update rand grid distortions

docs/source/transforms.rst

@@ -632,6 +632,8 @@ Spatial
 
 `RandGridDistortion`
 """"""""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/RandGridDistortion.png
+    :alt: example of RandGridDistortion
 .. autoclass:: RandGridDistortion
     :members:
     :special-members: __call__
@@ -1466,6 +1468,8 @@ Spatial (Dict)
 
 `RandGridDistortiond`
 """""""""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/RandGridDistortiond.png
+    :alt: example of RandGridDistortiond
 .. autoclass:: RandGridDistortiond
     :members:
     :special-members: __call__

monai/transforms/spatial/array.py

@@ -2067,8 +2067,8 @@ class GridDistortion(Transform):
 
     def __init__(
         self,
-        num_cells: int,
-        distort_steps: List[Tuple],
+        num_cells: Union[Tuple[int], int],
+        distort_steps: Sequence[Sequence[float]],
         mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
         padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
         device: Optional[torch.device] = None,
@@ -2091,22 +2091,15 @@ def __init__(
             device: device on which the tensor will be allocated.
 
         """
-        self.resampler = Resample(
-            mode=mode,
-            padding_mode=padding_mode,
-            device=device,
-        )
-        for dim_steps in distort_steps:
-            if len(dim_steps) != num_cells + 1:
-                raise ValueError("the length of each tuple in `distort_steps` must equal to `num_cells + 1`.")
+        self.resampler = Resample(mode=mode, padding_mode=padding_mode, device=device)
         self.num_cells = num_cells
         self.distort_steps = distort_steps
         self.device = device
 
     def __call__(
         self,
         img: NdarrayOrTensor,
-        distort_steps: Optional[List[Tuple]] = None,
+        distort_steps: Optional[Sequence[Sequence]] = None,
         mode: Optional[Union[GridSampleMode, str]] = None,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
     ) -> NdarrayOrTensor:
@@ -2129,12 +2122,13 @@ def __call__(
             raise ValueError("the spatial size of `img` does not match with the length of `distort_steps`")
 
         all_ranges = []
+        num_cells = ensure_tuple_rep(self.num_cells, len(img.shape) - 1)
         for dim_idx, dim_size in enumerate(img.shape[1:]):
             dim_distort_steps = distort_steps[dim_idx]
             ranges = torch.zeros(dim_size, dtype=torch.float32)
-            cell_size = dim_size // self.num_cells
+            cell_size = dim_size // num_cells[dim_idx]
             prev = 0
-            for idx in range(self.num_cells + 1):
+            for idx in range(num_cells[dim_idx] + 1):
                 start = int(idx * cell_size)
                 end = start + cell_size
                 if end > dim_size:
@@ -2159,9 +2153,8 @@ class RandGridDistortion(RandomizableTransform):
 
     def __init__(
         self,
-        num_cells: int = 5,
+        num_cells: Union[Tuple[int], int] = 5,
         prob: float = 0.1,
-        spatial_dims: int = 2,
         distort_limit: Union[Tuple[float, float], float] = (-0.03, 0.03),
         mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
         padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
@@ -2174,7 +2167,6 @@ def __init__(
         Args:
             num_cells: number of grid cells on each dimension.
             prob: probability of returning a randomized grid distortion transform. Defaults to 0.1.
-            spatial_dims: spatial dimension of input data. The value should be 2 or 3. Defaults to 2.
             distort_limit: range to randomly distort.
                 If single number, distort_limit is picked from (-distort_limit, distort_limit).
                 Defaults to (-0.03, 0.03).
@@ -2188,17 +2180,12 @@ def __init__(
 
