resnet python代码

ResNet是一种深度神经网络架构，主要解决深度网络训练过程中梯度消失的问题。它的核心是残差学习，通过跨层连接将输入直接传递到输出，避免了梯度在深层网络中消失的问题。

以下是ResNet的Python代码实现示例：

import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, 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.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):
        out = nn.functional.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = nn.functional.relu(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10):
        super(ResNet, self).__init__()
        self.in_channels = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self.make_layer(block, 64, layers, stride=1)
        self.layer2 = self.make_layer(block, 128, layers, stride=2)
        self.layer3 = self.make_layer(block, 256, layers, stride=2)
        self.layer4 = self.make_layer(block, 512, layers, stride=2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def make_layer(self, block, out_channels, blocks, stride):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels
        for i in range(blocks-1):
            layers.append(block(out_channels, out_channels))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = nn.functional.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

def ResNet18(num_classes=10):
    return ResNet(ResidualBlock, [2, 2, 2, 2], num_classes=num_classes)

def ResNet34(num_classes=10):
    return ResNet(ResidualBlock, [3, 4, 6, 3], num_classes=num_classes)

def ResNet50(num_classes=10):
    return ResNet(BottleneckBlock, [3, 4, 6, 3], num_classes=num_classes)

def ResNet101(num_classes=10):
    return ResNet(BottleneckBlock, [3, 4, 23, 3], num_classes=num_classes)

def ResNet152(num_classes=10):
    return ResNet(BottleneckBlock, [3, 8, 36, 3], num_classes=num_classes)

以上代码实现了ResNet的基本结构，并提供了ResNet18、ResNet34、ResNet50、ResNet101、ResNet152等不同深度的模型供选择。通过调用不同的函数即可构建不同深度的ResNet模型。