From 8b2a5f5ffbdf4acab968926b13ac158c10617278 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 17 Nov 2021 19:46:23 +0800
Subject: [PATCH 1/5] [DLMED] simiplify AsDiscrete transform

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/transforms/post/array.py | 63 +++++++++-------------------------
 1 file changed, 17 insertions(+), 46 deletions(-)

diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py
index 7cbb6aad44..444980478b 100644
--- a/monai/transforms/post/array.py
+++ b/monai/transforms/post/array.py
@@ -122,56 +122,36 @@ class AsDiscrete(Transform):
     Args:
         argmax: whether to execute argmax function on input data before transform.
             Defaults to ``False``.
-        to_onehot: whether to convert input data into the one-hot format.
-            Defaults to ``False``.
-        num_classes: the number of classes to convert to One-Hot format.
+        to_onehot: if not None, convert input data into the one-hot format with specified number of classes.
+            Defaults to ``None``.
+        threshold_values: if not None, threshold the float values to int number 0 or 1 with specified theashold.
             Defaults to ``None``.
-        threshold_values: whether threshold the float value to int number 0 or 1.
-            Defaults to ``False``.
-        logit_thresh: the threshold value for thresholding operation..
-            Defaults to ``0.5``.
         rounding: if not None, round the data according to the specified option,
             available options: ["torchrounding"].
 
-    .. deprecated:: 0.6.0
-        ``n_classes`` is deprecated, use ``num_classes`` instead.
-
     """
 
     backend = [TransformBackends.TORCH]
 
-    @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,
         argmax: bool = False,
-        to_onehot: bool = False,
-        num_classes: Optional[int] = None,
-        threshold_values: bool = False,
-        logit_thresh: float = 0.5,
+        to_onehot: Optional[int] = None,
+        threshold_values: Optional[float] = None,
         rounding: Optional[str] = None,
-        n_classes: Optional[int] = None,
     ) -> None:
-        # in case the new num_classes is default but you still call deprecated n_classes
-        if n_classes is not None and num_classes is None:
-            num_classes = n_classes
         self.argmax = argmax
         self.to_onehot = to_onehot
-        self.num_classes = num_classes
         self.threshold_values = threshold_values
-        self.logit_thresh = logit_thresh
         self.rounding = rounding
 
-    @deprecated_arg("n_classes", since="0.6")
     def __call__(
         self,
         img: NdarrayOrTensor,
         argmax: Optional[bool] = None,
-        to_onehot: Optional[bool] = None,
-        num_classes: Optional[int] = None,
-        threshold_values: Optional[bool] = None,
-        logit_thresh: Optional[float] = None,
+        to_onehot: Optional[int] = None,
+        threshold_values: Optional[float] = None,
         rounding: Optional[str] = None,
-        n_classes: Optional[int] = None,
     ) -> NdarrayOrTensor:
         """
         Args:
@@ -179,37 +159,28 @@ def __call__(
                 will automatically add it.
             argmax: whether to execute argmax function on input data before transform.
                 Defaults to ``self.argmax``.
-            to_onehot: whether to convert input data into the one-hot format.
+            to_onehot: if not None, convert input data into the one-hot format with specified number of classes.
                 Defaults to ``self.to_onehot``.
-            num_classes: the number of classes to convert to One-Hot format.
-                Defaults to ``self.num_classes``.
-            threshold_values: whether threshold the float value to int number 0 or 1.
+            threshold_values: if not None, threshold the float values to int number 0 or 1 with specified theashold.
                 Defaults to ``self.threshold_values``.
-            logit_thresh: the threshold value for thresholding operation..
-                Defaults to ``self.logit_thresh``.
             rounding: if not None, round the data according to the specified option,
                 available options: ["torchrounding"].
 
-        .. deprecated:: 0.6.0
-            ``n_classes`` is deprecated, use ``num_classes`` instead.
-
         """
-        # in case the new num_classes is default but you still call deprecated n_classes
-        if n_classes is not None and num_classes is None:
-            num_classes = n_classes
         img_t: torch.Tensor
         img_t, *_ = convert_data_type(img, torch.Tensor)  # type: ignore
         if argmax or self.argmax:
             img_t = torch.argmax(img_t, dim=0, keepdim=True)
 
-        if to_onehot or self.to_onehot:
-            _nclasses = self.num_classes if num_classes is None else num_classes
-            if not isinstance(_nclasses, int):
-                raise AssertionError("One of self.num_classes or num_classes must be an integer")
-            img_t = one_hot(img_t, num_classes=_nclasses, dim=0)
+        to_onehot = to_onehot or self.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)
 
-        if threshold_values or self.threshold_values:
-            img_t = img_t >= (self.logit_thresh if logit_thresh is None else logit_thresh)
+        threshold_values = threshold_values or self.threshold_values
+        if threshold_values is not None:
+            img_t = img_t >= threshold_values
 
         rounding = self.rounding if rounding is None else rounding
         if rounding is not None:

From 17bb3d9af75893dcfb867416ce6bf438f0f7e673 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 17 Nov 2021 20:28:13 +0800
Subject: [PATCH 2/5] [DLMED] update tests

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/data/test_time_augmentation.py          |  2 +-
 monai/transforms/post/array.py                |  2 +-
 monai/transforms/post/dictionary.py           | 33 ++++++-------------
 .../transforms/utils_create_transform_ims.py  | 11 ++-----
 tests/test_as_discrete.py                     |  8 ++---
 tests/test_as_discreted.py                    | 27 ++-------------
 tests/test_compute_roc_auc.py                 | 18 +++++-----
 tests/test_handler_decollate_batch.py         |  2 +-
 tests/test_handler_post_processing.py         |  2 +-
 tests/test_handler_rocauc.py                  |  2 +-
 tests/test_handler_rocauc_dist.py             |  2 +-
 tests/test_integration_classification_2d.py   |  2 +-
 tests/test_integration_fast_train.py          |  4 +--
 tests/test_integration_segmentation_3d.py     |  4 +--
 tests/test_integration_workflows.py           |  6 ++--
 tests/test_testtimeaugmentation.py            |  2 +-
 16 files changed, 43 insertions(+), 84 deletions(-)

diff --git a/monai/data/test_time_augmentation.py b/monai/data/test_time_augmentation.py
index 948e85e131..9c1a25d5b9 100644
--- a/monai/data/test_time_augmentation.py
+++ b/monai/data/test_time_augmentation.py
@@ -86,7 +86,7 @@ class TestTimeAugmentation:
         .. code-block:: python
 
             transform = RandAffined(keys, ...)
-            post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True)])
+            post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=0.5)])
 
             tt_aug = TestTimeAugmentation(
                 transform, batch_size=5, num_workers=0, inferrer_fn=lambda x: post_trans(model(x)), device=device
diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py
index 444980478b..67a591101b 100644
--- a/monai/transforms/post/array.py
+++ b/monai/transforms/post/array.py
@@ -25,7 +25,7 @@
 from monai.transforms.transform import Transform
 from monai.transforms.utils import fill_holes, get_largest_connected_component_mask
 from monai.transforms.utils_pytorch_numpy_unification import unravel_index
-from monai.utils import TransformBackends, convert_data_type, deprecated_arg, ensure_tuple, look_up_option
+from monai.utils import TransformBackends, convert_data_type, ensure_tuple, look_up_option
 from monai.utils.type_conversion import convert_to_dst_type
 
