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# Ultralytics YOLO 🚀, AGPL-3.0 license
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING
from ultralytics.utils.torch_utils import model_info_for_loggers
try:
import neptune
from neptune.types import File
assert not TESTS_RUNNING # do not log pytest
assert hasattr(neptune, '__version__')
assert SETTINGS['neptune'] is True # verify integration is enabled
except (ImportError, AssertionError):
neptune = None
run = None # NeptuneAI experiment logger instance
def _log_scalars(scalars, step=0):
"""Log scalars to the NeptuneAI experiment logger."""
if run:
for k, v in scalars.items():
run[k].append(value=v, step=step)
def _log_images(imgs_dict, group=''):
"""Log scalars to the NeptuneAI experiment logger."""
if run:
for k, v in imgs_dict.items():
run[f'{group}/{k}'].upload(File(v))
def _log_plot(title, plot_path):
"""Log plots to the NeptuneAI experiment logger."""
"""
Log image as plot in the plot section of NeptuneAI
arguments:
title (str) Title of the plot
plot_path (PosixPath or str) Path to the saved image file
"""
img = mpimg.imread(plot_path)
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[]) # no ticks
ax.imshow(img)
run[f'Plots/{title}'].upload(fig)
def on_pretrain_routine_start(trainer):
"""Callback function called before the training routine starts."""
try:
global run
run = neptune.init_run(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, tags=['YOLOv8'])
run['Configuration/Hyperparameters'] = {k: '' if v is None else v for k, v in vars(trainer.args).items()}
except Exception as e:
LOGGER.warning(f'WARNING ⚠️ NeptuneAI installed but not initialized correctly, not logging this run. {e}')
def on_train_epoch_end(trainer):
"""Callback function called at end of each training epoch."""
_log_scalars(trainer.label_loss_items(trainer.tloss, prefix='train'), trainer.epoch + 1)
_log_scalars(trainer.lr, trainer.epoch + 1)
if trainer.epoch == 1:
_log_images({f.stem: str(f) for f in trainer.save_dir.glob('train_batch*.jpg')}, 'Mosaic')
def on_fit_epoch_end(trainer):
"""Callback function called at end of each fit (train+val) epoch."""
if run and trainer.epoch == 0:
run['Configuration/Model'] = model_info_for_loggers(trainer)
_log_scalars(trainer.metrics, trainer.epoch + 1)
def on_val_end(validator):
"""Callback function called at end of each validation."""
if run:
# Log val_labels and val_pred
_log_images({f.stem: str(f) for f in validator.save_dir.glob('val*.jpg')}, 'Validation')
def on_train_end(trainer):
"""Callback function called at end of training."""
if run:
# Log final results, CM matrix + PR plots
files = [
'results.png', 'confusion_matrix.png', 'confusion_matrix_normalized.png',
*(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()] # filter
for f in files:
_log_plot(title=f.stem, plot_path=f)
# Log the final model
run[f'weights/{trainer.args.name or trainer.args.task}/{str(trainer.best.name)}'].upload(File(str(
trainer.best)))
callbacks = {
'on_pretrain_routine_start': on_pretrain_routine_start,
'on_train_epoch_end': on_train_epoch_end,
'on_fit_epoch_end': on_fit_epoch_end,
'on_val_end': on_val_end,
'on_train_end': on_train_end} if neptune else {}
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