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https://github.com/open-mmlab/mmclassification.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>
65 lines
1.7 KiB
Python
65 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 mmpretrain.models import SimSiam
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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_simsiam():
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data_preprocessor = {
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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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'to_rgb': True,
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}
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backbone = dict(
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type='ResNet',
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depth=18,
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norm_cfg=dict(type='BN'),
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zero_init_residual=True)
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neck = dict(
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type='NonLinearNeck',
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in_channels=512,
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hid_channels=2,
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out_channels=2,
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num_layers=3,
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with_last_bn_affine=False,
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with_avg_pool=True,
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norm_cfg=dict(type='BN1d'))
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head = dict(
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type='LatentPredictHead',
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loss=dict(type='CosineSimilarityLoss'),
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predictor=dict(
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type='NonLinearNeck',
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in_channels=2,
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hid_channels=2,
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out_channels=2,
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with_avg_pool=False,
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with_last_bn=False,
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with_last_bias=True,
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norm_cfg=dict(type='BN1d')))
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alg = SimSiam(
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backbone=backbone,
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neck=neck,
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head=head,
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data_preprocessor=copy.deepcopy(data_preprocessor))
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fake_data = {
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'inputs':
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[torch.randn((2, 3, 224, 224)),
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torch.randn((2, 3, 224, 224))],
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'data_samples': [DataSample() for _ in range(2)]
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}
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fake_inputs = alg.data_preprocessor(fake_data)
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fake_loss = alg(**fake_inputs, mode='loss')
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assert fake_loss['loss'] > -1
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# test extract
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fake_feat = alg(fake_inputs['inputs'][0], mode='tensor')
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assert fake_feat[0].size() == torch.Size([2, 512, 7, 7])
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