Apply ruff==0.9.0 formatting (#18624)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/utils/__init__.py

@@ -1243,7 +1243,7 @@ class SettingsManager(JSONDict):
         """Updates settings, validating keys and types."""
         for arg in args:
             if isinstance(arg, dict):
-                kwargs.update(arg)
+                kwargs |= arg
         for k, v in kwargs.items():
             if k not in self.defaults:
                 raise KeyError(f"No Ultralytics setting '{k}'. {self.help_msg}")

ultralytics/utils/callbacks/hub.py

@@ -15,16 +15,14 @@ def on_pretrain_routine_start(trainer):
 
 def on_pretrain_routine_end(trainer):
     """Logs info before starting timer for upload rate limit."""
-    session = getattr(trainer, "hub_session", None)
-    if session:
+    if session := getattr(trainer, "hub_session", None):
         # Start timer for upload rate limit
         session.timers = {"metrics": time(), "ckpt": time()}  # start timer on session.rate_limit
 
 
 def on_fit_epoch_end(trainer):
     """Uploads training progress metrics at the end of each epoch."""
-    session = getattr(trainer, "hub_session", None)
-    if session:
+    if session := getattr(trainer, "hub_session", None):
         # Upload metrics after val end
         all_plots = {
             **trainer.label_loss_items(trainer.tloss, prefix="train"),
@@ -49,8 +47,7 @@ def on_fit_epoch_end(trainer):
 
 def on_model_save(trainer):
     """Saves checkpoints to Ultralytics HUB with rate limiting."""
-    session = getattr(trainer, "hub_session", None)
-    if session:
+    if session := getattr(trainer, "hub_session", None):
         # Upload checkpoints with rate limiting
         is_best = trainer.best_fitness == trainer.fitness
         if time() - session.timers["ckpt"] > session.rate_limits["ckpt"]:
@@ -61,8 +58,7 @@ def on_model_save(trainer):
 
 def on_train_end(trainer):
     """Upload final model and metrics to Ultralytics HUB at the end of training."""
-    session = getattr(trainer, "hub_session", None)
-    if session:
+    if session := getattr(trainer, "hub_session", None):
         # Upload final model and metrics with exponential standoff
         LOGGER.info(f"{PREFIX}Syncing final model...")
         session.upload_model(

ultralytics/utils/checks.py

@@ -75,8 +75,7 @@ def parse_requirements(file_path=ROOT.parent / "requirements.txt", package=""):
         line = line.strip()
         if line and not line.startswith("#"):
             line = line.split("#")[0].strip()  # ignore inline comments
-            match = re.match(r"([a-zA-Z0-9-_]+)\s*([<>!=~]+.*)?", line)
-            if match:
+            if match := re.match(r"([a-zA-Z0-9-_]+)\s*([<>!=~]+.*)?", line):
                 requirements.append(SimpleNamespace(name=match[1], specifier=match[2].strip() if match[2] else ""))
 
     return requirements

ultralytics/utils/downloads.py

@@ -269,8 +269,7 @@ def get_google_drive_file_info(link):
         for k, v in response.cookies.items():
             if k.startswith("download_warning"):
                 drive_url += f"&confirm={v}"  # v is token
-        cd = response.headers.get("content-disposition")
-        if cd:
+        if cd := response.headers.get("content-disposition"):
             filename = re.findall('filename="(.+)"', cd)[0]
     return drive_url, filename
 

ultralytics/utils/loss.py

@@ -189,8 +189,7 @@ class v8DetectionLoss:
             out = torch.zeros(batch_size, counts.max(), ne - 1, device=self.device)
             for j in range(batch_size):
                 matches = i == j
-                n = matches.sum()
-                if n:
+                if n := matches.sum():
                     out[j, :n] = targets[matches, 1:]
             out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
         return out
@@ -630,8 +629,7 @@ class v8OBBLoss(v8DetectionLoss):
             out = torch.zeros(batch_size, counts.max(), 6, device=self.device)
             for j in range(batch_size):
                 matches = i == j
-                n = matches.sum()
-                if n:
+                if n := matches.sum():
                     bboxes = targets[matches, 2:]
                     bboxes[..., :4].mul_(scale_tensor)
                     out[j, :n] = torch.cat([targets[matches, 1:2], bboxes], dim=-1)