KeypointEndPointError

class mmeval.metrics.KeypointEndPointError(dataset_meta: Optional[Dict] = None, dist_collect_mode: str = 'unzip', dist_backend: Optional[str] = None, logger: Optional[logging.Logger] = None)

EPE evaluation metric.

Calculate the end-point error (EPE) of keypoints.

Note

• length of dataset: N

• num_keypoints: K

• number of keypoint dimensions: D (typically D = 2)

Examples

>>> from mmeval.metrics import KeypointEndPointError
>>> import numpy as np
>>> output = np.array([[[10.,  4.],
...     [10., 18.],
...     [ 0.,  0.],
...     [40., 40.],
...     [20., 10.]]])
>>> target = np.array([[[10.,  0.],
...     [10., 10.],
...     [ 0., -1.],
...     [30., 30.],
...     [ 0., 10.]]])
>>> keypoints_visible = np.array([[True, True, False, True, True]])
>>> predictions = [{'coords': output}]
>>> groundtruths = [{'coords': target, 'mask': keypoints_visible}]
>>> epe_metric = KeypointEndPointError()
>>> epe_metric(predictions, groundtruths)
{'EPE': 11.535533905029297}

add(predictions: Sequence[Dict], groundtruths: Sequence[Dict]) → None

Process one batch of predictions and groundtruths and add the intermediate results to self._results.

Parameters

• predictions (Sequence[dict]) –

  Predictions from the model. Each prediction dict has the following keys:

  • coords (np.ndarray, [1, K, D]): predicted keypoints coordinates

• groundtruths (Sequence[dict]) –

  The ground truth labels. Each groundtruth dict has the following keys:

  • coords (np.ndarray, [1, K, D]): ground truth keypoints coordinates

  • mask (np.ndarray, [1, K]): ground truth keypoints_visible

compute_metric(results: list) → Dict[str, float]

Compute the metrics from processed results.

Parameters

results (list) – The processed results of each batch.

Returns

The computed metrics. The keys are the names of the metrics, and the values are corresponding results.

Return type

Dict[str, float]