debug

nnet/xvector.py

-# coding: utf-8 -*-
+#coding: utf-8 -*-
 #
 # This file is part of SIDEKIT.
 #
@@ -1492,22 +1492,22 @@ def xtrain(dataset_description,
                                                           validation_non_indices,
                                                           training_opts["mixed_precision"])
 
-            #test_eer = None
-            #if training_opts["compute_test_eer"] and local_rank < 1:
-            #    test_eer = test_metrics(model, device, model_opts, dataset_opts, training_opts)
+            test_eer = None
+            if training_opts["compute_test_eer"] and local_rank < 1:
+                test_eer = test_metrics(model, device, model_opts, dataset_opts, training_opts)
 
-            monitor.update(test_eer=test_eer,
-                           val_eer=val_eer,
-                           val_loss=val_loss,
-                           val_acc=val_acc)
+            #monitor.update(test_eer=test_eer,
+            #               val_eer=val_eer,
+            #               val_loss=val_loss,
+            #               val_acc=val_acc)
 
             #if local_rank < 1:
             #    monitor.display()
 
             # Save the current model and if needed update the best one
             # TODO ajouter une option qui garde les modèles à certaines époques (par exemple avant le changement de LR
-            if local_rank < 1:
-                save_model(model, monitor, model_opts, training_opts, optimizer, scheduler, epoch)
+            #if local_rank < 1:
+            #    save_model(model, monitor, model_opts, training_opts, optimizer, scheduler, epoch)
 
 
     for ii in range(torch.cuda.device_count()):
@@ -1681,6 +1681,7 @@ def cross_validation(model, validation_loader, device, validation_shape, tar_ind
     return (100. * accuracy.cpu().numpy() / validation_shape[0],
             loss.cpu().numpy() / ((batch_idx + 1) * batch_size),
             equal_error_rate)
+    return 0, 0, 0 
 
 
 def extract_embeddings(idmap_name,