 __all__ = [
diff --git a/monai/transforms/post/dictionary.py b/monai/transforms/post/dictionary.py
index 226f5953e2..88c11d8c45 100644
--- a/monai/transforms/post/dictionary.py
+++ b/monai/transforms/post/dictionary.py
@@ -38,7 +38,7 @@
 from monai.transforms.transform import MapTransform
 from monai.transforms.utility.array import ToTensor
 from monai.transforms.utils import allow_missing_keys_mode, convert_inverse_interp_mode
-from monai.utils import deprecated_arg, ensure_tuple, ensure_tuple_rep
+from monai.utils import ensure_tuple, ensure_tuple_rep
 
 __all__ = [
     "ActivationsD",
@@ -128,18 +128,14 @@ class AsDiscreted(MapTransform):
 
     backend = AsDiscrete.backend
 
-    @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,
         keys: KeysCollection,
         argmax: Union[Sequence[bool], bool] = False,
-        to_onehot: Union[Sequence[bool], bool] = False,
-        num_classes: Optional[Union[Sequence[int], int]] = None,
-        threshold_values: Union[Sequence[bool], bool] = False,
-        logit_thresh: Union[Sequence[float], float] = 0.5,
+        to_onehot: Union[Sequence[Optional[int]], Optional[int]] = None,
+        threshold_values: Union[Sequence[Optional[float]], Optional[float]] = None,
         rounding: Union[Sequence[Optional[str]], Optional[str]] = None,
         allow_missing_keys: bool = False,
-        n_classes: Optional[int] = None,
     ) -> None:
         """
         Args:
@@ -147,14 +143,10 @@ def __init__(
                 See also: :py:class:`monai.transforms.compose.MapTransform`
             argmax: whether to execute argmax function on input data before transform.
                 it also can be a sequence of bool, each element corresponds to a key in ``keys``.
-            to_onehot: whether to convert input data into the one-hot format. Defaults to False.
-                it also can be a sequence of bool, each element corresponds to a key in ``keys``.
-            num_classes: the number of classes to convert to One-Hot format. it also can be a
-                sequence of int, each element corresponds to a key in ``keys``.
-            threshold_values: whether threshold the float value to int number 0 or 1, default is False.
-                it also can be a sequence of bool, each element corresponds to a key in ``keys``.
-            logit_thresh: the threshold value for thresholding operation, default is 0.5.
-                it also can be a sequence of float, each element corresponds to a key in ``keys``.
+            to_onehot: if not None, convert input data into the one-hot format with specified number of classes.
+                defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.
+            threshold_values: if not None, threshold the float values to int number 0 or 1 with specified theashold.
+                defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.
             rounding: if not None, round the data according to the specified option,
                 available options: ["torchrounding"]. it also can be a sequence of str or None,
                 each element corresponds to a key in ``keys``.
@@ -164,24 +156,19 @@ def __init__(
             ``n_classes`` is deprecated, use ``num_classes`` instead.
 
         """
-        # in case the new num_classes is default but you still call deprecated n_classes
-        if n_classes is not None and num_classes is None:
-            num_classes = n_classes
         super().__init__(keys, allow_missing_keys)
         self.argmax = ensure_tuple_rep(argmax, len(self.keys))
         self.to_onehot = ensure_tuple_rep(to_onehot, len(self.keys))
-        self.num_classes = ensure_tuple_rep(num_classes, len(self.keys))
         self.threshold_values = ensure_tuple_rep(threshold_values, len(self.keys))
-        self.logit_thresh = ensure_tuple_rep(logit_thresh, len(self.keys))
         self.rounding = ensure_tuple_rep(rounding, len(self.keys))
         self.converter = AsDiscrete()
 
     def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
         d = dict(data)
-        for key, argmax, to_onehot, num_classes, threshold_values, logit_thresh, rounding in self.key_iterator(
-            d, self.argmax, self.to_onehot, self.num_classes, self.threshold_values, self.logit_thresh, self.rounding
+        for key, argmax, to_onehot, threshold_values, rounding in self.key_iterator(
+            d, self.argmax, self.to_onehot, self.threshold_values, self.rounding
         ):
-            d[key] = self.converter(d[key], argmax, to_onehot, num_classes, threshold_values, logit_thresh, rounding)
+            d[key] = self.converter(d[key], argmax, to_onehot, threshold_values, rounding)
         return d
 
 
diff --git a/monai/transforms/utils_create_transform_ims.py b/monai/transforms/utils_create_transform_ims.py
index 84aaa348fe..2abbc0628b 100644
--- a/monai/transforms/utils_create_transform_ims.py
+++ b/monai/transforms/utils_create_transform_ims.py
@@ -640,15 +640,8 @@ def create_transform_im(
     create_transform_im(RandScaleCropd, dict(keys=keys, roi_scale=0.4), data)
     create_transform_im(CenterScaleCrop, dict(roi_scale=0.4), data)
     create_transform_im(CenterScaleCropd, dict(keys=keys, roi_scale=0.4), data)
-    create_transform_im(
-        AsDiscrete, dict(num_classes=2, threshold_values=True, logit_thresh=10), data, is_post=True, colorbar=True
-    )
-    create_transform_im(
-        AsDiscreted,
-        dict(keys=CommonKeys.LABEL, num_classes=2, threshold_values=True, logit_thresh=10),
-        data,
-        is_post=True,
-    )
+    create_transform_im(AsDiscrete, dict(to_onehot=2, threshold_values=10), data, is_post=True, colorbar=True)
+    create_transform_im(AsDiscreted, dict(keys=CommonKeys.LABEL, to_onehot=2, threshold_values=10), data, is_post=True)
     create_transform_im(LabelFilter, dict(applied_labels=(1, 2, 3, 4, 5, 6)), data, is_post=True)
     create_transform_im(
         LabelFilterd, dict(keys=CommonKeys.LABEL, applied_labels=(1, 2, 3, 4, 5, 6)), data, is_post=True
diff --git a/tests/test_as_discrete.py b/tests/test_as_discrete.py
index 75f6a38d3c..ecaed68f41 100644
--- a/tests/test_as_discrete.py
+++ b/tests/test_as_discrete.py
@@ -20,7 +20,7 @@
 for p in TEST_NDARRAYS:
     TEST_CASES.append(
         [
-            {"argmax": True, "to_onehot": False, "num_classes": None, "threshold_values": False, "logit_thresh": 0.5},
+            {"argmax": True, "to_onehot": None, "threshold_values": 0.5},
             p([[[0.0, 1.0]], [[2.0, 3.0]]]),
             p([[[1.0, 1.0]]]),
             (1, 1, 2),
@@ -29,7 +29,7 @@
 
