Decrease verbosity of unit tests (#1201)

pyproject.toml

@@ -128,3 +128,11 @@ markers = [
   "slow: marks tests as slow (deselect with '-m \"not slow\"')",
   "serial",
 ]
+
+filterwarnings = [
+    # Ignore SimpleITK Swig warnings
+    "ignore:builtin type .* has no __module__ attribute",
+    "ignore:Casting complex values to real discards the imaginary part",
+    # Raised by SimpleITK on CI
+    "ignore:invalid escape sequence",
+]

src/torchio/data/io.py

@@ -220,23 +220,25 @@ def write_matrix(matrix: torch.Tensor, path: TypePath):
         _write_niftyreg_matrix(matrix, path)
 
 
-def _to_itk_convention(matrix):
+def _to_itk_convention(matrix: TypeData) -> np.ndarray:
     """RAS to LPS."""
+    if isinstance(matrix, torch.Tensor):
+        matrix = matrix.numpy()
     matrix = np.dot(FLIPXY_44, matrix)
     matrix = np.dot(matrix, FLIPXY_44)
     matrix = np.linalg.inv(matrix)
     return matrix
 
 
-def _from_itk_convention(matrix):
+def _from_itk_convention(matrix: TypeData) -> np.ndarray:
     """LPS to RAS."""
     matrix = np.dot(matrix, FLIPXY_44)
     matrix = np.dot(FLIPXY_44, matrix)
     matrix = np.linalg.inv(matrix)
     return matrix
 
 
-def _read_itk_matrix(path):
+def _read_itk_matrix(path: TypePath) -> torch.Tensor:
     """Read an affine transform in ITK's .tfm format."""
     transform = sitk.ReadTransform(str(path))
     parameters = transform.GetParameters()
@@ -250,28 +252,31 @@ def _read_itk_matrix(path):
     return torch.as_tensor(homogeneous_matrix_ras)
 
 
-def _write_itk_matrix(matrix, tfm_path):
+def _write_itk_matrix(matrix: TypeData, tfm_path: TypePath) -> None:
     """The tfm file contains the matrix from floating to reference."""
     transform = _matrix_to_itk_transform(matrix)
     transform.WriteTransform(str(tfm_path))
 
 
-def _matrix_to_itk_transform(matrix, dimensions=3):
+def _matrix_to_itk_transform(
+    matrix: TypeData,
+    dimensions: int = 3,
+) -> sitk.AffineTransform:
     matrix = _to_itk_convention(matrix)
     rotation = matrix[:dimensions, :dimensions].ravel().tolist()
     translation = matrix[:dimensions, 3].tolist()
     transform = sitk.AffineTransform(rotation, translation)
     return transform
 
 
-def _read_niftyreg_matrix(trsf_path):
+def _read_niftyreg_matrix(trsf_path: TypePath) -> torch.Tensor:
     """Read a NiftyReg matrix and return it as a NumPy array."""
     matrix = np.loadtxt(trsf_path)
     matrix = np.linalg.inv(matrix)
     return torch.as_tensor(matrix)
 
 
-def _write_niftyreg_matrix(matrix, txt_path):
+def _write_niftyreg_matrix(matrix: TypeData, txt_path: TypePath) -> None:
     """Write an affine transform in NiftyReg's .txt format (ref -> flo)"""
     matrix = np.linalg.inv(matrix)
     np.savetxt(txt_path, matrix, fmt='%.8f')

src/torchio/transforms/preprocessing/intensity/histogram_standardization.py

@@ -1,6 +1,7 @@
 from pathlib import Path
 from typing import Callable
 from typing import Dict
+from typing import Iterable
 from typing import Optional
 from typing import Sequence
 from typing import Tuple
@@ -107,6 +108,8 @@ def train(
         mask_path: Optional[Union[Sequence[TypePath], TypePath]] = None,
         masking_function: Optional[Callable] = None,
         output_path: Optional[TypePath] = None,
+        *,
+        progress: bool = True,
     ) -> np.ndarray:
         """Extract average histogram landmarks from images used for training.
 
@@ -171,7 +174,9 @@ def train(
         percentiles_database = []
         a, b = percentiles_cutoff  # for mypy
         percentiles = _get_percentiles((a, b))
-        for i, image_file_path in enumerate(tqdm(images_paths)):
+        iterable: Iterable[TypePath]
+        iterable = tqdm(images_paths) if progress else images_paths  # type: ignore[assignment]
+        for i, image_file_path in enumerate(iterable):
             tensor, _ = read_image(image_file_path)
             if masking_function is not None:
                 mask = masking_function(tensor)

tests/data/test_io.py

@@ -52,8 +52,10 @@ def test_dicom_dir_missing(self):
     def test_dicom_dir_no_files(self):
         empty = self.dir / 'empty'
         empty.mkdir()
+        sitk.ProcessObject_SetGlobalWarningDisplay(False)
         with pytest.raises(FileNotFoundError):
             io._read_dicom(empty)
+        sitk.ProcessObject_SetGlobalWarningDisplay(True)
 
     def write_read_matrix(self, suffix):
         out_path = self.dir / f'matrix{suffix}'

tests/transforms/preprocessing/test_histogram_standardization.py

@@ -36,19 +36,22 @@ def test_train_histogram(self):
             paths,
             masking_function=HistogramStandardization.mean,
             output_path=(self.dir / 'landmarks.txt'),
+            progress=False,
         )
         # Use a file to mask
         HistogramStandardization.train(
             paths,
             mask_path=self.dataset[0].label.path,
             output_path=(self.dir / 'landmarks.npy'),
+            progress=False,
         )
         # Use files to mask
         masks = [subject.label.path for subject in self.dataset]
         HistogramStandardization.train(
             paths,
             mask_path=masks,
             output_path=(self.dir / 'landmarks_masks.npy'),
+            progress=False,
         )
 
     def test_bad_paths_lengths(self):
@@ -74,8 +77,9 @@ def test_wrong_image_key(self):
     def test_with_saved_dict(self):
         landmarks = np.linspace(0, 100, 13)
         landmarks_dict = {'image': landmarks}
-        torch.save(landmarks_dict, self.dir / 'landmarks_dict.pth')
-        landmarks_dict = torch.load(self.dir / 'landmarks_dict.pth')
+        landmarks_path = self.dir / 'landmarks_dict.pth'
+        torch.save(landmarks_dict, landmarks_path)
+        landmarks_dict = torch.load(landmarks_path, weights_only=False)
         transform = HistogramStandardization(landmarks_dict)
         transform(self.dataset[0])
 

tox.ini

@@ -20,7 +20,7 @@ commands =
 [testenv:mypy]
 deps =
     mypy
-    pandas-stubs
+    pip
 commands =
     mypy \
         --install-types \