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# Ultralytics YOLO 🚀, AGPL-3.0 license
from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr
try:
from torch.utils.tensorboard import SummaryWriter
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['tensorboard'] is True # verify integration is enabled
# TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows
except (ImportError, AssertionError, TypeError):
SummaryWriter = None
WRITER = None # TensorBoard SummaryWriter instance
def _log_scalars(scalars, step=0):
"""Logs scalar values to TensorBoard."""
if WRITER:
for k, v in scalars.items():
WRITER.add_scalar(k, v, step)
def _log_tensorboard_graph(trainer):
"""Log model graph to TensorBoard."""
try:
import warnings
from ultralytics.utils.torch_utils import de_parallel, torch
imgsz = trainer.args.imgsz
imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz
p = next(trainer.model.parameters()) # for device, type
im = torch.zeros((1, 3, *imgsz), device=p.device, dtype=p.dtype) # input image (must be zeros, not empty)
with warnings.catch_warnings():
warnings.simplefilter('ignore', category=UserWarning) # suppress jit trace warning
WRITER.add_graph(torch.jit.trace(de_parallel(trainer.model), im, strict=False), [])
except Exception as e:
LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}')
def on_pretrain_routine_start(trainer):
"""Initialize TensorBoard logging with SummaryWriter."""
if SummaryWriter:
try:
global WRITER
WRITER = SummaryWriter(str(trainer.save_dir))
prefix = colorstr('TensorBoard: ')
LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/")
except Exception as e:
LOGGER.warning(f'WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}')
def on_train_start(trainer):
"""Log TensorBoard graph."""
if WRITER:
_log_tensorboard_graph(trainer)
def on_batch_end(trainer):
"""Logs scalar statistics at the end of a training batch."""
_log_scalars(trainer.label_loss_items(trainer.tloss, prefix='train'), trainer.epoch + 1)
def on_fit_epoch_end(trainer):
"""Logs epoch metrics at end of training epoch."""
_log_scalars(trainer.metrics, trainer.epoch + 1)
callbacks = {
'on_pretrain_routine_start': on_pretrain_routine_start,
'on_train_start': on_train_start,
'on_fit_epoch_end': on_fit_epoch_end,
'on_batch_end': on_batch_end}
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