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350 lines
15 KiB
350 lines
15 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing AutoContrast op in DE
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"""
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import numpy as np
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import mindspore.dataset.engine as de
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import mindspore.dataset.transforms.py_transforms
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import mindspore.dataset.vision.py_transforms as F
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import mindspore.dataset.vision.c_transforms as C
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from mindspore import log as logger
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from util import visualize_list, visualize_one_channel_dataset, diff_mse, save_and_check_md5
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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MNIST_DATA_DIR = "../data/dataset/testMnistData"
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GENERATE_GOLDEN = False
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def test_auto_contrast_py(plot=False):
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"""
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Test AutoContrast
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"""
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logger.info("Test AutoContrast Python Op")
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# Original Images
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
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F.Resize((224, 224)),
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F.ToTensor()])
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ds_original = ds.map(operations=transforms_original, input_columns="image")
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ds_original = ds_original.batch(512)
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = np.transpose(image.asnumpy(), (0, 2, 3, 1))
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else:
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images_original = np.append(images_original,
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np.transpose(image.asnumpy(), (0, 2, 3, 1)),
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axis=0)
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# AutoContrast Images
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_auto_contrast = \
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mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
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F.Resize((224, 224)),
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F.AutoContrast(cutoff=10.0, ignore=[10, 20]),
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F.ToTensor()])
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ds_auto_contrast = ds.map(operations=transforms_auto_contrast, input_columns="image")
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ds_auto_contrast = ds_auto_contrast.batch(512)
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for idx, (image, _) in enumerate(ds_auto_contrast):
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if idx == 0:
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images_auto_contrast = np.transpose(image.asnumpy(), (0, 2, 3, 1))
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else:
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images_auto_contrast = np.append(images_auto_contrast,
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np.transpose(image.asnumpy(), (0, 2, 3, 1)),
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axis=0)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_auto_contrast[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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# Compare with expected md5 from images
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filename = "autocontrast_01_result_py.npz"
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save_and_check_md5(ds_auto_contrast, filename, generate_golden=GENERATE_GOLDEN)
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if plot:
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visualize_list(images_original, images_auto_contrast)
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def test_auto_contrast_c(plot=False):
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"""
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Test AutoContrast C Op
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"""
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logger.info("Test AutoContrast C Op")
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# AutoContrast Images
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"])
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python_op = F.AutoContrast(cutoff=10.0, ignore=[10, 20])
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c_op = C.AutoContrast(cutoff=10.0, ignore=[10, 20])
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transforms_op = mindspore.dataset.transforms.py_transforms.Compose([lambda img: F.ToPIL()(img.astype(np.uint8)),
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python_op,
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np.array])
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ds_auto_contrast_py = ds.map(operations=transforms_op, input_columns="image")
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ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
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for idx, (image, _) in enumerate(ds_auto_contrast_py):
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if idx == 0:
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images_auto_contrast_py = image.asnumpy()
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else:
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images_auto_contrast_py = np.append(images_auto_contrast_py,
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image.asnumpy(),
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axis=0)
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"])
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ds_auto_contrast_c = ds.map(operations=c_op, input_columns="image")
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ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
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for idx, (image, _) in enumerate(ds_auto_contrast_c):
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if idx == 0:
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images_auto_contrast_c = image.asnumpy()
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else:
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images_auto_contrast_c = np.append(images_auto_contrast_c,
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image.asnumpy(),
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axis=0)
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num_samples = images_auto_contrast_c.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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np.testing.assert_equal(np.mean(mse), 0.0)
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# Compare with expected md5 from images
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filename = "autocontrast_01_result_c.npz"
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save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN)
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if plot:
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visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
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def test_auto_contrast_one_channel_c(plot=False):
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"""
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Test AutoContrast C op with one channel
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"""
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logger.info("Test AutoContrast C Op With One Channel Images")
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# AutoContrast Images
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"])
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python_op = F.AutoContrast()
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c_op = C.AutoContrast()
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# not using F.ToTensor() since it converts to floats
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transforms_op = mindspore.dataset.transforms.py_transforms.Compose(
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[lambda img: (np.array(img)[:, :, 0]).astype(np.uint8),
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F.ToPIL(),
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python_op,
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np.array])
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ds_auto_contrast_py = ds.map(operations=transforms_op, input_columns="image")
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ds_auto_contrast_py = ds_auto_contrast_py.batch(512)
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for idx, (image, _) in enumerate(ds_auto_contrast_py):
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if idx == 0:
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images_auto_contrast_py = image.asnumpy()
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else:
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images_auto_contrast_py = np.append(images_auto_contrast_py,
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image.asnumpy(),
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axis=0)
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[C.Decode(), C.Resize((224, 224)), lambda img: np.array(img[:, :, 0])],
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input_columns=["image"])
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ds_auto_contrast_c = ds.map(operations=c_op, input_columns="image")
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ds_auto_contrast_c = ds_auto_contrast_c.batch(512)
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for idx, (image, _) in enumerate(ds_auto_contrast_c):
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if idx == 0:
