Project import generated by Copybara.

PiperOrigin-RevId: 177165761

research/gan/cifar/util_test.py

@@ -37,20 +37,19 @@ class UtilTest(tf.test.TestCase):
         num_classes=3,
         num_images_per_class=1)
 
-  def test_get_inception_scores(self):
   # Mock `inception_score` which is expensive.
-    with mock.patch.object(
-        util.tfgan.eval, 'inception_score') as mock_inception_score:
+  @mock.patch.object(util.tfgan.eval, 'inception_score', autospec=True)
+  def test_get_inception_scores(self, mock_inception_score):
     mock_inception_score.return_value = 1.0
     util.get_inception_scores(
         tf.placeholder(tf.float32, shape=[None, 28, 28, 3]),
         batch_size=100,
         num_inception_images=10)
 
-  def test_get_frechet_inception_distance(self):
   # Mock `frechet_inception_distance` which is expensive.
-    with mock.patch.object(
-        util.tfgan.eval, 'frechet_inception_distance') as mock_fid:
+  @mock.patch.object(util.tfgan.eval, 'frechet_inception_distance',
+                     autospec=True)
+  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]),

research/gan/image_compression/data_provider.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.
+# ==============================================================================
+"""Contains code for loading and preprocessing the compression image data."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+import tensorflow as tf
+
+from slim.datasets import dataset_factory as datasets
+
+slim = tf.contrib.slim
+
+
+def provide_data(split_name, batch_size, dataset_dir,
+                 dataset_name='imagenet', num_readers=1, num_threads=1,
+                 patch_size=128):
+  """Provides batches of image data for compression.
+
+  Args:
+    split_name: Either 'train' or 'validation'.
+    batch_size: The number of images in each batch.
+    dataset_dir: The directory where the data can be found. If `None`, use
+      default.
+    dataset_name: Name of the dataset.
+    num_readers: Number of dataset readers.
+    num_threads: Number of prefetching threads.
+    patch_size: Size of the path to extract from the image.
+
+  Returns:
+    images: A `Tensor` of size [batch_size, patch_size, patch_size, channels]
+  """
+  randomize = split_name == 'train'
+  dataset = datasets.get_dataset(
+      dataset_name, split_name, dataset_dir=dataset_dir)
+  provider = slim.dataset_data_provider.DatasetDataProvider(
+      dataset,
+      num_readers=num_readers,
+      common_queue_capacity=5 * batch_size,
+      common_queue_min=batch_size,
+      shuffle=randomize)
+  [image] = provider.get(['image'])
+
+  # Sample a patch of fixed size.
+  patch = tf.image.resize_image_with_crop_or_pad(image, patch_size, patch_size)
+  patch.shape.assert_is_compatible_with([patch_size, patch_size, 3])
+
+  # Preprocess the images. Make the range lie in a strictly smaller range than
+  # [-1, 1], so that network outputs aren't forced to the extreme ranges.
+  patch = (tf.to_float(patch) - 128.0) / 142.0
+
+  if randomize:
+    image_batch = tf.train.shuffle_batch(
+        [patch],
+        batch_size=batch_size,
+        num_threads=num_threads,
+        capacity=5 * batch_size,
+        min_after_dequeue=batch_size)
+  else:
+    image_batch = tf.train.batch(
+        [patch],
+        batch_size=batch_size,
+        num_threads=1,  # no threads so it's deterministic
+        capacity=5 * batch_size)
+
+  return image_batch
+
+
+def float_image_to_uint8(image):
+  """Convert float image in ~[-0.9, 0.9) to [0, 255] uint8.
+
+  Args:
+    image: An image tensor. Values should be in [-0.9, 0.9).
+
+  Returns:
+    Input image cast to uint8 and with integer values in [0, 255].
+  """
+  image = (image * 142.0) + 128.0
+  return tf.cast(image, tf.uint8)

