def get_common_transforms(): """ Get the transform object with the common transforms (Flips, rotation90) Args: None Returns: transformation: the transformation """ common_transforms = transforms.Compose([ transforms.RandomFlip(p=0.5, flip_plane=(1, 2)), transforms.RandomFlip(p=0.5, flip_plane=(2, 1)), transforms.RandomRotation90(p=1.0, mult_90=[0, 1, 2, 3], rot_plane=(1, 2)) ]) return common_transforms
input_shape = (140, 140, 1) output_shape = (140, 140, 1) data_split = DataSplit(train=0.85, validation=0.15, test=0.00) cache_RAM = True cache_HDD = True cache_root = os.path.join(run_root, '.cache/') batch_size = 256 num_workers = 8 data_sources = WkwData.datasources_from_json(datasources_json_path) transforms = transforms.Compose([ transforms.RandomFlip(p=0.5, flip_plane=(1, 2)), transforms.RandomFlip(p=0.5, flip_plane=(2, 1)), transforms.RandomRotation90(p=1.0, mult_90=[0, 1, 2, 3], rot_plane=(1, 2)) ]) dataset = WkwData(input_shape=input_shape, target_shape=output_shape, data_sources=data_sources, data_split=data_split, transforms=transforms, cache_RAM=cache_RAM, cache_HDD=cache_HDD, cache_HDD_root=cache_HDD_root) # Create the weighted samplers which create imbalance given the factor imbalance_factor = 20 data_loaders = subsetWeightedSampler.get_data_loaders( dataset, imbalance_factor=imbalance_factor,