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ultralytics 8.0.168 save Tuner results every iteration (#4680)

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Glenn Jocher 2023-08-31 23:40:42 +02:00 • committed by GitHub
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5 changed files with 31 additions and 16 deletions
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37
ultralytics/engine/tuner.py
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@ -159,16 +159,18 @@ class Tuner:
"""
t0 = time.time()
best_save_dir, best_metrics = None, None
self.tune_dir.mkdir(parents=True, exist_ok=True)
for i in range(iterations):
# Mutate hyperparameters
mutated_hyp = self._mutate()
LOGGER.info(f'{prefix} Starting iteration {i + 1}/{iterations} with hyperparameters: {mutated_hyp}')
# Initialize and train YOLOv8 model
try:
# Train YOLO model with mutated hyperparameters
train_args = {**vars(self.args), **mutated_hyp}
fitness = (deepcopy(model) or YOLO(self.args.model)).train(**train_args).fitness # results.fitness
results = (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
@ -179,14 +181,25 @@ class Tuner:
with open(self.evolve_csv, 'a') as f:
f.write(headers + ','.join(map(str, log_row)) + '\n')
# 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}')
# Print tuning results
x = np.loadtxt(self.evolve_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 = results.save_dir
best_metrics = {k: round(v, 5) for k, v in results.results_dict.items()}
header = (f'{prefix} {i + 1} iterations complete ✅ ({time.time() - t0:.2f}s)\n'
f'{prefix} Results saved to {colorstr("bold", self.tune_dir)}\n'
f'{prefix} Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n'
f'{prefix} Best fitness metrics are {best_metrics}\n'
f'{prefix} Best fitness model is {best_save_dir}\n'
f'{prefix} Best fitness hyperparameters are printed below.\n')
# 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')
LOGGER.info('\n' + header)
# Save turning results
data = {k: float(x[0, i + 1]) for i, k in enumerate(self.space.keys())}
header = header.replace(prefix, '#').replace('/', '').replace('', '') + '\n'
yaml_save(self.tune_dir / 'best.yaml', data=data, header=header)
yaml_print(self.tune_dir / 'best.yaml')