From ef7b3a47d422eb65093499e6af197687d807df3e Mon Sep 17 00:00:00 2001
From: dohe0342 <kimdohe1070@gmail.com>
Date: Tue, 10 Jan 2023 01:12:13 +0900
Subject: [PATCH] from local

---
 .../ASR/incremental_transf/.train.py.swp      | Bin 61440 -> 61440 bytes
 .../ASR/incremental_transf/train.py           |   8 +++-----
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/egs/librispeech/ASR/incremental_transf/.train.py.swp b/egs/librispeech/ASR/incremental_transf/.train.py.swp
index afab38765d44e9b9bb2e47a833988428a5e7e54e..21ccc11589a9d54050b3bd1c863902e846f84250 100644
GIT binary patch
delta 442
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D=FC&D

delta 446
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diff --git a/egs/librispeech/ASR/incremental_transf/train.py b/egs/librispeech/ASR/incremental_transf/train.py
index 588c56091..4786bb6a0 100755
--- a/egs/librispeech/ASR/incremental_transf/train.py
+++ b/egs/librispeech/ASR/incremental_transf/train.py
@@ -977,7 +977,7 @@ def run(rank, world_size, args):
     for n, p in model.named_parameters():
         if 'layer' not in n:
             try: p.data = pre_trained_model1[n]
-            except: print(f'pre-trained model has no parameter named {n}.')
+            except: print(f'1: pre-trained model has no parameter named {n}.')
         else:
             layer_name_splited = n.split('.')
             if int(layer_name_splited[3]) % 2 == 0:
@@ -985,21 +985,19 @@ def run(rank, world_size, args):
                 layer_name_splited[3] = str(int(layer_name_splited[3])//2)
                 old_name = '.'.join(layer_name_splited)
                 try: p.data = pre_trained_model2[old_name]
-                except: print(f'pre-trained model has no parameter named {n}.')
+                except: print(f'2: pre-trained model has no parameter named {n}.')
             else:
                 layer_name_splited[0] = 'inter_encoder'
                 layer_name_splited[3] = str(int(layer_name_splited[3])//2+1)
                 old_name = '.'.join(layer_name_splited)
                 try: p.data = pre_trained_model2[old_name]
-                except: print(f'pre-trained model has no parameter named {n}.')
+                except: print(f'3: pre-trained model has no parameter named {n}.')
     
     del pre_trained1
     del pre_trained2
     del pre_trained_model1
     del pre_trained_model2
 
-            
-
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")