mirror of
https://github.com/open-mmlab/mmpretrain.git
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08dc8c75d3
* remove basehead * add moco series * add byol simclr simsiam * add ut * update configs * add simsiam hook * add and refactor beit * update ut * add cae * update extract_feat * refactor cae * add mae * refactor data preprocessor * update heads * add maskfeat * add milan * add simmim * add mixmim * fix lint * fix ut * fix lint * add eva * add densecl * add barlowtwins * add swav * fix lint * update readtherdocs rst * update docs * update * Decrease UT memory usage * Fix docstring * update DALLEEncoder * Update model docs * refactor dalle encoder * update docstring * fix ut * fix config error * add val_cfg and test_cfg * refactor clip generator * fix lint * pass check * fix ut * add lars * update type of BEiT in configs * Use MMEngine style momentum in EMA. * apply mmpretrain solarize --------- Co-authored-by: mzr1996 <mzr1996@163.com>
71 lines
2.1 KiB
Python
71 lines
2.1 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 mmpretrain.models import SimMIM, SimMIMSwinTransformer
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from mmpretrain.structures import DataSample
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_simmim_swin():
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backbone = dict(
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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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simmim_backbone = SimMIMSwinTransformer(**backbone)
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simmim_backbone.init_weights()
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fake_inputs = torch.randn((2, 3, 192, 192))
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fake_mask = torch.rand((2, 48, 48))
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# test with mask
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fake_outputs = simmim_backbone(fake_inputs, fake_mask)[0]
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assert fake_outputs.shape == torch.Size([2, 1024, 6, 6])
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# test without mask
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fake_outputs = simmim_backbone(fake_inputs, None)
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assert len(fake_outputs) == 1
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assert fake_outputs[0].shape == torch.Size([2, 1024, 6, 6])
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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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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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'to_rgb': True
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}
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# model config
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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='SimMIMLinearDecoder', 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='PixelReconstructionLoss', criterion='L1', channel=3))
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model = SimMIM(
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backbone=backbone,
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neck=neck,
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head=head,
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data_preprocessor=data_preprocessor)
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# test forward_train
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fake_data_sample = DataSample()
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fake_mask = torch.rand((48, 48))
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fake_data_sample.set_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_samples': [fake_data_sample for _ in range(2)]
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}
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fake_inputs = model.data_preprocessor(fake_data)
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fake_outputs = model(**fake_inputs, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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