Make mypy happy

src/torchattack/admix.py

@@ -103,13 +103,13 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
             grad = torch.autograd.grad(loss, x_admixs)[0]
 
             # Split gradients and compute mean
-            grads = torch.tensor_split(grad, 5, dim=0)
-            grads = [g * s for g, s in zip(grads, scales, strict=True)]
+            split_grads = torch.tensor_split(grad, 5, dim=0)
+            grads = [g * s for g, s in zip(split_grads, scales, strict=True)]
             grad = torch.mean(torch.stack(grads), dim=0)
 
             # Gather gradients
-            grads = torch.tensor_split(grad, self.size)
-            grad = torch.sum(torch.stack(grads), dim=0)
+            split_grads = torch.tensor_split(grad, self.size)
+            grad = torch.sum(torch.stack(split_grads), dim=0)
 
             # Apply momentum term
             g = self.decay * g + grad / torch.mean(

src/torchattack/dataset.py

@@ -16,7 +16,7 @@ def __init__(
         self,
         image_root: str,
         pairs_path: str,
-        transform: Callable[[torch.Tensor], torch.Tensor] | None = None,
+        transform: Callable[[torch.Tensor | Image.Image], torch.Tensor] | None = None,
         max_samples: int | None = None,
     ) -> None:
         """Initialize the NIPS 2017 Adversarial Learning Challenge dataset.
@@ -73,10 +73,10 @@ def __getitem__(self, index: int) -> tuple[Any, int, str]:
         name = self.names[index]
         label = int(self.labels[index]) - 1
 
-        image = Image.open(f'{self.image_root}/{name}.png').convert('RGB')
-        # image = np.array(image, dtype=np.uint8)
-        # image = torch.from_numpy(image).permute((2, 0, 1)).contiguous().float().div(255)
-        image = self.transform(image) if self.transform else image
+        pil_image = Image.open(f'{self.image_root}/{name}.png').convert('RGB')
+        # np_image = np.array(pil_image, dtype=np.uint8)
+        # image = torch.from_numpy(np_image).permute((2, 0, 1)).contiguous().float().div(255)
+        image = self.transform(pil_image) if self.transform else pil_image
         return image, label, name
 
 
@@ -89,9 +89,9 @@ class NIPSLoader(DataLoader):
 
         >>> from torchvision.transforms import transforms
         >>> from torchattack.dataset import NIPSLoader
-        >>> transform = transforms.Resize([224])
+        >>> transform = transforms.Compose([transforms.Resize([224]), transforms.ToTensor()])
         >>> dataloader = NIPSLoader(
-        >>>     path="data/nips2017", batch_size=16, transform=transform
+        >>>     path="data/nips2017", batch_size=16, transform=transform, max_samples=100
         >>> )
 
         You can specify a custom image root directory and CSV file location by
@@ -107,7 +107,7 @@ def __init__(
         batch_size: int = 1,
         shuffle: bool = False,
         num_workers: int = 4,
-        transform: Callable[[torch.Tensor], torch.Tensor] | None = None,
+        transform: Callable[[torch.Tensor | Image.Image], torch.Tensor] | None = None,
         max_samples: int | None = None,
     ):
         # Specifing a custom image root directory is useful when evaluating

src/torchattack/runner.py

@@ -1,5 +1,5 @@
 from contextlib import suppress
-from typing import Any, Optional
+from typing import Any
 
 import torch
 import torchvision as tv
@@ -11,9 +11,9 @@ class FoolingRateMetric:
     """Fooling rate metric tracker."""
 
     def __init__(self) -> None:
-        self.total_count = 0
-        self.clean_count = 0
-        self.adv_count = 0
+        self.total_count = torch.tensor(0)
+        self.clean_count = torch.tensor(0)
+        self.adv_count = torch.tensor(0)
 
     def update(
         self, labels: torch.Tensor, clean_logits: torch.Tensor, adv_logits: torch.Tensor
@@ -42,7 +42,7 @@ def compute_clean_accuracy(self) -> torch.Tensor:
 
 def run_attack(
     attack: Any,
-    attack_cfg: Optional[dict] = None,
+    attack_cfg: dict | None = None,
     model_name: str = 'resnet50',
     max_samples: int = 100,
     batch_size: int = 8,