基于pytorch的vgg16的源代码

以下是基于PyTorch的VGG16的源代码：

import torch
import torch.nn as nn

class VGG16(nn.Module):
    def __init__(self, num_classes=1000):
        super(VGG16, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.fc1 = nn.Linear(512 * 7 * 7, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.fc3 = nn.Linear(4096, num_classes)
        self.relu = nn.ReLU(inplace=True)
    
    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.pool1(x)
        x = self.relu(self.conv3(x))
        x = self.relu(self.conv4(x))
        x = self.pool2(x)
        x = self.relu(self.conv5(x))
        x = self.relu(self.conv6(x))
        x = self.relu(self.conv7(x))
        x = self.pool3(x)
        x = self.relu(self.conv8(x))
        x = self.relu(self.conv9(x))
        x = self.relu(self.conv10(x))
        x = self.pool4(x)
        x = self.relu(self.conv11(x))
        x = self.relu(self.conv12(x))
        x = self.relu(self.conv13(x))
        x = self.pool5(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

该模型包括VGG16的所有层（共13层卷积层和3个全连接层），并且使用ReLU激活函数。在前向传播时，输入数据首先通过卷积层和池化层进行特征提取，然后通过全连接层进行分类。最后的输出是一个含有num_classes个元素的向量，表示每个类别的概率分数。