research/gan/image_compression/data_provider_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 data_provider."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+
+import numpy as np
+
+import tensorflow as tf
+
+import data_provider
+
+
+class DataProviderTest(tf.test.TestCase):
+
+  def _test_data_provider_helper(self, split_name):
+    dataset_dir = os.path.join(
+        tf.flags.FLAGS.test_srcdir,
+        'google3/third_party/tensorflow_models/gan/image_compression/testdata/')
+
+    batch_size = 3
+    patch_size = 8
+    images = data_provider.provide_data(
+        split_name, batch_size, dataset_dir, patch_size=8)
+    self.assertListEqual([batch_size, patch_size, patch_size, 3],
+                         images.shape.as_list())
+
+    with self.test_session(use_gpu=True) as sess:
+      with tf.contrib.slim.queues.QueueRunners(sess):
+        images_out = sess.run(images)
+        self.assertEqual((batch_size, patch_size, patch_size, 3),
+                         images_out.shape)
+        # Check range.
+        self.assertTrue(np.all(np.abs(images_out) <= 1.0))
+
+  def test_data_provider_train(self):
+    self._test_data_provider_helper('train')
+
+  def test_data_provider_validation(self):
+    self._test_data_provider_helper('validation')
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/gan/image_compression/eval.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.
+# ==============================================================================
+"""Evaluates a TFGAN trained compression model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+
+import tensorflow as tf
+
+import data_provider
+import networks
+import summaries
+
+flags = tf.flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')
+
+flags.DEFINE_string('checkpoint_dir', '/tmp/compression/',
+                    'Directory where the model was written to.')
+
+flags.DEFINE_string('eval_dir', '/tmp/compression/',
+                    'Directory where the results are saved to.')
+
+flags.DEFINE_integer('max_number_of_evaluations', None,
+                     'Number of times to run evaluation. If `None`, run '
+                     'forever.')
+
+flags.DEFINE_string('dataset_dir', None, 'Location of data.')
+
+# Compression-specific flags.
+flags.DEFINE_integer('batch_size', 32, 'The number of images in each batch.')
+
+flags.DEFINE_integer('patch_size', 32, 'The size of the patches to train on.')
+
+flags.DEFINE_integer('bits_per_patch', 1230,
+                     'The number of bits to produce per patch.')
+
+flags.DEFINE_integer('model_depth', 64,
+                     'Number of filters for compression model')
+
+
+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)
+
+
+if __name__ == '__main__':
+  tf.app.run()

research/gan/image_compression/eval_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 gan.image_compression.eval."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+import eval  # pylint:disable=redefined-builtin
+
+
+class EvalTest(tf.test.TestCase):
+
+  def test_build_graph(self):
+    eval.main(None, run_eval_loop=False)
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/gan/image_compression/launch_jobs.sh

+# Copyright 2016 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.
+# ==============================================================================
+#!/bin/bash
+#
+# This script performs the following operations:
+# 1. Downloads the Imagenet dataset.
+# 2. Trains image compression model on patches from Imagenet.
+# 3. Evaluates the models and writes sample images to disk.
+#
+# Usage:
+# cd models/research/gan/image_compression
+# ./launch_jobs.sh ${weight_factor} ${git_repo}
+set -e
+
+# Weight of the adversarial loss.
+weight_factor=$1
+if [[ "$weight_factor" == "" ]]; then
+  echo "'weight_factor' must not be empty."
+  exit
+fi
+
+# Location of the git repository.
+git_repo=$2
+if [[ "$git_repo" == "" ]]; then
+  echo "'git_repo' must not be empty."
+  exit
+fi
+
+# Base name for where the checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/compression-model
+
+# Base name for where the evaluation images will be saved to.
+EVAL_DIR=/tmp/compression-model/eval
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/imagenet-data
+
+export PYTHONPATH=$PYTHONPATH:$git_repo:$git_repo/research:$git_repo/research/slim:$git_repo/research/slim/nets
+
+# A helper function for printing pretty output.
+Banner () {
+  local text=$1
+  local green='\033[0;32m'
+  local nc='\033[0m'  # No color.
+  echo -e "${green}${text}${nc}"
+}
+
+# Download the dataset.
+"${git_repo}/research/slim/datasets/download_and_convert_imagenet.sh" ${DATASET_DIR}
+
+# Run the compression model.
+NUM_STEPS=10000
+MODEL_TRAIN_DIR="${TRAIN_DIR}/wt${weight_factor}"
+Banner "Starting training an image compression model for ${NUM_STEPS} steps..."
+python "${git_repo}/research/gan/image_compression/train.py" \
+  --train_log_dir=${MODEL_TRAIN_DIR} \
+  --dataset_dir=${DATASET_DIR} \
+  --max_number_of_steps=${NUM_STEPS} \
+  --weight_factor=${weight_factor} \
+  --alsologtostderr
+Banner "Finished training image compression model ${NUM_STEPS} steps."
+
+# Run evaluation.
+MODEL_EVAL_DIR="${TRAIN_DIR}/eval/wt${weight_factor}"
+Banner "Starting evaluation of image compression model..."
+python "${git_repo}/research/gan/image_compression/eval.py" \
+  --checkpoint_dir=${MODEL_TRAIN_DIR} \
+  --eval_dir=${MODEL_EVAL_DIR} \
+  --dataset_dir=${DATASET_DIR} \
+  --max_number_of_evaluation=1
+Banner "Finished evaluation. See ${MODEL_EVAL_DIR} for output images."

