text: # 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
  
  
  @manager.LOSSES.add_component
  class CrossEntropyLoss(nn.Layer):
      """
      Implements the cross entropy loss function.
  
      Args:
          weight (tuple|list|ndarray|Tensor, optional): A manual rescaling weight
              given to each class. Its length must be equal to the number of classes.
              Default ``None``.
          ignore_index (int64, optional): Specifies a target value that is ignored
              and does not contribute to the input gradient. Default ``255``.
          top_k_percent_pixels (float, optional): the value lies in [0.0, 1.0]. When its value < 1.0, only compute the loss for
              the top k percent pixels (e.g., the top 20% pixels). This is useful for hard pixel mining.
      """
  
      def __init__(self, weight=None, ignore_index=255, top_k_percent_pixels=1.0):
          super(CrossEntropyLoss, self).__init__()
          if weight is not None:
              weight = paddle.to_tensor(weight, dtype='float32')
          self.weight = weight
          self.ignore_index = ignore_index
          self.top_k_percent_pixels = top_k_percent_pixels
          self.EPS = 1e-8
  
      def forward(self, logit, label, semantic_weights=None):
          """
          Forward computation.
  
          Args:
              logit (Tensor): Logit tensor, the data type is float32, float64. 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.
              label (Tensor): Label tensor, the data type is int64. 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.
          """
          if self.weight is not None and logit.shape[1] != len(self.weight):
              raise ValueError(
                  'The number of weights = {} must be the same as the number of classes = {}.'
                  .format(len(self.weight), logit.shape[1]))
  
          logit = paddle.transpose(logit, [0, 2, 3, 1])
          if self.weight is None:
              loss = F.cross_entropy(
                  logit, label, ignore_index=self.ignore_index, reduction='none')
          else:
              label_one_hot = F.one_hot(label, logit.shape[-1])
              loss = F.cross_entropy(
                  logit,
                  label_one_hot * self.weight,
                  soft_label=True,
                  ignore_index=self.ignore_index,
                  reduction='none')
              loss = loss.squeeze(-1)
  
          mask = label != self.ignore_index
          mask = paddle.cast(mask, 'float32')
          loss = loss * mask
          if semantic_weights is not None:
              loss = loss * semantic_weights
  
          label.stop_gradient = True
          mask.stop_gradient = True
          if self.top_k_percent_pixels == 1.0:
              avg_loss = paddle.mean(loss) / (paddle.mean(mask) + self.EPS)
              return avg_loss
  
          loss = loss.reshape((-1, ))
          top_k_pixels = int(self.top_k_percent_pixels * loss.numel())
          loss, _ = paddle.topk(loss, top_k_pixels)
  
          return loss.mean()
  