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https://github.com/open-mmlab/mmclassification.git
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e453a45d31
* add heads * add losses * fix * remove mim head * add modified backbones and target generators * add unittest * refactor caevit * add window_size check * fix lint * apply new DataSample * fix ut error * update ut * fix ut * fix lint * Update base modules. --------- Co-authored-by: mzr1996 <mzr1996@163.com>
29 lines
695 B
Python
29 lines
695 B
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 mmpretrain.models import BEiTPretrainViT
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backbone = dict(
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arch='base',
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patch_size=16,
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drop_path_rate=0.1,
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final_norm=True,
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layer_scale_init_value=0.1,
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)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_beit_pretrain_vit():
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beit_backbone = BEiTPretrainViT(**backbone)
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beit_backbone.init_weights()
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fake_inputs = torch.randn((2, 3, 224, 224))
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fake_mask = torch.zeros((2, 196))
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fake_mask[:, 75:150] = 1
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fake_outputs = beit_backbone(fake_inputs, fake_mask)
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assert list(fake_outputs[0].shape) == [2, 197, 768]
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