backends -> backend

monai/transforms/__init__.py

@@ -500,6 +500,7 @@
     get_extreme_points,
     get_largest_connected_component_mask,
     get_number_image_type_conversions,
+    get_transform_backends,
     img_bounds,
     in_bounds,
     is_empty,

monai/transforms/intensity/array.py

@@ -131,7 +131,7 @@ class RandRicianNoise(RandomizableTransform):
             uniformly from 0 to std.
     """
 
-    backends = [TransformBackends.TORCH, TransformBackends.NUMPY]
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
     def __init__(
         self,
@@ -197,7 +197,7 @@ class ShiftIntensity(Transform):
         offset: offset value to shift the intensity of image.
     """
 
-    backends = [TransformBackends.TORCH, TransformBackends.NUMPY]
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
     def __init__(self, offset: float) -> None:
         self.offset = offset
@@ -219,7 +219,7 @@ class RandShiftIntensity(RandomizableTransform):
     Randomly shift intensity with randomly picked offset.
     """
 
-    backends = [TransformBackends.TORCH, TransformBackends.NUMPY]
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
     def __init__(self, offsets: Union[Tuple[float, float], float], prob: float = 0.1) -> None:
         """
@@ -273,7 +273,7 @@ class StdShiftIntensity(Transform):
         dtype: output data type, defaults to float32.
     """
 
-    backends = [TransformBackends.TORCH, TransformBackends.NUMPY]
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
     def __init__(
         self, factor: float, nonzero: bool = False, channel_wise: bool = False, dtype: DtypeLike = np.float32
@@ -318,7 +318,7 @@ class RandStdShiftIntensity(RandomizableTransform):
     by: ``v = v + factor * std(v)`` where the `factor` is randomly picked.
     """
 
-    backends = [TransformBackends.TORCH, TransformBackends.NUMPY]
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
     def __init__(
         self,

monai/transforms/utility/array.py

@@ -291,7 +291,7 @@ class CastToType(Transform):
     specified PyTorch data type.
     """
 
-    backends = [TransformBackends.TORCH, TransformBackends.NUMPY]
+    backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
     def __init__(self, dtype=np.float32) -> None:
         """

monai/transforms/utils.py

@@ -38,6 +38,7 @@
     min_version,
     optional_import,
 )
+from monai.utils.enums import TransformBackends
 from monai.utils.type_conversion import convert_data_type
 
 measure, _ = optional_import("skimage.measure", "0.14.2", min_version)
@@ -81,6 +82,7 @@
     "zero_margins",
     "equalize_hist",
     "get_number_image_type_conversions",
+    "get_transform_backends",
     "print_transform_backends",
 ]
 
@@ -1158,22 +1160,17 @@ def _get_data(obj, key):
     return num_conversions
 
 
-def print_transform_backends():
-    """Prints a list of backends of all MONAI transforms."""
-
-    class Colours:
-        red = "91"
-        green = "92"
-        yellow = "93"
-
-    def print_colour(t, colour):
-        print(f"\033[{colour}m{t}\033[00m")
+def get_transform_backends():
+    """Get the backends of all MONAI transforms.
 
-    tr_total = 0
-    tr_t_or_np = 0
-    tr_t = 0
-    tr_np = 0
-    tr_uncategorised = 0
+    Returns:
+        Dictionary, where each key is a transform, and its
+        corresponding values are a boolean list, stating
+        whether that transform supports (1) `torch.Tensor`,
+        and (2) `np.ndarray` as input without needing to
+        convert.
+    """
+    backends = {}
     unique_transforms = []
     for n, obj in getmembers(monai.transforms):
         # skip aliases
@@ -1194,21 +1191,54 @@ def print_colour(t, colour):
                 "InverteD",
             ]:
                 continue
-            tr_total += 1
-            if obj.backend == ["torch", "numpy"]:
-                tr_t_or_np += 1
-                print_colour(f"TorchOrNumpy:  {n}", Colours.green)
-            elif obj.backend == ["torch"]:
-                tr_t += 1
-                print_colour(f"Torch:         {n}", Colours.green)
-            elif obj.backend == ["numpy"]:
-                tr_np += 1
-                print_colour(f"Numpy:         {n}", Colours.yellow)
-            else:
-                tr_uncategorised += 1
-                print_colour(f"Uncategorised: {n}", Colours.red)
-    print("Total number of transforms:", tr_total)
-    print_colour(f"Number transforms allowing both torch and numpy: {tr_t_or_np}", Colours.green)
-    print_colour(f"Number of TorchTransform: {tr_t}", Colours.green)
-    print_colour(f"Number of NumpyTransform: {tr_np}", Colours.yellow)
-    print_colour(f"Number of uncategorised: {tr_uncategorised}", Colours.red)
+
+            backends[n] = [
+                TransformBackends.TORCH in obj.backend,
+                TransformBackends.NUMPY in obj.backend,
+            ]
+    return backends
+
+
+def print_transform_backends():
+    """Prints a list of backends of all MONAI transforms."""
+
+    class Colors:
+        none = ""
+        red = "91"
+        green = "92"
+        yellow = "93"
+
+    def print_color(t, color):
+        print(f"\033[{color}m{t}\033[00m")
+
+    def print_table_column(name, torch, numpy, color=Colors.none):
+        print_color("{:<50} {:<8} {:<8}".format(name, torch, numpy), color)
+
+    backends = get_transform_backends()
+    n_total = len(backends)
+    n_t_or_np, n_t, n_np, n_uncategorized = 0, 0, 0, 0
+    print_table_column("Transform", "Torch?", "Numpy?")
+    for k, v in backends.items():
+        if all(v):
+            color = Colors.green
+            n_t_or_np += 1
+        elif v[0]:
+            color = Colors.green
+            n_t += 1
+        elif v[1]:
+            color = Colors.yellow
+            n_np += 1
+        else:
+            color = Colors.red
+            n_uncategorized += 1
+        print_table_column(k, *v, color)
+
+    print("Total number of transforms:", n_total)
+    print_color(f"Number transforms allowing both torch and numpy: {n_t_or_np}", Colors.green)
+    print_color(f"Number of TorchTransform: {n_t}", Colors.green)
+    print_color(f"Number of NumpyTransform: {n_np}", Colors.yellow)
+    print_color(f"Number of uncategorised: {n_uncategorized}", Colors.red)
+
+
+if __name__ == "__main__":
+    print_transform_backends()

tests/test_print_transform_backends.py

@@ -11,13 +11,17 @@
 
 import unittest
 
-from monai.transforms.utils import print_transform_backends
+from monai.transforms.utils import get_transform_backends, print_transform_backends
 
 
 class TestPrintTransformBackends(unittest.TestCase):
     def test_get_number_of_conversions(self):
+        tr_t_or_np, *_ = get_transform_backends()
+        self.assertGreater(len(tr_t_or_np), 0)
         print_transform_backends()
 
 
 if __name__ == "__main__":
-    unittest.main()
+    # unittest.main()
+    a = TestPrintTransformBackends()
+    a.test_get_number_of_conversions()