用Pycharm写一个深度残差网络的代码

在PyCharm中编写深度残差网络（Residual Network, ResNet）的代码通常涉及使用深度学习框架，如TensorFlow或PyTorch。这里我将以PyTorch为例给出一个简单的ResNet模型结构的代码：

import torch
import torch.nn as nn

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += self.shortcut(residual)  # Residual connection
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000):
        super(ResNet, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self.make_layer(block, 64, layers[0])
        self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self.make_layer(block, 512, layers[3], stride=2)
        
        self.avg_pool = nn.AvgPool2d(7)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def make_layer(self, block, out_channels, blocks, stride=1):
        layer = []
        layer.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, blocks):
            layer.append(block(out_channels, out_channels))

        return nn.Sequential(*layer)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

# 使用示例
resnet18 = ResNet(BasicBlock, [2, 2, 2, 2])  # 创建一个ResNet18模型
input_image = torch.randn(1, 3, 224, 224)  # 假设输入图片大小是224x224
output = resnet18(input_image)