4856 resampling cupy backend for cuda-based spline interpolation

monai/engines/evaluator.py

@@ -161,7 +161,7 @@ def get_stats(self, *vars):
         stats = {
             ESKeys.RANK: self.state.rank,
             ESKeys.BEST_VALIDATION_EPOCH: self.state.best_metric_epoch,
-            ESKeys.BEST_VALIDATION_METRTC: self.state.best_metric,
+            ESKeys.BEST_VALIDATION_METRIC: self.state.best_metric,
         }
         for k in vars:
             stats[k] = getattr(self.state, k, None)

monai/transforms/utils.py

@@ -586,6 +586,9 @@ def create_grid(
     """
     compute a `spatial_size` mesh.
 
+        - when ``homogeneous=True``, the output shape is (N+1, dim_size_1, dim_size_2, ..., dim_size_N)
+        - when ``homogeneous=False``, the output shape is (N, dim_size_1, dim_size_2, ..., dim_size_N)
+
     Args:
         spatial_size: spatial size of the grid.
         spacing: same len as ``spatial_size``, defaults to 1.0 (dense grid).

monai/utils/__init__.py

@@ -36,12 +36,14 @@
     MetaKeys,
     Method,
     MetricReduction,
+    NdimageMode,
     NumpyPadMode,
     PostFix,
     ProbMapKeys,
     PytorchPadMode,
     SkipMode,
     SpaceKeys,
+    SplineMode,
     StrEnum,
     TraceKeys,
     TransformBackends,

monai/utils/enums.py

@@ -19,10 +19,12 @@
     "StrEnum",
     "NumpyPadMode",
     "GridSampleMode",
+    "SplineMode",
     "InterpolateMode",
     "UpsampleMode",
     "BlendMode",
     "PytorchPadMode",
+    "NdimageMode",
     "GridSamplePadMode",
     "Average",
     "MetricReduction",
@@ -92,6 +94,22 @@ class NumpyPadMode(StrEnum):
     EMPTY = "empty"
 
 
+class NdimageMode(StrEnum):
+    """
+    The available options determine how the input array is extended beyond its boundaries when interpolating.
+    See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
+    """
+
+    REFLECT = "reflect"
+    GRID_MIRROR = "grid-mirror"
+    CONSTANT = "constant"
+    GRID_CONSTANT = "grid-constant"
+    NEAREST = "nearest"
+    MIRROR = "mirror"
+    GRID_WRAP = "grid-wrap"
+    WRAP = "wrap"
+
+
 class GridSampleMode(StrEnum):
     """
     See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html
@@ -110,6 +128,21 @@ class GridSampleMode(StrEnum):
     BICUBIC = "bicubic"
 
 
+class SplineMode(StrEnum):
+    """
+    Order of spline interpolation.
+
+    See also: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.map_coordinates.html
+    """
+
+    ZERO = 0
+    ONE = 1
+    TWO = 2
+    THREE = 3
+    FOUR = 4
+    FIVE = 5
+
+
 class InterpolateMode(StrEnum):
     """
     See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.interpolate.html
@@ -492,4 +525,4 @@ class EngineStatsKeys(StrEnum):
     TOTAL_EPOCHS = "total_epochs"
     TOTAL_ITERATIONS = "total_iterations"
     BEST_VALIDATION_EPOCH = "best_validation_epoch"
-    BEST_VALIDATION_METRTC = "best_validation_metric"
+    BEST_VALIDATION_METRIC = "best_validation_metric"

tests/min_tests.py

@@ -136,6 +136,7 @@ def run_testsuit():
         "test_rand_zoom",
         "test_rand_zoomd",
         "test_randtorchvisiond",
+        "test_resample_backends",
         "test_resize",
         "test_resized",
         "test_resample_to_match",

