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91 lines
2.8 KiB
91 lines
2.8 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import os
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import numpy as np
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from paddle.vision.datasets import voc2012, VOC2012
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# VOC2012 is too large for unittest to download, stub a small dataset here
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voc2012.VOC_URL = 'https://paddlemodels.bj.bcebos.com/voc2012_stub/VOCtrainval_11-May-2012.tar'
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voc2012.VOC_MD5 = '34cb1fe5bdc139a5454b25b16118fff8'
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class TestVOC2012Train(unittest.TestCase):
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def test_main(self):
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voc2012 = VOC2012(mode='train')
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self.assertTrue(len(voc2012) == 3)
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# traversal whole dataset may cost a
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# long time, randomly check 1 sample
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idx = np.random.randint(0, 3)
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image, label = voc2012[idx]
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image = np.array(image)
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label = np.array(label)
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self.assertTrue(len(image.shape) == 3)
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self.assertTrue(len(label.shape) == 2)
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class TestVOC2012Valid(unittest.TestCase):
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def test_main(self):
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voc2012 = VOC2012(mode='valid')
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self.assertTrue(len(voc2012) == 1)
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# traversal whole dataset may cost a
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# long time, randomly check 1 sample
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idx = np.random.randint(0, 1)
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image, label = voc2012[idx]
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image = np.array(image)
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label = np.array(label)
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self.assertTrue(len(image.shape) == 3)
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self.assertTrue(len(label.shape) == 2)
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class TestVOC2012Test(unittest.TestCase):
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def test_main(self):
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voc2012 = VOC2012(mode='test')
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self.assertTrue(len(voc2012) == 2)
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# traversal whole dataset may cost a
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# long time, randomly check 1 sample
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idx = np.random.randint(0, 1)
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image, label = voc2012[idx]
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image = np.array(image)
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label = np.array(label)
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self.assertTrue(len(image.shape) == 3)
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self.assertTrue(len(label.shape) == 2)
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# test cv2 backend
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voc2012 = VOC2012(mode='test', backend='cv2')
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self.assertTrue(len(voc2012) == 2)
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# traversal whole dataset may cost a
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# long time, randomly check 1 sample
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idx = np.random.randint(0, 1)
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image, label = voc2012[idx]
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self.assertTrue(len(image.shape) == 3)
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self.assertTrue(len(label.shape) == 2)
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with self.assertRaises(ValueError):
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voc2012 = VOC2012(mode='test', backend=1)
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if __name__ == '__main__':
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unittest.main()
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