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43 lines
2.2 KiB
43 lines
2.2 KiB
from ultralytics.yolo.utils import LOGGER |
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try: |
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from ray import tune |
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from ray.air import RunConfig, session # noqa |
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from ray.air.integrations.wandb import WandbLoggerCallback # noqa |
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from ray.tune.schedulers import ASHAScheduler # noqa |
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from ray.tune.schedulers import AsyncHyperBandScheduler as AHB # noqa |
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except ImportError: |
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LOGGER.info("Tuning hyperparameters requires ray/tune. Install using `pip install 'ray[tune]'`") |
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tune = None |
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default_space = { |
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# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'RMSProp']), |
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'lr0': tune.uniform(1e-5, 1e-1), |
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'lrf': tune.uniform(0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) |
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'momentum': tune.uniform(0.6, 0.98), # SGD momentum/Adam beta1 |
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'weight_decay': tune.uniform(0.0, 0.001), # optimizer weight decay 5e-4 |
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'warmup_epochs': tune.uniform(0.0, 5.0), # warmup epochs (fractions ok) |
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'warmup_momentum': tune.uniform(0.0, 0.95), # warmup initial momentum |
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'box': tune.uniform(0.02, 0.2), # box loss gain |
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'cls': tune.uniform(0.2, 4.0), # cls loss gain (scale with pixels) |
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'fl_gamma': tune.uniform(0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) |
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'hsv_h': tune.uniform(0.0, 0.1), # image HSV-Hue augmentation (fraction) |
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'hsv_s': tune.uniform(0.0, 0.9), # image HSV-Saturation augmentation (fraction) |
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'hsv_v': tune.uniform(0.0, 0.9), # image HSV-Value augmentation (fraction) |
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'degrees': tune.uniform(0.0, 45.0), # image rotation (+/- deg) |
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'translate': tune.uniform(0.0, 0.9), # image translation (+/- fraction) |
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'scale': tune.uniform(0.0, 0.9), # image scale (+/- gain) |
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'shear': tune.uniform(0.0, 10.0), # image shear (+/- deg) |
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'perspective': tune.uniform(0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 |
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'flipud': tune.uniform(0.0, 1.0), # image flip up-down (probability) |
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'fliplr': tune.uniform(0.0, 1.0), # image flip left-right (probability) |
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'mosaic': tune.uniform(0.0, 1.0), # image mixup (probability) |
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'mixup': tune.uniform(0.0, 1.0), # image mixup (probability) |
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'copy_paste': tune.uniform(0.0, 1.0)} # segment copy-paste (probability) |
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task_metric_map = { |
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'detect': 'metrics/mAP50-95(B)', |
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'segment': 'metrics/mAP50-95(M)', |
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'classify': 'top1_acc', |
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'pose': None}
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