tests/test_resample_backends.py

@@ -0,0 +1,62 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import torch
+from parameterized import parameterized
+
+from monai.config import USE_COMPILED
+from monai.data import MetaTensor
+from monai.transforms import Resample
+from monai.transforms.utils import create_grid
+from monai.utils import GridSampleMode, GridSamplePadMode, NdimageMode, SplineMode, convert_to_numpy
+from tests.utils import assert_allclose, is_tf32_env
+
+_rtol = 1e-3 if is_tf32_env() else 1e-4
+
+TEST_IDENTITY = []
+for interp in GridSampleMode if not USE_COMPILED else ("nearest", "bilinear"):  # type: ignore
+    for pad in GridSamplePadMode:
+        for p in (np.float32, np.float64):
+            for device in [None, "cpu", "cuda"] if torch.cuda.is_available() else [None, "cpu"]:
+                TEST_IDENTITY.append([dict(device=device), p, interp, pad, (1, 3, 4)])
+                if interp != "bicubic":
+                    TEST_IDENTITY.append([dict(device=device), p, interp, pad, (1, 3, 5, 8)])
+for interp_s in SplineMode if not USE_COMPILED else []:  # type: ignore
+    for pad_s in NdimageMode:
+        for p_s in (int, float, np.float32, np.float64):
+            for device in [None, "cpu", "cuda"] if torch.cuda.is_available() else [None, "cpu"]:
+                TEST_IDENTITY.append([dict(device=device), p_s, interp_s, pad_s, (1, 20, 21)])
+                TEST_IDENTITY.append([dict(device=device), p_s, interp_s, pad_s, (1, 21, 23, 24)])
+
+
+class TestResampleBackends(unittest.TestCase):
+    @parameterized.expand(TEST_IDENTITY)
+    def test_resample_identity(self, input_param, im_type, interp, padding, input_shape):
+        """test resampling of an identity grid with padding 2, im_type, interp, padding, input_shape"""
+        xform = Resample(dtype=im_type, **input_param)
+        n_elem = np.prod(input_shape)
+        img = convert_to_numpy(np.arange(n_elem).reshape(input_shape), dtype=im_type)
+        grid = create_grid(input_shape[1:], homogeneous=True, backend="numpy")
+        grid_p = np.stack([np.pad(g, 2, "constant") for g in grid])  # testing pad
+        output = xform(img=img, grid=grid_p, mode=interp, padding_mode=padding)
+        self.assertTrue(not torch.any(torch.isinf(output) | torch.isnan(output)))
+        self.assertIsInstance(output, MetaTensor)
+        slices = [slice(None)]
+        slices.extend([slice(2, -2) for _ in img.shape[1:]])
+        output_c = output[slices]
+        assert_allclose(output_c, img, rtol=_rtol, atol=1e-3, type_test="tensor")
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_spatial_resample.py

@@ -15,7 +15,6 @@
 import torch
 from parameterized import parameterized
 
-from monai.config import USE_COMPILED
 from monai.data.meta_obj import set_track_meta
 from monai.data.meta_tensor import MetaTensor
 from monai.data.utils import to_affine_nd
@@ -37,7 +36,7 @@
 for dst, expct in zip(destinations_3d, expected_3d):
     for device in TEST_DEVICES:
         for align in (False, True):
-            interp = ("nearest", "bilinear", 0, 1) if align and USE_COMPILED else ("nearest", "bilinear")
+            interp = ("nearest", "bilinear")
             for interp_mode in interp:
                 for padding_mode in ("zeros", "border", "reflection"):
                     TESTS.append(

tests/test_spatial_resampled.py

@@ -15,7 +15,6 @@
 import torch
 from parameterized import parameterized
 
-from monai.config import USE_COMPILED
 from monai.data.meta_tensor import MetaTensor
 from monai.data.utils import to_affine_nd
 from monai.transforms.spatial.dictionary import SpatialResampled
@@ -37,7 +36,7 @@
     for device in TEST_DEVICES:
         for align in (True, False):
             for dtype in (torch.float32, torch.float64):
-                interp = ("nearest", "bilinear", 0, 1) if align and USE_COMPILED else ("nearest", "bilinear")
+                interp = ("nearest", "bilinear")
                 for interp_mode in interp:
                     for padding_mode in ("zeros", "border", "reflection"):
                         TESTS.append(