array([madd], dtype=np.int64))\n            return output\n\n        for module in self._model.modules():\n            if len(list(module.children())) == 0 and module.__class__ not in self._origin_call:\n                self._origin_call[module.__class__] = module.__class__.__call__\n                module.__class__.__call__ = wrap_call\n\n    def _hook_model(self):\n        self._model.apply(self._register_buffer)\n        self._sub_module_call_hook()\n\n    @staticmethod\n    def _retrieve_leaf_modules(model):\n        leaf_modules = []\n        for name, m in model.named_modules():\n            if len(list(m.children())) == 0:\n                leaf_modules.append((name, m))\n        return leaf_modules\n\n    def retrieve_leaf_modules(self):\n        return OrderedDict(self._retrieve_leaf_modules(self._model))\n"
  },
  {
    "path": "summary/model_summary.py",
    "content": "# -*- coding: utf-8 -*-\n\nimport queue\nfrom collections import OrderedDict\nimport pandas as pd\nimport torch.nn as nn\n\nfrom summary.model_hook import CModelHook\nfrom summary.summary_tree import CSummaryTree, CSummaryNode\n\npd.set_option('display.width', 1000)\npd.set_option('display.max_rows', 10000)\n\n\ndef get_parent_node(root_node, summary_node_name):\n    assert isinstance(root_node, CSummaryNode)\n\n    node = root_node\n    names = summary_node_name.split('.')\n    for i in range(len(names) - 1):\n        node_name = '.'.join(names[0:i+1])\n        child_index = node.find_child_index(node_name)\n        assert child_index != -1\n        node = node.children[child_index]\n    return node\n\n\ndef convert_leaf_modules_to_summary_tree(leaf_modules):\n    assert isinstance(leaf_modules, OrderedDict)\n\n    create_index = 1\n    root_node = CSummaryNode(name='root', parent=None)\n    for leaf_module_name, leaf_module in leaf_modules.items():\n        names = leaf_module_name.split('.')\n        for i in range(len(names)):\n            create_index += 1\n            summary_node_name = '.'.join(names[0:i+1])\n            parent_node = get_parent_node(root_node, summary_node_name)\n            node = CSummaryNode(name=summary_node_name, parent=parent_node)\n            parent_node.add_child(node)\n            if i == len(names) - 1:  # leaf module itself\n                input_shape = leaf_module.input_shape.numpy().tolist()\n                output_shape = leaf_module.output_shape.numpy().tolist()\n                node.input_shape = input_shape\n                node.output_shape = output_shape\n                node.parameter_quantity = leaf_module.parameter_quantity.numpy()[0]\n                node.inference_memory = leaf_module.inference_memory.numpy()[0]\n                node.MAdd = leaf_module.MAdd.numpy()[0]\n                node.duration = leaf_module.duration.numpy()[0]\n    return CSummaryTree(root_node)\n\n\ndef get_collected_summary_nodes(root_node, query_granularity):\n    assert isinstance(root_node, CSummaryNode)\n\n    collected_nodes = []\n    q = queue.Queue()\n    q.put(root_node)\n    while not q.empty():\n        node = q.get()\n        for child in node.children:\n            q.put(child)\n        if node.depth == query_granularity:\n            collected_nodes.append(node)\n        if node.depth < query_granularity <= node.granularity:\n            collected_nodes.append(node)\n    collected_nodes = sorted(collected_nodes, key=lambda x: x.create_index)\n    return collected_nodes\n\n\ndef pretty_format(collected_nodes):\n    data = list()\n    for node in collected_nodes:\n        name = node.name\n        input_shape = ' '.join(['{:>3d}'] * len(node.input_shape)).format(\n            *[e for e in node.input_shape])\n        output_shape = ' '.join(['{:>3d}'] * len(node.output_shape)).format(\n            *[e for e in node.output_shape])\n        parameter_quantity = node.parameter_quantity\n        inference_memory = node.inference_memory\n        MAdd = node.MAdd\n        duration = node.duration\n        data.append([name, input_shape, output_shape,\n                     parameter_quantity, inference_memory, MAdd, duration])\n    df = pd.DataFrame(data)\n    df.columns = ['module name', 'input shape', 'output shape',\n                  'parameter quantity', 'inference memory(MB)',\n                  'MAdd', 'duration']\n    df['duration percent'] = df['duration'] / df['duration'].sum()\n    total_parameters_quantity = df['parameter quantity'].sum()\n    total_memory = df['inference memory(MB)'].sum()\n    total_operation_quantity = df['MAdd'].sum()\n    del df['duration']\n    df = df.fillna(' ')\n    df['inference memory(MB)'] = df['inference memory(MB)'].apply(\n        lambda x: '{:.2f}MB'.format(x))\n    df['duration percent'] = df['duration percent'].apply(lambda x: '{:.2%}'.format(x))\n    df['MAdd'] = df['MAdd'].apply(lambda x: '{:,}'.format(x))\n\n    summary = str(df) + '\\n'\n    summary += \"=\" * len(str(df).split('\\n')[0])\n    summary += '\\n'\n    summary += \"total parameters quantity: {:,}\\n\".format(total_parameters_quantity)\n    summary += \"total memory: {:.2f}MB\\n\".format(total_memory)\n    summary += \"total MAdd: {:,}\\n\".format(total_operation_quantity)\n    print(summary)\n    return summary\n\n\ndef model_summary(model, input_size, query_granularity=1):\n    assert isinstance(model, nn.Module)\n    assert isinstance(input_size, (list, tuple)) and len(input_size) == 3\n\n    model_hook = CModelHook(model, input_size)\n    leaf_modules = model_hook.retrieve_leaf_modules()\n    summary_tree = convert_leaf_modules_to_summary_tree(leaf_modules)\n    collected_nodes = summary_tree.get_collected_summary_nodes(query_granularity)\n    summary = pretty_format(collected_nodes)\n    return summary\n"
  },
  {
    "path": "summary/module_mac.py",
    "content": "#-*- coding:utf-8 -*-\n\"\"\"\ncompute Multiply-Adds(MAdd) of each leaf module\n\"\"\"\n\nimport torch.nn as nn\n\n\ndef compute_Conv2d_mac(module, inp, out):\n    assert isinstance(module, nn.Conv2d)\n    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())\n\n    in_c = inp.size(1)\n    k_h, k_w = module.kernel_size\n    out_c, out_h, out_w = out.size()[1:]\n    groups = module.groups\n\n    # ops per output element\n    kernel_mul = k_h * k_w * (in_c // groups)\n    kernel_add = kernel_mul - 1 + (0 if module.bias is None else 1)\n\n    kernel_mul_group = kernel_mul * out_h * out_w * (out_c // groups)\n    kernel_add_group = kernel_add * out_h * out_w * (out_c // groups)\n\n    total_mul = kernel_mul_group * groups\n    total_add = kernel_add_group * groups\n\n    # return total_mul + total_add\n    return total_mul\n\n\ndef compute_ConvTranspose2d_mac(module, inp, out):\n    assert isinstance(module, nn.ConvTranspose2d)\n    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())\n\n    in_c, in_h, in_w = inp.size()[1:]\n    k_h, k_w = module.kernel_size\n    out_c, out_h, out_w = out.size()[1:]\n    groups = module.groups\n\n    kernel_mul = k_h * k_w * (in_c // groups)\n    kernel_add = kernel_mul - 1 + (0 if module.bias is None else 1)\n\n    kernel_mul_group = kernel_mul * in_h * in_w * (out_c // groups)\n    kernel_add_group = kernel_add * in_h * in_w * (out_c // groups)\n\n    total_mul = kernel_mul_group * groups\n    total_add = kernel_add_group * groups\n\n    # return total_mul + total_add\n    return total_mul\n\n\ndef compute_BatchNorm2d_mac(module, inp, out):\n    assert isinstance(module, nn.BatchNorm2d)\n    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())\n\n    in_c, in_h, in_w = inp.size()[1:]\n\n    # 1. sub mean\n    # 2. div standard deviation\n    # 3. mul alpha\n    # 4. add beta\n    return 4 * in_c * in_h * in_w\n\n\ndef compute_MaxPool2d_mac(module, inp, out):\n    assert isinstance(module, nn.MaxPool2d)\n    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())\n\n    if isinstance(module.kernel_size, (tuple, list)):\n        k_h, k_w = module.kernel_size\n    else:\n        k_h, k_w = module.kernel_size, module.kernel_size\n    out_c, out_h, out_w = out.size()[1:]\n\n    return (k_h * k_w - 1) * out_h * out_w * out_c\n\n\ndef compute_AvgPool2d_mac(module, inp, out):\n    assert isinstance(module, nn.AvgPool2d)\n    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())\n\n    if isinstance(module.kernel_size, (tuple, list)):\n        k_h, k_w = module.kernel_size\n    else:\n        k_h, k_w = module.kernel_size, module.kernel_size\n    out_c, out_h, out_w = out.size()[1:]\n\n    kernel_add = k_h * k_w - 1\n    kernel_avg = 1\n\n    return (kernel_add + kernel_avg) * (out_h * out_w) * out_c\n\n\ndef compute_ReLU_mac(module, inp, out):\n    assert isinstance(module, (nn.ReLU, nn.ReLU6))\n\n    count = 1\n    for i in inp.size()[1:]:\n        count *= i\n    return count\n\n\ndef compute_Softmax_mac(module, inp, out):\n    assert isinstance(module, nn.Softmax)\n    assert len(inp.size()) > 1\n\n    count = 1\n    for s in inp.size()[1:]:\n        count *= s\n    exp = count\n    add = count - 1\n    div = count\n    return exp + add + div\n\n\ndef compute_Linear_mac(module, inp, out):\n    assert isinstance(module, nn.Linear)\n    assert len(inp.size()) == 2 and len(out.size()) == 2\n\n    num_in_features = inp.size(1)\n    num_out_features = out.size(1)\n\n    mul = num_in_features\n    add = num_in_features - 1\n    # return num_out_features * (mul + add)\n    return num_out_features * mul\n\n\ndef compute_module_mac(module, inp, out):\n    if isinstance(module, nn.Conv2d):\n        return compute_Conv2d_mac(module, inp, out)\n    elif isinstance(module, nn.ConvTranspose2d):\n        return compute_ConvTranspose2d_mac(module, inp, out)\n    # elif isinstance(module, nn.BatchNorm2d):\n    #     return compute_BatchNorm2d_mac(module, inp, out)\n    # elif isinstance(module, nn.MaxPool2d):\n    #     return compute_MaxPool2d_mac(module, inp, out)\n    # elif isinstance(module, nn.AvgPool2d):\n    #     return compute_AvgPool2d_mac(module, inp, out)\n    # elif isinstance(module, (nn.ReLU, nn.ReLU6)):\n    #     return compute_ReLU_mac(module, inp, out)\n    # elif isinstance(module, nn.Softmax):\n    #     return compute_Softmax_mac(module, inp, out)\n    elif isinstance(module, nn.Linear):\n        return compute_Linear_mac(module, inp, out)\n    else:\n        return 0\n"
  },
  {
    "path": "summary/summary_tree.py",
