| import tensorflow as tf | |
| import numpy as np | |
| import tensorflow.contrib.slim as slim | |
| def resblock(inputs, out_channel=32, name='resblock'): | |
| with tf.variable_scope(name): | |
| x = slim.convolution2d(inputs, out_channel, [3, 3], | |
| activation_fn=None, scope='conv1') | |
| x = tf.nn.leaky_relu(x) | |
| x = slim.convolution2d(x, out_channel, [3, 3], | |
| activation_fn=None, scope='conv2') | |
| return x + inputs | |
| def unet_generator(inputs, channel=32, num_blocks=4, name='generator', reuse=False): | |
| with tf.variable_scope(name, reuse=reuse): | |
| x0 = slim.convolution2d(inputs, channel, [7, 7], activation_fn=None) | |
| x0 = tf.nn.leaky_relu(x0) | |
| x1 = slim.convolution2d(x0, channel, [3, 3], stride=2, activation_fn=None) | |
| x1 = tf.nn.leaky_relu(x1) | |
| x1 = slim.convolution2d(x1, channel*2, [3, 3], activation_fn=None) | |
| x1 = tf.nn.leaky_relu(x1) | |
| x2 = slim.convolution2d(x1, channel*2, [3, 3], stride=2, activation_fn=None) | |
| x2 = tf.nn.leaky_relu(x2) | |
| x2 = slim.convolution2d(x2, channel*4, [3, 3], activation_fn=None) | |
| x2 = tf.nn.leaky_relu(x2) | |
| for idx in range(num_blocks): | |
| x2 = resblock(x2, out_channel=channel*4, name='block_{}'.format(idx)) | |
| x2 = slim.convolution2d(x2, channel*2, [3, 3], activation_fn=None) | |
| x2 = tf.nn.leaky_relu(x2) | |
| h1, w1 = tf.shape(x2)[1], tf.shape(x2)[2] | |
| x3 = tf.image.resize_bilinear(x2, (h1*2, w1*2)) | |
| x3 = slim.convolution2d(x3+x1, channel*2, [3, 3], activation_fn=None) | |
| x3 = tf.nn.leaky_relu(x3) | |
| x3 = slim.convolution2d(x3, channel, [3, 3], activation_fn=None) | |
| x3 = tf.nn.leaky_relu(x3) | |
| h2, w2 = tf.shape(x3)[1], tf.shape(x3)[2] | |
| x4 = tf.image.resize_bilinear(x3, (h2*2, w2*2)) | |
| x4 = slim.convolution2d(x4+x0, channel, [3, 3], activation_fn=None) | |
| x4 = tf.nn.leaky_relu(x4) | |
| x4 = slim.convolution2d(x4, 3, [7, 7], activation_fn=None) | |
| return x4 | |
| if __name__ == '__main__': | |
| pass |