From 48a8c9533499e428b07767d4a991531943575d3a Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期二, 30 四月 2024 16:28:02 +0800
Subject: [PATCH] Dev gzf exp (#1684)
---
funasr/train_utils/trainer.py | 20 +++++++++++++++-----
funasr/bin/train.py | 2 +-
2 files changed, 16 insertions(+), 6 deletions(-)
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index d20915c..7695e51 100644
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -198,7 +198,7 @@
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):
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index a28ca51..dd0ac7a 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -169,6 +169,8 @@
"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 @@
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 @@
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 @@
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()
--
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