Make mypy happy

requirements.txt

@@ -1,7 +1,9 @@
 # This file was autogenerated by uv via the following command:
 #    uv pip compile pyproject.toml -o requirements.txt
 filelock==3.16.1
-    # via torch
+    # via
+    #   torch
+    #   triton
 fsspec==2024.10.0
     # via torch
 jinja2==3.1.4
@@ -17,10 +19,44 @@ numpy==2.2.0
     #   torchattack (pyproject.toml)
     #   scipy
     #   torchvision
+nvidia-cublas-cu12==12.4.5.8
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.4.127
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.4.127
+    # via torch
+nvidia-cuda-runtime-cu12==12.4.127
+    # via torch
+nvidia-cudnn-cu12==9.1.0.70
+    # via torch
+nvidia-cufft-cu12==11.2.1.3
+    # via torch
+nvidia-curand-cu12==10.3.5.147
+    # via torch
+nvidia-cusolver-cu12==11.6.1.9
+    # via torch
+nvidia-cusparse-cu12==12.3.1.170
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-nccl-cu12==2.21.5
+    # via torch
+nvidia-nvjitlink-cu12==12.4.127
+    # via
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+    #   torch
+nvidia-nvtx-cu12==12.4.127
+    # via torch
 pillow==11.0.0
     # via torchvision
 scipy==1.14.1
     # via torchattack (pyproject.toml)
+setuptools==75.8.0
+    # via torch
 sympy==1.13.1
     # via torch
 torch==2.5.1
@@ -29,5 +65,7 @@ torch==2.5.1
     #   torchvision
 torchvision==0.20.1
     # via torchattack (pyproject.toml)
+triton==3.1.0
+    # via torch
 typing-extensions==4.12.2
     # via torch

torchattack/att.py

@@ -59,7 +59,7 @@ def __init__(
         elif hasattr(model, 'model_name'):
             # If model is initialized via `torchattack.AttackModel`, the model_name
             # is automatically attached to the model during instantiation.
-            self.hook_cfg = model.model_name
+            self.hook_cfg = model.model_name  # type: ignore
 
         self.eps = eps
         self.steps = steps

torchattack/decowa.py

@@ -122,6 +122,9 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
                 # Compute gradient
                 loss.backward()
 
+                if delta.grad is None:
+                    continue
+
                 # Accumulate gradient
                 g += delta.grad
 

torchattack/dr.py

@@ -57,7 +57,7 @@ def __init__(
         # If model is initialized via `torchattack.AttackModel`, infer its model_name
         # from automatically attached attribute during instantiation.
         if not model_name and hasattr(model, 'model_name'):
-            model_name = model.model_name
+            model_name = model.model_name  # type: ignore
 
         self.eps = eps
         self.steps = steps

torchattack/pna_patchout.py

@@ -62,7 +62,7 @@ def __init__(
         elif hasattr(model, 'model_name'):
             # If model is initialized via `torchattack.AttackModel`, the model_name
             # is automatically attached to the model during instantiation.
-            self.hook_cfg = model.model_name
+            self.hook_cfg = model.model_name  # type: ignore
 
         self.eps = eps
         self.steps = steps

torchattack/ssa.py

@@ -92,6 +92,9 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
                 # Compute gradient
                 loss.backward()
 
+                if delta.grad is None:
+                    continue
+
                 # Accumulate gradient
                 g += delta.grad
 

torchattack/tgr.py

@@ -58,7 +58,7 @@ def __init__(
         elif hasattr(model, 'model_name'):
             # If model is initialized via `torchattack.AttackModel`, the model_name
             # is automatically attached to the model during instantiation.
-            self.hook_cfg = model.model_name
+            self.hook_cfg = model.model_name  # type: ignore
 
         self.eps = eps
         self.steps = steps

torchattack/vdc.py

@@ -60,7 +60,7 @@ def __init__(
         elif hasattr(model, 'model_name'):
             # If model is initialized via `torchattack.AttackModel`, the model_name
             # is automatically attached to the model during instantiation.
-            self.hook_cfg = model.model_name
+            self.hook_cfg = model.model_name  # type: ignore
 
         self.eps = eps
         self.steps = steps

torchattack/vmifgsm.py

@@ -117,6 +117,9 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
 
                 neighbor_loss.backward()
 
+                if neighbors.grad is None:
+                    continue
+
                 gv_grad += neighbors.grad
 
             # Accumulate gradient variance into v

torchattack/vnifgsm.py

@@ -121,6 +121,9 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
 
                 neighbor_loss.backward()
 
+                if neighbors.grad is None:
+                    continue
+
                 gv_grad += neighbors.grad
 
             # Accumulate gradient variance into v