12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758 |
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import paddle
- from paddle import nn
- from .rec_ctc_loss import CTCLoss
- from .rec_sar_loss import SARLoss
- class MultiLoss(nn.Layer):
- def __init__(self, **kwargs):
- super().__init__()
- self.loss_funcs = {}
- self.loss_list = kwargs.pop('loss_config_list')
- self.weight_1 = kwargs.get('weight_1', 1.0)
- self.weight_2 = kwargs.get('weight_2', 1.0)
- self.gtc_loss = kwargs.get('gtc_loss', 'sar')
- for loss_info in self.loss_list:
- for name, param in loss_info.items():
- if param is not None:
- kwargs.update(param)
- loss = eval(name)(**kwargs)
- self.loss_funcs[name] = loss
- def forward(self, predicts, batch):
- self.total_loss = {}
- total_loss = 0.0
-
- for name, loss_func in self.loss_funcs.items():
- if name == 'CTCLoss':
- loss = loss_func(predicts['ctc'],
- batch[:2] + batch[3:])['loss'] * self.weight_1
- elif name == 'SARLoss':
- loss = loss_func(predicts['sar'],
- batch[:1] + batch[2:])['loss'] * self.weight_2
- else:
- raise NotImplementedError(
- '{} is not supported in MultiLoss yet'.format(name))
- self.total_loss[name] = loss
- total_loss += loss
- self.total_loss['loss'] = total_loss
- return self.total_loss
|