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284 lines
10 KiB
284 lines
10 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 Equalize op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.py_transforms
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import mindspore.dataset.vision.c_transforms as C
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import mindspore.dataset.vision.py_transforms as F
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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_equalize_py(plot=False):
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"""
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Test Equalize py op
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"""
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logger.info("Test Equalize")
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# Original Images
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data_set = ds.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 = data_set.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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# Color Equalized Images
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_equalize = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
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F.Resize((224, 224)),
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F.Equalize(),
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F.ToTensor()])
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ds_equalize = data_set.map(operations=transforms_equalize, input_columns="image")
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ds_equalize = ds_equalize.batch(512)
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for idx, (image, _) in enumerate(ds_equalize):
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if idx == 0:
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images_equalize = np.transpose(image.asnumpy(), (0, 2, 3, 1))
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else:
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images_equalize = np.append(images_equalize,
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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_equalize[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize_list(images_original, images_equalize)
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def test_equalize_c(plot=False):
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"""
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Test Equalize Cpp op
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"""
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logger.info("Test Equalize cpp op")
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# Original Images
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = [C.Decode(), C.Resize(size=[224, 224])]
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ds_original = data_set.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 = image.asnumpy()
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else:
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images_original = np.append(images_original,
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image.asnumpy(),
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axis=0)
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# Equalize Images
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transform_equalize = [C.Decode(), C.Resize(size=[224, 224]),
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C.Equalize()]
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ds_equalize = data_set.map(operations=transform_equalize, input_columns="image")
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ds_equalize = ds_equalize.batch(512)
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for idx, (image, _) in enumerate(ds_equalize):
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if idx == 0:
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images_equalize = image.asnumpy()
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else:
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images_equalize = np.append(images_equalize,
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image.asnumpy(),
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axis=0)
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if plot:
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visualize_list(images_original, images_equalize)
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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_equalize[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_equalize_py_c(plot=False):
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"""
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Test Equalize Cpp op and python op
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"""
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logger.info("Test Equalize cpp and python op")
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# equalize Images in cpp
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"])
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ds_c_equalize = data_set.map(operations=C.Equalize(), input_columns="image")
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ds_c_equalize = ds_c_equalize.batch(512)
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for idx, (image, _) in enumerate(ds_c_equalize):
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if idx == 0:
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images_c_equalize = image.asnumpy()
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else:
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images_c_equalize = np.append(images_c_equalize,
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image.asnumpy(),
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axis=0)
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# Equalize images in python
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.map(operations=[C.Decode(), C.Resize((224, 224))], input_columns=["image"])
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transforms_p_equalize = mindspore.dataset.transforms.py_transforms.Compose([lambda img: img.astype(np.uint8),
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F.ToPIL(),
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F.Equalize(),
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np.array])
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ds_p_equalize = data_set.map(operations=transforms_p_equalize, input_columns="image")
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ds_p_equalize = ds_p_equalize.batch(512)
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for idx, (image, _) in enumerate(ds_p_equalize):
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if idx == 0:
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images_p_equalize = image.asnumpy()
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else:
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images_p_equalize = np.append(images_p_equalize,
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image.asnumpy(),
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axis=0)
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num_samples = images_c_equalize.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_p_equalize[i], images_c_equalize[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize_list(images_c_equalize, images_p_equalize, visualize_mode=2)
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def test_equalize_one_channel():
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"""
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Test Equalize cpp op with one channel image
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"""
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logger.info("Test Equalize C Op With One Channel Images")
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c_op = C.Equalize()
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try:
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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data_set = data_set.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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data_set.map(operations=c_op, input_columns="image")
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "The shape" in str(e)
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def test_equalize_mnist_c(plot=False):
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"""
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Test Equalize C op with MNIST dataset (Grayscale images)
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"""
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logger.info("Test Equalize C Op With MNIST Images")
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data_set = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR, num_samples=2, shuffle=False)
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ds_equalize_c = data_set.map(operations=C.Equalize(), input_columns="image")
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ds_orig = ds.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_equalize_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 = "equalize_mnist_result_c.npz"
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save_and_check_md5(ds_equalize_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_equalize_md5_py():
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"""
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Test Equalize py op with md5 check
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"""
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logger.info("Test Equalize")
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# First dataset
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data1 = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms = mindspore.dataset.transforms.py_transforms.Compose([F.Decode(),
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F.Equalize(),
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F.ToTensor()])
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data1 = data1.map(operations=transforms, input_columns="image")
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# Compare with expected md5 from images
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filename = "equalize_01_result.npz"
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save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
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def test_equalize_md5_c():
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"""
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Test Equalize cpp op with md5 check
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"""
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logger.info("Test Equalize cpp op with md5 check")
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# Generate dataset
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data_set = ds.ImageFolderDataset(dataset_dir=DATA_DIR, shuffle=False)
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transforms_equalize = [C.Decode(),
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C.Resize(size=[224, 224]),
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C.Equalize(),
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F.ToTensor()]
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data = data_set.map(operations=transforms_equalize, input_columns="image")
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# Compare with expected md5 from images
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filename = "equalize_01_result_c.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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if __name__ == "__main__":
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test_equalize_py(plot=False)
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test_equalize_c(plot=False)
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test_equalize_py_c(plot=False)
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test_equalize_mnist_c(plot=True)
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test_equalize_one_channel()
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test_equalize_md5_py()
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test_equalize_md5_c()
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