Merge pull request #633 from rsepassi/push

v1.5.4

.travis.yml

@@ -10,13 +10,13 @@ env:
   matrix:
     - TF_VERSION="1.4.*"
     - TF_VERSION="1.5.*"
-    - TF_VERSION="1.6.0rc1"
+    - TF_VERSION="1.6.*"
 matrix:
   exclude:
     - python: "3.6"
       env: TF_VERSION="1.4.*"
     - python: "3.6"
-      env: TF_VERSION="1.6.0rc1"
+      env: TF_VERSION="1.6.*"
 before_install:
   - echo "deb [arch=amd64] http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" | sudo tee /etc/apt/sources.list.d/tensorflow-serving.list
   - curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | sudo apt-key add -

README.md

@@ -12,10 +12,10 @@ welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CO
 
 [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor), or
 [T2T](https://github.com/tensorflow/tensor2tensor) for short, is a library
-of deep learning models and datasets designed to [accelerate deep learning
-research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html) and make it more accessible.
-
-T2T is actively used and maintained by researchers and engineers within the
+of deep learning models and datasets designed to make deep learning more
+accessible and [accelerate ML
+research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html).
+ is actively used and maintained by researchers and engineers within the
 [Google Brain team](https://research.google.com/teams/brain/) and a community
 of users. We're eager to collaborate with you too, so feel free to
 [open an issue on GitHub](https://github.com/tensorflow/tensor2tensor/issues)
@@ -368,6 +368,7 @@ T2T](https://research.googleblog.com/2017/06/accelerating-deep-learning-research
 * [Discrete Autoencoders for Sequence Models](https://arxiv.org/abs/1801.09797)
 * [Generating Wikipedia by Summarizing Long
    Sequences](https://arxiv.org/abs/1801.10198)
-* [Image Transformer](https://openreview.net/forum?id=r16Vyf-0-)
+* [Image Transformer](https://arxiv.org/abs/1802.05751)
+* [Training Tips for the Transformer Model](http://ufallab.ms.mff.cuni.cz/~popel/training-tips-transformer.pdf)
 
 *Note: This is not an official Google product.*

docs/index.md

@@ -11,8 +11,9 @@ welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CO
 
 [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor), or
 [T2T](https://github.com/tensorflow/tensor2tensor) for short, is a library
-of deep learning models and datasets designed to [accelerate deep learning
-research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html) and make it more accessible.
+of deep learning models and datasets designed to make deep learning more
+accessible and [accelerate ML
+research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html).
 
 
 ## Basics

docs/new_problem.md

@@ -9,6 +9,10 @@ welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CO
 [![Gitter](https://img.shields.io/gitter/room/nwjs/nw.js.svg)](https://gitter.im/tensor2tensor/Lobby)
 [![License](https://img.shields.io/badge/License-Apache%202.0-brightgreen.svg)](https://opensource.org/licenses/Apache-2.0)
 
+Another good overview of this part together with training is given in
+[The Cloud ML Poetry Blog
+Post](https://cloud.google.com/blog/big-data/2018/02/cloud-poetry-training-and-hyperparameter-tuning-custom-text-models-on-cloud-ml-engine)
+
 Let's add a new dataset together and train the
 [Transformer](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/models/transformer.py)
 model on it. We'll give the model a line of poetry, and it will learn to

docs/walkthrough.md

@@ -12,10 +12,10 @@ welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg)](CO
 
 [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor), or
 [T2T](https://github.com/tensorflow/tensor2tensor) for short, is a library
-of deep learning models and datasets designed to [accelerate deep learning
-research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html) and make it more accessible.
-
-T2T is actively used and maintained by researchers and engineers within the
+of deep learning models and datasets designed to make deep learning more
+accessible and [accelerate ML
+research](https://research.googleblog.com/2017/06/accelerating-deep-learning-research.html).
+ is actively used and maintained by researchers and engineers within the
 [Google Brain team](https://research.google.com/teams/brain/) and a community
 of users. We're eager to collaborate with you too, so feel free to
 [open an issue on GitHub](https://github.com/tensorflow/tensor2tensor/issues)
@@ -368,6 +368,7 @@ T2T](https://research.googleblog.com/2017/06/accelerating-deep-learning-research
 * [Discrete Autoencoders for Sequence Models](https://arxiv.org/abs/1801.09797)
 * [Generating Wikipedia by Summarizing Long
    Sequences](https://arxiv.org/abs/1801.10198)
-* [Image Transformer](https://openreview.net/forum?id=r16Vyf-0-)
+* [Image Transformer](https://arxiv.org/abs/1802.05751)
+* [Training Tips for the Transformer Model](http://ufallab.ms.mff.cuni.cz/~popel/training-tips-transformer.pdf)
 
