71 lines
2.8 KiB
Python
71 lines
2.8 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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"""
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This code is refer from:
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https://github.com/wangyuxin87/VisionLAN
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"""
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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 VLLoss(nn.Layer):
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def __init__(self, mode='LF_1', weight_res=0.5, weight_mas=0.5, **kwargs):
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super(VLLoss, self).__init__()
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self.loss_func = paddle.nn.loss.CrossEntropyLoss(reduction="mean")
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assert mode in ['LF_1', 'LF_2', 'LA']
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self.mode = mode
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self.weight_res = weight_res
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self.weight_mas = weight_mas
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def flatten_label(self, target):
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label_flatten = []
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label_length = []
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for i in range(0, target.shape[0]):
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cur_label = target[i].tolist()
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label_flatten += cur_label[:cur_label.index(0) + 1]
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label_length.append(cur_label.index(0) + 1)
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label_flatten = paddle.to_tensor(label_flatten, dtype='int64')
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label_length = paddle.to_tensor(label_length, dtype='int32')
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return (label_flatten, label_length)
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def _flatten(self, sources, lengths):
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return paddle.concat([t[:l] for t, l in zip(sources, lengths)])
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def forward(self, predicts, batch):
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text_pre = predicts[0]
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target = batch[1].astype('int64')
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label_flatten, length = self.flatten_label(target)
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text_pre = self._flatten(text_pre, length)
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if self.mode == 'LF_1':
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loss = self.loss_func(text_pre, label_flatten)
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else:
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text_rem = predicts[1]
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text_mas = predicts[2]
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target_res = batch[2].astype('int64')
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target_sub = batch[3].astype('int64')
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label_flatten_res, length_res = self.flatten_label(target_res)
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label_flatten_sub, length_sub = self.flatten_label(target_sub)
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text_rem = self._flatten(text_rem, length_res)
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text_mas = self._flatten(text_mas, length_sub)
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loss_ori = self.loss_func(text_pre, label_flatten)
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loss_res = self.loss_func(text_rem, label_flatten_res)
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loss_mas = self.loss_func(text_mas, label_flatten_sub)
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loss = loss_ori + loss_res * self.weight_res + loss_mas * self.weight_mas
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return {'loss': loss}
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