From 37c9d5382481b51835c245d5cc0a743466b8e681 Mon Sep 17 00:00:00 2001
From: Ikko Eltociear Ashimine <eltociear@gmail.com>
Date: Mon, 20 Feb 2023 23:55:00 +0900
Subject: [PATCH] Fix typo in metrics.py

Wether -> Whether
---
 annotator/uniformer/mmseg/core/evaluation/metrics.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/annotator/uniformer/mmseg/core/evaluation/metrics.py b/annotator/uniformer/mmseg/core/evaluation/metrics.py
index 16c7dd47ca..ec4819e60e 100644
--- a/annotator/uniformer/mmseg/core/evaluation/metrics.py
+++ b/annotator/uniformer/mmseg/core/evaluation/metrics.py
@@ -39,7 +39,7 @@ def intersect_and_union(pred_label,
         ignore_index (int): Index that will be ignored in evaluation.
         label_map (dict): Mapping old labels to new labels. The parameter will
             work only when label is str. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. The parameter will
+        reduce_zero_label (bool): Whether ignore zero label. The parameter will
             work only when label is str. Default: False.
 
      Returns:
@@ -101,7 +101,7 @@ def total_intersect_and_union(results,
         num_classes (int): Number of categories.
         ignore_index (int): Index that will be ignored in evaluation.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
 
      Returns:
          ndarray: The intersection of prediction and ground truth histogram
@@ -149,7 +149,7 @@ def mean_iou(results,
         nan_to_num (int, optional): If specified, NaN values will be replaced
             by the numbers defined by the user. Default: None.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
 
      Returns:
         dict[str, float | ndarray]:
@@ -188,7 +188,7 @@ def mean_dice(results,
         nan_to_num (int, optional): If specified, NaN values will be replaced
             by the numbers defined by the user. Default: None.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
 
      Returns:
         dict[str, float | ndarray]: Default metrics.
@@ -229,7 +229,7 @@ def mean_fscore(results,
         nan_to_num (int, optional): If specified, NaN values will be replaced
             by the numbers defined by the user. Default: None.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
         beta (int): Determines the weight of recall in the combined score.
             Default: False.
 
@@ -275,7 +275,7 @@ def eval_metrics(results,
         nan_to_num (int, optional): If specified, NaN values will be replaced
             by the numbers defined by the user. Default: None.
         label_map (dict): Mapping old labels to new labels. Default: dict().
-        reduce_zero_label (bool): Wether ignore zero label. Default: False.
+        reduce_zero_label (bool): Whether ignore zero label. Default: False.
      Returns:
         float: Overall accuracy on all images.
         ndarray: Per category accuracy, shape (num_classes, ).