Merge pull request #255 from rsepassi/push

v1.2.0

README.md

@@ -217,7 +217,7 @@ and are encoded in
 [`tf.contrib.training.HParams`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/training/python/training/hparam.py)
 objects. The `HParams` are available to both the problem specification and the
 model. A basic set of hyperparameters are defined in
-[`common_hparams.py`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/models/common_hparams.py)
+[`common_hparams.py`](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/layers/common_hparams.py)
 and hyperparameter set functions can compose other hyperparameter set functions.
 
 ### Trainer

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.1.9',
+    version='1.2.0',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='[email protected]',
@@ -26,8 +26,8 @@
         'six',
     ],
     extras_require={
-        'tensorflow': ['tensorflow>=1.2.0rc1'],
-        'tensorflow_gpu': ['tensorflow-gpu>=1.2.0rc1'],
+        'tensorflow': ['tensorflow>=1.3.0'],
+        'tensorflow_gpu': ['tensorflow-gpu>=1.3.0'],
     },
     tests_require=['nose'],
     test_suite='nose.collector',

tensor2tensor/bin/t2t-datagen

@@ -42,7 +42,6 @@ from tensor2tensor.data_generators import algorithmic_math
 from tensor2tensor.data_generators import all_problems  # pylint: disable=unused-import
 from tensor2tensor.data_generators import audio
 from tensor2tensor.data_generators import generator_utils
-from tensor2tensor.data_generators import image
 from tensor2tensor.data_generators import lm1b
 from tensor2tensor.data_generators import snli
 from tensor2tensor.data_generators import wmt
@@ -106,9 +105,6 @@ _SUPPORTED_PROBLEM_GENERATORS = {
         lambda: lm1b.generator(FLAGS.tmp_dir, True, characters=True),
         lambda: lm1b.generator(FLAGS.tmp_dir, False, characters=True)
     ),
-    "image_celeba_tune": (
-        lambda: image.celeba_generator(FLAGS.tmp_dir, 162770),
-        lambda: image.celeba_generator(FLAGS.tmp_dir, 19867, 162770)),
     "inference_snli32k": (
         lambda: snli.snli_token_generator(FLAGS.tmp_dir, True, 2**15),
         lambda: snli.snli_token_generator(FLAGS.tmp_dir, False, 2**15),

tensor2tensor/data_generators/algorithmic_test.py

@@ -21,7 +21,8 @@
 
 # Dependency imports
 
-from six.moves import xrange
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
 from tensor2tensor.data_generators import algorithmic
 
 import tensorflow as tf

tensor2tensor/data_generators/all_problems.py

@@ -43,3 +43,4 @@
   pass
 # pylint: enable=g-import-not-at-top
 # pylint: enable=unused-import
+

tensor2tensor/data_generators/generator_utils.py

@@ -301,9 +301,24 @@ def gunzip_file(gz_path, new_path):
 
 def get_or_generate_vocab_inner(data_dir, vocab_filename, vocab_size,
                                 generator_fn):
-  """Inner implementation for vocab generators."""
-  vocab_filepath = os.path.join(data_dir, vocab_filename)
-  if tf.gfile.Exists(vocab_filepath):
+  """Inner implementation for vocab generators.
+
+  Args:
+    data_dir: The base directory where data and vocab files are stored. If None,
+        then do not save the vocab even if it doesn't exist.
+    vocab_filename: relative filename where vocab file is stored
+    vocab_size: target size of the vocabulary constructed by SubwordTextEncoder
+    generator_fn: a generator that produces tokens from the vocabulary
+
+  Returns:
+    A SubwordTextEncoder vocabulary object.
+  """
+  if data_dir is None:
+    vocab_filepath = None
+  else:
+    vocab_filepath = os.path.join(data_dir, vocab_filename)
+
+  if vocab_filepath is not None and tf.gfile.Exists(vocab_filepath):
     tf.logging.info("Found vocab file: %s", vocab_filepath)
     vocab = text_encoder.SubwordTextEncoder(vocab_filepath)
     return vocab
@@ -316,7 +331,9 @@ def get_or_generate_vocab_inner(data_dir, vocab_filename, vocab_size,
 
   vocab = text_encoder.SubwordTextEncoder.build_to_target_size(
       vocab_size, token_counts, 1, 1e3)
-  vocab.store_to_file(vocab_filepath)
+
+  if vocab_filepath is not None:
+    vocab.store_to_file(vocab_filepath)
   return vocab
 