 *Note: This is not an official Google product.*

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.5.3',
+    version='1.5.4',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='no-reply@google.com',

tensor2tensor/data_generators/generator_utils.py

@@ -147,8 +147,9 @@ def generate_files(generator, output_filenames, max_cases=None):
   if outputs_exist(output_filenames):
     tf.logging.info("Skipping generator because outputs files exist")
     return
+  tmp_filenames = [fname + ".incomplete" for fname in output_filenames]
   num_shards = len(output_filenames)
-  writers = [tf.python_io.TFRecordWriter(fname) for fname in output_filenames]
+  writers = [tf.python_io.TFRecordWriter(fname) for fname in tmp_filenames]
   counter, shard = 0, 0
   for case in generator:
     if case is None:
@@ -165,6 +166,9 @@ def generate_files(generator, output_filenames, max_cases=None):
   for writer in writers:
     writer.close()
 
+  for tmp_name, final_name in zip(tmp_filenames, output_filenames):
+    tf.gfile.Rename(tmp_name, final_name)
+
   tf.logging.info("Generated %s Examples", counter)
 
 

tensor2tensor/data_generators/gym.py

@@ -19,39 +19,30 @@
 from __future__ import division
 from __future__ import print_function
 
-import os
+import functools
 
 # Dependency imports
 
-import numpy as np
-import functools
 import gym
+import numpy as np
 
-from tensor2tensor.rl import rl_trainer_lib
-from tensor2tensor.rl.envs import atari_wrappers
-from tensor2tensor.models.research import rl
 from tensor2tensor.data_generators import generator_utils
 from tensor2tensor.data_generators import problem
+from tensor2tensor.models.research import rl
+from tensor2tensor.rl.envs import atari_wrappers
 from tensor2tensor.utils import registry
 
 import tensorflow as tf
 
 
+
+
 flags = tf.flags
 FLAGS = flags.FLAGS
 
 flags.DEFINE_string("model_path", "", "File with model for pong")
 
 
-def gym_lib():
-  """Access to gym to allow for import of this file without a gym install."""
-  try:
-    import gym  # pylint: disable=g-import-not-at-top
-  except ImportError:
-    raise ImportError("pip install gym to use gym-based Problems")
-  return gym
-
-
 class GymDiscreteProblem(problem.Problem):
   """Gym environment with discrete actions and rewards."""
 
@@ -67,7 +58,7 @@ def env_name(self):
   @property
   def env(self):
     if self._env is None:
-      self._env = gym_lib().make(self.env_name)
+      self._env = gym.make(self.env_name)
     return self._env
 