    "content": "# -*- coding: utf-8 -*-\n\nimport queue\n\n\nclass CSummaryTree(object):\n    def __init__(self, root_node):\n        assert isinstance(root_node, CSummaryNode)\n\n        self.root_node = root_node\n\n    def get_same_level_max_node_depth(self, query_node):\n        if query_node.name == self.root_node.name:\n            return 0\n        same_level_depth = max([child.depth for child in query_node.parent.children])\n        return same_level_depth\n\n    def update_summary_nodes_granularity(self):\n        q = queue.Queue()\n        q.put(self.root_node)\n        while not q.empty():\n            node = q.get()\n            node.granularity = self.get_same_level_max_node_depth(node)\n            for child in node.children:\n                q.put(child)\n\n    def get_collected_summary_nodes(self, query_granularity):\n        self.update_summary_nodes_granularity()\n\n        collected_nodes = []\n        stack = list()\n        stack.append(self.root_node)\n        while len(stack) > 0:\n            node = stack.pop()\n            for child in reversed(node.children):\n                stack.append(child)\n            if node.depth == query_granularity:\n                collected_nodes.append(node)\n            if node.depth < query_granularity <= node.granularity:\n                collected_nodes.append(node)\n        return collected_nodes\n\n\nclass CSummaryNode(object):\n    def __init__(self, name=str(), parent=None):\n        self._name = name\n        self._input_shape = None\n        self._output_shape = None\n        self._parameter_quantity = 0\n        self._inference_memory = 0\n        self._MAdd = 0\n        self._duration = 0\n        self._duration_percent = 0\n\n        self._granularity = 1\n        self._depth = 1\n        self.parent = parent\n        self.children = list()\n\n    @property\n    def name(self):\n        return self._name\n\n    @name.setter\n    def name(self, name):\n        self._name = name\n\n    @property\n    def granularity(self):\n        return self._granularity\n\n    @granularity.setter\n    def granularity(self, g):\n        self._granularity = g\n\n    @property\n    def depth(self):\n        d = self._depth\n        if len(self.children) > 0:\n            d += max([child.depth for child in self.children])\n        return d\n\n    @property\n    def input_shape(self):\n        if len(self.children) == 0:  # leaf\n            return self._input_shape\n        else:\n            return self.children[0].input_shape\n\n    @input_shape.setter\n    def input_shape(self, input_shape):\n        assert isinstance(input_shape, (list, tuple))\n        self._input_shape = input_shape\n\n    @property\n    def output_shape(self):\n        if len(self.children) == 0:  # leaf\n            return self._output_shape\n        else:\n            return self.children[-1].output_shape\n\n    @output_shape.setter\n    def output_shape(self, output_shape):\n        assert isinstance(output_shape, (list, tuple))\n        self._output_shape = output_shape\n\n    @property\n    def parameter_quantity(self):\n        # return self.parameters_quantity\n        total_parameter_quantity = self._parameter_quantity\n        for child in self.children:\n            total_parameter_quantity += child.parameter_quantity\n        return total_parameter_quantity\n\n    @parameter_quantity.setter\n    def parameter_quantity(self, parameter_quantity):\n        assert parameter_quantity >= 0\n        self._parameter_quantity = parameter_quantity\n\n    @property\n    def inference_memory(self):\n        total_inference_memory = self._inference_memory\n        for child in self.children:\n            total_inference_memory += child.inference_memory\n        return total_inference_memory\n\n    @inference_memory.setter\n    def inference_memory(self, inference_memory):\n        self._inference_memory = inference_memory\n\n    @property\n    def MAdd(self):\n        total_MAdd = self._MAdd\n        for child in self.children:\n            total_MAdd += child.MAdd\n        return total_MAdd\n\n    @MAdd.setter\n    def MAdd(self, MAdd):\n        self._MAdd = MAdd\n\n    @property\n    def duration(self):\n        total_duration = self._duration\n        for child in self.children:\n            total_duration += child.duration\n        return total_duration\n\n    @duration.setter\n    def duration(self, duration):\n        self._duration = duration\n\n    def find_child_index(self, child_name):\n        assert isinstance(child_name, str)\n\n        index = -1\n        for i in range(len(self.children)):\n            if child_name == self.children[i].name:\n                index = i\n        return index\n\n    def add_child(self, node):\n        assert isinstance(node, CSummaryNode)\n\n        if self.find_child_index(node.name) == -1:  # not exist\n            self.children.append(node)\n"
  },
  {
    "path": "test/model/haarcascade_frontalface_default.xml",
    "content": "<?xml version=\"1.0\"?>\n<!--\n    Stump-based 24x24 discrete(?) adaboost frontal face detector.\n    Created by Rainer Lienhart.\n\n////////////////////////////////////////////////////////////////////////////////////////\n\n  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.\n\n  By downloading, copying, installing or using the software you agree to this license.\n  If you do not agree to this license, do not download, install,\n  copy or use the software.\n\n\n                        Intel License Agreement\n                For Open Source Computer Vision Library\n\n Copyright (C) 2000, Intel Corporation, all rights reserved.\n Third party copyrights are property of their respective owners.\n\n Redistribution and use in source and binary forms, with or without modification,\n are permitted provided that the following conditions are met:\n\n   * Redistribution's of source code must retain the above copyright notice,\n     this list of conditions and the following disclaimer.\n\n   * Redistribution's in binary form must reproduce the above copyright notice,\n     this list of conditions and the following disclaimer in the documentation\n     and/or other materials provided with the distribution.\n\n   * The name of Intel Corporation may not be used to endorse or promote products\n     derived from this software without specific prior written permission.\n\n This software is provided by the copyright holders and contributors \"as is\" and\n any express or implied warranties, including, but not limited to, the implied\n warranties of merchantability and fitness for a particular purpose are disclaimed.\n In no event shall the Intel Corporation or contributors be liable for any direct,\n indirect, incidental, special, exemplary, or consequential damages\n (including, but not limited to, procurement of substitute goods or services;\n loss of use, data, or profits; or business interruption) however caused\n and on any theory of liability, 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  },
  {
    "path": "test/shufflenetv2.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom collections import OrderedDict\nfrom torch.nn import init\nimport math\n\n\ndef conv_bn(inp, oup, stride):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n        nn.BatchNorm2d(oup),\n        nn.ReLU(inplace=True)\n    )\n\n\ndef conv_1x1_bn(inp, oup):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        nn.BatchNorm2d(oup),\n        nn.ReLU(inplace=True)\n    )\n\n\ndef channel_shuffle(x, groups):\n    batchsize, num_channels, height, width = x.data.size()\n\n    channels_per_group = num_channels // groups\n\n    # reshape\n    x = x.view(batchsize, groups, channels_per_group, height, width)\n\n    x = torch.transpose(x, 1, 2).contiguous()\n\n    # flatten\n    x = x.view(batchsize, -1, height, width)\n\n    return x\n\n\nclass InvertedResidual(nn.Module):\n    def __init__(self, inp, oup, stride, benchmodel):\n        super(InvertedResidual, self).__init__()\n        self.benchmodel = benchmodel\n        self.stride = stride\n        assert stride in [1, 2]\n\n        oup_inc = oup // 2\n\n        if self.benchmodel == 1:\n            # assert inp == oup_inc\n            self.banch2 = nn.Sequential(\n                # pw\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n                # dw\n                nn.Conv2d(oup_inc, oup_inc, 3, stride,\n                          1, groups=oup_inc, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                # pw-linear\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n        else:\n            self.banch1 = nn.Sequential(\n                # dw\n                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n                nn.BatchNorm2d(inp),\n                # pw-linear\n                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n\n            self.banch2 = nn.Sequential(\n                # pw\n                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n                # dw\n                nn.Conv2d(oup_inc, oup_inc, 3, stride,\n                          1, groups=oup_inc, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                # pw-linear\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n\n    @staticmethod\n    def _concat(x, out):\n        # concatenate along channel axis\n        return torch.cat((x, out), 1)\n\n    def forward(self, x):\n        if 1 == self.benchmodel:\n            # x1 = x[:, :(x.shape[1] // 2), :, :]\n            # x2 = x[:, (x.shape[1] // 2):, :, :]\n            x1, x2 = torch.split(x, x.shape[1] // 2, dim=1)\n            out = self._concat(x1, self.banch2(x2))\n        elif 2 == self.benchmodel:\n            out = self._concat(self.banch1(x), self.banch2(x))\n\n        return channel_shuffle(out, 2)\n\n\nclass ShuffleNetV2(nn.Module):\n    def __init__(self, input_size, n_class=200,  width_mult=1): #1,0\n        super(ShuffleNetV2, self).__init__()\n\n        assert input_size % 32 == 0\n\n        self.stage_repeats = [4, 8, 4]\n        # index 0 is invalid and should never be called.\n        # only used for indexing convenience.\n        if width_mult == 0.5:\n            self.stage_out_channels = [-1, 24, 48, 96, 192, 192] #175\n        elif width_mult == 1.0:\n            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024] # 1024\n        elif width_mult == 1.5:\n            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]\n        elif width_mult == 2.0:\n            self.stage_out_channels = [-1, 24, 224, 488, 976, 2048]\n        else:\n            raise ValueError(\n                \"\"\"width_mult = {} is not supported\"\"\".format(width_mult))\n\n        # building first layer\n        input_channel = self.stage_out_channels[1]\n        self.conv1 = conv_bn(3, input_channel, 2)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        self.features = []\n        # building inverted residual blocks\n        for idxstage in range(len(self.stage_repeats)):\n            numrepeat = self.stage_repeats[idxstage]\n            output_channel = self.stage_out_channels[idxstage + 2]\n            for i in range(numrepeat):\n                if i == 0:\n                    # inp, oup, stride, benchmodel):\n                    self.features.append(InvertedResidual(\n                        input_channel, output_channel, 2, 2))\n                else:\n                    self.features.append(InvertedResidual(\n                        input_channel, output_channel, 1, 1))\n                input_channel = output_channel\n\n        # make it nn.Sequential\n        self.features = nn.Sequential(*self.features)\n\n        # building last several layers\n        self.conv_last = conv_1x1_bn(\n            input_channel, self.stage_out_channels[-1])\n        self.globalpool = nn.Sequential(nn.AvgPool2d(int(input_size / 32)))\n\n        # building classifier\n        self.classifier = nn.Sequential(\n            nn.Linear(self.stage_out_channels[-1], n_class))\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.maxpool(x)\n        x = self.features(x)\n        x = self.conv_last(x)\n        x = self.globalpool(x)\n        x = x.view(-1, self.stage_out_channels[-1])\n        x = self.classifier(x)\n        return x\n\n\ndef shufflenetv2(width_mult=1.):\n    model = ShuffleNetV2(width_mult=width_mult)\n    return model\n\n\nif __name__ == \"__main__\":\n    \"\"\"Testing\n    \"\"\"\n    model = ShuffleNetV2()\n    print(model)\n"
  },
  {
    "path": "test/test.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.autograd import Variable\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport time\nimport os\nfrom PIL import Image\nimport sys\nimport torch.nn.functional as F\nfrom shufflenetv2 import ShuffleNetV2\nimport scipy.io as scio\nimport cv2\n\n#config\nmatFile = 'pScores.mat'\nfiiqaWeight = './model/97_160_2.pth'\ndetectFace = './model/haarcascade_frontalface_default.xml'\nimagePath = './image/00921_960627_fa_illu_cafe-sunshade-2pm-2_extracted.jpg'\nfacePath = './image/crop/test_face.jpg'\ninputSize = 160\n\n#crop face from img\nfaceCascade = cv2.CascadeClassifier(detectFace)\nimage = cv2.imread(imagePath)\nfaces = faceCascade.detectMultiScale(\n    image,\n    scaleFactor=1.1,\n    minNeighbors=5,\n    minSize=(10, 10)\n)\nfor (x, y, w, h) in faces:\n    cv2.imwrite('./image/crop/' + 'test_face.jpg',image[y:y+h,x:x+w])\ncv2.waitKey(0)\n\n#transfer img to tensor\ndataTransforms =  transforms.Compose([\n               transforms.Resize(inputSize),\n               transforms.ToTensor(),\n               transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n])\n\n#load net \nnet = ShuffleNetV2(inputSize)\ncheckpoint = torch.load(fiiqaWeight)\nnet.load_state_dict(checkpoint['net'])\nnet.eval()\n\n#load face and get expect num\nface = Image.open(facePath)\nimgblob = dataTransforms(face).unsqueeze(0)\n#imgblob = Variable(imgblob)\ntorch.no_grad()\npredict = F.softmax(net(imgblob),dim=1)\nexpect = torch.sum(Variable(torch.arange(0,200)).float()*predict, 1)\nprint('expect: %.4f' % expect)\nexpect = int(expect)\n\n#load matFile and get score\ndata = scio.loadmat(matFile)\nscores = data['pScores']\nscore = scores[:,expect]\n\nprint('expect: %d' % expect)\nprint('score: %.3f' %  score)\n"
