退购1.1定位算法
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jiajie555
144
ultralytics/yolo/utils/benchmarks.py
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144
ultralytics/yolo/utils/benchmarks.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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"""
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Benchmark a YOLO model formats for speed and accuracy
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Usage:
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from ultralytics.yolo.utils.benchmarks import run_benchmarks
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run_benchmarks(model='yolov8n.pt', imgsz=160)
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Format | `format=argument` | Model
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--- | --- | ---
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PyTorch | - | yolov8n.pt
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TorchScript | `torchscript` | yolov8n.torchscript
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ONNX | `onnx` | yolov8n.onnx
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OpenVINO | `openvino` | yolov8n_openvino_model/
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TensorRT | `engine` | yolov8n.engine
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CoreML | `coreml` | yolov8n.mlmodel
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TensorFlow SavedModel | `saved_model` | yolov8n_saved_model/
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TensorFlow GraphDef | `pb` | yolov8n.pb
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TensorFlow Lite | `tflite` | yolov8n.tflite
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TensorFlow Edge TPU | `edgetpu` | yolov8n_edgetpu.tflite
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TensorFlow.js | `tfjs` | yolov8n_web_model/
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PaddlePaddle | `paddle` | yolov8n_paddle_model/
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"""
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import platform
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import time
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from pathlib import Path
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from ultralytics import YOLO
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from ultralytics.yolo.engine.exporter import export_formats
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from ultralytics.yolo.utils import LINUX, LOGGER, MACOS, ROOT, SETTINGS
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from ultralytics.yolo.utils.checks import check_yolo
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from ultralytics.yolo.utils.downloads import download
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from ultralytics.yolo.utils.files import file_size
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from ultralytics.yolo.utils.torch_utils import select_device
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def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
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imgsz=160,
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half=False,
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int8=False,
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device='cpu',
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hard_fail=False):
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"""
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Benchmark a YOLO model across different formats for speed and accuracy.
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Args:
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model (Union[str, Path], optional): Path to the model file or directory. Default is
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Path(SETTINGS['weights_dir']) / 'yolov8n.pt'.
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imgsz (int, optional): Image size for the benchmark. Default is 160.
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half (bool, optional): Use half-precision for the model if True. Default is False.
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int8 (bool, optional): Use int8-precision for the model if True. Default is False.
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device (str, optional): Device to run the benchmark on, either 'cpu' or 'cuda'. Default is 'cpu'.
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hard_fail (Union[bool, float], optional): If True or a float, assert benchmarks pass with given metric.
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Default is False.
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Returns:
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df (pandas.DataFrame): A pandas DataFrame with benchmark results for each format, including file size,
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metric, and inference time.
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"""
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import pandas as pd
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pd.options.display.max_columns = 10
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pd.options.display.width = 120
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device = select_device(device, verbose=False)
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if isinstance(model, (str, Path)):
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model = YOLO(model)
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y = []
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t0 = time.time()
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for i, (name, format, suffix, cpu, gpu) in export_formats().iterrows(): # index, (name, format, suffix, CPU, GPU)
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emoji, filename = '❌', None # export defaults
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try:
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assert i != 9 or LINUX, 'Edge TPU export only supported on Linux'
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if i == 10:
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assert MACOS or LINUX, 'TF.js export only supported on macOS and Linux'
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if 'cpu' in device.type:
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assert cpu, 'inference not supported on CPU'
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if 'cuda' in device.type:
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assert gpu, 'inference not supported on GPU'
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# Export
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if format == '-':
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filename = model.ckpt_path or model.cfg
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export = model # PyTorch format
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else:
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filename = model.export(imgsz=imgsz, format=format, half=half, int8=int8, device=device) # all others
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export = YOLO(filename, task=model.task)
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assert suffix in str(filename), 'export failed'
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emoji = '❎' # indicates export succeeded
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# Predict
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assert i not in (9, 10), 'inference not supported' # Edge TPU and TF.js are unsupported
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assert i != 5 or platform.system() == 'Darwin', 'inference only supported on macOS>=10.13' # CoreML
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if not (ROOT / 'assets/bus.jpg').exists():
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download(url='https://ultralytics.com/images/bus.jpg', dir=ROOT / 'assets')
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export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half)
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# Validate
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if model.task == 'detect':
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data, key = 'coco8.yaml', 'metrics/mAP50-95(B)'
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elif model.task == 'segment':
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data, key = 'coco8-seg.yaml', 'metrics/mAP50-95(M)'
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elif model.task == 'classify':
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data, key = 'imagenet100', 'metrics/accuracy_top5'
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elif model.task == 'pose':
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data, key = 'coco8-pose.yaml', 'metrics/mAP50-95(P)'
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results = export.val(data=data,
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batch=1,
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imgsz=imgsz,
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plots=False,
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device=device,
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half=half,
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int8=int8,
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verbose=False)
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metric, speed = results.results_dict[key], results.speed['inference']
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y.append([name, '✅', round(file_size(filename), 1), round(metric, 4), round(speed, 2)])
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except Exception as e:
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if hard_fail:
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assert type(e) is AssertionError, f'Benchmark hard_fail for {name}: {e}'
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LOGGER.warning(f'ERROR ❌️ Benchmark failure for {name}: {e}')
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y.append([name, emoji, round(file_size(filename), 1), None, None]) # mAP, t_inference
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# Print results
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check_yolo(device=device) # print system info
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df = pd.DataFrame(y, columns=['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)'])
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name = Path(model.ckpt_path).name
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s = f'\nBenchmarks complete for {name} on {data} at imgsz={imgsz} ({time.time() - t0:.2f}s)\n{df}\n'
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LOGGER.info(s)
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with open('benchmarks.log', 'a', errors='ignore', encoding='utf-8') as f:
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f.write(s)
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if hard_fail and isinstance(hard_fail, float):
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metrics = df[key].array # values to compare to floor
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floor = hard_fail # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
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assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: one or more metric(s) < floor {floor}'
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return df
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if __name__ == '__main__':
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benchmark()
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