mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
100 lines
2.6 KiB
Python
100 lines
2.6 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 mmselfsup.models.algorithms import CAE
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from mmengine.data import InstanceData
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from mmselfsup.core.data_structures.selfsup_data_sample import \
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SelfSupDataSample
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from mmselfsup.models.algorithms.cae import CAE
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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(type='CAEHead', tokenizer_path='cae_ckpt/encoder_stat_dict.pth')
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loss = dict(type='CAELoss', lambd=2)
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preprocess_cfg = {
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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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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_cae():
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with pytest.raises(AssertionError):
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model = CAE(
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backbone=None,
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neck=neck,
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head=head,
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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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with pytest.raises(AssertionError):
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model = CAE(
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backbone=backbone,
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neck=None,
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head=head,
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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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with pytest.raises(AssertionError):
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model = CAE(
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backbone=backbone,
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neck=neck,
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head=None,
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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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with pytest.raises(AssertionError):
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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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loss=None,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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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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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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# model.init_weights()
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fake_img = torch.rand((3, 224, 224))
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fake_target_img = torch.rand((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 = [{
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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_loss = model(fake_data, return_loss=True)
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# test forward_train
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assert isinstance(fake_loss['loss'].item(), float)
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# test extract_feat
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fake_inputs, fake_data_samples = model.preprocss_data(fake_data)
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fake_feat = model.extract_feat(fake_inputs, fake_data_samples)
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assert list(fake_feat.shape) == [1, 122, 768]
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