From d7d8a10c1ffccb04e3f92ae552bbdb1c7e82c026 Mon Sep 17 00:00:00 2001
From: Siqi Liu <lsqshr@gmail.com>
Date: Sat, 18 Nov 2017 21:03:04 +1100
Subject: [PATCH] remove if True: in train.py

---
 train.py | 61 ++++++++++++++++++++++++++++----------------------------
 1 file changed, 30 insertions(+), 31 deletions(-)

diff --git a/train.py b/train.py
index 7a626977..eafa5b4b 100644
--- a/train.py
+++ b/train.py
@@ -161,40 +161,39 @@ def train(opt):
                 current_score = - val_loss
 
             best_flag = False
-            if True: # if true
-                if best_val_score is None or current_score > best_val_score:
-                    best_val_score = current_score
-                    best_flag = True
-                checkpoint_path = os.path.join(opt.checkpoint_path, 'model.pth')
+            if best_val_score is None or current_score > best_val_score:
+                best_val_score = current_score
+                best_flag = True
+            checkpoint_path = os.path.join(opt.checkpoint_path, 'model.pth')
+            torch.save(model.state_dict(), checkpoint_path)
+            print("model saved to {}".format(checkpoint_path))
+            optimizer_path = os.path.join(opt.checkpoint_path, 'optimizer.pth')
+            torch.save(optimizer.state_dict(), optimizer_path)
+
+            # Dump miscalleous informations
+            infos['iter'] = iteration
+            infos['epoch'] = epoch
+            infos['iterators'] = loader.iterators
+            infos['split_ix'] = loader.split_ix
+            infos['best_val_score'] = best_val_score
+            infos['opt'] = opt
+            infos['vocab'] = loader.get_vocab()
+
+            histories['val_result_history'] = val_result_history
+            histories['loss_history'] = loss_history
+            histories['lr_history'] = lr_history
+            histories['ss_prob_history'] = ss_prob_history
+            with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'.pkl'), 'wb') as f:
+                cPickle.dump(infos, f)
+            with open(os.path.join(opt.checkpoint_path, 'histories_'+opt.id+'.pkl'), 'wb') as f:
+                cPickle.dump(histories, f)
+
+            if best_flag:
+                checkpoint_path = os.path.join(opt.checkpoint_path, 'model-best.pth')
                 torch.save(model.state_dict(), checkpoint_path)
                 print("model saved to {}".format(checkpoint_path))
-                optimizer_path = os.path.join(opt.checkpoint_path, 'optimizer.pth')
-                torch.save(optimizer.state_dict(), optimizer_path)
-
-                # Dump miscalleous informations
-                infos['iter'] = iteration
-                infos['epoch'] = epoch
-                infos['iterators'] = loader.iterators
-                infos['split_ix'] = loader.split_ix
-                infos['best_val_score'] = best_val_score
-                infos['opt'] = opt
-                infos['vocab'] = loader.get_vocab()
-
-                histories['val_result_history'] = val_result_history
-                histories['loss_history'] = loss_history
-                histories['lr_history'] = lr_history
-                histories['ss_prob_history'] = ss_prob_history
-                with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'.pkl'), 'wb') as f:
+                with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'-best.pkl'), 'wb') as f:
                     cPickle.dump(infos, f)
-                with open(os.path.join(opt.checkpoint_path, 'histories_'+opt.id+'.pkl'), 'wb') as f:
-                    cPickle.dump(histories, f)
-
-                if best_flag:
-                    checkpoint_path = os.path.join(opt.checkpoint_path, 'model-best.pth')
-                    torch.save(model.state_dict(), checkpoint_path)
-                    print("model saved to {}".format(checkpoint_path))
-                    with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'-best.pkl'), 'wb') as f:
-                        cPickle.dump(infos, f)
 
         # Stop if reaching max epochs
         if epoch >= opt.max_epochs and opt.max_epochs != -1: