Merging internal updates.

.travis.yml

@@ -11,12 +11,15 @@ env:
     - TF_VERSION="1.5.*"
     - TF_VERSION="1.6.*"
     - TF_VERSION="1.7.*"
+    - TF_VERSION="1.8.*"
 matrix:
   exclude:
     - python: "3.6"
       env: TF_VERSION="1.5.*"
     - python: "3.6"
       env: TF_VERSION="1.6.*"
+    - python: "3.6"
+      env: TF_VERSION="1.7.*"
 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

@@ -26,10 +26,10 @@ You can chat with us on
 
 ### Quick Start
 
-[This iPython notebook](https://goo.gl/wkHexj) explains T2T and runs in your
-browser using a free VM from Google, no installation needed.
-Alternatively, here is a one-command version that installs T2T, downloads MNIST,
-trains a model and evaluates it:
+[This iPython notebook](https://colab.research.google.com/github/tensorflow/tensor2tensor/blob/master/tensor2tensor/notebooks/hello_t2t.ipynb)
+explains T2T and runs in your browser using a free VM from Google,
+no installation needed. Alternatively, here is a one-command version that
+installs T2T, downloads MNIST, trains a model and evaluates it:
 
 ```
 pip install tensor2tensor && t2t-trainer \

docs/index.md

@@ -19,7 +19,7 @@ research](https://research.googleblog.com/2017/06/accelerating-deep-learning-res
 ## Basics
 
 * [Walkthrough](walkthrough.md): Install and run.
-* [IPython notebook](https://goo.gl/wkHexj): Get a hands-on experience.
+* [IPython notebook](https://colab.research.google.com/github/tensorflow/tensor2tensor/blob/master/tensor2tensor/notebooks/hello_t2t.ipynb): Get a hands-on experience.
 * [Overview](overview.md): How all parts of T2T code are connected.
 * [New Problem](new_problem.md): Train T2T models on your data.
 * [New Model](new_model.md): Create your own T2T model.

docs/walkthrough.md

@@ -26,10 +26,10 @@ You can chat with us on
 
 ### Quick Start
 
-[This iPython notebook](https://goo.gl/wkHexj) explains T2T and runs in your
-browser using a free VM from Google, no installation needed.
-Alternatively, here is a one-command version that installs T2T, downloads MNIST,
-trains a model and evaluates it:
+[This iPython notebook](https://colab.research.google.com/github/tensorflow/tensor2tensor/blob/master/tensor2tensor/notebooks/hello_t2t.ipynb)
+explains T2T and runs in your browser using a free VM from Google,
+no installation needed. Alternatively, here is a one-command version that
+installs T2T, downloads MNIST, trains a model and evaluates it:
 
 ```
 pip install tensor2tensor && t2t-trainer \

tensor2tensor/data_generators/all_problems.py

@@ -46,6 +46,7 @@
     "tensor2tensor.data_generators.ptb",
     "tensor2tensor.data_generators.snli",
     "tensor2tensor.data_generators.squad",
+    "tensor2tensor.data_generators.subject_verb_agreement",
     "tensor2tensor.data_generators.translate_encs",
     "tensor2tensor.data_generators.translate_ende",
     "tensor2tensor.data_generators.translate_enet",
@@ -56,6 +57,7 @@
     "tensor2tensor.data_generators.twentybn",
     "tensor2tensor.data_generators.wiki",
     "tensor2tensor.data_generators.wikisum.wikisum",
+    "tensor2tensor.data_generators.wikitext103",
     "tensor2tensor.data_generators.wsj_parsing",
 ]
 

tensor2tensor/data_generators/generator_utils.py

@@ -44,7 +44,7 @@ def to_example(dictionary):
   features = {}
   for (k, v) in six.iteritems(dictionary):
     if not v:
-      raise ValueError("Empty generated field: %s", str((k, v)))
+      raise ValueError("Empty generated field: %s" % str((k, v)))
     if isinstance(v[0], six.integer_types):
       features[k] = tf.train.Feature(int64_list=tf.train.Int64List(value=v))
     elif isinstance(v[0], float):
@@ -130,7 +130,8 @@ def outputs_exist(filenames):
       return out_fname
 
 
-def generate_files(generator, output_filenames, max_cases=None):
+def generate_files(generator, output_filenames,
+                   max_cases=None, cycle_every_n=1):
   """Generate cases from a generator and save as TFRecord files.
 
   Generated cases are transformed to tf.Example protos and saved as TFRecords
@@ -141,6 +142,8 @@ def generate_files(generator, output_filenames, max_cases=None):
     output_filenames: List of output file paths.
     max_cases: maximum number of cases to get from the generator;
       if None (default), we use the generator until StopIteration is raised.
+    cycle_every_n: how many cases from the generator to take before
+      switching to the next shard; by default set to 1, switch every case.
   """
   if outputs_exist(output_filenames):
     tf.logging.info("Skipping generator because outputs files exist")
@@ -159,7 +162,8 @@ def generate_files(generator, output_filenames, max_cases=None):
       break
     example = to_example(case)
     writers[shard].write(example.SerializeToString())
-    shard = (shard + 1) % num_shards
+    if counter % cycle_every_n == 0:
+      shard = (shard + 1) % num_shards
 
   for writer in writers:
     writer.close()
@@ -341,6 +345,7 @@ def get_or_generate_vocab(data_dir, tmp_dir, vocab_filename, vocab_size,
   """Generate a vocabulary from the datasets in sources."""
 
   def generate():
+    """Generate lines for vocabulary generation."""
     tf.logging.info("Generating vocab from: %s", str(sources))
     for source in sources:
       url = source[0]

tensor2tensor/data_generators/gym.py

@@ -18,10 +18,11 @@
 from __future__ import division
 from __future__ import print_function
 
-from collections import deque
-
 import functools
+import os
+
 # Dependency imports
+
 import gym
 
 from tensor2tensor.data_generators import problem
@@ -62,9 +63,7 @@ def num_target_frames(self):
     return 1
 
   def eval_metrics(self):
-    eval_metrics = [
-        metrics.Metrics.ACC, metrics.Metrics.ACC_PER_SEQ,
-        metrics.Metrics.NEG_LOG_PERPLEXITY]
+    eval_metrics = [metrics.Metrics.ACC, metrics.Metrics.ACC_PER_SEQ]
     return eval_metrics
 
   @property
@@ -108,6 +107,10 @@ def num_rewards(self):
   def num_steps(self):
     raise NotImplementedError()
 
+  @property
+  def total_number_of_frames(self):
+    return self.num_steps
+
   @property
   def min_reward(self):
     raise NotImplementedError()
@@ -126,13 +129,13 @@ def hparams(self, defaults, unused_model_hparams):
     p.target_space_id = problem.SpaceID.IMAGE
 
   def generate_samples(self, data_dir, tmp_dir, unused_dataset_split):
-    next_obs = self.env.reset()
+    next_observation = self.env.reset()
     for _ in range(self.num_steps):
-      observation = next_obs
+      observation = next_observation
       action = self.get_action(observation)
-      next_obs, reward, done, _ = self.env.step(action)
+      next_observation, reward, done, _ = self.env.step(action)
       if done:
-        next_obs = self.env.reset()
+        next_observation = self.env.reset()
       yield {"frame": observation,
              "action": [action],
              "done": [done],
@@ -184,23 +187,22 @@ class GymDiscreteProblemWithAgent(GymPongRandom5k):
   def __init__(self, *args, **kwargs):
     super(GymDiscreteProblemWithAgent, self).__init__(*args, **kwargs)
     self._env = None
-    self.history_size = 2
+    self.debug_dump_frames_path = "debug_frames_env"
 
     # defaults
     self.environment_spec = lambda: gym.make("PongDeterministic-v4")
-    self.in_graph_wrappers = [(atari.MaxAndSkipWrapper, {"skip": 4})]
+    self.in_graph_wrappers = []
     self.collect_hparams = rl.atari_base()
-    self.settable_num_steps = 1000
+    self.settable_num_steps = 20000
     self.simulated_environment = None
-    self.warm_up = 70
+    self.warm_up = 10
 
