TaiwanOCR_CertificateofDiagnosis / ppocr /losses /rec_enhanced_ctc_loss.py
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# copyright (c) 2021 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle import nn
from .ace_loss import ACELoss
from .center_loss import CenterLoss
from .rec_ctc_loss import CTCLoss
class EnhancedCTCLoss(nn.Layer):
def __init__(self,
use_focal_loss=False,
use_ace_loss=False,
ace_loss_weight=0.1,
use_center_loss=False,
center_loss_weight=0.05,
num_classes=6625,
feat_dim=96,
init_center=False,
center_file_path=None,
**kwargs):
super(EnhancedCTCLoss, self).__init__()
self.ctc_loss_func = CTCLoss(use_focal_loss=use_focal_loss)
self.use_ace_loss = False
if use_ace_loss:
self.use_ace_loss = use_ace_loss
self.ace_loss_func = ACELoss()
self.ace_loss_weight = ace_loss_weight
self.use_center_loss = False
if use_center_loss:
self.use_center_loss = use_center_loss
self.center_loss_func = CenterLoss(
num_classes=num_classes,
feat_dim=feat_dim,
init_center=init_center,
center_file_path=center_file_path)
self.center_loss_weight = center_loss_weight
def __call__(self, predicts, batch):
loss = self.ctc_loss_func(predicts, batch)["loss"]
if self.use_center_loss:
center_loss = self.center_loss_func(
predicts, batch)["loss_center"] * self.center_loss_weight
loss = loss + center_loss
if self.use_ace_loss:
ace_loss = self.ace_loss_func(
predicts, batch)["loss_ace"] * self.ace_loss_weight
loss = loss + ace_loss
return {'enhanced_ctc_loss': loss}