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37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from mmselfsup.models.algorithms import SimCLR
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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='NonLinearNeck', # SimCLR non-linear neck
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in_channels=2048,
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hid_channels=4,
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out_channels=4,
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num_layers=2,
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with_avg_pool=True)
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head = dict(type='ContrastiveHead', temperature=0.1)
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def test_simclr():
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with pytest.raises(AssertionError):
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alg = SimCLR(backbone=backbone, neck=None, head=head)
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with pytest.raises(AssertionError):
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alg = SimCLR(backbone=backbone, neck=neck, head=None)
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alg = SimCLR(backbone=backbone, neck=neck, head=head)
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with pytest.raises(AssertionError):
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fake_input = torch.randn((16, 3, 224, 224))
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alg.forward_train(fake_input)
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fake_input = torch.randn((16, 3, 224, 224))
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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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