 

tensor2tensor/data_generators/image.py

@@ -66,7 +66,137 @@ def example_reading_spec(self, label_key=None):
     return data_fields, data_items_to_decoders
 
 
-# French street names dataset.
+@registry.register_problem("image_celeba_tune")
+class ImageCeleba(ImageProblem):
+  """CelebA dataset, aligned and cropped images."""
+  IMG_DATA = ("img_align_celeba.zip",
+              "https://drive.google.com/uc?export=download&"
+              "id=0B7EVK8r0v71pZjFTYXZWM3FlRnM")
+  LANDMARKS_DATA = ("celeba_landmarks_align",
+                    "https://drive.google.com/uc?export=download&"
+                    "id=0B7EVK8r0v71pd0FJY3Blby1HUTQ")
+  ATTR_DATA = ("celeba_attr", "https://drive.google.com/uc?export=download&"
+               "id=0B7EVK8r0v71pblRyaVFSWGxPY0U")
+
+  LANDMARK_HEADINGS = ("lefteye_x lefteye_y righteye_x righteye_y "
+                       "nose_x nose_y leftmouth_x leftmouth_y rightmouth_x "
+                       "rightmouth_y").split()
+  ATTR_HEADINGS = (
+      "5_o_Clock_Shadow Arched_Eyebrows Attractive Bags_Under_Eyes Bald Bangs "
+      "Big_Lips Big_Nose Black_Hair Blond_Hair Blurry Brown_Hair "
+      "Bushy_Eyebrows Chubby Double_Chin Eyeglasses Goatee Gray_Hair "
+      "Heavy_Makeup High_Cheekbones Male Mouth_Slightly_Open Mustache "
+      "Narrow_Eyes No_Beard Oval_Face Pale_Skin Pointy_Nose Receding_Hairline "
+      "Rosy_Cheeks Sideburns Smiling Straight_Hair Wavy_Hair Wearing_Earrings "
+      "Wearing_Hat Wearing_Lipstick Wearing_Necklace Wearing_Necktie Young"
+  ).split()
+
+  def preprocess_examples(self, examples, unused_mode, unused_hparams):
+
+    def resize(img, size):
+      return tf.to_int64(
+          tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA))
+
+    inputs = examples["inputs"]
+    # Remove boundaries in CelebA images. Remove 40 pixels each side
+    # vertically and 20 pixels each side horizontally.
+    inputs = tf.image.crop_to_bounding_box(inputs, 40, 20, 218 - 80, 178 - 40)
+    examples["inputs"] = resize(inputs, 8)
+    examples["targets"] = resize(inputs, 32)
+    return examples
+
+  def hparams(self, defaults, model_hparams):
+    p = defaults
+    p.input_modality = {"inputs": ("image:identity_no_pad", None)}
+    p.target_modality = ("image:identity_no_pad", None)
+    p.batch_size_multiplier = 256
+    p.max_expected_batch_size_per_shard = 4
+    p.input_space_id = 1
+    p.target_space_id = 1
+
+  def generator(self, tmp_dir, how_many, start_from=0):
+    """Image generator for CELEBA dataset.
+
+    Args:
+      tmp_dir: path to temporary storage directory.
+      how_many: how many images and labels to generate.
+      start_from: from which image to start.
+
+    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,
+    """
+    out_paths = []
+    for fname, url in [self.IMG_DATA, self.LANDMARKS_DATA, self.ATTR_DATA]:
+      path = generator_utils.maybe_download_from_drive(tmp_dir, fname, url)
+      out_paths.append(path)
+
+    img_path, landmarks_path, attr_path = out_paths  # pylint: disable=unbalanced-tuple-unpacking
+    unzipped_folder = img_path[:-4]
+    if not tf.gfile.Exists(unzipped_folder):
+      zipfile.ZipFile(img_path, "r").extractall(tmp_dir)
+
+    with tf.gfile.Open(landmarks_path) as f:
+      landmarks_raw = f.read()
+
+    with tf.gfile.Open(attr_path) as f:
+      attr_raw = f.read()
+
+    def process_landmarks(raw_data):
+      landmarks = {}
+      lines = raw_data.split("\n")
+      headings = lines[1].strip().split()
+      for line in lines[2:-1]:
+        values = line.strip().split()
+        img_name = values[0]
+        landmark_values = [int(v) for v in values[1:]]
+        landmarks[img_name] = landmark_values
+      return landmarks, headings
+
+    def process_attrs(raw_data):
+      attrs = {}
+      lines = raw_data.split("\n")
+      headings = lines[1].strip().split()
+      for line in lines[2:-1]:
+        values = line.strip().split()
+        img_name = values[0]
+        attr_values = [int(v) for v in values[1:]]
+        attrs[img_name] = attr_values
+      return attrs, headings
+
+    img_landmarks, _ = process_landmarks(landmarks_raw)
+    img_attrs, _ = process_attrs(attr_raw)
+
+    image_files = tf.gfile.Glob(unzipped_folder + "/*.jpg")
+    for filename in image_files[start_from:start_from + how_many]:
+      img_name = os.path.basename(filename)
+      landmarks = img_landmarks[img_name]
+      attrs = img_attrs[img_name]
+
+      with tf.gfile.Open(filename, "r") as f:
+        encoded_image_data = f.read()
+        yield {
+            "image/encoded": [encoded_image_data],
+            "image/format": ["jpeg"],
+            "attributes": attrs,
+            "landmarks": landmarks,
+        }
+
+  @property
+  def train_shards(self):
+    return 100
+
+  @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(tmp_dir, 162770),  # train
+        self.training_filepaths(data_dir, self.train_shards, shuffled=False),
+        self.generator(tmp_dir, 19867, 162770),  # dev
+        self.dev_filepaths(data_dir, self.dev_shards, shuffled=False))
 
