76 lines
2.8 KiB
Python
76 lines
2.8 KiB
Python
# Ultralytics YOLO 🚀, AGPL-3.0 license
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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 import due to protobuf issue in 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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except (ImportError, AssertionError, TypeError):
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# TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows
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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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try:
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import warnings
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from ultralytics.utils.torch_utils import de_parallel, torch
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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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WRITER.add_graph(torch.jit.trace(de_parallel(trainer.model), im, strict=False), [])
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except Exception as e:
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LOGGER.warning(f'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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prefix = colorstr('TensorBoard: ')
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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'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_batch_end(trainer):
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"""Logs scalar statistics at the end of a training batch."""
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_log_scalars(trainer.label_loss_items(trainer.tloss, prefix='train'), 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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'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_batch_end': on_batch_end} if SummaryWriter else {}
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