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https://github.com/open-mmlab/mmselfsup.git
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ff5f5ae2a9
* update circleci * update github action * fix windows error * update * update pr and merge state * fix lint * update * update * update circle ci * fix * fix ut
67 lines
1.9 KiB
Python
67 lines
1.9 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 DeepCluster
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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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num_classes = 5
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with_sobel = True,
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backbone = dict(
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type='ResNetSobel',
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depth=18,
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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(type='AvgPool2dNeck')
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head = dict(
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type='ClsHead',
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loss=dict(type='mmcls.CrossEntropyLoss'),
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with_avg_pool=False, # already has avgpool in the neck
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in_channels=512,
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num_classes=num_classes)
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@pytest.mark.skipif(
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not torch.cuda.is_available() or platform.system() == 'Windows',
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reason='CUDA is not available or Windows mem limit')
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def test_deepcluster():
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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 = DeepCluster(
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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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assert alg.num_classes == num_classes
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assert hasattr(alg, 'neck')
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assert hasattr(alg, 'head')
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fake_data_sample = SelfSupDataSample()
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clustering_label = InstanceData(clustering_label=torch.tensor([1]))
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fake_data_sample.pseudo_label = clustering_label
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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': [fake_data_sample 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 fake_loss['loss'] > 0
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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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