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参考 ultralytics/utils/callbacks/comet.py

備考

このファイルはhttps://github.com/ultralytics/ultralytics/blob/main/ ultralytics/utils/callbacks/ comet.py にあります。もし問題を発見したら、Pull Request🛠️ を投稿して修正にご協力ください。ありがとうございました!



ultralytics.utils.callbacks.comet._get_comet_mode()

環境変数で設定されているcomet のモードを返す。設定されていない場合のデフォルトは「online」。

ソースコード ultralytics/utils/callbacks/comet.py
def _get_comet_mode():
    """Returns the mode of comet set in the environment variables, defaults to 'online' if not set."""
    return os.getenv("COMET_MODE", "online")



ultralytics.utils.callbacks.comet._get_comet_model_name()

環境変数 'COMET_MODEL_NAME' からComet のモデル名を返すか、デフォルトを 'YOLOv8' とする。

ソースコード ultralytics/utils/callbacks/comet.py
def _get_comet_model_name():
    """Returns the model name for Comet from the environment variable 'COMET_MODEL_NAME' or defaults to 'YOLOv8'."""
    return os.getenv("COMET_MODEL_NAME", "YOLOv8")



ultralytics.utils.callbacks.comet._get_eval_batch_logging_interval()

評価バッチのロギング間隔を環境変数から取得するか、デフォルト値の1を使用する。

ソースコード ultralytics/utils/callbacks/comet.py
def _get_eval_batch_logging_interval():
    """Get the evaluation batch logging interval from environment variable or use default value 1."""
    return int(os.getenv("COMET_EVAL_BATCH_LOGGING_INTERVAL", 1))



ultralytics.utils.callbacks.comet._get_max_image_predictions_to_log()

ログに記録する画像予測数の最大値を環境変数から取得する。

ソースコード ultralytics/utils/callbacks/comet.py
def _get_max_image_predictions_to_log():
    """Get the maximum number of image predictions to log from the environment variables."""
    return int(os.getenv("COMET_MAX_IMAGE_PREDICTIONS", 100))



ultralytics.utils.callbacks.comet._scale_confidence_score(score)

与えられた信頼度スコアを環境変数で指定された係数でスケーリングする。

ソースコード ultralytics/utils/callbacks/comet.py
def _scale_confidence_score(score):
    """Scales the given confidence score by a factor specified in an environment variable."""
    scale = float(os.getenv("COMET_MAX_CONFIDENCE_SCORE", 100.0))
    return score * scale



ultralytics.utils.callbacks.comet._should_log_confusion_matrix()

環境変数の設定に基づいて、混同行列をログに記録するかどうかを決定する。

ソースコード ultralytics/utils/callbacks/comet.py
def _should_log_confusion_matrix():
    """Determines if the confusion matrix should be logged based on the environment variable settings."""
    return os.getenv("COMET_EVAL_LOG_CONFUSION_MATRIX", "false").lower() == "true"



ultralytics.utils.callbacks.comet._should_log_image_predictions()

指定された環境変数に基づいて画像予測ログを記録するかどうかを決定する。

ソースコード ultralytics/utils/callbacks/comet.py
def _should_log_image_predictions():
    """Determines whether to log image predictions based on a specified environment variable."""
    return os.getenv("COMET_EVAL_LOG_IMAGE_PREDICTIONS", "true").lower() == "true"



ultralytics.utils.callbacks.comet._get_experiment_type(mode, project_name)

モードとプロジェクト名に基づいた実験を返す。

ソースコード ultralytics/utils/callbacks/comet.py
def _get_experiment_type(mode, project_name):
    """Return an experiment based on mode and project name."""
    if mode == "offline":
        return comet_ml.OfflineExperiment(project_name=project_name)

    return comet_ml.Experiment(project_name=project_name)



ultralytics.utils.callbacks.comet._create_experiment(args)

