monitor display

nnet/xvector.py

@@ -1319,8 +1319,16 @@ def new_xtrain(dataset_description,
                                                                          training_description,
                                                                          kwargs)
 
+    # Initialize the training monitor
+    monitor = TrainingMonitor(output_file=training_opts["log_file"],
+                              patience=training_opts["patience"],
+                              best_accuracy=0.0,
+                              best_eer_epoch=1,
+                              best_eer=100,
+                              compute_test_eer=training_opts["compute_test_eer"])
+
     # Initialize the logger in file and console
-    init_logging(filename=training_opts["log_file"])
+    #init_logging(filename=training_opts["log_file"])
 
     # Display the entire configurations as YAML dictionnaries
     logging.info(yaml.dump(dataset_opts, default_flow_style=False))
@@ -1365,14 +1373,6 @@ def new_xtrain(dataset_description,
     if training_opts["mixed_precision"]:
         scaler = torch.cuda.amp.GradScaler()
 
-    # Initialize the training monitor
-    monitor = TrainingMonitor(output_file="log/training_xv.log",
-                              patience=training_opts["patience"],
-                              best_accuracy=0.0,
-                              best_eer_epoch=1,
-                              best_eer=100,
-                              compute_test_eer=training_opts["compute_test_eer"])
-
     for epoch in range(1, training_opts["epochs"] + 1):
 
         monitor.update(epoch)
@@ -1383,11 +1383,10 @@ def new_xtrain(dataset_description,
             break
 
         model = new_train_epoch(model,
-                            epoch,
+                            monitor,
                             training_loader,
                             optimizer,
                             scheduler,
-                            training_opts["log_interval"],
                             device,
                             scaler=scaler
                             )