32 lines
1018 B
Python
32 lines
1018 B
Python
import torch
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from mmseg.models import MultiLevelNeck
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def test_multilevel_neck():
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# Test init_weights
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MultiLevelNeck([266], 256).init_weights()
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# Test multi feature maps
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in_channels = [256, 512, 1024, 2048]
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inputs = [torch.randn(1, c, 14, 14) for i, c in enumerate(in_channels)]
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neck = MultiLevelNeck(in_channels, 256)
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outputs = neck(inputs)
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assert outputs[0].shape == torch.Size([1, 256, 7, 7])
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assert outputs[1].shape == torch.Size([1, 256, 14, 14])
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assert outputs[2].shape == torch.Size([1, 256, 28, 28])
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assert outputs[3].shape == torch.Size([1, 256, 56, 56])
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# Test one feature map
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in_channels = [768]
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inputs = [torch.randn(1, 768, 14, 14)]
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neck = MultiLevelNeck(in_channels, 256)
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outputs = neck(inputs)
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assert outputs[0].shape == torch.Size([1, 256, 7, 7])
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assert outputs[1].shape == torch.Size([1, 256, 14, 14])
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assert outputs[2].shape == torch.Size([1, 256, 28, 28])
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assert outputs[3].shape == torch.Size([1, 256, 56, 56])
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