Merge pull request #204 from rsepassi/push

v1.1.5

Author: lukaszkaiser

.gitignore

@@ -6,6 +6,7 @@ _pycache__/
 
 # Python egg metadata, regenerated from source files by setuptools.
 /*.egg-info
+/*.egg
 
 # PyPI distribution artifacts.
 build/

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.1.4',
+    version='1.1.5',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='no-reply@google.com',
@@ -20,6 +20,7 @@
     ],
     install_requires=[
         'numpy',
+        'requests',
         'sympy',
         'six',
     ],

tensor2tensor/bin/t2t-datagen

@@ -16,14 +16,15 @@
 
 """Produces the training and dev data for --problem into --data_dir.
 
-generator.py produces sharded and shuffled TFRecord files of tensorflow.Example
-protocol buffers for a variety of datasets registered in this file.
+Produces sharded and shuffled TFRecord files of tensorflow.Example protocol
+buffers for a variety of registered datasets.
 
-All datasets are registered in _SUPPORTED_PROBLEM_GENERATORS. Each entry maps a
-string name (selectable on the command-line with --problem) to a function that
-takes 2 arguments - input_directory and mode (one of "train" or "dev") - and
-yields for each training example a dictionary mapping string feature names to
-lists of {string, int, float}. The generator will be run once for each mode.
+All Problems are registered with @registry.register_problem or are in
+_SUPPORTED_PROBLEM_GENERATORS in this file. Each entry maps a string name
+(selectable on the command-line with --problem) to a function that takes 2
+arguments - input_directory and mode (one of "train" or "dev") - and yields for
+each training example a dictionary mapping string feature names to lists of
+{string, int, float}. The generator will be run once for each mode.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -229,8 +230,7 @@ def generate_data_for_problem(problem):
     num_shards = FLAGS.num_shards or 10
     tf.logging.info("Generating training data for %s.", problem)
     train_output_files = generator_utils.train_data_filenames(
-        problem + generator_utils.UNSHUFFLED_SUFFIX, FLAGS.data_dir,
-        num_shards)
+        problem + generator_utils.UNSHUFFLED_SUFFIX, FLAGS.data_dir, num_shards)
     generator_utils.generate_files(training_gen(), train_output_files,
                                    FLAGS.max_cases)
     tf.logging.info("Generating development data for %s.", problem)
@@ -250,9 +250,10 @@ def generate_data_for_registered_problem(problem_name):
     raise ValueError("--num_shards should not be set for registered Problem.")
   problem = registry.problem(problem_name)
   task_id = None if FLAGS.task_id < 0 else FLAGS.task_id
-  problem.generate_data(os.path.expanduser(FLAGS.data_dir),
-                        os.path.expanduser(FLAGS.tmp_dir),
-                        task_id=task_id)
+  problem.generate_data(
+      os.path.expanduser(FLAGS.data_dir),
+      os.path.expanduser(FLAGS.tmp_dir),
+      task_id=task_id)
 
 
 if __name__ == "__main__":

tensor2tensor/data_generators/all_problems.py

@@ -30,6 +30,7 @@
 from tensor2tensor.data_generators import wmt
 from tensor2tensor.data_generators import wsj_parsing
 
+
 # Problem modules that require optional dependencies
 # pylint: disable=g-import-not-at-top
 try:

tensor2tensor/data_generators/image.py

@@ -36,7 +36,7 @@
 from tensor2tensor.data_generators import generator_utils
 from tensor2tensor.data_generators import problem
 from tensor2tensor.data_generators import text_encoder
-from tensor2tensor.models import common_layers
+from tensor2tensor.layers import common_layers
 from tensor2tensor.utils import registry
 
 import tensorflow as tf
@@ -76,10 +76,11 @@ class ImageFSNS(ImageProblem):
   def generate_data(self, data_dir, tmp_dir, task_id=-1):
     list_url = ("https://raw.githubusercontent.com/tensorflow/models/master/"
                 "street/python/fsns_urls.txt")
-    fsns_urls = generator_utils.maybe_download(
-        tmp_dir, "fsns_urls.txt", list_url)
-    fsns_files = [f.strip() for f in open(fsns_urls, "r")
-                  if f.startswith("http://")]
+    fsns_urls = generator_utils.maybe_download(tmp_dir, "fsns_urls.txt",
+                                               list_url)
+    fsns_files = [
+        f.strip() for f in open(fsns_urls, "r") if f.startswith("http://")
+    ]
     for url in fsns_files:
       if "/train/train" in url:
         generator_utils.maybe_download(
@@ -88,8 +89,7 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
         generator_utils.maybe_download(
             data_dir, "image_fsns-dev" + url[-len("-00100-of-00512"):], url)
       elif "charset" in url:
-        generator_utils.maybe_download(
-            data_dir, "charset_size134.txt", url)
+        generator_utils.maybe_download(data_dir, "charset_size134.txt", url)
 