     TEST_CASES.append(
         [
-            {"argmax": True, "to_onehot": True, "num_classes": 2, "threshold_values": False, "logit_thresh": 0.5},
+            {"argmax": True, "to_onehot": 2, "threshold_values": 0.5},
             p([[[0.0, 1.0]], [[2.0, 3.0]]]),
             p([[[0.0, 0.0]], [[1.0, 1.0]]]),
             (2, 1, 2),
@@ -38,14 +38,14 @@
 
     TEST_CASES.append(
         [
-            {"argmax": False, "to_onehot": False, "num_classes": None, "threshold_values": True, "logit_thresh": 0.6},
+            {"argmax": False, "to_onehot": None, "threshold_values": 0.6},
             p([[[0.0, 1.0], [2.0, 3.0]]]),
             p([[[0.0, 1.0], [1.0, 1.0]]]),
             (1, 2, 2),
         ]
     )
 
-    TEST_CASES.append([{"argmax": False, "to_onehot": True, "num_classes": 3}, p(1), p([0.0, 1.0, 0.0]), (3,)])
+    TEST_CASES.append([{"argmax": False, "to_onehot": 3}, p(1), p([0.0, 1.0, 0.0]), (3,)])
 
     TEST_CASES.append(
         [{"rounding": "torchrounding"}, p([[[0.123, 1.345], [2.567, 3.789]]]), p([[[0.0, 1.0], [3.0, 4.0]]]), (1, 2, 2)]
diff --git a/tests/test_as_discreted.py b/tests/test_as_discreted.py
index dc160d5e46..d577fb9f22 100644
--- a/tests/test_as_discreted.py
+++ b/tests/test_as_discreted.py
@@ -20,14 +20,7 @@
 for p in TEST_NDARRAYS:
     TEST_CASES.append(
         [
-            {
-                "keys": ["pred", "label"],
-                "argmax": [True, False],
-                "to_onehot": True,
-                "num_classes": 2,
-                "threshold_values": False,
-                "logit_thresh": 0.5,
-            },
+            {"keys": ["pred", "label"], "argmax": [True, False], "to_onehot": 2, "threshold_values": 0.5},
             {"pred": p([[[0.0, 1.0]], [[2.0, 3.0]]]), "label": p([[[0, 1]]])},
             {"pred": p([[[0.0, 0.0]], [[1.0, 1.0]]]), "label": p([[[1.0, 0.0]], [[0.0, 1.0]]])},
             (2, 1, 2),
@@ -36,14 +29,7 @@
 
     TEST_CASES.append(
         [
-            {
-                "keys": ["pred", "label"],
-                "argmax": False,
-                "to_onehot": False,
-                "num_classes": None,
-                "threshold_values": [True, False],
-                "logit_thresh": 0.6,
-            },
+            {"keys": ["pred", "label"], "argmax": False, "to_onehot": None, "threshold_values": [0.6, None]},
             {"pred": p([[[0.0, 1.0], [2.0, 3.0]]]), "label": p([[[0, 1], [1, 1]]])},
             {"pred": p([[[0.0, 1.0], [1.0, 1.0]]]), "label": p([[[0.0, 1.0], [1.0, 1.0]]])},
             (1, 2, 2),
@@ -52,14 +38,7 @@
 
     TEST_CASES.append(
         [
-            {
-                "keys": ["pred"],
-                "argmax": True,
-                "to_onehot": True,
-                "num_classes": 2,
-                "threshold_values": False,
-                "logit_thresh": 0.5,
-            },
+            {"keys": ["pred"], "argmax": True, "to_onehot": 2, "threshold_values": 0.5},
             {"pred": p([[[0.0, 1.0]], [[2.0, 3.0]]])},
             {"pred": p([[[0.0, 0.0]], [[1.0, 1.0]]])},
             (2, 1, 2),
diff --git a/tests/test_compute_roc_auc.py b/tests/test_compute_roc_auc.py
index bfecb4ce5b..02e2f2b24f 100644
--- a/tests/test_compute_roc_auc.py
+++ b/tests/test_compute_roc_auc.py
@@ -23,7 +23,7 @@
     torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),
     torch.tensor([[0], [1], [0], [1]]),
     True,
-    True,
+    2,
     "macro",
     0.75,
 ]
@@ -32,20 +32,20 @@
     torch.tensor([[0.5], [0.5], [0.2], [8.3]]),
     torch.tensor([[0], [1], [0], [1]]),
     False,
-    False,
+    None,
     "macro",
     0.875,
 ]
 
-TEST_CASE_3 = [torch.tensor([[0.5], [0.5], [0.2], [8.3]]), torch.tensor([0, 1, 0, 1]), False, False, "macro", 0.875]
+TEST_CASE_3 = [torch.tensor([[0.5], [0.5], [0.2], [8.3]]), torch.tensor([0, 1, 0, 1]), False, None, "macro", 0.875]
 
-TEST_CASE_4 = [torch.tensor([0.5, 0.5, 0.2, 8.3]), torch.tensor([0, 1, 0, 1]), False, False, "macro", 0.875]
+TEST_CASE_4 = [torch.tensor([0.5, 0.5, 0.2, 8.3]), torch.tensor([0, 1, 0, 1]), False, None, "macro", 0.875]
 
