style: reformat with black 25.1.0 to match CI

Author: garciadias

monai/apps/detection/transforms/dictionary.py

@@ -125,8 +125,10 @@ def __init__(self, box_keys: KeysCollection, box_ref_image_keys: str, allow_miss
         super().__init__(box_keys, allow_missing_keys)
         box_ref_image_keys_tuple = ensure_tuple(box_ref_image_keys)
         if len(box_ref_image_keys_tuple) > 1:
-            raise ValueError("Please provide a single key for box_ref_image_keys.\
-                All boxes of box_keys are attached to box_ref_image_keys.")
+            raise ValueError(
+                "Please provide a single key for box_ref_image_keys.\
+                All boxes of box_keys are attached to box_ref_image_keys."
+            )
         self.box_ref_image_keys = box_ref_image_keys
 
     def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> dict[Hashable, NdarrayOrTensor]:
@@ -287,8 +289,10 @@ def __init__(
         super().__init__(box_keys, allow_missing_keys)
         box_ref_image_keys_tuple = ensure_tuple(box_ref_image_keys)
         if len(box_ref_image_keys_tuple) > 1:
-            raise ValueError("Please provide a single key for box_ref_image_keys.\
-                All boxes of box_keys are attached to box_ref_image_keys.")
+            raise ValueError(
+                "Please provide a single key for box_ref_image_keys.\
+                All boxes of box_keys are attached to box_ref_image_keys."
+            )
         self.box_ref_image_keys = box_ref_image_keys
         self.image_meta_key = image_meta_key or f"{box_ref_image_keys}_{image_meta_key_postfix}"
         self.converter_to_image_coordinate = AffineBox()
@@ -306,8 +310,10 @@ def extract_affine(self, data: Mapping[Hashable, torch.Tensor]) -> tuple[Ndarray
         else:
             raise ValueError(f"{meta_key} is not found. Please check whether it is the correct the image meta key.")
         if "affine" not in meta_dict:
-            raise ValueError(f"'affine' is not found in {meta_key}. \
-                Please check whether it is the correct the image meta key.")
+            raise ValueError(
+                f"'affine' is not found in {meta_key}. \
+                Please check whether it is the correct the image meta key."
+            )
         affine: NdarrayOrTensor = meta_dict["affine"]
 
         if self.affine_lps_to_ras:  # RAS affine
@@ -809,12 +815,16 @@ def __init__(
     ) -> None:
         box_keys_tuple = ensure_tuple(box_keys)
         if len(box_keys_tuple) != 1:
-            raise ValueError("Please provide a single key for box_keys.\
-                All label_keys are attached to this box_keys.")
+            raise ValueError(
+                "Please provide a single key for box_keys.\
+                All label_keys are attached to this box_keys."
+            )
         box_ref_image_keys_tuple = ensure_tuple(box_ref_image_keys)
         if len(box_ref_image_keys_tuple) != 1:
-            raise ValueError("Please provide a single key for box_ref_image_keys.\
-                All box_keys and label_keys are attached to this box_ref_image_keys.")
+            raise ValueError(
+                "Please provide a single key for box_ref_image_keys.\
+                All box_keys and label_keys are attached to this box_ref_image_keys."
+            )
         self.label_keys = ensure_tuple(label_keys)
         super().__init__(box_keys_tuple, allow_missing_keys)
 
@@ -1081,8 +1091,10 @@ def __init__(
 
         box_keys_tuple = ensure_tuple(box_keys)
         if len(box_keys_tuple) != 1:
-            raise ValueError("Please provide a single key for box_keys.\
-                All label_keys are attached to this box_keys.")
+            raise ValueError(
+                "Please provide a single key for box_keys.\
+                All label_keys are attached to this box_keys."
+            )
         self.box_keys = box_keys_tuple[0]
         self.label_keys = ensure_tuple(label_keys)
 

monai/apps/detection/utils/anchor_utils.py

@@ -124,8 +124,10 @@ def __init__(
             aspect_ratios = (aspect_ratios,) * len(self.sizes)
 
         if len(self.sizes) != len(aspect_ratios):
-            raise ValueError("len(sizes) and len(aspect_ratios) should be equal. \
-                It represents the number of feature maps.")
+            raise ValueError(
+                "len(sizes) and len(aspect_ratios) should be equal. \
+                It represents the number of feature maps."
+            )
 
