Merge pull request #2002 from dmrd/master

Open sourcing PixelDA code

CODEOWNERS

@@ -5,7 +5,7 @@ autoencoders/* @snurkabill
 cognitive_mapping_and_planning/* @s-gupta
 compression/* @nmjohn
 differential_privacy/* @panyx0718
-domain_adaptation/* @bousmalis
+domain_adaptation/* @bousmalis @ddohan
 im2txt/* @cshallue
 inception/* @shlens @vincentvanhoucke
 learning_to_remember_rare_events/* @lukaszkaiser @ofirnachum

domain_adaptation/README.md

-# Domain Separation Networks
+## Introduction
+This is the code used for two domain adaptation papers.
 
+The `domain_separation` directory contains code for the "Domain Separation
+Networks" paper by Bousmalis K., Trigeorgis G., et al. which was presented at
+NIPS 2016. The paper can be found here: https://arxiv.org/abs/1608.06019.
 
-## Introduction
-This code is the code used for the "Domain Separation Networks" paper
-by Bousmalis K., Trigeorgis G., et al. which was presented at NIPS 2016. The
-paper can be found here: https://arxiv.org/abs/1608.06019.
+The `pixel_domain_adaptation` directory contains the code used for the
+"Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial
+Networks" paper by Bousmalis K., et al. (presented at CVPR 2017). The paper can
+be found here: https://arxiv.org/abs/1612.05424. PixelDA aims to perform domain
+adaptation by transfering the visual style of the target domain (which has few
+or no labels) to a source domain (which has many labels). This is accomplished
+using a Generative Adversarial Network (GAN).
 
 ## Contact
-This code was open-sourced by [Konstantinos Bousmalis](https://github.com/bousmalis) (konstantinos@google.com).
+The domain separation code was open-sourced
+by [Konstantinos Bousmalis](https://github.com/bousmalis)
+(konstantinos@google.com), while the pixel level domain adaptation code was
+open-sourced by [David Dohan](https://github.com/dmrd) (ddohan@google.com).
 
 ## Installation
 You will need to have the following installed on your machine before trying out the DSN code.
@@ -16,26 +26,70 @@ You will need to have the following installed on your machine before trying out
 *  Bazel: https://bazel.build/
 
 ## Important Note
-Although we are making the code available, you are only able to use the MNIST
-provider for now. We will soon provide a script to download and convert MNIST-M
-as well. Check back here in a few weeks or wait for a relevant announcement from
-[@bousmalis](https://twitter.com/bousmalis).
+We are working to open source the pose estimation dataset. For now, the MNIST to
+MNIST-M dataset is available. Check back here in a few weeks or wait for a
+relevant announcement from [@bousmalis](https://twitter.com/bousmalis).
 
-## Running the code for adapting MNIST to MNIST-M
-In order to run the MNIST to MNIST-M experiments with DANNs and/or DANNs with
-domain separation (DSNs) you will need to set the directory you used to download
-MNIST and MNIST-M:
+## Initial setup
+In order to run the MNIST to MNIST-M experiments, you will need to set the
+data directory:
 
 ```
 $ export DSN_DATA_DIR=/your/dir
 ```
 
-Add models and models/slim to your `$PYTHONPATH`:
+Add models and models/slim to your `$PYTHONPATH` (assumes $PWD is /models):
 
 ```
 $ export PYTHONPATH=$PYTHONPATH:$PWD:$PWD/slim
 ```
 
+## Getting the datasets
+
+You can fetch the MNIST data by running
+
+```
+ $ bazel run slim:download_and_convert_data -- --dataset_dir $DSN_DATA_DIR --dataset_name=mnist
+```
+
+The MNIST-M dataset is available online [here](http://bit.ly/2nrlUAJ).  Once it is downloaded and extracted into your data directory, create TFRecord files by running:
+```
+$ bazel run domain_adaptation/datasets:download_and_convert_mnist_m -- --dataset_dir $DSN_DATA_DIR
+```
+
+
+
+# Running PixelDA from MNIST to MNIST-M
+You can run PixelDA as follows (using Tensorboard to examine the results):
+
+```
+$ bazel run domain_adaptation/pixel_domain_adaptation:pixelda_train -- --dataset_dir $DSN_DATA_DIR --source_dataset mnist --target_dataset mnist_m
+```
+
+And evaluation as:
+```
+$ bazel run domain_adaptation/pixel_domain_adaptation:pixelda_eval -- --dataset_dir $DSN_DATA_DIR --source_dataset mnist --target_dataset mnist_m --target_split_name test
+```
+
+The MNIST-M results in the paper were run with the following hparams flag:
+```
+--hparams arch=resnet,domain_loss_weight=0.135603587834,num_training_examples=16000000,style_transfer_loss_weight=0.0113173311334,task_loss_in_g_weight=0.0100959947002,task_tower=mnist,task_tower_in_g_step=true
+```
+
+### A note on terminology/language of the code:
+
+The components of the network can be grouped into two parts
+which correspond to elements which are jointly optimized: The generator
+component and the discriminator component.
+
+The generator component takes either an image or noise vector and produces an
+output image. 
+
+The discriminator component takes the generated images and the target images
+and attempts to discriminate between them.
+
+## Running DSN code for adapting MNIST to MNIST-M
+
 Then you need to build the binaries with Bazel:
 
 ```

domain_adaptation/datasets/BUILD

@@ -26,10 +26,20 @@ py_library(
     ],
 )
 
+py_binary(
+    name = "download_and_convert_mnist_m",
+    srcs = ["download_and_convert_mnist_m.py"],
+    deps = [
+        
+        "//slim:dataset_utils",
+    ],
+)
+
 py_binary(
     name = "mnist_m",
     srcs = ["mnist_m.py"],
     deps = [
+        
         "//slim:dataset_utils",
     ],
 )

domain_adaptation/datasets/dataset_factory.py

-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2017 Google Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,13 +11,14 @@
 # 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.
-# ==============================================================================
+
 """A factory-pattern class which returns image/label pairs."""
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+# Dependency imports
 import tensorflow as tf
 
 from slim.datasets import mnist

domain_adaptation/datasets/download_and_convert_mnist_m.py

+# Copyright 2017 Google Inc.
+#
+# 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.
+
+r"""Downloads and converts MNIST-M data to TFRecords of TF-Example protos.
+
+This module downloads the MNIST-M data, uncompresses it, reads the files
+that make up the MNIST-M data and creates two TFRecord datasets: one for train
+and one for test. Each TFRecord dataset is comprised of a set of TF-Example
+protocol buffers, each of which contain a single image and label.
+
+The script should take about a minute to run.
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import random
+import sys
+
+# Dependency imports
+import numpy as np
+from six.moves import urllib
+import tensorflow as tf
+
+from slim.datasets import dataset_utils
+
+tf.app.flags.DEFINE_string(
+    'dataset_dir', None,
+    'The directory where the output TFRecords and temporary files are saved.')
+
+FLAGS = tf.app.flags.FLAGS
+
+_IMAGE_SIZE = 32
+_NUM_CHANNELS = 3
+
+# The number of images in the training set.
+_NUM_TRAIN_SAMPLES = 59001
+
+# The number of images to be kept from the training set for the validation set.
+_NUM_VALIDATION = 1000
+
+# The number of images in the test set.
+_NUM_TEST_SAMPLES = 9001
+
+# Seed for repeatability.
+_RANDOM_SEED = 0
+
+# The names of the classes.
+_CLASS_NAMES = [
+    'zero',
+    'one',
+    'two',
+    'three',
+    'four',
+    'five',
+    'size',
+    'seven',
+    'eight',
+    'nine',
+]
+
+
+class ImageReader(object):
+  """Helper class that provides TensorFlow image coding utilities."""
+
+  def __init__(self):
+    # Initializes function that decodes RGB PNG data.
+    self._decode_png_data = tf.placeholder(dtype=tf.string)
+    self._decode_png = tf.image.decode_png(self._decode_png_data, channels=3)
+
+  def read_image_dims(self, sess, image_data):
+    image = self.decode_png(sess, image_data)
+    return image.shape[0], image.shape[1]
+
+  def decode_png(self, sess, image_data):
+    image = sess.run(
+        self._decode_png, feed_dict={self._decode_png_data: image_data})
+    assert len(image.shape) == 3
+    assert image.shape[2] == 3
+    return image
+
+
+def _convert_dataset(split_name, filenames, filename_to_class_id, dataset_dir):
+  """Converts the given filenames to a TFRecord dataset.
+
+  Args:
+    split_name: The name of the dataset, either 'train' or 'valid'.
+    filenames: A list of absolute paths to png images.
+    filename_to_class_id: A dictionary from filenames (strings) to class ids
+      (integers).
+    dataset_dir: The directory where the converted datasets are stored.
+  """
+  print('Converting the {} split.'.format(split_name))
+  # Train and validation splits are both in the train directory.
+  if split_name in ['train', 'valid']:
+    png_directory = os.path.join(dataset_dir, 'mnist_m', 'mnist_m_train')
+  elif split_name == 'test':
+    png_directory = os.path.join(dataset_dir, 'mnist_m', 'mnist_m_test')
+
+  with tf.Graph().as_default():
+    image_reader = ImageReader()
+
+    with tf.Session('') as sess:
+      output_filename = _get_output_filename(dataset_dir, split_name)
+
+      with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:
+        for filename in filenames:
+          # Read the filename:
+          image_data = tf.gfile.FastGFile(
+              os.path.join(png_directory, filename), 'r').read()
+          height, width = image_reader.read_image_dims(sess, image_data)
+
+          class_id = filename_to_class_id[filename]
+          example = dataset_utils.image_to_tfexample(image_data, 'png', height,
+                                                     width, class_id)
+          tfrecord_writer.write(example.SerializeToString())
+
+  sys.stdout.write('\n')
+  sys.stdout.flush()
+
+
+def _extract_labels(label_filename):
+  """Extract the labels into a dict of filenames to int labels.
+
+  Args:
+    labels_filename: The filename of the MNIST-M labels.
+
+  Returns:
+    A dictionary of filenames to int labels.
+  """
+  print('Extracting labels from: ', label_filename)
+  label_file = tf.gfile.FastGFile(label_filename, 'r').readlines()
+  label_lines = [line.rstrip('\n').split() for line in label_file]
+  labels = {}
+  for line in label_lines:
+    assert len(line) == 2
+    labels[line[0]] = int(line[1])
+  return labels
+
+
+def _get_output_filename(dataset_dir, split_name):
+  """Creates the output filename.
+
+  Args:
+    dataset_dir: The directory where the temporary files are stored.
+    split_name: The name of the train/test split.
+
+  Returns:
+    An absolute file path.
+  """
+  return '%s/mnist_m_%s.tfrecord' % (dataset_dir, split_name)
+
+
+def _get_filenames(dataset_dir):
+  """Returns a list of filenames and inferred class names.
+
+  Args:
+    dataset_dir: A directory containing a set PNG encoded MNIST-M images.
+
+  Returns:
+    A list of image file paths, relative to `dataset_dir`.
+  """
+  photo_filenames = []
+  for filename in os.listdir(dataset_dir):
+    photo_filenames.append(filename)
+  return photo_filenames
+
+
+def run(dataset_dir):
+  """Runs the download and conversion operation.
+
+  Args:
+    dataset_dir: The dataset directory where the dataset is stored.
