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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 numpy as np | |
| import paddle | |
| import paddle.nn as nn | |
| import paddle.nn.functional as F | |
| from paddleseg.cvlibs import manager | |
| class FocalLoss(nn.Layer): | |
| """ | |
| The implement of focal loss. | |
| The focal loss requires the label is 0 or 1 for now. | |
| Args: | |
| alpha (float, list, optional): The alpha of focal loss. alpha is the weight | |
| of class 1, 1-alpha is the weight of class 0. Default: 0.25 | |
| gamma (float, optional): The gamma of Focal Loss. Default: 2.0 | |
| ignore_index (int64, optional): Specifies a target value that is ignored | |
| and does not contribute to the input gradient. Default ``255``. | |
| """ | |
| def __init__(self, alpha=0.25, gamma=2.0, ignore_index=255): | |
| super().__init__() | |
| self.alpha = alpha | |
| self.gamma = gamma | |
| self.ignore_index = ignore_index | |
| self.EPS = 1e-10 | |
| def forward(self, logit, label): | |
| """ | |
| Forward computation. | |
| Args: | |
| logit (Tensor): Logit tensor, the data type is float32, float64. Shape is | |
| (N, C, H, W), where C is number of classes. | |
| label (Tensor): Label tensor, the data type is int64. Shape is (N, W, W), | |
| where each value is 0 <= label[i] <= C-1. | |
| Returns: | |
| (Tensor): The average loss. | |
| """ | |
| assert logit.ndim == 4, "The ndim of logit should be 4." | |
| assert logit.shape[1] == 2, "The channel of logit should be 2." | |
| assert label.ndim == 3, "The ndim of label should be 3." | |
| class_num = logit.shape[1] # class num is 2 | |
| logit = paddle.transpose(logit, [0, 2, 3, 1]) # N,C,H,W => N,H,W,C | |
| mask = label != self.ignore_index # N,H,W | |
| mask = paddle.unsqueeze(mask, 3) | |
| mask = paddle.cast(mask, 'float32') | |
| mask.stop_gradient = True | |
| label = F.one_hot(label, class_num) # N,H,W,C | |
| label = paddle.cast(label, logit.dtype) | |
| label.stop_gradient = True | |
| loss = F.sigmoid_focal_loss( | |
| logit=logit, | |
| label=label, | |
| alpha=self.alpha, | |
| gamma=self.gamma, | |
| reduction='none') | |
| loss = loss * mask | |
| avg_loss = paddle.sum(loss) / ( | |
| paddle.sum(paddle.cast(mask != 0., 'int32')) * class_num + self.EPS) | |
| return avg_loss | |
| class MultiClassFocalLoss(nn.Layer): | |
| """ | |
| The implement of focal loss for multi class. | |
| Args: | |
| alpha (float, list, optional): The alpha of focal loss. alpha is the weight | |
| of class 1, 1-alpha is the weight of class 0. Default: 0.25 | |
| gamma (float, optional): The gamma of Focal Loss. Default: 2.0 | |
| ignore_index (int64, optional): Specifies a target value that is ignored | |
| and does not contribute to the input gradient. Default ``255``. | |
| """ | |
| def __init__(self, num_class, alpha=1.0, gamma=2.0, ignore_index=255): | |
| super().__init__() | |
| self.num_class = num_class | |
| self.alpha = alpha | |
| self.gamma = gamma | |
| self.ignore_index = ignore_index | |
| self.EPS = 1e-10 | |
| def forward(self, logit, label): | |
| """ | |
| Forward computation. | |
| Args: | |
| logit (Tensor): Logit tensor, the data type is float32, float64. Shape is | |
| (N, C, H, W), where C is number of classes. | |
| label (Tensor): Label tensor, the data type is int64. Shape is (N, W, W), | |
| where each value is 0 <= label[i] <= C-1. | |
| Returns: | |
| (Tensor): The average loss. | |
| """ | |
| assert logit.ndim == 4, "The ndim of logit should be 4." | |
| assert label.ndim == 3, "The ndim of label should be 3." | |
| logit = paddle.transpose(logit, [0, 2, 3, 1]) | |
| label = label.astype('int64') | |
| ce_loss = F.cross_entropy( | |
| logit, label, ignore_index=self.ignore_index, reduction='none') | |
| pt = paddle.exp(-ce_loss) | |
| focal_loss = self.alpha * ((1 - pt)**self.gamma) * ce_loss | |
| mask = paddle.cast(label != self.ignore_index, 'float32') | |
| focal_loss *= mask | |
| avg_loss = paddle.mean(focal_loss) / (paddle.mean(mask) + self.EPS) | |
| return avg_loss | |