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29 lines
888 B
Python
29 lines
888 B
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from mmselfsup.models.algorithms import Classification
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with_sobel = True,
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backbone = dict(
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type='ResNet',
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depth=50,
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in_channels=2,
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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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frozen_stages=4)
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head = dict(
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type='ClsHead', with_avg_pool=True, in_channels=2048, num_classes=4)
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def test_classification():
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alg = Classification(backbone=backbone, with_sobel=with_sobel, head=head)
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assert hasattr(alg, 'sobel_layer')
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assert hasattr(alg, 'head')
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fake_input = torch.randn((16, 3, 224, 224))
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fake_labels = torch.ones(16, dtype=torch.long)
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fake_backbone_out = alg.extract_feat(fake_input)
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assert fake_backbone_out[0].size() == torch.Size([16, 2048, 7, 7])
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fake_out = alg.forward_train(fake_input, fake_labels)
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assert fake_out['loss'].item() > 0
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