+  """
+  if not tf.gfile.Exists(dataset_dir):
+    tf.gfile.MakeDirs(dataset_dir)
+
+  train_filename = _get_output_filename(dataset_dir, 'train')
+  testing_filename = _get_output_filename(dataset_dir, 'test')
+
+  if tf.gfile.Exists(train_filename) and tf.gfile.Exists(testing_filename):
+    print('Dataset files already exist. Exiting without re-creating them.')
+    return
+
+  # TODO(konstantinos): Add download and cleanup functionality
+
+  train_validation_filenames = _get_filenames(
+      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_train'))
+  test_filenames = _get_filenames(
+      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_test'))
+
+  # Divide into train and validation:
+  random.seed(_RANDOM_SEED)
+  random.shuffle(train_validation_filenames)
+  train_filenames = train_validation_filenames[_NUM_VALIDATION:]
+  validation_filenames = train_validation_filenames[:_NUM_VALIDATION]
+
+  train_validation_filenames_to_class_ids = _extract_labels(
+      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_train_labels.txt'))
+  test_filenames_to_class_ids = _extract_labels(
+      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_test_labels.txt'))
+
+  # Convert the train, validation, and test sets.
+  _convert_dataset('train', train_filenames,
+                   train_validation_filenames_to_class_ids, dataset_dir)
+  _convert_dataset('valid', validation_filenames,
+                   train_validation_filenames_to_class_ids, dataset_dir)
+  _convert_dataset('test', test_filenames, test_filenames_to_class_ids,
+                   dataset_dir)
+
+  # Finally, write the labels file:
+  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
+  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
+
+  print('\nFinished converting the MNIST-M dataset!')
+
+
+def main(_):
+  assert FLAGS.dataset_dir
+  run(FLAGS.dataset_dir)
+
+
+if __name__ == '__main__':
+  tf.app.run()

domain_adaptation/datasets/mnist_m.py

-# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+# Copyright 2017 Google Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,8 +11,11 @@
 # 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.
-# ==============================================================================
+
 """Provides data for the MNIST-M dataset.
+
+The dataset scripts used to create the dataset can be found at:
+tensorflow_models/domain_adaptation_/datasets/download_and_convert_mnist_m_dataset.py
 """
 
 from __future__ import absolute_import
@@ -20,6 +23,7 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+# Dependency imports
 import tensorflow as tf
 
 from slim.datasets import dataset_utils

domain_adaptation/pixel_domain_adaptation/BUILD

+# Description:
+#   Contains code for domain-adaptation style transfer.
+
+package(
+    default_visibility = [
+        ":internal",
+    ],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+package_group(
+    name = "internal",
+    packages = [
+        "//domain_adaptation/...",
+    ],
+)
+
+py_library(
+    name = "pixelda_preprocess",
+    srcs = ["pixelda_preprocess.py"],
+    deps = [
+        
+    ],
+)
+
+py_test(
+    name = "pixelda_preprocess_test",
+    srcs = ["pixelda_preprocess_test.py"],
+    deps = [
+        ":pixelda_preprocess",
+        
+    ],
+)
+
+py_library(
+    name = "pixelda_model",
+    srcs = [
+        "pixelda_model.py",
+        "pixelda_task_towers.py",
+        "hparams.py",
+    ],
+    deps = [
+        
+    ],
+)
+
+py_library(
+    name = "pixelda_utils",
+    srcs = ["pixelda_utils.py"],
+    deps = [
+        
+    ],
+)
+
+py_library(
+    name = "pixelda_losses",
+    srcs = ["pixelda_losses.py"],
+    deps = [
+        
+    ],
+)
+
+py_binary(
+    name = "pixelda_train",
+    srcs = ["pixelda_train.py"],
+    deps = [
+        ":pixelda_losses",
+        ":pixelda_model",
+        ":pixelda_preprocess",
+        ":pixelda_utils",
+        
+        "//domain_adaptation/datasets:dataset_factory",
+    ],
+)
+
+py_binary(
+    name = "pixelda_eval",
+    srcs = ["pixelda_eval.py"],
+    deps = [
+        ":pixelda_losses",
+        ":pixelda_model",
+        ":pixelda_preprocess",
+        ":pixelda_utils",
+        
+        "//domain_adaptation/datasets:dataset_factory",
+    ],
+)

domain_adaptation/pixel_domain_adaptation/baselines/BUILD

+licenses(["notice"])  # Apache 2.0
+
+py_binary(
+    name = "baseline_train",
+    srcs = ["baseline_train.py"],
+    deps = [
+        
+        "//domain_adaptation/datasets:dataset_factory",
+        "//domain_adaptation/pixel_domain_adaptation:pixelda_model",
+        "//domain_adaptation/pixel_domain_adaptation:pixelda_preprocess",
+    ],
+)
+
+py_binary(
+    name = "baseline_eval",
+    srcs = ["baseline_eval.py"],
+    deps = [
+        
+        "//domain_adaptation/datasets:dataset_factory",
+        "//domain_adaptation/pixel_domain_adaptation:pixelda_model",
+        "//domain_adaptation/pixel_domain_adaptation:pixelda_preprocess",
+    ],
+)

domain_adaptation/pixel_domain_adaptation/baselines/README.md

+The best baselines are obtainable via the following configuration:
+
+
+## MNIST => MNIST_M
+
+Accuracy:
+MNIST-Train: 99.9
+MNIST_M-Train: 63.9
+MNIST_M-Valid: 63.9
+MNIST_M-Test: 63.6
+
+Learning Rate = 0.0001
+Weight Decay = 0.0
+Number of Steps: 105,000
+
+## MNIST => USPS
+
+Accuracy:
+MNIST-Train: 100.0
+USPS-Train: 82.8
+USPS-Valid: 82.8
+USPS-Test: 78.9
+
+Learning Rate = 0.0001
+Weight Decay = 0.0
+Number of Steps: 22,000
+
+## MNIST_M => MNIST
+
+Accuracy:
+MNIST_M-Train: 100
+MNIST-Train: 98.5
+MNIST-Valid: 98.5
+MNIST-Test: 98.1
+
+Learning Rate = 0.001
+Weight Decay = 0.0
+Number of Steps: 604,400
+
+## MNIST_M => MNIST_M
+
+Accuracy:
+MNIST_M-Train: 100.0
+MNIST_M-Valid: 96.6
+MNIST_M-Test: 96.4
+
+Learning Rate = 0.001
+Weight Decay = 0.0
+Number of Steps: 139,400
+
+## USPS => USPS
+
+Accuracy:
+USPS-Train: 100.0
+USPS-Valid: 100.0
+USPS-Test: 96.5
+
+Learning Rate = 0.001
+Weight Decay = 0.0
+Number of Steps: 67,000

domain_adaptation/pixel_domain_adaptation/baselines/baseline_eval.py

+# Copyright 2017 Google Inc.
+#
+# 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.
+
+r"""Evals the classification/pose baselines."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import partial
+
+import math
+
+# Dependency imports
+
+import tensorflow as tf
+
+from domain_adaptation.datasets import dataset_factory
+from domain_adaptation.pixel_domain_adaptation import pixelda_preprocess
+from domain_adaptation.pixel_domain_adaptation import pixelda_task_towers
+
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+
+slim = tf.contrib.slim
+
+flags.DEFINE_string('master', '', 'BNS name of the tensorflow server')
+
+flags.DEFINE_string(
+    'checkpoint_dir', None, 'The location of the checkpoint files.')
+
+flags.DEFINE_string(
+    'eval_dir', None, 'The directory where evaluation logs are written.')
+
+flags.DEFINE_integer('batch_size', 32, 'The number of samples per batch.')
+
+flags.DEFINE_string('dataset_name', None, 'The name of the dataset.')
+
+flags.DEFINE_string('dataset_dir', None,
+                    'The directory where the data is stored.')
+
+flags.DEFINE_string('split_name', None, 'The name of the train/test split.')
+
+flags.DEFINE_integer('eval_interval_secs', 60 * 5,
+                     'How often (in seconds) to run evaluation.')
+
+flags.DEFINE_integer(
+    'num_readers', 4,
+    'The number of parallel readers that read data from the dataset.')
+
+def main(unused_argv):
+  tf.logging.set_verbosity(tf.logging.INFO)
+  hparams = tf.contrib.training.HParams()
+  hparams.weight_decay_task_classifier = 0.0
+
+  if FLAGS.dataset_name in ['mnist', 'mnist_m', 'usps']:
+    hparams.task_tower = 'mnist'
+  else:
+    raise ValueError('Unknown dataset %s' % FLAGS.dataset_name)
+
+  if not tf.gfile.Exists(FLAGS.eval_dir):
+    tf.gfile.MakeDirs(FLAGS.eval_dir)
+
+  with tf.Graph().as_default():
+    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, FLAGS.split_name,
+                                          FLAGS.dataset_dir)
+    num_classes = dataset.num_classes
+    num_samples = dataset.num_samples
+
+    preprocess_fn = partial(pixelda_preprocess.preprocess_classification,
+                            is_training=False)
+
+    images, labels = dataset_factory.provide_batch(
+        FLAGS.dataset_name,
+        FLAGS.split_name,
+        dataset_dir=FLAGS.dataset_dir,
+        num_readers=FLAGS.num_readers,
+        batch_size=FLAGS.batch_size,
+        num_preprocessing_threads=FLAGS.num_readers)
+
+    # Define the model
+    logits, _ = pixelda_task_towers.add_task_specific_model(
+        images, hparams, num_classes=num_classes, is_training=True)
+
+    #####################
+    # Define the losses #
+    #####################
+    if 'classes' in labels:
+      one_hot_labels = labels['classes']
+      loss = tf.losses.softmax_cross_entropy(
+          onehot_labels=one_hot_labels, logits=logits)
+      tf.summary.scalar('losses/Classification_Loss', loss)
+    else:
+      raise ValueError('Only support classification for now.')
+
+    total_loss = tf.losses.get_total_loss()
+
+    predictions = tf.reshape(tf.argmax(logits, 1), shape=[-1])
+    class_labels = tf.argmax(labels['classes'], 1)
+
+    metrics_to_values, metrics_to_updates = slim.metrics.aggregate_metric_map({
+        'Mean_Loss':
+            tf.contrib.metrics.streaming_mean(total_loss),
+        'Accuracy':
+            tf.contrib.metrics.streaming_accuracy(predictions,
+                                                  tf.reshape(
+                                                      class_labels,
+                                                      shape=[-1])),
+        'Recall_at_5':
+            tf.contrib.metrics.streaming_recall_at_k(logits, class_labels, 5),
+    })
+
+    tf.summary.histogram('outputs/Predictions', predictions)
+    tf.summary.histogram('outputs/Ground_Truth', class_labels)
+
+    for name, value in metrics_to_values.iteritems():
+      tf.summary.scalar(name, value)
+
+    num_batches = int(math.ceil(num_samples / float(FLAGS.batch_size)))
+
+    slim.evaluation.evaluation_loop(
+        master=FLAGS.master,
+        checkpoint_dir=FLAGS.checkpoint_dir,
+        logdir=FLAGS.eval_dir,
+        num_evals=num_batches,
+        eval_op=metrics_to_updates.values(),
+        eval_interval_secs=FLAGS.eval_interval_secs)
+
+
+if __name__ == '__main__':
+  tf.app.run()

domain_adaptation/pixel_domain_adaptation/baselines/baseline_train.py

+# Copyright 2017 Google Inc.
