Merge pull request #228 from rsepassi/push

v1.1.9

Author: lukaszkaiser

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.1.8',
+    version='1.1.9',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='no-reply@google.com',

tensor2tensor/bin/t2t-datagen

@@ -66,6 +66,8 @@ flags.DEFINE_integer("num_shards", 0, "How many shards to use. Ignored for "
                      "registered Problems.")
 flags.DEFINE_integer("max_cases", 0,
                      "Maximum number of cases to generate (unbounded if 0).")
+flags.DEFINE_bool("only_list", False,
+                  "If true, we only list the problems that will be generated.")
 flags.DEFINE_integer("random_seed", 429459, "Random seed to use.")
 flags.DEFINE_integer("task_id", -1, "For distributed data generation.")
 flags.DEFINE_string("t2t_usr_dir", "",
@@ -81,33 +83,33 @@ _SUPPORTED_PROBLEM_GENERATORS = {
     "algorithmic_algebra_inverse": (
         lambda: algorithmic_math.algebra_inverse(26, 0, 2, 100000),
         lambda: algorithmic_math.algebra_inverse(26, 3, 3, 10000)),
-    "wmt_parsing_tokens_8k": (
+    "parsing_english_ptb8k": (
         lambda: wmt.parsing_token_generator(
             FLAGS.data_dir, FLAGS.tmp_dir, True, 2**13),
         lambda: wmt.parsing_token_generator(
             FLAGS.data_dir, FLAGS.tmp_dir, False, 2**13)),
-    "wsj_parsing_tokens_16k": (
+    "parsing_english_ptb16k": (
         lambda: wsj_parsing.parsing_token_generator(
             FLAGS.data_dir, FLAGS.tmp_dir, True, 2**14, 2**9),
         lambda: wsj_parsing.parsing_token_generator(
             FLAGS.data_dir, FLAGS.tmp_dir, False, 2**14, 2**9)),
-    "wmt_ende_bpe32k": (
+    "translate_ende_wmt_bpe32k": (
         lambda: wmt.ende_bpe_token_generator(
             FLAGS.data_dir, FLAGS.tmp_dir, True),
         lambda: wmt.ende_bpe_token_generator(
             FLAGS.data_dir, FLAGS.tmp_dir, False)),
-    "lm1b_32k": (
+    "languagemodel_1b32k": (
         lambda: lm1b.generator(FLAGS.tmp_dir, True),
         lambda: lm1b.generator(FLAGS.tmp_dir, False)
     ),
-    "lm1b_characters": (
+    "languagemodel_1b_characters": (
         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)),
-    "snli_32k": (
+    "inference_snli32k": (
         lambda: snli.snli_token_generator(FLAGS.tmp_dir, True, 2**15),
         lambda: snli.snli_token_generator(FLAGS.tmp_dir, False, 2**15),
     ),
@@ -181,7 +183,11 @@ def main(_):
                        "Data will be written to default data_dir=%s.",
                        FLAGS.data_dir)
 
-  tf.logging.info("Generating problems:\n  * %s\n" % "\n  * ".join(problems))
+  tf.logging.info("Generating problems:\n%s"
+                  % registry.display_list_by_prefix(problems,
+                                                    starting_spaces=4))
+  if FLAGS.only_list:
+    return
   for problem in problems:
     set_random_seed()
 
@@ -210,7 +216,7 @@ def generate_data_for_problem(problem):
 
 
 def generate_data_for_registered_problem(problem_name):
-  tf.logging.info("Generating training data for %s.", problem_name)
+  tf.logging.info("Generating data for %s.", problem_name)
   if FLAGS.num_shards:
     raise ValueError("--num_shards should not be set for registered Problem.")
   problem = registry.problem(problem_name)

tensor2tensor/data_generators/cipher.py

@@ -29,7 +29,7 @@
 
 
 @registry.register_problem
-class CipherShift5(algorithmic.AlgorithmicProblem):
+class AlgorithmicCipherShift5(algorithmic.AlgorithmicProblem):
   """Shift cipher."""
 
   @property
@@ -62,7 +62,7 @@ def dev_length(self):
 
 
 @registry.register_problem
-class CipherVigenere5(algorithmic.AlgorithmicProblem):
+class AlgorithmicCipherVigenere5(algorithmic.AlgorithmicProblem):
   """Vinegre cipher."""
 
   @property
@@ -95,7 +95,7 @@ def dev_length(self):
 
 
 @registry.register_problem
-class CipherShift200(CipherShift5):
+class AlgorithmicCipherShift200(AlgorithmicCipherShift5):
   """Shift cipher."""
 
   @property
@@ -110,7 +110,7 @@ def distribution(self):
 
 
 @registry.register_problem
-class CipherVigenere200(CipherVigenere5):
+class AlgorithmicCipherVigenere200(AlgorithmicCipherVigenere5):
   """Vinegre cipher."""
 
   @property

tensor2tensor/data_generators/desc2code.py

@@ -209,8 +209,8 @@ def generator_target():
       }
 
 
-@registry.register_problem("desc2code_py")
-class Desc2CodePyProblem(Desc2CodeProblem):
+@registry.register_problem
+class ProgrammingDesc2codePy(Desc2CodeProblem):
   """Description2Code for python problem."""
 
