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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
-
-
- @manager.LOSSES.add_component
- 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
-
-
- @manager.LOSSES.add_component
- 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
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