40 lines
1.5 KiB
Python
40 lines
1.5 KiB
Python
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# copyright (c) 2021 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 AttentionLoss(nn.Layer):
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def __init__(self, **kwargs):
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super(AttentionLoss, self).__init__()
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self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none')
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def forward(self, predicts, batch):
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targets = batch[1].astype("int64")
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label_lengths = batch[2].astype('int64')
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batch_size, num_steps, num_classes = predicts.shape[0], predicts.shape[
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1], predicts.shape[2]
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assert len(targets.shape) == len(list(predicts.shape)) - 1, \
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"The target's shape and inputs's shape is [N, d] and [N, num_steps]"
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inputs = paddle.reshape(predicts, [-1, predicts.shape[-1]])
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targets = paddle.reshape(targets, [-1])
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return {'loss': paddle.sum(self.loss_func(inputs, targets))}
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