Repository: yangyuke001/FIIQA-PyTorch
Branch: master
Commit: 29ab6af25a4e
Files: 50
Total size: 22.1 MB

Directory structure:
gitextract_bsk4agu9/

├── AffectNet/
│   ├── train.txt
│   └── val.txt
├── checkpoint/
│   ├── 87.73517581419489_96_2.pth
│   ├── 92.75009731413002_1.pth
│   ├── 95.45543012845465_32_2.pth
│   └── 97_160_2.pth
├── datagen.py
├── flops_counter_pytorch/
│   ├── LICENSE
│   ├── README.md
│   ├── ptflops/
│   │   ├── __init__.py
│   │   └── flops_counter.py
│   ├── sample.py
│   └── setup.py
├── loss.py
├── model/
│   ├── 97_160_2.pth
│   └── haarcascade_frontalface_default.xml
├── net.py
├── pScores.mat
├── readme.md
├── result.csv
├── scripts/
│   └── get_state_dict.py
├── shufflenetv2.py
├── summary/
│   ├── __init__.py
│   ├── example.py
│   ├── model_hook.py
│   ├── model_summary.py
│   ├── module_mac.py
│   └── summary_tree.py
├── test/
│   ├── model/
│   │   ├── 97_160_2.pth
│   │   └── haarcascade_frontalface_default.xml
│   ├── pScores.mat
│   ├── shufflenetv2.py
│   ├── test.py
│   └── test_without_crop.py
├── test.py
├── test_convert_to_caffe/
│   ├── httpClient.py
│   ├── model/
│   │   ├── 97_160_2.pth
│   │   ├── fiiqa_shufflenetv2_1.0x.caffemodel
│   │   ├── fiiqa_shufflenetv2_1.0x.prototxt
│   │   └── haarcascade_frontalface_default.xml
│   ├── pScores.mat
│   ├── shufflenetv2.py
│   ├── shufflenetv2_to_caffe.py
│   ├── test.py
│   └── utils/
│       ├── get_topk.py
│       ├── load.py
│       └── load_test_image.py
├── test_model.py
├── train.py
└── train_affectnet.py

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FILE CONTENTS
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FILE: AffectNet/train.txt
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[File too large to display: 18.2 MB]

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FILE: AffectNet/val.txt
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================================================
FILE: datagen.py
================================================
from __future__ import print_function
import io
import os
import sys
import random
import torch
import torch.utils.data as data
import torchvision.transforms as transforms
from PIL import Image

class ListDataset(data.Dataset):
    def __init__(self, root, list_file, transform):
        '''
        Args:
          root: (str) ditectory to images.
          list_file: (str) path to index file.
          transform: ([transforms]) image transforms.
        '''
        self.root = root
        self.transform = transform

        self.fname = []
        self.fiiqa = []

        with io.open(list_file, encoding='gbk') as f:
            lines = f.readlines()
            self.num_imgs = len(lines)

        for line in lines:
            sp = line.strip().split()
            self.fname.append(sp[0])
            self.fiiqa.append(int(sp[1]))

    def __getitem__(self, idx):
        '''Load image.

        Args:
          idx: (int) image index.

        Returns:
          img: (tensor) image tensor.
          fiiqa: (float) fiiqa.
        '''
        # Load image and bbox locations.
        fname = self.fname[idx]
        fiiqa = self.fiiqa[idx]

        img = Image.open(os.path.join(self.root, fname)).convert('RGB')
        img = self.transform(img)
        return img, fiiqa

    def __len__(self):
        return self.num_imgs


def test():
    import torchvision
    transform = transforms.Compose([
        transforms.RandomSizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
    ])
    dataset = ListDataset(root='./data/validationset/val-faces/', list_file='./data/validationset/val-faces/new_4people_val_standard.txt', transform=transform)
    dataloader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=True, num_workers=1)
    for img, fiiqa in dataloader:
        print(img.size())
        print(fiiqa.size())



================================================
FILE: flops_counter_pytorch/LICENSE
================================================
MIT License

Copyright (c) 2018 Vladislav Sovrasov

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: flops_counter_pytorch/README.md
================================================
# Flops counter for convolutional networks in pytorch framework
[![Pypi version](https://img.shields.io/pypi/v/ptflops.svg)](https://pypi.org/project/ptflops/)

This script is designed to compute the theoretical amount of multiply-add operations
in convolutional neural networks. It also can compute the number of parameters and
print per-layer computational cost of a given network.

Supported layers:
- Convolution2d (including grouping)
- BatchNorm2d
- Activations (ReLU, PReLU, ELU, ReLU6, LeakyReLU)
- Linear
- Upsample
- Poolings (AvgPool2d, MaxPool2d and adaptive ones)

Requirements: Pytorch 0.4.1 or 1.0, torchvision 0.2.1

Thanks to @warmspringwinds for the initial version of script.

## Install the latest version
```bash
pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git
```

## Example
```python
import torchvision.models as models
import torch
from ptflops import get_model_complexity_info

with torch.cuda.device(0):
  net = models.densenet161()
  flops, params = get_model_complexity_info(net, (224, 224), as_strings=True, print_per_layer_stat=True)
  print('Flops:  ' + flops)
  print('Params: ' + params)
```


================================================
FILE: flops_counter_pytorch/ptflops/__init__.py
================================================
from .flops_counter import get_model_complexity_info


================================================
FILE: flops_counter_pytorch/ptflops/flops_counter.py
================================================
import torch.nn as nn
import torch
import numpy as np

def get_model_complexity_info(model, input_res, print_per_layer_stat=True, as_strings=True,
                              input_constructor=None):
    assert type(input_res) is tuple
    assert len(input_res) == 2
    flops_model = add_flops_counting_methods(model)
    flops_model.eval().start_flops_count()
    if input_constructor:
        input = input_constructor(input_res)
        _ = flops_model(**input)
    else:
        batch = torch.FloatTensor(1, 3, *input_res)
        _ = flops_model(batch)

    if print_per_layer_stat:
        print_model_with_flops(flops_model)
    flops_count = flops_model.compute_average_flops_cost()
    params_count = get_model_parameters_number(flops_model)
    flops_model.stop_flops_count()

    if as_strings:
        return flops_to_string(flops_count), params_to_string(params_count)

    return flops_count, params_count

def flops_to_string(flops, units='GMac', precision=12):
    if units is None:
        if flops // 10**9 > 0:
            return str(round(flops / 10.**9, precision)) + ' GMac'
        elif flops // 10**6 > 0:
            return str(round(flops / 10.**6, precision)) + ' MMac'
        elif flops // 10**3 > 0:
            return str(round(flops / 10.**3, precision)) + ' KMac'
        else:
            return str(flops) + ' Mac'
    else:
        if units == 'GMac':
            return str(round(flops / 10.**9, precision)) + ' ' + units
        elif units == 'MMac':
            return str(round(flops / 10.**6, precision)) + ' ' + units
        elif units == 'KMac':
            return str(round(flops / 10.**3, precision)) + ' ' + units
        else:
            return str(flops) + ' Mac'

def params_to_string(params_num):
    if params_num // 10 ** 6 > 0:
        return str(round(params_num / 10 ** 6, 2)) + ' M'
    elif params_num // 10 ** 3:
        return str(round(params_num / 10 ** 3, 2)) + ' k'

def print_model_with_flops(model, units='GMac', precision=3):
    total_flops = model.compute_average_flops_cost()

    def accumulate_flops(self):
        if is_supported_instance(self):
            return self.__flops__ / model.__batch_counter__
        else:
            sum = 0
            for m in self.children():
                sum += m.accumulate_flops()
            return sum

    def flops_repr(self):
        accumulated_flops_cost = self.accumulate_flops()
        return ', '.join([flops_to_string(accumulated_flops_cost, units=units, precision=precision),
                          '{:.3%} MACs'.format(accumulated_flops_cost / total_flops),
                          self.original_extra_repr()])

    def add_extra_repr(m):
        m.accumulate_flops = accumulate_flops.__get__(m)
        flops_extra_repr = flops_repr.__get__(m)
        if m.extra_repr != flops_extra_repr:
            m.original_extra_repr = m.extra_repr
            m.extra_repr = flops_extra_repr
            assert m.extra_repr != m.original_extra_repr

    def del_extra_repr(m):
        if hasattr(m, 'original_extra_repr'):
            m.extra_repr = m.original_extra_repr
            del m.original_extra_repr
        if hasattr(m, 'accumulate_flops'):
            del m.accumulate_flops

    model.apply(add_extra_repr)
    print(model)
    model.apply(del_extra_repr)

def get_model_parameters_number(model):
    params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
    return params_num

def add_flops_counting_methods(net_main_module):
    # adding additional methods to the existing module object,
    # this is done this way so that each function has access to self object
    net_main_module.start_flops_count = start_flops_count.__get__(net_main_module)
    net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module)
    net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module)
    net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(net_main_module)

    net_main_module.reset_flops_count()

    # Adding variables necessary for masked flops computation
    net_main_module.apply(add_flops_mask_variable_or_reset)

    return net_main_module


def compute_average_flops_cost(self):
    """
    A method that will be available after add_flops_counting_methods() is called
    on a desired net object.

    Returns current mean flops consumption per image.

    """

    batches_count = self.__batch_counter__
    flops_sum = 0
    for module in self.modules():
        if is_supported_instance(module):
            flops_sum += module.__flops__

    return flops_sum / batches_count


def start_flops_count(self):
    """
    A method that will be available after add_flops_counting_methods() is called
    on a desired net object.

    Activates the computation of mean flops consumption per image.
    Call it before you run the network.

    """
    add_batch_counter_hook_function(self)
    self.apply(add_flops_counter_hook_function)


def stop_flops_count(self):
    """
    A method that will be available after add_flops_counting_methods() is called
    on a desired net object.

    Stops computing the mean flops consumption per image.
    Call whenever you want to pause the computation.

    """
    remove_batch_counter_hook_function(self)
    self.apply(remove_flops_counter_hook_function)


def reset_flops_count(self):
    """
    A method that will be available after add_flops_counting_methods() is called
    on a desired net object.

    Resets statistics computed so far.

    """
    add_batch_counter_variables_or_reset(self)
    self.apply(add_flops_counter_variable_or_reset)


def add_flops_mask(module, mask):
    def add_flops_mask_func(module):
        if isinstance(module, torch.nn.Conv2d):
            module.__mask__ = mask
    module.apply(add_flops_mask_func)


def remove_flops_mask(module):
    module.apply(add_flops_mask_variable_or_reset)


# ---- Internal functions
def is_supported_instance(module):
    if isinstance(module, (torch.nn.Conv2d, torch.nn.ReLU, torch.nn.PReLU, torch.nn.ELU, \
                           torch.nn.LeakyReLU, torch.nn.ReLU6, torch.nn.Linear, \
                           torch.nn.MaxPool2d, torch.nn.AvgPool2d, torch.nn.BatchNorm2d, \
                           torch.nn.Upsample, nn.AdaptiveMaxPool2d, nn.AdaptiveAvgPool2d)):
        return True

    return False


def empty_flops_counter_hook(module, input, output):
    module.__flops__ += 0


def upsample_flops_counter_hook(module, input, output):
    output_size = output[0]
    batch_size = output_size.shape[0]
    output_elements_count = batch_size
    for val in output_size.shape[1:]:
        output_elements_count *= val
    module.__flops__ += int(output_elements_count)


def relu_flops_counter_hook(module, input, output):
    active_elements_count = output.numel()
    module.__flops__ += int(active_elements_count)


def linear_flops_counter_hook(module, input, output):
    input = input[0]
    batch_size = input.shape[0]
    module.__flops__ += int(batch_size * input.shape[1] * output.shape[1])


def pool_flops_counter_hook(module, input, output):
    input = input[0]
    module.__flops__ += int(np.prod(input.shape))

def bn_flops_counter_hook(module, input, output):
    module.affine
    input = input[0]

    batch_flops = np.prod(input.shape)
    if module.affine:
        batch_flops *= 2
    module.__flops__ += int(batch_flops)

def conv_flops_counter_hook(conv_module, input, output):
    # Can have multiple inputs, getting the first one
    input = input[0]

    batch_size = input.shape[0]
    output_height, output_width = output.shape[2:]

    kernel_height, kernel_width = conv_module.kernel_size
    in_channels = conv_module.in_channels
    out_channels = conv_module.out_channels
    groups = conv_module.groups

    filters_per_channel = out_channels // groups
    conv_per_position_flops = kernel_height * kernel_width * in_channels * filters_per_channel

    active_elements_count = batch_size * output_height * output_width

    if conv_module.__mask__ is not None:
        # (b, 1, h, w)
        flops_mask = conv_module.__mask__.expand(batch_size, 1, output_height, output_width)
        active_elements_count = flops_mask.sum()

    overall_conv_flops = conv_per_position_flops * active_elements_count

    bias_flops = 0

    if conv_module.bias is not None:

        bias_flops = out_channels * active_elements_count

    overall_flops = overall_conv_flops + bias_flops

    conv_module.__flops__ += int(overall_flops)


def batch_counter_hook(module, input, output):
    batch_size = 1
    if len(input) > 0:
        # Can have multiple inputs, getting the first one
        input = input[0]
        batch_size = len(input)
    else:
        pass
        print('Warning! No positional inputs found for a module, assuming batch size is 1.')
    module.__batch_counter__ += batch_size


def add_batch_counter_variables_or_reset(module):

    module.__batch_counter__ = 0


def add_batch_counter_hook_function(module):
    if hasattr(module, '__batch_counter_handle__'):
        return

    handle = module.register_forward_hook(batch_counter_hook)
    module.__batch_counter_handle__ = handle


def remove_batch_counter_hook_function(module):
    if hasattr(module, '__batch_counter_handle__'):
        module.__batch_counter_handle__.remove()
        del module.__batch_counter_handle__


def add_flops_counter_variable_or_reset(module):
    if is_supported_instance(module):
        module.__flops__ = 0


def add_flops_counter_hook_function(module):
    if is_supported_instance(module):
        if hasattr(module, '__flops_handle__'):
            return

        if isinstance(module, torch.nn.Conv2d):
            handle = module.register_forward_hook(conv_flops_counter_hook)
        elif isinstance(module, (torch.nn.ReLU, torch.nn.PReLU, torch.nn.ELU, \
                                 torch.nn.LeakyReLU, torch.nn.ReLU6)):
            handle = module.register_forward_hook(relu_flops_counter_hook)
        elif isinstance(module, torch.nn.Linear):
            handle = module.register_forward_hook(linear_flops_counter_hook)
        elif isinstance(module, (torch.nn.AvgPool2d, torch.nn.MaxPool2d, nn.AdaptiveMaxPool2d, \
                                 nn.AdaptiveAvgPool2d)):
            handle = module.register_forward_hook(pool_flops_counter_hook)
        elif isinstance(module, torch.nn.BatchNorm2d):
            handle = module.register_forward_hook(bn_flops_counter_hook)
        elif isinstance(module, torch.nn.Upsample):
            handle = module.register_forward_hook(upsample_flops_counter_hook)
        else:
            handle = module.register_forward_hook(empty_flops_counter_hook)
        module.__flops_handle__ = handle


def remove_flops_counter_hook_function(module):
    if is_supported_instance(module):
        if hasattr(module, '__flops_handle__'):
            module.__flops_handle__.remove()
            del module.__flops_handle__
# --- Masked flops counting


# Also being run in the initialization
def add_flops_mask_variable_or_reset(module):
    if is_supported_instance(module):
        module.__mask__ = None


================================================
FILE: flops_counter_pytorch/sample.py
================================================
import argparse
import torchvision.models as models
import torch
from ptflops import get_model_complexity_info

pt_models = { 'resnet18': models.resnet18, 'resnet50': models.resnet50,
              'alexnet': models.alexnet,
              'vgg16': models.vgg16, 'squeezenet': models.squeezenet1_0,
              'densenet': models.densenet161}

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Evaluation script for Face Recognition in PyTorch')
    parser.add_argument('--device', type=int, default=-1, help='Device to store the model.')
    parser.add_argument('--model', choices=list(pt_models.keys()), type=str, default='resnet18')
    args = parser.parse_args()

    with torch.cuda.device(args.device):
        net = pt_models[args.model]()
        flops, params = get_model_complexity_info(net, (224, 224), as_strings=True, print_per_layer_stat=True)
        print('Flops: ' + flops)
        print('Params: ' + params)


================================================
FILE: flops_counter_pytorch/setup.py
================================================
import os
import shutil
import sys
from setuptools import setup, find_packages

readme = open('README.md').read()

VERSION = '0.5'

requirements = [
    'torch',
]

setup(
    # Metadata
    name='ptflops',
    version=VERSION,
    author='Vladislav Sovrasov',
    author_email='sovrasov.vlad@gmail.com',
    url='https://github.com/sovrasov/flops-counter.pytorch',
    description='Flops counter for convolutional networks in pytorch framework',
    long_description=readme,
    license='MIT',

    # Package info
    packages=find_packages(exclude=('*test*',)),

    #
    zip_safe=True,
    install_requires=requirements,

    # Classifiers
    classifiers=[
        'Programming Language :: Python :: 3',
    ],
)


================================================
FILE: loss.py
================================================
from __future__ import print_function

import torch
import torch.nn as nn
import torch.nn.functional as F

from torch.autograd import Variable


class FIIQALoss(nn.Module):
    def __init__(self):
        super(AGLoss, self).__init__()

    def forward(self, fiiqa_preds, fiiqa_targets):
        '''Compute loss (fiiqa_preds, fiiqa_targets) .