分散トレーニング中、実験オブジェクトが単一のプロセスでのみ作成されるようにする。

ソースコード ultralytics/utils/callbacks/comet.py
def _create_experiment(args):
    """Ensures that the experiment object is only created in a single process during distributed training."""
    if RANK not in (-1, 0):
        return
    try:
        comet_mode = _get_comet_mode()
        _project_name = os.getenv("COMET_PROJECT_NAME", args.project)
        experiment = _get_experiment_type(comet_mode, _project_name)
        experiment.log_parameters(vars(args))
        experiment.log_others(
            {
                "eval_batch_logging_interval": _get_eval_batch_logging_interval(),
                "log_confusion_matrix_on_eval": _should_log_confusion_matrix(),
                "log_image_predictions": _should_log_image_predictions(),
                "max_image_predictions": _get_max_image_predictions_to_log(),
            }
        )
        experiment.log_other("Created from", "yolov8")

    except Exception as e:
        LOGGER.warning(f"WARNING ⚠️ Comet installed but not initialized correctly, not logging this run. {e}")



ultralytics.utils.callbacks.comet._fetch_trainer_metadata(trainer)

エポックやアセット保存状況など、YOLO トレーニングのメタデータを返します。

ソースコード ultralytics/utils/callbacks/comet.py
def _fetch_trainer_metadata(trainer):
    """Returns metadata for YOLO training including epoch and asset saving status."""
    curr_epoch = trainer.epoch + 1

    train_num_steps_per_epoch = len(trainer.train_loader.dataset) // trainer.batch_size
    curr_step = curr_epoch * train_num_steps_per_epoch
    final_epoch = curr_epoch == trainer.epochs

    save = trainer.args.save
    save_period = trainer.args.save_period
    save_interval = curr_epoch % save_period == 0
    save_assets = save and save_period > 0 and save_interval and not final_epoch

    return dict(curr_epoch=curr_epoch, curr_step=curr_step, save_assets=save_assets, final_epoch=final_epoch)



ultralytics.utils.callbacks.comet._scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad)

YOLOv8 学習中に画像のサイズが変更され、ラベル値はこのサイズ変更された形状に基づいて正規化される。

この関数は、バウンディングボックスのラベルを元の画像形状に再スケーリングする。

ソースコード ultralytics/utils/callbacks/comet.py
def _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad):
    """
    YOLOv8 resizes images during training and the label values are normalized based on this resized shape.

    This function rescales the bounding box labels to the original image shape.
    """

    resized_image_height, resized_image_width = resized_image_shape

    # Convert normalized xywh format predictions to xyxy in resized scale format
    box = ops.xywhn2xyxy(box, h=resized_image_height, w=resized_image_width)
    # Scale box predictions from resized image scale back to original image scale
    box = ops.scale_boxes(resized_image_shape, box, original_image_shape, ratio_pad)
    # Convert bounding box format from xyxy to xywh for Comet logging
    box = ops.xyxy2xywh(box)
    # Adjust xy center to correspond top-left corner
    box[:2] -= box[2:] / 2
    box = box.tolist()

    return box



ultralytics.utils.callbacks.comet._format_ground_truth_annotations_for_detection(img_idx, image_path, batch, class_name_map=None)

検出のためのグランドトゥルース注釈をフォーマットする。

ソースコード ultralytics/utils/callbacks/comet.py
def _format_ground_truth_annotations_for_detection(img_idx, image_path, batch, class_name_map=None):
    """Format ground truth annotations for detection."""
    indices = batch["batch_idx"] == img_idx
    bboxes = batch["bboxes"][indices]
    if len(bboxes) == 0:
        LOGGER.debug(f"COMET WARNING: Image: {image_path} has no bounding boxes labels")
        return None

    cls_labels = batch["cls"][indices].squeeze(1).tolist()
    if class_name_map:
        cls_labels = [str(class_name_map[label]) for label in cls_labels]

    original_image_shape = batch["ori_shape"][img_idx]
    resized_image_shape = batch["resized_shape"][img_idx]
    ratio_pad = batch["ratio_pad"][img_idx]

    data = []
    for box, label in zip(bboxes, cls_labels):
        box = _scale_bounding_box_to_original_image_shape(box, resized_image_shape, original_image_shape, ratio_pad)
        data.append(
            {
                "boxes": [box],
                "label": f"gt_{label}",
                "score": _scale_confidence_score(1.0),
            }
        )

    return {"name": "ground_truth", "data": data}



ultralytics.utils.callbacks.comet._format_prediction_annotations_for_detection(image_path, metadata, class_label_map=None)

