100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
# 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 CosineEmbeddingLoss(nn.Layer):
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def __init__(self, margin=0.):
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super(CosineEmbeddingLoss, self).__init__()
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self.margin = margin
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self.epsilon = 1e-12
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def forward(self, x1, x2, target):
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similarity = paddle.fluid.layers.reduce_sum(
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x1 * x2, dim=-1) / (paddle.norm(
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x1, axis=-1) * paddle.norm(
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x2, axis=-1) + self.epsilon)
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one_list = paddle.full_like(target, fill_value=1)
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out = paddle.fluid.layers.reduce_mean(
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paddle.where(
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paddle.equal(target, one_list), 1. - similarity,
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paddle.maximum(
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paddle.zeros_like(similarity), similarity - self.margin)))
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return out
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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_sem = CosineEmbeddingLoss()
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self.is_cosin_loss = True
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self.loss_func_rec = 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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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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if not self.is_cosin_loss:
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sem_loss = paddle.sum(self.loss_sem(embedding_vectors, sem_target))
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else:
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label_target = paddle.ones([embedding_vectors.shape[0]])
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sem_loss = paddle.sum(
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self.loss_sem(embedding_vectors, sem_target, label_target))
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# rec loss
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batch_size, def_max_length = targets.shape[0], targets.shape[1]
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mask = paddle.zeros([batch_size, def_max_length])
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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[2]])
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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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output = -paddle.index_sample(input, index=targets) * 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 + sem_loss * 0.1
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return {'loss': loss}
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