PyCharm Code Inspect fixes (#18392)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/cfg/__init__.py

@@ -86,7 +86,7 @@ SOLUTIONS_HELP_MSG = f"""
         yolo solutions count source="path/to/video/file.mp4" region=[(20, 400), (1080, 400), (1080, 360), (20, 360)]
 
     2. Call heatmaps solution
-        yolo solutions heatmap colormap=cv2.COLORMAP_PARAULA model=yolo11n.pt
+        yolo solutions heatmap colormap=cv2.COLORMAP_PARULA model=yolo11n.pt
 
     3. Call queue management solution
         yolo solutions queue region=[(20, 400), (1080, 400), (1080, 360), (20, 360)] model=yolo11n.pt

ultralytics/cfg/datasets/lvis.yaml

@@ -11,7 +11,7 @@
 path: ../datasets/lvis # dataset root dir
 train: train.txt # train images (relative to 'path') 100170 images
 val: val.txt # val images (relative to 'path') 19809 images
-minival: minival.txt # minval images (relative to 'path') 5000 images
+minival: minival.txt # minival images (relative to 'path') 5000 images
 
 names:
   0: aerosol can/spray can

ultralytics/cfg/solutions/default.yaml

@@ -12,7 +12,7 @@ colormap: #  (int | str) colormap for heatmap, Only OPENCV supported colormaps c
 # Workouts monitoring settings -----------------------------------------------------------------------------------------
 up_angle: 145.0 # (float) Workouts up_angle for counts, 145.0 is default value.
 down_angle: 90 # (float) Workouts down_angle for counts, 90 is default value. Y
-kpts: [6, 8, 10] # (list[int]) keypoints for workouts monitoring, i.e. for pushups kpts have values of [6, 8, 10].
+kpts: [6, 8, 10] # (list[int]) keypoints for workouts monitoring, i.e. for push-ups kpts have values of [6, 8, 10].
 
 # Analytics settings ---------------------------------------------------------------------------------------------------
 analytics_type: "line" # (str) analytics type i.e "line", "pie", "bar" or "area" charts.

ultralytics/data/augment.py

@@ -441,7 +441,8 @@ class BaseMixTransform:
         """
         raise NotImplementedError
 
-    def _update_label_text(self, labels):
+    @staticmethod
+    def _update_label_text(labels):
         """
         Updates label text and class IDs for mixed labels in image augmentation.
 
@@ -1259,7 +1260,8 @@ class RandomPerspective:
         labels["resized_shape"] = img.shape[:2]
         return labels
 
-    def box_candidates(self, box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
+    @staticmethod
+    def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
         """
         Compute candidate boxes for further processing based on size and aspect ratio criteria.
 
@@ -1598,7 +1600,8 @@ class LetterBox:
         else:
             return img
 
-    def _update_labels(self, labels, ratio, padw, padh):
+    @staticmethod
+    def _update_labels(labels, ratio, padw, padh):
         """
         Updates labels after applying letterboxing to an image.
 

ultralytics/data/dataset.py

@@ -68,7 +68,7 @@ class YOLODataset(BaseDataset):
         Cache dataset labels, check images and read shapes.
 
         Args:
-            path (Path): Path where to save the cache file. Default is Path('./labels.cache').
+            path (Path): Path where to save the cache file. Default is Path("./labels.cache").
 
         Returns:
             (dict): labels.
@@ -219,7 +219,7 @@ class YOLODataset(BaseDataset):
         segment_resamples = 100 if self.use_obb else 1000
         if len(segments) > 0:
             # make sure segments interpolate correctly if original length is greater than segment_resamples
-            max_len = max([len(s) for s in segments])
+            max_len = max(len(s) for s in segments)
             segment_resamples = (max_len + 1) if segment_resamples < max_len else segment_resamples
             # list[np.array(segment_resamples, 2)] * num_samples
             segments = np.stack(resample_segments(segments, n=segment_resamples), axis=0)

ultralytics/data/scripts/download_weights.sh

@@ -11,8 +11,8 @@
 python - <<EOF
 from ultralytics.utils.downloads import attempt_download_asset
 
-assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '-cls', '-seg', '-pose')]
+assets = [f"yolov8{size}{suffix}.pt" for size in "nsmlx" for suffix in ("", "-cls", "-seg", "-pose")]
 for x in assets:
-    attempt_download_asset(f'weights/{x}')
+    attempt_download_asset(f"weights/{x}")
 
