class BasicBlock(nn.Module): expansion = 1 def __init__(self, in_channels, out_channels, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels , kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU() self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.downsample = downsample

这是一个PyTorch中定义ResNet的BasicBlock的代码。BasicBlock是ResNet中的基本残差块，包含两个卷积层和一个跳跃连接。参数in_channels和out_channels分别表示输入通道数和输出通道数。stride表示卷积核的步长，downsample表示是否需要对输入进行下采样。

在BasicBlock的构造函数中，首先调用父类的构造函数，然后定义了两个卷积层。其中，第一个卷积层使用3×3的卷积核，stride为stride，padding为1，不使用偏置项；第二个卷积层与第一个卷积层类似，但stride为1。在卷积层之间，使用BatchNorm2d和ReLU激活函数。最后，如果需要进行下采样，则使用downsample对输入进行下采样。

此外，expansion=1表示残差块中第二个卷积层的输出通道数与输入通道数相同。在ResNet中还有Bottleneck残差块，它的输出通道数是输入通道数的4倍，expansion=4。

希望这可以帮助回答你的问题。

相关问题

class BasicBlock2D(nn.Module): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock2D, 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 != self.expansion * out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * out_channels) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out # 定义二维ResNet-18模型 class ResNet18_2D(nn.Module): def __init__(self, num_classes=1000): super(ResNet18_2D, self).__init__() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) # print(out.shape) out = out.view(out.size(0), -1) out = self.fc(out) return out改为用稀疏表示替换全连接层

为了用稀疏表示替换全连接层，需要先将特征图展平，然后使用稀疏矩阵与其进行乘法运算。

以下是修改后的代码：

import torch.sparse as sparse

class ResNet18_2D(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet18_2D, self).__init__()

        self.in_channels = 64

        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1)
        self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2)
        self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2)
        self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        # 替换全连接层
        self.fc = nn.Identity()

    def _make_layer(self, block, out_channels, num_blocks, stride):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * block.expansion
        for _ in range(1, num_blocks):
            layers.append(block(self.in_channels, out_channels))
        return nn.Sequential(*layers)

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

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

        out = self.avgpool(out)
        out = out.view(out.size(0), -1)

        # 使用稀疏矩阵与展平后的特征图进行乘法运算
        out = sparse.mm(out, nn.Parameter(torch.randn(out.size(1), 512)))

        return out

class ResNet18_2D(nn.Module): def __init__(self, num_classes=1000): super(ResNet18_2D, self).__init__() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.LeakyReLU = nn.LeakyReLU(negative_slope=0.1) self.relu = nn.ReLU(inplace=True) self.elu = nn.ELU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): # out = F.ReLU(self.bn1(self.conv1(x))) out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) out = out.view(out.size(0), -1) return out 在self.layer4(out)和 self.avgpool(out)之间加CBAM

要在 `self.layer4(out)` 和 `self.avgpool(out)` 之间CBAM模块，可以按照以下步骤进行修改：

首先，导入CBAM模块的相关库：

from cbam import CBAM

然后，在ResNet18_2D类中添加CBAM模块：

self.cbam = CBAM(512)  # 添加CBAM模块，输入通道数为512

最后，在forward方法中使用CBAM模块：

out = self.layer4(out)
out = self.cbam(out)  # 使用CBAM模块
out = self.avgpool(out)

确保在使用CBAM模块之前，已经定义了CBAM类并导入相应的库。

这样，你就在ResNet18_2D模型中成功添加了CBAM模块。请注意，这仅仅是示例代码，你可能需要根据实际情况自行进行调整和修改。