Perf attack save

Author: klchen0112

torchattacks/attack.py

@@ -248,6 +248,7 @@ def save(
         self,
         data_loader,
         save_path=None,
+        save_every_iter=False,
         verbose=True,
         return_verbose=False,
         save_predictions=False,
@@ -260,6 +261,7 @@ def save(
         Arguments:
             save_path (str): save_path.
             data_loader (torch.utils.data.DataLoader): data loader.
+            save_every_iter (bool): True for save every results every iter. (Default: False)
             verbose (bool): True for displaying detailed information. (Default: True)
             return_verbose (bool): True for returning detailed information. (Default: False)
             save_predictions (bool): True for saving predicted labels (Default: False)
@@ -319,45 +321,33 @@ def save(
             if save_path is not None:
                 adv_input_list.append(adv_inputs.detach().cpu())
                 label_list.append(labels.detach().cpu())
-
-                adv_input_list_cat = torch.cat(adv_input_list, 0)
-                label_list_cat = torch.cat(label_list, 0)
-
-                save_dict = {
-                    "adv_inputs": adv_input_list_cat,
-                    "labels": label_list_cat,
-                }  # nopep8
-
                 if save_predictions:
                     pred_list.append(pred.detach().cpu())
-                    pred_list_cat = torch.cat(pred_list, 0)
-                    save_dict["preds"] = pred_list_cat
-
                 if save_clean_inputs:
                     input_list.append(inputs.detach().cpu())
-                    input_list_cat = torch.cat(input_list, 0)
-                    save_dict["clean_inputs"] = input_list_cat
-
-                if self.normalization_used is not None:
-                    save_dict["adv_inputs"] = self.inverse_normalize(
-                        save_dict["adv_inputs"]
-                    )  # nopep8
-                    if save_clean_inputs:
-                        save_dict["clean_inputs"] = self.inverse_normalize(
-                            save_dict["clean_inputs"]
-                        )  # nopep8
-
-                if save_type == "int":
-                    save_dict["adv_inputs"] = self.to_type(
-                        save_dict["adv_inputs"], "int"
-                    )  # nopep8
-                    if save_clean_inputs:
-                        save_dict["clean_inputs"] = self.to_type(
-                            save_dict["clean_inputs"], "int"
-                        )  # nopep8
-
-                save_dict["save_type"] = save_type
-                torch.save(save_dict, save_path)
+                if save_every_iter:
+                    self._save_adv_examples(
+                        save_type,
+                        save_path,
+                        adv_input_list,
+                        label_list,
+                        save_predictions = save_predictions,
+                        pred_list = pred_list if save_predictions else None,
+                        save_clean_inputs = save_clean_inputs,
+                        input_list = input_list if save_clean_inputs else None,
+                    )
+
+        if save_path is not None and not save_every_iter:
+            self._save_adv_examples(
+                        save_type,
+                        save_path,
+                        adv_input_list,
+                        label_list,
+                        save_predictions = save_predictions,
+                        pred_list = pred_list if save_predictions else None,
+                        save_clean_inputs = save_clean_inputs,
+                        input_list = input_list if save_clean_inputs else None,
+                    )
 
         # To avoid erasing the printed information.
         if verbose:
@@ -388,6 +378,57 @@ def to_type(inputs, type):
             raise ValueError(type + " is not a valid type. [Options: float, int]")
         return inputs
 
+
+    def _save_adv_examples(
+        self,
+        save_type,
+        save_path,
+        adv_input_list,
+        label_list,
+        save_predictions = False,
+        pred_list = [],
+        save_clean_inputs = False,
+        input_list = [],
+        ):
+
+
+        adv_input_list_cat = torch.cat(adv_input_list, 0)
+        label_list_cat = torch.cat(label_list, 0)
+
+        save_dict = {
+            "adv_inputs": adv_input_list_cat,
+            "labels": label_list_cat,
+        }
+
+        if save_predictions:
+            pred_list_cat = torch.cat(pred_list, 0)
+            save_dict["preds"] = pred_list_cat
+
+        if save_clean_inputs:
+            input_list_cat = torch.cat(input_list, 0)
+            save_dict["clean_inputs"] = input_list_cat
+
+        if self.normalization_used is not None:
+            save_dict["adv_inputs"] = self.inverse_normalize(
+                save_dict["adv_inputs"]
+            )  # nopep8
+            if save_clean_inputs:
+                save_dict["clean_inputs"] = self.inverse_normalize(
+                    save_dict["clean_inputs"]
+                )  # nopep8
+
+        if save_type == "int":
+            save_dict["adv_inputs"] = self.to_type(
+                save_dict["adv_inputs"], "int"
+            )  # nopep8
+            if save_clean_inputs:
+                save_dict["clean_inputs"] = self.to_type(
+                    save_dict["clean_inputs"], "int"
+                )  # nopep8
+
+        save_dict["save_type"] = save_type
+        torch.save(save_dict, save_path)
+
     @staticmethod
     def _save_print(progress, rob_acc, l2, elapsed_time, end):
         print(