I am trying to write a custom dataset and data loader for a large dataset(SIDD-Full). The dataset contains 200 scenes and there are 150 different image captures of each of these 150 scenes, and each of these captures has a noisy and clean version. for more details, please refer to the dataset website here.
In each epoch, I want to sample one index from the 150 different captures for every 200 different instances and load them to the code, and then, I want to extract
n patches from each image with size
(32, 32). I don’t know how to handle this in
__getitem__(self, idx) function since I want to return instances of size
(1, 4, 32, 32) (the raw images have 4 channels), but if I do patching inside
__getitem__() function, I will have a tensor of size
(n, 1, 4, 32, 32), and I don’ know how can I handle that when creating batches out of the dataset with
I also have no idea of how to iterate on the patches inside the
__getitem__() function. Can anybody help me with this?