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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | |
| # | |
| # 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. | |
| import paddle | |
| import paddle.nn as nn | |
| from .rec_ctc_loss import CTCLoss | |
| from .center_loss import CenterLoss | |
| from .ace_loss import ACELoss | |
| from .rec_sar_loss import SARLoss | |
| from .distillation_loss import DistillationCTCLoss, DistillCTCLogits | |
| from .distillation_loss import DistillationSARLoss, DistillationNRTRLoss | |
| from .distillation_loss import DistillationDMLLoss, DistillationKLDivLoss, DistillationDKDLoss | |
| from .distillation_loss import DistillationDistanceLoss, DistillationDBLoss, DistillationDilaDBLoss | |
| from .distillation_loss import DistillationVQASerTokenLayoutLMLoss, DistillationSERDMLLoss | |
| from .distillation_loss import DistillationLossFromOutput | |
| from .distillation_loss import DistillationVQADistanceLoss | |
| class CombinedLoss(nn.Layer): | |
| """ | |
| CombinedLoss: | |
| a combionation of loss function | |
| """ | |
| def __init__(self, loss_config_list=None): | |
| super().__init__() | |
| self.loss_func = [] | |
| self.loss_weight = [] | |
| assert isinstance(loss_config_list, list), ( | |
| 'operator config should be a list') | |
| for config in loss_config_list: | |
| assert isinstance(config, | |
| dict) and len(config) == 1, "yaml format error" | |
| name = list(config)[0] | |
| param = config[name] | |
| assert "weight" in param, "weight must be in param, but param just contains {}".format( | |
| param.keys()) | |
| self.loss_weight.append(param.pop("weight")) | |
| self.loss_func.append(eval(name)(**param)) | |
| def forward(self, input, batch, **kargs): | |
| loss_dict = {} | |
| loss_all = 0. | |
| for idx, loss_func in enumerate(self.loss_func): | |
| loss = loss_func(input, batch, **kargs) | |
| if isinstance(loss, paddle.Tensor): | |
| loss = {"loss_{}_{}".format(str(loss), idx): loss} | |
| weight = self.loss_weight[idx] | |
| loss = {key: loss[key] * weight for key in loss} | |
| if "loss" in loss: | |
| loss_all += loss["loss"] | |
| else: | |
| loss_all += paddle.add_n(list(loss.values())) | |
| loss_dict.update(loss) | |
| loss_dict["loss"] = loss_all | |
| return loss_dict | |