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[Feature] Support classwise weight in losses (#388) 

* Add classwise weight in losses:CE,BCE,softBCE

* Update unit test

* rm some extra code

* rm some extra code

* fix broadcast

* fix broadcast

* update unit tests

* use new_tensor

* fix lint
pull/426/head
Ezra-Yu 2021-08-31 10:44:12 +08:00 committed by GitHub
parent 6a0a76af0c
commit 192b79eea0
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2 changed files with 96 additions and 21 deletions
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43
mmcls/models/losses/cross_entropy_loss.py
View File

@ -6,7 +6,12 @@ from ..builder import LOSSES
from .utils import weight_reduce_loss
def cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=None):
def cross_entropy(pred,
label,
weight=None,
reduction='mean',
avg_factor=None,
class_weight=None):
"""Calculate the CrossEntropy loss.
Args:
@ -17,12 +22,14 @@ def cross_entropy(pred, label, weight=None, reduction='mean', avg_factor=None):
reduction (str): The method used to reduce the loss.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
class_weight (torch.Tensor, optional): The weight for each class with
shape (C), C is the number of classes. Default None.
Returns:
torch.Tensor: The calculated loss
"""
# element-wise losses
loss = F.cross_entropy(pred, label, reduction='none')
loss = F.cross_entropy(pred, label, weight=class_weight, reduction='none')
# apply weights and do the reduction
if weight is not None:
@ -37,6 +44,7 @@ def soft_cross_entropy(pred,
label,
weight=None,
reduction='mean',
class_weight=None,
avg_factor=None):
"""Calculate the Soft CrossEntropy loss. The label can be float.
@ -49,12 +57,16 @@ def soft_cross_entropy(pred,
reduction (str): The method used to reduce the loss.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
class_weight (torch.Tensor, optional): The weight for each class with
shape (C), C is the number of classes. Default None.
Returns:
torch.Tensor: The calculated loss
"""
# element-wise losses
loss = -label * F.log_softmax(pred, dim=-1)
if class_weight is not None:
loss *= class_weight
loss = loss.sum(dim=-1)
# apply weights and do the reduction
@ -70,7 +82,8 @@ def binary_cross_entropy(pred,
label,
weight=None,
reduction='mean',
avg_factor=None):
avg_factor=None,
class_weight=None):
r"""Calculate the binary CrossEntropy loss with logits.
Args:
@ -83,13 +96,20 @@ def binary_cross_entropy(pred,
is same shape as pred and label. Defaults to 'mean'.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
class_weight (torch.Tensor, optional): The weight for each class with
shape (C), C is the number of classes. Default None.
Returns:
torch.Tensor: The calculated loss
"""
assert pred.dim() == label.dim()
loss = F.binary_cross_entropy_with_logits(pred, label, reduction='none')
# Ensure that the size of class_weight is consistent with pred and label to
# avoid automatic boracast,
if class_weight is not None:
N = pred.size()[0]
class_weight = class_weight.repeat(N, 1)
loss = F.binary_cross_entropy_with_logits(
pred, label, weight=class_weight, reduction='none')
# apply weights and do the reduction
if weight is not None:
@ -114,13 +134,16 @@ class CrossEntropyLoss(nn.Module):
reduction (str): The method used to reduce the loss.
Options are "none", "mean" and "sum". Defaults to 'mean'.
loss_weight (float): Weight of the loss. Defaults to 1.0.
class_weight (List[float], optional): The weight for each class with
shape (C), C is the number of classes. Default None.
"""
def __init__(self,
use_sigmoid=False,
use_soft=False,
reduction='mean',
loss_weight=1.0):
loss_weight=1.0,
class_weight=None):
super(CrossEntropyLoss, self).__init__()
self.use_sigmoid = use_sigmoid
self.use_soft = use_soft
@ -130,6 +153,7 @@ class CrossEntropyLoss(nn.Module):
self.reduction = reduction
self.loss_weight = loss_weight
self.class_weight = class_weight
if self.use_sigmoid:
self.cls_criterion = binary_cross_entropy
@ -148,10 +172,17 @@ class CrossEntropyLoss(nn.Module):
assert reduction_override in (None, 'none', 'mean', 'sum')
reduction = (
reduction_override if reduction_override else self.reduction)
if self.class_weight is not None:
class_weight = cls_score.new_tensor(self.class_weight)
else:
class_weight = None
loss_cls = self.loss_weight * self.cls_criterion(
cls_score,
label,
weight,
class_weight=class_weight,
reduction=reduction,
avg_factor=avg_factor,
**kwargs)

