mirror of
https://github.com/open-mmlab/mmpretrain.git
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62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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import platform
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import pytest
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import torch
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from mmpretrain.models import SwAV
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from mmpretrain.structures import DataSample
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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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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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'to_rgb': True
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}
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backbone = dict(
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type='ResNet',
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depth=18,
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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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head = dict(
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type='SwAVHead',
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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=[2, 6]))
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alg = SwAV(
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backbone=backbone,
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neck=neck,
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head=head,
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data_preprocessor=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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torch.randn((2, 3, 96, 96)),
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torch.randn((2, 3, 96, 96)),
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torch.randn((2, 3, 96, 96)),
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torch.randn((2, 3, 96, 96)),
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torch.randn((2, 3, 96, 96)),
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torch.randn((2, 3, 96, 96))
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],
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'data_samples': [DataSample() for _ in range(2)]
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}
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fake_inputs = alg.data_preprocessor(fake_data)
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fake_outputs = alg(**fake_inputs, mode='loss')
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assert isinstance(fake_outputs['loss'].item(), float)
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