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[Feature] Support multiple losses during training #818

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merged 15 commits into from
Sep 24, 2021
Merged

[Feature] Support multiple losses during training #818

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876d408
multiple losses
MengzhangLI Aug 24, 2021
849137d
fix lint error
MengzhangLI Aug 24, 2021
73980c4
fix typos
MengzhangLI Aug 31, 2021
84e7371
fix typos
MengzhangLI Aug 31, 2021
2b1ece5
fix version conflict
MengzhangLI Aug 31, 2021
a30749a
Adding Attribute
MengzhangLI Sep 18, 2021
c4d88ed
Fixing loss_ prefix
MengzhangLI Sep 22, 2021
4dba853
Fixing loss_ prefix
MengzhangLI Sep 22, 2021
a38b570
Fixing loss_ prefix
MengzhangLI Sep 22, 2021
f3472da
Add Same
MengzhangLI Sep 23, 2021
96213d3
loss_name must has 'loss_' prefix
MengzhangLI Sep 23, 2021
156c36c
Fix unittest
MengzhangLI Sep 24, 2021
4a1996f
Fix unittest
MengzhangLI Sep 24, 2021
67d3862
Fix unittest
MengzhangLI Sep 24, 2021
0b3a22b
Update mmseg/models/decode_heads/decode_head.py
Junjun2016 Sep 24, 2021
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Fixing loss_ prefix
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@MengzhangLI
MengzhangLI committed Sep 22, 2021
commit 4dba853c0974baf2c816b870bd708dda1d29493a
2 changes: 1 addition & 1 deletion tests/test_models/test_heads/test_decode_head.py
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Expand Up @@ -152,4 +152,4 @@ def test_decode_head():
loss = head.losses(seg_logit=inputs, seg_label=target)
assert 'loss_ce' in loss
assert 'loss_1' in loss
assert 'loss_2' in loss
assert 'loss_2' in loss
13 changes: 9 additions & 4 deletions tests/test_models/test_losses/test_lovasz_loss.py
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Expand Up @@ -18,13 +18,18 @@ def test_lovasz_loss():

# reduction should be 'none' when per_image is False.
with pytest.raises(AssertionError):
loss_cfg = dict(type='LovaszLoss', loss_type='multi_class',
loss_name='loss_lovasz')
loss_cfg = dict(
type='LovaszLoss',
loss_type='multi_class',
loss_name='loss_lovasz')
build_loss(loss_cfg)

# test lovasz loss with loss_type = 'multi_class' and per_image = False
loss_cfg = dict(type='LovaszLoss', reduction='none', loss_weight=1.0,
loss_name='loss_lovasz')
loss_cfg = dict(
type='LovaszLoss',
reduction='none',
loss_weight=1.0,
loss_name='loss_lovasz')
lovasz_loss = build_loss(loss_cfg)
logits = torch.rand(1, 3, 4, 4)
labels = (torch.rand(1, 4, 4) * 2).long()
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