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1.Add a backbone: deitiii. 2.Add an optimizer: lamb. 3.Add a sampler: RASampler. 4.Add a lr update hook: CosineAnnealingWarmupByEpochLrUpdaterHook. 5.In easycv/models/classification/classification.py, I remove the default mixup_cfg to keep the classification.py clean.
43 lines
1.5 KiB
Python
43 lines
1.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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import numpy as np
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import torch
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from numpy.testing import assert_array_almost_equal
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class DeiTIIITest(unittest.TestCase):
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def setUp(self):
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print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
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@unittest.skip('skip DeiT III unittest')
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def test_deitiii(self):
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model_path = 'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/classification/deitiii/epoch_800.pth'
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config_path = 'configs/classification/imagenet/vit/imagenet_deitiii_large_patch16_192_jpg.py'
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img = 'https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/demo/deitiii_demo.JPEG'
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# deitiii = ClsPredictor(model_path, config_path)
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deitiii = []
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output = deitiii.predict(img)
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self.assertIn('prob', output)
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self.assertIn('class', output)
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self.assertEqual(len(output['prob'][0]), 1000)
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assert_array_almost_equal(
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output['prob'][0][:10],
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torch.Tensor([
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2.04629918698628899e-06, 5.27398606209317222e-06,
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5.52915162188583054e-06, 3.60625563189387321e-06,
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3.29447357216849923e-06, 5.61309570912271738e-06,
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8.93703327164985240e-06, 4.89157764604897238e-06,
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4.39371024185675196e-06, 5.21611764270346612e-06
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]),
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decimal=8)
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self.assertEqual(int(output['class']), 948)
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if __name__ == '__main__':
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unittest.main()
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