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# ------------------------------------------------------------------------
# Copyright (c) 2023-present, BAAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
# pyre-unsafe
"""Loss layers."""
from torch import nn
def reduce_loss(loss, reduction="mean"):
"""Reduce the loss."""
if reduction == "mean" or reduction == "sum":
return getattr(loss, reduction)()
if reduction == "batch_mean":
return loss.sum().mul_(1.0 / loss.size(0))
return loss
class BinaryFocalLoss(nn.Module):
"""Binary focal loss."""
def __init__(self, alpha=0.25, reduction="none"):
super(BinaryFocalLoss, self).__init__()
self.alpha = alpha
self.reduction = reduction
def forward(self, input, target):
alpha, p = self.alpha, input.sigmoid()
neg_alpha, neg_target = 1.0 - alpha, 1.0 - target
alpha_weight = target.mul(alpha).add_(neg_target.mul(neg_alpha))
focal_weight = (1.0 - p).mul_(target).add_(p.mul(neg_target)).square()
loss = nn.functional.binary_cross_entropy_with_logits(
input, target, reduction="none"
)
return reduce_loss(loss * focal_weight.mul_(alpha_weight), self.reduction)
class BinaryDiceLoss(nn.Module):
"""Binary dice loss."""
def __init__(self, eps=1.0, reduction="none"):
super(BinaryDiceLoss, self).__init__()
self.eps = eps
self.reduction = reduction
def forward(self, input, target):
input = input.sigmoid()
num = input.mul(target).sum(-1).mul_(2).add_(self.eps)
den = input.add(target).sum(-1).add_(self.eps)
return reduce_loss(1.0 - num / den, self.reduction)
class CrossEntropyLoss(nn.Module):
"""Cross entropy loss with label smoothing."""
def __init__(self, epsilon=0, reduction="none"):
super(CrossEntropyLoss, self).__init__()
self.epsilon = epsilon
self.reduction = reduction
def forward_dense(self, input, target):
dim, target = input.shape[-1], target.squeeze_()
x = nn.functional.log_softmax(input, dim=-1)
y = nn.functional.one_hot(target, dim).float()
x = (
x.permute([0, x.dim() - 1] + list(range(x.dim()))[1:-1])
if x.dim() > 2
else x
)
y = (
y.permute([0, y.dim() - 1] + list(range(y.dim()))[1:-1])
if y.dim() > 2
else y
)
loss = nn.functional.cross_entropy(
x, y, reduction="none", label_smoothing=self.epsilon
)
return reduce_loss(loss, self.reduction)
def forward(self, input, target):
if self.epsilon > 0:
return self.forward_dense(input, target)
return nn.functional.cross_entropy(input, target, reduction=self.reduction)
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