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108 lines
3.5 KiB
108 lines
3.5 KiB
5 years ago
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# 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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import numpy as np
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import matplotlib.pyplot as plt
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from mindspore import log as logger
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import mindspore.dataset.engine as de
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import mindspore.dataset.transforms.vision.py_transforms as F
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DATA_DIR = "../data/dataset/testImageNetData/train/"
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def visualize(image_original, image_ua):
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"""
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visualizes the image using DE op and Numpy op
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"""
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num = len(image_ua)
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for i in range(num):
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plt.subplot(2, num, i + 1)
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plt.imshow(image_original[i])
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plt.title("Original image")
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plt.subplot(2, num, i + num + 1)
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plt.imshow(image_ua[i])
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plt.title("DE UniformAugment image")
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plt.show()
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def test_uniform_augment(plot=False, num_ops=2):
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"""
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Test UniformAugment
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"""
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logger.info("Test UniformAugment")
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# Original Images
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ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
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transforms_original = F.ComposeOp([F.Decode(),
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F.Resize((224,224)),
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F.ToTensor()])
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ds_original = ds.map(input_columns="image",
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operations=transforms_original())
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ds_original = ds_original.batch(512)
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for idx, (image,label) in enumerate(ds_original):
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if idx == 0:
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images_original = np.transpose(image, (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, (0, 2,3,1)),
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axis=0)
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# UniformAugment Images
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ds = de.ImageFolderDatasetV2(dataset_dir=DATA_DIR, shuffle=False)
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transform_list = [F.RandomRotation(45),
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F.RandomColor(),
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F.RandomSharpness(),
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F.Invert(),
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F.AutoContrast(),
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F.Equalize()]
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transforms_ua = F.ComposeOp([F.Decode(),
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F.Resize((224,224)),
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F.UniformAugment(transforms=transform_list, num_ops=num_ops),
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F.ToTensor()])
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ds_ua = ds.map(input_columns="image",
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operations=transforms_ua())
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ds_ua = ds_ua.batch(512)
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for idx, (image,label) in enumerate(ds_ua):
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if idx == 0:
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images_ua = np.transpose(image, (0, 2,3,1))
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else:
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images_ua = np.append(images_ua,
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np.transpose(image, (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] = np.mean((images_ua[i]-images_original[i])**2)
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logger.info("MSE= {}".format(str(np.mean(mse))))
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if plot:
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visualize(images_original, images_ua)
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if __name__ == "__main__":
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test_uniform_augment(num_ops=1)
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