Re-definition found for builtin function (#325)

tests/bases/test_ddp.py

@@ -146,9 +146,9 @@ def compute(self):
  metric(i)
  state_dict = metric.state_dict()
 
- sum = i * (i + 1) / 2
- assert state_dict["x"] == sum * worldsize
- assert metric.x == sum
+ exp_sum = i * (i + 1) / 2
+ assert state_dict["x"] == exp_sum * worldsize
+ assert metric.x == exp_sum
  assert metric.c == (i + 1)
  assert state_dict["c"] == metric.c * worldsize
 

tests/regression/test_cosine_similarity.py

@@ -46,11 +46,11 @@ def _multi_target_sk_metric(preds, target, reduction, sk_fn=sk_cosine):
  sk_target = target.view(-1, num_targets).numpy()
  result_array = sk_fn(sk_target, sk_preds)
  col = np.diagonal(result_array)
- sum = col.sum()
+ col_sum = col.sum()
  if reduction == 'sum':
- to_return = sum
+ to_return = col_sum
  elif reduction == 'mean':
- mean = sum / len(col)
+ mean = col_sum / len(col)
  to_return = mean
  else:
  to_return = col
@@ -62,11 +62,11 @@ def _single_target_sk_metric(preds, target, reduction, sk_fn=sk_cosine):
  sk_target = target.view(-1).numpy()
  result_array = sk_fn(np.expand_dims(sk_preds, axis=0), np.expand_dims(sk_target, axis=0))
  col = np.diagonal(result_array)
- sum = col.sum()
+ col_sum = col.sum()
  if reduction == 'sum':
- to_return = sum
+ to_return = col_sum
  elif reduction == 'mean':
- mean = sum / len(col)
+ mean = col_sum / len(col)
  to_return = mean
  else:
  to_return = col

torchmetrics/collections.py

@@ -232,9 +232,9 @@ def _check_arg(arg: Optional[str], name: str) -> Optional[str]:
  raise ValueError(f'Expected input `{name}` to be a string, but got {type(arg)}')
 
  def __repr__(self) -> Optional[str]:
- repr = super().__repr__()[:-2]
+ repr_str = super().__repr__()[:-2]
  if self.prefix:
- repr += f",\n prefix={self.prefix}{',' if self.postfix else ''}"
+ repr_str += f",\n prefix={self.prefix}{',' if self.postfix else ''}"
  if self.postfix:
- repr += f"{',' if not self.prefix else ''}\n postfix={self.postfix}"
- return repr + "\n)"
+ repr_str += f"{',' if not self.prefix else ''}\n postfix={self.postfix}"
+ return repr_str + "\n)"

torchmetrics/image/fid.py

@@ -60,14 +60,14 @@ class MatrixSquareRoot(Function):
  """
 
  @staticmethod
- def forward(ctx: Any, input: Tensor) -> Tensor:
+ def forward(ctx: Any, input_data: Tensor) -> Tensor:
  import scipy
 
  # TODO: update whenever pytorch gets an matrix square root function
  # Issue: https://github.com/pytorch/pytorch/issues/9983
- m = input.detach().cpu().numpy().astype(np.float_)
+ m = input_data.detach().cpu().numpy().astype(np.float_)
  scipy_res, _ = scipy.linalg.sqrtm(m, disp=False)
- sqrtm = torch.from_numpy(scipy_res.real).to(input)
+ sqrtm = torch.from_numpy(scipy_res.real).to(input_data)
  ctx.save_for_backward(sqrtm)
  return sqrtm