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images_auto_contrast_c = image.asnumpy()
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else:
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images_auto_contrast_c = np.append(images_auto_contrast_c,
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image.asnumpy(),
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axis=0)
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num_samples = images_auto_contrast_c.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_auto_contrast_c[i], images_auto_contrast_py[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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np.testing.assert_equal(np.mean(mse), 0.0)
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if plot:
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visualize_list(images_auto_contrast_c, images_auto_contrast_py, visualize_mode=2)
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def test_auto_contrast_mnist_c(plot=False):
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"""
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Test AutoContrast C op with MNIST dataset (Grayscale images)
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"""
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logger.info("Test AutoContrast C Op With MNIST Images")
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ds = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
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ds_auto_contrast_c = ds.map(operations=C.AutoContrast(cutoff=1, ignore=(0, 255)), input_columns="image")
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ds_orig = de.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
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images = []
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images_trans = []
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labels = []
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for _, (data_orig, data_trans) in enumerate(zip(ds_orig, ds_auto_contrast_c)):
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image_orig, label_orig = data_orig
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image_trans, _ = data_trans
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images.append(image_orig.asnumpy())
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labels.append(label_orig.asnumpy())
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images_trans.append(image_trans.asnumpy())
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# Compare with expected md5 from images
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filename = "autocontrast_mnist_result_c.npz"
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save_and_check_md5(ds_auto_contrast_c, filename, generate_golden=GENERATE_GOLDEN)
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if plot:
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visualize_one_channel_dataset(images, images_trans, labels)
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def test_auto_contrast_invalid_ignore_param_c():
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"""
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Test AutoContrast C Op with invalid ignore parameter
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"""
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logger.info("Test AutoContrast C Op with invalid ignore parameter")
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try:
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[C.Decode(),
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C.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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# invalid ignore
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ds = ds.map(operations=C.AutoContrast(ignore=255.5), input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Argument ignore with value 255.5 is not of type" in str(error)
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try:
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[C.Decode(), C.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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# invalid ignore
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ds = ds.map(operations=C.AutoContrast(ignore=(10, 100)), input_columns="image")
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except TypeError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Argument ignore with value (10,100) is not of type" in str(error)
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def test_auto_contrast_invalid_cutoff_param_c():
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"""
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Test AutoContrast C Op with invalid cutoff parameter
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"""
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logger.info("Test AutoContrast C Op with invalid cutoff parameter")
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try:
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[C.Decode(),
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C.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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# invalid ignore
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ds = ds.map(operations=C.AutoContrast(cutoff=-10.0), input_columns="image")
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
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try:
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[C.Decode(),
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C.Resize((224, 224)),
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lambda img: np.array(img[:, :, 0])], input_columns=["image"])
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# invalid ignore
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ds = ds.map(operations=C.AutoContrast(cutoff=120.0), input_columns="image")
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
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def test_auto_contrast_invalid_ignore_param_py():
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"""
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Test AutoContrast python Op with invalid ignore parameter
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"""
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logger.info("Test AutoContrast python Op with invalid ignore parameter")
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try:
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
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F.Resize((224, 224)),
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F.AutoContrast(ignore=255.5),
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F.ToTensor()])],
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input_columns=["image"])
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except TypeError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Argument ignore with value 255.5 is not of type" in str(error)
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try:
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
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F.Resize((224, 224)),
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F.AutoContrast(ignore=(10, 100)),
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F.ToTensor()])],
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input_columns=["image"])
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except TypeError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Argument ignore with value (10,100) is not of type" in str(error)
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def test_auto_contrast_invalid_cutoff_param_py():
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"""
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Test AutoContrast python Op with invalid cutoff parameter
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"""
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logger.info("Test AutoContrast python Op with invalid cutoff parameter")
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try:
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(operations=[mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
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F.Resize((224, 224)),
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F.AutoContrast(cutoff=-10.0),
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F.ToTensor()])],
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input_columns=["image"])
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
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try:
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ds = de.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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ds = ds.map(
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operations=[mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
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F.Resize((224, 224)),
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F.AutoContrast(cutoff=120.0),
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F.ToTensor()])],
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input_columns=["image"])
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Input cutoff is not within the required interval of (0 to 100)." in str(error)
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if __name__ == "__main__":
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test_auto_contrast_py(plot=True)
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test_auto_contrast_c(plot=True)
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test_auto_contrast_one_channel_c(plot=True)
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test_auto_contrast_mnist_c(plot=True)
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test_auto_contrast_invalid_ignore_param_c()
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test_auto_contrast_invalid_ignore_param_py()
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test_auto_contrast_invalid_cutoff_param_c()
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test_auto_contrast_invalid_cutoff_param_py()
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