def test_five_crop_op(plot=False): """ Test FiveCrop """ logger.info("test_five_crop") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_1 = [ vision.Decode(), vision.ToTensor(), ] 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.FiveCrop(200), lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 5 images ] 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"] 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(np.array([image_1] * 5), (image_2 * 255).astype(np.uint8).transpose( 0, 2, 3, 1)) # The output data should be of a 4D tensor shape, a stack of 5 images. assert len(image_2.shape) == 4 assert image_2.shape[0] == 5
def test_five_crop_error_msg(): """ Test FiveCrop error message. """ logger.info("test_five_crop_error_msg") data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [vision.Decode(), vision.FiveCrop(200), vision.ToTensor()] transform = vision.ComposeOp(transforms) data = data.map(input_columns=["image"], operations=transform()) with pytest.raises(RuntimeError): data.create_tuple_iterator().__next__()
def test_five_crop_error_msg(): """ Test FiveCrop error message. """ logger.info("test_five_crop_error_msg") data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [vision.Decode(), vision.FiveCrop(200), vision.ToTensor()] transform = vision.ComposeOp(transforms) data = data.map(input_columns=["image"], operations=transform()) with pytest.raises(RuntimeError) as info: data.create_tuple_iterator().get_next() error_msg = "TypeError: img should be PIL Image or Numpy array. Got <class 'tuple'>" # error msg comes from ToTensor() assert error_msg in str(info.value)
def test_five_crop_md5(): """ Test FiveCrop with md5 check """ logger.info("test_five_crop_md5") # First dataset data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ vision.Decode(), vision.FiveCrop(100), lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 5 images ] transform = vision.ComposeOp(transforms) data = data.map(input_columns=["image"], operations=transform()) # Compare with expected md5 from images filename = "five_crop_01_result.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)