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| # Copyright (c) 2020 PaddlePaddle 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. | |
| import paddle | |
| from paddle import nn | |
| import paddle.nn.functional as F | |
| from paddleseg.cvlibs import manager | |
| class BootstrappedCrossEntropyLoss(nn.Layer): | |
| """ | |
| Implements the cross entropy loss function. | |
| Args: | |
| min_K (int): the minimum number of pixels to be counted in loss computation. | |
| loss_th (float): the loss threshold. Only loss that is larger than the threshold | |
| would be calculated. | |
| weight (tuple|list, optional): The weight for different classes. Default: None. | |
| ignore_index (int, optional): Specifies a target value that is ignored | |
| and does not contribute to the input gradient. Default: 255. | |
| """ | |
| def __init__(self, min_K, loss_th, weight=None, ignore_index=255): | |
| super().__init__() | |
| self.ignore_index = ignore_index | |
| self.K = min_K | |
| self.threshold = loss_th | |
| if weight is not None: | |
| weight = paddle.to_tensor(weight, dtype='float32') | |
| self.weight = weight | |
| def forward(self, logit, label): | |
| n, c, h, w = logit.shape | |
| total_loss = 0.0 | |
| if len(label.shape) != len(logit.shape): | |
| label = paddle.unsqueeze(label, 1) | |
| for i in range(n): | |
| x = paddle.unsqueeze(logit[i], 0) | |
| y = paddle.unsqueeze(label[i], 0) | |
| x = paddle.transpose(x, (0, 2, 3, 1)) | |
| y = paddle.transpose(y, (0, 2, 3, 1)) | |
| x = paddle.reshape(x, shape=(-1, c)) | |
| y = paddle.reshape(y, shape=(-1, )) | |
| loss = F.cross_entropy( | |
| x, | |
| y, | |
| weight=self.weight, | |
| ignore_index=self.ignore_index, | |
| reduction="none") | |
| sorted_loss = paddle.sort(loss, descending=True) | |
| if sorted_loss[self.K] > self.threshold: | |
| new_indices = paddle.nonzero(sorted_loss > self.threshold) | |
| loss = paddle.gather(sorted_loss, new_indices) | |
| else: | |
| loss = sorted_loss[:self.K] | |
| total_loss += paddle.mean(loss) | |
| return total_loss / float(n) | |