rec_ctc_loss.py

# 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, use_focal_loss=False, **kwargs):
        super(CTCLoss, self).__init__()
        self.loss_func = nn.CTCLoss(blank=0, reduction='none')
        self.use_focal_loss = use_focal_loss

    def forward(self, predicts, batch):
        if isinstance(predicts, (list, tuple)):
            predicts = predicts[-1]
        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)
        if self.use_focal_loss:
            weight = paddle.exp(-loss)
            weight = paddle.subtract(paddle.to_tensor([1.0]), weight)
            weight = paddle.square(weight)
            loss = paddle.multiply(loss, weight)
        loss = loss.mean()
        return {'loss': loss}