How to get the input and output feature maps of a CNN model?

Question

I am trying to find the dimensions of an image as it goes through a convolutional neural network at each layer. So for instance, if there is max-pooling or convolution being applied, I'd like to know the shape of the image at that layer, for all layers. I know I can use the nOut=image+2p-f / s + 1 formula but it would be too tedious and complex given the size of the PyTorch model. Is there a simple way to do this, perhaps a visualization tool/script or something?

Answer 1

You can use the torchinfo library: https://github.com/TylerYep/torchinfo

Let's take their example:

from torchinfo import summary

model = ConvNet()
batch_size = 16
summary(model, input_size=(batch_size, 1, 28, 28))

Here (1, 28, 28) is the input's size, which is (Channel, Width, Height) of the image respectively.

The library will print:

================================================================================================================
Layer (type:depth-idx)          Input Shape          Output Shape         Param #            Mult-Adds
================================================================================================================
SingleInputNet                  --                   --                   --                  --
├─Conv2d: 1-1                   [7, 1, 28, 28]       [7, 10, 24, 24]      260                1,048,320
├─Conv2d: 1-2                   [7, 10, 12, 12]      [7, 20, 8, 8]        5,020              2,248,960
├─Dropout2d: 1-3                [7, 20, 8, 8]        [7, 20, 8, 8]        --                 --
├─Linear: 1-4                   [7, 320]             [7, 50]              16,050             112,350
├─Linear: 1-5                   [7, 50]              [7, 10]              510                3,570
================================================================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
Total mult-adds (M): 3.41
================================================================================================================
Input size (MB): 0.02
Forward/backward pass size (MB): 0.40
Params size (MB): 0.09
Estimated Total Size (MB): 0.51
================================================================================================================

I think 7 is wrong in this output though. It should be 16 .

Answer 2

访问https://deeplearning.neuromatch.io/tutorials/W2D1_ConvnetsAndRecurrentNeuralNetworks/student/W2D1_Tutorial1.html它是由神经匹配学院提供的包含大量 CNN 视觉表示的免费教程。