mirror of
https://github.com/open-mmlab/mmselfsup.git
synced 2025-06-03 14:59:38 +08:00
88 lines
2.4 KiB
Python
88 lines
2.4 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.core.data_structures.selfsup_data_sample import \
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SelfSupDataSample
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from mmselfsup.models.algorithms.swav import SwAV
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nmb_crops = [2, 6]
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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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zero_init_residual=True)
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neck = dict(
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type='SwAVNeck',
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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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norm_cfg=dict(type='BN1d'),
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with_avg_pool=True)
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loss = dict(
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type='SwAVLoss',
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feat_dim=2, # equal to neck['out_channels']
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epsilon=0.05,
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temperature=0.1,
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num_crops=nmb_crops)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_swav():
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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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with pytest.raises(AssertionError):
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alg = SwAV(
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backbone=backbone,
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neck=neck,
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loss=None,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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with pytest.raises(AssertionError):
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alg = SwAV(
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backbone=backbone,
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neck=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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alg = SwAV(
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backbone=None,
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neck=neck,
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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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alg = SwAV(
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backbone=backbone,
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neck=neck,
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loss=loss,
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preprocess_cfg=copy.deepcopy(preprocess_cfg))
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fake_data = [{
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'inputs': [
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torch.randn((3, 224, 224)),
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torch.randn((3, 224, 224)),
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torch.randn((3, 96, 96)),
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torch.randn((3, 96, 96)),
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torch.randn((3, 96, 96)),
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torch.randn((3, 96, 96)),
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torch.randn((3, 96, 96)),
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torch.randn((3, 96, 96))
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],
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'data_sample':
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SelfSupDataSample()
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} for _ in range(2)]
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fake_outputs = alg(fake_data, return_loss=True)
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assert isinstance(fake_outputs['loss'].item(), float)
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fake_inputs, fake_data_samples = alg.preprocss_data(fake_data)
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fake_feat = alg.extract_feat(
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inputs=fake_inputs, data_samples=fake_data_samples)
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assert list(fake_feat[0].shape) == [2, 512, 7, 7]
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