ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com> Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
e795277391
commit
fe27db2f6e
139 changed files with 6870 additions and 5125 deletions
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@ -6,12 +6,9 @@ from ultralytics.cfg import TASK2DATA, TASK2METRIC, get_save_dir
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from ultralytics.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, LOGGER, NUM_THREADS
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def run_ray_tune(model,
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space: dict = None,
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grace_period: int = 10,
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gpu_per_trial: int = None,
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max_samples: int = 10,
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**train_args):
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def run_ray_tune(
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model, space: dict = None, grace_period: int = 10, gpu_per_trial: int = None, max_samples: int = 10, **train_args
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):
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"""
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Runs hyperparameter tuning using Ray Tune.
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@ -38,12 +35,12 @@ def run_ray_tune(model,
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```
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"""
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LOGGER.info('💡 Learn about RayTune at https://docs.ultralytics.com/integrations/ray-tune')
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LOGGER.info("💡 Learn about RayTune at https://docs.ultralytics.com/integrations/ray-tune")
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if train_args is None:
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train_args = {}
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try:
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subprocess.run('pip install ray[tune]'.split(), check=True)
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subprocess.run("pip install ray[tune]".split(), check=True)
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import ray
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from ray import tune
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@ -56,33 +53,34 @@ def run_ray_tune(model,
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try:
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import wandb
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assert hasattr(wandb, '__version__')
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assert hasattr(wandb, "__version__")
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except (ImportError, AssertionError):
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wandb = False
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default_space = {
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# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', '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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'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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"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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"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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}
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# Put the model in ray store
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task = model.task
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@ -107,35 +105,39 @@ def run_ray_tune(model,
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# Get search space
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if not space:
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space = default_space
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LOGGER.warning('WARNING ⚠️ search space not provided, using default search space.')
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LOGGER.warning("WARNING ⚠️ search space not provided, using default search space.")
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# Get dataset
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data = train_args.get('data', TASK2DATA[task])
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space['data'] = data
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if 'data' not in train_args:
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data = train_args.get("data", TASK2DATA[task])
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space["data"] = data
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if "data" not in train_args:
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LOGGER.warning(f'WARNING ⚠️ data not provided, using default "data={data}".')
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# Define the trainable function with allocated resources
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trainable_with_resources = tune.with_resources(_tune, {'cpu': NUM_THREADS, 'gpu': gpu_per_trial or 0})
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trainable_with_resources = tune.with_resources(_tune, {"cpu": NUM_THREADS, "gpu": gpu_per_trial or 0})
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# Define the ASHA scheduler for hyperparameter search
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asha_scheduler = ASHAScheduler(time_attr='epoch',
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metric=TASK2METRIC[task],
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mode='max',
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max_t=train_args.get('epochs') or DEFAULT_CFG_DICT['epochs'] or 100,
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grace_period=grace_period,
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reduction_factor=3)
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asha_scheduler = ASHAScheduler(
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time_attr="epoch",
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metric=TASK2METRIC[task],
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mode="max",
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max_t=train_args.get("epochs") or DEFAULT_CFG_DICT["epochs"] or 100,
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grace_period=grace_period,
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reduction_factor=3,
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)
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# Define the callbacks for the hyperparameter search
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tuner_callbacks = [WandbLoggerCallback(project='YOLOv8-tune')] if wandb else []
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tuner_callbacks = [WandbLoggerCallback(project="YOLOv8-tune")] if wandb else []
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# Create the Ray Tune hyperparameter search tuner
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tune_dir = get_save_dir(DEFAULT_CFG, name='tune').resolve() # must be absolute dir
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tune_dir = get_save_dir(DEFAULT_CFG, name="tune").resolve() # must be absolute dir
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tune_dir.mkdir(parents=True, exist_ok=True)
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tuner = tune.Tuner(trainable_with_resources,
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param_space=space,
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tune_config=tune.TuneConfig(scheduler=asha_scheduler, num_samples=max_samples),
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run_config=RunConfig(callbacks=tuner_callbacks, storage_path=tune_dir))
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tuner = tune.Tuner(
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trainable_with_resources,
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param_space=space,
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tune_config=tune.TuneConfig(scheduler=asha_scheduler, num_samples=max_samples),
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run_config=RunConfig(callbacks=tuner_callbacks, storage_path=tune_dir),
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)
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# Run the hyperparameter search
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tuner.fit()
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