research/gan/image_compression/networks.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.
+# ==============================================================================
+"""Networks for GAN compression example using TFGAN."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+from slim.nets import dcgan
+from slim.nets import pix2pix
+
+
+def _last_conv_layer(end_points):
+  """"Returns the last convolutional layer from an endpoints dictionary."""
+  conv_list = [k if k[:4] == 'conv' else None for k in end_points.keys()]
+  conv_list.sort()
+  return end_points[conv_list[-1]]
+
+
+def _encoder(img_batch, is_training=True, bits=64, depth=64):
+  """Maps images to internal representation.
+
+  Args:
+    img_batch: Stuff
+    is_training: Stuff
+    bits: Number of bits per patch.
+    depth: Stuff
+
+  Returns:
+    Real-valued 2D Tensor of size [batch_size, bits].
+  """
+  _, end_points = dcgan.discriminator(
+      img_batch, depth=depth, is_training=is_training, scope='Encoder')
+
+  # (joelshor): Make the DCGAN convolutional layer that converts to logits
+  # not trainable, since it doesn't affect the encoder output.
+
+  # Get the pre-logit layer, which is the last conv.
+  net = _last_conv_layer(end_points)
+
+  # Transform the features to the proper number of bits.
+  with tf.variable_scope('EncoderTransformer'):
+    encoded = tf.contrib.layers.conv2d(net, bits, kernel_size=1, stride=1,
+                                       padding='VALID', normalizer_fn=None,
+                                       activation_fn=None)
+  encoded = tf.squeeze(encoded, [1, 2])
+  encoded.shape.assert_has_rank(2)
+
+  # Map encoded to the range [-1, 1].
+  return tf.nn.softsign(encoded)
+
+
+def _binarizer(prebinary_codes, is_training):
+  """Binarize compression logits.
+
+  During training, add noise, as in https://arxiv.org/pdf/1611.01704.pdf. During
+  eval, map [-1, 1] -> {-1, 1}.
+
+  Args:
+    prebinary_codes: Floating-point tensors corresponding to pre-binary codes.
+      Shape is [batch, code_length].
+    is_training: A python bool. If True, add noise. If false, binarize.
+
+  Returns:
+    Binarized codes. Shape is [batch, code_length].
+
+  Raises:
+    ValueError: If the shape of `prebinary_codes` isn't static.
+  """
+  if is_training:
+    # In order to train codes that can be binarized during eval, we add noise as
+    # in https://arxiv.org/pdf/1611.01704.pdf. Another option is to use a
+    # stochastic node, as in https://arxiv.org/abs/1608.05148.
+    noise = tf.random_uniform(
+        prebinary_codes.shape,
+        minval=-1.0,
+        maxval=1.0)
+    return prebinary_codes + noise
+  else:
+    return tf.sign(prebinary_codes)
+
+
+def _decoder(codes, final_size, is_training, depth=64):
+  """Compression decoder."""
+  decoded_img, _ = dcgan.generator(
+      codes,
+      depth=depth,
+      final_size=final_size,
+      num_outputs=3,
+      is_training=is_training,
+      scope='Decoder')
+
+  # Map output to [-1, 1].
+  # Use softsign instead of tanh, as per empirical results of
+  # http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.
+  return tf.nn.softsign(decoded_img)
+
+
+def _validate_image_inputs(image_batch):
+  image_batch.shape.assert_has_rank(4)
+  image_batch.shape[1:].assert_is_fully_defined()
+
+
+def compression_model(image_batch, num_bits=64, depth=64, is_training=True):
+  """Image compression model.
+
+  Args:
+    image_batch: A batch of images to compress and reconstruct. Images should
+      be normalized already. Shape is [batch, height, width, channels].
+    num_bits: Desired number of bits per image in the compressed representation.
+    depth: The base number of filters for the encoder and decoder networks.
+    is_training: A python bool. If False, run in evaluation mode.
+
+  Returns:
+    uncompressed images, binary codes, prebinary codes
+  """
+  image_batch = tf.convert_to_tensor(image_batch)
+  _validate_image_inputs(image_batch)
+  final_size = image_batch.shape.as_list()[1]
+
+  prebinary_codes = _encoder(image_batch, is_training, num_bits, depth)
+  binary_codes = _binarizer(prebinary_codes, is_training)
+  uncompressed_imgs = _decoder(binary_codes, final_size, is_training, depth)
+  return uncompressed_imgs, binary_codes, prebinary_codes
+
+
+def discriminator(image_batch, unused_conditioning=None, depth=64):
+  """A thin wrapper around the pix2pix discriminator to conform to TFGAN API."""
+  logits, _ = pix2pix.pix2pix_discriminator(
+      image_batch, num_filters=[depth, 2 * depth, 4 * depth, 8 * depth])
+  return tf.layers.flatten(logits)

