prep_mentormix

main.py

@@ -13,6 +13,7 @@
 plt.clf()
 
 import torch
+import wandb
 from torch.utils.tensorboard import SummaryWriter
 
 from dataset import import_dataset, add_impurity
@@ -61,12 +62,18 @@ def train_epoch(config:Dict[str, Union[str,int, List[int]]], model:torch.nn.Modu
         sig_mask = labels==1
         if writer is not None:
             key = "test" if is_testing else "train"
-            writer.add_scalar("Loss_"+ key, loss, (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
-            writer.add_scalar("Metrics_"+ key +"/Accuracy", accuracy, (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
-            writer.add_scalar("Metrics_"+ key +"/TPR", torch.mean(torch.where(torch.round(outputs[sig_mask])==1, 1., 0.)), (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
-            writer.add_scalar("Metrics_"+ key +"/TNR", torch.mean(torch.where(torch.round(outputs[~sig_mask])==0, 1., 0.)), (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
-            writer.add_scalar("Metrics_"+ key +"/TNR", torch.mean(torch.where(torch.round(outputs[~sig_mask])==0, 1., 0.)), (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
+            writer.add_scalar(f"Loss_{key}", loss, (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
+            writer.add_scalar(f"Metrics_{key}/Accuracy", accuracy, (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
+            writer.add_scalar(f"Metrics_{key}/TPR", torch.mean(torch.where(torch.round(outputs[sig_mask])==1, 1., 0.)), (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
+            writer.add_scalar(f"Metrics_{key}/TNR", torch.mean(torch.where(torch.round(outputs[~sig_mask])==0, 1., 0.)), (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
             writer.add_scalar("lr", lr_scheduler.get_last_lr()[0], (int(size/batch_size) + 1 ) * epoch + i, new_style=True if i==0 else False)
+
+            wandb.log({f"Loss_{key}": loss, 
+                        f"Metrics_{key}/Accuracy": accuracy,
+                        f"Metrics_{key}/TPR": torch.mean(torch.where(torch.round(outputs[sig_mask])==1, 1., 0.)),
+                        f"Metrics_{key}/TNR": torch.mean(torch.where(torch.round(outputs[~sig_mask])==0, 1., 0.)),
+                        "lr": lr_scheduler.get_last_lr()[0],
+            })
 
         mean_loss = (mean_loss * mean_counter + loss )/(mean_counter + 1)
         mean_accuracy = (mean_accuracy * mean_counter + accuracy )/(mean_counter + 1)
@@ -96,6 +103,9 @@ def train_epoch(config:Dict[str, Union[str,int, List[int]]], model:torch.nn.Modu
     #Gather configs from config file
     config = read_config(args["config"])
 
+    #initiate wandb
+    wandb.init(project="trend", entity="liphos", config=config, name=config["comment"],)
+    wandb.config = config
     #Set seed
     np.random.seed(config["seed"])
     print("Using " + config["device"] + " device")
@@ -214,15 +224,16 @@ def train_epoch(config:Dict[str, Union[str,int, List[int]]], model:torch.nn.Modu
                 new_labels[labels_mean>=0.8] = 1
                 new_labels[labels_mean<=1-0.8] = 0
             elif config["training"]["extra_args"]["mode"] == "unanimity":
-                new_labels[logical_and_arrays([torch.where(model(data_tensor)[:, 0]>=0.7, True, False) for model in models])] = 1
-                new_labels[logical_and_arrays([torch.where(model(data_tensor)[:, 0]<=1-0.7, True, False) for model in models])] = 0
+                new_labels[logical_and_arrays([np.where(model(data_tensor)[:, 0].detach().cpu().numpy()>=0.7, True, False) for model in models])] = 1
+                new_labels[logical_and_arrays([np.where(model(data_tensor)[:, 0].detach().cpu().numpy()<=1-0.7, True, False) for model in models])] = 0
             else:
                 raise ValueError("This mode for relabelling doesn't exist.")
             new_nb_correct = np.where(label_train_clean==new_labels)[0].shape[0]
 
             print(f"Previous labels:{np.where(new_labels[:,0]==labels_train[:,0])[0].shape}/{len(labels_train)}")            
             print(f"Correct labels: {new_nb_correct}/{len(new_labels)}")
-            print(f"Pourcentage of correct labels in labels that were changed: {(1+(new_nb_correct-old_nb_correct)/(len(labels_train) - np.where(new_labels[:,0]==labels_train[:,0])[0].shape[0]))/2}")
+            if (len(labels_train) - np.where(new_labels[:,0]==labels_train[:,0])[0].shape[0]) >0:
+                print(f"Pourcentage of correct labels in labels that were changed: {(1+(new_nb_correct-old_nb_correct)/(len(labels_train) - np.where(new_labels[:,0]==labels_train[:,0])[0].shape[0]))/2}")
 
             old_nb_correct = new_nb_correct
             labels_train = new_labels