CV之IG：基于CNN网络架构+ResNet网络进行DIY图像生成网络

CV之IG：基于CNN网络架构+ResNet网络进行DIY图像生成网络

目录

设计思路

实现代码

设计思路

实现代码

  

   

    

     

    
    

     

      # 定义图像生成网络：image, training，两个参数
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # Less border effects when padding a little before passing through ..
     
    
   

    

     

    
    

     

       image = tf.pad(image, [[0, 0], [10, 10], [10, 10], [0, 0]], mode='REFLECT')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 with tf.variable_scope('conv1'):
     
    
   

    

     

    
    

     

       conv1 = relu(instance_norm(conv2d(image, 3, 32, 9, 1)))
     
    
   

    

     

    
    

     
 with tf.variable_scope('conv2'):
     
    
   

    

     

    
    

     

       conv2 = relu(instance_norm(conv2d(conv1, 32, 64, 3, 2)))
     
    
   

    

     

    
    

     
 with tf.variable_scope('conv3'):
     
    
   

    

     

    
    

     

       conv3 = relu(instance_norm(conv2d(conv2, 64, 128, 3, 2)))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 with tf.variable_scope('res1'):
     
    
   

    

     

    
    

     

       res1 = residual(conv3, 128, 3, 1)
     
    
   

    

     

    
    

     
 with tf.variable_scope('res2'):
     
    
   

    

     

    
    

     

       res2 = residual(res1, 128, 3, 1)
     
    
   

    

     

    
    

     
 with tf.variable_scope('res3'):
     
    
   

    

     

    
    

     

       res3 = residual(res2, 128, 3, 1)
     
    
   

    

     

    
    

     
 with tf.variable_scope('res4'):
     
    
   

    

     

    
    

     

       res4 = residual(res3, 128, 3, 1)
     
    
   

    

     

    
    

     
 with tf.variable_scope('res5'):
     
    
   

    

     

    
    

     

       res5 = residual(res4, 128, 3, 1)
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 # print(res5.get_shape())
     
    
   

    

     

    
    

     
 with tf.variable_scope('deconv1'):
     
    
   

    

     

    
    

     
 # deconv1 = relu(instance_norm(conv2d_transpose(res5, 128, 64, 3, 2)))
     
    
   

    

     

    
    

     

       deconv1 = relu(instance_norm(resize_conv2d(res5, 128, 64, 3, 2, training)))
     
    
   

    

     

    
    

     
 with tf.variable_scope('deconv2'):
     
    
   

    

     

    
    

     
 # deconv2 = relu(instance_norm(conv2d_transpose(deconv1, 64, 32, 3, 2)))
     
    
   

    

     

    
    

     

       deconv2 = relu(instance_norm(resize_conv2d(deconv1, 64, 32, 3, 2, training)))
     
    
   

    

     

    
    

     
 with tf.variable_scope('deconv3'):
     
    
   

    

     

    
    

     
 # deconv_test = relu(instance_norm(conv2d(deconv2, 32, 32, 2, 1)))
     
    
   

    

     

    
    

     

       deconv3 = tf.nn.tanh(instance_norm(conv2d(deconv2, 32, 3, 9, 1)))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       y = (deconv3 + 1) * 127.5
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

       height = tf.shape(y)[1]
     
    
   

    

     

    
    

     

       width = tf.shape(y)[2]
     
    
   

    

     

    
    

     

       y = tf.slice(y, [0, 10, 10, 0], tf.stack([-1, height - 20, width - 20, -1]))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 return y
     
    
  
 

文章来源: yunyaniu.blog.csdn.net，作者：一个处女座的程序猿，版权归原作者所有，如需转载，请联系作者。

原文链接：yunyaniu.blog.csdn.net/article/details/82941106