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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
60 lines
1.7 KiB
Python
60 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 mmengine.structures import InstanceData
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from mmselfsup.models.algorithms import NPID
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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(
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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='LinearNeck', in_channels=512, out_channels=2, with_avg_pool=True)
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head = dict(
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type='ContrastiveHead',
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loss=dict(type='mmcls.CrossEntropyLoss'),
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temperature=0.07)
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memory_bank = dict(type='SimpleMemory', length=8, feat_dim=2, momentum=0.5)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_npid():
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data_preprocessor = {
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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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}
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alg = NPID(
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backbone=backbone,
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neck=neck,
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head=head,
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memory_bank=memory_bank,
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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_samples': [
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SelfSupDataSample(
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sample_idx=InstanceData(value=torch.randint(0, 7, (1, ))))
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for _ in range(2)
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]
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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 fake_loss['loss'] > -1
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# test extract
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fake_feats = alg(fake_inputs, fake_data_samples, mode='tensor')
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assert fake_feats[0].size() == torch.Size([2, 512, 7, 7])
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