Use SciPy's KDTree instead of deprecated cKDTree

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -44,7 +44,7 @@ dependencies:
 - pywavelets>=1.0
 - recommonmark
 - scikit-image>=0.19.0,<0.23.0a0
-- scipy
+- scipy>=1.6.0
 - sphinx<6
 - sysroot_linux-64==2.17
 - tifffile>=2022.7.28

conda/environments/all_cuda-122_arch-x86_64.yaml

@@ -43,7 +43,7 @@ dependencies:
 - pywavelets>=1.0
 - recommonmark
 - scikit-image>=0.19.0,<0.23.0a0
-- scipy
+- scipy>=1.6.0
 - sphinx<6
 - sysroot_linux-64==2.17
 - tifffile>=2022.7.28

conda/recipes/cucim/meta.yaml

@@ -66,7 +66,7 @@ requirements:
     - numpy 1.23
     - python
     - scikit-image >=0.19.0,<0.23.0a0
-    - scipy
+    - scipy >=1.6
   run:
     - {{ pin_compatible('cuda-version', max_pin='x', min_pin='x') }}
     {% if cuda_major != "11" %}
@@ -79,7 +79,7 @@ requirements:
     - libcucim ={{ version }}
     - python
     - scikit-image >=0.19.0,<0.23.0a0
-    - scipy
+    - scipy >=1.6
   run_constrained:
     - openslide-python >=1.3.0
 

dependencies.yaml

@@ -248,7 +248,7 @@ dependencies:
           - lazy_loader>=0.1
           - numpy>=1.23.4,<2.0a0
           - scikit-image>=0.19.0,<0.23.0a0
-          - scipy
+          - scipy>=1.6.0
       - output_types: conda
         packages:
           - cupy>=12.0.0

python/cucim/pyproject.toml

@@ -29,7 +29,7 @@ dependencies = [
     "lazy_loader>=0.1",
     "numpy>=1.23.4,<2.0a0",
     "scikit-image>=0.19.0,<0.23.0a0",
-    "scipy",
+    "scipy>=1.6.0",
 ] # This list was generated by `rapids-dependency-file-generator`. To make changes, edit ../../dependencies.yaml and run `rapids-dependency-file-generator`.
 classifiers = [
     "Development Status :: 4 - Beta",

python/cucim/src/cucim/skimage/_shared/coord.py

@@ -1,9 +1,9 @@
 import cupy as cp
 import numpy as np
-from scipy.spatial import cKDTree, distance
+from scipy.spatial import KDTree, distance
 
 
-# TODO: avoid host/device transfers (currently needed for cKDTree)
+# TODO: avoid host/device transfers (currently needed for KDTree)
 def _ensure_spacing(coord, spacing, p_norm, max_out):
     """Returns a subset of coord where a minimum spacing is guaranteed.
 
@@ -30,7 +30,7 @@ def _ensure_spacing(coord, spacing, p_norm, max_out):
     """
 
     # Use KDtree to find the peaks that are too close to each other
-    tree = cKDTree(coord)
+    tree = KDTree(coord)
 
     indices = tree.query_ball_point(coord, r=spacing, p=p_norm)
     rejected_peaks_indices = set()
@@ -106,6 +106,8 @@ def ensure_spacing(
     if len(coords):
         coords = cp.atleast_2d(coords)
         coords = cp.asnumpy(coords)
+        if not np.isscalar(spacing):
+            spacing = cp.asnumpy(spacing)
         if min_split_size is None:
             batch_list = [coords]
         else:

