79 lines
2.9 KiB
Python
79 lines
2.9 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.core import OHEMPixelSampler
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from mmseg.models.decode_heads import FCNHead
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def _context_for_ohem():
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return FCNHead(in_channels=32, channels=16, num_classes=19)
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def _context_for_ohem_multiple_loss():
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return 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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def test_ohem_sampler():
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with pytest.raises(AssertionError):
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# seg_logit and seg_label must be of the same size
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sampler = OHEMPixelSampler(context=_context_for_ohem())
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seg_logit = torch.randn(1, 19, 45, 45)
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seg_label = torch.randint(0, 19, size=(1, 1, 89, 89))
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sampler.sample(seg_logit, seg_label)
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# test with thresh
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sampler = OHEMPixelSampler(
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context=_context_for_ohem(), thresh=0.7, min_kept=200)
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seg_logit = torch.randn(1, 19, 45, 45)
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seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
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seg_weight = sampler.sample(seg_logit, seg_label)
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assert seg_weight.shape[0] == seg_logit.shape[0]
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assert seg_weight.shape[1:] == seg_logit.shape[2:]
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assert seg_weight.sum() > 200
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# test w.o thresh
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sampler = OHEMPixelSampler(context=_context_for_ohem(), min_kept=200)
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seg_logit = torch.randn(1, 19, 45, 45)
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seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
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seg_weight = sampler.sample(seg_logit, seg_label)
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assert seg_weight.shape[0] == seg_logit.shape[0]
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assert seg_weight.shape[1:] == seg_logit.shape[2:]
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assert seg_weight.sum() == 200
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# test multiple losses case
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with pytest.raises(AssertionError):
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# seg_logit and seg_label must be of the same size
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sampler = OHEMPixelSampler(context=_context_for_ohem_multiple_loss())
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seg_logit = torch.randn(1, 19, 45, 45)
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seg_label = torch.randint(0, 19, size=(1, 1, 89, 89))
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sampler.sample(seg_logit, seg_label)
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# test with thresh in multiple losses case
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sampler = OHEMPixelSampler(
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context=_context_for_ohem_multiple_loss(), thresh=0.7, min_kept=200)
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seg_logit = torch.randn(1, 19, 45, 45)
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seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
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seg_weight = sampler.sample(seg_logit, seg_label)
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assert seg_weight.shape[0] == seg_logit.shape[0]
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assert seg_weight.shape[1:] == seg_logit.shape[2:]
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assert seg_weight.sum() > 200
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# test w.o thresh in multiple losses case
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sampler = OHEMPixelSampler(
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context=_context_for_ohem_multiple_loss(), min_kept=200)
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seg_logit = torch.randn(1, 19, 45, 45)
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seg_label = torch.randint(0, 19, size=(1, 1, 45, 45))
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seg_weight = sampler.sample(seg_logit, seg_label)
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assert seg_weight.shape[0] == seg_logit.shape[0]
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assert seg_weight.shape[1:] == seg_logit.shape[2:]
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assert seg_weight.sum() == 200
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