109 lines
4.0 KiB
Python
109 lines
4.0 KiB
Python
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import contextlib
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from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr
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try:
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# WARNING: do not move SummaryWriter import due to protobuf bug https://github.com/ultralytics/ultralytics/pull/4674
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from torch.utils.tensorboard import SummaryWriter
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assert not TESTS_RUNNING # do not log pytest
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assert SETTINGS["tensorboard"] is True # verify integration is enabled
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WRITER = None # TensorBoard SummaryWriter instance
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PREFIX = colorstr("TensorBoard: ")
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# Imports below only required if TensorBoard enabled
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import warnings
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from copy import deepcopy
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from ultralytics.utils.torch_utils import de_parallel, torch
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except (ImportError, AssertionError, TypeError, AttributeError):
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# TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows
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# AttributeError: module 'tensorflow' has no attribute 'io' if 'tensorflow' not installed
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SummaryWriter = None
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def _log_scalars(scalars, step=0):
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"""Logs scalar values to TensorBoard."""
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if WRITER:
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for k, v in scalars.items():
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WRITER.add_scalar(k, v, step)
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def _log_tensorboard_graph(trainer):
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"""Log model graph to TensorBoard."""
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# Input image
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imgsz = trainer.args.imgsz
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imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz
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p = next(trainer.model.parameters()) # for device, type
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im = torch.zeros((1, 3, *imgsz), device=p.device, dtype=p.dtype) # input image (must be zeros, not empty)
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with warnings.catch_warnings():
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warnings.simplefilter("ignore", category=UserWarning) # suppress jit trace warning
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warnings.simplefilter("ignore", category=torch.jit.TracerWarning) # suppress jit trace warning
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# Try simple method first (YOLO)
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with contextlib.suppress(Exception):
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trainer.model.eval() # place in .eval() mode to avoid BatchNorm statistics changes
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WRITER.add_graph(torch.jit.trace(de_parallel(trainer.model), im, strict=False), [])
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LOGGER.info(f"{PREFIX}model graph visualization added ✅")
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return
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# Fallback to TorchScript export steps (RTDETR)
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try:
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model = deepcopy(de_parallel(trainer.model))
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model.eval()
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model = model.fuse(verbose=False)
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for m in model.modules():
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if hasattr(m, "export"): # Detect, RTDETRDecoder (Segment and Pose use Detect base class)
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m.export = True
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m.format = "torchscript"
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model(im) # dry run
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WRITER.add_graph(torch.jit.trace(model, im, strict=False), [])
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LOGGER.info(f"{PREFIX}model graph visualization added ✅")
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except Exception as e:
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LOGGER.warning(f"{PREFIX}WARNING ⚠️ TensorBoard graph visualization failure {e}")
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def on_pretrain_routine_start(trainer):
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"""Initialize TensorBoard logging with SummaryWriter."""
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if SummaryWriter:
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try:
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global WRITER
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WRITER = SummaryWriter(str(trainer.save_dir))
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LOGGER.info(f"{PREFIX}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/")
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except Exception as e:
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LOGGER.warning(f"{PREFIX}WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}")
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def on_train_start(trainer):
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"""Log TensorBoard graph."""
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if WRITER:
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_log_tensorboard_graph(trainer)
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def on_train_epoch_end(trainer):
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"""Logs scalar statistics at the end of a training epoch."""
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_log_scalars(trainer.label_loss_items(trainer.tloss, prefix="train"), trainer.epoch + 1)
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_log_scalars(trainer.lr, trainer.epoch + 1)
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def on_fit_epoch_end(trainer):
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"""Logs epoch metrics at end of training epoch."""
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_log_scalars(trainer.metrics, trainer.epoch + 1)
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callbacks = (
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{
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"on_pretrain_routine_start": on_pretrain_routine_start,
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"on_train_start": on_train_start,
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"on_fit_epoch_end": on_fit_epoch_end,
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"on_train_epoch_end": on_train_epoch_end,
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}
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if SummaryWriter
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else {}
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)
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