vgg16代码实现pytorch

以下是VGG16的PyTorch代码实现：

import torch
import torch.nn as nn

class VGG16(nn.Module):
    def __init__(self, num_classes=1000):
        super(VGG16, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

注释：

- `nn.Conv2d(in_channels, out_channels, kernel_size, padding)`：二维卷积层，其中`in_channels`表示输入通道数，`out_channels`表示输出通道数，`kernel_size`表示卷积核大小，`padding`表示填充大小。
- `nn.ReLU(inplace=True)`：ReLU激活函数，`inplace`为True表示原地操作，即直接修改输入的数据，节省内存。
- `nn.MaxPool2d(kernel_size, stride)`：最大值池化层，`kernel_size`表示池化核大小，`stride`表示步长。
- `nn.AdaptiveAvgPool2d(output_size)`：自适应平均值池化层，`output_size`表示输出的大小，可以是一个整数或一个元组。
- `nn.Linear(in_features, out_features)`：全连接层，其中`in_features`表示输入特征数，`out_features`表示输出特征数。
- `nn.Dropout(p=0.5)`：随机失活层，`p`表示失活概率。
- `torch.flatten(input, start_dim=0, end_dim=-1)`：展平输入张量。
- `forward(self, x)`：前向传播函数，其中`x`表示输入张量。