code cleaning & fixing semantic mistakes

solver.py

@@ -169,7 +169,7 @@ class Solver():
 
                 for batch_index, sampled_batch in enumerate(dataloaders[phase]):
                     X = sampled_batch[0].type(torch.FloatTensor)
-                    y = sampled_batch[1].type(torch.LondTensor)
+                    y = sampled_batch[1].type(torch.LongTensor)
 
                     if model.is_cuda():
                         X = X.cuda(self.device, non_blocking= True)
@@ -186,7 +186,7 @@ class Solver():
 
                         if batch_index % self.loss_log_period == 0:
                             
-                            self.LogWriter.loss_per_iteration(self, loss.item(), batch_index, iteration)
+                            self.LogWriter.loss_per_iteration(loss.item(), batch_index, iteration)
 
                         iteration += 1         
 
@@ -219,7 +219,7 @@ class Solver():
                     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
+                                                            phase= phase,
                                                             epoch= epoch)
 
             print("Epoch {}/{} DONE!".format(epoch, self.number_epochs))
@@ -318,10 +318,10 @@ class Solver():
         # We are not loading the model_name as we might want to pre-train a model and then use it. 
         self.model.load_state_dict = checkpoint['state_dict']
         self.optimizer.load_state_dict = checkpoint['optimizer']
-        self.scheduler.load_state_dict = checkpoint['scheduler']
+        self.learning_rate_scheduler.load_state_dict = checkpoint['scheduler']
 
         for state in self.optimizer.state.values():
-            for key, value in state.items{}:
+            for key, value in state.items():
                 if torch.is_tensor(value):
                     state[key] = value.to(self.device)
 

utils/data_logging_utils.py

@@ -20,6 +20,7 @@ import logging
 import numpy as np
 import re
 from textwrap import wrap
+import torch
 
 # The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it.
 # More here: https://tensorboardx.readthedocs.io/en/latest/tensorboard.html
@@ -139,7 +140,7 @@ class LogWriter():
         """
 
         print("Loss for Iteration {} is: {}".format(batch_index, loss_per_iteration))
-        self.log_writer['train'].add_scalar(tag= 'loss / iteration', loss_per_iteration, iteration)
+        self.log_writer['train'].add_scalar('loss / iteration', loss_per_iteration, iteration)
 
     def loss_per_epoch(self, losses, phase, epoch):
         """Log function
@@ -164,7 +165,7 @@ class LogWriter():
             loss = np.mean(losses)
 
         print("Loss for Epoch {} of {} is: {}".format(epoch, phase, loss))
-        self.log_writer[phase].add_scalar(tag= 'loss / iteration', loss, epoch)
+        self.log_writer[phase].add_scalar('loss / iteration', loss, epoch)
 
     # Currently, no confusion matrix is required
     # TODO: add a confusion matrix per epoch and confusion matrix plot functions if required
@@ -190,12 +191,11 @@ class LogWriter():
         print("Dice Score is being calculated...", end='', flush= True)
         dice_score = evaluation.dice_score_calculator(outputs, correct_labels, self.number_of_classes)
         mean_dice_score = torch.mean(dice_score)
-        self.plot_dice_score(dice_score, phase, plot_name='dice_score_per_epoch', title='Dice Score', epoch)
-        self.log_writer[phase].add_scalar(tag= 'loss / iteration', loss, epoch)
+        self.plot_dice_score(dice_score, phase, plot_name='dice_score_per_epoch', title='Dice Score', epochs=epoch)
         print("Dice Score calculated successfully")
-        return mean_dice_score
+        return mean_dice_score.item()
 
-    def plot_dice_score(self, dice_score, phase, plot_name, title, epochs):
+    def plot_dice_score(self, dice_score, phase, plot_name, title, epochs=None):
         """Function plotting dice score for multiple epochs
 
         This function plots the dice score for each epoch.
@@ -223,7 +223,7 @@ class LogWriter():
         ax.set_xticklabels(self.labels)
         ax.xaxis.tick_bottom()
 
-        if step:
+        if epochs:
             self.log_writer[phase].add_figure(plot_name + '/' + phase, figure, global_step= epochs)
         else:
             self.log_writer[phase].add_figure(plot_name + '/' + phase, figure)