Meaning of output shapes of ResNet9 model layers
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)))

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!

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
