ENH: support additional dtypes in pad_nd

monai/transforms/croppad/functional.py

@@ -96,19 +96,24 @@ def pad_nd(
         return _np_pad(img, pad_width=to_pad, mode=mode, **kwargs)
     try:
         _pad = _np_pad
-        if mode in {"constant", "reflect", "edge", "replicate", "wrap", "circular"} and img.dtype not in {
-            torch.int16,
-            torch.int64,
-            torch.bool,
-            torch.uint8,
-        }:
+        if mode in {"constant", "reflect", "edge", "replicate", "wrap", "circular"}:
+            # Try PyTorch pad for these modes; fallback to NumPy on error.
             _pad = _pt_pad
-        return _pad(img, pad_width=to_pad, mode=mode, **kwargs)
+        call_kwargs = dict(kwargs)
+        if mode != "constant" and "value" in call_kwargs:
+            raise ValueError("'value' argument is only valid when mode='constant'")
+        return _pad(img, pad_width=to_pad, mode=mode, **call_kwargs)
+    except NotImplementedError:
+        # PyTorch does not support this combination, fall back to NumPy
+        return _np_pad(img, pad_width=to_pad, mode=mode, **call_kwargs)
     except (ValueError, TypeError, RuntimeError) as err:
-        if isinstance(err, NotImplementedError) or any(
+        # PyTorch may raise generic errors for unsupported modes/dtypes or kwargs.
+        # Since there are no stable exception types for these cases, we fall back
+        # to NumPy by matching known error message patterns.
+        if any(
             k in str(err) for k in ("supported", "unexpected keyword", "implemented", "value")
         ):
-            return _np_pad(img, pad_width=to_pad, mode=mode, **kwargs)
+            return _np_pad(img, pad_width=to_pad, mode=mode, **call_kwargs)
         raise ValueError(
             f"{img.shape} {to_pad} {mode} {kwargs} {img.dtype} {img.device if isinstance(img, torch.Tensor) else None}"
         ) from err

tests/transforms/croppad/test_pad_nd_dtypes.py

@@ -0,0 +1,105 @@
+# 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.
+"""
+Tests for pad_nd dtype support and backend selection.
+Validates PyTorch padding preference and NumPy fallback behavior.
+"""
+from __future__ import annotations
+
+import unittest
+from unittest.mock import Mock, patch
+
+from parameterized.parameterized import parameterized
+
+import torch
+
+import monai.transforms.croppad.functional as F
+from monai.transforms.croppad.functional import pad_nd
+
+
+class TestPadNdDtypes(unittest.TestCase):
+    def test_pad_uses_pt_for_bool(self):
+        """Test that pad_nd uses PyTorch backend for bool dtype in constant mode."""
+        img = torch.ones((1, 4, 4), dtype=torch.bool)
+        to_pad = [(0, 0), (1, 1), (2, 2)]
+        with patch.object(F, "_pt_pad", wraps=F._pt_pad) as mock_pt, patch.object(F, "_np_pad", wraps=F._np_pad) as mock_np:
+            out = pad_nd(img, to_pad, mode="constant", value=0)
+
+        self.assertTrue(mock_pt.called)
+        self.assertFalse(mock_np.called)
+        self.assertEqual(out.dtype, img.dtype)
+        self.assertEqual(out.shape, (1, 6, 8))
+
+    def test_pad_falls_back_to_np_if_pt_raises(self):
+        """Test that pad_nd falls back to NumPy when PyTorch raises NotImplementedError."""
+        img = torch.ones((1, 4, 4), dtype=torch.bool)
+        to_pad = [(0, 0), (1, 1), (2, 2)]
+        with (
+            patch.object(F, "_pt_pad", new=Mock(side_effect=NotImplementedError("no"))) as mock_pt,
+            patch.object(F, "_np_pad", wraps=F._np_pad) as mock_np,
+        ):
+            out = pad_nd(img, to_pad, mode="constant", value=0)
+
+        self.assertTrue(mock_pt.called)
+        self.assertTrue(mock_np.called)
+        self.assertEqual(out.dtype, img.dtype)
+        self.assertEqual(out.shape, (1, 6, 8))
+
+    @parameterized.expand([
+        torch.bool,
+        torch.int8,
+        torch.int16,
+        torch.int32,
+        torch.int64,
+        torch.uint8,
+        torch.float32,
+    ])
+    def test_pad_dtype_no_error_and_dtype_preserved(self, dtype):
+        """Test that pad_nd handles various dtypes without error and preserves dtype."""
+        img = torch.ones((1, 4, 4), dtype=dtype)
+        to_pad = [(0, 0), (1, 1), (2, 2)]
+        out = pad_nd(img, to_pad, mode="constant", value=0)
+
+        self.assertEqual(out.shape, (1, 6, 8))
+        self.assertEqual(out.dtype, img.dtype)
+
+    @parameterized.expand([
+        ("constant", torch.bool),
+        ("constant", torch.int8),
+        ("constant", torch.float32),
+        ("reflect", torch.bool),
+        ("reflect", torch.int8),
+        ("reflect", torch.float32),
+        ("replicate", torch.bool),
+        ("replicate", torch.int8),
+        ("replicate", torch.float32),
+    ])
+    def test_pad_multiple_modes_dtype_preserved(self, mode, dtype):
+        """Test that pad_nd preserves dtype across multiple padding modes."""
+        img = torch.ones((1, 4, 4), dtype=dtype)
+        to_pad = [(0, 0), (1, 1), (2, 2)]
+
+        kwargs = {"value": 0} if mode == "constant" else {}
+        out = pad_nd(img, to_pad, mode=mode, **kwargs)
+
+        self.assertEqual(out.shape, (1, 6, 8))
+        self.assertEqual(out.dtype, img.dtype)
+
+    def test_value_with_non_constant_mode_raises(self):
+        """Test that pad_nd raises ValueError when 'value' is provided with non-constant mode."""
+        img = torch.ones((1, 4, 4))
+        to_pad = [(0, 0), (1, 1), (2, 2)]
+        with self.assertRaises(ValueError):
+            pad_nd(img, to_pad, mode="reflect", **{"value": 0})
+
+
+if __name__ == "__main__":
+    unittest.main()