research/gan/image_compression/networks_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 gan.image_compression.networks."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+import networks
+
+
+class NetworksTest(tf.test.TestCase):
+
+  def test_last_conv_layer(self):
+    x = tf.constant(1.0)
+    y = tf.constant(0.0)
+    end_points = {
+        'silly': y,
+        'conv2': y,
+        'conv4': x,
+        'logits': y,
+        'conv-1': y,
+    }
+    self.assertEqual(x, networks._last_conv_layer(end_points))
+
+  def test_generator_run(self):
+    img_batch = tf.zeros([3, 16, 16, 3])
+    model_output = networks.compression_model(img_batch)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      sess.run(model_output)
+
+  def test_generator_graph(self):
+    for i, batch_size in zip(xrange(3, 7), xrange(3, 11, 2)):
+      tf.reset_default_graph()
+      patch_size = 2 ** i
+      bits = 2 ** i
+      img = tf.ones([batch_size, patch_size, patch_size, 3])
+      uncompressed, binary_codes, prebinary = networks.compression_model(
+          img, bits)
+
+      self.assertAllEqual([batch_size, patch_size, patch_size, 3],
+                          uncompressed.shape.as_list())
+      self.assertEqual([batch_size, bits], binary_codes.shape.as_list())
+      self.assertEqual([batch_size, bits], prebinary.shape.as_list())
+
+  def test_generator_invalid_input(self):
+    wrong_dim_input = tf.zeros([5, 32, 32])
+    with self.assertRaisesRegexp(ValueError, 'Shape .* must have rank 4'):
+      networks.compression_model(wrong_dim_input)
+
+    not_fully_defined = tf.placeholder(tf.float32, [3, None, 32, 3])
+    with self.assertRaisesRegexp(ValueError, 'Shape .* is not fully defined'):
+      networks.compression_model(not_fully_defined)
+
+  def test_discriminator_run(self):
+    img_batch = tf.zeros([3, 70, 70, 3])
+    disc_output = networks.discriminator(img_batch)
+    with self.test_session() as sess:
+      sess.run(tf.global_variables_initializer())
+      sess.run(disc_output)
+
+  def test_discriminator_graph(self):
+    # Check graph construction for a number of image size/depths and batch
+    # sizes.
+    for batch_size, patch_size in zip([3, 6], [70, 128]):
+      tf.reset_default_graph()
+      img = tf.ones([batch_size, patch_size, patch_size, 3])
+      disc_output = networks.discriminator(img)
+
+      self.assertEqual(2, disc_output.shape.ndims)
+      self.assertEqual(batch_size, disc_output.shape[0])
+
+  def test_discriminator_invalid_input(self):
+    wrong_dim_input = tf.zeros([5, 32, 32])
+    with self.assertRaisesRegexp(ValueError, 'Shape must be rank 4'):
+      networks.discriminator(wrong_dim_input)
+
+    not_fully_defined = tf.placeholder(tf.float32, [3, None, 32, 3])
+    with self.assertRaisesRegexp(ValueError, 'Shape .* is not fully defined'):
+      networks.compression_model(not_fully_defined)
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/gan/image_compression/summaries.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.
+# ==============================================================================
+"""Summaries utility file to share between train and eval."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+
+import tensorflow as tf
+
+tfgan = tf.contrib.gan
+
+
+def add_reconstruction_summaries(images, reconstructions, prebinary,
+                                 num_imgs_to_visualize=8):
+  """Adds image summaries."""
+  reshaped_img = stack_images(images, reconstructions, num_imgs_to_visualize)
+
+  tf.summary.image('real_vs_reconstruction', reshaped_img, max_outputs=1)
+  if prebinary is not None:
+    tf.summary.histogram('prebinary_codes', prebinary)
+
+
+def stack_images(images, reconstructions, num_imgs_to_visualize=8):
+  """Stack and reshape images to see compression effects."""
+  to_reshape = (tf.unstack(images)[:num_imgs_to_visualize] +
+                tf.unstack(reconstructions)[:num_imgs_to_visualize])
+  reshaped_img = tfgan.eval.image_reshaper(
+      to_reshape, num_cols=num_imgs_to_visualize)
+  return reshaped_img