   def feature_encoders(self, data_dir):
     # This vocab file must be present within the data directory.
@@ -111,8 +111,8 @@ def hparams(self, defaults, model_hparams):
 
   def example_reading_spec(self):
     label_key = "image/unpadded_label"
-    return super(ImageFSNS, self).example_reading_spec(self,
-                                                       label_key=label_key)
+    return super(ImageFSNS, self).example_reading_spec(
+        self, label_key=label_key)
 
 
 class Image2ClassProblem(ImageProblem):
@@ -161,6 +161,7 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
 
 def imagenet_preprocess_examples(examples, mode):
   """Preprocessing used for Imagenet and similar problems."""
+
   def preprocess(img):
     img = tf.image.resize_images(img, [360, 360])
     img = common_layers.image_augmentation(tf.to_float(img) / 255.)
@@ -215,8 +216,8 @@ def is_small(self):
 
   def preprocess_examples(self, examples, mode):
     examples = imagenet_preprocess_examples(examples, mode)
-    examples["inputs"] = tf.to_int64(tf.image.resize_images(
-        examples["inputs"], [32, 32]))
+    examples["inputs"] = tf.to_int64(
+        tf.image.resize_images(examples["inputs"], [32, 32]))
 
 
 def image_generator(images, labels):
@@ -665,12 +666,20 @@ def generator(self, data_dir, tmp_dir, is_training):
     vocab_filename = "vocab.endefr.%d" % self.targeted_vocab_size
     if is_training:
       return mscoco_generator(
-          data_dir, tmp_dir, True, 80000,
-          vocab_filename=vocab_filename, vocab_size=self.targeted_vocab_size)
+          data_dir,
+          tmp_dir,
+          True,
+          80000,
+          vocab_filename=vocab_filename,
+          vocab_size=self.targeted_vocab_size)
     else:
       return mscoco_generator(
-          data_dir, tmp_dir, False, 40000,
-          vocab_filename=vocab_filename, vocab_size=self.targeted_vocab_size)
+          data_dir,
+          tmp_dir,
+          False,
+          40000,
+          vocab_filename=vocab_filename,
+          vocab_size=self.targeted_vocab_size)
 
 
 @registry.register_problem
@@ -690,8 +699,8 @@ def targeted_vocab_size(self):
 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)
+  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)
 
@@ -711,7 +720,7 @@ def celeba_generator(tmp_dir, how_many, start_from=0):
   """
   _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]:
+  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 {

tensor2tensor/data_generators/problem_hparams.py

@@ -25,7 +25,7 @@
 # Dependency imports
 
 from tensor2tensor.data_generators import text_encoder
-from tensor2tensor.models import modalities  # pylint: disable=unused-import
+from tensor2tensor.layers import modalities  # pylint: disable=unused-import
 from tensor2tensor.utils import registry
 
 import tensorflow as tf
@@ -202,8 +202,7 @@ def default_problem_hparams():
       # the targets. For instance `problem_copy` will copy the inputs, but
       # `problem_rev_copy` will copy the targets.
       was_reversed=False,
-      was_copy=False,
-  )
+      was_copy=False,)
 
 
 def test_problem_hparams(unused_model_hparams, input_vocab_size,
@@ -327,9 +326,7 @@ def lm1b_32k(model_hparams):
   encoder = text_encoder.SubwordTextEncoder(
       os.path.join(model_hparams.data_dir, "lm1b_32k.subword_text_encoder"))
   p.target_modality = (registry.Modalities.SYMBOL, encoder.vocab_size)
-  p.vocabulary = {
-      "targets": encoder
-  }
+  p.vocabulary = {"targets": encoder}
   p.target_space_id = 3
   return p
 
@@ -343,9 +340,7 @@ def lm1b_characters(unused_model_hparams):
   p.input_modality = {}
   encoder = text_encoder.ByteTextEncoder()
   p.target_modality = (registry.Modalities.SYMBOL, encoder.vocab_size)
-  p.vocabulary = {
-      "targets": encoder
-  }
+  p.vocabulary = {"targets": encoder}
   p.target_space_id = 2
   return p
 
@@ -358,10 +353,7 @@ def wiki_32k(model_hparams):
   modality_spec = (registry.Modalities.SYMBOL, encoder.vocab_size)
   p.input_modality = {"inputs": modality_spec}
   p.target_modality = modality_spec
-  p.vocabulary = {
-      "inputs": encoder,
-      "targets": encoder
-  }
+  p.vocabulary = {"inputs": encoder, "targets": encoder}
   p.target_space_id = 3
   return p
 
@@ -430,9 +422,7 @@ def wmt_parsing_tokens(model_hparams, wrong_vocab_size):
   return p
 
 
-def wsj_parsing_tokens(model_hparams,
-                       prefix,
-                       wrong_source_vocab_size,
+def wsj_parsing_tokens(model_hparams, prefix, wrong_source_vocab_size,
                        wrong_target_vocab_size):
   """English to parse tree translation benchmark.
 
