Merge pull request tensorflow#2918 from joel-shor/master

Project import generated by Copybara.

research/gan/cifar/util_test.py

@@ -37,26 +37,25 @@ def test_get_image_grid(self):
         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_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_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)
+  # Mock `inception_score` which is expensive.
+  @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)
+
+  # Mock `frechet_inception_distance` which is expensive.
+  @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]),
+        tf.placeholder(tf.float32, shape=[None, 28, 28, 3]),
+        batch_size=100,
+        num_inception_images=10)
 
 
 if __name__ == '__main__':

research/gan/image_compression/data_provider.py

@@ -0,0 +1,93 @@
+# 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

@@ -0,0 +1,60 @@
+# 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

@@ -0,0 +1,101 @@
+# 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

@@ -0,0 +1,32 @@
+# 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()