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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 ProfileModels, benchmark |
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ProfileModels(['yolov8n.yaml', 'yolov8s.yaml']).profile() |
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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 glob |
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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 numpy as np |
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import torch.cuda |
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from tqdm import tqdm |
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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_requirements, 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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class ProfileModels: |
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""" |
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ProfileModels class for profiling different models on ONNX and TensorRT. |
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This class profiles the performance of different models, provided their paths. The profiling includes parameters such as |
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model speed and FLOPs. |
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Attributes: |
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paths (list): Paths of the models to profile. |
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num_timed_runs (int): Number of timed runs for the profiling. Default is 100. |
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num_warmup_runs (int): Number of warmup runs before profiling. Default is 10. |
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min_time (float): Minimum number of seconds to profile for. Default is 60. |
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imgsz (int): Image size used in the models. Default is 640. |
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Methods: |
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profile(): Profiles the models and prints the result. |
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""" |
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def __init__(self, |
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paths: list, |
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num_timed_runs=100, |
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num_warmup_runs=10, |
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min_time=60, |
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imgsz=640, |
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trt=True, |
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device=None): |
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self.paths = paths |
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self.num_timed_runs = num_timed_runs |
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self.num_warmup_runs = num_warmup_runs |
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self.min_time = min_time |
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self.imgsz = imgsz |
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self.trt = trt # run TensorRT profiling |
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self.device = device or torch.device(0 if torch.cuda.is_available() else 'cpu') |
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def profile(self): |
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files = self.get_files() |
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if not files: |
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print('No matching *.pt or *.onnx files found.') |
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return |
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table_rows = [] |
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output = [] |
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for file in files: |
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engine_file = file.with_suffix('.engine') |
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if file.suffix in ('.pt', '.yaml'): |
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model = YOLO(str(file)) |
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model.fuse() # to report correct params and GFLOPs in model.info() |
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model_info = model.info() |
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if self.trt and self.device.type != 'cpu' and not engine_file.is_file(): |
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engine_file = model.export(format='engine', half=True, imgsz=self.imgsz, device=self.device) |
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onnx_file = model.export(format='onnx', half=True, imgsz=self.imgsz, simplify=True, device=self.device) |
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elif file.suffix == '.onnx': |
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model_info = self.get_onnx_model_info(file) |
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onnx_file = file |
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else: |
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continue |
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t_engine = self.profile_tensorrt_model(str(engine_file)) |
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t_onnx = self.profile_onnx_model(str(onnx_file)) |
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table_rows.append(self.generate_table_row(file.stem, t_onnx, t_engine, model_info)) |
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output.append(self.generate_results_dict(file.stem, t_onnx, t_engine, model_info)) |
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self.print_table(table_rows) |
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return output |
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def get_files(self): |
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files = [] |
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for path in self.paths: |
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path = Path(path) |
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if path.is_dir(): |
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extensions = ['*.pt', '*.onnx', '*.yaml'] |
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files.extend([file for ext in extensions for file in glob.glob(str(path / ext))]) |
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elif path.suffix in {'.pt', '.yaml'}: # add non-existing |
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files.append(str(path)) |
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else: |
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files.extend(glob.glob(str(path))) |
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print(f'Profiling: {sorted(files)}') |
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return [Path(file) for file in sorted(files)] |
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def get_onnx_model_info(self, onnx_file: str): |
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# return (num_layers, num_params, num_gradients, num_flops) |
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return 0.0, 0.0, 0.0, 0.0 |
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def iterative_sigma_clipping(self, data, sigma=2, max_iters=3): |
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data = np.array(data) |
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for _ in range(max_iters): |
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mean, std = np.mean(data), np.std(data) |
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clipped_data = data[(data > mean - sigma * std) & (data < mean + sigma * std)] |
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if len(clipped_data) == len(data): |
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break |
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data = clipped_data |
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return data |
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def profile_tensorrt_model(self, engine_file: str): |
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if not self.trt or not Path(engine_file).is_file(): |
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return 0.0, 0.0 |