  },
  {
    "path": "test/test_without_crop.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.autograd import Variable\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport time\nimport os\nfrom PIL import Image\nimport sys\nimport torch.nn.functional as F\nfrom shufflenetv2 import ShuffleNetV2\nimport scipy.io as scio\nimport cv2\n\n#config\nmatFile = 'pScores.mat'\nfiiqaWeight = './model/97_160_2.pth'\ndetectFace = './model/haarcascade_frontalface_default.xml'\nimagePath = './image/00921_960627_fa_illu_cafe-sunshade-2pm-2_extracted.jpg'\nfacePath = './image/crop/00921_960627_fa_illu_cafe-inside-2_extracted.jpg'\ninputSize = 160\n\n'''\n#crop face from img\nfaceCascade = cv2.CascadeClassifier(detectFace)\nimage = cv2.imread(imagePath)\nfaces = faceCascade.detectMultiScale(\n    image,\n    scaleFactor=1.1,\n    minNeighbors=5,\n    minSize=(10, 10)\n)\nfor (x, y, w, h) in faces:\n    cv2.imwrite('./image/crop/' + 'test_face.jpg',image[y:y+h,x:x+w])\ncv2.waitKey(0)\n'''\n\n#transfer img to tensor\ndataTransforms =  transforms.Compose([\n               transforms.Resize(inputSize),\n               transforms.ToTensor(),\n               transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n])\n\n#load net \nnet = ShuffleNetV2(inputSize)\ncheckpoint = torch.load(fiiqaWeight)\nnet.load_state_dict(checkpoint['net'])\nnet.eval()\n\n#load face and get expect num\nface = Image.open(facePath)\nimgblob = dataTransforms(face).unsqueeze(0)\nimgblob = Variable(imgblob)\ntorch.no_grad()\npredict = F.softmax(net(imgblob),dim=1)\nexpect = torch.sum(Variable(torch.arange(0,200)).float()*predict, 1)\nprint('expect: %.4f' % expect)\nexpect = int(expect)\n\n#load matFile and get score\ndata = scio.loadmat(matFile)\nscores = data['pScores']\nscore = scores[:,expect]\n\nprint('expect: %d' % expect)\nprint('score: %.3f' %  score)\n"
  },
  {
    "path": "test.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.autograd import Variable\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport time\nimport os\nfrom PIL import Image\nimport sys\nimport torch.nn.functional as F\nfrom shufflenetv2 import ShuffleNetV2\nimport scipy.io as scio\nimport cv2\n\n#config\nmatFile = 'pScores.mat'\nfiiqaWeight = './model/97_160_2.pth'\ndetectFace = './model/haarcascade_frontalface_default.xml'\nimagePath = './image/test.jpg'\nfacePath = './image/crop/test_face.jpg'\ninputSize = 160\n\n#crop face from img\nfaceCascade = cv2.CascadeClassifier(detectFace)\nimage = cv2.imread(imagePath)\nfaces = faceCascade.detectMultiScale(\n    image,\n    scaleFactor=1.1,\n    minNeighbors=5,\n    minSize=(10, 10)\n)\nfor (x, y, w, h) in faces:\n    cv2.imwrite('./image/crop/' + 'test_face.jpg',image[y:y+h,x:x+w])\ncv2.waitKey(0)\n\n#transfer img to tensor\ndataTransforms =  transforms.Compose([\n               transforms.Resize(inputSize),\n               transforms.ToTensor(),\n               transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n])\n\n#load net \nnet = ShuffleNetV2(inputSize)\ncheckpoint = torch.load(fiiqaWeight)\nnet.load_state_dict(checkpoint['net'])\nnet.eval()\n\n#load face and get expect num\nface = Image.open(facePath)\nimgblob = dataTransforms(face).unsqueeze(0)\nimgblob = Variable(imgblob)\ntorch.no_grad()\npredict = F.softmax(net(imgblob),dim=1)\nexpect = torch.sum(Variable(torch.arange(0,200)).float()*predict, 1)\nexpect = int(expect)\n\n#load matFile and get score\ndata = scio.loadmat(matFile)\nscores = data['pScores']\nscore = scores[:,expect]\n\nprint('expect: %d' % expect)\nprint('score: %.3f' %  score)\n"
  },
  {
    "path": "test_convert_to_caffe/httpClient.py",
    "content": "import urllib.request\nimport json\n   \nif __name__ == \"__main__\":\n    restUri = 'http://172.16.16.169:8081/facedetect'\n    PostParam = './image/test2.jpg'\n    DATA = PostParam.encode('utf8')\n    req = urllib.request.Request(url = restUri, data=DATA, method='POST')\n    req.add_header('Content-type', 'application/form-data')\n    r = urllib.request.urlopen(req).read()\n    print(r.decode('utf8'))\n    org_obj = json.loads(r.decode('utf8'))\n    print(org_obj['token'])"
  },
  {
    "path": "test_convert_to_caffe/model/fiiqa_shufflenetv2_1.0x.prototxt",
    "content": "name: \"shufflenetv2_1.0x\"\nlayer {\n  name: \"data1\"\n  type: \"Input\"\n  top: \"data1\"\n  input_param {\n    shape {\n      dim: 1\n      dim: 3\n      dim: 160\n      dim: 160\n    }\n  }\n}\nlayer {\n  name: \"conv1\"\n  type: \"Convolution\"\n  bottom: \"data1\"\n  top: \"conv1\"\n  convolution_param {\n    num_output: 24\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    group: 1\n    stride: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm1\"\n  type: \"BatchNorm\"\n  bottom: \"conv1\"\n  top: \"batch_norm1\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale1\"\n  type: \"Scale\"\n  bottom: \"batch_norm1\"\n  top: \"batch_norm1\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu1\"\n  type: \"ReLU\"\n  bottom: \"batch_norm1\"\n  top: \"relu1\"\n}\nlayer {\n  name: \"max_pool1\"\n  type: \"Pooling\"\n  bottom: \"relu1\"\n  top: \"max_pool1\"\n  pooling_param {\n    pool: MAX\n    kernel_size: 3\n    stride: 2\n    pad: 1\n    round_mode: FLOOR\n  }\n}\nlayer {\n  name: \"conv2\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"max_pool1\"\n  top: \"conv2\"\n  convolution_param {\n    num_output: 24\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm2\"\n  type: \"BatchNorm\"\n  bottom: \"conv2\"\n  top: \"batch_norm2\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale2\"\n  type: \"Scale\"\n  bottom: \"batch_norm2\"\n  top: \"batch_norm2\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv3\"\n  type: \"Convolution\"\n  bottom: \"batch_norm2\"\n  top: \"conv3\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm3\"\n  type: \"BatchNorm\"\n  bottom: \"conv3\"\n  top: \"batch_norm3\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale3\"\n  type: \"Scale\"\n  bottom: \"batch_norm3\"\n  top: \"batch_norm3\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu2\"\n  type: \"ReLU\"\n  bottom: \"batch_norm3\"\n  top: \"relu2\"\n}\nlayer {\n  name: \"conv4\"\n  type: \"Convolution\"\n  bottom: \"max_pool1\"\n  top: \"conv4\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm4\"\n  type: \"BatchNorm\"\n  bottom: \"conv4\"\n  top: \"batch_norm4\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale4\"\n  type: \"Scale\"\n  bottom: \"batch_norm4\"\n  top: \"batch_norm4\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu3\"\n  type: \"ReLU\"\n  bottom: \"batch_norm4\"\n  top: \"relu3\"\n}\nlayer {\n  name: \"conv5\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu3\"\n  top: \"conv5\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm5\"\n  type: \"BatchNorm\"\n  bottom: \"conv5\"\n  top: \"batch_norm5\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale5\"\n  type: \"Scale\"\n  bottom: \"batch_norm5\"\n  top: \"batch_norm5\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv6\"\n  type: \"Convolution\"\n  bottom: \"batch_norm5\"\n  top: \"conv6\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm6\"\n  type: \"BatchNorm\"\n  bottom: \"conv6\"\n  top: \"batch_norm6\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale6\"\n  type: \"Scale\"\n  bottom: \"batch_norm6\"\n  top: \"batch_norm6\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu4\"\n  type: \"ReLU\"\n  bottom: \"batch_norm6\"\n  top: \"relu4\"\n}\nlayer {\n  name: \"concat1\"\n  type: \"Concat\"\n  bottom: \"relu2\"\n  bottom: \"relu4\"\n  top: \"concat1\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel1\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat1\"\n  top: \"shuffle_channel1\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split1\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel1\"\n  top: \"split1\"\n  top: \"split2\"\n  slice_param {\n    slice_point: 58\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv7\"\n  type: \"Convolution\"\n  bottom: \"split2\"\n  top: \"conv7\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm7\"\n  type: \"BatchNorm\"\n  bottom: \"conv7\"\n  top: \"batch_norm7\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale7\"\n  type: \"Scale\"\n  bottom: \"batch_norm7\"\n  top: \"batch_norm7\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu5\"\n  type: \"ReLU\"\n  bottom: \"batch_norm7\"\n  top: \"relu5\"\n}\nlayer {\n  name: \"conv8\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu5\"\n  top: \"conv8\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm8\"\n  type: \"BatchNorm\"\n  bottom: \"conv8\"\n  top: \"batch_norm8\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale8\"\n  type: \"Scale\"\n  bottom: \"batch_norm8\"\n  top: \"batch_norm8\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv9\"\n  type: \"Convolution\"\n  bottom: \"batch_norm8\"\n  top: \"conv9\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm9\"\n  type: \"BatchNorm\"\n  bottom: \"conv9\"\n  top: \"batch_norm9\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale9\"\n  type: \"Scale\"\n  bottom: \"batch_norm9\"\n  top: \"batch_norm9\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu6\"\n  type: \"ReLU\"\n  bottom: \"batch_norm9\"\n  top: \"relu6\"\n}\nlayer {\n  name: \"concat2\"\n  type: \"Concat\"\n  bottom: \"split1\"\n  bottom: \"relu6\"\n  top: \"concat2\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel2\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat2\"\n  top: \"shuffle_channel2\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split2\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel2\"\n  top: \"split3\"\n  top: \"split4\"\n  slice_param {\n    slice_point: 58\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv10\"\n  type: \"Convolution\"\n  bottom: \"split4\"\n  top: \"conv10\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm10\"\n  type: \"BatchNorm\"\n  bottom: \"conv10\"\n  top: \"batch_norm10\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale10\"\n  type: \"Scale\"\n  bottom: \"batch_norm10\"\n  top: \"batch_norm10\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu7\"\n  type: \"ReLU\"\n  bottom: \"batch_norm10\"\n  top: \"relu7\"\n}\nlayer {\n  name: \"conv11\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu7\"\n  top: \"conv11\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm11\"\n  type: \"BatchNorm\"\n  bottom: \"conv11\"\n  top: \"batch_norm11\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale11\"\n  type: \"Scale\"\n  bottom: \"batch_norm11\"\n  top: \"batch_norm11\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv12\"\n  type: \"Convolution\"\n  bottom: \"batch_norm11\"\n  top: \"conv12\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm12\"\n  type: \"BatchNorm\"\n  bottom: \"conv12\"\n  top: \"batch_norm12\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale12\"\n  type: \"Scale\"\n  bottom: \"batch_norm12\"\n  top: \"batch_norm12\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu8\"\n  type: \"ReLU\"\n  bottom: \"batch_norm12\"\n  top: \"relu8\"\n}\nlayer {\n  name: \"concat3\"\n  type: \"Concat\"\n  bottom: \"split3\"\n  bottom: \"relu8\"\n  top: \"concat3\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel3\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat3\"\n  top: \"shuffle_channel3\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split3\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel3\"\n  top: \"split5\"\n  top: \"split6\"\n  slice_param {\n    slice_point: 58\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv13\"\n  type: \"Convolution\"\n  bottom: \"split6\"\n  top: \"conv13\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm13\"\n  type: \"BatchNorm\"\n  bottom: \"conv13\"\n  top: \"batch_norm13\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale13\"\n  type: \"Scale\"\n  bottom: \"batch_norm13\"\n  top: \"batch_norm13\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu9\"\n  type: \"ReLU\"\n  bottom: \"batch_norm13\"\n  top: \"relu9\"\n}\nlayer {\n  name: \"conv14\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu9\"\n  top: \"conv14\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm14\"\n  type: \"BatchNorm\"\n  bottom: \"conv14\"\n  top: \"batch_norm14\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale14\"\n  type: \"Scale\"\n  bottom: \"batch_norm14\"\n  top: \"batch_norm14\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv15\"\n  type: \"Convolution\"\n  bottom: \"batch_norm14\"\n  top: \"conv15\"\n  convolution_param {\n    num_output: 58\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm15\"\n  type: \"BatchNorm\"\n  bottom: \"conv15\"\n  top: \"batch_norm15\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale15\"\n  type: \"Scale\"\n  bottom: \"batch_norm15\"\n  top: \"batch_norm15\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu10\"\n  type: \"ReLU\"\n  bottom: \"batch_norm15\"\n  top: \"relu10\"\n}\nlayer {\n  name: \"concat4\"\n  type: \"Concat\"\n  bottom: \"split5\"\n  bottom: \"relu10\"\n  top: \"concat4\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel4\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat4\"\n  top: \"shuffle_channel4\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"conv16\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"shuffle_channel4\"\n  top: \"conv16\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm16\"\n  type: \"BatchNorm\"\n  bottom: \"conv16\"\n  top: \"batch_norm16\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale16\"\n  type: \"Scale\"\n  bottom: \"batch_norm16\"\n  top: \"batch_norm16\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv17\"\n  type: \"Convolution\"\n  bottom: \"batch_norm16\"\n  top: \"conv17\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm17\"\n  type: \"BatchNorm\"\n  bottom: \"conv17\"\n  top: \"batch_norm17\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale17\"\n  type: \"Scale\"\n  bottom: \"batch_norm17\"\n  top: \"batch_norm17\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu11\"\n  type: \"ReLU\"\n  bottom: \"batch_norm17\"\n  top: \"relu11\"\n}\nlayer {\n  name: \"conv18\"\n  type: \"Convolution\"\n  bottom: \"shuffle_channel4\"\n  top: \"conv18\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm18\"\n  type: \"BatchNorm\"\n  bottom: \"conv18\"\n  top: \"batch_norm18\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale18\"\n  type: \"Scale\"\n  bottom: \"batch_norm18\"\n  top: \"batch_norm18\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu12\"\n  type: \"ReLU\"\n  bottom: \"batch_norm18\"\n  top: \"relu12\"\n}\nlayer {\n  name: \"conv19\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu12\"\n  top: \"conv19\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm19\"\n  type: \"BatchNorm\"\n  bottom: \"conv19\"\n  top: \"batch_norm19\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale19\"\n  type: \"Scale\"\n  bottom: \"batch_norm19\"\n  top: \"batch_norm19\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv20\"\n  type: \"Convolution\"\n  bottom: \"batch_norm19\"\n  top: \"conv20\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm20\"\n  type: \"BatchNorm\"\n  bottom: \"conv20\"\n  top: \"batch_norm20\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale20\"\n  type: \"Scale\"\n  bottom: \"batch_norm20\"\n  top: \"batch_norm20\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu13\"\n  type: \"ReLU\"\n  bottom: \"batch_norm20\"\n  top: \"relu13\"\n}\nlayer {\n  name: \"concat5\"\n  type: \"Concat\"\n  bottom: \"relu11\"\n  bottom: \"relu13\"\n  top: \"concat5\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel5\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat5\"\n  top: \"shuffle_channel5\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split4\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel5\"\n  top: \"split7\"\n  top: \"split8\"\n  slice_param {\n    slice_point: 116\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv21\"\n  type: \"Convolution\"\n  bottom: \"split8\"\n  top: \"conv21\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm21\"\n  type: \"BatchNorm\"\n  bottom: \"conv21\"\n  top: \"batch_norm21\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale21\"\n  type: \"Scale\"\n  bottom: \"batch_norm21\"\n  top: \"batch_norm21\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu14\"\n  type: \"ReLU\"\n  bottom: \"batch_norm21\"\n  top: \"relu14\"\n}\nlayer {\n  name: \"conv22\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu14\"\n  top: \"conv22\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm22\"\n  type: \"BatchNorm\"\n  bottom: \"conv22\"\n  top: \"batch_norm22\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale22\"\n  type: \"Scale\"\n  bottom: \"batch_norm22\"\n  top: \"batch_norm22\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv23\"\n  type: \"Convolution\"\n  bottom: \"batch_norm22\"\n  top: \"conv23\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm23\"\n  type: \"BatchNorm\"\n  bottom: \"conv23\"\n  top: \"batch_norm23\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale23\"\n  type: \"Scale\"\n  bottom: \"batch_norm23\"\n  top: \"batch_norm23\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu15\"\n  type: \"ReLU\"\n  bottom: \"batch_norm23\"\n  top: \"relu15\"\n}\nlayer {\n  name: \"concat6\"\n  type: \"Concat\"\n  bottom: \"split7\"\n  bottom: \"relu15\"\n  top: \"concat6\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel6\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat6\"\n  top: \"shuffle_channel6\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split5\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel6\"\n  top: \"split9\"\n  top: \"split10\"\n  slice_param {\n    slice_point: 116\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv24\"\n  type: \"Convolution\"\n  bottom: \"split10\"\n  top: \"conv24\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm24\"\n  type: \"BatchNorm\"\n  bottom: \"conv24\"\n  top: \"batch_norm24\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale24\"\n  type: \"Scale\"\n  bottom: \"batch_norm24\"\n  top: \"batch_norm24\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu16\"\n  type: \"ReLU\"\n  bottom: \"batch_norm24\"\n  top: \"relu16\"\n}\nlayer {\n  name: \"conv25\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu16\"\n  top: \"conv25\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm25\"\n  type: \"BatchNorm\"\n  bottom: \"conv25\"\n  top: \"batch_norm25\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale25\"\n  type: \"Scale\"\n  bottom: \"batch_norm25\"\n  top: \"batch_norm25\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv26\"\n  type: \"Convolution\"\n  bottom: \"batch_norm25\"\n  top: \"conv26\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm26\"\n  type: \"BatchNorm\"\n  bottom: \"conv26\"\n  top: \"batch_norm26\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale26\"\n  type: \"Scale\"\n  bottom: \"batch_norm26\"\n  top: \"batch_norm26\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu17\"\n  type: \"ReLU\"\n  bottom: \"batch_norm26\"\n  top: \"relu17\"\n}\nlayer {\n  name: \"concat7\"\n  type: \"Concat\"\n  bottom: \"split9\"\n  bottom: \"relu17\"\n  top: \"concat7\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel7\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat7\"\n  top: \"shuffle_channel7\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split6\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel7\"\n  top: \"split11\"\n  top: \"split12\"\n  slice_param {\n    slice_point: 116\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv27\"\n  type: \"Convolution\"\n  bottom: \"split12\"\n  top: \"conv27\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm27\"\n  type: \"BatchNorm\"\n  bottom: \"conv27\"\n  top: \"batch_norm27\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale27\"\n  type: \"Scale\"\n  bottom: \"batch_norm27\"\n  top: \"batch_norm27\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu18\"\n  type: \"ReLU\"\n  bottom: \"batch_norm27\"\n  top: \"relu18\"\n}\nlayer {\n  name: \"conv28\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu18\"\n  top: \"conv28\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm28\"\n  type: \"BatchNorm\"\n  bottom: \"conv28\"\n  top: \"batch_norm28\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale28\"\n  type: \"Scale\"\n  bottom: \"batch_norm28\"\n  top: \"batch_norm28\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv29\"\n  type: \"Convolution\"\n  bottom: \"batch_norm28\"\n  top: \"conv29\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm29\"\n  type: \"BatchNorm\"\n  bottom: \"conv29\"\n  top: \"batch_norm29\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale29\"\n  type: \"Scale\"\n  bottom: \"batch_norm29\"\n  top: \"batch_norm29\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu19\"\n  type: \"ReLU\"\n  bottom: \"batch_norm29\"\n  top: \"relu19\"\n}\nlayer {\n  name: \"concat8\"\n  type: \"Concat\"\n  bottom: \"split11\"\n  bottom: \"relu19\"\n  top: \"concat8\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel8\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat8\"\n  top: \"shuffle_channel8\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split7\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel8\"\n  top: \"split13\"\n  top: \"split14\"\n  slice_param {\n    slice_point: 116\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv30\"\n  type: \"Convolution\"\n  bottom: \"split14\"\n  top: \"conv30\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm30\"\n  type: \"BatchNorm\"\n  bottom: \"conv30\"\n  top: \"batch_norm30\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale30\"\n  type: \"Scale\"\n  bottom: \"batch_norm30\"\n  top: \"batch_norm30\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu20\"\n  type: \"ReLU\"\n  bottom: \"batch_norm30\"\n  top: \"relu20\"\n}\nlayer {\n  name: \"conv31\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu20\"\n  top: \"conv31\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm31\"\n  type: \"BatchNorm\"\n  bottom: \"conv31\"\n  top: \"batch_norm31\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale31\"\n  type: \"Scale\"\n  bottom: \"batch_norm31\"\n  top: \"batch_norm31\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv32\"\n  type: \"Convolution\"\n  bottom: \"batch_norm31\"\n  top: \"conv32\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm32\"\n  type: \"BatchNorm\"\n  bottom: \"conv32\"\n  top: \"batch_norm32\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale32\"\n  type: \"Scale\"\n  bottom: \"batch_norm32\"\n  top: \"batch_norm32\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu21\"\n  type: \"ReLU\"\n  bottom: \"batch_norm32\"\n  top: \"relu21\"\n}\nlayer {\n  name: \"concat9\"\n  type: \"Concat\"\n  bottom: \"split13\"\n  bottom: \"relu21\"\n  top: \"concat9\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel9\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat9\"\n  top: \"shuffle_channel9\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split8\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel9\"\n  top: \"split15\"\n  top: \"split16\"\n  slice_param {\n    slice_point: 116\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv33\"\n  type: \"Convolution\"\n  bottom: \"split16\"\n  top: \"conv33\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm33\"\n  type: \"BatchNorm\"\n  bottom: \"conv33\"\n  top: \"batch_norm33\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale33\"\n  type: \"Scale\"\n  bottom: \"batch_norm33\"\n  top: \"batch_norm33\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu22\"\n  type: \"ReLU\"\n  bottom: \"batch_norm33\"\n  top: \"relu22\"\n}\nlayer {\n  name: \"conv34\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu22\"\n  top: \"conv34\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm34\"\n  type: \"BatchNorm\"\n  bottom: \"conv34\"\n  top: \"batch_norm34\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale34\"\n  type: \"Scale\"\n  bottom: \"batch_norm34\"\n  top: \"batch_norm34\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv35\"\n  type: \"Convolution\"\n  bottom: \"batch_norm34\"\n  top: \"conv35\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm35\"\n  type: \"BatchNorm\"\n  bottom: \"conv35\"\n  top: \"batch_norm35\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale35\"\n  type: \"Scale\"\n  bottom: \"batch_norm35\"\n  top: \"batch_norm35\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu23\"\n  type: \"ReLU\"\n  bottom: \"batch_norm35\"\n  top: \"relu23\"\n}\nlayer {\n  name: \"concat10\"\n  type: \"Concat\"\n  bottom: \"split15\"\n  bottom: \"relu23\"\n  top: \"concat10\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel10\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat10\"\n  top: \"shuffle_channel10\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split9\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel10\"\n  top: \"split17\"\n  top: \"split18\"\n  slice_param {\n    slice_point: 116\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv36\"\n  type: \"Convolution\"\n  bottom: \"split18\"\n  top: \"conv36\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm36\"\n  type: \"BatchNorm\"\n  bottom: \"conv36\"\n  top: \"batch_norm36\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale36\"\n  type: \"Scale\"\n  bottom: \"batch_norm36\"\n  top: \"batch_norm36\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu24\"\n  type: \"ReLU\"\n  bottom: \"batch_norm36\"\n  top: \"relu24\"\n}\nlayer {\n  name: \"conv37\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu24\"\n  top: \"conv37\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm37\"\n  type: \"BatchNorm\"\n  bottom: \"conv37\"\n  top: \"batch_norm37\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale37\"\n  type: \"Scale\"\n  bottom: \"batch_norm37\"\n  top: \"batch_norm37\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv38\"\n  type: \"Convolution\"\n  bottom: \"batch_norm37\"\n  top: \"conv38\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm38\"\n  type: \"BatchNorm\"\n  bottom: \"conv38\"\n  top: \"batch_norm38\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale38\"\n  type: \"Scale\"\n  bottom: \"batch_norm38\"\n  top: \"batch_norm38\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu25\"\n  type: \"ReLU\"\n  bottom: \"batch_norm38\"\n  top: \"relu25\"\n}\nlayer {\n  name: \"concat11\"\n  type: \"Concat\"\n  bottom: \"split17\"\n  bottom: \"relu25\"\n  top: \"concat11\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel11\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat11\"\n  top: \"shuffle_channel11\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split10\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel11\"\n  top: \"split19\"\n  top: \"split20\"\n  slice_param {\n    slice_point: 116\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv39\"\n  type: \"Convolution\"\n  bottom: \"split20\"\n  top: \"conv39\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm39\"\n  type: \"BatchNorm\"\n  bottom: \"conv39\"\n  top: \"batch_norm39\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale39\"\n  type: \"Scale\"\n  bottom: \"batch_norm39\"\n  top: \"batch_norm39\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu26\"\n  type: \"ReLU\"\n  bottom: \"batch_norm39\"\n  top: \"relu26\"\n}\nlayer {\n  name: \"conv40\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu26\"\n  top: \"conv40\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm40\"\n  type: \"BatchNorm\"\n  bottom: \"conv40\"\n  top: \"batch_norm40\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale40\"\n  type: \"Scale\"\n  bottom: \"batch_norm40\"\n  top: \"batch_norm40\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv41\"\n  type: \"Convolution\"\n  bottom: \"batch_norm40\"\n  top: \"conv41\"\n  convolution_param {\n    num_output: 116\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm41\"\n  type: \"BatchNorm\"\n  bottom: \"conv41\"\n  top: \"batch_norm41\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale41\"\n  type: \"Scale\"\n  bottom: \"batch_norm41\"\n  top: \"batch_norm41\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu27\"\n  type: \"ReLU\"\n  bottom: \"batch_norm41\"\n  top: \"relu27\"\n}\nlayer {\n  name: \"concat12\"\n  type: \"Concat\"\n  bottom: \"split19\"\n  bottom: \"relu27\"\n  top: \"concat12\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel12\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat12\"\n  top: \"shuffle_channel12\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"conv42\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"shuffle_channel12\"\n  top: \"conv42\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm42\"\n  type: \"BatchNorm\"\n  bottom: \"conv42\"\n  top: \"batch_norm42\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale42\"\n  type: \"Scale\"\n  bottom: \"batch_norm42\"\n  top: \"batch_norm42\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv43\"\n  type: \"Convolution\"\n  bottom: \"batch_norm42\"\n  top: \"conv43\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm43\"\n  type: \"BatchNorm\"\n  bottom: \"conv43\"\n  top: \"batch_norm43\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale43\"\n  type: \"Scale\"\n  bottom: \"batch_norm43\"\n  top: \"batch_norm43\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu28\"\n  type: \"ReLU\"\n  bottom: \"batch_norm43\"\n  top: \"relu28\"\n}\nlayer {\n  name: \"conv44\"\n  type: \"Convolution\"\n  bottom: \"shuffle_channel12\"\n  top: \"conv44\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm44\"\n  type: \"BatchNorm\"\n  bottom: \"conv44\"\n  top: \"batch_norm44\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale44\"\n  type: \"Scale\"\n  bottom: \"batch_norm44\"\n  top: \"batch_norm44\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu29\"\n  type: \"ReLU\"\n  bottom: \"batch_norm44\"\n  top: \"relu29\"\n}\nlayer {\n  name: \"conv45\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu29\"\n  top: \"conv45\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 2\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm45\"\n  type: \"BatchNorm\"\n  bottom: \"conv45\"\n  top: \"batch_norm45\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale45\"\n  type: \"Scale\"\n  bottom: \"batch_norm45\"\n  top: \"batch_norm45\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv46\"\n  type: \"Convolution\"\n  bottom: \"batch_norm45\"\n  top: \"conv46\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm46\"\n  type: \"BatchNorm\"\n  bottom: \"conv46\"\n  top: \"batch_norm46\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale46\"\n  type: \"Scale\"\n  bottom: \"batch_norm46\"\n  top: \"batch_norm46\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu30\"\n  type: \"ReLU\"\n  bottom: \"batch_norm46\"\n  top: \"relu30\"\n}\nlayer {\n  name: \"concat13\"\n  type: \"Concat\"\n  bottom: \"relu28\"\n  bottom: \"relu30\"\n  top: \"concat13\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel13\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat13\"\n  top: \"shuffle_channel13\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split11\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel13\"\n  top: \"split21\"\n  top: \"split22\"\n  slice_param {\n    slice_point: 232\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv47\"\n  type: \"Convolution\"\n  bottom: \"split22\"\n  top: \"conv47\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm47\"\n  type: \"BatchNorm\"\n  bottom: \"conv47\"\n  top: \"batch_norm47\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale47\"\n  type: \"Scale\"\n  bottom: \"batch_norm47\"\n  top: \"batch_norm47\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu31\"\n  type: \"ReLU\"\n  bottom: \"batch_norm47\"\n  top: \"relu31\"\n}\nlayer {\n  name: \"conv48\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu31\"\n  top: \"conv48\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm48\"\n  type: \"BatchNorm\"\n  bottom: \"conv48\"\n  top: \"batch_norm48\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale48\"\n  type: \"Scale\"\n  bottom: \"batch_norm48\"\n  top: \"batch_norm48\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv49\"\n  type: \"Convolution\"\n  bottom: \"batch_norm48\"\n  top: \"conv49\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm49\"\n  type: \"BatchNorm\"\n  bottom: \"conv49\"\n  top: \"batch_norm49\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale49\"\n  type: \"Scale\"\n  bottom: \"batch_norm49\"\n  top: \"batch_norm49\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu32\"\n  type: \"ReLU\"\n  bottom: \"batch_norm49\"\n  top: \"relu32\"\n}\nlayer {\n  name: \"concat14\"\n  type: \"Concat\"\n  bottom: \"split21\"\n  bottom: \"relu32\"\n  top: \"concat14\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel14\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat14\"\n  top: \"shuffle_channel14\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split12\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel14\"\n  top: \"split23\"\n  top: \"split24\"\n  slice_param {\n    slice_point: 232\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv50\"\n  type: \"Convolution\"\n  bottom: \"split24\"\n  top: \"conv50\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm50\"\n  type: \"BatchNorm\"\n  bottom: \"conv50\"\n  top: \"batch_norm50\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale50\"\n  type: \"Scale\"\n  bottom: \"batch_norm50\"\n  top: \"batch_norm50\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu33\"\n  type: \"ReLU\"\n  bottom: \"batch_norm50\"\n  top: \"relu33\"\n}\nlayer {\n  name: \"conv51\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu33\"\n  top: \"conv51\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm51\"\n  type: \"BatchNorm\"\n  bottom: \"conv51\"\n  top: \"batch_norm51\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale51\"\n  type: \"Scale\"\n  bottom: \"batch_norm51\"\n  top: \"batch_norm51\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv52\"\n  type: \"Convolution\"\n  bottom: \"batch_norm51\"\n  top: \"conv52\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm52\"\n  type: \"BatchNorm\"\n  bottom: \"conv52\"\n  top: \"batch_norm52\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale52\"\n  type: \"Scale\"\n  bottom: \"batch_norm52\"\n  top: \"batch_norm52\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu34\"\n  type: \"ReLU\"\n  bottom: \"batch_norm52\"\n  top: \"relu34\"\n}\nlayer {\n  name: \"concat15\"\n  type: \"Concat\"\n  bottom: \"split23\"\n  bottom: \"relu34\"\n  top: \"concat15\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel15\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat15\"\n  