Merge pull request #5401 from joel-shor/master

Project import generated by Copybara.

research/gan/stargan_estimator/data_provider.py

+"""StarGAN Estimator data provider."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import numpy as np
+import data_provider
+from google3.pyglib import resources
+
+
+provide_data = data_provider.provide_data
+
+
+def provide_celeba_test_set():
+  """Provide one example of every class, and labels.
+
+  Returns:
+    An `np.array` of shape (num_domains, H, W, C) representing the images.
+      Values are in [-1, 1].
+    An `np.array` of shape (num_domains, num_domains) representing the labels.
+
+  Raises:
+    ValueError: If test data is inconsistent or malformed.
+  """
+  base_dir = 'google3/third_party/tensorflow_models/gan/stargan_estimator/data'
+  images_fn = os.path.join(base_dir, 'celeba_test_split_images.npy')
+  with resources.GetResourceAsFile(images_fn) as f:
+    images_np = np.load(f)
+  labels_fn = os.path.join(base_dir, 'celeba_test_split_labels.npy')
+  with resources.GetResourceAsFile(labels_fn) as f:
+    labels_np = np.load(f)
+  if images_np.shape[0] != labels_np.shape[0]:
+    raise ValueError('Test data is malformed.')
+
+  return images_np, labels_np

research/gan/stargan_estimator/train.py

+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Trains a StarGAN model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import io
+import os
+
+from absl import flags
+import numpy as np
+import scipy.misc
+import tensorflow as tf
+
+import network
+import data_provider
+
+# FLAGS for data.
+flags.DEFINE_multi_string(
+    'image_file_patterns', None,
+    'List of file pattern for different domain of images. '
+    '(e.g.[\'black_hair\', \'blond_hair\', \'brown_hair\']')
+flags.DEFINE_integer('batch_size', 6, 'The number of images in each batch.')
+flags.DEFINE_integer('patch_size', 128, 'The patch size of images.')
+
+# Write-to-disk flags.
+flags.DEFINE_string('output_dir', '/tmp/stargan/out/',
+                    'Directory where to write summary image.')
+
+# FLAGS for training hyper-parameters.
+flags.DEFINE_float('generator_lr', 1e-4, 'The generator learning rate.')
+flags.DEFINE_float('discriminator_lr', 1e-4, 'The discriminator learning rate.')
+flags.DEFINE_integer('max_number_of_steps', 1000000,
+                     'The maximum number of gradient steps.')
+flags.DEFINE_integer('steps_per_eval', 1000,
+                     'The number of steps after which we write eval to disk.')
+flags.DEFINE_float('adam_beta1', 0.5, 'Adam Beta 1 for the Adam optimizer.')
+flags.DEFINE_float('adam_beta2', 0.999, 'Adam Beta 2 for the Adam optimizer.')
+flags.DEFINE_float('gen_disc_step_ratio', 0.2,
+                   'Generator:Discriminator training step ratio.')
+
+# FLAGS for distributed training.
+flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')
+flags.DEFINE_integer(
+    'ps_tasks', 0,
+    'The number of parameter servers. If the value is 0, then the parameters '
+    'are handled locally by the worker.')
+flags.DEFINE_integer(
+    'task', 0,
+    'The Task ID. This value is used when training with multiple workers to '
+    'identify each worker.')
+
+FLAGS = flags.FLAGS
+tfgan = tf.contrib.gan
+
+
+def _get_optimizer(gen_lr, dis_lr):
+  """Returns generator optimizer and discriminator optimizer.
+
+  Args:
+    gen_lr: A scalar float `Tensor` or a Python number.  The Generator learning
+        rate.
+    dis_lr: A scalar float `Tensor` or a Python number.  The Discriminator
+        learning rate.
+
+  Returns:
+    A tuple of generator optimizer and discriminator optimizer.
+  """
+  gen_opt = tf.train.AdamOptimizer(
+      gen_lr, beta1=FLAGS.adam_beta1, beta2=FLAGS.adam_beta2, use_locking=True)
+  dis_opt = tf.train.AdamOptimizer(
+      dis_lr, beta1=FLAGS.adam_beta1, beta2=FLAGS.adam_beta2, use_locking=True)
+  return gen_opt, dis_opt
+
+
+def _define_train_step():
+  """Get the training step for generator and discriminator for each GAN step.
+
+  Returns:
+    GANTrainSteps namedtuple representing the training step configuration.
+  """
+
+  if FLAGS.gen_disc_step_ratio <= 1:
+    discriminator_step = int(1 / FLAGS.gen_disc_step_ratio)
+    return tfgan.GANTrainSteps(1, discriminator_step)
+  else:
+    generator_step = int(FLAGS.gen_disc_step_ratio)
+    return tfgan.GANTrainSteps(generator_step, 1)
+
+
+def _get_summary_image(estimator, test_images_np):
+  """Returns a numpy image of the generate on the test images."""
+  num_domains = len(test_images_np)
+
+  img_rows = []
+  for img_np in test_images_np:
+    def test_input_fn():
+      dataset_imgs = [img_np] * num_domains  # pylint:disable=cell-var-from-loop
+      dataset_lbls = [tf.one_hot([d], num_domains) for d in xrange(num_domains)]
+
+      # Make into a dataset.
+      dataset_imgs = np.stack(dataset_imgs)
+      dataset_imgs = np.expand_dims(dataset_imgs, 1)
+      dataset_lbls = tf.stack(dataset_lbls)
+      unused_tensor = tf.zeros(num_domains)
+      return tf.data.Dataset.from_tensor_slices(
+          ((dataset_imgs, dataset_lbls), unused_tensor))
+
+    prediction_iterable = estimator.predict(test_input_fn)
+    predictions = [prediction_iterable.next() for _ in xrange(num_domains)]
+    transform_row = np.concatenate([img_np] + predictions, 1)
+    img_rows.append(transform_row)
+
+  all_rows = np.concatenate(img_rows, 0)
+  # Normalize` [-1, 1] to [0, 1].
+  normalized_summary = (all_rows + 1.0) / 2.0
+  return normalized_summary
+
+
+def _write_to_disk(summary_image, filename):
+  """Write to disk."""
+  buf = io.BytesIO()
+  scipy.misc.imsave(buf, summary_image, format='png')
+  buf.seek(0)
+  with tf.gfile.GFile(filename, 'w') as f:
+    f.write(buf.getvalue())
+
+
+def main(_, override_generator_fn=None, override_discriminator_fn=None):
+  # Create directories if not exist.
+  if not tf.gfile.Exists(FLAGS.output_dir):
+    tf.gfile.MakeDirs(FLAGS.output_dir)
+
+  # Make sure steps integers are consistent.
+  if FLAGS.max_number_of_steps % FLAGS.steps_per_eval != 0:
+    raise ValueError('`max_number_of_steps` must be divisible by '
+                     '`steps_per_eval`.')
+
+  # Create optimizers.
+  gen_opt, dis_opt = _get_optimizer(FLAGS.generator_lr, FLAGS.discriminator_lr)
+
+  # Create estimator.
+  # (joelshor): Add optional distribution strategy here.
+  stargan_estimator = tfgan.estimator.StarGANEstimator(
+      generator_fn=override_generator_fn or network.generator,
+      discriminator_fn=override_discriminator_fn or network.discriminator,
+      loss_fn=tfgan.stargan_loss,
+      generator_optimizer=gen_opt,
+      discriminator_optimizer=dis_opt,
+      get_hooks_fn=tfgan.get_sequential_train_hooks(_define_train_step()),
+      add_summaries=tfgan.estimator.SummaryType.IMAGES)
+
+  # Get input function for training and test images.
+  train_input_fn = lambda: data_provider.provide_data(  # pylint:disable=g-long-lambda
+      FLAGS.image_file_patterns, FLAGS.batch_size, FLAGS.patch_size)
+  test_images_np, _ = data_provider.provide_celeba_test_set()
+  filename_str = os.path.join(FLAGS.output_dir, 'summary_image_%i.png')
+
+  # Periodically train and write prediction output to disk.
+  cur_step = 0
+  while cur_step < FLAGS.max_number_of_steps:
+    stargan_estimator.train(train_input_fn, steps=FLAGS.steps_per_eval)
+    cur_step += FLAGS.steps_per_eval
+    summary_img = _get_summary_image(stargan_estimator, test_images_np)
+    _write_to_disk(summary_img, filename_str % cur_step)
+
+
+if __name__ == '__main__':
+  tf.flags.mark_flag_as_required('image_file_patterns')
+  tf.app.run()