         spatial_dims = len(ensure_tuple(aspect_ratios[0][0])) + 1
         spatial_dims = look_up_option(spatial_dims, [2, 3])
@@ -170,12 +172,16 @@ def generate_anchors(
         scales_t = torch.as_tensor(scales, dtype=dtype, device=device)  # sized (N,)
         aspect_ratios_t = torch.as_tensor(aspect_ratios, dtype=dtype, device=device)  # sized (M,) or (M,2)
         if (self.spatial_dims >= 3) and (len(aspect_ratios_t.shape) != 2):
-            raise ValueError(f"In {self.spatial_dims}-D image, aspect_ratios for each level should be \
-                {len(aspect_ratios_t.shape) - 1}-D. But got aspect_ratios with shape {aspect_ratios_t.shape}.")
+            raise ValueError(
+                f"In {self.spatial_dims}-D image, aspect_ratios for each level should be \
+                {len(aspect_ratios_t.shape) - 1}-D. But got aspect_ratios with shape {aspect_ratios_t.shape}."
+            )
 
         if (self.spatial_dims >= 3) and (aspect_ratios_t.shape[1] != self.spatial_dims - 1):
-            raise ValueError(f"In {self.spatial_dims}-D image, aspect_ratios for each level should has \
-                shape (_,{self.spatial_dims - 1}). But got aspect_ratios with shape {aspect_ratios_t.shape}.")
+            raise ValueError(
+                f"In {self.spatial_dims}-D image, aspect_ratios for each level should has \
+                shape (_,{self.spatial_dims - 1}). But got aspect_ratios with shape {aspect_ratios_t.shape}."
+            )
 
         # if 2d, w:h = 1:aspect_ratios
         if self.spatial_dims == 2:

monai/apps/reconstruction/transforms/array.py

@@ -61,8 +61,10 @@ def __init__(
                 real/imaginary parts.
         """
         if len(center_fractions) != len(accelerations):
-            raise ValueError("Number of center fractions \
-                should match number of accelerations")
+            raise ValueError(
+                "Number of center fractions \
+                should match number of accelerations"
+            )
 
         self.center_fractions = center_fractions
         self.accelerations = accelerations

monai/bundle/utils.py

@@ -124,8 +124,10 @@
         "run_name": None,
         # may fill it at runtime
         "save_execute_config": True,
-        "is_not_rank0": ("$torch.distributed.is_available() \
-                and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0"),
+        "is_not_rank0": (
+            "$torch.distributed.is_available() \
+                and torch.distributed.is_initialized() and torch.distributed.get_rank() > 0"
+        ),
         # MLFlowHandler config for the trainer
         "trainer": {
             "_target_": "MLFlowHandler",

monai/losses/dice.py

@@ -203,9 +203,11 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
                 self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
             else:
                 if self.class_weight.shape[0] != num_of_classes:
-                    raise ValueError("""the length of the `weight` sequence should be the same as the number of classes.
+                    raise ValueError(
+                        """the length of the `weight` sequence should be the same as the number of classes.
                         If `include_background=False`, the weight should not include
-                        the background category class 0.""")
+                        the background category class 0."""
+                    )
             if self.class_weight.min() < 0:
                 raise ValueError("the value/values of the `weight` should be no less than 0.")
             # apply class_weight to loss

monai/losses/focal_loss.py

@@ -183,9 +183,11 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
                 self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
             else:
                 if self.class_weight.shape[0] != num_of_classes:
-                    raise ValueError("""the length of the `weight` sequence should be the same as the number of classes.
+                    raise ValueError(
+                        """the length of the `weight` sequence should be the same as the number of classes.
                         If `include_background=False`, the weight should not include
-                        the background category class 0.""")
+                        the background category class 0."""
+                    )
             if self.class_weight.min() < 0:
                 raise ValueError("the value/values of the `weight` should be no less than 0.")
             # apply class_weight to loss

monai_quick.txt

@@ -62,7 +62,9 @@ def create_semantic_data(shape: list, semantic_regions: int):
                 start_point[0] : (start_point[0] + shape_square[0]),
                 start_point[1] : (start_point[1] + shape_square[1]),
                 start_point[2] : (start_point[2] + shape_square[2]),
-            ] = (base_intensity + torch.randn(shape_square) * 0.1)
+            ] = (
+                base_intensity + torch.randn(shape_square) * 0.1
+            )
         else:
             ValueError("Supports only 2D and 3D tensors")
 

tests/networks/nets/test_spade_vaegan.py

@@ -429,7 +429,9 @@ def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=
     return stdout, process.returncode
 
 
-LONG_VERSION_PY["git"] = r'''
+LONG_VERSION_PY[
+    "git"
+] = r'''
 # This file helps to compute a version number in source trees obtained from
 # git-archive tarball (such as those provided by githubs download-from-tag
 # feature). Distribution tarballs (built by setup.py sdist) and build