Refactor unnecessary else / elif when if block has a return statement

docs/source/conf.py

@@ -107,16 +107,8 @@ def generate_apidocs(*args):
 html_theme_options = {
     "external_links": [{"url": "https://github.com/Project-MONAI/tutorials", "name": "Tutorials"}],
     "icon_links": [
-        {
-            "name": "GitHub",
-            "url": "https://github.com/project-monai/monai",
-            "icon": "fab fa-github-square",
-        },
-        {
-            "name": "Twitter",
-            "url": "https://twitter.com/projectmonai",
-            "icon": "fab fa-twitter-square",
-        },
+        {"name": "GitHub", "url": "https://github.com/project-monai/monai", "icon": "fab fa-github-square"},
+        {"name": "Twitter", "url": "https://twitter.com/projectmonai", "icon": "fab fa-twitter-square"},
     ],
     "collapse_navigation": True,
     "navigation_depth": 3,

monai/__init__.py

@@ -30,7 +30,7 @@
     raise RuntimeError(
         "MONAI requires Python {}.{} or higher. But the current Python is: {}".format(
             PY_REQUIRED_MAJOR, PY_REQUIRED_MINOR, sys.version
-        ),
+        )
     )
 
 from .utils.module import load_submodules  # noqa: E402

monai/_extensions/loader.py

@@ -84,11 +84,7 @@ def load_module(
         # This will either run the build or return the existing .so object.
         name = module_name + platform_str.replace(".", "_")
         module = load(
-            name=name,
-            sources=source,
-            extra_cflags=define_args,
-            extra_cuda_cflags=define_args,
-            verbose=verbose_build,
+            name=name, sources=source, extra_cflags=define_args, extra_cuda_cflags=define_args, verbose=verbose_build
         )
 
     return module

monai/apps/datasets.py

@@ -147,11 +147,7 @@ def _generate_data_list(self, dataset_dir: str) -> List[Dict]:
             )
 
         return [
-            {
-                "image": image_files_list[i],
-                "label": image_class[i],
-                "class_name": class_name[i],
-            }
+            {"image": image_files_list[i], "label": image_class[i], "class_name": class_name[i]}
             for i in section_indices
         ]
 
@@ -366,13 +362,7 @@ class CrossValidation:
 
     """
 
-    def __init__(
-        self,
-        dataset_cls,
-        nfolds: int = 5,
-        seed: int = 0,
-        **dataset_params,
-    ) -> None:
+    def __init__(self, dataset_cls, nfolds: int = 5, seed: int = 0, **dataset_params) -> None:
         if not hasattr(dataset_cls, "_split_datalist"):
             raise ValueError("dataset class must have _split_datalist API.")
         self.dataset_cls = dataset_cls

monai/apps/deepedit/transforms.py

@@ -15,12 +15,7 @@
 
 
 class DiscardAddGuidanced(MapTransform):
-    def __init__(
-        self,
-        keys: KeysCollection,
-        probability: float = 1.0,
-        allow_missing_keys: bool = False,
-    ):
+    def __init__(self, keys: KeysCollection, probability: float = 1.0, allow_missing_keys: bool = False):
         """
         Discard positive and negative points randomly or Add the two channels for inference time
 
@@ -54,11 +49,7 @@ class ResizeGuidanceCustomd(Transform):
     Resize the guidance based on cropped vs resized image.
     """
 
-    def __init__(
-        self,
-        guidance: str,
-        ref_image: str,
-    ) -> None:
+    def __init__(self, guidance: str, ref_image: str) -> None:
         self.guidance = guidance
         self.ref_image = ref_image
 

monai/apps/deepgrow/dataset.py

@@ -165,9 +165,7 @@ def _save_data_2d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
         # Test Data
         if vol_label is None:
             data_list.append(
-                {
-                    "image": image_file.replace(dataset_dir + os.pathsep, "") if relative_path else image_file,
-                }
+                {"image": image_file.replace(dataset_dir + os.pathsep, "") if relative_path else image_file}
             )
             continue
 
