Dense_Transformer_Networks_3D.py 文件源码

def _makeT(self,cp):
        with tf.variable_scope('_makeT'):
            cp = tf.reshape(cp,(-1,3,self.X_controlP_number*self.Y_controlP_number*self.Z_controlP_number))
            cp = tf.cast(cp,'float32')       
            N_f = tf.shape(cp)[0]         
            #c_s
            x,y,z = tf.linspace(-1.,1.,self.X_controlP_number),tf.linspace(-1.,1.,self.Y_controlP_number),tf.linspace(-1.,1.,self.Z_controlP_number)
            x   = tf.tile(x,[self.Y_controlP_number*self.Z_controlP_number])
            y   = tf.tile(self._repeat(y,self.X_controlP_number,'float32'),[self.Z_controlP_number])
            z   = self._repeat(z,self.X_controlP_number*self.Y_controlP_number,'float32')
            xs,ys,zs = tf.transpose(tf.reshape(x,(-1,1))),tf.transpose(tf.reshape(y,(-1,1))),tf.transpose(tf.reshape(z,(-1,1)))
            cp_s = tf.concat([xs,ys,zs],0)
            cp_s_trans = tf.transpose(cp_s)
            # (4*4*4)*3 -> 64 * 3
            ##===Compute distance R
            xs_trans,ys_trans,zs_trans = tf.transpose(tf.stack([xs],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([ys],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([zs],axis=2),perm=[1,0,2])        
            xs, xs_trans = tf.meshgrid(xs,xs_trans);ys, ys_trans = tf.meshgrid(ys,ys_trans);zs, zs_trans = tf.meshgrid(zs,zs_trans)
            Rx,Ry, Rz = tf.square(tf.subtract(xs,xs_trans)),tf.square(tf.subtract(ys,ys_trans)),tf.square(tf.subtract(zs,zs_trans))
            R = tf.add_n([Rx,Ry,Rz])
            #print("R",sess.run(R))
            R = tf.multiply(R,tf.log(tf.clip_by_value(R,1e-10,1e+10)))
            #print("R",sess.run(R))
            ones = tf.ones([self.Y_controlP_number*self.X_controlP_number*self.Z_controlP_number,1],tf.float32)
            ones_trans = tf.transpose(ones)
            zeros = tf.zeros([4,4],tf.float32)
            Deltas1 = tf.concat([ones, cp_s_trans, R],1)
            Deltas2 = tf.concat([ones_trans,cp_s],0)
            Deltas2 = tf.concat([zeros,Deltas2],1)          
            Deltas = tf.concat([Deltas1,Deltas2],0)
            #print("Deltas",sess.run(Deltas))
            ##get deltas_inv
            Deltas_inv = tf.matrix_inverse(Deltas)
            Deltas_inv = tf.expand_dims(Deltas_inv,0)
            Deltas_inv = tf.reshape(Deltas_inv,[-1])
            Deltas_inv_f = tf.tile(Deltas_inv,tf.stack([N_f]))
            Deltas_inv_f = tf.reshape(Deltas_inv_f,tf.stack([N_f,self.X_controlP_number*self.Y_controlP_number*self.Z_controlP_number+4, -1]))
            cp_trans =tf.transpose(cp,perm=[0,2,1])
            zeros_f_In = tf.zeros([N_f,4,3],tf.float32)
            cp = tf.concat([cp_trans,zeros_f_In],1)
            #print("cp",sess.run(cp))
            #print("Deltas_inv_f",sess.run(Deltas_inv_f))
            T = tf.transpose(tf.matmul(Deltas_inv_f,cp),[0,2,1])
            #print("T",sess.run(T))
            return T