+#
+# 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.
+
+r"""Trains the classification/pose baselines."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import partial
+
+# Dependency imports
+
+import tensorflow as tf
+
+from domain_adaptation.datasets import dataset_factory
+from domain_adaptation.pixel_domain_adaptation import pixelda_preprocess
+from domain_adaptation.pixel_domain_adaptation import pixelda_task_towers
+
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+
+slim = tf.contrib.slim
+
+flags.DEFINE_string('master', '', 'BNS name of the tensorflow server')
+
+flags.DEFINE_integer('task', 0, 'The task ID.')
+
+flags.DEFINE_integer('num_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('batch_size', 32, 'The number of samples per batch.')
+
+flags.DEFINE_string('dataset_name', None, 'The name of the dataset.')
+
+flags.DEFINE_string('dataset_dir', None,
+                    'The directory where the data is stored.')
+
+flags.DEFINE_string('split_name', None, 'The name of the train/test split.')
+
+flags.DEFINE_float('learning_rate', 0.001, 'The initial learning rate.')
+
+flags.DEFINE_integer(
+    'learning_rate_decay_steps', 20000,
+    'The frequency, in steps, at which the learning rate is decayed.')
+
+flags.DEFINE_float('learning_rate_decay_factor',
+                   0.95,
+                   'The factor with which the learning rate is decayed.')
+
+flags.DEFINE_float('adam_beta1', 0.5, 'The beta1 value for the AdamOptimizer')
+
+flags.DEFINE_float('weight_decay', 1e-5,
+                   'The L2 coefficient on the model weights.')
+
+flags.DEFINE_string(
+    'logdir', None, 'The location of the logs and checkpoints.')
+
+flags.DEFINE_integer('save_interval_secs', 600,
+                     'How often, in seconds, we save the model to disk.')
+
+flags.DEFINE_integer('save_summaries_secs', 600,
+                     'How often, in seconds, we compute the summaries.')
+
+flags.DEFINE_integer(
+    'num_readers', 4,
+    'The number of parallel readers that read data from the dataset.')
+
+flags.DEFINE_float(
+    'moving_average_decay', 0.9999,
+    'The amount of decay to use for moving averages.')
+
+
+def main(unused_argv):
+  tf.logging.set_verbosity(tf.logging.INFO)
+  hparams = tf.contrib.training.HParams()
+  hparams.weight_decay_task_classifier = FLAGS.weight_decay
+
+  if FLAGS.dataset_name in ['mnist', 'mnist_m', 'usps']:
+    hparams.task_tower = 'mnist'
+  else:
+    raise ValueError('Unknown dataset %s' % FLAGS.dataset_name)
+
+  with tf.Graph().as_default():
+    with tf.device(
+        tf.train.replica_device_setter(FLAGS.num_ps_tasks, merge_devices=True)):
+      dataset = dataset_factory.get_dataset(FLAGS.dataset_name,
+                                            FLAGS.split_name, FLAGS.dataset_dir)
+      num_classes = dataset.num_classes
+
+      preprocess_fn = partial(pixelda_preprocess.preprocess_classification,
+                              is_training=True)
+
+      images, labels = dataset_factory.provide_batch(
+          FLAGS.dataset_name,
+          FLAGS.split_name,
+          dataset_dir=FLAGS.dataset_dir,
+          num_readers=FLAGS.num_readers,
+          batch_size=FLAGS.batch_size,
+          num_preprocessing_threads=FLAGS.num_readers)
+      # preprocess_fn=preprocess_fn)
+
+      # Define the model
+      logits, _ = pixelda_task_towers.add_task_specific_model(
+          images, hparams, num_classes=num_classes, is_training=True)
+
+      # Define the losses
+      if 'classes' in labels:
+        one_hot_labels = labels['classes']
+        loss = tf.losses.softmax_cross_entropy(
+            onehot_labels=one_hot_labels, logits=logits)
+        tf.summary.scalar('losses/Classification_Loss', loss)
+      else:
+        raise ValueError('Only support classification for now.')
+
+      total_loss = tf.losses.get_total_loss()
+      tf.summary.scalar('losses/Total_Loss', total_loss)
+
+      # Setup the moving averages
+      moving_average_variables = slim.get_model_variables()
+      variable_averages = tf.train.ExponentialMovingAverage(
+          FLAGS.moving_average_decay, slim.get_or_create_global_step())
+      tf.add_to_collection(
+          tf.GraphKeys.UPDATE_OPS,
+          variable_averages.apply(moving_average_variables))
+
+      # Specify the optimization scheme:
+      learning_rate = tf.train.exponential_decay(
+          FLAGS.learning_rate,
+          slim.get_or_create_global_step(),
+          FLAGS.learning_rate_decay_steps,
+          FLAGS.learning_rate_decay_factor,
+          staircase=True)
+
+      optimizer = tf.train.AdamOptimizer(learning_rate, beta1=FLAGS.adam_beta1)
+
+      train_op = slim.learning.create_train_op(total_loss, optimizer)
+
+      slim.learning.train(
+          train_op,
+          FLAGS.logdir,
+          master=FLAGS.master,
+          is_chief=(FLAGS.task == 0),
+          save_summaries_secs=FLAGS.save_summaries_secs,
+          save_interval_secs=FLAGS.save_interval_secs)
+
+
+if __name__ == '__main__':
+  tf.app.run()

domain_adaptation/pixel_domain_adaptation/hparams.py

+# Copyright 2017 Google Inc.
+#
+# 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.
+
+"""Define model HParams."""
+import tensorflow as tf
+
+
+def create_hparams(hparam_string=None):
+  """Create model hyperparameters. Parse nondefault from given string."""
+  hparams = tf.contrib.training.HParams(
+      # The name of the architecture to use.
+      arch='resnet',
+      lrelu_leakiness=0.2,
+      batch_norm_decay=0.9,
+      weight_decay=1e-5,
+      normal_init_std=0.02,
+      generator_kernel_size=3,
+      discriminator_kernel_size=3,
+
+      # Stop training after this many examples are processed
+      # If none, train indefinitely
+      num_training_examples=0,
+
+      # Apply data augmentation to datasets
+      # Applies only in training job
+      augment_source_images=False,
+      augment_target_images=False,
+
+      # Discriminator
+      # Number of filters in first layer of discriminator
+      num_discriminator_filters=64,
+      discriminator_conv_block_size=1,  # How many convs to have at each size
+      discriminator_filter_factor=2.0,  # Multiply # filters by this each layer
+      # Add gaussian noise with this stddev to every hidden layer of D
+      discriminator_noise_stddev=0.2,  # lmetz: Start seeing results at >= 0.1
+      # If true, add this gaussian noise to input images to D as well
+      discriminator_image_noise=False,
+      discriminator_first_stride=1,  # Stride in first conv of discriminator
+      discriminator_do_pooling=False,  # If true, replace stride 2 with avg pool
+      discriminator_dropout_keep_prob=0.9,  # keep probability for dropout
+
+      # DCGAN Generator
+      # Number of filters in generator decoder last layer (repeatedly halved
+      # from 1st layer)
+      num_decoder_filters=64,
+      # Number of filters in generator encoder 1st layer (repeatedly doubled
+      # after 1st layer)
+      num_encoder_filters=64,
+
+      # This is the shape to which the noise vector is projected (if we're
+      # transferring from noise).
+      # Write this way instead of [4, 4, 64] for hparam search flexibility
+      projection_shape_size=4,
+      projection_shape_channels=64,
+
+      # Indicates the method by which we enlarge the spatial representation
+      # of an image. Possible values include:
+      # - resize_conv: Performs a nearest neighbor resize followed by a conv.
+      # - conv2d_transpose: Performs a conv2d_transpose.
+      upsample_method='resize_conv',
+
+      # Visualization
+      summary_steps=500,  # Output image summary every N steps
+
+      ###################################
+      # Task Classifier Hyperparameters #
+      ###################################
+
+      # Which task-specific prediction tower to use. Possible choices are:
+      #  none: No task tower.
+      #  doubling_pose_estimator: classifier + quaternion regressor.
+      #    [conv + pool]* + FC
+      # Classifiers used in DSN paper:
+      #  gtsrb: Classifier used for GTSRB
+      #  svhn: Classifier used for SVHN
+      #  mnist: Classifier used for MNIST
+      #  pose_mini: Classifier + regressor used for pose_mini
+      task_tower='doubling_pose_estimator',
+      weight_decay_task_classifier=1e-5,
+      source_task_loss_weight=1.0,
+      transferred_task_loss_weight=1.0,
+
+      # Number of private layers in doubling_pose_estimator task tower
+      num_private_layers=2,
+
+      # The weight for the log quaternion loss we use for source and transferred
+      # samples of the cropped_linemod dataset.
+      # In the DSN work, 1/8 of the classifier weight worked well for our log
+      # quaternion loss
+      source_pose_weight=0.125 * 2.0,
+      transferred_pose_weight=0.125 * 1.0,
+
+      # If set to True, the style transfer network also attempts to change its
+      # weights to maximize the performance of the task tower. If set to False,
+      # then the style transfer network only attempts to change its weights to
+      # make the transferred images more likely according to the domain
+      # classifier.
+      task_tower_in_g_step=True,
+      task_loss_in_g_weight=1.0,  # Weight of task loss in G
+
+      #########################################
+      # 'simple` generator arch model hparams #
+      #########################################
+      simple_num_conv_layers=1,
+      simple_conv_filters=8,
+
+      #########################
+      # Resnet Hyperparameters#
+      #########################
+      resnet_blocks=6,  # Number of resnet blocks
+      resnet_filters=64,  # Number of filters per conv in resnet blocks
+      # If true, add original input back to result of convolutions inside the
+      # resnet arch. If false, it turns into a simple stack of conv/relu/BN
+      # layers.
+      resnet_residuals=True,
+
+      #######################################
+      # The residual / interpretable model. #
+      #######################################
+      res_int_blocks=2,  # The number of residual blocks.
+      res_int_convs=2,  # The number of conv calls inside each block.
+      res_int_filters=64,  # The number of filters used by each convolution.
+
+      ####################
+      # Latent variables #
+      ####################
+      # if true, then generate random noise and project to input for generator
+      noise_channel=True,
+      # The number of dimensions in the input noise vector.
+      noise_dims=10,
+
+      # If true, then one hot encode source image class and project as an
+      # additional channel for the input to generator. This gives the generator
+      # access to the class, which may help generation performance.
+      condition_on_source_class=False,
+
+      ########################
+      # Loss Hyperparameters #
+      ########################
+      domain_loss_weight=1.0,
+      style_transfer_loss_weight=1.0,
+
+      ########################################################################
+      # Encourages the transferred images to be similar to the source images #
+      # using a configurable metric.                                         #
+      ########################################################################
+
+      # The weight of the loss function encouraging the source and transferred
+      # images to be similar. If set to 0, then the loss function is not used.
+      transferred_similarity_loss_weight=0.0,
+
+      # The type of loss used to encourage transferred and source image
+      # similarity. Valid values include:
+      #   mpse: Mean Pairwise Squared Error
+      #   mse: Mean Squared Error
+      #   hinged_mse: Computes the mean squared error using squared differences
+      #     greater than hparams.transferred_similarity_max_diff
+      #   hinged_mae: Computes the mean absolute error using absolute
+      #     differences greater than hparams.transferred_similarity_max_diff.