        Args:
          fiiqa_preds: (tensor) predicted fiiqa, sized [batch_size,100].
          fiiqa_targets: (tensor) target fiiqa, sized [batch_size,].

        loss:
          (tensor) loss = SmoothL1Loss(fiiqa_preds, fiiqa_targets)
        '''
        #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。
        #先求得分类概率
        fiiqa_prob = F.softmax(fiiqa_preds,dim=1)
        #利用分类概率与对应预测值相乘后累加求和，求得期望值
        fiiqa_expect = torch.sum(Variable(torch.arange(0,200)).cuda().float()*fiiqa_prob, 1)
        #loss是期望值与ground trouth 之间的误差
        fiiqa_loss = F.smooth_l1_loss(fiiqa_expect, fiiqa_targets.float())
        return fiiqa_loss 


================================================
FILE: model/haarcascade_frontalface_default.xml
================================================
<?xml version="1.0"?>
<!--
    Stump-based 24x24 discrete(?) adaboost frontal face detector.
    Created by Rainer Lienhart.

////////////////////////////////////////////////////////////////////////////////////////

  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.

  By downloading, copying, installing or using the software you agree to this license.
  If you do not agree to this license, do not download, install,
  copy or use the software.


                        Intel License Agreement
                For Open Source Computer Vision Library

 Copyright (C) 2000, Intel Corporation, all rights reserved.
 Third party copyrights are property of their respective owners.

 Redistribution and use in source and binary forms, with or without modification,
 are permitted provided that the following conditions are met:

   * Redistribution's of source code must retain the above copyright notice,
     this list of conditions and the following disclaimer.

   * Redistribution's in binary form must reproduce the above copyright notice,
     this list of conditions and the following disclaimer in the documentation
     and/or other materials provided with the distribution.

   * The name of Intel Corporation may not be used to endorse or promote products
     derived from this software without specific prior written permission.

 This software is provided by the copyright holders and contributors "as is" and
 any express or implied warranties, including, but not limited to, the implied
 warranties of merchantability and fitness for a particular purpose are disclaimed.
 In no event shall the Intel Corporation or contributors be liable for any direct,
 indirect, incidental, special, exemplary, or consequential damages
 (including, but not limited to, procurement of substitute goods or services;
 loss of use, data, or profits; or business interruption) however caused
 and on any theory of liability, whether in contract, strict liability,
 or tort (including negligence or otherwise) arising in any way out of
 the use of this software, even if advised of the possibility of such damage.
-->
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================================================
FILE: net.py
================================================
'''Predict age in one shot.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.downsample = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.downsample(x)
        out = F.relu(out)
        return out

    def freeze_bn(self):
        '''Freeze BatchNorm layers.'''
        for layer in self.modules():
            if isinstance(layer, nn.BatchNorm2d):
                layer.training = False

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion*planes)

        self.downsample = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.downsample(x)
        out = F.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block,  64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        # self.avg_pool = nn.AvgPool2d(kernel_size=7)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.max_pool(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        return out

def ResNet18():
    return ResNet(BasicBlock, [2,2,2,2])

def ResNet50():
    return ResNet(Bottleneck, [3,4,6,3])

def ResNet101():
    return ResNet(Bottleneck, [2,4,23,3])


class AGNet(nn.Module):
    def __init__(self):
        super(AGNet, self).__init__()
        self.backbone = ResNet18()
        self.age_head = self._make_head()
        self.linear1 = nn.Linear(256,200)

    def _make_head(self):
        return nn.Sequential(
            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
        )

    def forward(self, x):
        N = x.size(0)
        y = self.backbone(x)
        y = F.avg_pool2d(y,5)
        # Age head
        y_age = self.age_head(y)
        y_age = self.linear1(y_age.view(N,-1))

        return y_age


def test():
    net = AGNet()
    age = net(Variable(torch.randn(2,3,150,150)))
    print(age.size())


# test()


================================================
FILE: readme.md
================================================
-----------------------------------------------------------------
Face Image Illumination Quality Assessment  implements by pytorch
-----------------------------------------------------------------
This 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




Overview:
![image](https://github.com/yangyuke001/FIIQA_pytorch/blob/master/image/1679052023.jpg)
![image](https://github.com/yangyuke001/FIIQA_pytorch/blob/master/image/996012808.jpg)


Dependencies
------------------------------------------------------------------------------------
*python 3.6+

*pytorch 1.0.1+


Usage
------------------------------------------------------------------------------------
1.cloning the respository
git clone https://github.com/yangyuke001/FIIQA-PyTorch

cd FIIQA-PyTorch

2.Downloading the dataset

Face Image Illumination Quality Dataset 1.0

We 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.
2.1 Source illumination patterns set (http://pan.baidu.com/s/1hrYayXI)

Unzip 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".
2.2 Training Set (http://pan.baidu.com/s/1mhFBusg)

Unzip 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".
2.3 Validation Set (http://pan.baidu.com/s/1miMDkt6)

Unzip 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".
2.4 Testing Set (http://pan.baidu.com/s/1nuXQjH3)

Unzip 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".

3. Training

To train FIIQA , run the script below:


python train.py

4. Testing

To test on FIIQA,run the script below:

python test.py





================================================
FILE: result.csv
================================================
F,I,I,Q,A
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tensor(94),tensor(94.0886)
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tensor(121),tensor(120.7858)
tensor(99),tensor(98.9918)
tensor(103),tensor(103.3867)
tensor(125),tensor(125.0813)
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tensor(29),tensor(28.8233)
tensor(141),tensor(141.1742)
tensor(49),tensor(49.0508)
tensor(21),tensor(21.0049)
tensor(185),tensor(185.0007)
tensor(23),tensor(23.0143)
tensor(50),tensor(49.9610)
tensor(90),tensor(90.0887)
tensor(98),tensor(98.0835)
tensor(73),tensor(73.0869)
tensor(186),tensor(185.8177)
tensor(44),tensor(43.9666)
tensor(7),tensor(6.6171)
tensor(51),tensor(51.3242)
tensor(171),tensor(171.3192)
tensor(62),tensor(61.9054)
tensor(12),tensor(12.2186)
tensor(9),tensor(8.7714)
tensor(30),tensor(29.7616)
tensor(31),tensor(30.9348)
tensor(82),tensor(81.9502)
tensor(60),tensor(60.2189)
tensor(45),tensor(45.1851)
tensor(17),tensor(16.7547)
tensor(0),tensor(3.0000)
tensor(3),tensor(3.0010)
tensor(24),tensor(23.9576)
tensor(10),tensor(9.9703)
tensor(11),tensor(11.3060)
tensor(67),tensor(67.1293)
tensor(101),tensor(101.0096)
tensor(97),tensor(96.8615)
tensor(164),tensor(164.1143)
tensor(179),tensor(179.2731)
tensor(183),tensor(183.1962)
tensor(184),tensor(184.2389)
tensor(136),tensor(136.0905)
tensor(109),tensor(108.7626)
tensor(22),tensor(21.9393)
tensor(91),tensor(90.8485)
tensor(187),tensor(186.8612)
tensor(188),tensor(188.0843)
tensor(189),tensor(188.8834)
tensor(28),tensor(28.0763)
tensor(137),tensor(136.9486)
tensor(175),tensor(175.2729)
tensor(146),tensor(146.0060)
tensor(167),tensor(166.9573)
tensor(190),tensor(190.0770)
tensor(46),tensor(45.9412)
tensor(36),tensor(36.0712)
tensor(147),tensor(146.8219)
tensor(148),tensor(147.9823)
tensor(191),tensor(191.1787)
tensor(156),tensor(154.9999)
tensor(134),tensor(133.9307)
tensor(149),tensor(148.9504)
tensor(157),tensor(157.0845)
tensor(192),tensor(192.0593)
tensor(159),tensor(159.1477)
tensor(63),tensor(63.0823)
tensor(64),tensor(63.8294)
tensor(110),tensor(109.8765)
tensor(111),tensor(111.0156)
tensor(144),tensor(144.0040)
tensor(85),tensor(85.2162)
tensor(145),tensor(144.9085)
tensor(126),tensor(125.5005)
tensor(139),tensor(139.2002)
tensor(193),tensor(193.2003)
tensor(194),tensor(193.9875)
tensor(47),tensor(47.0481)
tensor(37),tensor(36.8168)
tensor(26),tensor(25.7444)
tensor(112),tensor(111.8681)
tensor(135),tensor(135.0092)
tensor(100),tensor(99.9199)
tensor(127),tensor(126.8059)
tensor(74),tensor(73.8863)
tensor(195),tensor(194.7376)
tensor(92),tensor(91.8978)
tensor(4),tensor(4.2684)
tensor(5),tensor(5.3302)
tensor(32),tensor(31.8842)
tensor(61),tensor(61.2715)
tensor(128),tensor(127.8354)
tensor(150),tensor(150.0876)
tensor(120),tensor(119.8498)
tensor(68),tensor(68.0309)
tensor(69),tensor(69.1709)
tensor(113),tensor(112.7137)
tensor(86),tensor(85.9036)
tensor(48),tensor(48.0766)
tensor(70),tensor(70.3225)
tensor(71),tensor(70.7445)
tensor(114),tensor(114.1373)
tensor(89),tensor(88.9483)
tensor(107),tensor(107.1543)
tensor(151),tensor(151.4293)
tensor(196),tensor(195.9590)
tensor(160),tensor(159.8318)
tensor(115),tensor(115.0814)
tensor(116),tensor(116.4704)
tensor(161),tensor(160.5904)
tensor(166),tensor(165.9165)
tensor(129),tensor(128.9751)
tensor(117),tensor(116.9984)
tensor(93),tensor(93.1340)
tensor(78),tensor(77.7330)
tensor(87),tensor(87.0538)
tensor(118),tensor(118.0088)
tensor(59),tensor(58.8473)
tensor(130),tensor(129.7286)
tensor(158),tensor(157.8748)
tensor(124),tensor(124.0527)
tensor(79),tensor(79.2708)
tensor(123),tensor(123.1056)
tensor(152),tensor(152.0329)
tensor(94),tensor(94.0868)
tensor(52),tensor(52.3047)
tensor(121),tensor(120.7973)
tensor(99),tensor(98.9195)
tensor(103),tensor(103.3047)
tensor(125),tensor(124.9867)
tensor(168),tensor(167.9795)
tensor(65),tensor(65.2421)
tensor(15),tensor(15.0188)
tensor(39),tensor(38.9978)
tensor(142),tensor(142.2858)
tensor(16),tensor(15.8434)
tensor(1),tensor(3.0000)
tensor(8),tensor(7.4602)
tensor(40),tensor(40.0661)
tensor(42),tensor(41.9541)
tensor(13),tensor(12.9919)
tensor(176),tensor(176.0816)
tensor(177),tensor(176.9284)
tensor(140),tensor(140.1801)
tensor(143),tensor(143.1878)
tensor(131),tensor(130.8659)
tensor(172),tensor(172.1649)
tensor(181),tensor(180.7474)
tensor(83),tensor(83.3148)
tensor(84),tensor(83.8334)
tensor(95),tensor(94.6963)
tensor(25),tensor(24.6859)
tensor(153),tensor(152.9077)
tensor(132),tensor(131.6143)
tensor(173),tensor(173.2106)
tensor(197),tensor(197.2361)
tensor(75),tensor(75.0937)
tensor(154),tensor(154.0061)
tensor(41),tensor(40.9286)
tensor(53),tensor(53.0685)
tensor(169),tensor(169.0186)
tensor(162),tensor(161.4585)
tensor(122),tensor(122.1410)
tensor(174),tensor(173.9960)
tensor(198),tensor(197.9998)
tensor(199),tensor(197.9996)
tensor(178),tensor(178.0237)
tensor(182),tensor(179.9994)
tensor(77),tensor(77.1959)
tensor(19),tensor(18.9621)
tensor(38),tensor(37.7737)
tensor(138),tensor(138.2349)
tensor(81),tensor(80.9727)
tensor(66),tensor(65.8498)
tensor(96),tensor(95.8084)
tensor(80),tensor(80.1938)
tensor(163),tensor(163.0920)
tensor(170),tensor(169.7315)
tensor(155),tensor(154.9814)
tensor(133),tensor(133.3952)
tensor(102),tensor(101.9167)
tensor(35),tensor(35.1712)
tensor(104),tensor(103.7048)
tensor(72),tensor(71.8675)
tensor(43),tensor(43.0050)
tensor(14),tensor(14.2709)
tensor(20),tensor(19.7845)
tensor(55),tensor(55.0310)
tensor(108),tensor(108.0108)
tensor(119),tensor(118.8568)
tensor(165),tensor(165.1831)
tensor(76),tensor(76.0944)
tensor(105),tensor(104.8968)
tensor(2),tensor(3.0000)
tensor(18),tensor(17.7746)
tensor(33),tensor(32.9496)
tensor(34),tensor(33.9959)
tensor(88),tensor(88.0172)
tensor(106),tensor(106.2281)
tensor(6),tensor(6.0117)
tensor(54),tensor(53.9771)
tensor(57),tensor(57.1200)
tensor(58),tensor(58.0148)
tensor(29),tensor(28.7627)
tensor(141),tensor(141.2923)
tensor(49),tensor(49.0693)
tensor(21),tensor(21.1004)
tensor(185),tensor(184.9998)
tensor(23),tensor(23.0143)
tensor(50),tensor(49.9845)
tensor(90),tensor(89.8883)
tensor(98),tensor(98.0716)
tensor(73),tensor(72.9535)
tensor(186),tensor(185.8846)
tensor(44),tensor(44.0065)
tensor(7),tensor(6.6780)
tensor(51),tensor(51.3438)
tensor(171),tensor(171.1246)
tensor(62),tensor(61.9333)
tensor(12),tensor(12.2588)
tensor(9),tensor(8.8324)
tensor(30),tensor(29.4568)
tensor(31),tensor(30.9834)
tensor(82),tensor(82.1658)
tensor(60),tensor(60.2344)
tensor(45),tensor(45.0143)
tensor(17),tensor(17.0001)
tensor(0),tensor(3.0001)
tensor(3),tensor(3.0024)
tensor(24),tensor(23.8885)
tensor(10),tensor(9.9645)
tensor(11),tensor(10.8783)
tensor(67),tensor(67.1789)
tensor(101),tensor(100.9435)
tensor(97),tensor(96.7067)
tensor(164),tensor(163.9680)
tensor(179),tensor(179.2622)
tensor(183),tensor(183.2784)
tensor(184),tensor(184.3226)
tensor(136),tensor(136.1009)
tensor(109),tensor(108.9526)
tensor(22),tensor(21.9459)
tensor(91),tensor(90.9837)
tensor(187),tensor(186.8762)
tensor(188),tensor(187.8643)
tensor(189),tensor(188.8142)
tensor(28),tensor(27.8457)
tensor(137),tensor(137.2563)
tensor(175),tensor(175.0145)
tensor(146),tensor(146.0095)
tensor(167),tensor(166.9978)
tensor(190),tensor(190.0220)
tensor(46),tensor(46.1978)
tensor(36),tensor(36.0715)
tensor(147),tensor(146.7097)
tensor(148),tensor(148.0436)
tensor(191),tensor(191.0040)
tensor(156),tensor(155.0000)
tensor(134),tensor(134.0721)
tensor(149),tensor(148.9626)
tensor(192),tensor(192.0598)
tensor(159),tensor(159.1005)
tensor(64),tensor(63.7447)
tensor(110),tensor(109.7988)
tensor(111),tensor(110.9440)
tensor(144),tensor(144.0036)
tensor(85),tensor(85.1325)
tensor(145),tensor(144.9088)
tensor(126),tensor(125.7051)
tensor(139),tensor(139.2271)
tensor(193),tensor(193.1090)
tensor(194),tensor(193.9699)
tensor(47),tensor(47.1776)
tensor(37),tensor(36.7918)
tensor(26),tensor(25.7477)
tensor(112),tensor(111.5567)
tensor(135),tensor(135.1579)
tensor(100),tensor(99.8723)
tensor(127),tensor(126.7329)
tensor(74),tensor(74.2930)
tensor(195),tensor(195.1046)
tensor(92),tensor(91.9471)
tensor(4),tensor(4.7773)
tensor(5),tensor(5.6000)
tensor(32),tensor(31.6819)
tensor(61),tensor(61.0282)
tensor(128),tensor(127.8879)
tensor(150),tensor(150.0698)
tensor(120),tensor(119.7980)
tensor(68),tensor(68.1360)
tensor(69),tensor(69.0765)
tensor(113),tensor(112.6671)
tensor(86),tensor(85.9962)
tensor(48),tensor(48.1793)
tensor(70),tensor(69.9023)
tensor(71),tensor(71.6110)
tensor(89),tensor(89.0713)
tensor(107),tensor(106.9279)
tensor(151),tensor(150.7560)
tensor(196),tensor(195.9088)
tensor(160),tensor(159.7892)
tensor(115),tensor(115.0614)
tensor(116),tensor(117.0872)
tensor(161),tensor(159.7145)
tensor(166),tensor(165.6711)
tensor(129),tensor(128.6642)
tensor(117),tensor(116.5497)
tensor(93),tensor(92.8882)
tensor(78),tensor(78.2873)
tensor(87),tensor(86.9999)
tensor(118),tensor(117.8073)
tensor(59),tensor(58.9878)
tensor(130),tensor(129.8360)
tensor(158),tensor(157.8539)
tensor(124),tensor(123.9232)
tensor(123),tensor(122.9126)
tensor(152),tensor(152.0553)
tensor(94),tensor(93.9758)
tensor(52),tensor(51.9299)
tensor(121),tensor(120.8131)
tensor(99),tensor(99.0969)
tensor(103),tensor(103.4066)
tensor(125),tensor(124.9183)
tensor(168),tensor(167.9268)
tensor(15),tensor(15.0115)
tensor(39),tensor(39.0000)
tensor(142),tensor(142.1839)
tensor(16),tensor(15.9217)
tensor(1),tensor(3.0000)
tensor(8),tensor(7.4774)
tensor(40),tensor(39.9070)
tensor(42),tensor(41.7777)
tensor(176),tensor(176.0446)
tensor(177),tensor(176.7879)
tensor(140),tensor(140.1034)
tensor(143),tensor(143.2380)
tensor(131),tensor(130.9071)
tensor(172),tensor(172.3320)
tensor(181),tensor(181.8424)
tensor(83),tensor(82.9930)
tensor(84),tensor(83.8778)
tensor(95),tensor(95.1510)
tensor(25),tensor(24.8277)
tensor(153),tensor(152.8784)
tensor(132),tensor(131.6945)
tensor(173),tensor(172.9939)
tensor(75),tensor(75.1322)
tensor(154),tensor(153.8237)
tensor(41),tensor(40.6333)
tensor(53),tensor(52.9659)
tensor(169),tensor(169.2990)
tensor(162),tensor(161.7367)
tensor(122),tensor(121.7951)
tensor(174),tensor(173.8615)
tensor(198),tensor(197.9993)
tensor(199),tensor(198.0000)
tensor(178),tensor(178.0021)
tensor(182),tensor(179.9999)
tensor(77),tensor(75.9892)
tensor(19),tensor(18.7690)
tensor(138),tensor(138.2532)
tensor(81),tensor(80.9487)
tensor(66),tensor(65.9959)
tensor(96),tensor(95.9090)
tensor(80),tensor(80.2604)
tensor(163),tensor(163.2926)
tensor(170),tensor(170.2601)
tensor(155),tensor(154.9843)
tensor(133),tensor(133.0640)