オブジェクト検出の視覚化のためのYOLO 予測のフォーマット。

ソースコード ultralytics/utils/callbacks/comet.py
def _format_prediction_annotations_for_detection(image_path, metadata, class_label_map=None):
    """Format YOLO predictions for object detection visualization."""
    stem = image_path.stem
    image_id = int(stem) if stem.isnumeric() else stem

    predictions = metadata.get(image_id)
    if not predictions:
        LOGGER.debug(f"COMET WARNING: Image: {image_path} has no bounding boxes predictions")
        return None

    data = []
    for prediction in predictions:
        boxes = prediction["bbox"]
        score = _scale_confidence_score(prediction["score"])
        cls_label = prediction["category_id"]
        if class_label_map:
            cls_label = str(class_label_map[cls_label])

        data.append({"boxes": [boxes], "label": cls_label, "score": score})

    return {"name": "prediction", "data": data}



ultralytics.utils.callbacks.comet._fetch_annotations(img_idx, image_path, batch, prediction_metadata_map, class_label_map)

グランドトゥルースと予測アノテーションが存在する場合は、それらを結合する。

ソースコード ultralytics/utils/callbacks/comet.py
def _fetch_annotations(img_idx, image_path, batch, prediction_metadata_map, class_label_map):
    """Join the ground truth and prediction annotations if they exist."""
    ground_truth_annotations = _format_ground_truth_annotations_for_detection(
        img_idx, image_path, batch, class_label_map
    )
    prediction_annotations = _format_prediction_annotations_for_detection(
        image_path, prediction_metadata_map, class_label_map
    )

    annotations = [
        annotation for annotation in [ground_truth_annotations, prediction_annotations] if annotation is not None
    ]
    return [annotations] if annotations else None



ultralytics.utils.callbacks.comet._create_prediction_metadata_map(model_predictions)

画像IDに基づいてグループ化し、モデル予測用のメタデータマップを作成する。

ソースコード ultralytics/utils/callbacks/comet.py
def _create_prediction_metadata_map(model_predictions):
    """Create metadata map for model predictions by groupings them based on image ID."""
    pred_metadata_map = {}
    for prediction in model_predictions:
        pred_metadata_map.setdefault(prediction["image_id"], [])
        pred_metadata_map[prediction["image_id"]].append(prediction)

    return pred_metadata_map



ultralytics.utils.callbacks.comet._log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)

Comet 、混同行列を記録する。

ソースコード ultralytics/utils/callbacks/comet.py
def _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch):
    """Log the confusion matrix to Comet experiment."""
    conf_mat = trainer.validator.confusion_matrix.matrix
    names = list(trainer.data["names"].values()) + ["background"]
    experiment.log_confusion_matrix(
        matrix=conf_mat, labels=names, max_categories=len(names), epoch=curr_epoch, step=curr_step
    )



ultralytics.utils.callbacks.comet._log_images(experiment, image_paths, curr_step, annotations=None)

オプションの注釈付きで画像を実験に記録する。

ソースコード ultralytics/utils/callbacks/comet.py
def _log_images(experiment, image_paths, curr_step, annotations=None):
    """Logs images to the experiment with optional annotations."""
    if annotations:
        for image_path, annotation in zip(image_paths, annotations):
            experiment.log_image(image_path, name=image_path.stem, step=curr_step, annotations=annotation)

    else:
        for image_path in image_paths:
            experiment.log_image(image_path, name=image_path.stem, step=curr_step)



ultralytics.utils.callbacks.comet._log_image_predictions(experiment, validator, curr_step)

トレーニング中に1つの画像に対して予測されたボックスのログ。

ソースコード ultralytics/utils/callbacks/comet.py
def _log_image_predictions(experiment, validator, curr_step):
    """Logs predicted boxes for a single image during training."""
    global _comet_image_prediction_count

    task = validator.args.task
    if task not in COMET_SUPPORTED_TASKS:
        return

    jdict = validator.jdict
    if not jdict:
        return

    predictions_metadata_map = _create_prediction_metadata_map(jdict)
    dataloader = validator.dataloader
    class_label_map = validator.names

    batch_logging_interval = _get_eval_batch_logging_interval()
    max_image_predictions = _get_max_image_predictions_to_log()

    for batch_idx, batch in enumerate(dataloader):
        if (batch_idx + 1) % batch_logging_interval != 0:
            continue

        image_paths = batch["im_file"]
        for img_idx, image_path in enumerate(image_paths):
            if _comet_image_prediction_count >= max_image_predictions:
                return

            image_path = Path(image_path)
            annotations = _fetch_annotations(
                img_idx,
                image_path,
                batch,
                predictions_metadata_map,
                class_label_map,
            )
            _log_images(
                experiment,
                [image_path],
                curr_step,
                annotations=annotations,
            )
            _comet_image_prediction_count += 1



ultralytics.utils.callbacks.comet._log_plots(experiment, trainer)

