add unittest

Signed-off-by: YunLiu <[email protected]>

monai/transforms/spatial/array.py

@@ -687,7 +687,7 @@ def __init__(self, spatial_axis: Sequence[int] | int | None = None, lazy: bool =
         self.spatial_axis = spatial_axis
         self.operators = [flip_point, flip_image]
 
-    def __call__(self, img: torch.Tensor, lazy: bool | None = None) -> torch.Tensor:
+    def __call__(self, img: torch.Tensor, lazy: bool | None = None) -> torch.Tensor:  # type: ignore[return]
         """
         Args:
             img: channel first array, must have shape: (num_channels, H[, W, ..., ])
@@ -698,7 +698,7 @@ def __call__(self, img: torch.Tensor, lazy: bool | None = None) -> torch.Tensor:
         img = convert_to_tensor(img, track_meta=get_track_meta())
         lazy_ = self.lazy if lazy is None else lazy
         for operator in self.operators:
-            ret = operator(img, self.spatial_axis, lazy=lazy_, transform_info=self.get_transform_info())
+            ret: torch.Tensor = operator(img, self.spatial_axis, lazy=lazy_, transform_info=self.get_transform_info())
             if ret is not None:
                 return ret
 

monai/transforms/spatial/functional.py

@@ -286,7 +286,8 @@ def flip_point(points, sp_axes, lazy, transform_info):
     This function operates eagerly or lazily according to
     ``lazy`` (default ``False``).
     Args:
-        points: point coordinates, Nx2 or Nx3 torch tensor or ndarray
+        points: point coordinates, represented by a torch tensor or ndarray with dimensions of 1xNx2 or 1xNx3.
+            Here 1 represents the channel dimension.
         sp_axes: spatial axes along which to flip over. Default is None.
             The default `axis=None` will flip over all of the axes of the input array.
             If axis is negative it counts from the last to the first axis.
@@ -320,10 +321,10 @@ def flip_point(points, sp_axes, lazy, transform_info):
     meta_info = TraceableTransform.track_transform_meta(
         points, affine=xform, extra_info=extra_info, lazy=lazy, transform_info=transform_info
     )
-
     # flip box
     out = deepcopy(_maybe_new_metatensor(points))
     if lazy:
+        raise NotImplementedError
         return out.copy_meta_from(meta_info) if isinstance(out, MetaTensor) else meta_info
     if sp_size is None:
         warnings.warn("''sp_size'' is None, will flip in the world coordinates.")

tests/test_flip.py

@@ -12,6 +12,7 @@
 from __future__ import annotations
 
 import unittest
+from copy import deepcopy
 
 import numpy as np
 import torch
@@ -32,6 +33,15 @@
     for device in TEST_DEVICES:
         TORCH_CASES.append([[0, 1], torch.zeros((1, 3, 2)), track_meta, *device])
 
+POINT_2D_WITH_REFER = MetaTensor(
+    [[[3, 4], [5, 7], [6, 2], [7, 8]]], meta={"kind": "point", "refer_meta": {"spatial_shape": (10, 10)}}
+)
+POINT_3D = MetaTensor([[[3, 4, 5], [5, 7, 6], [6, 2, 7]]], meta={"kind": "point"})
+POINT_CASES = []
+for spatial_axis in [[0], [1], [0, 1]]:
+    for point in [POINT_2D_WITH_REFER, POINT_3D]:
+        POINT_CASES.append([spatial_axis, point])
+
 
 class TestFlip(NumpyImageTestCase2D):
 
@@ -73,6 +83,22 @@ def test_torch(self, spatial_axis, img: torch.Tensor, track_meta: bool, device):
             with self.assertRaisesRegex(ValueError, "MetaTensor"):
                 xform.inverse(res)
 
+    @parameterized.expand(POINT_CASES)
+    def test_points(self, spatial_axis, point):
+        init_param = {"spatial_axis": spatial_axis}
+        xform = Flip(**init_param)
+        res = xform(point)  # type: ignore[arg-type]
+        self.assertEqual(point.shape, res.shape)
+        expected = deepcopy(point)
+        if point.meta.get("refer_meta", None) is not None:
+            for _axes in spatial_axis:
+                expected[..., _axes] = (10, 10)[_axes] - point[..., _axes]
+        else:
+            for _axes in spatial_axis:
+                expected[..., _axes] = -point[..., _axes]
+        assert_allclose(res, expected, type_test="tensor")
+        test_local_inversion(xform, res, point)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/test_flipd.py

@@ -12,6 +12,7 @@
 from __future__ import annotations
 
 import unittest
+from copy import deepcopy
 
 import numpy as np
 import torch
@@ -33,6 +34,15 @@
     for device in TEST_DEVICES:
         TORCH_CASES.append([[0, 1], torch.zeros((1, 3, 2)), track_meta, *device])
 
+POINT_2D_WITH_REFER = MetaTensor(
+    [[[3, 4], [5, 7], [6, 2], [7, 8]]], meta={"kind": "point", "refer_meta": {"spatial_shape": (10, 10)}}
+)
+POINT_3D = MetaTensor([[[3, 4, 5], [5, 7, 6], [6, 2, 7]]], meta={"kind": "point"})
+POINT_CASES = []
+for spatial_axis in [[0], [1], [0, 1]]:
+    for point in [POINT_2D_WITH_REFER, POINT_3D]:
+        POINT_CASES.append([spatial_axis, point])
+
 
 class TestFlipd(NumpyImageTestCase2D):
 
@@ -80,6 +90,22 @@ def test_meta_dict(self):
         res = xform({"image": torch.zeros(1, 3, 4)})
         self.assertTrue(res["image"].applied_operations == res["image_transforms"])
 
+    @parameterized.expand(POINT_CASES)
+    def test_points(self, spatial_axis, point):
+        init_param = {"keys": "point", "spatial_axis": spatial_axis}
+        xform = Flipd(**init_param)
+        res = xform({"point": point})  # type: ignore[arg-type]
+        self.assertEqual(point.shape, res["point"].shape)
+        expected = deepcopy(point)
+        if point.meta.get("refer_meta", None) is not None:
+            for _axes in spatial_axis:
+                expected[..., _axes] = (10, 10)[_axes] - point[..., _axes]
+        else:
+            for _axes in spatial_axis:
+                expected[..., _axes] = -point[..., _axes]
+        assert_allclose(res["point"], expected, type_test="tensor")
+        test_local_inversion(xform, {"point": res["point"]}, {"point": point}, "point")
+
 
 if __name__ == "__main__":
     unittest.main()