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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 | |