Dense_Transformer_Network.py 文件源码

def _meshgrid(self):
        with tf.variable_scope('_meshgrid'):
            x_t = tf.matmul(tf.ones(shape=tf.stack([self.out_height, 1])),
                            tf.transpose(tf.expand_dims(tf.linspace(-1.0, 1.0, self.out_width), 1), [1, 0]))
            y_t = tf.matmul(tf.expand_dims(tf.linspace(-1.0, 1.0, self.out_height), 1),
                            tf.ones(shape=tf.stack([1, self.out_width])))
            x_t_flat = tf.reshape(x_t, (1, -1))
            y_t_flat = tf.reshape(y_t, (1, -1))
            px,py = tf.stack([x_t_flat],axis=2),tf.stack([y_t_flat],axis=2)
            #source control points
            x,y = tf.linspace(-1.,1.,self.Column_controlP_number),tf.linspace(-1.,1.,self.Row_controlP_number)
            x,y = tf.meshgrid(x,y)
            xs,ys = tf.transpose(tf.reshape(x,(-1,1))),tf.transpose(tf.reshape(y,(-1,1)))
            cpx,cpy = tf.transpose(tf.stack([xs],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([ys],axis=2),perm=[1,0,2])
            px, cpx = tf.meshgrid(px,cpx);py, cpy = tf.meshgrid(py,cpy)           
            #Compute distance R
            Rx,Ry = tf.square(tf.subtract(px,cpx)),tf.square(tf.subtract(py,cpy))
            R = tf.add(Rx,Ry)          
            R = tf.multiply(R,tf.log(tf.clip_by_value(R,1e-10,1e+10)))
            #Source coordinates
            ones = tf.ones_like(x_t_flat) 
            grid = tf.concat([ones, x_t_flat, y_t_flat,R],0)
            grid = tf.reshape(grid,[-1])
            grid = tf.reshape(grid,[self.Column_controlP_number*self.Row_controlP_number+3,self.out_height*self.out_width])
            return grid