論文：Very Deep Convolutional Networks for Large-Scale Image Recognition

簡單介紹
意義： 證明了增加小卷積核的個數以及網絡深度可以提高分類結果的正確率。
預處理： 各通道減去RGB在訓練集上的均值。
特點：
1）使用小的卷積核（3 × 3）疊加的形式代替大的卷積核（5 × 5 or 7 × 7）
2）卷積層不會改變layer大小，而是通過max pooling減小layer大小
3）網絡層數比較深
優點：
1）網絡結構簡潔：整個網絡都使用了同樣大小的卷積核（3x3）和最大池化尺寸（2x2）
2）使用小的卷積核疊加的形式代替大的卷積核，表達能力更強，網絡性能更佳
缺點：
1）網絡參數比較多，訓練時間過長，調參難度大。
2）需要的存儲容量大，不利于部署。例如存儲VGG16權重值文件的大小為500多MB。
其中需要說明的是2個3 x 3的卷積核是可以代替一個5 x 5的卷積核的，其示意圖如下：

網絡結構

其網絡結構圖如上圖所示，均還有5個block，其中VGG系列包含了vgg11、vgg13、vgg16以及vgg19，其中后面的數字代表的是對應的網絡結構中卷積層和全連接層的數量，例如vgg16中含有13個卷積層和3個全連接層。其中vgg11中的LRN（Local Response Normalization）表示局部響應歸一化。

源碼講解
首先要在電腦中安裝torchvision，其源碼可在torchvision下的models文件夾中找到，名為vgg.py
torchvision是pytorch框架中一個非常重要且好用的包，該包主要由三個子包組成，分別是：
torchvision.datasets；
torchvision.models；
torchvision.transforms

1）導入相應的包

import torch import torch.nn as nn from .utils import load_state_dict_from_url

2）所有的網絡名稱及其預訓練好的參數文件

__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', }

3）vgg類的定義，其中features表示對應的所有卷積以及池化層，avgpool表示平均池化（池化分為平均池化以及最大池化），classifier表示全連接層，共三層，_initialize_weights函數表示對網絡參數進行初始化

class VGG(nn.Module):def __init__(self, features, num_classes=1000, init_weights=True):super(VGG, self).__init__()self.features = featuresself.avgpool = nn.AdaptiveAvgPool2d((7, 7))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 = torch.flatten(x, 1)x = self.classifier(x)return xdef _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)

4）該函數表示添加相應的卷積層以及池化層，其中nn.Sequential表示一個有序的容器，神經網絡模塊將按照在傳入nn.Sequential的順序依次被添加到計算圖中執行。

def make_layers(cfg, batch_norm=False):layers = []in_channels = 3for 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 = vreturn nn.Sequential(*layers)

5）其中A、B、D、E分別表示vgg11、vgg13、vgg16以及vgg19，其中數字表示一個卷積層對應的輸出通道數， ’ M ’ 表示池化層

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'], }

6）下面表示不同的vgg網絡接口

def _vgg(arch, cfg, batch_norm, pretrained, progress, **kwargs):if pretrained:kwargs['init_weights'] = Falsemodel = 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 modeldef vgg11(pretrained=False, progress=True, **kwargs):r"""VGG 11-layer model (configuration "A") from`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (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):r"""VGG 11-layer model (configuration "A") with batch normalization`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (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):r"""VGG 13-layer model (configuration "B")`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (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):r"""VGG 13-layer model (configuration "B") with batch normalization`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (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):r"""VGG 16-layer model (configuration "D")`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (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):r"""VGG 16-layer model (configuration "D") with batch normalization`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (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):r"""VGG 19-layer model (configuration "E")`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (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):r"""VGG 19-layer model (configuration 'E') with batch normalization`"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_Args:pretrained (bool): If True, returns a model pre-trained on ImageNetprogress (bool): If True, displays a progress bar of the download to stderr"""return _vgg('vgg19_bn', 'E', True, pretrained, progress, **kwargs)

參考鏈接：https://zhuanlan.zhihu.com/p/41423739

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