angular_loss.py 文件源码

def angular_mc_loss(f, f_p, alpha=45, in_degree=True):
    '''
    Args:
        f (chainer.Variable or xp.npdarray):
            Anchor vectors. Each vectors in f must be l2 normalized.
        f_p (chainer.Variable or xp.npdarray):
            Positive vectors. Each vectors in f must be l2 normalized.
    '''
    xp = cuda.get_array_module(f)

    if in_degree:
        alpha = np.deg2rad(alpha)
    sq_tan_alpha = np.tan(alpha) ** 2
    n_pairs = len(f)

    # first and second term of f_{a,p,n}
    term1 = 4 * sq_tan_alpha + matmul(f + f_p, transpose(f_p))
    term2 = 2 * (1 + sq_tan_alpha) * F.sum(f * f_p, axis=1, keepdims=True)
#    term2 = 2 * (1 + sq_tan_alpha) * F.batch_matmul(f, f_p, transa=True).reshape(n_pairs, 1)

    f_apn = term1 - F.broadcast_to(term2, (n_pairs, n_pairs))
    # multiply zero to diagonal components of f_apn
    mask = xp.ones_like(f_apn.data) - xp.eye(n_pairs, dtype=f.dtype)
    f_apn = f_apn * mask

    return F.average(F.logsumexp(f_apn, axis=1))