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* [Fix]: Set qkv bias to False for cae and True for mae (#303) * [Fix]: Add mmcls transformer layer choice * [Fix]: Fix transformer encoder layer bug * [Fix]: Change UT of cae * [Feature]: Change the file name of cosine annealing hook (#304) * [Feature]: Change cosine annealing hook file name * [Feature]: Add UT for cosine annealing hook * [Fix]: Fix lint * read tutorials and fix typo (#308) * [Fix] fix config errors in MAE (#307) * update readthedocs algorithm readme (#310) * [Docs] Replace markdownlint with mdformat (#311) * Replace markdownlint with mdformat to avoid installing ruby * fix typo * add 'ba' to codespell ignore-words-list * Configure Myst-parser to parse anchor tag (#309) * [Docs] rewrite install.md (#317) * rewrite the install.md * add faq.md * fix lint * add FAQ to README * add Chinese version * fix typo * fix format * remove modification * fix format * [Docs] refine README.md file (#318) * refine README.md file * fix lint * format language button * rename getting_started.md * revise index.rst * add model_zoo.md to index.rst * fix lint * refine readme Co-authored-by: Jiahao Xie <52497952+Jiahao000@users.noreply.github.com> * [Enhance] update byol models and results (#319) * Update version information (#321) Co-authored-by: Yuan Liu <30762564+YuanLiuuuuuu@users.noreply.github.com> Co-authored-by: Yi Lu <21515006@zju.edu.cn> Co-authored-by: RenQin <45731309+soonera@users.noreply.github.com> Co-authored-by: Jiahao Xie <52497952+Jiahao000@users.noreply.github.com>
49 lines
1.4 KiB
Python
49 lines
1.4 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 CAE
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# model settings
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backbone = dict(
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type='CAEViT', arch='b', patch_size=16, init_values=0.1, qkv_bias=False)
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neck = dict(
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type='CAENeck',
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patch_size=16,
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embed_dims=768,
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num_heads=12,
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regressor_depth=4,
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decoder_depth=4,
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mlp_ratio=4,
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init_values=0.1,
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)
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head = dict(
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type='CAEHead', tokenizer_path='cae_ckpt/encoder_stat_dict.pth', lambd=2)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_cae():
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with pytest.raises(AssertionError):
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model = CAE(backbone=None, neck=neck, head=head)
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with pytest.raises(AssertionError):
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model = CAE(backbone=backbone, neck=None, head=head)
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with pytest.raises(AssertionError):
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model = CAE(backbone=backbone, neck=neck, head=None)
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model = CAE(backbone=backbone, neck=neck, head=head)
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model.init_weights()
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fake_input = torch.rand((1, 3, 224, 224))
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fake_target = torch.rand((1, 3, 112, 112))
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fake_mask = torch.zeros((1, 196)).bool()
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fake_mask[:, 75:150] = 1
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inputs = (fake_input, fake_target, fake_mask)
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fake_loss = model.forward_train(inputs)
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fake_feat = model.extract_feat(fake_input, fake_mask)
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assert isinstance(fake_loss['loss'].item(), float)
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assert list(fake_feat.shape) == [1, 122, 768]
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