80 lines
3.0 KiB
Python
80 lines
3.0 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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import fasttext
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class AsterLoss(nn.Layer):
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def __init__(self,
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weight=None,
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size_average=True,
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ignore_index=-100,
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sequence_normalize=False,
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sample_normalize=True,
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**kwargs):
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super(AsterLoss, self).__init__()
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self.weight = weight
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self.size_average = size_average
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self.ignore_index = ignore_index
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self.sequence_normalize = sequence_normalize
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self.sample_normalize = sample_normalize
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self.loss_func = paddle.nn.CosineSimilarity()
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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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# sem_target = batch[3].astype('float32')
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embedding_vectors = predicts['embedding_vectors']
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rec_pred = predicts['rec_pred']
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# semantic loss
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# print(embedding_vectors)
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# print(embedding_vectors.shape)
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# targets = fasttext[targets]
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# sem_loss = 1 - self.loss_func(embedding_vectors, targets)
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# rec loss
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batch_size, num_steps, num_classes = rec_pred.shape[0], rec_pred.shape[
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1], rec_pred.shape[2]
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assert len(targets.shape) == len(list(rec_pred.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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mask = paddle.zeros([batch_size, num_steps])
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for i in range(batch_size):
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mask[i, :label_lengths[i]] = 1
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mask = paddle.cast(mask, "float32")
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max_length = max(label_lengths)
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assert max_length == rec_pred.shape[1]
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targets = targets[:, :max_length]
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mask = mask[:, :max_length]
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rec_pred = paddle.reshape(rec_pred, [-1, rec_pred.shape[-1]])
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input = nn.functional.log_softmax(rec_pred, axis=1)
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targets = paddle.reshape(targets, [-1, 1])
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mask = paddle.reshape(mask, [-1, 1])
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# print("input:", input)
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output = -paddle.gather(input, index=targets, axis=1) * mask
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output = paddle.sum(output)
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if self.sequence_normalize:
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output = output / paddle.sum(mask)
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if self.sample_normalize:
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output = output / batch_size
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loss = output
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return {'loss': loss} # , 'sem_loss':sem_loss}
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