1452 adjust-warp-layer-grid-pull-user-case

monai/networks/blocks/warp.py

@@ -4,9 +4,7 @@
 from torch import nn
 from torch.nn import functional as F
 
-from monai.config import USE_COMPILED
-from monai.networks.layers import grid_pull
-from monai.utils import GridSampleMode, GridSamplePadMode
+from monai.utils import GridSamplePadMode
 
 
 class Warp(nn.Module):
@@ -17,30 +15,38 @@ class Warp(nn.Module):
     def __init__(
         self,
         spatial_dims: int,
-        mode: Optional[Union[GridSampleMode, str]] = GridSampleMode.BILINEAR,
+        mode: int = 1,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = GridSamplePadMode.ZEROS,
     ):
         """
         Args:
             spatial_dims: {2, 3}. number of spatial dimensions
-            mode: {``"bilinear"``, ``"nearest"``}
-                Interpolation mode to calculate output values. Defaults to ``"bilinear"``.
-                See also: https://pytorch.org/docs/stable/nn.functional.html#grid-sample
+            mode: interpolation mode to calculate output values, defaults to 1.
+                Possible values are::
+
+                    - 0 or 'nearest'    or InterpolationType.nearest
+                    - 1 or 'linear'     or InterpolationType.linear
+                    - 2 or 'quadratic'  or InterpolationType.quadratic
+                    - 3 or 'cubic'      or InterpolationType.cubic
+                    - 4 or 'fourth'     or InterpolationType.fourth
+                    - etc.
             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
         """
         super(Warp, self).__init__()
         if spatial_dims not in [2, 3]:
-            raise ValueError(f"got unsupported spatial_dims = {spatial_dims}, only support 2-d and 3-d input")
+            raise ValueError(f"got unsupported spatial_dims={spatial_dims}, only support 2-d and 3-d input")
         self.spatial_dims = spatial_dims
-        self.mode: GridSampleMode = GridSampleMode(mode)
+        if mode < 0:
+            raise ValueError(f"do not support negative mode, got mode={mode}")
+        self.mode = mode
         self.padding_mode: GridSamplePadMode = GridSamplePadMode(padding_mode)
 
     @staticmethod
     def get_reference_grid(ddf: torch.Tensor) -> torch.Tensor:
         mesh_points = [torch.arange(0, dim) for dim in ddf.shape[2:]]
-        grid = torch.stack(torch.meshgrid(*mesh_points[::-1]), dim=0)  # (spatial_dims, ...)
+        grid = torch.stack(torch.meshgrid(*mesh_points), dim=0)  # (spatial_dims, ...)
         grid = torch.stack([grid] * ddf.shape[0], dim=0)  # (batch, spatial_dims, ...)
         grid = grid.to(ddf)
         return grid
@@ -77,27 +83,31 @@ def forward(self, image: torch.Tensor, ddf: torch.Tensor) -> torch.Tensor:
         grid = self.get_reference_grid(ddf) + ddf
         grid = grid.permute([0] + list(range(2, 2 + self.spatial_dims)) + [1])  # (batch, ..., self.spatial_dims)
 
-        if USE_COMPILED:
-            _padding_mode = self.padding_mode.value
-            if _padding_mode == "zeros":
-                bound = 7
-            elif _padding_mode == "border":
-                bound = 0
-            else:
-                bound = 1
-            _interp_mode = self.mode.value
-            warped_image: torch.Tensor = grid_pull(
-                image,
-                grid,
-                bound=bound,
-                extrapolate=True,
-                interpolation=1 if _interp_mode == "bilinear" else _interp_mode,
-            )
+        if self.mode > 1:
+            raise ValueError(f"{self.mode}-order interpolation not yet implemented.")
+            # if not USE_COMPILED:
+            #     raise ValueError(f"cannot perform {self.mode}-order interpolation without C compile.")
+            # _padding_mode = self.padding_mode.value
+            # if _padding_mode == "zeros":
+            #     bound = 7
+            # elif _padding_mode == "border":
+            #     bound = 0
+            # else:
+            #     bound = 1
+            # warped_image: torch.Tensor = grid_pull(
+            #     image,
+            #     grid,
+            #     bound=bound,
+            #     extrapolate=True,
+            #     interpolation=self.mode,
+            # )
         else:
             grid = self.normalize_grid(grid)
             index_ordering: List[int] = list(range(self.spatial_dims - 1, -1, -1))
             grid = grid[..., index_ordering]  # z, y, x -> x, y, z
+            _interp_mode = "bilinear" if self.mode == 1 else "nearest"
             warped_image = F.grid_sample(
-                image, grid, mode=self.mode.value, padding_mode=self.padding_mode.value, align_corners=True
+                image, grid, mode=_interp_mode, padding_mode=self.padding_mode.value, align_corners=True
             )
+
         return warped_image

