ThresholdIntensity, ThresholdIntensityd

monai/transforms/__init__.py

@@ -524,4 +524,4 @@
     weighted_patch_samples,
     zero_margins,
 )
-from .utils_pytorch_numpy_unification import in1d, moveaxis
+from .utils_pytorch_numpy_unification import in1d, moveaxis, where

monai/transforms/intensity/array.py

@@ -28,6 +28,7 @@
 from monai.networks.layers import GaussianFilter, HilbertTransform, SavitzkyGolayFilter
 from monai.transforms.transform import RandomizableTransform, Transform
 from monai.transforms.utils import Fourier, equalize_hist, is_positive, rescale_array
+from monai.transforms.utils_pytorch_numpy_unification import where
 from monai.utils import (
     PT_BEFORE_1_7,
     InvalidPyTorchVersionError,
@@ -655,20 +656,23 @@ class ThresholdIntensity(Transform):
         cval: value to fill the remaining parts of the image, default is 0.
     """
 
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
+
     def __init__(self, threshold: float, above: bool = True, cval: float = 0.0) -> None:
         if not isinstance(threshold, (int, float)):
             raise ValueError("threshold must be a float or int number.")
         self.threshold = threshold
         self.above = above
         self.cval = cval
 
-    def __call__(self, img: np.ndarray) -> np.ndarray:
+    def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
         """
         Apply the transform to `img`.
         """
-        return np.asarray(
-            np.where(img > self.threshold if self.above else img < self.threshold, img, self.cval), dtype=img.dtype
-        )
+        mask = img > self.threshold if self.above else img < self.threshold
+        res = where(mask, img, self.cval)
+        res, *_ = convert_data_type(res, dtype=img.dtype)
+        return res
 
 
 class ScaleIntensityRange(Transform):

monai/transforms/intensity/dictionary.py

@@ -664,6 +664,8 @@ class ThresholdIntensityd(MapTransform):
         allow_missing_keys: don't raise exception if key is missing.
     """
 
+    backend = ThresholdIntensity.backend
+
     def __init__(
         self,
         keys: KeysCollection,
@@ -675,7 +677,7 @@ def __init__(
         super().__init__(keys, allow_missing_keys)
         self.filter = ThresholdIntensity(threshold, above, cval)
 
-    def __call__(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndarray]:
+    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
         d = dict(data)
         for key in self.key_iterator(d):
             d[key] = self.filter(d[key])

monai/transforms/utils_pytorch_numpy_unification.py

@@ -17,6 +17,7 @@
 __all__ = [
     "moveaxis",
     "in1d",
+    "where",
 ]
 
 
@@ -50,3 +51,17 @@ def in1d(x, y):
     if isinstance(x, np.ndarray):
         return np.in1d(x, y)
     return (x[..., None] == torch.tensor(y, device=x.device)).any(-1).view(-1)
+
+
+def where(condition: NdarrayOrTensor, x, y) -> NdarrayOrTensor:
+    """
+    Note that `torch.where` may convert y.dtype to x.dtype.
+    """
+    result: NdarrayOrTensor
+    if isinstance(condition, np.ndarray):
+        result = np.where(condition, x, y)
+    else:
+        x = torch.as_tensor(x, device=condition.device)
+        y = torch.as_tensor(y, device=condition.device, dtype=x.dtype)
+        result = torch.where(condition, x, y)
+    return result

tests/test_threshold_intensity.py

@@ -15,20 +15,21 @@
 from parameterized import parameterized
 
 from monai.transforms import ThresholdIntensity
+from tests.utils import TEST_NDARRAYS, assert_allclose
 
