mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-11-03 19:29:18 +00:00
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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import paddle
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import paddle.nn as nn
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from .rec_ctc_loss import CTCLoss
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from .center_loss import CenterLoss
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from .ace_loss import ACELoss
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from .distillation_loss import DistillationCTCLoss
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from .distillation_loss import DistillationDMLLoss
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from .distillation_loss import DistillationDistanceLoss, DistillationDBLoss, DistillationDilaDBLoss
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class CombinedLoss(nn.Layer):
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"""
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CombinedLoss:
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a combionation of loss function
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"""
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def __init__(self, loss_config_list=None):
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super().__init__()
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self.loss_func = []
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self.loss_weight = []
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assert isinstance(loss_config_list, list), (
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'operator config should be a list')
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for config in loss_config_list:
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assert isinstance(config,
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dict) and len(config) == 1, "yaml format error"
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name = list(config)[0]
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param = config[name]
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assert "weight" in param, "weight must be in param, but param just contains {}".format(
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param.keys())
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self.loss_weight.append(param.pop("weight"))
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self.loss_func.append(eval(name)(**param))
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def forward(self, input, batch, **kargs):
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loss_dict = {}
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loss_all = 0.
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for idx, loss_func in enumerate(self.loss_func):
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loss = loss_func(input, batch, **kargs)
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if isinstance(loss, paddle.Tensor):
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loss = {"loss_{}_{}".format(str(loss), idx): loss}
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weight = self.loss_weight[idx]
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loss = {key: loss[key] * weight for key in loss}
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if "loss" in loss:
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loss_all += loss["loss"]
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else:
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loss_all += paddle.add_n(list(loss.values()))
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loss_dict.update(loss)
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loss_dict["loss"] = loss_all
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return loss_dict
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