在PyTorch中提取CNN的中间层输出

如何解决在PyTorch中提取CNN的中间层输出

我正在使用Resnet18模型。

ResNet(
  (conv1): Conv2d(3,64,kernel_size=(7,7),stride=(2,2),padding=(3,3),bias=False)
  (bn1): BatchNorm2d(64,eps=1e-05,momentum=0.1,affine=True,track_running_stats=True)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3,stride=2,padding=1,dilation=1,ceil_mode=False)
  (layer1): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64,kernel_size=(3,stride=(1,1),padding=(1,bias=False)
      (bn1): BatchNorm2d(64,track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(64,bias=False)
      (bn2): BatchNorm2d(64,track_running_stats=True)
    )
    (1): BasicBlock(
      (conv1): Conv2d(64,track_running_stats=True)
    )
  )
  (layer2): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64,128,bias=False)
      (bn1): BatchNorm2d(128,track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(128,bias=False)
      (bn2): BatchNorm2d(128,track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(64,kernel_size=(1,bias=False)
        (1): BatchNorm2d(128,track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(128,track_running_stats=True)
    )
  )
  (layer3): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(128,256,bias=False)
      (bn1): BatchNorm2d(256,track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(256,bias=False)
      (bn2): BatchNorm2d(256,track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(128,bias=False)
        (1): BatchNorm2d(256,track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(256,track_running_stats=True)
    )
  )
  (layer4): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(256,512,bias=False)
      (bn1): BatchNorm2d(512,track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(512,bias=False)
      (bn2): BatchNorm2d(512,track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(256,bias=False)
        (1): BatchNorm2d(512,track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(512,track_running_stats=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1,1))
  (fc): Linear(in_features=512,out_features=1000,bias=True)
)

我只想从layer2，layer3，layer4提取输出，而我不希望avgpool和fc的输出。 我该如何实现？

class BasicBlock(nn.Module):
    def __init__(self,in_channels,out_channels,stride=1,padding=1) -> None:
        super(BasicBlock,self).__init__()
        self.conv1 = nn.Conv2d(in_channels,3,stride,padding=padding,bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels,bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        if in_channels != out_channels:
            l1 = nn.Conv2d(in_channels,kernel_size=1,stride=stride,bias=False)
            l2 = nn.BatchNorm2d(out_channels)
            self.downsample = nn.Sequential(l1,l2)
        else:
            self.downsample = None

    def forward(self,xb):
        prev = xb
        x = self.relu(self.bn1(self.conv1(xb)))
        x = self.bn2(self.conv2(x))
        if self.downsample is not None:
            prev = self.downsample(xb)
        x = x + prev
        return self.relu(x)

class CustomResnet(nn.Module):
    def __init__(self,pretrained:bool=True) -> None:
        super(CustomResnet,self).__init__()
        self.conv1 = nn.Conv2d(3,kernel_size=7,padding=3,bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3,padding=1)
        self.layer1 = nn.Sequential(BasicBlock( 64,stride=1),BasicBlock(64,64))
        self.layer2 = nn.Sequential(BasicBlock(64,stride=2),BasicBlock(128,128))
        self.layer3 = nn.Sequential(BasicBlock(128,BasicBlock(256,256))
        self.layer4 = nn.Sequential(BasicBlock(256,BasicBlock(512,512))

    def forward(self,xb):
        x = self.maxpool(self.relu(self.bn1(self.conv1(xb))))
        x = self.layer1(x)
        x2 = x = self.layer2(x)
        x3 = x = self.layer3(x)
        x4 = x = self.layer4(x)
        return [x2,x3,x4]

我想一种解决方案是..但是，如果在编写大量代码时没有编写此代码，还有其他方法吗？在上述经过修改的torchvision模型中，也可以加载ResNet给定的预训练权重。

解决方法

如果您知道如何实现forward方法，则可以对模型进行子类化，并仅覆盖forward方法。

如果您在PyTorch中使用模型的预训练权重，则您已经可以访问模型的代码。因此，找到模型代码的位置，将其导入，对该模型进行子类化，并覆盖forward方法。

例如：

class MyResNet18(Resnet):

  def __init__(self,*args,**kwargs):
    super().__init__(*args,**kwargs)

  def forward(self,xb):
    x = self.maxpool(self.relu(self.bn1(self.conv1(xb))))
    x = self.layer1(x)
    x2 = x = self.layer2(x)
    x3 = x = self.layer3(x)
    x4 = x = self.layer4(x)
    return [x2,x3,x4]

您已经完成。

,

为了将来参考，有一个pytorch实用程序可以轻松获得中间结果https://pypi.org/project/torch-intermediate-layer-getter/