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# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr
try:
# WARNING: do not move SummaryWriter import due to protobuf bug https://github.com/ultralytics/ultralytics/pull/4674
from torch.utils.tensorboard import SummaryWriter
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS["tensorboard"] is True # verify integration is enabled
WRITER = None # TensorBoard SummaryWriter instance
PREFIX = colorstr("TensorBoard: ")
# Imports below only required if TensorBoard enabled
import warnings
from copy import deepcopy
from ultralytics.utils.torch_utils import de_parallel, torch
except (ImportError, AssertionError, TypeError, AttributeError):
# TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows
# AttributeError: module 'tensorflow' has no attribute 'io' if 'tensorflow' not installed
SummaryWriter = None
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."""
# Input image
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
warnings.simplefilter("ignore", category=torch.jit.TracerWarning) # suppress jit trace warning
# Try simple method first (YOLO)
with contextlib.suppress(Exception):
trainer.model.eval() # place in .eval() mode to avoid BatchNorm statistics changes
WRITER.add_graph(torch.jit.trace(de_parallel(trainer.model), im, strict=False), [])
LOGGER.info(f"{PREFIX}model graph visualization added ✅")
return
# Fallback to TorchScript export steps (RTDETR)
try:
model = deepcopy(de_parallel(trainer.model))
model.eval()
model = model.fuse(verbose=False)
for m in model.modules():
if hasattr(m, "export"): # Detect, RTDETRDecoder (Segment and Pose use Detect base class)
m.export = True
m.format = "torchscript"
model(im) # dry run
WRITER.add_graph(torch.jit.trace(model, im, strict=False), [])
LOGGER.info(f"{PREFIX}model graph visualization added ✅")
except Exception as e:
LOGGER.warning(f"{PREFIX}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))
LOGGER.info(f"{PREFIX}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/")
except Exception as e:
LOGGER.warning(f"{PREFIX}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_train_epoch_end(trainer):
"""Logs scalar statistics at the end of a training epoch."""
_log_scalars(trainer.label_loss_items(trainer.tloss, prefix="train"), trainer.epoch + 1)
_log_scalars(trainer.lr, 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_train_epoch_end": on_train_epoch_end,
}
if SummaryWriter
else {}
)
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