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
