Python util.get_image_grid() Examples

The following are 8 code examples of util.get_image_grid(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module util , or try the search function .
Example #1
Source File: util_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def test_get_image_grid(self):
    util.get_image_grid(
        tf.zeros([6, 28, 28, 1]),
        batch_size=6,
        num_classes=3,
        num_images_per_class=1)

  # Mock `inception_score` which is expensive.
Example #2
Source File: util_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def test_get_image_grid(self):
    util.get_image_grid(
        tf.zeros([6, 28, 28, 1]),
        batch_size=6,
        num_classes=3,
        num_images_per_class=1)

  # Mock `inception_score` which is expensive.
Example #3
Source File: util_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def test_get_image_grid(self):
    util.get_image_grid(
        tf.zeros([6, 28, 28, 1]),
        batch_size=6,
        num_classes=3,
        num_images_per_class=1)

  # Mock `inception_score` which is expensive.
Example #4
Source File: util_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def test_get_image_grid(self):
    util.get_image_grid(
        tf.zeros([6, 28, 28, 1]),
        batch_size=6,
        num_classes=3,
        num_images_per_class=1)

  # Mock `inception_score` which is expensive.
Example #5
Source File: util_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def test_get_image_grid(self):
    util.get_image_grid(
        tf.zeros([6, 28, 28, 1]),
        batch_size=6,
        num_classes=3,
        num_images_per_class=1)

  # Mock `inception_score` which is expensive.
Example #6
Source File: eval.py    From yolo_v2 with Apache License 2.0 4 votes vote down vote up 
def main(_, run_eval_loop=True):
  # Fetch and generate images to run through Inception.
  with tf.name_scope('inputs'):
    real_data, num_classes = _get_real_data(
        FLAGS.num_images_generated, FLAGS.dataset_dir)
    generated_data = _get_generated_data(
        FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes)

  # Compute Frechet Inception Distance.
  if FLAGS.eval_frechet_inception_distance:
    fid = util.get_frechet_inception_distance(
        real_data, generated_data, FLAGS.num_images_generated,
        FLAGS.num_inception_images)
    tf.summary.scalar('frechet_inception_distance', fid)

  # Compute normal Inception scores.
  if FLAGS.eval_real_images:
    inc_score = util.get_inception_scores(
        real_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
  else:
    inc_score = util.get_inception_scores(
        generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
  tf.summary.scalar('inception_score', inc_score)

  # If conditional, display an image grid of difference classes.
  if FLAGS.conditional_eval and not FLAGS.eval_real_images:
    reshaped_imgs = util.get_image_grid(
        generated_data, FLAGS.num_images_generated, num_classes,
        FLAGS.num_images_per_class)
    tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)

  # Create ops that write images to disk.
  image_write_ops = None
  if FLAGS.conditional_eval:
    reshaped_imgs = util.get_image_grid(
        generated_data, FLAGS.num_images_generated, num_classes,
        FLAGS.num_images_per_class)
    uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
    image_write_ops = tf.write_file(
        '%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'),
        tf.image.encode_png(uint8_images[0]))
  else:
    if FLAGS.num_images_generated >= 100:
      reshaped_imgs = tfgan.eval.image_reshaper(
          generated_data[:100], num_cols=FLAGS.num_images_per_class)
      uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'),
          tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      master=FLAGS.master,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)
Example #7
Source File: eval.py    From Gun-Detector with Apache License 2.0 4 votes vote down vote up 
def main(_, run_eval_loop=True):
  # Fetch and generate images to run through Inception.
  with tf.name_scope('inputs'):
    real_data, num_classes = _get_real_data(
        FLAGS.num_images_generated, FLAGS.dataset_dir)
    generated_data = _get_generated_data(
        FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes)

  # Compute Frechet Inception Distance.
  if FLAGS.eval_frechet_inception_distance:
    fid = util.get_frechet_inception_distance(
        real_data, generated_data, FLAGS.num_images_generated,
        FLAGS.num_inception_images)
    tf.summary.scalar('frechet_inception_distance', fid)

