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Reference for ultralytics/utils/callbacks/dvc.py

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Full source code for this file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/dvc.py. Help us fix any issues you see by submitting a Pull Request 🛠️. Thank you 🙏!

ultralytics.utils.callbacks.dvc._log_images(path, prefix='')

Source code in ultralytics/utils/callbacks/dvc.py 
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def _log_images(path, prefix=''):
    if live:
        name = path.name

        # Group images by batch to enable sliders in UI
        if m := re.search(r'_batch(\d+)', name):
            ni = m[1]
            new_stem = re.sub(r'_batch(\d+)', '_batch', path.stem)
            name = (Path(new_stem) / ni).with_suffix(path.suffix)

        live.log_image(os.path.join(prefix, name), path)



ultralytics.utils.callbacks.dvc._log_plots(plots, prefix='')

Source code in ultralytics/utils/callbacks/dvc.py 
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def _log_plots(plots, prefix=''):
    for name, params in plots.items():
        timestamp = params['timestamp']
        if _processed_plots.get(name) != timestamp:
            _log_images(name, prefix)
            _processed_plots[name] = timestamp



ultralytics.utils.callbacks.dvc._log_confusion_matrix(validator)

Source code in ultralytics/utils/callbacks/dvc.py 
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def _log_confusion_matrix(validator):
    targets = []
    preds = []
    matrix = validator.confusion_matrix.matrix
    names = list(validator.names.values())
    if validator.confusion_matrix.task == 'detect':
        names += ['background']

    for ti, pred in enumerate(matrix.T.astype(int)):
        for pi, num in enumerate(pred):
            targets.extend([names[ti]] * num)
            preds.extend([names[pi]] * num)

    live.log_sklearn_plot('confusion_matrix', targets, preds, name='cf.json', normalized=True)



ultralytics.utils.callbacks.dvc.on_pretrain_routine_start(trainer)

Source code in ultralytics/utils/callbacks/dvc.py 
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def on_pretrain_routine_start(trainer):
    try:
        global live
        live = dvclive.Live(save_dvc_exp=True, cache_images=True)
        LOGGER.info("DVCLive is detected and auto logging is enabled (run 'yolo settings dvc=False' to disable).")
    except Exception as e:
        LOGGER.warning(f'WARNING ⚠️ DVCLive installed but not initialized correctly, not logging this run. {e}')



ultralytics.utils.callbacks.dvc.on_pretrain_routine_end(trainer)

Source code in ultralytics/utils/callbacks/dvc.py 
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def on_pretrain_routine_end(trainer):
    _log_plots(trainer.plots, 'train')



ultralytics.utils.callbacks.dvc.on_train_start(trainer)

Source code in ultralytics/utils/callbacks/dvc.py 
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def on_train_start(trainer):
    if live:
        live.log_params(trainer.args)



ultralytics.utils.callbacks.dvc.on_train_epoch_start(trainer)

Source code in ultralytics/utils/callbacks/dvc.py 
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def on_train_epoch_start(trainer):
    global _training_epoch
    _training_epoch = True



ultralytics.utils.callbacks.dvc.on_fit_epoch_end(trainer)

Source code in ultralytics/utils/callbacks/dvc.py 
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def on_fit_epoch_end(trainer):
    global _training_epoch
    if live and _training_epoch:
        all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}
        for metric, value in all_metrics.items():
            live.log_metric(metric, value)

        if trainer.epoch == 0:
            from ultralytics.utils.torch_utils import model_info_for_loggers
            for metric, value in model_info_for_loggers(trainer).items():
                live.log_metric(metric, value, plot=False)

        _log_plots(trainer.plots, 'train')
        _log_plots(trainer.validator.plots, 'val')

        live.next_step()
        _training_epoch = False



ultralytics.utils.callbacks.dvc.on_train_end(trainer)

Source code in ultralytics/utils/callbacks/dvc.py 
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def on_train_end(trainer):
    if live:
        # At the end log the best metrics. It runs validator on the best model internally.
        all_metrics = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics, **trainer.lr}
        for metric, value in all_metrics.items():
            live.log_metric(metric, value, plot=False)

        _log_plots(trainer.plots, 'val')
        _log_plots(trainer.validator.plots, 'val')
        _log_confusion_matrix(trainer.validator)

        if trainer.best.exists():
            live.log_artifact(trainer.best, copy=True, type='model')

        live.end()




Created 2023-07-16, Updated 2023-08-14
Authors: glenn-jocher (6), Laughing-q (1)