Merge branch 'master' into refactoring/remove_deprecated_compute_on_s…

…tep_image

torchmetrics/utilities/checks.py

@@ -243,7 +243,7 @@ def _check_classification_inputs(
             either from the shape of inputs, or the maximum label in the ``target`` and ``preds``
             tensor, where applicable.
         top_k:
-            Number of highest probability entries for each sample to convert to 1s - relevant
+            Number of the highest probability entries for each sample to convert to 1s - relevant
             only for inputs with probability predictions. The default value (``None``) will be
             interpreted as 1 for these inputs. If this parameter is set for multi-label inputs,
             it will take precedence over threshold.
@@ -342,7 +342,7 @@ def _input_format_classification(
 
     In binary case, targets are normally returned as ``(N,1)`` tensor, while preds are transformed
     into a binary tensor (elements become 1 if the probability is greater than or equal to
-    ``threshold`` or 0 otherwise). If ``multiclass=True``, then then both targets are preds
+    ``threshold`` or 0 otherwise). If ``multiclass=True``, then both targets are preds
     become ``(N, 2)`` tensors by a one-hot transformation; with the thresholding being applied to
     preds first.
 
@@ -461,7 +461,7 @@ def _input_format_classification_one_hot(
     Args:
         num_classes: number of classes
         preds: either tensor with labels, tensor with probabilities/logits or multilabel tensor
-        target: tensor with ground true labels
+        target: tensor with ground-true labels
         threshold: float used for thresholding multilabel input
         multilabel: boolean flag indicating if input is multilabel
 
@@ -503,7 +503,7 @@ def _check_retrieval_functional_inputs(
     target: Tensor,
     allow_non_binary_target: bool = False,
 ) -> Tuple[Tensor, Tensor]:
-    """Check ``preds`` and ``target`` tensors are of the same shape and of the correct dtype.
+    """Check ``preds`` and ``target`` tensors are of the same shape and of the correct data type.
 
     Args:
         preds: either tensor with scores/logits
@@ -535,7 +535,7 @@ def _check_retrieval_inputs(
     allow_non_binary_target: bool = False,
     ignore_index: Optional[int] = None,
 ) -> Tuple[Tensor, Tensor, Tensor]:
-    """Check ``indexes``, ``preds`` and ``target`` tensors are of the same shape and of the correct dtype.
+    """Check ``indexes``, ``preds`` and ``target`` tensors are of the same shape and of the correct data type.
 
     Args:
         indexes: tensor with queries indexes
@@ -580,7 +580,7 @@ def _check_retrieval_target_and_prediction_types(
     target: Tensor,
     allow_non_binary_target: bool = False,
 ) -> Tuple[Tensor, Tensor]:
-    """Check ``preds`` and ``target`` tensors are of the same shape and of the correct dtype.
+    """Check ``preds`` and ``target`` tensors are of the same shape and of the correct data type.
 
     Args:
         preds: either tensor with scores/logits

torchmetrics/utilities/data.py

@@ -157,10 +157,10 @@ def apply_to_collection(
         data: the collection to apply the function to
         dtype: the given function will be applied to all elements of this dtype
         function: the function to apply
-        *args: positional arguments (will be forwarded to calls of ``function``)
+        *args: positional arguments (will be forwarded to call of ``function``)
         wrong_dtype: the given function won't be applied if this type is specified and the given collections is of
             the :attr:`wrong_type` even if it is of type :attr`dtype`
-        **kwargs: keyword arguments (will be forwarded to calls of ``function``)
+        **kwargs: keyword arguments (will be forwarded to call of ``function``)
 
     Returns:
         the resulting collection

torchmetrics/utilities/distributed.py

@@ -19,11 +19,11 @@
 from typing_extensions import Literal
 
 
-def reduce(to_reduce: Tensor, reduction: Literal["elementwise_mean", "sum", "none", None]) -> Tensor:
+def reduce(x: Tensor, reduction: Literal["elementwise_mean", "sum", "none", None]) -> Tensor:
     """Reduces a given tensor by a given reduction method.
 
     Args:
-        to_reduce: the tensor, which shall be reduced
+        x: the tensor, which shall be reduced
         reduction:  a string specifying the reduction method ('elementwise_mean', 'none', 'sum')
 
     Return:
@@ -33,11 +33,11 @@ def reduce(to_reduce: Tensor, reduction: Literal["elementwise_mean", "sum", "non
         ValueError if an invalid reduction parameter was given
     """
     if reduction == "elementwise_mean":
-        return torch.mean(to_reduce)
+        return torch.mean(x)
     if reduction == "none" or reduction is None:
-        return to_reduce
+        return x
     if reduction == "sum":
-        return torch.sum(to_reduce)
+        return torch.sum(x)
     raise ValueError("Reduction parameter unknown.")
 
 

torchmetrics/utilities/imports.py

@@ -14,6 +14,7 @@
 """Import utilities."""
 import operator
 from collections import OrderedDict  # noqa: F401
+from functools import lru_cache
 from importlib import import_module
 from importlib.util import find_spec
 from typing import Callable, Optional
@@ -22,6 +23,7 @@
 from pkg_resources import DistributionNotFound, get_distribution
 
 
+@lru_cache()
 def _package_available(package_name: str) -> bool:
     """Check if a package is available in your environment.
 
@@ -40,6 +42,7 @@ def _package_available(package_name: str) -> bool:
         return False
 
 
+@lru_cache()
 def _module_available(module_path: str) -> bool:
     """Check if a module path is available in your environment.
 
@@ -64,6 +67,7 @@ def _module_available(module_path: str) -> bool:
     return True
 
 
+@lru_cache()
 def _compare_version(package: str, op: Callable, version: str) -> Optional[bool]:
     """Compare package version with some requirements.