-TEST_CASE_1 = [{"threshold": 5, "above": True, "cval": 0}, (0, 0, 0, 0, 0, 0, 6, 7, 8, 9)]
-
-TEST_CASE_2 = [{"threshold": 5, "above": False, "cval": 0}, (0, 1, 2, 3, 4, 0, 0, 0, 0, 0)]
-
-TEST_CASE_3 = [{"threshold": 5, "above": True, "cval": 5}, (5, 5, 5, 5, 5, 5, 6, 7, 8, 9)]
+TESTS = []
+for p in TEST_NDARRAYS:
+    TESTS.append([p, {"threshold": 5, "above": True, "cval": 0}, (0, 0, 0, 0, 0, 0, 6, 7, 8, 9)])
+    TESTS.append([p, {"threshold": 5, "above": False, "cval": 0}, (0, 1, 2, 3, 4, 0, 0, 0, 0, 0)])
+    TESTS.append([p, {"threshold": 5, "above": True, "cval": 5}, (5, 5, 5, 5, 5, 5, 6, 7, 8, 9)])
 
 
 class TestThresholdIntensity(unittest.TestCase):
-    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
-    def test_value(self, input_param, expected_value):
-        test_data = np.arange(10)
+    @parameterized.expand(TESTS)
+    def test_value(self, in_type, input_param, expected_value):
+        test_data = in_type(np.arange(10))
         result = ThresholdIntensity(**input_param)(test_data)
-        np.testing.assert_allclose(result, expected_value)
+        assert_allclose(result, expected_value)
 
 
 if __name__ == "__main__":

tests/test_threshold_intensityd.py

@@ -15,31 +15,41 @@
 from parameterized import parameterized
 
 from monai.transforms import ThresholdIntensityd
-
-TEST_CASE_1 = [
-    {"keys": ["image", "label", "extra"], "threshold": 5, "above": True, "cval": 0},
-    (0, 0, 0, 0, 0, 0, 6, 7, 8, 9),
-]
-
-TEST_CASE_2 = [
-    {"keys": ["image", "label", "extra"], "threshold": 5, "above": False, "cval": 0},
-    (0, 1, 2, 3, 4, 0, 0, 0, 0, 0),
-]
-
-TEST_CASE_3 = [
-    {"keys": ["image", "label", "extra"], "threshold": 5, "above": True, "cval": 5},
-    (5, 5, 5, 5, 5, 5, 6, 7, 8, 9),
-]
+from tests.utils import TEST_NDARRAYS, assert_allclose
+
+TESTS = []
+for p in TEST_NDARRAYS:
+    TESTS.append(
+        [
+            p,
+            {"keys": ["image", "label", "extra"], "threshold": 5, "above": True, "cval": 0},
+            (0, 0, 0, 0, 0, 0, 6, 7, 8, 9),
+        ]
+    )
+    TESTS.append(
+        [
+            p,
+            {"keys": ["image", "label", "extra"], "threshold": 5, "above": False, "cval": 0},
+            (0, 1, 2, 3, 4, 0, 0, 0, 0, 0),
+        ]
+    )
+    TESTS.append(
+        [
+            p,
+            {"keys": ["image", "label", "extra"], "threshold": 5, "above": True, "cval": 5},
+            (5, 5, 5, 5, 5, 5, 6, 7, 8, 9),
+        ]
+    )
 
 
 class TestThresholdIntensityd(unittest.TestCase):
-    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
-    def test_value(self, input_param, expected_value):
-        test_data = {"image": np.arange(10), "label": np.arange(10), "extra": np.arange(10)}
+    @parameterized.expand(TESTS)
+    def test_value(self, in_type, input_param, expected_value):
+        test_data = {"image": in_type(np.arange(10)), "label": in_type(np.arange(10)), "extra": in_type(np.arange(10))}
         result = ThresholdIntensityd(**input_param)(test_data)
-        np.testing.assert_allclose(result["image"], expected_value)
-        np.testing.assert_allclose(result["label"], expected_value)
-        np.testing.assert_allclose(result["extra"], expected_value)
+        assert_allclose(result["image"], expected_value)
+        assert_allclose(result["label"], expected_value)
+        assert_allclose(result["extra"], expected_value)
 
 
 if __name__ == "__main__":