================================================
FILE: scripts/get_state_dict.py
================================================
'''Init AGNet18 with pretrained ResNet18 model.'''
import math
import torch
import torch.nn as nn
import torch.nn.init as init

from net import AGNet, ResNet18


print('Loading pretrained ResNet18 model..')
d = torch.load('./model/resnet18.pth')

print('Loading into backbone..')
backbone = ResNet18()
dd = backbone.state_dict()
for k in d.keys():
    if not k.startswith('fc'):  # skip fc layers
        dd[k] = d[k]

print('Saving AGNet..')
net = AGNet()
net.backbone.load_state_dict(dd)
torch.save(net.state_dict(), 'net.pth')
print('Done!')


================================================
FILE: shufflenetv2.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math


def conv_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU(inplace=True)
    )


def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU(inplace=True)
    )


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()

    channels_per_group = num_channels // groups

    # reshape
    x = x.view(batchsize, groups, channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, benchmodel):
        super(InvertedResidual, self).__init__()
        self.benchmodel = benchmodel
        self.stride = stride
        assert stride in [1, 2]

        oup_inc = oup // 2

        if self.benchmodel == 1:
            # assert inp == oup_inc
            self.banch2 = nn.Sequential(
                # pw
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(oup_inc, oup_inc, 3, stride,
                          1, groups=oup_inc, bias=False),
                nn.BatchNorm2d(oup_inc),
                # pw-linear
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )
        else:
            self.banch1 = nn.Sequential(
                # dw
                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                nn.BatchNorm2d(inp),
                # pw-linear
                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )

            self.banch2 = nn.Sequential(
                # pw
                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(oup_inc, oup_inc, 3, stride,
                          1, groups=oup_inc, bias=False),
                nn.BatchNorm2d(oup_inc),
                # pw-linear
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )

    @staticmethod
    def _concat(x, out):
        # concatenate along channel axis
        return torch.cat((x, out), 1)

    def forward(self, x):
        if 1 == self.benchmodel:
            # x1 = x[:, :(x.shape[1] // 2), :, :]
            # x2 = x[:, (x.shape[1] // 2):, :, :]
            x1, x2 = torch.split(x, x.shape[1] // 2, dim=1)
            out = self._concat(x1, self.banch2(x2))
        elif 2 == self.benchmodel:
            out = self._concat(self.banch1(x), self.banch2(x))

        return channel_shuffle(out, 2)


class ShuffleNetV2(nn.Module):
    def __init__(self, n_class=200, input_size=160, width_mult=1): #1,0
        super(ShuffleNetV2, self).__init__()

        assert input_size % 32 == 0

        self.stage_repeats = [4, 8, 4]
        # index 0 is invalid and should never be called.
        # only used for indexing convenience.
        if width_mult == 0.5:
            self.stage_out_channels = [-1, 24, 48, 96, 192, 192] #175
        elif width_mult == 1.0:
            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024] # 1024
        elif width_mult == 1.5:
            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]
        elif width_mult == 2.0:
            self.stage_out_channels = [-1, 24, 224, 488, 976, 2048]
        else:
            raise ValueError(
                """width_mult = {} is not supported""".format(width_mult))

        # building first layer
        input_channel = self.stage_out_channels[1]
        self.conv1 = conv_bn(3, input_channel, 2)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.features = []
        # building inverted residual blocks
        for idxstage in range(len(self.stage_repeats)):
            numrepeat = self.stage_repeats[idxstage]
            output_channel = self.stage_out_channels[idxstage + 2]
            for i in range(numrepeat):
                if i == 0:
                    # inp, oup, stride, benchmodel):
                    self.features.append(InvertedResidual(
                        input_channel, output_channel, 2, 2))
                else:
                    self.features.append(InvertedResidual(
                        input_channel, output_channel, 1, 1))
                input_channel = output_channel

        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)

        # building last several layers
        self.conv_last = conv_1x1_bn(
            input_channel, self.stage_out_channels[-1])
        self.globalpool = nn.Sequential(nn.AvgPool2d(int(input_size / 32)))

        # building classifier
        self.classifier = nn.Sequential(
            nn.Linear(self.stage_out_channels[-1], n_class))

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.features(x)
        x = self.conv_last(x)
        x = self.globalpool(x)
        x = x.view(-1, self.stage_out_channels[-1])
        x = self.classifier(x)
        return x


def shufflenetv2(width_mult=1.):
    model = ShuffleNetV2(width_mult=width_mult)
    return model


if __name__ == "__main__":
    """Testing
    """
    model = ShuffleNetV2()
    print(model)


================================================
FILE: summary/__init__.py
================================================
#-*- coding:utf-8 -*-
from summary.model_summary import model_summary


================================================
FILE: summary/example.py
================================================
import torch
from misc.summary import model_summary
from model.MobileNetV2 import MobileNetV2


if __name__ == '__main__':
    net = MobileNetV2()
    model_summary(net, input_size=(3, 224, 224))

================================================
FILE: summary/model_hook.py
================================================
# -*- coding: utf-8 -*-

import time
from collections import OrderedDict
import numpy as np
import torch
import torch.nn as nn

from summary.module_mac import compute_module_mac


class CModelHook(object):
    def __init__(self, model, input_size):
        assert isinstance(model, nn.Module)
        assert isinstance(input_size, (list, tuple))

        self._model = model
        self._input_size = input_size
        self._origin_call = dict()  # sub module call hook

        self._hook_model()
        self._model.eval()
        self._model(torch.rand(1, *self._input_size))

    @staticmethod
    def _register_buffer(module):
        assert isinstance(module, nn.Module)

        if len(list(module.children())) > 0:
            return

        module.register_buffer('input_shape', torch.zeros(3).int())
        module.register_buffer('output_shape', torch.zeros(3).int())
        module.register_buffer('parameter_quantity', torch.zeros(1).int())
        module.register_buffer('inference_memory', torch.zeros(1).long())
        module.register_buffer('MAdd', torch.zeros(1).long())
        module.register_buffer('duration', torch.zeros(1).float())

    def _sub_module_call_hook(self):
        def wrap_call(module, *input, **kwargs):
            assert module.__class__ in self._origin_call

            start = time.time()
            output = self._origin_call[module.__class__](module, *input, **kwargs)
            end = time.time()
            module.duration = torch.from_numpy(
                np.array([end - start], dtype=np.float32))

            module.input_shape = torch.from_numpy(
                np.array(input[0].size()[1:], dtype=np.int32))
            module.output_shape = torch.from_numpy(
                np.array(output.size()[1:], dtype=np.int32))

            parameter_quantity = 0
            # iterate through parameters and count num params
            for name, p in module._parameters.items():
                parameter_quantity += (0 if p is None else torch.numel(p.data))
            module.parameter_quantity = torch.from_numpy(
                np.array([parameter_quantity], dtype=np.long))

            inference_memory = 1
            for s in output.size()[1:]:
                inference_memory *= s
            # memory += parameters_number  # exclude parameter memory
            inference_memory = inference_memory * 4 / (1024 ** 2)  # shown as MB unit
            module.inference_memory = torch.from_numpy(
                np.array([inference_memory], dtype=np.float32))

            madd = compute_module_mac(module, input[0], output)
            module.MAdd = torch.from_numpy(
                np.array([madd], dtype=np.int64))
            return output

        for module in self._model.modules():
            if len(list(module.children())) == 0 and module.__class__ not in self._origin_call:
                self._origin_call[module.__class__] = module.__class__.__call__
                module.__class__.__call__ = wrap_call

    def _hook_model(self):
        self._model.apply(self._register_buffer)
        self._sub_module_call_hook()

    @staticmethod
    def _retrieve_leaf_modules(model):
        leaf_modules = []
        for name, m in model.named_modules():
            if len(list(m.children())) == 0:
                leaf_modules.append((name, m))
        return leaf_modules

    def retrieve_leaf_modules(self):
        return OrderedDict(self._retrieve_leaf_modules(self._model))


