layers.py

import tensorflow as tf


def lrelu(x, leak=0.2, name="lrelu"):
        return tf.maximum(x, leak * x)


def instance_norm(x):
    with tf.variable_scope("instance_norm"):
        epsilon = 1e-5
        mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
        scale = tf.get_variable('scale', [x.get_shape()[-1]],
                                initializer=tf.truncated_normal_initializer(mean=1.0, stddev=0.02))
        offset = tf.get_variable('offset', [x.get_shape()[-1]], initializer=tf.constant_initializer(0.0))
        out = scale * tf.div(x - mean, tf.sqrt(var + epsilon)) + offset
        return out


def conv2d(inputconv, o_d=64, f_h=7, f_w=7, s_h=1, s_w=1, stddev=0.02, padding="VALID", name="conv2d",
           do_norm=True, do_relu=True, relufactor=0):
    with tf.variable_scope(name):
        conv = tf.contrib.layers.conv2d(inputconv, o_d, f_w, s_w, padding, activation_fn=None,
                                        weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
                                        biases_initializer=tf.constant_initializer(0.0))
        if do_norm:
            conv = instance_norm(conv)
        relu_conv = conv
        if do_relu:
            if (relufactor == 0):
                relu_conv = tf.nn.relu(conv, "relu")
            else:
                relu_conv = lrelu(conv, relufactor, "lrelu")
        return conv, relu_conv


def deconv2d(inputconv, o_d=64, f_h=7, f_w=7, s_h=1, s_w=1, stddev=0.02, padding="VALID",
             name="deconv2d", do_norm=True, do_relu=True, relufactor=0):
    with tf.variable_scope(name):
        conv = tf.contrib.layers.conv2d_transpose(inputconv, o_d, [f_h, f_w], [s_h, s_w], padding, activation_fn=None,
                                                  weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
                                                  biases_initializer=tf.constant_initializer(0.0))
        if do_norm:
            conv = instance_norm(conv)
        relu_conv = conv
        if do_relu:
            if (relufactor == 0):
                relu_conv = tf.nn.relu(conv, "relu")
            else:
                relu_conv = lrelu(conv, relufactor, "lrelu")
        return conv, relu_conv