DCO Remediation Commit for sewon.jeon <irocks0922@gmail.com>

I, sewon.jeon <irocks0922@gmail.com>, hereby add my Signed-off-by to this commit: 95222f3

Signed-off-by: sewon.jeon <irocks0922@gmail.com>

Author: eclipse0922

monai/apps/detection/transforms/box_ops.py

@@ -267,7 +267,7 @@ def convert_box_to_mask(
             boxes_only_mask = np.ones(box_size, dtype=np.int16) * np.int16(labels_np[b])
         # apply to global mask
         slicing = [b]
-        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type:ignore
+        slicing.extend(slice(boxes_np[b, d], boxes_np[b, d + spatial_dims]) for d in range(spatial_dims))  # type: ignore
         boxes_mask_np[tuple(slicing)] = boxes_only_mask
     return convert_to_dst_type(src=boxes_mask_np, dst=boxes, dtype=torch.int16)[0]
 

monai/apps/detection/utils/anchor_utils.py

@@ -124,10 +124,8 @@ 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])
@@ -172,16 +170,12 @@ 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

@@ -61,10 +61,8 @@ 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/auto3dseg/analyzer.py

@@ -105,7 +105,7 @@ def update_ops_nested_label(self, nested_key: str, op: Operations) -> None:
             raise ValueError("Nested_key input format is wrong. Please ensure it is like key1#0#key2")
         root: str
         child_key: str
-        (root, _, child_key) = keys
+        root, _, child_key = keys
         if root not in self.ops:
             self.ops[root] = [{}]
         self.ops[root][0].update({child_key: None})

monai/bundle/scripts.py

@@ -1948,7 +1948,7 @@ def create_workflow(
 
     """
     _args = update_kwargs(args=args_file, workflow_name=workflow_name, config_file=config_file, **kwargs)
-    (workflow_name, config_file) = _pop_args(
+    workflow_name, config_file = _pop_args(
         _args, workflow_name=ConfigWorkflow, config_file=None
     )  # the default workflow name is "ConfigWorkflow"
     if isinstance(workflow_name, str):

monai/bundle/utils.py

@@ -124,10 +124,8 @@
         "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/data/dataset.py

@@ -139,7 +139,7 @@ class DatasetFunc(Dataset):
     """
 
     def __init__(self, data: Any, func: Callable, **kwargs) -> None:
-        super().__init__(data=None, transform=None)  # type:ignore
+        super().__init__(data=None, transform=None)  # type: ignore
         self.src = data
         self.func = func
         self.kwargs = kwargs
@@ -1635,7 +1635,7 @@ def _cachecheck(self, item_transformed):
                         return (_data, _meta)
                     return _data
                 else:
-                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type:ignore
+                    item: list[dict[Any, Any]] = [{} for _ in range(len(item_transformed))]  # type: ignore
                     for i, _item in enumerate(item_transformed):
                         for k in _item:
                             meta_i_k = self._load_meta_cache(meta_hash_file_name=f"{hashfile.name}-{k}-meta-{i}")

monai/handlers/utils.py

@@ -48,7 +48,7 @@ def stopping_fn_from_loss() -> Callable[[Engine], Any]:
     """
 
     def stopping_fn(engine: Engine) -> Any:
-        return -engine.state.output  # type:ignore
+        return -engine.state.output  # type: ignore
 
     return stopping_fn
 

monai/losses/dice.py

@@ -204,11 +204,9 @@ 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

@@ -183,11 +183,9 @@ 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