64 lines
1.8 KiB
Python
64 lines
1.8 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 mmseg.models.decode_heads import SETRMLAHead
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from .utils import to_cuda
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def test_setr_mla_head(capsys):
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with pytest.raises(AssertionError):
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# MLA requires input multiple stage feature information.
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SETRMLAHead(in_channels=8, channels=4, num_classes=19, in_index=1)
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with pytest.raises(AssertionError):
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# multiple in_indexs requires multiple in_channels.
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SETRMLAHead(
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in_channels=8, channels=4, num_classes=19, in_index=(0, 1, 2, 3))
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with pytest.raises(AssertionError):
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# channels should be len(in_channels) * mla_channels
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SETRMLAHead(
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in_channels=(8, 8, 8, 8),
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channels=8,
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mla_channels=4,
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in_index=(0, 1, 2, 3),
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num_classes=19)
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# test inference of MLA head
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img_size = (8, 8)
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patch_size = 4
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head = SETRMLAHead(
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in_channels=(8, 8, 8, 8),
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channels=16,
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mla_channels=4,
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in_index=(0, 1, 2, 3),
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num_classes=19,
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norm_cfg=dict(type='BN'))
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h, w = img_size[0] // patch_size, img_size[1] // patch_size
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# Input square NCHW format feature information
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x = [
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torch.randn(1, 8, h, w),
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torch.randn(1, 8, h, w),
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torch.randn(1, 8, h, w),
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torch.randn(1, 8, h, w)
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]
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if torch.cuda.is_available():
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head, x = to_cuda(head, x)
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out = head(x)
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assert out.shape == (1, head.num_classes, h * 4, w * 4)
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# Input non-square NCHW format feature information
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x = [
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torch.randn(1, 8, h, w * 2),
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torch.randn(1, 8, h, w * 2),
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torch.randn(1, 8, h, w * 2),
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torch.randn(1, 8, h, w * 2)
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]
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if torch.cuda.is_available():
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head, x = to_cuda(head, x)
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out = head(x)
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assert out.shape == (1, head.num_classes, h * 4, w * 8)
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