mt_transform.py

# transformer
composed_transform = transforms.Compose([
            mt_transforms.Resample(0.25, 0.25),
            mt_transforms.CenterCrop2D((200, 200)),
            mt_transforms.ToTensor(),
])

# load data
train_dataset = mt_datasets.SCGMChallenge2DTrain(root_dir=ROOT_DIR_GMCHALLENGE, transform=composed_transform)

# PyTorch data loader
dataloader = DataLoader(train_dataset, batch_size=4,
                        shuffle=True, num_workers=4,
                        collate_fn=mt_datasets.mt_collate)

# get minibatch
minibatch = next(iter(dataloader))

# check minibatch size
minibatch['input'].size()

### output
# torch.Size([4, 1, 200, 200])