fix some minor typos in docs (PL^5369)

* fix docs typos

* Apply suggestions from code review

Co-authored-by: Wansoo Kim <rladhkstn8@gmail.com>

* flake8

Co-authored-by: Wansoo Kim <rladhkstn8@gmail.com>
(cherry picked from commit a047323cc796dcf5d4709399083bfc4b3b411de9)

pytorch_lightning/metrics/functional/average_precision.py

@@ -67,7 +67,7 @@ def average_precision(
             which for binary problem is translate to 1. For multiclass problems
             this argument should not be set as we iteratively change it in the
             range [0,num_classes-1]
-        sample_weight: sample weights for each data point
+        sample_weights: sample weights for each data point
 
     Returns:
         tensor with average precision. If multiclass will return list

pytorch_lightning/metrics/functional/explained_variance.py

@@ -62,7 +62,7 @@ def explained_variance(
     Computes explained variance.
 
     Args:
-        pred: estimated labels
+        preds: estimated labels
         target: ground truth labels
         multioutput: Defines aggregation in the case of multiple output scores. Can be one
             of the following strings (default is `'uniform_average'`.):

pytorch_lightning/metrics/functional/f_beta.py

@@ -75,7 +75,7 @@ def fbeta(
     If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
 
     Args:
-        pred: estimated probabilities
+        preds: estimated probabilities
         target: ground-truth labels
         num_classes: Number of classes in the dataset.
         beta: Beta coefficient in the F measure.
@@ -128,7 +128,7 @@ def f1(
     If preds has an extra dimension as in the case of multi-class scores we perform an argmax on ``dim=1``.
 
     Args:
-        pred: estimated probabilities
+        preds: estimated probabilities
         target: ground-truth labels
         num_classes: Number of classes in the dataset.
         threshold:

pytorch_lightning/metrics/functional/mean_squared_error.py

@@ -34,7 +34,7 @@ def mean_squared_error(preds: torch.Tensor, target: torch.Tensor) -> torch.Tenso
     Computes mean squared error
 
     Args:
-        pred: estimated labels
+        preds: estimated labels
         target: ground truth labels
 
     Return:

pytorch_lightning/metrics/functional/mean_squared_log_error.py

@@ -34,7 +34,7 @@ def mean_squared_log_error(preds: torch.Tensor, target: torch.Tensor) -> torch.T
     Computes mean squared log error
 
     Args:
-        pred: estimated labels
+        preds: estimated labels
         target: ground truth labels
 
     Return:

pytorch_lightning/metrics/functional/precision_recall_curve.py

@@ -173,7 +173,7 @@ def precision_recall_curve(
             which for binary problem is translate to 1. For multiclass problems
             this argument should not be set as we iteratively change it in the
             range [0,num_classes-1]
-        sample_weight: sample weights for each data point
+        sample_weights: sample weights for each data point
 
     Returns: 3-element tuple containing
 

pytorch_lightning/metrics/functional/psnr.py

@@ -46,8 +46,6 @@ def psnr(
             - ``'elementwise_mean'``: takes the mean (default)
             - ``'sum'``: takes the sum
             - ``'none'``: no reduction will be applied
-        return_state: returns a internal state that can be ddp reduced
-            before doing the final calculation
 
     Return:
         Tensor with PSNR score

pytorch_lightning/metrics/functional/r2score.py

@@ -98,7 +98,7 @@ def r2score(
     be provided as the ``adjusted`` argument.
 
     Args:
-        pred: estimated labels
+        preds: estimated labels
         target: ground truth labels
         adjusted: number of independent regressors for calculating adjusted r2 score.
             Default 0 (standard r2 score).

pytorch_lightning/metrics/functional/roc.py

@@ -98,7 +98,7 @@ def roc(
             which for binary problem is translate to 1. For multiclass problems
             this argument should not be set as we iteratively change it in the
             range [0,num_classes-1]
-        sample_weight: sample weights for each data point
+        sample_weights: sample weights for each data point
 
     Returns: 3-element tuple containing
 

pytorch_lightning/metrics/functional/ssim.py

@@ -125,7 +125,7 @@ def ssim(
     Computes Structual Similarity Index Measure
 
     Args:
-        pred: estimated image
+        preds: estimated image
         target: ground truth image
         kernel_size: size of the gaussian kernel (default: (11, 11))
         sigma: Standard deviation of the gaussian kernel (default: (1.5, 1.5))

pytorch_lightning/metrics/utils.py

@@ -232,7 +232,7 @@ def class_reduce(
 
     Args:
         num: numerator tensor
-        decom: denominator tensor
+        denom: denominator tensor
         weights: weights for each class
         class_reduction: reduction method for multiclass problems