ops.py

"""
Some code used from from https://github.com/Newmu/dcgan_code
"""

import math
import numpy as np
import tensorflow as tf

from tensorflow.python.framework import ops

from utils import *

try:
  image_summary = tf.image_summary
  scalar_summary = tf.scalar_summary
  histogram_summary = tf.histogram_summary
  merge_summary = tf.merge_summary
  SummaryWriter = tf.train.SummaryWriter
except:
  image_summary = tf.summary.image
  scalar_summary = tf.summary.scalar
  histogram_summary = tf.summary.histogram
  merge_summary = tf.summary.merge
  SummaryWriter = tf.summary.FileWriter

if "concat_v2" in dir(tf):
  def concat(tensors, axis, *args, **kwargs):
    return tf.concat_v2(tensors, axis, *args, **kwargs)
else:
  def concat(tensors, axis, *args, **kwargs):
    return tf.concat(tensors, axis, *args, **kwargs)

class batch_norm(object):
  def __init__(self, epsilon=1e-6, decay = 0.9, name="batch_norm",train=True):
    with tf.variable_scope(name):
      self.epsilon  = epsilon
      self.decay = decay
      self.name = name
      self.train = train

  def __call__(self, x, train=True):
    return tf.contrib.layers.batch_norm(x,
                      decay=self.decay,
                      updates_collections=None,
                      epsilon=self.epsilon,
                      scale=True,
                      is_training=self.train,
                      scope=self.name)

def conv_cond_concat(x, y):
  """Concatenate conditioning vector on feature map axis."""
  x_shapes = x.get_shape()
  y_shapes = y.get_shape()
  return concat([
    x, y*tf.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])], 3)

def conv2d(input_, output_dim, k_h=5, k_w=5, d_h=1, d_w=1, stddev=0.02, name="conv2d", padding = "VALID"):

  '''
  : params k_h : kernel_height
  : params k_w : kernel_width
  : params d_h : stride_height
  : params d_w : stride_width
  '''
  with tf.variable_scope(name):

    w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],initializer=tf.contrib.layers.xavier_initializer())

    conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding=padding)

    biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))

    conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())

    return conv



def deconv2d(input_, output_shape, k_h=5, k_w=5, d_h=1, d_w=1, stddev=0.02, name="deconv2d", with_w=False,padding='VALID'):

  with tf.variable_scope(name):

    # filter : [height, width, output_channels, in_channels]
    w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],initializer=tf.contrib.layers.xavier_initializer())
    if padding=='VALID':
        # input_ = tf.pad(input_,paddings = [[0,0], [4,4], [4,4], [0,0]], mode='SYMMETRIC', name = "pad")
        deconv = tf.nn.conv2d_transpose(input_, w, output_shape = output_shape, strides=[1, d_h, d_w, 1], padding = 'VALID')
    else:
        deconv = tf.nn.conv2d_transpose(input_, w, output_shape = output_shape, strides=[1, d_h, d_w, 1], padding = 'SAME')
    biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
    deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())

    return deconv

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

def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):

  shape = input_.get_shape().as_list()

  with tf.variable_scope(scope or "Linear"):

    matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,tf.contrib.layers.xavier_initializer())

    bias = tf.get_variable("bias", [output_size],initializer=tf.constant_initializer(bias_start))

    if with_w:
      return tf.matmul(input_, matrix) + bias, matrix, bias
    else:
      return tf.matmul(input_, matrix) + bias