   @property
@@ -157,8 +148,6 @@ def num_steps(self):
     return 5000
 
 
-
-
 @registry.register_problem
 class GymPongTrajectoriesFromPolicy(GymDiscreteProblem):
   """Pong game, loaded actions."""
@@ -167,28 +156,34 @@ def __init__(self, event_dir, *args, **kwargs):
     super(GymPongTrajectoriesFromPolicy, self).__init__(*args, **kwargs)
     self._env = None
     self._event_dir = event_dir
-    env_spec = lambda: atari_wrappers.wrap_atari(
-      gym.make("PongNoFrameskip-v4"), warp=False, frame_skip=4, frame_stack=False)
+    env_spec = lambda: atari_wrappers.wrap_atari(  # pylint: disable=g-long-lambda
+        gym.make("PongNoFrameskip-v4"),
+        warp=False,
+        frame_skip=4,
+        frame_stack=False)
     hparams = rl.atari_base()
     with tf.variable_scope("train"):
       policy_lambda = hparams.network
       policy_factory = tf.make_template(
-        "network",
-        functools.partial(policy_lambda, env_spec().action_space, hparams))
-      self._max_frame_pl = tf.placeholder(tf.float32, self.env.observation_space.shape)
-      actor_critic = policy_factory(tf.expand_dims(tf.expand_dims(self._max_frame_pl, 0), 0))
+          "network",
+          functools.partial(policy_lambda, env_spec().action_space, hparams))
+      self._max_frame_pl = tf.placeholder(
+          tf.float32, self.env.observation_space.shape)
+      actor_critic = policy_factory(tf.expand_dims(tf.expand_dims(
+          self._max_frame_pl, 0), 0))
       policy = actor_critic.policy
       self._last_policy_op = policy.mode()
     self._last_action = self.env.action_space.sample()
     self._skip = 4
     self._skip_step = 0
-    self._obs_buffer = np.zeros((2,) + self.env.observation_space.shape, dtype=np.uint8)
+    self._obs_buffer = np.zeros((2,) + self.env.observation_space.shape,
+                                dtype=np.uint8)
     self._sess = tf.Session()
     model_saver = tf.train.Saver(tf.global_variables(".*network_parameters.*"))
     model_saver.restore(self._sess, FLAGS.model_path)
 
   # TODO(blazej0): For training of atari agents wrappers are usually used.
-  # Below we have a hacky solution which is a temporary workaround to be used together
+  # Below we have a hacky solution which is a workaround to be used together
   # with atari_wrappers.MaxAndSkipEnv.
   def get_action(self, observation=None):
     if self._skip_step == self._skip - 2: self._obs_buffer[0] = observation
@@ -197,7 +192,8 @@ def get_action(self, observation=None):
     if self._skip_step == 0:
       max_frame = self._obs_buffer.max(axis=0)
       self._last_action = int(self._sess.run(
-        self._last_policy_op, feed_dict={self._max_frame_pl: max_frame})[0, 0])
+          self._last_policy_op,
+          feed_dict={self._max_frame_pl: max_frame})[0, 0])
     return self._last_action
 
   @property

tensor2tensor/data_generators/imagenet.py

@@ -19,19 +19,84 @@
 from __future__ import division
 from __future__ import print_function
 
+import os
 # Dependency imports
 
+from tensor2tensor.data_generators import generator_utils
 from tensor2tensor.data_generators import image_utils
 from tensor2tensor.utils import registry
 
 import tensorflow as tf
 
+# URLs and filenames for IMAGENET 32x32 data from
+# https://arxiv.org/abs/1601.06759.
+_IMAGENET_SMALL_ROOT_URL = "http://image-net.org/small/"
+_IMAGENET_SMALL_URLS = [
+    "train_32x32.tar", "valid_32x32.tar"]
+_IMAGENET_SMALL_TRAIN_PREFIX = "train_32x32"
+_IMAGENET_SMALL_EVAL_PREFIX = "valid_32x32"
+_IMAGENET_SMALL_IMAGE_SIZE = 32
+
+
+# URLs and filenames for IMAGENET 64x64 data.
+_IMAGENET_MEDIUM_ROOT_URL = "http://image-net.org/small/"
+_IMAGENET_MEDIUM_URLS = [
+    "train_64x64.tar", "valid_64x64.tar"]
+_IMAGENET_MEDIUM_TRAIN_PREFIX = "train_64x64"
+_IMAGENET_MEDIUM_EVAL_PREFIX = "valid_64x64"
+_IMAGENET_MEDIUM_IMAGE_SIZE = 64
+
 
