use logsigmoid at multilabel_soft_margin_loss, and change output from shape=(N, C)to (N,) #9965

weiyangfb · 2018-07-27T23:35:02Z

fixes Make torch.nn.functional.multilabel_soft_margin_loss more stable #9141, bug of multilable loss in v0.4 #9301
use logsigmoid at multilabel_soft_margin_loss to make it more stable (NOT fixing legacy MultiLabelSoftMarginCriterion)
return (N) instead of (N, C) to match the same behavior as MultiMarginLoss
Note that with this PR, the following behavior is expected:

loss = F.multilabel_soft_margin_loss(outputs, labels, reduction='none')
loss_mean = F.multilabel_soft_margin_loss(outputs, labels, reduction='elementwise_mean')
loss_sum = F.multilabel_soft_margin_loss(outputs, labels, reduction='sum')

loss.sum() == loss_sum  # True
loss.mean() == loss_mean  # True

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torch/nn/functional.py

soumith

approved. change comment.

torch/nn/functional.py

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torch/nn/functional.py

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li-roy · 2018-08-02T16:25:21Z

Why are we changing the behavior of all these losses?

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torch/nn/functional.py

-    return binary_cross_entropy(input, target, weight, None, None, reduction)
+
+    loss = -(target * logsigmoid(input) + (1 - target) * logsigmoid(-input))
+    loss.sum(dim=0)  # only return N loss values


weiyangfb · 2018-08-03T03:12:34Z

@li-roy I made changes accordingly, maybe take a look when you get a chance?

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li-roy

Looks good for the most part, just need to be consistent in the behavior.

torch/nn/functional.py

+    loss = loss.sum(dim=1)  # only return N loss values
+
+    if reduction == 'none':
+        return loss


li-roy · 2018-08-03T21:27:22Z

If you're planning to go with averaging across classes, don't forget to change the formula in the doc in torch/nn/modules/loss.py

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… shape=(N, C)to (N,) (pytorch#9965) Summary: - fixes pytorch#9141, pytorch#9301 - use logsigmoid at multilabel_soft_margin_loss to make it more stable (NOT fixing legacy MultiLabelSoftMarginCriterion) - return (N) instead of (N, C) to match the same behavior as MultiMarginLoss - Note that with this PR, the following behavior is expected: ``` loss = F.multilabel_soft_margin_loss(outputs, labels, reduction='none') loss_mean = F.multilabel_soft_margin_loss(outputs, labels, reduction='elementwise_mean') loss_sum = F.multilabel_soft_margin_loss(outputs, labels, reduction='sum') loss.sum() == loss_sum # True loss.mean() == loss_mean # True ``` Pull Request resolved: pytorch#9965 Differential Revision: D9038402 Pulled By: weiyangfb fbshipit-source-id: 0fa94c7b3cd370ea62bd6333f1a0e9bd0b8ccbb9

vadimkantorov · 2023-12-11T22:36:40Z

torch/nn/functional.py

-    input = torch.sigmoid(input)
-    return binary_cross_entropy(input, target, weight, None, None, reduction)
+
+    loss = -(target * logsigmoid(input) + (1 - target) * logsigmoid(-input))


this could probably use torch.lerp (yet AFAIK it fails export to ONNX and has NaN problems: #71701)

weiyangfb requested review from apaszke, colesbury, ezyang, gchanan, soumith and zdevito as code owners July 27, 2018 23:35

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weiyangfb force-pushed the stable_multilabel_soft_margin_loss branch from 1376a8b to 210f88e Compare July 31, 2018 02:39

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li-roy mentioned this pull request Jul 31, 2018

bug of multilable loss in v0.4 #9301

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weiyangfb changed the title ~~use logsigmoid at multilabel_soft_margin_loss~~ [wip] use logsigmoid at multilabel_soft_margin_loss, fix 'elementwise_mean' Aug 1, 2018

weiyangfb mentioned this pull request Aug 2, 2018

MSELoss wrongly sums instead of averages when reduction='elementwise_mean' #10148

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weiyangfb added 2 commits August 2, 2018 08:21

use logsigmoid at multilabel_soft_margin_loss

4c2b1b8

elementwise_mean should be global mean

f778ce0

weiyangfb force-pushed the stable_multilabel_soft_margin_loss branch from 9ea862e to 5a10812 Compare August 2, 2018 15:23

changed output of MultiLabelMarginLoss from (N,C) to (N)

c2b71e0

weiyangfb force-pushed the stable_multilabel_soft_margin_loss branch from 5a10812 to c2b71e0 Compare August 2, 2018 20:39

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weiyangfb changed the title ~~[wip] use logsigmoid at multilabel_soft_margin_loss, fix 'elementwise_mean'~~ use logsigmoid at multilabel_soft_margin_loss, and change output from shape=(N, C)to (N,) Aug 2, 2018

li-roy suggested changes Aug 2, 2018

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weiyangfb added 2 commits August 2, 2018 16:30

addresses comment

41cf0bb

dim=1

55c48e0

fix tests

f8fb411

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torch/nn/functional.py

loss = loss.sum(dim=1) # only return N loss values

if reduction == 'none':

return loss

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weiyangfb added 2 commits August 3, 2018 13:38

addresses comments, follow legacy behavior for reduction

58fcb2e

lint

2106e3f

update loss formula in doc

5ae40c4

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facebook-github-bot closed this in 149d4f7 Aug 4, 2018

ezyang added the merged label Jun 26, 2019

vadimkantorov reviewed Dec 11, 2023

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use logsigmoid at multilabel_soft_margin_loss, and change output from shape=(N, C)to (N,) #9965

use logsigmoid at multilabel_soft_margin_loss, and change output from shape=(N, C)to (N,) #9965

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weiyangfb commented Jul 27, 2018 •

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