Merge pull request #361 from rsepassi/push

v1.2.5

.travis.yml

@@ -24,6 +24,6 @@ script:
   - mkdir $T2T_TRAIN_DIR
   - t2t-datagen --problem=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR
   - t2t-trainer --problems=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR --model=transformer --hparams_set=transformer_tiny --train_steps=5 --eval_steps=5 --output_dir=$T2T_TRAIN_DIR
-  - t2t-decoder --problems=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR --model=transformer --hparams_set=transformer_tiny --output_dir=$T2T_TRAIN_DIR
+  - t2t-decoder --problems=$T2T_PROBLEM --data_dir=$T2T_DATA_DIR --model=transformer --hparams_set=transformer_tiny --output_dir=$T2T_TRAIN_DIR --decode_hparams='num_samples=10'
 git:
   depth: 3

setup.py

@@ -5,7 +5,7 @@
 
 setup(
     name='tensor2tensor',
-    version='1.2.4',
+    version='1.2.5',
     description='Tensor2Tensor',
     author='Google Inc.',
     author_email='[email protected]',

tensor2tensor/data_generators/cnn_dailymail.py

@@ -74,7 +74,7 @@ def story_generator(tmp_dir):
   for path in paths:
     for story_file in tf.gfile.Glob(path + "*"):
       story = u""
-      for line in tf.gfile.Open(story_file, 'rb'):
+      for line in tf.gfile.Open(story_file, "rb"):
         line = unicode(line, "utf-8") if six.PY2 else line.decode("utf-8")
         story += line
       yield story

tensor2tensor/data_generators/generator_utils.py

@@ -355,6 +355,8 @@ def generate():
       for lang_file in source[1]:
         tf.logging.info("Reading file: %s" % lang_file)
         filepath = os.path.join(tmp_dir, lang_file)
+
+        # Extract from tar if needed.
         if not tf.gfile.Exists(filepath):
           read_type = "r:gz" if filename.endswith("tgz") else "r"
           with tarfile.open(compressed_file, read_type) as corpus_tar:
@@ -411,7 +413,7 @@ def generate():
       for line in source_file:
         line = line.strip()
         if line and "\t" in line:
-          parts = line.split("\t", maxsplit=1)
+          parts = line.split("\t", 1)
           part = parts[index].strip()
           yield part
 

tensor2tensor/data_generators/image.py

@@ -42,6 +42,12 @@
 import tensorflow as tf
 
 
+def resize_by_area(img, size):
+  """image resize function used by quite a few image problems."""
+  return tf.to_int64(
+      tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA))
+
+
 class ImageProblem(problem.Problem):
 
   def example_reading_spec(self, label_key=None):
@@ -93,16 +99,12 @@ class ImageCeleba(ImageProblem):
 
   def preprocess_example(self, example, unused_mode, unused_hparams):
 
-    def resize(img, size):
-      return tf.to_int64(
-          tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA))
-
     inputs = example["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)
-    example["inputs"] = resize(inputs, 8)
-    example["targets"] = resize(inputs, 32)
+    example["inputs"] = resize_by_area(inputs, 8)
+    example["targets"] = resize_by_area(inputs, 32)
     return example
 
   def hparams(self, defaults, unused_model_hparams):
@@ -388,14 +390,10 @@ def dataset_filename(self):
 
   def preprocess_example(self, example, unused_mode, unused_hparams):
 
-    def resize(img, size):
-      return tf.to_int64(
-          tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA))
-
     inputs = example["inputs"]
     # For Img2Img resize input and output images as desired.
-    example["inputs"] = resize(inputs, 8)
-    example["targets"] = resize(inputs, 32)
+    example["inputs"] = resize_by_area(inputs, 8)
+    example["targets"] = resize_by_area(inputs, 32)
     return example
 
   def hparams(self, defaults, unused_model_hparams):
@@ -654,6 +652,43 @@ def preprocess_example(self, example, mode, unused_hparams):
     return example
 
 
+@registry.register_problem
+class ImageCifar10Plain8(ImageCifar10):
+  """CIFAR-10 rescaled to 8x8 for output: Conditional image generation."""
+
+  def dataset_filename(self):
+    return "image_cifar10_plain"  # Reuse CIFAR-10 plain data.
+
+  def preprocess_example(self, example, mode, unused_hparams):
+    example["inputs"] = resize_by_area(example["inputs"], 8)
+    return example
+
+
+@registry.register_problem
+class Img2imgCifar10(ImageCifar10):
+  """CIFAR-10 rescaled to 8x8 for input and 32x32 for output."""
+
+  def dataset_filename(self):
+    return "image_cifar10_plain"  # Reuse CIFAR-10 plain data.
+
+  def preprocess_example(self, example, unused_mode, unused_hparams):
+
+    inputs = example["inputs"]
+    # For Img2Img resize input and output images as desired.
+    example["inputs"] = resize_by_area(inputs, 8)
+    example["targets"] = resize_by_area(inputs, 32)
+    return example
+
+  def hparams(self, defaults, unused_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
+
+
 # URLs and filenames for MSCOCO data.
 _MSCOCO_ROOT_URL = "http://msvocds.blob.core.windows.net/"
 _MSCOCO_URLS = [

tensor2tensor/data_generators/wmt.py

@@ -19,9 +19,7 @@
 from __future__ import division
 from __future__ import print_function
 
