Apply Ruff 0.9.0 (#18622)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
GitHub
parent
cc1e77138c
commit
3902e740cf
22 changed files with 69 additions and 65 deletions
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@ -357,7 +357,7 @@ class Exporter:
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)
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self.pretty_name = Path(self.model.yaml.get("yaml_file", self.file)).stem.replace("yolo", "YOLO")
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data = model.args["data"] if hasattr(model, "args") and isinstance(model.args, dict) else ""
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description = f'Ultralytics {self.pretty_name} model {f"trained on {data}" if data else ""}'
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description = f"Ultralytics {self.pretty_name} model {f'trained on {data}' if data else ''}"
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self.metadata = {
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"description": description,
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"author": "Ultralytics",
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@ -377,7 +377,7 @@ class Exporter:
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LOGGER.info(
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f"\n{colorstr('PyTorch:')} starting from '{file}' with input shape {tuple(im.shape)} BCHW and "
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f'output shape(s) {self.output_shape} ({file_size(file):.1f} MB)'
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f"output shape(s) {self.output_shape} ({file_size(file):.1f} MB)"
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)
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# Exports
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@ -427,11 +427,11 @@ class Exporter:
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predict_data = f"data={data}" if model.task == "segment" and fmt == "pb" else ""
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q = "int8" if self.args.int8 else "half" if self.args.half else "" # quantization
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LOGGER.info(
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f'\nExport complete ({time.time() - t:.1f}s)'
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f"\nExport complete ({time.time() - t:.1f}s)"
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f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
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f'\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {q} {predict_data}'
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f'\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={data} {q} {s}'
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f'\nVisualize: https://netron.app'
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f"\nPredict: yolo predict task={model.task} model={f} imgsz={imgsz} {q} {predict_data}"
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f"\nValidate: yolo val task={model.task} model={f} imgsz={imgsz} data={data} {q} {s}"
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f"\nVisualize: https://netron.app"
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)
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self.run_callbacks("on_export_end")
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@ -680,16 +680,16 @@ class Exporter:
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shutil.rmtree(unzip_dir) # delete unzip dir
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ncnn_args = [
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f'ncnnparam={f / "model.ncnn.param"}',
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f'ncnnbin={f / "model.ncnn.bin"}',
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f'ncnnpy={f / "model_ncnn.py"}',
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f"ncnnparam={f / 'model.ncnn.param'}",
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f"ncnnbin={f / 'model.ncnn.bin'}",
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f"ncnnpy={f / 'model_ncnn.py'}",
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]
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pnnx_args = [
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f'pnnxparam={f / "model.pnnx.param"}',
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f'pnnxbin={f / "model.pnnx.bin"}',
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f'pnnxpy={f / "model_pnnx.py"}',
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f'pnnxonnx={f / "model.pnnx.onnx"}',
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f"pnnxparam={f / 'model.pnnx.param'}",
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f"pnnxbin={f / 'model.pnnx.bin'}",
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f"pnnxpy={f / 'model_pnnx.py'}",
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f"pnnxonnx={f / 'model.pnnx.onnx'}",
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]
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cmd = [
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@ -1139,7 +1139,9 @@ class Exporter:
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def export_imx(self, prefix=colorstr("IMX:")):
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"""YOLO IMX export."""
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gptq = False
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assert LINUX, "export only supported on Linux. See https://developer.aitrios.sony-semicon.com/en/raspberrypi-ai-camera/documentation/imx500-converter"
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assert LINUX, (
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"export only supported on Linux. See https://developer.aitrios.sony-semicon.com/en/raspberrypi-ai-camera/documentation/imx500-converter"
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)
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if getattr(self.model, "end2end", False):
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raise ValueError("IMX export is not supported for end2end models.")
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if "C2f" not in self.model.__str__():
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@ -367,7 +367,7 @@ class BasePredictor:
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# Save videos and streams
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if self.dataset.mode in {"stream", "video"}:
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fps = self.dataset.fps if self.dataset.mode == "video" else 30
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frames_path = f'{save_path.split(".", 1)[0]}_frames/'
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frames_path = f"{save_path.split('.', 1)[0]}_frames/"
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if save_path not in self.vid_writer: # new video
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if self.args.save_frames:
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Path(frames_path).mkdir(parents=True, exist_ok=True)
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@ -196,7 +196,7 @@ class BaseTrainer:
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# Command
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cmd, file = generate_ddp_command(world_size, self)
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try:
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LOGGER.info(f'{colorstr("DDP:")} debug command {" ".join(cmd)}')
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LOGGER.info(f"{colorstr('DDP:')} debug command {' '.join(cmd)}")
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subprocess.run(cmd, check=True)
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except Exception as e:
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raise e
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@ -329,10 +329,10 @@ class BaseTrainer:
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self.train_time_start = time.time()
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self.run_callbacks("on_train_start")
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LOGGER.info(
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f'Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n'
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f'Using {self.train_loader.num_workers * (world_size or 1)} dataloader workers\n'
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f"Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n"
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f"Using {self.train_loader.num_workers * (world_size or 1)} dataloader workers\n"
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f"Logging results to {colorstr('bold', self.save_dir)}\n"
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f'Starting training for ' + (f"{self.args.time} hours..." if self.args.time else f"{self.epochs} epochs...")
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f"Starting training for " + (f"{self.args.time} hours..." if self.args.time else f"{self.epochs} epochs...")
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)
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if self.args.close_mosaic:
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base_idx = (self.epochs - self.args.close_mosaic) * nb
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@ -814,6 +814,6 @@ class BaseTrainer:
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optimizer.add_param_group({"params": g[1], "weight_decay": 0.0}) # add g1 (BatchNorm2d weights)
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LOGGER.info(
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f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}, momentum={momentum}) with parameter groups "
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f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias(decay=0.0)'
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f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias(decay=0.0)"
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)
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return optimizer
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@ -224,12 +224,12 @@ class Tuner:
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# Save and print tune results
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header = (
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f'{self.prefix}{i + 1}/{iterations} iterations complete ✅ ({time.time() - t0:.2f}s)\n'
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f'{self.prefix}Results saved to {colorstr("bold", self.tune_dir)}\n'
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f'{self.prefix}Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n'
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f'{self.prefix}Best fitness metrics are {best_metrics}\n'
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f'{self.prefix}Best fitness model is {best_save_dir}\n'
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f'{self.prefix}Best fitness hyperparameters are printed below.\n'
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f"{self.prefix}{i + 1}/{iterations} iterations complete ✅ ({time.time() - t0:.2f}s)\n"
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f"{self.prefix}Results saved to {colorstr('bold', self.tune_dir)}\n"
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f"{self.prefix}Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n"
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f"{self.prefix}Best fitness metrics are {best_metrics}\n"
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f"{self.prefix}Best fitness model is {best_save_dir}\n"
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f"{self.prefix}Best fitness hyperparameters are printed below.\n"
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)
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LOGGER.info("\n" + header)
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data = {k: float(x[best_idx, i + 1]) for i, k in enumerate(self.space.keys())}
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