When creating a convolution layer in Pytorch, the function takes an argument called in_channels. I am wondering if there is a formal definition of what in_channels actually means, especially in the case of the first layer, where in_channels depends on what your data looks like?
When working with grayscale and colored images, I understand in_channels is set to 1 and 3, respectively, where 3 corresponds to red, green, and blue.
Say you have a colored image that is 200x200 pixels. The standard would be to set in_channels = 3 in the first conv layer. Would it be possible to structure the input data differently, and set in_channels equal to the width or height dimension of the input image, i.e., in_channels = 200?