67 lines
1.8 KiB
Python
67 lines
1.8 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.byol import BYOL
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from mmselfsup.structures import SelfSupDataSample
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backbone = dict(
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type='ResNet',
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depth=18,
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in_channels=3,
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out_indices=[4], # 0: conv-1, x: stage-x
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norm_cfg=dict(type='BN'))
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neck = dict(
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type='NonLinearNeck',
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in_channels=512,
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hid_channels=2,
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out_channels=2,
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with_bias=True,
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with_last_bn=False,
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with_avg_pool=True,
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norm_cfg=dict(type='BN1d'))
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head = dict(
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type='LatentPredictHead',
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loss=dict(type='CosineSimilarityLoss'),
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predictor=dict(
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type='NonLinearNeck',
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in_channels=2,
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hid_channels=2,
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out_channels=2,
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with_bias=True,
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with_last_bn=False,
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with_avg_pool=False,
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norm_cfg=dict(type='BN1d')))
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_byol():
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data_preprocessor = dict(
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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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bgr_to_rgb=True)
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alg = BYOL(
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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':
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[torch.randn((2, 3, 224, 224)),
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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_inputs, fake_data_samples = alg.data_preprocessor(fake_data)
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fake_loss = alg(fake_inputs, fake_data_samples, mode='loss')
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assert isinstance(fake_loss['loss'].item(), float)
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assert fake_loss['loss'].item() > -4
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fake_feats = alg(fake_inputs, fake_data_samples, mode='tensor')
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assert list(fake_feats[0].shape) == [2, 512, 7, 7]
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