+      transferred_similarity_loss='mpse',
+
+      # The maximum allowable difference between the source and target images.
+      # This value is used, in effect, to produce a hinge loss. Note that the
+      # range of values should be between 0 and 1.
+      transferred_similarity_max_diff=0.4,
+
+      ################################
+      # Optimization Hyperparameters #
+      ################################
+      learning_rate=0.001,
+      batch_size=32,
+      lr_decay_steps=20000,
+      lr_decay_rate=0.95,
+
+      # Recomendation from the DCGAN paper:
+      adam_beta1=0.5,
+      clip_gradient_norm=5.0,
+
+      # The number of times we run the discriminator train_op in a row.
+      discriminator_steps=1,
+
+      # The number of times we run the generator train_op in a row.
+      generator_steps=1)
+
+  if hparam_string:
+    tf.logging.info('Parsing command line hparams: %s', hparam_string)
+    hparams.parse(hparam_string)
+
+  tf.logging.info('Final parsed hparams: %s', hparams.values())
+  return hparams

domain_adaptation/pixel_domain_adaptation/pixelda_eval.py

+# Copyright 2017 Google Inc.
+#
+# 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.
+
+r"""Evaluates the PIXELDA model.
+
+-- Compiles the model for CPU.
+$ bazel build -c opt third_party/tensorflow_models/domain_adaptation/pixel_domain_adaptation:pixelda_eval
+
+-- Compile the model for GPU.
+$ bazel build -c opt --copt=-mavx --config=cuda \
+    third_party/tensorflow_models/domain_adaptation/pixel_domain_adaptation:pixelda_eval
+
+-- Runs the training.
+$ ./bazel-bin/third_party/tensorflow_models/domain_adaptation/pixel_domain_adaptation/pixelda_eval \
+    --source_dataset=mnist \
+    --target_dataset=mnist_m \
+    --dataset_dir=/tmp/datasets/ \
+    --alsologtostderr
+
+-- Visualize the results.
+$ bash learning/brain/tensorboard/tensorboard.sh \
+    --port 2222 --logdir=/tmp/pixelda/
+"""
+from functools import partial
+import math
+
+# Dependency imports
+
+import tensorflow as tf
+
+from domain_adaptation.datasets import dataset_factory
+from domain_adaptation.pixel_domain_adaptation import pixelda_model
+from domain_adaptation.pixel_domain_adaptation import pixelda_preprocess
+from domain_adaptation.pixel_domain_adaptation import pixelda_utils
+from domain_adaptation.pixel_domain_adaptation import pixelda_losses
+from domain_adaptation.pixel_domain_adaptation.hparams import create_hparams
+
+slim = tf.contrib.slim
+
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('master', '', 'BNS name of the TensorFlow master to use.')
+
+flags.DEFINE_string('checkpoint_dir', '/tmp/pixelda/',
+                    'Directory where the model was written to.')
+
+flags.DEFINE_string('eval_dir', '/tmp/pixelda/',
+                    'Directory where the results are saved to.')
+
+flags.DEFINE_integer('eval_interval_secs', 60,
+                     'The frequency, in seconds, with which evaluation is run.')
+
+flags.DEFINE_string('target_split_name', 'test',
+                    'The name of the train/test split.')
+flags.DEFINE_string('source_split_name', 'train', 'Split for source dataset.'
+                    ' Defaults to train.')
+
+flags.DEFINE_string('source_dataset', 'mnist',
+                    'The name of the source dataset.')
+
+flags.DEFINE_string('target_dataset', 'mnist_m',
+                    'The name of the target dataset.')
+
+flags.DEFINE_string(
+    'dataset_dir',
+    '',  # None,
+    'The directory where the datasets can be found.')
+
+flags.DEFINE_integer(
+    'num_readers', 4,
+    'The number of parallel readers that read data from the dataset.')
+
+flags.DEFINE_integer('num_preprocessing_threads', 4,
+                     'The number of threads used to create the batches.')
+
+# HParams
+
+flags.DEFINE_string('hparams', '', 'Comma separated hyperparameter values')
+
+
+def run_eval(run_dir, checkpoint_dir, hparams):
+  """Runs the eval loop.
+
+  Args:
+    run_dir: The directory where eval specific logs are placed
+    checkpoint_dir: The directory where the checkpoints are stored
+    hparams: The hyperparameters struct.
+
+  Raises:
+    ValueError: if hparams.arch is not recognized.
+  """
+  for checkpoint_path in slim.evaluation.checkpoints_iterator(
+      checkpoint_dir, FLAGS.eval_interval_secs):
+    with tf.Graph().as_default():
+      #########################
+      # Preprocess the inputs #
+      #########################
+      target_dataset = dataset_factory.get_dataset(
+          FLAGS.target_dataset,
+          split_name=FLAGS.target_split_name,
+          dataset_dir=FLAGS.dataset_dir)
+      target_images, target_labels = dataset_factory.provide_batch(
+          FLAGS.target_dataset, FLAGS.target_split_name, FLAGS.dataset_dir,
+          FLAGS.num_readers, hparams.batch_size,
+          FLAGS.num_preprocessing_threads)
+      num_target_classes = target_dataset.num_classes
+      target_labels['class'] = tf.argmax(target_labels['classes'], 1)
+      del target_labels['classes']
+
+      if hparams.arch not in ['dcgan']:
+        source_dataset = dataset_factory.get_dataset(
+            FLAGS.source_dataset,
+            split_name=FLAGS.source_split_name,
+            dataset_dir=FLAGS.dataset_dir)
+        num_source_classes = source_dataset.num_classes
+        source_images, source_labels = dataset_factory.provide_batch(
+            FLAGS.source_dataset, FLAGS.source_split_name, FLAGS.dataset_dir,
+            FLAGS.num_readers, hparams.batch_size,
+            FLAGS.num_preprocessing_threads)
+        source_labels['class'] = tf.argmax(source_labels['classes'], 1)
+        del source_labels['classes']
+        if num_source_classes != num_target_classes:
+          raise ValueError(
+              'Input and output datasets must have same number of classes')
+      else:
+        source_images = None
+        source_labels = None
+
+      ####################
+      # Define the model #
+      ####################
+      end_points = pixelda_model.create_model(
+          hparams,
+          target_images,
+          source_images=source_images,
+          source_labels=source_labels,
+          is_training=False,
+          num_classes=num_target_classes)
+
+      #######################
+      # Metrics & Summaries #
+      #######################
+      names_to_values, names_to_updates = create_metrics(end_points,
+                                                         source_labels,
+                                                         target_labels, hparams)
+      pixelda_utils.summarize_model(end_points)
+      pixelda_utils.summarize_transferred_grid(
+          end_points['transferred_images'], source_images, name='Transferred')
+      if 'source_images_recon' in end_points:
+        pixelda_utils.summarize_transferred_grid(
+            end_points['source_images_recon'],
+            source_images,
+            name='Source Reconstruction')
+      pixelda_utils.summarize_images(target_images, 'Target')
+
+      for name, value in names_to_values.iteritems():
+        tf.summary.scalar(name, value)
+
+      # Use the entire split by default
+      num_examples = target_dataset.num_samples
+
+      num_batches = math.ceil(num_examples / float(hparams.batch_size))
+      global_step = slim.get_or_create_global_step()
+
+      result = slim.evaluation.evaluate_once(
+          master=FLAGS.master,
+          checkpoint_path=checkpoint_path,
+          logdir=run_dir,
+          num_evals=num_batches,
+          eval_op=names_to_updates.values(),
+          final_op=names_to_values)
+
+
+def to_degrees(log_quaternion_loss):
+  """Converts a log quaternion distance to an angle.
+
+  Args:
+    log_quaternion_loss: The log quaternion distance between two
+      unit quaternions (or a batch of pairs of quaternions).
+
+  Returns:
+    The angle in degrees of the implied angle-axis representation.
+  """
+  return tf.acos(-(tf.exp(log_quaternion_loss) - 1)) * 2 * 180 / math.pi
+
+
+def create_metrics(end_points, source_labels, target_labels, hparams):
+  """Create metrics for the model.
+
+  Args:
+    end_points: A dictionary of end point name to tensor
+    source_labels: Labels for source images. batch_size x 1
+    target_labels: Labels for target images. batch_size x 1
+    hparams: The hyperparameters struct.
+
+  Returns:
+    Tuple of (names_to_values, names_to_updates), dictionaries that map a metric
+    name to its value and update op, respectively
+
+  """
+  ###########################################
+  # Evaluate the Domain Prediction Accuracy #
+  ###########################################
+  batch_size = hparams.batch_size
+  names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
+      ('eval/Domain_Accuracy-Transferred'):
+          tf.contrib.metrics.streaming_accuracy(
+              tf.to_int32(
+                  tf.round(tf.sigmoid(end_points[
+                      'transferred_domain_logits']))),
+              tf.zeros(batch_size, dtype=tf.int32)),
+      ('eval/Domain_Accuracy-Target'):
+          tf.contrib.metrics.streaming_accuracy(
+              tf.to_int32(
+                  tf.round(tf.sigmoid(end_points['target_domain_logits']))),
+              tf.ones(batch_size, dtype=tf.int32))
+  })
+
+  ################################
+  # Evaluate the task classifier #
+  ################################
+  if 'source_task_logits' in end_points:
+    metric_name = 'eval/Task_Accuracy-Source'
+    names_to_values[metric_name], names_to_updates[
+        metric_name] = tf.contrib.metrics.streaming_accuracy(
+            tf.argmax(end_points['source_task_logits'], 1),
+            source_labels['class'])
+
+  if 'transferred_task_logits' in end_points:
+    metric_name = 'eval/Task_Accuracy-Transferred'
+    names_to_values[metric_name], names_to_updates[
+        metric_name] = tf.contrib.metrics.streaming_accuracy(
+            tf.argmax(end_points['transferred_task_logits'], 1),
+            source_labels['class'])
+
+  if 'target_task_logits' in end_points:
+    metric_name = 'eval/Task_Accuracy-Target'
+    names_to_values[metric_name], names_to_updates[
+        metric_name] = tf.contrib.metrics.streaming_accuracy(
+            tf.argmax(end_points['target_task_logits'], 1),
+            target_labels['class'])
+
+  ##########################################################################
+  # Pose data-specific losses.
+  ##########################################################################
+  if 'quaternion' in source_labels.keys():
+    params = {}
+    params['use_logging'] = False
+    params['batch_size'] = batch_size
+
+    angle_loss_source = to_degrees(
+        pixelda_losses.log_quaternion_loss_batch(end_points[
+            'source_quaternion'], source_labels['quaternion'], params))
+    angle_loss_transferred = to_degrees(
+        pixelda_losses.log_quaternion_loss_batch(end_points[
+            'transferred_quaternion'], source_labels['quaternion'], params))
+    angle_loss_target = to_degrees(
+        pixelda_losses.log_quaternion_loss_batch(end_points[
+            'target_quaternion'], target_labels['quaternion'], params))
+
+    metric_name = 'eval/Angle_Loss-Source'
+    names_to_values[metric_name], names_to_updates[
+        metric_name] = slim.metrics.mean(angle_loss_source)
+
+    metric_name = 'eval/Angle_Loss-Transferred'
+    names_to_values[metric_name], names_to_updates[
+        metric_name] = slim.metrics.mean(angle_loss_transferred)
+
+    metric_name = 'eval/Angle_Loss-Target'
+    names_to_values[metric_name], names_to_updates[
+        metric_name] = slim.metrics.mean(angle_loss_target)
+
+  return names_to_values, names_to_updates
+
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+  hparams = create_hparams(FLAGS.hparams)
+  run_eval(
+      run_dir=FLAGS.eval_dir,
+      checkpoint_dir=FLAGS.checkpoint_dir,
+      hparams=hparams)
+
+
+if __name__ == '__main__':
+  tf.app.run()

domain_adaptation/pixel_domain_adaptation/pixelda_losses.py

+# Copyright 2017 Google Inc.