@@ -236,11 +234,7 @@ def _save_data_3d(vol_idx, vol_image, vol_label, dataset_dir, relative_path):
 
     # Test Data
     if vol_label is None:
-        data_list.append(
-            {
-                "image": image_file.replace(dataset_dir + os.pathsep, "") if relative_path else image_file,
-            }
-        )
+        data_list.append({"image": image_file.replace(dataset_dir + os.pathsep, "") if relative_path else image_file})
     else:
         # For all Labels
         unique_labels = np.unique(vol_label.flatten())

monai/apps/deepgrow/transforms.py

@@ -163,13 +163,7 @@ class AddGuidanceSignald(Transform):
 
     """
 
-    def __init__(
-        self,
-        image: str = "image",
-        guidance: str = "guidance",
-        sigma: int = 2,
-        number_intensity_ch: int = 1,
-    ):
+    def __init__(self, image: str = "image", guidance: str = "guidance", sigma: int = 2, number_intensity_ch: int = 1):
         self.image = image
         self.guidance = guidance
         self.sigma = sigma
@@ -276,12 +270,7 @@ class AddRandomGuidanced(Randomizable, Transform):
 
     """
 
-    def __init__(
-        self,
-        guidance: str = "guidance",
-        discrepancy: str = "discrepancy",
-        probability: str = "probability",
-    ):
+    def __init__(self, guidance: str = "guidance", discrepancy: str = "discrepancy", probability: str = "probability"):
         self.guidance = guidance
         self.discrepancy = discrepancy
         self.probability = probability

monai/apps/pathology/data/datasets.py

@@ -293,11 +293,7 @@ def _load_a_patch(self, index):
         location_on_image = sample["image_locations"][patch_num]
         location_on_mask = sample["mask_locations"][patch_num]
 
-        image, _ = self.image_reader.get_data(
-            img=sample["image"],
-            location=location_on_image,
-            size=self.patch_size,
-        )
+        image, _ = self.image_reader.get_data(img=sample["image"], location=location_on_image, size=self.patch_size)
         processed_sample = {"image": image, "name": sample["name"], "mask_location": location_on_mask}
         return processed_sample
 

monai/apps/pathology/metrics/lesion_froc.py

@@ -78,11 +78,7 @@ def __init__(
         self.itc_diameter = itc_diameter
         self.eval_thresholds = eval_thresholds
         self.image_reader = WSIReader(image_reader_name)
-        self.nms = PathologyProbNMS(
-            sigma=nms_sigma,
-            prob_threshold=nms_prob_threshold,
-            box_size=nms_box_size,
-        )
+        self.nms = PathologyProbNMS(sigma=nms_sigma, prob_threshold=nms_prob_threshold, box_size=nms_box_size)
 
     def prepare_inference_result(self, sample: Dict):
         """
@@ -151,12 +147,7 @@ def compute_fp_tp(self):
             total_tp_probs.extend(tp_probs)
             total_num_targets += num_targets
 
-        return (
-            np.array(total_fp_probs),
-            np.array(total_tp_probs),
-            total_num_targets,
-            num_images,
-        )
+        return (np.array(total_fp_probs), np.array(total_tp_probs), total_num_targets, num_images)
 
     def evaluate(self):
         """
@@ -168,17 +159,12 @@ def evaluate(self):
 
         # compute FROC curve given the evaluation of all images
         fps_per_image, total_sensitivity = compute_froc_curve_data(
-            fp_probs=fp_probs,
-            tp_probs=tp_probs,
-            num_targets=num_targets,
-            num_images=num_images,
+            fp_probs=fp_probs, tp_probs=tp_probs, num_targets=num_targets, num_images=num_images
         )
 