python/cucim/src/cucim/skimage/_shared/tests/test_coord.py

@@ -0,0 +1,98 @@
+import time
+
+import cupy as cp
+import numpy as np
+import pytest
+from scipy.spatial.distance import minkowski, pdist
+
+from cucim.skimage._shared.coord import ensure_spacing
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [30, 50, None])
+def test_ensure_spacing_trivial(p, size):
+    # --- Empty input
+    assert ensure_spacing(cp.asarray([]), p_norm=p).size == 0
+
+    # --- A unique point
+    coord = cp.random.randn(1, 2)
+    assert cp.array_equal(
+        coord, ensure_spacing(coord, p_norm=p, min_split_size=size)
+    )
+
+    # --- Verified spacing
+    coord = cp.random.randn(100, 2)
+
+    # --- 0 spacing
+    assert cp.array_equal(
+        coord, ensure_spacing(coord, spacing=0, p_norm=p, min_split_size=size)
+    )
+
+    # Spacing is chosen to be half the minimum distance
+    coord_cpu = cp.asnumpy(coord)
+    spacing = cp.asarray(pdist(coord_cpu, metric=minkowski, p=p).min() * 0.5)
+
+    out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)
+
+    assert cp.array_equal(coord, out)
+
+
+@pytest.mark.parametrize("ndim", [1, 2, 3, 4, 5])
+@pytest.mark.parametrize("size", [2, 10, None])
+def test_ensure_spacing_nD(ndim, size):
+    coord = cp.ones((5, ndim))
+
+    expected = cp.ones((1, ndim))
+
+    assert cp.array_equal(ensure_spacing(coord, min_split_size=size), expected)
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [50, 100, None])
+def test_ensure_spacing_batch_processing(p, size):
+    coord_cpu = np.random.randn(100, 2)
+
+    # --- Consider the average distance btween the point as spacing
+    spacing = cp.asarray(np.median(pdist(coord_cpu, metric=minkowski, p=p)))
+    coord = cp.asarray(coord_cpu)
+
+    expected = ensure_spacing(coord, spacing=spacing, p_norm=p)
+
+    cp.testing.assert_array_equal(
+        ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size),
+        expected,
+    )
+
+
+def test_max_batch_size():
+    """Small batches are slow, large batches -> large allocations -> also slow.
+
+    https://github.com/scikit-image/scikit-image/pull/6035#discussion_r751518691
+    """
+    coords = cp.random.randint(low=0, high=1848, size=(40000, 2))
+    tstart = time.time()
+    ensure_spacing(coords, spacing=100, min_split_size=50, max_split_size=2000)
+    dur1 = time.time() - tstart
+
+    tstart = time.time()
+    ensure_spacing(coords, spacing=100, min_split_size=50, max_split_size=20000)
+    dur2 = time.time() - tstart
+
+    # Originally checked dur1 < dur2 to assert that the default batch size was
+    # faster than a much larger batch size. However, on rare occasion a CI test
+    # case would fail with dur1 ~5% larger than dur2. To be more robust to
+    # variable load or differences across architectures, we relax this here.
+    assert dur1 < 1.33 * dur2
+
+
+@pytest.mark.parametrize("p", [1, 2, np.inf])
+@pytest.mark.parametrize("size", [30, 50, None])
+def test_ensure_spacing_p_norm(p, size):
+    coord_cpu = np.random.randn(100, 2)
+
+    # --- Consider the average distance btween the point as spacing
+    spacing = cp.asarray(np.median(pdist(coord_cpu, metric=minkowski, p=p)))
+    coord = cp.asarray(coord_cpu)
+    out = ensure_spacing(coord, spacing=spacing, p_norm=p, min_split_size=size)
+
+    assert pdist(cp.asnumpy(out), metric=minkowski, p=p).min() > spacing

python/cucim/src/cucim/skimage/feature/corner.py

@@ -4,7 +4,7 @@
 
 import cupy as cp
 import numpy as np
-from scipy import spatial  # TODO: use cuSpatial if cKDTree becomes available
+from scipy import spatial  # TODO: use cuSpatial if KDTree becomes available
 
 import cucim.skimage._vendored.ndimage as ndi
 from cucim.skimage.util import img_as_float
@@ -1370,7 +1370,7 @@ def corner_peaks(
         coords = cp.asnumpy(coords)
 
         # Use KDtree to find the peaks that are too close to each other
-        tree = spatial.cKDTree(coords)
+        tree = spatial.KDTree(coords)
 
         rejected_peaks_indices = set()
         for idx, point in enumerate(coords):