research/gan/image_compression/testdata/labels.txt

+

research/gan/image_compression/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.
+# ==============================================================================
+# pylint:disable=line-too-long
+"""Trains an image compression network with an adversarial loss."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+
+import tensorflow as tf
+
+import data_provider
+import networks
+import summaries
+
+
+tfgan = tf.contrib.gan
+flags = tf.flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_integer('batch_size', 32, 'The number of images in each batch.')
+
+flags.DEFINE_integer('patch_size', 32, 'The size of the patches to train on.')
+
+flags.DEFINE_integer('bits_per_patch', 1230,
+                     'The number of bits to produce per patch.')
+
+flags.DEFINE_integer('model_depth', 64,
+                     'Number of filters for compression model')
+
+flags.DEFINE_string('master', '', 'Name of the TensorFlow master to use.')
+
+flags.DEFINE_string('train_log_dir', '/tmp/compression/',
+                    'Directory where to write event logs.')
+
+flags.DEFINE_float('generator_lr', 1e-5,
+                   'The compression model learning rate.')
+
+flags.DEFINE_float('discriminator_lr', 1e-6,
+                   'The discriminator learning rate.')
+
+flags.DEFINE_integer('max_number_of_steps', 2000000,
+                     'The maximum number of gradient steps.')
+
+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.DEFINE_float(
+    'weight_factor', 10000.0,
+    'How much to weight the adversarial loss relative to pixel loss.')
+
+flags.DEFINE_string('dataset_dir', None, 'Location of data.')
+
+
+def main(_):
+  if not tf.gfile.Exists(FLAGS.train_log_dir):
+    tf.gfile.MakeDirs(FLAGS.train_log_dir)
+
+  with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
+    # Put input pipeline on CPU to reserve GPU for training.
+    with tf.name_scope('inputs'), tf.device('/cpu:0'):
+      images = data_provider.provide_data(
+          'train', FLAGS.batch_size, dataset_dir=FLAGS.dataset_dir,
+          patch_size=FLAGS.patch_size)
+
+    # Manually define a GANModel tuple. This is useful when we have custom
+    # code to track variables. Note that we could replace all of this with a
+    # call to `tfgan.gan_model`, but we don't in order to demonstrate some of
+    # TFGAN's flexibility.
+    with tf.variable_scope('generator') as gen_scope:
+      reconstructions, _, prebinary = networks.compression_model(
+          images,
+          num_bits=FLAGS.bits_per_patch,
+          depth=FLAGS.model_depth)
+    gan_model = _get_gan_model(
+        generator_inputs=images,
+        generated_data=reconstructions,
+        real_data=images,
+        generator_scope=gen_scope)
+    summaries.add_reconstruction_summaries(images, reconstructions, prebinary)
+    tfgan.eval.add_gan_model_summaries(gan_model)
+
+    # Define the GANLoss tuple using standard library functions.
+    with tf.name_scope('loss'):
+      gan_loss = tfgan.gan_loss(
+          gan_model,
+          generator_loss_fn=tfgan.losses.least_squares_generator_loss,
+          discriminator_loss_fn=tfgan.losses.least_squares_discriminator_loss,
+          add_summaries=FLAGS.weight_factor > 0)
+
+      # Define the standard pixel loss.
+      l1_pixel_loss = tf.norm(gan_model.real_data - gan_model.generated_data,
+                              ord=1)
+
+      # Modify the loss tuple to include the pixel loss. Add summaries as well.
+      gan_loss = tfgan.losses.combine_adversarial_loss(
+          gan_loss, gan_model, l1_pixel_loss, weight_factor=FLAGS.weight_factor)
