mindspore/tests/ut/python/dataset/test_invert.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 Invert op in DE
"""
import numpy as np

import mindspore.dataset.engine as de
import mindspore.dataset.transforms.vision.py_transforms as F
from mindspore import log as logger
from util import visualize_list

DATA_DIR = "../data/dataset/testImageNetData/train/"


def test_invert(plot=False):
    """
    Test Invert
    """
    logger.info("Test Invert")

    # Original Images
    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)

    transforms_original = F.ComposeOp([F.Decode(),
                                       F.Resize((224, 224)),
                                       F.ToTensor()])

    ds_original = ds.map(input_columns="image",
                         operations=transforms_original())

    ds_original = ds_original.batch(512)

    for idx, (image, _) in enumerate(ds_original):
        if idx == 0:
            images_original = np.transpose(image, (0, 2, 3, 1))
        else:
            images_original = np.append(images_original,
                                        np.transpose(image, (0, 2, 3, 1)),
                                        axis=0)

            # Color Inverted Images
    ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)

    transforms_invert = F.ComposeOp([F.Decode(),
                                     F.Resize((224, 224)),
                                     F.Invert(),
                                     F.ToTensor()])

    ds_invert = ds.map(input_columns="image",
                       operations=transforms_invert())

    ds_invert = ds_invert.batch(512)

    for idx, (image, _) in enumerate(ds_invert):
        if idx == 0:
            images_invert = np.transpose(image, (0, 2, 3, 1))
        else:
            images_invert = np.append(images_invert,
                                      np.transpose(image, (0, 2, 3, 1)),
                                      axis=0)

    num_samples = images_original.shape[0]
    mse = np.zeros(num_samples)
    for i in range(num_samples):
        mse[i] = np.mean((images_invert[i] - images_original[i]) ** 2)
    logger.info("MSE= {}".format(str(np.mean(mse))))

    if plot:
        visualize_list(images_original, images_invert)


if __name__ == "__main__":
    test_invert(plot=True)