mirror of
https://github.com/open-mmlab/mmsegmentation.git
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25d8d77fab
* delete markdownlint * Support MobileNetV3 * fix import * add mobilenetv3 head and configs * Modify MobileNetV3 to semantic segmentation version * modify mobilenetv3 configs * add std configs * fix Conv2dAdaptivePadding bug * add configs * add unitest and fix bugs * fix lraspp unitest bugs * restore * fix unitest * add MobileNetV3 docstring * add mmcv * add mmcv * fix syntax bug * fix unitest bug * fix unitest bug * fix unitest bugs * fix docstring * add configs * restore * delete unnecessary assert * modify unitest * delete benchmark
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
import mmcv
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import pytest
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import torch
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from mmseg.models.utils.se_layer import SELayer
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def test_se_layer():
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with pytest.raises(AssertionError):
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# test act_cfg assertion.
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SELayer(32, act_cfg=(dict(type='ReLU'), ))
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# test config with channels = 16.
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se_layer = SELayer(16)
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assert se_layer.conv1.conv.kernel_size == (1, 1)
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assert se_layer.conv1.conv.stride == (1, 1)
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assert se_layer.conv1.conv.padding == (0, 0)
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assert isinstance(se_layer.conv1.activate, torch.nn.ReLU)
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assert se_layer.conv2.conv.kernel_size == (1, 1)
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assert se_layer.conv2.conv.stride == (1, 1)
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assert se_layer.conv2.conv.padding == (0, 0)
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assert isinstance(se_layer.conv2.activate, mmcv.cnn.HSigmoid)
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x = torch.rand(1, 16, 64, 64)
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output = se_layer(x)
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assert output.shape == (1, 16, 64, 64)
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# test config with channels = 16, act_cfg = dict(type='ReLU').
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se_layer = SELayer(16, act_cfg=dict(type='ReLU'))
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assert se_layer.conv1.conv.kernel_size == (1, 1)
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assert se_layer.conv1.conv.stride == (1, 1)
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assert se_layer.conv1.conv.padding == (0, 0)
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assert isinstance(se_layer.conv1.activate, torch.nn.ReLU)
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assert se_layer.conv2.conv.kernel_size == (1, 1)
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assert se_layer.conv2.conv.stride == (1, 1)
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assert se_layer.conv2.conv.padding == (0, 0)
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assert isinstance(se_layer.conv2.activate, torch.nn.ReLU)
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x = torch.rand(1, 16, 64, 64)
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output = se_layer(x)
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assert output.shape == (1, 16, 64, 64)
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