For example the task of transformation, the model consist convolutional layer and pooling layer only, take input of image, and output a feature map (loss MSE, trying to produce feature map that exactly match feature map in label)

The forward pass can take any image size and output a feature with size = [w/(n_max_pooling2), h/(n_max_pooling2)]

Thus, with batch size of 1, there are no "dimension mismatch error" when we feed different image size, different ratio. The network just output a feature map, due to the nature of convolutional layer and max pooling layer.

For example: (batch_size, channel, width, height) Sample 1: 1x3x128x128 => target 64x64 Sample 2: 1x3x768x1024 => target 368x512 ....

I would like to know in that case of inconsist input shape, will the network be able to train and update it weight correctly ?

For example the task of transformation, the model consist convolutional layer and pooling layer only, take input of image, and output a feature map (loss MSE, trying to produce feature map that exactly match feature map in label)

The forward pass can take any image size and output a feature with size = [w/(n_max_pooling2), h/(n_max_pooling2)]

Thus, with batch size of 1, there are no "dimension mismatch error" when we feed different image size, different ratio. The network just output a feature map, due to the nature of convolutional layer and max pooling layer.

For example: (batch_size, channel, width, height)

• Sample 1: 1x3x128x128 => target 64x64
• Sample 2: 1x3x768x1024 => target 368x512

....

I would like to know in that case of inconsist input shape, will the network be able to train and update it weight correctly ?