vis4d.eval.common.binary

Binary occupancy evaluator.

Functions

threshold_and_flatten(prediction, target, ...)

Thresholds the predictions based on the provided treshold value.

Classes

Creates a new Evaluater that evaluates binary predictions.

threshold_and_flatten(prediction, target, threshold_value)[source]

Thresholds the predictions based on the provided treshold value.

Applies the following actions:: prediction -> prediction >= threshold_value pred, gt = pred.ravel().bool(), gt.ravel().bool()

Parameters:

prediction (Union[ndarray[Any, dtype[bool_]], ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[int32]], ndarray[Any, dtype[int64]], ndarray[Any, dtype[uint8]], ndarray[Any, dtype[uint16]], ndarray[Any, dtype[uint32]]]) – Prediction array with continuous values
target (Union[ndarray[Any, dtype[bool_]], ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[int32]], ndarray[Any, dtype[int64]], ndarray[Any, dtype[uint8]], ndarray[Any, dtype[uint16]], ndarray[Any, dtype[uint32]]]) – Grondgtruth values {0,1}
threshold_value (float) – Value to use to convert the continuous prediction into binary.

Return type:

tuple[ndarray[Any, dtype[bool_]], ndarray[Any, dtype[bool_]]]

Returns:

tuple of two boolean arrays, prediction and target

class BinaryEvaluator(threshold=0.5)[source]

Creates a new Evaluater that evaluates binary predictions.

__init__(threshold=0.5)[source]

Creates a new binary evaluator.

Parameters:: threshold (float) – Threshold for prediction to convert to binary. All prediction that are higher than this value will be assigned the ‘True’ label

Reset the saved predictions to start new round of evaluation.

process_batch(prediction, groundtruth)[source]

Processes a new (batch) of predictions.

Calculates the metrics and caches them internally.

Parameters:

prediction (ArrayLike) – the prediction(continuous values or bin) (Batch x Pts)
groundtruth (ArrayLike) – the groundtruth (binary) (Batch x Pts)

Return type:

None

evaluate(metric)[source]

Evaluate predictions.

Returns a dict containing the raw data and a short description string containing a readable result.

Parameters:

metric (str) – Metric to use. See @property metric

Return type:

tuple[Dict[str, Union[float, int, Tensor]], str]

Returns:

metric_data, description tuple containing the metric data (dict with metric name and value) as well as a short string with shortened information.

Raises: