68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
import pytest
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import torch
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from mmseg.models.decode_heads import LRASPPHead
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def test_lraspp_head():
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with pytest.raises(ValueError):
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# check invalid input_transform
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LRASPPHead(
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in_channels=(16, 16, 576),
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in_index=(0, 1, 2),
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channels=128,
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input_transform='resize_concat',
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dropout_ratio=0.1,
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num_classes=19,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
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with pytest.raises(AssertionError):
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# check invalid branch_channels
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LRASPPHead(
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in_channels=(16, 16, 576),
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in_index=(0, 1, 2),
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channels=128,
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branch_channels=64,
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input_transform='multiple_select',
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dropout_ratio=0.1,
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num_classes=19,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
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# test with default settings
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lraspp_head = LRASPPHead(
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in_channels=(16, 16, 576),
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in_index=(0, 1, 2),
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channels=128,
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input_transform='multiple_select',
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dropout_ratio=0.1,
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num_classes=19,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='ReLU'),
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align_corners=False,
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loss_decode=dict(
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type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0))
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inputs = [
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torch.randn(2, 16, 45, 45),
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torch.randn(2, 16, 28, 28),
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torch.randn(2, 576, 14, 14)
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]
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with pytest.raises(RuntimeError):
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# check invalid inputs
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output = lraspp_head(inputs)
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inputs = [
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torch.randn(2, 16, 111, 111),
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torch.randn(2, 16, 77, 77),
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torch.randn(2, 576, 55, 55)
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]
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output = lraspp_head(inputs)
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assert output.shape == (2, 19, 111, 111)
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