2420 Add image_only option to several spatial transforms

monai/transforms/spatial/array.py

@@ -88,6 +88,7 @@ def __init__(
         padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
         align_corners: bool = False,
         dtype: DtypeLike = np.float64,
+        image_only: bool = False,
     ) -> None:
         """
         Args:
@@ -114,13 +115,16 @@ def __init__(
             dtype: data type for resampling computation. Defaults to ``np.float64`` for best precision.
                 If None, use the data type of input data. To be compatible with other modules,
                 the output data type is always ``np.float32``.
+            image_only: if True return only the image volume, otherwise return (image, original affine, new affine).
+
         """
         self.pixdim = np.array(ensure_tuple(pixdim), dtype=np.float64)
         self.diagonal = diagonal
         self.mode: GridSampleMode = GridSampleMode(mode)
         self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
         self.align_corners = align_corners
         self.dtype = dtype
+        self.image_only = image_only
 
     def __call__(
         self,
@@ -131,7 +135,7 @@ def __call__(
         align_corners: Optional[bool] = None,
         dtype: DtypeLike = None,
         output_spatial_shape: Optional[np.ndarray] = None,
-    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray, np.ndarray]]:
         """
         Args:
             data_array: in shape (num_channels, H[, W, ...]).
@@ -204,7 +208,8 @@ def __call__(
         )
         output_data = np.asarray(output_data.squeeze(0).detach().cpu().numpy(), dtype=np.float32)  # type: ignore
         new_affine = to_affine_nd(affine, new_affine)
-        return output_data, affine, new_affine
+
+        return output_data if self.image_only else (output_data, affine, new_affine)
 
 
 class Orientation(Transform):
@@ -217,6 +222,7 @@ def __init__(
         axcodes: Optional[str] = None,
         as_closest_canonical: bool = False,
         labels: Optional[Sequence[Tuple[str, str]]] = tuple(zip("LPI", "RAS")),
+        image_only: bool = False,
     ) -> None:
         """
         Args:
@@ -229,6 +235,7 @@ def __init__(
             labels: optional, None or sequence of (2,) sequences
                 (2,) sequences are labels for (beginning, end) of output axis.
                 Defaults to ``(('L', 'R'), ('P', 'A'), ('I', 'S'))``.
+            image_only: if True return only the image volume, otherwise return (image, original affine, new affine).
 
         Raises:
             ValueError: When ``axcodes=None`` and ``as_closest_canonical=True``. Incompatible values.
@@ -243,10 +250,11 @@ def __init__(
         self.axcodes = axcodes
         self.as_closest_canonical = as_closest_canonical
         self.labels = labels
+        self.image_only = image_only
 
     def __call__(
         self, data_array: np.ndarray, affine: Optional[np.ndarray] = None
-    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray, np.ndarray]]:
         """
         original orientation of `data_array` is defined by `affine`.
 
@@ -289,7 +297,8 @@ def __call__(
         data_array = np.ascontiguousarray(nib.orientations.apply_orientation(data_array, ornt))
         new_affine = affine_ @ nib.orientations.inv_ornt_aff(spatial_ornt, shape)
         new_affine = to_affine_nd(affine, new_affine)
-        return data_array, affine, new_affine
+
+        return data_array if self.image_only else (data_array, affine, new_affine)
 
 
 class Flip(Transform):
@@ -1270,6 +1279,7 @@ def __init__(
         padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.REFLECTION,
         as_tensor_output: bool = False,
         device: Optional[torch.device] = None,
+        image_only: bool = False,
     ) -> None:
         """
         The affine transformations are applied in rotate, shear, translate, scale order.
@@ -1296,6 +1306,7 @@ def __init__(
             as_tensor_output: the computation is implemented using pytorch tensors, this option specifies
                 whether to convert it back to numpy arrays.
             device: device on which the tensor will be allocated.
+            image_only: if True return only the image volume, otherwise return (image, affine).
         """
         self.affine_grid = AffineGrid(
             rotate_params=rotate_params,
@@ -1305,6 +1316,7 @@ def __init__(
             as_tensor_output=True,
             device=device,
         )
+        self.image_only = image_only
         self.resampler = Resample(as_tensor_output=as_tensor_output, device=device)
         self.spatial_size = spatial_size
         self.mode: GridSampleMode = GridSampleMode(mode)
@@ -1316,7 +1328,7 @@ def __call__(
         spatial_size: Optional[Union[Sequence[int], int]] = None,
         mode: Optional[Union[GridSampleMode, str]] = None,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
-    ) -> Tuple[Union[np.ndarray, torch.Tensor], Union[np.ndarray, torch.Tensor]]:
+    ):
         """
         Args:
             img: shape must be (num_channels, H, W[, D]),
@@ -1334,10 +1346,9 @@ def __call__(
         """
         sp_size = fall_back_tuple(spatial_size or self.spatial_size, img.shape[1:])
         grid, affine = self.affine_grid(spatial_size=sp_size)
-        return (
-            self.resampler(img=img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode),
-            affine,
-        )
+        ret = self.resampler(img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode)
+
+        return ret if self.image_only else (ret, affine)
 
