tfma.metrics.SemanticSegmentationTruePositive

Calculates the true postive for semantic segmentation.

Inherits From: Metric

tfma.metrics.SemanticSegmentationTruePositive(
    class_ids: List[int],
    ground_truth_key: str,
    prediction_key: str,
    decode_ground_truth: bool = True,
    decode_prediction: bool = False,
    ignore_ground_truth_id: Optional[int] = None,
    name: Optional[str] = None
)

Args
class_ids	the class ids for calculating metrics.
ground_truth_key	the key for storing the ground truth of encoded image with class ids.
prediction_key	the key for storing the predictions of encoded image with class ids.
decode_ground_truth	If true, the ground truth is assumed to be bytes of images and will be decoded. By default it is true assuming the label is the bytes of image.
decode_prediction	If true, the prediction is assumed to be bytes of images and will be decoded. By default it is false assuming the model outputs numpy arrays or tensors.
ignore_ground_truth_id	(Optional) The id of ground truth to be ignored.
name	(Optional) string name of the metric instance.

Attributes
compute_confidence_interval	Whether to compute confidence intervals for this metric. Note that this may not completely remove the computational overhead involved in computing a given metric. This is only respected by the jackknife confidence interval method.

Methods

computations

View source

computations(
    eval_config: Optional[tfma.EvalConfig] = None,
    schema: Optional[schema_pb2.Schema] = None,
    model_names: Optional[List[str]] = None,
    output_names: Optional[List[str]] = None,
    sub_keys: Optional[List[Optional[SubKey]]] = None,
    aggregation_type: Optional[AggregationType] = None,
    class_weights: Optional[Dict[int, float]] = None,
    example_weighted: bool = False,
    query_key: Optional[str] = None
) -> tfma.metrics.MetricComputations

Creates computations associated with metric.

from_config

View source

@classmethod
from_config(
    config: Dict[str, Any]
) -> 'Metric'

get_config

View source

get_config() -> Dict[str, Any]

Returns serializable config.