text: # copyright (c) 2019 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.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import paddle
  from paddle import nn
  
  
  class CTCLoss(nn.Layer):
      def __init__(self, **kwargs):
          super(CTCLoss, self).__init__()
          self.loss_func = nn.CTCLoss(blank=0, reduction='none')
  
      def __call__(self, predicts, batch):
          predicts = predicts.transpose((1, 0, 2))
          N, B, _ = predicts.shape
          preds_lengths = paddle.to_tensor([N] * B, dtype='int64')
          labels = batch[1].astype("int32")
          label_lengths = batch[2].astype('int64')
          loss = self.loss_func(predicts, labels, preds_lengths, label_lengths)
          loss = loss.mean()  # sum
          return {'loss': loss}
  
  
  class FocalCTCLoss(nn.Layer):
      def __init__(self, alpha=0.5, gamma=2.0, **kwargs):
          super(FocalCTCLoss, self).__init__()
          self.alpha = alpha
          self.gamma = gamma
          self.loss_func = nn.CTCLoss(blank=0, reduction='none')
  
      def __call__(self, predicts, batch):
          predicts = predicts.transpose((1, 0, 2))
          N, B, _ = predicts.shape
          preds_lengths = paddle.to_tensor([N] * B, dtype='int64')
          labels = batch[1].astype("int32")
          label_lengths = batch[2].astype('int64')
          loss = self.loss_func(predicts, labels, preds_lengths, label_lengths)
          p = paddle.exp(-loss)
          focal_ctc_loss = self.alpha * paddle.pow((1 - p), self.gamma) * loss
          focal_ctc_loss = focal_ctc_loss.mean()  # sum
          return {'loss': focal_ctc_loss}
  