update spacing inverse

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

.github/workflows/pythonapp.yml

@@ -285,7 +285,7 @@ jobs:
     - name: Run quick tests (GPU)
       run: |
         nvidia-smi
-        export LAUNCH_DELAY=$(python -c "import numpy; print(numpy.random.randint(30) * 5)")
+        export LAUNCH_DELAY=$(python -c "import numpy; print(numpy.random.randint(30) * 10)")
         echo "Sleep $LAUNCH_DELAY"
         sleep $LAUNCH_DELAY
         export CUDA_VISIBLE_DEVICES=$(coverage run -m tests.utils)

docs/source/installation.md

@@ -59,11 +59,11 @@ for the latest features:
 
 ### Option 1 (as a part of your system-wide module):
 ```bash
-pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI
+pip install git+https://github.com/Project-MONAI/MONAI#egg=monai
 ```
 or, to build with MONAI Cpp/CUDA extensions:
 ```bash
-BUILD_MONAI=1 pip install git+https://github.com/Project-MONAI/MONAI#egg=MONAI
+BUILD_MONAI=1 pip install git+https://github.com/Project-MONAI/MONAI#egg=monai
 ```
 this command will download and install the current master branch of [MONAI from
 GitHub](https://github.com/Project-MONAI/MONAI).

monai/transforms/spatial/array.py

@@ -129,6 +129,7 @@ def __call__(
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
         align_corners: Optional[bool] = None,
         dtype: DtypeLike = None,
+        output_spatial_shape: Optional[np.ndarray] = None,
     ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         """
         Args:
@@ -145,6 +146,9 @@ def __call__(
             dtype: data type for resampling computation. Defaults to ``self.dtype``.
                 If None, use the data type of input data. To be compatible with other modules,
                 the output data type is always ``np.float32``.
+            output_spatial_shape: specify the shape of the output data_array. This is typically useful for
+                the inverse of `Spacingd` where sometimes we could not compute the exact shape due to the quantization
+                error with the affines.
 
         Raises:
             ValueError: When ``data_array`` has no spatial dimensions.
@@ -195,7 +199,7 @@ def __call__(
             # AffineTransform requires a batch dim
             torch.as_tensor(np.ascontiguousarray(data_array).astype(_dtype)).unsqueeze(0),
             torch.as_tensor(np.ascontiguousarray(transform).astype(_dtype)),
-            spatial_size=output_shape,
+            spatial_size=output_shape if output_spatial_shape is None else output_spatial_shape,
         )
         output_data = np.asarray(output_data.squeeze(0).detach().cpu().numpy(), dtype=np.float32)  # type: ignore
         new_affine = to_affine_nd(affine, new_affine)

monai/transforms/spatial/dictionary.py

@@ -201,6 +201,7 @@ def __call__(
             meta_data = d[meta_data_key]
             # resample array of each corresponding key
             # using affine fetched from d[affine_key]
+            original_spatial_shape = d[key].shape[1:]
             d[key], old_affine, new_affine = self.spacing_transform(
                 data_array=np.asarray(d[key]),
                 affine=meta_data["affine"],
@@ -219,6 +220,7 @@ def __call__(
                     "padding_mode": padding_mode.value if isinstance(padding_mode, Enum) else padding_mode,
                     "align_corners": align_corners if align_corners is not None else "none",
                 },
+                orig_size=original_spatial_shape,
             )
             # set the 'affine' key
             meta_data["affine"] = new_affine
@@ -239,6 +241,7 @@ def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndar
             mode = transform[InverseKeys.EXTRA_INFO]["mode"]
             padding_mode = transform[InverseKeys.EXTRA_INFO]["padding_mode"]
             align_corners = transform[InverseKeys.EXTRA_INFO]["align_corners"]
+            orig_size = transform[InverseKeys.ORIG_SIZE]
             orig_pixdim = np.sqrt(np.sum(np.square(old_affine), 0))[:-1]
             inverse_transform = Spacing(orig_pixdim, diagonal=self.spacing_transform.diagonal)
             # Apply inverse
@@ -249,6 +252,7 @@ def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndar
                 padding_mode=padding_mode,
                 align_corners=False if align_corners == "none" else align_corners,
                 dtype=dtype,
+                output_spatial_shape=orig_size,
             )
             meta_data["affine"] = new_affine
             # Remove the applied transform
@@ -1482,7 +1486,7 @@ def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndar
                 align_corners=None if align_corners == "none" else align_corners,
             )
             # Size might be out by 1 voxel so pad
-            d[key] = SpatialPad(transform[InverseKeys.ORIG_SIZE])(d[key])
+            d[key] = SpatialPad(transform[InverseKeys.ORIG_SIZE], mode="edge")(d[key])
             # Remove the applied transform
             self.pop_transform(d, key)
 
@@ -1607,7 +1611,7 @@ def inverse(self, data: Mapping[Hashable, np.ndarray]) -> Dict[Hashable, np.ndar
                     align_corners=None if align_corners == "none" else align_corners,
                 )
                 # Size might be out by 1 voxel so pad
-                d[key] = SpatialPad(transform[InverseKeys.ORIG_SIZE])(d[key])
+                d[key] = SpatialPad(transform[InverseKeys.ORIG_SIZE], mode="edge")(d[key])
             # Remove the applied transform
             self.pop_transform(d, key)
 

tests/test_handler_transform_inverter.py

@@ -24,6 +24,7 @@
     CastToTyped,
     Compose,
     LoadImaged,
+    Orientationd,
     RandAffined,
     RandAxisFlipd,
     RandFlipd,
@@ -49,7 +50,8 @@ def test_invert(self):
             [
                 LoadImaged(KEYS),
                 AddChanneld(KEYS),
-                Spacingd(KEYS, pixdim=(1.1, 1.01, 0.9), mode=["bilinear", "nearest"], dtype=np.float32),
+                Orientationd(KEYS, "RPS"),
+                Spacingd(KEYS, pixdim=(1.2, 1.01, 0.9), mode=["bilinear", "nearest"], dtype=np.float32),
                 ScaleIntensityd("image", minv=1, maxv=10),
                 RandFlipd(KEYS, prob=0.5, spatial_axis=[1, 2]),
                 RandAxisFlipd(KEYS, prob=0.5),
@@ -105,7 +107,7 @@ def _train_func(engine, batch):
         original_name = data[-1]["label"]
         self.assertEqual(reverted_name, original_name)
         print("invert diff", reverted.size - n_good)
-        self.assertTrue((reverted.size - n_good) in (0, 981), "diff. in two possible values")
+        self.assertTrue((reverted.size - n_good) in (25300, 1812), "diff. in two possible values")
 
 
 if __name__ == "__main__":