From 9fa2b2128d3935b2edff2a2a3f1b8fd430a7e272 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期四, 30 十一月 2023 11:15:47 +0800
Subject: [PATCH] rm log.h for wins-websocket
---
funasr/train/trainer.py | 16 ++++------------
1 files changed, 4 insertions(+), 12 deletions(-)
diff --git a/funasr/train/trainer.py b/funasr/train/trainer.py
index f066909..a5069d0 100644
--- a/funasr/train/trainer.py
+++ b/funasr/train/trainer.py
@@ -26,7 +26,6 @@
import torch
import torch.nn
import torch.optim
-from typeguard import check_argument_types
from funasr.iterators.abs_iter_factory import AbsIterFactory
from funasr.main_funcs.average_nbest_models import average_nbest_models
@@ -127,7 +126,6 @@
@classmethod
def build_options(cls, args: argparse.Namespace) -> TrainerOptions:
"""Build options consumed by train(), eval()"""
- assert check_argument_types()
return build_dataclass(TrainerOptions, args)
@classmethod
@@ -188,7 +186,6 @@
distributed_option: DistributedOption,
) -> None:
"""Perform training. This method performs the main process of training."""
- assert check_argument_types()
# NOTE(kamo): Don't check the type more strictly as far trainer_options
assert is_dataclass(trainer_options), type(trainer_options)
assert len(optimizers) == len(schedulers), (len(optimizers), len(schedulers))
@@ -281,14 +278,11 @@
for iepoch in range(start_epoch, trainer_options.max_epoch + 1):
if iepoch != start_epoch:
logging.info(
- "{}/{}epoch started. Estimated time to finish: {}".format(
+ "{}/{}epoch started. Estimated time to finish: {} hours".format(
iepoch,
trainer_options.max_epoch,
- humanfriendly.format_timespan(
- (time.perf_counter() - start_time)
- / (iepoch - start_epoch)
- * (trainer_options.max_epoch - iepoch + 1)
- ),
+ (time.perf_counter() - start_time) / 3600.0 / (iepoch - start_epoch) * (
+ trainer_options.max_epoch - iepoch + 1),
)
)
else:
@@ -372,7 +366,7 @@
],
"scaler": scaler.state_dict() if scaler is not None else None,
"ema_model": model.encoder.ema.model.state_dict()
- if hasattr(model.encoder, "ema") and model.encoder.ema is not None else None,
+ if hasattr(model, "encoder") and hasattr(model.encoder, "ema") and model.encoder.ema is not None else None,
},
buffer,
)
@@ -551,7 +545,6 @@
options: TrainerOptions,
distributed_option: DistributedOption,
) -> Tuple[bool, bool]:
- assert check_argument_types()
grad_noise = options.grad_noise
accum_grad = options.accum_grad
@@ -845,7 +838,6 @@
options: TrainerOptions,
distributed_option: DistributedOption,
) -> None:
- assert check_argument_types()
ngpu = options.ngpu
no_forward_run = options.no_forward_run
distributed = distributed_option.distributed
--
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