+#
+# 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.
+
+"""Defines the various loss functions in use by the PIXELDA model."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def add_domain_classifier_losses(end_points, hparams):
+  """Adds losses related to the domain-classifier.
+
+  Args:
+    end_points: A map of network end point names to `Tensors`.
+    hparams: The hyperparameters struct.
+
+  Returns:
+    loss: A `Tensor` representing the total task-classifier loss.
+  """
+  if hparams.domain_loss_weight == 0:
+    tf.logging.info(
+        'Domain classifier loss weight is 0, so not creating losses.')
+    return 0
+
+  # The domain prediction loss is minimized with respect to the domain
+  # classifier features only. Its aim is to predict the domain of the images.
+  # Note: 1 = 'real image' label, 0 = 'fake image' label
+  transferred_domain_loss = tf.losses.sigmoid_cross_entropy(
+      multi_class_labels=tf.zeros_like(end_points['transferred_domain_logits']),
+      logits=end_points['transferred_domain_logits'])
+  tf.summary.scalar('Domain_loss_transferred', transferred_domain_loss)
+
+  target_domain_loss = tf.losses.sigmoid_cross_entropy(
+      multi_class_labels=tf.ones_like(end_points['target_domain_logits']),
+      logits=end_points['target_domain_logits'])
+  tf.summary.scalar('Domain_loss_target', target_domain_loss)
+
+  # Compute the total domain loss:
+  total_domain_loss = transferred_domain_loss + target_domain_loss
+  total_domain_loss *= hparams.domain_loss_weight
+  tf.summary.scalar('Domain_loss_total', total_domain_loss)
+
+  return total_domain_loss
+
+def log_quaternion_loss_batch(predictions, labels, params):
+  """A helper function to compute the error between quaternions.
+
+  Args:
+    predictions: A Tensor of size [batch_size, 4].
+    labels: A Tensor of size [batch_size, 4].
+    params: A dictionary of parameters. Expecting 'use_logging', 'batch_size'.
+
+  Returns:
+    A Tensor of size [batch_size], denoting the error between the quaternions.
+  """
+  use_logging = params['use_logging']
+  assertions = []
+  if use_logging:
+    assertions.append(
+        tf.Assert(
+            tf.reduce_all(
+                tf.less(
+                    tf.abs(tf.reduce_sum(tf.square(predictions), [1]) - 1),
+                    1e-4)),
+            ['The l2 norm of each prediction quaternion vector should be 1.']))
+    assertions.append(
+        tf.Assert(
+            tf.reduce_all(
+                tf.less(
+                    tf.abs(tf.reduce_sum(tf.square(labels), [1]) - 1), 1e-4)),
+            ['The l2 norm of each label quaternion vector should be 1.']))
+
+  with tf.control_dependencies(assertions):
+    product = tf.multiply(predictions, labels)
+  internal_dot_products = tf.reduce_sum(product, [1])
+
+  if use_logging:
+    internal_dot_products = tf.Print(internal_dot_products, [
+        internal_dot_products,
+        tf.shape(internal_dot_products)
+    ], 'internal_dot_products:')
+
+  logcost = tf.log(1e-4 + 1 - tf.abs(internal_dot_products))
+  return logcost
+
+
+def log_quaternion_loss(predictions, labels, params):
+  """A helper function to compute the mean error between batches of quaternions.
+
+  The caller is expected to add the loss to the graph.
+
+  Args:
+    predictions: A Tensor of size [batch_size, 4].
+    labels: A Tensor of size [batch_size, 4].
+    params: A dictionary of parameters. Expecting 'use_logging', 'batch_size'.
+
+  Returns:
+    A Tensor of size 1, denoting the mean error between batches of quaternions.
+  """
+  use_logging = params['use_logging']
+  logcost = log_quaternion_loss_batch(predictions, labels, params)
+  logcost = tf.reduce_sum(logcost, [0])
+  batch_size = params['batch_size']
+  logcost = tf.multiply(logcost, 1.0 / batch_size, name='log_quaternion_loss')
+  if use_logging:
+    logcost = tf.Print(
+        logcost, [logcost], '[logcost]', name='log_quaternion_loss_print')
+  return logcost
+
+def _quaternion_loss(labels, predictions, weight, batch_size, domain,
+                     add_summaries):
+  """Creates a Quaternion Loss.
+
+  Args:
+    labels: The true quaternions.
+    predictions: The predicted quaternions.
+    weight: A scalar weight.
+    batch_size: The size of the batches.
+    domain: The name of the domain from which the labels were taken.
+    add_summaries: Whether or not to add summaries for the losses.
+
+  Returns:
+    A `Tensor` representing the loss.
+  """
+  assert domain in ['Source', 'Transferred']
+
+  params = {'use_logging': False, 'batch_size': batch_size}
+  loss = weight * log_quaternion_loss(labels, predictions, params)
+
+  if add_summaries:
+    assert_op = tf.Assert(tf.is_finite(loss), [loss])
+    with tf.control_dependencies([assert_op]):
+      tf.summary.histogram(
+          'Log_Quaternion_Loss_%s' % domain, loss, collections='losses')
+      tf.summary.scalar(
+          'Task_Quaternion_Loss_%s' % domain, loss, collections='losses')
+
+  return loss
+
+
+def _add_task_specific_losses(end_points, source_labels, num_classes, hparams,
+                              add_summaries=False):
+  """Adds losses related to the task-classifier.
+
+  Args:
+    end_points: A map of network end point names to `Tensors`.
+    source_labels: A dictionary of output labels to `Tensors`.
+    num_classes: The number of classes used by the classifier.
+    hparams: The hyperparameters struct.
+    add_summaries: Whether or not to add the summaries.
+
+  Returns:
+    loss: A `Tensor` representing the total task-classifier loss.
+  """
+  # TODO(ddohan): Make sure the l2 regularization is added to the loss
+
+  one_hot_labels = slim.one_hot_encoding(source_labels['class'], num_classes)
+  total_loss = 0
+
+  if 'source_task_logits' in end_points:
+    loss = tf.losses.softmax_cross_entropy(
+        onehot_labels=one_hot_labels,
+        logits=end_points['source_task_logits'],
+        weights=hparams.source_task_loss_weight)
+    if add_summaries:
+      tf.summary.scalar('Task_Classifier_Loss_Source', loss)
+    total_loss += loss
+
+  if 'transferred_task_logits' in end_points:
+    loss = tf.losses.softmax_cross_entropy(
+        onehot_labels=one_hot_labels,
+        logits=end_points['transferred_task_logits'],
+        weights=hparams.transferred_task_loss_weight)
+    if add_summaries:
+      tf.summary.scalar('Task_Classifier_Loss_Transferred', loss)
+    total_loss += loss
+
+  #########################
+  # Pose specific losses. #
+  #########################
+  if 'quaternion' in source_labels:
+    total_loss += _quaternion_loss(
+        source_labels['quaternion'],
+        end_points['source_quaternion'],
+        hparams.source_pose_weight,
+        hparams.batch_size,
+        'Source',
+        add_summaries)
+
+    total_loss += _quaternion_loss(
+        source_labels['quaternion'],
+        end_points['transferred_quaternion'],
+        hparams.transferred_pose_weight,
+        hparams.batch_size,
+        'Transferred',
+        add_summaries)
+
+  if add_summaries:
+    tf.summary.scalar('Task_Loss_Total', total_loss)
+
+  return total_loss
+
+
+def _transferred_similarity_loss(reconstructions,
+                                 source_images,
+                                 weight=1.0,
+                                 method='mse',
+                                 max_diff=0.4,
+                                 name='similarity'):
+  """Computes a loss encouraging similarity between source and transferred.
+
+  Args:
+    reconstructions: A `Tensor` of shape [batch_size, height, width, channels]
+    source_images: A `Tensor` of shape [batch_size, height, width, channels].
+    weight: Multiple similarity loss by this weight before returning
+    method: One of:
+      mpse = Mean Pairwise Squared Error
+      mse = Mean Squared Error
+      hinged_mse = Computes the mean squared error using squared differences
+        greater than hparams.transferred_similarity_max_diff
+      hinged_mae = Computes the mean absolute error using absolute
+        differences greater than hparams.transferred_similarity_max_diff.
+    max_diff: Maximum unpenalized difference for hinged losses
+    name: Identifying name to use for creating summaries
+
+
+  Returns:
+    A `Tensor` representing the transferred similarity loss.
+
+  Raises:
+    ValueError: if `method` is not recognized.
+  """
+  if weight == 0:
+    return 0
+
+  source_channels = source_images.shape.as_list()[-1]
+  reconstruction_channels = reconstructions.shape.as_list()[-1]
+
+  # Convert grayscale source to RGB if target is RGB
+  if source_channels == 1 and reconstruction_channels != 1:
+    source_images = tf.tile(source_images, [1, 1, 1, reconstruction_channels])
+  if reconstruction_channels == 1 and source_channels != 1:
+    reconstructions = tf.tile(reconstructions, [1, 1, 1, source_channels])
+
+  if method == 'mpse':
+    reconstruction_similarity_loss_fn = (
+        tf.contrib.losses.mean_pairwise_squared_error)
+  elif method == 'masked_mpse':
+
+    def masked_mpse(predictions, labels, weight):
+      """Masked mpse assuming we have a depth to create a mask from."""
+      assert labels.shape.as_list()[-1] == 4
+      mask = tf.to_float(tf.less(labels[:, :, :, 3:4], 0.99))
+      mask = tf.tile(mask, [1, 1, 1, 4])
+      predictions *= mask
+      labels *= mask
+      tf.image_summary('masked_pred', predictions)
+      tf.image_summary('masked_label', labels)
+      return tf.contrib.losses.mean_pairwise_squared_error(
+          predictions, labels, weight)
+
+    reconstruction_similarity_loss_fn = masked_mpse
+  elif method == 'mse':
+    reconstruction_similarity_loss_fn = tf.contrib.losses.mean_squared_error
+  elif method == 'hinged_mse':
+
+    def hinged_mse(predictions, labels, weight):
+      diffs = tf.square(predictions - labels)
+      diffs = tf.maximum(0.0, diffs - max_diff)
+      return tf.reduce_mean(diffs) * weight
+
+    reconstruction_similarity_loss_fn = hinged_mse
+  elif method == 'hinged_mae':
+
+    def hinged_mae(predictions, labels, weight):
+      diffs = tf.abs(predictions - labels)
+      diffs = tf.maximum(0.0, diffs - max_diff)
+      return tf.reduce_mean(diffs) * weight
+
+    reconstruction_similarity_loss_fn = hinged_mae
+  else:
+    raise ValueError('Unknown reconstruction loss %s' % method)
+
+  reconstruction_similarity_loss = reconstruction_similarity_loss_fn(
+      reconstructions, source_images, weight)
+
+  name = '%s_Similarity_(%s)' % (name, method)
+  tf.summary.scalar(name, reconstruction_similarity_loss)
+  return reconstruction_similarity_loss
+
+
+def g_step_loss(source_images, source_labels, end_points, hparams, num_classes):
+  """Configures the loss function which runs during the g-step.
