| import copy |
| from importlib import import_module |
| from torch import nn |
|
|
| name_to_module = { |
| 'ABINetLoss': '.abinet_loss', |
| 'ARLoss': '.ar_loss', |
| 'CDistNetLoss': '.cdistnet_loss', |
| 'CELoss': '.ce_loss', |
| 'CPPDLoss': '.cppd_loss', |
| 'CTCLoss': '.ctc_loss', |
| 'IGTRLoss': '.igtr_loss', |
| 'LISTERLoss': '.lister_loss', |
| 'LPVLoss': '.lpv_loss', |
| 'MGPLoss': '.mgp_loss', |
| 'PARSeqLoss': '.parseq_loss', |
| 'RobustScannerLoss': '.robustscanner_loss', |
| 'SEEDLoss': '.seed_loss', |
| 'SMTRLoss': '.smtr_loss', |
| 'SRNLoss': '.srn_loss', |
| 'VisionLANLoss': '.visionlan_loss', |
| 'CAMLoss': '.cam_loss', |
| 'MDiffLoss': '.mdiff_loss', |
| } |
|
|
|
|
| def build_loss(config): |
| config = copy.deepcopy(config) |
| module_name = config.pop('name') |
|
|
| if module_name in globals(): |
| module_class = globals()[module_name] |
| else: |
| assert module_name in name_to_module, Exception( |
| '{} is not supported. The losses in {} are supportes'.format( |
| module_name, list(name_to_module.keys()))) |
| module_path = name_to_module[module_name] |
| module = import_module(module_path, package=__package__) |
| module_class = getattr(module, module_name) |
|
|
| return module_class(**config) |
|
|
|
|
| class GTCLoss(nn.Module): |
|
|
| def __init__(self, |
| gtc_loss, |
| gtc_weight=1.0, |
| ctc_weight=1.0, |
| zero_infinity=True, |
| **kwargs): |
| super(GTCLoss, self).__init__() |
| |
| ctc_config = {'name': 'CTCLoss', 'zero_infinity': zero_infinity} |
| self.ctc_loss = build_loss(ctc_config) |
| |
| self.gtc_loss = build_loss(gtc_loss) |
| self.gtc_weight = gtc_weight |
| self.ctc_weight = ctc_weight |
|
|
| def forward(self, predicts, batch): |
| ctc_loss = self.ctc_loss(predicts['ctc_pred'], |
| [None] + batch[-2:])['loss'] |
| gtc_loss = self.gtc_loss(predicts['gtc_pred'], batch[:-2])['loss'] |
| return { |
| 'loss': self.ctc_weight * ctc_loss + self.gtc_weight * gtc_loss, |
| 'ctc_loss': ctc_loss, |
| 'gtc_loss': gtc_loss |
| } |
|
|
