37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
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# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import paddle
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from paddle import nn
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class CTCLoss(nn.Layer):
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def __init__(self, **kwargs):
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super(CTCLoss, self).__init__()
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self.loss_func = nn.CTCLoss(blank=0, reduction='none')
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def __call__(self, predicts, batch):
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predicts = predicts.transpose((1, 0, 2))
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N, B, _ = predicts.shape
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preds_lengths = paddle.to_tensor([N] * B, dtype='int64')
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labels = batch[1].astype("int32")
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label_lengths = batch[2].astype('int64')
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loss = self.loss_func(predicts, labels, preds_lengths, label_lengths)
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loss = loss.mean()
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return {'loss': loss}
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