auto updates (Project-MONAI#6153)

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>

---------

Signed-off-by: monai-bot <monai.miccai2019@gmail.com>
Signed-off-by: Wenqi Li <wenqil@nvidia.com>
Co-authored-by: Wenqi Li <wenqil@nvidia.com>

.github/workflows/codeql-analysis.yml

@@ -64,6 +64,7 @@ jobs:
 
     - name: Build
       run: |
+        python -m pip install -U pip wheel
         python -m pip install -r requirements-dev.txt
         BUILD_MONAI=1 ./runtests.sh --build
 

.github/workflows/docker.yml

@@ -32,6 +32,7 @@ jobs:
       - shell: bash
         run: |
           git describe
+          python -m pip install -U pip wheel setuptools
           python setup.py build
           cat build/lib/monai/_version.py
       - name: Upload version

monai/bundle/properties.py

@@ -8,7 +8,6 @@
 # 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.
-
 """
 The predefined properties for a bundle workflow, other applications can leverage the properties
 to interact with the bundle workflow.
@@ -149,7 +148,6 @@
     },
 }
 
-
 InferProperties = {
     "bundle_root": {
         BundleProperty.DESC: "root path of the bundle.",

monai/data/utils.py

@@ -667,7 +667,7 @@ def worker_init_fn(worker_id: int) -> None:
 
     """
     worker_info = torch.utils.data.get_worker_info()
-    set_rnd(worker_info.dataset, seed=worker_info.seed)
+    set_rnd(worker_info.dataset, seed=worker_info.seed)  # type: ignore[union-attr]
 
 
 def set_rnd(obj, seed: int) -> int:

monai/networks/layers/filtering.py

@@ -49,6 +49,7 @@ class BilateralFilter(torch.autograd.Function):
 
     @staticmethod
     def forward(ctx, input, spatial_sigma=5, color_sigma=0.5, fast_approx=True):
+        """autograd forward"""
         ctx.ss = spatial_sigma
         ctx.cs = color_sigma
         ctx.fa = fast_approx
@@ -57,6 +58,7 @@ def forward(ctx, input, spatial_sigma=5, color_sigma=0.5, fast_approx=True):
 
     @staticmethod
     def backward(ctx, grad_output):
+        """autograd backward"""
         spatial_sigma, color_sigma, fast_approx = ctx.ss, ctx.cs, ctx.fa
         grad_input = _C.bilateral_filter(grad_output, spatial_sigma, color_sigma, fast_approx)
         return grad_input, None, None, None

monai/networks/utils.py

@@ -610,7 +610,7 @@ def convert_to_torchscript(
         for r1, r2 in zip(torch_out, torchscript_out):
             if isinstance(r1, torch.Tensor) or isinstance(r2, torch.Tensor):
                 assert_fn = torch.testing.assert_close if pytorch_after(1, 11) else torch.testing.assert_allclose
-                assert_fn(r1, r2, rtol=rtol, atol=atol)
+                assert_fn(r1, r2, rtol=rtol, atol=atol)  # type: ignore
 
     return script_module
 

monai/transforms/spatial/array.py

@@ -1836,7 +1836,7 @@ def resolve_modes(interp_mode, padding_mode):
             elif interp_mode == "bilinear":
                 _interp_mode = 1  # type: ignore
             else:
-                _interp_mode = GridSampleMode(interp_mode)  # type: ignore
+                _interp_mode = GridSampleMode(interp_mode)
         else:  # TransformBackends.NUMPY
             _interp_mode = int(interp_mode)  # type: ignore
             _padding_mode = look_up_option(padding_mode, NdimageMode)
@@ -1898,7 +1898,7 @@ def __call__(
 
