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ultralytics 8.0.126 Ray Tune refactoring (#3511)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-07-05 00:30:17 +02:00
committed by GitHub
parent 69a2d70a78
commit 4c22273198
8 changed files with 223 additions and 151 deletions
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75
ultralytics/yolo/engine/model.py
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@ -9,8 +9,8 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, PoseModel
attempt_load_one_weight, guess_model_task, nn, yaml_model_load)
from ultralytics.yolo.cfg import get_cfg
from ultralytics.yolo.engine.exporter import Exporter
from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, NUM_THREADS, RANK, ROOT,
callbacks, is_git_dir, yaml_load)
from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
is_git_dir, yaml_load)
from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update_available, check_yaml
from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
@ -387,13 +387,7 @@ class YOLO:
self._check_is_pytorch_model()
self.model.to(device)
def tune(self,
data: str,
space: dict = None,
grace_period: int = 10,
gpu_per_trial: int = None,
max_samples: int = 10,
train_args: dict = None):
def tune(self, *args, **kwargs):
"""
Runs hyperparameter tuning using Ray Tune.
@ -411,66 +405,9 @@ class YOLO:
Raises:
ModuleNotFoundError: If Ray Tune is not installed.
"""
if train_args is None:
train_args = {}
try:
from ultralytics.yolo.utils.tuner import (ASHAScheduler, RunConfig, WandbLoggerCallback, default_space,
task_metric_map, tune)
except ImportError:
raise ModuleNotFoundError("Install Ray Tune: `pip install 'ray[tune]'`")
try:
import wandb
from wandb import __version__ # noqa
except ImportError:
wandb = False
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.
"""
self._reset_callbacks()
config.update(train_args)
self.train(**config)
if not space:
LOGGER.warning('WARNING: search space not provided. Using default search space')
space = default_space
space['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=task_metric_map[self.task],
mode='max',
max_t=train_args.get('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, local_dir='./runs'))
# Run the hyperparameter search
tuner.fit()
# Return the results of the hyperparameter search
return tuner.get_results()
self._check_is_pytorch_model()
from ultralytics.yolo.utils.tuner import run_ray_tune
return run_ray_tune(self, *args, **kwargs)
@property
def names(self):