53 lines
1.7 KiB
Python
53 lines
1.7 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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# This code is refer from: https://github.com/viig99/LS-ACELoss
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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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import paddle.nn as nn
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class ACELoss(nn.Layer):
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def __init__(self, **kwargs):
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super().__init__()
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self.loss_func = nn.CrossEntropyLoss(
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weight=None,
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ignore_index=0,
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reduction='none',
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soft_label=True,
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axis=-1)
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def __call__(self, predicts, batch):
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if isinstance(predicts, (list, tuple)):
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predicts = predicts[-1]
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B, N = predicts.shape[:2]
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div = paddle.to_tensor([N]).astype('float32')
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predicts = nn.functional.softmax(predicts, axis=-1)
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aggregation_preds = paddle.sum(predicts, axis=1)
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aggregation_preds = paddle.divide(aggregation_preds, div)
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length = batch[2].astype("float32")
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batch = batch[3].astype("float32")
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batch[:, 0] = paddle.subtract(div, length)
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batch = paddle.divide(batch, div)
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loss = self.loss_func(aggregation_preds, batch)
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return {"loss_ace": loss}
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