Beispiel #1
0
def test_pad_md5():
    """
    Test Pad with md5 check
    """
    logger.info("test_pad_md5")

    # First dataset
    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    decode_op = c_vision.Decode()
    pad_op = c_vision.Pad(150)
    ctrans = [decode_op,
              pad_op,
              ]

    data1 = data1.map(input_columns=["image"], operations=ctrans)

    # Second dataset
    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    pytrans = [
        py_vision.Decode(),
        py_vision.Pad(150),
        py_vision.ToTensor(),
    ]
    transform = py_vision.ComposeOp(pytrans)
    data2 = data2.map(input_columns=["image"], operations=transform())
    # Compare with expected md5 from images
    filename1 = "pad_01_c_result.npz"
    save_and_check_md5(data1, filename1, generate_golden=GENERATE_GOLDEN)
    filename2 = "pad_01_py_result.npz"
    save_and_check_md5(data2, filename2, generate_golden=GENERATE_GOLDEN)
Beispiel #2
0
def test_pad_op():
    """
    Test Pad op
    """
    logger.info("test_random_color_jitter_op")

    # First dataset
    data1 = ds.TFRecordDataset(DATA_DIR,
                               SCHEMA_DIR,
                               columns_list=["image"],
                               shuffle=False)
    decode_op = c_vision.Decode()

    pad_op = c_vision.Pad((100, 100, 100, 100))
    ctrans = [
        decode_op,
        pad_op,
    ]

    data1 = data1.map(input_columns=["image"], operations=ctrans)

    # Second dataset
    transforms = [
        py_vision.Decode(),
        py_vision.Pad(100),
        py_vision.ToTensor(),
    ]
    transform = py_vision.ComposeOp(transforms)
    data2 = ds.TFRecordDataset(DATA_DIR,
                               SCHEMA_DIR,
                               columns_list=["image"],
                               shuffle=False)
    data2 = data2.map(input_columns=["image"], operations=transform())

    num_iter = 0
    for item1, item2 in zip(data1.create_dict_iterator(),
                            data2.create_dict_iterator()):
        num_iter += 1
        c_image = item1["image"]
        py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)

        logger.info("shape of c_image: {}".format(c_image.shape))
        logger.info("shape of py_image: {}".format(py_image.shape))

        logger.info("dtype of c_image: {}".format(c_image.dtype))
        logger.info("dtype of py_image: {}".format(py_image.dtype))

        diff = c_image - py_image
        mse = diff_mse(c_image, py_image)
        logger.info("mse is {}".format(mse))
        assert mse < 0.01