         # compute FROC score give specific evaluation threshold
         froc_score = compute_froc_score(
-            fps_per_image=fps_per_image,
-            total_sensitivity=total_sensitivity,
-            eval_thresholds=self.eval_thresholds,
+            fps_per_image=fps_per_image, total_sensitivity=total_sensitivity, eval_thresholds=self.eval_thresholds
         )
 
         return froc_score

monai/apps/pathology/transforms/spatial/array.py

@@ -34,9 +34,7 @@ class SplitOnGrid(Transform):
     """
 
     def __init__(
-        self,
-        grid_size: Union[int, Tuple[int, int]] = (2, 2),
-        patch_size: Optional[Union[int, Tuple[int, int]]] = None,
+        self, grid_size: Union[int, Tuple[int, int]] = (2, 2), patch_size: Optional[Union[int, Tuple[int, int]]] = None
     ):
         # Grid size
         if isinstance(grid_size, int):

monai/apps/pathology/utils.py

@@ -62,11 +62,7 @@ class PathologyProbNMS(ProbNMS):
     Pathology.
     """
 
-    def __call__(
-        self,
-        probs_map: Union[np.ndarray, torch.Tensor],
-        resolution_level: int = 0,
-    ):
+    def __call__(self, probs_map: Union[np.ndarray, torch.Tensor], resolution_level: int = 0):
         """
         probs_map: the input probabilities map, it must have shape (H[, W, ...]).
         resolution_level: the level at which the probabilities map is made.

monai/apps/utils.py

@@ -31,12 +31,7 @@
 else:
     tqdm, has_tqdm = optional_import("tqdm", "4.47.0", min_version, "tqdm")
 
-__all__ = [
-    "check_hash",
-    "download_url",
-    "extractall",
-    "download_and_extract",
-]
+__all__ = ["check_hash", "download_url", "extractall", "download_and_extract"]
 
 
 def _basename(p):
@@ -69,13 +64,7 @@ def update_to(self, b: int = 1, bsize: int = 1, tsize: Optional[int] = None):
                         self.total = tsize
                     self.update(b * bsize - self.n)  # will also set self.n = b * bsize
 
-            with TqdmUpTo(
-                unit="B",
-                unit_scale=True,
-                unit_divisor=1024,
-                miniters=1,
-                desc=_basename(filepath),
-            ) as t:
+            with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=_basename(filepath)) as t:
                 urlretrieve(url, filepath, reporthook=t.update_to)
         else:
             if not has_tqdm and progress:

monai/data/csv_saver.py

@@ -32,11 +32,7 @@ class CSVSaver:
     """
 
     def __init__(
-        self,
-        output_dir: str = "./",
-        filename: str = "predictions.csv",
-        overwrite: bool = True,
-        flush: bool = False,
+        self, output_dir: str = "./", filename: str = "predictions.csv", overwrite: bool = True, flush: bool = False
     ) -> None:
         """
         Args:

monai/data/dataloader.py

@@ -81,8 +81,4 @@ def __init__(self, dataset: Dataset, num_workers: int = 0, **kwargs) -> None:
         if "worker_init_fn" not in kwargs:
             kwargs.update({"worker_init_fn": worker_init_fn})
 
-        super().__init__(  # type: ignore[call-overload]
-            dataset=dataset,
-            num_workers=num_workers,
-            **kwargs,
-        )
+        super().__init__(dataset=dataset, num_workers=num_workers, **kwargs)  # type: ignore[call-overload]

monai/data/dataset.py

@@ -1218,11 +1218,6 @@ def __init__(
         files = ensure_tuple(filename)
         dfs = [pd.read_csv(f) for f in files]
         data = convert_tables_to_dicts(
-            dfs=dfs,
-            row_indices=row_indices,
-            col_names=col_names,
-            col_types=col_types,
-            col_groups=col_groups,
-            **kwargs,
+            dfs=dfs, row_indices=row_indices, col_names=col_names, col_types=col_types, col_groups=col_groups, **kwargs
         )
         super().__init__(data=data, transform=transform)

monai/data/decathlon_datalist.py

@@ -126,10 +126,7 @@ def load_decathlon_datalist(
     return _append_paths(base_dir, is_segmentation, expected_data)
 
 
-def load_decathlon_properties(
-    data_property_file_path: str,
-    property_keys: Union[Sequence[str], str],
-) -> Dict:
+def load_decathlon_properties(data_property_file_path: str, property_keys: Union[Sequence[str], str]) -> Dict:
     """Load the properties from the JSON file contains data property with specified `property_keys`.
 
