Merge pull request #396 from rsepassi/push

v1.2.7

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.2.6',
+    version='1.2.7',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='[email protected]',

tensor2tensor/data_generators/generator_utils.py

@@ -21,9 +21,9 @@
 
 from collections import defaultdict
 import gzip
-import io
 import os
 import random
+import stat
 import tarfile
 
 # Dependency imports
@@ -190,8 +190,8 @@ def maybe_download(directory, filename, url):
     print()
     tf.gfile.Rename(inprogress_filepath, filepath)
     statinfo = os.stat(filepath)
-    tf.logging.info("Successfully downloaded %s, %s bytes." % (filename,
-                                                              statinfo.st_size))
+    tf.logging.info("Successfully downloaded %s, %s bytes." %
+                    (filename, statinfo.st_size))
   else:
     tf.logging.info("Not downloading, file already found: %s" % filepath)
   return filepath
@@ -243,7 +243,7 @@ def maybe_download_from_drive(directory, filename, url):
   print()
   statinfo = os.stat(filepath)
   tf.logging.info("Successfully downloaded %s, %s bytes." % (filename,
-                                                            statinfo.st_size))
+                                                             statinfo.st_size))
   return filepath
 
 
@@ -258,8 +258,11 @@ def gunzip_file(gz_path, new_path):
     tf.logging.info("File %s already exists, skipping unpacking" % new_path)
     return
   tf.logging.info("Unpacking %s to %s" % (gz_path, new_path))
+  # We may be unpacking into a newly created directory, add write mode.
+  mode = stat.S_IRWXU or stat.S_IXGRP or stat.S_IRGRP or stat.S_IROTH
+  os.chmod(os.path.dirname(new_path), mode)
   with gzip.open(gz_path, "rb") as gz_file:
-    with io.open(new_path, "wb") as new_file:
+    with tf.gfile.GFile(new_path, mode="wb") as new_file:
       for line in gz_file:
         new_file.write(line)
 

tensor2tensor/data_generators/image.py

@@ -24,6 +24,7 @@
 import json
 import os
 import random
+import struct
 import tarfile
 import zipfile
 
@@ -925,3 +926,58 @@ class ImageMsCocoTokens32k(ImageMsCocoTokens8k):
   @property
   def targeted_vocab_size(self):
     return 2**15  # 32768
+
+
+@registry.register_problem
+class OcrTest(Image2TextProblem):
+  """OCR test problem."""
+
+  @property
+  def is_small(self):
+    return True
+
+  @property
+  def is_character_level(self):
+    return True
+
+  @property
+  def target_space_id(self):
+    return problem.SpaceID.EN_CHR
+
+  @property
+  def train_shards(self):
+    return 1
+
+  @property
+  def dev_shards(self):
+    return 1
+
+  def preprocess_example(self, example, mode, _):
+    # Resize from usual size ~1350x60 to 90x4 in this test.
+    img = example["inputs"]
+    example["inputs"] = tf.to_int64(
+        tf.image.resize_images(img, [90, 4], tf.image.ResizeMethod.AREA))
+    return example
+
+  def generator(self, data_dir, tmp_dir, is_training):
+    # In this test problem, we assume that the data is in tmp_dir/ocr/ in
+    # files names 0.png, 0.txt, 1.png, 1.txt and so on until num_examples.
+    num_examples = 2
+    ocr_dir = os.path.join(tmp_dir, "ocr/")
+    tf.logging.info("Looking for OCR data in %s." % ocr_dir)
+    for i in xrange(num_examples):
+      image_filepath = os.path.join(ocr_dir, "%d.png" % i)
+      text_filepath = os.path.join(ocr_dir, "%d.txt" % i)
+      with tf.gfile.Open(text_filepath, "rb") as f:
+        label = f.read()
+      with tf.gfile.Open(image_filepath, "rb") as f:
+        encoded_image_data = f.read()
+      # In PNG files width and height are stored in these bytes.
+      width, height = struct.unpack(">ii", encoded_image_data[16:24])
+      yield {
+          "image/encoded": [encoded_image_data],
+          "image/format": ["png"],
+          "image/class/label": label.strip(),
+          "image/height": [height],
+          "image/width": [width]
+      }

