Python data_provider.float_image_to_uint8方法代码示例

# 需要导入模块: import data_provider [as 别名]
# 或者: from data_provider import float_image_to_uint8 [as 别名]
def _get_write_image_ops(eval_dir, filename, images):
  """Create Ops that write images to disk."""
  return tf.write_file(
      '%s/%s'% (eval_dir, filename),
      tf.image.encode_png(data_provider.float_image_to_uint8(images))) 

# 需要导入模块: import data_provider [as 别名]
# 或者: from data_provider import float_image_to_uint8 [as 别名]
def main(_, run_eval_loop=True):
  # Fetch real images.
  with tf.name_scope('inputs'):
    real_images, _, _ = data_provider.provide_data(
        'train', FLAGS.num_images_generated, FLAGS.dataset_dir)

  image_write_ops = None
  if FLAGS.eval_real_images:
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(real_images, FLAGS.classifier_filename))
  else:
    # In order for variables to load, use the same variable scope as in the
    # train job.
    with tf.variable_scope('Generator'):
      images = networks.unconditional_generator(
          tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]))
    tf.summary.scalar('MNIST_Frechet_distance',
                      util.mnist_frechet_distance(
                          real_images, images, FLAGS.classifier_filename))
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(images, FLAGS.classifier_filename))
    if FLAGS.num_images_generated >= 100:
      reshaped_images = tfgan.eval.image_reshaper(
          images[:100, ...], num_cols=10)
      uint8_images = data_provider.float_image_to_uint8(reshaped_images)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.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,
      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) 

# 需要导入模块: import data_provider [as 别名]
# 或者: from data_provider import float_image_to_uint8 [as 别名]
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise, one_hot_labels = _get_generator_inputs(
        FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)

  # Generate images.
  with tf.variable_scope('Generator'):  # Same scope as in train job.
    images = networks.conditional_generator((noise, one_hot_labels))

  # Visualize images.
  reshaped_img = tfgan.eval.image_reshaper(
      images, num_cols=FLAGS.num_images_per_class)
  tf.summary.image('generated_images', reshaped_img, max_outputs=1)

  # Calculate evaluation metrics.
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(images, FLAGS.classifier_filename))
  tf.summary.scalar('MNIST_Cross_entropy',
                    util.mnist_cross_entropy(
                        images, one_hot_labels, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = tf.write_file(
      '%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
      tf.image.encode_png(data_provider.float_image_to_uint8(reshaped_img[0])))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      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) 

# 需要导入模块: import data_provider [as 别名]
# 或者: from data_provider import float_image_to_uint8 [as 别名]
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    images = data_provider.provide_data(
        'validation', FLAGS.batch_size, dataset_dir=FLAGS.dataset_dir,
        patch_size=FLAGS.patch_size)

  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('generator'):
    reconstructions, _, prebinary = networks.compression_model(
        images,
        num_bits=FLAGS.bits_per_patch,
        depth=FLAGS.model_depth,
        is_training=False)
  summaries.add_reconstruction_summaries(images, reconstructions, prebinary)

  # Visualize losses.
  pixel_loss_per_example = tf.reduce_mean(
      tf.abs(images - reconstructions), axis=[1, 2, 3])
  pixel_loss = tf.reduce_mean(pixel_loss_per_example)
  tf.summary.histogram('pixel_l1_loss_hist', pixel_loss_per_example)
  tf.summary.scalar('pixel_l1_loss', pixel_loss)

  # Create ops to write images to disk.
  uint8_images = data_provider.float_image_to_uint8(images)
  uint8_reconstructions = data_provider.float_image_to_uint8(reconstructions)
  uint8_reshaped = summaries.stack_images(uint8_images, uint8_reconstructions)
  image_write_ops = tf.write_file(
      '%s/%s'% (FLAGS.eval_dir, 'compression.png'),
      tf.image.encode_png(uint8_reshaped[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) 

# 需要导入模块: import data_provider [as 别名]
# 或者: from data_provider import float_image_to_uint8 [as 别名]
def main(_, run_eval_loop=True):
  # Fetch real images.
  with tf.name_scope('inputs'):
    real_images, _, _ = data_provider.provide_data(
        'train', FLAGS.num_images_generated, FLAGS.dataset_dir)

  image_write_ops = None
  if FLAGS.eval_real_images:
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(real_images, FLAGS.classifier_filename))
  else:
    # In order for variables to load, use the same variable scope as in the
    # train job.
    with tf.variable_scope('Generator'):
      images = networks.unconditional_generator(
          tf.random_normal([FLAGS.num_images_generated, FLAGS.noise_dims]),
          is_training=False)
    tf.summary.scalar('MNIST_Frechet_distance',
                      util.mnist_frechet_distance(
                          real_images, images, FLAGS.classifier_filename))
    tf.summary.scalar('MNIST_Classifier_score',
                      util.mnist_score(images, FLAGS.classifier_filename))
    if FLAGS.num_images_generated >= 100 and FLAGS.write_to_disk:
      reshaped_images = tfgan.eval.image_reshaper(
          images[:100, ...], num_cols=10)
      uint8_images = data_provider.float_image_to_uint8(reshaped_images)
      image_write_ops = tf.write_file(
          '%s/%s'% (FLAGS.eval_dir, 'unconditional_gan.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,
      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) 

# 需要导入模块: import data_provider [as 别名]
# 或者: from data_provider import float_image_to_uint8 [as 别名]
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise, one_hot_labels = _get_generator_inputs(
        FLAGS.num_images_per_class, NUM_CLASSES, FLAGS.noise_dims)

  # Generate images.
  with tf.variable_scope('Generator'):  # Same scope as in train job.
    images = networks.conditional_generator(
        (noise, one_hot_labels), is_training=False)

  # Visualize images.
  reshaped_img = tfgan.eval.image_reshaper(
      images, num_cols=FLAGS.num_images_per_class)
  tf.summary.image('generated_images', reshaped_img, max_outputs=1)

  # Calculate evaluation metrics.
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(images, FLAGS.classifier_filename))
  tf.summary.scalar('MNIST_Cross_entropy',
                    util.mnist_cross_entropy(
                        images, one_hot_labels, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = None
  if FLAGS.write_to_disk:
    image_write_ops = tf.write_file(
        '%s/%s'% (FLAGS.eval_dir, 'conditional_gan.png'),
        tf.image.encode_png(data_provider.float_image_to_uint8(
            reshaped_img[0])))

  # For unit testing, use `run_eval_loop=False`.
  if not run_eval_loop: return
  tf.contrib.training.evaluate_repeatedly(
      FLAGS.checkpoint_dir,
      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) 

# 需要导入模块: import data_provider [as 别名]
# 或者: from data_provider import float_image_to_uint8 [as 别名]
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) 

# 需要导入模块: import data_provider [as 别名]
# 或者: from data_provider import float_image_to_uint8 [as 别名]
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)