resnet101 网络结构代码

以下是ResNet101网络结构的代码，使用PyTorch实现：

import torch.nn as nn

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != planes * self.expansion:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, planes * self.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * self.expansion)
            )

    def forward(self, x):
        out = nn.ReLU()(self.bn1(self.conv1(x)))
        out = nn.ReLU()(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = nn.ReLU()(out)
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=1000):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = nn.ReLU()(self.bn1(self.conv1(x)))
        out = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = nn.AvgPool2d(kernel_size=7, stride=1)(out)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def ResNet101():
    return ResNet(Bottleneck, [3, 4, 23, 3])

这个代码实现了ResNet101的网络结构。其中Bottleneck类定义了ResNet中的一个基本块，ResNet类定义了整个网络结构，包括多个Bottleneck块和全连接层。最后的ResNet101函数用于创建ResNet101网络。