class Tuner:
"""
Class responsible for hyperparameter tuning of YOLO models.
The class evolves YOLO model hyperparameters over a given number of iterations
by mutating them according to the search space and retraining the model to evaluate their performance.
Attributes:
space (dict): Hyperparameter search space containing bounds and scaling factors for mutation.
tune_dir (Path): Directory where evolution logs and results will be saved.
tune_csv (Path): Path to the CSV file where evolution logs are saved.
Methods:
_mutate(hyp: dict) -> dict:
Mutates the given hyperparameters within the bounds specified in `self.space`.
__call__():
Executes the hyperparameter evolution across multiple iterations.
Example:
Tune hyperparameters for YOLOv8n on COCO8 at imgsz=640 and epochs=30 for 300 tuning iterations.
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
```
Tune with custom search space.
```python
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.tune(space={key1: val1, key2: val2}) # custom search space dictionary
```
"""
def __init__(self, args=DEFAULT_CFG, _callbacks=None):
"""
Initialize the Tuner with configurations.
Args:
args (dict, optional): Configuration for hyperparameter evolution.
"""
self.space = args.pop("space", None) or { # key: (min, max, gain(optional))
# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
"lr0": (1e-5, 1e-1), # initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
"lrf": (0.0001, 0.1), # final OneCycleLR learning rate (lr0 * lrf)
"momentum": (0.7, 0.98, 0.3), # SGD momentum/Adam beta1
"weight_decay": (0.0, 0.001), # optimizer weight decay 5e-4
"warmup_epochs": (0.0, 5.0), # warmup epochs (fractions ok)
"warmup_momentum": (0.0, 0.95), # warmup initial momentum
"box": (1.0, 20.0), # box loss gain
"cls": (0.2, 4.0), # cls loss gain (scale with pixels)
"dfl": (0.4, 6.0), # dfl loss gain
"hsv_h": (0.0, 0.1), # image HSV-Hue augmentation (fraction)
"hsv_s": (0.0, 0.9), # image HSV-Saturation augmentation (fraction)
"hsv_v": (0.0, 0.9), # image HSV-Value augmentation (fraction)
"degrees": (0.0, 45.0), # image rotation (+/- deg)
"translate": (0.0, 0.9), # image translation (+/- fraction)
"scale": (0.0, 0.95), # image scale (+/- gain)
"shear": (0.0, 10.0), # image shear (+/- deg)
"perspective": (0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
"flipud": (0.0, 1.0), # image flip up-down (probability)
"fliplr": (0.0, 1.0), # image flip left-right (probability)
"bgr": (0.0, 1.0), # image channel bgr (probability)
"mosaic": (0.0, 1.0), # image mixup (probability)
"mixup": (0.0, 1.0), # image mixup (probability)
"copy_paste": (0.0, 1.0), # segment copy-paste (probability)
}
self.args = get_cfg(overrides=args)
self.tune_dir = get_save_dir(self.args, name="tune")
self.tune_csv = self.tune_dir / "tune_results.csv"
self.callbacks = _callbacks or callbacks.get_default_callbacks()
self.prefix = colorstr("Tuner: ")
callbacks.add_integration_callbacks(self)
LOGGER.info(
f"{self.prefix}Initialized Tuner instance with 'tune_dir={self.tune_dir}'\n"
f"{self.prefix}💡 Learn about tuning at https://docs.ultralytics.com/guides/hyperparameter-tuning"
)
def _mutate(self, parent="single", n=5, mutation=0.8, sigma=0.2):
"""
Mutates the hyperparameters based on bounds and scaling factors specified in `self.space`.
Args:
parent (str): Parent selection method: 'single' or 'weighted'.
n (int): Number of parents to consider.
mutation (float): Probability of a parameter mutation in any given iteration.
sigma (float): Standard deviation for Gaussian random number generator.
Returns:
(dict): A dictionary containing mutated hyperparameters.
