rm useless

deepspeech/exps/u2/model.py

@@ -41,8 +41,6 @@ from deepspeech.utils import mp_tools
 from deepspeech.utils import text_grid
 from deepspeech.utils import utility
 from deepspeech.utils.log import Log
-# from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
-# from deepspeech.training.scheduler import WarmupLR
 
 logger = Log(__name__).getlog()
 
@@ -324,25 +322,6 @@ class U2Trainer(Trainer):
         lr_scheduler = LRSchedulerFactory.from_args(scheduler_type,
                                                     scheduler_args)
 
-        # if scheduler_type == 'expdecaylr':
-        #     lr_scheduler = paddle.optimizer.lr.ExponentialDecay(
-        #         learning_rate=optim_conf.lr,
-        #         gamma=scheduler_conf.lr_decay,
-        #         verbose=False)
-        # elif scheduler_type == 'warmuplr':
-        #     lr_scheduler = WarmupLR(
-        #         learning_rate=optim_conf.lr,
-        #         warmup_steps=scheduler_conf.warmup_steps,
-        #         verbose=False)
-        # elif scheduler_type == 'noam':
-        #     lr_scheduler = paddle.optimizer.lr.NoamDecay(
-        #         learning_rate=optim_conf.lr,
-        #         d_model=model_conf.encoder_conf.output_size,
-        #         warmup_steps=scheduler_conf.warmup_steps,
-        #         verbose=False)
-        # else:
-        #     raise ValueError(f"Not support scheduler: {scheduler_type}")
-
         def optimizer_args(
                 config,
                 parameters,
@@ -366,17 +345,6 @@ class U2Trainer(Trainer):
         optimzer_args = optimizer_args(config, model.parameters(), lr_scheduler)
         optimizer = OptimizerFactory.from_args(optim_type, optimzer_args)
 
-        # grad_clip = ClipGradByGlobalNormWithLog(train_config.global_grad_clip)
-        # weight_decay = paddle.regularizer.L2Decay(optim_conf.weight_decay)
-        # if optim_type == 'adam':
-        #     optimizer = paddle.optimizer.Adam(
-        #         learning_rate=lr_scheduler,
-        #         parameters=model.parameters(),
-        #         weight_decay=weight_decay,
-        #         grad_clip=grad_clip)
-        # else:
-        #     raise ValueError(f"Not support optim: {optim_type}")
-
         self.model = model
         self.optimizer = optimizer
         self.lr_scheduler = lr_scheduler