auto updates

.github/workflows/docker.yml

@@ -85,12 +85,11 @@ jobs:
     container:
       image: docker://projectmonai/monai:latest
       options: "--shm-size=4g --ipc=host"
-    runs-on: ubuntu-latest
+    runs-on: [self-hosted, linux, X64, docker]
     steps:
     - name: Import
       run: |
-        export CUDA_VISIBLE_DEVICES=$(python -m tests.utils | tail -n 1)
-        echo $CUDA_VISIBLE_DEVICES
+        export CUDA_VISIBLE_DEVICES=  # cpu-only
         python -c 'import monai; monai.config.print_debug_info()'
         cd /opt/monai
         ls -al

monai/apps/deepgrow/transforms.py

@@ -441,8 +441,8 @@ def __call__(self, data):
 
         if np.all(np.less(current_size, self.spatial_size)):
             cropper = SpatialCrop(roi_center=center, roi_size=self.spatial_size)
-            box_start = np.array([s.start for s in cropper.slices])  # type: ignore
-            box_end = np.array([s.stop for s in cropper.slices])  # type: ignore
+            box_start = np.array([s.start for s in cropper.slices])
+            box_end = np.array([s.stop for s in cropper.slices])
         else:
             cropper = SpatialCrop(roi_start=box_start, roi_end=box_end)
 

monai/data/ultrasound_confidence_map.py

@@ -301,7 +301,7 @@ def confidence_estimation(self, img, seeds, labels, beta, gamma):
         m[:, 0] = labels == 1
 
         # Right-handside (-B^T*M)
-        rhs = -b @ m  # type: ignore
+        rhs = -b @ m
 
         # Solve linear system
         x = self._solve_linear_system(lap, rhs)
@@ -334,7 +334,7 @@ def __call__(self, data: NDArray, sink_mask: NDArray | None = None) -> NDArray:
 
         if self.mode == "RF":
             # MATLAB hilbert applies the Hilbert transform to columns
-            data = np.abs(hilbert(data, axis=0)).astype("float64")  # type: ignore
+            data = np.abs(hilbert(data, axis=0)).astype("float64")
 
         seeds, labels = self.get_seed_and_labels(data, self.sink_mode, sink_mask)
 

monai/transforms/intensity/array.py

@@ -39,7 +39,6 @@
 
 skimage, _ = optional_import("skimage", "0.19.0", min_version)
 
-
 __all__ = [
     "RandGaussianNoise",
     "RandRicianNoise",

tests/test_ultrasound_confidence_map_transform.py

@@ -49,7 +49,6 @@
     ]
 )
 
-
 SINK_ALL_OUTPUT = np.array(
     [
         [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],