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# coding=utf-8
# Copyright 2022 The IDEA Authors. 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.
# ------------------------------------------------------------------------------------------------
# Copyright (c) OpenMMLab. All rights reserved.
# ------------------------------------------------------------------------------------------------
# Modified from:
# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/cross_entropy_loss.py
# ------------------------------------------------------------------------------------------------
import warnings
import torch.nn as nn
import torch.nn.functional as F
from .utils import weight_reduce_loss
def cross_entropy(
preds,
targets,
weight=None,
class_weight=None,
reduction="mean",
avg_factor=None,
ignore_index=-100,
avg_non_ignore=False,
):
# The default value of ignore_index is the same as F.cross_entropy
ignore_index = -100 if ignore_index is None else ignore_index
loss = F.cross_entropy(
preds,
targets,
weight=class_weight,
reduction="none",
ignore_index=ignore_index,
)
# average loss over non-ignored elements
# pytorch's official cross_entropy average loss over non-ignored elements
# refer to https://github.com/pytorch/pytorch/blob/56b43f4fec1f76953f15a627694d4bba34588969/torch/nn/functional.py#L2660 # noqa
if (avg_factor is None) and avg_non_ignore and reduction == "mean":
avg_factor = targets.numel() - (targets == ignore_index).sum().item()
# apply weights and do the reduction
if weight is not None:
weight = weight.float()
loss = weight_reduce_loss(loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
return loss
class CrossEntropyLoss(nn.Module):
def __init__(
self,
reduction="mean",
loss_weight=1.0,
ignore_index=None,
avg_non_ignore=False,
):
super(CrossEntropyLoss, self).__init__()
self.reduction = reduction
self.loss_weight = loss_weight
self.ignore_index = ignore_index
self.avg_non_ignore = avg_non_ignore
if (ignore_index is not None) and not self.avg_non_ignore and self.reduction == "mean":
warnings.warn(
"Default ``avg_non_ignore`` is False, if you would like to "
"ignore the certain label and average loss over non-ignore "
"labels, which is the same with PyTorch official "
"cross_entropy, set ``avg_non_ignore=True``."
)
def forward(
self,
preds,
targets,
weight=None,
avg_factor=None,
class_weight=None,
ignore_index=None,
**kwargs,
):
if ignore_index is None:
ignore_index = self.ignore_index
loss_class = self.loss_weight * cross_entropy(
preds,
targets,
weight,
class_weight,
reduction=self.reduction,
avg_factor=avg_factor,
avg_non_ignore=self.avg_non_ignore,
)
return loss_class
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