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45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
import torch
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from mmseg.models.decode_heads import DNLHead
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from .utils import to_cuda
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def test_dnl_head():
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# DNL with 'embedded_gaussian' mode
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head = DNLHead(in_channels=32, channels=16, num_classes=19)
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assert len(head.convs) == 2
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assert hasattr(head, 'dnl_block')
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assert head.dnl_block.temperature == 0.05
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inputs = [torch.randn(1, 32, 45, 45)]
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if torch.cuda.is_available():
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head, inputs = to_cuda(head, inputs)
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outputs = head(inputs)
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assert outputs.shape == (1, head.num_classes, 45, 45)
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# NonLocal2d with 'dot_product' mode
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head = DNLHead(
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in_channels=32, channels=16, num_classes=19, mode='dot_product')
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inputs = [torch.randn(1, 32, 45, 45)]
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if torch.cuda.is_available():
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head, inputs = to_cuda(head, inputs)
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outputs = head(inputs)
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assert outputs.shape == (1, head.num_classes, 45, 45)
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# NonLocal2d with 'gaussian' mode
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head = DNLHead(
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in_channels=32, channels=16, num_classes=19, mode='gaussian')
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inputs = [torch.randn(1, 32, 45, 45)]
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if torch.cuda.is_available():
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head, inputs = to_cuda(head, inputs)
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outputs = head(inputs)
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assert outputs.shape == (1, head.num_classes, 45, 45)
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# NonLocal2d with 'concatenation' mode
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head = DNLHead(
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in_channels=32, channels=16, num_classes=19, mode='concatenation')
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inputs = [torch.randn(1, 32, 45, 45)]
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if torch.cuda.is_available():
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head, inputs = to_cuda(head, inputs)
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outputs = head(inputs)
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assert outputs.shape == (1, head.num_classes, 45, 45)
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