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62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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import torch
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from easycv.models.builder import build_model
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_base_model_cfg = dict(
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type='BYOL',
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pretrained=None,
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base_momentum=0.996,
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backbone=dict(
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type='ResNet',
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depth=50,
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in_channels=3,
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out_indices=[4], # 0: conv-1, x: stage-x
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norm_cfg=dict(type='BN')),
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neck=dict(
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type='NonLinearNeckV2',
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in_channels=2048,
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hid_channels=4096,
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out_channels=256,
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with_avg_pool=True),
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head=dict(
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type='LatentPredictHead',
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size_average=True,
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predictor=dict(
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type='NonLinearNeckV2',
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in_channels=256,
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hid_channels=4096,
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out_channels=256,
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with_avg_pool=False)))
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class BYOLTest(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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def test_byol_train(self):
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model = build_model(_base_model_cfg)
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model.train()
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model.init_weights()
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batch_size = 3
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imgs = [torch.randn(batch_size, 3, 640, 640)] * 2
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output = model(imgs, mode='train')
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self.assertIn('loss', output)
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def test_byol_extract(self):
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model = build_model(_base_model_cfg)
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batch_size = 3
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imgs = torch.randn(batch_size, 3, 640, 640)
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output = model(imgs, mode='extract')
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self.assertEqual(len(output), 1)
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self.assertEqual(output[0].shape, torch.Size([3, 2048, 20, 20]))
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if __name__ == '__main__':
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unittest.main()
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