[Deps] make scipy as a runtime dependency

mmeval/metrics/hmean_iou.py

@@ -1,17 +1,16 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import numpy as np
+from scipy import sparse
 from typing import TYPE_CHECKING, Dict, List, Sequence, Tuple, Union
 
 from mmeval.core import BaseMetric
 from mmeval.metrics.utils import poly_intersection, poly_iou, polys2shapely
 from mmeval.utils import try_import
 
 if TYPE_CHECKING:
-    import scipy.sparse as scipy_sparse
     import shapely.geometry as geometry
     from shapely.geometry import Polygon
 else:
-    scipy_sparse = try_import('scipy.sparse')
     geometry = try_import('shapely.geometry')
     if geometry is not None:
         Polygon = geometry.Polygon
@@ -143,10 +142,6 @@ def __init__(
         strategy: str = 'vanilla',
         **kwargs,
     ) -> None:
-        if strategy == 'max_matching' and scipy_sparse is None:
-            raise RuntimeError(
-                'scipy is not installed, please run "pip install scipy" to use'
-                ' HmeanIoUMetric with "max_matching" strategy.')
         if geometry is None:
             raise RuntimeError(
                 'shapely is not installed, please run "pip install shapely" to'
@@ -265,8 +260,8 @@ def _max_matching(self, iou_metric: np.ndarray) -> int:
         Returns:
             int: The hits by max matching policy.
         """
-        csr_matched_metric = scipy_sparse.csr_matrix(iou_metric)
-        matched_preds = scipy_sparse.csgraph.maximum_bipartite_matching(
+        csr_matched_metric = sparse.csr_matrix(iou_metric)
+        matched_preds = sparse.csgraph.maximum_bipartite_matching(
             csr_matched_metric, perm_type='row')
         # -1 denotes unmatched pred polygons
         return np.sum(matched_preds != -1).item()

requirements/optional.txt

@@ -1,4 +1,3 @@
 opencv-python!=4.5.5.62,!=4.5.5.64
 pycocotools
-scipy
 shapely

requirements/runtime.txt

@@ -1,3 +1,4 @@
 numpy
 plum-dispatch<2.0.0
 pyyaml
+scipy