         """
         RandomizableTransform.__init__(self, prob)
-        if num_cells <= 0:
-            raise ValueError("num_cells should be no less than 1.")
         self.num_cells = num_cells
-        if spatial_dims not in [2, 3]:
-            raise ValueError("spatial_size should be 2 or 3.")
-        self.spatial_dims = spatial_dims
         if isinstance(distort_limit, (int, float)):
             self.distort_limit = (min(-distort_limit, distort_limit), max(-distort_limit, distort_limit))
         else:
             self.distort_limit = (min(distort_limit), max(distort_limit))
-        self.distort_steps = [tuple([1 + self.distort_limit[0]] * (self.num_cells + 1)) for _ in range(spatial_dims)]
+        self.distort_steps: Sequence[Sequence[float]] = ((1.0,),)
         self.grid_distortion = GridDistortion(
             num_cells=num_cells,
             distort_steps=self.distort_steps,
@@ -2207,21 +2194,21 @@ def __init__(
             device=device,
         )
 
-    def randomize(self, data: Optional[Any] = None) -> None:
+    def randomize(self, spatial_shape: Sequence[int]) -> None:
         super().randomize(None)
-        self.distort_steps = [
-            tuple(
-                1 + self.R.uniform(low=self.distort_limit[0], high=self.distort_limit[1])
-                for _ in range(self.num_cells + 1)
-            )
-            for _dim in range(self.spatial_dims)
-        ]
+        if not self._do_transform:
+            return
+        self.distort_steps = tuple(
+            tuple(1.0 + self.R.uniform(low=self.distort_limit[0], high=self.distort_limit[1], size=n_cells + 1))
+            for n_cells in ensure_tuple_rep(self.num_cells, len(spatial_shape))
+        )
 
     def __call__(
         self,
         img: NdarrayOrTensor,
         mode: Optional[Union[GridSampleMode, str]] = None,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
+        randomize: bool = True,
     ) -> NdarrayOrTensor:
         """
         Args:
@@ -2232,9 +2219,10 @@ def __call__(
             padding_mode: {``"zeros"``, ``"border"``, ``"reflection"``}
                 Padding mode for outside grid values. Defaults to ``"border"``.
                 See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
-
+            randomize: whether to shuffle the random factors using `randomize()`, default to True.
         """
-        self.randomize()
+        if randomize:
+            self.randomize(img.shape[1:])
         if not self._do_transform:
             return img
         return self.grid_distortion(img, distort_steps=self.distort_steps, mode=mode, padding_mode=padding_mode)

monai/transforms/spatial/dictionary.py

@@ -1787,7 +1787,7 @@ class GridDistortiond(MapTransform):
     def __init__(
         self,
         keys: KeysCollection,
-        num_cells: int,
+        num_cells: Union[Tuple[int], int],
         distort_steps: List[Tuple],
         mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
         padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
@@ -1839,9 +1839,8 @@ class RandGridDistortiond(RandomizableTransform, MapTransform):
     def __init__(
         self,
         keys: KeysCollection,
-        num_cells: int = 5,
+        num_cells: Union[Tuple[int], int] = 5,
         prob: float = 0.1,
-        spatial_dims: int = 2,
         distort_limit: Union[Tuple[float, float], float] = (-0.03, 0.03),
         mode: Union[GridSampleMode, str] = GridSampleMode.BILINEAR,
         padding_mode: Union[GridSamplePadMode, str] = GridSamplePadMode.BORDER,
@@ -1853,7 +1852,6 @@ def __init__(
             keys: keys of the corresponding items to be transformed.
             num_cells: number of grid cells on each dimension.
             prob: probability of returning a randomized grid distortion transform. Defaults to 0.1.
-            spatial_dims: spatial dimension of input data. Defaults to 2.
             distort_limit: range to randomly distort.
                 If single number, distort_limit is picked from (-distort_limit, distort_limit).
                 Defaults to (-0.03, 0.03).
@@ -1874,7 +1872,6 @@ def __init__(
         self.rand_grid_distortion = RandGridDistortion(
             num_cells=num_cells,
             prob=1.0,
-            spatial_dims=spatial_dims,
             distort_limit=distort_limit,
             device=device,
         )
@@ -1894,9 +1891,9 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
         if not self._do_transform:
             return d
 