-import glob
 import os
-import stat
 import tarfile
 
 # Dependency imports
@@ -115,7 +113,7 @@ def tabbed_generator(source_path, source_vocab, target_vocab, eos=None):
   with tf.gfile.GFile(source_path, mode="r") as source_file:
     for line in source_file:
       if line and "\t" in line:
-        parts = line.split("\t", maxsplit=1)
+        parts = line.split("\t", 1)
         source, target = parts[0].strip(), parts[1].strip()
         source_ints = source_vocab.encode(source) + eos_list
         target_ints = target_vocab.encode(target) + eos_list
@@ -267,8 +265,9 @@ def bi_vocabs_token_generator(source_path,
 # English-Czech datasets
 _ENCS_TRAIN_DATASETS = [
     [
-        "https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-1458/data-plaintext-format.tar",
-        ('tsv', 3, 2, 'data.plaintext-format/*train.gz')
+        ("https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/"
+         "11234/1-1458/data-plaintext-format.tar"),
+        ("tsv", 3, 2, "data.plaintext-format/*train.gz")
     ],
     [
         "http://data.statmt.org/wmt17/translation-task/training-parallel-nc-v12.tgz",  # pylint: disable=line-too-long
@@ -375,25 +374,22 @@ def _compile_data(tmp_dir, datasets, filename):
         url = dataset[0]
         compressed_filename = os.path.basename(url)
         compressed_filepath = os.path.join(tmp_dir, compressed_filename)
+
         generator_utils.maybe_download(tmp_dir, compressed_filename, url)
 
-        if dataset[1][0] == 'tsv':
+        if dataset[1][0] == "tsv":
           _, src_column, trg_column, glob_pattern = dataset[1]
-          filenames = glob.glob(os.path.join(tmp_dir, glob_pattern))
+          filenames = tf.gfile.Glob(os.path.join(tmp_dir, glob_pattern))
           if not filenames:
-            mode = "r:gz" if compressed_filepath.endswith("gz") else "r"  # *.tgz *.tar.gz
+            # Capture *.tgz and *.tar.gz too.
+            mode = "r:gz" if compressed_filepath.endswith("gz") else "r"
             with tarfile.open(compressed_filepath, mode) as corpus_tar:
               corpus_tar.extractall(tmp_dir)
-            filenames = glob.glob(os.path.join(tmp_dir, glob_pattern))
+            filenames = tf.gfile.Glob(os.path.join(tmp_dir, glob_pattern))
           for tsv_filename in filenames:
             if tsv_filename.endswith(".gz"):
               new_filename = tsv_filename.strip(".gz")
-              try:
-                generator_utils.gunzip_file(tsv_filename, new_filename)
-              except PermissionError:
-                tsvdir = os.path.dirname(tsv_filename)
-                os.chmod(tsvdir, os.stat(tsvdir).st_mode | stat.S_IWRITE)
-                generator_utils.gunzip_file(tsv_filename, new_filename)
+              generator_utils.gunzip_file(tsv_filename, new_filename)
               tsv_filename = new_filename
             with tf.gfile.GFile(tsv_filename, mode="r") as tsv_file:
               for line in tsv_file:
@@ -663,17 +659,19 @@ def vocab_name(self):
   def generator(self, data_dir, tmp_dir, train):
     datasets = _ENCS_TRAIN_DATASETS if train else _ENCS_TEST_DATASETS
     tag = "train" if train else "dev"
-    data_path = _compile_data(tmp_dir, datasets, "wmt_encs_tok_%s" % tag)
     vocab_datasets = []
+    data_path = _compile_data(tmp_dir, datasets, "wmt_encs_tok_%s" % tag)
     # CzEng contains 100 gz files with tab-separated columns, so let's expect
-    # it is the first dataset in datasets and use the newly created *.lang{1,2} files instead.
+    # it is the first dataset in datasets and use the newly created *.lang{1,2}
+    # files for vocab construction.
     if datasets[0][0].endswith("data-plaintext-format.tar"):
-      vocab_datasets.append([datasets[0][0],
-                            ["wmt_encs_tok_%s.lang1" % tag, "wmt_encs_tok_%s.lang2" % tag]])
+      vocab_datasets.append([datasets[0][0], ["wmt_encs_tok_%s.lang1" % tag,
+                                              "wmt_encs_tok_%s.lang2" % tag]])
       datasets = datasets[1:]
     vocab_datasets += [[item[0], [item[1][0], item[1][1]]] for item in datasets]
     symbolizer_vocab = generator_utils.get_or_generate_vocab(
-        data_dir, tmp_dir, self.vocab_file, self.targeted_vocab_size, vocab_datasets)
+        data_dir, tmp_dir, self.vocab_file, self.targeted_vocab_size,
+        vocab_datasets)
     return token_generator(data_path + ".lang1", data_path + ".lang2",
                            symbolizer_vocab, EOS)