 TEST_CASE_5 = [
     torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5]]),
     torch.tensor([[0], [1], [0], [1]]),
     True,
-    True,
+    2,
     "none",
     [0.75, 0.75],
 ]
@@ -54,7 +54,7 @@
     torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5], [0.1, 0.5]]),
     torch.tensor([[1, 0], [0, 1], [0, 0], [1, 1], [0, 1]]),
     True,
-    False,
+    None,
     "weighted",
     0.56667,
 ]
@@ -63,7 +63,7 @@
     torch.tensor([[0.1, 0.9], [0.3, 1.4], [0.2, 0.1], [0.1, 0.5], [0.1, 0.5]]),
     torch.tensor([[1, 0], [0, 1], [0, 0], [1, 1], [0, 1]]),
     True,
-    False,
+    None,
     "micro",
     0.62,
 ]
@@ -73,7 +73,7 @@ class TestComputeROCAUC(unittest.TestCase):
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])
     def test_value(self, y_pred, y, softmax, to_onehot, average, expected_value):
         y_pred_trans = Compose([ToTensor(), Activations(softmax=softmax)])
-        y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=to_onehot, num_classes=2)])
+        y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=to_onehot)])
         y_pred = torch.stack([y_pred_trans(i) for i in decollate_batch(y_pred)], dim=0)
         y = torch.stack([y_trans(i) for i in decollate_batch(y)], dim=0)
         result = compute_roc_auc(y_pred=y_pred, y=y, average=average)
@@ -82,7 +82,7 @@ def test_value(self, y_pred, y, softmax, to_onehot, average, expected_value):
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6, TEST_CASE_7])
     def test_class_value(self, y_pred, y, softmax, to_onehot, average, expected_value):
         y_pred_trans = Compose([ToTensor(), Activations(softmax=softmax)])
-        y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=to_onehot, num_classes=2)])
+        y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=to_onehot)])
         y_pred = [y_pred_trans(i) for i in decollate_batch(y_pred)]
         y = [y_trans(i) for i in decollate_batch(y)]
         metric = ROCAUCMetric(average=average)
diff --git a/tests/test_handler_decollate_batch.py b/tests/test_handler_decollate_batch.py
index 8f0ffb2b5c..584fb33116 100644
--- a/tests/test_handler_decollate_batch.py
+++ b/tests/test_handler_decollate_batch.py
@@ -32,7 +32,7 @@ def test_compute(self):
                     [
                         Activationsd(keys="pred", sigmoid=True),
                         CopyItemsd(keys="filename", times=1, names="filename_bak"),
-                        AsDiscreted(keys="pred", threshold_values=True, to_onehot=True, num_classes=2),
+                        AsDiscreted(keys="pred", threshold_values=0.5, to_onehot=2),
                     ]
                 )
             ),
diff --git a/tests/test_handler_post_processing.py b/tests/test_handler_post_processing.py
index e9d57128cb..fd670113b7 100644
--- a/tests/test_handler_post_processing.py
+++ b/tests/test_handler_post_processing.py
@@ -26,7 +26,7 @@
         "transform": Compose(
             [
                 CopyItemsd(keys="filename", times=1, names="filename_bak"),
-                AsDiscreted(keys="pred", threshold_values=True, to_onehot=True, num_classes=2),
+                AsDiscreted(keys="pred", threshold_values=0.5, to_onehot=2),
             ]
         ),
         "event": "iteration_completed",
diff --git a/tests/test_handler_rocauc.py b/tests/test_handler_rocauc.py
index 5b80bc43eb..bd32922777 100644
--- a/tests/test_handler_rocauc.py
+++ b/tests/test_handler_rocauc.py
@@ -22,7 +22,7 @@ class TestHandlerROCAUC(unittest.TestCase):
     def test_compute(self):
         auc_metric = ROCAUC()
         act = Activations(softmax=True)
-        to_onehot = AsDiscrete(to_onehot=True, num_classes=2)
+        to_onehot = AsDiscrete(to_onehot=2)
 
         y_pred = [torch.Tensor([0.1, 0.9]), torch.Tensor([0.3, 1.4])]
         y = [torch.Tensor([0]), torch.Tensor([1])]
diff --git a/tests/test_handler_rocauc_dist.py b/tests/test_handler_rocauc_dist.py
index 8316d4c4b6..0905816868 100644
--- a/tests/test_handler_rocauc_dist.py
+++ b/tests/test_handler_rocauc_dist.py
@@ -26,7 +26,7 @@ class DistributedROCAUC(DistTestCase):
     def test_compute(self):
         auc_metric = ROCAUC()
         act = Activations(softmax=True)
-        to_onehot = AsDiscrete(to_onehot=True, num_classes=2)
+        to_onehot = AsDiscrete(to_onehot=2)
 
         device = f"cuda:{dist.get_rank()}" if torch.cuda.is_available() else "cpu"
         if dist.get_rank() == 0:
diff --git a/tests/test_integration_classification_2d.py b/tests/test_integration_classification_2d.py
index 7a94780f82..cafad9dcf0 100644
--- a/tests/test_integration_classification_2d.py
+++ b/tests/test_integration_classification_2d.py
@@ -80,7 +80,7 @@ def run_training_test(root_dir, train_x, train_y, val_x, val_y, device="cuda:0",
         [LoadImage(image_only=True), AddChannel(), Transpose(indices=[0, 2, 1]), ScaleIntensity(), ToTensor()]
     )
     y_pred_trans = Compose([ToTensor(), Activations(softmax=True)])
-    y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=True, num_classes=len(np.unique(train_y)))])
+    y_trans = Compose([ToTensor(), AsDiscrete(to_onehot=len(np.unique(train_y)))])
     auc_metric = ROCAUCMetric()
 
     # create train, val data loaders
diff --git a/tests/test_integration_fast_train.py b/tests/test_integration_fast_train.py
index 9fd37a0897..b2706dbb47 100644
--- a/tests/test_integration_fast_train.py
+++ b/tests/test_integration_fast_train.py
@@ -170,8 +170,8 @@ def test_train_timing(self):
         optimizer = Novograd(model.parameters(), learning_rate * 10)
         scaler = torch.cuda.amp.GradScaler()
 
-        post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=True, num_classes=2)])
-        post_label = Compose([EnsureType(), AsDiscrete(to_onehot=True, num_classes=2)])
+        post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=2)])
+        post_label = Compose([EnsureType(), AsDiscrete(to_onehot=2)])
 
         dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
 
diff --git a/tests/test_integration_segmentation_3d.py b/tests/test_integration_segmentation_3d.py
index 215a5b3f9a..197dfeecd5 100644
--- a/tests/test_integration_segmentation_3d.py
+++ b/tests/test_integration_segmentation_3d.py
@@ -95,7 +95,7 @@ def run_training_test(root_dir, device="cuda:0", cachedataset=0, readers=(None,
     # create a validation data loader
     val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
     val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
-    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold_values=True)])
+    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold_values=0.5)])
     dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
 