+
+  Args:
+    source_images: A `Tensor` of shape [batch_size, height, width, channels].
+    source_labels: A dictionary of `Tensors` of shape [batch_size]. Valid keys
+      are 'class' and 'quaternion'.
+    end_points: A map of the network end points.
+    hparams: The hyperparameters struct.
+    num_classes: Number of classes for classifier loss
+
+  Returns:
+    A `Tensor` representing a loss function.
+
+  Raises:
+    ValueError: if hparams.transferred_similarity_loss_weight is non-zero but
+      hparams.transferred_similarity_loss is invalid.
+  """
+  generator_loss = 0
+
+  ################################################################
+  # Adds a loss which encourages the discriminator probabilities #
+  # to be high (near one).
+  ################################################################
+
+  # As per the GAN paper, maximize the log probs, instead of minimizing
+  # log(1-probs). Since we're minimizing, we'll minimize -log(probs) which is
+  # the same thing.
+  style_transfer_loss = tf.losses.sigmoid_cross_entropy(
+      logits=end_points['transferred_domain_logits'],
+      multi_class_labels=tf.ones_like(end_points['transferred_domain_logits']),
+      weights=hparams.style_transfer_loss_weight)
+  tf.summary.scalar('Style_transfer_loss', style_transfer_loss)
+  generator_loss += style_transfer_loss
+
+  # Optimizes the style transfer network to produce transferred images similar
+  # to the source images.
+  generator_loss += _transferred_similarity_loss(
+      end_points['transferred_images'],
+      source_images,
+      weight=hparams.transferred_similarity_loss_weight,
+      method=hparams.transferred_similarity_loss,
+      name='transferred_similarity')
+
+  # Optimizes the style transfer network to maximize classification accuracy.
+  if source_labels is not None and hparams.task_tower_in_g_step:
+    generator_loss += _add_task_specific_losses(
+        end_points, source_labels, num_classes,
+        hparams) * hparams.task_loss_in_g_weight
+
+  return generator_loss
+
+
+def d_step_loss(end_points, source_labels, num_classes, hparams):
+  """Configures the losses during the D-Step.
+
+  Note that during the D-step, the model optimizes both the domain (binary)
+  classifier and the task classifier.
+
+  Args:
+    end_points: A map of the network end points.
+    source_labels: A dictionary of output labels to `Tensors`.
+    num_classes: The number of classes used by the classifier.
+    hparams: The hyperparameters struct.
+
+  Returns:
+    A `Tensor` representing the value of the D-step loss.
+  """
+  domain_classifier_loss = add_domain_classifier_losses(end_points, hparams)
+
+  task_classifier_loss = 0
+  if source_labels is not None:
+    task_classifier_loss = _add_task_specific_losses(
+        end_points, source_labels, num_classes, hparams, add_summaries=True)
+
+  return domain_classifier_loss + task_classifier_loss

domain_adaptation/pixel_domain_adaptation/pixelda_preprocess.py

+# Copyright 2017 Google Inc.
+#
+# 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 functions for preprocessing the inputs."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+import tensorflow as tf
+
+
+def preprocess_classification(image, labels, is_training=False):
+  """Preprocesses the image and labels for classification purposes.
+
+  Preprocessing includes shifting the images to be 0-centered between -1 and 1.
+  This is not only a popular method of preprocessing (inception) but is also
+  the mechanism used by DSNs.
+
+  Args:
+    image: A `Tensor` of size [height, width, 3].
+    labels: A dictionary of labels.
+    is_training: Whether or not we're training the model.
+
+  Returns:
+    The preprocessed image and labels.
+  """
+  # If the image is uint8, this will scale it to 0-1.
+  image = tf.image.convert_image_dtype(image, tf.float32)
+  image -= 0.5
+  image *= 2
+
+  return image, labels
+
+
+def preprocess_style_transfer(image,
+                              labels,
+                              augment=False,
+                              size=None,
+                              is_training=False):
+  """Preprocesses the image and labels for style transfer purposes.
+
+  Args:
+    image: A `Tensor` of size [height, width, 3].
+    labels: A dictionary of labels.
+    augment: Whether to apply data augmentation to inputs
+    size: The height and width to which images should be resized. If left as
+      `None`, then no resizing is performed
+    is_training: Whether or not we're training the model
+
+  Returns:
+    The preprocessed image and labels. Scaled to [-1, 1]
+  """
+  # If the image is uint8, this will scale it to 0-1.
+  image = tf.image.convert_image_dtype(image, tf.float32)
+  if augment and is_training:
+    image = image_augmentation(image)
+
+  if size:
+    image = resize_image(image, size)
+
+  image -= 0.5
+  image *= 2
+
+  return image, labels
+
+
+def image_augmentation(image):
+  """Performs data augmentation by randomly permuting the inputs.
+
+  Args:
+    image: A float `Tensor` of size [height, width, channels] with values
+      in range[0,1].
+
+  Returns:
+    The mutated batch of images
+  """
+  # Apply photometric data augmentation (contrast etc.)
+  num_channels = image.shape_as_list()[-1]
+  if num_channels == 4:
+    # Only augment image part
+    image, depth = image[:, :, 0:3], image[:, :, 3:4]
+  elif num_channels == 1:
+    image = tf.image.grayscale_to_rgb(image)
+  image = tf.image.random_brightness(image, max_delta=0.1)
+  image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
+  image = tf.image.random_hue(image, max_delta=0.032)
+  image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
+  image = tf.clip_by_value(image, 0, 1.0)
+  if num_channels == 4:
+    image = tf.concat(2, [image, depth])
+  elif num_channels == 1:
+    image = tf.image.rgb_to_grayscale(image)
+  return image
+
+
+def resize_image(image, size=None):
+  """Resize image to target size.
+
+  Args:
+    image: A `Tensor` of size [height, width, 3].
+    size: (height, width) to resize image to.
+
+  Returns:
+    resized image
+  """
+  if size is None:
+    raise ValueError('Must specify size')
+
+  if image.shape_as_list()[:2] == size:
+    # Don't resize if not necessary
+    return image
+  image = tf.expand_dims(image, 0)
+  image = tf.image.resize_images(image, size)
+  image = tf.squeeze(image, 0)
+  return image

domain_adaptation/pixel_domain_adaptation/pixelda_preprocess_test.py

+# Copyright 2017 Google Inc.
+#
+# 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 domain_adaptation.pixel_domain_adaptation.pixelda_preprocess."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+# Dependency imports
+
+import tensorflow as tf
+
+from domain_adaptation.pixel_domain_adaptation import pixelda_preprocess
+
+
+class PixelDAPreprocessTest(tf.test.TestCase):
+
+  def assert_preprocess_classification_is_centered(self, dtype, is_training):
+    tf.set_random_seed(0)
+
+    if dtype == tf.uint8:
+      image = tf.random_uniform((100, 200, 3), maxval=255, dtype=tf.int64)
+      image = tf.cast(image, tf.uint8)
+    else:
+      image = tf.random_uniform((100, 200, 3), maxval=1.0, dtype=dtype)
+
+    labels = {}
+    image, labels = pixelda_preprocess.preprocess_classification(
+        image, labels, is_training=is_training)
+
+    with self.test_session() as sess:
+      np_image = sess.run(image)
+
+      self.assertTrue(np_image.min() <= -0.95)
+      self.assertTrue(np_image.min() >= -1.0)
+      self.assertTrue(np_image.max() >= 0.95)
+      self.assertTrue(np_image.max() <= 1.0)
+
+  def testPreprocessClassificationZeroCentersUint8DuringTrain(self):
+    self.assert_preprocess_classification_is_centered(
+        tf.uint8, is_training=True)
+
+  def testPreprocessClassificationZeroCentersUint8DuringTest(self):
+    self.assert_preprocess_classification_is_centered(
+        tf.uint8, is_training=False)
+
+  def testPreprocessClassificationZeroCentersFloatDuringTrain(self):
+    self.assert_preprocess_classification_is_centered(
+        tf.float32, is_training=True)
+
+  def testPreprocessClassificationZeroCentersFloatDuringTest(self):
+    self.assert_preprocess_classification_is_centered(
+        tf.float32, is_training=False)
+
+
+if __name__ == '__main__':
+  tf.test.main()

domain_adaptation/pixel_domain_adaptation/pixelda_task_towers.py

+# Copyright 2017 Google Inc.
+#
+# 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.
+
+"""Task towers for PixelDA model."""
+import tensorflow as tf
+
+slim = tf.contrib.slim
+
+
+def add_task_specific_model(images,
+                            hparams,
+                            num_classes=10,
+                            is_training=False,
+                            reuse_private=False,
+                            private_scope=None,
+                            reuse_shared=False,
+                            shared_scope=None):
+  """Create a classifier for the given images.
+
+  The classifier is composed of a few 'private' layers followed by a few
+  'shared' layers. This lets us account for different image 'style', while
+  sharing the last few layers as 'content' layers.
+
+  Args:
+    images: A `Tensor` of size [batch_size, height, width, 3].
+    hparams: model hparams
+    num_classes: The number of output classes.
+    is_training: whether model is training
+    reuse_private: Whether or not to reuse the private weights, which are the
+      first few layers in the classifier
+    private_scope: The name of the variable_scope for the private (unshared)
+      components of the classifier.
+    reuse_shared: Whether or not to reuse the shared weights, which are the last
+      few layers in the classifier
+    shared_scope: The name of the variable_scope for the shared components of
+      the classifier.
+
+  Returns:
+    The logits, a `Tensor` of shape [batch_size, num_classes].
+
+  Raises:
+    ValueError: If hparams.task_classifier is an unknown value
+  """
+
+  model = hparams.task_tower
+  # Make sure the classifier name shows up in graph
+  shared_scope = shared_scope or (model + '_shared')
+  kwargs = {
+      'num_classes': num_classes,
+      'is_training': is_training,
+      'reuse_private': reuse_private,
+      'reuse_shared': reuse_shared,
+  }
+
+  if private_scope:
+    kwargs['private_scope'] = private_scope
+  if shared_scope:
+    kwargs['shared_scope'] = shared_scope
+
+  quaternion_pred = None
+  with slim.arg_scope(
+      [slim.conv2d, slim.fully_connected],
+      activation_fn=tf.nn.relu,
+      weights_regularizer=tf.contrib.layers.l2_regularizer(
+          hparams.weight_decay_task_classifier)):
+    with slim.arg_scope([slim.conv2d], padding='SAME'):
+      if model == 'doubling_pose_estimator':
+        logits, quaternion_pred = doubling_cnn_class_and_quaternion(
+            images, num_private_layers=hparams.num_private_layers, **kwargs)
+      elif model == 'mnist':
+        logits, _ = mnist_classifier(images, **kwargs)
+      elif model == 'svhn':
+        logits, _ = svhn_classifier(images, **kwargs)
+      elif model == 'gtsrb':
+        logits, _ = gtsrb_classifier(images, **kwargs)
+      elif model == 'pose_mini':
+        logits, quaternion_pred = pose_mini_tower(images, **kwargs)
+      else:
+        raise ValueError('Unknown task classifier %s' % model)
+
+  return logits, quaternion_pred
+
+
+#####################################
+# Classifiers used in the DSN paper #
+#####################################
+
+
+def mnist_classifier(images,
+                     is_training=False,
+                     num_classes=10,
+                     reuse_private=False,
+                     private_scope='mnist',
+                     reuse_shared=False,
+                     shared_scope='task_model'):
+  """Creates the convolutional MNIST model from the gradient reversal paper.
