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48 lines
1.5 KiB
Python
48 lines
1.5 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.data import BaseDataElement as PixelData
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from mmselfsup.core import SelfSupDataSample
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from mmselfsup.models.algorithms import SimMIM
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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(type='SimMIMHead', patch_size=4, encoder_in_channels=3),
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preprocess_cfg={
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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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'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 = PixelData(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((3, 192, 192))],
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'data_sample': fake_data_sample
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} for _ in range(2)]
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outputs = model(fake_data, return_loss=True)
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assert isinstance(outputs['loss'], torch.Tensor)
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# test extract_feat
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fake_inputs, fake_data_samples = model.preprocss_data(fake_data)
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fake_feat = model.extract_feat(
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inputs=fake_inputs, data_samples=fake_data_samples)
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assert list(fake_feat[0].shape) == [2, 1024, 6, 6]
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