# Copyright 2020-2021 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 Invert op in DE """ import numpy as np 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, save_and_check_md5, diff_mse DATA_DIR = "../data/dataset/testImageNetData/train/" GENERATE_GOLDEN = False def test_invert_callable(): """ Test Invert is callable """ logger.info("Test Invert callable") img = np.fromfile("../data/dataset/apple.jpg", dtype=np.uint8) logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape)) img = C.Decode()(img) img = C.Invert()(img) logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape)) assert img.shape == (2268, 4032, 3) def test_invert_py(plot=False): """ Test Invert python op """ logger.info("Test Invert Python op") # Original Images data_set = 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_set.map(operations=transforms_original, input_columns="image") ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_original = np.append(images_original, np.transpose(image.asnumpy(), (0, 2, 3, 1)), axis=0) # Color Inverted Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_invert = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Resize((224, 224)), F.Invert(), F.ToTensor()]) ds_invert = data_set.map(operations=transforms_invert, input_columns="image") ds_invert = ds_invert.batch(512) for idx, (image, _) in enumerate(ds_invert): if idx == 0: images_invert = np.transpose(image.asnumpy(), (0, 2, 3, 1)) else: images_invert = np.append(images_invert, np.transpose(image.asnumpy(), (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) def test_invert_c(plot=False): """ Test Invert Cpp op """ logger.info("Test Invert cpp op") # Original Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_original = [C.Decode(), C.Resize(size=[224, 224])] ds_original = data_set.map(operations=transforms_original, input_columns="image") ds_original = ds_original.batch(512) for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = image.asnumpy() else: images_original = np.append(images_original, image.asnumpy(), axis=0) # Invert Images data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transform_invert = [C.Decode(), C.Resize(size=[224, 224]), C.Invert()] ds_invert = data_set.map(operations=transform_invert, input_columns="image") ds_invert = ds_invert.batch(512) for idx, (image, _) in enumerate(ds_invert): if idx == 0: images_invert = image.asnumpy() else: images_invert = np.append(images_invert, image.asnumpy(), axis=0) if plot: visualize_list(images_original, images_invert) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_invert[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) def test_invert_py_c(plot=False): """ Test Invert Cpp op and python op """ logger.info("Test Invert cpp and python op") # Invert Images in cpp data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"]) ds_c_invert = data_set.map(operations=C.Invert(), input_columns="image") ds_c_invert = ds_c_invert.batch(512) for idx, (image, _) in enumerate(ds_c_invert): if idx == 0: images_c_invert = image.asnumpy() else: images_c_invert = np.append(images_c_invert, image.asnumpy(), axis=0) # invert images in python data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"]) transforms_p_invert = mindspore.dataset.transforms.py_transforms.Compose([lambda img: img.astype(np.uint8), F.ToPIL(), F.Invert(), np.array]) ds_p_invert = data_set.map(operations=transforms_p_invert, input_columns="image") ds_p_invert = ds_p_invert.batch(512) for idx, (image, _) in enumerate(ds_p_invert): if idx == 0: images_p_invert = image.asnumpy() else: images_p_invert = np.append(images_p_invert, image.asnumpy(), axis=0) num_samples = images_c_invert.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_p_invert[i], images_c_invert[i]) logger.info("MSE= {}".format(str(np.mean(mse)))) if plot: visualize_list(images_c_invert, images_p_invert, visualize_mode=2) def test_invert_one_channel(): """ Test Invert cpp op with one channel image """ logger.info("Test Invert C Op With One Channel Images") c_op = C.Invert() try: data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])], input_columns=["image"]) data_set.map(operations=c_op, input_columns="image") except RuntimeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "The shape" in str(e) def test_invert_md5_py(): """ Test Invert python op with md5 check """ logger.info("Test Invert python op with md5 check") # Generate dataset data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_invert = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(), F.Invert(), F.ToTensor()]) data = data_set.map(operations=transforms_invert, input_columns="image") # Compare with expected md5 from images filename = "invert_01_result_py.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN) def test_invert_md5_c(): """ Test Invert cpp op with md5 check """ logger.info("Test Invert cpp op with md5 check") # Generate dataset data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False) transforms_invert = [C.Decode(), C.Resize(size=[224, 224]), C.Invert(), F.ToTensor()] data = data_set.map(operations=transforms_invert, input_columns="image") # Compare with expected md5 from images filename = "invert_01_result_c.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN) if __name__ == "__main__": test_invert_callable() test_invert_py(plot=False) test_invert_c(plot=False) test_invert_py_c(plot=False) test_invert_one_channel() test_invert_md5_py() test_invert_md5_c()