53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
# copyright (c) 2022 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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"""This code is refer from:
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https://github.com/hikopensource/DAVAR-Lab-OCR
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"""
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class SPINAttentionLoss(nn.Layer):
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def __init__(self, reduction="mean", ignore_index=-100, **kwargs):
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super(SPINAttentionLoss, self).__init__()
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self.loss_func = nn.CrossEntropyLoss(
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weight=None, reduction=reduction, ignore_index=ignore_index
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)
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def forward(self, predicts, batch):
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targets = batch[1].astype("int64")
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targets = targets[:, 1:] # remove [eos] in label
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label_lengths = batch[2].astype("int64")
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batch_size, num_steps, num_classes = (
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predicts.shape[0],
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predicts.shape[1],
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predicts.shape[2],
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)
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assert (
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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": self.loss_func(inputs, targets)}
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