mindspore/tests/ut/python/dataset/test_ten_crop.py

# Copyright 2020 Huawei Technologies Co., Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Testing TenCrop in DE
"""
import pytest
import numpy as np

import mindspore.dataset as ds
import mindspore.dataset.transforms.vision.py_transforms as vision
from mindspore import log as logger
from util import visualize_list, save_and_check_md5

GENERATE_GOLDEN = False

DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"


def util_test_ten_crop(crop_size, vertical_flip=False, plot=False):
    """
    Utility function for testing TenCrop. Input arguments are given by other tests
    """
    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.TenCrop(crop_size, use_vertical_flip=vertical_flip),
        lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 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] * 10), (image_2 * 255).astype(np.uint8).transpose(0, 2, 3, 1))

        # The output data should be of a 4D tensor shape, a stack of 10 images.
        assert len(image_2.shape) == 4
        assert image_2.shape[0] == 10


def test_ten_crop_op_square(plot=False):
    """
    Tests TenCrop for a square crop
    """

    logger.info("test_ten_crop_op_square")
    util_test_ten_crop(200, plot=plot)


def test_ten_crop_op_rectangle(plot=False):
    """
    Tests TenCrop for a rectangle crop
    """

    logger.info("test_ten_crop_op_rectangle")
    util_test_ten_crop((200, 150), plot=plot)


def test_ten_crop_op_vertical_flip(plot=False):
    """
    Tests TenCrop with vertical flip set to True
    """

    logger.info("test_ten_crop_op_vertical_flip")
    util_test_ten_crop(200, vertical_flip=True, plot=plot)


def test_ten_crop_md5():
    """
    Tests TenCrops for giving the same results in multiple runs.
    Since TenCrop is a deterministic function, we expect it to return the same result for a specific input every time
    """
    logger.info("test_ten_crop_md5")

    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    transforms_2 = [
        vision.Decode(),
        vision.TenCrop((200, 100), use_vertical_flip=True),
        lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
    ]
    transform_2 = vision.ComposeOp(transforms_2)
    data2 = data2.map(input_columns=["image"], operations=transform_2())
    # Compare with expected md5 from images
    filename = "ten_crop_01_result.npz"
    save_and_check_md5(data2, filename, generate_golden=GENERATE_GOLDEN)


def test_ten_crop_list_size_error_msg():
    """
    Tests TenCrop error message when the size arg has more than 2 elements
    """
    logger.info("test_ten_crop_list_size_error_msg")

    with pytest.raises(TypeError) as info:
        _ = [
            vision.Decode(),
            vision.TenCrop([200, 200, 200]),
            lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
        ]
    error_msg = "Size should be a single integer or a list/tuple (h, w) of length 2."
    assert error_msg == str(info.value)


def test_ten_crop_invalid_size_error_msg():
    """
    Tests TenCrop error message when the size arg is not positive
    """
    logger.info("test_ten_crop_invalid_size_error_msg")

    with pytest.raises(ValueError) as info:
        _ = [
            vision.Decode(),
            vision.TenCrop(0),
            lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
        ]
    error_msg = "Input is not within the required interval of (1 to 16777216)."
    assert error_msg == str(info.value)

    with pytest.raises(ValueError) as info:
        _ = [
            vision.Decode(),
            vision.TenCrop(-10),
            lambda images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 10 images
        ]

    assert error_msg == str(info.value)


def test_ten_crop_wrong_img_error_msg():
    """
    Tests TenCrop error message when the image is not in the correct format.
    """
    logger.info("test_ten_crop_wrong_img_error_msg")

    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    transforms = [
        vision.Decode(),
        vision.TenCrop(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)


if __name__ == "__main__":
    test_ten_crop_op_square(plot=True)
    test_ten_crop_op_rectangle(plot=True)
    test_ten_crop_op_vertical_flip(plot=True)
    test_ten_crop_md5()
    test_ten_crop_list_size_error_msg()
    test_ten_crop_invalid_size_error_msg()
    test_ten_crop_wrong_img_error_msg()