394 lines
18 KiB
Python
394 lines
18 KiB
Python
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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.utils.benchmarks import ProfileModels, benchmark
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ProfileModels(['yolov8n.yaml', 'yolov8s.yaml']).profile()
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benchmark(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.mlpackage
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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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ncnn | `ncnn` | yolov8n_ncnn_model/
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"""
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import glob
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import platform
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import sys
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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 ultralytics import YOLO
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from ultralytics.cfg import TASK2DATA, TASK2METRIC
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from ultralytics.engine.exporter import export_formats
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from ultralytics.utils import ASSETS, LINUX, LOGGER, MACOS, TQDM, WEIGHTS_DIR
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from ultralytics.utils.checks import check_requirements, check_yolo
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from ultralytics.utils.files import file_size
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from ultralytics.utils.torch_utils import select_device
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def benchmark(model=WEIGHTS_DIR / 'yolov8n.pt',
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data=None,
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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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verbose=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 (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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data (str, optional): Dataset to evaluate on, inherited from TASK2DATA if not passed. Default is None.
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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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verbose (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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Example:
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```python
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from ultralytics.utils.benchmarks import benchmark
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benchmark(model='yolov8n.pt', imgsz=640)
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```
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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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elif i == 11:
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assert sys.version_info < (3, 11), 'PaddlePaddle export only supported on Python<=3.10'
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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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exported_model = 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, verbose=False)
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exported_model = 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 model.task != 'pose' or i != 7, 'GraphDef Pose inference is not supported'
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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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exported_model.predict(ASSETS / 'bus.jpg', imgsz=imgsz, device=device, half=half)
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# Validate
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data = data or TASK2DATA[model.task] # task to dataset, i.e. coco8.yaml for task=detect
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key = TASK2METRIC[model.task] # task to metric, i.e. metrics/mAP50-95(B) for task=detect
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results = exported_model.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 verbose:
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assert type(e) is AssertionError, f'Benchmark failure 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 verbose and isinstance(verbose, float):
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metrics = df[key].array # values to compare to floor
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floor = verbose # 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'Benchmark failure: 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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Example:
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```python
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from ultralytics.utils.benchmarks import ProfileModels
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ProfileModels(['yolov8n.yaml', 'yolov8s.yaml'], imgsz=640).profile()
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```
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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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half=True,
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trt=True,
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device=None):
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"""
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Initialize the ProfileModels class for profiling models.
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Args:
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paths (list): List of paths of the models to be profiled.
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num_timed_runs (int, optional): Number of timed runs for the profiling. Default is 100.
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num_warmup_runs (int, optional): Number of warmup runs before the actual profiling starts. Default is 10.
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min_time (float, optional): Minimum time in seconds for profiling a model. Default is 60.
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imgsz (int, optional): Size of the image used during profiling. Default is 640.
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half (bool, optional): Flag to indicate whether to use half-precision floating point for profiling. Default is True.
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trt (bool, optional): Flag to indicate whether to profile using TensorRT. Default is True.
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device (torch.device, optional): Device used for profiling. If None, it is determined automatically. Default is None.
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"""
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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.half = half
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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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"""Logs the benchmarking results of a model, checks metrics against floor and returns the results."""
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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', '.yml'):
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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',
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half=self.half,
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imgsz=self.imgsz,
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device=self.device,
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verbose=False)
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onnx_file = model.export(format='onnx',
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half=self.half,
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imgsz=self.imgsz,
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simplify=True,
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device=self.device,
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verbose=False)
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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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"""Returns a list of paths for all relevant model files given by the user."""
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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', '.yml'}: # 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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"""Retrieves the information including number of layers, parameters, gradients and FLOPs for an ONNX model
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file.
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"""
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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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"""Applies an iterative sigma clipping algorithm to the given data times number of iterations."""
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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, eps: float = 1e-3):
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"""Profiles the TensorRT model, measuring average run time and standard deviation among runs."""
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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, imgsz=self.imgsz, 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 + eps) * 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, imgsz=self.imgsz, 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, eps: float = 1e-3):
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"""Profiles an ONNX model by executing it multiple times and returns the mean and standard deviation of run
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times.
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"""
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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
|
||
|
|
elif 'float' in input_type:
|
||
|
|
input_dtype = np.float32
|
||
|
|
elif 'double' in input_type:
|
||
|
|
input_dtype = np.float64
|
||
|
|
elif 'int64' in input_type:
|
||
|
|
input_dtype = np.int64
|
||
|
|
elif 'int32' in input_type:
|
||
|
|
input_dtype = np.int32
|
||
|
|
else:
|
||
|
|
raise ValueError(f'Unsupported ONNX datatype {input_type}')
|
||
|
|
|
||
|
|
input_data = np.random.rand(*input_tensor.shape).astype(input_dtype)
|
||
|
|
input_name = input_tensor.name
|
||
|
|
output_name = sess.get_outputs()[0].name
|
||
|
|
|
||
|
|
# Warmup runs
|
||
|
|
elapsed = 0.0
|
||
|
|
for _ in range(3):
|
||
|
|
start_time = time.time()
|
||
|
|
for _ in range(self.num_warmup_runs):
|
||
|
|
sess.run([output_name], {input_name: input_data})
|
||
|
|
elapsed = time.time() - start_time
|
||
|
|
|
||
|
|
# Compute number of runs as higher of min_time or num_timed_runs
|
||
|
|
num_runs = max(round(self.min_time / (elapsed + eps) * self.num_warmup_runs), self.num_timed_runs)
|
||
|
|
|
||
|
|
# Timed runs
|
||
|
|
run_times = []
|
||
|
|
for _ in TQDM(range(num_runs), desc=onnx_file):
|
||
|
|
start_time = time.time()
|
||
|
|
sess.run([output_name], {input_name: input_data})
|
||
|
|
run_times.append((time.time() - start_time) * 1000) # Convert to milliseconds
|
||
|
|
|
||
|
|
run_times = self.iterative_sigma_clipping(np.array(run_times), sigma=2, max_iters=5) # sigma clipping
|
||
|
|
return np.mean(run_times), np.std(run_times)
|
||
|
|
|
||
|
|
def generate_table_row(self, model_name, t_onnx, t_engine, model_info):
|
||
|
|
"""Generates a formatted string for a table row that includes model performance and metric details."""
|
||
|
|
layers, params, gradients, flops = model_info
|
||
|
|
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} |'
|
||
|
|
|
||
|
|
def generate_results_dict(self, model_name, t_onnx, t_engine, model_info):
|
||
|
|
"""Generates a dictionary of model details including name, parameters, GFLOPS and speed metrics."""
|
||
|
|
layers, params, gradients, flops = model_info
|
||
|
|
return {
|
||
|
|
'model/name': model_name,
|
||
|
|
'model/parameters': params,
|
||
|
|
'model/GFLOPs': round(flops, 3),
|
||
|
|
'model/speed_ONNX(ms)': round(t_onnx[0], 3),
|
||
|
|
'model/speed_TensorRT(ms)': round(t_engine[0], 3)}
|
||
|
|
|
||
|
|
def print_table(self, table_rows):
|
||
|
|
"""Formats and prints a comparison table for different models with given statistics and performance data."""
|
||
|
|
gpu = torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'GPU'
|
||
|
|
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) |'
|
||
|
|
separator = '|-------------|---------------------|--------------------|------------------------------|-----------------------------------|------------------|-----------------|'
|
||
|
|
|
||
|
|
print(f'\n\n{header}')
|
||
|
|
print(separator)
|
||
|
|
for row in table_rows:
|
||
|
|
print(row)
|