PyUpgrade 3.8 updates (#15941)

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

docs/build_docs.py

@@ -164,7 +164,7 @@ def update_docs_html():
     # Convert plaintext links to HTML hyperlinks
     files_modified = 0
     for html_file in tqdm(SITE.rglob("*.html"), desc="Converting plaintext links"):
-        with open(html_file, "r", encoding="utf-8") as file:
+        with open(html_file, encoding="utf-8") as file:
             content = file.read()
         updated_content = convert_plaintext_links_to_html(content)
         if updated_content != content:

ultralytics/data/converter.py

@@ -490,7 +490,7 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
                 normalized_coords = [
                     coords[i] / image_width if i % 2 == 0 else coords[i] / image_height for i in range(8)
                 ]
-                formatted_coords = ["{:.6g}".format(coord) for coord in normalized_coords]
+                formatted_coords = [f"{coord:.6g}" for coord in normalized_coords]
                 g.write(f"{class_idx} {' '.join(formatted_coords)}\n")
 
     for phase in ["train", "val"]:

ultralytics/data/dataset.py

@@ -296,7 +296,7 @@ class GroundingDataset(YOLODataset):
         """Loads annotations from a JSON file, filters, and normalizes bounding boxes for each image."""
         labels = []
         LOGGER.info("Loading annotation file...")
-        with open(self.json_file, "r") as f:
+        with open(self.json_file) as f:
             annotations = json.load(f)
         images = {f'{x["id"]:d}': x for x in annotations["images"]}
         img_to_anns = defaultdict(list)

ultralytics/data/split_dota.py

@@ -193,7 +193,7 @@ def crop_and_save(anno, windows, window_objs, im_dir, lb_dir, allow_background_i
 
             with open(Path(lb_dir) / f"{new_name}.txt", "w") as f:
                 for lb in label:
-                    formatted_coords = ["{:.6g}".format(coord) for coord in lb[1:]]
+                    formatted_coords = [f"{coord:.6g}" for coord in lb[1:]]
                     f.write(f"{int(lb[0])} {' '.join(formatted_coords)}\n")
 
 

ultralytics/engine/predictor.py

@@ -328,7 +328,7 @@ class BasePredictor:
             frame = int(match[1]) if match else None  # 0 if frame undetermined
 
         self.txt_path = self.save_dir / "labels" / (p.stem + ("" if self.dataset.mode == "image" else f"_{frame}"))
-        string += "%gx%g " % im.shape[2:]
+        string += "{:g}x{:g} ".format(*im.shape[2:])
         result = self.results[i]
         result.save_dir = self.save_dir.__str__()  # used in other locations
         string += f"{result.verbose()}{result.speed['inference']:.1f}ms"

ultralytics/engine/validator.py

@@ -202,8 +202,9 @@ class BaseValidator:
             return {k: round(float(v), 5) for k, v in results.items()}  # return results as 5 decimal place floats
         else:
             LOGGER.info(
-                "Speed: %.1fms preprocess, %.1fms inference, %.1fms loss, %.1fms postprocess per image"
+                "Speed: {:.1f}ms preprocess, {:.1f}ms inference, {:.1f}ms loss, {:.1f}ms postprocess per image".format(
-                % tuple(self.speed.values())
+                    *tuple(self.speed.values())
+                )
             )
             if self.args.save_json and self.jdict:
                 with open(str(self.save_dir / "predictions.json"), "w") as f:

ultralytics/hub/utils.py

@@ -55,23 +55,22 @@ def request_with_credentials(url: str) -> any:
 
     display.display(
         display.Javascript(
-            """
+            f"""
-            window._hub_tmp = new Promise((resolve, reject) => {
+            window._hub_tmp = new Promise((resolve, reject) => {{
                 const timeout = setTimeout(() => reject("Failed authenticating existing browser session"), 5000)
-                fetch("%s", {
+                fetch("{url}", {{
                     method: 'POST',
                     credentials: 'include'
-                })
+                }})
                     .then((response) => resolve(response.json()))
-                    .then((json) => {
+                    .then((json) => {{
                     clearTimeout(timeout);
-                    }).catch((err) => {
+                    }}).catch((err) => {{
                     clearTimeout(timeout);
                     reject(err);
-                });
+                }});
-            });
+            }});
             """
-            % url
         )
     )
     return output.eval_js("_hub_tmp")