================================================
FILE: summary/model_summary.py
================================================
# -*- coding: utf-8 -*-

import queue
from collections import OrderedDict
import pandas as pd
import torch.nn as nn

from summary.model_hook import CModelHook
from summary.summary_tree import CSummaryTree, CSummaryNode

pd.set_option('display.width', 1000)
pd.set_option('display.max_rows', 10000)


def get_parent_node(root_node, summary_node_name):
    assert isinstance(root_node, CSummaryNode)

    node = root_node
    names = summary_node_name.split('.')
    for i in range(len(names) - 1):
        node_name = '.'.join(names[0:i+1])
        child_index = node.find_child_index(node_name)
        assert child_index != -1
        node = node.children[child_index]
    return node


def convert_leaf_modules_to_summary_tree(leaf_modules):
    assert isinstance(leaf_modules, OrderedDict)

    create_index = 1
    root_node = CSummaryNode(name='root', parent=None)
    for leaf_module_name, leaf_module in leaf_modules.items():
        names = leaf_module_name.split('.')
        for i in range(len(names)):
            create_index += 1
            summary_node_name = '.'.join(names[0:i+1])
            parent_node = get_parent_node(root_node, summary_node_name)
            node = CSummaryNode(name=summary_node_name, parent=parent_node)
            parent_node.add_child(node)
            if i == len(names) - 1:  # leaf module itself
                input_shape = leaf_module.input_shape.numpy().tolist()
                output_shape = leaf_module.output_shape.numpy().tolist()
                node.input_shape = input_shape
                node.output_shape = output_shape
                node.parameter_quantity = leaf_module.parameter_quantity.numpy()[0]
                node.inference_memory = leaf_module.inference_memory.numpy()[0]
                node.MAdd = leaf_module.MAdd.numpy()[0]
                node.duration = leaf_module.duration.numpy()[0]
    return CSummaryTree(root_node)


def get_collected_summary_nodes(root_node, query_granularity):
    assert isinstance(root_node, CSummaryNode)

    collected_nodes = []
    q = queue.Queue()
    q.put(root_node)
    while not q.empty():
        node = q.get()
        for child in node.children:
            q.put(child)
        if node.depth == query_granularity:
            collected_nodes.append(node)
        if node.depth < query_granularity <= node.granularity:
            collected_nodes.append(node)
    collected_nodes = sorted(collected_nodes, key=lambda x: x.create_index)
    return collected_nodes


def pretty_format(collected_nodes):
    data = list()
    for node in collected_nodes:
        name = node.name
        input_shape = ' '.join(['{:>3d}'] * len(node.input_shape)).format(
            *[e for e in node.input_shape])
        output_shape = ' '.join(['{:>3d}'] * len(node.output_shape)).format(
            *[e for e in node.output_shape])
        parameter_quantity = node.parameter_quantity
        inference_memory = node.inference_memory
        MAdd = node.MAdd
        duration = node.duration
        data.append([name, input_shape, output_shape,
                     parameter_quantity, inference_memory, MAdd, duration])
    df = pd.DataFrame(data)
    df.columns = ['module name', 'input shape', 'output shape',
                  'parameter quantity', 'inference memory(MB)',
                  'MAdd', 'duration']
    df['duration percent'] = df['duration'] / df['duration'].sum()
    total_parameters_quantity = df['parameter quantity'].sum()
    total_memory = df['inference memory(MB)'].sum()
    total_operation_quantity = df['MAdd'].sum()
    del df['duration']
    df = df.fillna(' ')
    df['inference memory(MB)'] = df['inference memory(MB)'].apply(
        lambda x: '{:.2f}MB'.format(x))
    df['duration percent'] = df['duration percent'].apply(lambda x: '{:.2%}'.format(x))
    df['MAdd'] = df['MAdd'].apply(lambda x: '{:,}'.format(x))

    summary = str(df) + '\n'
    summary += "=" * len(str(df).split('\n')[0])
    summary += '\n'
    summary += "total parameters quantity: {:,}\n".format(total_parameters_quantity)
    summary += "total memory: {:.2f}MB\n".format(total_memory)
    summary += "total MAdd: {:,}\n".format(total_operation_quantity)
    print(summary)
    return summary


def model_summary(model, input_size, query_granularity=1):
    assert isinstance(model, nn.Module)
    assert isinstance(input_size, (list, tuple)) and len(input_size) == 3

    model_hook = CModelHook(model, input_size)
    leaf_modules = model_hook.retrieve_leaf_modules()
    summary_tree = convert_leaf_modules_to_summary_tree(leaf_modules)
    collected_nodes = summary_tree.get_collected_summary_nodes(query_granularity)
    summary = pretty_format(collected_nodes)
    return summary


================================================
FILE: summary/module_mac.py
================================================
#-*- coding:utf-8 -*-
"""
compute Multiply-Adds(MAdd) of each leaf module
"""

import torch.nn as nn


def compute_Conv2d_mac(module, inp, out):
    assert isinstance(module, nn.Conv2d)
    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())

    in_c = inp.size(1)
    k_h, k_w = module.kernel_size
    out_c, out_h, out_w = out.size()[1:]
    groups = module.groups

    # ops per output element
    kernel_mul = k_h * k_w * (in_c // groups)
    kernel_add = kernel_mul - 1 + (0 if module.bias is None else 1)

    kernel_mul_group = kernel_mul * out_h * out_w * (out_c // groups)
    kernel_add_group = kernel_add * out_h * out_w * (out_c // groups)

    total_mul = kernel_mul_group * groups
    total_add = kernel_add_group * groups

    # return total_mul + total_add
    return total_mul


def compute_ConvTranspose2d_mac(module, inp, out):
    assert isinstance(module, nn.ConvTranspose2d)
    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())

    in_c, in_h, in_w = inp.size()[1:]
    k_h, k_w = module.kernel_size
    out_c, out_h, out_w = out.size()[1:]
    groups = module.groups

    kernel_mul = k_h * k_w * (in_c // groups)
    kernel_add = kernel_mul - 1 + (0 if module.bias is None else 1)

    kernel_mul_group = kernel_mul * in_h * in_w * (out_c // groups)
    kernel_add_group = kernel_add * in_h * in_w * (out_c // groups)

    total_mul = kernel_mul_group * groups
    total_add = kernel_add_group * groups

    # return total_mul + total_add
    return total_mul


def compute_BatchNorm2d_mac(module, inp, out):
    assert isinstance(module, nn.BatchNorm2d)
    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())

    in_c, in_h, in_w = inp.size()[1:]

    # 1. sub mean
    # 2. div standard deviation
    # 3. mul alpha
    # 4. add beta
    return 4 * in_c * in_h * in_w


def compute_MaxPool2d_mac(module, inp, out):
    assert isinstance(module, nn.MaxPool2d)
    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())

    if isinstance(module.kernel_size, (tuple, list)):
        k_h, k_w = module.kernel_size
    else:
        k_h, k_w = module.kernel_size, module.kernel_size
    out_c, out_h, out_w = out.size()[1:]

    return (k_h * k_w - 1) * out_h * out_w * out_c


def compute_AvgPool2d_mac(module, inp, out):
    assert isinstance(module, nn.AvgPool2d)
    assert len(inp.size()) == 4 and len(inp.size()) == len(out.size())

    if isinstance(module.kernel_size, (tuple, list)):
        k_h, k_w = module.kernel_size
    else:
        k_h, k_w = module.kernel_size, module.kernel_size
    out_c, out_h, out_w = out.size()[1:]

    kernel_add = k_h * k_w - 1
    kernel_avg = 1

    return (kernel_add + kernel_avg) * (out_h * out_w) * out_c


def compute_ReLU_mac(module, inp, out):
    assert isinstance(module, (nn.ReLU, nn.ReLU6))

    count = 1
    for i in inp.size()[1:]:
        count *= i
    return count


def compute_Softmax_mac(module, inp, out):
    assert isinstance(module, nn.Softmax)
    assert len(inp.size()) > 1

    count = 1
    for s in inp.size()[1:]:
        count *= s
    exp = count
    add = count - 1
    div = count
    return exp + add + div


def compute_Linear_mac(module, inp, out):
    assert isinstance(module, nn.Linear)
    assert len(inp.size()) == 2 and len(out.size()) == 2

    num_in_features = inp.size(1)
    num_out_features = out.size(1)

    mul = num_in_features
    add = num_in_features - 1
    # return num_out_features * (mul + add)
    return num_out_features * mul


def compute_module_mac(module, inp, out):
    if isinstance(module, nn.Conv2d):
        return compute_Conv2d_mac(module, inp, out)
    elif isinstance(module, nn.ConvTranspose2d):
        return compute_ConvTranspose2d_mac(module, inp, out)
    # elif isinstance(module, nn.BatchNorm2d):
    #     return compute_BatchNorm2d_mac(module, inp, out)
    # elif isinstance(module, nn.MaxPool2d):
    #     return compute_MaxPool2d_mac(module, inp, out)
    # elif isinstance(module, nn.AvgPool2d):
    #     return compute_AvgPool2d_mac(module, inp, out)
    # elif isinstance(module, (nn.ReLU, nn.ReLU6)):
    #     return compute_ReLU_mac(module, inp, out)
    # elif isinstance(module, nn.Softmax):
    #     return compute_Softmax_mac(module, inp, out)
    elif isinstance(module, nn.Linear):
        return compute_Linear_mac(module, inp, out)
    else:
        return 0


================================================
FILE: summary/summary_tree.py
================================================
# -*- coding: utf-8 -*-

import queue


class CSummaryTree(object):
    def __init__(self, root_node):
        assert isinstance(root_node, CSummaryNode)

        self.root_node = root_node

    def get_same_level_max_node_depth(self, query_node):
        if query_node.name == self.root_node.name:
            return 0
        same_level_depth = max([child.depth for child in query_node.parent.children])
        return same_level_depth

    def update_summary_nodes_granularity(self):
        q = queue.Queue()
        q.put(self.root_node)
        while not q.empty():
            node = q.get()
            node.granularity = self.get_same_level_max_node_depth(node)
            for child in node.children:
                q.put(child)

    def get_collected_summary_nodes(self, query_granularity):
        self.update_summary_nodes_granularity()

        collected_nodes = []
        stack = list()
        stack.append(self.root_node)
        while len(stack) > 0:
            node = stack.pop()
            for child in reversed(node.children):
                stack.append(child)
            if node.depth == query_granularity:
                collected_nodes.append(node)
            if node.depth < query_granularity <= node.granularity:
                collected_nodes.append(node)
        return collected_nodes


class CSummaryNode(object):
    def __init__(self, name=str(), parent=None):
        self._name = name
        self._input_shape = None
        self._output_shape = None
        self._parameter_quantity = 0
        self._inference_memory = 0
        self._MAdd = 0
        self._duration = 0
        self._duration_percent = 0

        self._granularity = 1
        self._depth = 1
        self.parent = parent
        self.children = list()

    @property
    def name(self):
        return self._name

    @name.setter
    def name(self, name):
        self._name = name

    @property
    def granularity(self):
        return self._granularity

    @granularity.setter
    def granularity(self, g):
        self._granularity = g

    @property
    def depth(self):
        d = self._depth
        if len(self.children) > 0:
            d += max([child.depth for child in self.children])
        return d

    @property
    def input_shape(self):
        if len(self.children) == 0:  # leaf
            return self._input_shape
        else:
            return self.children[0].input_shape

    @input_shape.setter
    def input_shape(self, input_shape):
        assert isinstance(input_shape, (list, tuple))
        self._input_shape = input_shape

    @property
    def output_shape(self):
        if len(self.children) == 0:  # leaf
            return self._output_shape
        else:
            return self.children[-1].output_shape

    @output_shape.setter
    def output_shape(self, output_shape):
        assert isinstance(output_shape, (list, tuple))
        self._output_shape = output_shape

    @property
    def parameter_quantity(self):
        # return self.parameters_quantity
        total_parameter_quantity = self._parameter_quantity
        for child in self.children:
            total_parameter_quantity += child.parameter_quantity
        return total_parameter_quantity

    @parameter_quantity.setter
    def parameter_quantity(self, parameter_quantity):
        assert parameter_quantity >= 0
        self._parameter_quantity = parameter_quantity

    @property
    def inference_memory(self):
        total_inference_memory = self._inference_memory
        for child in self.children:
            total_inference_memory += child.inference_memory
        return total_inference_memory

    @inference_memory.setter
    def inference_memory(self, inference_memory):
        self._inference_memory = inference_memory

    @property
    def MAdd(self):
        total_MAdd = self._MAdd
        for child in self.children:
            total_MAdd += child.MAdd
        return total_MAdd

    @MAdd.setter
    def MAdd(self, MAdd):
        self._MAdd = MAdd

    @property
    def duration(self):
        total_duration = self._duration
        for child in self.children:
            total_duration += child.duration
        return total_duration

    @duration.setter
    def duration(self, duration):
        self._duration = duration

    def find_child_index(self, child_name):
        assert isinstance(child_name, str)

        index = -1
        for i in range(len(self.children)):
            if child_name == self.children[i].name:
                index = i
        return index

    def add_child(self, node):
        assert isinstance(node, CSummaryNode)

        if self.find_child_index(node.name) == -1:  # not exist
            self.children.append(node)


================================================
FILE: test/model/haarcascade_frontalface_default.xml
================================================
<?xml version="1.0"?>
<!--
    Stump-based 24x24 discrete(?) adaboost frontal face detector.
    Created by Rainer Lienhart.

////////////////////////////////////////////////////////////////////////////////////////

  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.

  By downloading, copying, installing or using the software you agree to this license.
  If you do not agree to this license, do not download, install,
  copy or use the software.


                        Intel License Agreement
                For Open Source Computer Vision Library

 Copyright (C) 2000, Intel Corporation, all rights reserved.
 Third party copyrights are property of their respective owners.

 Redistribution and use in source and binary forms, with or without modification,
 are permitted provided that the following conditions are met:

   * Redistribution's of source code must retain the above copyright notice,
     this list of conditions and the following disclaimer.

   * Redistribution's in binary form must reproduce the above copyright notice,
     this list of conditions and the following disclaimer in the documentation
     and/or other materials provided with the distribution.

   * The name of Intel Corporation may not be used to endorse or promote products
     derived from this software without specific prior written permission.

 This software is provided by the copyright holders and contributors "as is" and
 any express or implied warranties, including, but not limited to, the implied
 warranties of merchantability and fitness for a particular purpose are disclaimed.
 In no event shall the Intel Corporation or contributors be liable for any direct,
 indirect, incidental, special, exemplary, or consequential damages
 (including, but not limited to, procurement of substitute goods or services;
 loss of use, data, or profits; or business interruption) however caused
 and on any theory of liability, whether in contract, strict liability,
 or tort (including negligence or otherwise) arising in any way out of
 the use of this software, even if advised of the possibility of such damage.
-->
<opencv_storage>
<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
  <featureType>HAAR</featureType>
  <height>24</height>
  <width>24</width>
  <stageParams>
    <maxWeakCount>211</maxWeakCount></stageParams>
  <featureParams>
    <maxCatCount>0</maxCatCount></featureParams>
  <stageNum>25</stageNum>
  <stages>
    <_>
      <maxWeakCount>9</maxWeakCount>
      <stageThreshold>-5.0425500869750977e+00</stageThreshold>
      <weakClassifiers>
        <_>
          <internalNodes>
            0 -1 0 -3.1511999666690826e-02</internalNodes>
          <leafValues>
            2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_>
        <_>
          <internalNodes>
            0 -1 1 1.2396000325679779e-02</internalNodes>
          <leafValues>
            -1.8633940219879150e+00 1.3272049427032471e+00</leafValues></_>
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          <internalNodes>
            0 -1 2 2.1927999332547188e-02</internalNodes>
          <leafValues>
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          <leafValues>
            -8.5909199714660645e-01 8.5255599021911621e-01</leafValues></_></weakClassifiers></_>
    <_>
      <maxWeakCount>16</maxWeakCount>
      <stageThreshold>-4.9842400550842285e+00</stageThreshold>
      <weakClassifiers>
        <_>
          <internalNodes>
            0 -1 9 -2.1110000088810921e-02</internalNodes>
          <leafValues>
            1.2435649633407593e+00 -1.5713009834289551e+00</leafValues></_>
        <_>
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        <_>
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            -3.5127198696136475e-01 1.1440860033035278e+00</leafValues></_>
        <_>
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          <leafValues>
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        <_>
          <internalNodes>
            0 -1 24 9.1508001089096069e-02</internalNodes>
          <leafValues>
            3.8817799091339111e-01 -1.5122940540313721e+00</leafValues></_></weakClassifiers></_>
    <_>
      <maxWeakCount>27</maxWeakCount>
      <stageThreshold>-4.6551899909973145e+00</stageThreshold>
      <weakClassifiers>
        <_>
          <internalNodes>
            0 -1 25 6.9747000932693481e-02</internalNodes>
          <leafValues>
            -1.0130879878997803e+00 1.4687349796295166e+00</leafValues></_>
        <_>
          <internalNodes>
            0 -1 26 3.1502999365329742e-02</internalNodes>
          <leafValues>
            -1.6463639736175537e+00 1.0000629425048828e+00</leafValues></_>
        <_>
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          <leafValues>
            4.6480301022529602e-01 -1.5959889888763428e+00</leafValues></_>
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          <internalNodes>
            0 -1 28 1.4453000389039516e-02</internalNodes>
          <leafValues>
            -6.5511900186538696e-01 8.3021801710128784e-01</leafValues></_>
        <_>
          <internalNodes>
            0 -1 29 -3.0509999487549067e-03</internalNodes>
          <leafValues>
            -1.3982310295104980e+00 4.2550599575042725e-01</leafValues></_>
        <_>
          <internalNodes>
            0 -1 30 3.2722998410463333e-02</internalNodes>
          <leafValues>
            -5.0702601671218872e-01 1.0526109933853149e+00</leafValues></_>
        <_>
          <internalNodes>
            0 -1 31 -7.2960001416504383e-03</internalNodes>
          <leafValues>
            3.6356899142265320e-01 -1.3464889526367188e+00</leafValues></_>
        <_>
          <internalNodes>
            0 -1 32 5.0425000488758087e-02</internalNodes>
          <leafValues>
            -3.0461400747299194e-01 1.4504129886627197e+00</leafValues></_>
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            -4.0286201238632202e-01 1.2145609855651855e+00</leafValues></_>
        <_>
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            1.0539360046386719e+00 -4.5719701051712036e-01</leafValues></_>
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================================================
FILE: test/shufflenetv2.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math


def conv_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU(inplace=True)
    )


def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU(inplace=True)
    )


