diff --git a/MTVulnerability/experiments/attacks/__init__.py b/MTVulnerability/experiments/attacks/__init__.py
index 01e4da0..a632e0e 100644
--- a/MTVulnerability/experiments/attacks/__init__.py
+++ b/MTVulnerability/experiments/attacks/__init__.py
@@ -39,16 +39,17 @@ def load_model(args):
 
         epoch = args.epoch
 
-        path_model = os.path.join(args.model_root, "savecheckpoint","checkpoint_{epoch}.pth.tar").format(
-                arch=args.arch,
-                dataset=args.dataset,
-                train="".join(train),
-                aux="".join(aux),
-                test="".join(test),
-                epoch=epoch
-        )
-
-        path_model=  path_model.replace("\r","")
+        # path_model = os.path.join(args.model_root, "savecheckpoint","checkpoint_{epoch}.pth.tar").format(
+        #         arch=args.arch,
+        #         dataset=args.dataset,
+        #         train="".join(train),
+        #         aux="".join(aux),
+        #         test="".join(test),
+        #         epoch=epoch
+        # )
+
+        # path_model=  path_model.replace("\r","")
+        path_model = args.model_root
 
         if args.dataset == "taskonomy" and args.arch == "resnet18":
                 model = resnet18_taskonomy(pretrained=False, tasks=args.test_task_set)
@@ -96,7 +97,7 @@ def load_model(args):
                 for k in ks:
                         state[k[7:]] = state.pop(k)
 
-        model.load_state_dict(state)  # , strict=False
+        model.load_state_dict(state, strict=False)  # , strict=False
         model = torch.nn.DataParallel(model)
         if torch.cuda.is_available():
                 model.cuda()
diff --git a/MTVulnerability/utils/xception_taskonomy_new.py b/MTVulnerability/utils/xception_taskonomy_new.py
index 36531f4..f7cb521 100644
--- a/MTVulnerability/utils/xception_taskonomy_new.py
+++ b/MTVulnerability/utils/xception_taskonomy_new.py
@@ -320,7 +320,7 @@ def __init__(self,size=1, tasks=None,num_classes=None, ozan=False,half_sized_out
         if tasks is not None:
 
             task_to_params = {
-                'autoencoder': {'output_channels': 3},
+                'autoencoder': {'output_chanels': 3},
                 'class_object'      : {'output_chanels' : 0, 'num_cla sses':1000},
                 'class_places'       	: {'output_chanels' : 0, 'num_cla sses':63},
                 'depth_eucln'   	: {'output_chanels' : 1},
@@ -343,7 +343,7 @@ def __init__(self,size=1, tasks=None,num_classes=None, ozan=False,half_sized_out
             self.final_conv_bn = nn.BatchNorm2d(512)
             for task in tasks:
 
-                output_channels = task_to_params.get(task).get("output_channels", 0)
+                output_channels = task_to_params.get(task).get("output_chanels", 0)
                 nb_classes = task_to_params.get(task).get("num_classes", num_classes)
                 decoder = Decoder(output_channels, nb_classes,half_sized_output=half_sized_output)
                 self.task_to_decoder[task] = decoder