monitor display

nnet/xvector.py

@@ -1330,9 +1330,9 @@ def new_xtrain(dataset_description,
                               compute_test_eer=training_opts["compute_test_eer"])
 
     # Display the entire configurations as YAML dictionnaries
-    logging.info(yaml.dump(dataset_opts, default_flow_style=False))
-    logging.info(yaml.dump(model_opts, default_flow_style=False))
-    logging.info(yaml.dump(training_opts, default_flow_style=False))
+    monitor.logger.info(yaml.dump(dataset_opts, default_flow_style=False))
+    monitor.logger.info(yaml.dump(model_opts, default_flow_style=False))
+    monitor.logger.info(yaml.dump(training_opts, default_flow_style=False))
 
     # Set all the seeds
     numpy.random.seed(training_opts["seed"]) # Set the random seed of numpy for the data split.
@@ -1352,18 +1352,18 @@ def new_xtrain(dataset_description,
 
     model.to(device)
 
-    logging.info(f"Start process at {time.strftime('%H:%M:%S', time.localtime())}")
-    logging.info(f"Use \t{torch.cuda.device_count()} \tgpus")
-    logging.info(f"Use \t{training_opts['num_cpu']} \tcpus")
+    monitor.logger.info(f"Start process at {time.strftime('%H:%M:%S', time.localtime())}")
+    monitor.logger.info(f"Use \t{torch.cuda.device_count()} \tgpus")
+    monitor.logger.info(f"Use \t{training_opts['num_cpu']} \tcpus")
 
     # Initialise data loaders
     training_loader, validation_loader, validation_tar_indices, validation_non_indices = get_loaders(dataset_opts,
                                                                                                      training_opts,
                                                                                                      speaker_number)
 
-    logging.info(f"Validation EER will be measured using")
-    logging.info(f"\t {numpy.sum(validation_tar_indices)} target trials and")
-    logging.info(f"\t {numpy.sum(validation_non_indices)} non-target trials")
+    monitor.logger.info(f"Validation EER will be measured using")
+    monitor.logger.info(f"\t {numpy.sum(validation_tar_indices)} target trials and")
+    monitor.logger.info(f"\t {numpy.sum(validation_non_indices)} non-target trials")
 
     # Create optimizer and scheduler
     optimizer, scheduler = get_optimizer(model, model_opts, training_opts)
@@ -1416,7 +1416,7 @@ def new_xtrain(dataset_description,
             save_model(model, monitor, model_opts, training_opts, optimizer, scheduler)
 
     for ii in range(torch.cuda.device_count()):
-        logging.debug(torch.cuda.memory_summary(ii))
+        monitor.logger.info(torch.cuda.memory_summary(ii))
 
     # TODO gérer l'affichage en utilisant le training_monitor
     monitor.display_final()