utils.py

"""
Some codes from https://github.com/Newmu/dcgan_code
"""
from __future__ import division

import math
import pprint
import random
from time import gmtime, strftime

import numpy as np
import scipy.misc
import tensorflow as tf
import tensorflow.contrib.slim as slim
from six.moves import xrange

pp = pprint.PrettyPrinter()

get_stddev = lambda x, k_h, k_w: 1/math.sqrt(k_w*k_h*x.get_shape()[-1])

def show_all_variables():
  model_vars = tf.trainable_variables()
  slim.model_analyzer.analyze_vars(model_vars, print_info=True)

def get_image(image_path, input_height, input_width,
              resize_height=64, resize_width=64,
              crop=True, grayscale=False):
  image = imread(image_path, grayscale)
  return transform(image, input_height, input_width,
                   resize_height, resize_width, crop)

def save_images(images, size, image_path):
  return imsave(inverse_transform(images), size, image_path)

def imread(path, grayscale = False):
  if (grayscale):
    return scipy.misc.imread(path, flatten = True).astype(np.float)
  else:
    return scipy.misc.imread(path).astype(np.float)

def merge_images(images, size):
  return inverse_transform(images)

def merge(images, size):
  h, w = images.shape[1], images.shape[2]
  if (images.shape[3] in (3,4)):
    c = images.shape[3]
    img = np.zeros((h * size[0], w * size[1], c))
    for idx, image in enumerate(images):
      i = idx % size[1]
      j = idx // size[1]
      img[j * h:j * h + h, i * w:i * w + w, :] = image
    return img
  elif images.shape[3]==1:
    img = np.zeros((h * size[0], w * size[1]))
    for idx, image in enumerate(images):
      i = idx % size[1]
      j = idx // size[1]
      img[j * h:j * h + h, i * w:i * w + w] = image[:,:,0]
    return img
  else:
    raise ValueError('in merge(images,size) images parameter '
                     'must have dimensions: HxW or HxWx3 or HxWx4')

def imsave(images, size, path):
  image = np.squeeze(merge(images, size))
  return scipy.misc.imsave(path, image)

def center_crop(x, crop_h, crop_w,
                resize_h=64, resize_w=64):
  if crop_w is None:
    crop_w = crop_h
  h, w = x.shape[:2]
  j = int(round((h - crop_h)/2.))
  i = int(round((w - crop_w)/2.))
  return scipy.misc.imresize(
      x[j:j+crop_h, i:i+crop_w], [resize_h, resize_w])

def transform(image, input_height, input_width, 
              resize_height=64, resize_width=64, crop=True):
  if crop:
    cropped_image = center_crop(
      image, input_height, input_width, 
      resize_height, resize_width)
  else:
    cropped_image = scipy.misc.imresize(image, [resize_height, resize_width])
  return np.array(cropped_image)/127.5 - 1.

def inverse_transform(images):
  return (images+1.)/2.

def to_json(output_path, *layers):
  with open(output_path, "w") as layer_f:
    lines = ""
    for w, b, bn in layers:
      layer_idx = w.name.split('/')[0].split('h')[1]

      B = b.eval()

      if "lin/" in w.name:
        W = w.eval()
        depth = W.shape[1]
      else:
        W = np.rollaxis(w.eval(), 2, 0)
        depth = W.shape[0]

      biases = {"sy": 1, "sx": 1, "depth": depth, "w": ['%.2f' % elem for elem in list(B)]}
      if bn != None:
        gamma = bn.gamma.eval()
        beta = bn.beta.eval()

        gamma = {"sy": 1, "sx": 1, "depth": depth, "w": ['%.2f' % elem for elem in list(gamma)]}
        beta = {"sy": 1, "sx": 1, "depth": depth, "w": ['%.2f' % elem for elem in list(beta)]}
      else:
        gamma = {"sy": 1, "sx": 1, "depth": 0, "w": []}
        beta = {"sy": 1, "sx": 1, "depth": 0, "w": []}

      if "lin/" in w.name:
        fs = []
        for w in W.T:
          fs.append({"sy": 1, "sx": 1, "depth": W.shape[0], "w": ['%.2f' % elem for elem in list(w)]})

        lines += """
          var layer_%s = {
            "layer_type": "fc", 
            "sy": 1, "sx": 1, 
            "out_sx": 1, "out_sy": 1,
            "stride": 1, "pad": 0,
            "out_depth": %s, "in_depth": %s,
            "biases": %s,
            "gamma": %s,
            "beta": %s,
            "filters": %s
          };""" % (layer_idx.split('_')[0], W.shape[1], W.shape[0], biases, gamma, beta, fs)
      else:
        fs = []
        for w_ in W:
          fs.append({"sy": 5, "sx": 5, "depth": W.shape[3], "w": ['%.2f' % elem for elem in list(w_.flatten())]})

        lines += """
          var layer_%s = {
            "layer_type": "deconv", 
            "sy": 5, "sx": 5,
            "out_sx": %s, "out_sy": %s,
            "stride": 2, "pad": 1,
            "out_depth": %s, "in_depth": %s,
            "biases": %s,
            "gamma": %s,
            "beta": %s,
            "filters": %s
          };""" % (layer_idx, 2**(int(layer_idx)+2), 2**(int(layer_idx)+2),
               W.shape[0], W.shape[3], biases, gamma, beta, fs)
    layer_f.write(" ".join(lines.replace("'","").split()))

def make_gif(images, fname, duration=2, true_image=False):
  import moviepy.editor as mpy

  def make_frame(t):
    try:
      x = images[int(len(images)/duration*t)]
    except:
      x = images[-1]

    if true_image:
      return x.astype(np.uint8)
    else:
      return ((x+1)/2*255).astype(np.uint8)

  clip = mpy.VideoClip(make_frame, duration=duration)
  clip.write_gif(fname, fps = len(images) / duration)

def visualize(sess, dcgan, config, batch_size, option):
  print('dcgan.z_dim:', dcgan.z_dim)
  print('xrange(dcgan.z_dim):', xrange(dcgan.z_dim))
  print('config.generate_test_images:', config.generate_test_images)

  if option == 0:
    z_sample = np.random.uniform(-0.5, 0.5, size=(batch_size, dcgan.z_dim))
    samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
    save_images(samples, [config.grid_height, config.grid_width], './samples/test_%s.png' % strftime("%Y-%m-%d-%H-%M-%S", gmtime()))
  elif option == 1:
    values = np.arange(0, 1, 1./batch_size)
    for idx in xrange(config.generate_test_images):
      print(" [*] %d" % idx)
      z_sample = np.random.uniform(-1, 1, size=(batch_size, dcgan.z_dim))
      for kdx, z in enumerate(z_sample):
        z[idx] = values[kdx]

      if config.dataset == "mnist":
        y = np.random.choice(10, batch_size)
        y_one_hot = np.zeros((batch_size, 10))
        y_one_hot[np.arange(batch_size), y] = 1

        samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample, dcgan.y: y_one_hot})
      else:
        samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})

      save_images(samples, [config.grid_height, config.grid_width], './samples/test_arange_%s.png' % (idx))
  elif option == 2:
    values = np.arange(0, 1, 1./batch_size)
    for idx in [random.randint(0, dcgan.z_dim - 1) for _ in xrange(dcgan.z_dim)]:
      print(" [*] %d" % idx)
      z = np.random.uniform(-0.2, 0.2, size=(dcgan.z_dim))
      z_sample = np.tile(z, (batch_size, 1))
      #z_sample = np.zeros([batch_size, dcgan.z_dim])
      for kdx, z in enumerate(z_sample):
        z[idx] = values[kdx]

      if config.dataset == "mnist":
        y = np.random.choice(10, batch_size)
        y_one_hot = np.zeros((batch_size, 10))
        y_one_hot[np.arange(batch_size), y] = 1

        samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample, dcgan.y: y_one_hot})
      else:
        samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})

      try:
        make_gif(samples, './samples/test_gif_%s.gif' % (idx))
      except:
        save_images(samples, [config.grid_height, config.grid_width], './samples/test_%s.png' % strftime("%Y-%m-%d-%H-%M-%S", gmtime()))
  elif option == 3:
    values = np.arange(0, 1, 1./batch_size)
    for idx in xrange(dcgan.z_dim):
      print(" [*] %d" % idx)
      z_sample = np.zeros([batch_size, dcgan.z_dim])
      for kdx, z in enumerate(z_sample):
        z[idx] = values[kdx]

      samples = sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample})
      make_gif(samples, './samples/test_gif_%s.gif' % (idx))
  elif option == 4:
    image_set = []
    values = np.arange(0, 1, 1./batch_size)

    for idx in xrange(dcgan.z_dim):
      print(" [*] %d" % idx)
      z_sample = np.zeros([batch_size, dcgan.z_dim])
      for kdx, z in enumerate(z_sample): z[idx] = values[kdx]

      image_set.append(sess.run(dcgan.sampler, feed_dict={dcgan.z: z_sample}))
      make_gif(image_set[-1], './samples/test_gif_%s.gif' % (idx))

    new_image_set = [merge(np.array([images[idx] for images in image_set]), [10, 10]) \
        for idx in range(64) + range(63, -1, -1)]
    make_gif(new_image_set, './samples/test_gif_merged.gif', duration=8)