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# Ultralytics YOLO 🚀, AGPL-3.0 license
from ultralytics.yolo.cfg import TASK2DATA, TASK2METRIC
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, LOGGER, NUM_THREADS
def run_ray_tune(model,
space: dict = None,
grace_period: int = 10,
gpu_per_trial: int = None,
max_samples: int = 10,
**train_args):
"""
Runs hyperparameter tuning using Ray Tune.
Args:
model (YOLO): Model to run the tuner on.
space (dict, optional): The hyperparameter search space. Defaults to None.
grace_period (int, optional): The grace period in epochs of the ASHA scheduler. Defaults to 10.
gpu_per_trial (int, optional): The number of GPUs to allocate per trial. Defaults to None.
max_samples (int, optional): The maximum number of trials to run. Defaults to 10.
train_args (dict, optional): Additional arguments to pass to the `train()` method. Defaults to {}.
Returns:
(dict): A dictionary containing the results of the hyperparameter search.
Raises:
ModuleNotFoundError: If Ray Tune is not installed.
"""
if train_args is None:
train_args = {}
try:
from ray import tune
from ray.air import RunConfig
from ray.air.integrations.wandb import WandbLoggerCallback
from ray.tune.schedulers import ASHAScheduler
except ImportError:
raise ModuleNotFoundError("Tuning hyperparameters requires Ray Tune. Install with: pip install 'ray[tune]'")
try:
import wandb
assert hasattr(wandb, '__version__')
except (ImportError, AssertionError):
wandb = False
default_space = {
# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
'lr0': tune.uniform(1e-5, 1e-1),
'lrf': tune.uniform(0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
'momentum': tune.uniform(0.6, 0.98), # SGD momentum/Adam beta1
'weight_decay': tune.uniform(0.0, 0.001), # optimizer weight decay 5e-4
'warmup_epochs': tune.uniform(0.0, 5.0), # warmup epochs (fractions ok)
'warmup_momentum': tune.uniform(0.0, 0.95), # warmup initial momentum
'box': tune.uniform(0.02, 0.2), # box loss gain
'cls': tune.uniform(0.2, 4.0), # cls loss gain (scale with pixels)
'hsv_h': tune.uniform(0.0, 0.1), # image HSV-Hue augmentation (fraction)
'hsv_s': tune.uniform(0.0, 0.9), # image HSV-Saturation augmentation (fraction)
'hsv_v': tune.uniform(0.0, 0.9), # image HSV-Value augmentation (fraction)
'degrees': tune.uniform(0.0, 45.0), # image rotation (+/- deg)
'translate': tune.uniform(0.0, 0.9), # image translation (+/- fraction)
'scale': tune.uniform(0.0, 0.9), # image scale (+/- gain)
'shear': tune.uniform(0.0, 10.0), # image shear (+/- deg)
'perspective': tune.uniform(0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
'flipud': tune.uniform(0.0, 1.0), # image flip up-down (probability)
'fliplr': tune.uniform(0.0, 1.0), # image flip left-right (probability)
'mosaic': tune.uniform(0.0, 1.0), # image mixup (probability)
'mixup': tune.uniform(0.0, 1.0), # image mixup (probability)
'copy_paste': tune.uniform(0.0, 1.0)} # segment copy-paste (probability)
def _tune(config):
"""
Trains the YOLO model with the specified hyperparameters and additional arguments.
Args:
config (dict): A dictionary of hyperparameters to use for training.
Returns:
None.
"""
model._reset_callbacks()
config.update(train_args)
model.train(**config)
# Get search space
if not space:
space = default_space
LOGGER.warning('WARNING ⚠️ search space not provided, using default search space.')
# Get dataset
data = train_args.get('data', TASK2DATA[model.task])
space['data'] = data
if 'data' not in train_args:
LOGGER.warning(f'WARNING ⚠️ data not provided, using default "data={data}".')
# Define the trainable function with allocated resources
trainable_with_resources = tune.with_resources(_tune, {'cpu': NUM_THREADS, 'gpu': gpu_per_trial or 0})
# Define the ASHA scheduler for hyperparameter search
asha_scheduler = ASHAScheduler(time_attr='epoch',
metric=TASK2METRIC[model.task],
mode='max',
max_t=train_args.get('epochs') or DEFAULT_CFG_DICT['epochs'] or 100,
grace_period=grace_period,
reduction_factor=3)
# Define the callbacks for the hyperparameter search
tuner_callbacks = [WandbLoggerCallback(project='YOLOv8-tune')] if wandb else []
# Create the Ray Tune hyperparameter search tuner
tuner = tune.Tuner(trainable_with_resources,
param_space=space,
tune_config=tune.TuneConfig(scheduler=asha_scheduler, num_samples=max_samples),
run_config=RunConfig(callbacks=tuner_callbacks, storage_path='./runs/tune'))
# Run the hyperparameter search
tuner.fit()
# Return the results of the hyperparameter search
return tuner.get_results()
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