   @property
@@ -222,8 +222,8 @@ def preprocess_target(self, target):
     return target.replace("\t", "    ")
 
 
-@registry.register_problem("desc2code_cpp")
-class Desc2CodeCppProblem(Desc2CodeProblem):
+@registry.register_problem
+class ProgrammingDesc2codeCpp(Desc2CodeProblem):
   """Description2Code for C++ problem."""
 
   @property

tensor2tensor/data_generators/desc2code_test.py

@@ -47,7 +47,7 @@ class Desc2codeTest(tf.test.TestCase):
 
   def testCppPreprocess(self):
     """Check that the file correctly preprocess the code source."""
-    cpp_pb = desc2code.Desc2CodeCppProblem()
+    cpp_pb = desc2code.ProgrammingDesc2codeCpp()
 
     self.assertEqual(  # Add space beween two lines
         cpp_pb.preprocess_target("firstline//comm1\nsecondline//comm2\n"),

tensor2tensor/data_generators/gene_expression.py

@@ -176,8 +176,8 @@ def eval_metrics(self):
     return [metrics.Metrics.LOG_POISSON, metrics.Metrics.R2]
 
 
-@registry.register_problem("gene_expression_cage10")
-class GeneExpressionCAGE10(GeneExpressionProblem):
+@registry.register_problem
+class GenomicsExpressionCage10(GeneExpressionProblem):
 
   @property
   def download_url(self):
@@ -188,8 +188,8 @@ def h5_file(self):
     return "cage10.h5"
 
 
-@registry.register_problem("gene_expression_gm12878")
-class GeneExpressionGM12878(GeneExpressionProblem):
+@registry.register_problem
+class GenomicsExpressionGm12878(GeneExpressionProblem):
 
   @property
   def download_url(self):
@@ -200,8 +200,8 @@ def h5_file(self):
     return "gm12878.h5"
 
 
-@registry.register_problem("gene_expression_l262k")
-class GeneExpressionL262k(GeneExpressionProblem):
+@registry.register_problem
+class GenomicsExpressionL262k(GeneExpressionProblem):
 
   @property
   def h5_file(self):

tensor2tensor/data_generators/ice_parsing.py

@@ -62,8 +62,8 @@ def tabbed_parsing_character_generator(tmp_dir, train):
   return tabbed_generator(pair_filepath, character_vocab, character_vocab, EOS)
 
 
-@registry.register_problem("ice_parsing_tokens")
-class IceParsingTokens(problem.Problem):
+@registry.register_problem
+class ParsingIcelandic16k(problem.Problem):
   """Problem spec for parsing tokenized Icelandic text to constituency trees."""
 
   @property

tensor2tensor/data_generators/image.py

@@ -214,10 +214,21 @@ def dataset_filename(self):
   def is_small(self):
     return True  # Modalities like for CIFAR.
 
-  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]))
+  @property
+  def num_classes(self):
+    return 1000
+
+  def preprocess_examples(self, examples, mode, hparams):
+    # Just resize with area.
+    if self._was_reversed:
+      examples["inputs"] = tf.to_int64(
+          tf.image.resize_images(examples["inputs"], [32, 32],
+                                 tf.image.ResizeMethod.AREA))
+    else:
+      examples = imagenet_preprocess_examples(examples, mode)
+      examples["inputs"] = tf.to_int64(
+          tf.image.resize_images(examples["inputs"], [32, 32]))
+    return examples
 
 
 def image_generator(images, labels):

tensor2tensor/data_generators/problem.py

@@ -98,7 +98,7 @@ def preprocess_examples_common(examples, hparams):
     examples["inputs"] = examples["inputs"][:hparams.max_input_seq_length]
   if hparams.max_target_seq_length > 0:
     examples["targets"] = examples["targets"][:hparams.max_target_seq_length]
-  if hparams.prepend_inputs_to_targets:
+  if hparams.prepend_mode != "none":
     examples["targets"] = tf.concat(
         [examples["inputs"], [0], examples["targets"]], 0)
   return examples
@@ -410,11 +410,12 @@ def generate_data(self, data_dir, tmp_dir, task_id=-1):
       generator_utils.generate_files(
           self.generator(data_dir, tmp_dir, True), all_paths)
       generator_utils.shuffle_dataset(all_paths)
-    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))
+    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))
 
   def feature_encoders(self, data_dir):
     if self.is_character_level:

tensor2tensor/data_generators/problem_hparams.py

@@ -492,16 +492,16 @@ def image_celeba(unused_model_hparams):
         lambda p: audio_wsj_tokens(p, 2**13),
     "audio_wsj_tokens_8k_test":
         lambda p: audio_wsj_tokens(p, 2**13),
-    "lm1b_characters":
+    "languagemodel_1b_characters":
         lm1b_characters,
-    "lm1b_32k":
+    "languagemodel_1b32k":
         lm1b_32k,
-    "wmt_parsing_tokens_8k":
+    "parsing_english_ptb8k":
         lambda p: wmt_parsing_tokens(p, 2**13),
-    "wsj_parsing_tokens_16k":
+    "parsing_english_ptb16k":
         lambda p: wsj_parsing_tokens(  # pylint: disable=g-long-lambda
             p, "wsj", 2**14, 2**9),
-    "wmt_ende_bpe32k":
+    "translate_ende_wmt_bpe32k":
         wmt_ende_bpe32k,
     "image_celeba_tune":
         image_celeba,