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https://github.com/open-mmlab/mmselfsup.git
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7a7b048f23
* [Feature]: Add BEiT Support * [Fix]: fix bugs after update * [Fix]: fix bugs in backbone * [Refactor]: refactor config * [Feature]: Support BEiTv2 * [Fix]: Fix UT * [Fix]: rename some configs * [Fix]: fix beitv2neck * [Refactor]: refactor beitv2 * [Fix]: fix lint * refactor configs * refactor beitv2 * update configs * add dalle target generator * refactor for beitv1 * refactor rel_pos_bias of beit * update configs * update configs * update v1 configs * update v2 configs * refactoe layer decay * update unittest * fix lint * fix ut * add docstrings * rename * fix lint * add beit model and log links * fix lint * update according to review * update * update * update LearningRateDecayOptimWrapperConstructor related configs * update init and backbone * update neck and vqkd * refactor neck * fix lint * add some comments * fix typo Co-authored-by: 任琴 <PJLAB\renqin@shai14001114l.pjlab.org> Co-authored-by: fangyixiao18 <fangyx18@hotmail.com>
66 lines
1.7 KiB
Python
66 lines
1.7 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 mmengine.structures import InstanceData
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from mmselfsup.models import BEiT
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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data_preprocessor = dict(
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type='TwoNormDataPreprocessor',
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mean=(123.675, 116.28, 103.53),
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std=(58.395, 57.12, 57.375),
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second_mean=(-20.4, -20.4, -20.4),
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second_std=(204., 204., 204.),
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bgr_to_rgb=True)
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# model settings
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backbone = dict(
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type='BEiTViT',
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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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neck = None
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head = dict(
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type='BEiTV1Head',
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embed_dims=768,
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num_embed=8192,
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loss=dict(type='BEiTLoss'))
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target_generator = dict(type='DALL-E')
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_beitv1():
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register_all_modules()
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model = BEiT(
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backbone=backbone,
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neck=neck,
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head=head,
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target_generator=target_generator,
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data_preprocessor=data_preprocessor)
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fake_img = torch.rand((1, 3, 224, 224))
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fake_target_img = torch.rand((1, 3, 112, 112))
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fake_mask = torch.zeros((196)).bool()
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fake_mask[75:150] = 1
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fake_data_sample = SelfSupDataSample()
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fake_mask = InstanceData(value=fake_mask)
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fake_data_sample.mask = fake_mask
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fake_data_sample = [fake_data_sample]
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fake_data = {
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'inputs': [fake_img, fake_target_img],
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'data_sample': fake_data_sample
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}
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fake_batch_inputs, fake_data_samples = model.data_preprocessor(fake_data)
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fake_outputs = model(fake_batch_inputs, fake_data_samples, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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