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48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
# copyright (c) 2024 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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"""
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This code is refer from:
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https://github.com/lucidrains/x-transformers/blob/main/x_transformers/autoregressive_wrapper.py
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"""
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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import numpy as np
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class LaTeXOCRLoss(nn.Layer):
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"""
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LaTeXOCR adopt CrossEntropyLoss for network training.
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"""
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def __init__(self):
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super(LaTeXOCRLoss, self).__init__()
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self.ignore_index = -100
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self.cross = nn.CrossEntropyLoss(
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reduction="mean", ignore_index=self.ignore_index
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)
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def forward(self, preds, batch):
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word_probs = preds
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labels = batch[1][:, 1:]
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word_loss = self.cross(
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paddle.reshape(word_probs, [-1, word_probs.shape[-1]]),
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paddle.reshape(labels, [-1]),
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)
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loss = word_loss
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return {"loss": loss}
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