Python networks.infogan_generator() Examples

The following are 5 code examples of networks.infogan_generator(). 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 networks , or try the search function .
Example #1
Source File: infogan_eval.py    From yolo_v2 with Apache License 2.0 4 votes vote down vote up 
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise_args = (FLAGS.noise_samples, CAT_SAMPLE_POINTS, CONT_SAMPLE_POINTS,
                  FLAGS.unstructured_noise_dims, FLAGS.continuous_noise_dims)
    # Use fixed noise vectors to illustrate the effect of each dimension.
    display_noise1 = util.get_eval_noise_categorical(*noise_args)
    display_noise2 = util.get_eval_noise_continuous_dim1(*noise_args)
    display_noise3 = util.get_eval_noise_continuous_dim2(*noise_args)
    _validate_noises([display_noise1, display_noise2, display_noise3])

  # Visualize the effect of each structured noise dimension on the generated
  # image.
  generator_fn = lambda x: networks.infogan_generator(x, len(CAT_SAMPLE_POINTS))
  with tf.variable_scope('Generator') as genscope:  # Same scope as in training.
    categorical_images = generator_fn(display_noise1)
  reshaped_categorical_img = tfgan.eval.image_reshaper(
      categorical_images, num_cols=len(CAT_SAMPLE_POINTS))
  tf.summary.image('categorical', reshaped_categorical_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous1_images = generator_fn(display_noise2)
  reshaped_continuous1_img = tfgan.eval.image_reshaper(
      continuous1_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous1', reshaped_continuous1_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous2_images = generator_fn(display_noise3)
  reshaped_continuous2_img = tfgan.eval.image_reshaper(
      continuous2_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous2', reshaped_continuous2_img, max_outputs=1)

  # Evaluate image quality.
  all_images = tf.concat(
      [categorical_images, continuous1_images, continuous2_images], 0)
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(all_images, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = []
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'categorical_infogan.png', reshaped_categorical_img[0]))
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'continuous1_infogan.png', reshaped_continuous1_img[0]))
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'continuous2_infogan.png', reshaped_continuous2_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)
Example #2
Source File: infogan_eval.py    From Gun-Detector with Apache License 2.0 4 votes vote down vote up 
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise_args = (FLAGS.noise_samples, CAT_SAMPLE_POINTS, CONT_SAMPLE_POINTS,
                  FLAGS.unstructured_noise_dims, FLAGS.continuous_noise_dims)
    # Use fixed noise vectors to illustrate the effect of each dimension.
    display_noise1 = util.get_eval_noise_categorical(*noise_args)
    display_noise2 = util.get_eval_noise_continuous_dim1(*noise_args)
    display_noise3 = util.get_eval_noise_continuous_dim2(*noise_args)
    _validate_noises([display_noise1, display_noise2, display_noise3])

  # Visualize the effect of each structured noise dimension on the generated
  # image.
  def generator_fn(inputs):
    return networks.infogan_generator(
        inputs, len(CAT_SAMPLE_POINTS), is_training=False)
  with tf.variable_scope('Generator') as genscope:  # Same scope as in training.
    categorical_images = generator_fn(display_noise1)
  reshaped_categorical_img = tfgan.eval.image_reshaper(
      categorical_images, num_cols=len(CAT_SAMPLE_POINTS))
  tf.summary.image('categorical', reshaped_categorical_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous1_images = generator_fn(display_noise2)
  reshaped_continuous1_img = tfgan.eval.image_reshaper(
      continuous1_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous1', reshaped_continuous1_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous2_images = generator_fn(display_noise3)
  reshaped_continuous2_img = tfgan.eval.image_reshaper(
      continuous2_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous2', reshaped_continuous2_img, max_outputs=1)

  # Evaluate image quality.
  all_images = tf.concat(
      [categorical_images, continuous1_images, continuous2_images], 0)
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(all_images, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = []
  if FLAGS.write_to_disk:
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'categorical_infogan.png', reshaped_categorical_img[0]))
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'continuous1_infogan.png', reshaped_continuous1_img[0]))
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'continuous2_infogan.png', reshaped_continuous2_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)
Example #3
Source File: infogan_eval.py    From object_detection_with_tensorflow with MIT License 4 votes vote down vote up 
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise_args = (FLAGS.noise_samples, CAT_SAMPLE_POINTS, CONT_SAMPLE_POINTS,
                  FLAGS.unstructured_noise_dims, FLAGS.continuous_noise_dims)
    # Use fixed noise vectors to illustrate the effect of each dimension.
    display_noise1 = util.get_eval_noise_categorical(*noise_args)
    display_noise2 = util.get_eval_noise_continuous_dim1(*noise_args)
    display_noise3 = util.get_eval_noise_continuous_dim2(*noise_args)
    _validate_noises([display_noise1, display_noise2, display_noise3])

  # Visualize the effect of each structured noise dimension on the generated
  # image.
  generator_fn = lambda x: networks.infogan_generator(x, len(CAT_SAMPLE_POINTS))
  with tf.variable_scope('Generator') as genscope:  # Same scope as in training.
    categorical_images = generator_fn(display_noise1)
  reshaped_categorical_img = tfgan.eval.image_reshaper(
      categorical_images, num_cols=len(CAT_SAMPLE_POINTS))
  tf.summary.image('categorical', reshaped_categorical_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous1_images = generator_fn(display_noise2)
  reshaped_continuous1_img = tfgan.eval.image_reshaper(
      continuous1_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous1', reshaped_continuous1_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous2_images = generator_fn(display_noise3)
  reshaped_continuous2_img = tfgan.eval.image_reshaper(
      continuous2_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous2', reshaped_continuous2_img, max_outputs=1)