 
 @registry.register_problem
@@ -199,7 +329,7 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
           "instructions at https://github.com/tensorflow/models/blob/master"
           "/inception/README.md#getting-started")
 
-  def preprocess_examples(self, examples, mode):
+  def preprocess_examples(self, examples, mode, _):
     return imagenet_preprocess_examples(examples, mode)
 
 
@@ -638,7 +768,7 @@ def train_shards(self):
   def dev_shards(self):
     return 10
 
-  def preprocess_examples(self, examples, mode):
+  def preprocess_examples(self, examples, mode, _):
     return imagenet_preprocess_examples(examples, mode)
 
   def generator(self, data_dir, tmp_dir, is_training):
@@ -700,41 +830,3 @@ class ImageMsCocoTokens32k(ImageMsCocoTokens8k):
   @property
   def targeted_vocab_size(self):
     return 2**15  # 32768
-
-
-# URL and filename for CELEBA data.
-_CELEBA_NAME = "img_align_celeba"
-_CELEBA_URL = "https://drive.google.com/uc?export=download&id=0B7EVK8r0v71pZjFTYXZWM3FlRnM"
-
-
-def _get_celeba(directory):
-  """Download and extract CELEBA to directory unless it is there."""
-  # path = os.path.join(directory, _CELEBA_NAME)
-  path = generator_utils.maybe_download_from_drive(directory, _CELEBA_NAME,
-                                                   _CELEBA_URL)
-  if not tf.gfile.Exists(path):
-    zipfile.ZipFile(path + ".zip", "r").extractall(directory)
-
-
-def celeba_generator(tmp_dir, how_many, start_from=0):
-  """Image generator for CELEBA dataset.
-
-  Args:
-    tmp_dir: path to temporary storage directory.
-    how_many: how many images and labels to generate.
-    start_from: from which image to start.
-
-  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,
-  """
-  _get_celeba(tmp_dir)
-  image_files = tf.gfile.Glob(os.path.join(tmp_dir, _CELEBA_NAME) + "/*.jpg")
-  for filename in image_files[start_from:start_from + how_many]:
-    with tf.gfile.Open(filename, "r") as f:
-      encoded_image_data = f.read()
-      yield {
-          "image/encoded": [encoded_image_data],
-          "image/format": ["jpeg"],
-      }

