Merge branch 'dev' into fix/8239-cldice-loss-enhancements

Author: ericspod

monai/transforms/utils.py

@@ -955,14 +955,15 @@ def create_shear(
 
     Args:
         spatial_dims: spatial rank
-        coefs: shearing factors, a tuple of 2 floats for 2D, a tuple of 6 floats for 3D),
-            take a 3D affine as example::
+        coefs: shearing factors, a tuple of 2 floats for 2D, a tuple of 6 floats for 3D).
+            Individual single-axis shear matrices are composed (multiplied) in
+            coefficient order so that the result is a proper shear with determinant 1.
+            For 2D with coefs ``(Sx, Sy)`` the composed matrix is::
 
                 [
-                    [1.0, coefs[0], coefs[1], 0.0],
-                    [coefs[2], 1.0, coefs[3], 0.0],
-                    [coefs[4], coefs[5], 1.0, 0.0],
-                    [0.0, 0.0, 0.0, 1.0],
+                    [1.0,  Sx,          0.0],
+                    [Sy,   1.0 + Sx*Sy, 0.0],
+                    [0.0,  0.0,         1.0],
                 ]
 
         device: device to compute and store the output (when the backend is "torch").
@@ -985,17 +986,30 @@ def create_shear(
 def _create_shear(spatial_dims: int, coefs: Sequence[float] | float, eye_func=np.eye) -> NdarrayOrTensor:
     if spatial_dims == 2:
         coefs = ensure_tuple_size(coefs, dim=2, pad_val=0.0)
-        out = eye_func(3)
-        out[0, 1], out[1, 0] = coefs[0], coefs[1]
-        return out  # type: ignore
-    if spatial_dims == 3:
+        rank = 3
+        shear_indices = [(0, 1, coefs[0]), (1, 0, coefs[1])]
+    elif spatial_dims == 3:
         coefs = ensure_tuple_size(coefs, dim=6, pad_val=0.0)
-        out = eye_func(4)
-        out[0, 1], out[0, 2] = coefs[0], coefs[1]
-        out[1, 0], out[1, 2] = coefs[2], coefs[3]
-        out[2, 0], out[2, 1] = coefs[4], coefs[5]
-        return out  # type: ignore
-    raise NotImplementedError("Currently only spatial_dims in [2, 3] are supported.")
+        rank = 4
+        shear_indices = [
+            (0, 1, coefs[0]),
+            (0, 2, coefs[1]),
+            (1, 0, coefs[2]),
+            (1, 2, coefs[3]),
+            (2, 0, coefs[4]),
+            (2, 1, coefs[5]),
+        ]
+    else:
+        raise NotImplementedError("Currently only spatial_dims in [2, 3] are supported.")
+    # Compose individual single-axis shear matrices so that the result is a
+    # proper (area/volume-preserving) shear with determinant 1.  Each elementary
+    # shear is pre-multiplied, so the first coefficient is applied first.
+    out = eye_func(rank)
+    for i, j, c in shear_indices:
+        s = eye_func(rank)
+        s[i, j] = c
+        out = s @ out
+    return out  # type: ignore
 
 
 def create_scale(

tests/transforms/test_create_grid_and_affine.py

@@ -224,13 +224,31 @@ def test_create_rotate(self):
 
     def test_create_shear(self):
         test_assert(create_shear, (2, 1.0), np.array([[1.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))
-        test_assert(create_shear, (2, (2.0, 3.0)), np.array([[1.0, 2.0, 0.0], [3.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))
+        test_assert(create_shear, (2, (2.0, 3.0)), np.array([[1.0, 2.0, 0.0], [3.0, 7.0, 0.0], [0.0, 0.0, 1.0]]))
         test_assert(
             create_shear,
             (3, 1.0),
             np.array([[1.0, 1.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 1.0]]),
         )
 
