Merge branch 'master' into fix_pesq_nan

CHANGELOG.md

@@ -44,7 +44,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
--
+- Fixed for Pearson changes inputs ([#2765](https://github.com/Lightning-AI/torchmetrics/pull/2765))
 
 
 ## [1.4.2] - 2022-09-12

src/torchmetrics/detection/mean_ap.py

@@ -124,19 +124,23 @@ class MeanAveragePrecision(Metric):
 
  - ``map_dict``: A dictionary containing the following key-values:
 
- - map: (:class:`~torch.Tensor`), global mean average precision
- - map_small: (:class:`~torch.Tensor`), mean average precision for small objects
- - map_medium:(:class:`~torch.Tensor`), mean average precision for medium objects
- - map_large: (:class:`~torch.Tensor`), mean average precision for large objects
+ - map: (:class:`~torch.Tensor`), global mean average precision which by default is defined as mAP50-95 e.g. the
+ mean average precision for IoU thresholds 0.50, 0.55, 0.60, ..., 0.95 averaged over all classes and areas. If
+ the IoU thresholds are changed this value will be calculated with the new thresholds.
+ - map_small: (:class:`~torch.Tensor`), mean average precision for small objects (area < 32^2 pixels)
+ - map_medium:(:class:`~torch.Tensor`), mean average precision for medium objects (32^2 pixels < area < 96^2
+ pixels)
+ - map_large: (:class:`~torch.Tensor`), mean average precision for large objects (area > 96^2 pixels)
  - mar_{mdt[0]}: (:class:`~torch.Tensor`), mean average recall for `max_detection_thresholds[0]` (default 1)
  detection per image
  - mar_{mdt[1]}: (:class:`~torch.Tensor`), mean average recall for `max_detection_thresholds[1]` (default 10)
  detection per image
  - mar_{mdt[1]}: (:class:`~torch.Tensor`), mean average recall for `max_detection_thresholds[2]` (default 100)
  detection per image
- - mar_small: (:class:`~torch.Tensor`), mean average recall for small objects
- - mar_medium: (:class:`~torch.Tensor`), mean average recall for medium objects
- - mar_large: (:class:`~torch.Tensor`), mean average recall for large objects
+ - mar_small: (:class:`~torch.Tensor`), mean average recall for small objects (area < 32^2 pixels)
+ - mar_medium: (:class:`~torch.Tensor`), mean average recall for medium objects (32^2 pixels < area < 96^2
+ pixels)
+ - mar_large: (:class:`~torch.Tensor`), mean average recall for large objects (area > 96^2 pixels)
  - map_50: (:class:`~torch.Tensor`) (-1 if 0.5 not in the list of iou thresholds), mean average precision at
  IoU=0.50
  - map_75: (:class:`~torch.Tensor`) (-1 if 0.75 not in the list of iou thresholds), mean average precision at
@@ -150,8 +154,11 @@ class MeanAveragePrecision(Metric):
  For an example on how to use this metric check the `torchmetrics mAP example`_.
 
  .. note::
- ``map`` score is calculated with @[ IoU=self.iou_thresholds | area=all | max_dets=max_detection_thresholds ].
- Caution: If the initialization parameters are changed, dictionary keys for mAR can change as well.
+ ``map`` score is calculated with @[ IoU=self.iou_thresholds | area=all | max_dets=max_detection_thresholds ]
+ e.g. the mean average precision for IoU thresholds 0.50, 0.55, 0.60, ..., 0.95 averaged over all classes and
+ all areas and all max detections per image. If the IoU thresholds are changed this value will be calculated with
+ the new thresholds. Caution: If the initialization parameters are changed, dictionary keys for mAR can change as
+ well.
 