-        self.rand_grid_distortion.randomize(None)
+        self.rand_grid_distortion.randomize(d[self.keys[0]].shape[1:])
         for key, mode, padding_mode in self.key_iterator(d, self.mode, self.padding_mode):
-            d[key] = self.rand_grid_distortion(d[key], mode=mode, padding_mode=padding_mode)
+            d[key] = self.rand_grid_distortion(d[key], mode=mode, padding_mode=padding_mode, randomize=False)
         return d
 
 

monai/transforms/utils_create_transform_ims.py

@@ -141,11 +141,20 @@
 )
 from monai.transforms.post.array import KeepLargestConnectedComponent, LabelFilter, LabelToContour
 from monai.transforms.post.dictionary import AsDiscreted, KeepLargestConnectedComponentd, LabelFilterd, LabelToContourd
-from monai.transforms.spatial.array import Rand2DElastic, RandAffine, RandAxisFlip, RandRotate90, Resize, Spacing
+from monai.transforms.spatial.array import (
+    Rand2DElastic,
+    RandAffine,
+    RandAxisFlip,
+    RandGridDistortion,
+    RandRotate90,
+    Resize,
+    Spacing,
+)
 from monai.transforms.spatial.dictionary import (
     Rand2DElasticd,
     RandAffined,
     RandAxisFlipd,
+    RandGridDistortiond,
     RandRotate90d,
     Resized,
     Spacingd,
@@ -672,3 +681,5 @@ def create_transform_im(
     create_transform_im(
         KeepLargestConnectedComponentd, dict(keys=CommonKeys.LABEL, applied_labels=1), data_binary, is_post=True, ndim=2
     )
+    create_transform_im(RandGridDistortion, dict(num_cells=3, prob=1.0, distort_limit=(-0.1, 0.1)), data)
+    create_transform_im(RandGridDistortiond, dict(keys=keys, num_cells=5, prob=1.0, distort_limit=(-0.1, 0.1)), data)

tests/test_efficientnet.py

@@ -382,8 +382,12 @@ class TestExtractFeatures(unittest.TestCase):
     def test_shape(self, input_param, input_shape, expected_shapes):
         device = "cuda" if torch.cuda.is_available() else "cpu"
 
-        # initialize model
-        net = EfficientNetBNFeatures(**input_param).to(device)
+        try:
+            # initialize model
+            net = EfficientNetBNFeatures(**input_param).to(device)
+        except (ContentTooShortError, HTTPError, RuntimeError) as e:
+            print(str(e))
+            return  # skipping the tests because of http errors
 
         # run inference with random tensor
         with eval_mode(net):

tests/test_grid_distortion.py

@@ -19,13 +19,11 @@
 
 TESTS = []
 for p in TEST_NDARRAYS:
-    num_cells = 3
-    distort_steps = [(1.5,) * (1 + num_cells)] * 2
     TESTS.append(
         [
             dict(
-                num_cells=num_cells,
-                distort_steps=distort_steps,
+                num_cells=3,
+                distort_steps=[(1.5,) * 4] * 2,
                 mode="nearest",
                 padding_mode="zeros",
             ),
@@ -54,8 +52,8 @@
             ),
         ]
     )
-    num_cells = 2
-    distort_steps = [(1.5,) * (1 + num_cells), (1.0,) * (1 + num_cells)]
+    num_cells = (2, 2)
+    distort_steps = [(1.5,) * (1 + num_cells[0]), (1.0,) * (1 + num_cells[1])]
     TESTS.append(
         [
             dict(
@@ -89,13 +87,11 @@
             ),
         ]
     )
-    num_cells = 2
-    distort_steps = [(1.25,) * (1 + num_cells)] * 3
     TESTS.append(
         [
             dict(
-                num_cells=num_cells,
-                distort_steps=distort_steps,
+                num_cells=2,
+                distort_steps=[(1.25,) * 3] * 3,
                 mode="nearest",
                 padding_mode="zeros",
             ),

