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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.
# ------------------------------------------------------------------------------------------------
# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/utils.py
# ------------------------------------------------------------------------------------------------
import torch
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified
Args:
loss (nn.Tensor): Elementwise loss tensor.
reduction (str): Specified reduction function chosen from "none",
"mean" and "sum".
Return:
nn.Tensor: Reduced loss tensor.
"""
reduction_enum = F._Reduction.get_enum(reduction)
# none: 0, elementwise_mean:1, sum: 2
if reduction_enum == 0:
return loss
elif reduction_enum == 1:
return loss.mean()
elif reduction_enum == 2:
return loss.sum()
def weight_reduce_loss(loss, weight=None, reduction="mean", avg_factor=None):
"""Apply element-wise weight and reduce loss.
Args:
loss (Tensor): Element-wise loss.
weight (Tensor): Element-wise weights.
reduction (str): Same as built-in losses of PyTorch.
avg_factor (float): Average factor when computing the mean of losses.
Returns:
Tensor: Processed loss values.
"""
# if weight is specified, apply element-wise weight
if weight is not None:
loss = loss * weight
# if avg_factor is not specified, just reduce the loss
if avg_factor is None:
loss = reduce_loss(loss, reduction)
else:
# if reduction is mean, then average the loss by avg_factor
if reduction == "mean":
# Avoid causing ZeroDivisionError when avg_factor is 0.0.
eps = torch.finfo(loss.dtype).eps
loss = loss.sum() / (avg_factor + eps)
# if reduction is 'none', then do nothing, otherwise raise an error
elif reduction != "none":
raise ValueError('avg_factor can not be used with reduction="sum"')
return loss
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