src/Baselines/radarcam-depth/utils/eval_utils.py

"""Metric utilities used by the RadarCam-Depth evaluation backend."""
import numpy as np


def root_mean_sq_err(src, tgt):
    '''
    Root mean squared error
    Arg(s):
        src : numpy[float32]
            source array
        tgt : numpy[float32]
            target array
    Returns:
        float : root mean squared error
    '''

    return np.sqrt(np.mean((tgt - src) ** 2))

def mean_abs_err(src, tgt):
    '''
    Mean absolute error
    Arg(s):
        src : numpy[float32]
            source array
        tgt : numpy[float32]
            target array
    Returns:
        float : mean absolute error
    '''

    return np.mean(np.abs(tgt - src))

def inv_root_mean_sq_err(src, tgt):
    '''
    Inverse root mean squared error
    Arg(s):
        src : numpy[float32]
            source array
        tgt : numpy[float32]
            target array
    Returns:
        float : inverse root mean squared error
    '''

    return np.sqrt(np.mean(((1.0 / tgt) - (1.0 / src)) ** 2))

def inv_mean_abs_err(src, tgt):
    '''
    Inverse mean absolute error
    Arg(s):
        src : numpy[float32]
            source array
        tgt : numpy[float32]
            target array
    Returns:
        float : inverse mean absolute error
    '''

    return np.mean(np.abs((1.0 / tgt) - (1.0 / src)))

def mean_abs_rel_err(src, tgt):
    '''
    Mean absolute relative error (normalize absolute error)
    Arg(s):
        src : numpy[float32]
            source array
        tgt : numpy[float32]
            target array
    Returns:
        float : mean absolute relative error between source and target
    '''

    return np.mean(np.abs(src - tgt) / tgt)


def mean_sq_rel_err(src, tgt):
    '''
    Mean squared relative error (normalize squared error)
    Arg(s):
        src : numpy[float32]
            source array
        tgt : numpy[float32]
            target array
    Returns:
        float : mean squared relative error between source and target
    '''

    return np.mean(((src - tgt) ** 2) / tgt)


def thr_acc(src, tgt, thr=1.25):
    '''
    Threshold accuracy
    Arg(s):
        src : numpy[float32]
            source array
        tgt : numpy[float32]
            target array
        thr : float
            threshold
    Returns:
        float : threshold accuracy
    '''

    return np.mean(np.maximum((tgt / src), (src / tgt)) < thr)