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https://github.com/open-mmlab/mmselfsup.git
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20488d01b4
* [Refactor]: Refactor data flow * [Fix]: Change data sample to data samples * [Fix]: Change batch_inputs to inputs * [Fix]: Fix lint and UT * [Fix]: Fix UT * [Fix]: Fix lint * [Fix]: Fix docstring * [Fix]: Fix UT * [Refactor]: Add assert in data preprocessor * [Fix]: Fix lint
64 lines
1.7 KiB
Python
64 lines
1.7 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.structures import InstanceData
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from mmselfsup.models.algorithms import CAE
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from mmselfsup.structures import SelfSupDataSample
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from mmselfsup.utils import register_all_modules
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register_all_modules()
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# model settings
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backbone = dict(type='CAEViT', arch='b', patch_size=16, init_values=0.1)
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neck = dict(
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type='CAENeck',
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patch_size=16,
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embed_dims=768,
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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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init_values=0.1,
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)
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head = dict(
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type='CAEHead',
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tokenizer_path='cae_ckpt/encoder_stat_dict.pth',
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loss=dict(type='CAELoss', lambd=2))
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data_preprocessor = dict(
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type='mmselfsup.CAEDataPreprocessor',
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mean=[124, 117, 104],
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std=[59, 58, 58],
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bgr_to_rgb=True)
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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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model = CAE(
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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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# model.init_weights()
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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 = SelfSupDataSample()
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fake_mask = InstanceData(value=fake_mask)
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fake_data_sample.mask = fake_mask
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fake_data_sample = [fake_data_sample]
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fake_data = {
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'inputs': [fake_img, fake_target_img],
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'data_sample': fake_data_sample
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}
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fake_batch_inputs, fake_data_samples = model.data_preprocessor(fake_data)
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fake_outputs = model(fake_batch_inputs, fake_data_samples, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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