74
tests/test_metrics/test_losses.py
View File

@ -45,48 +45,92 @@ def test_cross_entropy_loss():
loss = build_loss(loss_cfg)
# test ce_loss
cls_score = torch.Tensor([[100, -100]])
label = torch.Tensor([1]).long()
weight = torch.tensor(0.5)
cls_score = torch.Tensor([[-1000, 1000], [100, -100]])
label = torch.Tensor([0, 1]).long()
class_weight = [0.3, 0.7] # class 0 : 0.3, class 1 : 0.7
weight = torch.tensor([0.6, 0.4])
# test ce_loss without class weight
loss_cfg = dict(type='CrossEntropyLoss', reduction='mean', loss_weight=1.0)
loss = build_loss(loss_cfg)
assert torch.allclose(loss(cls_score, label), torch.tensor(200.))
assert torch.allclose(loss(cls_score, label), torch.tensor(1100.))
# test ce_loss with weight
assert torch.allclose(
loss(cls_score, label, weight=weight), torch.tensor(100.))
loss(cls_score, label, weight=weight), torch.tensor(640.))
# test ce_loss with class weight
loss_cfg = dict(
type='CrossEntropyLoss',
reduction='mean',
loss_weight=1.0,
class_weight=class_weight)
loss = build_loss(loss_cfg)
assert torch.allclose(loss(cls_score, label), torch.tensor(370.))
# test ce_loss with weight
assert torch.allclose(
loss(cls_score, label, weight=weight), torch.tensor(208.))
# test bce_loss
cls_score = torch.Tensor([[100, -100], [100, -100]])
label = torch.Tensor([[1, 0], [0, 1]])
weight = torch.Tensor([0.5, 0.5])
cls_score = torch.Tensor([[-200, 100], [500, -1000], [300, -300]])
label = torch.Tensor([[1, 0], [0, 1], [1, 0]])
weight = torch.Tensor([0.6, 0.4, 0.5])
class_weight = [0.1, 0.9] # class 0: 0.1, class 1: 0.9
# test bce_loss without class weight
loss_cfg = dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=1.0)
loss = build_loss(loss_cfg)
assert torch.allclose(loss(cls_score, label), torch.tensor(50.))
assert torch.allclose(loss(cls_score, label), torch.tensor(300.))
# test ce_loss with weight
assert torch.allclose(
loss(cls_score, label, weight=weight), torch.tensor(25.))
loss(cls_score, label, weight=weight), torch.tensor(130.))
# test bce_loss with class weight
loss_cfg = dict(
type='CrossEntropyLoss',
use_sigmoid=True,
reduction='mean',
loss_weight=1.0,
class_weight=class_weight)
loss = build_loss(loss_cfg)
assert torch.allclose(loss(cls_score, label), torch.tensor(176.667))
# test bce_loss with weight
assert torch.allclose(
loss(cls_score, label, weight=weight), torch.tensor(74.333))
# test soft_ce_loss
cls_score = torch.Tensor([[100, -100]])
label = torch.Tensor([[1, 0], [0, 1]])
weight = torch.tensor(0.5)
cls_score = torch.Tensor([[-1000, 1000], [100, -100]])
label = torch.Tensor([[1.0, 0.0], [0.0, 1.0]])
class_weight = [0.3, 0.7] # class 0 : 0.3, class 1 : 0.7
weight = torch.tensor([0.6, 0.4])
# test soft_ce_loss without class weight
loss_cfg = dict(
type='CrossEntropyLoss',
use_soft=True,
reduction='mean',
loss_weight=1.0)
loss = build_loss(loss_cfg)
assert torch.allclose(loss(cls_score, label), torch.tensor(100.))
assert torch.allclose(loss(cls_score, label), torch.tensor(1100.))
# test soft_ce_loss with weight
assert torch.allclose(
loss(cls_score, label, weight=weight), torch.tensor(50.))
loss(cls_score, label, weight=weight), torch.tensor(640.))
# test soft_ce_loss with class weight
loss_cfg = dict(
type='CrossEntropyLoss',
use_soft=True,
reduction='mean',
loss_weight=1.0,
class_weight=class_weight)
loss = build_loss(loss_cfg)
assert torch.allclose(loss(cls_score, label), torch.tensor(370.))
# test soft_ce_loss with weight
assert torch.allclose(
loss(cls_score, label, weight=weight), torch.tensor(208.))
def test_focal_loss():