From 82ee0385e444861e8ec6b1a245dcd83d35d71c6a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Fernando=20P=C3=A9rez-Garc=C3=ADa?=
 <fperezgarcia@microsoft.com>
Date: Wed, 1 Nov 2023 22:47:13 +0000
Subject: [PATCH] Make RescaleIntensity non-invertible again

---
 .../preprocessing/intensity/rescale.py        | 27 ++++++++-----------
 .../transforms/preprocessing/test_rescale.py  | 11 --------
 2 files changed, 11 insertions(+), 27 deletions(-)

diff --git a/src/torchio/transforms/preprocessing/intensity/rescale.py b/src/torchio/transforms/preprocessing/intensity/rescale.py
index 86e8d720..de121aef 100644
--- a/src/torchio/transforms/preprocessing/intensity/rescale.py
+++ b/src/torchio/transforms/preprocessing/intensity/rescale.py
@@ -65,16 +65,11 @@ def __init__(
             max_constraint=100,
         )
 
-        self.in_min: Optional[float]
-        self.in_max: Optional[float]
         if self.in_min_max is not None:
-            self.in_min, self.in_max = self._parse_range(
+            self.in_min_max = self._parse_range(
                 self.in_min_max,
                 'in_min_max',
             )
-        else:
-            self.in_min = None
-            self.in_max = None
 
         self.args_names = [
             'out_min_max',
@@ -109,18 +104,16 @@ def rescale(
             )
             warnings.warn(message, RuntimeWarning, stacklevel=2)
             return tensor
+
         values = array[mask]
         cutoff = np.percentile(values, self.percentiles)
         np.clip(array, *cutoff, out=array)  # type: ignore[call-overload]
+
         if self.in_min_max is None:
-            self.in_min_max = self._parse_range(
-                (array.min(), array.max()),
-                'in_min_max',
-            )
-            self.in_min, self.in_max = self.in_min_max
-        assert self.in_min is not None
-        assert self.in_max is not None
-        in_range = self.in_max - self.in_min
+            in_min, in_max = array.min(), array.max()
+        else:
+            in_min, in_max = self.in_min_max
+        in_range = in_max - in_min
         if in_range == 0:  # should this be compared using a tolerance?
             message = (
                 f'Rescaling image "{image_name}" not possible'
@@ -128,14 +121,16 @@ def rescale(
             )
             warnings.warn(message, RuntimeWarning, stacklevel=2)
             return tensor
+
         out_range = self.out_max - self.out_min
+
         if self.invert_transform:
             array -= self.out_min
             array /= out_range
             array *= in_range
-            array += self.in_min
+            array += in_min
         else:
-            array -= self.in_min
+            array -= in_min
             array /= in_range
             array *= out_range
             array += self.out_min
diff --git a/tests/transforms/preprocessing/test_rescale.py b/tests/transforms/preprocessing/test_rescale.py
index 8cb00485..b9d205a8 100644
--- a/tests/transforms/preprocessing/test_rescale.py
+++ b/tests/transforms/preprocessing/test_rescale.py
@@ -109,17 +109,6 @@ def test_empty_mask(self):
         with pytest.warns(RuntimeWarning):
             rescale(subject)
 
-    def test_invert_rescaling(self):
-        torch.manual_seed(0)
-        transform = tio.RescaleIntensity(out_min_max=(0, 1))
-        data = torch.rand(1, 2, 3, 4).double()
-        subject = tio.Subject(t1=tio.ScalarImage(tensor=data))
-        transformed = transform(subject)
-        assert transformed.t1.data.min() == 0
-        assert transformed.t1.data.max() == 1
-        inverted = transformed.apply_inverse_transform()
-        self.assert_tensor_almost_equal(inverted.t1.data, data)
-
     def test_persistent_in_min_max(self):
         # see https://github.com/fepegar/torchio/issues/1115
         img1 = torch.tensor([[[[0, 1]]]])