build(deps-dev): bump ruff from 0.5.2 to 0.5.5

.github/collect_env.py

@@ -159,14 +159,13 @@ def get_nvidia_smi():
 def get_platform():
  if sys.platform.startswith("linux"):
  return "linux"
- elif sys.platform.startswith("win32"):
+ if sys.platform.startswith("win32"):
  return "win32"
- elif sys.platform.startswith("cygwin"):
+ if sys.platform.startswith("cygwin"):
  return "cygwin"
- elif sys.platform.startswith("darwin"):
+ if sys.platform.startswith("darwin"):
  return "darwin"
- else:
- return sys.platform
+ return sys.platform
 
 
 def get_mac_version(run_lambda):

.github/verify_labels.py

@@ -78,9 +78,7 @@ def parse_args():
  )
 
  parser.add_argument("pr", type=int, help="PR number")
- args = parser.parse_args()
-
- return args
+ return parser.parse_args()
 
 
 if __name__ == "__main__":

.github/workflows/style.yml

@@ -22,7 +22,7 @@ jobs:
  - name: Run ruff
  run: |
  python -m pip install --upgrade uv
- uv pip install --system ruff==0.3.0
+ uv pip install --system --upgrade -e ".[quality]"
  ruff --version
  ruff check --diff .
 
@@ -62,7 +62,7 @@ jobs:
  - name: Run ruff
  run: |
  python -m pip install --upgrade uv
- uv pip install --system ruff==0.3.0
+ uv pip install --system --upgrade -e ".[quality]"
  ruff --version
  ruff format --check --diff .
 

.pre-commit-config.yaml

@@ -22,7 +22,7 @@ repos:
  - id: requirements-txt-fixer
  - id: trailing-whitespace
  - repo: https://github.com/charliermarsh/ruff-pre-commit
- rev: 'v0.5.2'
+ rev: 'v0.5.5'
  hooks:
  - id: ruff
  args:

pyproject.toml

@@ -49,7 +49,7 @@ test = [
  "pytest-pretty>=1.0.0,<2.0.0",
 ]
 quality = [
- "ruff==0.5.2",
+ "ruff==0.5.5",
  "mypy==1.10.0",
  "types-Pillow",
  "pre-commit>=3.0.0,<4.0.0",
@@ -77,7 +77,7 @@ dev = [
  "pytest-cov>=4.0.0,<5.0.0",
  "pytest-pretty>=1.0.0,<2.0.0",
  # style
- "ruff==0.5.2",
+ "ruff==0.5.5",
  "mypy==1.10.0",
  "types-Pillow",
  "pre-commit>=3.0.0,<4.0.0",
@@ -116,37 +116,43 @@ preview = true
 
 [tool.ruff.lint]
 select = [
+ "F", # pyflakes
  "E", # pycodestyle errors
  "W", # pycodestyle warnings
- "D101", "D103", # pydocstyle missing docstring in public function/class
- "D201","D202","D207","D208","D214","D215","D300","D301","D417", "D419", # pydocstyle
- "F", # pyflakes
  "I", # isort
- "C4", # flake8-comprehensions
- "B", # flake8-bugbear
- "CPY001", # flake8-copyright
- "ISC", # flake8-implicit-str-concat
- "PYI", # flake8-pyi
- "NPY", # numpy
- "PERF", # perflint
- "RUF", # ruff specific
- "PTH", # flake8-use-pathlib
- "S", # flake8-bandit
  "N", # pep8-naming
- "T10", # flake8-debugger
- "T20", # flake8-print
- "PT", # flake8-pytest-style
- "LOG", # flake8-logging
- "SIM", # flake8-simplify
+ "D101", "D103", # pydocstyle missing docstring in public function/class
+ "D201","D202","D207","D208","D214","D215","D300","D301","D417", "D419", # pydocstyle
  "YTT", # flake8-2020
  "ANN", # flake8-annotations
  "ASYNC", # flake8-async
+ "S", # flake8-bandit
  "BLE", # flake8-blind-except
+ "B", # flake8-bugbear
  "A", # flake8-builtins
+ "COM", # flake8-commas
+ "CPY", # flake8-copyright
+ "C4", # flake8-comprehensions
+ "T10", # flake8-debugger
+ "ISC", # flake8-implicit-str-concat
  "ICN", # flake8-import-conventions
+ "LOG", # flake8-logging
  "PIE", # flake8-pie
+ "T20", # flake8-print
+ "PYI", # flake8-pyi
+ "PT", # flake8-pytest-style
+ "Q", # flake8-quotes
+ "RET", # flake8-return
+ "SLF", # flake8-self
+ "SIM", # flake8-simplify
  "ARG", # flake8-unused-arguments
+ "PTH", # flake8-use-pathlib
+ "PERF", # perflint
+ "NPY", # numpy
+ "FAST", # fastapi
  "FURB", # refurb
+ "RUF", # ruff specific
+ "N", # pep8-naming
 ]
 ignore = [
  "E501", # line too long, handled by black
@@ -179,7 +185,7 @@ known-third-party = ["torch", "torchvision"]
 "scripts/**.py" = ["D", "T201", "N812", "S101", "ANN"]
 ".github/**.py" = ["D", "T201", "S602", "S101", "ANN"]
 "docs/**.py" = ["E402", "D103", "ANN", "A001", "ARG001"]
-"tests/**.py" = ["D103", "CPY001", "S101", "PT011", "ANN"]
+"tests/**.py" = ["D103", "CPY001", "S101", "PT011", "ANN", "SLF001"]
 "demo/**.py" = ["D103", "ANN"]
 "setup.py" = ["T201"]
 

