54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import platform
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import pytest
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import torch
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from mmselfsup.models.algorithms import BYOL
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backbone = dict(
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type='ResNet',
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depth=18,
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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',
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in_channels=512,
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hid_channels=2,
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out_channels=2,
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with_bias=True,
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with_last_bn=False,
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with_avg_pool=True,
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norm_cfg=dict(type='BN1d'))
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head = dict(
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type='LatentPredictHead',
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predictor=dict(
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type='NonLinearNeck',
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in_channels=2,
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hid_channels=2,
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out_channels=2,
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with_bias=True,
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with_last_bn=False,
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with_avg_pool=False,
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norm_cfg=dict(type='BN1d')))
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_byol():
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with pytest.raises(AssertionError):
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alg = BYOL(backbone=backbone, neck=None, head=head)
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with pytest.raises(AssertionError):
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alg = BYOL(backbone=backbone, neck=neck, head=None)
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alg = BYOL(backbone=backbone, neck=neck, head=head)
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fake_input = torch.randn((2, 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([2, 512, 7, 7])
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with pytest.raises(AssertionError):
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fake_out = alg.forward_train(fake_input)
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fake_input = [torch.randn((2, 3, 224, 224)), torch.randn((2, 3, 224, 224))]
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fake_out = alg.forward_train(fake_input)
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assert fake_out['loss'].item() > -4
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