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5de480ea9e
* Support HRNet * Add HRNet configs * Fix a bug in backward * Add configs and update docs. * Not use bias in conv before batch norm * Defaults to use `norm_eval=False` * Add unit tests and support out_channels in HRFuseScales * Update checkpoint path * Update docstring. * Remove incorrect files * Improve according to comments
61 lines
1.5 KiB
Python
61 lines
1.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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from mmcls.models.necks import GlobalAveragePooling, HRFuseScales
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def test_gap_neck():
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# test 1d gap_neck
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neck = GlobalAveragePooling(dim=1)
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# batch_size, num_features, feature_size
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fake_input = torch.rand(1, 16, 24)
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output = neck(fake_input)
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# batch_size, num_features
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assert output.shape == (1, 16)
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# test 1d gap_neck
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neck = GlobalAveragePooling(dim=2)
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# batch_size, num_features, feature_size(2)
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fake_input = torch.rand(1, 16, 24, 24)
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output = neck(fake_input)
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# batch_size, num_features
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assert output.shape == (1, 16)
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# test 1d gap_neck
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neck = GlobalAveragePooling(dim=3)
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# batch_size, num_features, feature_size(3)
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fake_input = torch.rand(1, 16, 24, 24, 5)
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output = neck(fake_input)
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# batch_size, num_features
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assert output.shape == (1, 16)
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with pytest.raises(AssertionError):
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# dim must in [1, 2, 3]
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GlobalAveragePooling(dim='other')
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def test_hr_fuse_scales():
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in_channels = (18, 32, 64, 128)
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neck = HRFuseScales(in_channels=in_channels, out_channels=1024)
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feat_size = 56
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inputs = []
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for in_channel in in_channels:
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input_tensor = torch.rand(3, in_channel, feat_size, feat_size)
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inputs.append(input_tensor)
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feat_size = feat_size // 2
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with pytest.raises(AssertionError):
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neck(inputs)
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outs = neck(tuple(inputs))
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assert isinstance(outs, tuple)
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assert len(outs) == 1
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assert outs[0].shape == (3, 1024, 7, 7)
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