top: \"shuffle_channel15\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"split13\"\n  type: \"Slice\"\n  bottom: \"shuffle_channel15\"\n  top: \"split25\"\n  top: \"split26\"\n  slice_param {\n    slice_point: 232\n    axis: 1\n  }\n}\nlayer {\n  name: \"conv53\"\n  type: \"Convolution\"\n  bottom: \"split26\"\n  top: \"conv53\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm53\"\n  type: \"BatchNorm\"\n  bottom: \"conv53\"\n  top: \"batch_norm53\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale53\"\n  type: \"Scale\"\n  bottom: \"batch_norm53\"\n  top: \"batch_norm53\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu35\"\n  type: \"ReLU\"\n  bottom: \"batch_norm53\"\n  top: \"relu35\"\n}\nlayer {\n  name: \"conv54\"\n  type: \"ConvolutionDepthwise\"\n  bottom: \"relu35\"\n  top: \"conv54\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 1\n    kernel_size: 3\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm54\"\n  type: \"BatchNorm\"\n  bottom: \"conv54\"\n  top: \"batch_norm54\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale54\"\n  type: \"Scale\"\n  bottom: \"batch_norm54\"\n  top: \"batch_norm54\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"conv55\"\n  type: \"Convolution\"\n  bottom: \"batch_norm54\"\n  top: \"conv55\"\n  convolution_param {\n    num_output: 232\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm55\"\n  type: \"BatchNorm\"\n  bottom: \"conv55\"\n  top: \"batch_norm55\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale55\"\n  type: \"Scale\"\n  bottom: \"batch_norm55\"\n  top: \"batch_norm55\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu36\"\n  type: \"ReLU\"\n  bottom: \"batch_norm55\"\n  top: \"relu36\"\n}\nlayer {\n  name: \"concat16\"\n  type: \"Concat\"\n  bottom: \"split25\"\n  bottom: \"relu36\"\n  top: \"concat16\"\n  concat_param {\n    axis: 1\n  }\n}\nlayer {\n  name: \"shuffle_channel16\"\n  type: \"ShuffleChannel\"\n  bottom: \"concat16\"\n  top: \"shuffle_channel16\"\n  shuffle_channel_param {\n    group: 2\n  }\n}\nlayer {\n  name: \"conv56\"\n  type: \"Convolution\"\n  bottom: \"shuffle_channel16\"\n  top: \"conv56\"\n  convolution_param {\n    num_output: 1024\n    bias_term: false\n    pad: 0\n    kernel_size: 1\n    group: 1\n    stride: 1\n    weight_filler {\n      type: \"xavier\"\n    }\n    dilation: 1\n  }\n}\nlayer {\n  name: \"batch_norm56\"\n  type: \"BatchNorm\"\n  bottom: \"conv56\"\n  top: \"batch_norm56\"\n  batch_norm_param {\n    use_global_stats: true\n    eps: 9.999999747378752e-06\n  }\n}\nlayer {\n  name: \"bn_scale56\"\n  type: \"Scale\"\n  bottom: \"batch_norm56\"\n  top: \"batch_norm56\"\n  scale_param {\n    bias_term: true\n  }\n}\nlayer {\n  name: \"relu37\"\n  type: \"ReLU\"\n  bottom: \"batch_norm56\"\n  top: \"relu37\"\n}\nlayer {\n  name: \"ave_pool1\"\n  type: \"Pooling\"\n  bottom: \"relu37\"\n  top: \"ave_pool1\"\n  pooling_param {\n    pool: AVE\n    global_pooling: true\n  }\n}\nlayer {\n  name: \"reshape1\"\n  type: \"Reshape\"\n  bottom: \"ave_pool1\"\n  top: \"reshape1\"\n  reshape_param {\n    shape {\n      dim: 0\n      dim: 1024\n    }\n  }\n}\nlayer {\n  name: \"fc1\"\n  type: \"InnerProduct\"\n  bottom: \"reshape1\"\n  top: \"fc1\"\n  inner_product_param {\n    num_output: 200\n    bias_term: true\n    weight_filler {\n      type: \"xavier\"\n    }\n    bias_filler {\n      type: \"constant\"\n    }\n  }\n}\nlayer {\n  name: \"softmax1\"\n  type: \"Softmax\"\n  bottom: \"fc1\"\n  top: \"softmax1\"\n  softmax_param {\n    axis: 1\n  }\n}\n"
  },
  {
    "path": "test_convert_to_caffe/model/haarcascade_frontalface_default.xml",
    "content": "<?xml version=\"1.0\"?>\n<!--\n    Stump-based 24x24 discrete(?) adaboost frontal face detector.\n    Created by Rainer Lienhart.\n\n////////////////////////////////////////////////////////////////////////////////////////\n\n  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.\n\n  By downloading, copying, installing or using the software you agree to this license.\n  If you do not agree to this license, do not download, install,\n  copy or use the software.\n\n\n                        Intel License Agreement\n                For Open Source Computer Vision Library\n\n Copyright (C) 2000, Intel Corporation, all rights reserved.\n Third party copyrights are property of their respective owners.\n\n Redistribution and use in source and binary forms, with or without modification,\n are permitted provided that the following conditions are met:\n\n   * Redistribution's of source code must retain the above copyright notice,\n     this list of conditions and the following disclaimer.\n\n   * Redistribution's in binary form must reproduce the above copyright notice,\n     this list of conditions and the following disclaimer in the documentation\n     and/or other materials provided with the distribution.\n\n   * The name of Intel Corporation may not be used to endorse or promote products\n     derived from this software without specific prior written permission.\n\n This software is provided by the copyright holders and contributors \"as is\" and\n any express or implied warranties, including, but not limited to, the implied\n warranties of merchantability and fitness for a particular purpose are disclaimed.\n In no event shall the Intel Corporation or contributors be liable for any direct,\n indirect, incidental, special, exemplary, or consequential damages\n (including, but not limited to, procurement of substitute goods or services;\n loss of use, data, or profits; or business interruption) however caused\n and on any theory of liability, whether in contract, strict liability,\n or tort (including negligence or otherwise) arising in any way out of\n the use of this software, even if advised of the possibility of such damage.\n-->\n<opencv_storage>\n<cascade type_id=\"opencv-cascade-classifier\"><stageType>BOOST</stageType>\n  <featureType>HAAR</featureType>\n  <height>24</height>\n  <width>24</width>\n  <stageParams>\n    <maxWeakCount>211</maxWeakCount></stageParams>\n  <featureParams>\n    <maxCatCount>0</maxCatCount></featureParams>\n  <stageNum>25</stageNum>\n  <stages>\n    <_>\n      <maxWeakCount>9</maxWeakCount>\n      <stageThreshold>-5.0425500869750977e+00</stageThreshold>\n      <weakClassifiers>\n        <_>\n          <internalNodes>\n            0 -1 0 -3.1511999666690826e-02</internalNodes>\n          <leafValues>\n            2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_>\n        <_>\n          <internalNodes>\n            0 -1 1 1.2396000325679779e-02</internalNodes>\n          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  },
  {
    "path": "test_convert_to_caffe/shufflenetv2.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom collections import OrderedDict\nfrom torch.nn import init\nimport math\n\n\ndef conv_bn(inp, oup, stride):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),\n        nn.BatchNorm2d(oup),\n        nn.ReLU(inplace=True)\n    )\n\n\ndef conv_1x1_bn(inp, oup):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        nn.BatchNorm2d(oup),\n        nn.ReLU(inplace=True)\n    )\n\n\ndef channel_shuffle(x, groups):\n    batchsize, num_channels, height, width = x.data.size()\n\n    channels_per_group = num_channels // groups\n\n    # reshape\n    x = x.view(batchsize, groups, channels_per_group, height, width)\n\n    x = torch.transpose(x, 1, 2).contiguous()\n\n    # flatten\n    x = x.view(batchsize, -1, height, width)\n\n    return x\n\n\nclass InvertedResidual(nn.Module):\n    def __init__(self, inp, oup, stride, benchmodel):\n        super(InvertedResidual, self).__init__()\n        self.benchmodel = benchmodel\n        self.stride = stride\n        assert stride in [1, 2]\n\n        oup_inc = oup // 2\n\n        if self.benchmodel == 1:\n            # assert inp == oup_inc\n            self.banch2 = nn.Sequential(\n                # pw\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n                # dw\n                nn.Conv2d(oup_inc, oup_inc, 3, stride,\n                          1, groups=oup_inc, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                # pw-linear\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n        else:\n            self.banch1 = nn.Sequential(\n                # dw\n                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),\n                nn.BatchNorm2d(inp),\n                # pw-linear\n                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n\n            self.banch2 = nn.Sequential(\n                # pw\n                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n                # dw\n                nn.Conv2d(oup_inc, oup_inc, 3, stride,\n                          1, groups=oup_inc, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                # pw-linear\n                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup_inc),\n                nn.ReLU(inplace=True),\n            )\n\n    @staticmethod\n    def _concat(x, out):\n        # concatenate along channel axis\n        return torch.cat((x, out), 1)\n\n    def forward(self, x):\n        if 1 == self.benchmodel:\n            # x1 = x[:, :(x.shape[1] // 2), :, :]\n            # x2 = x[:, (x.shape[1] // 2):, :, :]\n            x1, x2 = torch.split(x, x.shape[1] // 2, dim=1)\n            out = self._concat(x1, self.banch2(x2))\n        elif 2 == self.benchmodel:\n            out = self._concat(self.banch1(x), self.banch2(x))\n\n        return channel_shuffle(out, 2)\n\n\nclass ShuffleNetV2(nn.Module):\n    def __init__(self, input_size, n_class=200,  width_mult=1): #1,0\n        super(ShuffleNetV2, self).__init__()\n\n        assert input_size % 32 == 0\n\n        self.stage_repeats = [4, 8, 4]\n        # index 0 is invalid and should never be called.\n        # only used for indexing convenience.\n        if width_mult == 0.5:\n            self.stage_out_channels = [-1, 24, 48, 96, 192, 192] #175\n        elif width_mult == 1.0:\n            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024] # 1024\n        elif width_mult == 1.5:\n            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]\n        elif width_mult == 2.0:\n            self.stage_out_channels = [-1, 24, 224, 488, 976, 2048]\n        else:\n            raise ValueError(\n                \"\"\"width_mult = {} is not supported\"\"\".format(width_mult))\n\n        # building first layer\n        input_channel = self.stage_out_channels[1]\n        self.conv1 = conv_bn(3, input_channel, 2)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        self.features = []\n        # building inverted residual blocks\n        for idxstage in range(len(self.stage_repeats)):\n            numrepeat = self.stage_repeats[idxstage]\n            output_channel = self.stage_out_channels[idxstage + 2]\n            for i in range(numrepeat):\n                if i == 0:\n                    # inp, oup, stride, benchmodel):\n                    self.features.append(InvertedResidual(\n                        input_channel, output_channel, 2, 2))\n                else:\n                    self.features.append(InvertedResidual(\n                        input_channel, output_channel, 1, 1))\n                input_channel = output_channel\n\n        # make it nn.Sequential\n        self.features = nn.Sequential(*self.features)\n\n        # building last several layers\n        self.conv_last = conv_1x1_bn(\n            input_channel, self.stage_out_channels[-1])\n        self.globalpool = nn.Sequential(nn.AdaptiveAvgPool2d(1))\n\n        # building classifier\n        self.classifier = nn.Sequential(\n            nn.Linear(self.stage_out_channels[-1], n_class))\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.maxpool(x)\n        x = self.features(x)\n        x = self.conv_last(x)\n        x = self.globalpool(x)\n        x = x.view(-1, self.stage_out_channels[-1])\n        x = self.classifier(x)\n        return x\n\n\ndef shufflenetv2(width_mult=1.):\n    model = ShuffleNetV2(width_mult=width_mult)\n    return model\n\n\nif __name__ == \"__main__\":\n    \"\"\"Testing\n    \"\"\"\n    model = ShuffleNetV2()\n    print(model)\n"