         if USE_COMPILED or backend == TransformBackends.NUMPY:
             grid_t, *_ = convert_to_dst_type(grid[:sr], img_t, dtype=grid.dtype, wrap_sequence=True)
-            if hasattr(grid, "storage") and grid_t.storage().data_ptr() == grid.storage().data_ptr():
+            if isinstance(grid, torch.Tensor) and grid_t.data_ptr() == grid.data_ptr():
                 grid_t = grid_t.clone(memory_format=torch.contiguous_format)
             for i, dim in enumerate(img_t.shape[1 : 1 + sr]):
                 _dim = max(2, dim)
@@ -1928,7 +1928,7 @@ def __call__(
         else:
             grid_t = moveaxis(grid[list(range(sr - 1, -1, -1))], 0, -1)  # type: ignore
             grid_t = convert_to_dst_type(grid_t, img_t, wrap_sequence=True)[0].unsqueeze(0)
-            if hasattr(grid, "storage") and grid_t.storage().data_ptr() == grid.storage().data_ptr():
+            if isinstance(grid, torch.Tensor) and grid_t.data_ptr() == grid.data_ptr():
                 grid_t = grid_t.clone(memory_format=torch.contiguous_format)
             if self.norm_coords:
                 for i, dim in enumerate(img_t.shape[sr + 1 : 0 : -1]):
@@ -2079,7 +2079,7 @@ def __call__(
         sp_size = fall_back_tuple(self.spatial_size if spatial_size is None else spatial_size, img_size)
         _mode = mode if mode is not None else self.mode
         _padding_mode = padding_mode if padding_mode is not None else self.padding_mode
-        grid, affine = self.affine_grid(spatial_size=sp_size)  # type: ignore
+        grid, affine = self.affine_grid(spatial_size=sp_size)
 
         return affine_func(  # type: ignore
             img,
@@ -2327,15 +2327,15 @@ def __call__(
         img = convert_to_tensor(img, track_meta=get_track_meta())
         if self.lazy_evaluation:
             if self._do_transform:
-                affine = self.rand_affine_grid.get_transformation_matrix()  # type: ignore
+                affine = self.rand_affine_grid.get_transformation_matrix()
             else:
                 affine = convert_to_dst_type(torch.eye(len(sp_size) + 1), img, dtype=self.rand_affine_grid.dtype)[0]
         else:
             if grid is None:
                 grid = self.get_identity_grid(sp_size)
                 if self._do_transform:
                     grid = self.rand_affine_grid(grid=grid, randomize=randomize)
-            affine = self.rand_affine_grid.get_transformation_matrix()  # type: ignore
+            affine = self.rand_affine_grid.get_transformation_matrix()
         return affine_func(  # type: ignore
             img,
             affine,

tests/test_handler_clearml_image.py

@@ -22,6 +22,7 @@
 
 @unittest.skipUnless(has_clearml, "Requires 'clearml' installation")
 @unittest.skipUnless(has_tb, "Requires SummaryWriter installation")
+@unittest.skip("temp mute clearml tests https://github.com/Project-MONAI/MONAI/issues/6148")
 class TestHandlerClearMLImageHandler(unittest.TestCase):
     def test_task_init(self):
         Task.set_offline(offline_mode=True)

tests/test_handler_clearml_stats.py

@@ -22,6 +22,7 @@
 
 @unittest.skipUnless(has_clearml, "Requires 'clearml' installation")
 @unittest.skipUnless(has_tb, "Requires SummaryWriter installation")
+@unittest.skip("temp mute clearml tests https://github.com/Project-MONAI/MONAI/issues/6148")
 class TestHandlerClearMLStatsHandler(unittest.TestCase):
     def test_task_init(self):
         Task.set_offline(offline_mode=True)

tests/test_spacingd.py

@@ -117,7 +117,7 @@ def test_spacingd(self, _, data, kw_args, expected_shape, expected_affine, devic
     @parameterized.expand(TESTS_TORCH)
     def test_orntd_torch(self, init_param, img: torch.Tensor, track_meta: bool, device):
         set_track_meta(track_meta)
-        tr = Spacingd(**init_param)  # type: ignore
+        tr = Spacingd(**init_param)
         call_param = {"data": {"seg": img.to(device)}}
         res_data = tr(**call_param)  # type: ignore
         res = res_data["seg"]