Refactor Python code (#13448)

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

ultralytics/utils/benchmarks.py

@@ -208,9 +208,10 @@ class RF100Benchmark:
 
         return self.ds_names, self.ds_cfg_list
 
-    def fix_yaml(self, path):
+    @staticmethod
+    def fix_yaml(path):
         """
-        Function to fix yaml train and val path.
+        Function to fix YAML train and val path.
 
         Args:
             path (str): YAML file path.
@@ -245,32 +246,19 @@ class RF100Benchmark:
                 entries = line.split(" ")
                 entries = list(filter(lambda val: val != "", entries))
                 entries = [e.strip("\n") for e in entries]
-                start_class = False
-                for e in entries:
-                    if e == "all":
-                        if "(AP)" not in entries:
-                            if "(AR)" not in entries:
-                                # parse all
-                                eval = {}
-                                eval["class"] = entries[0]
-                                eval["images"] = entries[1]
-                                eval["targets"] = entries[2]
-                                eval["precision"] = entries[3]
-                                eval["recall"] = entries[4]
-                                eval["map50"] = entries[5]
-                                eval["map95"] = entries[6]
-                                eval_lines.append(eval)
-
-                    if e in class_names:
-                        eval = {}
-                        eval["class"] = entries[0]
-                        eval["images"] = entries[1]
-                        eval["targets"] = entries[2]
-                        eval["precision"] = entries[3]
-                        eval["recall"] = entries[4]
-                        eval["map50"] = entries[5]
-                        eval["map95"] = entries[6]
-                        eval_lines.append(eval)
+                eval_lines.extend(
+                    {
+                        "class": entries[0],
+                        "images": entries[1],
+                        "targets": entries[2],
+                        "precision": entries[3],
+                        "recall": entries[4],
+                        "map50": entries[5],
+                        "map95": entries[6],
+                    }
+                    for e in entries
+                    if e in class_names or (e == "all" and "(AP)" not in entries and "(AR)" not in entries)
+                )
         map_val = 0.0
         if len(eval_lines) > 1:
             print("There's more dicts")

ultralytics/utils/callbacks/mlflow.py

@@ -103,22 +103,22 @@ def on_fit_epoch_end(trainer):
 
 def on_train_end(trainer):
     """Log model artifacts at the end of the training."""
-    if mlflow:
-        mlflow.log_artifact(str(trainer.best.parent))  # log save_dir/weights directory with best.pt and last.pt
-        for f in trainer.save_dir.glob("*"):  # log all other files in save_dir
-            if f.suffix in {".png", ".jpg", ".csv", ".pt", ".yaml"}:
-                mlflow.log_artifact(str(f))
-        keep_run_active = os.environ.get("MLFLOW_KEEP_RUN_ACTIVE", "False").lower() == "true"
-        if keep_run_active:
-            LOGGER.info(f"{PREFIX}mlflow run still alive, remember to close it using mlflow.end_run()")
-        else:
-            mlflow.end_run()
-            LOGGER.debug(f"{PREFIX}mlflow run ended")
+    if not mlflow:
+        return
+    mlflow.log_artifact(str(trainer.best.parent))  # log save_dir/weights directory with best.pt and last.pt
+    for f in trainer.save_dir.glob("*"):  # log all other files in save_dir
+        if f.suffix in {".png", ".jpg", ".csv", ".pt", ".yaml"}:
+            mlflow.log_artifact(str(f))
+    keep_run_active = os.environ.get("MLFLOW_KEEP_RUN_ACTIVE", "False").lower() == "true"
+    if keep_run_active:
+        LOGGER.info(f"{PREFIX}mlflow run still alive, remember to close it using mlflow.end_run()")
+    else:
+        mlflow.end_run()
+        LOGGER.debug(f"{PREFIX}mlflow run ended")
 
-        LOGGER.info(
-            f"{PREFIX}results logged to {mlflow.get_tracking_uri()}\n"
-            f"{PREFIX}disable with 'yolo settings mlflow=False'"
-        )
+    LOGGER.info(
+        f"{PREFIX}results logged to {mlflow.get_tracking_uri()}\n{PREFIX}disable with 'yolo settings mlflow=False'"
+    )
 
 
 callbacks = (

ultralytics/utils/callbacks/wb.py

@@ -19,7 +19,7 @@ def _custom_table(x, y, classes, title="Precision Recall Curve", x_title="Recall
     """
     Create and log a custom metric visualization to wandb.plot.pr_curve.
 
-    This function crafts a custom metric visualization that mimics the behavior of wandb's default precision-recall
+    This function crafts a custom metric visualization that mimics the behavior of the default wandb precision-recall
     curve while allowing for enhanced customization. The visual metric is useful for monitoring model performance across
     different classes.
 

ultralytics/utils/checks.py

@@ -434,10 +434,9 @@ def check_torchvision():
 
     # Extract only the major and minor versions
     v_torch = ".".join(torch.__version__.split("+")[0].split(".")[:2])
-    v_torchvision = ".".join(TORCHVISION_VERSION.split("+")[0].split(".")[:2])
-
     if v_torch in compatibility_table:
         compatible_versions = compatibility_table[v_torch]
+        v_torchvision = ".".join(TORCHVISION_VERSION.split("+")[0].split(".")[:2])
         if all(v_torchvision != v for v in compatible_versions):
             print(
                 f"WARNING ⚠️ torchvision=={v_torchvision} is incompatible with torch=={v_torch}.\n"

ultralytics/utils/plotting.py

@@ -493,7 +493,7 @@ class Annotator:
             angle = 360 - angle
         return angle
 
-    def draw_specific_points(self, keypoints, indices=[2, 5, 7], shape=(640, 640), radius=2, conf_thres=0.25):
+    def draw_specific_points(self, keypoints, indices=None, shape=(640, 640), radius=2, conf_thres=0.25):
         """
         Draw specific keypoints for gym steps counting.
 
@@ -503,6 +503,8 @@ class Annotator:
             shape (tuple): imgsz for model inference
             radius (int): Keypoint radius value
         """
+        if indices is None:
+            indices = [2, 5, 7]
         for i, k in enumerate(keypoints):
             if i in indices:
                 x_coord, y_coord = k[0], k[1]