tensor2tensor/data_generators/translate_enfr.py

@@ -34,50 +34,54 @@
 # End-of-sentence marker.
 EOS = text_encoder.EOS_ID
 
-_ENFR_TRAIN_DATASETS = [
+_ENFR_TRAIN_SMALL_DATA = [
     [
         "https://s3.amazonaws.com/opennmt-trainingdata/baseline-1M-enfr.tgz",
         ("baseline-1M-enfr/baseline-1M_train.en",
          "baseline-1M-enfr/baseline-1M_train.fr")
     ],
-    #    [
-    #        "http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz",
-    #        ("commoncrawl.fr-en.en", "commoncrawl.fr-en.fr")
-    #    ],
-    #    [
-    #        "http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz",
-    #        ("training/europarl-v7.fr-en.en", "training/europarl-v7.fr-en.fr")
-    #    ],
-    #    [
-    #        "http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz",
-    #        ("training/news-commentary-v9.fr-en.en",
-    #         "training/news-commentary-v9.fr-en.fr")
-    #    ],
-    #    [
-    #        "http://www.statmt.org/wmt10/training-giga-fren.tar",
-    #        ("giga-fren.release2.fixed.en.gz",
-    #         "giga-fren.release2.fixed.fr.gz")
-    #    ],
-    #    [
-    #        "http://www.statmt.org/wmt13/training-parallel-un.tgz",
-    #        ("un/undoc.2000.fr-en.en", "un/undoc.2000.fr-en.fr")
-    #    ],
 ]
-_ENFR_TEST_DATASETS = [
+_ENFR_TEST_SMALL_DATA = [
     [
         "https://s3.amazonaws.com/opennmt-trainingdata/baseline-1M-enfr.tgz",
         ("baseline-1M-enfr/baseline-1M_valid.en",
          "baseline-1M-enfr/baseline-1M_valid.fr")
     ],
-    #    [
-    #        "http://data.statmt.org/wmt17/translation-task/dev.tgz",
-    #        ("dev/newstest2013.en", "dev/newstest2013.fr")
-    #    ],
+]
+_ENFR_TRAIN_LARGE_DATA = [
+    [
+        "http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz",
+        ("commoncrawl.fr-en.en", "commoncrawl.fr-en.fr")
+    ],
+    [
+        "http://www.statmt.org/wmt13/training-parallel-europarl-v7.tgz",
+        ("training/europarl-v7.fr-en.en", "training/europarl-v7.fr-en.fr")
+    ],
+    [
+        "http://www.statmt.org/wmt14/training-parallel-nc-v9.tgz",
+        ("training/news-commentary-v9.fr-en.en",
+         "training/news-commentary-v9.fr-en.fr")
+    ],
+    [
+        "http://www.statmt.org/wmt10/training-giga-fren.tar",
+        ("giga-fren.release2.fixed.en.gz",
+         "giga-fren.release2.fixed.fr.gz")
+    ],
+    [
+        "http://www.statmt.org/wmt13/training-parallel-un.tgz",
+        ("un/undoc.2000.fr-en.en", "un/undoc.2000.fr-en.fr")
+    ],
+]
+_ENFR_TEST_LARGE_DATA = [
+    [
+        "http://data.statmt.org/wmt17/translation-task/dev.tgz",
+        ("dev/newstest2013.en", "dev/newstest2013.fr")
+    ],
 ]
 
 
 @registry.register_problem
-class TranslateEnfrWmt8k(translate.TranslateProblem):
+class TranslateEnfrWmtSmall8k(translate.TranslateProblem):
   """Problem spec for WMT En-Fr translation."""
 
   @property
@@ -88,11 +92,18 @@ def targeted_vocab_size(self):
   def vocab_name(self):
     return "vocab.enfr"
 
+  @property
+  def use_small_dataset(self):
+    return True
+
   def generator(self, data_dir, tmp_dir, train):
     symbolizer_vocab = generator_utils.get_or_generate_vocab(
         data_dir, tmp_dir, self.vocab_file, self.targeted_vocab_size,
-        _ENFR_TRAIN_DATASETS)
-    datasets = _ENFR_TRAIN_DATASETS if train else _ENFR_TEST_DATASETS
+        _ENFR_TRAIN_SMALL_DATA)
+    if self.use_small_dataset:
+      datasets = _ENFR_TRAIN_SMALL_DATA if train else _ENFR_TEST_SMALL_DATA
+    else:
+      datasets = _ENFR_TRAIN_LARGE_DATA if train else _ENFR_TEST_LARGE_DATA
     tag = "train" if train else "dev"
     data_path = translate.compile_data(tmp_dir, datasets,
                                        "wmt_enfr_tok_%s" % tag)
@@ -109,15 +120,31 @@ def target_space_id(self):
 
