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مرجع ل ultralytics/utils/callbacks/comet.py

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ultralytics.utils.callbacks.comet._get_comet_mode()

إرجاع وضع comet تعيين في متغيرات البيئة ، افتراضيا إلى "متصل" إذا لم يتم تعيينها.

شفرة المصدر في 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()

إرجاع اسم طراز ل Comet من متغير البيئة 'COMET_MODEL_NAME' أو الإعدادات الافتراضية إلى 'YOLOv8'.

شفرة المصدر في 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()

احصل على الفاصل الزمني لتسجيل دفعة التقييم من متغير البيئة أو استخدم القيمة الافتراضية 1.

شفرة المصدر في 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()

احصل على الحد الأقصى لعدد تنبؤات الصور للتسجيل من متغيرات البيئة.

شفرة المصدر في 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)

يقيس درجة الثقة المعطاة بواسطة عامل محدد في متغير بيئة.

شفرة المصدر في 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()

يحدد ما إذا كان يجب تسجيل مصفوفة الارتباك استنادا إلى إعدادات متغير البيئة.

شفرة المصدر في 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()

يحدد ما إذا كان سيتم تسجيل تنبؤات الصور استنادا إلى متغير بيئة محدد.

شفرة المصدر في 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)

إرجاع تجربة استنادا إلى الوضع واسم المشروع.

شفرة المصدر في 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)

يضمن أن كائن التجربة يتم إنشاؤه في عملية واحدة فقط أثناء التدريب الموزع.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

إرجاع البيانات الأولية ل YOLO التدريب بما في ذلك الحقبة وحالة توفير الأصول.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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 يغير حجم الصور أثناء التدريب ويتم تسوية قيم التسمية بناء على هذا الشكل الذي تم تغيير حجمه.

تعيد هذه الدالة قياس تسميات المربع المحيط إلى شكل الصورة الأصلي.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

تنسيق التعليقات التوضيحية للحقيقة الأساسية للكشف.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

تنسيق YOLO تنبؤات لتصور اكتشاف الكائنات.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

انضم إلى الحقيقة الأساسية والتعليقات التوضيحية للتنبؤ إن وجدت.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

قم بإنشاء خريطة بيانات تعريف لتنبؤات النموذج عن طريق تجميعها بناء على معرف الصورة.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

تسجيل مصفوفة الارتباك إلى Comet التجربه.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

يسجل الصور إلى التجربة باستخدام التعليقات التوضيحية الاختيارية.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

توقعت السجلات مربعات لصورة واحدة أثناء التدريب.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

يسجل مخططات التقييم ومخططات التسمية للتجربة.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

تسجيل النموذج الأفضل تدريبا على Comet.مل.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

إنشاء تجربة CometML أو استئنافها في بداية YOLO روتين ما قبل التدريب.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

سجل المقاييس واحفظ الصور المجمعة في نهاية فترات التدريب.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

يسجل أصول النموذج في نهاية كل حقبة.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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)

إجراء العمليات في نهاية التدريب.

شفرة المصدر في ultralytics/utils/callbacks/comet.py
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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





تم إنشاء 2023-11-12, اخر تحديث 2023-11-25
المؤلفون: جلين جوشر (3) ، يضحك س (1)