mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
89 lines
2.2 KiB
Python
89 lines
2.2 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.core import SelfSupDataSample
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from mmselfsup.models.algorithms import ODC
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num_classes = 5
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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='ODCNeck',
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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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norm_cfg=dict(type='BN1d'),
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with_avg_pool=True)
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head = dict(
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type='ClsHead',
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with_avg_pool=False,
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in_channels=2,
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num_classes=num_classes)
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loss = dict(type='CrossEntropyLoss')
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memory_bank = dict(
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type='ODCMemory',
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length=8,
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feat_dim=2,
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momentum=0.5,
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num_classes=num_classes,
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min_cluster=2,
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debug=False)
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preprocess_cfg = {
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'mean': [0.5, 0.5, 0.5],
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'std': [0.5, 0.5, 0.5],
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'to_rgb': True
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}
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@pytest.mark.skipif(
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not torch.cuda.is_available() or platform.system() == 'Windows',
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reason='CUDA is not available or Windows mem limit')
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def test_odc():
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with pytest.raises(AssertionError):
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alg = ODC(
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backbone=backbone,
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neck=neck,
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head=head,
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loss=loss,
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memory_bank=None,
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preprocess_cfg=preprocess_cfg)
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with pytest.raises(AssertionError):
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alg = ODC(
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backbone=backbone,
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neck=neck,
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head=None,
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memory_bank=memory_bank,
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preprocess_cfg=preprocess_cfg)
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with pytest.raises(AssertionError):
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alg = ODC(
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backbone=backbone,
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neck=neck,
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head=head,
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loss=loss,
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memory_bank=memory_bank,
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preprocess_cfg=preprocess_cfg)
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alg = ODC(
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backbone=backbone,
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neck=neck,
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head=head,
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loss=loss,
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memory_bank=memory_bank,
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preprocess_cfg=preprocess_cfg)
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alg.set_reweight()
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fake_data = [{
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'inputs': torch.randn((3, 224, 224)),
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'data_sample': SelfSupDataSample()
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} for _ in range(2)]
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fake_out = alg(fake_data, return_loss=False)
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assert hasattr(fake_out[0].prediction, 'head0')
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assert fake_out[0].prediction.head0.size() == torch.Size([num_classes])
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