|
|
|
@ -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
|
|
|
|
|