tensor2tensor/data_generators/lm1b.py

@@ -26,7 +26,8 @@
 
 # Dependency imports
 
-from six.moves import xrange
+from six.moves import xrange  # pylint: disable=redefined-builtin
+
 from tensor2tensor.data_generators import generator_utils
 from tensor2tensor.data_generators import text_encoder
 from tensor2tensor.data_generators import tokenizer

tensor2tensor/data_generators/problem.py

@@ -116,7 +116,10 @@ class Problem(object):
     * generate_data(data_dir, tmp_dir)
         - Generate training and dev datasets into data_dir.
         - Additonal files, e.g. vocabulary files, should also be written to
-          data_dir.
+          data_dir. Vocab files are newline-separated files with each line
+          containing a token. The standard convention for the filename is to
+          set it to be
+                  ${Problem.vocab_name}.${Problem.targeted_vocab_size}
         - Downloads and other files can be written to tmp_dir
         - If you have a training and dev generator, you can generate the
           training and dev datasets with
@@ -200,22 +203,22 @@ def training_filepaths(self, data_dir, num_shards, shuffled):
     file_basename = self.dataset_filename()
     if not shuffled:
       file_basename += generator_utils.UNSHUFFLED_SUFFIX
-    return generator_utils.train_data_filenames(
-        file_basename, data_dir, num_shards)
+    return generator_utils.train_data_filenames(file_basename, data_dir,
+                                                num_shards)
 
   def dev_filepaths(self, data_dir, num_shards, shuffled):
     file_basename = self.dataset_filename()
     if not shuffled:
       file_basename += generator_utils.UNSHUFFLED_SUFFIX
-    return generator_utils.dev_data_filenames(
-        file_basename, data_dir, num_shards)
+    return generator_utils.dev_data_filenames(file_basename, data_dir,
+                                              num_shards)
 
   def test_filepaths(self, data_dir, num_shards, shuffled):
     file_basename = self.dataset_filename()
     if not shuffled:
       file_basename += generator_utils.UNSHUFFLED_SUFFIX
-    return generator_utils.test_data_filenames(
-        file_basename, data_dir, num_shards)
+    return generator_utils.test_data_filenames(file_basename, data_dir,
+                                               num_shards)
 
   def __init__(self, was_reversed=False, was_copy=False):
     """Create a Problem.
@@ -412,10 +415,8 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
       generator_utils.shuffle_dataset(all_paths)
     else:
       generator_utils.generate_dataset_and_shuffle(
-          self.generator(data_dir, tmp_dir, True),
-          self.training_filepaths(data_dir, self.num_shards, shuffled=False),
-          self.generator(data_dir, tmp_dir, False),
-          self.dev_filepaths(data_dir, self.num_dev_shards, shuffled=False))
+          self.generator(data_dir, tmp_dir, True), train_paths,
+          self.generator(data_dir, tmp_dir, False), dev_paths)
 
   def feature_encoders(self, data_dir):
     if self.is_character_level:
@@ -435,8 +436,9 @@ def hparams(self, defaults, unused_model_hparams):
 
     if self.has_inputs:
       source_vocab_size = self._encoders["inputs"].vocab_size
-      p.input_modality = {"inputs": (registry.Modalities.SYMBOL,
-                                     source_vocab_size)}
+      p.input_modality = {
+          "inputs": (registry.Modalities.SYMBOL, source_vocab_size)
+      }
     target_vocab_size = self._encoders["targets"].vocab_size
     p.target_modality = (registry.Modalities.SYMBOL, target_vocab_size)
     if self.has_inputs: