Perf: single-pass remap_instance_id (unique + bincount + LUT gather)

monai/metrics/utils.py

@@ -12,7 +12,7 @@
 from __future__ import annotations
 
 import warnings
-from collections.abc import Iterable, Sequence
+from collections.abc import Sequence
 from functools import cache, partial
 from types import ModuleType
 from typing import Any, overload
@@ -418,23 +418,22 @@ def remap_instance_id(pred: torch.Tensor, by_size: bool = False) -> torch.Tensor
             value of the tensor should be an integer, and represents the prediction of its corresponding instance id.
         by_size: if True, largest instance will be assigned a smaller id.
 
+    Returns:
+        tensor of the same shape as ``pred`` with ids remapped to ``1..K`` (``torch.int`` dtype),
+        or ``pred`` unchanged (original dtype) when it contains no foreground ids.
     """
-    pred_id: Iterable[Any] = list(pred.unique())
-    # the original implementation has the limitation that if there is no 0 in pred, error will happen
-    pred_id = [i for i in pred_id if i != 0]
-
-    if not pred_id:
+    uniq, inverse = torch.unique(pred, return_inverse=True)
+    order = torch.nonzero(uniq != 0).flatten()
+    if order.numel() == 0:
         return pred
     if by_size:
-        instance_size = [(pred == instance_id).sum() for instance_id in pred_id]
-        pair_data = zip(pred_id, instance_size)
-        pair_list = sorted(pair_data, key=lambda x: x[1], reverse=True)
-        pred_id, _ = zip(*pair_list)
-
-    new_pred = torch.zeros_like(pred, dtype=torch.int)
-    for idx, instance_id in enumerate(pred_id):
-        new_pred[pred == instance_id] = idx + 1
-    return new_pred
+        counts = torch.bincount(inverse.flatten(), minlength=uniq.numel())[order]
+        # stable sort keeps ascending-id order for equal-size instances, matching the
+        # original python `sorted` tie-breaking
+        order = order[torch.argsort(counts, descending=True, stable=True)]
+    lut = torch.zeros(uniq.numel(), dtype=torch.int, device=pred.device)
+    lut[order] = torch.arange(1, order.numel() + 1, dtype=torch.int, device=pred.device)
+    return lut[inverse]
 
 
 def prepare_spacing(

tests/metrics/test_remap_instance_id.py

@@ -0,0 +1,93 @@
+# 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 import parameterized
+
+from monai.metrics.utils import remap_instance_id
+
+_device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+TEST_CASES = [
+    # (description, input, by_size, expected)
+    ["non_contiguous_ids", [[0, 2, 2], [5, 5, 0]], False, [[0, 1, 1], [2, 2, 0]]],
+    ["already_contiguous", [[0, 1], [2, 2]], False, [[0, 1], [2, 2]]],
+    # id 7 covers 3 pixels, id 3 covers 2, id 9 covers 1 -> sizes decide new ids
+    ["by_size_largest_first", [[7, 7, 7, 0], [3, 3, 9, 0]], True, [[1, 1, 1, 0], [2, 2, 3, 0]]],
+    # equal sizes: ascending original id order wins (stable tie-breaking)
+    ["by_size_ties_stable", [[5, 5, 0], [2, 2, 0]], True, [[2, 2, 0], [1, 1, 0]]],
+    ["no_background", [[4, 4], [6, 6]], False, [[1, 1], [2, 2]]],
+    ["single_instance", [[0, 0], [3, 3]], True, [[0, 0], [1, 1]]],
+]
+
+
+def _reference_remap(pred: torch.Tensor, by_size: bool = False) -> torch.Tensor:
+    """The original per-instance-loop implementation, kept as the behavioral reference."""
+    pred_id = [i for i in pred.unique() if i != 0]
+    if not pred_id:
+        return pred
+    if by_size:
+        instance_size = [(pred == instance_id).sum() for instance_id in pred_id]
+        pair_list = sorted(zip(pred_id, instance_size, strict=True), key=lambda x: x[1], reverse=True)
+        pred_id = [p[0] for p in pair_list]
+    new_pred = torch.zeros_like(pred, dtype=torch.int)
+    for idx, instance_id in enumerate(pred_id):
+        new_pred[pred == instance_id] = idx + 1
+    return new_pred
+
+
+class TestRemapInstanceId(unittest.TestCase):
+    """Tests for `remap_instance_id` covering expected values, pass-through cases, and reference equivalence."""
+
+    @parameterized.expand(TEST_CASES)
+    def test_expected_value(self, _, pred, by_size, expected):
+        """Remapping produces the hand-computed contiguous ids, including `by_size` ordering and ties."""
+        result = remap_instance_id(torch.as_tensor(pred, device=_device), by_size=by_size)
+        torch.testing.assert_close(result.cpu(), torch.as_tensor(expected, dtype=torch.int), check_dtype=False)
+
+    @parameterized.expand([["all_background_2d", (4, 4)], ["all_background_3d", (2, 3, 4)], ["empty", (0,)]])
+    def test_passthrough(self, _, shape):
+        """Inputs without foreground ids are returned unchanged, keeping their original dtype."""
+        pred = torch.zeros(shape, dtype=torch.int64, device=_device)
+        result = remap_instance_id(pred, by_size=True)
+        self.assertEqual(result.dtype, pred.dtype)
+        torch.testing.assert_close(result, pred)
+
+    def test_output_dtype(self):
+        """Remapped outputs use the `torch.int` dtype regardless of input dtype."""
+        pred = torch.as_tensor([[0, 9]], dtype=torch.int64, device=_device)
+        self.assertEqual(remap_instance_id(pred).dtype, torch.int)
+
+    @parameterized.expand(
+        [
+            ["2d", (64, 64), 20, False],
+            ["2d_by_size", (64, 64), 20, True],
+            ["3d_by_size", (16, 16, 16), 12, True],
+            ["sparse_ids_by_size", (48, 48), 7, True],
+        ]
+    )
+    def test_matches_reference(self, name, shape, n_inst, by_size):
+        """Randomized inputs produce output identical to the previous per-instance-loop implementation."""
+        generator = torch.Generator().manual_seed(0)
+        pred = torch.randint(0, n_inst + 1, shape, generator=generator).to(_device)
+        if name.startswith("sparse"):
+            pred = pred * 1000 + 17  # large, non-contiguous, no-background ids
+        result = remap_instance_id(pred, by_size=by_size)
+        expected = _reference_remap(pred, by_size=by_size)
+        torch.testing.assert_close(result, expected)
+
+
+if __name__ == "__main__":
+    unittest.main()