Spaces:
Runtime error
Runtime error
| # copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """ | |
| This code is refer from: | |
| https://github.com/hikopensource/DAVAR-Lab-OCR/blob/main/davarocr/davar_common/models/loss/cross_entropy_loss.py | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import paddle | |
| from paddle import nn | |
| from .basic_loss import CELoss, DistanceLoss | |
| class RFLLoss(nn.Layer): | |
| def __init__(self, ignore_index=-100, **kwargs): | |
| super().__init__() | |
| self.cnt_loss = nn.MSELoss(**kwargs) | |
| self.seq_loss = nn.CrossEntropyLoss(ignore_index=ignore_index) | |
| def forward(self, predicts, batch): | |
| self.total_loss = {} | |
| total_loss = 0.0 | |
| if isinstance(predicts, tuple) or isinstance(predicts, list): | |
| cnt_outputs, seq_outputs = predicts | |
| else: | |
| cnt_outputs, seq_outputs = predicts, None | |
| # batch [image, label, length, cnt_label] | |
| if cnt_outputs is not None: | |
| cnt_loss = self.cnt_loss(cnt_outputs, | |
| paddle.cast(batch[3], paddle.float32)) | |
| self.total_loss['cnt_loss'] = cnt_loss | |
| total_loss += cnt_loss | |
| if seq_outputs is not None: | |
| targets = batch[1].astype("int64") | |
| label_lengths = batch[2].astype('int64') | |
| batch_size, num_steps, num_classes = seq_outputs.shape[ | |
| 0], seq_outputs.shape[1], seq_outputs.shape[2] | |
| assert len(targets.shape) == len(list(seq_outputs.shape)) - 1, \ | |
| "The target's shape and inputs's shape is [N, d] and [N, num_steps]" | |
| inputs = seq_outputs[:, :-1, :] | |
| targets = targets[:, 1:] | |
| inputs = paddle.reshape(inputs, [-1, inputs.shape[-1]]) | |
| targets = paddle.reshape(targets, [-1]) | |
| seq_loss = self.seq_loss(inputs, targets) | |
| self.total_loss['seq_loss'] = seq_loss | |
| total_loss += seq_loss | |
| self.total_loss['loss'] = total_loss | |
| return self.total_loss | |