ultralytics/models/fastsam/predict.py

@@ -100,7 +100,7 @@ class FastSAMPredictor(SegmentationPredictor):
                     texts = [texts]
                 crop_ims, filter_idx = [], []
                 for i, b in enumerate(result.boxes.xyxy.tolist()):
-                    x1, y1, x2, y2 = [int(x) for x in b]
+                    x1, y1, x2, y2 = (int(x) for x in b)
                     if masks[i].sum() <= 100:
                         filter_idx.append(i)
                         continue

ultralytics/models/sam/modules/blocks.py

@@ -35,7 +35,7 @@ class DropPath(nn.Module):
 
     def __init__(self, drop_prob=0.0, scale_by_keep=True):
         """Initialize DropPath module for stochastic depth regularization during training."""
-        super(DropPath, self).__init__()
+        super().__init__()
         self.drop_prob = drop_prob
         self.scale_by_keep = scale_by_keep
 

ultralytics/nn/modules/block.py

@@ -672,7 +672,7 @@ class CBLinear(nn.Module):
 
     def __init__(self, c1, c2s, k=1, s=1, p=None, g=1):
         """Initializes the CBLinear module, passing inputs unchanged."""
-        super(CBLinear, self).__init__()
+        super().__init__()
         self.c2s = c2s
         self.conv = nn.Conv2d(c1, sum(c2s), k, s, autopad(k, p), groups=g, bias=True)
 
@@ -686,7 +686,7 @@ class CBFuse(nn.Module):
 
     def __init__(self, idx):
         """Initializes CBFuse module with layer index for selective feature fusion."""
-        super(CBFuse, self).__init__()
+        super().__init__()
         self.idx = idx
 
     def forward(self, xs):

ultralytics/solutions/parking_management.py

@@ -210,7 +210,7 @@ class ParkingManagement:
         Args:
             json_file (str): file that have all parking slot points
         """
-        with open(json_file, "r") as f:
+        with open(json_file) as f:
             return json.load(f)
 
     def process_data(self, json_data, im0, boxes, clss):

ultralytics/utils/benchmarks.py

@@ -198,7 +198,7 @@ class RF100Benchmark:
         os.mkdir("ultralytics-benchmarks")
         safe_download("https://github.com/ultralytics/assets/releases/download/v0.0.0/datasets_links.txt")
 
-        with open(ds_link_txt, "r") as file:
+        with open(ds_link_txt) as file:
             for line in file:
                 try:
                     _, url, workspace, project, version = re.split("/+", line.strip())
@@ -222,7 +222,7 @@ class RF100Benchmark:
         Args:
             path (str): YAML file path.
         """
-        with open(path, "r") as file:
+        with open(path) as file:
             yaml_data = yaml.safe_load(file)
         yaml_data["train"] = "train/images"
         yaml_data["val"] = "valid/images"
@@ -242,7 +242,7 @@ class RF100Benchmark:
         skip_symbols = ["🚀", "⚠️", "💡", "❌"]
         with open(yaml_path) as stream:
             class_names = yaml.safe_load(stream)["names"]
-        with open(val_log_file, "r", encoding="utf-8") as f:
+        with open(val_log_file, encoding="utf-8") as f:
             lines = f.readlines()
             eval_lines = []
             for line in lines:

ultralytics/utils/metrics.py

@@ -460,7 +460,7 @@ def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names={}, on_plot=N
     else:
         ax.plot(px, py, linewidth=1, color="grey")  # plot(recall, precision)
 
-    ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean())
+    ax.plot(px, py.mean(1), linewidth=3, color="blue", label=f"all classes {ap[:, 0].mean():.3f} mAP@0.5")
     ax.set_xlabel("Recall")
     ax.set_ylabel("Precision")
     ax.set_xlim(0, 1)