!26738 fix comments in metric

Merge pull request !26738 from liutongtong9/code_docs_fixmetric

mindspore/nn/metrics/confusion_matrix.py

@@ -162,15 +162,15 @@ class ConfusionMatrixMetric(Metric):
         skip_channel (bool): Whether to skip the measurement calculation on the first channel of the predicted output.
                              Default: True.
         metric_name (str): Names of supported metrics , users can also set the industry common aliases for them.  Choose
-                            from: ["sensitivity", "specificity", "precision", "negative predictive value", "miss rate",
+                           from: ["sensitivity", "specificity", "precision", "negative predictive value", "miss rate",
                            "fall out", "false discovery rate", "false omission rate", "prevalence threshold",
                            "threat score", "accuracy", "balanced accuracy", "f1 score",
                            "matthews correlation coefficient", "fowlkes mallows index", "informedness", "markedness"].
         calculation_method (bool): If true, the measurement for each sample will be calculated first.
-                                   If not, the confusion matrix of all samples will be accumulated first.
-                                   As for classification task, 'calculation_method' should be False. Default: False.
+                           If not, the confusion matrix of all samples will be accumulated first.
+                           As for classification task, 'calculation_method' should be False. Default: False.
         decrease (str): The reduction method on data batch. `decrease` takes effect only when calculation_method
-                            is True. Default: "mean". Choose from:
+                        is True. Default: "mean". Choose from:
                         ["none", "mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel"].
 
     Supported Platforms:

mindspore/nn/metrics/mean_surface_distance.py

@@ -25,7 +25,7 @@ class MeanSurfaceDistance(Metric):
     on average, the surface varies between the segmentation and the GT (ground truth).
 
     Given two sets A and B, S(A) denotes the set of surface voxels of A. The shortest distance of an arbitrary voxel v
-     to S(A) is defined as:
+    to S(A) is defined as:
 
     .. math::
         {\text{dis}}\left (v, S(A)\right ) = \underset{s_{A}  \in S(A)}{\text{min }}\rVert v - s_{A} \rVert \
@@ -36,7 +36,7 @@ class MeanSurfaceDistance(Metric):
         AvgSurDis(B\rightarrow A) = \frac{\sum_{s_{B}  \in S(B)}^{} {\text{dis}  \
         left ( s_{B}, S(A) \right )} } {\left | S(B) \right |}
 
-    Where the ||*|| denotes a distance measure. |*| denotes the number of elements.
+    Where the \|\|\*\|\| denotes a distance measure. \|\*\| denotes the number of elements.
 
     The mean of surface distance form set(B) to set(A) and from set(A) to set(B) is:
 

mindspore/nn/metrics/recall.py

@@ -38,7 +38,7 @@ class Recall(EvaluationBase):
 
     Args:
         eval_type (str): 'classification' or 'multilabel' are supported. Default: 'classification'.
-        Default: 'classification'.
+            Default: 'classification'.
 
     Examples:
         >>> import numpy as np

mindspore/nn/metrics/root_mean_square_surface_distance.py

@@ -35,7 +35,7 @@ class RootMeanSquareDistance(Metric):
         RmsSurDis(B \rightarrow A) = \sqrt{\frac{\sum_{s_{B}  \in S(B)}^{} {\text{dis}^2  \left ( s_{B}, S(A)
         \right )} }{\left | S(B) \right |}}
 
-    Where the ||\*|| denotes a distance measure. |\*| denotes the number of elements.
+    Where the \|\|\*\|\| denotes a distance measure.\ |\*\| denotes the number of elements.
 
     The Root Mean Square Surface Distance form set(B) to set(A) and from set(A) to set(B) is:
 

mindspore/nn/metrics/topk.py

@@ -68,7 +68,7 @@ class TopKCategoricalAccuracy(Metric):
         Updates the internal evaluation result `y_pred` and `y`.
 
         Args:
-            inputs: Input `y_pred` and `y`. ` y_pred` and `y` are Tensor, list or numpy.ndarray.
+            inputs: Input `y_pred` and `y`. `y_pred` and `y` are Tensor, list or numpy.ndarray.
                 `y_pred` is in most cases (not strictly) a list of floating numbers in range :math:`[0, 1]`
                 and the shape is :math:`(N, C)`, where :math:`N` is the number of cases and :math:`C`
                 is the number of categories. `y` contains values of integers. The shape is :math:`(N, C)`