用稀疏连接代替ResNet 18的全连接的pytorch代码

以下是使用稀疏连接代替 ResNet 18 全连接层的 PyTorch 代码示例：

import torch.nn as nn
import torch.nn.functional as F

class SparseResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(SparseResidualBlock, 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.sparse = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
        self.bn_sparse = nn.BatchNorm2d(out_channels)
        
    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        sparse = self.bn_sparse(self.sparse(x))
        out += sparse
        out = F.relu(out)
        return out

class SparseResNet18(nn.Module):
    def __init__(self, num_classes=10):
        super(SparseResNet18, 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(SparseResidualBlock, 64, 2, stride=1)
        self.layer2 = self.make_layer(SparseResidualBlock, 128, 2, stride=2)
        self.layer3 = self.make_layer(SparseResidualBlock, 256, 2, stride=2)
        self.layer4 = self.make_layer(SparseResidualBlock, 512, 2, stride=2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)
        
    def make_layer(self, block, out_channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels
        return nn.Sequential(*layers)
        
    def forward(self, x):
        out = F.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

在这个代码中，我们使用了 `nn.Conv2d` 和 `nn.BatchNorm2d` 来代替全连接层，同时引入了一个 `SparseResidualBlock` 类来实现稀疏连接。`SparseResidualBlock` 实现了一个残差块，其中包含了一个卷积层和一个扩展层，扩展层使用了 `nn.Conv2d` 来实现稀疏连接。`make_layer` 方法用来构建不同层的残差块，这里使用了四个残差块层，分别是 `layer1`、`layer2`、`layer3`、`layer4`。

最后，我们使用 `nn.AdaptiveAvgPool2d` 来进行自适应平均池化，然后使用一个全连接层 `nn.Linear` 将特征向量映射到类别空间。