transfer.py 文件源码

def buildTVNorm(model):
    adjustedImage = model.bgr


    yPlusOne = tf.slice(adjustedImage, [0,0,1,0], [1,imageShape[0],(imageShape[1]-1),imageShape[2]])
    xPlusOne = tf.slice(adjustedImage, [0,1,0,0], [1,(imageShape[0]-1),imageShape[1],imageShape[2]])

    inputNoiseYadj = tf.slice(adjustedImage,[0,0,0,0],[1,imageShape[0],(imageShape[1]-1),imageShape[2]])
    inputNoiseXadj = tf.slice(adjustedImage, [0,0,0,0], [1,(imageShape[0]-1),imageShape[1],imageShape[2]])


    lambdaBeta = (sigma**beta) / (imageShape[0]*imageShape[1]*((a*B)**beta))
    error1 = tf.slice(tf.square(yPlusOne-inputNoiseYadj), [0,0,0,0], [1,(imageShape[0]-1),(imageShape[1]-1), imageShape[2]])
    error2 = tf.slice(tf.square(xPlusOne-inputNoiseXadj), [0,0,0,0], [1,(imageShape[0]-1),(imageShape[1]-1), imageShape[2]])

    return lambdaBeta*tf.reduce_sum( tf.pow((error1+error2),(beta/2) ))