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 SeCrossEntropyLoss(nn.Layer):
      def __init__(self, num_classes, ignore_index=255):
          super(SeCrossEntropyLoss, self).__init__()
          self.num_classes = num_classes
  
      def forward(self, logit, label):
          if logit.ndim == 4:
              logit_ = logit.sequeeze(2).sequeeze(3)
              assert logit_.ndim == 2, "The shape of logit should be [N, C, 1, 1] or [N, C], but got {}.".format(
                  logit.ndim)
              logit = logit_
          assert logit.ndim == 2, "The dimension of logit should be 2, but got {}.".format(
              logit.ndim)
          batch_size = paddle.shape(label)[0]
          se_label = paddle.zeros([batch_size, self.num_classes])
          for i in range(batch_size):
              hist = paddle.histogram(label[i],
                                      bins=self.num_classes,
                                      min=0,
                                      max=self.num_classes - 1)
              hist = hist.astype('float32') / hist.sum().astype('float32')
              se_label[i] = (hist > 0).astype('float32')
          loss = F.binary_cross_entropy_with_logits(logit, se_label)
          return loss
  