debugging + commenting out functions deprecated

solver.py

@@ -50,7 +50,7 @@ class Solver():
         logs_directory (str): Directory for outputing training logs
 
     Returns:
-        trained model(?) - working on this!
+        trained model - working on this!
 
     """
 
@@ -105,8 +105,8 @@ class Solver():
 
         self.start_epoch = 1
         self.start_iteration = 1
-        self.best_mean_score = 0
-        self.best_mean_score_epoch = 0
+        # self.best_mean_score = 0
+        # self.best_mean_score_epoch = 0
 
         self.LogWriter = LogWriter(number_of_classes=number_of_classes,
                                    logs_directory=logs_directory,
@@ -159,8 +159,8 @@ class Solver():
                 print('-> Phase: {}'.format(phase))
 
                 losses = []
-                outputs = []
-                y_values = []
+                # outputs = []
+                # y_values = []
 
                 if phase == 'train':
                     model.train()
@@ -196,9 +196,11 @@ class Solver():
 
                         iteration += 1
 
+                    
+
                     losses.append(loss.item())
-                    outputs.append(torch.max(y_hat, dim=1)[1].cpu())
-                    y_values.append(y.cpu())
+                    # outputs.append(torch.max(y_hat, dim=1)[1].cpu())
+                    # y_values.append(y.cpu())
 
                     # Clear the memory
 
@@ -212,24 +214,25 @@ class Solver():
                             print("100%", flush=True)
 
                 with torch.no_grad():
-                    output_array, y_array = torch.cat(
-                        outputs), torch.cat(y_values)
+                    # output_array, y_array = torch.cat(
+                    #     outputs), torch.cat(y_values)
 
                     self.LogWriter.loss_per_epoch(losses, phase, epoch)
 
-                    dice_score_mean = self.LogWriter.dice_score_per_epoch(
-                        phase, output_array, y_array, epoch)
-                    if phase == 'test':
-                        if dice_score_mean > self.best_mean_score:
-                            self.best_mean_score = dice_score_mean
-                            self.best_mean_score_epoch = epoch
-
-                    index = np.random.choice(
-                        len(dataloaders[phase].dataset.X), size=3, replace=False)
-                    self.LogWriter.sample_image_per_epoch(prediction=model.predict(dataloaders[phase].dataset.X[index], self.device),
-                                                          ground_truth=dataloaders[phase].dataset.y[index],
-                                                          phase=phase,
-                                                          epoch=epoch)
+                    # dice_score_mean = self.LogWriter.dice_score_per_epoch(
+                    #     phase, output_array, y_array, epoch)
+
+                    # if phase == 'test':
+                    #     if dice_score_mean > self.best_mean_score:
+                    #         self.best_mean_score = dice_score_mean
+                    #         self.best_mean_score_epoch = epoch
+
+                    # index = np.random.choice(
+                    #     len(dataloaders[phase].dataset.X), size=3, replace=False)
+                    # self.LogWriter.sample_image_per_epoch(prediction=model.predict(dataloaders[phase].dataset.X[index], self.device),
+                    #                                       ground_truth=dataloaders[phase].dataset.y[index],
+                    #                                       phase=phase,
+                    #                                       epoch=epoch)
 
             print("Epoch {}/{} DONE!".format(epoch, self.number_epochs))