53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
# 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 SimCLR
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from mmpretrain.structures import DataSample
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backbone = dict(type='ResNet', depth=18, norm_cfg=dict(type='BN'))
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neck = dict(
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type='NonLinearNeck', # SimCLR non-linear neck
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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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num_layers=2,
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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='ContrastiveHead',
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loss=dict(type='CrossEntropyLoss'),
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temperature=0.1)
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@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
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def test_simclr():
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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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alg = SimCLR(
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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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'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_loss = alg(**fake_inputs, mode='loss')
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assert isinstance(fake_loss['loss'].item(), float)
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# test extract
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fake_feat = alg(fake_inputs['inputs'][0], mode='tensor')
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assert fake_feat[0].size() == torch.Size([2, 512, 7, 7])
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