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Référence pour ultralytics/utils/callbacks/comet.py

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ultralytics.utils.callbacks.comet._get_comet_mode()

Renvoie le mode de comet défini dans les variables d'environnement. La valeur par défaut est 'online' si elle n'est pas définie.

Code source dans 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()

Renvoie le nom du modèle pour Comet à partir de la variable d'environnement 'COMET_MODEL_NAME' ou par défaut 'YOLOv8'.

Code source dans 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()

Obtiens l'intervalle d'enregistrement du lot d'évaluation à partir de la variable d'environnement ou utilise la valeur par défaut 1.

Code source dans 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()

Récupère le nombre maximum de prédictions d'images à enregistrer dans les variables d'environnement.

Code source dans 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)

Échelle le score de confiance donné par un facteur spécifié dans une variable d'environnement.

Code source dans 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()

Détermine si la matrice de confusion doit être enregistrée en fonction des paramètres de la variable d'environnement.

Code source dans 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()

Détermine s'il faut enregistrer les prédictions d'images en fonction d'une variable d'environnement spécifiée.

Code source dans 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)

Renvoie une expérience basée sur le mode et le nom du projet.

Code source dans 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)

Veille à ce que l'objet d'expérience ne soit créé que dans un seul processus lors de la formation distribuée.

Code source dans 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)

Renvoie les métadonnées de l'entraînement YOLO , y compris l'époque et l'état de sauvegarde des actifs.

Code source dans 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 redimensionne les images pendant la formation et les valeurs des étiquettes sont normalisées en fonction de cette forme redimensionnée.

Cette fonction redimensionne les étiquettes de la boîte de délimitation en fonction de la forme originale de l'image.

Code source dans 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)

Formate les annotations de vérité terrain pour la détection.

Code source dans 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)

Format YOLO prédictions pour la visualisation de la détection d'objets.

Code source dans 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)

Joins les annotations de vérité terrain et de prédiction si elles existent.

Code source dans 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)

Crée une carte de métadonnées pour les prédictions du modèle en les regroupant sur la base de l'identifiant de l'image.

Code source dans 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)

Enregistre la matrice de confusion Ă  l'adresse Comet .

Code source dans 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)

Enregistre les images dans l'expérience avec des annotations facultatives.

Code source dans 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)

Logs des boîtes prédites pour une seule image pendant l'entraînement.

Code source dans 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)

Enregistre les parcelles d'évaluation et les parcelles d'étiquettes pour l'expérience.

Code source dans 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)

Enregistre le modèle le mieux formé sur Comet.ml.

Code source dans 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)

Crée ou reprend une expérience CometML au début d'une routine de pré-entraînement YOLO .

Code source dans 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)

Consigne les métriques et enregistre les images du lot à la fin des époques d'apprentissage.

Code source dans 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)

Enregistre les actifs du modèle à la fin de chaque époque.

Code source dans 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)

Effectue les opérations à la fin de la formation.

Code source dans 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





Créé le 2023-11-12, Mis à jour le 2023-11-25
Auteurs : glenn-jocher (3), Laughing-q (1)