+
+    # Get the GANTrain ops using the custom optimizers and optional
+    # discriminator weight clipping.
+    with tf.name_scope('train_ops'):
+      gen_lr, dis_lr = _lr(FLAGS.generator_lr, FLAGS.discriminator_lr)
+      gen_opt, dis_opt = _optimizer(gen_lr, dis_lr)
+      train_ops = tfgan.gan_train_ops(
+          gan_model,
+          gan_loss,
+          generator_optimizer=gen_opt,
+          discriminator_optimizer=dis_opt,
+          summarize_gradients=True,
+          colocate_gradients_with_ops=True,
+          aggregation_method=tf.AggregationMethod.EXPERIMENTAL_ACCUMULATE_N)
+      tf.summary.scalar('generator_lr', gen_lr)
+      tf.summary.scalar('discriminator_lr', dis_lr)
+
+    # Determine the number of generator vs discriminator steps.
+    train_steps = tfgan.GANTrainSteps(
+        generator_train_steps=1,
+        discriminator_train_steps=int(FLAGS.weight_factor > 0))
+
+    # Run the alternating training loop. Skip it if no steps should be taken
+    # (used for graph construction tests).
+    status_message = tf.string_join(
+        ['Starting train step: ',
+         tf.as_string(tf.train.get_or_create_global_step())],
+        name='status_message')
+    if FLAGS.max_number_of_steps == 0: return
+    tfgan.gan_train(
+        train_ops,
+        FLAGS.train_log_dir,
+        tfgan.get_sequential_train_hooks(train_steps),
+        hooks=[tf.train.StopAtStepHook(num_steps=FLAGS.max_number_of_steps),
+               tf.train.LoggingTensorHook([status_message], every_n_iter=10)],
+        master=FLAGS.master,
+        is_chief=FLAGS.task == 0)
+
+
+def _optimizer(gen_lr, dis_lr):
+  # First is generator optimizer, second is discriminator.
+  adam_kwargs = {
+      'epsilon': 1e-8,
+      'beta1': 0.5,
+  }
+  return (tf.train.AdamOptimizer(gen_lr, **adam_kwargs),
+          tf.train.AdamOptimizer(dis_lr, **adam_kwargs))
+
+
+def _lr(gen_lr_base, dis_lr_base):
+  """Return the generator and discriminator learning rates."""
+  gen_lr_kwargs = {
+      'decay_steps': 60000,
+      'decay_rate': 0.9,
+      'staircase': True,
+  }
+  gen_lr = tf.train.exponential_decay(
+      learning_rate=gen_lr_base,
+      global_step=tf.train.get_or_create_global_step(),
+      **gen_lr_kwargs)
+  dis_lr = dis_lr_base
+
+  return gen_lr, dis_lr
+
+
+def _get_gan_model(generator_inputs, generated_data, real_data,
+                   generator_scope):
+  """Manually construct and return a GANModel tuple."""
+  generator_vars = tf.contrib.framework.get_trainable_variables(generator_scope)
+
+  discriminator_fn = networks.discriminator
+  with tf.variable_scope('discriminator') as dis_scope:
+    discriminator_gen_outputs = discriminator_fn(generated_data)
+  with tf.variable_scope(dis_scope, reuse=True):
+    discriminator_real_outputs = discriminator_fn(real_data)
+  discriminator_vars = tf.contrib.framework.get_trainable_variables(
+      dis_scope)
+
+  # Manually construct GANModel tuple.
+  gan_model = tfgan.GANModel(
+      generator_inputs=generator_inputs,
+      generated_data=generated_data,
+      generator_variables=generator_vars,
+      generator_scope=generator_scope,
+      generator_fn=None,  # not necessary
+      real_data=real_data,
+      discriminator_real_outputs=discriminator_real_outputs,
+      discriminator_gen_outputs=discriminator_gen_outputs,
+      discriminator_variables=discriminator_vars,
+      discriminator_scope=dis_scope,
+      discriminator_fn=discriminator_fn)
+
+  return gan_model
+
+
+if __name__ == '__main__':
+  tf.logging.set_verbosity(tf.logging.INFO)
+  tf.app.run()
+