     # create UNet, DiceLoss and Adam optimizer
@@ -195,7 +195,7 @@ def run_inference_test(root_dir, device="cuda:0"):
     val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
     # sliding window inference need to input 1 image in every iteration
     val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
-    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold_values=True)])
+    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold_values=0.5)])
     dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
 
     model = UNet(
diff --git a/tests/test_integration_workflows.py b/tests/test_integration_workflows.py
index 0ffef2935b..b3d80c8b84 100644
--- a/tests/test_integration_workflows.py
+++ b/tests/test_integration_workflows.py
@@ -114,7 +114,7 @@ def run_training_test(root_dir, device="cuda:0", amp=False, num_workers=4):
         [
             ToTensord(keys=["pred", "label"]),
             Activationsd(keys="pred", sigmoid=True),
-            AsDiscreted(keys="pred", threshold_values=True),
+            AsDiscreted(keys="pred", threshold_values=0.5),
             KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
         ]
     )
@@ -155,7 +155,7 @@ def _forward_completed(self, engine):
         [
             ToTensord(keys=["pred", "label"]),
             Activationsd(keys="pred", sigmoid=True),
-            AsDiscreted(keys="pred", threshold_values=True),
+            AsDiscreted(keys="pred", threshold_values=0.5),
             KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
         ]
     )
@@ -242,7 +242,7 @@ def run_inference_test(root_dir, model_file, device="cuda:0", amp=False, num_wor
         [
             ToTensord(keys=["pred", "label"]),
             Activationsd(keys="pred", sigmoid=True),
-            AsDiscreted(keys="pred", threshold_values=True),
+            AsDiscreted(keys="pred", threshold_values=0.5),
             KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
             # test the case that `pred` in `engine.state.output`, while `image_meta_dict` in `engine.state.batch`
             SaveImaged(keys="pred", meta_keys="image_meta_dict", output_dir=root_dir, output_postfix="seg_transform"),
diff --git a/tests/test_testtimeaugmentation.py b/tests/test_testtimeaugmentation.py
index e7303ca524..568571b380 100644
--- a/tests/test_testtimeaugmentation.py
+++ b/tests/test_testtimeaugmentation.py
@@ -122,7 +122,7 @@ def test_test_time_augmentation(self):
 
             epoch_loss /= len(train_loader)
 
-        post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=True)])
+        post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=0.5)])
 
         def inferrer_fn(x):
             return post_trans(model(x))

From 70919d3a7d3ddce0f6c2dc0dce1e2794999694e7 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Wed, 17 Nov 2021 22:27:26 +0800
Subject: [PATCH 3/5] [DLMED] update according to comments

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/data/test_time_augmentation.py          |  2 +-
 monai/transforms/post/array.py                | 23 +++++++++++--------
 monai/transforms/post/dictionary.py           | 15 ++++++------
 .../transforms/utils_create_transform_ims.py  |  4 ++--
 tests/test_as_discrete.py                     |  6 ++---
 tests/test_as_discreted.py                    |  6 ++---
 tests/test_handler_decollate_batch.py         |  2 +-
 tests/test_handler_post_processing.py         |  2 +-
 tests/test_integration_segmentation_3d.py     |  4 ++--
 tests/test_integration_workflows.py           |  6 ++---
 tests/test_testtimeaugmentation.py            |  2 +-
 11 files changed, 38 insertions(+), 34 deletions(-)

diff --git a/monai/data/test_time_augmentation.py b/monai/data/test_time_augmentation.py
index 9c1a25d5b9..4df3283ab6 100644
--- a/monai/data/test_time_augmentation.py
+++ b/monai/data/test_time_augmentation.py
@@ -86,7 +86,7 @@ class TestTimeAugmentation:
         .. code-block:: python
 
             transform = RandAffined(keys, ...)
-            post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=0.5)])
+            post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
 
             tt_aug = TestTimeAugmentation(
                 transform, batch_size=5, num_workers=0, inferrer_fn=lambda x: post_trans(model(x)), device=device
diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py
index 67a591101b..71cd349fba 100644
--- a/monai/transforms/post/array.py
+++ b/monai/transforms/post/array.py
@@ -25,7 +25,7 @@
 from monai.transforms.transform import Transform
 from monai.transforms.utils import fill_holes, get_largest_connected_component_mask
 from monai.transforms.utils_pytorch_numpy_unification import unravel_index
-from monai.utils import TransformBackends, convert_data_type, ensure_tuple, look_up_option
+from monai.utils import TransformBackends, convert_data_type, deprecated_arg, ensure_tuple, look_up_option
 from monai.utils.type_conversion import convert_to_dst_type
 
 __all__ = [
@@ -124,7 +124,7 @@ class AsDiscrete(Transform):
             Defaults to ``False``.
         to_onehot: if not None, convert input data into the one-hot format with specified number of classes.
             Defaults to ``None``.
-        threshold_values: if not None, threshold the float values to int number 0 or 1 with specified theashold.
+        threshold: if not None, threshold the float values to int number 0 or 1 with specified theashold.
             Defaults to ``None``.
         rounding: if not None, round the data according to the specified option,
             available options: ["torchrounding"].
@@ -133,16 +133,19 @@ class AsDiscrete(Transform):
 
     backend = [TransformBackends.TORCH]
 
+    @deprecated_arg(name="threshold_values", new_name="threshold", since="0.7")
     def __init__(
         self,
         argmax: bool = False,
         to_onehot: Optional[int] = None,
-        threshold_values: Optional[float] = None,
+        threshold: Optional[float] = None,
         rounding: Optional[str] = None,
     ) -> None:
         self.argmax = argmax
         self.to_onehot = to_onehot
-        self.threshold_values = threshold_values
+        if threshold is True:
+            raise ValueError("`threshold_values=True` is deprecated, please use `threashold=value` instead.")
+        self.threshold = threshold
         self.rounding = rounding
 
     def __call__(
@@ -150,7 +153,7 @@ def __call__(
         img: NdarrayOrTensor,
         argmax: Optional[bool] = None,
         to_onehot: Optional[int] = None,
-        threshold_values: Optional[float] = None,
+        threshold: Optional[float] = None,
         rounding: Optional[str] = None,
     ) -> NdarrayOrTensor:
         """
@@ -161,8 +164,8 @@ def __call__(
                 Defaults to ``self.argmax``.
             to_onehot: if not None, convert input data into the one-hot format with specified number of classes.
                 Defaults to ``self.to_onehot``.
-            threshold_values: if not None, threshold the float values to int number 0 or 1 with specified theashold.
-                Defaults to ``self.threshold_values``.
+            threshold: if not None, threshold the float values to int number 0 or 1 with specified theashold value.
+                Defaults to ``self.threshold``.
             rounding: if not None, round the data according to the specified option,
                 available options: ["torchrounding"].
 
