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44 lines
2.2 KiB
44 lines
2.2 KiB
2 years ago
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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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