実験の評価プロットとラベルプロット。

ソースコード ultralytics/utils/callbacks/comet.py
def _log_plots(experiment, trainer):
    """Logs evaluation plots and label plots for the experiment."""
    plot_filenames = [trainer.save_dir / f"{plots}.png" for plots in EVALUATION_PLOT_NAMES]
    _log_images(experiment, plot_filenames, None)

    label_plot_filenames = [trainer.save_dir / f"{labels}.jpg" for labels in LABEL_PLOT_NAMES]
    _log_images(experiment, label_plot_filenames, None)



ultralytics.utils.callbacks.comet._log_model(experiment, trainer)

最適に訓練されたモデルをComet.ml にログ出力する。

ソースコード ultralytics/utils/callbacks/comet.py
def _log_model(experiment, trainer):
    """Log the best-trained model to Comet.ml."""
    model_name = _get_comet_model_name()
    experiment.log_model(model_name, file_or_folder=str(trainer.best), file_name="best.pt", overwrite=True)



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

YOLO プレトレーニングルーチンの開始時に CometML 実験を作成または再開する。

ソースコード ultralytics/utils/callbacks/comet.py
def on_pretrain_routine_start(trainer):
    """Creates or resumes a CometML experiment at the start of a YOLO pre-training routine."""
    experiment = comet_ml.get_global_experiment()
    is_alive = getattr(experiment, "alive", False)
    if not experiment or not is_alive:
        _create_experiment(trainer.args)



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

学習エポック終了時にメトリクスを記録し、バッチ画像を保存します。

ソースコード ultralytics/utils/callbacks/comet.py
def on_train_epoch_end(trainer):
    """Log metrics and save batch images at the end of training epochs."""
    experiment = comet_ml.get_global_experiment()
    if not experiment:
        return

    metadata = _fetch_trainer_metadata(trainer)
    curr_epoch = metadata["curr_epoch"]
    curr_step = metadata["curr_step"]

    experiment.log_metrics(trainer.label_loss_items(trainer.tloss, prefix="train"), step=curr_step, epoch=curr_epoch)

    if curr_epoch == 1:
        _log_images(experiment, trainer.save_dir.glob("train_batch*.jpg"), curr_step)



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

各エポック終了時にモデル資産をログに記録する。

ソースコード ultralytics/utils/callbacks/comet.py
def on_fit_epoch_end(trainer):
    """Logs model assets at the end of each epoch."""
    experiment = comet_ml.get_global_experiment()
    if not experiment:
        return

    metadata = _fetch_trainer_metadata(trainer)
    curr_epoch = metadata["curr_epoch"]
    curr_step = metadata["curr_step"]
    save_assets = metadata["save_assets"]

    experiment.log_metrics(trainer.metrics, step=curr_step, epoch=curr_epoch)
    experiment.log_metrics(trainer.lr, step=curr_step, epoch=curr_epoch)
    if curr_epoch == 1:
        from ultralytics.utils.torch_utils import model_info_for_loggers

        experiment.log_metrics(model_info_for_loggers(trainer), step=curr_step, epoch=curr_epoch)

    if not save_assets:
        return

    _log_model(experiment, trainer)
    if _should_log_confusion_matrix():
        _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
    if _should_log_image_predictions():
        _log_image_predictions(experiment, trainer.validator, curr_step)



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

トレーニングの最後にオペレーションを行う。

ソースコード ultralytics/utils/callbacks/comet.py
def on_train_end(trainer):
    """Perform operations at the end of training."""
    experiment = comet_ml.get_global_experiment()
    if not experiment:
        return

    metadata = _fetch_trainer_metadata(trainer)
    curr_epoch = metadata["curr_epoch"]
    curr_step = metadata["curr_step"]
    plots = trainer.args.plots

    _log_model(experiment, trainer)
    if plots:
        _log_plots(experiment, trainer)

    _log_confusion_matrix(experiment, trainer, curr_step, curr_epoch)
    _log_image_predictions(experiment, trainer.validator, curr_step)
    experiment.end()

    global _comet_image_prediction_count
    _comet_image_prediction_count = 0





作成日:2023-11-12 更新日:2023-11-25
作成者:glenn-jocher(3),Laughing-q(1)