@@ -178,9 +181,9 @@ def __call__(
                 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_values = threshold_values or self.threshold_values
-        if threshold_values is not None:
-            img_t = img_t >= threshold_values
+        threshold = threshold or self.threshold
+        if threshold is not None:
+            img_t = img_t >= threshold
 
         rounding = self.rounding if rounding is None else rounding
         if rounding is not None:
diff --git a/monai/transforms/post/dictionary.py b/monai/transforms/post/dictionary.py
index 88c11d8c45..e7119dc2e5 100644
--- a/monai/transforms/post/dictionary.py
+++ b/monai/transforms/post/dictionary.py
@@ -38,7 +38,7 @@
 from monai.transforms.transform import MapTransform
 from monai.transforms.utility.array import ToTensor
 from monai.transforms.utils import allow_missing_keys_mode, convert_inverse_interp_mode
-from monai.utils import ensure_tuple, ensure_tuple_rep
+from monai.utils import deprecated_arg, ensure_tuple, ensure_tuple_rep
 
 __all__ = [
     "ActivationsD",
@@ -128,12 +128,13 @@ class AsDiscreted(MapTransform):
 
     backend = AsDiscrete.backend
 
+    @deprecated_arg(name="threshold_values", new_name="threshold", since="0.7")
     def __init__(
         self,
         keys: KeysCollection,
         argmax: Union[Sequence[bool], bool] = False,
         to_onehot: Union[Sequence[Optional[int]], Optional[int]] = None,
-        threshold_values: Union[Sequence[Optional[float]], Optional[float]] = None,
+        threshold: Union[Sequence[Optional[float]], Optional[float]] = None,
         rounding: Union[Sequence[Optional[str]], Optional[str]] = None,
         allow_missing_keys: bool = False,
     ) -> None:
@@ -145,7 +146,7 @@ def __init__(
                 it also can be a sequence of bool, each element corresponds to a key in ``keys``.
             to_onehot: if not None, convert input data into the one-hot format with specified number of classes.
                 defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.
-            threshold_values: if not None, threshold the float values to int number 0 or 1 with specified theashold.
+            threshold: if not None, threshold the float values to int number 0 or 1 with specified theashold value.
                 defaults to ``None``. it also can be a sequence, each element corresponds to a key in ``keys``.
             rounding: if not None, round the data according to the specified option,
                 available options: ["torchrounding"]. it also can be a sequence of str or None,
@@ -159,16 +160,16 @@ def __init__(
         super().__init__(keys, allow_missing_keys)
         self.argmax = ensure_tuple_rep(argmax, len(self.keys))
         self.to_onehot = ensure_tuple_rep(to_onehot, len(self.keys))
-        self.threshold_values = ensure_tuple_rep(threshold_values, len(self.keys))
+        self.threshold = ensure_tuple_rep(threshold, len(self.keys))
         self.rounding = ensure_tuple_rep(rounding, len(self.keys))
         self.converter = AsDiscrete()
 
     def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
         d = dict(data)
-        for key, argmax, to_onehot, threshold_values, rounding in self.key_iterator(
-            d, self.argmax, self.to_onehot, self.threshold_values, self.rounding
+        for key, argmax, to_onehot, threshold, rounding in self.key_iterator(
+            d, self.argmax, self.to_onehot, self.threshold, self.rounding
         ):
-            d[key] = self.converter(d[key], argmax, to_onehot, threshold_values, rounding)
+            d[key] = self.converter(d[key], argmax, to_onehot, threshold, rounding)
         return d
 
 
diff --git a/monai/transforms/utils_create_transform_ims.py b/monai/transforms/utils_create_transform_ims.py
index 2abbc0628b..59d359639b 100644
--- a/monai/transforms/utils_create_transform_ims.py
+++ b/monai/transforms/utils_create_transform_ims.py
@@ -640,8 +640,8 @@ def create_transform_im(
     create_transform_im(RandScaleCropd, dict(keys=keys, roi_scale=0.4), data)
     create_transform_im(CenterScaleCrop, dict(roi_scale=0.4), data)
     create_transform_im(CenterScaleCropd, dict(keys=keys, roi_scale=0.4), data)
-    create_transform_im(AsDiscrete, dict(to_onehot=2, threshold_values=10), data, is_post=True, colorbar=True)
-    create_transform_im(AsDiscreted, dict(keys=CommonKeys.LABEL, to_onehot=2, threshold_values=10), data, is_post=True)
+    create_transform_im(AsDiscrete, dict(to_onehot=2, threshold=10), data, is_post=True, colorbar=True)
+    create_transform_im(AsDiscreted, dict(keys=CommonKeys.LABEL, to_onehot=2, threshold=10), data, is_post=True)
     create_transform_im(LabelFilter, dict(applied_labels=(1, 2, 3, 4, 5, 6)), data, is_post=True)
     create_transform_im(
         LabelFilterd, dict(keys=CommonKeys.LABEL, applied_labels=(1, 2, 3, 4, 5, 6)), data, is_post=True
diff --git a/tests/test_as_discrete.py b/tests/test_as_discrete.py
index ecaed68f41..e8db2052f0 100644
--- a/tests/test_as_discrete.py
+++ b/tests/test_as_discrete.py
@@ -20,7 +20,7 @@
 for p in TEST_NDARRAYS:
     TEST_CASES.append(
         [
-            {"argmax": True, "to_onehot": None, "threshold_values": 0.5},
+            {"argmax": True, "to_onehot": None, "threshold": 0.5},
             p([[[0.0, 1.0]], [[2.0, 3.0]]]),
             p([[[1.0, 1.0]]]),
             (1, 1, 2),
@@ -29,7 +29,7 @@
 
     TEST_CASES.append(
         [
-            {"argmax": True, "to_onehot": 2, "threshold_values": 0.5},
+            {"argmax": True, "to_onehot": 2, "threshold": 0.5},
             p([[[0.0, 1.0]], [[2.0, 3.0]]]),
             p([[[0.0, 0.0]], [[1.0, 1.0]]]),
             (2, 1, 2),
@@ -38,7 +38,7 @@
 
     TEST_CASES.append(
         [
-            {"argmax": False, "to_onehot": None, "threshold_values": 0.6},
+            {"argmax": False, "to_onehot": None, "threshold": 0.6},
             p([[[0.0, 1.0], [2.0, 3.0]]]),
             p([[[0.0, 1.0], [1.0, 1.0]]]),
             (1, 2, 2),
diff --git a/tests/test_as_discreted.py b/tests/test_as_discreted.py
index d577fb9f22..8532f84d33 100644
--- a/tests/test_as_discreted.py
+++ b/tests/test_as_discreted.py
@@ -20,7 +20,7 @@
 for p in TEST_NDARRAYS:
     TEST_CASES.append(
         [
-            {"keys": ["pred", "label"], "argmax": [True, False], "to_onehot": 2, "threshold_values": 0.5},
+            {"keys": ["pred", "label"], "argmax": [True, False], "to_onehot": 2, "threshold": 0.5},
             {"pred": p([[[0.0, 1.0]], [[2.0, 3.0]]]), "label": p([[[0, 1]]])},
             {"pred": p([[[0.0, 0.0]], [[1.0, 1.0]]]), "label": p([[[1.0, 0.0]], [[0.0, 1.0]]])},
             (2, 1, 2),
@@ -29,7 +29,7 @@
 
