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https://github.com/open-mmlab/mmselfsup.git
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41747f73c7
* [Refactor]: refactor MAE visualization * [Fix]: fix lint * [Refactor]: refactor MAE visualization * [Feature]: add mae_visualization.py * [UT]: add unit test * [Refactor]: move mae_visualization.py to tools/analysis_tools * [Docs]: Add the purpose of the function unpatchify() * [Fix]: fix lint
57 lines
1.7 KiB
Python
57 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 mmselfsup.models.algorithms.mae import MAE
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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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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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mlp_ratio=4.,
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)
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loss = dict(type='MAEReconstructionLoss')
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head = dict(type='MAEPretrainHead', norm_pix=False, patch_size=16, loss=loss)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_mae():
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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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'bgr_to_rgb': True
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}
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alg = MAE(
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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': [torch.randn((2, 3, 224, 224))],
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'data_sample': [SelfSupDataSample() for _ in range(2)]
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}
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fake_batch_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
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fake_outputs = alg(fake_batch_inputs, fake_data_samples, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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fake_feats = alg(fake_batch_inputs, fake_data_samples, mode='tensor')
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assert list(fake_feats.shape) == [2, 196, 768]
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results = alg(fake_batch_inputs, fake_data_samples, mode='predict')
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assert list(results.mask.value.shape) == [2, 224, 224, 3]
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assert list(results.pred.value.shape) == [2, 224, 224, 3]
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