ops.py

import tensorflow as tf
import numpy as np
from tensorflow.python.ops import math_ops
from tensorflow.python.framework import ops
import math


def Conv(name, x, filter_size, in_filters, out_filters, strides, padding):

    with tf.variable_scope(name):
        n= filter_size * filter_size * out_filters
        kernel = tf.get_variable('filter', [filter_size, filter_size, in_filters, out_filters],tf.float32, initializer=tf.random_normal_initializer(stddev = 0.01))
        bias = tf.get_variable('bias',[out_filters],tf.float32, initializer = tf.zeros_initializer())
        
        return tf.nn.conv2d(x, kernel, [1,strides,strides,1], padding = padding) + bias
    


def Conv_transpose(name, x, filter_size, in_filters, out_filters, fraction = 2, padding = "SAME"):
    
    with tf.variable_scope(name):
        n = filter_size * filter_size * out_filters
        kernel = tf.get_variable('filter', [filter_size, filter_size, out_filters, in_filters], tf.float32, initializer=tf.random_normal_initializer(stddev=np.sqrt(2.0/n)) )
        size = tf.shape(x)
        output_shape = tf.stack([size[0], size[1] * fraction, size[2] * fraction, out_filters])
        x = tf.nn.conv2d_transpose(x, kernel, output_shape, [1, fraction, fraction, 1], padding)
        
        return x
        
def instance_norm(name, x, dim, affine = False, BN_decay = 0.999, BN_epsilon = 1e-3):

    mean, variance = tf.nn.moments(x, axes = [1, 2])
    x = (x - mean) / ((variance + BN_epsilon) ** 0.5)
    
    if affine :
        beta = tf.get_variable(name = name + "beta", shape = dim, dtype = tf.float32,
                               initializer = tf.constant_initializer(0.0, tf.float32))
        gamma = tf.get_variable(name + "gamma", dim, tf.float32, 
                                initializer = tf.constant_initializer(1.0, tf.float32))
        x = gamma * x + beta 
    
    return x