Hello all, I have 10 MRI image type of .nii with size of 128x256x256 (CxHxW). I have converted it to hdf5 files such as
1.h5
2.h5
...
9.h5
10.h5

I am using pytorch 0.4 to create a dataloader (read .h5 files). The dataloader has to randomly read the h5 files with crop size of 64x64x64. Could you tell me the steps to do it? This is my current solution
import h5py
import torch
import torch.utils.data as data
import glob
import os
#BCHW order
class H5Dataset(data.Dataset):

    def __init__(self, root_path, crop_size=(64,64,64)):
        self.hdf5_list = [x for x in glob.glob(os.path.join(root_path, '*.h5'))]
        self.crop_size = crop_size


    def __getitem__(self, index):
        h5_file = h5py.File(self.hdf5_list[index])
        self.data = h5_file.get('data')
        self.target = h5_file.get('label')
        return (torch.from_numpy(self.data[index,:,:,:,:]).float(),
                torch.from_numpy(self.target[index,:,:,:,:]).float())

    def __len__(self):
        return len(self.hdf5_list)

if __name__ == '__main__':
    mri_Data = H5Dataset("./data")
    trainloader = data.DataLoader(mri_Data, batch_size=4)
    for i, data in enumerate(trainloader):
        imgs, labels = data