 # Derived from ImageNet data
 MEAN_RGB = [0.485, 0.456, 0.406]
 STDDEV_RGB = [0.229, 0.224, 0.225]
 
 
+def imagenet_pixelrnn_generator(tmp_dir,
+                                training,
+                                size=_IMAGENET_SMALL_IMAGE_SIZE):
+  """Image generator for Imagenet 64x64 downsampled images.
+
+  It assumes that the data has been downloaded from
+  http://image-net.org/small/*_32x32.tar or
+  http://image-net.org/small/*_64x64.tar into tmp_dir.
+  Args:
+    tmp_dir: path to temporary storage directory.
+    training: a Boolean; if true, we use the train set, otherwise the test set.
+    size: image size (assumes height and width are same)
+
+  Yields:
+    A dictionary representing the images with the following fields:
+    * image/encoded: the string encoding the image as JPEG,
+    * image/format: the string "jpeg" representing image format,
+    * image/height: an integer representing the height,
+    * image/width: an integer representing the width.
+    Every field is actually a list of the corresponding type.
+  """
+  if size == _IMAGENET_SMALL_IMAGE_SIZE:
+    train_prefix = _IMAGENET_SMALL_TRAIN_PREFIX
+    eval_prefix = _IMAGENET_SMALL_EVAL_PREFIX
+  else:
+    train_prefix = _IMAGENET_MEDIUM_TRAIN_PREFIX
+    eval_prefix = _IMAGENET_MEDIUM_EVAL_PREFIX
+  prefix = train_prefix if training else eval_prefix
+  images_filepath = os.path.join(tmp_dir, prefix)
+  image_files = tf.gfile.Glob(images_filepath + "/*")
+  height = size
+  width = size
+  const_label = 0
+  for filename in image_files:
+    with tf.gfile.Open(filename, "r") as f:
+      encoded_image = f.read()
+      yield {
+          "image/encoded": [encoded_image],
+          "image/format": ["png"],
+          "image/class/label": [const_label],
+          "image/height": [height],
+          "image/width": [width]
+      }
+
+
 def imagenet_preprocess_example(example, mode, resize_size=None):
   """Preprocessing used for Imagenet and similar problems."""
   resize_size = resize_size or [299, 299]
@@ -123,6 +188,40 @@ def preprocess_example(self, example, mode, _):
     return example
 
 
+@registry.register_problem
+class ImageImagenet64Gen(ImageImagenet):
+  """Cifar-10 Tune."""
+
+  @property
+  def train_shards(self):
+    return 1024
+
+  @property
+  def dev_shards(self):
+    return 10
+
+  def generate_data(self, data_dir, tmp_dir, task_id=-1):
+    generator_utils.generate_dataset_and_shuffle(
+        self.generator(data_dir, tmp_dir, True),
+        self.training_filepaths(data_dir, self.train_shards, shuffled=True),
+        self.generator(data_dir, tmp_dir, False),
+        self.dev_filepaths(data_dir, self.dev_shards, shuffled=True))
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    if is_training:
+      return imagenet_pixelrnn_generator(
+          tmp_dir, int(True), size=_IMAGENET_MEDIUM_IMAGE_SIZE)
+    else:
+      return imagenet_pixelrnn_generator(
+          tmp_dir, int(False), size=_IMAGENET_MEDIUM_IMAGE_SIZE)
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    example["inputs"].set_shape([_IMAGENET_MEDIUM_IMAGE_SIZE,
+                                 _IMAGENET_MEDIUM_IMAGE_SIZE, 3])
+    example["inputs"] = tf.to_int64(example["inputs"])
+    return example
+
+
 @registry.register_problem
 class ImageImagenet64(ImageImagenet32):
   """Imagenet rescaled to 64x64."""

tensor2tensor/data_generators/inspect.py

@@ -60,6 +60,8 @@ def main(_):
   total_sequences = 0
   total_input_tokens = 0
   total_target_tokens = 0
+  nonpadding_input_tokens = 0
+  nonpadding_target_tokens = 0
   max_input_length = 0
   max_target_length = 0
   for record in reader:
@@ -71,6 +73,8 @@ def main(_):
       print("INPUTS:\n" + encoder.decode(inputs) if encoder else inputs)
     if FLAGS.print_targets:
       print("TARGETS:\n" + encoder.decode(targets) if encoder else targets)
+    nonpadding_input_tokens += len(inputs) - inputs.count(0)
+    nonpadding_target_tokens += len(targets) - targets.count(0)
     total_input_tokens += len(inputs)
     total_target_tokens += len(targets)
     total_sequences += 1
@@ -83,6 +87,8 @@ def main(_):
   print("total_sequences: %d" % total_sequences)
   print("total_input_tokens: %d" % total_input_tokens)
   print("total_target_tokens: %d" % total_target_tokens)
+  print("nonpadding_input_tokens: %d" % nonpadding_input_tokens)
+  print("nonpadding_target_tokens: %d" % nonpadding_target_tokens)
   print("max_input_length: %d" % max_input_length)
   print("max_target_length: %d" % max_target_length)