Apply ruff==0.9.0 formatting (ultralytics#18624)

Co-authored-by: UltralyticsAssistant <[email protected]>

ultralytics/cfg/__init__.py

@@ -473,8 +473,7 @@ def check_dict_alignment(base: Dict, custom: Dict, e=None):
     """
     custom = _handle_deprecation(custom)
     base_keys, custom_keys = (set(x.keys()) for x in (base, custom))
-    mismatched = [k for k in custom_keys if k not in base_keys]
-    if mismatched:
+    if mismatched := [k for k in custom_keys if k not in base_keys]:
         from difflib import get_close_matches
 
         string = ""

ultralytics/data/utils.py

@@ -60,8 +60,7 @@ def exif_size(img: Image.Image):
     s = img.size  # (width, height)
     if img.format == "JPEG":  # only support JPEG images
         try:
-            exif = img.getexif()
-            if exif:
+            if exif := img.getexif():
                 rotation = exif.get(274, None)  # the EXIF key for the orientation tag is 274
                 if rotation in {6, 8}:  # rotation 270 or 90
                     s = s[1], s[0]
@@ -125,8 +124,7 @@ def verify_image_label(args):
                     segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
                     lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                 lb = np.array(lb, dtype=np.float32)
-            nl = len(lb)
-            if nl:
+            if nl := len(lb):
                 if keypoint:
                     assert lb.shape[1] == (5 + nkpt * ndim), f"labels require {(5 + nkpt * ndim)} columns each"
                     points = lb[:, 5:].reshape(-1, ndim)[:, :2]

ultralytics/engine/results.py

@@ -652,12 +652,11 @@ def verbose(self):
         """
         log_string = ""
         probs = self.probs
-        boxes = self.boxes
         if len(self) == 0:
             return log_string if probs is not None else f"{log_string}(no detections), "
         if probs is not None:
             log_string += f"{', '.join(f'{self.names[j]} {probs.data[j]:.2f}' for j in probs.top5)}, "
-        if boxes:
+        if boxes := self.boxes:
             for c in boxes.cls.unique():
                 n = (boxes.cls == c).sum()  # detections per class
                 log_string += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "

ultralytics/models/sam/model.py

@@ -106,7 +106,7 @@ def predict(self, source, stream=False, bboxes=None, points=None, labels=None, *
             ...     print(f"Detected {len(r.masks)} masks")
         """
         overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024)
-        kwargs = {**overrides, **kwargs}
+        kwargs = overrides | kwargs
         prompts = dict(bboxes=bboxes, points=points, labels=labels)
         return super().predict(source, stream, prompts=prompts, **kwargs)
 

ultralytics/models/sam/modules/utils.py

@@ -54,7 +54,7 @@ def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num
             (t for t in cond_frame_outputs if t not in selected_outputs),
             key=lambda x: abs(x - frame_idx),
         )[:num_remain]
-        selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain)
+        selected_outputs |= ((t, cond_frame_outputs[t]) for t in inds_remain)
         unselected_outputs = {t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs}
 
     return selected_outputs, unselected_outputs

ultralytics/models/utils/loss.py

@@ -244,7 +244,7 @@ def _get_loss(
             gt_scores[idx] = bbox_iou(pred_bboxes.detach(), gt_bboxes, xywh=True).squeeze(-1)
 
         loss = {}
-        loss.update(self._get_loss_class(pred_scores, targets, gt_scores, len(gt_bboxes), postfix))
+        loss |= self._get_loss_class(pred_scores, targets, gt_scores, len(gt_bboxes), postfix)
         loss.update(self._get_loss_bbox(pred_bboxes, gt_bboxes, postfix))
         # if masks is not None and gt_mask is not None:
         #     loss.update(self._get_loss_mask(masks, gt_mask, match_indices, postfix))

ultralytics/solutions/streamlit_inference.py

@@ -62,8 +62,7 @@ def __init__(self, **kwargs: Any):
         self.selected_ind = []  # List of selected classes for detection or tracking
         self.model = None  # Container for the loaded model instance
 
-        self.temp_dict = {"model": None}  # Temporary dict to store the model path
-        self.temp_dict.update(kwargs)
+        self.temp_dict = {"model": None} | kwargs
         self.model_path = None  # Store model file name with path
         if self.temp_dict["model"] is not None:
             self.model_path = self.temp_dict["model"]

ultralytics/utils/__init__.py

@@ -1243,7 +1243,7 @@ def update(self, *args, **kwargs):
         """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 @@ def preprocess(self, targets, batch_size, scale_tensor):
             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 @@ def preprocess(self, targets, batch_size, scale_tensor):
             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)