mindspore2022/tests/ut/python/dataset/test_random_lighting.py

# Copyright 2021 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 RandomLighting op in DE
"""
import numpy as np
import pytest

import mindspore.dataset as ds
import mindspore.dataset.transforms.py_transforms
import mindspore.dataset.vision.py_transforms as F
import mindspore.dataset.vision.c_transforms as C
from mindspore import log as logger
from util import visualize_list, diff_mse, save_and_check_md5, \
    config_get_set_seed, config_get_set_num_parallel_workers

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

GENERATE_GOLDEN = False


def test_random_lighting_py(alpha=1, plot=False):
    """
    Feature: RandomLighting
    Description: test RandomLighting python op
    Expectation: equal results
    """
    logger.info("Test RandomLighting python op")

    # Original Images
    data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)

    transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
                                                                              F.Resize((224, 224)),
                                                                              F.ToTensor()])

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

    ds_original = ds_original.batch(512)

    for idx, (image, _) in enumerate(ds_original.create_tuple_iterator(num_epochs=1, output_numpy=True)):
        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)

    # Random Lighting Adjusted Images
    data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)

    alpha = alpha if alpha is not None else 0.05
    py_op = F.RandomLighting(alpha)

    transforms_random_lighting = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
                                                                                     F.Resize((224, 224)),
                                                                                     py_op,
                                                                                     F.ToTensor()])
    ds_random_lighting = data.map(operations=transforms_random_lighting, input_columns="image")

    ds_random_lighting = ds_random_lighting.batch(512)

    for idx, (image, _) in enumerate(ds_random_lighting.create_tuple_iterator(num_epochs=1, output_numpy=True)):
        if idx == 0:
            images_random_lighting = np.transpose(image, (0, 2, 3, 1))
        else:
            images_random_lighting = np.append(images_random_lighting, 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] = diff_mse(images_random_lighting[i], images_original[i])

    logger.info("MSE= {}".format(str(np.mean(mse))))

    if plot:
        visualize_list(images_original, images_random_lighting)


def test_random_lighting_py_md5():
    """
    Feature: RandomLighting
    Description: test RandomLighting python op with md5 comparison
    Expectation: same MD5
    """
    logger.info("Test RandomLighting python op with md5 comparison")
    original_seed = config_get_set_seed(140)
    original_num_parallel_workers = config_get_set_num_parallel_workers(1)

    # define map operations
    transforms = [
        F.Decode(),
        F.Resize((224, 224)),
        F.RandomLighting(1),
        F.ToTensor()
    ]
    transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)

    #  Generate dataset
    data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
    data = data.map(operations=transform, input_columns=["image"])

    # check results with md5 comparison
    filename = "random_lighting_py_01_result.npz"
    save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)

    # Restore configuration
    ds.config.set_seed(original_seed)
    ds.config.set_num_parallel_workers(original_num_parallel_workers)


def test_random_lighting_c(alpha=1, plot=False):
    """
    Feature: RandomLighting
    Description: test RandomLighting cpp op
    Expectation: equal results from Mindspore and benchmark
    """
    logger.info("Test RandomLighting cpp op")
    # Original Images
    data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)

    transforms_original = [C.Decode(), C.Resize((224, 224))]

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

    ds_original = ds_original.batch(512)

    for idx, (image, _) in enumerate(ds_original.create_tuple_iterator(num_epochs=1, output_numpy=True)):
        if idx == 0:
            images_original = image
        else:
            images_original = np.append(images_original, image, axis=0)

    # Random Lighting Adjusted Images
    data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)

    alpha = alpha if alpha is not None else 0.05
    c_op = C.RandomLighting(alpha)

    transforms_random_lighting = [C.Decode(), C.Resize((224, 224)), c_op]

    ds_random_lighting = data.map(operations=transforms_random_lighting, input_columns="image")

    ds_random_lighting = ds_random_lighting.batch(512)

    for idx, (image, _) in enumerate(ds_random_lighting.create_tuple_iterator(num_epochs=1, output_numpy=True)):
        if idx == 0:
            images_random_lighting = image
        else:
            images_random_lighting = np.append(images_random_lighting, image, axis=0)

    num_samples = images_original.shape[0]
    mse = np.zeros(num_samples)
    for i in range(num_samples):
        mse[i] = diff_mse(images_random_lighting[i], images_original[i])

    logger.info("MSE= {}".format(str(np.mean(mse))))

    if plot:
        visualize_list(images_original, images_random_lighting)


def test_random_lighting_c_py(alpha=1, plot=False):
    """
    Feature: RandomLighting
    Description: test Random Lighting Cpp and Python Op
    Expectation: equal results from Cpp and Python
    """
    logger.info("Test RandomLighting Cpp and python Op")

    # RandomLighting Images
    data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
    data = data.map(operations=[C.Decode(), C.Resize((200, 300))], input_columns=["image"])

    python_op = F.RandomLighting(alpha)
    c_op = C.RandomLighting(alpha)

    transforms_op = mindspore.dataset.transforms.py_transforms.Compose([lambda img: F.ToPIL()(img.astype(np.uint8)),
                                                                        python_op,
                                                                        np.array])

    ds_random_lighting_py = data.map(operations=transforms_op, input_columns="image")

    ds_random_lighting_py = ds_random_lighting_py.batch(512)

    for idx, (image, _) in enumerate(ds_random_lighting_py.create_tuple_iterator(num_epochs=1, output_numpy=True)):
        if idx == 0:
            images_random_lighting_py = image

        else:
            images_random_lighting_py = np.append(images_random_lighting_py, image, axis=0)

    data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
    data = data.map(operations=[C.Decode(), C.Resize((200, 300))], input_columns=["image"])

    ds_images_random_lighting_c = data.map(operations=c_op, input_columns="image")

    ds_random_lighting_c = ds_images_random_lighting_c.batch(512)

    for idx, (image, _) in enumerate(ds_random_lighting_c.create_tuple_iterator(num_epochs=1, output_numpy=True)):
        if idx == 0:
            images_random_lighting_c = image
        else:
            images_random_lighting_c = np.append(images_random_lighting_c, image, axis=0)

    num_samples = images_random_lighting_c.shape[0]
    mse = np.zeros(num_samples)
    for i in range(num_samples):
        mse[i] = diff_mse(images_random_lighting_c[i], images_random_lighting_py[i])
    logger.info("MSE= {}".format(str(np.mean(mse))))
    if plot:
        visualize_list(images_random_lighting_c, images_random_lighting_py, visualize_mode=2)


def test_random_lighting_invalid_params():
    """
    Feature: RandomLighting
    Description: test RandomLighting with invalid input parameters
    Expectation: throw ValueError or TypeError
    """
    logger.info("Test RandomLighting with invalid input parameters.")
    with pytest.raises(ValueError) as error_info:
        data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
        data = data.map(operations=[C.Decode(), C.Resize((224, 224)),
                                    C.RandomLighting(-2)], input_columns=["image"])
    assert "Input alpha is not within the required interval of [0, 16777216]." in str(error_info.value)

    with pytest.raises(TypeError) as error_info:
        data = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
        data = data.map(operations=[C.Decode(), C.Resize((224, 224)),
                                    C.RandomLighting('1')], input_columns=["image"])
    err_msg = "Argument alpha with value 1 is not of type [<class 'float'>, <class 'int'>], but got <class 'str'>."
    assert err_msg in str(error_info.value)


if __name__ == "__main__":
    test_random_lighting_py()
    test_random_lighting_py_md5()
    test_random_lighting_c()
    test_random_lighting_c_py()
    test_random_lighting_invalid_params()