+
+  Note that since the output is a set of 'logits', the values fall in the
+  interval of (-infinity, infinity). Consequently, to convert the outputs to a
+  probability distribution over the characters, one will need to convert them
+  using the softmax function:
+        logits, endpoints = conv_mnist(images, is_training=False)
+        predictions = tf.nn.softmax(logits)
+
+  Args:
+    images: the MNIST digits, a tensor of size [batch_size, 28, 28, 1].
+    is_training: specifies whether or not we're currently training the model.
+      This variable will determine the behaviour of the dropout layer.
+    num_classes: the number of output classes to use.
+
+  Returns:
+    the output logits, a tensor of size [batch_size, num_classes].
+    a dictionary with key/values the layer names and tensors.
+  """
+
+  net = {}
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    net['conv1'] = slim.conv2d(images, 32, [5, 5], scope='conv1')
+    net['pool1'] = slim.max_pool2d(net['conv1'], [2, 2], 2, scope='pool1')
+
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    net['conv2'] = slim.conv2d(net['pool1'], 48, [5, 5], scope='conv2')
+    net['pool2'] = slim.max_pool2d(net['conv2'], [2, 2], 2, scope='pool2')
+    net['fc3'] = slim.fully_connected(
+        slim.flatten(net['pool2']), 100, scope='fc3')
+    net['fc4'] = slim.fully_connected(
+        slim.flatten(net['fc3']), 100, scope='fc4')
+    logits = slim.fully_connected(
+        net['fc4'], num_classes, activation_fn=None, scope='fc5')
+  return logits, net
+
+
+def svhn_classifier(images,
+                    is_training=False,
+                    num_classes=10,
+                    reuse_private=False,
+                    private_scope=None,
+                    reuse_shared=False,
+                    shared_scope='task_model'):
+  """Creates the convolutional SVHN model from the gradient reversal paper.
+
+  Note that since the output is a set of 'logits', the values fall in the
+  interval of (-infinity, infinity). Consequently, to convert the outputs to a
+  probability distribution over the characters, one will need to convert them
+  using the softmax function:
+        logits = mnist.Mnist(images, is_training=False)
+        predictions = tf.nn.softmax(logits)
+
+  Args:
+    images: the SVHN digits, a tensor of size [batch_size, 40, 40, 3].
+    is_training: specifies whether or not we're currently training the model.
+      This variable will determine the behaviour of the dropout layer.
+    num_classes: the number of output classes to use.
+
+  Returns:
+    the output logits, a tensor of size [batch_size, num_classes].
+    a dictionary with key/values the layer names and tensors.
+  """
+
+  net = {}
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    net['conv1'] = slim.conv2d(images, 64, [5, 5], scope='conv1')
+    net['pool1'] = slim.max_pool2d(net['conv1'], [3, 3], 2, scope='pool1')
+
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    net['conv2'] = slim.conv2d(net['pool1'], 64, [5, 5], scope='conv2')
+    net['pool2'] = slim.max_pool2d(net['conv2'], [3, 3], 2, scope='pool2')
+    net['conv3'] = slim.conv2d(net['pool2'], 128, [5, 5], scope='conv3')
+
+    net['fc3'] = slim.fully_connected(
+        slim.flatten(net['conv3']), 3072, scope='fc3')
+    net['fc4'] = slim.fully_connected(
+        slim.flatten(net['fc3']), 2048, scope='fc4')
+
+    logits = slim.fully_connected(
+        net['fc4'], num_classes, activation_fn=None, scope='fc5')
+
+  return logits, net
+
+
+def gtsrb_classifier(images,
+                     is_training=False,
+                     num_classes=43,
+                     reuse_private=False,
+                     private_scope='gtsrb',
+                     reuse_shared=False,
+                     shared_scope='task_model'):
+  """Creates the convolutional GTSRB model from the gradient reversal paper.
+
+  Note that since the output is a set of 'logits', the values fall in the
+  interval of (-infinity, infinity). Consequently, to convert the outputs to a
+  probability distribution over the characters, one will need to convert them
+  using the softmax function:
+        logits = mnist.Mnist(images, is_training=False)
+        predictions = tf.nn.softmax(logits)
+
+  Args:
+    images: the SVHN digits, a tensor of size [batch_size, 40, 40, 3].
+    is_training: specifies whether or not we're currently training the model.
+      This variable will determine the behaviour of the dropout layer.
+    num_classes: the number of output classes to use.
+    reuse_private: Whether or not to reuse the private components of the model.
+    private_scope: The name of the private scope.
+    reuse_shared: Whether or not to reuse the shared components of the model.
+    shared_scope: The name of the shared scope.
+
+  Returns:
+    the output logits, a tensor of size [batch_size, num_classes].
+    a dictionary with key/values the layer names and tensors.
+  """
+
+  net = {}
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    net['conv1'] = slim.conv2d(images, 96, [5, 5], scope='conv1')
+    net['pool1'] = slim.max_pool2d(net['conv1'], [2, 2], 2, scope='pool1')
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    net['conv2'] = slim.conv2d(net['pool1'], 144, [3, 3], scope='conv2')
+    net['pool2'] = slim.max_pool2d(net['conv2'], [2, 2], 2, scope='pool2')
+    net['conv3'] = slim.conv2d(net['pool2'], 256, [5, 5], scope='conv3')
+    net['pool3'] = slim.max_pool2d(net['conv3'], [2, 2], 2, scope='pool3')
+
+    net['fc3'] = slim.fully_connected(
+        slim.flatten(net['pool3']), 512, scope='fc3')
+    logits = slim.fully_connected(
+        net['fc3'], num_classes, activation_fn=None, scope='fc4')
+
+    return logits, net
+
+
+#########################
+# pose_mini task towers #
+#########################
+
+
+def pose_mini_tower(images,
+                    num_classes=11,
+                    is_training=False,
+                    reuse_private=False,
+                    private_scope='pose_mini',
+                    reuse_shared=False,
+                    shared_scope='task_model'):
+  """Task tower for the pose_mini dataset."""
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    net = slim.conv2d(images, 32, [5, 5], scope='conv1')
+    net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool1')
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    net = slim.conv2d(net, 64, [5, 5], scope='conv2')
+    net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool2')
+    net = slim.flatten(net)
+
+    net = slim.fully_connected(net, 128, scope='fc3')
+    net = slim.dropout(net, 0.5, is_training=is_training, scope='dropout')
+    with tf.variable_scope('quaternion_prediction'):
+      quaternion_pred = slim.fully_connected(
+          net, 4, activation_fn=tf.tanh, scope='fc_q')
+      quaternion_pred = tf.nn.l2_normalize(quaternion_pred, 1)
+
+    logits = slim.fully_connected(
+        net, num_classes, activation_fn=None, scope='fc4')
+
+    return logits, quaternion_pred
+
+
+def doubling_cnn_class_and_quaternion(images,
+                                      num_private_layers=1,
+                                      num_classes=10,
+                                      is_training=False,
+                                      reuse_private=False,
+                                      private_scope='doubling_cnn',
+                                      reuse_shared=False,
+                                      shared_scope='task_model'):
+  """Alternate conv, pool while doubling filter count."""
+  net = images
+  depth = 32
+  layer_id = 1
+
+  with tf.variable_scope(private_scope, reuse=reuse_private):
+    while num_private_layers > 0 and net.shape.as_list()[1] > 5:
+      net = slim.conv2d(net, depth, [3, 3], scope='conv%s' % layer_id)
+      net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool%s' % layer_id)
+      depth *= 2
+      layer_id += 1
+      num_private_layers -= 1
+
+  with tf.variable_scope(shared_scope, reuse=reuse_shared):
+    while net.shape.as_list()[1] > 5:
+      net = slim.conv2d(net, depth, [3, 3], scope='conv%s' % layer_id)
+      net = slim.max_pool2d(net, [2, 2], stride=2, scope='pool%s' % layer_id)
+      depth *= 2
+      layer_id += 1
+
+    net = slim.flatten(net)
+    net = slim.fully_connected(net, 100, scope='fc1')
+    net = slim.dropout(net, 0.5, is_training=is_training, scope='dropout')
+    quaternion_pred = slim.fully_connected(
+        net, 4, activation_fn=tf.tanh, scope='fc_q')
+    quaternion_pred = tf.nn.l2_normalize(quaternion_pred, 1)
+
+    logits = slim.fully_connected(
+        net, num_classes, activation_fn=None, scope='fc_logits')
+
+    return logits, quaternion_pred

domain_adaptation/pixel_domain_adaptation/pixelda_train.py

+# Copyright 2017 Google Inc.
+#
+# 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.
+
+r"""Trains the PixelDA model."""
+
+from functools import partial
+import os
+
+# Dependency imports
+
+import tensorflow as tf
+
+from domain_adaptation.datasets import dataset_factory
+from domain_adaptation.pixel_domain_adaptation import pixelda_losses
+from domain_adaptation.pixel_domain_adaptation import pixelda_model
+from domain_adaptation.pixel_domain_adaptation import pixelda_preprocess
+from domain_adaptation.pixel_domain_adaptation import pixelda_utils
+from domain_adaptation.pixel_domain_adaptation.hparams import create_hparams
+
+slim = tf.contrib.slim
+
+flags = tf.app.flags
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string('master', '', 'BNS 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.DEFINE_string('train_log_dir', '/tmp/pixelda/',
+                    'Directory where to write event logs.')
+
+flags.DEFINE_integer(
+    'save_summaries_steps', 500,
+    'The frequency with which summaries are saved, in seconds.')
+
+flags.DEFINE_integer('save_interval_secs', 300,
+                     'The frequency with which the model is saved, in seconds.')
+
+flags.DEFINE_boolean('summarize_gradients', False,
+                     'Whether to summarize model gradients')
+
+flags.DEFINE_integer(
+    'print_loss_steps', 100,
+    'The frequency with which the losses are printed, in steps.')
+
+flags.DEFINE_string('source_dataset', 'mnist', 'The name of the source dataset.'
+                    ' If hparams="arch=dcgan", this flag is ignored.')
+
+flags.DEFINE_string('target_dataset', 'mnist_m',
+                    'The name of the target dataset.')
+
+flags.DEFINE_string('source_split_name', 'train',
+                    'Name of the train split for the source.')
+
+flags.DEFINE_string('target_split_name', 'train',
+                    'Name of the train split for the target.')
+
+flags.DEFINE_string('dataset_dir', '',
+                    'The directory where the datasets can be found.')
+
+flags.DEFINE_integer(
+    'num_readers', 4,
+    'The number of parallel readers that read data from the dataset.')
+
+flags.DEFINE_integer('num_preprocessing_threads', 4,
+                     'The number of threads used to create the batches.')
+
+# HParams
+
+flags.DEFINE_string('hparams', '', 'Comma separated hyperparameter values')
+
+
+def _get_vars_and_update_ops(hparams, scope):
+  """Returns the variables and update ops for a particular variable scope.
+
+  Args:
+    hparams: The hyperparameters struct.
+    scope: The variable scope.
