detregionloss_bnum.py 文件源码

def yoloconfidloss(y_true, y_pred, t):
    real_y_true = tf.select(t, y_true, K.zeros_like(y_true))
    pobj = K.sigmoid(y_pred)
    lo = K.square(real_y_true-pobj)
    value_if_true = lamda_confid_obj*(lo)
    value_if_false = lamda_confid_noobj*(lo)
    loss1 = tf.select(t, value_if_true, value_if_false)

    loss = K.mean(loss1) 
    #
    noobj = tf.select(t, K.zeros_like(y_pred), pobj)
    noobjcount = tf.select(t, K.zeros_like(y_pred), K.ones_like(y_pred))
    ave_anyobj = K.sum(noobj) / K.sum(noobjcount)
    #ave_anyobj = K.mean(pobj)
    obj = tf.select(t, pobj, K.zeros_like(y_pred))
    objcount = tf.select(t, K.ones_like(y_pred), K.zeros_like(y_pred))
    #ave_obj = K.mean( K.sum(obj, axis=1) / (K.sum(objcount, axis=1)+0.000001) ) # prevent div 0
    ave_obj =  K.sum(obj) / (K.sum(objcount)+0.000001)  # prevent div 0
    return loss, ave_anyobj, ave_obj

# shape is (gridcells*2,)