speech_recognition/FunASR
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* resume from step

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* batch

* train_loss_avg train_acc_avg

* train_loss_avg train_acc_avg
This commit is contained in:
zhifu gao 2024-04-30 16:28:02 +08:00 committed by GitHub
parent a09aba419f
commit 48a8c95334
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2 changed files with 16 additions and 6 deletions
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2
funasr/bin/train.py
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@ -198,7 +198,7 @@ def main(**kwargs):
writer = None
dataloader_tr, dataloader_val = None, None
for epoch in range(trainer.start_epoch, trainer.max_epoch + 1):
for epoch in range(trainer.start_epoch, trainer.max_epoch):
time1 = time.perf_counter()
for data_split_i in range(trainer.start_data_split_i, dataloader.data_split_num):

20
funasr/train_utils/trainer.py
View File

@ -169,6 +169,8 @@ class Trainer:
"data_split_i": kwargs.get("data_split_i", 0),
"data_split_num": kwargs.get("data_split_num", 1),
"batch_total": self.batch_total,
"train_loss_avg": kwargs.get("train_loss_avg", 0),
"train_acc_avg": kwargs.get("train_acc_avg", 0),
}
step = step_in_epoch
if hasattr(model, "module"):
@ -306,7 +308,12 @@ class Trainer:
checkpoint["step_in_epoch"] if "step_in_epoch" in checkpoint else 0
)
self.step_in_epoch = 0 if self.step_in_epoch is None else self.step_in_epoch
self.train_acc_avg = (
checkpoint["train_acc_avg"] if "train_acc_avg" in checkpoint else 0
)
self.train_loss_avg = (
checkpoint["train_loss_avg"] if "train_loss_avg" in checkpoint else 0
)
model.to(self.device)
print(f"Checkpoint loaded successfully from '{ckpt}'")
else:
@ -400,12 +407,13 @@ class Trainer:
speed_stats["backward_time"] = f"{time4 - time3:0.3f}"
self.train_loss_avg = (
self.train_loss_avg * batch_idx + loss.detach().cpu().item()
) / (batch_idx + 1)
self.train_loss_avg * (self.step_in_epoch - 1) + loss.detach().cpu().item()
) / self.step_in_epoch
if "acc" in stats:
self.train_acc_avg = (
self.train_acc_avg * batch_idx + stats["acc"].detach().cpu().item()
) / (batch_idx + 1)
self.train_acc_avg * (self.step_in_epoch - 1)
+ stats["acc"].detach().cpu().item()
) / self.step_in_epoch
if self.use_ddp or self.use_fsdp:
train_loss_avg = torch.tensor(self.train_loss_avg, dtype=torch.float32).to(
self.device
@ -490,6 +498,8 @@ class Trainer:
step_in_epoch=self.step_in_epoch,
data_split_i=kwargs.get("data_split_i", 0),
data_split_num=kwargs.get("data_split_num", 1),
train_loss_avg=self.train_loss_avg,
train_acc_avg=self.train_acc_avg,
)
time_beg = time.perf_counter()