"""
if self.tune_csv.exists(): # if CSV file exists: select best hyps and mutate
# Select parent(s)
x = np.loadtxt(self.tune_csv, ndmin=2, delimiter=",", skiprows=1)
fitness = x[:, 0] # first column
n = min(n, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness)][:n] # top n mutations
w = x[:, 0] - x[:, 0].min() + 1e-6 # weights (sum > 0)
if parent == "single" or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
elif parent == "weighted":
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate
r = np.random # method
r.seed(int(time.time()))
g = np.array([v[2] if len(v) == 3 else 1.0 for k, v in self.space.items()]) # gains 0-1
ng = len(self.space)
v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates)
v = (g * (r.random(ng) < mutation) * r.randn(ng) * r.random() * sigma + 1).clip(0.3, 3.0)
hyp = {k: float(x[i + 1] * v[i]) for i, k in enumerate(self.space.keys())}
else:
hyp = {k: getattr(self.args, k) for k in self.space.keys()}
# Constrain to limits
for k, v in self.space.items():
hyp[k] = max(hyp[k], v[0]) # lower limit
hyp[k] = min(hyp[k], v[1]) # upper limit
hyp[k] = round(hyp[k], 5) # significant digits
return hyp
def __call__(self, model=None, iterations=10, cleanup=True):
"""
Executes the hyperparameter evolution process when the Tuner instance is called.
This method iterates through the number of iterations, performing the following steps in each iteration:
1. Load the existing hyperparameters or initialize new ones.
2. Mutate the hyperparameters using the `mutate` method.
3. Train a YOLO model with the mutated hyperparameters.
4. Log the fitness score and mutated hyperparameters to a CSV file.
Args:
model (Model): A pre-initialized YOLO model to be used for training.
iterations (int): The number of generations to run the evolution for.
cleanup (bool): Whether to delete iteration weights to reduce storage space used during tuning.
Note:
The method utilizes the `self.tune_csv` Path object to read and log hyperparameters and fitness scores.
Ensure this path is set correctly in the Tuner instance.
"""
t0 = time.time()
best_save_dir, best_metrics = None, None
(self.tune_dir / "weights").mkdir(parents=True, exist_ok=True)
for i in range(iterations):
# Mutate hyperparameters
mutated_hyp = self._mutate()
LOGGER.info(f"{self.prefix}Starting iteration {i + 1}/{iterations} with hyperparameters: {mutated_hyp}")
metrics = {}
train_args = {**vars(self.args), **mutated_hyp}
save_dir = get_save_dir(get_cfg(train_args))
weights_dir = save_dir / "weights"
try:
# Train YOLO model with mutated hyperparameters (run in subprocess to avoid dataloader hang)
cmd = ["yolo", "train", *(f"{k}={v}" for k, v in train_args.items())]
return_code = subprocess.run(cmd, check=True).returncode
ckpt_file = weights_dir / ("best.pt" if (weights_dir / "best.pt").exists() else "last.pt")
metrics = torch.load(ckpt_file)["train_metrics"]
assert return_code == 0, "training failed"
except Exception as e:
LOGGER.warning(f"WARNING ❌️ training failure for hyperparameter tuning iteration {i + 1}\n{e}")
# Save results and mutated_hyp to CSV
fitness = metrics.get("fitness", 0.0)
log_row = [round(fitness, 5)] + [mutated_hyp[k] for k in self.space.keys()]
headers = "" if self.tune_csv.exists() else (",".join(["fitness"] + list(self.space.keys())) + "\n")
with open(self.tune_csv, "a") as f:
f.write(headers + ",".join(map(str, log_row)) + "\n")
# Get best results
x = np.loadtxt(self.tune_csv, ndmin=2, delimiter=",", skiprows=1)
fitness = x[:, 0] # first column
best_idx = fitness.argmax()
best_is_current = best_idx == i
if best_is_current:
best_save_dir = save_dir
best_metrics = {k: round(v, 5) for k, v in metrics.items()}
for ckpt in weights_dir.glob("*.pt"):
shutil.copy2(ckpt, self.tune_dir / "weights")
elif cleanup:
shutil.rmtree(weights_dir, ignore_errors=True) # remove iteration weights/ dir to reduce storage space
# Plot tune results
plot_tune_results(self.tune_csv)
# Save and print tune results
header = (
f'{self.prefix}{i + 1}/{iterations} iterations complete ✅ ({time.time() - t0:.2f}s)\n'
f'{self.prefix}Results saved to {colorstr("bold", self.tune_dir)}\n'
f'{self.prefix}Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n'
f'{self.prefix}Best fitness metrics are {best_metrics}\n'
f'{self.prefix}Best fitness model is {best_save_dir}\n'
f'{self.prefix}Best fitness hyperparameters are printed below.\n'
)
LOGGER.info("\n" + header)
data = {k: float(x[best_idx, i + 1]) for i, k in enumerate(self.space.keys())}
yaml_save(
self.tune_dir / "best_hyperparameters.yaml",
data=data,
header=remove_colorstr(header.replace(self.prefix, "# ")) + "\n",
)
yaml_print(self.tune_dir / "best_hyperparameters.yaml")