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()

    channels_per_group = num_channels // groups

    # reshape
    x = x.view(batchsize, groups, channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, benchmodel):
        super(InvertedResidual, self).__init__()
        self.benchmodel = benchmodel
        self.stride = stride
        assert stride in [1, 2]

        oup_inc = oup // 2

        if self.benchmodel == 1:
            # assert inp == oup_inc
            self.banch2 = nn.Sequential(
                # pw
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(oup_inc, oup_inc, 3, stride,
                          1, groups=oup_inc, bias=False),
                nn.BatchNorm2d(oup_inc),
                # pw-linear
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )
        else:
            self.banch1 = nn.Sequential(
                # dw
                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                nn.BatchNorm2d(inp),
                # pw-linear
                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )

            self.banch2 = nn.Sequential(
                # pw
                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(oup_inc, oup_inc, 3, stride,
                          1, groups=oup_inc, bias=False),
                nn.BatchNorm2d(oup_inc),
                # pw-linear
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )

    @staticmethod
    def _concat(x, out):
        # concatenate along channel axis
        return torch.cat((x, out), 1)

    def forward(self, x):
        if 1 == self.benchmodel:
            # x1 = x[:, :(x.shape[1] // 2), :, :]
            # x2 = x[:, (x.shape[1] // 2):, :, :]
            x1, x2 = torch.split(x, x.shape[1] // 2, dim=1)
            out = self._concat(x1, self.banch2(x2))
        elif 2 == self.benchmodel:
            out = self._concat(self.banch1(x), self.banch2(x))

        return channel_shuffle(out, 2)


class ShuffleNetV2(nn.Module):
    def __init__(self, input_size, n_class=200,  width_mult=1): #1,0
        super(ShuffleNetV2, self).__init__()

        assert input_size % 32 == 0

        self.stage_repeats = [4, 8, 4]
        # index 0 is invalid and should never be called.
        # only used for indexing convenience.
        if width_mult == 0.5:
            self.stage_out_channels = [-1, 24, 48, 96, 192, 192] #175
        elif width_mult == 1.0:
            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024] # 1024
        elif width_mult == 1.5:
            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]
        elif width_mult == 2.0:
            self.stage_out_channels = [-1, 24, 224, 488, 976, 2048]
        else:
            raise ValueError(
                """width_mult = {} is not supported""".format(width_mult))

        # building first layer
        input_channel = self.stage_out_channels[1]
        self.conv1 = conv_bn(3, input_channel, 2)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.features = []
        # building inverted residual blocks
        for idxstage in range(len(self.stage_repeats)):
            numrepeat = self.stage_repeats[idxstage]
            output_channel = self.stage_out_channels[idxstage + 2]
            for i in range(numrepeat):
                if i == 0:
                    # inp, oup, stride, benchmodel):
                    self.features.append(InvertedResidual(
                        input_channel, output_channel, 2, 2))
                else:
                    self.features.append(InvertedResidual(
                        input_channel, output_channel, 1, 1))
                input_channel = output_channel

        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)

        # building last several layers
        self.conv_last = conv_1x1_bn(
            input_channel, self.stage_out_channels[-1])
        self.globalpool = nn.Sequential(nn.AvgPool2d(int(input_size / 32)))

        # building classifier
        self.classifier = nn.Sequential(
            nn.Linear(self.stage_out_channels[-1], n_class))

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.features(x)
        x = self.conv_last(x)
        x = self.globalpool(x)
        x = x.view(-1, self.stage_out_channels[-1])
        x = self.classifier(x)
        return x


def shufflenetv2(width_mult=1.):
    model = ShuffleNetV2(width_mult=width_mult)
    return model


if __name__ == "__main__":
    """Testing
    """
    model = ShuffleNetV2()
    print(model)


================================================
FILE: test/test.py
================================================
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torchvision
from torchvision import datasets, models, transforms
import time
import os
from PIL import Image
import sys
import torch.nn.functional as F
from shufflenetv2 import ShuffleNetV2
import scipy.io as scio
import cv2

#config
matFile = 'pScores.mat'
fiiqaWeight = './model/97_160_2.pth'
detectFace = './model/haarcascade_frontalface_default.xml'
imagePath = './image/00921_960627_fa_illu_cafe-sunshade-2pm-2_extracted.jpg'
facePath = './image/crop/test_face.jpg'
inputSize = 160

#crop face from img
faceCascade = cv2.CascadeClassifier(detectFace)
image = cv2.imread(imagePath)
faces = faceCascade.detectMultiScale(
    image,
    scaleFactor=1.1,
    minNeighbors=5,
    minSize=(10, 10)
)
for (x, y, w, h) in faces:
    cv2.imwrite('./image/crop/' + 'test_face.jpg',image[y:y+h,x:x+w])
cv2.waitKey(0)

#transfer img to tensor
dataTransforms =  transforms.Compose([
               transforms.Resize(inputSize),
               transforms.ToTensor(),
               transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])

#load net 
net = ShuffleNetV2(inputSize)
checkpoint = torch.load(fiiqaWeight)
net.load_state_dict(checkpoint['net'])
net.eval()

#load face and get expect num
face = Image.open(facePath)
imgblob = dataTransforms(face).unsqueeze(0)
#imgblob = Variable(imgblob)
torch.no_grad()
predict = F.softmax(net(imgblob),dim=1)
expect = torch.sum(Variable(torch.arange(0,200)).float()*predict, 1)
print('expect: %.4f' % expect)
expect = int(expect)

#load matFile and get score
data = scio.loadmat(matFile)
scores = data['pScores']
score = scores[:,expect]

print('expect: %d' % expect)
print('score: %.3f' %  score)


================================================
FILE: test/test_without_crop.py
================================================
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torchvision
from torchvision import datasets, models, transforms
import time
import os
from PIL import Image
import sys
import torch.nn.functional as F
from shufflenetv2 import ShuffleNetV2
import scipy.io as scio
import cv2

#config
matFile = 'pScores.mat'
fiiqaWeight = './model/97_160_2.pth'
detectFace = './model/haarcascade_frontalface_default.xml'
imagePath = './image/00921_960627_fa_illu_cafe-sunshade-2pm-2_extracted.jpg'
facePath = './image/crop/00921_960627_fa_illu_cafe-inside-2_extracted.jpg'
inputSize = 160

'''
#crop face from img
faceCascade = cv2.CascadeClassifier(detectFace)
image = cv2.imread(imagePath)
faces = faceCascade.detectMultiScale(
    image,
    scaleFactor=1.1,
    minNeighbors=5,
    minSize=(10, 10)
)
for (x, y, w, h) in faces:
    cv2.imwrite('./image/crop/' + 'test_face.jpg',image[y:y+h,x:x+w])
cv2.waitKey(0)
'''

#transfer img to tensor
dataTransforms =  transforms.Compose([
               transforms.Resize(inputSize),
               transforms.ToTensor(),
               transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])

#load net 
net = ShuffleNetV2(inputSize)
checkpoint = torch.load(fiiqaWeight)
net.load_state_dict(checkpoint['net'])
net.eval()

#load face and get expect num
face = Image.open(facePath)
imgblob = dataTransforms(face).unsqueeze(0)
imgblob = Variable(imgblob)
torch.no_grad()
predict = F.softmax(net(imgblob),dim=1)
expect = torch.sum(Variable(torch.arange(0,200)).float()*predict, 1)
print('expect: %.4f' % expect)
expect = int(expect)

#load matFile and get score
data = scio.loadmat(matFile)
scores = data['pScores']
score = scores[:,expect]

print('expect: %d' % expect)
print('score: %.3f' %  score)


================================================
FILE: test.py
================================================
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torchvision
from torchvision import datasets, models, transforms
import time
import os
from PIL import Image
import sys
import torch.nn.functional as F
from shufflenetv2 import ShuffleNetV2
import scipy.io as scio
import cv2

#config
matFile = 'pScores.mat'
fiiqaWeight = './model/97_160_2.pth'
detectFace = './model/haarcascade_frontalface_default.xml'
imagePath = './image/test.jpg'
facePath = './image/crop/test_face.jpg'
inputSize = 160

#crop face from img
faceCascade = cv2.CascadeClassifier(detectFace)
image = cv2.imread(imagePath)
faces = faceCascade.detectMultiScale(
    image,
    scaleFactor=1.1,
    minNeighbors=5,
    minSize=(10, 10)
)
for (x, y, w, h) in faces:
    cv2.imwrite('./image/crop/' + 'test_face.jpg',image[y:y+h,x:x+w])
cv2.waitKey(0)

#transfer img to tensor
dataTransforms =  transforms.Compose([
               transforms.Resize(inputSize),
               transforms.ToTensor(),
               transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])

#load net 
net = ShuffleNetV2(inputSize)
checkpoint = torch.load(fiiqaWeight)
net.load_state_dict(checkpoint['net'])
net.eval()

#load face and get expect num
face = Image.open(facePath)
imgblob = dataTransforms(face).unsqueeze(0)
imgblob = Variable(imgblob)
torch.no_grad()
predict = F.softmax(net(imgblob),dim=1)
expect = torch.sum(Variable(torch.arange(0,200)).float()*predict, 1)
expect = int(expect)

#load matFile and get score
data = scio.loadmat(matFile)
scores = data['pScores']
score = scores[:,expect]

print('expect: %d' % expect)
print('score: %.3f' %  score)


================================================
FILE: test_convert_to_caffe/httpClient.py
================================================
import urllib.request
import json
   
if __name__ == "__main__":
    restUri = 'http://172.16.16.169:8081/facedetect'
    PostParam = './image/test2.jpg'
    DATA = PostParam.encode('utf8')
    req = urllib.request.Request(url = restUri, data=DATA, method='POST')
    req.add_header('Content-type', 'application/form-data')
    r = urllib.request.urlopen(req).read()
    print(r.decode('utf8'))
    org_obj = json.loads(r.decode('utf8'))
    print(org_obj['token'])

