|
|
# Ultralytics YOLO 🚀, AGPL-3.0 license |
|
|
""" |
|
|
This module provides functionalities for hyperparameter tuning of the Ultralytics YOLO models for object detection, |
|
|
instance segmentation, image classification, pose estimation, and multi-object tracking. |
|
|
|
|
|
Hyperparameter tuning is the process of systematically searching for the optimal set of hyperparameters |
|
|
that yield the best model performance. This is particularly crucial in deep learning models like YOLO, |
|
|
where small changes in hyperparameters can lead to significant differences in model accuracy and efficiency. |
|
|
|
|
|
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) |
|
|
``` |
|
|
""" |
|
|
|
|
|
import random |
|
|
import shutil |
|
|
import subprocess |
|
|
import time |
|
|
|
|
|
import numpy as np |
|
|
import torch |
|
|
|
|
|
from ultralytics.cfg import get_cfg, get_save_dir |
|
|
from ultralytics.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, remove_colorstr, yaml_print, yaml_save |
|
|
from ultralytics.utils.plotting import plot_tune_results |
|
|
|
|
|
|
|
|
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")
|
|
|
|
