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https://github.com/open-mmlab/mmselfsup.git
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73cd764b5f
* refactor reconstruction visualization * support simmim visualization * fix reconstruction bug of MAE * support visualization of MaskFeat * refaction mae visualization demo * add unit test * fix lint and ut * update * add docs * set random seed * update * update docstring * add torch version check * update * rename * update version * update * fix lint * add docstring * update docs
60 lines
2.0 KiB
Python
60 lines
2.0 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.algorithms import SimMIM
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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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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_simmim():
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# model config
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model_config = dict(
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backbone=dict(
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type='SimMIMSwinTransformer',
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arch='B',
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img_size=192,
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stage_cfgs=dict(block_cfgs=dict(window_size=6))),
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neck=dict(
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type='SimMIMNeck', in_channels=128 * 2**3, encoder_stride=32),
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head=dict(
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type='SimMIMHead',
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patch_size=4,
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loss=dict(type='SimMIMReconstructionLoss', encoder_in_channels=3)),
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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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model = SimMIM(**model_config)
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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((48, 48)))
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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, 192, 192))],
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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 = 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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# test extract_feat
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fake_inputs, fake_data_samples = model.data_preprocessor(fake_data)
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fake_feats = model.extract_feat(fake_inputs, fake_data_samples)
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assert list(fake_feats.shape) == [2, 3, 192, 192]
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# test reconstruct
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results = model.reconstruct(fake_feats, fake_data_samples)
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assert list(results.mask.value.shape) == [2, 192, 192, 3]
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assert list(results.pred.value.shape) == [2, 192, 192, 3]
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