| 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 |
|
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|
|
| 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 |
