Python networks.conditional_generator() Examples

The following are 15 code examples of networks.conditional_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: eval.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def _get_generated_data(num_images_generated, conditional_eval, num_classes):
  """Get generated images."""
  noise = tf.random_normal([num_images_generated, 64])
  # If conditional, generate class-specific images.
  if conditional_eval:
    conditioning = util.get_generator_conditioning(
        num_images_generated, num_classes)
    generator_inputs = (noise, conditioning)
    generator_fn = networks.conditional_generator
  else:
    generator_inputs = noise
    generator_fn = networks.generator
  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('Generator'):
    data = generator_fn(generator_inputs)

  return data
Example #2
Source File: eval.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def _get_generated_data(num_images_generated, conditional_eval, num_classes):
  """Get generated images."""
  noise = tf.random_normal([num_images_generated, 64])
  # If conditional, generate class-specific images.
  if conditional_eval:
    conditioning = util.get_generator_conditioning(
        num_images_generated, num_classes)
    generator_inputs = (noise, conditioning)
    generator_fn = networks.conditional_generator
  else:
    generator_inputs = noise
    generator_fn = networks.generator
  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('Generator'):
    data = generator_fn(generator_inputs, is_training=False)

  return data
Example #3
Source File: eval.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def _get_generated_data(num_images_generated, conditional_eval, num_classes):
  """Get generated images."""
  noise = tf.random_normal([num_images_generated, 64])
  # If conditional, generate class-specific images.
  if conditional_eval:
    conditioning = util.get_generator_conditioning(
        num_images_generated, num_classes)
    generator_inputs = (noise, conditioning)
    generator_fn = networks.conditional_generator
  else:
    generator_inputs = noise
    generator_fn = networks.generator
  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('Generator'):
    data = generator_fn(generator_inputs)

  return data
Example #4
Source File: eval.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def _get_generated_data(num_images_generated, conditional_eval, num_classes):
  """Get generated images."""
  noise = tf.random_normal([num_images_generated, 64])
  # If conditional, generate class-specific images.
  if conditional_eval:
    conditioning = util.get_generator_conditioning(
        num_images_generated, num_classes)
    generator_inputs = (noise, conditioning)
    generator_fn = networks.conditional_generator
  else:
    generator_inputs = noise
    generator_fn = networks.generator
  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('Generator'):
    data = generator_fn(generator_inputs, is_training=False)

  return data
Example #5
Source File: eval.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def _get_generated_data(num_images_generated, conditional_eval, num_classes):
  """Get generated images."""
  noise = tf.random_normal([num_images_generated, 64])
  # If conditional, generate class-specific images.
  if conditional_eval:
    conditioning = util.get_generator_conditioning(
        num_images_generated, num_classes)
    generator_inputs = (noise, conditioning)
    generator_fn = networks.conditional_generator
  else:
    generator_inputs = noise
    generator_fn = networks.generator
  # In order for variables to load, use the same variable scope as in the
  # train job.
  with tf.variable_scope('Generator'):
    data = generator_fn(generator_inputs, is_training=False)

  return data
Example #6
Source File: networks_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def test_generator_conditional(self):
    tf.set_random_seed(1234)
    batch_size = 100
    noise = tf.random_normal([batch_size, 64])
    conditioning = tf.one_hot([0] * batch_size, 10)
    image = networks.conditional_generator((noise, conditioning))
    with self.test_session(use_gpu=True) as sess:
      sess.run(tf.global_variables_initializer())
      image_np = image.eval()

    self.assertAllEqual([batch_size, 32, 32, 3], image_np.shape)
    self.assertTrue(np.all(np.abs(image_np) <= 1))
Example #7
Source File: conditional_eval.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
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)
Example #8
Source File: networks_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def test_generator_conditional(self):
    tf.set_random_seed(1234)
    batch_size = 100
    noise = tf.random_normal([batch_size, 64])
    conditioning = tf.one_hot([0] * batch_size, 10)
    image = networks.conditional_generator((noise, conditioning))
    with self.test_session(use_gpu=True) as sess:
      sess.run(tf.global_variables_initializer())
      image_np = image.eval()

    self.assertAllEqual([batch_size, 32, 32, 3], image_np.shape)
    self.assertTrue(np.all(np.abs(image_np) <= 1))
Example #9
Source File: conditional_eval.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
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)
Example #10
Source File: networks_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def test_generator_conditional(self):
    tf.set_random_seed(1234)
    batch_size = 100
    noise = tf.random_normal([batch_size, 64])
    conditioning = tf.one_hot([0] * batch_size, 10)
    image = networks.conditional_generator((noise, conditioning))
    with self.test_session(use_gpu=True) as sess:
      sess.run(tf.global_variables_initializer())
      image_np = image.eval()

    self.assertAllEqual([batch_size, 32, 32, 3], image_np.shape)
    self.assertTrue(np.all(np.abs(image_np) <= 1))
Example #11
Source File: conditional_eval.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
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)
Example #12
Source File: networks_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def test_generator_conditional(self):
    tf.set_random_seed(1234)
    batch_size = 100
    noise = tf.random_normal([batch_size, 64])
    conditioning = tf.one_hot([0] * batch_size, 10)
    image = networks.conditional_generator((noise, conditioning))
    with self.test_session(use_gpu=True) as sess:
      sess.run(tf.global_variables_initializer())
      image_np = image.eval()

    self.assertAllEqual([batch_size, 32, 32, 3], image_np.shape)
    self.assertTrue(np.all(np.abs(image_np) <= 1))
Example #13
Source File: conditional_eval.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
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)
Example #14
Source File: networks_test.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def test_generator_conditional(self):
    tf.set_random_seed(1234)
    batch_size = 100
    noise = tf.random_normal([batch_size, 64])
    conditioning = tf.one_hot([0] * batch_size, 10)
    image = networks.conditional_generator((noise, conditioning))
    with self.test_session(use_gpu=True) as sess:
      sess.run(tf.global_variables_initializer())
      image_np = image.eval()

    self.assertAllEqual([batch_size, 32, 32, 3], image_np.shape)
    self.assertTrue(np.all(np.abs(image_np) <= 1))
Example #15
Source File: conditional_eval.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
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)