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Update val.py speed and study tasks (#5608)

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Accepts all arguments now by default resolving https://github.com/ultralytics/yolov5/issues/5600
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Glenn Jocher 2021-11-10 16:48:38 +01:00 committed by GitHub
parent 61c50199a2
commit 30bc089cbb
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1 changed files with 20 additions and 19 deletions
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val.py
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@ -339,22 +339,23 @@ def main(opt):
LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} >> 0.001 will produce invalid mAP values.')
run(**vars(opt))
elif opt.task == 'speed': # speed benchmarks
else:
weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
opt.half = True # FP16 for fastest results
if opt.task == 'speed': # speed benchmarks
# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=opt.imgsz, conf_thres=.25, iou_thres=.45,
device=opt.device, save_json=False, plots=False)
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
for opt.weights in weights:
run(**vars(opt), plots=False)
elif opt.task == 'study': # run over a range of settings and save/plot
elif opt.task == 'study': # speed vs mAP benchmarks
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
x = list(range(256, 1536 + 128, 128)) # x axis (image sizes)
for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to
y = [] # y axis
for i in x: # img-size
LOGGER.info(f'\nRunning {f} point {i}...')
r, _, t = run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=i, conf_thres=opt.conf_thres,
iou_thres=opt.iou_thres, device=opt.device, save_json=opt.save_json, plots=False)
for opt.weights in weights:
f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to
x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
for opt.imgsz in x: # img-size
LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')
r, _, t = run(**vars(opt), plots=False)
y.append(r + t) # results and times
np.savetxt(f, y, fmt='%10.4g') # save
os.system('zip -r study.zip study_*.txt')