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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>
52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import platform
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from unittest.mock import MagicMock
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import pytest
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import torch
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from mmpretrain.models import EVA
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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_eva():
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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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backbone = dict(type='MAEViT', arch='b', patch_size=16, mask_ratio=0.75)
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neck = dict(
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type='MAEPretrainDecoder',
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patch_size=16,
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in_chans=3,
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embed_dim=768,
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decoder_embed_dim=512,
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decoder_depth=8,
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decoder_num_heads=16,
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predict_feature_dim=512,
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mlp_ratio=4.)
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head = dict(
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type='MIMHead',
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loss=dict(
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type='CosineSimilarityLoss', shift_factor=1.0, scale_factor=1.0))
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alg = EVA(
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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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target_generator = MagicMock(
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return_value=(torch.ones(2, 197, 512), torch.ones(2, 197, 197)))
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alg.target_generator = target_generator
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fake_data = {
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'inputs': 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_outputs = alg(**fake_inputs, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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