research/gan/image_compression/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 image_compression.train."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+import numpy as np
+import tensorflow as tf
+import train
+
+FLAGS = tf.flags.FLAGS
+mock = tf.test.mock
+
+
+class TrainTest(tf.test.TestCase):
+
+  def _test_build_graph_helper(self, weight_factor):
+    FLAGS.max_number_of_steps = 0
+    FLAGS.weight_factor = weight_factor
+
+    batch_size = 3
+    patch_size = 16
+
+    FLAGS.batch_size = batch_size
+    FLAGS.patch_size = patch_size
+    mock_imgs = np.zeros([batch_size, patch_size, patch_size, 3],
+                         dtype=np.float32)
+
+    with mock.patch.object(train, 'data_provider') as mock_data_provider:
+      mock_data_provider.provide_data.return_value = mock_imgs
+      train.main(None)
+
+  def test_build_graph_noadversarialloss(self):
+    self._test_build_graph_helper(0.0)
+
+  def test_build_graph_adversarialloss(self):
+    self._test_build_graph_helper(1.0)
+
+
+if __name__ == '__main__':
+  tf.test.main()
+

research/gan/mnist/eval_test.py

@@ -21,7 +21,7 @@ from __future__ import print_function
 
 
 import tensorflow as tf
-from google3.third_party.tensorflow_models.gan.mnist  import eval  # pylint:disable=redefined-builtin
+import eval  # pylint:disable=redefined-builtin
 
 
 class EvalTest(tf.test.TestCase):

research/gan/mnist/train_test.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Tests for tfgan.examples.mnist.train."""
+"""Tests for mnist.train."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -30,7 +30,8 @@ mock = tf.test.mock
 
 class TrainTest(tf.test.TestCase):
 