   @property
   def num_steps(self):
     return self.settable_num_steps
 
   def _setup(self):
-    in_graph_wrappers = [(atari.ShiftRewardWrapper, {"add_value": 2}),
-                         (atari.MemoryWrapper, {})] + self.in_graph_wrappers
+    in_graph_wrappers = [(atari.MemoryWrapper, {})] + self.in_graph_wrappers
     env_hparams = tf.contrib.training.HParams(
         in_graph_wrappers=in_graph_wrappers,
         simulated_environment=self.simulated_environment)
@@ -229,41 +231,41 @@ def _setup(self):
 
     self.avilable_data_size_op = atari.MemoryWrapper.singleton.speculum.size()
     self.data_get_op = atari.MemoryWrapper.singleton.speculum.dequeue()
-    self.history_buffer = deque(maxlen=self.history_size+1)
 
   def restore_networks(self, sess):
     if FLAGS.agent_policy_path:
       model_saver = tf.train.Saver(
-        tf.global_variables(".*network_parameters.*"))
+          tf.global_variables(".*network_parameters.*"))
       model_saver.restore(sess, FLAGS.agent_policy_path)
 
   def generate_encoded_samples(self, data_dir, tmp_dir, unused_dataset_split):
     self._setup()
+    self.debug_dump_frames_path = os.path.join(
+        data_dir, self.debug_dump_frames_path)
 
     with tf.Session() as sess:
       sess.run(tf.global_variables_initializer())
       self.restore_networks(sess)
-
+      # Actions are shifted by 1 by MemoryWrapper, compensate here.
+      avilable_data_size = sess.run(self.avilable_data_size_op)
+      if avilable_data_size < 1:
+        sess.run(self.collect_trigger_op)
       pieces_generated = 0
+      observ, reward, _, _ = sess.run(self.data_get_op)
       while pieces_generated < self.num_steps + self.warm_up:
         avilable_data_size = sess.run(self.avilable_data_size_op)
-        if avilable_data_size > 0:
-          observ, reward, action, _ = sess.run(self.data_get_op)
-          self.history_buffer.append(observ)
-
-          if len(self.history_buffer) == self.history_size + 1:
-            pieces_generated += 1
-            ret_dict = {"image/encoded": [observ],
-                        "image/format": ["png"],
-                        "image/height": [self.frame_height],
-                        "image/width": [self.frame_width],
-                        "action": [int(action)],
-                        "done": [int(False)],
-                        "reward": [int(reward) - self.min_reward]}
-            if pieces_generated > self.warm_up:
-              yield ret_dict
-        else:
+        if avilable_data_size < 1:
           sess.run(self.collect_trigger_op)
+        next_observ, next_reward, action, _ = sess.run(self.data_get_op)
+        yield {"image/encoded": [observ],
+               "image/format": ["png"],
+               "image/height": [self.frame_height],
+               "image/width": [self.frame_width],
+               "action": [int(action)],
+               "done": [int(False)],
+               "reward": [int(reward) - self.min_reward]}
+        pieces_generated += 1
+        observ, reward = next_observ, next_reward
 
 
 @registry.register_problem
@@ -273,7 +275,7 @@ class GymSimulatedDiscreteProblemWithAgent(GymDiscreteProblemWithAgent):
   def __init__(self, *args, **kwargs):
     super(GymSimulatedDiscreteProblemWithAgent, self).__init__(*args, **kwargs)
     self.simulated_environment = True
-    self.debug_dump_frames_path = "/tmp/t2t_debug_dump_frames"
+    self.debug_dump_frames_path = "debug_frames_sim"
 
   def restore_networks(self, sess):
     super(GymSimulatedDiscreteProblemWithAgent, self).restore_networks(sess)

tensor2tensor/data_generators/imagenet.py

@@ -189,7 +189,7 @@ def preprocess_example(self, example, mode, _):
 
 @registry.register_problem
 class ImageImagenet64Gen(ImageImagenet):
-  """Cifar-10 Tune."""
+  """Imagenet 64 from the pixen cnn paper"""
 
   @property
   def train_shards(self):
@@ -264,6 +264,33 @@ def preprocess_example(self, example, mode, hparams):
     return example
 
 
+@registry.register_problem
+class ImageImagenet32Small(ImageImagenet):
+  """Imagenet small from the pixel cnn paper"""
+
+  @property
+  def is_small(self):
+    return False  # Modalities like for CIFAR.
+
+  @property
+  def num_classes(self):
+    return 1000
+
+  @property
+  def train_shards(self):
+    return 1024
+
+  @property
+  def dev_shards(self):
+    return 10
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    example["inputs"].set_shape([_IMAGENET_SMALL_IMAGE_SIZE,
+                                 _IMAGENET_SMALL_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/squad.py

@@ -143,5 +143,5 @@ def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
     for sample in samples:
       sample['targets'] = self.generate_targets(sample['targets'],
                                                 sample['context'])
-      if not sample['targets']:
+      if sample['targets']:
         yield sample