forked from mindspore-Ecosystem/mindspore
130 lines
5.0 KiB
Python
130 lines
5.0 KiB
Python
# Copyright 2021 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 RandomEqualize 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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from mindspore.dataset.vision.transforms import Decode, Resize, RandomEqualize, Equalize
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from mindspore import log as logger
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from util import visualize_list, visualize_image, diff_mse
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image_file = "../data/dataset/testImageNetData/train/class1/1_1.jpg"
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data_dir = "../data/dataset/testImageNetData/train/"
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def test_random_equalize_pipeline(plot=False):
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"""
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Test RandomEqualize pipeline
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"""
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logger.info("Test RandomEqualize pipeline")
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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 = [Decode(), 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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# Randomly Equalized Images
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data_set1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
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transform_random_equalize = [Decode(), Resize(size=[224, 224]), RandomEqualize(0.6)]
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ds_random_equalize = data_set1.map(operations=transform_random_equalize, input_columns="image")
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ds_random_equalize = ds_random_equalize.batch(512)
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for idx, (image, _) in enumerate(ds_random_equalize):
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if idx == 0:
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images_random_equalize = image.asnumpy()
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else:
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images_random_equalize = np.append(images_random_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_random_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_random_equalize[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_random_equalize_eager():
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"""
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Test RandomEqualize eager.
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"""
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img = np.fromfile(image_file, dtype=np.uint8)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))
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img = Decode()(img)
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img_equalized = Equalize()(img)
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img_random_equalized = RandomEqualize(1.0)(img)
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logger.info("Image.type: {}, Image.shape: {}".format(type(img_random_equalized), img_random_equalized.shape))
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assert img_random_equalized.all() == img_equalized.all()
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def test_random_equalize_comp(plot=False):
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"""
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Test RandomEqualize op compared with Equalize op.
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"""
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random_equalize_op = RandomEqualize(prob=1.0)
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equalize_op = Equalize()
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dataset1 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
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for item in dataset1.create_dict_iterator(num_epochs=1, output_numpy=True):
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image = item['image']
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dataset1.map(operations=random_equalize_op, input_columns=['image'])
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dataset2 = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
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dataset2.map(operations=equalize_op, input_columns=['image'])
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for item1, item2 in zip(dataset1.create_dict_iterator(num_epochs=1, output_numpy=True),
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dataset2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image_random_equalized = item1['image']
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image_equalized = item2['image']
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mse = diff_mse(image_equalized, image_random_equalized)
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assert mse == 0
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logger.info("mse: {}".format(mse))
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if plot:
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visualize_image(image, image_random_equalized, mse, image_equalized)
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def test_random_equalize_invalid_prob():
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"""
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Test eager. prob out of range.
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"""
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logger.info("test_random_equalize_invalid_prob")
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dataset = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
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try:
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random_equalize_op = RandomEqualize(1.5)
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dataset = dataset.map(operations=random_equalize_op, input_columns=['image'])
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Input prob is not within the required interval of [0.0, 1.0]." in str(e)
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if __name__ == "__main__":
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test_random_equalize_pipeline(plot=True)
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test_random_equalize_eager()
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test_random_equalize_comp(plot=True)
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test_random_equalize_invalid_prob()
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