Add a test case in test_metrics() of tests/test_metrics.py to test th…

…e bug caused by torch.histc;

tests/test_metrics.py

@@ -64,30 +64,36 @@ 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,
  ignore_index)
  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,
  ignore_index)
  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
  assert np.allclose(acc, acc_l)
  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)