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objdetpredictionhandler

objdetpredictionhandler

Module with a prediction handler for object detection and also supportive functions

Classes

ObjDetPredictionHandler 

ObjDetPredictionHandler(
    prediction_filter,
    prediction_prefix="pred",
    pred_identifier="image_location",
    detection_idx_col=DETECTION_INDEX_COLUMN_NAME,
    apply_sigmoid=True,
)

Bases: PredictionHandler

Prediction handler for object detection predictions (BoundingBox, class prediction)

Initializes the ObjDetPredictionHandler

Source code in niceml/mlcomponents/predictionhandlers/objdetpredictionhandler.py 
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def __init__(  # noqa: PLR0913
    self,
    prediction_filter: PredictionFilter,
    prediction_prefix: str = "pred",
    pred_identifier: str = "image_location",
    detection_idx_col: str = DETECTION_INDEX_COLUMN_NAME,
    apply_sigmoid: bool = True,
):
    """Initializes the ObjDetPredictionHandler"""
    super().__init__()
    self.prediction_filter = prediction_filter
    self.prediction_prefix = prediction_prefix
    self.apply_sigmoid = apply_sigmoid
    self.pred_identifier = pred_identifier
    self.detection_idx_col = detection_idx_col
    self.data = None
    self.data_columns = [pred_identifier, detection_idx_col]
    self.data_columns += list(asdict(BoundingBox(0, 0, 0, 0)).keys())

    self.anchor_generator = AnchorGenerator()
    self.anchors = None
    self.anchor_array = None
Functions
__enter__ 
__enter__()

Init self.data after the context is entered

Source code in niceml/mlcomponents/predictionhandlers/objdetpredictionhandler.py 
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def __enter__(self):
    """Init `self.data` after the context is entered"""
    self.data = []
    if isinstance(self.data_description, OutputObjDetDataDescription):
        for class_count in range(self.data_description.get_output_class_count()):
            self.data_columns.append(f"{self.prediction_prefix}_{class_count:04d}")
    return self
__exit__ 
__exit__(exc_type, exc_value, exc_traceback)

Save the data in self.data as a parquet file

Source code in niceml/mlcomponents/predictionhandlers/objdetpredictionhandler.py 
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def __exit__(self, exc_type, exc_value, exc_traceback):
    """Save the data in `self.data` as a parquet file"""
    if self.data is None:
        logging.getLogger(__name__).warning(
            "PredictionHandler: %s has no data to write!",
            self.filename,
        )
    else:
        data_frame: pd.DataFrame = pd.DataFrame(self.data)
        self.exp_context.write_parquet(
            data_frame,
            join(ExperimentFilenames.PREDICTION_FOLDER, self.filename + ".parq"),
        )
add_prediction 
add_prediction(data_info_list, prediction_batch)

Gets the results of an object detection model, de

Source code in niceml/mlcomponents/predictionhandlers/objdetpredictionhandler.py 
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def add_prediction(
    self, data_info_list: List[ObjDetDataInfo], prediction_batch: np.ndarray
):
    """Gets the results of an object detection model, de"""
    output_dd: OutputObjDetDataDescription = check_instance(
        self.data_description, OutputObjDetDataDescription
    )
    for curr_batch, curr_data_info in zip(prediction_batch, data_info_list):
        decoded_box_predictions = self._decode_box_predictions(
            box_predictions=curr_batch
        )

        if self.apply_sigmoid:
            decoded_box_predictions = apply_sigmoid_on_cls_predictions(
                decoded_box_predictions,
                output_dd.get_coordinates_count(),
            )

        filtered_box_predictions = self.prediction_filter.filter(
            decoded_box_predictions
        )

        if len(filtered_box_predictions) > 0:
            for curr_index, prediction in enumerate(filtered_box_predictions):
                self._add_data(
                    identifier=curr_data_info.get_identifier(),
                    prediction=prediction,
                    detection_index=curr_index,
                )
        else:
            prediction = np.zeros(
                (
                    output_dd.get_coordinates_count()
                    + output_dd.get_output_class_count(),
                )
            )
            self._add_data(
                curr_data_info.get_identifier(),
                prediction=prediction,
                detection_index=NO_PREDICTIONS_DETECTION_VALUE,
            )
initialize 
initialize()

Initializes the prediction handler

Source code in niceml/mlcomponents/predictionhandlers/objdetpredictionhandler.py 
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def initialize(self):
    """Initializes the prediction handler"""
    self.anchors: List[BoundingBox] = self.anchor_generator.generate_anchors(
        data_description=self.data_description
    )
    self.anchor_array = np.array([box.get_absolute_xywh() for box in self.anchors])
    self.prediction_filter.initialize(data_description=self.data_description)

Functions

apply_sigmoid_on_cls_predictions 

apply_sigmoid_on_cls_predictions(
    box_predictions, coordinates_count
)

Applies sigmoid only on the classification part

Source code in niceml/mlcomponents/predictionhandlers/objdetpredictionhandler.py 
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def apply_sigmoid_on_cls_predictions(
    box_predictions: np.ndarray, coordinates_count: int
) -> np.ndarray:
    """Applies sigmoid only on the classification part"""
    box_predictions[:, coordinates_count:] = 1 / (
        1 + np.exp(-box_predictions[:, coordinates_count:])
    )
    return box_predictions