  },
  {
    "path": "test_convert_to_caffe/shufflenetv2_to_caffe.py",
    "content": "import sys\nimport os.path as osp\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport argparse\nimport caffe\nimport numpy as np\n\nthis_dir = '.'\n\ncpath = '/home/jinsy/2T/jinsy/code/tuya_sdk/Pytorch2Caffe'\nif cpath not in sys.path:\n    sys.path.append(cpath)\n\nimport converter\nfrom utils.load_test_image import load_test_image\nfrom utils.get_topk import get_topk\nfrom utils.load import load_troch_model2\nimport shufflenetv2 as ShuffleNetV2\n\n\ndef _channel_shuffle(raw, input, groups):\n    x = input.clone()\n    name = converter.log.add_layer(name='shuffle_channel')\n    converter.log.add_blobs([x], name='shuffle_channel')\n    layer = converter.caffe_net.Layer_param(name=name, type='ShuffleChannel',\n                                            bottom=[converter.log.blobs(input)], top=[converter.log.blobs(x)])\n    layer.channel_shuffle_param(groups)\n    converter.log.cnet.add_layer(layer)\n    return x\n\n\nShuffleNetV2.channel_shuffle = converter.Rp(ShuffleNetV2.channel_shuffle, _channel_shuffle)\n\n\ndef parse():\n    parser = argparse.ArgumentParser(description='convert pytorch model to caffe')\n    parser.add_argument('-c', '--convert', action='store_true', help='convert pytorch model to caffe')\n    parser.add_argument('-t', '--test', action='store_true', help='test converted model')\n    parser.add_argument('-wm', '--width-mult', type=float, choices=[0.5, 1.0], default=1.0,\n                        help='width mult')\n    return parser.parse_args()\n\n\ndef convert(pytorch_net, caffe_prototxt, caffe_model_file, name):\n    pytorch_net.eval()\n    input = torch.ones(1, 3, 160, 160)\n    converter.trans_net(pytorch_net, input, name)\n    converter.save_prototxt(caffe_prototxt)\n    converter.save_caffemodel(caffe_model_file)\n\n\ndef test(caffe_prototxt, caffe_model_file, pytorch_net, topk=5):\n    pytorch_net.eval()\n    input = load_test_image()\n    with torch.no_grad():\n        x1 = pytorch_net(input)\n        x1 = x1.numpy()\n\n    caffe_net = caffe.Net(caffe_prototxt, caffe_model_file, caffe.TEST)\n    caffe.set_mode_cpu()\n    caffe_net.blobs['data1'].data.reshape(*input.size())\n    caffe_net.blobs['data1'].data[...] = input.numpy()\n    x2 = caffe_net.forward()['softmax1']\n    idx = 0\n\n    expect = torch.dot(torch.arange(0,200).float() ,torch.from_numpy(x1.flatten()))\n    print('pytorch: ', expect)\n\n    expect = torch.dot(torch.arange(0,200).float() ,torch.from_numpy(x2.flatten()))\n    print('caffe: ', expect)\n\n\nclass SoftmaxWrapper(nn.Module):\n    def __init__(self, net):\n        super(SoftmaxWrapper, self).__init__()\n        self.net = net\n\n    def forward(self, x):\n        y = self.net(x)\n        return F.softmax(y, dim=1)\n\n\ndef shufflenetv2(width_mult=1., pretrained=True):\n    model = ShuffleNetV2.ShuffleNetV2(input_size=160, width_mult=width_mult)\n    if pretrained:\n        if width_mult == 1.:\n            model_file = osp.join(this_dir, 'model/97_160_2.pth')\n        elif width_mult == .5:\n            model_file = osp.join(this_dir, '../models/ShuffleNetV2/shufflenetv2_x0.5_60.646_81.696.pth.tar')\n        else:\n            raise ValueError('width_mult = {} is not support pretrained model'.format(width_mult))\n        assert osp.exists(model_file), '{} is not exists!'.format(model_file)\n        print('shufflenetv2 load state dict: ', model_file)\n        state = torch.load(model_file)\n        model.load_state_dict(state['net'], strict=False)\n\n    return model\n\n\nif __name__ == '__main__':\n    args = parse()\n\n    name = 'shufflenetv2_{:.1f}x'.format(args.width_mult)\n\n    caffe_prototxt = osp.join(this_dir, 'model/') + '{}.prototxt'.format(name)\n    caffe_model_file = osp.join(this_dir, 'model/') + '{}.caffemodel'.format(name)\n    net = shufflenetv2(width_mult=args.width_mult, pretrained=True)\n    net = SoftmaxWrapper(net)\n\n    if args.convert:\n        convert(net, caffe_prototxt, caffe_model_file, name)\n\n    if args.test:\n        test(caffe_prototxt, caffe_model_file, net)\n"
  },
  {
    "path": "test_convert_to_caffe/test.py",
    "content": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nfrom torch.autograd import Variable\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport time\nimport os\nfrom PIL import Image\nimport sys\nimport torch.nn.functional as F\nfrom shufflenetv2 import ShuffleNetV2\nimport scipy.io as scio\nimport cv2\n\n#config\nmatFile = 'pScores.mat'\nfiiqaWeight = './model/97_160_2.pth'\ndetectFace = './model/haarcascade_frontalface_default.xml'\nimagePath = './image/test1.jpg'\nfacePath = './image/crop/test_face.jpg'\ninputSize = 160\n\n#crop face from img\nfaceCascade = cv2.CascadeClassifier(detectFace)\nimage = cv2.imread(imagePath)\nfaces = faceCascade.detectMultiScale(\n    image,\n    scaleFactor=1.1,\n    minNeighbors=5,\n    minSize=(10, 10)\n)\nfor (x, y, w, h) in faces:\n    cv2.imwrite('./image/crop/' + 'test_face.jpg',image[y:y+h,x:x+w])\ncv2.waitKey(0)\n\n#transfer img to tensor\ndataTransforms =  transforms.Compose([\n               transforms.Resize(inputSize),\n               transforms.ToTensor(),\n               transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n])\n\n#load net \nnet = ShuffleNetV2(inputSize)\ncheckpoint = torch.load(fiiqaWeight)\nnet.load_state_dict(checkpoint['net'])\nnet.eval()\n\n#load face and get expect num\nface = Image.open(facePath)\nimgblob = dataTransforms(face).unsqueeze(0)\nimgblob = Variable(imgblob)\ntorch.no_grad()\npredict = F.softmax(net(imgblob),dim=1)\nexpect = torch.sum(Variable(torch.arange(0,200)).float()*predict, 1)\n\nprint(predict, expect)\n\nexpect = int(expect)\n\n#load matFile and get score\ndata = scio.loadmat(matFile)\nscores = data['pScores']\nscore = scores[:,expect]\n\nprint('expect: %d' % expect)\nprint('score: %.3f' %  score)\n"
  },
  {
    "path": "test_convert_to_caffe/utils/get_topk.py",
    "content": "import numpy as np\n\n\ndef get_topk(prob, k=5):\n    size = prob.shape[0]\n    k = min(k, size)\n    sorted_idx = np.argsort(prob)[::-1]\n    topk_idx = sorted_idx[:k]\n    topk_prob = prob[sorted_idx[:k]]\n    return topk_idx, topk_prob\n"
  },
  {
    "path": "test_convert_to_caffe/utils/load.py",
    "content": "import torch\nfrom collections import OrderedDict\n\n\ndef load_troch_model(filename):\n    def _process_state(state):\n        new_state_dict = OrderedDict()\n        for k, v in state.items():\n            if k.startswith('module.'):\n                k = k.replace('module.', '')\n            new_state_dict[k] = v\n        return new_state_dict\n    state = torch.load(filename, map_location=lambda storage, location: storage)\n    state['state_dict'] = _process_state(state['state_dict'])\n    return state\n\n\ndef load_troch_model2(filename):\n    def _process_state(state):\n        new_state_dict = OrderedDict()\n        for k, v in state.items():\n            if k.startswith('module.'):\n                k = k.replace('module.', '')\n            new_state_dict[k] = v\n        return new_state_dict\n    state = torch.load(filename, map_location=lambda storage, location: storage)\n    state = _process_state(state)\n    return state\n"
  },
  {
    "path": "test_convert_to_caffe/utils/load_test_image.py",
    "content": "import os.path as osp\nimport cv2\nfrom torchvision import transforms\nimport torch\nimport numpy as np\nfrom PIL import Image\n\nthis_dir = osp.abspath(osp.dirname(__file__))\n\n\ndef load_test_image(filename='test_face.jpg', image_size=(3, 160, 160)):\n    full_filename = osp.join(this_dir, '../image/crop', filename)\n    print(full_filename)\n    rgb_image = Image.open(full_filename)\n    tf = transforms.Compose([\n        transforms.Resize(160),\n        transforms.ToTensor(),\n        transforms.Normalize(mean=[0.485, 0.456, 0.406],\n                             std=[0.229, 0.224, 0.225])\n\n    ])\n    tensor = tf(rgb_image)\n    return tensor.view(1, *image_size)\n\n\ndef load_test_image2(filename='cat_224x224.jpg', image_size=(3, 224, 224)):\n    full_filename = osp.join(this_dir, '../data', filename)\n    bgr_image = cv2.imread(full_filename)\n    bgr_image2 = torch.from_numpy(cv2.resize(bgr_image, image_size[1:]).astype(np.float32))\n    return bgr_image2.view(1, *image_size)\n"
  },
  {
    "path": "test_model.py",
    "content": "import torch \nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\n\nfrom shufflenetv2 import ShuffleNetV2\nimport torchvision\nimport torchvision.transforms as transforms\nfrom datagen import ListDataset\nimport csv\nimport os\nfrom PIL import Image\n\ntest_data = './data/test-faces/'\nlist_file = './data/new_4people_test_standard.txt'\ncheckpoint = torch.load('./checkpoint/97_160_2.pth')\n\ndef write_csv(header, write_data, filename):\n    # header-标题 write_data-写入数据 filename-文件名 \n    with open(filename, 'a', newline='',encoding='utf-8-sig') as csvFile:\n        writer = csv.writer(csvFile)\n        if os.path.getsize(filename) == False:\n            # 先写columns_name\n            writer.writerow(header)\n        # 写入多行用writerows\n        writer.writerows(write_data)\n\ndef test():\n    # configure model\n    net= ShuffleNetV2(input_size=160)\n    net.load_state_dict(checkpoint['net'])\n    net.eval()\n\n    transform_test = transforms.Compose([\n        transforms.Resize(160),\n        transforms.CenterCrop(160),\n        transforms.ToTensor(),\n        transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n    ])\n    testset = ListDataset(root=test_data, list_file=list_file, \\\n        transform=transform_test)\n    testloader = torch.utils.data.DataLoader(testset, batch_size=64, \\\n        shuffle=False, num_workers=4, pin_memory = True)\n    torch.multiprocessing.set_sharing_strategy('file_system')\n\n    results = []\n    for ii,(data,path) in enumerate(testloader):\n\n        #input = torch.autograd.Variable(data)\n        with torch.no_grad():\n            score = net(data)\n        \n        probability = torch.nn.functional.softmax(score,dim=1)#[:,0].data.tolist()\n        # label = score.max(dim = 1)[1].data.tolist()\n        expect = torch.sum(torch.arange(0,200).float()*probability, 1)\n        #print('expect: %.4f' % expect.numpy())\n        batch_results = [(path_,expect_) for path_,expect_ in zip(path,expect) ]\n        print('batch_results: ',batch_results)\n        results += batch_results\n    write_csv('FIIQA',results,'result.csv')\n\n    return results\n\n\nif __name__ == \"__main__\":\n    \"\"\"Testing\n    \"\"\"\n    test()"
  },
  {
    "path": "train.py",