 EOF

ultralytics/engine/exporter.py

@@ -813,7 +813,7 @@ class Exporter:
         workspace = int(self.args.workspace * (1 << 30)) if self.args.workspace is not None else 0
         if is_trt10 and workspace > 0:
             config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace)
-        elif workspace > 0 and not is_trt10:  # TensorRT versions 7, 8
+        elif workspace > 0:  # TensorRT versions 7, 8
             config.max_workspace_size = workspace
         flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
         network = builder.create_network(flag)

ultralytics/engine/model.py

@@ -1170,6 +1170,4 @@ class Model(nn.Module):
             >>> print(model.stride)
             >>> print(model.task)
         """
-        if name == "model":
-            return self._modules["model"]
-        return getattr(self.model, name)
+        return self._modules["model"] if name == "model" else getattr(self.model, name)

ultralytics/engine/validator.py

@@ -245,7 +245,7 @@ class BaseValidator:
 
                 cost_matrix = iou * (iou >= threshold)
                 if cost_matrix.any():
-                    labels_idx, detections_idx = scipy.optimize.linear_sum_assignment(cost_matrix, maximize=True)
+                    labels_idx, detections_idx = scipy.optimize.linear_sum_assignment(cost_matrix)
                     valid = cost_matrix[labels_idx, detections_idx] > 0
                     if valid.any():
                         correct[detections_idx[valid], i] = True

ultralytics/models/sam/modules/tiny_encoder.py

@@ -955,7 +955,8 @@ class TinyViT(nn.Module):
 
         self.apply(_check_lr_scale)
 
-    def _init_weights(self, m):
+    @staticmethod
+    def _init_weights(m):
         """Initializes weights for linear and normalization layers in the TinyViT model."""
         if isinstance(m, nn.Linear):
             # NOTE: This initialization is needed only for training.

ultralytics/models/sam/predict.py

@@ -1377,7 +1377,7 @@ class SAM2VideoPredictor(SAM2Predictor):
             if "maskmem_pos_enc" not in model_constants:
                 assert isinstance(out_maskmem_pos_enc, list)
                 # only take the slice for one object, since it's same across objects
-                maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc]
+                maskmem_pos_enc = [x[:1].clone() for x in out_maskmem_pos_enc]
                 model_constants["maskmem_pos_enc"] = maskmem_pos_enc
             else:
                 maskmem_pos_enc = model_constants["maskmem_pos_enc"]

ultralytics/nn/autobackend.py

@@ -429,10 +429,7 @@ class AutoBackend(nn.Module):
 
             import MNN
 
-            config = {}
-            config["precision"] = "low"
-            config["backend"] = "CPU"
-            config["numThread"] = (os.cpu_count() + 1) // 2
+            config = {"precision": "low", "backend": "CPU", "numThread": (os.cpu_count() + 1) // 2}
             rt = MNN.nn.create_runtime_manager((config,))
             net = MNN.nn.load_module_from_file(w, [], [], runtime_manager=rt, rearrange=True)
 

ultralytics/solutions/streamlit_inference.py

@@ -181,12 +181,8 @@ class Inference:
 if __name__ == "__main__":
     import sys  # Import the sys module for accessing command-line arguments
 
-    model = None  # Initialize the model variable as None
-
     # Check if a model name is provided as a command-line argument
     args = len(sys.argv)
-    if args > 1:
-        model = args  # Assign the first argument as the model name
-
+    model = args if args > 1 else None
     # Create an instance of the Inference class and run inference
     Inference(model=model).inference()

ultralytics/utils/benchmarks.py

@@ -440,7 +440,8 @@ class ProfileModels:
         print(f"Profiling: {sorted(files)}")
         return [Path(file) for file in sorted(files)]
 
-    def get_onnx_model_info(self, onnx_file: str):
+    @staticmethod
+    def get_onnx_model_info(onnx_file: str):
         """Extracts metadata from an ONNX model file including parameters, GFLOPs, and input shape."""
         return 0.0, 0.0, 0.0, 0.0  # return (num_layers, num_params, num_gradients, num_flops)
 

ultralytics/utils/downloads.py

@@ -138,7 +138,7 @@ def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX"), exist_ok=Fals
     If a path is not provided, the function will use the parent directory of the zipfile as the default path.
 
     Args:
-        file (str): The path to the zipfile to be extracted.
+        file (str | Path): The path to the zipfile to be extracted.
         path (str, optional): The path to extract the zipfile to. Defaults to None.
         exclude (tuple, optional): A tuple of filename strings to be excluded. Defaults to ('.DS_Store', '__MACOSX').
         exist_ok (bool, optional): Whether to overwrite existing contents if they exist. Defaults to False.

ultralytics/utils/instance.py

@@ -28,7 +28,7 @@ to_4tuple = _ntuple(4)
 # `ltwh` means left top and width, height(COCO format)
 _formats = ["xyxy", "xywh", "ltwh"]
 
-__all__ = ("Bboxes",)  # tuple or list
+__all__ = ("Bboxes", "Instances")  # tuple or list
 
 
 class Bboxes:

ultralytics/utils/plotting.py

@@ -545,7 +545,8 @@ class Annotator:
         """Save the annotated image to 'filename'."""
         cv2.imwrite(filename, np.asarray(self.im))
 
-    def get_bbox_dimension(self, bbox=None):
+    @staticmethod
+    def get_bbox_dimension(bbox=None):
         """
         Calculate the area of a bounding box.