MeanAbsoluteError¶
- class mmeval.metrics.MeanAbsoluteError(**kwargs)[source]¶
Mean Absolute Error metric for image.
Formula: mean(abs(a-b)).
- Parameters
**kwargs – Keyword parameters passed to
BaseMetric
.
Examples
>>> from mmeval import MeanAbsoluteError as MAE >>> import numpy as np >>> >>> mae = MAE() >>> gts = np.random.randint(0, 255, size=(3, 32, 32)) >>> preds = np.random.randint(0, 255, size=(3, 32, 32)) >>> mae(preds, gts) {'mae': ...}
Calculate MeanAbsoluteError between 2 images with mask:
>>> img1 = np.ones((32, 32, 3)) >>> img2 = np.ones((32, 32, 3)) * 2 >>> mask = np.ones((32, 32, 3)) * 2 >>> mask[:16] *= 0 >>> MAE.compute_mae(img1, img2, mask) 0.003921568627
- add(predictions: Sequence[numpy.ndarray], groundtruths: Sequence[numpy.ndarray], masks: Optional[Sequence[numpy.ndarray]] = None) → None[source]¶
Add MeanAbsoluteError score of batch to
self._results
- Parameters
predictions (Sequence[np.ndarray]) – Predictions of the model.
groundtruths (Sequence[np.ndarray]) – The ground truth images.
masks (Sequence[np.ndarray], optional) – Mask images. Defaults to None.
- static compute_mae(prediction: numpy.ndarray, groundtruth: numpy.ndarray, mask: Optional[numpy.ndarray] = None) → numpy.float32[source]¶
Calculate MeanAbsoluteError (Mean Absolute Error).
- Parameters
prediction (np.ndarray) – Images with range [0, 255].
groundtruth (np.ndarray) – Images with range [0, 255].
mask (np.ndarray, optional) – Mask of evaluation.
- Returns
MeanAbsoluteError result.
- Return type
np.float32
- compute_metric(results: List[numpy.float32]) → Dict[str, float][source]¶
Compute the MeanAbsoluteError metric.
This method would be invoked in
BaseMetric.compute
after distributed synchronization.- Parameters
results (List[np.float32]) – A list that consisting the MeanAbsoluteError score. This list has already been synced across all ranks.
- Returns
The computed MeanAbsoluteError metric.
- Return type
Dict[str, float]