3387 Fix 0.0 or None value in AsDiscrete

monai/transforms/post/array.py

@@ -237,13 +237,13 @@ def __call__(
         if argmax or self.argmax:
             img_t = torch.argmax(img_t, dim=0, keepdim=True)
 
-        to_onehot = to_onehot or self.to_onehot
+        to_onehot = self.to_onehot if to_onehot is None else to_onehot
         if to_onehot is not None:
             if not isinstance(to_onehot, int):
                 raise AssertionError("the number of classes for One-Hot must be an integer.")
             img_t = one_hot(img_t, num_classes=to_onehot, dim=0)
 
-        threshold = threshold or self.threshold
+        threshold = self.threshold if threshold is None else threshold
         if threshold is not None:
             img_t = img_t >= threshold
 

tests/test_as_discrete.py

@@ -45,6 +45,16 @@
         ]
     )
 
+    # test threshold = 0.0
+    TEST_CASES.append(
+        [
+            {"argmax": False, "to_onehot": None, "threshold": 0.0},
+            p([[[0.0, -1.0], [-2.0, 3.0]]]),
+            p([[[1.0, 0.0], [0.0, 1.0]]]),
+            (1, 2, 2),
+        ]
+    )
+
     TEST_CASES.append([{"argmax": False, "to_onehot": 3}, p(1), p([0.0, 1.0, 0.0]), (3,)])
 
     TEST_CASES.append(

tests/test_as_discreted.py

@@ -70,6 +70,16 @@
         ]
     )
 
+    # test threshold = 0.0
+    TEST_CASES.append(
+        [
+            {"keys": ["pred", "label"], "argmax": False, "to_onehot": None, "threshold": [0.0, None]},
+            {"pred": p([[[0.0, -1.0], [-2.0, 3.0]]]), "label": p([[[0, 1], [1, 1]]])},
+            {"pred": p([[[1.0, 0.0], [0.0, 1.0]]]), "label": p([[[0.0, 1.0], [1.0, 1.0]]])},
+            (1, 2, 2),
+        ]
+    )
+
 
 class TestAsDiscreted(unittest.TestCase):
     @parameterized.expand(TEST_CASES)