From cdca62d933c4e0766a05044c6cba7cfa0596e615 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 21 二月 2024 19:22:59 +0800
Subject: [PATCH] Dev gzf (#1377)
---
funasr/train_utils/trainer.py | 22 +++++++++++-----------
1 files changed, 11 insertions(+), 11 deletions(-)
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index d24023d..6a59f91 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -108,7 +108,7 @@
filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
torch.save(state, filename)
- print(f'Checkpoint saved to {filename}')
+ print(f'\nCheckpoint saved to {filename}\n')
latest = Path(os.path.join(self.output_dir, f'model.pt'))
torch.save(state, latest)
@@ -181,7 +181,7 @@
time2 = time.perf_counter()
time_escaped = (time2 - time1)/3600.0
- print(f"time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish: {(self.max_epoch-epoch)*time_escaped:.3f}")
+ print(f"\nrank: {self.local_rank}, time_escaped_epoch: {time_escaped:.3f} hours, estimated to finish {self.max_epoch} epoch: {(self.max_epoch-epoch)*time_escaped:.3f}\n")
if self.rank == 0:
average_checkpoints(self.output_dir, self.avg_nbest_model)
@@ -302,17 +302,14 @@
)
pbar.set_description(description)
if self.writer:
- self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(),
- epoch*len(self.dataloader_train) + batch_idx)
+ self.writer.add_scalar(f'rank{self.local_rank}_Loss/train', loss.item(), self.batch_total)
+ self.writer.add_scalar(f'rank{self.local_rank}_lr/train', lr, self.batch_total)
for key, var in stats.items():
- self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(),
- epoch * len(self.dataloader_train) + batch_idx)
+ self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', var.item(), self.batch_total)
for key, var in speed_stats.items():
- self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var),
- epoch * len(self.dataloader_train) + batch_idx)
-
- # if batch_idx == 2:
- # break
+ self.writer.add_scalar(f'rank{self.local_rank}_{key}/train', eval(var), self.batch_total)
+
+
pbar.close()
def _validate_epoch(self, epoch):
@@ -356,7 +353,10 @@
if (batch_idx+1) % self.log_interval == 0 or (batch_idx+1) == len(self.dataloader_val):
pbar.update(self.log_interval)
+ time_now = datetime.now()
+ time_now = time_now.strftime("%Y-%m-%d %H:%M:%S")
description = (
+ f"{time_now}, "
f"rank: {self.local_rank}, "
f"validation epoch: {epoch}/{self.max_epoch}, "
f"step: {batch_idx+1}/{len(self.dataloader_val)}, "
--
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