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Fix Pad.inverse channel-dimension back-crop #8900

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Fix Pad.inverse channel-dimension back-crop #8900

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5510a4d
Fix Pad.inverse channel-dimension back-crop
aymuos15 Jun 7, 2026
7fccf52
Add regression test for Pad.inverse channel-dim crop
aymuos15 Jun 7, 2026
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2 changes: 1 addition & 1 deletion monai/transforms/croppad/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -170,7 +170,7 @@ def inverse(self, data: MetaTensor) -> MetaTensor:
padded = transform[TraceKeys.EXTRA_INFO]["padded"]
if padded[0][0] > 0 or padded[0][1] > 0: # slicing the channel dimension
s = padded[0][0]
e = min(max(padded[0][1], s + 1), len(data))
e = min(padded[0][1], len(data) - s)
data = data[s : len(data) - e] # type: ignore
roi_start = [i[0] for i in padded[1:]]
roi_end = [i - j[1] for i, j in zip(data.shape[1:], padded[1:])]
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44 changes: 44 additions & 0 deletions tests/transforms/croppad/test_pad_inverse_channel.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,44 @@
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations

import unittest

import torch
from parameterized.parameterized import parameterized

from monai.data import MetaTensor, set_track_meta
from monai.transforms.croppad.array import Pad

# "symmetric" routes through the NumPy backend, which pads the channel dim.
CHANNEL_PAD_CASES = [
[[(2, 0), (0, 0), (0, 0)]],
[[(0, 2), (0, 0), (0, 0)]],
[[(2, 2), (0, 0), (0, 0)]],
[[(1, 3), (1, 1), (0, 2)]],
]


class TestPadInverseChannel(unittest.TestCase):
@parameterized.expand(CHANNEL_PAD_CASES)
def test_inverse_roundtrip_channel_pad(self, to_pad):
set_track_meta(True)
img = MetaTensor(torch.arange(3 * 4 * 4).reshape(3, 4, 4).float())
pad = Pad(to_pad=to_pad, mode="symmetric")
padded = pad(img.clone())
self.assertEqual(padded.shape[0], img.shape[0] + to_pad[0][0] + to_pad[0][1])
inv = pad.inverse(padded)
self.assertEqual(inv.shape, img.shape)
self.assertTrue(torch.equal(inv.as_tensor(), img.as_tensor()))
Comment on lines +32 to +40

@coderabbitai coderabbitai Bot Jun 7, 2026

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⚠️ Potential issue | 🟡 Minor | ⚡ Quick win

Add docstring to test method.

Per coding guidelines, all definitions require Google-style docstrings describing parameters and purpose.

📝 Suggested docstring 
     `@parameterized.expand`(CHANNEL_PAD_CASES)
     def test_inverse_roundtrip_channel_pad(self, to_pad):
+        """Test Pad.inverse correctly restores original tensor after channel-dimension padding.
+
+        Args:
+            to_pad: Padding specification [(ch_pad), (h_pad), (w_pad)].
+        """
         set_track_meta(True)
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
def test_inverse_roundtrip_channel_pad(self, to_pad):
set_track_meta(True)
img = MetaTensor(torch.arange(3 * 4 * 4).reshape(3, 4, 4).float())
pad = Pad(to_pad=to_pad, mode="symmetric")
padded = pad(img.clone())
self.assertEqual(padded.shape[0], img.shape[0] + to_pad[0][0] + to_pad[0][1])
inv = pad.inverse(padded)
self.assertEqual(inv.shape, img.shape)
self.assertTrue(torch.equal(inv.as_tensor(), img.as_tensor()))
def test_inverse_roundtrip_channel_pad(self, to_pad):
"""Test Pad.inverse correctly restores original tensor after channel-dimension padding.
Args:
to_pad: Padding specification [(ch_pad), (h_pad), (w_pad)].
"""
set_track_meta(True)
img = MetaTensor(torch.arange(3 * 4 * 4).reshape(3, 4, 4).float())
pad = Pad(to_pad=to_pad, mode="symmetric")
padded = pad(img.clone())
self.assertEqual(padded.shape[0], img.shape[0] + to_pad[0][0] + to_pad[0][1])
inv = pad.inverse(padded)
self.assertEqual(inv.shape, img.shape)
self.assertTrue(torch.equal(inv.as_tensor(), img.as_tensor()))
🤖 Prompt for AI Agents 
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.

In `@tests/transforms/croppad/test_pad_inverse_channel.py` around lines 32 - 40,
Add a Google-style docstring to the test method
test_inverse_roundtrip_channel_pad describing the test's purpose and its
parameter to_pad (e.g., what padding configuration is being tested and the
expected round-trip behavior), placed immediately below the def line; ensure it
follows Google style with a short description and an Args section for to_pad.

Source: Coding guidelines



if __name__ == "__main__":
unittest.main()
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