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TF框架利用ResNet算法网络DIY自定义图像生成网络(定义卷积和反卷积)实现代码

目录

基于TF框架利用ResNet算法网络DIY自定义图像生成网络(定义卷积和反卷积)实现代码

设计思路

实现代码

基于TF框架利用ResNet算法网络DIY自定义图像生成网络(定义卷积和反卷积)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