tests/test_grid_distortiond.py

@@ -18,9 +18,9 @@
 from tests.utils import TEST_NDARRAYS, assert_allclose
 
 TESTS = []
+num_cells = (2, 2)
+distort_steps = [(1.5,) * (1 + n_c) for n_c in num_cells]
 for p in TEST_NDARRAYS:
-    num_cells = 2
-    distort_steps = [(1.5,) * (1 + num_cells)] * 2
     img = np.indices([6, 6]).astype(np.float32)
     TESTS.append(
         [

tests/test_rand_grid_distortion.py

@@ -19,14 +19,12 @@
 
 TESTS = []
 for p in TEST_NDARRAYS:
-    num_cells = 2
     seed = 0
     TESTS.append(
         [
             dict(
-                num_cells=num_cells,
+                num_cells=2,
                 prob=1.0,
-                spatial_dims=2,
                 distort_limit=0.5,
                 mode="nearest",
                 padding_mode="zeros",
@@ -57,14 +55,12 @@
             ),
         ]
     )
-    num_cells = 2
     seed = 1
     TESTS.append(
         [
             dict(
-                num_cells=num_cells,
+                num_cells=(2, 2),
                 prob=1.0,
-                spatial_dims=2,
                 distort_limit=0.1,
                 mode="bilinear",
                 padding_mode="reflection",

tests/test_rand_grid_distortiond.py

@@ -18,17 +18,16 @@
 from tests.utils import TEST_NDARRAYS, assert_allclose
 
 TESTS = []
+num_cells = 2
+seed = 0
 for p in TEST_NDARRAYS:
-    num_cells = 2
-    seed = 0
     img = np.indices([6, 6]).astype(np.float32)
     TESTS.append(
         [
             dict(
                 keys=["img", "mask"],
                 num_cells=num_cells,
                 prob=1.0,
-                spatial_dims=2,
                 distort_limit=(-0.1, 0.1),
                 mode=["bilinear", "nearest"],
                 padding_mode="zeros",
@@ -40,18 +39,18 @@
                     [
                         [
                             [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
-                            [1.6390989, 1.6390989, 1.6390989, 1.6390989, 1.6390989, 0.0],
-                            [3.2781978, 3.2781978, 3.2781978, 3.2781978, 3.2781978, 0.0],
-                            [3.2781978, 3.2781978, 3.2781978, 3.2781978, 3.2781978, 0.0],
-                            [4.74323, 4.74323, 4.74323, 4.74323, 4.74323, 0.0],
+                            [1.5645568, 1.5645568, 1.5645568, 1.5645568, 1.5645568, 0.0],
+                            [3.1291137, 3.1291137, 3.1291137, 3.1291137, 3.1291137, 0.0],
+                            [3.1291137, 3.1291137, 3.1291137, 3.1291137, 3.1291137, 0.0],
+                            [4.6599426, 4.6599426, 4.6599426, 4.6599426, 4.6599426, 0.0],
                             [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                         ],
                         [
-                            [0.0, 1.5086684, 3.0173368, 3.0173368, 4.5377502, 0.0],
-                            [0.0, 1.5086684, 3.0173368, 3.0173368, 4.5377502, 0.0],
-                            [0.0, 1.5086684, 3.0173368, 3.0173368, 4.5377502, 0.0],
-                            [0.0, 1.5086684, 3.0173368, 3.0173368, 4.5377502, 0.0],
-                            [0.0, 1.5086684, 3.0173368, 3.0173368, 4.5377502, 0.0],
+                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],
+                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],
+                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],
+                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],
+                            [0.0, 1.4770963, 2.9541926, 2.9541926, 4.497961, 0.0],
                             [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                         ],
                     ]
@@ -66,7 +65,7 @@
                             [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
                             [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
                             [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
-                            [1.0, 1.0, 1.0, 1.0, 1.0, 0.0],
+                            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                         ]
                     ]
                 )