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fc69e38009
* [Feature] Add MoCo v3 (#194) * [Feature] add position embedding function * [Fature] modify nonlinear neck for vit backbone * [Feature] add mocov3 head * [Feature] modify cls_head for vit backbone * [Feature] add ViT backbone * [Feature] add mocov3 algorithm * [Docs] revise BYOL hook docstring * [Feature] add mocov3 vit small config files * [Feature] add mocov3 vit small linear eval config files * [Fix] solve conflict * [Fix] add mmcls * [Fix] fix docstring format * [Fix] fix isort * [Fix] add mmcls to runtime requirements * [Feature] remove duplicated codes * [Feature] add mocov3 related unit test * [Feature] revise position embedding function * [Feature] add UT codes * [Docs] add README.md * [Docs] add model links and results to model zoo * [Docs] fix model links * [Docs] add metafile * [Docs] modify install.md and add mmcls requirements * [Docs] modify description * [Fix] using specific arch name `mocov3-small` rather than general arch name `small` * [Fix] add mmcls * [Fix] fix arch name * [Feature] change name to `MoCoV3` * [Fix] fix unit test bug * [Feature] change `BYOLHook` name to `MomentumUpdateHook` * [Feature] change name to MoCoV3 * [Docs] modify description Co-authored-by: fangyixiao18 <fangyx18@hotmail.com> Co-authored-by: Yixiao Fang <36138628+fangyixiao18@users.noreply.github.com> * [Docs] update model zoo results (#195) * Bump version to v0.6.0 (#198) * [Docs] update model zoo results * Bump version to v0.6.0 Co-authored-by: fangyixiao18 <fangyx18@hotmail.com> Co-authored-by: Yixiao Fang <36138628+fangyixiao18@users.noreply.github.com>
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
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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def test_classification():
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# test ResNet
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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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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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# test ViT
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backbone = dict(
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type='VisionTransformer',
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arch='mocov3-small', # embed_dim = 384
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img_size=224,
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patch_size=16,
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stop_grad_conv1=True)
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head = dict(
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type='ClsHead',
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in_channels=384,
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num_classes=1000,
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vit_backbone=True,
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)
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alg = Classification(backbone=backbone, head=head)
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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_out = alg.forward_train(fake_input, fake_labels)
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assert fake_out['loss'].item() > 0
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