scripts/cam_example.py

@@ -74,7 +74,7 @@ def main(args):
  ax.set_title("Input", size=8)
 
  for idx, extractor in zip(range(1, len(cam_extractors) + 1), cam_extractors):
- extractor._hooks_enabled = True
+ extractor.enable_hooks()
  model.zero_grad()
  scores = model(img_tensor.unsqueeze(0))
 
@@ -85,8 +85,8 @@ def main(args):
  activation_map = extractor(class_idx, scores)[0].squeeze(0).cpu()
 
  # Clean data
+ extractor.disable_hooks()
  extractor.remove_hooks()
- extractor._hooks_enabled = False
  # Convert it to PIL image
  # The indexing below means first image in batch
  heatmap = to_pil_image(activation_map, mode="F")

torchcam/methods/activation.py

@@ -90,8 +90,7 @@ def _get_weights(
  # Take the FC weights of the target class
  if isinstance(class_idx, int):
  return [self._fc_weights[class_idx, :].unsqueeze(0)]
- else:
- return [self._fc_weights[class_idx, :]]
+ return [self._fc_weights[class_idx, :]]
 
 
 class ScoreCAM(_CAM):
@@ -214,15 +213,15 @@ def _get_weights(
  ]
 
  # Disable hook updates
- self._hooks_enabled = False
+ self.disable_hooks()
  # Switch to eval
  origin_mode = self.model.training
  self.model.eval()
 
  weights: List[Tensor] = self._get_score_weights(upsampled_a, class_idx)
 
  # Reenable hook updates
- self._hooks_enabled = True
+ self.enable_hooks()
  # Put back the model in the correct mode
  self.model.training = origin_mode
 

torchcam/methods/core.py

@@ -78,6 +78,14 @@ def __init__(
  # Model output is used by the extractor
  self._score_used = False
 
+ def enable_hooks(self) -> None:
+ """Enable hooks."""
+ self._hooks_enabled = True
+
+ def disable_hooks(self) -> None:
+ """Disable hooks."""
+ self._hooks_enabled = False
+
  def __enter__(self) -> "_CAM":
  return self
 
@@ -236,17 +244,16 @@ def fuse_cams(cls, cams: List[Tensor], target_shape: Optional[Tuple[int, int]] =
 
  if len(cams) == 0:
  raise ValueError("argument `cams` cannot be an empty list")
- elif len(cams) == 1:
+ if len(cams) == 1:
  return cams[0]
+ # Resize to the biggest CAM if no value was provided for `target_shape`
+ if isinstance(target_shape, tuple):
+ _shape = target_shape
  else:
- # Resize to the biggest CAM if no value was provided for `target_shape`
- if isinstance(target_shape, tuple):
- _shape = target_shape
- else:
- _shape = tuple(map(max, zip(*[tuple(cam.shape[1:]) for cam in cams])))
- # Scale cams
- scaled_cams = cls._scale_cams(cams)
- return cls._fuse_cams(scaled_cams, _shape)
+ _shape = tuple(map(max, zip(*[tuple(cam.shape[1:]) for cam in cams])))
+ # Scale cams
+ scaled_cams = cls._scale_cams(cams)
+ return cls._fuse_cams(scaled_cams, _shape)
 
  @staticmethod
  def _scale_cams(cams: List[Tensor]) -> List[Tensor]:

torchcam/metrics.py

@@ -97,7 +97,7 @@ def update(
  cams = self.cam_extractor(preds.cpu().numpy().tolist(), probs)
  cam = self.cam_extractor.fuse_cams(cams)
  probs = probs.gather(1, preds.unsqueeze(1)).squeeze(1)
- self.cam_extractor._hooks_enabled = False
+ self.cam_extractor.disable_hooks()
  # Safeguard: replace NaNs
  cam[torch.isnan(cam)] = 0
  # Resize the CAM
@@ -116,7 +116,7 @@ def update(
  # Increase
  increase = probs < masked_probs
 
- self.cam_extractor._hooks_enabled = True
+ self.cam_extractor.enable_hooks()
 
  self.drop += drop.sum().item()
  self.increase += increase.sum().item()