     TEST_CASES.append(
         [
-            {"keys": ["pred", "label"], "argmax": False, "to_onehot": None, "threshold_values": [0.6, None]},
+            {"keys": ["pred", "label"], "argmax": False, "to_onehot": None, "threshold": [0.6, None]},
             {"pred": p([[[0.0, 1.0], [2.0, 3.0]]]), "label": p([[[0, 1], [1, 1]]])},
             {"pred": p([[[0.0, 1.0], [1.0, 1.0]]]), "label": p([[[0.0, 1.0], [1.0, 1.0]]])},
             (1, 2, 2),
@@ -38,7 +38,7 @@
 
     TEST_CASES.append(
         [
-            {"keys": ["pred"], "argmax": True, "to_onehot": 2, "threshold_values": 0.5},
+            {"keys": ["pred"], "argmax": True, "to_onehot": 2, "threshold": 0.5},
             {"pred": p([[[0.0, 1.0]], [[2.0, 3.0]]])},
             {"pred": p([[[0.0, 0.0]], [[1.0, 1.0]]])},
             (2, 1, 2),
diff --git a/tests/test_handler_decollate_batch.py b/tests/test_handler_decollate_batch.py
index 584fb33116..d2282a971f 100644
--- a/tests/test_handler_decollate_batch.py
+++ b/tests/test_handler_decollate_batch.py
@@ -32,7 +32,7 @@ def test_compute(self):
                     [
                         Activationsd(keys="pred", sigmoid=True),
                         CopyItemsd(keys="filename", times=1, names="filename_bak"),
-                        AsDiscreted(keys="pred", threshold_values=0.5, to_onehot=2),
+                        AsDiscreted(keys="pred", threshold=0.5, to_onehot=2),
                     ]
                 )
             ),
diff --git a/tests/test_handler_post_processing.py b/tests/test_handler_post_processing.py
index fd670113b7..4b47ece063 100644
--- a/tests/test_handler_post_processing.py
+++ b/tests/test_handler_post_processing.py
@@ -26,7 +26,7 @@
         "transform": Compose(
             [
                 CopyItemsd(keys="filename", times=1, names="filename_bak"),
-                AsDiscreted(keys="pred", threshold_values=0.5, to_onehot=2),
+                AsDiscreted(keys="pred", threshold=0.5, to_onehot=2),
             ]
         ),
         "event": "iteration_completed",
diff --git a/tests/test_integration_segmentation_3d.py b/tests/test_integration_segmentation_3d.py
index 197dfeecd5..8898bcdbf8 100644
--- a/tests/test_integration_segmentation_3d.py
+++ b/tests/test_integration_segmentation_3d.py
@@ -95,7 +95,7 @@ def run_training_test(root_dir, device="cuda:0", cachedataset=0, readers=(None,
     # create a validation data loader
     val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
     val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
-    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold_values=0.5)])
+    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
     dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
 
     # create UNet, DiceLoss and Adam optimizer
@@ -195,7 +195,7 @@ def run_inference_test(root_dir, device="cuda:0"):
     val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
     # sliding window inference need to input 1 image in every iteration
     val_loader = monai.data.DataLoader(val_ds, batch_size=1, num_workers=4)
-    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold_values=0.5)])
+    val_post_tran = Compose([ToTensor(), Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
     dice_metric = DiceMetric(include_background=True, reduction="mean", get_not_nans=False)
 
     model = UNet(
diff --git a/tests/test_integration_workflows.py b/tests/test_integration_workflows.py
index b3d80c8b84..f188ab626f 100644
--- a/tests/test_integration_workflows.py
+++ b/tests/test_integration_workflows.py
@@ -114,7 +114,7 @@ def run_training_test(root_dir, device="cuda:0", amp=False, num_workers=4):
         [
             ToTensord(keys=["pred", "label"]),
             Activationsd(keys="pred", sigmoid=True),
-            AsDiscreted(keys="pred", threshold_values=0.5),
+            AsDiscreted(keys="pred", threshold=0.5),
             KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
         ]
     )
@@ -155,7 +155,7 @@ def _forward_completed(self, engine):
         [
             ToTensord(keys=["pred", "label"]),
             Activationsd(keys="pred", sigmoid=True),
-            AsDiscreted(keys="pred", threshold_values=0.5),
+            AsDiscreted(keys="pred", threshold=0.5),
             KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
         ]
     )
@@ -242,7 +242,7 @@ def run_inference_test(root_dir, model_file, device="cuda:0", amp=False, num_wor
         [
             ToTensord(keys=["pred", "label"]),
             Activationsd(keys="pred", sigmoid=True),
-            AsDiscreted(keys="pred", threshold_values=0.5),
+            AsDiscreted(keys="pred", threshold=0.5),
             KeepLargestConnectedComponentd(keys="pred", applied_labels=[1]),
             # test the case that `pred` in `engine.state.output`, while `image_meta_dict` in `engine.state.batch`
             SaveImaged(keys="pred", meta_keys="image_meta_dict", output_dir=root_dir, output_postfix="seg_transform"),
diff --git a/tests/test_testtimeaugmentation.py b/tests/test_testtimeaugmentation.py
index 568571b380..09a7f1c2ed 100644
--- a/tests/test_testtimeaugmentation.py
+++ b/tests/test_testtimeaugmentation.py
@@ -122,7 +122,7 @@ def test_test_time_augmentation(self):
 
             epoch_loss /= len(train_loader)
 
-        post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold_values=0.5)])
+        post_trans = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])
 
         def inferrer_fn(x):
             return post_trans(model(x))

From e348f84165651f995a8734bd480fe8b883c71931 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Thu, 18 Nov 2021 16:09:47 +0800
Subject: [PATCH 4/5] [DLMED] update according to comments

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/transforms/post/array.py      | 44 +++++++++++++++++++++++++++--
 monai/transforms/post/dictionary.py | 14 ++++++++-
 2 files changed, 55 insertions(+), 3 deletions(-)

diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py
index 71cd349fba..683735940b 100644
--- a/monai/transforms/post/array.py
+++ b/monai/transforms/post/array.py
@@ -129,10 +129,21 @@ class AsDiscrete(Transform):
         rounding: if not None, round the data according to the specified option,
             available options: ["torchrounding"].
 
