diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 2033617c2a..1f4330df24 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -64,7 +64,11 @@ def test_metrics():
     ignore_index = 255
     results = np.random.randint(0, num_classes, size=pred_size)
     label = np.random.randint(0, num_classes, size=pred_size)
+
+    # Test the availability of arg: ignore_index.
     label[:, 2, 5:10] = ignore_index
+
+    # Test the correctness of the implementation of mIoU calculation.
     all_acc, acc, iou = eval_metrics(
         results, label, num_classes, ignore_index, metrics='mIoU')
     all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes,
@@ -72,7 +76,7 @@ def test_metrics():
     assert all_acc == all_acc_l
     assert np.allclose(acc, acc_l)
     assert np.allclose(iou, iou_l)
-
+    # Test the correctness of the implementation of mDice calculation.
     all_acc, acc, dice = eval_metrics(
         results, label, num_classes, ignore_index, metrics='mDice')
     all_acc_l, acc_l, dice_l = legacy_mean_dice(results, label, num_classes,
@@ -80,7 +84,7 @@ def test_metrics():
     assert all_acc == all_acc_l
     assert np.allclose(acc, acc_l)
     assert np.allclose(dice, dice_l)
-
+    # Test the correctness of the implementation of joint calculation.
     all_acc, acc, iou, dice = eval_metrics(
         results, label, num_classes, ignore_index, metrics=['mIoU', 'mDice'])
     assert all_acc == all_acc_l
@@ -88,6 +92,8 @@ def test_metrics():
     assert np.allclose(iou, iou_l)
     assert np.allclose(dice, dice_l)
 
+    # Test the correctness of calculation when arg: num_classes is larger
+    # than the maximum value of input maps.
     results = np.random.randint(0, 5, size=pred_size)
     label = np.random.randint(0, 4, size=pred_size)
     all_acc, acc, iou = eval_metrics(
@@ -121,6 +127,17 @@ def test_metrics():
     assert dice[-1] == -1
     assert iou[-1] == -1
 
+    # Test the bug which is caused by torch.histc.
+    # torch.histc:  https://pytorch.org/docs/stable/generated/torch.histc.html
+    # When the arg:bins is set to be same as arg:max,
+    # some channels of mIoU may be nan.
+    results = np.array([np.repeat(31, 59)])
+    label = np.array([np.arange(59)])
+    num_classes = 59
+    all_acc, acc, iou = eval_metrics(
+        results, label, num_classes, ignore_index=255, metrics='mIoU')
+    assert np.sum(np.isnan(iou)) == 0
+
 
 def test_mean_iou():
     pred_size = (10, 30, 30)
@@ -182,7 +199,7 @@ def save_arr(input_arrays: list, title: str, is_image: bool, dir: str):
             filenames.append(filename)
         return filenames
 
-    pred_size = (10, 512, 1024)
+    pred_size = (10, 30, 30)
     num_classes = 19
     ignore_index = 255
     results = np.random.randint(0, num_classes, size=pred_size)