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ultralytics 8.0.167 Tuner updates and HUB Pose and Classify fixes (#4656)

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Glenn Jocher 2023-08-31 01:38:42 +02:00 • committed by GitHub
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45
ultralytics/engine/tuner.py
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@ -18,6 +18,7 @@ Example:
"""
import random
import time
from copy import deepcopy
import numpy as np
@ -51,7 +52,7 @@ class Tuner:
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.tune(data='coco8.yaml', imgsz=640, epochs=100, iterations=10)
model.tune(data='coco8.yaml', imgsz=640, epochs=100, iterations=10, val=False, cache=True)
```
"""
@ -63,11 +64,11 @@ class Tuner:
args (dict, optional): Configuration for hyperparameter evolution.
"""
self.args = get_cfg(overrides=args)
self.space = {
self.space = { # key: (min, max, gain(optionaL))
# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
'lr0': (1e-5, 1e-1),
'lrf': (0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
'momentum': (0.6, 0.98), # SGD momentum/Adam beta1
'momentum': (0.6, 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
@ -86,13 +87,13 @@ class Tuner:
'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.tune_dir = get_save_dir(self.args, name='tune')
self.tune_dir = get_save_dir(self.args, name='_tune')
self.evolve_csv = self.tune_dir / 'evolve.csv'
self.callbacks = _callbacks or callbacks.get_default_callbacks()
callbacks.add_integration_callbacks(self)
LOGGER.info(f"Initialized Tuner instance with 'tune_dir={self.tune_dir}'.")
def _mutate(self, parent='single', n=5, mutation=0.8, sigma=0.2, return_best=False):
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`.
@ -111,10 +112,7 @@ class Tuner:
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
if return_best:
return {k: float(x[0, i + 1]) for i, k in enumerate(self.space.keys())}
fitness = x[:, 0] # first column
w = fitness - fitness.min() + 1E-6 # weights (sum > 0)
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
@ -124,7 +122,7 @@ class Tuner:
# Mutate
r = np.random # method
r.seed(int(time.time()))
g = np.array([self.space[k][0] for k in self.space.keys()]) # gains 0-1
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)
@ -152,7 +150,7 @@ class Tuner:
4. Log the fitness score and mutated hyperparameters to a CSV file.
Args:
model (YOLO): A pre-initialized YOLO model to be used for training.
model (Model): A pre-initialized YOLO model to be used for training.
iterations (int): The number of generations to run the evolution for.
Note:
@ -160,6 +158,7 @@ class Tuner:
Ensure this path is set correctly in the Tuner instance.
"""
t0 = time.time()
self.tune_dir.mkdir(parents=True, exist_ok=True)
for i in range(iterations):
# Mutate hyperparameters
@ -167,17 +166,27 @@ class Tuner:
LOGGER.info(f'{prefix} Starting iteration {i + 1}/{iterations} with hyperparameters: {mutated_hyp}')
# Initialize and train YOLOv8 model
model = YOLO('yolov8n.pt')
train_args = {**vars(self.args), **mutated_hyp}
results = model.train(**train_args)
try:
train_args = {**vars(self.args), **mutated_hyp}
fitness = (deepcopy(model) or YOLO(self.args.model)).train(**train_args).fitness # results.fitness
except Exception as e:
LOGGER.warning(f'WARNING ❌️ training failure for hyperparameter tuning iteration {i}\n{e}')
fitness = 0.0
# Save results and mutated_hyp to evolve_csv
log_row = [round(fitness, 5)] + [mutated_hyp[k] for k in self.space.keys()]
headers = '' if self.evolve_csv.exists() else (','.join(['fitness_score'] + list(self.space.keys())) + '\n')
log_row = [results.fitness] + [mutated_hyp[k] for k in self.space.keys()]
with open(self.evolve_csv, 'a') as f:
f.write(headers + ','.join(map(str, log_row)) + '\n')
LOGGER.info(f'{prefix} All iterations complete. Results saved to {colorstr("bold", self.tune_dir)}')
best_hyp = self._mutate(return_best=True) # best hyps
yaml_save(self.tune_dir / 'best.yaml', best_hyp)
# Print tuning results
x = np.loadtxt(self.evolve_csv, ndmin=2, delimiter=',', skiprows=1)
fitness = x[:, 0] # first column
i = np.argsort(-fitness)[0] # best fitness index
LOGGER.info(f'\n{prefix} All iterations complete ✅ ({time.time() - t0:.2f}s)\n'
f'{prefix} Results saved to {colorstr("bold", self.tune_dir)}\n'
f'{prefix} Best fitness={fitness[i]} observed at iteration {i}')
# Save turning results
yaml_save(self.tune_dir / 'best.yaml', data={k: float(x[0, i + 1]) for i, k in enumerate(self.space.keys())})
yaml_print(self.tune_dir / 'best.yaml')