     Args:

monai/data/image_reader.py

@@ -637,12 +637,7 @@ def _get_meta_dict(self, img) -> Dict:
             img: a PIL Image object loaded from an image file.
 
         """
-        return {
-            "format": img.format,
-            "mode": img.mode,
-            "width": img.width,
-            "height": img.height,
-        }
+        return {"format": img.format, "mode": img.mode, "width": img.width, "height": img.height}
 
     def _get_spatial_shape(self, img):
         """
@@ -729,10 +724,7 @@ def get_data(
 
         if self.reader_lib == "openslide" and size is None:
             # the maximum size is set to WxH
-            size = (
-                img.shape[0] // (2 ** level) - location[0],
-                img.shape[1] // (2 ** level) - location[1],
-            )
+            size = (img.shape[0] // (2 ** level) - location[0], img.shape[1] // (2 ** level) - location[1])
 
         region = self._extract_region(img, location=location, size=size, level=level, dtype=dtype)
 
@@ -745,10 +737,7 @@ def get_data(
         else:
             tuple_patch_size = ensure_tuple_rep(patch_size, 2)
             patches = self._extract_patches(
-                region,
-                patch_size=tuple_patch_size,  # type: ignore
-                grid_shape=grid_shape,
-                dtype=dtype,
+                region, patch_size=tuple_patch_size, grid_shape=grid_shape, dtype=dtype  # type: ignore
             )
 
         return patches, metadata
@@ -772,11 +761,7 @@ def _extract_region(
         region = self.convert_to_rgb_array(region, dtype)
         return region
 
-    def convert_to_rgb_array(
-        self,
-        raw_region,
-        dtype: DtypeLike = np.uint8,
-    ):
+    def convert_to_rgb_array(self, raw_region, dtype: DtypeLike = np.uint8):
         """Convert to RGB mode and numpy array"""
         if self.reader_lib == "openslide":
             # convert to RGB

monai/data/png_saver.py

@@ -134,11 +134,7 @@ def save(self, data: Union[torch.Tensor, np.ndarray], meta_data: Optional[Dict]
             raise ValueError(f"Unsupported number of channels: {data.shape[0]}, available options are [1, 3, 4]")
 
         write_png(
-            np.asarray(data),
-            file_name=path,
-            output_spatial_shape=spatial_shape,
-            mode=self.mode,
-            scale=self.scale,
+            np.asarray(data), file_name=path, output_spatial_shape=spatial_shape, mode=self.mode, scale=self.scale
         )
 
         if self.print_log:

monai/data/test_time_augmentation.py

@@ -184,9 +184,7 @@ def __call__(
             transform_info = batch_data[transform_key]
             if self.nearest_interp:
                 transform_info = convert_inverse_interp_mode(
-                    trans_info=deepcopy(transform_info),
-                    mode="nearest",
-                    align_corners=None,
+                    trans_info=deepcopy(transform_info), mode="nearest", align_corners=None
                 )
 
             # create a dictionary containing the inferred batch and their transforms

monai/data/thread_buffer.py

@@ -92,12 +92,7 @@ class ThreadDataLoader(DataLoader):
     """
 
     def __init__(
-        self,
-        dataset: Dataset,
-        buffer_size: int = 1,
-        buffer_timeout: float = 0.01,
-        num_workers: int = 0,
-        **kwargs,
+        self, dataset: Dataset, buffer_size: int = 1, buffer_timeout: float = 0.01, num_workers: int = 0, **kwargs
     ):
         super().__init__(dataset, num_workers, **kwargs)
         self.buffer_size = buffer_size