tests/test_warp.py

@@ -6,66 +6,39 @@
 
 from monai.networks.blocks.warp import Warp
 
-TEST_CASE = [
+LOW_POWER_TEST_CASES = [
     [
-        {"spatial_dims": 2, "mode": "bilinear", "padding_mode": "zeros"},
+        {"spatial_dims": 2, "mode": 0, "padding_mode": "zeros"},
         {"image": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), "ddf": torch.zeros(1, 2, 2, 2)},
         torch.arange(4).reshape((1, 1, 2, 2)),
     ],
     [
-        {"spatial_dims": 2, "mode": "nearest", "padding_mode": "zeros"},
-        {"image": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), "ddf": torch.zeros(1, 2, 4, 4)},
-        torch.tensor([[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0], [0.0, 2.0, 3.0, 0.0], [0.0, 0.0, 0.0, 0.0]])
-        .unsqueeze(0)
-        .unsqueeze(0),
-    ],
-    [
-        {"spatial_dims": 2, "mode": "bilinear", "padding_mode": "border"},
-        {"image": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), "ddf": torch.zeros(1, 2, 4, 4)},
-        torch.tensor([[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0], [2.0, 2.0, 3, 3.0], [2.0, 2.0, 3.0, 3.0]])
-        .unsqueeze(0)
-        .unsqueeze(0),
-    ],
-    [
-        {"spatial_dims": 2, "mode": "nearest", "padding_mode": "reflection"},
-        {"image": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), "ddf": torch.zeros(1, 2, 4, 4)},
-        torch.tensor([[3.0, 2.0, 3.0, 2.0], [1.0, 0.0, 1.0, 0.0], [3.0, 2.0, 3.0, 2.0], [1.0, 0.0, 1.0, 0.0]])
-        .unsqueeze(0)
-        .unsqueeze(0),
-    ],
-    [
-        {"spatial_dims": 3, "mode": "bilinear", "padding_mode": "zeros"},
-        {"image": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float), "ddf": torch.zeros(1, 3, 2, 2, 2)},
-        torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),
-    ],
-]
-
-TEST_CASES = [
-    [
-        {"spatial_dims": 2, "mode": "bilinear", "padding_mode": "zeros"},
-        {"image": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), "ddf": torch.zeros(1, 2, 2, 2)},
-        torch.arange(4).reshape((1, 1, 2, 2)),
-    ],
-    [
-        {"spatial_dims": 2, "mode": "bilinear", "padding_mode": "zeros"},
+        {"spatial_dims": 2, "mode": 1, "padding_mode": "zeros"},
         {"image": torch.arange(4).reshape((1, 1, 2, 2)).to(dtype=torch.float), "ddf": torch.ones(1, 2, 2, 2)},
         torch.tensor([[[[3, 0], [0, 0]]]]),
     ],
+]
+
+HIGH_POWER_TEST_CASES = [
     [
-        {"spatial_dims": 3, "mode": "nearest", "padding_mode": "border"},
+        {"spatial_dims": 3, "mode": 2, "padding_mode": "border"},
         {
             "image": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float),
             "ddf": torch.ones(1, 3, 2, 2, 2) * -1,
         },
         torch.tensor([[[[[0, 0], [0, 0]], [[0, 0], [0, 0]]]]]),
     ],
     [
-        {"spatial_dims": 3, "mode": "nearest", "padding_mode": "reflection"},
+        {"spatial_dims": 3, "mode": 3, "padding_mode": "reflection"},
         {"image": torch.arange(8).reshape((1, 1, 2, 2, 2)).to(dtype=torch.float), "ddf": torch.ones(1, 3, 2, 2, 2)},
         torch.tensor([[[[[7, 6], [5, 4]], [[3, 2], [1, 0]]]]]),
     ],
 ]
 
+TEST_CASES = LOW_POWER_TEST_CASES
+# if USE_COMPILED:
+#     TEST_CASES += HIGH_POWER_TEST_CASES
+
 
 class TestWarp(unittest.TestCase):
     @parameterized.expand(TEST_CASES)