@@ -487,11 +477,9 @@ def ice_parsing_tokens(model_hparams, wrong_source_vocab_size):
   p = default_problem_hparams()
   # This vocab file must be present within the data directory.
   source_vocab_filename = os.path.join(
-      model_hparams.data_dir,
-      "ice_source.vocab.%d" % wrong_source_vocab_size)
-  target_vocab_filename = os.path.join(
-      model_hparams.data_dir,
-      "ice_target.vocab.256")
+      model_hparams.data_dir, "ice_source.vocab.%d" % wrong_source_vocab_size)
+  target_vocab_filename = os.path.join(model_hparams.data_dir,
+                                       "ice_target.vocab.256")
   source_subtokenizer = text_encoder.SubwordTextEncoder(source_vocab_filename)
   target_subtokenizer = text_encoder.SubwordTextEncoder(target_vocab_filename)
   p.input_modality = {
@@ -502,7 +490,7 @@ def ice_parsing_tokens(model_hparams, wrong_source_vocab_size):
       "inputs": source_subtokenizer,
       "targets": target_subtokenizer,
   }
-  p.input_space_id = 18   # Icelandic tokens
+  p.input_space_id = 18  # Icelandic tokens
   p.target_space_id = 19  # Icelandic parse tokens
   return p
 
@@ -534,23 +522,41 @@ def image_celeba(unused_model_hparams):
 # Dictionary of named hyperparameter settings for various problems.
 # This is only accessed through the problem_hparams function below.
 PROBLEM_HPARAMS_MAP = {
-    "audio_timit_characters_tune": audio_timit_characters,
-    "audio_timit_characters_test": audio_timit_characters,
-    "audio_timit_tokens_8k_tune": lambda p: audio_timit_tokens(p, 2**13),
-    "audio_timit_tokens_8k_test": lambda p: audio_timit_tokens(p, 2**13),
-    "audio_wsj_characters_tune": audio_wsj_characters,
-    "audio_wsj_characters_test": audio_wsj_characters,
-    "audio_wsj_tokens_8k_tune": lambda p: audio_wsj_tokens(p, 2**13),
-    "audio_wsj_tokens_8k_test": lambda p: audio_wsj_tokens(p, 2**13),
-    "lm1b_characters": lm1b_characters,
-    "lm1b_32k": lm1b_32k,
-    "wiki_32k": wiki_32k,
-    "ice_parsing_characters": wmt_parsing_characters,
-    "ice_parsing_tokens": lambda p: ice_parsing_tokens(p, 2**13),
-    "wmt_parsing_tokens_8k": lambda p: wmt_parsing_tokens(p, 2**13),
-    "wsj_parsing_tokens_16k": lambda p: wsj_parsing_tokens(  # pylint: disable=g-long-lambda
-        p, "wsj", 2**14, 2**9),
-    "wmt_ende_bpe32k": wmt_ende_bpe32k,
-    "image_celeba_tune": image_celeba,
-    "img2img_imagenet": img2img_imagenet,
+    "audio_timit_characters_tune":
+        audio_timit_characters,
+    "audio_timit_characters_test":
+        audio_timit_characters,
+    "audio_timit_tokens_8k_tune":
+        lambda p: audio_timit_tokens(p, 2**13),
+    "audio_timit_tokens_8k_test":
+        lambda p: audio_timit_tokens(p, 2**13),
+    "audio_wsj_characters_tune":
+        audio_wsj_characters,
+    "audio_wsj_characters_test":
+        audio_wsj_characters,
+    "audio_wsj_tokens_8k_tune":
+        lambda p: audio_wsj_tokens(p, 2**13),
+    "audio_wsj_tokens_8k_test":
+        lambda p: audio_wsj_tokens(p, 2**13),
+    "lm1b_characters":
+        lm1b_characters,
+    "lm1b_32k":
+        lm1b_32k,
+    "wiki_32k":
+        wiki_32k,
+    "ice_parsing_characters":
+        wmt_parsing_characters,
+    "ice_parsing_tokens":
+        lambda p: ice_parsing_tokens(p, 2**13),
+    "wmt_parsing_tokens_8k":
+        lambda p: wmt_parsing_tokens(p, 2**13),
+    "wsj_parsing_tokens_16k":
+        lambda p: wsj_parsing_tokens(  # pylint: disable=g-long-lambda
+            p, "wsj", 2**14, 2**9),
+    "wmt_ende_bpe32k":
+        wmt_ende_bpe32k,
+    "image_celeba_tune":
+        image_celeba,
+    "img2img_imagenet":
+        img2img_imagenet,
 }