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I have a ResNet9 model, implemented in Pytorch which I am using for multi-class image classification. My total number of classes is 6. Using the following code, from torchsummary library, I am able to show the summary of the model, seen in the attached image:

INPUT_SHAPE = (3, 256, 256) #input shape of my image

print(summary(model.cuda(), (INPUT_SHAPE)))

enter image description here

However, I am quite confused about the -1 values in all layers of the ResNet9 model. Also, for Conv2d-1 layer, I am confused about the 64 value in the output shape [-1, 64, 256, 256] as I believe the n_channels value of the input image is 3. Can anyone please help me with the explanation of the output shape values? Thanks!

Slwd-wave540
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1 Answers1

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Yes
your INPUT_SHAPE is torch.Size([3, 256, 256]) if it's channel first format AND (256, 256, 3) if it's channel last format.
As Pytorch model accepts it in channel first format , for you it shows torch.Size([3, 256, 256])

and talking about our output shape [-1, 64, 256, 256], this is the output shape of your first conv output which has 64 filter each of 256x256 dim and not your input_shape. -1 represents your variable batch_size which can be fixed in dataloader

Prajot Kuvalekar
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