Update ablation_cam.py (#440)

Updated the comments for better understanding and suggested some syntax modification.

pytorch_grad_cam/ablation_cam.py

@@ -10,7 +10,7 @@
 """ Implementation of AblationCAM
 https://openaccess.thecvf.com/content_WACV_2020/papers/Desai_Ablation-CAM_Visual_Explanations_for_Deep_Convolutional_Network_via_Gradient-free_Localization_WACV_2020_paper.pdf
 
-Ablate individual activations, and then measure the drop in the target score.
+Ablate individual activations, and then measure the drop in the target scores.
 
 In the current implementation, the target layer activations is cached, so it won't be re-computed.
 However layers before it, if any, will not be cached.
@@ -88,8 +88,8 @@ def get_cam_weights(self,
                 [target(output).cpu().item() for target, output in zip(targets, outputs)])
 
         # Replace the layer with the ablation layer.
-        # When we finish, we will replace it back, so the original model is
-        # unchanged.
+        # When we finish, we will replace it back, so the 
+        # original model is unchanged.
         ablation_layer = self.ablation_layer
         replace_layer_recursive(self.model, target_layer, ablation_layer)
 
@@ -122,9 +122,9 @@ def get_cam_weights(self,
                     # Change the state of the ablation layer so it ablates the next channels.
                     # TBD: Move this into the ablation layer forward pass.
                     ablation_layer.set_next_batch(
-                        input_batch_index=batch_index,
-                        activations=self.activations,
-                        num_channels_to_ablate=batch_tensor.size(0))
+                        input_batch_index = batch_index,
+                        activations = self.activations,
+                        num_channels_to_ablate = batch_tensor.size(0))
                     score = [target(o).cpu().item()
                              for o in self.model(batch_tensor)]
                     new_scores.extend(score)
@@ -145,4 +145,5 @@ def get_cam_weights(self,
 
         # Replace the model back to the original state
         replace_layer_recursive(self.model, ablation_layer, target_layer)
+        # Returning the weights from new_scores
         return weights