diff --git a/docs/build_docs.py b/docs/build_docs.py index 77e808a7dc..e342312bd6 100644 --- a/docs/build_docs.py +++ b/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: diff --git a/ultralytics/data/converter.py b/ultralytics/data/converter.py index 0dab1fe399..400e928bc0 100644 --- a/ultralytics/data/converter.py +++ b/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"]: diff --git a/ultralytics/data/dataset.py b/ultralytics/data/dataset.py index ca2fb2662c..7216fa006a 100644 --- a/ultralytics/data/dataset.py +++ b/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) diff --git a/ultralytics/data/split_dota.py b/ultralytics/data/split_dota.py index 1460a46d9a..f9acffe9bb 100644 --- a/ultralytics/data/split_dota.py +++ b/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") diff --git a/ultralytics/engine/predictor.py b/ultralytics/engine/predictor.py index c5e1d16dea..8ace18f611 100644 --- a/ultralytics/engine/predictor.py +++ b/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" diff --git a/ultralytics/engine/validator.py b/ultralytics/engine/validator.py index 5a91e5b8b5..655f2455ca 100644 --- a/ultralytics/engine/validator.py +++ b/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" - % tuple(self.speed.values()) + "Speed: {:.1f}ms preprocess, {:.1f}ms inference, {:.1f}ms loss, {:.1f}ms postprocess per image".format( + *tuple(self.speed.values()) + ) ) if self.args.save_json and self.jdict: with open(str(self.save_dir / "predictions.json"), "w") as f: diff --git a/ultralytics/hub/utils.py b/ultralytics/hub/utils.py index 6005609f81..2fc956fb34 100644 --- a/ultralytics/hub/utils.py +++ b/ultralytics/hub/utils.py @@ -55,23 +55,22 @@ def request_with_credentials(url: str) -> any: display.display( display.Javascript( - """ - window._hub_tmp = new Promise((resolve, reject) => { + f""" + 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") diff --git a/ultralytics/models/fastsam/predict.py b/ultralytics/models/fastsam/predict.py index 8095239e3c..0dce968a9b 100644 --- a/ultralytics/models/fastsam/predict.py +++ b/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 diff --git a/ultralytics/models/sam/modules/blocks.py b/ultralytics/models/sam/modules/blocks.py index 008185a29d..0615037467 100644 --- a/ultralytics/models/sam/modules/blocks.py +++ b/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 diff --git a/ultralytics/nn/modules/block.py b/ultralytics/nn/modules/block.py index ec236dff7f..07be2b8845 100644 --- a/ultralytics/nn/modules/block.py +++ b/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): diff --git a/ultralytics/solutions/parking_management.py b/ultralytics/solutions/parking_management.py index 7ee9f9a126..19a8ef1689 100644 --- a/ultralytics/solutions/parking_management.py +++ b/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): diff --git a/ultralytics/utils/benchmarks.py b/ultralytics/utils/benchmarks.py index c33a89064e..212c2f7bfe 100644 --- a/ultralytics/utils/benchmarks.py +++ b/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: diff --git a/ultralytics/utils/metrics.py b/ultralytics/utils/metrics.py index ec1295aefc..fc9862dd36 100644 --- a/ultralytics/utils/metrics.py +++ b/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)