vis4d.eval.common.depth

Depth estimation evaluator.

Classes

DepthEvaluator([min_depth, max_depth, ...])

Depth estimation evaluator.

class DepthEvaluator(min_depth=0.0, max_depth=80.0, scale=1.0, epsilon=0.001)

Depth estimation evaluator.

__init__(min_depth=0.0, max_depth=80.0, scale=1.0, epsilon=0.001)

Initialize the optical flow evaluator.

Parameters:

• min_depth (float) – Minimum depth to evaluate. Defaults to 0.001.

• max_depth (float) – Maximum depth to evaluate. Defaults to 80.0.

• scale (float) – Scale factor for depth. Defaults to 1.0.

• epsilon (float) – Small value to avoid logarithms of small values. Defaults to 1e-3.

__repr__()

Concise representation of the evaluator.

Return type:

str

property metrics: list[str]

Supported metrics.

reset()

Reset evaluator for new round of evaluation.

Return type:

None

gather(gather_func)

Accumulate predictions across processes.

Return type:

None

process_batch(prediction, groundtruth)

Process a batch of data.

Parameters:

• prediction (np.array) – Prediction optical flow, in shape (B, H, W).

• groundtruth (np.array) – Target optical flow, in shape (B, H, W).

Return type:

None

evaluate(metric)

Evaluate predictions.

Returns a dict containing the raw data and a short description string containing a readablae 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:

• RuntimeError – if no data has been registered to be evaluated.

• ValueError – if metric is not supported.