网络骨架Backbone

神经网络基本组成：卷积层、激活函数层、池化层、Droupout层、BN层、全连接层。

VGG16经典网络结构：

from torch import nn
class VGG(nn.Module):
    def __init__(self, num_classes=1000):
        super(VGG, self).__init__()
        layers = []
        in_dim = 3
        out_dim = 64
        for i in range(13):
            layers += [nn.Conv2d(in_dim, out_dim, 3, 1, 1), nn.ReLU(inplace=True)]
            in_dim = out_dim
            if i==1 or i==3 or i==6 or i==9 or i==12:
                layers += [nn.MaxPool2d(2, 2)]
                if i!=9:
                    out_dim*=2
        self.features = nn.Sequential(*layers)
        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),
        )
    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x