 
 @registry.register_problem
-class TranslateEnfrWmt32k(TranslateEnfrWmt8k):
+class TranslateEnfrWmtSmall32k(TranslateEnfrWmtSmall8k):
 
   @property
   def targeted_vocab_size(self):
     return 2**15  # 32768
 
 
 @registry.register_problem
-class TranslateEnfrWmtCharacters(translate.TranslateProblem):
+class TranslateEnfrWmt8k(TranslateEnfrWmtSmall8k):
+
+  @property
+  def use_small_dataset(self):
+    return False
+
+
+@registry.register_problem
+class TranslateEnfrWmt32k(TranslateEnfrWmtSmall32k):
+
+  @property
+  def use_small_dataset(self):
+    return False
+
+
+@registry.register_problem
+class TranslateEnfrWmtSmallCharacters(translate.TranslateProblem):
   """Problem spec for WMT En-Fr translation."""
 
   @property
@@ -130,7 +157,10 @@ def vocab_name(self):
 
   def generator(self, data_dir, tmp_dir, train):
     character_vocab = text_encoder.ByteTextEncoder()
-    datasets = _ENFR_TRAIN_DATASETS if train else _ENFR_TEST_DATASETS
+    if self.use_small_dataset:
+      datasets = _ENFR_TRAIN_SMALL_DATA if train else _ENFR_TEST_SMALL_DATA
+    else:
+      datasets = _ENFR_TRAIN_LARGE_DATA if train else _ENFR_TEST_LARGE_DATA
     tag = "train" if train else "dev"
     data_path = translate.compile_data(tmp_dir, datasets,
                                        "wmt_enfr_chr_%s" % tag)
@@ -144,3 +174,11 @@ def input_space_id(self):
   @property
   def target_space_id(self):
     return problem.SpaceID.FR_CHR
+
+
+@registry.register_problem
+class TranslateEnfrWmtCharacters(TranslateEnfrWmtSmallCharacters):
+
+  @property
+  def use_small_dataset(self):
+    return False

tensor2tensor/data_generators/translate_enzh.py

@@ -35,9 +35,13 @@
 
 # End-of-sentence marker.
 EOS = text_encoder.EOS_ID
+
+# End-of-sentence marker.
+EOS = text_encoder.EOS_ID
+
 # This is far from being the real WMT17 task - only toyset here
-# you need to register to get UN data and CWT data
-# also by convention this is EN to ZH - use translate_enzh_wmt8k_rev for ZH to EN task
+# you need to register to get UN data and CWT data. Also, by convention,
+# this is EN to ZH - use translate_enzh_wmt8k_rev for ZH to EN task
 _ENZH_TRAIN_DATASETS = [[("http://data.statmt.org/wmt17/translation-task/"
                           "training-parallel-nc-v12.tgz"),
                          ("training/news-commentary-v12.zh-en.en",

tensor2tensor/layers/common_attention.py

@@ -2958,15 +2958,20 @@ def pad_and_reshape(x):
 
 @expert_utils.add_var_scope()
 def multihead_self_attention_reduced(
-    x, factor, nonlinearity, reduction_type, multihead_params):
+    x,
+    factor,
+    multihead_params,
+    nonlinearity="none",
+    reduction_type="conv",
+):
   """Reduce the length dimension by compressing with conv.
 
   Args:
     x (tf.Tensor): float32 of shape [batch, length, depth]
     factor (int): compression factor for the memory sequence
+    multihead_params (dict): parameters for multihead attention
     nonlinearity (str): Add some non-linearity after the memory block
     reduction_type (str): type of compression
-    multihead_params (dict): parameters for multihead attention
 
   Returns:
     (tf.Tensor): float32 of shape [batch, length, depth]

tensor2tensor/layers/common_hparams.py

@@ -116,12 +116,15 @@ def basic_params1():
       # If set to True, drop sequences longer than max_length during eval.
       # This affects the validity of the evaluation metrics.
       eval_drop_long_sequences=int(False),
+      # TODO(lukaszkaiser): these parameters should probably be set elsewhere.
       # in SymbolModality, share the output embeddings and the softmax
       # variables.
       # You can also share the input embeddings with the output embeddings
       # by using a problem_hparams that uses the same modality object for
       # the input_modality and target_modality.
       shared_embedding_and_softmax_weights=int(False),
+      # In SymbolModality, skip the top layer, assume we're providing logits.
+      symbol_modality_skip_top=int(False),
       # For each feature for which you want to override the default input
       # modality, add an entry to this semicolon-separated string. Entries are
       # formatted "feature_name:modality_type:modality_name", e.g.