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| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve. | |
| # | |
| # 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. | |
| import paddle | |
| import paddle.nn as nn | |
| import paddle.nn.functional as F | |
| from paddleseg.cvlibs import manager | |
| class KLLoss(nn.Layer): | |
| """ | |
| The implementation of Kullback-Leibler divergence Loss. | |
| Refer to https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence. | |
| Args: | |
| ignore_index (int64): Specifies a target value that is ignored | |
| and does not contribute to the input gradient. Default ``255``. | |
| temperature (float): the coefficient of kl_loss. | |
| """ | |
| def __init__(self, ignore_index=255, temperature=1): | |
| super().__init__() | |
| self.ignore_index = ignore_index | |
| self.temperature = temperature | |
| self.kl_loss = nn.KLDivLoss(reduction="none") | |
| self.EPS = 1e-8 | |
| def forward(self, logit_1, logit_2, label=None): | |
| """ | |
| Calculate the KL loss. If the label is not None, it considers the | |
| ignore_index in label and calculates the masked loss. | |
| Args: | |
| logit_1 (Tensor): Logit tensor, the data type is float32 or float64. | |
| The shape is (N, C), where C is number of classes, and if shape is | |
| more than 2D, this is (N, C, D1, D2,..., Dk), k >= 1. | |
| logit_2 (Tensor): Logit tensor, the data type is float32 or float64. | |
| The shape of logit_2 and logit_1 are the same. | |
| label (Tensor, optional): Label tensor, the data type is int64. | |
| The shape is (N), where each value is 0 <= label[i] <= C-1, and | |
| if shape is more than 2D, this is (N, D1, D2,..., Dk), k >= 1. | |
| Returns: | |
| (Tensor): The average loss. | |
| """ | |
| if logit_1.shape != logit_2.shape: | |
| raise ValueError( | |
| 'The shape of logit_1 = {} must be the same as the shape of logit_2 = {}.' | |
| .format(logit_1.shape, logit_2.shape)) | |
| logit_1 = F.log_softmax(logit_1 / self.temperature, axis=1) | |
| logit_2 = F.softmax(logit_2 / self.temperature, axis=1) | |
| loss = self.kl_loss(logit_1, logit_2) | |
| loss = loss * self.temperature * self.temperature | |
| if label is None: | |
| avg_loss = paddle.mean(loss) | |
| else: | |
| mask = label != self.ignore_index | |
| mask = paddle.cast(mask, 'float32') | |
| mask = paddle.unsqueeze(mask, axis=1) | |
| label.stop_gradient = True | |
| mask.stop_gradient = True | |
| loss = loss * mask | |
| avg_loss = paddle.mean(loss) / (paddle.mean(mask) + self.EPS) | |
| return avg_loss | |