Pythorch：如何打印网络中每层的输出blob大小？

Question

我可以像打印网络结构一样（又如何打印每个“简单”层的位置索引？因为在本例中，我们为Fire模块提供了3，它的内容（“简单”层）没有索引）：

net = models.squeezenet1_1(pretrained=True)
print(net)



SqueezeNet(
  (features): Sequential(
    (0): Conv2d (3, 64, kernel_size=(3, 3), stride=(2, 2))
    (1): ReLU(inplace)
    (2): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))
    (3): Fire(
      (squeeze): Conv2d (64, 16, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d (16, 64, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d (16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (4): Fire(
      (squeeze): Conv2d (128, 16, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d (16, 64, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d (16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (5): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))
    (6): Fire(
      (squeeze): Conv2d (128, 32, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d (32, 128, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d (32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (7): Fire(
      (squeeze): Conv2d (256, 32, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d (32, 128, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d (32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (8): MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))
    (9): Fire(
      (squeeze): Conv2d (256, 48, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d (48, 192, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d (48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (10): Fire(
      (squeeze): Conv2d (384, 48, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d (48, 192, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d (48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (11): Fire(
      (squeeze): Conv2d (384, 64, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d (64, 256, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d (64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
    (12): Fire(
      (squeeze): Conv2d (512, 64, kernel_size=(1, 1), stride=(1, 1))
      (squeeze_activation): ReLU(inplace)
      (expand1x1): Conv2d (64, 256, kernel_size=(1, 1), stride=(1, 1))
      (expand1x1_activation): ReLU(inplace)
      (expand3x3): Conv2d (64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (expand3x3_activation): ReLU(inplace)
    )
  )
  (classifier): Sequential(
    (0): Dropout(p=0.5)
    (1): Conv2d (512, 1000, kernel_size=(1, 1), stride=(1, 1))
    (2): ReLU(inplace)
    (3): AvgPool2d(kernel_size=13, stride=1, padding=0, ceil_mode=False, count_include_pad=True)
  )
)

我可以打印重量大小如下：

^{pr2}$

如何打印网络中各层的输出blob大小？在

Answer 1

您可以注册一个钩子（回调函数），它将打印出输入和输出张量的形状，如手册中所述：Forward and Backward Function Hooks
示例：

net.register_forward_hook(your_print_blobs_function)

在这之后，你需要对一些输入张量做一次向前传球。在

^{pr2}$