================================================
FILE: test_convert_to_caffe/model/fiiqa_shufflenetv2_1.0x.prototxt
================================================
name: "shufflenetv2_1.0x"
layer {
  name: "data1"
  type: "Input"
  top: "data1"
  input_param {
    shape {
      dim: 1
      dim: 3
      dim: 160
      dim: 160
    }
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data1"
  top: "conv1"
  convolution_param {
    num_output: 24
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm1"
  type: "BatchNorm"
  bottom: "conv1"
  top: "batch_norm1"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale1"
  type: "Scale"
  bottom: "batch_norm1"
  top: "batch_norm1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "batch_norm1"
  top: "relu1"
}
layer {
  name: "max_pool1"
  type: "Pooling"
  bottom: "relu1"
  top: "max_pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 1
    round_mode: FLOOR
  }
}
layer {
  name: "conv2"
  type: "ConvolutionDepthwise"
  bottom: "max_pool1"
  top: "conv2"
  convolution_param {
    num_output: 24
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm2"
  type: "BatchNorm"
  bottom: "conv2"
  top: "batch_norm2"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale2"
  type: "Scale"
  bottom: "batch_norm2"
  top: "batch_norm2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "batch_norm2"
  top: "conv3"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm3"
  type: "BatchNorm"
  bottom: "conv3"
  top: "batch_norm3"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale3"
  type: "Scale"
  bottom: "batch_norm3"
  top: "batch_norm3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "batch_norm3"
  top: "relu2"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "max_pool1"
  top: "conv4"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm4"
  type: "BatchNorm"
  bottom: "conv4"
  top: "batch_norm4"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale4"
  type: "Scale"
  bottom: "batch_norm4"
  top: "batch_norm4"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "batch_norm4"
  top: "relu3"
}
layer {
  name: "conv5"
  type: "ConvolutionDepthwise"
  bottom: "relu3"
  top: "conv5"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm5"
  type: "BatchNorm"
  bottom: "conv5"
  top: "batch_norm5"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale5"
  type: "Scale"
  bottom: "batch_norm5"
  top: "batch_norm5"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "batch_norm5"
  top: "conv6"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm6"
  type: "BatchNorm"
  bottom: "conv6"
  top: "batch_norm6"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale6"
  type: "Scale"
  bottom: "batch_norm6"
  top: "batch_norm6"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "batch_norm6"
  top: "relu4"
}
layer {
  name: "concat1"
  type: "Concat"
  bottom: "relu2"
  bottom: "relu4"
  top: "concat1"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel1"
  type: "ShuffleChannel"
  bottom: "concat1"
  top: "shuffle_channel1"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split1"
  type: "Slice"
  bottom: "shuffle_channel1"
  top: "split1"
  top: "split2"
  slice_param {
    slice_point: 58
    axis: 1
  }
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "split2"
  top: "conv7"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm7"
  type: "BatchNorm"
  bottom: "conv7"
  top: "batch_norm7"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale7"
  type: "Scale"
  bottom: "batch_norm7"
  top: "batch_norm7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "batch_norm7"
  top: "relu5"
}
layer {
  name: "conv8"
  type: "ConvolutionDepthwise"
  bottom: "relu5"
  top: "conv8"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm8"
  type: "BatchNorm"
  bottom: "conv8"
  top: "batch_norm8"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale8"
  type: "Scale"
  bottom: "batch_norm8"
  top: "batch_norm8"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv9"
  type: "Convolution"
  bottom: "batch_norm8"
  top: "conv9"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm9"
  type: "BatchNorm"
  bottom: "conv9"
  top: "batch_norm9"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale9"
  type: "Scale"
  bottom: "batch_norm9"
  top: "batch_norm9"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "batch_norm9"
  top: "relu6"
}
layer {
  name: "concat2"
  type: "Concat"
  bottom: "split1"
  bottom: "relu6"
  top: "concat2"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel2"
  type: "ShuffleChannel"
  bottom: "concat2"
  top: "shuffle_channel2"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split2"
  type: "Slice"
  bottom: "shuffle_channel2"
  top: "split3"
  top: "split4"
  slice_param {
    slice_point: 58
    axis: 1
  }
}
layer {
  name: "conv10"
  type: "Convolution"
  bottom: "split4"
  top: "conv10"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm10"
  type: "BatchNorm"
  bottom: "conv10"
  top: "batch_norm10"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale10"
  type: "Scale"
  bottom: "batch_norm10"
  top: "batch_norm10"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "batch_norm10"
  top: "relu7"
}
layer {
  name: "conv11"
  type: "ConvolutionDepthwise"
  bottom: "relu7"
  top: "conv11"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm11"
  type: "BatchNorm"
  bottom: "conv11"
  top: "batch_norm11"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale11"
  type: "Scale"
  bottom: "batch_norm11"
  top: "batch_norm11"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv12"
  type: "Convolution"
  bottom: "batch_norm11"
  top: "conv12"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm12"
  type: "BatchNorm"
  bottom: "conv12"
  top: "batch_norm12"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale12"
  type: "Scale"
  bottom: "batch_norm12"
  top: "batch_norm12"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "batch_norm12"
  top: "relu8"
}
layer {
  name: "concat3"
  type: "Concat"
  bottom: "split3"
  bottom: "relu8"
  top: "concat3"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel3"
  type: "ShuffleChannel"
  bottom: "concat3"
  top: "shuffle_channel3"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split3"
  type: "Slice"
  bottom: "shuffle_channel3"
  top: "split5"
  top: "split6"
  slice_param {
    slice_point: 58
    axis: 1
  }
}
layer {
  name: "conv13"
  type: "Convolution"
  bottom: "split6"
  top: "conv13"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm13"
  type: "BatchNorm"
  bottom: "conv13"
  top: "batch_norm13"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale13"
  type: "Scale"
  bottom: "batch_norm13"
  top: "batch_norm13"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu9"
  type: "ReLU"
  bottom: "batch_norm13"
  top: "relu9"
}
layer {
  name: "conv14"
  type: "ConvolutionDepthwise"
  bottom: "relu9"
  top: "conv14"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm14"
  type: "BatchNorm"
  bottom: "conv14"
  top: "batch_norm14"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale14"
  type: "Scale"
  bottom: "batch_norm14"
  top: "batch_norm14"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv15"
  type: "Convolution"
  bottom: "batch_norm14"
  top: "conv15"
  convolution_param {
    num_output: 58
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm15"
  type: "BatchNorm"
  bottom: "conv15"
  top: "batch_norm15"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale15"
  type: "Scale"
  bottom: "batch_norm15"
  top: "batch_norm15"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu10"
  type: "ReLU"
  bottom: "batch_norm15"
  top: "relu10"
}
layer {
  name: "concat4"
  type: "Concat"
  bottom: "split5"
  bottom: "relu10"
  top: "concat4"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel4"
  type: "ShuffleChannel"
  bottom: "concat4"
  top: "shuffle_channel4"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "conv16"
  type: "ConvolutionDepthwise"
  bottom: "shuffle_channel4"
  top: "conv16"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm16"
  type: "BatchNorm"
  bottom: "conv16"
  top: "batch_norm16"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale16"
  type: "Scale"
  bottom: "batch_norm16"
  top: "batch_norm16"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv17"
  type: "Convolution"
  bottom: "batch_norm16"
  top: "conv17"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm17"
  type: "BatchNorm"
  bottom: "conv17"
  top: "batch_norm17"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale17"
  type: "Scale"
  bottom: "batch_norm17"
  top: "batch_norm17"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu11"
  type: "ReLU"
  bottom: "batch_norm17"
  top: "relu11"
}
layer {
  name: "conv18"
  type: "Convolution"
  bottom: "shuffle_channel4"
  top: "conv18"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm18"
  type: "BatchNorm"
  bottom: "conv18"
  top: "batch_norm18"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale18"
  type: "Scale"
  bottom: "batch_norm18"
  top: "batch_norm18"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu12"
  type: "ReLU"
  bottom: "batch_norm18"
  top: "relu12"
}
layer {
  name: "conv19"
  type: "ConvolutionDepthwise"
  bottom: "relu12"
  top: "conv19"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm19"
  type: "BatchNorm"
  bottom: "conv19"
  top: "batch_norm19"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale19"
  type: "Scale"
  bottom: "batch_norm19"
  top: "batch_norm19"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv20"
  type: "Convolution"
  bottom: "batch_norm19"
  top: "conv20"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm20"
  type: "BatchNorm"
  bottom: "conv20"
  top: "batch_norm20"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale20"
  type: "Scale"
  bottom: "batch_norm20"
  top: "batch_norm20"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu13"
  type: "ReLU"
  bottom: "batch_norm20"
  top: "relu13"
}
layer {
  name: "concat5"
  type: "Concat"
  bottom: "relu11"
  bottom: "relu13"
  top: "concat5"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel5"
  type: "ShuffleChannel"
  bottom: "concat5"
  top: "shuffle_channel5"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split4"
  type: "Slice"
  bottom: "shuffle_channel5"
  top: "split7"
  top: "split8"
  slice_param {
    slice_point: 116
    axis: 1
  }
}
layer {
  name: "conv21"
  type: "Convolution"
  bottom: "split8"
  top: "conv21"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm21"
  type: "BatchNorm"
  bottom: "conv21"
  top: "batch_norm21"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale21"
  type: "Scale"
  bottom: "batch_norm21"
  top: "batch_norm21"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu14"
  type: "ReLU"
  bottom: "batch_norm21"
  top: "relu14"
}
layer {
  name: "conv22"
  type: "ConvolutionDepthwise"
  bottom: "relu14"
  top: "conv22"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm22"
  type: "BatchNorm"
  bottom: "conv22"
  top: "batch_norm22"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale22"
  type: "Scale"
  bottom: "batch_norm22"
  top: "batch_norm22"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv23"
  type: "Convolution"
  bottom: "batch_norm22"
  top: "conv23"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm23"
  type: "BatchNorm"
  bottom: "conv23"
  top: "batch_norm23"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale23"
  type: "Scale"
  bottom: "batch_norm23"
  top: "batch_norm23"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu15"
  type: "ReLU"
  bottom: "batch_norm23"
  top: "relu15"
}
layer {
  name: "concat6"
  type: "Concat"
  bottom: "split7"
  bottom: "relu15"
  top: "concat6"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel6"
  type: "ShuffleChannel"
  bottom: "concat6"
  top: "shuffle_channel6"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split5"
  type: "Slice"
  bottom: "shuffle_channel6"
  top: "split9"
  top: "split10"
  slice_param {
    slice_point: 116
    axis: 1
  }
}
layer {
  name: "conv24"
  type: "Convolution"
  bottom: "split10"
  top: "conv24"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm24"
  type: "BatchNorm"
  bottom: "conv24"
  top: "batch_norm24"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale24"
  type: "Scale"
  bottom: "batch_norm24"
  top: "batch_norm24"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu16"
  type: "ReLU"
  bottom: "batch_norm24"
  top: "relu16"
}
layer {
  name: "conv25"
  type: "ConvolutionDepthwise"
  bottom: "relu16"
  top: "conv25"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm25"
  type: "BatchNorm"
  bottom: "conv25"
  top: "batch_norm25"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale25"
  type: "Scale"
  bottom: "batch_norm25"
  top: "batch_norm25"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv26"
  type: "Convolution"
  bottom: "batch_norm25"
  top: "conv26"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm26"
  type: "BatchNorm"
  bottom: "conv26"
  top: "batch_norm26"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale26"
  type: "Scale"
  bottom: "batch_norm26"
  top: "batch_norm26"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu17"
  type: "ReLU"
  bottom: "batch_norm26"
  top: "relu17"
}
layer {
  name: "concat7"
  type: "Concat"
  bottom: "split9"
  bottom: "relu17"
  top: "concat7"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel7"
  type: "ShuffleChannel"
  bottom: "concat7"
  top: "shuffle_channel7"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split6"
  type: "Slice"
  bottom: "shuffle_channel7"
  top: "split11"
  top: "split12"
  slice_param {
    slice_point: 116
    axis: 1
  }
}
layer {
  name: "conv27"
  type: "Convolution"
  bottom: "split12"
  top: "conv27"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm27"
  type: "BatchNorm"
  bottom: "conv27"
  top: "batch_norm27"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale27"
  type: "Scale"
  bottom: "batch_norm27"
  top: "batch_norm27"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu18"
  type: "ReLU"
  bottom: "batch_norm27"
  top: "relu18"
}
layer {
  name: "conv28"
  type: "ConvolutionDepthwise"
  bottom: "relu18"
  top: "conv28"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm28"
  type: "BatchNorm"
  bottom: "conv28"
  top: "batch_norm28"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale28"
  type: "Scale"
  bottom: "batch_norm28"
  top: "batch_norm28"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv29"
  type: "Convolution"
  bottom: "batch_norm28"
  top: "conv29"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm29"
  type: "BatchNorm"
  bottom: "conv29"
  top: "batch_norm29"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale29"
  type: "Scale"
  bottom: "batch_norm29"
  top: "batch_norm29"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu19"
  type: "ReLU"
  bottom: "batch_norm29"
  top: "relu19"
}
layer {
  name: "concat8"
  type: "Concat"
  bottom: "split11"
  bottom: "relu19"
  top: "concat8"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel8"
  type: "ShuffleChannel"
  bottom: "concat8"
  top: "shuffle_channel8"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split7"
  type: "Slice"
  bottom: "shuffle_channel8"
  top: "split13"
  top: "split14"
  slice_param {
    slice_point: 116
    axis: 1
  }
}
layer {
  name: "conv30"
  type: "Convolution"
  bottom: "split14"
  top: "conv30"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm30"
  type: "BatchNorm"
  bottom: "conv30"
  top: "batch_norm30"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale30"
  type: "Scale"
  bottom: "batch_norm30"
  top: "batch_norm30"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu20"
  type: "ReLU"
  bottom: "batch_norm30"
  top: "relu20"
}
layer {
  name: "conv31"
  type: "ConvolutionDepthwise"
  bottom: "relu20"
  top: "conv31"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm31"
  type: "BatchNorm"
  bottom: "conv31"
  top: "batch_norm31"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale31"
  type: "Scale"
  bottom: "batch_norm31"
  top: "batch_norm31"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv32"
  type: "Convolution"
  bottom: "batch_norm31"
  top: "conv32"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm32"
  type: "BatchNorm"
  bottom: "conv32"
  top: "batch_norm32"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale32"
  type: "Scale"
  bottom: "batch_norm32"
  top: "batch_norm32"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu21"
  type: "ReLU"
  bottom: "batch_norm32"
  top: "relu21"
}
layer {
  name: "concat9"
  type: "Concat"
  bottom: "split13"
  bottom: "relu21"
  top: "concat9"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel9"
  type: "ShuffleChannel"
  bottom: "concat9"
  top: "shuffle_channel9"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split8"
  type: "Slice"
  bottom: "shuffle_channel9"
  top: "split15"
  top: "split16"
  slice_param {
    slice_point: 116
    axis: 1
  }
}
layer {
  name: "conv33"
  type: "Convolution"
  bottom: "split16"
  top: "conv33"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm33"
  type: "BatchNorm"
  bottom: "conv33"
  top: "batch_norm33"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale33"
  type: "Scale"
  bottom: "batch_norm33"
  top: "batch_norm33"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu22"
  type: "ReLU"
  bottom: "batch_norm33"
  top: "relu22"
}
layer {
  name: "conv34"
  type: "ConvolutionDepthwise"
  bottom: "relu22"
  top: "conv34"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm34"
  type: "BatchNorm"
  bottom: "conv34"
  top: "batch_norm34"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale34"
  type: "Scale"
  bottom: "batch_norm34"
  top: "batch_norm34"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv35"
  type: "Convolution"
  bottom: "batch_norm34"
  top: "conv35"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm35"
  type: "BatchNorm"
  bottom: "conv35"
  top: "batch_norm35"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale35"
  type: "Scale"
  bottom: "batch_norm35"
  top: "batch_norm35"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu23"
  type: "ReLU"
  bottom: "batch_norm35"
  top: "relu23"
}
layer {
  name: "concat10"
  type: "Concat"
  bottom: "split15"
  bottom: "relu23"
  top: "concat10"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel10"
  type: "ShuffleChannel"
  bottom: "concat10"
  top: "shuffle_channel10"
  shuffle_channel_param {
    group: 2
  }
}
layer {
  name: "split9"
  type: "Slice"
  bottom: "shuffle_channel10"
  top: "split17"
  top: "split18"
  slice_param {
    slice_point: 116
    axis: 1
  }
}
layer {
  name: "conv36"
  type: "Convolution"
  bottom: "split18"
  top: "conv36"
  convolution_param {
    num_output: 116
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm36"
  type: "BatchNorm"
  bottom: "conv36"
  top: "batch_norm36"
  batch_norm_param {
    use_global_stats: true
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}
layer {
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layer {
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}
layer {
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layer {
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}
layer {
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  top: "batch_norm37"
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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  top: "batch_norm42"
  batch_norm_param {
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}
layer {
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}
layer {
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}
layer {
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  batch_norm_param {
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}
layer {
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  scale_param {
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}
layer {
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}
layer {
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  convolution_param {
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}
layer {
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  batch_norm_param {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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  batch_norm_param {
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}
layer {
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  scale_param {
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}
layer {
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  convolution_param {
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}
layer {
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  batch_norm_param {
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}
layer {
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  type: "Scale"
  bottom: "batch_norm46"
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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}
layer {
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  convolution_param {
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    weight_filler {
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}
layer {
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  batch_norm_param {
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}
layer {
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  type: "Scale"
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}
layer {
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}
layer {
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    weight_filler {
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}
layer {
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  type: "BatchNorm"
  bottom: "conv48"
  top: "batch_norm48"
  batch_norm_param {
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}
layer {
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  type: "Scale"
  bottom: "batch_norm48"
  top: "batch_norm48"
  scale_param {
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}
layer {
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  type: "Convolution"
  bottom: "batch_norm48"
  top: "conv49"
  convolution_param {
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    pad: 0
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    group: 1
    stride: 1
    weight_filler {
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}
layer {
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  type: "BatchNorm"
  bottom: "conv49"
  top: "batch_norm49"
  batch_norm_param {
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    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "batch_norm49"
  top: "batch_norm49"
  scale_param {
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}
layer {
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  type: "ReLU"
  bottom: "batch_norm49"
  top: "relu32"
}
layer {
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  type: "Concat"
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  bottom: "relu32"
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  concat_param {
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}
layer {
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  type: "ShuffleChannel"
  bottom: "concat14"
  top: "shuffle_channel14"
  shuffle_channel_param {
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}
layer {
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  bottom: "shuffle_channel14"
  top: "split23"
  top: "split24"
  slice_param {
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}
layer {
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  type: "Convolution"
  bottom: "split24"
  top: "conv50"
  convolution_param {
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    pad: 0
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    group: 1
    stride: 1
    weight_filler {
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    dilation: 1
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}
layer {
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  type: "BatchNorm"
  bottom: "conv50"
  top: "batch_norm50"
  batch_norm_param {
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    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "batch_norm50"
  top: "batch_norm50"
  scale_param {
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}
layer {
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  type: "ReLU"
  bottom: "batch_norm50"
  top: "relu33"
}
layer {
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  type: "ConvolutionDepthwise"
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  top: "conv51"
  convolution_param {
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    stride: 1
    weight_filler {
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    dilation: 1
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}
layer {
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  type: "BatchNorm"
  bottom: "conv51"
  top: "batch_norm51"
  batch_norm_param {
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    eps: 9.999999747378752e-06
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}
layer {
  name: "bn_scale51"
  type: "Scale"
  bottom: "batch_norm51"
  top: "batch_norm51"
  scale_param {
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}
layer {
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  type: "Convolution"
  bottom: "batch_norm51"
  top: "conv52"
  convolution_param {
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    pad: 0
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    group: 1
    stride: 1
    weight_filler {
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    dilation: 1
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}
layer {
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  type: "BatchNorm"
  bottom: "conv52"
  top: "batch_norm52"
  batch_norm_param {
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    eps: 9.999999747378752e-06
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}
layer {
  name: "bn_scale52"
  type: "Scale"
  bottom: "batch_norm52"
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  scale_param {
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}
layer {
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  type: "ReLU"
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  top: "relu34"
}
layer {
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  type: "Concat"
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  concat_param {
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}
layer {
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  type: "ShuffleChannel"
  bottom: "concat15"
  top: "shuffle_channel15"
  shuffle_channel_param {
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}
layer {
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  type: "Slice"
  bottom: "shuffle_channel15"
  top: "split25"
  top: "split26"
  slice_param {
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    axis: 1
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}
layer {
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  type: "Convolution"
  bottom: "split26"
  top: "conv53"
  convolution_param {
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    pad: 0
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    stride: 1
    weight_filler {
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    dilation: 1
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}
layer {
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  type: "BatchNorm"
  bottom: "conv53"
  top: "batch_norm53"
  batch_norm_param {
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    eps: 9.999999747378752e-06
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}
layer {
  name: "bn_scale53"
  type: "Scale"
  bottom: "batch_norm53"
  top: "batch_norm53"
  scale_param {
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}
layer {
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  type: "ReLU"
  bottom: "batch_norm53"
  top: "relu35"
}
layer {
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  type: "ConvolutionDepthwise"
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  convolution_param {
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    pad: 1
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    stride: 1
    weight_filler {
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    }
    dilation: 1
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}
layer {
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  type: "BatchNorm"
  bottom: "conv54"
  top: "batch_norm54"
  batch_norm_param {
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    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale54"
  type: "Scale"
  bottom: "batch_norm54"
  top: "batch_norm54"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv55"
  type: "Convolution"
  bottom: "batch_norm54"
  top: "conv55"
  convolution_param {
    num_output: 232
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm55"
  type: "BatchNorm"
  bottom: "conv55"
  top: "batch_norm55"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale55"
  type: "Scale"
  bottom: "batch_norm55"
  top: "batch_norm55"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu36"
  type: "ReLU"
  bottom: "batch_norm55"
  top: "relu36"
}
layer {
  name: "concat16"
  type: "Concat"
  bottom: "split25"
  bottom: "relu36"
  top: "concat16"
  concat_param {
    axis: 1
  }
}
layer {
  name: "shuffle_channel16"
  type: "ShuffleChannel"
  bottom: "concat16"
  top: "shuffle_channel16"
  shuffle_channel_param {
    group: 2
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}
layer {
  name: "conv56"
  type: "Convolution"
  bottom: "shuffle_channel16"
  top: "conv56"
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
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}
layer {
  name: "batch_norm56"
  type: "BatchNorm"
  bottom: "conv56"
  top: "batch_norm56"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale56"
  type: "Scale"
  bottom: "batch_norm56"
  top: "batch_norm56"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu37"
  type: "ReLU"
  bottom: "batch_norm56"
  top: "relu37"
}
layer {
  name: "ave_pool1"
  type: "Pooling"
  bottom: "relu37"
  top: "ave_pool1"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "reshape1"
  type: "Reshape"
  bottom: "ave_pool1"
  top: "reshape1"
  reshape_param {
    shape {
      dim: 0
      dim: 1024
    }
  }
}
layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "reshape1"
  top: "fc1"
  inner_product_param {
    num_output: 200
    bias_term: true
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "softmax1"
  type: "Softmax"
  bottom: "fc1"
  top: "softmax1"
  softmax_param {
    axis: 1
  }
}


