VGG网络的Pytorch实现

1.文章原文地址

Very Deep Convolutional Networks for Large-Scale Image Recognition

2.文章摘要

在这项工作中，我们研究了在大规模的图像识别数据集上卷积神经网络的深度对准确率的影响。我们主要贡献是使用非常小（3×3）卷积核的架构对深度增加的网络进行全面的评估，其结果表明将深度增大到16-19层时网络的性能会显著提升。这些发现是基于我们在ImageNet Challenge 2014的目标检测和分类任务分别获得了第一名和第二名的成绩而得出的。另外该网络也可以很好的推广到其他数据集上，在这些数据集上获得了当前最好结果。我们已经公开了性能最佳的ConvNet模型，为了促进在计算机视觉中使用深度视觉表征的进一步研究。

3.网络结构

4.Pytorch实现

 import torch.nn as nn
 try:
     from torch.hub import load_state_dict_from_url
 except ImportError:
     from torch.utils.model_zoo import load_url as load_state_dict_from_url

 __all__ = [
     'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
     'vgg19_bn', 'vgg19',
 ]

 model_urls = {
     'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
     'vgg13': 'https://download.pytorch.org/models/vgg13-c768596a.pth',
     'vgg16': 'https://download.pytorch.org/models/vgg16-397923af.pth',
     'vgg19': 'https://download.pytorch.org/models/vgg19-dcbb9e9d.pth',
     'vgg11_bn': 'https://download.pytorch.org/models/vgg11_bn-6002323d.pth',
     'vgg13_bn': 'https://download.pytorch.org/models/vgg13_bn-abd245e5.pth',
     'vgg16_bn': 'https://download.pytorch.org/models/vgg16_bn-6c64b313.pth',
     'vgg19_bn': 'https://download.pytorch.org/models/vgg19_bn-c79401a0.pth',
 }

 class VGG(nn.Module):

     def __init__(self, features, num_classes=1000, init_weights=True):
         super(VGG, self).__init__()
         self.features = features
         self.avgpool = nn.AdaptiveAvgPool2d((7, 7))　　#固定全连接层的输入
         self.classifier = nn.Sequential(
             nn.Linear(512 * 7 * 7, 4096),
             nn.ReLU(True),
             nn.Dropout(),
             nn.Linear(4096, 4096),
             nn.ReLU(True),
             nn.Dropout(),
             nn.Linear(4096, num_classes),
         )
         if init_weights:
             self._initialize_weights()

     def forward(self, x):
         x = self.features(x)
         x = self.avgpool(x)
         x = x.view(x.size(0), -1)
         x = self.classifier(x)
         return x

     def _initialize_weights(self):
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
                 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                 if m.bias is not None:
                     nn.init.constant_(m.bias, 0)
             elif isinstance(m, nn.BatchNorm2d):
                 nn.init.constant_(m.weight, 1)
                 nn.init.constant_(m.bias, 0)
             elif isinstance(m, nn.Linear):
                 nn.init.normal_(m.weight, 0, 0.01)
                 nn.init.constant_(m.bias, 0)

 def make_layers(cfg, batch_norm=False):
     layers = []
     in_channels = 3
     for v in cfg:
         if v == 'M':
             layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
         else:
             conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
             if batch_norm:
                 layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
             else:
                 layers += [conv2d, nn.ReLU(inplace=True)]
             in_channels = v
     return nn.Sequential(*layers)

 cfgs = {
     'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
     'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
     'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
     'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
 }

 def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs):
     if pretrained:
         kwargs['init_weights'] = False
     model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
     if pretrained:
         state_dict = load_state_dict_from_url(model_urls[arch],
                                               progress=progress)
         model.load_state_dict(state_dict)
     return model

 def vgg11(pretrained=False, progress=True, **kwargs):
     """VGG 11-layer model (configuration "A")
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     return _vgg('vgg11', 'A', False, pretrained, progress, **kwargs)

 def vgg11_bn(pretrained=False, progress=True, **kwargs):
     """VGG 11-layer model (configuration "A") with batch normalization
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     return _vgg('vgg11_bn', 'A', True, pretrained, progress, **kwargs)

 def vgg13(pretrained=False, progress=True, **kwargs):
     """VGG 13-layer model (configuration "B")
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     return _vgg('vgg13', 'B', False, pretrained, progress, **kwargs)

 def vgg13_bn(pretrained=False, progress=True, **kwargs):
     """VGG 13-layer model (configuration "B") with batch normalization
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     return _vgg('vgg13_bn', 'B', True, pretrained, progress, **kwargs)

 def vgg16(pretrained=False, progress=True, **kwargs):
     """VGG 16-layer model (configuration "D")
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     return _vgg('vgg16', 'D', False, pretrained, progress, **kwargs)

 def vgg16_bn(pretrained=False, progress=True, **kwargs):
     """VGG 16-layer model (configuration "D") with batch normalization
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     return _vgg('vgg16_bn', 'D', True, pretrained, progress, **kwargs)

 def vgg19(pretrained=False, progress=True, **kwargs):
     """VGG 19-layer model (configuration "E")
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     return _vgg('vgg19', 'E', False, pretrained, progress, **kwargs)

 def vgg19_bn(pretrained=False, progress=True, **kwargs):
     """VGG 19-layer model (configuration 'E') with batch normalization
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
         progress (bool): If True, displays a progress bar of the download to stderr
     """
     return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs)

参考

https://github.com/pytorch/vision/tree/master/torchvision/models