From 4cf44a89f808411a0616c8ed92c3afae3d3e371a Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 21 二月 2024 00:43:27 +0800
Subject: [PATCH] bugfix

---
 funasr/train_utils/trainer.py |   14 ++++++++------
 1 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 10f7f80..b3c9953 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -188,7 +188,7 @@
             epoch (int): The current epoch number.
         """
         self.model.train()
-        pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
+        pbar = tqdm(colour="blue", desc=f"rank: {self.local_rank}, Training Epoch: {epoch + 1}", total=len(self.dataloader_train),
                     dynamic_ncols=True)
         
         # Set the number of steps for gradient accumulation
@@ -273,12 +273,14 @@
                                              torch.cuda.memory_reserved()/1024/1024/1024,
                                              torch.cuda.max_memory_reserved()/1024/1024/1024,
                                              )
+                lr = self.scheduler.get_last_lr()[0]
                 description = (
                     f"rank: {self.local_rank}, "
                     f"epoch: {epoch}/{self.max_epoch}, "
-                    f"step: {batch_idx}/{len(self.dataloader_train)}, total: {self.batch_total}, "
+                    f"step: {batch_idx+1}/{len(self.dataloader_train)}, total: {self.batch_total}, "
                     f"(loss: {loss.detach().cpu().item():.3f}), "
-                    f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
+                    f"(lr: {lr:.3e}), "
+                    f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
                     f"{speed_stats}, "
                     f"{gpu_info}"
                 )
@@ -307,7 +309,7 @@
         """
         self.model.eval()
         with torch.no_grad():
-            pbar = tqdm(colour="red", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_val),
+            pbar = tqdm(colour="red", desc=f"rank: {self.local_rank}, Validation Epoch: {epoch + 1}", total=len(self.dataloader_val),
                         dynamic_ncols=True)
             speed_stats = {}
             time5 = time.perf_counter()
@@ -341,9 +343,9 @@
                     description = (
                         f"rank: {self.local_rank}, "
                         f"validation epoch: {epoch}/{self.max_epoch}, "
-                        f"step: {batch_idx}/{len(self.dataloader_val)}, "
+                        f"step: {batch_idx+1}/{len(self.dataloader_val)}, "
                         f"(loss: {loss.detach().cpu().item():.3f}), "
-                        f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
+                        f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}, "
                         f"{speed_stats}, "
                     )
                     pbar.set_description(description)

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