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ultralytics 8.0.81 single-line docstring updates (#2061)

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Glenn Jocher 2023-04-17 00:45:36 +02:00 • committed by GitHub
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36
ultralytics/yolo/utils/benchmarks.py
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@ -35,7 +35,30 @@ from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.torch_utils import select_device
def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, half=False, device='cpu', hard_fail=False):
def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
imgsz=160,
half=False,
int8=False,
device='cpu',
hard_fail=False):
"""
Benchmark a YOLO model across different formats for speed and accuracy.
Args:
model (Union[str, Path], optional): Path to the model file or directory. Default is
Path(SETTINGS['weights_dir']) / 'yolov8n.pt'.
imgsz (int, optional): Image size for the benchmark. Default is 160.
half (bool, optional): Use half-precision for the model if True. Default is False.
int8 (bool, optional): Use int8-precision for the model if True. Default is False.
device (str, optional): Device to run the benchmark on, either 'cpu' or 'cuda'. Default is 'cpu'.
hard_fail (Union[bool, float], optional): If True or a float, assert benchmarks pass with given metric.
Default is False.
Returns:
df (pandas.DataFrame): A pandas DataFrame with benchmark results for each format, including file size,
metric, and inference time.
"""
import pandas as pd
pd.options.display.max_columns = 10
pd.options.display.width = 120
@ -61,7 +84,7 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, hal
filename = model.ckpt_path or model.cfg
export = model # PyTorch format
else:
filename = model.export(imgsz=imgsz, format=format, half=half, device=device) # all others
filename = model.export(imgsz=imgsz, format=format, half=half, int8=int8, device=device) # all others
export = YOLO(filename, task=model.task)
assert suffix in str(filename), 'export failed'
emoji = '❎' # indicates export succeeded
@ -83,7 +106,14 @@ def benchmark(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt', imgsz=160, hal
elif model.task == 'pose':
data, key = 'coco8-pose.yaml', 'metrics/mAP50-95(P)'
results = export.val(data=data, batch=1, imgsz=imgsz, plots=False, device=device, half=half, verbose=False)
results = export.val(data=data,
batch=1,
imgsz=imgsz,
plots=False,
device=device,
half=half,
int8=int8,
verbose=False)
metric, speed = results.results_dict[key], results.speed['inference']
y.append([name, '✅', round(file_size(filename), 1), round(metric, 4), round(speed, 2)])
except Exception as e: