# 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 AutoContrast op in DE """ import numpy as np import mindspore.dataset.engine as de import mindspore.dataset.transforms.vision.py_transforms as F import mindspore.dataset.transforms.vision.c_transforms as C from mindspore import log as logger from util import visualize_list, visualize_one_channel_dataset, diff_mse, save_and_check_md5 DATA_DIR = "../data/dataset/testImageNetData/train/" MNIST_DATA_DIR = "../data/dataset/testMnistData" GENERATE_GOLDEN = False def test_auto_contrast_py(plot=False): """ Test AutoContrast """ logger.info("Test AutoContrast Python Op") # 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) # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_auto_contrast = F.ComposeOp([F.Decode(), F.Resize((224, 224)), F.AutoContrast(cutoff=10.0, ignore=[10, 20]), F.ToTensor()]) ds_auto_contrast = ds.map(input_columns="image", operations=transforms_auto_contrast()) ds_auto_contrast = ds_auto_contrast.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast): if idx == 0: images_auto_contrast = np.transpose(image, (0, 2, 3, 1)) else: images_auto_contrast = np.append(images_auto_contrast, 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_auto_contrast[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) # Compare with expected md5 from images filename = "autocontrast_01_result_py.npz" save_and_check_md5(ds_auto_contrast, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_list(images_original, images_auto_contrast) def test_auto_contrast_c(plot=False): """ Test AutoContrast C Op """ logger.info("Test AutoContrast C Op") # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20]) c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20]) transforms_op = F.ComposeOp([lambda img: F.ToPIL()(img.astype(np.uint8)), python_op, np.array])() ds_auto_contrast_py = ds.map(input_columns="image", operations=transforms_op) ds_auto_contrast_py = ds_auto_contrast_py.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_py): if idx == 0: images_auto_contrast_py = image else: images_auto_contrast_py = np.append(images_auto_contrast_py, image, axis=0) ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op) ds_auto_contrast_c = ds_auto_contrast_c.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_c): if idx == 0: images_auto_contrast_c = image else: images_auto_contrast_c = np.append(images_auto_contrast_c, image, axis=0) num_samples = images_auto_contrast_c.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) np.testing.assert_equal(np.mean(mse), 0.0) # Compare with expected md5 from images filename = "autocontrast_01_result_c.npz" save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2) def test_auto_contrast_one_channel_c(plot=False): """ Test AutoContrast C op with one channel """ logger.info("Test AutoContrast C Op With One Channel Images") # AutoContrast Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224))]) python_op = F.AutoContrast() c_op = C.AutoContrast() # not using F.ToTensor() since it converts to floats transforms_op = F.ComposeOp([lambda img: (np.array(img)[:, :, 0]).astype(np.uint8), F.ToPIL(), python_op, np.array])() ds_auto_contrast_py = ds.map(input_columns="image", operations=transforms_op) ds_auto_contrast_py = ds_auto_contrast_py.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_py): if idx == 0: images_auto_contrast_py = image else: images_auto_contrast_py = np.append(images_auto_contrast_py, image, axis=0) ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])]) ds_auto_contrast_c = ds.map(input_columns="image", operations=c_op) ds_auto_contrast_c = ds_auto_contrast_c.batch(512) for idx, (image, _) in enumerate(ds_auto_contrast_c): if idx == 0: images_auto_contrast_c = image else: images_auto_contrast_c = np.append(images_auto_contrast_c, image, axis=0) num_samples = images_auto_contrast_c.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) np.testing.assert_equal(np.mean(mse), 0.0) if plot: visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2) def test_auto_contrast_mnist_c(plot=False): """ Test AutoContrast C op with MNIST dataset (Grayscale images) """ logger.info("Test AutoContrast C Op With MNIST Images") ds = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) ds_auto_contrast_c = ds.map(input_columns="image", operations=C.AutoContrast(cutoff=1, ignore=(0, 255))) ds_orig = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False) images = [] images_trans = [] labels = [] for _, (data_orig, data_trans) in enumerate(zip(ds_orig, ds_auto_contrast_c)): image_orig, label_orig = data_orig image_trans, _ = data_trans images.append(image_orig) labels.append(label_orig) images_trans.append(image_trans) # Compare with expected md5 from images filename = "autocontrast_mnist_result_c.npz" save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN) if plot: visualize_one_channel_dataset(images, images_trans, labels) def test_auto_contrast_invalid_ignore_param_c(): """ Test AutoContrast C Op with invalid ignore parameter """ logger.info("Test AutoContrast C Op with invalid ignore parameter") try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])]) # invalid ignore ds = ds.map(input_columns="image", operations=C.AutoContrast(ignore=255.5)) except TypeError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Argument ignore with value 255.5 is not of type" in str(error) try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])]) # invalid ignore ds = ds.map(input_columns="image", operations=C.AutoContrast(ignore=(10, 100))) except TypeError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Argument ignore with value (10,100) is not of type" in str(error) def test_auto_contrast_invalid_cutoff_param_c(): """ Test AutoContrast C Op with invalid cutoff parameter """ logger.info("Test AutoContrast C Op with invalid cutoff parameter") try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])]) # invalid ignore ds = ds.map(input_columns="image", operations=C.AutoContrast(cutoff=-10.0)) except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Input cutoff is not within the required interval of (0 to 100)." in str(error) try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])]) # invalid ignore ds = ds.map(input_columns="image", operations=C.AutoContrast(cutoff=120.0)) except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Input cutoff is not within the required interval of (0 to 100)." in str(error) def test_auto_contrast_invalid_ignore_param_py(): """ Test AutoContrast python Op with invalid ignore parameter """ logger.info("Test AutoContrast python Op with invalid ignore parameter") try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[F.ComposeOp([F.Decode(), F.Resize((224, 224)), F.AutoContrast(ignore=255.5), F.ToTensor()])]) except TypeError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Argument ignore with value 255.5 is not of type" in str(error) try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[F.ComposeOp([F.Decode(), F.Resize((224, 224)), F.AutoContrast(ignore=(10, 100)), F.ToTensor()])]) except TypeError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Argument ignore with value (10,100) is not of type" in str(error) def test_auto_contrast_invalid_cutoff_param_py(): """ Test AutoContrast python Op with invalid cutoff parameter """ logger.info("Test AutoContrast python Op with invalid cutoff parameter") try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[F.ComposeOp([F.Decode(), F.Resize((224, 224)), F.AutoContrast(cutoff=-10.0), F.ToTensor()])]) except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Input cutoff is not within the required interval of (0 to 100)." in str(error) try: ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) ds = ds.map(input_columns=["image"], operations=[F.ComposeOp([F.Decode(), F.Resize((224, 224)), F.AutoContrast(cutoff=120.0), F.ToTensor()])]) except ValueError as error: logger.info("Got an exception in DE: {}".format(str(error))) assert "Input cutoff is not within the required interval of (0 to 100)." in str(error) if __name__ == "__main__": test_auto_contrast_py(plot=True) test_auto_contrast_c(plot=True) test_auto_contrast_one_channel_c(plot=True) test_auto_contrast_mnist_c(plot=True) test_auto_contrast_invalid_ignore_param_c() test_auto_contrast_invalid_ignore_param_py() test_auto_contrast_invalid_cutoff_param_c() test_auto_contrast_invalid_cutoff_param_py()