-  def test_run_one_train_step(self):
+  @mock.patch.object(train, 'data_provider', autospec=True)
+  def test_run_one_train_step(self, mock_data_provider):
     FLAGS.max_number_of_steps = 1
     FLAGS.gan_type = 'unconditional'
     FLAGS.batch_size = 5
@@ -42,9 +43,8 @@ class TrainTest(tf.test.TestCase):
     mock_lbls = np.concatenate(
         (np.ones([FLAGS.batch_size, 1], dtype=np.int32),
          np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
-    with mock.patch.object(train, 'data_provider') as mock_data_provider:
-      mock_data_provider.provide_data.return_value = (
-          mock_imgs, mock_lbls, None)
+    mock_data_provider.provide_data.return_value = (mock_imgs, mock_lbls, None)
+
     train.main(None)
 
   def _test_build_graph_helper(self, gan_type):

research/gan/mnist_estimator/train_test.py

@@ -31,7 +31,8 @@ mock = tf.test.mock
 
 class TrainTest(tf.test.TestCase):
 
-  def test_full_flow(self):
+  @mock.patch.object(train, 'data_provider', autospec=True)
+  def test_full_flow(self, mock_data_provider):
     FLAGS.eval_dir = self.get_temp_dir()
     FLAGS.batch_size = 16
     FLAGS.max_number_of_steps = 2
@@ -42,9 +43,8 @@ class TrainTest(tf.test.TestCase):
     mock_lbls = np.concatenate(
         (np.ones([FLAGS.batch_size, 1], dtype=np.int32),
          np.zeros([FLAGS.batch_size, 9], dtype=np.int32)), axis=1)
-    with mock.patch.object(train, 'data_provider') as mock_data_provider:
-      mock_data_provider.provide_data.return_value = (
-          mock_imgs, mock_lbls, None)
+    mock_data_provider.provide_data.return_value = (mock_imgs, mock_lbls, None)
+
     train.main(None)
 
 

research/gan/tutorial.ipynb

@@ -23,48 +23,27 @@
    "metadata": {},
    "source": [
     "## Table of Contents\n",
-    "\n",
-    "<a href=#installation_and_setup>Installation and Setup</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#download_data>Download Data</a>\n",
-    "\n",
-    "<a href=#unconditional_example>Unconditional GAN example</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#unconditional_input>Input pipeline</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#unconditional_model>Model</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#unconditional_loss>Loss</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#unconditional_train>Train and evaluation</a>\n",
-    "\n",
-    "<a href=#ganestimator_example>GANEstimator example</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#ganestimator_input>Input pipeline</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#ganestimator_train>Train</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#ganestimator_eval>Eval</a>\n",
-    "\n",
-    "<a href=#conditional_example>Conditional GAN example</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#conditional_input>Input pipeline</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#conditional_model>Model</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#conditional_loss>Loss</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#conditional_train>Train and evaluation</a>\n",
-    "\n",
-    "<a href=#infogan_example>InfoGAN example</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#infogan_input>Input pipeline</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#infogan_model>Model</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#infogan_loss>Loss</a>\n",
-    "\n",
-    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href=#infogan_train>Train and evaluation</a>"
+    "<a href='#installation_and_setup'>Installation and Setup</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#download_data'>Download Data</a><br>\n",
+    "<a href='#unconditional_example'>Unconditional GAN example</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#unconditional_input'>Input pipeline</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#unconditional_model'>Model</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#unconditional_loss'>Loss</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#unconditional_train'>Train and evaluation</a><br>\n",
+    "<a href='#ganestimator_example'>GANEstimator example</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#ganestimator_input'>Input pipeline</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#ganestimator_train'>Train</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#ganestimator_eval'>Eval</a><br>\n",
+    "<a href='#conditional_example'>Conditional GAN example</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#conditional_input'>Input pipeline</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#conditional_model'>Model</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#conditional_loss'>Loss</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#conditional_train'>Train and evaluation</a><br>\n",
+    "<a href='#infogan_example'>InfoGAN example</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#infogan_input'>Input pipeline</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#infogan_model'>Model</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#infogan_loss'>Loss</a><br>\n",
+    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='#infogan_train'>Train and evaluation</a><br>"
    ]
   },
   {