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