+    def test_create_shear_determinant(self):
+        """Composed shear must be area/volume-preserving (determinant == 1)."""
+        for coefs in [(0.3, 0.5), (1.0, 2.0), (-0.5, 0.7)]:
+            m = create_shear(2, coefs, backend="numpy")
+            assert_allclose(np.linalg.det(m), 1.0, atol=1e-10)
+        for coefs in [(0.1, 0.2, 0.3, 0.4, 0.5, 0.6)]:
+            m = create_shear(3, coefs, backend="numpy")
+            assert_allclose(np.linalg.det(m), 1.0, atol=1e-10)
+
+    def test_create_shear_sequential_equivalence(self):
+        """Composing single-axis shears must equal a single multi-axis shear."""
+        sx, sy = 0.3, 0.5
+        shear_x = create_shear(2, (sx, 0.0), backend="numpy")
+        shear_y = create_shear(2, (0.0, sy), backend="numpy")
+        combined = create_shear(2, (sx, sy), backend="numpy")
+        # shear_y applied after shear_x
+        assert_allclose(shear_y @ shear_x, combined, atol=1e-10)
+
     def test_create_scale(self):
         test_assert(create_scale, (2, 2), np.array([[2.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]))
         test_assert(create_scale, (2, [2, 2, 2]), np.array([[2.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 1.0]]))

tests/transforms/test_rand_affine.py

@@ -102,7 +102,7 @@
                 {"img": p(torch.arange(64).reshape((1, 8, 8))), "spatial_size": (3, 3)},
                 p(
                     torch.tensor(
-                        [[[18.7362, 15.5820, 12.4278], [27.3988, 24.2446, 21.0904], [36.0614, 32.9072, 29.7530]]]
+                        [[[18.7575, 15.5552, 12.3528], [27.4202, 24.2178, 21.0154], [36.0828, 32.8805, 29.6781]]]
                     )
                 ),
             ]
@@ -122,7 +122,7 @@
                 {"img": p(torch.arange(64).reshape((1, 8, 8)))},
                 p(
                     torch.tensor(
-                        [[[18.7362, 15.5820, 12.4278], [27.3988, 24.2446, 21.0904], [36.0614, 32.9072, 29.7530]]]
+                        [[[18.7575, 15.5552, 12.3528], [27.4202, 24.2178, 21.0154], [36.0828, 32.8805, 29.6781]]]
                     )
                 ),
             ]

tests/transforms/test_rand_affined.py

@@ -93,7 +93,7 @@
                 "img": MetaTensor(torch.arange(64).reshape((1, 8, 8))),
                 "seg": MetaTensor(torch.arange(64).reshape((1, 8, 8))),
             },
-            torch.tensor([[[18.7362, 15.5820, 12.4278], [27.3988, 24.2446, 21.0904], [36.0614, 32.9072, 29.7530]]]),
+            torch.tensor([[[18.7575, 15.5552, 12.3528], [27.4202, 24.2178, 21.0154], [36.0828, 32.8805, 29.6781]]]),
         ]
     )
     TESTS.append(
@@ -118,14 +118,14 @@
                     torch.tensor(
                         [
                             [
-                                [18.736153, 15.581954, 12.4277525],
-                                [27.398798, 24.244598, 21.090399],
-                                [36.061443, 32.90724, 29.753046],
+                                [18.757534, 15.55517, 12.352802],
+                                [27.420181, 24.217815, 21.01545],
+                                [36.08283, 32.880463, 29.678097],
                             ]
                         ]
                     )
                 ),
-                "seg": MetaTensor(torch.tensor([[[19.0, 20.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),
+                "seg": MetaTensor(torch.tensor([[[19.0, 12.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),
             },
         ]
     )
@@ -168,14 +168,14 @@
                     np.array(
                         [
                             [
-                                [18.736153, 15.581954, 12.4277525],
-                                [27.398798, 24.244598, 21.090399],
-                                [36.061443, 32.90724, 29.753046],
+                                [18.757534, 15.55517, 12.352802],
+                                [27.420181, 24.217815, 21.01545],
+                                [36.08283, 32.880463, 29.678097],
                             ]
                         ]
                     )
                 ),
-                "seg": MetaTensor(np.array([[[19.0, 20.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),
+                "seg": MetaTensor(np.array([[[19.0, 12.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),
             },
         ]
     )
@@ -202,14 +202,14 @@
                     torch.tensor(
                         [
                             [
-                                [18.736153, 15.581954, 12.4277525],
-                                [27.398798, 24.244598, 21.090399],
-                                [36.061443, 32.90724, 29.753046],
+                                [18.757534, 15.55517, 12.352802],
+                                [27.420181, 24.217815, 21.01545],
+                                [36.08283, 32.880463, 29.678097],
                             ]
                         ]
                     )
                 ),
-                "seg": MetaTensor(torch.tensor([[[19.0, 20.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),
+                "seg": MetaTensor(torch.tensor([[[19.0, 12.0, 12.0], [27.0, 28.0, 20.0], [35.0, 36.0, 29.0]]])),
             },
         ]
     )