 
 class RandAffine(RandomizableTransform):

tests/test_affine.py

@@ -23,6 +23,11 @@
         {"img": np.arange(9).reshape((1, 3, 3)), "spatial_size": (-1, 0)},
         np.arange(9).reshape(1, 3, 3),
     ],
+    [
+        dict(padding_mode="zeros", as_tensor_output=False, device=None, image_only=True),
+        {"img": np.arange(9).reshape((1, 3, 3)), "spatial_size": (-1, 0)},
+        np.arange(9).reshape(1, 3, 3),
+    ],
     [
         dict(padding_mode="zeros", as_tensor_output=False, device=None),
         {"img": np.arange(4).reshape((1, 2, 2))},
@@ -78,7 +83,9 @@ class TestAffine(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
     def test_affine(self, input_param, input_data, expected_val):
         g = Affine(**input_param)
-        result, _ = g(**input_data)
+        result = g(**input_data)
+        if isinstance(result, tuple):
+            result = result[0]
         self.assertEqual(isinstance(result, torch.Tensor), isinstance(expected_val, torch.Tensor))
         np.testing.assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)
 

tests/test_orientation.py

@@ -25,6 +25,13 @@
         np.arange(12).reshape((2, 1, 2, 3)),
         "RAS",
     ],
+    [
+        {"axcodes": "RAS", "image_only": True},
+        np.arange(12).reshape((2, 1, 2, 3)),
+        {"affine": np.eye(4)},
+        np.arange(12).reshape((2, 1, 2, 3)),
+        "RAS",
+    ],
     [
         {"axcodes": "ALS"},
         np.arange(12).reshape((2, 1, 2, 3)),
@@ -114,6 +121,9 @@ class TestOrientationCase(unittest.TestCase):
     def test_ornt(self, init_param, img, data_param, expected_data, expected_code):
         ornt = Orientation(**init_param)
         res = ornt(img, **data_param)
+        if not isinstance(res, tuple):
+            np.testing.assert_allclose(res, expected_data)
+            return
         np.testing.assert_allclose(res[0], expected_data)
         original_affine = data_param["affine"]
         np.testing.assert_allclose(original_affine, res[1])

tests/test_spacing.py

@@ -30,6 +30,12 @@
         {"affine": np.eye(4)},
         np.array([[[[1.0, 1.0]], [[1.0, 1.0]]]]),
     ],
+    [
+        {"pixdim": 1.0, "padding_mode": "zeros", "dtype": float, "image_only": True},
+        np.ones((1, 2, 1, 2)),  # data
+        {"affine": np.eye(4)},
+        np.array([[[[1.0, 1.0]], [[1.0, 1.0]]]]),
+    ],
     [
         {"pixdim": (1.0, 1.0, 1.0), "padding_mode": "zeros", "dtype": float},
         np.ones((1, 2, 1, 2)),  # data
@@ -145,6 +151,9 @@ class TestSpacingCase(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
     def test_spacing(self, init_param, img, data_param, expected_output):
         res = Spacing(**init_param)(img, **data_param)
+        if not isinstance(res, tuple):
+            np.testing.assert_allclose(res, expected_output, atol=1e-6)
+            return
         np.testing.assert_allclose(res[0], expected_output, atol=1e-6)
         sr = len(res[0].shape) - 1
         if isinstance(init_param["pixdim"], float):