mirror of
https://github.com/open-mmlab/mmselfsup.git
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48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
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# 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 DenseCL
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queue_len = 65536
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feat_dim = 128
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momentum = 0.999
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loss_lambda = 0.5
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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='DenseCLNeck',
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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_grid=None)
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head = dict(type='ContrastiveHead', temperature=0.2)
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def test_densecl():
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with pytest.raises(AssertionError):
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alg = DenseCL(backbone=backbone, neck=None, head=head)
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with pytest.raises(AssertionError):
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alg = DenseCL(backbone=backbone, neck=neck, head=None)
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alg = DenseCL(
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backbone=backbone,
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neck=neck,
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head=head,
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queue_len=queue_len,
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feat_dim=feat_dim,
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momentum=momentum,
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loss_lambda=loss_lambda)
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assert alg.queue.size() == torch.Size([feat_dim, queue_len])
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assert alg.queue2.size() == torch.Size([feat_dim, queue_len])
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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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with pytest.raises(AssertionError):
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fake_backbone_out = alg.forward_train(fake_input)
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