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ultralytics 8.0.43 optimized Results class and fixes (#1069)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Alexander Duda <Alexander.Duda@me.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher 2023-02-21 20:37:59 +01:00 • committed by GitHub
parent f2a7a29e53
commit fe61018975
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22 changed files with 180 additions and 102 deletions
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17
ultralytics/yolo/utils/benchmarks.py
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@ -27,13 +27,14 @@ import time
from pathlib import Path
import pandas as pd
import torch
from ultralytics import YOLO
from ultralytics.yolo.engine.exporter import export_formats
from ultralytics.yolo.utils import LOGGER, SETTINGS
from ultralytics.yolo.utils import LOGGER, ROOT, SETTINGS
from ultralytics.yolo.utils.checks import check_yolo
from ultralytics.yolo.utils.downloads import download
from ultralytics.yolo.utils.files import file_size
from ultralytics.yolo.utils.torch_utils import select_device
def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
@ -41,8 +42,9 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
half=False,
device='cpu',
hard_fail=False):
device = torch.device(int(device) if device.isnumeric() else device)
model = YOLO(model)
device = select_device(device, verbose=False)
if isinstance(model, (str, Path)):
model = YOLO(model)
y = []
t0 = time.time()
@ -65,6 +67,11 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
export = YOLO(filename)
assert suffix in str(filename), 'export failed'
# Predict
if not (ROOT / 'assets/bus.jpg').exists():
download(url='https://ultralytics.com/images/bus.jpg', dir=ROOT / 'assets')
export.predict(ROOT / 'assets/bus.jpg', imgsz=imgsz, device=device, half=half) # test
# Validate
if model.task == 'detect':
data, key = 'coco128.yaml', 'metrics/mAP50-95(B)'
@ -96,6 +103,8 @@ def run_benchmarks(model=Path(SETTINGS['weights_dir']) / 'yolov8n.pt',
floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n
assert all(x > floor for x in metrics if pd.notna(x)), f'HARD FAIL: metric < floor {floor}'
return df
if __name__ == '__main__':
run_benchmarks()