Python util.get_frechet_inception_distance方法代碼示例

本文整理匯總了Python中util.get_frechet_inception_distance方法的典型用法代碼示例。如果您正苦於以下問題：Python util.get_frechet_inception_distance方法的具體用法？Python util.get_frechet_inception_distance怎麽用？Python util.get_frechet_inception_distance使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類util的用法示例。

在下文中一共展示了util.get_frechet_inception_distance方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: test_get_frechet_inception_distance

# 需要導入模塊: import util [as 別名]
# 或者: from util import get_frechet_inception_distance [as 別名]
def test_get_frechet_inception_distance(self, mock_fid):
    mock_fid.return_value = 1.0
    util.get_frechet_inception_distance(
        tf.placeholder(tf.float32, shape=[None, 28, 28, 3]),
        tf.placeholder(tf.float32, shape=[None, 28, 28, 3]),
        batch_size=100,
        num_inception_images=10)

開發者ID:rky0930，項目名稱:yolo_v2，代碼行數:9，代碼來源:util_test.py

示例2: main

# 需要導入模塊: import util [as 別名]
# 或者: from util import get_frechet_inception_distance [as 別名]
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)

開發者ID:rky0930，項目名稱:yolo_v2，代碼行數:61，代碼來源:eval.py

示例3: main

# 需要導入模塊: import util [as 別名]
# 或者: from util import get_frechet_inception_distance [as 別名]
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)

開發者ID:itsamitgoel，項目名稱:Gun-Detector，代碼行數:61，代碼來源:eval.py

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