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https://github.com/open-mmlab/mmselfsup.git
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e761acd1bd
* [Feature]: Add MaskfeatMaskGenerator Pipeline * [Feature]: Add MaskFeatMaskGenerator Pipeline * [Feature]: Add Backbone of MaskFeat * [Feature]: Add HogLayerC for MaskFeat * [Feature]: Add Loss of MaskFeat * [Feature]: Add Head of MaskFeat * [Feature]: Add Algorithms of MaskFeat * [Feature]: Add Config of MaskFeat * [Fix] fix ut of MaskFeatMaskGenerator * refine configs * update * refactor to support hog generator * update config * update * update config and metafiel * update maskfeat model link * fix ut * refine codes * fix lint * refine docstring * refactor maskfeat head * update model links * fix ut * refine docstring * update model-index * using BEiTMaskGenerator * refine configs * update ut * fix lint Co-authored-by: fangyixiao18 <fangyx18@hotmail.com>
55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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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.algorithms.maskfeat import MaskFeat
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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backbone = dict(type='MaskFeatViT', arch='b', patch_size=16)
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neck = dict(
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type='LinearNeck', in_channels=768, out_channels=108, with_avg_pool=False)
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head = dict(
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type='MaskFeatPretrainHead',
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loss=dict(type='PixelReconstructionLoss', criterion='L2'))
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target_generator = dict(
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type='HOGGenerator', nbins=9, pool=8, gaussian_window=16)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_maskfeat():
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data_preprocessor = {
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'mean': [0.5, 0.5, 0.5],
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'std': [0.5, 0.5, 0.5],
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'bgr_to_rgb': True
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}
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alg = MaskFeat(
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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=copy.deepcopy(data_preprocessor))
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# test forward_train
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fake_data_sample = SelfSupDataSample()
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fake_mask = InstanceData(value=torch.rand((14, 14)).bool())
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fake_data_sample.mask = fake_mask
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fake_data = {
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'inputs': [torch.randn((2, 3, 224, 224))],
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'data_sample': [fake_data_sample for _ in range(2)]
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}
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fake_batch_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
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fake_outputs = alg(fake_batch_inputs, fake_data_samples, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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fake_feats = alg.extract_feat(fake_batch_inputs, fake_data_samples)
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assert list(fake_feats.shape) == [2, 197, 768]
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