+
+  Returns:
+    A tuple consisting of trainable variables and update ops.
+  """
+  is_trainable = lambda x: x in tf.trainable_variables()
+  var_list = filter(is_trainable, slim.get_model_variables(scope))
+  global_step = slim.get_or_create_global_step()
+
+  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope)
+
+  tf.logging.info('All variables for scope: %s',
+                  slim.get_model_variables(scope))
+  tf.logging.info('Trainable variables for scope: %s', var_list)
+
+  return var_list, update_ops
+
+
+def _train(discriminator_train_op,
+           generator_train_op,
+           logdir,
+           master='',
+           is_chief=True,
+           scaffold=None,
+           hooks=None,
+           chief_only_hooks=None,
+           save_checkpoint_secs=600,
+           save_summaries_steps=100,
+           hparams=None):
+  """Runs the training loop.
+
+  Args:
+    discriminator_train_op: A `Tensor` that, when executed, will apply the
+      gradients and return the loss value for the discriminator.
+    generator_train_op: A `Tensor` that, when executed, will apply the
+      gradients and return the loss value for the generator.
+    logdir: The directory where the graph and checkpoints are saved.
+    master: The URL of the master.
+    is_chief: Specifies whether or not the training is being run by the primary
+      replica during replica training.
+    scaffold: An tf.train.Scaffold instance.
+    hooks: List of `tf.train.SessionRunHook` callbacks which are run inside the
+      training loop.
+    chief_only_hooks: List of `tf.train.SessionRunHook` instances which are run
+      inside the training loop for the chief trainer only.
+    save_checkpoint_secs: The frequency, in seconds, that a checkpoint is saved
+      using a default checkpoint saver. If `save_checkpoint_secs` is set to
+      `None`, then the default checkpoint saver isn't used.
+    save_summaries_steps: The frequency, in number of global steps, that the
+      summaries are written to disk using a default summary saver. If
+      `save_summaries_steps` is set to `None`, then the default summary saver
+      isn't used.
+    hparams: The hparams struct.
+
+  Returns:
+    the value of the loss function after training.
+
+  Raises:
+    ValueError: if `logdir` is `None` and either `save_checkpoint_secs` or
+    `save_summaries_steps` are `None.
+  """
+  global_step = slim.get_or_create_global_step()
+
+  scaffold = scaffold or tf.train.Scaffold()
+
+  hooks = hooks or []
+
+  if is_chief:
+    session_creator = tf.train.ChiefSessionCreator(
+        scaffold=scaffold, checkpoint_dir=logdir, master=master)
+
+    if chief_only_hooks:
+      hooks.extend(chief_only_hooks)
+    hooks.append(tf.train.StepCounterHook(output_dir=logdir))
+
+    if save_summaries_steps:
+      if logdir is None:
+        raise ValueError(
+            'logdir cannot be None when save_summaries_steps is None')
+      hooks.append(
+          tf.train.SummarySaverHook(
+              scaffold=scaffold,
+              save_steps=save_summaries_steps,
+              output_dir=logdir))
+
+    if save_checkpoint_secs:
+      if logdir is None:
+        raise ValueError(
+            'logdir cannot be None when save_checkpoint_secs is None')
+      hooks.append(
+          tf.train.CheckpointSaverHook(
+              logdir, save_secs=save_checkpoint_secs, scaffold=scaffold))
+  else:
+    session_creator = tf.train.WorkerSessionCreator(
+        scaffold=scaffold, master=master)
+
+  with tf.train.MonitoredSession(
+      session_creator=session_creator, hooks=hooks) as session:
+    loss = None
+    while not session.should_stop():
+      # Run the domain classifier op X times.
+      for _ in range(hparams.discriminator_steps):
+        if session.should_stop():
+          return loss
+        loss, np_global_step = session.run(
+            [discriminator_train_op, global_step])
+        if np_global_step % FLAGS.print_loss_steps == 0:
+          tf.logging.info('Step %d: Discriminator Loss = %.2f', np_global_step,
+                          loss)
+
+      # Run the generator op X times.
+      for _ in range(hparams.generator_steps):
+        if session.should_stop():
+          return loss
+        loss, np_global_step = session.run([generator_train_op, global_step])
+        if np_global_step % FLAGS.print_loss_steps == 0:
+          tf.logging.info('Step %d: Generator Loss = %.2f', np_global_step,
+                          loss)
+  return loss
+
+
+def run_training(run_dir, checkpoint_dir, hparams):
+  """Runs the training loop.
+
+  Args:
+    run_dir: The directory where training specific logs are placed
+    checkpoint_dir: The directory where the checkpoints and log files are
+      stored.
+    hparams: The hyperparameters struct.
+
+  Raises:
+    ValueError: if hparams.arch is not recognized.
+  """
+  for path in [run_dir, checkpoint_dir]:
+    if not tf.gfile.Exists(path):
+      tf.gfile.MakeDirs(path)
+
+  # Serialize hparams to log dir
+  hparams_filename = os.path.join(checkpoint_dir, 'hparams.json')
+  with tf.gfile.FastGFile(hparams_filename, 'w') as f:
+    f.write(hparams.to_json())
+
+  with tf.Graph().as_default():
+    with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
+      global_step = slim.get_or_create_global_step()
+
+      #########################
+      # Preprocess the inputs #
+      #########################
+      target_dataset = dataset_factory.get_dataset(
+          FLAGS.target_dataset,
+          split_name='train',
+          dataset_dir=FLAGS.dataset_dir)
+      target_images, _ = dataset_factory.provide_batch(
+          FLAGS.target_dataset, 'train', FLAGS.dataset_dir, FLAGS.num_readers,
+          hparams.batch_size, FLAGS.num_preprocessing_threads)
+      num_target_classes = target_dataset.num_classes
+
+      if hparams.arch not in ['dcgan']:
+        source_dataset = dataset_factory.get_dataset(
+            FLAGS.source_dataset,
+            split_name='train',
+            dataset_dir=FLAGS.dataset_dir)
+        num_source_classes = source_dataset.num_classes
+        source_images, source_labels = dataset_factory.provide_batch(
+            FLAGS.source_dataset, 'train', FLAGS.dataset_dir, FLAGS.num_readers,
+            hparams.batch_size, FLAGS.num_preprocessing_threads)
+        # Data provider provides 1 hot labels, but we expect categorical.
+        source_labels['class'] = tf.argmax(source_labels['classes'], 1)
+        del source_labels['classes']
+        if num_source_classes != num_target_classes:
+          raise ValueError(
+              'Source and Target datasets must have same number of classes. '
+              'Are %d and %d' % (num_source_classes, num_target_classes))
+      else:
+        source_images = None
+        source_labels = None
+
+      ####################
+      # Define the model #
+      ####################
+      end_points = pixelda_model.create_model(
+          hparams,
+          target_images,
+          source_images=source_images,
+          source_labels=source_labels,
+          is_training=True,
+          num_classes=num_target_classes)
+
+      #################################
+      # Get the variables to optimize #
+      #################################
+      generator_vars, generator_update_ops = _get_vars_and_update_ops(
+          hparams, 'generator')
+      discriminator_vars, discriminator_update_ops = _get_vars_and_update_ops(
+          hparams, 'discriminator')
+
+      ########################
+      # Configure the losses #
+      ########################
+      generator_loss = pixelda_losses.g_step_loss(
+          source_images,
+          source_labels,
+          end_points,
+          hparams,
+          num_classes=num_target_classes)
+      discriminator_loss = pixelda_losses.d_step_loss(
+          end_points, source_labels, num_target_classes, hparams)
+
+      ###########################
+      # Create the training ops #
+      ###########################
+      learning_rate = hparams.learning_rate
+      if hparams.lr_decay_steps:
+        learning_rate = tf.train.exponential_decay(
+            learning_rate,
+            slim.get_or_create_global_step(),
+            decay_steps=hparams.lr_decay_steps,
+            decay_rate=hparams.lr_decay_rate,
+            staircase=True)
+      tf.summary.scalar('Learning_rate', learning_rate)
+
+
+      if hparams.discriminator_steps == 0:
+        discriminator_train_op = tf.no_op()
+      else:
+        discriminator_optimizer = tf.train.AdamOptimizer(
+            learning_rate, beta1=hparams.adam_beta1)
+
+        discriminator_train_op = slim.learning.create_train_op(
+            discriminator_loss,
+            discriminator_optimizer,
+            update_ops=discriminator_update_ops,
+            variables_to_train=discriminator_vars,
+            clip_gradient_norm=hparams.clip_gradient_norm,
+            summarize_gradients=FLAGS.summarize_gradients)
+
+      if hparams.generator_steps == 0:
+        generator_train_op = tf.no_op()
+      else:
+        generator_optimizer = tf.train.AdamOptimizer(
+            learning_rate, beta1=hparams.adam_beta1)
+        generator_train_op = slim.learning.create_train_op(
+            generator_loss,
+            generator_optimizer,
+            update_ops=generator_update_ops,
+            variables_to_train=generator_vars,
+            clip_gradient_norm=hparams.clip_gradient_norm,
+            summarize_gradients=FLAGS.summarize_gradients)
+
+      #############
+      # Summaries #
+      #############
+      pixelda_utils.summarize_model(end_points)
+      pixelda_utils.summarize_transferred_grid(
+          end_points['transferred_images'], source_images, name='Transferred')
+      if 'source_images_recon' in end_points:
+        pixelda_utils.summarize_transferred_grid(
+            end_points['source_images_recon'],
+            source_images,
+            name='Source Reconstruction')
+      pixelda_utils.summaries_color_distributions(end_points['transferred_images'],
+                                               'Transferred')
+      pixelda_utils.summaries_color_distributions(target_images, 'Target')
+
+      if source_images is not None:
+        pixelda_utils.summarize_transferred(source_images,
+                                         end_points['transferred_images'])
+        pixelda_utils.summaries_color_distributions(source_images, 'Source')
+        pixelda_utils.summaries_color_distributions(
+            tf.abs(source_images - end_points['transferred_images']),
+            'Abs(Source_minus_Transferred)')
+
+      number_of_steps = None
+      if hparams.num_training_examples:
+        # Want to control by amount of data seen, not # steps
+        number_of_steps = hparams.num_training_examples / hparams.batch_size
+
+      hooks = [tf.train.StepCounterHook(),]
+
+      chief_only_hooks = [
+          tf.train.CheckpointSaverHook(
+              saver=tf.train.Saver(),
+              checkpoint_dir=run_dir,
+              save_secs=FLAGS.save_interval_secs)
+      ]
+
+      if number_of_steps:
+        hooks.append(tf.train.StopAtStepHook(last_step=number_of_steps))
+
+      _train(
+          discriminator_train_op,
+          generator_train_op,
+          logdir=run_dir,
+          master=FLAGS.master,
+          is_chief=FLAGS.task == 0,
+          hooks=hooks,
+          chief_only_hooks=chief_only_hooks,
+          save_checkpoint_secs=None,
+          save_summaries_steps=FLAGS.save_summaries_steps,
+          hparams=hparams)
+
+def main(_):
+  tf.logging.set_verbosity(tf.logging.INFO)
+  hparams = create_hparams(FLAGS.hparams)
+  run_training(
+      run_dir=FLAGS.train_log_dir,
+      checkpoint_dir=FLAGS.train_log_dir,
+      hparams=hparams)
+
+
+if __name__ == '__main__':
+  tf.app.run()