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| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING | |
| try: | |
| assert not TESTS_RUNNING # do not log pytest | |
| assert SETTINGS["neptune"] is True # verify integration is enabled | |
| import neptune | |
| from neptune.types import File | |
| assert hasattr(neptune, "__version__") | |
| run = None # NeptuneAI experiment logger instance | |
| except (ImportError, AssertionError): | |
| neptune = None | |
| 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) | |
| 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)) | |
| 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) | |
| 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}") | |
| 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") | |
| 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) | |
| 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") | |
| 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))) | |
| callbacks = ( | |
| { | |
| "on_pretrain_routine_start": on_pretrain_routine_start, | |
| "on_train_epoch_end": on_train_epoch_end, | |
| "on_fit_epoch_end": on_fit_epoch_end, | |
| "on_val_end": on_val_end, | |
| "on_train_end": on_train_end, | |
| } | |
| if neptune | |
| else {} | |
| ) | |