================================================
FILE: test_convert_to_caffe/model/haarcascade_frontalface_default.xml
================================================
<?xml version="1.0"?>
<!--
    Stump-based 24x24 discrete(?) adaboost frontal face detector.
    Created by Rainer Lienhart.

////////////////////////////////////////////////////////////////////////////////////////

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  If you do not agree to this license, do not download, install,
  copy or use the software.


                        Intel License Agreement
                For Open Source Computer Vision Library

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 are permitted provided that the following conditions are met:

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     this list of conditions and the following disclaimer.

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     this list of conditions and the following disclaimer in the documentation
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     derived from this software without specific prior written permission.

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 any express or implied warranties, including, but not limited to, the implied
 warranties of merchantability and fitness for a particular purpose are disclaimed.
 In no event shall the Intel Corporation or contributors be liable for any direct,
 indirect, incidental, special, exemplary, or consequential damages
 (including, but not limited to, procurement of substitute goods or services;
 loss of use, data, or profits; or business interruption) however caused
 and on any theory of liability, whether in contract, strict liability,
 or tort (including negligence or otherwise) arising in any way out of
 the use of this software, even if advised of the possibility of such damage.
-->
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================================================
FILE: test_convert_to_caffe/shufflenetv2.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from collections import OrderedDict
from torch.nn import init
import math


def conv_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU(inplace=True)
    )


def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        nn.ReLU(inplace=True)
    )


def channel_shuffle(x, groups):
    batchsize, num_channels, height, width = x.data.size()

    channels_per_group = num_channels // groups

    # reshape
    x = x.view(batchsize, groups, channels_per_group, height, width)

    x = torch.transpose(x, 1, 2).contiguous()

    # flatten
    x = x.view(batchsize, -1, height, width)

    return x


class InvertedResidual(nn.Module):
    def __init__(self, inp, oup, stride, benchmodel):
        super(InvertedResidual, self).__init__()
        self.benchmodel = benchmodel
        self.stride = stride
        assert stride in [1, 2]

        oup_inc = oup // 2

        if self.benchmodel == 1:
            # assert inp == oup_inc
            self.banch2 = nn.Sequential(
                # pw
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(oup_inc, oup_inc, 3, stride,
                          1, groups=oup_inc, bias=False),
                nn.BatchNorm2d(oup_inc),
                # pw-linear
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )
        else:
            self.banch1 = nn.Sequential(
                # dw
                nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
                nn.BatchNorm2d(inp),
                # pw-linear
                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )

            self.banch2 = nn.Sequential(
                # pw
                nn.Conv2d(inp, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(oup_inc, oup_inc, 3, stride,
                          1, groups=oup_inc, bias=False),
                nn.BatchNorm2d(oup_inc),
                # pw-linear
                nn.Conv2d(oup_inc, oup_inc, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup_inc),
                nn.ReLU(inplace=True),
            )

    @staticmethod
    def _concat(x, out):
        # concatenate along channel axis
        return torch.cat((x, out), 1)

    def forward(self, x):
        if 1 == self.benchmodel:
            # x1 = x[:, :(x.shape[1] // 2), :, :]
            # x2 = x[:, (x.shape[1] // 2):, :, :]
            x1, x2 = torch.split(x, x.shape[1] // 2, dim=1)
            out = self._concat(x1, self.banch2(x2))
        elif 2 == self.benchmodel:
            out = self._concat(self.banch1(x), self.banch2(x))

        return channel_shuffle(out, 2)


class ShuffleNetV2(nn.Module):
    def __init__(self, input_size, n_class=200,  width_mult=1): #1,0
        super(ShuffleNetV2, self).__init__()

        assert input_size % 32 == 0

        self.stage_repeats = [4, 8, 4]
        # index 0 is invalid and should never be called.
        # only used for indexing convenience.
        if width_mult == 0.5:
            self.stage_out_channels = [-1, 24, 48, 96, 192, 192] #175
        elif width_mult == 1.0:
            self.stage_out_channels = [-1, 24, 116, 232, 464, 1024] # 1024
        elif width_mult == 1.5:
            self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]
        elif width_mult == 2.0:
            self.stage_out_channels = [-1, 24, 224, 488, 976, 2048]
        else:
            raise ValueError(
                """width_mult = {} is not supported""".format(width_mult))

        # building first layer
        input_channel = self.stage_out_channels[1]
        self.conv1 = conv_bn(3, input_channel, 2)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.features = []
        # building inverted residual blocks
        for idxstage in range(len(self.stage_repeats)):
            numrepeat = self.stage_repeats[idxstage]
            output_channel = self.stage_out_channels[idxstage + 2]
            for i in range(numrepeat):
                if i == 0:
                    # inp, oup, stride, benchmodel):
                    self.features.append(InvertedResidual(
                        input_channel, output_channel, 2, 2))
                else:
                    self.features.append(InvertedResidual(
                        input_channel, output_channel, 1, 1))
                input_channel = output_channel

        # make it nn.Sequential
        self.features = nn.Sequential(*self.features)

        # building last several layers
        self.conv_last = conv_1x1_bn(
            input_channel, self.stage_out_channels[-1])
        self.globalpool = nn.Sequential(nn.AdaptiveAvgPool2d(1))

        # building classifier
        self.classifier = nn.Sequential(
            nn.Linear(self.stage_out_channels[-1], n_class))

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)
        x = self.features(x)
        x = self.conv_last(x)
        x = self.globalpool(x)
        x = x.view(-1, self.stage_out_channels[-1])
        x = self.classifier(x)
        return x


def shufflenetv2(width_mult=1.):
    model = ShuffleNetV2(width_mult=width_mult)
    return model


if __name__ == "__main__":
    """Testing
    """
    model = ShuffleNetV2()
    print(model)


================================================
FILE: test_convert_to_caffe/shufflenetv2_to_caffe.py
================================================
import sys
import os.path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
import caffe
import numpy as np

this_dir = '.'

cpath = '/home/jinsy/2T/jinsy/code/tuya_sdk/Pytorch2Caffe'
if cpath not in sys.path:
    sys.path.append(cpath)

import converter
from utils.load_test_image import load_test_image
from utils.get_topk import get_topk
from utils.load import load_troch_model2
import shufflenetv2 as ShuffleNetV2


def _channel_shuffle(raw, input, groups):
    x = input.clone()
    name = converter.log.add_layer(name='shuffle_channel')
    converter.log.add_blobs([x], name='shuffle_channel')
    layer = converter.caffe_net.Layer_param(name=name, type='ShuffleChannel',
                                            bottom=[converter.log.blobs(input)], top=[converter.log.blobs(x)])
    layer.channel_shuffle_param(groups)
    converter.log.cnet.add_layer(layer)
    return x


ShuffleNetV2.channel_shuffle = converter.Rp(ShuffleNetV2.channel_shuffle, _channel_shuffle)


def parse():
    parser = argparse.ArgumentParser(description='convert pytorch model to caffe')
    parser.add_argument('-c', '--convert', action='store_true', help='convert pytorch model to caffe')
    parser.add_argument('-t', '--test', action='store_true', help='test converted model')
    parser.add_argument('-wm', '--width-mult', type=float, choices=[0.5, 1.0], default=1.0,
                        help='width mult')
    return parser.parse_args()


def convert(pytorch_net, caffe_prototxt, caffe_model_file, name):
    pytorch_net.eval()
    input = torch.ones(1, 3, 160, 160)
    converter.trans_net(pytorch_net, input, name)
    converter.save_prototxt(caffe_prototxt)
    converter.save_caffemodel(caffe_model_file)


def test(caffe_prototxt, caffe_model_file, pytorch_net, topk=5):
    pytorch_net.eval()
    input = load_test_image()
    with torch.no_grad():
        x1 = pytorch_net(input)
        x1 = x1.numpy()

    caffe_net = caffe.Net(caffe_prototxt, caffe_model_file, caffe.TEST)
    caffe.set_mode_cpu()
    caffe_net.blobs['data1'].data.reshape(*input.size())
    caffe_net.blobs['data1'].data[...] = input.numpy()
    x2 = caffe_net.forward()['softmax1']
    idx = 0

    expect = torch.dot(torch.arange(0,200).float() ,torch.from_numpy(x1.flatten()))
    print('pytorch: ', expect)

    expect = torch.dot(torch.arange(0,200).float() ,torch.from_numpy(x2.flatten()))
    print('caffe: ', expect)


class SoftmaxWrapper(nn.Module):
    def __init__(self, net):
        super(SoftmaxWrapper, self).__init__()
        self.net = net

    def forward(self, x):
        y = self.net(x)
        return F.softmax(y, dim=1)


def shufflenetv2(width_mult=1., pretrained=True):
    model = ShuffleNetV2.ShuffleNetV2(input_size=160, width_mult=width_mult)
    if pretrained:
        if width_mult == 1.:
            model_file = osp.join(this_dir, 'model/97_160_2.pth')
        elif width_mult == .5:
            model_file = osp.join(this_dir, '../models/ShuffleNetV2/shufflenetv2_x0.5_60.646_81.696.pth.tar')
        else:
            raise ValueError('width_mult = {} is not support pretrained model'.format(width_mult))
        assert osp.exists(model_file), '{} is not exists!'.format(model_file)
        print('shufflenetv2 load state dict: ', model_file)
        state = torch.load(model_file)
        model.load_state_dict(state['net'], strict=False)

    return model


if __name__ == '__main__':
    args = parse()

    name = 'shufflenetv2_{:.1f}x'.format(args.width_mult)

    caffe_prototxt = osp.join(this_dir, 'model/') + '{}.prototxt'.format(name)
    caffe_model_file = osp.join(this_dir, 'model/') + '{}.caffemodel'.format(name)
    net = shufflenetv2(width_mult=args.width_mult, pretrained=True)
    net = SoftmaxWrapper(net)

    if args.convert:
        convert(net, caffe_prototxt, caffe_model_file, name)

    if args.test:
        test(caffe_prototxt, caffe_model_file, net)


================================================
FILE: test_convert_to_caffe/test.py
================================================
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torchvision
from torchvision import datasets, models, transforms
import time
import os
from PIL import Image
import sys
import torch.nn.functional as F
from shufflenetv2 import ShuffleNetV2
import scipy.io as scio
import cv2

#config
matFile = 'pScores.mat'
fiiqaWeight = './model/97_160_2.pth'
detectFace = './model/haarcascade_frontalface_default.xml'
imagePath = './image/test1.jpg'
facePath = './image/crop/test_face.jpg'
inputSize = 160

#crop face from img
faceCascade = cv2.CascadeClassifier(detectFace)
image = cv2.imread(imagePath)
faces = faceCascade.detectMultiScale(
    image,
    scaleFactor=1.1,
    minNeighbors=5,
    minSize=(10, 10)
)
for (x, y, w, h) in faces:
    cv2.imwrite('./image/crop/' + 'test_face.jpg',image[y:y+h,x:x+w])
cv2.waitKey(0)

#transfer img to tensor
dataTransforms =  transforms.Compose([
               transforms.Resize(inputSize),
               transforms.ToTensor(),
               transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])

#load net 
net = ShuffleNetV2(inputSize)
checkpoint = torch.load(fiiqaWeight)
net.load_state_dict(checkpoint['net'])
net.eval()

#load face and get expect num
face = Image.open(facePath)
imgblob = dataTransforms(face).unsqueeze(0)
imgblob = Variable(imgblob)
torch.no_grad()
predict = F.softmax(net(imgblob),dim=1)
expect = torch.sum(Variable(torch.arange(0,200)).float()*predict, 1)

print(predict, expect)

expect = int(expect)

#load matFile and get score
data = scio.loadmat(matFile)
scores = data['pScores']
score = scores[:,expect]

print('expect: %d' % expect)
print('score: %.3f' %  score)


================================================
FILE: test_convert_to_caffe/utils/get_topk.py
================================================
import numpy as np


def get_topk(prob, k=5):
    size = prob.shape[0]
    k = min(k, size)
    sorted_idx = np.argsort(prob)[::-1]
    topk_idx = sorted_idx[:k]
    topk_prob = prob[sorted_idx[:k]]
    return topk_idx, topk_prob