research/gan/stargan_estimator/train_test.py

+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for stargan_estimator.train."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+from absl import flags
+import tensorflow as tf
+
+import train
+
+FLAGS = flags.FLAGS
+mock = tf.test.mock
+tfgan = tf.contrib.gan
+
+TESTDATA_DIR = 'google3/third_party/tensorflow_models/gan/stargan_estimator/testdata/celeba'
+
+
+def _test_generator(input_images, _):
+  """Simple generator function."""
+  return input_images * tf.get_variable('dummy_g', initializer=2.0)
+
+
+def _test_discriminator(inputs, num_domains):
+  """Differentiable dummy discriminator for StarGAN."""
+
+  hidden = tf.contrib.layers.flatten(inputs)
+
+  output_src = tf.reduce_mean(hidden, axis=1)
+
+  output_cls = tf.contrib.layers.fully_connected(
+      inputs=hidden,
+      num_outputs=num_domains,
+      activation_fn=None,
+      normalizer_fn=None,
+      biases_initializer=None)
+  return output_src, output_cls
+
+
+class TrainTest(tf.test.TestCase):
+
+  def test_main(self):
+
+    FLAGS.image_file_patterns = [
+        os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'black/*.jpg'),
+        os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'blond/*.jpg'),
+        os.path.join(FLAGS.test_srcdir, TESTDATA_DIR, 'brown/*.jpg'),
+    ]
+    FLAGS.max_number_of_steps = 1
+    FLAGS.steps_per_eval = 1
+    FLAGS.batch_size = 1
+    train.main(None, _test_generator, _test_discriminator)
+
+
+if __name__ == '__main__':
+  tf.test.main()
+