  # Evaluate image quality.
  all_images = tf.concat(
      [categorical_images, continuous1_images, continuous2_images], 0)
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(all_images, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = []
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'categorical_infogan.png', reshaped_categorical_img[0]))
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'continuous1_infogan.png', reshaped_continuous1_img[0]))
  image_write_ops.append(_get_write_image_ops(
      FLAGS.eval_dir, 'continuous2_infogan.png', reshaped_continuous2_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)
Example #4
Source File: infogan_eval.py    From g-tensorflow-models with Apache License 2.0 4 votes vote down vote up 
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise_args = (FLAGS.noise_samples, CAT_SAMPLE_POINTS, CONT_SAMPLE_POINTS,
                  FLAGS.unstructured_noise_dims, FLAGS.continuous_noise_dims)
    # Use fixed noise vectors to illustrate the effect of each dimension.
    display_noise1 = util.get_eval_noise_categorical(*noise_args)
    display_noise2 = util.get_eval_noise_continuous_dim1(*noise_args)
    display_noise3 = util.get_eval_noise_continuous_dim2(*noise_args)
    _validate_noises([display_noise1, display_noise2, display_noise3])

  # Visualize the effect of each structured noise dimension on the generated
  # image.
  def generator_fn(inputs):
    return networks.infogan_generator(
        inputs, len(CAT_SAMPLE_POINTS), is_training=False)
  with tf.variable_scope('Generator') as genscope:  # Same scope as in training.
    categorical_images = generator_fn(display_noise1)
  reshaped_categorical_img = tfgan.eval.image_reshaper(
      categorical_images, num_cols=len(CAT_SAMPLE_POINTS))
  tf.summary.image('categorical', reshaped_categorical_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous1_images = generator_fn(display_noise2)
  reshaped_continuous1_img = tfgan.eval.image_reshaper(
      continuous1_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous1', reshaped_continuous1_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous2_images = generator_fn(display_noise3)
  reshaped_continuous2_img = tfgan.eval.image_reshaper(
      continuous2_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous2', reshaped_continuous2_img, max_outputs=1)

  # Evaluate image quality.
  all_images = tf.concat(
      [categorical_images, continuous1_images, continuous2_images], 0)
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(all_images, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = []
  if FLAGS.write_to_disk:
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'categorical_infogan.png', reshaped_categorical_img[0]))
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'continuous1_infogan.png', reshaped_continuous1_img[0]))
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'continuous2_infogan.png', reshaped_continuous2_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)
Example #5
Source File: infogan_eval.py    From multilabel-image-classification-tensorflow with MIT License 4 votes vote down vote up 
def main(_, run_eval_loop=True):
  with tf.name_scope('inputs'):
    noise_args = (FLAGS.noise_samples, CAT_SAMPLE_POINTS, CONT_SAMPLE_POINTS,
                  FLAGS.unstructured_noise_dims, FLAGS.continuous_noise_dims)
    # Use fixed noise vectors to illustrate the effect of each dimension.
    display_noise1 = util.get_eval_noise_categorical(*noise_args)
    display_noise2 = util.get_eval_noise_continuous_dim1(*noise_args)
    display_noise3 = util.get_eval_noise_continuous_dim2(*noise_args)
    _validate_noises([display_noise1, display_noise2, display_noise3])

  # Visualize the effect of each structured noise dimension on the generated
  # image.
  def generator_fn(inputs):
    return networks.infogan_generator(
        inputs, len(CAT_SAMPLE_POINTS), is_training=False)
  with tf.variable_scope('Generator') as genscope:  # Same scope as in training.
    categorical_images = generator_fn(display_noise1)
  reshaped_categorical_img = tfgan.eval.image_reshaper(
      categorical_images, num_cols=len(CAT_SAMPLE_POINTS))
  tf.summary.image('categorical', reshaped_categorical_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous1_images = generator_fn(display_noise2)
  reshaped_continuous1_img = tfgan.eval.image_reshaper(
      continuous1_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous1', reshaped_continuous1_img, max_outputs=1)

  with tf.variable_scope(genscope, reuse=True):
    continuous2_images = generator_fn(display_noise3)
  reshaped_continuous2_img = tfgan.eval.image_reshaper(
      continuous2_images, num_cols=len(CONT_SAMPLE_POINTS))
  tf.summary.image('continuous2', reshaped_continuous2_img, max_outputs=1)

  # Evaluate image quality.
  all_images = tf.concat(
      [categorical_images, continuous1_images, continuous2_images], 0)
  tf.summary.scalar('MNIST_Classifier_score',
                    util.mnist_score(all_images, FLAGS.classifier_filename))

  # Write images to disk.
  image_write_ops = []
  if FLAGS.write_to_disk:
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'categorical_infogan.png', reshaped_categorical_img[0]))
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'continuous1_infogan.png', reshaped_continuous1_img[0]))
    image_write_ops.append(_get_write_image_ops(
        FLAGS.eval_dir, 'continuous2_infogan.png', reshaped_continuous2_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)