41 lines
1.3 KiB
Python
41 lines
1.3 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 MAE
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backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
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neck = dict(
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type='MAEPretrainDecoder',
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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decoder_embed_dim=512,
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decoder_depth=8,
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decoder_num_heads=16,
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mlp_ratio=4.,
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)
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head = dict(type='MAEPretrainHead', norm_pix=False, patch_size=16)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_mae():
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with pytest.raises(AssertionError):
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alg = MAE(backbone=backbone, neck=None, head=head)
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with pytest.raises(AssertionError):
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alg = MAE(backbone=backbone, neck=neck, head=None)
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with pytest.raises(AssertionError):
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alg = MAE(backbone=None, neck=neck, head=head)
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alg = MAE(backbone=backbone, neck=neck, head=head)
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fake_input = torch.randn((2, 3, 224, 224))
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fake_loss = alg.forward_train(fake_input)
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fake_feature = alg.extract_feat(fake_input)
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mask, pred = alg.forward_test(fake_input)
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assert isinstance(fake_loss['loss'].item(), float)
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assert list(fake_feature[0].shape) == [2, 50, 768]
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assert list(mask.shape) == [2, 224, 224, 3]
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assert list(pred.shape) == [2, 224, 224, 3]
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