62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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from mmcv.utils import ConfigDict
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from mmseg.models.decode_heads import FCNHead, PointHead
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from .utils import to_cuda
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def test_point_head():
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inputs = [torch.randn(1, 32, 45, 45)]
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point_head = PointHead(
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in_channels=[32], in_index=[0], channels=16, num_classes=19)
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assert len(point_head.fcs) == 3
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fcn_head = FCNHead(in_channels=32, channels=16, num_classes=19)
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if torch.cuda.is_available():
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head, inputs = to_cuda(point_head, inputs)
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head, inputs = to_cuda(fcn_head, inputs)
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prev_output = fcn_head(inputs)
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test_cfg = ConfigDict(
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subdivision_steps=2, subdivision_num_points=8196, scale_factor=2)
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output = point_head.forward_test(inputs, prev_output, None, test_cfg)
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assert output.shape == (1, point_head.num_classes, 180, 180)
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# test multiple losses case
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inputs = [torch.randn(1, 32, 45, 45)]
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point_head_multiple_losses = PointHead(
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in_channels=[32],
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in_index=[0],
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channels=16,
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num_classes=19,
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loss_decode=[
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dict(type='CrossEntropyLoss', loss_name='loss_1'),
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dict(type='CrossEntropyLoss', loss_name='loss_2')
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])
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assert len(point_head_multiple_losses.fcs) == 3
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fcn_head_multiple_losses = FCNHead(
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in_channels=32,
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channels=16,
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num_classes=19,
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loss_decode=[
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dict(type='CrossEntropyLoss', loss_name='loss_1'),
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dict(type='CrossEntropyLoss', loss_name='loss_2')
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])
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if torch.cuda.is_available():
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head, inputs = to_cuda(point_head_multiple_losses, inputs)
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head, inputs = to_cuda(fcn_head_multiple_losses, inputs)
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prev_output = fcn_head_multiple_losses(inputs)
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test_cfg = ConfigDict(
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subdivision_steps=2, subdivision_num_points=8196, scale_factor=2)
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output = point_head_multiple_losses.forward_test(inputs, prev_output, None,
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test_cfg)
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assert output.shape == (1, point_head.num_classes, 180, 180)
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fake_label = torch.ones([1, 180, 180], dtype=torch.long)
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if torch.cuda.is_available():
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fake_label = fake_label.cuda()
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loss = point_head_multiple_losses.losses(output, fake_label)
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assert 'pointloss_1' in loss
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assert 'pointloss_2' in loss
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