VGG網絡詳解

VGG是在2014年由牛津大學著名研究組VGG（Visual Geometry Group）提出，斬獲該年ImageNet競賽中Localization Task（定位任務）第一名和Classification Task（分類任務）第二名。

具體的來說，論文中寫到通過堆疊兩個3×3的卷積核代替5×5的卷積核，堆疊三個3×3的卷積核替代7×7的卷積核。也就說小的多個卷積核堆疊和一個大的卷積核具有相同的感受野，提取的特徵是一樣的。

在卷積神經網絡中，決定某一層輸出結果中一個元素所對應的輸入層的區域大小，被稱為感受野（receptive field）。通俗的解釋是，輸出feature map上的一個單元對應輸入層上的區域大小。

下面使用深度學習框架pytorch搭建VGG網絡，IDE環境為Pycharm2020.2.3，Pytorch1.7.1，python3.6.8。數據集以花分類數據集為例，最後進訓練得到模型，並在網上隨機下載一張鬱金香圖片預測成功。

"""Author: LGDFileName: modelDateTime: 2021/1/24 21:30 SoftWare: PyCharm"""import torch.nn as nnimport torch

class VGG(nn.Module):    def __init__(self, features, num_class=1000, init_weights=False):        super(VGG, self).__init__()        self.features = features        self.classifier = nn.Sequential(            nn.Dropout(p=0.5),            nn.Linear(512 * 7 * 7, 2048),            nn.ReLU(True),            nn.Dropout(p=0.5),            nn.Linear(2048, 2048),            nn.ReLU(True),            nn.Linear(2048, num_class)        )        if init_weights:            self._initialize_weights()
    def forward(self, x):                x = self.features(x)                x = torch.flatten(x, start_dim=1)                x = self.classifier(x)
        return x
    def _initialize_weights(self):        for m in self.modules():            if isinstance(m, nn.Conv2d):                                nn.init.xavier_uniform_(m.weight)                if m.bias is not None:                    nn.init.constant_(m.bias, 0)            elif isinstance(m, nn.Linear):                nn.init.xavier_uniform_(m.weight)                                nn.init.constant_(m.bias, 0)

def make_features(cfg: list):    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=in_channels, out_channels=v, kernel_size=3, padding=1)            layers += [conv2d, nn.ReLU(True)]            in_channels = v
    return nn.Sequential(*layers)

cfgs = {    'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],    'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],    'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],    'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M']}

def vgg(model_name='vgg16', **kwargs):    try:        cfg = cfgs[model_name]    except:        print("Waring: model name {} not in cfgs dict!".format(model_name))        exit(-1)
    model = VGG(make_features(cfg), **kwargs)
    return model