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我正在尝试提取在 Densenet121 CNN 上训练的数据集（x 射线图像）的特征向量，以便使用 Pytorch 进行分类。我想从中间层之一中提取特征向量。

模型.eval() -->

DataParallel(
  (module): DenseNet121(
    (densenet121): DenseNet(
      (features): Sequential(
        (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
        (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu0): ReLU(inplace=True)
        (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        (denseblock1): _DenseBlock(
          (denselayer1): _DenseLayer(
            (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer2): _DenseLayer(
            (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer3): _DenseLayer(
            (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer4): _DenseLayer(
            (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer5): _DenseLayer(
            (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer6): _DenseLayer(
            (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
        )
        (transition1): _Transition(
          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
        )
        (denseblock2): _DenseBlock(
          (denselayer1): _DenseLayer(
            (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer2): _DenseLayer(
            (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer3): _DenseLayer(
            (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer4): _DenseLayer(
            (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer5): _DenseLayer(
            (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer6): _DenseLayer(
            (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer7): _DenseLayer(
            (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer8): _DenseLayer(
            (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer9): _DenseLayer(
            (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer10): _DenseLayer(
            (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer11): _DenseLayer(
            (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer12): _DenseLayer(
            (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
        )
        (transition2): _Transition(
          (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
        )

我想我必须在下面的代码块中做一些工作，但我需要帮助才能做到这一点。

class DenseNet121(nn.Module):

    def __init__(self, out_size):
        super(DenseNet121, self).__init__()
        self.densenet121 = torchvision.models.densenet121(pretrained = True)
        num_ftrs = self.densenet121.classifier.in_features
        self.densenet121.classifier = nn.Sequential(
            nn.Linear(num_ftrs, out_size),
            nn.Sigmoid()
        )


    def forward(self, x):
        x = self.densenet121(x)

        return x

我想获取特征向量，然后保存它们，以便以后将它们用作另一个函数的输入。

谢谢你。

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1 回答 1

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您可能想使用类似forward hook的东西。它基本上是一个你可以注册的函数调用，它在调用这个特定模块的转发时执行。因此，您可以在模型中要记录输入和/或输出的点注册前向钩子，并将特征向量写入文件或其他任何内容。

找出如何对正确的图层进行分类，它正在查看您发布的描述并沿着树向下走。因此，如果您想查看denseblock1.denselayer2.conv1. 应该是这样的

model.densenet121.features.denseblock1.denselayer2.conv1

不能保证它会起作用，最好在调试器中尝试一下。也许您还需要Sequential通过带有[]运算符或其他东西的索引来访问元素 os

于 2021-09-02T13:15:49.390 回答