[DLMED] update dict transform

Signed-off-by: Nic Ma <[email protected]>

Author: Nic-Ma

monai/transforms/intensity/dictionary.py

@@ -1431,7 +1431,8 @@ class RandCoarseDropoutd(RandomizableTransform, MapTransform):
             if some components of the `spatial_size` are non-positive values, the transform will use the
             corresponding components of input img size. For example, `spatial_size=(32, -1)` will be adapted
             to `(32, 64)` if the second spatial dimension size of img is `64`.
-        fill_value: target value to fill the dropout regions.
+        fill_value: target value to fill the dropout regions, if providing a tuple for the `min` and `max`,
+            will randomly select value for every pixel / voxel from the range `[min, max)`.
         max_holes: if not None, define the maximum number to randomly select the expected number of regions.
         max_spatial_size: if not None, define the maximum spatial size to randomly select size for every region.
             if some components of the `max_spatial_size` are non-positive values, the transform will use the
@@ -1447,7 +1448,7 @@ def __init__(
         keys: KeysCollection,
         holes: int,
         spatial_size: Union[Sequence[int], int],
-        fill_value: Union[float, int] = 0,
+        fill_value: Union[Tuple[Union[float, int]], Union[float, int]] = 0,
         max_holes: Optional[int] = None,
         max_spatial_size: Optional[Union[Sequence[int], int]] = None,
         prob: float = 0.1,
@@ -1483,7 +1484,12 @@ def __call__(self, data):
         if self._do_transform:
             for key in self.key_iterator(d):
                 for h in self.hole_coords:
-                    d[key][h] = self.fill_value
+                    if isinstance(self.fill_value, (tuple, list)):
+                        if len(self.fill_value) != 2:
+                            raise ValueError("fill_value should contain 2 numbers if providing the `min` and `max`.")
+                        d[key][h] = self.R.uniform(self.fill_value[0], self.fill_value[1], size=d[key][h].shape)
+                    else:
+                        d[key][h] = self.fill_value
         return d
 
 

tests/test_rand_coarse_dropout.py

@@ -65,7 +65,6 @@ def test_value(self, input_param, input_data):
             if isinstance(fill_value, (int, float)):
                 np.testing.assert_allclose(data, fill_value)
             else:
-                print("hole data:", data)
                 min_value = data.min()
                 max_value = data.max()
                 self.assertGreaterEqual(max_value, min_value)

tests/test_rand_coarse_dropoutd.py

@@ -70,7 +70,15 @@ def test_value(self, input_param, input_data):
 
         for h in dropout.hole_coords:
             data = result[h]
-            np.testing.assert_allclose(data, input_param.get("fill_value", 0))
+            fill_value = input_param.get("fill_value", 0)
+            if isinstance(fill_value, (int, float)):
+                np.testing.assert_allclose(data, fill_value)
+            else:
+                min_value = data.min()
+                max_value = data.max()
+                self.assertGreaterEqual(max_value, min_value)
+                self.assertGreaterEqual(min_value, fill_value[0])
+                self.assertLess(max_value, fill_value[1])
             if max_spatial_size is None:
                 self.assertTupleEqual(data.shape[1:], tuple(spatial_size))
             else: