Add option to use jax.image.resize in Interpolation decorator.

PiperOrigin-RevId: 646993039

connectomics/volume/decorators.py

@@ -788,22 +788,26 @@ class Interpolation(Decorator):
 
   def __init__(self,
                size: Sequence[int],
-               use_jax: bool = False,
+               backend: str = 'scipy_map_coordinates',
                context_spec: Optional[MutableJsonSpec] = None,
                **interpolation_args):
     """Interpolation.
 
     Args:
       size: New size of TensorStore.
-      use_jax: Whether to use scipy or jax version of `map_coordinates`.
-        Note that the jax version may only implement a subset of features, see
-        `jax.scipy.ndimage.map_coordinates` docs for current status.
+      backend: Backend to use for interpolation. One of 'scipy_map_coordinates',
+        'jax_map_coordinates', or 'jax_resize'. Defaults to the first.
       context_spec: Spec for virtual chunked context overriding its defaults.
-      **interpolation_args: Passed to `scipy.ndimage.map_coordinates`.
+      **interpolation_args: Passed to `scipy.ndimage.map_coordinates`,
+        `jax.scipy.ndimage.map_coordinates` , or`jax.image.resize` depending
+        on `backend`, respectively.
     """
     super().__init__(context_spec)
     self._size = size
-    self._use_jax = use_jax
+    backends = ('scipy_map_coordinates', 'jax_map_coordinates', 'jax_resize')
+    if backend not in backends:
+      raise ValueError(f'Unsupported backend: {backend} not in {backends}.')
+    self._backend = backend
     self._interpolation_args = interpolation_args
 
   def decorate(self, input_ts: ts.TensorStore) -> ts.TensorStore:
@@ -820,7 +824,7 @@ def decorate(self, input_ts: ts.TensorStore) -> ts.TensorStore:
           f'currently supported, but `inclusive_min` is: {inclusive_min}.')
 
     resize_dim = [d for d, s in enumerate(self._size) if s != input_ts.shape[d]]
-    map_coordinates = (scipy.ndimage.map_coordinates if not self._use_jax
+    map_coordinates = (scipy.ndimage.map_coordinates if 'scipy' in self._backend
                        else jax.scipy.ndimage.map_coordinates)
 
     def read_fn(domain: ts.IndexDomain, array: np.ndarray,
@@ -842,8 +846,14 @@ def read_fn(domain: ts.IndexDomain, array: np.ndarray,
         else:
           slices.append(slice(0, data.shape[d]))
 
-      array[...] = map_coordinates(data, np.mgrid[slices],
-                                   **self._interpolation_args)
+      if self._backend == 'jax_resize':
+        sub_size = [s if d in resize_dim else data.shape[d]
+                    for d, s in enumerate(self._size)]
+        array[...] = jax.image.resize(data, sub_size,
+                                      **self._interpolation_args)
+      else:
+        array[...] = map_coordinates(data, np.mgrid[slices],
+                                     **self._interpolation_args)
 
     json = input_ts.schema.to_json()
     json['domain']['exclusive_max'] = self._size

connectomics/volume/decorators_test.py

@@ -340,19 +340,20 @@ def test_interpolation(self):
     }).result()
     data_ts[...] = data
 
-    for use_jax in (True, False):
+    expected_res = np.array([
+        [[0., 0.],
+         [0., 0.]],
+        [[10.//2, 20.//2],
+         [30.//2, 40.//2]],
+        [[10., 20.],
+         [30., 40.]],])
+    backends = ('scipy_map_coordinates', 'jax_map_coordinates', 'jax_resize')
+    for backend in backends:
+      kwargs = {'method': 'linear'} if backend == 'jax_resize' else {'order': 1}
       dec = decorators.Interpolation(
-          size=(3, 2, 2), order=1, use_jax=use_jax,
+          size=(3, 2, 2), backend=backend, **kwargs
       )
       vc = dec.decorate(data_ts)
-
-      expected_res = np.array([
-          [[0., 0.],
-           [0., 0.]],
-          [[10.//2, 20.//2],
-           [30.//2, 40.//2]],
-          [[10., 20.],
-           [30., 40.]],])
       np.testing.assert_equal(vc[...].read().result(), expected_res)
 
   def test_multiply(self):