  .. note::
  This metric supports, at the moment, two different backends for the evaluation. The default backend is

src/torchmetrics/functional/image/rmse_sw.py

@@ -104,7 +104,8 @@ def _rmse_sw_compute(
  """
  rmse = rmse_val_sum / total_images if rmse_val_sum is not None else None
  if rmse_map is not None:
- rmse_map /= total_images
+ # prevent overwrite the inputs
+ rmse_map = rmse_map / total_images
  return rmse, rmse_map
 
 

src/torchmetrics/functional/regression/concordance.py

@@ -27,6 +27,8 @@ def _concordance_corrcoef_compute(
 ) -> Tensor:
  """Compute the final concordance correlation coefficient based on accumulated statistics."""
  pearson = _pearson_corrcoef_compute(var_x, var_y, corr_xy, nb)
+ var_x = var_x / (nb - 1)
+ var_y = var_y / (nb - 1)
  return 2.0 * pearson * var_x.sqrt() * var_y.sqrt() / (var_x + var_y + (mean_x - mean_y) ** 2)
 
 

src/torchmetrics/functional/regression/pearson.py

@@ -92,9 +92,10 @@ def _pearson_corrcoef_compute(
  nb: number of observations
 
  """
- var_x /= nb - 1
- var_y /= nb - 1
- corr_xy /= nb - 1
+ # prevent overwrite the inputs
+ var_x = var_x / (nb - 1)
+ var_y = var_y / (nb - 1)
+ corr_xy = corr_xy / (nb - 1)
  # if var_x, var_y is float16 and on cpu, make it bfloat16 as sqrt is not supported for float16
  # on cpu, remove this after https://github.com/pytorch/pytorch/issues/54774 is fixed
  if var_x.dtype == torch.float16 and var_x.device == torch.device("cpu"):

src/torchmetrics/regression/r2.py

@@ -38,8 +38,8 @@ class R2Score(Metric):
 
  where the parameter :math:`k` (the number of independent regressors) should be provided as the `adjusted` argument.
  The score is only proper defined when :math:`SS_{tot}\neq 0`, which can happen for near constant targets. In this
- case a score of 0 is returned. By definition the score is bounded between 0 and 1, where 1 corresponds to the
- predictions exactly matching the targets.
+ case a score of 0 is returned. By definition the score is bounded between :math:`-inf` and 1.0, with 1.0 indicating
+ perfect prediction, 0 indicating constant prediction and negative values indicating worse than constant prediction.
 
  As input to ``forward`` and ``update`` the metric accepts the following input:
 
@@ -99,7 +99,6 @@ class R2Score(Metric):
  is_differentiable: bool = True
  higher_is_better: bool = True
  full_state_update: bool = False
- plot_lower_bound: float = 0.0
  plot_upper_bound: float = 1.0
 
  sum_squared_error: Tensor

src/torchmetrics/regression/symmetric_mape.py

@@ -41,7 +41,7 @@ class SymmetricMeanAbsolutePercentageError(Metric):
 
  As output of ``forward`` and ``compute`` the metric returns the following output:
 
- - ``smape`` (:class:`~torch.Tensor`): A tensor with non-negative floating point smape value between 0 and 1
+ - ``smape`` (:class:`~torch.Tensor`): A tensor with non-negative floating point smape value between 0 and 2
 
  Args:
  kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
@@ -60,6 +60,7 @@ class SymmetricMeanAbsolutePercentageError(Metric):
  higher_is_better: bool = False
  full_state_update: bool = False
  plot_lower_bound: float = 0.0
+ plot_upper_bound: float = 2.0
 
  sum_abs_per_error: Tensor
  total: Tensor

tests/unittests/regression/test_pearson.py

@@ -164,3 +164,25 @@ def test_single_sample_update():
  metric(torch.tensor([7.0]), torch.tensor([8.0]))
  res2 = metric.compute()
  assert torch.allclose(res1, res2)
+
+
+def test_overwrite_reference_inputs():
+ """Test that the normalizations does not overwrite inputs.
+
+ Variables var_x, var_y, corr_xy are references to the object variables and get incorrectly scaled down such that
+ when you update again and compute you get very wrong values.
+
+ """
+ y = torch.randn(100)
+ y_pred = y + torch.randn(y.shape) / 5
+ # Initialize Pearson correlation coefficient metric
+ pearson = PearsonCorrCoef()
+ # Compute the Pearson correlation coefficient
+ correlation = pearson(y, y_pred)
+
+ pearson = PearsonCorrCoef()
+ for lower, upper in [(0, 33), (33, 66), (66, 99), (99, 100)]:
+ pearson.update(torch.tensor(y[lower:upper]), torch.tensor(y_pred[lower:upper]))
+ pearson.compute()
+
+ assert torch.isclose(pearson.compute(), correlation)