Add docs

.gitignore

@@ -38,6 +38,7 @@ pip-delete-this-directory.txt
 
 #codebase-specific
 datasets/*
+MAML*
 
 # Unit test / coverage reports
 htmlcov/

few_shot_learning_system.py

@@ -209,7 +209,7 @@ def forward(self, data_batch, epoch, use_second_order, optimize_final_target_los
                 support_loss, support_preds = self.net_forward(x=x_support_set_task,
                                                                y=y_support_set_task,
                                                                weights=names_weights_copy,
-                                                               reset_running_statistics=
+                                                               backup_running_statistics=
                                                                True if (num_step == 0) else False,
                                                                training=True, num_step=num_step)
 
@@ -222,13 +222,13 @@ def forward(self, data_batch, epoch, use_second_order, optimize_final_target_los
                     if num_step == (self.args.number_of_training_steps_per_iter - 1):
                         target_loss, target_preds = self.net_forward(x=x_target_set_task,
                                                                      y=y_target_set_task, weights=names_weights_copy,
-                                                                     reset_running_statistics=False, training=True,
+                                                                     backup_running_statistics=False, training=True,
                                                                      num_step=num_step)
                         task_losses.append(target_loss)
                 else:
                     target_loss, target_preds = self.net_forward(x=x_target_set_task,
                                                                  y=y_target_set_task, weights=names_weights_copy,
-                                                                 reset_running_statistics=False, training=True,
+                                                                 backup_running_statistics=False, training=True,
                                                                  num_step=num_step)
 
                     task_losses.append(per_step_loss_importance_vectors[num_step] * target_loss)
@@ -254,7 +254,7 @@ def forward(self, data_batch, epoch, use_second_order, optimize_final_target_los
 
         return losses
 
-    def net_forward(self, x, y, weights, reset_running_statistics, training, num_step):
+    def net_forward(self, x, y, weights, backup_running_statistics, training, num_step):
         """
         A base model forward pass on some data points x. Using the parameters in the weights dictionary. Also requires
         boolean flags indicating whether to reset the running statistics at the end of the run (if at evaluation phase).
@@ -263,7 +263,7 @@ def net_forward(self, x, y, weights, reset_running_statistics, training, num_ste
         :param x: A data batch of shape b, c, h, w
         :param y: A data targets batch of shape b, n_classes
         :param weights: A dictionary containing the weights to pass to the network.
-        :param reset_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
+        :param backup_running_statistics: A flag indicating whether to reset the batch norm running statistics to their
          previous values after the run (only for evaluation)
         :param training: A flag indicating whether the current process phase is a training or evaluation.
         :param num_step: An integer indicating the number of the step in the inner loop.
@@ -274,7 +274,7 @@ def net_forward(self, x, y, weights, reset_running_statistics, training, num_ste
 
         preds = self.classifier.forward(x=input_var, params=weights,
                                         training=training,
-                                        reset_running_statistics=reset_running_statistics, num_step=num_step)
+                                        backup_running_statistics=backup_running_statistics, num_step=num_step)
 
         loss = F.cross_entropy(input=preds, target=target_var)
 
@@ -330,9 +330,9 @@ def meta_update(self, loss):
     def run_train_iter(self, data_batch, epoch):
         """
         Runs an outer loop update step on the meta-model's parameters.
-        :param data_batch:
-        :param epoch:
-        :return:
+        :param data_batch: input data batch containing the support set and target set input, output pairs
+        :param epoch: the index of the current epoch
+        :return: The losses of the ran iteration.
         """
         self.scheduler.step(epoch=epoch)
         if self.current_epoch != epoch:
@@ -360,7 +360,12 @@ def run_train_iter(self, data_batch, epoch):
         return losses
 
     def run_validation_iter(self, data_batch):
-
+        """
+        Runs an outer loop evaluation step on the meta-model's parameters.
+        :param data_batch: input data batch containing the support set and target set input, output pairs
+        :param epoch: the index of the current epoch
+        :return: The losses of the ran iteration.
+        """
         if self.training:
             self.eval()
 
@@ -381,10 +386,24 @@ def run_validation_iter(self, data_batch):
         return losses
 
     def save_model(self, model_save_dir, state):
+        """
+        Save the network parameter state and experiment state dictionary.
+        :param model_save_dir: The directory to store the state at.
+        :param state: The state containing the experiment state and the network. It's in the form of a dictionary
+        object.
+        """
         state['network'] = self.state_dict()
         torch.save(state, f=model_save_dir)
 
     def load_model(self, model_save_dir, model_name, model_idx):
+        """
+        Load checkpoint and return the state dictionary containing the network state params and experiment state.
+        :param model_save_dir: The directory from which to load the files.
+        :param model_name: The model_name to be loaded from the direcotry.
+        :param model_idx: The index of the model (i.e. epoch number or 'latest' for the latest saved model of the current
+        experiment)
+        :return: A dictionary containing the experiment state and the saved model parameters.
+        """
         filepath = os.path.join(model_save_dir, "{}_{}".format(model_name, model_idx))
         checkpoint_state = torch.load(filepath)
         state_dict_loaded = checkpoint_state['network']