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# Ultralytics YOLO 🚀, GPL-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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import pandas as pd |
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import torch |
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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 LOGGER, SETTINGS |
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from ultralytics.yolo.utils.checks import check_yolo |
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from ultralytics.yolo.utils.files import file_size |
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def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', |
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imgsz=640, |
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half=False, |
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device='cpu', |
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hard_fail=False): |
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device = torch.device(int(device) if device.isnumeric() else device) |
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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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try: |
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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 '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 |
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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, device=device) # all others |
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export = YOLO(filename) |
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assert suffix in str(filename), 'export failed' |
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# Validate |
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if model.task == 'detect': |
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data, key = 'coco128.yaml', 'metrics/mAP50-95(B)' |
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elif model.task == 'segment': |
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data, key = 'coco128-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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results = export.val(data=data, batch=1, imgsz=imgsz, plots=False, device=device, half=half, 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, '❌', None, None, None]) # mAP, t_inference |
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# Print results |
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LOGGER.info('\n') |
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check_yolo(device=device) # print system info |
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c = ['Format', 'Status❔', 'Size (MB)', key, 'Inference time (ms/im)'] if map else ['Format', 'Export', '', ''] |
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df = pd.DataFrame(y, columns=c) |
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LOGGER.info(f'\nBenchmarks complete for {Path(model.ckpt_path).name} on {data} at imgsz={imgsz} ' |
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f'({time.time() - t0:.2f}s)') |
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LOGGER.info(str(df if map else df.iloc[:, :2])) |
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if hard_fail and isinstance(hard_fail, str): |
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metrics = df[key].array # values to compare to floor |
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floor = eval(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: metric < floor {floor}' |
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if __name__ == '__main__': |
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run_benchmarks()
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