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>
81 lines
2.4 KiB
Python
81 lines
2.4 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 CAE, CAEPretrainViT
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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_cae_vit():
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backbone = dict(
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arch='deit-tiny', patch_size=16, layer_scale_init_value=0.1)
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cae_backbone = CAEPretrainViT(**backbone)
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cae_backbone.init_weights()
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fake_inputs = torch.randn((1, 3, 224, 224))
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fake_mask = torch.zeros((1, 196)).bool()
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fake_mask[:, 75:150] = 1
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# test with mask
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fake_outputs = cae_backbone(fake_inputs, fake_mask)
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assert list(fake_outputs.shape) == [1, 122, 192]
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# test without mask
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fake_outputs = cae_backbone(fake_inputs, None)
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assert len(fake_outputs[0]) == 2
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assert fake_outputs[0][0].shape == torch.Size([1, 192, 14, 14])
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assert fake_outputs[0][1].shape == torch.Size([1, 192])
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_cae():
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data_preprocessor = dict(
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type='TwoNormDataPreprocessor',
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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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second_mean=[-31.875, -31.875, -31.875],
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second_std=[318.75, 318.75, 318.75],
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to_rgb=True)
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# model settings
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backbone = dict(
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type='CAEPretrainViT',
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arch='deit-tiny',
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patch_size=16,
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layer_scale_init_value=0.1)
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neck = dict(
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type='CAENeck',
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embed_dims=192,
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num_heads=12,
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regressor_depth=4,
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decoder_depth=4,
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mlp_ratio=4,
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layer_scale_init_value=0.1)
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head = dict(type='CAEHead', loss=dict(type='CAELoss', lambd=2))
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target_generator = dict(type='DALL-E')
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model = CAE(
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backbone=backbone,
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neck=neck,
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head=head,
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target_generator=target_generator,
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data_preprocessor=data_preprocessor)
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fake_img = torch.rand((1, 3, 224, 224))
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fake_target_img = torch.rand((1, 3, 112, 112))
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fake_mask = torch.zeros((196)).bool()
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fake_mask[75:150] = 1
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fake_data_sample = DataSample()
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fake_data_sample.set_mask(fake_mask)
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fake_data = {
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'inputs': [fake_img, fake_target_img],
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'data_samples': [fake_data_sample]
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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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