跳至内容

参考资料 ultralytics/utils/callbacks/neptune.py

备注

该文件可在https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/utils/callbacks/ neptune.py 上获取。如果您发现问题,请通过提交 Pull Request🛠️ 帮助修复。谢谢🙏!



ultralytics.utils.callbacks.neptune._log_scalars(scalars, step=0)

将标量记录到 NeptuneAI 实验记录器。

源代码 ultralytics/utils/callbacks/neptune.py
def _log_scalars(scalars, step=0):
    """Log scalars to the NeptuneAI experiment logger."""
    if run:
        for k, v in scalars.items():
            run[k].append(value=v, step=step)



ultralytics.utils.callbacks.neptune._log_images(imgs_dict, group='')

将标量记录到 NeptuneAI 实验记录器。

源代码 ultralytics/utils/callbacks/neptune.py
def _log_images(imgs_dict, group=""):
    """Log scalars to the NeptuneAI experiment logger."""
    if run:
        for k, v in imgs_dict.items():
            run[f"{group}/{k}"].upload(File(v))



ultralytics.utils.callbacks.neptune._log_plot(title, plot_path)

将绘图记录到 NeptuneAI 实验记录仪。

参数

名称 类型 说明 默认值
title str

情节名称

所需
plot_path PosixPath | str

保存图像文件的路径。

所需
源代码 ultralytics/utils/callbacks/neptune.py
def _log_plot(title, plot_path):
    """
    Log plots to the NeptuneAI experiment logger.

    Args:
        title (str): Title of the plot.
        plot_path (PosixPath | str): Path to the saved image file.
    """
    import matplotlib.image as mpimg
    import matplotlib.pyplot as plt

    img = mpimg.imread(plot_path)
    fig = plt.figure()
    ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect="auto", xticks=[], yticks=[])  # no ticks
    ax.imshow(img)
    run[f"Plots/{title}"].upload(fig)



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

在训练程序启动前调用的回调函数。

源代码 ultralytics/utils/callbacks/neptune.py
def on_pretrain_routine_start(trainer):
    """Callback function called before the training routine starts."""
    try:
        global run
        run = neptune.init_run(project=trainer.args.project or "YOLOv8", name=trainer.args.name, tags=["YOLOv8"])
        run["Configuration/Hyperparameters"] = {k: "" if v is None else v for k, v in vars(trainer.args).items()}
    except Exception as e:
        LOGGER.warning(f"WARNING ⚠️ NeptuneAI installed but not initialized correctly, not logging this run. {e}")



ultralytics.utils.callbacks.neptune.on_train_epoch_end(trainer)

每个训练期结束时调用的回调函数。

源代码 ultralytics/utils/callbacks/neptune.py
def on_train_epoch_end(trainer):
    """Callback function called at end of each training epoch."""
    _log_scalars(trainer.label_loss_items(trainer.tloss, prefix="train"), trainer.epoch + 1)
    _log_scalars(trainer.lr, trainer.epoch + 1)
    if trainer.epoch == 1:
        _log_images({f.stem: str(f) for f in trainer.save_dir.glob("train_batch*.jpg")}, "Mosaic")



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

在每次拟合(train+val)历时结束时调用的回调函数。

源代码 ultralytics/utils/callbacks/neptune.py
def on_fit_epoch_end(trainer):
    """Callback function called at end of each fit (train+val) epoch."""
    if run and trainer.epoch == 0:
        from ultralytics.utils.torch_utils import model_info_for_loggers

        run["Configuration/Model"] = model_info_for_loggers(trainer)
    _log_scalars(trainer.metrics, trainer.epoch + 1)



ultralytics.utils.callbacks.neptune.on_val_end(validator)

每次验证结束时调用的回调函数。

源代码 ultralytics/utils/callbacks/neptune.py
def on_val_end(validator):
    """Callback function called at end of each validation."""
    if run:
        # Log val_labels and val_pred
        _log_images({f.stem: str(f) for f in validator.save_dir.glob("val*.jpg")}, "Validation")



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

训练结束时调用的回调函数。

源代码 ultralytics/utils/callbacks/neptune.py
def on_train_end(trainer):
    """Callback function called at end of training."""
    if run:
        # Log final results, CM matrix + PR plots
        files = [
            "results.png",
            "confusion_matrix.png",
            "confusion_matrix_normalized.png",
            *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R")),
        ]
        files = [(trainer.save_dir / f) for f in files if (trainer.save_dir / f).exists()]  # filter
        for f in files:
            _log_plot(title=f.stem, plot_path=f)
        # Log the final model
        run[f"weights/{trainer.args.name or trainer.args.task}/{trainer.best.name}"].upload(File(str(trainer.best)))





创建于 2023-11-12,更新于 2023-11-25
作者:glenn-jocher(3),Laughing-q(1)