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Fix NormalizeIntensity inverse for non-contiguous inputs #9183

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Fix NormalizeIntensity inverse for non-contiguous inputs #9183

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4cb5df9
Fix NormalizeIntensity inverse for non-contiguous inputs
mottopanikeiku Oct 17, 2026
2e4d272
Document channel_wise in the new inverse tests
mottopanikeiku Oct 17, 2026
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2 changes: 1 addition & 1 deletion monai/transforms/intensity/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1055,7 +1055,7 @@ def _restore(x, sub, div, zeroed_idx=None):
div, *_ = convert_to_dst_type(div, x)
return x * div + sub
# nonzero=True: the forward mask is the output's non-zero voxels plus the recorded zeroed ones
mask = x != 0
mask = (x != 0).contiguous()
zeroed_idx, *_ = convert_to_dst_type(zeroed_idx, mask, dtype=torch.long)
mask.view(-1)[zeroed_idx] = True
vals = x[mask]
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17 changes: 17 additions & 0 deletions tests/transforms/test_normalize_intensity.py
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Expand Up @@ -176,6 +176,23 @@ def test_inverse_nonzero_value_equal_to_mean(self, _, args):
inv = normalizer.inverse(out)
assert_allclose(inv, img, type_test=False, rtol=0, atol=0)

@parameterized.expand([[False], [True]])
def test_inverse_nonzero_noncontiguous(self, channel_wise):
"""Restore transposed inputs, including non-zero voxels normalized to zero.

Args:
channel_wise: whether each channel is normalized independently.
"""
self.addCleanup(set_track_meta, get_track_meta())
set_track_meta(True)
img = MetaTensor(torch.tensor([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 0.0], [0.0, 2.0]]])).transpose(1, 2)
normalizer = NormalizeIntensity(nonzero=True, channel_wise=channel_wise)
out = normalizer(img.clone())
self.assertFalse(out.is_contiguous())
inv = normalizer.inverse(out)
assert_allclose(inv, img, type_test=False, rtol=1e-6, atol=1e-6)
self.assertEqual(len(inv.applied_operations), 0)

def test_inverse_nonzero_in_compose(self):
self.addCleanup(set_track_meta, get_track_meta())
set_track_meta(True)
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17 changes: 17 additions & 0 deletions tests/transforms/test_normalize_intensityd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -98,6 +98,23 @@ def test_inverse(self, _, args):
inv = normalizer.inverse(out)
assert_allclose(inv[key], original, type_test=False, rtol=1e-4, atol=1e-4)

@parameterized.expand([[False], [True]])
def test_inverse_nonzero_noncontiguous(self, channel_wise):
"""Restore transposed dictionary values without losing their zeroed voxels.

Args:
channel_wise: whether each channel is normalized independently.
"""
self.addCleanup(set_track_meta, get_track_meta())
set_track_meta(True)
img = MetaTensor(torch.tensor([[[0.0, 1.0], [2.0, 3.0]], [[0.0, 0.0], [0.0, 2.0]]])).transpose(1, 2)
normalizer = NormalizeIntensityd(keys="img", nonzero=True, channel_wise=channel_wise)
out = normalizer({"img": img.clone()})
self.assertFalse(out["img"].is_contiguous())
inv = normalizer.inverse(out)
assert_allclose(inv["img"], img, type_test=False, rtol=1e-6, atol=1e-6)
self.assertEqual(len(inv["img"].applied_operations), 0)

def test_invertd_nonzero(self):
self.addCleanup(set_track_meta, get_track_meta())
set_track_meta(True)
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