  # Compute normal Inception scores.
  if FLAGS.eval_real_images:
    inc_score = util.get_inception_scores(
        real_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
  else:
    inc_score = util.get_inception_scores(
        generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
  tf.summary.scalar('inception_score', inc_score)

  # If conditional, display an image grid of difference classes.
  if FLAGS.conditional_eval and not FLAGS.eval_real_images:
    reshaped_imgs = util.get_image_grid(
        generated_data, FLAGS.num_images_generated, num_classes,
        FLAGS.num_images_per_class)
    tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)

  # Create ops that write images to disk.
  image_write_ops = None
  if FLAGS.conditional_eval and FLAGS.write_to_disk:
    reshaped_imgs = util.get_image_grid(
        generated_data, FLAGS.num_images_generated, num_classes,
        FLAGS.num_images_per_class)
    uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
    image_write_ops = tf.write_file(
        '%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'),
        tf.image.encode_png(uint8_images[0]))
  else:
    if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
      reshaped_imgs = tfgan.eval.image_reshaper(
          generated_data[:100], num_cols=FLAGS.num_images_per_class)
      uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'),
          tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      master=FLAGS.master,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)
Example #8
Source File: eval.py    From object_detection_with_tensorflow with MIT License 4 votes vote down vote up 
def main(_, run_eval_loop=True):
  # Fetch and generate images to run through Inception.
  with tf.name_scope('inputs'):
    real_data, num_classes = _get_real_data(
        FLAGS.num_images_generated, FLAGS.dataset_dir)
    generated_data = _get_generated_data(
        FLAGS.num_images_generated, FLAGS.conditional_eval, num_classes)

  # Compute Frechet Inception Distance.
  if FLAGS.eval_frechet_inception_distance:
    fid = util.get_frechet_inception_distance(
        real_data, generated_data, FLAGS.num_images_generated,
        FLAGS.num_inception_images)
    tf.summary.scalar('frechet_inception_distance', fid)

  # Compute normal Inception scores.
  if FLAGS.eval_real_images:
    inc_score = util.get_inception_scores(
        real_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
  else:
    inc_score = util.get_inception_scores(
        generated_data, FLAGS.num_images_generated, FLAGS.num_inception_images)
  tf.summary.scalar('inception_score', inc_score)

  # If conditional, display an image grid of difference classes.
  if FLAGS.conditional_eval and not FLAGS.eval_real_images:
    reshaped_imgs = util.get_image_grid(
        generated_data, FLAGS.num_images_generated, num_classes,
        FLAGS.num_images_per_class)
    tf.summary.image('generated_data', reshaped_imgs, max_outputs=1)

  # Create ops that write images to disk.
  image_write_ops = None
  if FLAGS.conditional_eval:
    reshaped_imgs = util.get_image_grid(
        generated_data, FLAGS.num_images_generated, num_classes,
        FLAGS.num_images_per_class)
    uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
    image_write_ops = tf.write_file(
        '%s/%s'% (FLAGS.eval_dir, 'conditional_cifar10.png'),
        tf.image.encode_png(uint8_images[0]))
  else:
    if FLAGS.num_images_generated >= 100:
      reshaped_imgs = tfgan.eval.image_reshaper(
          generated_data[:100], num_cols=FLAGS.num_images_per_class)
      uint8_images = data_provider.float_image_to_uint8(reshaped_imgs)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_cifar10.png'),
          tf.image.encode_png(uint8_images[0]))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      master=FLAGS.master,
      hooks=[tf.contrib.training.SummaryAtEndHook(FLAGS.eval_dir),
             tf.contrib.training.StopAfterNEvalsHook(1)],
      eval_ops=image_write_ops,
      max_number_of_evaluations=FLAGS.max_number_of_evaluations)