    "content": "from __future__ import print_function\n\nimport os\nimport argparse\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\nimport torchvision\nimport torchvision.transforms as transforms\nfrom net import AGNet\nfrom loss import FIIQALoss\nfrom datagen import ListDataset\nfrom torch.autograd import Variable\nfrom adabound import AdaBound\nfrom shufflenetv2 import ShuffleNetV2\nfrom flops_counter_pytorch.ptflops import get_model_complexity_info\nfrom summary import model_summary\n\nparser = argparse.ArgumentParser(description='PyTorch AGNet Training')\nparser.add_argument('--lr', default=1e-3, type=float, help='learning rate')\nparser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')\nargs = parser.parse_args()\n\nassert torch.cuda.is_available(), 'Error: CUDA not found!'\nbest_correct = 0 # best number of fiiqa_correct \nstart_epoch = 0  # start from epoch 0 or last epoch\nbest_test_acc_epoch = 0\nbatch_size=128\npath = './checkpoint/'\nTOLERANCE = 2\ninput_size=32\ntrain_epoch=500\n\n# Data\nprint('==> Preparing data..')\ntransform_train = transforms.Compose([\n    transforms.Resize(input_size),\n    transforms.CenterCrop(input_size),\n    #transforms.RandomCrop(160, padding=4),\n    transforms.RandomHorizontalFlip(),\n    transforms.ToTensor(),\n    transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n])\n\ntransform_test = transforms.Compose([\n    transforms.Resize(input_size),\n    transforms.CenterCrop(input_size),\n    transforms.ToTensor(),\n    transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n])\n\ntrainset = ListDataset(root='./data/trainingset/train-faces/', list_file='./data/trainingset/new_4people_train_standard.txt', transform=transform_train)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size, shuffle=True, num_workers=12)\ntestset = ListDataset(root='./data/validationset/val-faces/', list_file='./data/validationset/new_4people_val_standard.txt', transform=transform_test)\ntestloader = torch.utils.data.DataLoader(testset, batch_size, shuffle=False, num_workers=12)\n\n# Model\nnet = ShuffleNetV2(input_size)\n'''\nmodel_summary(net,input_size=(3,input_size,input_size))\nflops, params = get_model_complexity_info(net, (input_size, input_size), as_strings=True, print_per_layer_stat=False)\nprint('Flops:  ' + flops)\nprint('Params: ' + params)\n'''\n\nif args.resume:\n    print('==> Resuming from checkpoint..')\n    checkpoint = torch.load('./checkpoint/ckpt.pth')\n    net.load_state_dict(checkpoint['net'])\n    best_correct = checkpoint['correct']\n    start_epoch = checkpoint['epoch']\nnet = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count())) #多gpu并行训练\nnet.cuda()\n\ncriterion = FIIQALoss()\n#criterion = nn.CrossEntropyLoss()\noptimizer = optim.AdaBound(net.parameters(),lr=args.lr,final_lr=0.1)\n\n#optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=1e-4)\n\n# Training\ndef train(epoch):\n    print('\\nEpoch: %d' % epoch)\n    net.train()\n    train_loss = 0\n    total = 0\n    fiiqa_correct = 0\n    for batch_idx, (inputs, fiiqa_targets) in enumerate(trainloader):\n        inputs = Variable(inputs.cuda())\n        fiiqa_targets = Variable(fiiqa_targets.cuda()) \n        optimizer.zero_grad()\n        fiiqa_preds = net(inputs)\n        loss = criterion(fiiqa_preds.float(), fiiqa_targets)\n        loss.backward()\n        optimizer.step()\n        train_loss += loss.item()\n        fiiqa_correct_i = accuracy(fiiqa_preds, fiiqa_targets)\n        fiiqa_correct += fiiqa_correct_i\n        total += len(inputs)\n        print('train_loss: %.3f | fiiqa_prec: %.3f (%d/%d) |  [%d/%d]'  \\\n            % (loss.item(),       \\\n               100.*fiiqa_correct/total, fiiqa_correct, total,  \\\n               batch_idx+1, len(trainloader)))\n\n# Test\ndef test(epoch):\n    global Test_acc\n    global best_correct\n    global best_test_acc_epoch\n    print('\\nTest')\n    net.eval()\n    test_loss = 0\n    total = 0\n    fiiqa_correct = 0\n    for batch_idx, (inputs, fiiqa_targets) in enumerate(testloader):\n        inputs = Variable(inputs.cuda())\n        fiiqa_targets = Variable(fiiqa_targets.cuda())\n        fiiqa_preds = net(inputs)\n        loss = criterion(fiiqa_preds, fiiqa_targets)\n        test_loss += loss.item()\n        fiiqa_correct_i = accuracy(fiiqa_preds, fiiqa_targets)\n        fiiqa_correct += fiiqa_correct_i\n        total += len(inputs)\n        print('test_loss: %.3f | fiiqa_prec: %.3f (%d/%d) | [%d/%d]' \\\n            % (loss.item(),       \\\n               100.*fiiqa_correct/total, fiiqa_correct, total,  \\\n               batch_idx+1, len(testloader)))\n\n    # Save checkpoint\n    Test_acc = 100.*fiiqa_correct/total\n\n    if Test_acc  > best_correct:\n        print('Saving..')\n        print(\"best_test_acc: %0.3f\" % Test_acc)\n        best_correct = Test_acc \n        best_test_acc_epoch = epoch\n\n        state = {\n            'net': net.module.state_dict(),\n            'correct': best_correct,\n            'epoch': epoch,\n        }\n\n\n        if not os.path.isdir('checkpoint'):\n            os.mkdir('checkpoint')\n        torch.save(state, os.path.join(path,str(best_correct)+'_'+str(input_size)+'_'+str(TOLERANCE) +'.pth'))\n\ndef accuracy(fiiqa_preds, fiiqa_targets):\n    '''Measure batch accuracy.'''\n    fiiqa_prob = F.softmax(fiiqa_preds,dim=1)\n    fiiqa_expect = torch.sum(Variable(torch.arange(0,200)).cuda().float()*fiiqa_prob, 1)\n    fiiqa_correct = ((fiiqa_expect-fiiqa_targets.float()).abs() < TOLERANCE).long().sum().cpu().item()\n    return fiiqa_correct\n\nfor epoch in range(start_epoch, start_epoch+train_epoch):\n    train(epoch)\n    test(epoch)\nprint('best_test_acc: %0.3f' % best_correct)\nprint('best_test_acc_epoch: %d' % best_test_acc_epoch)\n"
  },
  {
    "path": "train_affectnet.py",
    "content": "'''Train CK+ with PyTorch.'''\n# 10 crop for data enhancement\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\nimport torchvision\nfrom torchvision import transforms,utils\nfrom torch.utils.data import DataLoader\nimport transforms as transforms\nimport numpy as np\nimport os\nimport argparse\nimport utils\nfrom CK import CK\nfrom torch.autograd import Variable\nfrom models import *\nfrom ShuffleNetV2 import ShuffleNetV2\nfrom flops_counter_pytorch.ptflops import get_model_complexity_info\nfrom summary import model_summary\nfrom datagen import ListDataset\n\n\ntrain_data = '../train_val_imgs/Manually/Manually_train_croped'\ntest_data = '../train_val_imgs/Manually/Manually_validation_croped'\n\nparser = argparse.ArgumentParser(description='PyTorch CK+ CNN Training')\n#parser.add_argument('--model', type=str, default='VGG19', help='CNN architecture')\nparser.add_argument('--dataset', type=str, default='CK+', help='dataset')\nparser.add_argument('--fold', default=1, type=int, help='k fold number')\nparser.add_argument('--bs', default=128, type=int, help='batch_size')\nparser.add_argument('--lr', default=0.001, type=float, help='learning rate')\nparser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')\nopt = parser.parse_args()\n\nuse_cuda = torch.cuda.is_available()\n\nbest_Test_acc = 0  # best PrivateTest accuracy\nbest_Test_acc_epoch = 0\nstart_epoch = 0  # start from epoch 0 or last checkpoint epoch\n\nlearning_rate_decay_start = 20  # 50\nlearning_rate_decay_every = 1 # 5\nlearning_rate_decay_rate = 0.8 # 0.9\n\n\ntotal_epoch = 500\nbs = 128\ninput_size = 64\ncut_size = input_size - 1\nn_class=7\n\n#path = os.path.join(opt.dataset + '_' + opt.model, str(opt.fold))\npath = './AffectNet+ShuffleNetV2/'\n\n# Data\nprint('==> Preparing data..')\ntransform_train = transforms.Compose([\n    transforms.Resize(input_size),\n    transforms.RandomCrop(cut_size),\n    transforms.RandomHorizontalFlip(),\n    transforms.ToTensor(),\n    #transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n])\n\ntransform_test = transforms.Compose([\n    transforms.Resize(input_size),\n    #transforms.RandomCrop(cut_size),\n    #transforms.RandomHorizontalFlip(),\n    transforms.TenCrop(cut_size),\n    transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),\n    #transforms.ToTensor(),\n    #transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))\n])\n\n#trainset=torchvision.datasets.ImageFolder(train_data,transform_train)\ntrainset = ListDataset(root='../train_val_imgs/Manually/Manually_train_croped/', list_file='./AffectNet/train.txt', transform=transform_train)\ntrainloader=DataLoader(trainset,bs,shuffle=True, num_workers=12)\n#testset=torchvision.datasets.ImageFolder(test_data,transform_test)\ntestset = ListDataset(root='../train_val_imgs/Manually/Manually_validation_croped/', list_file='./AffectNet/val.txt', transform=transform_test)\ntestloader=DataLoader(testset,batch_size=128,shuffle=True, num_workers=12)\n\n\nnet = ShuffleNetV2(input_size,n_class)\n'''\nmodel_summary(net,input_size=(3,input_size,input_size))\nflops, params = get_model_complexity_info(net, (input_size, input_size), as_strings=True, print_per_layer_stat=False)\nprint('Flops:  ' + flops)\nprint('Params: ' + params)\n#net = net.to(device=my_device)\n'''\n\nif opt.resume:\n    # Load checkpoint.\n    print('==> Resuming from checkpoint..')\n    assert os.path.isdir(path), 'Error: no checkpoint directory found!'\n    checkpoint = torch.load(os.path.join(path,'ShuffleNetV2.pth'))\n    \n    net.load_state_dict(checkpoint['net'])\n    best_Test_acc = checkpoint['best_Test_acc']\n    best_Test_acc_epoch = checkpoint['best_Test_acc_epoch']\n    start_epoch = best_Test_acc_epoch + 1\nelse:\n    print('==> Building model..')\n\nif use_cuda:\n    net.cuda()\n\ncriterion = nn.CrossEntropyLoss()\noptimizer=optim.AdaBound(net.parameters(), lr=opt.lr, final_lr=0.1)\n#optimizer = optim.SGD(net.parameters(), lr=opt.lr, momentum=0.9, weight_decay=1e-4)\n\n# Training\ndef train(epoch):\n    print('\\nEpoch: %d' % epoch)\n    global Train_acc\n    net.train()\n    train_loss = 0\n    correct = 0\n    total = 0\n\n    for batch_idx, (inputs, targets) in enumerate(trainloader):\n        if use_cuda:\n            inputs, targets = inputs.cuda(), targets.cuda()\n        optimizer.zero_grad()\n        #inputs, targets = Variable(inputs), Variable(targets)\n        outputs = net(inputs)\n        loss = criterion(outputs, targets)\n        loss.backward()\n        utils.clip_gradient(optimizer, 0.1) #clip_gradient能有效了控制梯度爆炸的影响，使得最终的loss能下降到满意的结果 \n        optimizer.step()\n\n        train_loss += loss.item()\n        _, predicted = torch.max(outputs.data, 1)\n        total += targets.size(0)\n        correct += predicted.eq(targets.data).cpu().sum()\n\n        utils.progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n            % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))\n\n    Train_acc = 100.*correct/total\n\n# test\ndef test(epoch):\n    global Test_acc\n    global best_Test_acc\n    global best_Test_acc_epoch\n    net.eval()\n    PrivateTest_loss = 0\n    correct = 0\n    total = 0\n    for batch_idx, (inputs, targets) in enumerate(testloader):\n        bs, ncrops, c, h, w = np.shape(inputs)\n        inputs = inputs.view(-1, c, h, w)\n\n        if use_cuda:\n            inputs, targets = inputs.cuda(), targets.cuda()\n\n        inputs, targets = Variable(inputs), Variable(targets)\n        outputs = net(inputs)\n        outputs_avg = outputs.view(bs, ncrops, -1).mean(1)  # avg over crops\n\n        loss = criterion(outputs_avg, targets)\n        PrivateTest_loss += loss.item()\n        _, predicted = torch.max(outputs_avg.data, 1)\n        total += targets.size(0)\n        correct += predicted.eq(targets.data).cpu().sum()\n\n        utils.progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'\n            % (PrivateTest_loss / (batch_idx + 1), \\\n            100. * correct / total, correct, total))\n    # Save checkpoint.\n    Test_acc = 100.*correct/total\n\n    if Test_acc > best_Test_acc:\n        print('Saving..')\n        print(\"best_Test_acc: %0.3f\" % Test_acc)\n        state = {'net': net.state_dict() if use_cuda else net,\n            'best_Test_acc': Test_acc,\n            'best_Test_acc_epoch': epoch,\n        }\n        if not os.path.isdir(opt.dataset + '_' + 'ShuffleNetV2'):\n            os.mkdir(opt.dataset + '_' + 'ShuffleNetV2')\n        if not os.path.isdir(path):\n            os.mkdir(path)\n        #torch.save(state, os.path.join(path, str(best_Test_acc) + '_ShuffleNetV2.pth'))\n        best_Test_acc = Test_acc\n        best_Test_acc_epoch = epoch\n        torch.save(state, os.path.join(path, str(best_Test_acc) + '_'+str(input_size)+'_ShuffleNetV2.pth'))\n\nfor epoch in range(start_epoch, total_epoch):\n    train(epoch)\n    test(epoch)\n\nprint(\"best_Test_acc: %0.3f\" % best_Test_acc)\nprint(\"best_Test_acc_epoch: %d\" % best_Test_acc_epoch)"
  }
]