# 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. # ============================================================================== import matplotlib.pyplot as plt import numpy as np import mindspore.dataset.engine as de import mindspore.dataset.transforms.vision.c_transforms as C import mindspore.dataset.transforms.vision.py_transforms as F from mindspore import log as logger DATA_DIR = "../data/dataset/testImageNetData/train/" def visualize(image_original, image_ua): """ visualizes the image using DE op and Numpy op """ num = len(image_ua) for i in range(num): plt.subplot(2, num, i + 1) plt.imshow(image_original[i]) plt.title("Original image") plt.subplot(2, num, i + num + 1) plt.imshow(image_ua[i]) plt.title("DE UniformAugment image") plt.show() def test_uniform_augment(plot=False, num_ops=2): """ Test UniformAugment """ logger.info("Test UniformAugment") # 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, label) 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) # UniformAugment Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transform_list = [F.RandomRotation(45), F.RandomColor(), F.RandomSharpness(), F.Invert(), F.AutoContrast(), F.Equalize()] transforms_ua = F.ComposeOp([F.Decode(), F.Resize((224, 224)), F.UniformAugment(transforms=transform_list, num_ops=num_ops), F.ToTensor()]) ds_ua = ds.map(input_columns="image", operations=transforms_ua()) ds_ua = ds_ua.batch(512) for idx, (image, label) in enumerate(ds_ua): if idx == 0: images_ua = np.transpose(image, (0, 2, 3, 1)) else: images_ua = np.append(images_ua, 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_ua[i] - images_original[i]) ** 2) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize(images_original, images_ua) def test_cpp_uniform_augment(plot=False, num_ops=2): """ Test UniformAugment """ logger.info("Test CPP UniformAugment") # Original Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_original = [C.Decode(), C.Resize(size=[224, 224]), F.ToTensor()] ds_original = ds.map(input_columns="image", operations=transforms_original) ds_original = ds_original.batch(512) for idx, (image, label) 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) # UniformAugment Images ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False) transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45)] uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops) transforms_all = [C.Decode(), C.Resize(size=[224, 224]), uni_aug, F.ToTensor()] ds_ua = ds.map(input_columns="image", operations=transforms_all, num_parallel_workers=1) ds_ua = ds_ua.batch(512) for idx, (image, label) in enumerate(ds_ua): if idx == 0: images_ua = np.transpose(image, (0, 2, 3, 1)) else: images_ua = np.append(images_ua, np.transpose(image, (0, 2, 3, 1)), axis=0) if plot: visualize(images_original, images_ua) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = np.mean((images_ua[i] - images_original[i]) ** 2) logger.info("MSE= {}".format(str(np.mean(mse)))) def test_cpp_uniform_augment_exception_pyops(num_ops=2): """ Test UniformAugment invalid op in operations """ logger.info("Test CPP UniformAugment invalid OP exception") transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45), F.Invert()] try: uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops) except BaseException as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "operations" in str(e) def test_cpp_uniform_augment_exception_large_numops(num_ops=6): """ Test UniformAugment invalid large number of ops """ logger.info("Test CPP UniformAugment invalid large num_ops exception") transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45)] try: uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops) except BaseException as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "num_ops" in str(e) def test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0): """ Test UniformAugment invalid non-positive number of ops """ logger.info("Test CPP UniformAugment invalid non-positive num_ops exception") transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45)] try: uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops) except BaseException as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "num_ops" in str(e) def test_cpp_uniform_augment_exception_float_numops(num_ops=2.5): """ Test UniformAugment invalid float number of ops """ logger.info("Test CPP UniformAugment invalid float num_ops exception") transforms_ua = [C.RandomCrop(size=[224, 224], padding=[32, 32, 32, 32]), C.RandomHorizontalFlip(), C.RandomVerticalFlip(), C.RandomColorAdjust(), C.RandomRotation(degrees=45)] try: uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops) except BaseException as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "integer" in str(e) def test_cpp_uniform_augment_random_crop_badinput(num_ops=1): """ Test UniformAugment with greater crop size """ logger.info("Test CPP UniformAugment with random_crop bad input") batch_size=2 cifar10_dir = "../data/dataset/testCifar10Data" ds1 = de.Cifar10Dataset(cifar10_dir, shuffle=False) # shape = [32,32,3] transforms_ua = [ # Note: crop size [224, 224] > image size [32, 32] C.RandomCrop(size=[224, 224]), C.RandomHorizontalFlip() ] uni_aug = C.UniformAugment(operations=transforms_ua, num_ops=num_ops) ds1 = ds1.map(input_columns="image", operations=uni_aug) # apply DatasetOps ds1 = ds1.batch(batch_size, drop_remainder=True, num_parallel_workers=1) num_batches = 0 try: for data in ds1.create_dict_iterator(): num_batches += 1 except BaseException as e: assert "Crop size" in str(e) if __name__ == "__main__": test_uniform_augment(num_ops=1) test_cpp_uniform_augment(num_ops=1) test_cpp_uniform_augment_exception_pyops(num_ops=1) test_cpp_uniform_augment_exception_large_numops(num_ops=6) test_cpp_uniform_augment_exception_nonpositive_numops(num_ops=0) test_cpp_uniform_augment_exception_float_numops(num_ops=2.5) test_cpp_uniform_augment_random_crop_badinput(num_ops=1)