Yes exactly. The reason why I posed that question was to really see what the formal definition of in_channels is. So far I haven’t found anything.
Generally, should we order the dimensions of the input image such that channels corresponds to the smallest dimension, width and height corresponds to the larger 2 dimensions? As you said, this would allow for more flexibility with kernel sizing and strides.
For actual images, I think it is intuitive what width, height, and channels should correspond to. But CNNs can also be applied to non-image data (e.g., time series data), which is what I am working with. It is not clear to me what I should be using for in_channels in my application.