ops.py

import tensorflow as tf
import tensorflow.contrib as tf_contrib

weight_init = tf_contrib.layers.xavier_initializer()
weight_regularizer = None

def conv(x, channels, kernel=4, stride=2, pad=0, pad_type='zero', use_bias=True, scope='conv_0'):
    with tf.variable_scope(scope):
        if pad_type == 'zero':
            x = tf.pad(x, [[0, 0], [pad, pad], [pad, pad], [0, 0]])
        if pad_type == 'reflect':
            x = tf.pad(x, [[0, 0], [pad, pad], [pad, pad], [0, 0]], mode='REFLECT')

        x = tf.layers.conv2d(inputs=x, filters=channels,
                             kernel_size=kernel, kernel_initializer=weight_init,
                             kernel_regularizer=weight_regularizer,
                             strides=stride, use_bias=use_bias)
        return x


def deconv(x, channels, kernel=4, stride=2, use_bias=True, scope='deconv_0'):
    with tf.variable_scope(scope):
        x = tf.layers.conv2d_transpose(inputs=x, filters=channels,
                                       kernel_size=kernel, kernel_initializer=weight_init,
                                       kernel_regularizer=weight_regularizer,
                                       strides=stride, padding='SAME', use_bias=use_bias)
        return x


def resblock(x_init, channels, use_bias=True, scope='resblock'):
    with tf.variable_scope(scope):
        with tf.variable_scope('res1'):
            x = conv(x_init, channels, kernel=3, stride=1, pad=1, use_bias=use_bias)
            x = instance_norm(x)
            x = relu(x)
        with tf.variable_scope('res2'):
            x = conv(x, channels, kernel=3, stride=1, pad=1, use_bias=use_bias)
            x = instance_norm(x)
        return x + x_init

def lrelu(x, alpha=0.2):
    return tf.nn.leaky_relu(x, alpha)


def relu(x):
    return tf.nn.relu(x)

def tanh(x):
    return tf.tanh(x)

def instance_norm(x, scope='instance_norm'):
    return tf_contrib.layers.instance_norm(x,
                                           epsilon=1e-05,
                                           center=True, scale=True,
                                           scope=scope)