def test_cut_out_md5(): """ Test Cutout with md5 check """ logger.info("test_cut_out_md5") original_seed = config_get_set_seed(2) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c.Decode() cut_out_op = c.CutOut(100) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=cut_out_op) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [f.Decode(), f.ToTensor(), f.Cutout(100)] transform = f.ComposeOp(transforms) data2 = data2.map(input_columns=["image"], operations=transform()) # Compare with expected md5 from images filename1 = "cut_out_01_c_result.npz" save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN) filename2 = "cut_out_01_py_result.npz" save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN) # Restore config ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_cut_out_comp(plot=False): """ Test Cutout with c++ and python op comparison """ logger.info("test_cut_out_comp") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_1 = [f.Decode(), f.ToTensor(), f.Cutout(200)] transform_1 = f.ComposeOp(transforms_1) data1 = data1.map(input_columns=["image"], operations=transform_1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_2 = [c.Decode(), c.CutOut(200)] data2 = data2.map(input_columns=["image"], operations=transforms_2) num_iter = 0 image_list_1, image_list_2 = [], [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) # C image doesn't require transpose image_2 = item2["image"] image_list_1.append(image_1) image_list_2.append(image_2) logger.info("shape of image_1: {}".format(image_1.shape)) logger.info("shape of image_2: {}".format(image_2.shape)) logger.info("dtype of image_1: {}".format(image_1.dtype)) logger.info("dtype of image_2: {}".format(image_2.dtype)) if plot: visualize_list(image_list_1, image_list_2, visualize_mode=2)
def test_random_erasing_op(plot=False): """ Test RandomErasing and Cutout """ logger.info("test_random_erasing") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_1 = [ vision.Decode(), vision.ToTensor(), vision.RandomErasing(value='random') ] transform_1 = vision.ComposeOp(transforms_1) data1 = data1.map(input_columns=["image"], operations=transform_1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_2 = [vision.Decode(), vision.ToTensor(), vision.Cutout(80)] transform_2 = vision.ComposeOp(transforms_2) data2 = data2.map(input_columns=["image"], operations=transform_2()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape of image_1: {}".format(image_1.shape)) logger.info("shape of image_2: {}".format(image_2.shape)) logger.info("dtype of image_1: {}".format(image_1.dtype)) logger.info("dtype of image_2: {}".format(image_2.dtype)) mse = diff_mse(image_1, image_2) if plot: visualize_image(image_1, image_2, mse)