================================================
FILE: test_convert_to_caffe/utils/load.py
================================================
import torch
from collections import OrderedDict


def load_troch_model(filename):
    def _process_state(state):
        new_state_dict = OrderedDict()
        for k, v in state.items():
            if k.startswith('module.'):
                k = k.replace('module.', '')
            new_state_dict[k] = v
        return new_state_dict
    state = torch.load(filename, map_location=lambda storage, location: storage)
    state['state_dict'] = _process_state(state['state_dict'])
    return state


def load_troch_model2(filename):
    def _process_state(state):
        new_state_dict = OrderedDict()
        for k, v in state.items():
            if k.startswith('module.'):
                k = k.replace('module.', '')
            new_state_dict[k] = v
        return new_state_dict
    state = torch.load(filename, map_location=lambda storage, location: storage)
    state = _process_state(state)
    return state


================================================
FILE: test_convert_to_caffe/utils/load_test_image.py
================================================
import os.path as osp
import cv2
from torchvision import transforms
import torch
import numpy as np
from PIL import Image

this_dir = osp.abspath(osp.dirname(__file__))


def load_test_image(filename='test_face.jpg', image_size=(3, 160, 160)):
    full_filename = osp.join(this_dir, '../image/crop', filename)
    print(full_filename)
    rgb_image = Image.open(full_filename)
    tf = transforms.Compose([
        transforms.Resize(160),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])

    ])
    tensor = tf(rgb_image)
    return tensor.view(1, *image_size)


def load_test_image2(filename='cat_224x224.jpg', image_size=(3, 224, 224)):
    full_filename = osp.join(this_dir, '../data', filename)
    bgr_image = cv2.imread(full_filename)
    bgr_image2 = torch.from_numpy(cv2.resize(bgr_image, image_size[1:]).astype(np.float32))
    return bgr_image2.view(1, *image_size)


================================================
FILE: test_model.py
================================================
import torch 
from torch.utils.data import DataLoader
from torch.autograd import Variable

from shufflenetv2 import ShuffleNetV2
import torchvision
import torchvision.transforms as transforms
from datagen import ListDataset
import csv
import os
from PIL import Image

test_data = './data/test-faces/'
list_file = './data/new_4people_test_standard.txt'
checkpoint = torch.load('./checkpoint/97_160_2.pth')

def write_csv(header, write_data, filename):
    # header-标题 write_data-写入数据 filename-文件名 
    with open(filename, 'a', newline='',encoding='utf-8-sig') as csvFile:
        writer = csv.writer(csvFile)
        if os.path.getsize(filename) == False:
            # 先写columns_name
            writer.writerow(header)
        # 写入多行用writerows
        writer.writerows(write_data)

def test():
    # configure model
    net= ShuffleNetV2(input_size=160)
    net.load_state_dict(checkpoint['net'])
    net.eval()

    transform_test = transforms.Compose([
        transforms.Resize(160),
        transforms.CenterCrop(160),
        transforms.ToTensor(),
        transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
    ])
    testset = ListDataset(root=test_data, list_file=list_file, \
        transform=transform_test)
    testloader = torch.utils.data.DataLoader(testset, batch_size=64, \
        shuffle=False, num_workers=4, pin_memory = True)
    torch.multiprocessing.set_sharing_strategy('file_system')

    results = []
    for ii,(data,path) in enumerate(testloader):

        #input = torch.autograd.Variable(data)
        with torch.no_grad():
            score = net(data)
        
        probability = torch.nn.functional.softmax(score,dim=1)#[:,0].data.tolist()
        # label = score.max(dim = 1)[1].data.tolist()
        expect = torch.sum(torch.arange(0,200).float()*probability, 1)
        #print('expect: %.4f' % expect.numpy())
        batch_results = [(path_,expect_) for path_,expect_ in zip(path,expect) ]
        print('batch_results: ',batch_results)
        results += batch_results
    write_csv('FIIQA',results,'result.csv')

    return results


if __name__ == "__main__":
    """Testing
    """
    test()

================================================
FILE: train.py
================================================
from __future__ import print_function

import os
import argparse

import torch
import torch.nn as nn
import torch.optim as optim

import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
from net import AGNet
from loss import FIIQALoss
from datagen import ListDataset
from torch.autograd import Variable
from adabound import AdaBound
from shufflenetv2 import ShuffleNetV2
from flops_counter_pytorch.ptflops import get_model_complexity_info
from summary import model_summary

parser = argparse.ArgumentParser(description='PyTorch AGNet Training')
parser.add_argument('--lr', default=1e-3, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
args = parser.parse_args()

assert torch.cuda.is_available(), 'Error: CUDA not found!'
best_correct = 0 # best number of fiiqa_correct 
start_epoch = 0  # start from epoch 0 or last epoch
best_test_acc_epoch = 0
batch_size=128
path = './checkpoint/'
TOLERANCE = 2
input_size=32
train_epoch=500

# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
    transforms.Resize(input_size),
    transforms.CenterCrop(input_size),
    #transforms.RandomCrop(160, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])

transform_test = transforms.Compose([
    transforms.Resize(input_size),
    transforms.CenterCrop(input_size),
    transforms.ToTensor(),
    transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])

trainset = ListDataset(root='./data/trainingset/train-faces/', list_file='./data/trainingset/new_4people_train_standard.txt', transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size, shuffle=True, num_workers=12)
testset = ListDataset(root='./data/validationset/val-faces/', list_file='./data/validationset/new_4people_val_standard.txt', transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size, shuffle=False, num_workers=12)

# Model
net = ShuffleNetV2(input_size)
'''
model_summary(net,input_size=(3,input_size,input_size))
flops, params = get_model_complexity_info(net, (input_size, input_size), as_strings=True, print_per_layer_stat=False)
print('Flops:  ' + flops)
print('Params: ' + params)
'''

if args.resume:
    print('==> Resuming from checkpoint..')
    checkpoint = torch.load('./checkpoint/ckpt.pth')
    net.load_state_dict(checkpoint['net'])
    best_correct = checkpoint['correct']
    start_epoch = checkpoint['epoch']
net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count())) #多gpu并行训练
net.cuda()

criterion = FIIQALoss()
#criterion = nn.CrossEntropyLoss()
optimizer = optim.AdaBound(net.parameters(),lr=args.lr,final_lr=0.1)

#optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=1e-4)

# Training
def train(epoch):
    print('\nEpoch: %d' % epoch)
    net.train()
    train_loss = 0
    total = 0
    fiiqa_correct = 0
    for batch_idx, (inputs, fiiqa_targets) in enumerate(trainloader):
        inputs = Variable(inputs.cuda())
        fiiqa_targets = Variable(fiiqa_targets.cuda()) 
        optimizer.zero_grad()
        fiiqa_preds = net(inputs)
        loss = criterion(fiiqa_preds.float(), fiiqa_targets)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        fiiqa_correct_i = accuracy(fiiqa_preds, fiiqa_targets)
        fiiqa_correct += fiiqa_correct_i
        total += len(inputs)
        print('train_loss: %.3f | fiiqa_prec: %.3f (%d/%d) |  [%d/%d]'  \
            % (loss.item(),       \
               100.*fiiqa_correct/total, fiiqa_correct, total,  \
               batch_idx+1, len(trainloader)))

# Test
def test(epoch):
    global Test_acc
    global best_correct
    global best_test_acc_epoch
    print('\nTest')
    net.eval()
    test_loss = 0
    total = 0
    fiiqa_correct = 0
    for batch_idx, (inputs, fiiqa_targets) in enumerate(testloader):
        inputs = Variable(inputs.cuda())
        fiiqa_targets = Variable(fiiqa_targets.cuda())
        fiiqa_preds = net(inputs)
        loss = criterion(fiiqa_preds, fiiqa_targets)
        test_loss += loss.item()
        fiiqa_correct_i = accuracy(fiiqa_preds, fiiqa_targets)
        fiiqa_correct += fiiqa_correct_i
        total += len(inputs)
        print('test_loss: %.3f | fiiqa_prec: %.3f (%d/%d) | [%d/%d]' \
            % (loss.item(),       \
               100.*fiiqa_correct/total, fiiqa_correct, total,  \
               batch_idx+1, len(testloader)))

    # Save checkpoint
    Test_acc = 100.*fiiqa_correct/total

    if Test_acc  > best_correct:
        print('Saving..')
        print("best_test_acc: %0.3f" % Test_acc)
        best_correct = Test_acc 
        best_test_acc_epoch = epoch

        state = {
            'net': net.module.state_dict(),
            'correct': best_correct,
            'epoch': epoch,
        }


        if not os.path.isdir('checkpoint'):
            os.mkdir('checkpoint')
        torch.save(state, os.path.join(path,str(best_correct)+'_'+str(input_size)+'_'+str(TOLERANCE) +'.pth'))

def accuracy(fiiqa_preds, fiiqa_targets):
    '''Measure batch accuracy.'''
    fiiqa_prob = F.softmax(fiiqa_preds,dim=1)
    fiiqa_expect = torch.sum(Variable(torch.arange(0,200)).cuda().float()*fiiqa_prob, 1)
    fiiqa_correct = ((fiiqa_expect-fiiqa_targets.float()).abs() < TOLERANCE).long().sum().cpu().item()
    return fiiqa_correct

for epoch in range(start_epoch, start_epoch+train_epoch):
    train(epoch)
    test(epoch)
print('best_test_acc: %0.3f' % best_correct)
print('best_test_acc_epoch: %d' % best_test_acc_epoch)


================================================
FILE: train_affectnet.py
================================================
'''Train CK+ with PyTorch.'''
# 10 crop for data enhancement
from __future__ import print_function

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
from torchvision import transforms,utils
from torch.utils.data import DataLoader
import transforms as transforms
import numpy as np
import os
import argparse
import utils
from CK import CK
from torch.autograd import Variable
from models import *
from ShuffleNetV2 import ShuffleNetV2
from flops_counter_pytorch.ptflops import get_model_complexity_info
from summary import model_summary
from datagen import ListDataset


train_data = '../train_val_imgs/Manually/Manually_train_croped'
test_data = '../train_val_imgs/Manually/Manually_validation_croped'

parser = argparse.ArgumentParser(description='PyTorch CK+ CNN Training')
#parser.add_argument('--model', type=str, default='VGG19', help='CNN architecture')
parser.add_argument('--dataset', type=str, default='CK+', help='dataset')
parser.add_argument('--fold', default=1, type=int, help='k fold number')
parser.add_argument('--bs', default=128, type=int, help='batch_size')
parser.add_argument('--lr', default=0.001, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
opt = parser.parse_args()

use_cuda = torch.cuda.is_available()

best_Test_acc = 0  # best PrivateTest accuracy
best_Test_acc_epoch = 0
start_epoch = 0  # start from epoch 0 or last checkpoint epoch

learning_rate_decay_start = 20  # 50
learning_rate_decay_every = 1 # 5
learning_rate_decay_rate = 0.8 # 0.9


total_epoch = 500
bs = 128
input_size = 64
cut_size = input_size - 1
n_class=7

#path = os.path.join(opt.dataset + '_' + opt.model, str(opt.fold))
path = './AffectNet+ShuffleNetV2/'

# Data
print('==> Preparing data..')
transform_train = transforms.Compose([
    transforms.Resize(input_size),
    transforms.RandomCrop(cut_size),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    #transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])

transform_test = transforms.Compose([
    transforms.Resize(input_size),
    #transforms.RandomCrop(cut_size),
    #transforms.RandomHorizontalFlip(),
    transforms.TenCrop(cut_size),
    transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),
    #transforms.ToTensor(),
    #transforms.Normalize((0.485,0.456,0.406), (0.229,0.224,0.225))
])

#trainset=torchvision.datasets.ImageFolder(train_data,transform_train)
trainset = ListDataset(root='../train_val_imgs/Manually/Manually_train_croped/', list_file='./AffectNet/train.txt', transform=transform_train)
trainloader=DataLoader(trainset,bs,shuffle=True, num_workers=12)
#testset=torchvision.datasets.ImageFolder(test_data,transform_test)
testset = ListDataset(root='../train_val_imgs/Manually/Manually_validation_croped/', list_file='./AffectNet/val.txt', transform=transform_test)
testloader=DataLoader(testset,batch_size=128,shuffle=True, num_workers=12)


net = ShuffleNetV2(input_size,n_class)
'''
model_summary(net,input_size=(3,input_size,input_size))
flops, params = get_model_complexity_info(net, (input_size, input_size), as_strings=True, print_per_layer_stat=False)
print('Flops:  ' + flops)
print('Params: ' + params)
#net = net.to(device=my_device)
'''

if opt.resume:
    # Load checkpoint.
    print('==> Resuming from checkpoint..')
    assert os.path.isdir(path), 'Error: no checkpoint directory found!'
    checkpoint = torch.load(os.path.join(path,'ShuffleNetV2.pth'))
    
    net.load_state_dict(checkpoint['net'])
    best_Test_acc = checkpoint['best_Test_acc']
    best_Test_acc_epoch = checkpoint['best_Test_acc_epoch']
    start_epoch = best_Test_acc_epoch + 1
else:
    print('==> Building model..')

if use_cuda:
    net.cuda()

criterion = nn.CrossEntropyLoss()
optimizer=optim.AdaBound(net.parameters(), lr=opt.lr, final_lr=0.1)
#optimizer = optim.SGD(net.parameters(), lr=opt.lr, momentum=0.9, weight_decay=1e-4)

# Training
def train(epoch):
    print('\nEpoch: %d' % epoch)
    global Train_acc
    net.train()
    train_loss = 0
    correct = 0
    total = 0

    for batch_idx, (inputs, targets) in enumerate(trainloader):
        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()
        optimizer.zero_grad()
        #inputs, targets = Variable(inputs), Variable(targets)
        outputs = net(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        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 
        optimizer.step()

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += targets.size(0)
        correct += predicted.eq(targets.data).cpu().sum()

        utils.progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
            % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))

    Train_acc = 100.*correct/total

# test
def test(epoch):
    global Test_acc
    global best_Test_acc
    global best_Test_acc_epoch
    net.eval()
    PrivateTest_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(testloader):
        bs, ncrops, c, h, w = np.shape(inputs)
        inputs = inputs.view(-1, c, h, w)

        if use_cuda:
            inputs, targets = inputs.cuda(), targets.cuda()

        inputs, targets = Variable(inputs), Variable(targets)
        outputs = net(inputs)
        outputs_avg = outputs.view(bs, ncrops, -1).mean(1)  # avg over crops

        loss = criterion(outputs_avg, targets)
        PrivateTest_loss += loss.item()
        _, predicted = torch.max(outputs_avg.data, 1)
        total += targets.size(0)
        correct += predicted.eq(targets.data).cpu().sum()

        utils.progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
            % (PrivateTest_loss / (batch_idx + 1), \
            100. * correct / total, correct, total))
    # Save checkpoint.
    Test_acc = 100.*correct/total

    if Test_acc > best_Test_acc:
        print('Saving..')
        print("best_Test_acc: %0.3f" % Test_acc)
        state = {'net': net.state_dict() if use_cuda else net,
            'best_Test_acc': Test_acc,
            'best_Test_acc_epoch': epoch,
        }
        if not os.path.isdir(opt.dataset + '_' + 'ShuffleNetV2'):
            os.mkdir(opt.dataset + '_' + 'ShuffleNetV2')
        if not os.path.isdir(path):
            os.mkdir(path)
        #torch.save(state, os.path.join(path, str(best_Test_acc) + '_ShuffleNetV2.pth'))
        best_Test_acc = Test_acc
        best_Test_acc_epoch = epoch
        torch.save(state, os.path.join(path, str(best_Test_acc) + '_'+str(input_size)+'_ShuffleNetV2.pth'))

for epoch in range(start_epoch, total_epoch):
    train(epoch)
    test(epoch)

print("best_Test_acc: %0.3f" % best_Test_acc)
print("best_Test_acc_epoch: %d" % best_Test_acc_epoch)