+    .. deprecated:: 0.6.0
+        ``n_classes`` is deprecated, use ``to_onehot`` instead.
+
+    .. deprecated:: 0.7.0
+        ``num_classes`` is deprecated, use ``to_onehot`` instead.
+        ``logit_thresh`` is deprecated, use ``threshold`` instead.
+        ``threshold_values`` is deprecated, use ``threshold`` instead.
+
     """
 
     backend = [TransformBackends.TORCH]
 
+    @deprecated_arg("n_classes", since="0.6")
+    @deprecated_arg("num_classes", since="0.7")
+    @deprecated_arg("logit_thresh", since="0.7")
     @deprecated_arg(name="threshold_values", new_name="threshold", since="0.7")
     def __init__(
         self,
@@ -140,14 +151,26 @@ def __init__(
         to_onehot: Optional[int] = None,
         threshold: Optional[float] = None,
         rounding: Optional[str] = None,
+        n_classes: Optional[int] = None,
+        num_classes: Optional[int] = None,
+        logit_thresh: float = 0.5,
+        threshold_values: bool = False,
     ) -> None:
         self.argmax = argmax
+        if isinstance(to_onehot, bool):
+            raise ValueError("`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.")
         self.to_onehot = to_onehot
-        if threshold is True:
-            raise ValueError("`threshold_values=True` is deprecated, please use `threashold=value` instead.")
+
+        if isinstance(threshold, bool):
+            raise ValueError("`threshold_values=True/False` is deprecated, please use `threashold=value` instead.")
         self.threshold = threshold
+
         self.rounding = rounding
 
+    @deprecated_arg("n_classes", since="0.6")
+    @deprecated_arg("num_classes", since="0.7")
+    @deprecated_arg("logit_thresh", since="0.7")
+    @deprecated_arg(name="threshold_values", new_name="threshold", since="0.7")
     def __call__(
         self,
         img: NdarrayOrTensor,
@@ -155,6 +178,10 @@ def __call__(
         to_onehot: Optional[int] = None,
         threshold: Optional[float] = None,
         rounding: Optional[str] = None,
+        n_classes: Optional[int] = None,
+        num_classes: Optional[int] = None,
+        logit_thresh: Optional[float] = None,
+        threshold_values: Optional[bool] = None,
     ) -> NdarrayOrTensor:
         """
         Args:
@@ -169,7 +196,20 @@ def __call__(
             rounding: if not None, round the data according to the specified option,
                 available options: ["torchrounding"].
 
+        .. deprecated:: 0.6.0
+            ``n_classes`` is deprecated, use ``to_onehot`` instead.
+
+        .. deprecated:: 0.7.0
+            ``num_classes`` is deprecated, use ``to_onehot`` instead.
+            ``logit_thresh`` is deprecated, use ``threshold`` instead.
+            ``threshold_values`` is deprecated, use ``threshold`` instead.
+
         """
+        if isinstance(to_onehot, bool):
+            raise ValueError("`to_onehot=True/False` is deprecated, please use `to_onehot=num_classes` instead.")
+        if isinstance(threshold, bool):
+            raise ValueError("`threshold_values=True/False` is deprecated, please use `threashold=value` instead.")
+
         img_t: torch.Tensor
         img_t, *_ = convert_data_type(img, torch.Tensor)  # type: ignore
         if argmax or self.argmax:
diff --git a/monai/transforms/post/dictionary.py b/monai/transforms/post/dictionary.py
index e7119dc2e5..8f97114a69 100644
--- a/monai/transforms/post/dictionary.py
+++ b/monai/transforms/post/dictionary.py
@@ -128,6 +128,9 @@ class AsDiscreted(MapTransform):
 
     backend = AsDiscrete.backend
 
+    @deprecated_arg("n_classes", since="0.6")
+    @deprecated_arg("num_classes", since="0.7")
+    @deprecated_arg("logit_thresh", since="0.7")
     @deprecated_arg(name="threshold_values", new_name="threshold", since="0.7")
     def __init__(
         self,
@@ -137,6 +140,10 @@ def __init__(
         threshold: Union[Sequence[Optional[float]], Optional[float]] = None,
         rounding: Union[Sequence[Optional[str]], Optional[str]] = None,
         allow_missing_keys: bool = False,
+        n_classes: Optional[Union[Sequence[int], int]] = None,
+        num_classes: Optional[Union[Sequence[int], int]] = None,
+        logit_thresh: Union[Sequence[float], float] = 0.5,
+        threshold_values: Union[Sequence[bool], bool] = False,
     ) -> None:
         """
         Args:
@@ -154,7 +161,12 @@ def __init__(
             allow_missing_keys: don't raise exception if key is missing.
 
         .. deprecated:: 0.6.0
-            ``n_classes`` is deprecated, use ``num_classes`` instead.
+            ``n_classes`` is deprecated, use ``to_onehot`` instead.
+
+        .. deprecated:: 0.7.0
+            ``num_classes`` is deprecated, use ``to_onehot`` instead.
+            ``logit_thresh`` is deprecated, use ``threshold`` instead.
+            ``threshold_values`` is deprecated, use ``threshold`` instead.
 
         """
         super().__init__(keys, allow_missing_keys)

From e8acfe8e4aec2a55b56efcb25d4929dd2dac41b9 Mon Sep 17 00:00:00 2001
From: Nic Ma <nma@nvidia.com>
Date: Thu, 18 Nov 2021 22:02:54 +0800
Subject: [PATCH 5/5] [DLMED] add examples

Signed-off-by: Nic Ma <nma@nvidia.com>
---
 monai/transforms/post/array.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/monai/transforms/post/array.py b/monai/transforms/post/array.py
index 683735940b..0f9133037a 100644
--- a/monai/transforms/post/array.py
+++ b/monai/transforms/post/array.py
@@ -129,6 +129,20 @@ class AsDiscrete(Transform):
         rounding: if not None, round the data according to the specified option,
             available options: ["torchrounding"].
 
+    Example:
+
+        >>> transform = AsDiscrete(argmax=True)
+        >>> print(transform(np.array([[[0.0, 1.0]], [[2.0, 3.0]]])))
+        # [[[1.0, 1.0]]]
+
+        >>> transform = AsDiscrete(threshold=0.6)
+        >>> print(transform(np.array([[[0.0, 0.5], [0.8, 3.0]]])))
+        # [[[0.0, 0.0], [1.0, 1.0]]]
+
+        >>> transform = AsDiscrete(argmax=True, to_onehot=2, threshold=0.5)
+        >>> print(transform(np.array([[[0.0, 1.0]], [[2.0, 3.0]]])))
+        # [[[0.0, 0.0]], [[1.0, 1.0]]]
+
     .. deprecated:: 0.6.0
         ``n_classes`` is deprecated, use ``to_onehot`` instead.