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# Model and input |
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model = YOLO(engine_file) |
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input_data = np.random.rand(self.imgsz, self.imgsz, 3).astype(np.float32) # must be FP32 |
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# Warmup runs |
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elapsed = 0.0 |
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for _ in range(3): |
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start_time = time.time() |
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for _ in range(self.num_warmup_runs): |
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model(input_data, verbose=False) |
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elapsed = time.time() - start_time |
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# Compute number of runs as higher of min_time or num_timed_runs |
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num_runs = max(round(self.min_time / elapsed * self.num_warmup_runs), self.num_timed_runs * 50) |
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# Timed runs |
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run_times = [] |
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for _ in tqdm(range(num_runs), desc=engine_file): |
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results = model(input_data, verbose=False) |
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run_times.append(results[0].speed['inference']) # Convert to milliseconds |
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run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=3) # sigma clipping |
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return np.mean(run_times), np.std(run_times) |
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def profile_onnx_model(self, onnx_file: str): |
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check_requirements('onnxruntime') |
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import onnxruntime as ort |
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# Session with either 'TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider' |
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sess_options = ort.SessionOptions() |
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sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL |
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sess_options.intra_op_num_threads = 8 # Limit the number of threads |
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sess = ort.InferenceSession(onnx_file, sess_options, providers=['CPUExecutionProvider']) |
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input_tensor = sess.get_inputs()[0] |
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input_type = input_tensor.type |
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# Mapping ONNX datatype to numpy datatype |
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if 'float16' in input_type: |
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input_dtype = np.float16 |
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elif 'float' in input_type: |
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input_dtype = np.float32 |
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elif 'double' in input_type: |
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input_dtype = np.float64 |
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elif 'int64' in input_type: |
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input_dtype = np.int64 |
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elif 'int32' in input_type: |
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input_dtype = np.int32 |
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else: |
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raise ValueError(f'Unsupported ONNX datatype {input_type}') |
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input_data = np.random.rand(*input_tensor.shape).astype(input_dtype) |
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input_name = input_tensor.name |
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output_name = sess.get_outputs()[0].name |
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# Warmup runs |
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elapsed = 0.0 |
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for _ in range(3): |
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start_time = time.time() |
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for _ in range(self.num_warmup_runs): |
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sess.run([output_name], {input_name: input_data}) |
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elapsed = time.time() - start_time |
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# Compute number of runs as higher of min_time or num_timed_runs |
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num_runs = max(round(self.min_time / elapsed * self.num_warmup_runs), self.num_timed_runs) |
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# Timed runs |
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run_times = [] |
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for _ in tqdm(range(num_runs), desc=onnx_file): |
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start_time = time.time() |
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sess.run([output_name], {input_name: input_data}) |
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run_times.append((time.time() - start_time) * 1000) # Convert to milliseconds |
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run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=5) # sigma clipping |
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return np.mean(run_times), np.std(run_times) |
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def generate_table_row(self, model_name, t_onnx, t_engine, model_info): |
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layers, params, gradients, flops = model_info |
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return f'| {model_name:18s} | {self.imgsz} | - | {t_onnx[0]:.2f} ± {t_onnx[1]:.2f} ms | {t_engine[0]:.2f} ± {t_engine[1]:.2f} ms | {params / 1e6:.1f} | {flops:.1f} |' |
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def generate_results_dict(self, model_name, t_onnx, t_engine, model_info): |
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layers, params, gradients, flops = model_info |
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return { |
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'model/name': model_name, |
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'model/parameters': params, |
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'model/GFLOPs': round(flops, 3), |
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'model/speed_ONNX(ms)': round(t_onnx[0], 3), |
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'model/speed_TensorRT(ms)': round(t_engine[0], 3)} |
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def print_table(self, table_rows): |
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gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'GPU' |
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header = f'| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>{gpu} TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |' |
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separator = '|-------------|---------------------|--------------------|------------------------------|-----------------------------------|------------------|-----------------|' |
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print(f'\n\n{header}') |
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print(separator) |
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for row in table_rows: |
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print(row) |
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if __name__ == '__main__': |
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# Benchmark all export formats |
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benchmark() |
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# Profiling models on ONNX and TensorRT |
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ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'])
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